{'fraction_non_alphanumeric': 0.02437477502185502, 'fraction_numerical': 0.008382042887262381, 'mean_word_length': 4.835167033406681, 'pattern_counts': {'":': 0, '<': 2, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 1, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Burden of heart failure in Flemish general practices: a registry-based study in the Intego database

Objectives To assess the prevalence and incidence of heart failure (HF) stages A to C/D and their evolution over a 16-year period. Additionally, trends in comorbidities and cardiovascular (CV) treatment in patients with HF were studied in the same period. Design Registry-based study. Setting Primary care, Flanders, Belgium. Participants Data were obtained from Intego, a morbidity registration network in which 111 general practitioners of 48 practices collaborate. In the study period between 2000 and 2015, data from 165 796 unique patients aged 45 years and older were available. Outcome measures Prevalence and incidence were calculated for HF stage A, B and C/D by gender. Additionally, the trend in age-standardised prevalence and incidence rates between 2000 and 2015 was analysed with joint-point regression. The same model was used to study trends in comorbidity profiles in incident HF cases and trends in cardiovascular medication in prevalent HF cases. Results We found a downward trend in the incidence and prevalence of HF stage C/D in Flemish general practice between 2000 and 2015, whereas the prevalence and incidence of stage A and B increased. The burden of comorbidities in incident HF cases increased during the study period, as shown by an increasing disease count (p<0.001). The prescription of cardiovascular medication such as renin-angiotensin-aldosterone system blockade, β-blockers and statins showed a sharp increase in the first part of the study period (2000–2008). Conclusion Age-standardised incidence and prevalence of HF stage C/D showed a slightly downward trend over the past 16 years, probably due to the sharp increase in cardiovascular treatment. However, the increasing age-standardised incidence and prevalence of stage A and B, as precursors of symptomatic HF, together with a rising comorbid burden, highlights the challenges we are still facing.

years of risk are determined when there is not a registered population. The cohort study needs to be described using RECORD guidelines for reporting analysis of routine data. The cohort should have a description of the population under study (inclusion and exclusion criteria), the follow up, censoring events and definitions of outcome events. Outcome events need to be defined more clearly. Background The introduction focuses on multimorbidity but does not mention similar estimates of the prevalence and incidence of heart failure using electronic primary care records. Two recent (UK) references are mentioned below but there are probably more estimates of the incidence and prevalence of heart failure to consider. Conrad N et al Temporal trends and patterns in heart failure incidence: a population-based study of 4 million individuals. Lancet. 2017 Nov 21. pii: S0140-6736(17)32520-5. doi: 10.1016/S0140-6736(17)32520-5. Taylor CJ, Ryan R, Nichols L, Gale N, Hobbs R, Marshall T. Survival following a diagnosis of heart failure in primary care Family Practice 2017 Jan 30. pii: cmw145. doi: 10.1093/fampra/cmw145. Methods The Intego database is described but some detail is missing. It would be helpful to know how data are uploaded to the Intego database from participating practices. Is this in real time or periodically (e.g. daily, weekly, monthly)? The population denominator is not explained. I understand that unlike the Netherlands, patients in Belgium do not register with a GP or a primary care provider (general practice). This means there is not a registered population to be used as the denominator. The authors need to explain how the population denominator is determined for prevalence estimates and the person-years at risk for incidence estimates. Two separate studies are required to estimate prevalence and incidence, but this is not entirely clear. Prevalence is estimated from a series of cross-sectional studies. Incidence from following up a cohort over a period of time. There is no explanation of how patients enter a cohort study, whether it is an open or closed cohort, the age range, the exclusion criteria (e.g. existing heart failure), the dates on which follow up began, the censoring events (e.g. death, leaving database, last data upload, developing an outcome event). For example if hypertension is a criterion for becoming an incident case of HF stage A, this would be an exclusion criteria for a cohort to estimate incidence of HF stage A. Heart failure stages could be explained more clearly. For example it is not immediately apparent that stage A is not heart failure at all but the presence of any of a number of common cardiovascular risk factors. I am not very convinced that HF stage A or B are very useful measures in themselves. Heart failure risk factors are listed including hypertension, diabetes mellitus, obesity, and atherosclerotic diseases with exclusion of myocardial infarction. More detail should be provided on how these are defined. For example is hypertension defined as anyone with a clinical code indicating hypertension; anyone receiving an antihypertensive medication; anyone whose most recent blood pressure is above a threshold (e.g. 160/100 mm Hg); or some combination of these criteria. The definition of "on medication" as a single prescription in a calendar year seems very broad. Is there a reason for using such a broad definition? Results These are reported reasonably clearly. Essentially there is a rise in stage A (cardiovascular risk factors) and stage B (diagnosed cardiac impairment) but a fall in stages C/D (symptomatic heart failure). Discussion Could comment on the treatment of HF in the light of the evidence: RAAS antagonists and beta-blockers have an evidence base in HF with reduced ejection fraction; statins do not. The treatment of HF with preserved ejection fraction is less clear cut. Some comment on how the denominator is calculated needs to be included.
The manuscript is well written and of interest. this is the first time to my knowledge that anyone has examined temporal trends in the different stages of HF. I have a number of comments that will need to be addressed in the manuscript: 1) Has anyone validated diagnostic codes in the this dataset as that will be key. For example has anyone undertaken a note review in a practice to see how well / robust the coding for the various conditions are? Are other diagnostic codes such as ICD-9 captured in any of the practices? would it be possible to look for consistency at least between icd-9 codes and ICPC-2 codes?
