{'fraction_non_alphanumeric': 0.028831322918726518, 'fraction_numerical': 0.020961044097290885, 'mean_word_length': 5.085679903730445, 'pattern_counts': {'":': 0, '<': 17, '<?xml version=': 0, '>': 1, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, '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}}
Preoperative Frailty Assessment, Operative Severity Score, and Early Postoperative Loss of Independence in Surgical Patients Age 65 Years or Older

Background Preoperative discussions around postoperative discharge planning have been amplified by the COVID pandemic. We wished to determine whether our preoperative frailty screen would predict postoperative loss of independence (LOI). Study Design This single-institutional study included demographic, procedural, and outcomes data from patients 65 years or older who underwent frailty screening before a surgical procedure. Frailty was assessed using the Edmonton Frail Scale. The Operative Severity Score was used to categorize procedures. The Hierarchical Condition Category risk-adjustment score, as calculated by the Centers for Medicare and Medicaid Services, was included. LOI was defined as an increase in support outside of the home after discharge. Univariable, multivariable logistic regressions, and adjusted postestimation analyses for predictive probabilities of best fit were performed. Results Five hundred and thirty-five patients met inclusion criteria and LOI was seen in 38 patients (7%). Patients with LOI were older, had a lower BMI, a higher Edmonton Frail Scale score (7 vs 3.0; p < 0.001), and a higher Hierarchical Condition Category score than patients without LOI. Being frail and undergoing a procedure with an Operative Severity Score of 3 or higher was independently associated with an increased risk of LOI. In addition, social dependency, depression, and limited mobility were associated with an increased risk for LOI. On multivariable modeling, frailty status, undergoing an operation with an Operative Severity Score of 3 or higher, and having a Hierarchical Condition Category score ≥1 were the most predictive of LOI (odds ratio 12.72; 95% CI, 12.04 to 13.44; p < 0.001). In addition, self-reported depression, weight loss, and limited mobility were associated with a nearly 11-fold increased risk of postoperative LOI. Conclusions This study was novel, as it identified clear, generalizable risk factors for LOI. In addition, our findings support the implementation of preoperative assessments to aid in care coordination and provide specific targets for intervention.

It is currently estimated that >50% of operations performed in the US are in patients older than 65 years, and the prevalence of frailty in this older patient population ranges from 10% to 37%. [1][2][3] Frailty is an indicator of a patient's vulnerability to the physiologic stress of a surgical procedure and the potential for long-term postoperative effects. 4 It has become a well-established predictor of poor postoperative outcomes, including an increased risk for complication and longer length of stay, in many of the surgical subspecialties. [5][6][7] Recently, Berian and colleagues 8 reported that the increase in complication seen in our older patient population was significantly associated with increased rate of loss of independence (LOI) and early mortality. Frail surgical patients who experience LOI are often placed in skilled nursing facilities on discharge from the hospital. This has been particularly challenging, given the uncertainty around severe acute respiratory syndrome coronavirus 2 infection within skilled nursing facilities. Skilled nursing facilities are particularly vulnerable to this infection and residents are at risk for severe outcomes. 9 In 2013, the American College of Surgeons NSQIP, in collaboration with the American Geriatrics Society, issued best practice guidelines recommending the incorporation of frailty assessment into clinical practice to address the growing trend of a rapidly aging US population. 10 In a systematic review, Ko 5 identified 67 different frailty instruments frequently cited for their ability to identify this vulnerable population. The instrument types are categorized into 2 unique models, the Physical Frailty Phenotype, characterized by the 5 clinical features of a decline in lean body mass, grip strength, endurance, walking speed, and physical activity; and the Deficit Accumulations Model, characterized as a model of multimorbidity. Several of the instruments lack a cognitive assessment and fail to acknowledge psychosocial determinants of health. In 2018, we implemented the Edmonton Frail Scale (EFS) in our clinical setting to screen patients for the risk of frailty before a surgical intervention. This is an 11-item scale in which 9 items are self-reported. It has been reported to take, on average, 5 minutes to complete and does not require someone with geriatric expertise to administer. 11,12 The assessment does incorporate a cognitive screen and screens for psychosocial determinants and has been validated as a tool to evaluate frailty. [11][12][13] Understanding the interaction between frailty and surgical outcomes has been hindered by the high degree of heterogeneity of the frailty assessments. However, more important is the limitation imposed by the variability in the types of procedures performed in these patients. Most studies examining frailty have limited their patient populations to a select disease state (cancer) or procedure type, which greatly limits the generalizability of their findings. Recently, to address this issue, Shinall and colleagues 4 developed and validated the Operative Severity Score (OSS), which assigns an ordinal number (1 to 5) relative to the degree of physiologic stress experienced by patients from a large variety of surgical procedures. We wished to evaluate the use of the EFS to predict postoperative outcomes in our surgical population. To aid in the generalizability of our findings, we wished to examine the use of the OSS to categorize the elective surgical interventions in our older population. In particular, we wanted to understand the effect of our frailty screen and the physiologic severity of the planned procedure on postoperative loss of independence and mortality.

