Anticipating Poor Outcomes: A Prognostic Machine Learning Model of Unplanned Visits to the Emergency Department for Patients Undergoing Treatment for Head and Neck Cancer Using Comprehensive Multi-Factor Electronic Health Records.

PURPOSE/OBJECTIVE(S) Unplanned visits to the emergency department (ED) and hospital admissions (HA) are a significant problem in head neck cancer (HNC) with the literature suggesting that 20-50% patients will visit the ED during active cancer treatment or within 90 days of treatment completion. These unplanned visits compromise outcomes for patients with interruptions or discontinuation of care, and contribute large expense to the healthcare system. Previous work has identified static pretreatment clinical factors associated with ED visits and HA. Here, we used our custom warehouse system to evaluate pre- and during-treatment clinical, dosimetric and toxicity factors. MATERIALS/METHODS The records of 1285 HNC cancer patients treated in our department between 2014 and 2019 were examined to profile details of a comprehensive set of factors including: demographics, location, diagnosis and staging, chemotherapy, radiation therapy, opioid use, provider reported toxicities and laboratory values and changes to create a prognostic model for ED use during or within 90 days of end of radiation therapy. For continuous variables, Youden's Index was used to identify discriminatory thresholds. Odds ratios were calculated to gauge statistical evidence of relevance. Machine learning models were constructed using gradient boosting (GB), random forest (RF), support vector machines (SVM) and naive bayes (NB) using 10-fold cross validation. RESULTS Within our cohort, 17% visited the ED during or within 90 days of the end of radiation therapy. Factors associated with ED visits included female sex (OR = 1.2), single marital status (1.6), smoking history recorded in EHR (1.3), lack of significant alcohol history recorded in EHR (1.3), living within 34 miles of hospital (1.8), age < 59 (1.2), stage IV (1.4), chemotherapy (1.3) including cisplatin (1.5), ≥ 3 changes in opioid Rx (3.4) including morphine (2.0) or fentanyl (2.2), any provider graded toxicity ≥ 3 (2.1). RF had the highest accuracy 0.87 ± 0.005, and AUC of 0.75 ± 0.05. Accuracy was significantly (P < 0.01) less for NB than for RF or GB. Highest importance scoring factors in the model were number of opioid prescription records, body mass index values and changes, volume of low risk PTV volume, age, creatinine, changes in platelet and neutrophil counts, albumin-bilirubin score, and distance. CONCLUSION We were able to combine a comprehensive set of clinical, dosimetric and toxicity grading factors to identify an actionable clinical profile for anticipating ED. This forms the basis for next steps to develop and test interventions based on the model as reduction in unplanned acute care is a priority for clinical transformation in oncology. The scope of high-ranking inputs underscores the importance of constructing data ware house systems, that enable constructing multi-domain models. Significance of "distance of hospital" in our model and similar limitations for all ED studies underscores the need for integrated EHR across all hospital systems.