Fall incidents in nursing home residents: development of a predictive clinical rule (FINDER)

Development and External Validation of a Machine Learning-based Fall Prediction Model for Nursing Home Residents: A Prospective Cohort Study

OBJECTIVES

To develop and externally validate a machine learning-based fall prediction model for ambulatory nursing home residents. The focus is on predicting fall occurrences within 6 months after baseline assessment through a binary classification task, aiming to provide staff with an effective and user-friendly fall-risk assessment tool.

DESIGN

Prospective cohort study.

SETTING AND PARTICIPANTS

A total of 864 older residents living in 4 nursing homes between May 2022 and March 2023 in China.

METHODS

Potential fall-risk predictors were collected through in-person interviews and assessments of anthropometric and physical function. Participants were followed for 6 months, with falls recorded by trained nurses. Seven machine learning algorithms, including Logistic Regression (LR), Gradient Boosting Machine (GBM), eXtreme Gradient Boosting (XGBoost), Random Forest (RF), Support Vector Machine (SVM), Neural Networks (NN), and Decision Tree (DT), were used to develop prediction models. Performance was evaluated using the area under the receiver operating characteristic curve (ROC-AUC) and Precision-Recall curve (PR-AUC), with calibration assessed via a calibration curve. Feature importance was visualized using SHapley Additive exPlanations (SHAP).

RESULTS

The 6 selected predictors were balance, grip strength, fatigue, fall history, age, and comorbidity. The ROC-AUC for the models ranged from 0.710 to 0.750, PR-AUC from 0.415 to 0.473, sensitivity from 0.704 to 0.914, and specificity from 0.511 to 0.687 in the validation cohort. The LR model was converted into a nomogram.

CONCLUSIONS AND IMPLICATIONS

The machine learning-based fall-prediction models effectively identified nursing home residents at high risk of falls. The developed nomogram can be integrated into clinical practice to enhance fall risk assessment protocols, ultimately improving patient safety and care in nursing homes.

Development and internal validation of a prediction model for patients with hematologic diseases of fall risk: a cohort study

BACKGROUND

To develop and internally validate a prediction model for identifying patients with hematologic diseases of fall risk.

RESEARCH DESIGN AND METHODS

This is a prospective cohort study from a prospective collection of data for 6 months. We recruited 412 patients with hematologic diseases in medical institutions and home environment of China. The outcome of the prediction model was fall or not. These variables were filtered via univariable logistic analysis, LASSO, and multivariable logistic analysis. We adopt an internal validation method of K-fold cross validation. The area under the ROC curve and the H-L test were used to evaluate the discrimination and calibration of the model.

RESULTS

Five influencing factors were identified multivariable logistic regression analysis. The established model equation is as follows: the H-L goodness-of-fit test of the model p > 0.05. The area under the ROC curve of train is 0.957 (95% CI: 0.936 ~ 0.978), and the area under the ROC curve of test is 0.962 (95% CI: 0.884 ~ 1), so the model calibration and discriminant validity are good.

CONCLUSION

Our equation has good sensitivity and specificity in predicting the fall risk of patients with hematologic diseases, and has certain positive significance for clinical assessment of their fall risk.

TRIAL REGISTRATION NUMBER

ChiCTR2200063940.

Falls prediction using the nursing home minimum dataset

OBJECTIVE

The purpose of the study was to develop and validate a model to predict the risk of experiencing a fall for nursing home residents utilizing data that are electronically available at the more than 15 000 facilities in the United States.

MATERIALS AND METHODS

The fall prediction model was built and tested using 2 extracts of data (2011 through 2013 and 2016 through 2018) from the Long-term Care Minimum Dataset (MDS) combined with drug data from 5 skilled nursing facilities. The model was created using a hybrid Classification and Regression Tree (CART)-logistic approach.

RESULTS

The combined dataset consisted of 3985 residents with mean age of 77 years and 64% female. The model's area under the ROC curve was 0.668 (95% confidence interval: 0.643-0.693) on the validation subsample of the merged data.

DISCUSSION

Inspection of the model showed that antidepressant medications have a significant protective association where the resident has a fall history prior to admission, requires assistance to balance while walking, and some functional range of motion impairment in the lower body; even if the patient exhibits behavioral issues, unstable behaviors, and/or are exposed to multiple psychotropic drugs.

CONCLUSION

The novel hybrid CART-logit algorithm is an advance over the 22 fall risk assessment tools previously evaluated in the nursing home setting because it has a better performance characteristic for the fall prediction window of ≤90 days and it is the only model designed to use features that are easily obtainable at nearly every facility in the United States.