Comparison of Electronic Frailty Metrics for Prediction of Adverse Outcomes of Abdominal Surgery

Accuracy of frailty instruments in predicting outcomes following perioperative cardiac arrest

BACKGROUND

Frailty is associated with increased 30-day mortality and non-home discharge following perioperative cardiac arrest. We estimated the predictive accuracy of frailty when added to baseline risk prediction models.

METHODS

In this retrospective cohort study using 2015-2020 NSQIP data for 3048 patients aged 50+ undergoing non-cardiac surgery and resuscitation on post-operative day 0 (i.e., intraoperatively or postoperatively on the day of surgery), baseline models including age, sex, ASA physical status, preoperative sepsis or septic shock, and emergent surgery were compared to models that added frailty indices, either RAI or mFI-5, to predict 30-day mortality and non-home discharge. Predictive accuracy was characterized by area under the receiver operating characteristic curve (AUC-ROC), integrated calibration index (ICI), and continuous net reclassification index (NRI).

RESULTS

1786 patients (58.6%) died in the study cohort within 30 days, and 38.6% of eligible patients experienced non-home discharge. The baseline model showed good discrimination (AUC-ROC 0.77 for 30-day mortality and 0.74 for non-home discharge). AUC-ROC and ICI did not significantly change after adding frailty for 30-day mortality or non-home discharge. Adding RAI significantly improved NRI for 30-day mortality and non-home discharge; however, the magnitude was small and difficult to interpret, given other results including false positive and negative rates showing no difference in predictive accuracy.

CONCLUSIONS

Incorporating frailty did not significantly improve predictive accuracy of models for 30-day mortality and non-home discharge following perioperative resuscitation. Thus, demonstrated associations between frailty and outcomes of perioperative resuscitation may not translate into improved predictive accuracy. When engaging patients in shared decision-making regarding do-not-resuscitate orders perioperatively, providers should acknowledge uncertainty in anticipating resuscitation outcomes.

Automated CT Analysis of Body Composition as a Frailty Biomarker in Abdominal Surgery

Importance

Prior studies demonstrated consistent associations of low skeletal muscle mass assessed on surgical planning scans with postoperative morbidity and mortality. The increasing availability of imaging artificial intelligence enables development of more comprehensive imaging biomarkers to objectively phenotype frailty in surgical patients.

Objective

To evaluate the associations of body composition scores derived from multiple skeletal muscle and adipose tissue measurements from automated segmentation of computed tomography (CT) with the Hospital Frailty Risk Score (HFRS) and adverse outcomes after abdominal surgery.

Design, Setting, and Participants

This retrospective cohort study used CT imaging and electronic health record data from a random sample of adults who underwent abdominal surgery at 20 medical centers within Kaiser Permanente Northern California from January 1, 2010, to December 31, 2020. Data were analyzed from April 1, 2022, to December 1, 2023.

Exposure

Body composition derived from automated analysis of multislice abdominal CT scans.

Main Outcomes and Measures

The primary outcome of the study was all-cause 30-day postdischarge readmission or postoperative mortality. The secondary outcome was 30-day postoperative morbidity among patients undergoing abdominal surgery who were sampled for reporting to the National Surgical Quality Improvement Program.

Results

The study included 48 444 adults; mean [SD] age at surgery was 61 (17) years, and 51% were female. Using principal component analysis, 3 body composition scores were derived: body size, muscle quantity and quality, and distribution of adiposity. Higher muscle quantity and quality scores were inversely correlated (r = -0.42; 95% CI, -0.43 to -0.41) with the HFRS and associated with a reduced risk of 30-day readmission or mortality (quartile 4 vs quartile 1: relative risk, 0.61; 95% CI, 0.56-0.67) and 30-day postoperative morbidity (quartile 4 vs quartile 1: relative risk, 0.59; 95% CI, 0.52-0.67), independent of sex, age, comorbidities, body mass index, procedure characteristics, and the HFRS. In contrast to the muscle score, scores for body size and greater subcutaneous and intermuscular vs visceral adiposity had inconsistent associations with postsurgical outcomes and were attenuated and only associated with 30-day postoperative morbidity after adjustment for the HFRS.

