Hospital Frailty Risk Score and healthcare resource utilization after surgery for metastatic spinal column tumors

Hospital frailty risk score predicts high-value care outcomes following brain metastasis resection

OBJECTIVE

Brain metastases (BM) are the most common adult intracranial tumors, representing a significant source of morbidity in patients with systemic malignancy. Frailty indices, including 11- and 5-factor modified frailty indices (mFI-11 and mFI-5), American Society of Anesthesiologists (ASA) physical status classification, and Charlson Comorbidity Index (CCI), have recently demonstrated an important role in predicting high-value care outcomes in neurosurgery. This study aims to investigate the efficacy of the newly developed Hospital Frailty Risk Score (HFRS) on postoperative outcomes in BM patients.

METHODS

Adult patients with BM treated surgically at a single institution were identified (2017-2019). HFRS was calculated using ICD-10 codes, and patients were subsequently separated into low (<5), intermediate (5-15), and high (>15) HFRS cohorts. Multivariate logistic regressions were utilized to identify associations between HFRS and complications, length of stay (LOS), hospital charges, and discharge disposition. Model discrimination was assessed using receiver operating characteristic (ROC) curves.

RESULTS

A total of 356 patients (mean age: 61.81±11.63 years; 50.6 % female) were included. The mean±SD for HFRS, mFI-11, mFI-5, ASA, and CCI were 6.46±5.73, 1.31±1.24, 0.95±0.86, 2.94±0.48, and 8.69±2.07, respectively. On multivariate analysis, higher HFRS was significantly associated with greater complication rate (OR=1.10, p<0.001), extended LOS (OR=1.13, p<0.001), high hospital charges (OR=1.14, p<0.001), and nonroutine discharge disposition (OR=1.12, p<0.001), and comparing the ROC curves of mFI-11, mFI-5, ASA,and CCI, the predictive accuracy of HFRS was the most superior for all four outcomes assessed.

CONCLUSION

The predictive ability of HFRS on BM resection outcomes may be superior than other frailty indices, offering a new avenue for routine preoperative frailty assessment and for managing postoperative expectations.

Knowledge mapping of frailty and surgery: a bibliometric and visualized analysis

PURPOSE

Frailty is common in surgical patients and is closely associated with postoperative outcomes.

AIMS

This study employed bibliometric methods to summarize and analyze research related to frailty and surgery, comprehensively analyzing the research structure and providing visualized maps.

METHODS

This study analyzed the volume of publications, countries, institutions, authors, journals, references, and keywords related to perioperative frailty in the Web of Science Core Collection from 1978 to 2024. Visual bibliometric analyses were conducted from multiple perspectives, including collaboration networks, citation analysis, and keyword clustering.

RESULTS

From 1978 to 2024, 21,879 authors from 95 countries and regions published 4,119 papers on perioperative frailty in 973 journals worldwide. The United States has the most publications, while Italy has the highest degree of international collaboration. The University of California System has the highest number of publications. The University of Kansas Medical Center is the institution with the highest centrality. The top nine authors in terms of publication volume are all from the USA. Bowers Christian A. is the most prolific author. The Journal of Vascular Surgery is the journal with the most publications. Current research directions include preoperative risk assessment of frailty, the relationship between frailty and postoperative complications, elderly frailty, and the relationship between frailty and sarcopenia. Research hotspots include risk stratification, postoperative delirium, the elderly, and sarcopenia.

CONCLUSION

This study has identified the research hotspots and trends in perioperative frailty. Our findings will enable researchers to understand this field's knowledge structure better and identify future research directions.

Assessing a revised-risk analysis index for morbidity and mortality after spine surgery for metastatic spinal tumors

BACKGROUND

Risk Analysis Index (RAI) has been increasingly used to assess surgical frailty in various procedures, but its effectiveness in predicting mortality or in-patient hospital outcomes for spine surgery in metastatic disease remains unclear. The aim of this study was to compare the predictive values of the revised RAI (RAI-rev), the modified frailty index-5 (mFI-5), and advanced age for extended length of stay, 30-day readmission, complications, and mortality among patients undergoing spine surgery for metastatic spinal tumors.

METHODS

A retrospective cohort study was performed using the 2012-2022 ACS NSQIP database to identify adult patients who underwent spinal surgery for metastatic spinal pathologies. Using receiver operating characteristic (ROC) and multivariable analyses, we compared the discriminative thresholds and independent associations of RAI-rev, mFI-5, and greater patient age with extended length of stay (LOS), 30-day complications, hospital readmission, and mortality.