2) It is not clear from the methods what inclusion criteria were used for entry into the study. this is particularly important in describing incidences. For example did the patients need to be registered to a practice for a certain period of time before they could be included? ie 1 or 2 years? How long did a patient have to be registered at a practice for a new HF code to be counted as an "incident" code. For example patient could be newly registered at a practice and a new code of HF entered into their EHR. This wouldnt be an incident diagnosis of HF. Can patients be tracked across practices?
3) There are too many assumptions made for the diagnosis of charlson score, for example connective tissue could not be assessed from the registry and the differentiation between cancers with or without mets, DM with or without end organ damage and mild or moderate to severe liver disease could not be made. i feel that these assumptions do not lead to any robust calculation of charlson score and any score reported is significantly flawed. I would suggest removing charlson score data and just listing the number of comorbid conditions, which would allow a broader number of conditions to be considered, and arguably proivide more interesting information.
4) The temporal trends in medications could be explained by differences in the prevalances of HPEF vs HFREF over time. The authors need to discuss this more. Patients may not be precribed ACEi, B-Blockers etc as pts diagnosed with HFPEF. Are cardiac devices coded in this dataset? i think it would be interesting to look at device changes over time such as CRT/ICD implantation. 5) I am not convinced that cardiomyopathy should be coded as a stage B subtytpe of HF. should probably coded as Stage C.
Overall an interesting manuscript that provides new information. . Another important issue is the fact that only data from practices with optimal registration were included in the registry induce a huge doubt of the quality of the data.

REVIEWER
Epidemiological study but I think the results should be presented differently with more discussion and less affirmative conclusions.

Reviewer 1:
Overall this is an interesting paper but the methods are not clearly explained as a result it is hard to know what limitations of the analysis might be. 1. The authors need to explain more about the Intego database and in particular to explain how the population denominator and person years of risk are determined when there is not a registered population.
Response: The authors fully agree with this important remark. This is indeed a challenge in countries, such as Belgium, were patients are not registered with a particular GP. In the past years this issue was extensively studied, specifically for the INTEGO database, and described by Bartholomeeusen  . We used the yearly contact group (YCG) as the denominator in our study. These are the patients who had at least one contact with their GP in a given year. In Belgium, former research has shown that this is approximately 80% of the total practice population.1 Given the importance of this remark the Method section was revised to clarify how the denominator was calculated in the INTEGO database. We calculated the cumulative incidence in a given year based on the yearly contact group minus the prevalent cases on January 1st of that year (Supplemental file 2 added to clarify how we did this). "The denominator was the yearly contact group. These are the patients that visited the practice at least once in a given year.1 2" 2. The cohort study needs to be described using RECORD guidelines for reporting analysis of routine data.
Response: The authors thank the reviewer for this useful remark. The RECORD guidelines were followed and the RECORD checklist was added.
3. The cohort should have a description of the population under study (inclusion and exclusion criteria), the follow up, censoring events and definitions of outcome events. Outcome events need to be defined more clearly.
Response: The authors thank the reviewer for this important question because it led to an important clarification. We wrongfully described our study as a cohort study. However, INTEGO is a general practice registry and analyses from the INTEGO database should be described as registry-based studies. There are no inclusion and exclusion criteria for patients in the registry. Furthermore, INTEGO is a dynamic registry, so GP practices and patients come and go. In total, the INTEGO database contains approximately 440.000 unique patients, and approximately 120.000 unique patients that have at least one contact with their GP in a specific year. For each year the yearly contact group is determined (our denominator). In each yearly contact group the prevalence and incidence of the HF stages was determined by searching on the according ICPC-2 codes following a predefined flowchart (Supplemental file 2).
The title (and abstract) was adapted to clarify this ambiguity. "The Burden of Heart Failure in Flemish General Practices A registry-based study in the INTEGO database" 4. Background The introduction focuses on multimorbidity but does not mention similar estimates of the prevalence and incidence of heart failure using electronic primary care records. Two the incidence and prevalence of heart failure to consider. Response: The authors thank the reviewer for this helpful feedback. The Background section was revised to integrate what is already known about trends in heart failure incidence and prevalence in the community. Indeed, there are more estimates to consider, and we chose to discuss this more extensively in the Discussion section. Revised Background section: "Recently, a large population-based study confirmed the rising burden of HF stage C/D, highlighting the need of prevention3, however, general practice data about the number of patients at risk for HF and evolution of the CV burden over time are limited."

Methods
The Intego database is described but some detail is missing. It would be helpful to know how data are uploaded to the Intego database from participating practices. Is this in real time or periodically (e.g. daily, weekly, monthly)?
Response: The authors thank the reviewer for this interesting comment. The following information was added to the Method section to clarify this matter: "Using specially framed extraction software, new data were encrypted and collected from the GPs' personal computers and entered in a central database on a yearly basis." 6. The population denominator is not explained. I understand that unlike the Netherlands, patients in Belgium do not register with a GP or a primary care provider (general practice). This means there is not a registered population to be used as the denominator. The authors need to explain how the population denominator is determined for prevalence estimates and the person-years at risk for incidence estimates.
Response: The authors fully agree with this important remark. This is indeed a challenge in countries, such as Belgium, were patients are not registered with a particular GP. In the past years this issue was extensively studied, specifically for the INTEGO database, and described by Bartholomeeusen  . We used the yearly contact group (YCG) as the denominator in our study. These are the patients who had at least one contact with their GP in a given year. In Belgium, former research has shown that this is approximately 80% of the total practice population.1 Given the importance of this remark the Method section was revised to clarify how the denominator is calculated in the INTEGO database. We calculated the cumulative incidence in a given year based on the yearly contact group minus the prevalent cases on January 1st of that year (Supplemental file 2 added to clarify how we did this).
