Prediction of 30-Day Readmission in Hospitalized Older Adults Using Comprehensive Geriatric Assessment and LACE Index and HOSPITAL Score

Predicting 1 year readmission for heart failure: A comparative study of machine learning and the LACE index

AIMS

There is a lack of tools for accurately identifying the risk of readmission for heart failure in elderly patients with arrhythmia. The aim of this study was to establish and compare the performance of the LACE [length of stay ('L'), acute (emergent) admission ('A'), Charlson comorbidity index ('C') and visits to the emergency department during the previous 6 months ('E')] index and machine learning in predicting 1 year readmission for heart failure in elderly patients with arrhythmia.

METHODS

Elderly patients with arrhythmia who were hospitalized at Sichuan Provincial People's Hospital between 1 June 2018 and 31 May 2020 were enrolled. The LACE index was calculated for each patient, and the area under the receiver operating characteristic curve (AUROC) was calculated. Six machine learning algorithms, combined with three variable selection methods and clinically relevant features available at the time of hospital discharge, were used to develop machine learning models. AUROC and area under the precision-recall curve (AUPRC) were used to assess discrimination. Shapley additive explanations (SHAP) analysis was used to explain the contributions of the features.

RESULTS

A total of 523 patients were enrolled, and 108 patients experienced 1 year hospital readmission for heart failure. The AUROC of the LACE index was 0.5886. The complete machine learning model had the best predictive performance, with an AUROC of 0.7571 and an AUPRC of 0.4096. The most important predictors for 1 year readmission were educational level, total triiodothyronine (TT3), aspartate aminotransferase/alanine aminotransferase (AST/ALT), number of medications (NOM) and triglyceride (TG) level.

CONCLUSIONS

Compared with the LACE index, the machine learning model can accurately identify the risk of 1 year readmission for heart failure in elderly patients with arrhythmia.

Predictors of hospital readmission rate in geriatric patients

BACKGROUND

Hospital readmissions among older adults are associated with progressive functional worsening, increased institutionalization and mortality.

AIM

Identify the main predictors of readmission in older adults.

METHODS

We examined readmission predictors in 777 hospitalized subjects (mean age 84.40 ± 6.77 years) assessed with Comprehensive Geriatric Assessment (CGA), clinical, anthropometric and biochemical evaluations. Comorbidity burden was estimated by Charlson Comorbidity Index (CCI). Median follow-up was 365 days.

RESULTS

358 patients (46.1%) had a second admission within 365 days of discharge. Estimated probability of having a second admission was 0.119 (95%C.I. 0.095-0.141), 0.158 (95%C.I. 0.131-0.183), and 0.496 (95%C.I. 0.458-0.532) at 21, 30 and 356 days, respectively. Main predictors of readmission at 1 year were length of stay (LOS) > 14 days (p < 0.001), albumin level < 30 g/l (p 0.018), values of glomerular filtration rate (eGFR) < 40 ml/min (p < 0.001), systolic blood pressure < 115 mmHg (p < 0.001), CCI ≥ 6 (p < 0.001), and cardiovascular diagnoses. When the joint effects of selected prognostic variables were accounted for, LOS > 14 days, worse renal function, systolic blood pressure < 115 mmHg, higher comorbidity burden remained independently associated with higher readmission risk.

DISCUSSION

Selected predictors are associated with higher readmission risk, and the relationship evolves with time.

CONCLUSIONS

This study highlights the importance of performing an accurate CGA, since defined domains and variables contained in the CGA (i.e., LOS, lower albumin and systolic blood pressure, poor renal function, and greater comorbidity burden), when combined altogether, may offer a valid tool to identify the most fragile patients with clinical and functional impairment enhancing their risk of unplanned early and late readmission.

Short-term prognosis of polypharmacy in elderly patients treated in emergency departments: results from the EDEN project

Background

Polypharmacy is a growing phenomenon among elderly individuals. However, there is little information about the frequency of polypharmacy among the elderly population treated in emergency departments (EDs) and its prognostic effect. This study aims to determine the prevalence and short-term prognostic effect of polypharmacy in elderly patients treated in EDs.

Methods

A retrospective analysis of the Emergency Department Elderly in Needs (EDEN) project's cohort was performed. This registry included all elderly patients who attended 52 Spanish EDs for any condition. Mild and severe polypharmacy was defined as the use of 5-9 drugs and ⩾10 drugs, respectively. The assessed outcomes were ED revisits, hospital readmissions, and mortality 30 days after discharge. Crude and adjusted logistic regression analyses, including the patient's comorbidities, were performed.

Results

A total of 25,557 patients were evaluated [mean age: 78 (IQR: 71-84) years]; 10,534 (41.2%) and 5678 (22.2%) patients presented with mild and severe polypharmacy, respectively. In the adjusted analysis, mild polypharmacy and severe polypharmacy were associated with an increase in ED revisits [odds ratio (OR) 1.13 (95% confidence interval (CI): 1.04-1.23) and 1.38 (95% CI: 1.24-1.51)] and hospital readmissions [OR 1.18 (95% CI: 1.04-1.35) and 1.36 (95% CI: 1.16-1.60)], respectively, compared to non-polypharmacy. Mild and severe polypharmacy were not associated with increased 30-day mortality [OR 1.05 (95% CI: 0.89-2.26) and OR 0.89 (95% CI: 0.72-1.12)], respectively.

Conclusion

Polypharmacy was common among the elderly treated in EDs and associated with increased risks of ED revisits and hospital readmissions ⩽30 days but not with an increased risk of 30-day mortality. Patients with polypharmacy had a higher risk of ED revisits and hospital readmissions ⩽30 days after discharge.