Conclusions and Relevance

In this study, higher muscle quantity and quality scores were correlated with frailty and associated with 30-day readmission and postoperative mortality and morbidity, whereas body size and adipose tissue distribution scores were not correlated with patient frailty and had inconsistent associations with surgical outcomes. The findings suggest that assessment of muscle quantity and quality on CT can provide an objective measure of patient frailty that would not otherwise be clinically apparent and that may complement existing risk stratification tools to identify patients at high risk of mortality, morbidity, and readmission.

Impact of frailty on perioperative outcomes following percutaneous nephrolithotomy in older persons: evidence from the US Nationwide Inpatient Sample

To evaluate the impact of frailty on perioperative outcomes of older patients undergoing PCNL, utilizing the US Nationwide Inpatient Sample (NIS) database. Data of hospitalized patients ≥ 60 years who received PCNL were extracted from the 2010 to 2020 NIS database, and included demographics, clinical, and hospital-related information. Patients were assigned to low (< 5), medium (5-15), and high frailty risk (> 15) groups based on the hospital frailty risk score (HFRS). Associations between frailty risk and perioperative outcomes including total hospital cost were determined using population-weighted linear and logistic regression analyses. Data of 30,829 hospitalized patients were analyzed (mean age 72.5 years; 55% male; 78% white). Multivariable analyses revealed that compared to low frailty risk, increased frailty risk was significantly associated with elevated in-hospital mortality (adjusted odds ratio (aOR) = 10.70, 95% confidence interval (CI): 6.38-18.62), higher incidence of unfavorable discharge (aOR = 5.09, 95% CI: 4.43-5.86), prolonged hospital length of stay (LOS; aOR = 7.67, 95% CI: 6.38-9.22), increased transfusion risk (aOR = 8.05, 95% CI: 6.55-9.90), increased total hospital costs (adjusted Beta = 37.61, 95% CI: 36.39-38.83), and greater risk of complications (aOR = 8.52, 95% CI: 7.69-9.45). Frailty is a significant prognostic indicator of adverse perioperative outcomes in older patients undergoing PCNL, underscoring importance of recognizing and managing frailty in older patients.

Hospice and palliative care utilization in 16 004 232 medicare claims: comparing trauma to surgical and medical inpatients

Background

Hospice and palliative care (PC) utilization is increasing in geriatric inpatients, but limited research exists comparing rates among trauma, surgical and medical specialties. The goal of this study was to determine whether there are differences among these three groups in rates of hospice and PC utilization.

Methods

Patients from Centers for Medicare & Medicaid Services (CMS) Inpatient Standard Analytical Files for 2016-2020 aged ≥65 years were analyzed. Patients with a National Trauma Data Standard-qualifying ICD-10 injury code with abbreviated injury score ≥2 were classified as 'trauma'; the rest as 'surgical' or 'medical' using CMS MS-DRG definitions. Patients were classified as having PC if they had an ICD-10 diagnosis code for PC (Z51.5) and as hospice discharge (HD) if their hospital disposition was 'hospice' (home or inpatient). Use proportions for specialties were compared by group and by subgroups with increasing risk of poor outcome.

Results

There were 16M hospitalizations from 1024 hospitals (9.3% trauma, 26.3% surgical and 64.4% medical) with 53.7% women, 84.5% white and 38.7% >80 years. Overall, 6.2% received PC and 4.1% a HD. Both rates were higher in trauma patients (HD: 3.6%, PC: 6.3%) versus surgical patients (HD: 1.5%, PC: 3.0%), but lower than in medical patients (HD: 5.2%, PC: 7.5%). PC rates increased in higher risk patient subgroups and were highest for inpatient HD.

Conclusions

In this large study of Medicare patients, HD and PC rates varied significantly among specialties. Trauma patients had higher HD and PC utilization rates than surgical, but lower than medical. The presence of comorbidities, frailty and/or severe traumatic brain injury (in addition to advanced age) may be valuable criteria in selection of trauma patients for hospice and PC services. Further studies are needed to inform the most efficient use of hospice and PC resources, with particular focus on both timing and selection of subgroups most likely to benefit from these valuable yet limited resources.

Level of evidence

Level III, therapeutic/care management.

The Impact of Frailty on Adjuvant Therapies Not Offered to or Declined by Breast Cancer Surgery Patients

INTRODUCTION

The impact of frailty on adjuvant therapies not offered to or declined by elderly breast cancer surgery patients has been understudied.

