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Generic Artane (Trihexyphenidyl)
Artrane is used to treat symptoms of Parkinson's disease. |
Per Pills
Product Description
Common useArtrane is used to treat symptoms of Parkinson's disease. It can be prescribed alone or in combination with other medications. It controls rigidity, drooling, tremors and other Parkinson's disease. Also it can be prescribed to prevent similar symotoms caused by chlorpromazine (Thorazine), fluphenazine (Prolixin), perphenazine (Trilafon) and others.
Dosage and directions
Artane should be used only by the patient for whom it has been prescribed. Do not take less or more or take it more often than prescribed by your doctor.
Artane dosage depends on your conditions it should be taken exactly as prescribed. Usually Artane is prescribed as a capsule, tablet and liquid taken 3-4 times a day with food at bedtime. Controlled-released Artane should be taken once to twice a day every 12 hours.
Before taking Artane discuss with your doctor if you have liver o kidney disease. Use it with caution.
The medicine may cause dizziness or lightheadedness. Avoid drink alcohol o other medicines. It can make worse the side effects. Do not drive or perform other unsafely work until you know how you react to this drug. Do not eat grapefruit or drink grapefruit juice while you take Artane Tablets. Proper dental care is needed while you are taking Artane Tablets. Brush and floss your teeth and visit the dentist regularly. If your symptoms do not improve or if they worsen, contact your doctor.
You may need special dosage adjustment or you may not be able to take this drug if you have liver o kidney infection or if you have gastrointestinal problems.
Possible side effects
The most serious side effects are severe neutropenia (low white blood cells), hemorrhage (gastrointensial or cerebral), thrombotic thrombocytopenic pupura (TTP), headache, indigestion, flushing. Seek medical help immediately if your side effects do not ease off during several hours.
Drug interaction
Artane interacts with certain products phenytoin (Dilantin); tamoxifen (Nolvadex); tolbutamide (Orinase); torsemide (Demadex); fluvastatin (Lescol); a blood thinner such as warfarin (Coumadin), heparin, ardeparin (Normiflo), dalteparin (Fragmin), danaparoid (Orgaran), enoxaparin (Lovenox), or tinzaparin (Innohep); (Activase), anistreplase (Eminase), dipyridamole (Persantine), streptokinase (Kabikinase, Streptase), ticlopidine (Ticlid), and urokinase (Abbokinase). Disscuss with your doctor if you are taking the above mentioned drugs.
Missed dose
If you miss a dose take it as soon as you remember. If it is almost time for the next dose, skip the Missed Dose and continue your regular dosing schedule. Do not take a double dose to make up for a missed one.
The symptoms of Artane overdose aren't known. If you think that you overdosed the medicine seek medical help immediately.
Keep at the temperature between 59–86F (15–30 C), away from heat, moisture, light and children.
We provide only general information about medications which does not cover all directions, possible drug integrations, or precautions. Information at the site cannot be used for self-treatment and self-diagnosis. Any specific instructions for a particular patient should be agreed with your health care adviser or doctor in charge of the case. We disclaim reliability of this information and mistakes it could contain. We are not responsible for any direct, indirect, special or other indirect damage as a result of any use of the information on this site and also for consequences of self-treatment.


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The Best Songs of 2018 (So Far!)
Don't settle for the blare of an ice cream truck's nursery rhyme siren or the maddening hum of an air conditioning unit to serve as your official soundtrack to summer — in 2018, pop music has delivered more than enough to get you through — so long as you've got a trusty set of noise-canceling headphones, anyway.
Only five months into the year, veteran pop acts like the Backstreet Boys, new queen of hip-hop Cardi B and promising vocal powerhouse Louisa Johnson have all released tracks that'll have you buzzing en route to the beach (or, if you're very lucky, your backyard pool). Artists have made magic themselves (Nicki Minaj) and in pairs (Rita Ora and Liam Payne); collectively, pop's A-list have arranged a playlist that's pretty much bulletproof.
Sure, there will be plenty more tracks to discuss once the weather cools and winter approaches, but let's not get ahead of ourselves. Below, PopCrush has collected our favorite pop songs of 2018 so far — check 'em all out and get to streaming!
Didn't see a song you loved on our list this year? Let us know in the comments or hit us up on Twitter!

subset: commoncrawl


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Found 1 results
Author Title Type [ Year] Filters: First Letter Of Title is P and Author is P. Liu [Clear All Filters]
Printed Organic Electronics." In FLEXIBLE FLAT PANEL DISPLAYS, edited by GP Crawford, 219-243. Wiley-SID Series in Display Technology. OSNEY MEAD, OXFORD OX2 0EL, ENGLAND: BLACKWELL SCIENCE PUBL, 2005.


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Clean Brands represents a specialised range of unique cleaning and associated product formulations, managed by Chemical Solutions Ltd (CSL), a wholly NZ owned and operated manufacturing business, based at Airport Oaks.
The range of products are given high status within CSL's distribution offer due to their outstanding performance or licensing privileges. They are supported with a team of specialist advisors and 'how-to' information, with exclusive on-line support and training material.
- Unique brands and formulations
- Training and Support
- Expert advice
- Kiwi owned and operated
- Promotional Ideas
- Strategic Selling
See more Clean Brands products.


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The Kebra Nagast, by E.A. Wallis Budge, [1932], at sacred-texts.com
And behold, the Angel of the Lord appeared again to AZÂRYÂS and he stood up above him like a pillar of fire, and he filled the house with his light. And he raised up AZÂRYÂS and said unto him, "Stand up, be strong, and rouse up thy brother ÊLMĔYÂS, and 'ABĔSÂ, and MÂKARÎ, and take the pieces of wood and I will open for thee the doors of the sanctuary. And take thou the Tabernacle of the Law of God, and thou shalt carry it without trouble and discomfort. And I, inasmuch as I have been commanded by God to be with it for ever, will be thy guide when thou shalt carry it away."
And AZÂRYÂS rose up straightway, and woke up the three men his brethren, and they took the pieces of wood, and went into the house of God—now they found all the doors open, both those that were outside and those that were inside—to the actual place where AZÂRYÂS found ZION, the Tabernacle of the Law of God; and it was taken away by them forthwith, in the twinkling of an eye, the Angel of the Lord being present and directing. And had it not been that p. 72 God willed it ZION could not have been taken away forthwith. And the four of them carried ZION away, and they brought it into the house of AZÂRYÂS, and they went back into the house of God, and they set the pieces of wood on the place where ZION had been, and they covered them over with the covering of ZION, and they shut the doors, and went back to their houses. And they took lamps and set them in the place where [ZION] was hidden, and they sacrificed the sheep thereto, and burned offerings of incense thereto, and they spread purple cloths over it and set it in a secret place for seven days and seven nights.


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County Clare is renowned for its legacy in traditional Irish music and is home to an abundance of visual riches and spectacular scenery.
Clare is located on the far west coast of Ireland, bordered by Galway Bay to the north, Lough Derg to the east, the River Shannon to the south and the Atlantic Ocean to the west. In Clare you'll find some of Ireland's most recognisable picture postcard icons, from the stunning Cliffs of Moher overlooking the Atlantic, to the Neolithic tomb, the Poulnabrone Dolman and the famous Bunratty Castle. This 13th Century castle now forms the heart of a heritage park and host world famous medieval banquets and lively entertainments.
In the far north western region of Clare is the Burren, a unique landscape of limestone karst, that in spring is awash with colourful flora. This harsh but beautiful region is popular with walkers, cyclists and cave explorers. While the sleepy villages here and in the rest of County Clare such as Doolin, Lisdoonvarna, Ennis and Feakle are renowned for their lively entertainments and festivals of traditional Irish music.
Many of Ireland's best traditional musicians and balladists have come from the Banner County as Clare is known. These include, the piper Willie Clancy and contemporary Irish musician Christie Moore and the county was famously the royal home of the last High King of Ireland, Brian Boru.
Also in County Clare you'll find the world famous Lahinch Golf Links Course and Shannon Airport, Ireland's second largest airport behind Dublin.


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The following article will go though a drive mode where the catch diodes are conducting for a significant amount of time in each cycle. If you haven't read the introductory page of the series and you're not familiar with H-bridge concepts, I suggest you do and only than continue reading this article. I also strongly suggest you read the page on sign-magnitude drive because much of the discussion here will build on the material covered over there.