"The denominator was the yearly contact group. These are the patients that visited the practice at least once in a given year.1 2" 7. Two separate studies are required to estimate prevalence and incidence, but this is not entirely clear. Prevalence is estimated from a series of cross-sectional studies. Incidence from following up a cohort over a period of time. There is no explanation of how patients enter a cohort study, whether it is an open or closed cohort, the age range, the exclusion criteria (e.g. existing heart failure), the dates on which follow up began, the censoring events (e.g. death, leaving database, last data upload, developing an outcome event). For example if hypertension is a criterion for becoming an incident case of HF stage A, this would be an exclusion criteria for a cohort to estimate incidence of HF stage A.
Response: The authors thank the reviewer for these important questions. Hopefully, some of these questions were already clarified in the above-mentioned answers. We wrongfully described this study as a cohort study, however it is a registry-based study, and an open registry. New patients can consult participating GPs each year and by doing so enter the INTEGO database. There is no age range, however we focused in our study on patients aged 45 and older because younger HF patients often have different HF etiology. As mentioned above, each year the yearly contact group (YCG) was determined (denominator) and in this YCG a predefined flowchart was followed (supplemental file 2). For example, in the YCG of 2000, first all patients with a K77 ICPC-2 code before 01/01/2000 were identified (=prevalent HF patients). Subsequently, in the remaining patients first the prevalence of HF stage B was determined by searching on ICPC-2 codes K75-K83-K84 and within this group new K77 diagnoses between 01/01/2000 and 31/12/2000 were identified being incident HF cases. By following the flowchart the prevalence and incidence of all HF stages could be determined and this procedure was repeated in each YCG. One of the limitations of INTEGO is the lack of data on mortality. Therefore, patients in the yearly contact group were considered at risk until the diagnosis or until December 31th of that specific year to calculate the incidence. The latter was added to the Discussion section as a limitation.
8. Heart failure stages could be explained more clearly. For example it is not immediately apparent that stage A is not heart failure at all but the presence of any of a number of common cardiovascular risk factors. I am not very convinced that HF stage A or B are very useful measures in themselves. Response: The authors thank the reviewer for this helpful suggestion. The Method section was revised to clarify the concept of HF stage A. However, we do believe in the usefulness of HF stage A and B, especially on a population level. The concept of the HF stages is proposed by the ACCF/AHA guideline to emphasize the development and progression of the disease, in line with the concept of the cardiovascular continuum. 4 5 The strength of this concept is that progression in HF stages was associated with reduced 5-year survival and increased plasma natriuretic peptide concentrations. 6 Additionally, it has interesting therapeutic implications, facilitating the identification of patients at risk for HF/in need of prevention, which is important for pro-active population based management. 5 7 HF stage 0 was defined as absence of HF risk factors in healthy subjects. 5 HF stage A was defined as the presence of HF risk factors (cardiovascular risk factors) without a structural or functional cardiac abnormality. 5 In accordance with previous studies, only risk factors that have been shown to be predictive of HF in longitudinal studies were used (hypertension, diabetes mellitus, obesity and atherosclerotic diseases with exclusion of myocardial infarction).6 However, obesity could not reliably be assessed from the registry. HF stage B was defined as structural or functional heart disease without HF symptoms including acute myocardial infarction, heart valve disease, cardiomyopathy or tachyarrhythmia.5 9. Heart failure risk factors are listed including hypertension, diabetes mellitus, obesity, and atherosclerotic diseases with exclusion of myocardial infarction. More detail should be provided on how these are defined. For example is hypertension defined as anyone with a clinical code indicating hypertension; anyone receiving an antihypertensive medication; anyone whose most recent blood pressure is above a threshold (e.g. 160/100 mm Hg); or some combination of these criteria.
Response: The authors thank the reviewer for this useful remark. The Method section was revised to provide more detail on how patients with HF stage A or B were identified.
"Patients with HF stage A or B were identified based on an ICPC-2 coded diagnosis in their Electronic Medical Record (EMR). An overview of all used codes can be found in Supplement 1." 10. The definition of "on medication" as a single prescription in a calendar year seems very broad. Is there a reason for using such a broad definition?
Response: The authors fully agree with the reviewer on this remark. First, we used the following definition for chronic medication: "medication that is prescribed at least three times/year with the last prescription in the last 6 months in the year after". However, by looking at patient profiles including several years, we noticed that patients who were chronically prescribed the same medication did not meet this definition in some years, because they received only one or two prescriptions that year, followed by three prescriptions the next year. INTEGO only registers the electronic prescriptions made by GPs. But Belgian GPs also prescribe medication manually when they visit patients in longterm-care facilities or at home, and other doctors, like cardiologists, could have prescribed medication. These prescriptions are not registered in the INTEGO database. Consequently, a too strict definition did not reflect clinical reality in this case. Therefore, we opted for a more broad definition.
11. Results. These are reported reasonably clearly. Essentially there is a rise in stage A (cardiovascular risk factors) and stage B (diagnosed cardiac impairment) but a fall in stages C/D (symptomatic heart failure).
Response: The authors thank the reviewer for the thorough reading of our paper. This is indeed our main message.
12. Discussion. Could comment on the treatment of HF in the light of the evidence: RAAS antagonists and betablockers have an evidence base in HF with reduced ejection fraction; statins do not. The treatment of HF with preserved ejection fraction is less clear cut.
Response: The authors thank the reviewer for this helpful feedback. The Discussion section was revised to clarify this statement.
This could be explained by the lack of differentiation between HF with reduced ejection fraction (HFrEF) and HF with preserved ejection fraction (HFpEF), since no evidence-based treatment options exist for the latter. … In general, in the period we studied, the use of medications influencing survival in HFrEF patients (RAAS-blockade, B-blockers) has increased whereas the use of medications that were demonstrated not to improve survival (cardiac glycosides) has decreased.
