Hospital Frailty Risk Score Research Articles

Prediction of early death after atrial fibrillation diagnosis using a machine learning approach: A French nationwide cohort study

Atrial fibrillation is associated with important mortality but the usual clinical risk factor based scores only modestly predict mortality. This study aimed to develop machine learning models for the prediction of death occurrence within the year following atrial fibrillation diagnosis and compare predictive ability against usual clinical risk scores. We used a nationwide cohort of 2,435,541 newly diagnosed atrial fibrillation patients seen in French hospitals from 2011 to 2019. Three machine learning models were trained to predict mortality within the first year using a training set (70% of the cohort). The best model was selected to be evaluated and compared with previously published scores on the validation set (30% of the cohort). Discrimination of the best model was evaluated using the C index. Within the first year following atrial fibrillation diagnosis, 342,005 patients (14.4%) died after a period of 83 (SD 98) days (median 37 [10-129]). The best machine learning model selected was a deep neural network with a C index of 0.785 (95% CI, 0.781-0.789) on the validation set. Compared to clinical risk scores, the selected model was superior to the CHA2DS2-VASc and HAS-BLED risk scores and superior to dedicated scores such as Charlson Comorbidity Index and Hospital Frailty Risk Score to predict death within the year following atrial fibrillation diagnosis (C indexes: 0.597; 0.562; 0.643; 0.626 respectively. P < .0001). Machine learning algorithms predict early death after atrial fibrillation diagnosis and may help clinicians to better risk stratify atrial fibrillation patients at high risk of mortality.

A comparative analysis of the Hospital Frailty Risk Score in predicting postoperative outcomes among intracranial tumor patients.

In recent years, frailty indices such as the 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 been shown to be effective predictors of various postoperative outcomes in neurosurgical patients. The Hospital Frailty Risk Score (HFRS) is a well-validated tool for assessing frailty; however, its utility has not been evaluated in intracranial tumor surgery. In the present study, the authors investigated the accuracy of the HFRS in predicting outcomes following intracranial tumor resection and compared its utility to those of other validated frailty indices. A retrospective analysis was conducted using an intracranial tumor patient database at a single institution. Patients eligible for study inclusion were those who had undergone resection for an intracranial tumor between January 1, 2017, and December 31, 2019. ICD-10 codes were used to identify HFRS components and subsequently calculate risk scores. In addition to several postoperative variables, ASA class, CCI, and mFI-11 and mFI-5 scores were determined for each patient. Model discrimination was assessed using the area under the receiver operating characteristic curve (AUROC), and the DeLong test was used to assess for significant differences between AUROCs. Multivariate models for continuous outcomes were constructed using linear regression, whereas logistic regression models were used for categorical outcomes. A total of 2518 intracranial tumor patients (mean age 55.3 ± 15.1 years, 53.4% female, 70.4% White) were included in this study. The HFRS had a statistically significant greater AUROC than ASA status, CCI, mFI-11, and mFI-5 for postoperative complications, high hospital charges, nonroutine discharge, and 90-day readmission. In the multivariate analysis, the HFRS was significantly and independently associated with postoperative complications (OR 1.14, p < 0.0001), hospital length of stay (coefficient = 0.50, p < 0.0001), high hospital charges (coefficient = 1917.49, p < 0.0001), nonroutine discharge (OR 1.14, p < 0.0001), and 90-day readmission (OR 1.06, p < 0.0001). The study findings suggest that the HFRS is an effective predictor of postoperative outcomes in intracranial tumor patients and more effectively predicts adverse outcomes than other frailty indices. The HFRS may serve as an important tool for reducing patient morbidity and mortality in intracranial tumor surgery.

Impact of Frailty on Clinical Outcomes in Patients With Atrial Fibrillation Who Underwent Cardiac Ablation Using a Nationwide Database

This study aimed to assess the association between frailty and clinical outcomes in patients with atrial fibrillation (AF) who undergo catheter ablation. We conducted a retrospective cohort study using the National Inpatient Sample database from 2017 to 2019. Adult patients hospitalized with a primary diagnosis of AF who underwent catheter ablation were included. Frailty was assessed using the Hospital Frailty Risk Score. The primary outcome was the presence of any complication (vascular, cardiac, respiratory, neurologic, or infectious), and secondary outcomes were in-hospital mortality, length of hospital stay, and hospital charges. A total of 21,075 weighted hospitalizations were included, and 14% were classified as intermediate or great risk of frailty. Patients with intermediate (adjusted relative risk 2.86, 95% confidence interval 2.24 to 3.67) and great (adjusted relative risk 6.68, 95% confidence interval 3.77 to 11.84) risk of frailty were associated with a greater risk of any complication than that of the group at less risk. The in-hospital mortality rate was significantly higher among patients at intermediate risk than among those at less risk of frailty (2.6% vs 0.1%, p <0.001). Patients with great and intermediate risk had significantly longer hospital stays than did the group with less risk (median 14 vs 5 vs 2days, p <0.001), in addition to greater total charges (median $189,072 vs $161,598 vs $130,672, p <0.001), respectively. In conclusion, frailty was associated with a greater risk of poor short-term outcomes in patients with AF who underwent catheter ablation. The Hospital Frailty Risk Score is a useful tool for identifying patients at increased risk of adverse events and could aid in preoperative optimization and postoperative management.