Patient characteristics
Between June 2019 and June 2020, patients 65 years or older who underwent frailty screening before a surgical intervention were included in this study. All patient data were abstracted from the hospital's electronic medical record (EMR). Abstracted data included patients' demographic characteristics, including age, sex, and race; and lifestyle determinants of health, including BMI, alcohol use, and smoking status. In addition, access to advanced care directives within the EMR before the planned procedure was assessed. Procedural data included CPT coding designation and procedure duration. The patient class designation of elective surgical inpatient or outpatient procedure was used.

Risk assessment indices Edmonton Frail Scale
To evaluate for the presence of frailty in patients 65 years or older, deviations from normal physiologic functions were considered across several frailty domains, including independent capacity, cognition, nutrition, disease chronicity, and the presence of geriatric syndromes. Individual scores were calculated as the proportion of deficits out of the total number of the domain-specific items included in the scale to compare frailty levels. EFS was first dichotomized, with a score of 6 or higher considered frail. To understand the impact of the individual domains of the EFS on outcomes, each domain was tallied independently using the following algorithm: 1. Cognitive impairment: complete failure of the clockdraw or partial failure and self-reporting forgetting to take medication (2 points) 2. Social dependence: self-reported failure to perform 2 or more activities of daily living, self-reported lack of any social support, or self-reported failure to perform 1 or more activities of daily living and self-reported lack of some support (2 points) Operative Severity Score The OSS has been described previously, and the techniques for the score evaluation are reviewed. 4 Briefly, modified Delphi consensus methods using CPT codes were used to develop OSS. OSS is assigned using an ordinal scale of 1 to 5 relative to the degree of physiologic stress experienced by patients, with larger scores registering more physiologic stress levels. The research team assigned scores based on CPT and procedural description.
Only procedures with an established OSS score were included in this study.
The Hierarchical Condition Category risk adjustment score The Centers for Medicare and Medicaid Services (CMS) use patient demographic characteristics and diagnosisbased clinical measures to generate a summary riskadjustment scoring system. 14 The CMS-HCC model adjusts for patients' insurance capitation fees and predicts healthcare charges for which payment plans are subject.
The CMS-HCC model is prospective, as the mean expenditures collected in 1 year provide predictive data for the following year. In addition, beneficiaries rely on several parameters for coding diagnoses and, consequently, a comprehensive clinical profile prediction is generated. Five domains of clinical and diagnostic categories are used to assign CMS-HCC and include the following sources: primary hospital inpatient diagnosis, secondary hospital inpatient diagnosis, hospital outpatient, physician visit, and clinically trained nonphysician. For the individual HCC scores, we evaluated the most updated reported predictive ratio for each patient before the surgical intervention. Ratios approaching 1 are characteristic of an accurate prediction, ratios less than 1 underpredict expenditure, and ratios greater than 1 overpredict expenditure.

Outcomes measure
The main outcomes of interest evaluated included LOI and mortality. LOI was defined as discharge destination other than home. Patients discharged to skilled care facilities or other locations that differed from their point of origin were considered dependent and met the LOI definition. Mortality is defined as patients who died within a 30-day period from the primary procedure. Other outcomes of interest included length of hospital stay (days) after the surgical procedure and readmission occurring within 30 days after discharge.