METHODS

This is a retrospective review of a prospectively managed single-center database including all breast cancer patients >65 years undergoing surgery in 2021. Frailty was determined using an electronic frailty index (eFI) derived from electronic health data. Patients were categorized as Fit (eFI ≤ .10), Pre-frail (.10 < eFI ≤.21), or Frail (eFI > .21). Chart review was performed to collect data on adjuvant therapies not offered or declined. Descriptive statistics and logistic regression were performed.

RESULTS

Of 133 patients, 16.5% were frail, 46.6% were pre-frail, and 36.8% were fit. Demographics were similar among groups except age and comorbidities. Of those with adjuvant therapy indicated (n = 123), 15.4% were not offered at least one indicated therapy. Of those offered therapy, some therapy was declined in 22.7%. Frail patients more often were not offered or declined some therapy (frail: 63.2%, pre-frail 36.2%, fit: 28.2%, P = .03). Frailty was associated with having some therapy not offered or declined on univariate modeling (OR 4.4 95% CI 1.4-13.5, P = .01) but not on multivariate. Being frail was associated with higher odds of readmission at 6 months on multivariate analysis (OR 9.5, 95% CI: 1.7-54.2. P = .01).

CONCLUSION

Over half of frail patients are not offered or decline some adjuvant therapy. The impact of this requires further study. Given their higher odds of readmission, frail patients require close postoperative monitoring to prevent the interruption of adjuvant therapies.

Development and multicentre validation of the FLEX score: personalised preoperative surgical risk prediction using attention-based ICD-10 and Current Procedural Terminology set embeddings

BACKGROUND

Preoperative knowledge of surgical risks can improve perioperative care and patient outcomes. However, assessments requiring clinician examination of patients or manual chart review can be too burdensome for routine use.

METHODS

We conducted a multicentre retrospective study of 243 479 adult noncardiac surgical patients at four hospitals within the Mass General Brigham (MGB) system in the USA. We developed a machine learning method using routinely collected coding and patient characteristics data from the electronic health record which predicts 30-day mortality, 30-day readmission, discharge to long-term care, and hospital length of stay.

RESULTS

Our method, the Flexible Surgical Set Embedding (FLEX) score, achieved state-of-the-art performance to identify comorbidities that significantly contribute to the risk of each adverse outcome. The contributions of comorbidities are weighted based on patient-specific context, yielding personalised risk predictions. Understanding the significant drivers of risk of adverse outcomes for each patient can inform clinicians of potential targets for intervention.

CONCLUSIONS

FLEX utilises information from a wider range of medical diagnostic and procedural codes than previously possible and can adapt to different coding practices to accurately predict adverse postoperative outcomes.

A modified Hospital Frailty Risk Score for patients with cirrhosis undergoing abdominal operations

BACKGROUND AND AIMS

Existing tools for perioperative risk stratification in patients with cirrhosis do not incorporate measures of comorbidity. The Hospital Frailty Risk Score (HFRS) is a widely used measure of comorbidity burden in administrative dataset analyses. However, it is not specific to patients with cirrhosis, and application of this index is limited by its complexity.

APPROACH AND RESULTS

Adult patients with cirrhosis who underwent nontransplant abdominal operations were identified from the National Inpatient Sample, 2016-2018. Adjusted associations between HFRS and in-hospital mortality and length of stay were computed with logistic and Poisson regression. Lasso regularization was used to identify the components of the HFRS most predictive of mortality and develop a simplified index, the cirrhosis-HFRS. Of 10,714 patients with cirrhosis, the majority were male, the median age was 62 years, and 32% of operations were performed electively. HFRS was associated with an increased risk of both in-hospital mortality (OR=6.42; 95% CI: 4.93, 8.36) and length of stay (incidence rate ratio [IRR]=1.79; 95% CI: 1.72, 1.88), with adjustment. Using lasso, we found that a subset of 12 of the 109 ICD-10 codes within the HFRS resulted in superior prediction of mortality in this patient population (AUC = 0.89 vs. 0.79, p < 0.001).

CONCLUSIONS

While the 109-component HFRS was associated with adverse surgical outcomes, 12 components accounted for much of the association between the HFRS and mortality. We developed the cirrhosis-HFRS, a tool that demonstrates superior predictive accuracy for in-hospital mortality and more precisely reflects the specific comorbidity pattern of hospitalized patients with cirrhosis undergoing general surgery procedures.