Frailty, Treatments, and Outcomes in Older Patients With Myocardial Infarction: A Nationwide Registry-Based Study.

Background Guidelines recommend that patients with myocardial infarction (MI) receive equal care regardless of age. However, withholding treatment may be justified in elderly and frail patients. This study aimed to investigate trends in treatments and outcomes of older patients with MI according to frailty. Methods and Results All patients aged ≥75 years with first-time MI during 2002 to 2021 were identified through Danish nationwide registries. Frailty was categorized using the Hospital Frailty Risk Score. One-year risk and hazard ratios (HRs) for days 0 to 28 and 29 to 365 were calculated for all-cause death. A total of 51 022 patients with MI were included (median, 82 years; 50.2% women). Intermediate/high frailty increased from 26.7% in 2002 to 2006 to 37.1% in 2017 to 2021. Use of treatment increased substantially regardless of frailty: for example, 28.1% to 48.0% (statins), 21.8% to 33.7% (dual antiplatelet therapy), and 7.6% to 28.0% (percutaneous coronary intervention) for high frailty (all P-trend <0.001). One-year death decreased for low frailty (35.1%-17.9%), intermediate frailty (49.8%-31.0%), and high frailty (62.8%-45.6%), all P-trend <0.001. Age- and sex-adjusted 29- to 365-day HRs (2017-2021 versus 2002-2006) were 0.53 (0.48-0.59), 0.62 (0.55-0.70), and 0.62 (0.46-0.83) for low, intermediate, and high frailty, respectively (P-interaction=0.23). When additionally adjusted for treatment, HRs attenuated to 0.74 (0.67-0.83), 0.83 (0.74-0.94), and 0.78 (0.58-1.05), respectively, indicating that increased use of treatment may account partially for the observed improvements. Conclusions Use of guideline-based treatments and outcomes improved concomitantly in older patients with MI, irrespective of frailty. These results indicate that guideline-based management of MI may be reasonable in the elderly and frail.

Validation of Frail Scale and comparison with hospital frailty risk score to predict hospital use in a cohort of older Australian women.

With no standard frailty tool for clinical care, research and policymaking, identifying frail older people is a challenge. This study aimed to compare two validated scales, which are the Frail Scale and Hospital Frailty Risk Score (HFRS) for their ability in identifying frailty in older Australian women and predicting hospital use. This study included older Australian women aged 75-95years, who had unplanned overnight hospital admission as an index admission between 2001 and 2016. Data from the Australian Longitudinal Study on Women's Health (ALSWH) were linked with administrative hospital data to calculate HFRS (using the International Statistical Classification of Diseases, Australia Modification (ICD-10-AM) diagnostic codes) and the Frail Scale (using the ALSWH self-reported survey). The Frail Scale identified a higher proportion of older frail women (30.54%) compared to the HFRS (23.0%). Frail older women, classified by Frail Scale, were at higher risk of long hospital stay (adjusted odds ratio=1.28, 95% CI=1.02-1.60), repeated admission (adjusted hazard ratio [AHR]=1.30, 95% CI=1.03-1.41) and death (AHR=1.70, 95% CI=1.45-2.01). HFRS was associated with longer hospital stay and mortality. The proportion of older women classified as frail by the Frail Scale tool was higher than women classified as frail by HFRS. The Frail Scale and HFRS were not significantly associated with each other. While both tools were associated with the risk of long hospital stay and mortality, only the Frail Scale predicted the risk of repeated admission.

Using the Hospital Frailty Risk Score to assess mortality risk in older medical patients admitted to the intensive care unit.

