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ObjectiveDespite the strong association between gout and pre-diabetes, the role of metformin in gout among individuals with pre-diabetes remains uncertain. We compared the incidence rates of gout in adults with pre-diabetes starting metformin with those not using antidiabetic treatments.MethodsWe conducted a new-user, propensity score-matched cohort study using electronic health records from an academic health system (2007–2022). Pre-diabetes was defined based on haemoglobin A1c levels. Metformin users were identified and followed from the first metformin prescription date. Non-users of antidiabetic medications were matched to metformin users based on propensity score and the start of follow-up. The primary outcome was incident gout. Cox proportional hazards models estimated the HR for metformin. Linear regression analyses assessed the association between metformin use and changes in serum urate (SU) or C-reactive protein (CRP).ResultsWe identified 25 064 individuals with pre-diabetes and propensity score-matched 1154 metformin initiators to 13 877 non-users. Baseline characteristics were well balanced (all standardised mean differences <0.1). The median follow-up was 3.9 years. The incidence rate of gout per 1000 person-years was lower in metformin users 7.1 (95% CI 5.1 to 10) compared with non-users 9.5 (95% CI 8.8 to 10.2). Metformin initiation was associated with a reduced relative risk of gout (HR 0.68, 95% CI 0.48 to 0.96). No relationship was found between metformin and changes in SU or CRP.ConclusionsMetformin use was associated with a reduced risk of gout among adults with pre-diabetes, suggesting that metformin may be important in lowering gout risk in individuals with pre-diabetes.

Poor subjective sleep is associated with future depression in older adults, but there is limited consensus on which sleep features have the strongest associations. Moreover, composite scores incorporating multiple features may better represent sleep burden than individual sleep items. We analyzed older adults (age ≥ 60) without clinically relevant depressive symptoms from a multi-cohort United States sample (US; N = 4826) and the Netherlands’ Rotterdam Study (RS; N = 3663), with the goal of identifying individual and composite sleep features that are associated with future clinically relevant depressive symptoms 3–6 years later. Sleep-related daytime symptoms (Risk Ratio [95% CI] 2.10 [1.58, 2.80] in US; 2.10 [1.40, 3.14] in RS) and difficulty falling asleep (1.87 [1.49, 2.35] in US; RS = 1.90 [1.50, 2.43] in RS) were the strongest individual sleep features. Moreover, the combination of these features was most impactful (3.32 [2.33, 4.73] in US; 3.19 [2.64, 3.86] in RS), providing the largest effect size with the fewest number of items. Future studies should assess whether screening tools incorporating these features, paired with targeted sleep treatment, could reduce rates of incident depression in older adults. Examining mechanisms underlying these associations could improve the effectiveness of sleep-related treatments in older adults.

Background Chronic cough (CC) is difficult to identify in electronic health records (EHRs) due to the lack of specific diagnostic codes. We developed a natural language processing (NLP) model to identify cough in free-text provider notes in EHRs from multiple health care providers with the objective of using the model in a rules-based CC algorithm to identify individuals with CC from EHRs and to describe the demographic and clinical characteristics of individuals with CC. Methods This was a retrospective observational study of enrollees in Optum’s Integrated Clinical + Claims Database. Participants were 18–85 years of age with medical and pharmacy health insurance coverage between January 2016 and March 2017. A labeled reference standard data set was constructed by manually annotating 1000 randomly selected provider notes from the EHRs of enrollees with ≥ 1 cough mention. An NLP model was developed to extract positive or negated cough contexts. NLP, cough diagnosis and medications identified cough encounters. Patients with ≥ 3 encounters spanning at least 56 days within 120 days were defined as having CC. Results The positive predictive value and sensitivity of the NLP algorithm were 0.96 and 0.68, respectively, for positive cough contexts, and 0.96 and 0.84, respectively, for negated cough contexts. Among the 4818 individuals identified as having CC, 37% were identified using NLP-identified cough mentions in provider notes alone, 16% by diagnosis codes and/or written medication orders, and 47% through a combination of provider notes and diagnosis codes/medications. Chronic cough patients were, on average, 61.0 years and 67.0% were female. The most prevalent comorbidities were respiratory infections (75%) and other lower respiratory disease (82%). Conclusions Our EHR-based algorithm integrating NLP methodology with structured fields was able to identify a CC population. Machine learning based approaches can therefore aid in patient selection for future CC research studies.

