Using Machine Learning to Predict Unplanned Hospital Utilization and Chemotherapy Management From Patient-Reported Outcome Measures.

PURPOSE: Adverse effects of chemotherapy often require hospital admissions or treatment management. Identifying factors contributing to unplanned hospital utilization may improve health care quality and patients' well-being. This study aimed to assess if patient-reported outcome measures (PROMs) improve performance of machine learning (ML) models predicting hospital admissions, triage events (contacting helpline or attending hospital), and changes to chemotherapy. MATERIALS AND METHODS: Clinical trial data were used and contained responses to three PROMs (European Organisation for Research and Treatment of Cancer Core Quality of Life Questionnaire [QLQ-C30], EuroQol Five-Dimensional Visual Analogue Scale [EQ-5D], and Functional Assessment of Cancer Therapy-General [FACT-G]) and clinical information on 508 participants undergoing chemotherapy. Six feature sets (with following variables: [1] all available; [2] clinical; [3] PROMs; [4] clinical and QLQ-C30; [5] clinical and EQ-5D; [6] clinical and FACT-G) were applied in six ML models (logistic regression [LR], decision tree, adaptive boosting, random forest [RF], support vector machines [SVMs], and neural network) to predict admissions, triage events, and chemotherapy changes. RESULTS: The comprehensive analysis of predictive performances of the six ML models for each feature set in three different methods for handling class imbalance indicated that PROMs improved predictions of all outcomes. RF and SVMs had the highest performance for predicting admissions and changes to chemotherapy in balanced data sets, and LR in imbalanced data set. Balancing data led to the best performance compared with imbalanced data set or data set with balanced train set only. CONCLUSION: These results endorsed the view that ML can be applied on PROM data to predict hospital utilization and chemotherapy management. If further explored, this study may contribute to health care planning and treatment personalization. Rigorous comparison of model performance affected by different imbalanced data handling methods shows best practice in ML research.

Palliative Care Needs and Integration of Palliative Care Support in COPD: A Qualitative Study.

BACKGROUND: The provision of palliative care for severe COPD remains low, resulting in unmet needs in patients and carers. RESEARCH QUESTIONS: What are the palliative care needs of patients living with severe COPD and their caregivers? What views of accessing and providing palliative care and factors influence these experiences. To what extent have palliative care and COPD services been integrated? STUDY DESIGN AND METHODS: A multicentre qualitative study was undertaken in COPD services and specialist palliative care in the United Kingdom involving patients with severe COPD, their carers, and health professionals. Data were collected using semistructured interviews and were analyzed using framework analysis. Themes were integrated using the constant comparison process, enabling systematic data synthesis. RESULTS: Four themes were generated from interviews with 20 patients, six carers, and 25 health professionals: management of exacerbations, palliative care needs, access to palliative care and pathways, and integration of palliative care support. Uncertainty and fear were common in patients and carers, with identified needs for reassurance, rapid medical access, home care, and finance advice. Timely palliative care was perceived as important by health professionals. Palliative care was integrated into COPD services, although models of working varied across regions. Reliable screening tools and needs assessment, embedded psychological care, and enhanced training in palliative care and communication skills were perceived to be important by health professionals for timely palliative care referrals and optimized management. INTERPRETATION: Palliative care increasingly is being implemented for nonmalignant diseases including COPD throughout the United Kingdom, although models of working vary. A theoretical model was developed to illustrate the concept and pathway of the integration of palliative care support. A standardized screening and needs assessment tool is required to improve timely palliative care and to address the significant needs of this population.