Prognostic information at the time of hospital discharge can help guide goals-of-care discussions for future care. We sought to assess the association between the Hospital Frailty Risk Score (HFRS), which may highlight patients' risk of adverse outcomes at the time of hospital discharge, and in-hospital death among patients admitted to the intensive care unit (ICU) within 12 months of a previous hospital discharge. We conducted a multicentre retrospective cohort study that included patients aged 75 years or older admitted at least twice over a 12-month period to the general medicine service at 7 academic centres and large community-based teaching hospitals in Toronto and Mississauga, Ontario, Canada, from Apr. 1, 2010, to Dec. 31, 2019. The HFRS (categorized as low, moderate or high frailty risk) was calculated at the time of discharge from the first hospital admission. Outcomes included ICU admission and death during the second hospital admission. The cohort included 22 178 patients, of whom 1767 (8.0%) were categorized as having high frailty risk, 9464 (42.7%) as having moderate frailty risk, and 10 947 (49.4%) as having low frailty risk. One hundred patients (5.7%) with high frailty risk were admitted to the ICU, compared to 566 (6.0%) of those with moderate risk and 790 (7.2%) of those with low risk. After adjustment for age, sex, hospital, day of admission, time of admission and Laboratory-based Acute Physiology Score, the odds of ICU admission were not significantly different for patients with high (adjusted odds ratio [OR] 0.99, 95% confidence interval [CI] 0.78 to 1.23) or moderate (adjusted OR 0.97, 95% CI 0.86 to 1.09) frailty risk compared to those with low frailty risk. Among patients admitted to the ICU, 75 (75.0%) of those with high frailty risk died, compared to 317 (56.0%) of those with moderate risk and 416 (52.7%) of those with low risk. After multivariable adjustment, the risk of death after ICU admission was higher for patients with high frailty risk than for those with low frailty risk (adjusted OR 2.86, 95% CI 1.77 to 4.77). Among patients readmitted to hospital within 12 months, patients with high frailty risk were similarly likely as those with lower frailty risk to be admitted to the ICU but were more likely to die if admitted to ICU. The HFRS at hospital discharge can inform prognosis, which can help guide discussions for preferences for ICU care during future hospital stays.

Influence of Frailty in Patients Undergoing Endoscopic Retrograde Cholangiopancreatography for Biliary Stone Disease: A Nationwide Study.

Pancreaticobiliary diseases are common in the elderly. To this end, frailty represents a state of vulnerability that should be considered when assessing the risks and benefits of therapeutic endoscopic procedures. We aim to determine the rate of readmissions and clinical outcomes using the validated Hospital Frailty Risk Score in patients undergoing endoscopic retrograde cholangiopancreatography (ERCP). Using the National Readmissions Database, we identified patients with an admission diagnosis of cholangitis with obstructive stone from 2016 to 2019. Patients were determined to be of low frailty risk with a score of < 5, while patients of medium to high frailty risk had a score of > 5. During the study period, 5751 patients were identified with acute cholangitis with obstructing stone. Mean age of index admissions was 69.4years and 51.8% were female. From the total cohort, 5119 (89.2%) patients underwent therapeutic ERCP, 38.0% (n = 1947) of whom were regarded as frail (risk score > 5). Following ERCP, frail patients had a less but statistically insignificant readmission rate compared to non-frail patients (2.76% vs 4.05%, p = 0.450). However, compared to non-frail patients, frail patients experienced higher post-ERCP complications (6.20% vs 14.63%, p < 0.001). Frail patients were more likely to have longer lengths of stay, higher hospital cost, and mortality risk. ERCP is not a risk factor for readmission among frail patients. However, frail patients are at higher risk for procedure-related complications, healthcare utilization, and mortality.

Frailty in aneurysmal subarachnoid hemorrhage: the risk analysis index.

Few studies have evaluated frailty in the setting of aneurysmal subarachnoid hemorrhage (aSAH) using large-scale data. The risk analysis index (RAI) may be implemented at the bedside or assessed retrospectively, differentiating it from other indices used in administrative registry-based research. Adult aSAH hospitalizations were identified in the National Inpatient Sample (NIS) from 2015 to 2019. Complex samples statistical methods were performed to evaluate the comparative effect size and discriminative ability of the RAI, the modified frailty index (mFI), and the Hospital Frailty Risk Score (HFRS). Poor functional outcome was determined by the NIS-SAH Outcome Measure (NIS-SOM), shown to have high concordance with modified Rankin Scale scores > 2. 42,300 aSAH hospitalizations were identified in the NIS during the study period. By both ordinal [adjusted odds ratio (aOR) 3.20, 95% confidence interval (CI) 3.05, 3.36, p < 0.001] and categorical stratification [frail aOR 3.59, 95% CI 3.39, 3.80, p < 0.001; severely frail aOR 6.67, 95% CI 5.78, 7.69, p < 0.001], the RAI achieved the largest effect sizes for NIS-SOM in comparison with the mFI and HFRS. Discrimination of the RAI for NIS-SOM in high-grade aSAH was significantly greater than that of the HFRS (c-statistic 0.651 vs. 0.615). The mFI demonstrated the lowest discrimination in both high-grade and normal-grade patients. A combined Hunt and Hess-RAI model (c-statistic 0.837, 95% CI 0.828, 0.845) for NIS-SOM achieved significantly greater discrimination than both the combined models for mFI and HFRS (p < 0.001). The RAI was robustly associated with poor functional outcomes in aSAH independent of established risk factors.