13. Some comment on how the denominator is calculated needs to be included.
Response: The authors fully agree with the reviewer on this suggestion. The Discussion section was revised to discuss this limitation.
However, this study also has a few limitations. Patients are not registered with a particular GP in Belgium. The current study used the YCG as the denominator. Former research has shown that the YCG accounts for 80% of the total practice population. 1 The YCG is not the perfect denominator but it is the most realistic approach in countries without capitation. Additionally, one can assume that most HF patients will visit their GP at least once a year.
The authors want to thank the reviewer for this very useful and constructive review.

Reviewer 2:
The authors have undertaken a primary care study in Belgium using the Intego dataset to study the prevalence and incidence of heart failure (HF) stages A to C/D and their evolution over 15 yrs using electronic health data derived from 48 practices and 165,000 pts over the age of 45. The authors also studied trends in comorbidities and CV treatments over the same period. The manuscript is well written and of interest. This is the first time to my knowledge that anyone has examined temporal trends in the different stages of HF. I have a number of comments that will need to be addressed in the manuscript: 1) Has anyone validated diagnostic codes in this dataset, as that will be key. For example has anyone undertaken a note review in a practice to see how well / robust the coding for the various conditions are? Are other diagnostic codes such as ICD-9 captured in any of the practices? Would it be possible to look for consistency at least between icd-9 codes and ICPC-2 codes? Response: The authors thank the reviewer for this important remark. This is indeed crucial in a registry such as INTEGO. It is described in full in the publication by Truyers et al: the INTEGO database: background, methods and basic results of a Flemish general practice-based continuous morbidity registration network.2 In short, external validation of the INTEGO database has been examined by means of national and international comparisons (this sentence has been added to the Method section to clarify this matter for all readers). For example, overall cancer incidence was compared to the Limburg Cancer Registry (LIKAR), incidence rates of influenza and acute respiratory illness were compared to the European Influenza Surveillance Scheme (EISN), and all data is routinely compared to the tweede nationale studie, studying morbidity and care in Dutch general practice. Additionally, all INTEGO practices were Medidoc ® practices in our study period, because this was at that time one of the few electronic medical packages that facilitated routine input of coded data. The general practitioner records all data using keywords in a predetermined field of the electronic medical record (EMR). Each of the 67.500 keywords is associated with a unique programspecific internal code and can be linked to classifications as ICPC-2 and ICD-10. This linkage process is automated and based on a validated codebook. Looking at internal consistency between these codes therefore has little added value.
That these codes are also linked to ICD-10 was added to the Method section. New diagnoses were classified according to a very detailed thesaurus (Medidoc ® codes) automatically linked to the International Classification of Primary Care (ICPC-2) and International Statistical Classification of Diseases and Related Health Problems 10th Revision (ICD-10) 2) It is not clear from the methods what inclusion criteria were used for entry into the study. This is particularly important in describing incidences. For example did the patients need to be registered to a practice for a certain period of time before they could be included? ie 1 or 2 years? How long did a patient have to be registered at a practice for a new HF code to be counted as an "incident" code. For example patient could be newly registered at a practice and a new code of HF entered into their EHR. This wouldn't be an incident diagnosis of HF. Can patients be tracked across practices?
Response: The authors thank the reviewer for this important remark. The excellent remarks of the reviewers led to an important clarification. We wrongfully described our study as a cohort study in the title section while it is a registry-based study. Our denominator is the yearly contact group (YCG 3) There are too many assumptions made for the diagnosis of charlson score, for example connective tissue could not be assessed from the registry and the differentiation between cancers with or without metas, DM with or without end organ damage and mild or moderate to severe liver disease could not be made. i feel that these assumptions do not lead to any robust calculation of charlson score and any score reported is significantly flawed. I would suggest removing charlson score data and just listing the number of comorbid conditions, which would allow a broader number of conditions to be considered, and arguably proivide more interesting information.
Response: The authors understand the reviewer's concern. However, the mCCI was not studied for the first time. It was extensively studied and validated by Boeckxstaens et al in the BELFRAIL cohort8 and compared to other comorbidity measures, like a disease count. Even with an incomplete score, the mCCI remains a reflection of the burden of comorbidity in HF patients. Additionally, the changes in the score during the past 16 years concern us most. The trend analysis of the mCCI is probably not, to only mildly, affected by the missing variables.
4) The temporal trends in medications could be explained by differences in the prevalances of HPEF vs HFREF over time. The authors need to discuss this more. Patients may not be precribed ACEi, B-Blockers etc as pts diagnosed with HFPEF. Are cardiac devices coded in this dataset? i think it would be interesting to look at device changes over time such as CRT/ICD implantation. Response: The authors thank the reviewer for this useful remark. The Discussion section was revised to add this interesting suggestion. Temporal trends in CRT/ICD implantation would indeed be of great interest but this is unfortunately not coded in the INTEGO database. This could be explained by the lack of differentiation between HF with reduced ejection fraction (HFrEF) and HF with preserved ejection fraction (HFpEF), since no evidence-based treatment options exist for the latter. Additionally, the prevalence of HFpEF keeps increasing, especially in the community, compared to a decreasing HFrEF prevalence.9 5) I am not convinced that cardiomyopathy should be coded as a stage B subtype of HF. should probably coded as Stage C.
Response: The authors understand the reviewer's concern. However, cardiomyopathy is coded as ICPC-2 code K84 (Heart disease other) and the matching program-specific internal code is hypertensive cardiomyopathy, being a synonym for structural cardiac changes due to hypertension, fitting perfectly into the HF stage B subtype.
Overall an interesting manuscript that provides new information.
