A Narrowing Mortality Gap: Temporal Trends of Cause-Specific Mortality in a National Matched Cohort Study in US Veterans With Rheumatoid Arthritis
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Abstract: To examine temporal trends in all-cause and cause-specific mortality in patients with rheumatoid arthritis (RA) in the Veterans Health Administration (VHA). We conducted a matched cohort study in the VHA from January 1, 2000 to December 31, 2017. Incident RA patients were matched up to 1:10 on age, sex, and VHA enrollment year to non-RA patients, then followed until death or end of study period. Cause of death was obtained from the National Death Index. Multivariable Cox regression models stratified by RA diagnosis years were used to examine trends in RA-related risk of all-cause and cause-specific mortality. Among 29,779 incident RA patients (matched to 245,226 non-RA patients), 9,565 deaths occurred. RA patients were at increased risk of all-cause (adjusted hazard ratio [HRadj] 1.23 [95% confidence interval (95% CI) 1.20–1.26]), cardiovascular (HRadj 1.19 [95% CI 1.14–1.23]), cancer (HRadj 1.19 [95% CI 1.14–1.24]), respiratory (HRadj 1.46 [95% CI 1.38–1.55]), and infection-related mortality (HRadj 1.59 [95% CI 1.41–1.80]). Interstitial lung disease was the cause of death most strongly associated with RA (HRadj 3.39 [95% CI 2.88–3.99]). Nearly 70% of excess deaths in RA were attributable to cardiopulmonary disease. All-cause mortality risk related to RA was lower among those diagnosed during 2012–2017 (HRadj 1.10 [95% CI 1.05–1.15]) compared to 2000–2005 (HRadj 1.31 [95% CI 1.26–1.36]), but still higher than for non-RA controls (P < 0.001). Cause-specific mortality trends were similar. Excess RA-related mortality was driven by cardiovascular, cancer, respiratory, and infectious causes, particularly cardiopulmonary diseases. Although our findings support that RA-related mortality risk is decreasing over time, a mortality gap remains for all-cause and cause-specific mortality in RA.
Abstract: Novel and automated means of opioid use and relapse risk detection are needed. Unstructured electronic medical record data, including written progress notes, can be mined for clinically relevant information, including the presence of substance use and relapse-critical markers of risk and recovery from opioid use disorder (OUD). In this study, we used natural language processing (NLP) to automate the extraction of opioid relapses, and the timing of these occurrences, from veteran patients' electronic medical record. We then demonstrated the utility of our NLP tool via analysis of pre-/post-COVID-19 opioid relapse trends among veterans with OUD. For this demonstration, we analyzed data from 107,606 veterans OUD enrolled in Veterans Health Administration, comparing a pandemic-exposed cohort (n = 53,803; January 2019-March 2021) to a matched prepandemic cohort (n = 53,803; October 2017-December 2019). The recall of our NLP tool was 75% and our precision was 94%, demonstrating moderate sensitivity and excellent specificity. Using the NLP tool, we found that the odds of opioid relapse postpandemic onset were proportionally higher compared to prepandemic trends, despite patients having fewer mental health encounters from which to derive instances of relapse postpandemic onset. In this research application of the tool, and as hypothesized, we found that opioid relapse risk was elevated postpandemic. The application of NLP Methods: to identify and monitor relapse risk holds promise for future surveillance, risk prevention, and clinical outcome research.