Rapid identification of inflammatory arthritis and associated adverse events following immune checkpoint therapy: a machine learning approach.

Introduction: Immune checkpoint inhibitor-induced inflammatory arthritis (ICI-IA) poses a major clinical challenge to ICI therapy for cancer, with 13% of cases halting ICI therapy and ICI-IA being difficult to identify for timely referral to a rheumatologist. The objective of this study was to rapidly identify ICI-IA patients in clinical data and assess associated immune-related adverse events (irAEs) and risk factors. Methods: We conducted a retrospective study of the electronic health records (EHRs) of 89 patients who developed ICI-IA out of 2451 cancer patients who received ICI therapy at Northwestern University between March 2011 to January 2021. Logistic regression and random forest machine learning models were trained on all EHR diagnoses, labs, medications, and procedures to identify ICI-IA patients and EHR codes indicating ICI-IA. Multivariate logistic regression was then used to test associations between ICI-IA and cancer type, ICI regimen, and comorbid irAEs. Results: Logistic regression and random forest models identified ICI-IA patients with accuracies of 0.79 and 0.80, respectively. Key EHR features from the random forest model included ICI-IA relevant features (joint pain, steroid prescription, rheumatoid factor tests) and features suggesting comorbid irAEs (thyroid function tests, pruritus, triamcinolone prescription). Compared to 871 adjudicated ICI patients who did not develop arthritis, ICI-IA patients had higher odds of developing cutaneous (odds ratio [OR]=2.66; 95% Confidence Interval [CI] 1.63-4.35), endocrine (OR=2.09; 95% CI 1.15-3.80), or gastrointestinal (OR=2.88; 95% CI 1.76-4.72) irAEs adjusting for demographics, cancer type, and ICI regimen. Melanoma (OR=1.99; 95% CI 1.08-3.65) and renal cell carcinoma (OR=2.03; 95% CI 1.06-3.84) patients were more likely to develop ICI-IA compared to lung cancer patients. Patients on nivolumab+ipilimumab were more likely to develop ICI-IA compared to patients on pembrolizumab (OR=1.86; 95% CI 1.01-3.43). Discussion: Our machine learning models rapidly identified patients with ICI-IA in EHR data and elucidated clinical features indicative of comorbid irAEs. Patients with ICI-IA were significantly more likely to also develop cutaneous, endocrine, and gastrointestinal irAEs during their clinical course compared to ICI therapy patients without ICI-IA.

Impact of pre-ablation weight loss on the success of catheter ablation for atrial fibrillation.

INTRODUCTION: Obesity is an established risk factor for recurrent atrial fibrillation (AF) after ablation. The impact of pre-procedure weight changes on freedom from AF (FFAF) after ablation in obese and nonobese patients is unknown. METHODS: A single-center retrospective cohort study of patients undergoing pulmonary vein isolation was performed. Before ablation, all candidates were encouraged to adopt healthy lifestyle habits according to American Heart Association guidelines, including weight loss, by their physician. The primary endpoint was FFAF through 1-year after completion of the 3-month blanking period. RESULTS: Of the 601 patients (68% male; average age 62.1 ± 10.3 years) included in analysis, 234 patients (38.9%) were obese (body mass index ≥ 30) and 315 (52.4%) had paroxysmal AF. FFAF was observed in 420 patients (69.9%) at 15 months. Percent change in weight that occurred during the year before ablation independently predicted FFAF through 15-months in all patients (adjusted odds ratio = 1.17, 95% confidence interval: 1.11-1.23). Subgroup analyses based on paroxysmal vs persistent AF, presence of obesity, and history of prior ablation were performed. Percent change in weight over the year before ablation was independently associated with FFAF in all subgroups except nonobese patients with persistent AF. CONCLUSION: Pre-ablation weight loss was associated with FFAF in both obese and nonobese patients. Further studies are needed to define the optimal approach to weight loss before AF ablation.