Study Objectives Determine the association of insomnia symptoms with subsequent health services use, in a representative sample of U.S. older adults. Methods Participants were 4,289 community-dwelling Medicare beneficiaries who had continuous fee-for-service Medicare coverage 30 days before, and 1 year after the National Health and Aging Trends Study (NHATS) Round 1 interview. Participants reported past-month insomnia symptoms (i.e. sleep onset latency >30 min, difficulty returning to sleep) which we categorized as 0, 1, or 2 symptoms. Outcomes were health services use within 1 year of interviews from linked Medicare claims: emergency department (ED) visits, hospitalizations, 30-day readmissions, home health care (all measured as yes/no), and number of hospitalizations and ED visits. Results Overall, 18.5% of participants were hospitalized, 28.7% visited the ED, 2.5% had a 30-day readmission, and 11.3% used home health care. After adjustment for demographics, depressive and anxiety symptoms, medical comorbidities, and BMI, compared to participants with no insomnia symptoms, those with two insomnia symptoms had a higher odds of ED visits (odds ratio [OR) = 1.60, 95% confidence interval [CI] = 1.24–2.07, p < 0.001), hospitalizations (OR = 1.29, 95% CI = 1.01–1.65, p < 0.05), and 30-day readmissions (OR = 1.88, 95% CI = 1.88–3.29, p < 0.05). Reporting 2 insomnia symptoms, versus no insomnia symptoms, was associated with a greater number of ED visits and hospitalizations (incidence rate ratio (IRR) = 1.52, 95% CI = 1.23–1.87, p < 0.001; IRR = 1.21, 95% CI = 1.02–1.44, p < 0.05, respectively) after adjusting for demographic and health characteristics. Conclusions Among older adults, insomnia symptoms are associated with greater health services use, including emergency department use, hospitalization, and 30-day readmission. Targeting insomnia may lower health services use.

Accurate identification of patient populations is an essential component of clinical research, especially for medical conditions such as chronic cough that are inconsistently defined and diagnosed. We aimed to develop and compare machine learning models to identify chronic cough from medical and pharmacy claims data. In this retrospective observational study, we compared 3 machine learning algorithms based on XG Boost, logistic regression, and neural network approaches using a large claims and electronic health record database. Of the 327,423 patients who met the study criteria, 4,818 had chronic cough based on linked claims–electronic health record data. The XG Boost model showed the best performance, achieving a Receiver-Operator Characteristic Area Under the Curve (ROC-AUC) of 0.916. We selected a cutoff that favors a high positive predictive value (PPV) to minimize false positives, resulting in a sensitivity, specificity, PPV, and negative predictive value of 18.0%, 99.6%, 38.7%, and 98.8%, respectively on the held-out testing set (n = 82,262). Logistic regression and neural network models achieved slightly lower ROC-AUCs of 0.907 and 0.838, respectively. The XG Boost and logistic regression models maintained their robust performance in subgroups of individuals with higher rates of chronic cough. Machine learning algorithms are one way of identifying conditions that are not coded in medical records, and can help identify individuals with chronic cough from claims data with a high degree of classification value.

Chlamydia trachomatis (CT) and Neisseria gonorrhoeae (NG) are the most common bacterial causes of sexually transmitted infection (STI) in the United States (US). The purpose of this study was to determine the frequency of reinfection during a six-month study period and to evaluate the retesting interval for those infected with CT or NG. We conducted a prospective, six-month follow-up study among US military personnel with new onset, laboratory-confirmed CT or NG, recruited from an STI clinic at a large military base from January 2018 to January 2020. Each participant was randomly assigned to one of four groups, which differed only by the timing of the first study-associated follow-up visit after CT or NG diagnosis. Of the 347 initially recruited into the study, 267 participants completed a follow-up visit prior to their scheduled, final visit 6 months after initial infection. The median age at enrollment was 22 years and 41.0% were female. There were 32 (12.0%) reinfections (30 CT and 2 NG) after treatment of an index diagnosis of CT or NG within the six-month study period. Six of the CT reinfections were only detected at the final visit. A review of medical records revealed additional CT and NG reinfections. The probability of detecting a reinfection did not vary significantly by timing of follow-up. The likelihood of detecting CT or NG reinfection did not differ according to time of follow up visit among study participants, thus supporting CDC guidance to retest three months post treatment. Efforts should continue to focus on STI prevention and risk reduction.