Statistical analysis
Descriptive analyses were performed for the demographic and perioperative clinical variables. Reported results on variables are presented in frequencies and proportions for dichotomized and categorical variables. For continuous variables, we report the medians and interquartile range. Patient characteristics at inclusion are stratified and analyzed relative to their functional dependency status at discharge. The sample size with power set at 80% and significance at p 0.05 was used to compute the least number of patients required to detect a baseline change.
For all other tests, 2-sided tests with statistical significance at p 0.05 were performed. The comparisons were performed based on the variables assessed, that is, Wilcoxon rank sum and the Kruskal-Wallis analysis for medians, chi-square, and Fisher exact tests for dichotomized or categorical variables. Multivariable logistic regressions were performed, and odds ratios (ORs) with their corresponding 95% CIs are reported. Hosmer-Lemeshow statistics were used for model discrimination and calibration. To evaluate our hypothesis that the possible interplay of procedure complexities and the patients' clinical profile is associated with a higher likelihood of LOI and mortality, we tested interaction terms to understand the direct effect of the independent factors on LOI and mortality and the impact of other independent factors on their effect size. 15 In addition, we applied Stata's post-estimation predictive margins (PMs) to determine the mean predicted probability for all combinations of the different interactions (ie frailty status, OSS, and HCC). To identify specific EFS survey items that predict LOI and mortality, we calculated the mean predicted probability for all combinations of the different interactions (ie cognitive impairment, social support, depression, and weight loss). PM measures the marginal effects relative to the covariates' changes. Coefficients with their corresponding 95% CI and percent change are reported for PMs. 16 Sensitivity analyses were then performed for quality assurance of our statistical model selection by matching estimators to determine the magnitude of unobserved confounding. We applied propensity score matching, a quasi-experimental method to evaluate the covariates' impact on mortality by creating matched pairs for patients who died compared with patients with similar characteristics who did not die in the postoperative period (results not reported). 17 Statistical analysis was performed using Stata statistical software, version 16.0 (Stata Corp). . Using the OSS to define the case type, the majority of procedures performed were an OSS of 2 with 33.5% of the total population having a procedure with an OSS of 3 or higher performed. Comparisons between patients with LOI and those without LOI showed that patients with LOI were older (79 vs 72 years; p < 0.001), had a lower BMI (24.3 vs 28.1 kg/m 2 ; p ¼ 0.02), and a higher total EFS score (7.5 vs 3.0; p < 0.001). Inpatient procedures resulted in more LOI than outpatient procedures; however, it was noted that 7 patients (18.4%) who had LOI had a designated outpatient procedure. Procedures performed in patients with LOI had a significantly higher OSS score. Patients with LOI had an increased length of hospital stay compared with patients without LOI (4 vs 0 days; p < 0.001). The 30-day mortality rate among patients with LOI was higher than patients without LOI (10.5% vs 3.4%; p ¼ 0.05) ( Table 1).
When evaluating risk associated with LOI using EFS as an indicator of frail or not frail, several factors were identified (Table 2). There was a significant risk of LOI for patients 80 years or older (OR 1.69; 95% CI, 1.62 to 1.77; p < 0.001) and a decreased risk for LOI in patients with a BMI 25 kg/m 2 (OR 0.41; 95% CI, 0.38 to 0.43; p < 0.001). Being frail (EFS score 6; OR 6.98; 95% CI, 6.90 to 7.06; p < 0.001) and undergoing a procedure with an OSS 3 (OR 1.19; 95% CI, 1.12 to 1.25; p < 0.001) was associated with an increased risk of LOI. In addition, analyzing domains of the EFS assessment showed that reporting social dependency, presence of depression, and limited mobility were associated with an increased risk for LOI. Analyzing interactions between independent variables to develop a predictive model for LOI demonstrated that frailty status, undergoing an operation with an OSS 3, and having an HCC score 1 were the most predictive of LOI (OR 12.72; 95% CI, 12.04 to 13.44; p < 0.001) ( Table 3). Postestimation PMs of these variables suggested that age 80 years or older along with frailty status and undergoing an operation with an OSS 3 conferred a nearly 40% risk for postoperative LOI (PM 0.39; 95% CI, 0.13 to 0.66; p ¼ 0.004)  (Table 4). Finally, combining the domains of the EFS showed that self-reported depression and weight loss and measured limited mobility were associated with a nearly 11-fold increased risk in postoperative LOI (OR 10.88; 95% CI, 8.41 to 14.08; p < 0.001) ( Table 3). These findings were mirrored in our analysis of PMs,  where the combination of these same domains is associated with a 24% increased risk for postoperative LOI (PM 0.24; 95% CI, 0.09 to 0.39; p ¼ 0.002) ( Table 4).