If however you did read that introductory article, you might be a little surprised. There I put together a neat little taxonomy of available drive modes, and concluded that there's only two reasonable variants. Those, the sign-magnitude drive and the lock anti-phase drive' we've already covered in previous chapters. So what's all this about than?
Well, as it turns out, I've lied. To be more precise, I didn't tell the whole truth. Before we talk about the details and the reasons however, let's review the basics! Our motor control circuit is an H-bridge, with the following configuration: The load, the DC motor is a complex beast, but we will mostly use just a very simple model for it: For this discussion we won't be able to ignore the internal resistance for the most part.
Finally, a warning: while the previously discussed drive modes are in wide use, I haven't found too many resources that describe what I'm about to here. That means that many of the terms I'm using (including the name actually) I came up with and may not correspond to the industry-standard nomenclature. If you find that misleading or know the official term for something in here, please let me know so I can make the appropriate corrections.
The principle assumption leading to the original analysis and the resulting two possible drive modes was that we wanted the switches to conduct during both the on-time and the off-time. Our intention was to minimize heat dissipation on the switches and since in most cases the voltage drop on the switch would be less than that on the diode, it's better to use the switches for conducting current.
As it turns out, both the lock anti-drive drive and the sign-magnitude drive suffer from a serious problem: regenerative braking. I say problem, because this phenomena, while sounds cool at first – putting energy back into the battery – complicates system design quite a bit and pretty much forces closed-loop control of the bridge with additional sensing circuitry.
If we look back at what causes regeneration, we'll see that it happens whenever Vmot_avg and Vg are of the same polarity but Vg is the greater of the two. Under those circumstances, the average current starts flowing in the reverse direction, recharging the battery. The current could reverse direction, because our switches allow current to flow both ways. If there was a way to block the current from reversing – at least through the battery – we could potentially make an H-bridge which doesn't have any state with regeneration. Turns out, such reverse-current protected elements are diodes, and we already have four of them in the bridge. The question is, can we make use of them. And we'll investigate the answer in the next chapter.
Basic operation
The switching patterns of this drive mode are a modification of the sign-magnitude drive. The idea is that during the off-time we'll leave only one of the four switches closed, instead of two as with the sign-magnitude drive. This will force the off-time current to flow through one of the catch-diodes. There are four reasonable ways of doing that:
Mapping A | Q1 | Q2 | Q3 | Q4 |
on-time state | close | open | open | close |
off-time state | close | open | open | open |
Mapping B | Q1 | Q2 | Q3 | Q4 |
on-time state | close | open | open | close |
off-time state | open | open | open | close |
Mapping C | Q1 | Q2 | Q3 | Q4 |
on-time state | open | close | close | open |
off-time state | open | open | close | open |
Mapping D | Q1 | Q2 | Q3 | Q4 |
on-time state | open | close | close | open |
off-time state | open | close | open | open |
(If you are pedantic, you'll notice that there are four other possibilities, bur they are effectively identical to the four I have listed at least as far as the discussion here is concerned.)
The four combinations are the four possible answers to the two binary questions:
- Is the on-time voltage applied in the forward or reverse direction?
- Does the high- or low-side switch stay closed during the off-time?
There usually is a control signal deciding the answer to the first question, while the second one is usually answered during the design of the bridge (there are exceptions we'll talk about in a later article).
For the general discussion it doesn't matter too much which direction the on-time voltage is applied, so I'm going to stick with my old habit of choosing one where the motor is driven in the forward direction (mapping A or B). For the most part, I'll assume that we keep the high-side switch closed, so I'll use 'mapping A' from now on.
Let's take a look at the switching diagram first:
You see that during the off-time, I drew the 'b-side' motor potential being slightly above Vbat, and at the same time, the motor voltage being slightly negative. To understand the reasons for that, let's look at the current flow diagrams! During the on-time, the current does what it always does: flows through the two closed switching elements.
When the switch-over happens to the off-time, Q4 opens and only Q1 stays closed. At that point the motor current (being an inductive load) will have to continue flowing and the only way it can is through forward-biasing D3. So, the off-time current flow will be the following:
In order for current to flow through D3 however, the b-side lead of the motor needs to be at a slightly higher potential than Vbat. This voltage is the forward-bias voltage of the diode, and is in the range of 0.2 and 1V depending on construction, current, temperature among other things. In the following discussions I will disregard this little voltage difference though, and assume that the diode drop is 0. Just keep in mind, that for more precise calculations, you will have to take that forward drop into consideration as well.
In other drive modes (mapping B for example) it's the low-side diode and switch that conducts the current:
In both cases however, essentially the motor is short-circuited, the same way as in sign-magnitude drive. Consequently the current diagram of the motor doesn't change much either.
Continuous and Discontinuous Current
Now, before we go any further, we'll have to discuss the topic of current flow a little deeper. What happens, when (during the off-time) the current decreases to 0? In both of the sign-magnitude and lock anti-phase drive modes the current is free to turn negative, as the conducting elements are bi-directional. Not the case here! The conducting diode (D3 for example) will close as soon as the current reaches 0. If that happens, there's no current flow in the circuit for the rest of the cycle. So, depending on the slopes of the on-time and off-time currents (and the times spent in each state) the current might flow continuously or discontinuously in the system:
This phenomena has some nice benefits as we'll see later, but unfortunately means that our equations will become messier as their form changes depending on which of the two distinct operating mode the bridge is in.
You can see that I introduced three time-periods:
- ton is the on-time
- toff_conduct is the portion of the off-time when the bridge is conducting current
- toff_zero is the portion of the time when the current is 0.
There are some obvious relationships between these:
- toff_conduct+toff_zero = toff
- ton+toff = tcycle
Finally there's another time metric, worth introducing, which is the conduction time:
- tconduct = ton+toff_conduct
Using this terminology, the difference between continuous and discontinuous current modes is if toff_zero is zero or not.
While many things are different, some of the knowledge that we've gained by analyzing the simpler drive modes, carries over. For example, the slope of the current change during the on- and off-time (as long as it conducts) can be found by the following equations:
dI/dt = (Vbat – Vg – Imot_avg*Rm)/Lm during the on-time, and
dI/dt = (– Vg – Imot_avg*Rm)/Lm during the off-time.
As I promised, I am already involving the voltage drop on the internal resistance in these equations: we will need to make use of them pretty soon.
Average motor voltage
In order to be able to calculate the average motor voltage, there's one last thing we'll have to discuss: what is the voltage on the motor terminals in discontinuous current mode, when the current is 0? The answer is fairly simple: Since the current doesn't change, the inductor of the motor should see 0 voltage difference. The internal resistance drops 0 as well, since the current is 0. This means that the motor terminal voltage must cancel out the generator voltage, Vg. In other words the motor voltage during this period should be Vg. Now, using that knowledge, we can calculate the average motor voltage:
Vmot_avg = (Vbat*ton + Vg*toff_zero)/tcycle
It's also beneficial to introduce another voltage, which is the average voltage the motor sees during the time the bridge conducts current:
Vavg_conduct = Vbat*ton/tconduct
Note, that if the bridge is in continuous current mode, the two values are equal as tcycle = tconduct and toff_zero is 0.
Steady state
Our steady-state is defined when the parameters of the bridge stay constant from cycle-to-cycle. This means both electrical and mechanical characteristics: currents, voltages, torque, acceleration etc. If that's true, the current-change during the on-time and off-time must chancel each other out, and the change (the ripple-current) is the following:
Iripple = (Vbat – Vg – Imot_avg*Rm)/Lm*ton = (Vg + Imot_avg*Rm)/Lm*toff_conduct
Expressing Vg from this, we get:
Vg = Vbat * ton/tconduct – Imot_avg*Rm
which is the same as:
Vg = Vavg_conduct – Imot_avg*Rm
And solving it for the average motor current, we get:
Imot_avg = (Vavg_conduct – Vg) / Rm
Now, let's put this back into the ripple-current equation:
Iripple = 1/Lm * Vavg_conduct * toff_conduct
Finding the operating mode
Let's see if we can figure out if the bridge at a given operating point is in continuous or discontinuous mode. The external conditions that define the operating point are the following:
- The operating frequency represented by tcycle
- The duty-cycle, represented by D (D = ton/tcycle)
- The battery voltage Vbat
- The speed of the motor – or the generator voltage, represented by Vg
- The motor parameters Rm and Lm
Before we can attack this problem, we'll have to do one more thing. It turns out, we can arrive at the average current for discontinuous current mode in a different way as well. For this operating mode, that is when the current starts from 0 in each cycle, the area under the current 'triangle' should be the same as the area under the average current rectangle:
Note, that this approach doesn't work for continuous current mode, as the current doesn't reach zero during the cycle.