Training the next generation of delivery science researchers: 10-year experience of a post-doctoral research fellowship program within an integrated care system

Introduction

Learning health systems require a workforce of researchers trained in the methods of identifying and overcoming barriers to effective, evidence-based care. Most existing postdoctoral training programs, such as NIH-funded postdoctoral T32 awards, support basic and epidemiological science with very limited focus on rigorous delivery science methods for improving care. In this report, we present the 10-year experience of developing and implementing a Delivery Science postdoctoral fellowship embedded within an integrated health care delivery system.

Methods

In 2012, the Kaiser Permanente Northern California Division of Research designed and implemented a 2-year postdoctoral Delivery Science Fellowship research training program to foster research expertise in identifying and addressing barriers to evidence-based care within health care delivery systems.

Results

Since 2014, 20 fellows have completed the program. Ten fellows had PhD-level scientific training, and 10 fellows had clinical doctorates (eg, MD, RN/PhD, PharmD). Fellowship alumni have graduated to faculty research positions at academic institutions (9), and research or clinical organizations (4). Seven alumni now hold positions in Kaiser Permanente's clinical operations or medical group (7).

Conclusions

This delivery science fellowship program has succeeded in training graduates to address delivery science problems from both research and operational perspectives. In the next 10 years, additional goals of the program will be to expand its reach (eg, by developing joint research training models in collaboration with clinical fellowships) and strengthen mechanisms to support transition from fellowship to the workforce, especially for researchers from underrepresented groups.

Association of Frailty With Post-Operative Outcomes of Older Adults Undergoing Elective Ostomy Reversal

BACKGROUND

Ostomy reversal is a common surgical procedure; however, it is not without associated risks. Patient selection for this elective procedure is therefore critically important. Elderly patients represent a growing population and a substantial proportion of patients that present for evaluation after ostomy creation due to the most common etiologies. This study aims to assess the impact of frailty on the outcomes of ostomy reversal among older adults.

METHODS

Patients ≥65 years who underwent ostomy reversal from 2015 to 2019 were identified in the NSQIP database. Frailty was calculated using the 5-item Modified Frailty Index (MFI). Multivariate regression was performed to evaluate the association of frailty with post-operative 30-day mortality, 30-day serious complications, discharge to a facility, and 30-day readmission.

RESULTS

A total of 13,053 patients were included, of which 18.7% were frail (MFI ≥ 2). Patients who underwent colostomy reversal had higher rates of serious complications (P < .0001) and discharge to facility (P < .0001) compared to other reversals. In multivariate analysis, frailty was associated with increased odds of serious complications (OR 1.52, 95% CI 1.31-1.77), discharge to facility (OR 2.14, 95% CI 1.79-2.57), and readmission (OR 1.23, 95% CI 1.04-1.46), but not mortality. Frail patients had predicted probabilities 1.4 times higher for serious complications and 1.7-2.2 times greater for discharge to facility than non-frail patients.

CONCLUSIONS

Among older adults undergoing elective ostomy reversal, frailty is independently associated with increased odds of 30-day serious complications, discharge to facility, and 30-day readmission. As a potentially modifiable risk factor, identification of frailty offers the opportunity for shared decision-making and prehabilitation.

Interrater reliability of the Clinical Frailty Scale in the anesthesia preadmission clinic

PURPOSE

As many as 30% of patients with frailty die, are discharged to a nursing home, or have a new disability after surgery. The 2010 United Kingdom National Confidential Enquiry into Patient Outcome and Death recommended that frailty assessment be developed and included in the routine risk assessment of older surgical patients. The Clinical Frailty Scale (CFS) is a simple, clinically-assessed frailty measure; however, few studies have investigated interrater reliability of the CFS in the surgical setting. The objective of this study was to determine the interrater reliability of frailty classification between anesthesiologists and perioperative nurses.

METHODS

We conducted a cohort study assessing interrater reliability of the CFS between perioperative nurses and anesthesiologists for elective surgical patients aged ≥ 65 yr, admitted to a large regional university-affiliated hospital in Australia between July 2020 and February 2021. Agreement was measured via Cohen's kappa.