The authors want to thank the reviewer for this very useful and constructive review.
To the Editor in Chief of BMJ Open Dr. Trish Groves Leuven, Belgium, Dear Dr. Groves, We are pleased to send you a revised version of our manuscript "The burden of heart failure in Flemish general practices: a retrospective cohort study in the INTEGO database". Thank you for forwarding your suggestion and the suggestions of the reviewers to improve our manuscript.
We have made a revision of our paper in response to these suggestions and have provided a detailed description of our responses as below.
Thank you for your continued consideration of our manuscript.
Best regards, Miek Smeets On behalf of all authors: Bert Vaes, Pavlos Mamouris, Marjan Van den Akker, Gijs Van Pottelbergh, Geert Goderis, Stefan Janssens, Bert Aertgeerts, Séverine Henrard Reviewer 1: Overall this is an interesting paper but the methods are not clearly explained as a result it is hard to know what limitations of the analysis might be. 1. The authors need to explain more about the Intego database and in particular to explain how the population denominator and person years of risk are determined when there is not a registered population. Response: The authors fully agree with this important remark. This is indeed a challenge in countries, such as Belgium, were patients are not registered with a particular GP. In the past years this issue was extensively studied, specifically for the INTEGO database, and described by Bartholomeeusen  . We used the yearly contact group (YCG) as the denominator in our study. These are the patients who had at least one contact with their GP in a given year. In Belgium, former research has shown that this is approximately 80% of the total practice population.1 Given the importance of this remark the Method section was revised to clarify how the denominator was calculated in the INTEGO database. We calculated the cumulative incidence in a given year based on the yearly contact group minus the prevalent cases on January 1st of that year (Supplemental file 2 added to clarify how we did this). "The denominator was the yearly contact group. These are the patients that visited the practice at least once in a given year.1 2" 2. The cohort study needs to be described using RECORD guidelines for reporting analysis of routine data.
Response: The authors thank the reviewer for this useful remark. The RECORD guidelines were followed and the RECORD checklist was added.
3. The cohort should have a description of the population under study (inclusion and exclusion criteria), the follow up, censoring events and definitions of outcome events. Outcome events need to be defined more clearly.
Response: The authors thank the reviewer for this important question because it led to an important clarification. We wrongfully described our study as a cohort study. However, INTEGO is a general practice registry and analyses from the INTEGO database should be described as registry-based studies. There are no inclusion and exclusion criteria for patients in the registry. Furthermore, INTEGO is a dynamic registry, so GP practices and patients come and go. In total, the INTEGO database contains approximately 440.000 unique patients, and approximately 120.000 unique patients that have at least one contact with their GP in a specific year. For each year the yearly contact group is determined (our denominator). In each yearly contact group the prevalence and incidence of the HF stages was determined by searching on the according ICPC-2 codes following a predefined flowchart (Supplemental file 2).
The title (and abstract) was adapted to clarify this ambiguity. "The Burden of Heart Failure in Flemish General Practices A registry-based study in the INTEGO database" 4. Background The introduction focuses on multimorbidity but does not mention similar estimates of the prevalence and incidence of heart failure using electronic primary care records. Two the incidence and prevalence of heart failure to consider. Response: The authors thank the reviewer for this helpful feedback. The Background section was revised to integrate what is already known about trends in heart failure incidence and prevalence in the community. Indeed, there are more estimates to consider, and we chose to discuss this more extensively in the Discussion section. Revised Background section: "Recently, a large population-based study confirmed the rising burden of HF stage C/D, highlighting the need of prevention3, however, general practice data about the number of patients at risk for HF and evolution of the CV burden over time are limited."

Methods
The Intego database is described but some detail is missing. It would be helpful to know how data are uploaded to the Intego database from participating practices. Is this in real time or periodically (e.g. daily, weekly, monthly)?
Response: The authors thank the reviewer for this interesting comment. The following information was added to the Method section to clarify this matter: "Using specially framed extraction software, new data were encrypted and collected from the GPs' personal computers and entered in a central database on a yearly basis." 6. The population denominator is not explained. I understand that unlike the Netherlands, patients in Belgium do not register with a GP or a primary care provider (general practice). This means there is not a registered population to be used as the denominator. The authors need to explain how the population denominator is determined for prevalence estimates and the person-years at risk for incidence estimates.
Response: The authors fully agree with this important remark. This is indeed a challenge in countries, such as Belgium, were patients are not registered with a particular GP. In the past years this issue was extensively studied, specifically for the INTEGO database, and described by Bartholomeeusen  We calculated the cumulative incidence in a given year based on the yearly contact group minus the prevalent cases on January 1st of that year (Supplemental file 2 added to clarify how we did this).
"The denominator was the yearly contact group. These are the patients that visited the practice at least once in a given year.1 2" 7. Two separate studies are required to estimate prevalence and incidence, but this is not entirely clear. Prevalence is estimated from a series of cross-sectional studies. Incidence from following up a cohort over a period of time. There is no explanation of how patients enter a cohort study, whether it is an open or closed cohort, the age range, the exclusion criteria (e.g. existing heart failure), the dates on which follow up began, the censoring events (e.g. death, leaving database, last data upload, developing an outcome event). For example if hypertension is a criterion for becoming an incident case of HF stage A, this would be an exclusion criteria for a cohort to estimate incidence of HF stage A.