From this insight, we get this:
Imot_avg * tcycle = 1/2 * Iripple * tconduct
Putting the expressions for Imot_avg and Iripple into this and expressing Vg, we get:
Vg = Vavg_conduct * (1 – 1/2 * Rm/Lm * tconduct * toff_conduct / tcycle)
The final insight is this: there's a critical point where the continuous and discontinuous current modes meet: at this point the off-time current just reaches zero at the very end of the off-time. So in that case, the above equation is correct, while toff_zero is zero as well:
For this special-case we can do some simplifications to the previous equation, and after substituting Dcritical = ton_critical/tcycle, we get:
Vbat * Dcritical * (1 – 1/2*Rm/Lm*tcycle*(1-Dcritical)) = Vg
This is a quadratic equation for Dcritical:
Dcritical2 – Dcritical*(1+2 * Lm/Rm * 1/tcycle) + 2 * Lm/Rm * 1/tcycle * Vg/Vbat = 0
This equation has at most two roots. However, as long as Vg has the same polarity as Vbat (we'll talk about what happens if it's not in a minute), only one of the roots is between 0 and 1, leaving us with exactly one physically valid root:
Dcritical = 1/2 + Lm/Rm * 1/tcycle – sqrt[ (1/2 + Lm/Rm * 1/tcycle)2 + 2 * Lm/Rm * 1/tcycle * Vg/Vbat ]
Not a nice equation, but at least a solution. Finally we're at the point where we can figure out in what operating mode the bridge is in: With the operational parameters, we calculate the critical duty-cycle. If the actual duty-cycle is above this critical value, the bridge operates in continuous current mode. If it is below that, the bridge is in discontinuous current mode.
If you look carefully at the solution, you'll see that it depends on a few ratios only:
- First, it's the ratio of Vg/Vbat, in other words the relative speed of the motor.
- The second is Lm/Rm * 1/tcycle. Since Lm/Rm is the electrical time constant of the motor, this ratio is the relative time constant of the motor to the bridge cycle time.
Usually a bridge operates at lower cycle time as the motor time constant and even if it didn't the calculations above assumed that; We assumed the current change to be linear during the on-time and the off-time and that is only true if the bridge operates much faster than the motor time constant. So Lm/Rm * 1/tcycle must always be – for our purposes at least – bigger than 1.
I've plotted a few of these curves in the following graph:
You can see that each of the curves cut the square which represents all possible duty-cycle and relative motor speed combinations (in the forward direction) into two. The area above the curves is the land of continuous current mode operation, the area below them is for discontinuous current modes. You can see that the curves don't deviate too much from the diagonal, so a good first-order approximation is that if the relative motor speed is less than the duty cycle, the bridge operates in discontinuous current mode. This approximation has a roughly 15% error, but the error is less and less if you increase the operating frequency of the bridge.
Finally, the promised explanation for negative Vg: If Vg is negative (that is opposing Vbat), the off-time current will never be able to close the diode as Vg biases the diode in the forward direction. So in that case, the bridge is always in continuous current mode.
Finding tconduct
The last topic to discuss is this: let's say your bridge operates in discontinuous current mode. What is the portion of the time it spend conducting?
For the answer, we'll have to go back to the equation in the previous chapter:
Vg = Vavg_conduct * (1 – 1/2 * Rm/Lm * tconduct * toff_conduct / tcycle)
From this, we get (after expressing Vavg_conduct from Vbat and rearranging things):
Vg/Vbat*tconduct = ton * (1 – 1/2 * Rm/Lm * tconduct * (tconduct-ton) / tcycle)
This is a quadratic equation for tconduct. I'm not going to give you the closed-form solution, because it's an eye-sore and it's actually easier to substitute numbers into this equation and solve if for a particular case, than to derive the parametric solution. What is important to note here is that just as before, from the two possible solutions, only one will be physically meaningful, that is between 0 and tcycle.
Transient states and input capacitor
So far it seems it's impossible for the current to flow in the reverse direction, and we've really covered quite a bit of ground. The previous discussion not only applies to steady-speed operation but to acceleration, deceleration as well as long as the cycle-to-cycle averages don't change.
When one of the parameters (mainly D or Vg) changes, it takes time for the system to settle in the new steady-state. In that transient, the current can flow in the reverse direction during the on-time. Let's take a quick example to understand why!
Let's say we operate the bridge in the reverse direction at 50% duty-cycle and under relatively heavy loads. Let's also assume the motor has a really low Rm. In this case Vg is close to -Vbat/2 and the average motor current is a relatively high positive number. Because of the bridge operates in the reverse direction, the motor current flows through the battery in the positive direction as well, discharging the battery.
Now, let's imagine that we abruptly change the bridge top operate in the forward direction, still with 50% duty-cycle. The first cycle with the changed duty-cycle will see the motor current to be opposite to the battery voltage, just as Vg. This current will start increasing (decrease in absolute value) during the on-time, as Vbat is applied in the opposite direction to the motor current now, but for the moment it still is flowing into the battery, recharging it.
When the off-time comes along, something strange happens: As Q4 closes but Q1 stays open, the motor current (still flowing in the negative direction) will have to find a way to continue flowing. It can't flow through D3, since that diode can't conduct in the reverse direction. The only way the current can flow is through D4:
What that means, is that the bridge is essentially in the same mode for both the on- and the off-time. The only difference is that it's the diode and not the switch that conducts the current, but it flows through the same leg in the same direction.
This also means that the current will continue increasing (decrease in absolute value) during the off-time as well.
The next on-time will continue in the same way just as the next off-time and so on. This process continues as long as the motor current reaches 0 (coming from the negative side) at which point the normal operation of the bridge will resume: the off-time current will start flowing through D3 as usual.
From this example, the following can be derived: in the transient, the motor current increases through potentially several cycles from it's initial negative value to 0. The increase will not depend on the timing of the bridge as the current flow and voltages stay the same during the on- and off-times. The current change will follow an exponential curve (we can no longer assume linear process as the current change is too large to assume a constant voltage drop on Rm), according the following equation:
Imot(t) = Imot(0) + (Vbat/Rm – Imot(0)) * (1 – e-t/(Lm/Rm))
Note that Imot(0), the initial current through the motor when the change in the duty-cycle happens is negative, so is Imot(t). Of course only the portion of the curve where the current is negative is valid:
As we've discussed it several times, current flowing back to the battery is a problem in most implementations. The main goal with using this switching pattern to begin with was to get rid of this reverse-current! Well, apparently we weren't completely successful.
However, as long as we can put a limit on the charge that this reverse current pushes back into the battery, we can handle it by putting a capacitor on the input on the bridge that can temporarily soak up the extra charge.
So, let's figure out if there's an upper bound to this charge? The total charge delivered is the integral of the current over time, so the first order of business is to figure out how long does the current flow. It stops flowing when the motor current reaches 0, so:
Imot(0) + (Vbat/Rm – Imot(0)) * (1 – e-tstop/(Lm/Rm)) = 0
Now, solving it for tstop, we get:
tstop = – Lm/Rm * ln(1 – Imot(0) / (Vbat/Rm – Imot(0)) )
Next, to do the integral, we have to find the primitive function of the current curve. It is the following:
Vbat/Rm * t + (Lm/Rm) * (Vbat/Rm – Imot(0)) * e-t/(Lm/Rm) + C
With these two, we can calculate the integral. After some simplifications, the total charge released is the following:
Qreverse = – Lm/Rm * (Imot(0) – (Vbat/Rm) * ln(1 – Imot(0) / (Vbat/Rm – Imot(0)) )
As expected, the sign is negative, so the charge is coming out of the bridge, into the battery (or the capacitor we're trying to size).