RESULTS

A total of 1,796 patients were identified, of which 1,116 (62.1%) were male and 1,008 (70.7%) were non-Hispanic White. RAI-rev identified 1,291 (71.9%) frail and 208 (11.6%) very frail patients, while mFI-5 identified 272 (15.1%) frail and 49 (2.7%) very frail patients. In the ROC analysis for extended LOS, both RAI-rev and mFI-5 showed modest predictive capabilities with area under the curve (AUC) values of 0.5477 and 0.5329, respectively, and no significant difference in their predictive abilities (p = 0.446). When compared to age, RAI-rev demonstrated superior prediction (p = 0.015). With respect to predicting 30-day readmission, no significant difference was observed between RAI-rev and mFI-5 (AUC 0.5394 l respectively, p = 0.354). However, RAI-rev outperformed age (p = 0.001). When assessing the risk of 30-day complications, RAI-rev significantly outperformed mFI-5 (AUC: 0.6016 and 0.5542 respectively, p = 0.022) but not age. Notably, RAI-rev demonstrated superior ability for predicting 30-day mortality compared to mFI-5 and age (AUC: 0.6541, 0.5652, and 0.5515 respectively, p < 0.001). Multivariate analysis revealed RAI-rev as a significant predictor of extended LOS [aOR: 1.96, 95% CI: 1.13-3.38, p = 0.016] and 30-day mortality [aOR: 5.27, 95% CI: 1.73-16.06, p = 0.003] for very frail patients. Similarly, the RAI-rev significantly predicted 30-day complications for frail [aOR: 2.63, 95% CI: 1.21-5.72, p = 0.015] and very frail [aOR: 3.69, 95% CI: 1.60-8.51, p = 0.002] patients. However, the RAI did not significantly predict 30-day readmission [Very Frail aOR: 1.52, 95% CI: 0.75-3.07, p = 0.245; Frail aOR: 1.46, 95% CI: 0.79-2.68, p = 0.225].

CONCLUSION

Our study demonstrates the utility of RAI-rev in predicting morbidity and mortality in patients undergoing spine surgery for metastatic spinal pathologies. Particularly, the superiority that RAI-rev has in predicting 30-day mortality may have significant implications in multidisciplinary decision making.

Utilization of Machine Learning to Model Important Features of 30-day Readmissions following Surgery for Metastatic Spinal Column Tumors: The Influence of Frailty

STUDY DESIGN

Retrospective cohort study.

OBJECTIVE

The aim of this study was to determine the relative importance and predicative power of the Hospital Frailty Risk Score (HFRS) on unplanned 30-day readmission after surgical intervention for metastatic spinal column tumors.

METHODS

All adult patients undergoing surgery for metastatic spinal column tumor were identified in the Nationwide Readmission Database from the years 2016 to 2018. Patients were categorized into 3 cohorts based on the criteria of the HFRS: Low(<5), Intermediate(5-14.9), and High(≥ 15). Random Forest (RF) classification was used to construct predictive models for 30-day patient readmission. Model performance was examined using the area under the receiver operating curve (AUC), and the Mean Decrease Gini (MDG) metric was used to quantify and rank features by relative importance.

RESULTS

There were 4346 patients included. The proportion of patients who required any readmission were higher among the Intermediate and High frailty cohorts when compared to the Low frailty cohort (Low:33.9% vs. Intermediate:39.3% vs. High:39.2%, P < .001). An RF classifier was trained to predict 30-day readmission on all features (AUC = .60) and architecturally equivalent model trained using only ten features with highest MDG (AUC = .59). Both models found frailty to have the highest importance in predicting risk of readmission. On multivariate regression analysis, Intermediate frailty [OR:1.32, CI(1.06,1.64), P = .012] was found to be an independent predictor of unplanned 30-day readmission.

CONCLUSION

Our study utilizes machine learning approaches and predictive modeling to identify frailty as a significant risk-factor that contributes to unplanned 30-day readmission after spine surgery for metastatic spinal column metastases.

Validation of the Hospital Frailty Risk Score in older adults hospitalized with community-acquired pneumonia

AIM

Frailty results from age-associated declines in physiological reserve and function and is prevalent in older people. Our aim is to examine the association of the Hospital Frailty Risk Score (HFRS) with adverse events in older patients hospitalized with community-acquired pneumonia (CAP) and hypothesise that frailty is a comparable predictor of outcomes in CAP versus traditional severity indices such as CURB-65.

METHODS

Retrospective review of electronic medical records in patients ≥65 years with CAP admitted to a tertiary hospital from 1 January to 30 April 2021. Patients were identified using ICD codes for CAP and categorized as high risk (>15), intermediate risk (5-15) and low risk (<5) of frailty using the HFRS.

