Predictive Models for Palliative Care Needs of Advanced Cancer Patients Receiving Chemotherapy.

CONTEXT: Early palliative care is recommended within eight-week of diagnosing advanced cancer. Although guidelines suggest routine screening to identify cancer patients who could benefit from palliative care, implementing screening can be challenging due to understaffing and time constraints. OBJECTIVES: To develop and evaluate machine learning models for predicting specialist palliative care needs in advanced cancer patients undergoing chemotherapy, and to investigate if predictive models could substitute screening tools. METHODS: We conducted a retrospective cohort study using supervised machine learning. The study included patients aged 18 or older, diagnosed with metastatic or stage IV cancer, who underwent chemotherapy and distress screening at a designated cancer hospital in Japan from April 1, 2018, to March 31, 2023. Specialist palliative care needs were assessed based on distress screening scores and expert evaluations. Data sources were hospital's cancer registry, health claims database, and nursing admission records. The predictive model was developed using XGBoost, a machine learning algorithm. RESULTS: Out of the 1878 included patients, 561 were analyzed. Among them, 114 (20.3%) exhibited needs for specialist palliative care. After under-sampling to address data imbalance, the models achieved an Area Under the Curve (AUC) of 0.89 with 95.8% sensitivity and a specificity of 71.9%. After feature selection, the model retained five variables, including the patient-reported pain score, and showcased an 0.82 AUC. CONCLUSION: Our models could forecast specialist palliative care needs for advanced cancer patients on chemotherapy. Using five variables as predictors could replace screening tools and has the potential to contribute to earlier palliative care.

In-Hospital Fall Risk Prediction by Objective Measurement of Lower Extremity Function in a High-Risk Population.

OBJECTIVES: Limited data exist regarding association between physical performance and in-hospital falls. This study was performed to investigate the association between physical performance and in-hospital falls in a high-risk population. DESIGN: Retrospective cohort study. SETTING AND PARTICIPANTS: The study population consisted of 1200 consecutive patients with a median age of 74 years (50.8% men) admitted to a ward with high incidence rates of falls, primarily in the departments of geriatrics and neurology, in a university hospital between January 2019 and December 2021. METHODS: Short Physical Performance Battery (SPPB) was measured after treatment in the acute phase. As the primary end point of the study, the incidence of in-hospital falls was examined prospectively based on data from mandatory standardized incident report forms and electronic patient records. RESULTS: SPPB assessment was performed at a median of 3 days after admission, and the study population had a median SPPB score of 3 points. Falls occurred in 101 patients (8.4%) over a median hospital stay of 15 days. SPPB score showed a significant inverse association with the incidence of in-hospital falls after adjusting for possible confounders (adjusted odds ratio for each 1-point decrease in SPPB: 1.19, 95% CI 1.10-1.28; P < .001), and an SPPB score ≤6 was significantly associated with increased risk of in-hospital falls. Inclusion of SPPB with previously identified risk factors significantly increased the area under the curve for in-hospital falls (0.683 vs. 0.740, P = .003). CONCLUSION AND IMPLICATIONS: This study demonstrated an inverse association of SPPB score with risk of in-hospital falls in a high-risk population and showed that SPPB assessment is useful for accurate risk stratification in a hospital setting.