Response: The authors thank the reviewer for these important questions. Hopefully, some of these questions were already clarified in the above-mentioned answers. We wrongfully described this study as a cohort study, however it is a registry-based study, and an open registry. New patients can consult participating GPs each year and by doing so enter the INTEGO database. There is no age range, however we focused in our study on patients aged 45 and older because younger HF patients often have different HF etiology. As mentioned above, each year the yearly contact group (YCG) was determined (denominator) and in this YCG a predefined flowchart was followed (supplemental file 2). For example, in the YCG of 2000, first all patients with a K77 ICPC-2 code before 01/01/2000 were identified (=prevalent HF patients). Subsequently, in the remaining patients first the prevalence of HF stage B was determined by searching on ICPC-2 codes K75-K83-K84 and within this group new K77 diagnoses between 01/01/2000 and 31/12/2000 were identified being incident HF cases. By following the flowchart the prevalence and incidence of all HF stages could be determined and this procedure was repeated in each YCG. One of the limitations of INTEGO is the lack of data on mortality. Therefore, patients in the yearly contact group were considered at risk until the diagnosis or until December 31th of that specific year to calculate the incidence. The latter was added to the Discussion section as a limitation.
8. Heart failure stages could be explained more clearly. For example it is not immediately apparent that stage A is not heart failure at all but the presence of any of a number of common cardiovascular risk factors. I am not very convinced that HF stage A or B are very useful measures in themselves. Response: The authors thank the reviewer for this helpful suggestion. The Method section was revised to clarify the concept of HF stage A. However, we do believe in the usefulness of HF stage A and B, especially on a population level. The concept of the HF stages is proposed by the ACCF/AHA guideline to emphasize the development and progression of the disease, in line with the concept of the cardiovascular continuum. 4 5 The strength of this concept is that progression in HF stages was associated with reduced 5-year survival and increased plasma natriuretic peptide concentrations. 6 Additionally, it has interesting therapeutic implications, facilitating the identification of patients at risk for HF/in need of prevention, which is important for pro-active population based management. 5 7 HF stage 0 was defined as absence of HF risk factors in healthy subjects. 5 HF stage A was defined as the presence of HF risk factors (cardiovascular risk factors) without a structural or functional cardiac abnormality.5 In accordance with previous studies, only risk factors that have been shown to be predictive of HF in longitudinal studies were used (hypertension, diabetes mellitus, obesity and atherosclerotic diseases with exclusion of myocardial infarction).6 However, obesity could not reliably be assessed from the registry. HF stage B was defined as structural or functional heart disease without HF symptoms including acute myocardial infarction, heart valve disease, cardiomyopathy or tachyarrhythmia.5 9. Heart failure risk factors are listed including hypertension, diabetes mellitus, obesity, and atherosclerotic diseases with exclusion of myocardial infarction. More detail should be provided on how these are defined. For example is hypertension defined as anyone with a clinical code indicating hypertension; anyone receiving an antihypertensive medication; anyone whose most recent blood pressure is above a threshold (e.g. 160/100 mm Hg); or some combination of these criteria.
Response: The authors thank the reviewer for this useful remark. The Method section was revised to provide more detail on how patients with HF stage A or B were identified.
"Patients with HF stage A or B were identified based on an ICPC-2 coded diagnosis in their Electronic Medical Record (EMR). An overview of all used codes can be found in Supplement 1." 10. The definition of "on medication" as a single prescription in a calendar year seems very broad. Is there a reason for using such a broad definition?
Response: The authors fully agree with the reviewer on this remark. First, we used the following definition for chronic medication: "medication that is prescribed at least three times/year with the last prescription in the last 6 months in the year after". However, by looking at patient profiles including several years, we noticed that patients who were chronically prescribed the same medication did not meet this definition in some years, because they received only one or two prescriptions that year, followed by three prescriptions the next year. INTEGO only registers the electronic prescriptions made by GPs. But Belgian GPs also prescribe medication manually when they visit patients in longterm-care facilities or at home, and other doctors, like cardiologists, could have prescribed medication. These prescriptions are not registered in the INTEGO database. Consequently, a too strict definition did not reflect clinical reality in this case. Therefore, we opted for a more broad definition.
11. Results. These are reported reasonably clearly. Essentially there is a rise in stage A (cardiovascular risk factors) and stage B (diagnosed cardiac impairment) but a fall in stages C/D (symptomatic heart failure).
Response: The authors thank the reviewer for the thorough reading of our paper. This is indeed our main message.
12. Discussion. Could comment on the treatment of HF in the light of the evidence: RAAS antagonists and betablockers have an evidence base in HF with reduced ejection fraction; statins do not. The treatment of HF with preserved ejection fraction is less clear cut.
Response: The authors thank the reviewer for this helpful feedback. The Discussion section was revised to clarify this statement.
This could be explained by the lack of differentiation between HF with reduced ejection fraction (HFrEF) and HF with preserved ejection fraction (HFpEF), since no evidence-based treatment options exist for the latter. … In general, in the period we studied, the use of medications influencing survival in HFrEF patients (RAAS-blockade, B-blockers) has increased whereas the use of medications that were demonstrated not to improve survival (cardiac glycosides) has decreased.
13. Some comment on how the denominator is calculated needs to be included.
Response: The authors fully agree with the reviewer on this suggestion. The Discussion section was revised to discuss this limitation.
However, this study also has a few limitations. Patients are not registered with a particular GP in Belgium. The current study used the YCG as the denominator. Former research has shown that the YCG accounts for 80% of the total practice population. 1 The YCG is not the perfect denominator but it is the most realistic approach in countries without capitation. Additionally, one can assume that most HF patients will visit their GP at least once a year.
The authors want to thank the reviewer for this very useful and constructive review.