RESULTS

Of 429 patients with CAP, 53.8% male, mean age of 82.9 years, older patients (85 vs. 79.7 years, P < 0.001) were at higher risk of frailty. Using the HFRS, 47.6% were deemed at high risk, 35.9% at intermediate risk, and 16.6% at low risk of frailty. Multivariate logistic regression shows that HFRS was more strongly associated (≥7 days, OR 1.042, CI 1.017-1.069) than CURB-65 (OR 0.995, CI 0.810-1.222) with long hospital length of stay (LOS), while CURB-65 (Confusion, Urea >7mmol/L, Respiratory rate >30, Blood pressure, age => 65 years old) was more strongly associated with mortality at 30, 90 and 365 days, compared with the HFRS. Comparing the values for the area under the receiver operator characteristic curve, the HFRS was found to be a better predictor of long LOS, while CURB-65 remains a better predictor of mortality.

CONCLUSIONS

Patients with high risk of frailty have higher healthcare utilization and HFRS is a better predictor of long LOS than CURB-65 but CURB-65 was a better predictor of mortality. Geriatr Gerontol Int 2024; 24: 135-141.

Frailty-aware surgical care: Validation of Hospital Frailty Risk Score (HFRS) in older surgical patients

Introduction

Frailty has an important impact on the health outcomes of older patients, and frailty screening is recommended as part of perioperative evaluation. The Hospital Frailty Risk Score (HFRS) is a validated tool that highlights frailty risk using 109 International Classification of Diseases, 10th revision (ICD-10) codes. In this study, we aim to compare HFRS to the Charlson Comorbidity Index (CCI) and validate HFRS as a predictor of adverse outcomes in Asian patients admitted to surgical services.

Method

A retrospective study of electronic health records (EHR) was undertaken in patients aged 65 years and above who were discharged from surgical services between 1 April 2022 to 31 July 2022. Patients were stratified into low (HFRS <5), interme-diate (HFRS 5-15) and high (HFRS >15) risk of frailty.

Results

Those at high risk of frailty were older and more likely to be men. They were also likely to have more comorbidities and a higher CCI than those at low risk of frailty. High HFRS scores were associated with an increased risk of adverse outcomes, such as mortality, hospital length of stay (LOS) and 30-day readmission. When used in combination with CCI, there was better prediction of mortality at 90 and 270 days, and 30-day readmission.

Conclusion

To our knowledge, this is the first validation of HFRS in Singapore in surgical patients and confirms that high-risk HFRS predicts long LOS (≥7days), increased unplanned hospital readmissions (both 30-day and 270-day) and increased mortality (inpatient, 10-day, 30-day, 90-day, 270-day) compared with those at low risk of frailty.

The Limited Utility of the Hospital Frailty Risk Score as a Frailty Assessment Tool in Neurosurgery: A Systematic Review

BACKGROUND AND OBJECTIVES

The Hospital Frailty Risk Score (HFRS) is an International Classification of Disease 10th Revision-based scale that was originally designed for, and validated in, the assessment of patients 75 years or older presenting in an acute care setting. This study highlights central tenets inherent to the concept of frailty; questions the logic behind, and utility of, HFRS' recent implementation in the neurosurgical literature; and discusses why there is no useful role for HFRS as a frailty-based neurosurgical risk assessment (FBNRA) tool.

METHODS

The authors performed a systematic review of the literature per Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, including all cranial and spinal studies that used HFRS as their primary frailty tool. Seventeen (N = 17) studies used HFRS to assess frailty's impact on neurosurgical outcomes. Thirteen total journals, 10 of which were neurosurgical journals, including the highest impact factor journals, published the 17 papers.

RESULTS

Increasing HFRS score was associated with adverse outcomes, including prolonged length of stay (11 of 17 studies), nonroutine discharge (10 of 17 studies), and increased hospital costs (9 of 17 studies). Four different HFRS studies, of the 17, predicted one of the following 4 adverse outcomes: worse quality of life, worse functional outcomes, reoperation, or in-hospital mortality.

CONCLUSION

Despite its rapid acceptance and widespread proliferation through the leading neurosurgical journals, HFRS lacks any conceptual relationship to the frailty syndrome or FBNRA for individual patients. HFRS measures acute conditions using International Classification of Disease 10th Revision codes and awards "frailty" points for symptoms and examination findings unrelated to the impaired baseline physiological reserve inherent to the very definition of frailty. HFRS lacks clinical utility as it cannot be deployed point-of-care at the bedside to risk stratify patients. HFRS has never been validated in any patient population younger than 75 years or in any nonacute care setting. We recommend HFRS be discontinued as an individual FBNRA tool.