In this data set, the risk factors for 30-day mortality included frailty (EFS score 6; OR 3.36; 95% CI, 3.09 to 3.65; p < 0.001) and, more significantly, a higher HCC score (OR 21.83; 95% CI, 21.51 to 22.14; p < 0.001) ( Table 2). A higher OSS score was associated with a decreased risk of 30-day mortality (OR 0.80; 95% CI, 0.78 to 0.82; p < 0.001). As for the domains of the EFS, all domains except cognitive impairment were associated with a significant increased risk of mortality, with limited mobility being the highest risk (OR 3.04; 95% CI, 2.75 to 3.36; p < 0.001) ( Table 2). Multivariable analysis of variable interactions demonstrated age 80 years or older and frailty were associated with a 4.4-fold increase in mortality (OR 4.41; 95% CI, 4.22 to 4.61; p < 0.001) ( Table 3). For the domains of the EFS score, having self-reported weight loss and measured poor mobility was associated with an 8.7-fold increase in mortality (OR 8.78; 95% CI, 7.82 to 9.87; p < 0.001). This finding was confirmed with postestimation PMs that demonstrated a 12% increased risk for mortality in patients with self-reported weight loss and measured limited mobility (PM 0.12; 95% CI, 0.04 to 0.21; p ¼ 0.004) ( Table 4).

DISCUSSION
LOI is part of the conceptual pathway known as "the disablement process" that elderly people often experience. Verbrugge and Jette 18 define the disablement process as a description of how chronic and acute conditions affect functioning in specific body systems and how personal and environmental factors speed up or slow down the disablement process. One of the environmental factors that has been shown to facilitate the disablement process is subjecting an elderly person to the physiologic stress of an operation and the complications often associated with an operative intervention. It therefore is not surprising that LOI occurs in elderly patients after many surgical interventions for vascular disease, colorectal cancer, and general abdominal emergencies. 6,8,19,20 Frailty is an established risk factor for poor postoperative outcomes, including an increased risk of discharge to a skilled nursing facility. In addition, LOI identified on postoperative discharge has been shown to be predictive of longterm LOI. In patients 80 years or older undergoing operation for colorectal cancer, nearly one-half of patients with LOI at discharge continued to have LOI at 30 days. 19 Similarly, De Roo and associates 21 demonstrated that 33% of older adults undergoing major colorectal operation experienced a functional decline, as measured by the activities of daily living scale at 1 year. We therefore wanted to determine the ability of our frailty assessment tool, the EFS, to predict postoperative LOI at our institution.
We implemented the EFS into our multispecialty surgical clinic workflow through integration with our EMR. An EFS score of 6 or higher is an indicator of vulnerability and this score has been used to trigger intervention, including a referral to geriatric medicine for a comprehensive geriatric assessment. As our surgical population is heterogeneous, each surgical procedure was assigned an OSS. Our results demonstrated that frailty status (EFS score 6) was significantly associated with LOI in both the inpatient and outpatient surgical populations. This finding validates the independent use of this screening system to predict this end point and supports the use of existing resources and workflow to complete the screening assessment and the incorporation of the screening results into shared decision-making with the patient during the clinic visit. In addition, this finding supports the allocation of appropriate resources needed for coordination of perioperative care. In a similar study, Donald and colleagues 20 used the validated Clinical Frailty Score in the preoperative setting to successfully predict LOI in patients undergoing major vascular operation only. Our findings are more generalizable, as they apply to a variety of surgical procedures. In this data set, the CMS-HCC score was not associated with a risk for LOI, however, was significantly associated with the risk of 30-day mortality. Our findings are corroborated by Kumar and colleagues, 22 who recently reported that current risk adjustment and comorbidity index, including the CMS-HCC, were poor at predicting post-acute care skilled nursing facility use and readmission in patients undergoing joint replacement. This suggests that automated scoring systems that rely heavily on coding data and multimorbidity might not provide the granularity necessary to evaluate some of our important surgical outcomes.