Reviewer 2:
The authors have undertaken a primary care study in Belgium using the Intego dataset to study the prevalence and incidence of heart failure (HF) stages A to C/D and their evolution over 15 yrs using electronic health data derived from 48 practices and 165,000 pts over the age of 45. The authors also studied trends in comorbidities and CV treatments over the same period. The manuscript is well written and of interest. This is the first time to my knowledge that anyone has examined temporal trends in the different stages of HF. I have a number of comments that will need to be addressed in the manuscript: 1) Has anyone validated diagnostic codes in this dataset, as that will be key. For example has anyone undertaken a note review in a practice to see how well / robust the coding for the various conditions are? Are other diagnostic codes such as ICD-9 captured in any of the practices? Would it be possible to look for consistency at least between icd-9 codes and ICPC-2 codes? Response: The authors thank the reviewer for this important remark. This is indeed crucial in a registry such as INTEGO. It is described in full in the publication by Truyers et al: the INTEGO database: background, methods and basic results of a Flemish general practice-based continuous morbidity registration network.2 In short, external validation of the INTEGO database has been examined by means of national and international comparisons (this sentence has been added to the Method section to clarify this matter for all readers). For example, overall cancer incidence was compared to the Limburg Cancer Registry (LIKAR), incidence rates of influenza and acute respiratory illness were compared to the European Influenza Surveillance Scheme (EISN), and all data is routinely compared to the tweede nationale studie, studying morbidity and care in Dutch general practice. Additionally, all INTEGO practices were Medidoc ® practices in our study period, because this was at that time one of the few electronic medical packages that facilitated routine input of coded data. The general practitioner records all data using keywords in a predetermined field of the electronic medical record (EMR). Each of the 67.500 keywords is associated with a unique programspecific internal code and can be linked to classifications as ICPC-2 and ICD-10. This linkage process is automated and based on a validated codebook. Looking at internal consistency between these codes therefore has little added value. That these codes are also linked to ICD-10 was added to the Method section. New diagnoses were classified according to a very detailed thesaurus (Medidoc ® codes) automatically linked to the International Classification of Primary Care (ICPC-2) and International Statistical Classification of Diseases and Related Health Problems 10th Revision (ICD-10) 2) It is not clear from the methods what inclusion criteria were used for entry into the study. This is particularly important in describing incidences. For example did the patients need to be registered to a practice for a certain period of time before they could be included? ie 1 or 2 years? How long did a patient have to be registered at a practice for a new HF code to be counted as an "incident" code. For example patient could be newly registered at a practice and a new code of HF entered into their EHR. This wouldn't be an incident diagnosis of HF. Can patients be tracked across practices?
Response: The authors thank the reviewer for this important remark. The excellent remarks of the reviewers led to an important clarification. We wrongfully described our study as a cohort study in the title section while it is a registry-based study. Our denominator is the yearly contact group (YCG). These are all patients that visit a particular GP at least once in a given year. Furthermore, it is an open registry. New patients can consult participating GPs each year and by doing so enter the INTEGO database. However, an old HF diagnosis of a new patient is coded, together with the date of the diagnosis in the EHR and can be distinguished from a "real" new, incident HF diagnosis. Patients cannot be tracked on an individual level or across practices. To clarify how we distinguished between prevalent and incident diagnoses of all HF stages, we added a flowchart as supplemental file 2. Additionally, the Method section was revised to clarify how data was collected and analyzed in the INTEGO database.
3) There are too many assumptions made for the diagnosis of charlson score, for example connective tissue could not be assessed from the registry and the differentiation between cancers with or without metas, DM with or without end organ damage and mild or moderate to severe liver disease could not be made. i feel that these assumptions do not lead to any robust calculation of charlson score and any score reported is significantly flawed. I would suggest removing charlson score data and just listing the number of comorbid conditions, which would allow a broader number of conditions to be considered, and arguably proivide more interesting information.
Response: The authors understand the reviewer's concern. However, the mCCI was not studied for the first time. It was extensively studied and validated by Boeckxstaens et al in the BELFRAIL cohort8 and compared to other comorbidity measures, like a disease count. Even with an incomplete score, the mCCI remains a reflection of the burden of comorbidity in HF patients. Additionally, the changes in the score during the past 16 years concern us most. The trend analysis of the mCCI is probably not, to only mildly, affected by the missing variables.
4) The temporal trends in medications could be explained by differences in the prevalances of HPEF vs HFREF over time. The authors need to discuss this more. Patients may not be precribed ACEi, B-Blockers etc as pts diagnosed with HFPEF. Are cardiac devices coded in this dataset? i think it would be interesting to look at device changes over time such as CRT/ICD implantation. Response: The authors thank the reviewer for this useful remark. The Discussion section was revised to add this interesting suggestion. Temporal trends in CRT/ICD implantation would indeed be of great interest but this is unfortunately not coded in the INTEGO database. This could be explained by the lack of differentiation between HF with reduced ejection fraction (HFrEF) and HF with preserved ejection fraction (HFpEF), since no evidence-based treatment options exist for the latter. Additionally, the prevalence of HFpEF keeps increasing, especially in the community, compared to a decreasing HFrEF prevalence.9 5) I am not convinced that cardiomyopathy should be coded as a stage B subtype of HF. should probably coded as Stage C. Response: The authors understand the reviewer's concern. However, cardiomyopathy is coded as ICPC-2 code K84 (Heart disease other) and the matching program-specific internal code is hypertensive cardiomyopathy, being a synonym for structural cardiac changes due to hypertension, fitting perfectly into the HF stage B subtype.
Overall an interesting manuscript that provides new information.