So what is the maximum value of this charge? It is when Imot(0) is maximal (in absolute value). So how big this current can be? We can take two approaches, just as we did in the lock anti-phase drive. We can take either the short-circuit current of the motor or the current-limit of the bridge into account. The latter is still a very conservative estimation and is usually much lower than the short-circuit current of the motor, so let's use that. In the following equations, I've changed the signs so that we substitute the positive bridge current:
Qreverse_max = Lm/Rm * (Imax + (Vbat/Rm) * ln(1 + Imax / (Vbat/Rm + Imax) )
This charge changes the capacitor voltage following the familiar formula: Vripple_max = Qrelease_max/C. If we know how much ripple voltage the circuit can tolerate, we can calculate the size of the capacitor needed:
C = Lm/Rm * (Imax + (Vbat/Rm) * ln(1 + Imax / (Vbat/Rm + Imax) ) / Vripple_max
Let's see an example! Let's say our motor has a 30uH inductance, and a 1Ω resistance. Let's say we operate from a 20V supply and the bridge can sustain a 10A current. Finally let's assume that we can tolerate 5% ripple current. The result is a 473µF.
If we want to build a bridge with a 100A current delivery capability, the input capacitance needed would jump to almost 10x, or around 3500µF.
If we assume a much lower resistance, let's say 100mΩ only (and still 10A current limit), the capacitor value jumps to over 5000µF. That is a trend: for low resistances, the capacitance required is more or less proportional to 1/Rm.
You can also play around with the equation to get a feel to how the various parameters affect the solution.
Another interesting experiment is this: what if we want to calculate the capacitance needed for the stall-current of the motor? The stall current is Vbat/Rm, so if we put that in the place of Imax in the previous equation we get some nice simplifications:
C = 1.405 * Lm/Rm2 * Vbat/Vripple_max
This is much easier to comprehend, but as you can see it has an inverse quadratic relationship to Rm. That is understandable since in the previous calculations we've assumed Imax to be independent of Rm, while in here it's not the case anymore.
We started this article with the idea to construct an H-Bridge drive mechanism that doesn't suffer from regenerative braking. After a lot of ugly math I hope I convinced you that it is in deed possible. There are of course down-sides to this approach:
- The catch diodes conduct for a significant portion of the time and that is usually going to generate more heat than if the switches conduct the current.
- The bridge has two operating modes: continuous and discontinuous current mode and the operating parameters, including the motor torque (average motor current) behaves differently in those two modes. This change in behavior complicates closed-loop speed-control design.
- We still need to employ an input capacitor to handle transient states, and the capacitor value can be rather large, especially for high-current bridges.
- The analysis of the bridge is significantly more complicated due to the non-linear behavior of the diodes.
With all that, this drive mode has one huge advantage, one that in many cases outweighs the negatives: it is inherently safe, as it can't over-charge the battery during braking.
Where to go from here?
If you're really pedantic, you'll notice that there's another possible drive mode we haven't discussed so far: you can take the lock anti-phase drive and modify it to work in an asynchronous fashion just as we did with the sign-magnitude drive here. I will leave that operating mode alone at least for now as I don't see too much practical value to it.
So now that we've finished with the detailed analysis of all interesting drive modes, we're almost ready to move on and discuss drive circuitry and component selection. However before we jump into that topic, the next installment will start with a comparison of the drive modes we've discussed here to be able to see the differences and the similarities side-by-side.


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The World Health Organization recommends the screening of all people living with HIV for tuberculosis (TB) disease, followed by TB treatment, or isoniazid preventive therapy (IPT) when TB is excluded. However, the difficulty of reliably excluding TB disease has severely limited TB screening and IPT uptake in resource-limited settings. We conducted an individual participant data meta-analysis of primary studies, aiming to identify a sensitive TB screening rule.
Methods and Findings
We identified 12 studies that had systematically collected sputum specimens regardless of signs or symptoms, at least one mycobacterial culture, clinical symptoms, and HIV and TB disease status. Bivariate random-effects meta-analysis and the hierarchical summary relative operating characteristic curves were used to evaluate the screening performance of all combinations of variables of interest. TB disease was diagnosed in 557 (5.8%) of 9,626 people living with HIV. The primary analysis included 8,148 people living with HIV who could be evaluated on five symptoms from nine of the 12 studies. The median age was 34 years. The best performing rule was the presence of any one of: current cough (any duration), fever, night sweats, or weight loss. The overall sensitivity of this rule was 78.9% (95% confidence interval [CI] 58.3%–90.9%) and specificity was 49.6% (95% CI 29.2%–70.1%). Its sensitivity increased to 90.1% (95% CI 76.3%–96.2%) among participants selected from clinical settings and to 88.0% (95% CI 76.1%–94.4%) among those who were not previously screened for TB. Negative predictive value was 97.7% (95% CI 97.4%–98.0%) and 90.0% (95% CI 88.6%–91.3%) at 5% and 20% prevalence of TB among people living with HIV, respectively. Abnormal chest radiographic findings increased the sensitivity of the rule by 11.7% (90.6% versus 78.9%) with a reduction of specificity by 10.7% (49.6% versus 38.9%).
Absence of all of current cough, fever, night sweats, and weight loss can identify a subset of people living with HIV who have a very low probability of having TB disease. A simplified screening rule using any one of these symptoms can be used in resource-constrained settings to identify people living with HIV in need of further diagnostic assessment for TB. Use of this algorithm should result in earlier TB diagnosis and treatment, and should allow for substantial scale-up of IPT.
Citation: Getahun H, Kittikraisak W, Heilig CM, Corbett EL, Ayles H, Cain KP, et al. (2011) Development of a Standardized Screening Rule for Tuberculosis in People Living with HIV in Resource-Constrained Settings: Individual Participant Data Meta-analysis of Observational Studies. PLoS Med 8(1): e1000391. https://doi.org/10.1371/journal.pmed.1000391
Academic Editor: Megan Murray, Harvard School of Public Health, United States of America
Received: February 12, 2010; Accepted: December 2, 2010; Published: January 18, 2011
This is an open-access article distributed under the terms of the Creative Commons Public Domain declaration which stipulates that, once placed in the public domain, this work may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose.
Funding: No direct funding was received for this study. The authors were personally salaried by their institutions during the period of writing (although no specific salary was set aside or given for the writing of this paper). No funding bodies had any role in the study design, data collection, analysis, decision to publish or preparation of the manuscript. The World Health Organization covered the cost of the meeting of the principal investigators.
Competing interests: HG and RG are staff members of the World Health Organization. The authors alone are responsible for the views expressed in this publication and they do not necessarily represent the decisions or policies of the World Health Organization.
Abbreviations: AFB, acid-fast bacilli; ART, antiretroviral treatment; CD4, CD4 T-lymphocyte cell count; CI, confidence interval; IQR, interquartile range; IPT, isoniazid preventive therapy; TB, tuberculosis
Editors' Summary
In 2009, 1.7 million people died from tuberculosis (TB)—equating to 4,700 deaths a day—including 380,000 people living with HIV. TB remains the most common cause of death in people living with HIV and compared to people without HIV, people living with HIV are more than 20 times more likely to develop TB. Furthermore, TB infection may occur at any stage of HIV disease and is often the initial presentation of underlying HIV infection. Without antiretroviral treatment, up to 50% of people living with HIV who are diagnosed with TB die during the 6–8 months of TB treatment.
Although antiretroviral treatment can reduce the incidence of TB both at the individual and population level, people living with HIV on antiretroviral treatment still have higher TB incidence rates and a higher risk of dying from TB. Therefore, the World Health Organization recommends regular screening for active TB disease in all people living with HIV, so those identified as having active TB disease can be provided with appropriate treatment, and isoniazid preventive therapy (to help mitigate TB morbidity, mortality, and transmission) can be given to vulnerable individuals who do not yet have active TB.
Why Was This Study Done?
There is currently no internationally accepted evidence-based tool to screen for TB in people living with HIV—a serious gap given that the presenting signs and symptoms of TB in people living with HIV are different from those in people without HIV. Therefore, the researchers aimed to develop a simple, standardized TB screening rule for resource-constrained settings, on the basis of the best available evidence that would adequately distinguish between people living with HIV who are very unlikely to have TB from those who require further investigation for TB disease.
What Did the Researchers Do and Find?
The researchers selected 12 studies that met their strict criteria, then asked the authors of these studies for primary data so that they could map individual-level data to identify five symptoms common to most studies. Using a statistical model, the researchers devised 23 screening rules derived from these five symptoms and used meta-analysis methods (bivariate random-effects meta-analysis) and the association of study-level and individual-level correlates (hierarchical summary relative operating characteristic curves) to evaluate the sensitivity and specificity of each tool used in each individual study.
