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Developing a Parsimonious Frailty Index for Older, Multimorbid Adults With Heart Failure Using Machine Learning.

ABSTRACT: Frailty is associated with adverse outcomes in heart failure (HF). A parsimonious frailty index (FI) that predicts outcomes of older, multimorbid patients with HF could be a useful resource for clinicians. A retrospective study of veterans hospitalized from October 2015 to October 2018 with HF, aged ≥50 years, and discharged home developed a 10-item parsimonious FI using machine learning from diagnostic codes, laboratory results, vital signs, and ejection fraction (EF) from outpatient encounters. An unsupervised clustering technique identified 5 FI strata: severely frail, moderately frail, mildly frail, prefrail, and robust. We report hazard ratios (HRs) of mortality, adjusting for age, gender, race, and EF and odds ratios (ORs) for 30-day and 1-year emergency department visits and all-cause hospitalizations after discharge. We identified 37,431 veterans (age, 73 ± 10 years; co-morbidity index, 5 ± 3; 43.5% with EF ≤40%). All frailty groups had a higher mortality than the robust group: severely frail (HR 2.63, 95% confidence interval [CI] 2.42 to 2.86), moderately frail (HR 2.04, 95% CI 1.87 to 2.22), mildly frail (HR 1.60, 95% CI 1.47 to 1.74), and prefrail (HR 1.18, 95% CI: 1.07 to 1.29). The associations between frailty and mortality remained unchanged in the stratified analysis by age or EF. The combined (severely, moderately, and mildly) frail group had higher odds of 30-day emergency visits (OR 1.62, 95% CI 1.43 to 1.83), all-cause readmission (OR, 1.75, 95% CI 1.52 to 2.02), 1-year emergency visits (OR 1.70, 95% CI 1.53 to 1.89), rehospitalization (OR 2.18, 95% CI 1.97 to 2.41) than the robust group. In conclusion, a 10-item FI is associated with postdischarge outcomes among patients discharged home after a hospitalization for HF. A parsimonious FI may aid clinical prediction at the point of care.

SUBMITTER: Razjouyan J

PROVIDER: S-EPMC9951585 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature

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Developing a Parsimonious Frailty Index for Older, Multimorbid Adults With Heart Failure Using Machine Learning.

Razjouyan Javad J Horstman Molly J MJ Orkaby Ariela R AR Virani Salim S SS Intrator Orna O Goyal Parag P Amos Christopher I CI Naik Aanand D AD

The American journal of cardiology 20221223

Frailty is associated with adverse outcomes in heart failure (HF). A parsimonious frailty index (FI) that predicts outcomes of older, multimorbid patients with HF could be a useful resource for clinicians. A retrospective study of veterans hospitalized from October 2015 to October 2018 with HF, aged ≥50 years, and discharged home developed a 10-item parsimonious FI using machine learning from diagnostic codes, laboratory results, vital signs, and ejection fraction (EF) from outpatient encounters ...[more]

PMID: 36566620

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Project description:AimsFrailty may be found in heart failure patients especially in the elderly and is associated with a poor prognosis. However, assessment of frailty status is time-consuming, and the electronic frailty indices developed using health records have served as useful surrogates. We hypothesized that an electronic frailty index developed using machine learning can improve short-term mortality prediction in patients with heart failure.Methods and resultsThis was a retrospective observational study that included patients admitted to nine public hospitals for heart failure from Hong Kong between 2013 and 2017. Age, sex, variables in the modified frailty index, Deyo's Charlson co-morbidity index (≥2), neutrophil-to-lymphocyte ratio (NLR), and prognostic nutritional index at baseline were analysed. Gradient boosting, which is a supervised sequential ensemble learning algorithm with weak prediction submodels (typically decision trees), was applied to predict mortality. Variables were ranked in the order of importance with a total score of 100 and used to build the frailty models. Comparisons were made with decision tree and multivariable logistic regression. A total of 8893 patients (median: age 81, Q1-Q3: 71-87 years old) were included, in whom 9% had 30 day mortality and 17% had 90 day mortality. Prognostic nutritional index, age, and NLR were the most important variables predicting 30 day mortality (importance score: 37.4, 32.1, and 20.5, respectively) and 90 day mortality (importance score: 35.3, 36.3, and 14.6, respectively). Gradient boosting significantly outperformed decision tree and multivariable logistic regression. The area under the curve from a five-fold cross validation was 0.90 for gradient boosting and 0.87 and 0.86 for decision tree and logistic regression in predicting 30 day mortality. For the prediction of 90 day mortality, the area under the curve was 0.92, 0.89, and 0.86 for gradient boosting, decision tree, and logistic regression, respectively.ConclusionsThe electronic frailty index based on co-morbidities, inflammation, and nutrition information can readily predict mortality outcomes. Their predictive performances were significantly improved by gradient boosting techniques.

