Development and validation of a risk prediction model for acute care use among patients with advanced cancer on clinical trials.

277 Background: Patients with advanced cancer on clinical trials are at high risk for unplanned Emergency Room visits (ER_visits) and hospital stays (HS). Identifying patients at highest risk could inform personalized intervention strategies. Methods: The data were from the SWOG Cancer Research Network. Medicare claims data were linked to data from advanced cancer clinical trials. The occurrence of any HS or ER visit within 1 year after enrollment was the primary endpoint. Patients must have had 6 months of continuous Medicare claims prior to registration to determine comorbid conditions prior to treatment, and 12 months of claims after registration (or until death) to detect outcomes. We examined 23 sociodemographic (age, sex, race, ethnicity, insurance status), geographic (rural or urban, area-level deprivation), clinical (disease severity, performance status), treatment (line of therapy, therapy type), and individual comorbidity factors to establish a predictive risk model. Model derivation was conducted in a 60% random sample and tested in the remaining 40%. Best subset selection with logistic regression was used with iterative application of 3-, 4-, and 5-fold cross validation repeated 10 times. Results: Among N=1397 total patients from 6 trials (lung, 3; prostate, 2; pancreas, 1), 33.9% were 75+ years, 20.3% were female, and 8.2% were Black. The overall proportion of patients with ≥1 HS/ER visit was 68.8%. In the training set (n=839), a best 5-variable risk was identified with adverse risk factors including cancer type (prostate vs other), performance status (PS; 0 vs. >=1), coronary artery disease (CAD; yes vs. no), hypertension (yes vs. no), and liver disease (yes vs. no). As cancer type was a study-level variable, we derived a risk model using PS, CAD, hypertension, and liver disease and adjusted for cancer type. There were no differences in adverse risk between those in with and without prostate cancer (interaction p-value=.44), justifying combining across cancer types. In the training set, patients with ≥2 risk factors (high risk; n=303, 36.1%) vs. 0/1 risk factor (low risk; n=536, 63.9%) had nearly a 3-fold increased odds of experiencing HS/ER visit (82.2% vs. 59.1%, OR=2.85, 95% CI, 2.01-4.03, p<.001). Results were successfully validated in the test set (OR=1.93, p=.002). In all patients, those in the highest risk quartile (3/4 factors) vs the lowest risk (0 factors) had nearly a fourfold increased odds of HS/ER visits (OR=3.92, 95% CI, 2.25-6.83, p<.001). Conclusions: Rates of HS_ER visits were high within the first year after enrolling on a trial for advanced cancer. We derived and validated a risk model that delineated strong differences in risk. With expansion of eligibility to include patients with more comorbid conditions, personalized interventions aimed at preventing acute care use could decrease the cost and improve the quality of cancer care.