The authors of the selected studies were able to provide data for 29,523 participants, of whom 10,057 were people living with HIV. The dataset included 9,626 people living with HIV who had TB screening and sputum culture performed, of which 8,148 individuals could be evaluated on the five symptoms of interest from nine of 12 studies. TB disease was diagnosed in 5.8% of people living with HIV and the best performing rule was the presence of any one of the following: current cough (any duration), fever, night sweats, or weight loss. The overall sensitivity of the rule was 78.9% and the specificity was 49.6%. However, the sensitivity of the rule increased to 90.1% among participants selected from clinical settings and to 88.0% among those who were not previously screened for TB.
What Do These Findings Mean?
The results of this study suggest that in resource-constrained settings, the absence of current cough, fever, night sweats, and weight loss (all inclusive) can identify those people living with HIV who have a low probability of having TB disease. Furthermore, any one of these symptoms can be used in resource-constrained settings to identify people living with HIV who are in need of further diagnostic assessment for TB.
Despite the limitations of the methodology used in this study, until there are evidence-based and internationally recommended guidelines for the diagnosis and treatment of TB in people living with HIV, use of the algorithm developed and presented in this study could result in earlier TB diagnosis and treatment for people living with HIV and could help to substantially scale-up isoniazid preventive therapy.
Additional Information
Please access these websites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1000391.
- The World Health Organization has information about TB in people living with HIV
- The US Centers for Disease Control and Prevention also provide information about TB and HIV coinfection
- The World Health Organization also has information about isoniazid preventative therapy
- The Stop TB Partnership's TB/HIV Working Group provide information about TB and HIV co-infection
By the end of 2009, an estimated 33 million people were living with HIV, the vast majority in sub-Saharan Africa and Asia. Tuberculosis (TB) remains the most common cause of death in people living with HIV. Compared to people without HIV, people living with HIV have a more than 20-fold increased risk of developing TB [1]. TB disease may occur at any stage of HIV disease and is frequently the first recognized presentation of underlying HIV infection [2],[3]. Without antiretroviral treatment (ART), up to 50% of people living with HIV who are diagnosed with TB die during the 6–8 mo of TB treatment [4]–[6]. This risk increases to 72%–98% among those with multi-drug (MDR) or extensively drug-resistant (XDR) TB [7],[8]. Although ART can reduce the incidence of TB both at individual [9] and population [10] levels, people living with HIV on ART still have higher TB incidence rates and a higher risk of dying from TB [11]. Routine TB screening offers the opportunity to diagnose and promptly treat TB disease, and to identify those without TB disease who may be eligible for TB preventive therapy [12]. The use of TB preventive therapy can reduce TB incidence and is therefore of considerable benefit to patients [13].
For these reasons, the World Health Organization (WHO) recommends regular screening for active TB disease of all people living with HIV and providing either treatment for active disease or isoniazid preventive therapy (IPT) to mitigate TB morbidity, mortality, and transmission [14]. However, in 2009, of the estimated 33 million people living with HIV, only 1.7 million (5%) were screened for TB, and about 85,000 (0.2%) were offered IPT [15]. Currently there is no internationally accepted evidence-based tool to screen for TB in people living with HIV. Several studies have shown that the presenting signs and symptoms of TB in people living with HIV are different from those in people without HIV to diagnose TB; for example, many people living with HIV who have culture-confirmed TB do not report having a prolonged cough, which is one of the standard TB screening questions used by national TB control programs globally [16]. Moreover, the most widely available TB diagnostic tests such as smear microscopy and chest radiography perform poorly among people living with HIV; because most people living with HIV and TB have either sputum acid-fast bacilli (AFB) smear negative pulmonary or extrapulmonary TB [17].
We conducted an individual participant data meta-analysis of published and unpublished studies to develop a simple, standardized TB screening rule for resource-constrained settings that will adequately separate patients into two groups: (1) those for whom TB is reliably excluded, and IPT and ART, if indicated, can be initiated; and (2) those who require further investigation for TB disease. We describe the results of this meta-analysis and propose an algorithm for TB screening among people living with HIV in resource-constrained settings.
We proceeded through several steps. First, we prospectively enumerated criteria for studies to be included in our meta-analysis. Second, we searched for and selected studies that met these criteria. Third, we sought primary data from investigators and mapped individual-level data to common symptoms. Fourth, we identified five symptoms available in most studies and, within each study, computed the sensitivity and specificity of 23 screening rules derived from these five symptoms. Finally, we used meta-analysis methods to estimate the performance of all 23 rules, as well as the association of study-level and individual-level correlates with performance.
Inclusion of Studies
We defined studies as being eligible for inclusion in this analysis if they met the following criteria: (1) collected sputum specimens from people living with HIV regardless of signs or symptoms; (2) used mycobacterial culture of at least one specimen to diagnose TB; and (3) collected data about signs and symptoms.
Search Strategy and Selection of Studies
To identify studies eligible for the meta-analysis, we conducted a systematic literature review of studies related to TB screening among people living with HIV in June 2008 using PubMed and various combinations of the following keywords: "HIV," "Tuberculosis," "TB screening," "Smear-negative TB," "Sputum negative TB," "TB case finding," "Intensified TB case finding," "Isoniazid prevention treatment, trial or therapy." We also searched for abstracts presented at conferences organized by the International Union Against TB and Lung Diseases and the International AIDS Society between 2000–2008. No language restriction was placed on the search. We reviewed all retrieved titles and abstracts for relevance to the topic. The reference lists of retrieved studies were also reviewed to identify further studies that meet the eligibility criteria. In addition, recognized experts in the field were contacted to identify studies that were not available (e.g., unpublished) in the initial electronic search. Studies that involve concomitant HIV testing and mycobacterial culture on all patients are resource intensive and challenging to implement in countries with a high burden of TB and HIV. Therefore, we believe it is unlikely that eligible studies would have been completed but missed by our search strategy.
We found 2,119 publications and reviewed all their abstracts. Using the criteria above, we selected 53 for review of the full text. Twenty-one articles included information on signs and symptoms for TB screening in people living with HIV. A total of 14 studies (six published and eight unpublished at the time of the search) met the inclusion criteria of our meta-analysis (Figure 1). The corresponding authors or principal investigators were contacted for all 14 studies to confirm that their studies met all the eligibility criteria. One unpublished dataset was excluded for not meeting the inclusion criteria after verification with the principal investigator, and another one was excluded because the investigators could not submit the data within the agreed timeframe. A total of 12 investigators (representing six published and six unpublished studies at the time of the search) provided de-identified individual patient data for inclusion in the primary meta-analysis within an agreed time framework (Table 1) [18]–[29]. In November 2010, immediately preceding manuscript publication, we re-ran the search strategy again to look for additional studies that were reported since the initial search and should have been included in the meta-analysis. The search found seven eligible studies, of which all except one [30] were included in our meta-analysis as unpublished datasets.
Investigators for all included studies signed a data sharing and confidentiality agreement, and agreed to a data management, analysis, and publication plan. During design and analysis phases of the meta-analysis, the investigators of the studies and data managers of the included studies held multiple discussions by email, by teleconference, and in person in Geneva, Switzerland and Atlanta, Georgia, United States.
Data Abstraction and Management
The list of variables from the most comprehensive dataset [20] was used to construct an initial master list of variables. All the variables from each study included in the meta-analysis were mapped to this master list. Principal investigators and data managers for the 12 studies worked with the meta-analysis investigators to ensure accurate mapping of data from the primary studies to the master variable list. In the end, the final dataset of the meta-analysis included 159 variables that appeared in at least two of the studies. We identified five symptoms common to most studies and limited the meta-analysis to the nine studies with substantially complete information for those five symptoms.
Case Definitions
We defined a TB patient as any person living with HIV and at least one specimen culture positive for Mycobacterium tuberculosis (MTB). We defined participants as having no TB if cultures were negative for MTB and participants were judged not to have TB on the basis of the original study definition of the investigators. We excluded from the analysis: (1) patients who were receiving treatment for TB (infection or disease) at enrolment; (2) patients who were AFB smear positive, but whose culture grew non-tuberculous mycobacteria; and (3) patients who were AFB smear positive or scanty, but sputum culture negative.