In this study, we chose to examine all variables for an interaction to develop the best predictive model for LOI. After examining all potential variables for possible interactions, we did note that using all 3 scoring systemsdfrailty (EFS score 6), OSS 3, and HCC score 1dwas associated with a 13-fold increase risk for LOI. As predictive models vary in outcomes, we also chose to examine our findings using postestimation PMs. Using this modeling, we determined that frail (EFS score 6), older (80 years or older) patients undergoing a more physiologically stressful procedure (OSS 3) were at a nearly 40% risk of postoperative LOI. Our combined results suggest that knowledge of the preoperative frailty status and application of validated global score of operative stress provides the surgeon with valuable information to incorporate into the process of shared decision-making with the patient and should assist in the allocation of appropriate resources to coordinate perioperative care.
When examining the domains of the EFS to determine for possible areas of intervention, we noted that selfreported depression and weight loss and limited mobility were associated with LOI using both predictive modeling methods. This finding is similar to a recent study from Pederson and colleagues, 23 which demonstrated that a delay in mobilization, defined as out of bed 36 hours or more after operation, was independently associated with a 2-fold higher risk of 30-day readmission or mortality. In their study, 24% of patients undergoing emergent abdominal operation (the most common procedure was cholecystectomy) had delayed mobilization. In addition, Hirvensalo and colleagues 24 demonstrated similar findings in community-dwelling older adults. In their study, mobility impairments independently predicted a 5-fold increase in LOI among older men and a 3-fold increase among older women. It is also important to note that LaCroix and colleagues, 25 in a similar study, found that a higher level of physical activity was associated with better functioning in those with chronic disease. In addition, those patients with chronic disease who had a higher level of physical functioning were less likely to lose their mobility than those individuals with chronic disease who were sedentary. In aggregate, this work supports the implementation of a multimodal prehabilitation protocol, such as the Michigan Surgical Home and Optimization Program, 26 to optimize preoperative mobility and nutrition in a targeted population of frail patients undergoing a higher physiologically stressful procedure.
It is interesting to note that our data did not show that cognitive impairment assessed with the clock-draw was independently associated with LOI or mortality. Previous work using NSQIP data has shown cognitive impairment as well as postoperative delirium to be predictors of early postoperative LOI. 19 As we have recently implemented our delirium screening tool at our institution, we currently lack the ability to identify the rate of postoperative delirium, which is more likely to occur in patients with underlying cognitive impairment and is a known risk factor for poor surgical outcomes. 27 It is also plausible that early identification of these at-risk individuals with cognitive impairment in our patient population has prevented LOI, and additional work to examine the effect of cognitive impairment is necessary.
This study has several limitations. First, we used the EMR to obtain our data and, therefore, our data are dependent on correct documentation in the appropriate location within the EMR. Our EFS data might not have reflected the absolute condition of the patient at the time of operation, as we have allowed for a 6month gap in performance of the assessment. In addition, we internally validated administration of the EFS by our medical assistants, and there is always change in staffing, which could allow for some newer medical assistants to be less familiar with EFS scoring. We did develop an EFS learning tool and have performed several reeducation sessions, however, we currently do not have a method of assessing the performance of the medical assistants before they administer the EFS assessment. Second, although we were able to assess a variety of elective surgical procedures, some procedures might have been more urgent than others, given our current operating room release policy and the ability of some procedures in inpatients to be posted to the next day's schedule as an elective case. In addition, nearly one-half of our cases were outpatient procedures. As we noted that nearly 20% of our patients with LOI were scheduled for outpatient procedures, we thought it was important to include these cases in our study, as unplanned admissions can use a large amount of hospital resources. Finally, we were concerned about our low number of patients with LOI, which is most likely a result of including outpatient procedures. Low numbers might have made predictive models less accurate; however, we used 2 separate models to investigate our population.

CONCLUSIONS
Additional work in this area is necessary to better define measures to identify vulnerable individuals and implement strategies to prevent poor outcomes in this patient population.