#4146 LIMITATIONS AND UNCERTAINTIES OF ASSESSING AND MANAGING OLDER PATIENTS WITH ANCA ASSOCIATED VASCULITIS

Abstract Background and Aims ANCA associated vasculitis (AAV) has a peak incidence in 65 -74 yr olds. As a result, it affects an ageing population who are vulnerable to co-morbidities, polypharmacy and frailty. Due to the high burden of morbidity and mortality in these patients, it is increasingly recognised that individualised management is needed. Whilst current immunotherapies are effective, there remains caution in treating older patients due to the potential risks. Yet despite AAV being a condition that predominantly affects older people, few studies have looked specifically at the benefit of treating AAV in such age groups, or at frailty assessments tools to guide treatment. Method We aimed to evaluate treatment outcomes in older AAV patients, as well as frailty assessment tools to aid prognostication and management. A cohort study evaluating outcomes of induction immunosuppression in patients ≥75yrs was constructed from 2 centres, with subsequent meta-analysis of published data [1]. In addition, studies identifying frail patients, who may be at risk of adverse outcomes, were carried out using the quantitative tools; Hospital Frailty Risk Score (HFRS) [2] and the Clinical Frailty Scale (CFS). Primary outcomes were mortality and end stage kidney disease (ESKD). The HFRS was calculated using hospital coding data from any hospital admissions between a patient's AAV diagnosis to 2yr follow up. Patients were categorized into low, intermediate and high HFRS groups. CFS scores at presentation were documented prospectively and then repeated a minimum of 6 weeks from diagnosis. A CFS score of ≥5 was used to categorise frailty. Differences in the scores were assessed and compared to clinical outcomes. Results Retrospective analysis of our AAV cohort aged ≥75 yrs demonstrated that induction immunosuppression was associated with a significant reduction in the 2yr mortality risk [HR 0.29 (95% CI 0.09–0.93)]. Following systematic review, with a study population of 290 patients, the pooled HR by meta-analysis confirmed a significant reduction in the risk of death with induction treatment [HR 0.31 (95% CI 0.16 - 0.57)]. Treated patients had a lower rate of ESKD, but this was not statistically significant [HR 0.71 (95% CI 0.15 – 3.35)]. Looking separately at frailty assessments, mortality and ESKD were assessed according to HFRS and CFS. Thirty-four patients with AAV aged ≥75 yrs were assessed using HFRS and categorised into the low (n = 18), intermediate (n = 13) and high (n = 3) HFRS groups. All patients in the high HFRS groups had ≥1 episodes of hospitalization after diagnosis of AAV. However, there was no difference in mortality or duration of hospitalisation between the 3 categories. Forty-one patients with AAV aged ≥65yrs were assessed using the CFS. The median CFS at diagnosis and follow-up was 4. There was no significant interval change in CFS (P = 0.16) suggesting that patients did not become frailer. Instead, there was a tendency towards improved frailty scores at reassessment (n = 17, 43%), with some patients going from a CFS of 6 (moderately frail) to 3 (managing well). There was no significant difference in ESKD between those categorised as frail and non-frail (P = 1.0), although crude mortality was higher among those initially categorised as frail (P = 0.03). Conclusion Our research shows that older patients with AAV benefit from standard remission-induction therapy and those treated had favourable mortality outcomes compared to those who were not. Furthermore, frailty assessment tools do not correlate clearly with clinical outcomes. Whilst there is evidence to support the use of HFRS and CFS in a many diseases, its utility in determining frailty and potential susceptibility to adverse effects of therapy in patients with multifaceted, autoimmune inflammatory disease remains limited. Patients may present ‘frail’ as a result of significant disease burden, which has the potential to improve with appropriate treatment. We suggest that age alone and frailty at assessment should not be a limiting factor when considering treatment and prospective studies of this population are needed.