The authors want to thank the reviewer for this very useful and constructive review. There are major limitations: 1. no idea of the LVEF. Thus, you can not discuss the rate of HF drugs prescription since only HFrEF have guidelines and recommendations for dugs prescription and optimization. In the present study you can not conclude. Response: The authors agree with the reviewer on this concern. However, we do not intent to conclude on guideline-based HF treatment with our study, because indeed, without differentiation between HFrEF and HFpEF we cannot. The absolute prescription rates are of minor importance in our paper and indeed hampered by many limitations as discussed in the Discussion section. However, it does make sense to describe the trends in cardiovascular prescribing without making distinction between HFrEF and HFpEF because it informs us about guideline dissemination to general practice. To emphasize the difference between HFrEF and HFpEF and possibly influencing trends, the Discussion section was revised. This could be explained by the lack of differentiation between HF with reduced ejection fraction (HFrEF) and HF with preserved ejection fraction (HFpEF), since no evidence-based treatment options exist for the latter. Additionally, the prevalence of HFpEF keeps increasing, especially in the community, compared to a decreasing HFrEF prevalence.9 Furthermore, our data depend on the quality of registration. EMR registration of medication can be hampered in patients in long-term care facilities, in patients visited at home and in medication prescribed by specialists. Furthermore, you discuss statins, here prescribed probably after myocardial infarction, although this class is not recommended in HF patients since it does not have any benefit. Response: The authors fully agree with the reviewer and revised the Discussion section to correct this statement.
In general, in the period we studied, the use of medications influencing survival in HFrEF patients (RAAS-blockade, B-blockers) has increased whereas the use of medications that were demonstrated not to affect survival (cardiac glycosides) has decreased. 3. Due to the retrospective design there are some difficulties to conclude like do the authors despite important difficulties to identify the degree of comorbidities, the type of HF, no code for obesity ... Response: The authors understand the reviewer's concern and indeed, in line with other registries, the INTEGO database has its limitations. However, data from the INTEGO database has gone through extensive external validation to confirm its completeness.2 Additionally, specifically for our research topic, we found the exact HF stage C prevalence, Conrad et al found in a UK cohort of 4.000.000 patients, published in the Lancet in 2018.3 Our other results are also in line with internationally published papers, as discussed in the Discussion section, contributing to the trustworthiness of our data and results. 4. Another important issue is the fact that only data from practices with optimal registration were included in the registry induce a huge doubt of the quality of the data. Response: The authors fully agree on the importance of data quality but are not sure they completely understand the reviewer's concern. Since the quality of data in a registry such as INTEGO is highly dependent of the quality of registration, selection of practices with optimal registration is necessary to ensure the quality of the data and minimize the risk of recording bias, where general practitioners only register certain e.g. serious diagnoses. Epidemiological study but I think the results should be presented differently with more discussion and less affirmative conclusions. The authors want to thank the reviewer for this useful review. To meet the reviewer's concerns the limitations of this registry-based epidemiological study were discussed more extensively in the Discussion section. Additionally, we are completely aware that we can only describe trends based on this observational data and formulate careful hypotheses. Hopefully, our revisions meet the reviewer's expectations. Editorial Request: Along with your revised manuscript, please provide a completed copy of the RECORD checklist (http://www.record-statement.org/checklist.php). Thank you for your continued consideration of our manuscript. All requested revisions were done. A completed copy of the RECORD checklist was added.
score is missing to make it useful. given that comorbidity is a major part of this manuscript, i am afraid that they will need to model comorbidity differently. the easiest way may be to model individual comorbid conditions, or a simple count of comorbidities. I feel that using a "modified charlson score" that has formally never been validated and then referencing a paper that makes no formal reference re the modifications of the score, and simply refers to the score as charlson score throughout is dishonest. A second point that needs to be discussed better that has been highlighted by a number of reviewers is HFPEF vs HFREF in discussion and receipt of evidence based therapy. Evidence based therapy is ONLY indicated for the HFREF patiesnt, so comments cant be made re receipt of evidence based therapies, unless the % of HFREF and HFPEF are know. this is a major limitation and needs to be discussed as such.

Reviewer 2:
The authors have generally addressed my comments.
1. With the exception of 1 issue related to comorbidity which i believe should be addressed. The reference to prior work is disingenuous. My concerns relate around the proposed "modifed charlson comorbidity index" which they provide a reference to Boeckxstaens P et al 2015. The authors do not have data re whether Diabetes has end organ damage or not (which are scored differently) or whether the patient has metastases (which are scored differently, extent of liver disease (again scored differently) and have total absence of connective tissue disease. the reference they provide (a prior publication) makes no reference to them having modified the charlson score in the methods of the paper (they always refer to the score as a charlson score in that reference, and if they had modified as they have done in this paper, and not reported this modification, i would argue that this is incredibly poor research practice) and as such cannot provide justification for this "modifed charlson score" using this reference. In my view too much important information from this modified score is missing to make it useful. given that comorbidity is a major part of this manuscript, i am afraid that they will need to model comorbidity differently. the easiest way may be to model individual comorbid conditions, or a simple count of comorbidities. I feel that using a "modified charlson score" that has formally never been validated and then referencing a paper that makes no formal reference re the modifications of the score, and simply refers to the score as charlson score throughout is dishonest.
Response: The authors understand the reviewer's concern and followed the suggestion to model comorbidity differently. We chose to report a disease count of chronic diseases. The list of chronic diseases used for this disease count was based on a paper by Knottnerus et al and a full list of the used diseases can be found in Supplement 3.1 These new analyses resulted in a similar rising trend in the mean disease count between 2000 and 2015 (P<0.001). The Abstract, Method, Result, Discussion and Supplement section were adapted.

2.
A second point that needs to be discussed better that has been highlighted by a number of reviewers is HFPEF vs HFREF in discussion and receipt of evidence based therapy. Evidence based therapy is ONLY indicated for the HFREF patiesnt, so comments cant be made re receipt of evidence based therapies, unless the % of HFREF and HFPEF are know. this is a major limitation and needs to be discussed as such.