BackgroundTreatment with immune checkpoint inhibitors (ICIs) has been associated with an increased rate of cardiac events. There are limited data on the risk factors that predict cardiac events in patients treated with ICIs. Therefore, we created a machine learning (ML) model to predict cardiac events in this at-risk population.MethodsWe leveraged the CancerLinQ database curated by the American Society of Clinical Oncology and applied an XGBoosted decision tree to predict cardiac events in patients taking programmed death receptor-1 (PD-1) or programmed death ligand-1 (PD-L1) therapy. All curated data from patients with non-small cell lung cancer, melanoma, and renal cell carcinoma, and who were prescribed PD-1/PD-L1 therapy between 2013 and 2019, were used for training, feature interpretation, and model performance evaluation. A total of 356 potential risk factors were included in the model, including elements of patient medical history, social history, vital signs, common laboratory tests, oncological history, medication history and PD-1/PD-L1-specific factors like PD-L1 tumor expression.ResultsOur study population consisted of 4960 patients treated with PD-1/PD-L1 therapy, of whom 418 had a cardiac event. The following were key predictors of cardiac events: increased age, corticosteroids, laboratory abnormalities and medications suggestive of a history of heart disease, the extremes of weight, a lower baseline or on-treatment percentage of lymphocytes, and a higher percentage of neutrophils. The final model predicted cardiac events with an area under the curve–receiver operating characteristic of 0.65 (95% CI 0.58 to 0.75). Using our model, we divided patients into low-risk and high-risk subgroups. At 100 days, the cumulative incidence of cardiac events was 3.3% in the low-risk group and 6.1% in the high-risk group (p<0.001).ConclusionsML can be used to predict cardiac events in patients taking PD-1/PD-L1 therapy. Cardiac risk was driven by immunological factors (eg, percentage of lymphocytes), oncological factors (eg, low weight), and a cardiac history.
e21596 Background: There are ongoing efforts to understand and predict exceptional response to existing cancer therapies, but few clinical characteristics of these patients are known. We trained a machine learning model using the Concerto HealthAI database of oncology EMR data that includes clinical data from CancerLinQ Discovery to predict slow progression, a proxy for exceptional response, in aNSCLC in the second line setting. Methods: We trained an XGBoost model to predict patients with a progression free survival (PFS) greater than 180 days from the start of second line therapy (index date). This cutoff approximately determines the top 20% of PFS values in our database (median PFS = 86 days). Patients were included from the study if they (1) were pathologically confirmed aNSCLC without other primary cancer diagnoses and (2) started their second-line therapy between 2013 and 2017. Patients were labeled as slow progressors if they (1) had no evidence of progression or death within 180 days of index and (2) were evaluated for progression for at least 180 days post-index. The model considered data up to 120 days prior to index date. Risk factors in the model included demographics, vitals, common labs, common medical conditions, ECOG performance status, stage, histology, prior cancer treatment patterns, prior progression/response assessments, and medication history. Feature importance was evaluated using SHapley Additive exPlanations (SHAP). Results: 2205 patients met selection criteria of the study. Of these, 420 were labeled as slow progressors. 1776 patients were used for model training and 429 were set aside for model validation. The final model was able to predict slow progression with an AUCROC of 0.75 (F-score 0.48, precision 0.39, recall 0.6). The performance compares favorably to that of a logistic regression model (0.66 AUCROC). Top features that indicated slow progression included a low number of prior progression events or regimens, absence of metastatic disease, lower stage/t-stage/ECOG, absence of COPD, previous treatment with an EGFR inhibitor, normal Alk-Phos/WBC (versus elevated), absence of tachycardia, and a normal BMI (versus low). Conclusions: Machine learning and real world-data provided promising results in predicting slow progression in aNSCLC and may be useful in discovering novel drivers of favorable response.
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