RESULTS

Frailty assessment was conducted on 238 patients with a median [interquartile range] age of 74 [70-80] yr. Agreement was perfect between nursing and medical staff for CFS scores in 112 (47%) patients, with a further 99 (42%) differing by only one point. Interrater kappa was 0.70 (95% confidence interval, 0.63 to 0.77; P < 0.001), suggesting good agreement between anesthesiologists and perioperative nurses.

CONCLUSION

This study suggests that CFS assessment by either anesthesiologists or nursing staff is reliable across a population of patients from a range of surgical specialities, with an acceptable degree of agreement. The CFS measurement should be included in the normal preanesthesia clinic workflow.

Automated Electronic Frailty Index-Identified Frailty Status and Associated Postsurgical Adverse Events

Importance

Electronic frailty index (eFI) is an automated electronic health record (EHR)-based tool that uses a combination of clinical encounters, diagnosis codes, laboratory workups, medications, and Medicare annual wellness visit data as markers of frailty status. The association of eFI with postanesthesia adverse outcomes has not been evaluated.

Objective

To examine the association of frailty, calculated as eFI at the time of the surgical procedure and categorized as fit, prefrail, or frail, with adverse events after elective noncardiac surgery.

Design, Setting, and Participants

This cohort study was conducted at a tertiary care academic medical center in Winston-Salem, North Carolina. The cohort included patients 55 years or older who underwent noncardiac surgery of at least 1 hour in duration between October 1, 2017, and June 30, 2021.

Exposure

Frailty calculated by the eFI tool. Preoperative eFI scores were calculated based on available data 1 day prior to the procedure and categorized as fit (eFI score: ≤0.10), prefrail (eFI score: >0.10 to ≤0.21), or frail (eFI score: >0.21).

Main Outcomes and Measures

The primary outcome was a composite of the following 8 adverse component events: 90-item Patient Safety Indicators (PSI 90) score, hospital-acquired conditions, in-hospital mortality, 30-day mortality, 30-day readmission, 30-day emergency department visit after surgery, transfer to a skilled nursing facility after surgery, or unexpected intensive care unit admission after surgery. Secondary outcomes were each of the component events of the composite.

Results

Of the 33 449 patients (median [IQR] age, 67 [61-74] years; 17 618 females [52.7%]) included, 11 563 (34.6%) were classified as fit, 15 928 (47.6%) as prefrail, and 5958 (17.8%) as frail. Using logistic regression models that were adjusted for age, sex, race and ethnicity, and comorbidity burden, patients with prefrail (odds ratio [OR], 1.24; 95% CI, 1.18-1.30; P < .001) and frail (OR, 1.71; 95% CI, 1.58-1.82; P < .001) statuses were more likely to experience postoperative adverse events compared with patients with a fit status. Subsequent adjustment for all other potential confounders or covariates did not alter this association. For every increase in eFI of 0.03 units, the odds of a composite of postoperative adverse events increased by 1.06 (95% CI, 1.03-1.13; P < .001).

Conclusions and Relevance

This cohort study found that frailty, as measured by an automatically calculated index integrated within the EHR, was associated with increased risk of adverse events after noncardiac surgery. Deployment of eFI tools may support screening and possible risk modification, especially in patients who undergo high-risk surgery.

A systematic review of predictive accuracy via c-statistic of preoperative frailty tests for extended length of stay, post-operative complications, and mortality

Frailty, as an age-related syndrome of reduced physiological reserve, contributes significantly to post-operative outcomes. With the aging population, frailty poses a significant threat to patients and health systems. Since 2012, preoperative frailty assessment has been recommended, yet its implementation has been inhibited by the vast number of frailty tests and lack of consensus. Since the anesthesiologist is the best placed for perioperative care, an anesthesia-tailored preoperative frailty test must be simple, quick, universally applicable to all surgeries, accurate, and ideally available in an app or online form. This systematic review attempted to rank frailty tests by predictive accuracy using the c-statistic in the outcomes of extended length of stay, 3-month post-operative complications, and 3-month mortality, as well as feasibility outcomes including time to completion, equipment and training requirements, cost, and database compatibility. Presenting findings of all frailty tests as a future reference for anesthesiologists, Clinical Frailty Scale was found to have the best combination of accuracy and feasibility for mortality with speed of completion and phone app availability; Edmonton Frailty Scale had the best accuracy for post-operative complications with opportunity for self-reporting. Finally, extended length of stay had too little data for recommendation of a frailty test. This review also demonstrated the need for changing research emphasis from odds ratios to metrics that measure the accuracy of a test itself, such as the c-statistic.