A comparison of three approaches to measuring frailty to determine adverse health outcomes in critically ill patients.

studies comparing different frailty measures in intensive care unit settings are lacking. We aimed to compare the frailty index based on physiological and laboratory tests (FI-Lab), modified frailty index (MFI) and hospital frailty risk score (HFRS) to predict short-term outcomes for critically ill patients. we conducted a secondary analysis of data from the Medical Information Mart for Intensive Care IV database. Outcomes of interest included in-hospital mortality and discharge with need for nursing care. the primary analysis was conducted with 21,421 eligible critically ill patients. After adjusting for confounding variables, frailty as diagnosed by all three frailty measures was found to be significantly associated with increased in-hospital mortality. In addition, frail patients were more likely to receive further nursing care after being discharged. All three frailty scores could improve the discrimination ability of the initial model generated by baseline characteristics for adverse outcomes. The FI-Lab had the best predictive ability for in-hospital mortality, whereas the HFRS had the best predictive performance for discharge with need for nursing care amongst the three frailty measures. A combination of the FI-Lab with either the HFRS or MFI improved the identification of critically ill patients at increased risk of in-hospital mortality. frailty, as assessed by the HFRS, MFI and FI-Lab, was associated with short-term survival and discharge with need for nursing care amongst critically ill patients. The FI-Lab was a better predictor of in-hospital mortality than the HFRS and MFI. Future studies focusing on FI-Lab are warranted.

Frailty as a risk-stratification tool in patients undergoing transjugular intrahepatic portosystemic shunt (TIPS).

The concept of frailty has gained importance, especially in patients with liver disease. Our study systematically investigated the effect of frailty on post-procedural outcomes in patients undergoing transjugular intrahepatic portosystemic shunt (TIPS). We used National Inpatient Sample(NIS) 2016-2019 data to identify patients who underwent TIPS. Hospital frailty risk score (HFRS) was used to classify patients as frail (HFRS>=5) and non-frail (HFRS<5). The relationship between frailty and outcomes such as death, post-procedural shock, non-home discharge, length of stay (LOS), post-procedural LOS, and total hospitalization charges (THC) was assessed. A total of 13,700 patients underwent TIPS during 2016-2019. Of them, 5,995 (43.76%) patients were frail, while 7,705 (56.24%) were non-frail. There were no significant differences between the two groups based on age, gender, race, insurance, and income. Frail patients had higher mortality (15.18% vs. 2.07%, p<0.001), a higher incidence of non-home discharge (53.38% vs. 19.08%, p<0.001), a longer overall LOS (12.5 days vs. 3.35,p<0.001), longer post-procedural stay (8.2 days vs. 3.4 days, p<0.001), and higher THC ($240,746.7 vs. $121,763.1, p<0.001) compared to the non-frail patients. On multivariate analysis, frail patients had a statistically significant higher risk of mortality (aOR-3.22, 95% CI-1.98- 5.00, p<0.001). Frailty assessment can be beneficial in risk stratification in patients undergoing TIPS.

Prediction of early death after atrial fibrillation diagnosis: a french nationwide study

Abstract Funding Acknowledgements Type of funding sources: None. Background Atrial fibrillation (AF) is associated with important mortality. Dedicated clinical scores to predict mortality have been developed but perform modestly and are not specific for this population. Machine learning models are developing in the field of AF and may be able to outperform existing tools for the prediction of mortality. Purpose This study aimed to train and evaluate machine learning models for the prediction death occurrence within this critical period of the year following atrial fibrillation diagnosis and to compare predictive ability to usual clinical scores. Methods We used for this purpose a nationwide cohort of 2,435,541 newly diagnosed atrial fibrillation patients seen in all the French hospitals from 2011 to 2019. Three ML models (logistic regression, random forests, deep neural networks) were trained to predict mortality within the first year on a train set (70% of the cohort). The best model was selected to be evaluate on the test set (30% of the cohort). Discrimination of the best model was evaluated using the C index. We finally compared our best model with previously published scores. Results Within the first year following AF diagnosis, 342,005 patients (14.4%) died after a mean time of 83 (SD 98) days in whom 107,715 were from cardiovascular deaths (31.5%). Among 110 variables, the 18 most predictive variables were identified and selected using a Random Forest algorithm: age, metastasis, resuscitated cardiac arrest, cancer, congestive heart failure, decubitus ulcers, renal failure, pneumonia, lung disease, difficulty in walking, malnutrition, anemia, impaired mobility, liver disease, renal disease, blood transfusion and urinary tract infection. After training 3 ML algorithms, the best ML model selected was a deep neural network with a C index of 0.785 (95% CI, 0.781-0.789) on the test set. The incidence of all cause death at one year rises in a stepwise fashion from 13.8 per 1000 patients for the first quintile to 352.4 per 1000 for the fifth quintile. Compared to traditional clinical risk scores, the selected model was significantly superior to the CHA2DS2-VASc and HAS-BLED scores, and dedicated scores such as Charlson Comorbidity Index and Hospital Frailty Risk Score to predict death within the year following AF diagnosis (C indexes: 0.597; 0.562; 0.643; 0.626 respectively. P&amp;lt;0.0001) (Figure 1). The ability to predict AF was improved as shown by the net reclassification index and integrated discriminatory improvement increase (P&amp;lt;0.0001, respectively) and decision curve analysis (Figure 2). Conclusion Machine learning algorithms predict early death after AF diagnosis with a better ability than previously developed traditional clinical risk scores. A ML approach may help clinicians to better risk stratify AF patients at high risk of mortality.

