Traeger, Lara

Objective: To determine the prevalence of clinical significance reporting in contemporary comparative effectiveness research (CER). Background: In CER, a statistically significant difference between study groups may or may not be clinically significant. Misinterpreting statistically significant results could lead to inappropriate recommendations that increase health care costs and treatment toxicity. Methods: CER studies from 2022 issues of the Annals of Surgery , Journal of the American Medical Association , Journal of Clinical Oncology , Journal of Surgical Research , and Journal of the American College of Surgeons were systematically reviewed by 2 different investigators. The primary outcome of interest was whether the authors specified what they considered to be a clinically significant difference in the “Methods.” Results: Of 307 reviewed studies, 162 were clinical trials and 145 were observational studies. Authors specified what they considered to be a clinically significant difference in 26 studies (8.5%). Clinical significance was defined using clinically validated standards in 25 studies and subjectively in 1 study. Seven studies (2.3%) recommended a change in clinical decision-making, all with primary outcomes achieving statistical significance. Five (71.4%) of these studies did not have clinical significance defined in their methods. In randomized controlled trials with statistically significant results, sample size was inversely correlated with effect size ( r = −0.30, P = 0.038). Conclusions: In contemporary CER, most authors do not specify what they consider to be a clinically significant difference in study outcome. Most studies recommending a change in clinical decision-making did so based on statistical significance alone, and clinical significance was usually defined with clinically validated standards.

11514 Background: Cancer pain is a significant problem that impairs patient quality of life and increases healthcare utilization. ePAL is a smartphone application that utilizes patient-reported outcomes (PROs) and artificial intelligence (AI) to optimize cancer pain management. This randomized controlled trial examined the impact of ePAL on cancer pain severity, attitudes toward cancer pain, and healthcare utilization. Methods: Patients with pain from metastatic solid tumors (n = 112) undergoing treatment in a palliative care clinic were randomized to either a control group (n = 56) that received usual care or an intervention group (n = 56) that received ePAL in addition to usual care for 8 weeks. Measures of pain severity (Brief Pain Inventory), attitudes towards cancer treatment (Barriers Questionnaire II) and anxiety (General Anxiety Disorder-7) were assessed. We used repeated measures mixed modeling to assess change in outcome measures over time. We also conducted a chart review to identify pain-related hospital admissions and emergency department (ED) visits and compared risk between study groups. Results: Pain severity (BPI) and negative attitudes toward cancer treatment (BQ-II) decreased significantly for those assigned to ePAL compared to controls (ß = -0.09, p = 0.034 and ß = -0.037, p = 0.042, respectively). Patients assigned to ePAL reported higher anxiety scores compared to controls (ß = 0.21, p = 0.015). Patients assigned to ePAL had significantly fewer pain-related hospital admissions (n = 4 vs. n = 20, per patient risk ratio 0.31, p = 0.018) and fewer pain-related admissions through the ED (n = 2 vs. n = 14, per patient risk ratio 0.18, p = 0.008) compared to control group. Conclusions: To our knowledge, this is the first mobile app to utilize patient reported outcomes and artificial intelligence to significantly decrease pain scores and pain-related hospitalizations in patients with cancer-related pain. Future directions include examining the efficacy of ePAL in settings with limited access to palliative care.