Sources of Study Heterogeneity
All studies were reviewed to identify study-level characteristics that could substantially impact the findings of the meta-analysis. Two studies were conducted exclusively among gold miners living in South Africa [21],[23], a population that may not be broadly generalizable, because of its demographics, its high prevalence of non-TB illnesses (e.g., silicosis) that can produce cough, and the practice of annual TB radiological screening. Five studies [18],[19],[22],[25],[27] were conducted among individuals drawn from a community setting through prevalence surveys, which may lead to enrolment of patients with a different spectrum of TB and HIV disease than would be found among patients seeking care. Three studies [19],[22],[23] involved participants who were previously screened for TB or who had had access to routine TB screening before being enrolled into the studies. Finally, three studies exclusively used liquid media to culture specimens [26]–[28], two studies used both solid and liquid media [18],[20], and seven studies exclusively used solid media (Table 1). Liquid media have substantially increased sensitivity for growing MTB, particularly in patients with low levels of TB bacilli, as would be expected in a population of people living with HIV being screened for TB [31]. Studies that used liquid media, therefore, would have improved ability to classify patients correctly into those who have TB and those who do not. We explored the impact of these factors on the performance of the screening algorithms and analyzed subsets of the final dataset grouped according to these characteristics.
Data Analysis
We compared characteristics of patients with TB to those of patients without TB to derive a standardized rule for TB screening among people living with HIV. The goal of TB screening is to divide the population of people living with HIV into two groups: (1) those who do not have TB; and (2) those who need further evaluation for the diagnosis of TB (i.e., TB suspects). We restricted our analysis to clinical symptoms that could be readily assessed at any level of the health system and that were asked about in all studies: current cough (C), haemoptysis (H), fever (F), night sweats (S), and weight loss (W). Using the four studies that included chest radiography data [20],[23],[24],[26], we also evaluated the impact of adding abnormal chest radiography findings to the TB screening rule. Only observations with no missing data for the symptoms of interest were included in the analysis.
We considered "1-of-n" rules as candidates for screening for TB that could best classify patients into two groups (not TB and suspected TB) with high sensitivity [20]. The "1" represents the minimum number of symptoms that must be present in an individual to be classified as a suspected TB patient and the "n" represents the number of symptom(s) in a given rule. For example, a "1-of-3" rule could be a set of symptoms such as current cough, fever, and weight loss, abbreviated here as CFW. An individual with at least one symptom specified in this particular rule would be classified as a TB suspect; an individual without any of these symptoms would be classified as not having TB. We considered all combinations of the five candidate symptoms except for combinations that include both current cough and haemoptysis, yielding a total of 23 candidate rules: two 1-of-4 rules (CFSW, HFSW), seven 1-of-3 rules (CFS, CFW, CSW, HFS, HFW, HSW, FSW), nine 1-of-2 rules (CF, CS, CW, HF, HS, HW, FS, FW, SW), and five 1-of-1 rules (C, H, F, S, W) (see also Table 3.) The analysis with abnormal chest radiographic findings (X) considered these 23 rules, each augmented with this additional sign (e.g., CFSWX).
Other analyses have considered m-of-n rules with m>1 [20]. These rules cannot exceed the sensitivity of 1-of-n rules. Suppose that a positive screen requires the presence of at least two symptoms out of current cough, fever, and night sweats, the number of true positives for this 2-of-3 rule will not be greater than the number of true positives from the corresponding 1-of-3 rules. Because our aim in this analysis was to identify the most sensitive rule, we did not include rules of this kind in our analysis.
We applied two closely related methods for cross-study analysis of sensitivity and specificity of these 23 candidate screening rules: bivariate random-effects meta-analysis (BREMA) and the hierarchical summary relative operating characteristic (HSROC) curve [32],[33]. BREMA jointly models sensitivity and specificity while accommodating between-study heterogeneity, and HSROC models tradeoffs between sensitivity and specificity across study populations. Both methods can be unified in the same model. In addition to sensitivity and specificity, we calculated predictive value negative and likelihood ratio negative of each candidate rule [34]. Our goal was to identify a combination of symptoms that achieved the highest possible sensitivity and the lowest possible negative likelihood ratio for ruling out TB disease (without any predetermined cut-off points).
To further understand between-study heterogeneity and other factors associated with the diagnostic performance of the most sensitive rule, we analyzed several study-level predictors (setting, prior screening of study participants, culture medium used, and geographic region) and participant-level predictors (age, gender, CD4 T-lymphocyte cell count [CD4], and abnormal chest radiographic findings). Our analytic methods produced odds ratios that reflect the magnitude of association between each factor and the probability of correctly identifying persons with TB (sensitivity) or without TB (specificity). For a range of TB prevalence values, we calculated the negative predictive values at levels of each covariate. We calculated the ratio of the number of patients that screen positive but who actually have no TB (false positives) and hence unnecessarily require additional TB diagnostic evaluation (e.g., culture) to the number of patients that screen positive and actually have TB (true positives), which is referred to as the number needed to screen. We calculated this ratio for different rules using a theoretical population of 1,000 people living with HIV with different levels of TB prevalence. This ratio provides proxy information similar to a marginal cost-effectiveness analysis for different screening rules and it helps quantify how much a health program would need to invest (as measured by additional diagnostic tests) for every patient with TB identified through the screening rule [35].
Each observation with a missing covariate value was omitted from analysis of that covariate. BREMA models were fitted using SAS procedure Glimmix (SAS 9.22, SAS Institute), and further calculations were performed in R (R 2.10.1, R Development Core Team).
Ethical Review
All data collection included in the meta-analysis was approved by institutional ethical review boards at the respective institutions during the original study; if necessary, principal investigators requested additional approval from institutional review boards for the inclusion of the primary dataset in the meta-analysis. All data for the meta-analysis were provided completely de-identified. In the meta-analysis dataset, investigators were not able to link case records to individuals.
Investigators provided data about 29,523 participants, of whom 10,057 were people living with HIV. The dataset included 9,626 people living with HIV who had TB screening and sputum culture performed, of whom 8,148 could be evaluated on the five symptoms of interest from nine of 12 studies (Figure 2).
Most patients (77% [7,386/9,626]) were from sub-Saharan Africa; the rest were from Southeast Asian countries. The median age was 34 y (interquartile range [IQR], 27–41 y). Of the 9,626 patients with HIV in the 12 studies, CD4 cell count information was available for 3,489 (36%) and chest radiography information for 3,903 (41%). The median CD4 count was 248 cells/µl (IQR, 107–409).
The overall prevalence of TB disease was 5.8% (557/9,626), ranging across studies from 0.4% to 25.7% (Table 1). More than half of TB patients (52% [288/557]) had sputum smear negative pulmonary TB, whereas 39% (218/557) had sputum smear positive pulmonary, and 5% (28/557) had exclusively extrapulmonary TB. The anatomic site of TB was not specified in 4% (23/557) of patients.
Table 2 summarizes the distribution of common variables, and Table S1 summarizes how each question was actually asked in each study. Because duration of cough was included in many studies but was asked about in different ways, we were able to analyze data using three different cough variables: cough in the past 4 wk (information available for 39.3% of participants); cough lasting for 2 wk or more (information available for 47.1%); and cough present in the last 24 h, which is referred to as "current cough" (information available for 89.6%).
We analyzed the performance of individual and combinations of symptoms as screening rules using data from the 8,148 participants who could be evaluated based on the five candidate symptoms. Table 3 shows the diagnostic performance characteristics for the 23 candidate combinations of symptoms, sorted from highest sensitivity to lowest. The most sensitive rule was the presence of any one of the following symptoms: current cough, fever, night sweats, and weight loss (CFSW). The population-average sensitivity of this symptom combination was 78.9% (95% confidence interval [CI] 58.3%–90.9%) with the negative likelihood ratio of 0.426 (95% CI 0.349–0.520), which corresponds to a postscreening reduction in the probability of TB by 15%–20% [34].
The nine included studies demonstrated significant between-study heterogeneity on both sensitivity (p<0.001) and specificity (p<0.001) of the rule CFSW (see also Figure 3). The bivariate graphic shows that six studies have study-level specificities below and three above the population average specificity. Furthermore, this rule has the highest-ranking sensitivity in eight of the nine included studies (Table S2). The hierarchical summary relative operating characteristic curves (Figure S1) show slightly better overall diagnostic performance of the rules CFS and CF, but our application requires the highest sensitivity possible, allowing for some tradeoffs with lower specificity. Figure 3 shows that three studies are outliers, and they represent studies of patients who were previously screened for TB or studies in which much of the population likely had previous TB screening (e.g., miners); this can modify the performance characteristics of the screening rule.
BREMA, bivariate random-effects meta-analysis; HSROC, hierarchical summary relative operating characteristic.