Midlife Life's Simple 7, Psychosocial Health, and Physical Frailty, Hospital Frailty, and Comprehensive Frailty 10 Years Later.

This study aims to examine the associations between midlife Life's Simple 7 (LS7) status, psychosocial health (social isolation and loneliness), and late-life multidimensional frailty indicators, and to investigate their synergistic effect on frailty. We used cohort data from the UK Biobank. Frailty was assessed using physical frailty phenotype, hospital frailty risk score, and frailty index. Cox proportional-hazards models were used to estimate the hazard ratios (HRs) and 95% confidence intervals (CIs) on the association between the LS7 score, psychosocial health, and frailty. For the association of LS7 with physical and comprehensive frailty, 39,047 individuals were included. After a median follow-up of 9.0 years, 1329 (3.4%) people were identified with physical frailty, and 5699 (14.6%) with comprehensive frailty. For the association of LS7 with hospital frailty, 366,570 people were included. After a median follow-up of 12.0 years, 18,737 (5.1%) people were identified with hospital frailty. Compared to people with a poor LS7 score, those with an intermediate (physical frailty: 0.64, 0.54-0.77; hospital frailty: 0.60, 0.58-0.62; and comprehensive frailty: 0.77, 0.69-0.86) and optimal LS7 score (physical frailty: 0.31, 0.25-0.39; hospital frailty: 0.39, 0.37-0.41; and comprehensive frailty: 0.62, 0.55-0.69) were associated with a lower risk of frailty. Poor psychosocial health was associated with an increased risk of frailty. People who had a poor psychosocial status and poor LS7 score had the highest risk of frailty. A better LS7 score in midlife was associated with a reduced risk of physical, hospital, and comprehensive frailty. There was a synergistic effect of psychosocial status and LS7 on frailty.

All-Cause Readmission or Potentially Avoidable Readmission: Which Is More Predictable Using Frailty, Comorbidities, and ADL?

Readmission-related health care reforms have shifted their focus from all-cause readmissions (ACR) to potentially avoidable readmissions (PAR). However, little is known about the utility of analytic tools from administrative data in predicting PAR. This study determined whether 30-day ACR or 30-day PAR is more predictable using tools that assess frailty, comorbidities, and activities of daily living (ADL) from administrative data. This retrospective cohort study was conducted at a large general acute care hospital in Tokyo, Japan. We analyzed patients aged ≥70 years who had been admitted to and discharged from the subject hospital between July 2016 and February 2021. Using administrative data, we assessed each patient's Hospital Frailty Risk Score, Charlson Comorbidity Index, and Barthel Index on admission. To determine the influence of each tool on readmission predictions, we constructed logistic regression models with different combinations of independent variables for predicting unplanned ACR and PAR within 30 days of discharge. Among 16 313 study patients, 4.1% experienced 30-day ACR and 1.8% experienced 30-day PAR. The full model (including sex, age, annual household income, frailty, comorbidities, and ADL as independent variables) for 30-day PAR showed better discrimination (C-statistic: 0.79, 95% confidence interval: 0.77-0.82) than the full model for 30-day ACR (0.73, 0.71-0.75). The other prediction models for 30-day PAR also had consistently better discrimination than their corresponding models for 30-day ACR. PAR is more predictable than ACR when using tools that assess frailty, comorbidities, and ADL from administrative data. Our PAR prediction model may contribute to the accurate identification of at-risk patients in clinical settings who would benefit from transitional care interventions.

Hospital Frailty Risk Score (HFRS) Predicts Adverse Outcomes Among Hospitalized Patients with Chronic Pancreatitis.

The prevalence of frailty among patients with chronic pancreatitis (CP) and its impact on clinical outcomes is unclear. We report the impact of frailty on mortality, readmission rates, and healthcare utilization among patients with chronic pancreatitis in the United States. We extracted data on patients hospitalized with a primary or secondary diagnosis of CP from the Nationwide Readmissions Database 2019. We applied a previously validated hospital frailty risk scoring system to classify CP patients into frail and non-frail on index hospitalization and compared the characteristics of frail and non-frail patients. We studied the impact of frailty on mortality, readmission, and healthcare utilization. Of 56,072 patients with CP, 40.78% of patients were classified as frail. Frail patients experienced a higher rate of unplanned and preventable hospitalizations. Almost two-thirds of frail patients were younger than 65, and one-third had no or only single comorbidity. On multivariate analysis, frailty was independently associated with two times higher mortality risk (adjusted hazard ratio [aHR], 2.05; 95% CI 1.7-2.5). Frailty was also associated with a higher risk of all-cause readmission with an aHR of 1.07; (95% CI 1.03-1.1). Frail patients experienced a longer length of stay, higher hospitalization costs, and hospitalization charges. Infectious causes were the most common cause of readmission among frail patients compared to acute pancreatitis among non-frail patients. Frailty is independently associated with higher mortality, readmission rates, and healthcare utilization among patients with chronic pancreatitis in the US.

