Identifying Unexpected Deaths in Long-Term Care Homes

Physical function decline predictors in nursing home residents using new national quality indicators

AIM

To determine the predictors of physical function (PF) decline among nursing home residents using items from the Long-term care Information system For Evidence (LIFE), a system launched in 2021 to ensure the quality of long-term care.

METHODS

The LIFE data of 1648 residents from 45 nursing homes in Japan were retrospectively collected in July 2021 (T0) and January 2022 (T1), including demographics, PF assessed by the Barthel index (BI), nutrition and oral health, and cognitive function. The Dementia Behavior Disturbance scale was used to assess the frequency of certain behaviors, such as "waking at midnight." The predictors of PF decline, defined as a decrease ≥5 in the BI score at T1 compared with that at T0, were determined using mixed-effects logistic regression analyses. PF at T0 was classified into high (>60 BI) and low (≤60 BI) groups.

RESULTS

The participants' mean age was 87.2 ± 7.1 years, and 45.3% experienced PF decline. The significant predictors of PF decline were age ≥ 90 years, body mass index <18.5 kg/m2 , dementia diagnosis, moderate and severe cognitive impairments, not vocalizing reciprocal exchanges at will, always "waking at midnight," and high PF at T0.

CONCLUSIONS

The LIFE items predicted PF decline among nursing home residents, suggesting that LIFE data can be used to ensure the quality of long-term care. Geriatr Gerontol Int 2024; 24: 123-132.

Developing a Machine Learning Risk-adjustment Method for Hospitalizations and Emergency Department Visits of Nursing Home Residents With Dementia

BACKGROUND

Long-stay nursing home (NH) residents with Alzheimer disease and related dementias (ADRD) are at high risk of hospital transfers. Machine learning might improve risk-adjustment methods for NHs.

OBJECTIVES

The objective of this study was to develop and compare NH risk-adjusted rates of hospitalizations and emergency department (ED) visits among long-stay residents with ADRD using Extreme Gradient Boosting (XGBoost) and logistic regression.

RESEARCH DESIGN

Secondary analysis of national Medicare claims and NH assessment data in 2012 Q3. Data were equally split into the training and test sets. Both XGBoost and logistic regression predicted any hospitalization and ED visit using 58 predictors. NH-level risk-adjusted rates from XGBoost and logistic regression were constructed and compared. Multivariate regressions examined NH and market factors associated with rates of hospitalization and ED visits.

SUBJECTS

Long-stay Medicare residents with ADRD (N=413,557) from 14,057 NHs.

RESULTS

A total of 8.1% and 8.9% residents experienced any hospitalization and ED visit in a quarter, respectively. XGBoost slightly outperformed logistic regression in area under the curve (0.88 vs. 0.86 for hospitalization; 0.85 vs. 0.83 for ED visit). NH-level risk-adjusted rates from XGBoost were slightly lower than logistic regression (hospitalization=8.3% and 8.4%; ED=8.9% and 9.0%, respectively), but were highly correlated. Facility and market factors associated with the XGBoost and logistic regression-adjusted hospitalization and ED rates were similar. NHs serving more residents with ADRD and having a higher registered nurse-to-total nursing staff ratio had lower rates.

CONCLUSIONS

XGBoost and logistic regression provide comparable estimates of risk-adjusted hospitalization and ED rates.

A Risk Prediction Model for Physical Restraints Among Older Chinese Adults in Long-term Care Facilities: Machine Learning Study

BACKGROUND

Numerous studies have identified risk factors for physical restraint (PR) use in older adults in long-term care facilities. Nevertheless, there is a lack of predictive tools to identify high-risk individuals.

OBJECTIVE

We aimed to develop machine learning (ML)-based models to predict the risk of PR in older adults.

METHODS

This study conducted a cross-sectional secondary data analysis based on 1026 older adults from 6 long-term care facilities in Chongqing, China, from July 2019 to November 2019. The primary outcome was the use of PR (yes or no), identified by 2 collectors' direct observation. A total of 15 candidate predictors (older adults' demographic and clinical factors) that could be commonly and easily collected from clinical practice were used to build 9 independent ML models: Gaussian Naïve Bayesian (GNB), k-nearest neighbor (KNN), decision tree (DT), logistic regression (LR), support vector machine (SVM), random forest (RF), multilayer perceptron (MLP), extreme gradient boosting (XGBoost), and light gradient boosting machine (Lightgbm), as well as stacking ensemble ML. Performance was evaluated using accuracy, precision, recall, an F score, a comprehensive evaluation indicator (CEI) weighed by the above indicators, and the area under the receiver operating characteristic curve (AUC). A net benefit approach using the decision curve analysis (DCA) was performed to evaluate the clinical utility of the best model. Models were tested via 10-fold cross-validation. Feature importance was interpreted using Shapley Additive Explanations (SHAP).

RESULTS

A total of 1026 older adults (mean 83.5, SD 7.6 years; n=586, 57.1% male older adults) and 265 restrained older adults were included in the study. All ML models performed well, with an AUC above 0.905 and an F score above 0.900. The 2 best independent models are RF (AUC 0.938, 95% CI 0.914-0.947) and SVM (AUC 0.949, 95% CI 0.911-0.953). The DCA demonstrated that the RF model displayed better clinical utility than other models. The stacking model combined with SVM, RF, and MLP performed best with AUC (0.950) and CEI (0.943) values, as well as the DCA curve indicated the best clinical utility. The SHAP plots demonstrated that the significant contributors to model performance were related to cognitive impairment, care dependency, mobility decline, physical agitation, and an indwelling tube.

CONCLUSIONS

The RF and stacking models had high performance and clinical utility. ML prediction models for predicting the probability of PR in older adults could offer clinical screening and decision support, which could help medical staff in the early identification and PR management of older adults.