The CFSW rule has sensitivity of 90.1% (95% CI 76.3%–96.2%) and 67.1% (95% CI 41.7%–85.3%) among participants selected from clinical and community settings, respectively. Similarly the sensitivity of the rule among those who had not been previously screened for TB was higher at 88.0% (95% CI 76.1%–94.4%) compared to those who had been screened for TB at 40.5% (95% CI 16.6%–69.9%). At the 95% confidence level, the sensitivity of this rule could not be statistically distinguished from the sensitivity of the rule that substitutes haemoptysis for current cough (HFSW, 75.7% sensitive [95% CI 53.9–89.2%]) or the rule that drops night sweats (CFW, 74.0% sensitive [51.7–88.3%]). All other rules had lower sensitivity.
Regression analysis of study-level predictors revealed that studies in which TB screening was performed in clinical settings had 4.5 times the odds for a true-positive screening result compared to studies in which TB screening was performed in a community setting (95% CI 1.0–19.5). Studies of participants who had not previously been screened for TB had 10.8 times the odds for a true-positive screen (95% CI 2.4–47.8) compared with studies in which participants had previously been screened for TB. Participants with CD4 cell count <200 cells/µl had 6.4 times the odds of a true-positive screen (95% CI 2.9–14.2). Statistically significant predictors of true-negative results include prescreening, geographic region, participant age ≥33 y, CD4 cell count <200 cells/ml, and abnormal result on chest radiograph (Table 4).
Table 5 shows the negative predictive value and the numbers needed to screen for the CFSW rule adjusted for individual- and study-level covariates. In a setting with 5% TB prevalence among people living with HIV, the rule has a negative predictive value of 98.3% (95% CI 97.5%–98.8%) for patients screened in a clinical setting and 97.3% (95% CI 96.9%–97.7%) for patients screened in a community setting. The numbers needed to screen at the same prevalence of TB are 15 and 11 for clinical and community setting, respectively. The negative predictive value was similar in those having high (≥200) and low (<200) CD4 count at 96.9% (95% CI 95.1%–98.0%) and 98.9% (95% CI 97.5%–99.5%), respectively (see also Table S3).
Four studies [20],[23],[24],[26] consistently recorded information on chest radiograph, allowing screening rules with this sign to be evaluated using data from 2,805 participants The addition of abnormal chest radiographic findings into the CFSW rule increases the sensitivity to 90.6% (95% CI 66.7%–97.9%) with a specificity of 38.9% (95% CI 12.8%–73.3%), and a likelihood ratio negative of 0.242 (95% CI 0.102–0.571). Fifteen of the 23 rules included in our analysis outperform the symptom-based CFSW rule when abnormal chest radiographic findings are added (Table S4).
On the basis of our meta-analysis findings and incorporating current WHO recommendations on provision of IPT, we developed a simple TB screening algorithm for public health programmes to screen people living with HIV, and, depending on the outcome of screening, to either provide IPT or evaluate patients further for TB or other diseases (Figure 4).
* Every person living with HIV needs to be evaluated for ART eligibility, and all settings providing care should reduce TB transmission through proper measures. ** Chest radiography is not required to classify patients into the TB and not-TB groups, but can be done, if available, to increase the sensitivity of screening. *** Assess for contraindications, including active hepatitis (acute or chronic), regular and heavy alcohol consumption, and symptoms of peripheral neuropathy, is required prior to initiating IPT. Past history of TB is not a contraindication for starting IPT. Tuberculin skin test may be performed as part of eligibility screening in some settings. **** Investigations for TB should be done in accordance with existing national guidelines.
We found that the absence of all of current cough, fever, night sweats, and weight loss can identify a subset of people living with HIV who have low probability of having TB disease. This screening rule was superior over other candidate rules in eight of the nine studies included and had an overall favourable performance over competing rules in the hierarchical summary relative operating characteristic (HSROC) analysis. We also demonstrated that the negative predictive value of the rule was high across a range of TB disease prevalence estimates and across different population subsets, including those with low and high CD4 count, and those drawn from clinical and community settings and South African miners. We believe that these screening questions are likely to be acceptable to practitioners, because they are symptoms classically associated with TB disease.
Underdiagnosis and delayed diagnosis of TB contribute to excess mortality among people living with HIV [17]. Similarly, concerns about the ability to reliably rule out active TB before initiating IPT have been a major barrier for wider use of this intervention. In the absence of a rapid and effective TB diagnostic tool available at the point-of-care, simple clinical algorithms must be used to screen people living with HIV for TB, dividing them into those in whom active TB is excluded and those who require further evaluation. This meta-analysis synthesizes the best available evidence for how to do this by relying on individual patient data of culture-confirmed TB cases from people living with HIV in the two regions of the world with the most severe burden of the TB and HIV dual epidemic.
The major change to existing practice would be the replacement of chronic cough with current cough as a screening question and the addition of other symptoms to standard screening. National TB programs have traditionally defined a TB "suspect" as someone with cough lasting greater than 2 or 3 wk, and designed case-finding activities to investigate up to ten TB suspects for every TB case detected [36]. However, studies included in this analysis have shown that chronic cough is highly insensitive for TB disease in people living with HIV; using this symptom as a screening rule would miss cases and contribute to diagnostic delays [16],[20]. Using the combination of symptoms that we propose, in a population of people living with HIV with a 5% TB prevalence (excluding miners), requires the investigation of 13 extra patients for every TB case detected, a ratio of TB suspects to a TB case not much different from what national TB control programmes target in the general population.
There has been ongoing debate about the importance of chest radiography in screening people living with HIV for IPT eligibility [37],[38]. Our analysis showed that the addition of abnormal chest radiography findings into the screening rule of CFSW increases the sensitivity of the rule by 11.7% (90.6% versus 78.9%) with a reduction of specificity by 10.7% (49.6% versus 38.9%). However, for example at a 5% TB prevalence rate among people living with HIV, augmenting the CFSW rule with abnormal chest radiographic findings increases the negative predictive value by a margin of only 1% (98.7% versus 97.8%), albeit with the same number of cases needed to be screened. On the other hand, the addition of abnormal chest radiographic findings to the rule at TB prevalence of 20% among people living with HIV increases the negative predictive value by almost 4% (94.3% versus 90.4%) without additional cases needed to be screened. It is also worth noting that the CFSW screening rule has higher sensitivity among those who presented into a clinical setting (90%) and among those who have not been previously screened for TB (88%).
Our findings show that the utility of the proposed symptom-based screening rule have excellent performance in most settings with TB and HIV burden. However, the negative predictive value will fall in those settings with higher TB prevalence when symptom screening alone is used, as it depends on prevalence of disease. In particular settings (e.g., antiretroviral clinics with a very high TB burden [39]), consideration must be given to use of an algorithm that contains chest radiography, or even adding additional sensitive investigations (e.g., culture) while screening people living with HIV for TB [30],[40]. People living with HIV and receiving IPT should also be regularly screened for TB during their visit to a health facility or contact with health care provider so as to promptly detect active TB, if it develops. Programme managers need to weigh the financial, technical, and logistic difficulties, and patient cost and inconvenience associated with performing chest radiography or other additional sensitive investigations on all people living with HIV as part of a screening program compared with an approach that relies only on symptomatic screening.
When interpreting our results, one must bear in mind that only a few variables were common to all studies included in the meta-analysis. It is possible that the addition of one or more symptoms not included in our list of common symptoms could have improved the performance of our proposed screening rule. However, at least one study included in our meta-analysis explored over 80 million combinations of about 100 signs and symptoms and found a symptom combination (cough and fever of any duration and night sweats for 3 weeks or longer), which was similar to the one we propose as the best performing one [20]. Furthermore, questions were not asked in a uniform manner across all studies, and the reporting of symptoms can be highly dependent on factors such as the quality of the interview and interviewer, the circumstances under which questions are asked, and the social and cultural factors that shape individual perceptions of symptoms and disease [41]. We reviewed all questions carefully with principal investigators and data managers to ensure accurate mapping of differently phrased questions to common variables. Our study relied on patients drawn from multiple countries and multiple settings, and the variation in the performance of the proposed screening rule across these different settings suggests that variation in patient self-report of symptoms is unlikely to have major impact, at least at the population level. In some studies, only one sputum specimen was collected for culture, while multiple cultures are required to maximize sensitivity. Some patients with TB may have been incorrectly classified as not having TB. Extrapulmonary TB is an important cause of morbidity and mortality in people living with HIV, but most studies included in the meta-analysis focused on screening for pulmonary TB. Young children were not included in the studies. We did not specifically look into the role of tuberculin skin test in the proposed screening rule. Ideally, the utility of the algorithm we propose, based on the screening rule from our meta-analysis, should be studied prospectively using a standardized protocol in multiple diverse sites; this is particularly important as the studies included in our analysis came from only two geographical regions of the world. Similarly, because of the time required for the data aggregation, statistical analysis, manuscript preparation, and publication, there was one potentially eligible study that was not included in our analysis [30]. We believe that the exclusion of this single study from South Africa, a country from which we have included similar studies already, will not affect the interpretation of our data and conclusions. In the future, as more studies are reported, particularly from other regions, it will be important to repeat the meta-analysis.
