Development of Machine Learning Algorithms Incorporating Electronic Health Record Data, Patient-Reported Outcomes, or Both to Predict Mortality for Outpatients With Cancer.

Parikh RB, Hasler JS, Zhang Y, Liu M, Chivers C, Ferrell W, Gabriel PE, Lerman C, Bekelman JE, Chen J. Development of Machine Learning Algorithms Incorporating Electronic Health Record Data, Patient-Reported Outcomes, or Both to Predict Mortality for Outpatients With Cancer. JCO clinical cancer informatics. 2022 Dec 1; 6:e2200073.

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Abstract:

PURPOSE: Machine learning (ML) algorithms that incorporate routinely collected patient-reported outcomes (PROs) alongside electronic health record (EHR) variables may improve prediction of short-term mortality and facilitate earlier supportive and palliative care for patients with cancer. METHODS: We trained and validated two-phase ML algorithms that incorporated standard PRO assessments alongside approximately 200 routinely collected EHR variables, among patients with medical oncology encounters at a tertiary academic oncology and a community oncology practice. RESULTS: Among 12,350 patients, 5,870 (47.5%) completed PRO assessments. Compared with EHR- and PRO-only algorithms, the EHR + PRO model improved predictive performance in both tertiary oncology (EHR + PRO EHR PRO: area under the curve [AUC] 0.86 [0.85-0.87] 0.82 [0.81-0.83] 0.74 [0.74-0.74]) and community oncology (area under the curve 0.89 [0.88-0.90] 0.86 [0.85-0.88] 0.77 [0.76-0.79]) practices. CONCLUSION: Routinely collected PROs contain added prognostic information not captured by an EHR-based ML mortality risk algorithm. Augmenting an EHR-based algorithm with PROs resulted in a more accurate and clinically relevant model, which can facilitate earlier and targeted supportive care for patients with cancer.