An ensemble machine learning approach to predict postoperative mortality in older patients undergoing emergency surgery

BackgroundPrediction of preoperative frailty risk in the emergency setting is a challenging issue because preoperative evaluation cannot be done sufficiently. In a previous study, the preoperative frailty risk prediction model used only diagnostic and operation codes for emergency surgery and found poor predictive performance. This study developed a preoperative frailty prediction model using machine learning techniques that can be used in various clinical settings with improved predictive performance.MethodsThis is a national cohort study including 22,448 patients who were older than 75 years and visited the hospital for emergency surgery from the cohort of older patients among the retrieved sample from the Korean National Health Insurance Service. The diagnostic and operation codes were one-hot encoded and entered into the predictive model using the extreme gradient boosting (XGBoost) as a machine learning technique. The predictive performance of the model for postoperative 90-day mortality was compared with those of previous frailty evaluation tools such as Operation Frailty Risk Score (OFRS) and Hospital Frailty Risk Score (HFRS) using the receiver operating characteristic curve analysis.ResultsThe predictive performance of the XGBoost, OFRS, and HFRS for postoperative 90-day mortality was 0.840, 0.607, and 0.588 on a c-statistics basis, respectively.ConclusionsUsing machine learning techniques, XGBoost to predict postoperative 90-day mortality, using diagnostic and operation codes, the prediction performance was improved significantly over the previous risk assessment models such as OFRS and HFRS.

Association of frailty with clinical outcomes in chronic obstructive pulmonary disease: A retrospective longitudinal cohort study

BackgroundFrailty is a clinical syndrome and common phenomenon in the elderly, particularly when it coexists with chronic obstructive pulmonary disease (COPD). However, the relationship between frailty and its prognosis in COPD patients has not been clearly elucidated. MethodsWe collected electronic data of inpatients who were diagnosed with COPD in the First Affiliated Hospital with Nanjing Medical University (NJMU) from January 2018 to December 2020. In further, we divided them into different groups based on Frailty Index Common Laboratory Tests (FI-LAB). Binary logistic regression was performed to analyze the risk factors associated with COPD. The receiver operating characteristic (ROC) curve and area under the curve (AUC) were applied to validate FI-LAB's value in prognosis. Primary clinical outcomes contained 30-day mortality and readmission. Moreover, we also compared the prognositic value of FI-LAB with Hospital Frailty Risk Score (HRS) by ROC curve, significance was set at P < 0·05. FindingsThe final study included 826 COPD patients, among of them, 30-day mortality and readmission of frailty group was 11·2%, 25·9%, the robust group was 4·3%, 16·0%, and p value was 0·001, 0·004 respectively. Multivariate analysis revealed that smoking, CCI≥3, oral drug≥5, pneumonia, abnormal lymphocyte, abnormal haemoglobin were independent risk factors with frailty. As for the prediction of FI-LAB about frailty in 30-day mortality, the AUC was 0·832, and 30-day readmission was 0·661. As for the prognositic value, FI-LAB and HRS showed no difference in predicting clinical outcomes. InterpretationCOPD individuals have a higher rate of frailty and pre-frailty. There exists a strong correlation between frailty and 30-day mortality in COPD patients, and FI-LAB has good prognostic value in clinical outcomes of patients with COPD.

Does frailty should stop us now?

Key Points Despite being increasingly encountered in contemporary real‐world practice, due to a global aging of population, frail patients tend to be excluded from large clinical trials, so the impact of frailty on clinical outcomes is often underestimated. Frailty described by the Hospital Frailty Risk Score is associated with high readmission rate, increased healthcare source utilization and lower percutaneous coronary intervention treatment. Further studies are needed to understand if hospital frailty score could provide a prediction model for adverse outcomes, risk‐benefit assessments of interventions, allowing for goal‐oriented care of patients with STEMI.

Frailty and Recurrent Hospitalization After Transcatheter Aortic Valve Replacement.