Greatly improving TB screening, diagnosis, and treatment in people living with HIV will require deployment of a rapid, accurate, point-of-care TB diagnostic test. In the absence of such a test, we believe that a standardized algorithm employing symptoms, as we propose here, can improve the diagnosis and treatment of TB for people living with HIV, and by doing so would save many lives. Reliable exclusion of TB disease will facilitate safer initiation of antiretroviral therapy and will allow for broader use of IPT, which can substantially reduce TB incidence. Earlier and accurate HIV and TB screening and treatment may also help identify infectious cases earlier, thereby reducing both HIV and TB transmission. Evidence-based and internationally recommended guidelines should be used to expedite the diagnosis and treatment of TB in people living with HIV [42].
Supporting Information
Figure S1.
Hierarchical summary relative operating characteristic (HSROC) curves for the 23 candidate 1-of-n rules.
Table S1.
Phrasing of questions that were used in all studies to ask about five common symptoms.
Table S2.
Study-specific values and rankings of the sensitivity of each candidate screening rule in the nine studies included.
Table S3.
Diagnostic performance of all 23 candidate rules and number needed to screen in a hypothetical population of 1,000 people living with HIV stratified by TB prevalence among people living with HIV.
Table S4.
Diagnostic performance of 23 candidate rules that include abnormal chest radiograph and number needed to screen in a hypothetical population of 1,000 people living with HIV stratified by TB prevalence among people living with HIV.
The following data managers of the included studies provided invaluable help during the data analysis: Tsitsi Bandason, Carl Morrow, and Ab Schaap.
Author Contributions
ICMJE criteria for authorship read and met: HG WK CMH ELC HA KPC ADG GJC MK SS SDL RW GM RG AAD JKV. Agree with the manuscript's results and conclusions: HG WK CMH ELC HA KPC ADG GJC MK SS SDL RW GM RG AAD JKV. Designed the experiments/the study: HG CMH KPC GJC MK JKV. Analyzed the data: HG WK CMH KPC GJC SS AAD JKV. Collected data/did experiments for the study: HG ELC HA ADG GJC MK SS SDL RW GM JKV. Enrolled patients: ELC HA ADG GJC MK SS SDL RW GM. Wrote the first draft of the paper: HG JKV. Contributed to the writing of the paper: WK CMH ELC HA KPC ADG GJC MK SS SDL RW GM RG AAD JKV. Conducted the data retrieval and cleaning: WK. Designed the principal analytic method, supervised overall statistical analysis, and aided with interpretation of its results: CMH. Responsible for literature review to identify studies to be included in the meta-analysis: AAD. Member of the team to develop and finalize the data analysis plan: AAD.
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Posted December 5, 2010, 6:36 PM.The latest outlook from NOAA's Climate Prediction Center forecasts that December will be warmer and drier than normal through December. The drier than normal forecast is expected to hold through the winter, but longer term, temperatures are predicted to be cooler than normal. The predictions are largely based on cooler than normal temperatures in the equatorial Pacific Ocean, a condition called La Nina. La Nina decreases chances of seasonal rainfall in Southern California. For December, the chances of below normal rainfall was 33%. Likewise, the chances of above normal temperatures was 33%, except for along the coastline. In spite of predictions, rainfall for the season to date has been at or above normal in most inland valley locations. Temperatures have generally run below normal. Average rainfall for December is nearly double November's, usually running between 1.66" and 2.88". Along with January and February, December is typically one of the wettest months of the year. Average high temperatures are significantly colder, in the middle to upper 60's. Average low temperatures are in the upper 30's to middle 40's. Here's what's normal for the month of December, by community:
Note: normals are used for communities with five or more years of data. |
Other Recent Weather News for French Valley, California


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Press Release
NADCA Warns of Possible Health Dangers Left Behind by Hurricane Ike in Region's Ventilation Systems
WASHINGTON, DC--October 8, 2008-- NADCA -- The HVAC Inspection, Maintenance and Restoration Association, is alerting homeowners, commercial property managers, school boards and other facility operators of hidden health hazards potentially spreading in the ventilation systems of buildings affected by Hurricane Ike.
Heating, ventilation and air conditioning (HVAC) systems that were either flooded or simply exposed to unusual humidity or other environmental conditions during the hurricane may become breeding grounds for mold or other contaminants. While most molds are relatively harmless and usually cause little worse than hay fever-type symptoms, individuals with health conditions such as asthma, compromised auto-immune systems or severe mold allergies may experience potentially life-threatening reactions caused by mold spores spread throughout a building by a contaminated HVAC system.
The U.S. Occupational Safety & Health Administration (OSHA) has cautioned building managers that even the suspicion of mold in an HVAC system should be enough to get the system professionally inspected and cleaned if necessary. "Do not run the HVAC system if you know or suspect that it is contaminated with mold, as it could spread contamination throughout the building," says OSHA's "Brief Guide to Mold in the Workplace." (SHIB 03-10-10) The Centers for Disease Control agree, telling building owners that "…all components of the HVAC system that were contaminated with flood water or moisture should be thoroughly inspected, cleaned of dirt and debris, and disinfected by a qualified professional." While a simple visual inspection performed by removing a vent and looking into the ductwork for signs of mold is a good first step, molds grow best in the damp and hidden corners deep within HVAC ductwork, out of sight of the average homeowner or building manager. Generally, only an HVAC inspection professional can truly determine if an HVAC system is mold-free.
In Texas, HVAC inspection, maintenance and remediation professionals are required to be state-licensed Air Conditioning and Refrigeration Contractors. NADCA recommends that consumers choose licensed contractors that have also completed NADCA's industry-leading certification programs that ensure best practices and adherence to NADCA-developed standards. In addition to a NADCA-certified Air System Cleaning Specialist, consumers can also choose service providers who have completed extra training and testing and fulfill ongoing professional education requirements to become Certified Ventilation Inspectors or Certified Ventilation System Mold Remediators. More information on these certifications, as well as contact information for certified service providers in specific areas, can be found at www.NADCA.com. Texas Department of Licensing and Regulation contractor licensing information is available at http://www.license.state.tx.us/ or by calling 800-803-9202.
"With the terrible devastation caused by Hurricane Ike, our concern is that the 'out of sight, out of mind' issue of HVAC contamination may get lost in among every other legitimate cleanup and rebuilding project," explained NADCA Executive Director John Schulte. "We just want to make sure that the people of the Gulf Coast are aware of how to evaluate their risks for contaminated HVAC systems and know how to make contact with a licensed, knowledgeable and reliable professional if their situation requires it."
Hurricane Ike is not the first time that NADCA has reached out to educate consumers and others on the issues of post-disaster HVAC contamination. Most recently, following the record floods in Iowa in June 2008, NADCA experts performed free training for city building inspectors and other health and safety professionals in the affected region to help them recognize signs of HVAC contamination during their post-flood cleanup and inspection efforts.
About NADCA:
NADCA – The HVAC Inspection, Maintenance and Restoration Association was formed in 1989 as the National Air Duct Cleaners Association, and has since expanded its mission to become a trusted advocate for consumers and the industry on environmental and health issues surrounding heating, ventilation and air conditioning (HVAC) systems. NADCA was the first organization to develop industry-standard best practices and its "Standard for Assessment, Cleaning & Restoration of HVAC Systems" is in use in more than 30 countries worldwide as a best practice and/or basis for national law. NADCA has more than 1,000 corporate members and more than 1,500 individuals certified as Air System Cleaning Specialists, Ventilation Inspectors or Ventilation System Mold Remediators. For more information, visit www.NADCA.com
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