Preoperative Frailty Risk in Cranioplasty Patients: Risk Analysis Index Predicts Adverse Outcomes

Cranioplasty is a common surgical procedure used to repair cranial defects, and it is associated with significant morbidity and mortality. Although frailty is a strong predictor of poor postoperative outcomes across surgical specialties, little is known about frailty's impact on cranioplasty outcomes. This study examined the association between frailty and cranioplasty by comparing the effect of the Risk Analysis Index-Administrative (RAI-A) and the Modified Frailty Index-5 (mFI-5) on cranioplasty outcomes.

Methods

The National Surgical Quality Improvement Program was queried for patients undergoing cranioplasty between 2012 and 2020. Receiver operating characteristics and multivariable analyses were used to assess the relationship of postoperative outcomes and the RAI-A, mFI-5, and increasing patient age.

Results

There were 2864 included study patients with a median age of 57 years (IQR, 44-67), and a higher proportion of patients were women (57.0%) and White (68.5%). The RAI-A had a more robust predictive ability for 30-day mortality (C-Statistic, 0.741; 95% confidence interval (CI), 0.678-0.804) compared with mFI-5 (C-Statistic, 0.574; 95% CI, 0.489-0.659) and increasing patient age (C-Statistic, 0.671; 95% CI, 0.610-0.732). On multivariable analyses, frailty was independently associated with mortality and other poor postoperative outcomes (P < 0.05).

Conclusions

The RAI-A demonstrated superior discrimination than the mFI-5 and increasing patient age in predicting mortality. Additionally, the RAI-A showed independent associations with nonhome discharge and postoperative complications (CDII, CDIIIb, and CDIV). The high rates of operative morbidity (5.0%-36.5%) and mortality (0.4%-3.2%) after cranioplasty highlight the importance of identifying independent risk factors for poor cranioplasty outcomes.

Validating biologic age in selecting elderly patients with pancreatic cancer for surgical resection

BACKGROUND AND OBJECTIVES

Selecting frail elderly patients with pancreatic cancer (PC) for pancreas resection using biologic age has not been elucidated. This study determined the feasibility of the deficit accumulation frailty index (DAFI) in identifying such patients and its association with surgical outcomes.

METHODS

The DAFI, which assesses frailty based on biologic age, was used to identify frail patients using clinical and health-related quality-of-life data. The characteristics of frail and nonfrail patients were compared.

RESULTS

Of 242 patients (median age, 75.5 years), 61.2% were frail and 32.6% had undergone pancreas resection (surgery group). Median overall survival (mOS) decreased in frail patients (7.13 months, 95% confidence interval [CI]: 5.65-10.1) compared with nonfrail patients (16.1 months, 95% CI: 11.47-34.40, p = 0.001). In the surgery group, mOS improved in the nonfrail patients (49.4%; 49.2 months, 95% CI: 29.3-79.9) compared with frail patients (50.6%, 22.1 months, 95% CI: 18.3-52.4, p = 0.10). In the no-surgery group, mOS was better in nonfrail patients (54%; 10.81 months, CI 7.85-16.03) compared with frail patients (66%; 5.45 months, 95% CI: 4.34-7.03, p = 0.02).

CONCLUSIONS

The DAFI identified elderly patients with PC at risk of poor outcomes and can identify patients who can tolerate more aggressive treatments.

Identifying Frail Patients by Using Electronic Health Records in Primary Care: Current Status and Future Directions

With the rapidly aging population, frailty, characterized by an increased risk of adverse outcomes, has become a major public health problem globally. Several frailty guidelines or consensuses recommend screening for frailty, especially in primary care settings. However, most of the frailty assessment tools are based on questionnaires or physical examinations, adding to the clinical workload, which is the major obstacle to converting frailty research into clinical practice. Medical data naturally generated by routine clinical work containing frailty indicators are stored in electronic health records (EHRs) (also called electronic health record (EHR) data), which provide resources and possibilities for frailty assessment. We reviewed several frailty assessment tools based on primary care EHRs and summarized the features and novel usage of these tools, as well as challenges and trends. Further research is needed to develop and validate frailty assessment tools based on EHRs in primary care in other parts of the world.