Impact of Frailty on Short-Term Outcomes of Hepatic Lobectomy in Patients with Intrahepatic Cholangiocarcinoma: Evidence from the US Nationwide Inpatient Sample 2005-2018

INTRODUCTION

This study aimed to evaluate associations between frailty and outcomes in patients with intrahepatic cholangiocarcinoma (ICC) undergoing hepatic lobectomy using a large, nationally representative sample.

METHODS

This population-based, retrospective observational study extracted the data of adults ≥20 years old with ICC undergoing hepatic lobectomy from the US Nationwide Inpatient Sample database between 2005 and 2018. Frailty was assessed by the validated Hospital Frailty Risk Score (HFRS). Associations between frailty and surgical outcomes were analyzed using logistic regression analyses.

RESULTS

After exclusions, 777 patients were enrolled, including 427 frail and 350 non-frail. Patients' mean age was 64.5 (±0.4) years and the majority were males (51.1%) and whites (76.5%). Frailty was significantly associated with increased odds of in-hospital mortality (aOR: 18.51, 95% CI: 6.70, 51.18), non-home discharge (aOR: 3.58, 95% CI: 2.26, 5.66), prolonged LOS (aOR: 5.56, 95% CI: 3.87, 7.99), perioperative cardiac arrest/stroke (aOR: 5.44, 95% CI: 1.62, 18.24), acute respiratory distress syndrome (ARDS)/respiratory failure (aOR: 3.88, 95% CI: 2.40, 6.28), tracheostomy/ventilation (aOR: 3.83, 95% CI: 2.23, 6.58), bleeding/transfusion (aOR: 1.67, 95% CI: 1.24, 2.26), acute kidney injury (AKI) (aOR: 14.37, 95% CI: 7.13, 28.99), postoperative shock (aOR: 4.44, 95% CI: 2.54, 7.74), and sepsis (aOR: 11.94, 95% CI: 6.90, 20.67).

DISCUSSION/CONCLUSION

Among patients with ICC undergoing hepatic lobectomy, HFRS-defined frailty is a strong predictor of worse in-patient outcomes, including in-hospital death, prolonged LOS, unfavorable discharge, and complications (perioperative cardiac arrest/stroke, ARDS/respiratory failure, tracheostomy/ventilation, bleeding/transfusion, AKI, postoperative shock, and sepsis). Study results may help stratify risk in frail patients undergoing hepatic resection for ICC.

Frailty Indexes in Metastatic Spine Tumor Surgery: A Narrative Review

Quantification of preoperative frailty is an important prognostic tool in neurosurgical decision making. Metastatic spine tumor patients undergoing surgery are frail and have unfavorable outcomes that include an increased length of stay, unfavorable discharge disposition, and increased readmission rates. These undesirable outcomes result in higher treatment costs. A heterogeneous mixture of various frailty indexes is available with marked variance in their validation, leading to disparate clinical utility. The lack of a universally accepted definition for frailty, let alone in the method of creation or elements required in the formation of a frailty index, has resulted in a body of frailty literature lacking precision for predicting neurosurgical outcomes. In this review, we examine the role of reported frailty indexes in predicting postoperative outcomes after resection of metastatic spine tumors and aim to assist as a frailty guide for helping clinical decision making.

Quantifying care delivery team influences on the hospitalization outcomes of patients with multimorbidity: Implications for clinical informatics

The primary objective was to quantify the influences of care delivery teams on the outcomes of patients with multimorbidity. Electronic medical record data on 68,883 patient care encounters (i.e., 54,664 patients) were extracted from the Arkansas Clinical Data Repository. Social network analysis assessed the minimum care team size associated with improved care outcomes (i.e., hospitalizations, days between hospitalizations, and cost) of patients with multimorbidity. Binomial logistic regression further assessed the influence of the presence of seven specific clinical roles. When compared to patients without multimorbidity, patients with multimorbidity had a higher mean age (i.e., 47.49 v. 40.61), a higher mean dollar amount of cost per encounter (i.e., $3,068 v. $2,449), a higher number of hospitalizations (i.e., 25 v. 4), and a higher number of clinicians engaged in their care (i.e., 139,391 v. 7,514). Greater network density in care teams (i.e., any combination of two or more Physicians, Residents, Nurse Practitioners, Registered Nurses, or Care Managers) was associated with a 46-98% decreased odds of having a high number of hospitalizations. Greater network density (i.e., any combination of two or more Residents or Registered Nurses) was associated with 11-13% increased odds of having a high cost encounter. Greater network density was not significantly associated with having a high number of days between hospitalizations. Analyzing the social networks of care teams may fuel computational tools that better monitor and visualize real-time hospitalization risk and care cost that are germane to care delivery.