Project description:EscapeEvaluation of a patient-centred biopsychosocial blended collaborative care pathway for the treatment of multimorbid elderly patients.Therapeutic areaHealthcare interventions for the management of older patients with multiple morbidities.AimsMulti-morbidity treatment is an increasing challenge for healthcare systems in ageing societies. This comprehensive cohort study with embedded randomized controlled trial tests an integrated biopsychosocial care model for multimorbid elderly patients.HypothesisA holistic, patient-centred pro-active 9-month intervention based on the blended collaborative care (BCC) approach and enhanced by information and communication technologies can improve health-related quality of life (HRQoL) and disease outcomes as compared with usual care at 9 months.MethodsAcross six European countries, ESCAPE is recruiting patients with heart failure, mental distress/disorder plus ≥2 medical co-morbidities into an observational cohort study. Within the cohort study, 300 patients will be included in a randomized controlled assessor-blinded two-arm parallel group interventional clinical trial (RCT). In the intervention, trained care managers (CMs) regularly support patients and informal carers in managing their multiple health problems. Supervised by a clinical specialist team, CMs remotely support patients in implementing the treatment plan-customized to the patients' individual needs and preferences-into their daily lives and liaise with patients' healthcare providers. An eHealth platform with an integrated patient registry guides the intervention and helps to empower patients and informal carers. HRQoL measured with the EQ-5D-5L as primary endpoint, and secondary outcomes, that is, medical and patient-reported outcomes, healthcare costs, cost-effectiveness, and informal carer burden, will be assessed at 9 and ≥18 months.ConclusionsIf proven effective, the ESCAPE BCC intervention can be implemented in routine care for older patients with multiple morbidities across the participating countries and beyond.

Project description:AimsRelationship between body mass index (BMI), frailty, and clinical adverse events remains unclear in patients with heart failure (HF) with preserved ejection fraction (HFpEF) in different patient populations. We aimed to compare the association of BMI, frailty, and clinical adverse events between a US cohort from the Treatment of Preserved Cardiac Function Heart Failure with an Aldosterone Antagonist (TOPCAT) study and a Chinese cohort from the Heart Failure Registry of Patient Outcomes (HERO) study.Methods and resultsWe used data of 1715 participants enrolled from America in the TOPCAT study and 1487 patients with HFpEF in the Chinese registry study, the HERO. We evaluated the relationship between BMI and frailty using multivariate restricted cubic spline logistic regression. Association between frailty and BMI categories and primary outcomes including HF hospitalization, aborted sudden death, and cardiovascular death, all-cause mortality, and HF hospitalization were analysed by Cox proportional hazards models. The patients' mean age was 72 ± 11 years for both study populations, with 50% and 46% female for the TOPCAT study and the HERO study, respectively. Patients in the TOPCAT study had a higher mean BMI (33.9 vs. 24 kg/m2), with 72.3% vs. 52.9% defined as moderately to severely frail (frailty index > 0.3). In the TOPCAT study, risk of frailty rose as BMI increased, but not in the HERO study. Patients with frailty were at significant higher risk for the primary composite outcomes [hazard ratio (HR) 1.84 (95% confidence interval: 1.46-2.32)], all-cause mortality [HR 1.73 (1.34-2.25)], and HF hospitalization [HR 1.83 (1.40-2.40)] in the TOPCAT study. The corresponding numbers in the HERO study were 1.26 (1.01-1.57), 2.21 (1.45-3.35), and 1.15 (0.81-1.37), respectively. The association of frailty with clinical outcomes did not vary with BMI categories in the two studies.ConclusionsBMI distribution and association between BMI and frailty risk were different between the two study populations. Frailty was associated with clinical adverse events and this association was consistent across different BMI categories in both studies.

Dataset Information

Developing a Parsimonious Frailty Index for Older, Multimorbid Adults With Heart Failure Using Machine Learning.

Publications

Developing a Parsimonious Frailty Index for Older, Multimorbid Adults With Heart Failure Using Machine Learning.

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