Background For frail patients with limited life expectancy, time in hospital following transcatheter aortic valve replacement is an important measure of quality of life; however, data remain scarce. Thus, we aimed to investigate frailty and its relation to time in hospital during the first year after transcatheter aortic valve replacement. Methods and Results From 2008 to 2020, all Danish patients who underwent transcatheter aortic valve replacement and were alive at discharge were included. Using the validated Hospital Frailty Risk Score, patients were categorized in the low, intermediate, and high frailty groups. Time in hospital and mortality up to 1 year are reported according to frailty groups. In total, 3437 (57.6%), 2277 (38.1%), and 257 (4.3%) were categorized in the low, intermediate, and high frailty groups, respectively. Median age was ≈81 years. Female sex and comorbidity burden were incrementally higher across frailty groups (low frailty: heart failure, 24.1%; stroke, 7.2%; and chronic kidney disease, 4.5%; versus high frailty: heart failure, 42.8%; stroke, 34.2%; and chronic kidney disease, 29.2%). In the low frailty group, 50.5% survived 1 year without a hospital admission, 10.8% were hospitalized >15 days, and 5.8% of patients died. By contrast, 26.1% of patients in the high frailty group survived 1 year without a hospital admission, 26.4% were hospitalized >15 days, and 15.6% died within 1 year. Differences persisted in models adjusted for sex, age, frailty, and comorbidity burden (excluding overlapping comorbidities). Conclusions Among patients undergoing transcatheter aortic valve replacement, frailty is strongly associated with time in hospital and mortality. Prevention strategies for frail patients to reduce hospitalization burden could be beneficial.

PD08-02 APPLICATION OF HOSPITAL FRAILTY RISK SCORE IN RISK STRATIFYING ELDERLY PATIENTS UNDERGOING NEPHRECTOMY IN A NATIONAL INPATIENT DATABASE

You have accessJournal of UrologyCME1 Apr 2023PD08-02 APPLICATION OF HOSPITAL FRAILTY RISK SCORE IN RISK STRATIFYING ELDERLY PATIENTS UNDERGOING NEPHRECTOMY IN A NATIONAL INPATIENT DATABASE Zhiyu (Jason) Qian, Benjamin Stone, Yuzhe Tang, and Steven Chang Zhiyu (Jason) QianZhiyu (Jason) Qian More articles by this author , Benjamin StoneBenjamin Stone More articles by this author , Yuzhe TangYuzhe Tang More articles by this author , and Steven ChangSteven Chang More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003239.02AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: The majority of kidney cancer is diagnosed after age 65. Risk stratification of elderly patients by frailty is an integral element of surgical decision making. Hospital Frailty Risk Score (HFRS) is a novel instrument based on 109 International Classification of Diseases-10 (ICD-10) diagnosis codes. It was devised in 2018 via rigorous validation with multi-institutional data in the UK and provides an efficient way of screening for frailty in elderly patients using data readily available from electronic medical records. We aimed to examine the ability of HFRS to predict perioperative outcomes among elderly patients undergoing nephrectomy. METHODS: We identified patients over 65 years from the Premier Healthcare Database between 2016 and 2020 after the 2015 shift to ICD-10 coding. HFRS was calculated and stratified into low, moderate, and high frailty risks. Our primary outcomes were the odds of intensive care unit admission, 90-day overall complications, 90-day major complications, and 90-day mortality. We used separate multivariable logistic regression models to analyze each outcome of interest. Covariates adjusted for included age, sex, race, insurance, rural/urban location, region of residence, hospital size, teaching hospital status, minimally invasive technique, and partial or radical nephrectomy. RESULTS: Our cohort included 28,566 elderly patients. 22,071 (77.2%) scored low frailty risk on HFRS. 6,086 (21.3%) and 409 (1.4%) scored moderate and high frailty risk. Patients with moderate and high frailty risks on HFRS exhibited increasing odds of intensive care unit admission (aOR=3.3 95%CI [3.1, 3.6] and aOR=6.3 95%CI [5.1, 7.8]), 90-day overall complications (aOR=4.2 95%CI [3.9, 4.5] and aOR=8.0 95%CI [5.7, 11.3]), 90-day major complications (aOR=3.1 95%CI [2.9, 3.3] and aOR=6.9 95%CI [5.6, 8.4]), and 90-day mortality (aOR=6.6 95%CI [5.0, 8.7] and aOR = 12.4 95%CI [7.6, 20.1]). Figure 1 summarizes the predictive probabilities from these models. CONCLUSIONS: Higher HFRS frailty risk is associated with progressively worse short-term outcomes from nephrectomy. With one out of every seven elderly patients undergoing surgery being frail, HFRS offers a straightforward, effective, and contemporary means of pre-nephrectomy risk stratification. Source of Funding: n/a © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 209Issue Supplement 4April 2023Page: e232 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information Zhiyu (Jason) Qian More articles by this author Benjamin Stone More articles by this author Yuzhe Tang More articles by this author Steven Chang More articles by this author Expand All Advertisement PDF downloadLoading ...