Background Hepatitis C virus (HCV) coinfection may further compromise immunological and cognitive function in people with HIV (PWH). This study compared laboratory and neuropsychiatric measures across the periods of HCV seroconversion and direct-acting antiviral (DAA) therapy with sustained virologic response (SVR) among PWH who initiated antiretroviral therapy (ART) during acute HIV infection (AHI) and acquired HCV after 24 weeks of ART. Methods Participants from the RV254 AHI cohort underwent paired laboratory and neuropsychiatric assessments during follow-up visits. The former included measurements of CD4 + and CD8 + T-cell counts, HIV RNA, liver enzymes, and lipid profiles. The latter included the Patient Health Questionnaire-9 (PHQ-9), Distress Thermometer (DT), and a 4-test cognitive battery that evaluated psychomotor speed, executive function, fine motor speed, and dexterity. The raw scores in the battery were standardized and averaged to create an aggregate performance (NPZ-4) score. Parameters of HCV-coinfected participants were compared across the periods of HCV seroconversion and DAA treatment. Results Between 2009 and 2022, 79 of 703 RV254 participants acquired HCV after ≥ 24 weeks of ART; 53 received DAA, and 50 (94%) achieved SVR. All participants were Thai males (median age: 30 years); 34 (68%) denied past intravenous drug use, and 41 (82%) had a history of other sexually transmitted infections during follow-up. Following SVR, aspartate transferase (AST) and alanine transaminase (ALT) decreased ( p  < 0.001), while total cholesterol, low-density lipoprotein, and triglycerides increased ( p  < 0.01). The median CD4 + /CD8 + ratio increased from 0.91 to 0.97 ( p  = 0.012). NPZ-4 improved from 0.75 to 0.91 ( p  = 0.004). The median DT score increased from 1.7 to 2.7 ( p  = 0.045), but the PHQ-9 score remained unchanged. Conclusion HCV coinfection is common in this group of high-risk PWH, highlighting the need for regular screening, early diagnosis, and treatment. The study participants exhibited a modest improvement in the CD4 + /CD8 + T-cell ratio and cognitive performance following DAA therapy and SVR. Future studies should examine potential neuropsychiatric impacts during early HCV infection as well as the longer-term neuropsychiatric outcomes after DAA treatment with SVR.

Seasonal nadirs in 25-hydroxyvitamin D (25[OH]D) concentrations overlap with increased incidence and severity of obstructive sleep apnea (OSA) in winter. We hypothesized that, because lower 25(OH)D concentrations might lead to upper airway muscle dysfunction, low 25(OH)D would be associated with higher apnea-hypopnea index (AHI), a measure of OSA severity. To determine if lower 25(OH)D concentration is associated with greater prevalence and increased severity of OSA, independent of established OSA risk factors. Using unconditional logistic regression, we performed a cross-sectional analysis in the Outcomes of Sleep Disorders in Older Men study, which included in-home overnight polysomnography, serum 25(OH)D measurement, and collection of demographic and comorbidity data. The primary outcome was severe sleep apnea, as defined by AHI of 30/h or more. Among 2,827 community-dwelling, largely white (92.2%), elderly (aged 76.4 ± 5.5 yr [mean±SD]) males, mean 25(OH)D concentration was 28.8 (±8.8) ng/ml. Subjects within the lowest quartile of 25(OH)D (6-23 ng/ml) had greater odds of severe sleep apnea in unadjusted analyses (odds ratio = 1.45; 95% confidence interval = 1.02-2.07) when compared with the highest 25(OH)D quartile (35-84 ng/ml). However, further adjustment for established OSA risk factors strongly attenuated this association (multivariable adjusted odds ratio = 1.05; 95% confidence interval = 0.72-1.52), with body mass index and neck circumference as the main confounders. There was also no evidence of an independent association between lower 25(OH)D levels and increased odds of mild (AHI = 5.0-14.9/h) or moderate (AHI = 15.0-29.9/h) sleep apnea. Among community-dwelling older men, the association between lower 25(OH)D and sleep apnea was largely explained by confounding by larger body mass index and neck circumference.

ObjectiveTo compare longitudinal changes in spirometric measures between patients with rheumatoid arthritis (RA) and non-RA comparators.MethodsWe analysed longitudinal data from two prospective cohorts: the UK Biobank and COPDGene. Spirometry was conducted at baseline and a second visit after 5–7 years. RA was identified based on self-report and disease-modifying antirheumatic drug use; non-RA comparators reported neither. The primary outcomes were annual changes in the per cent-predicted forced expiratory volume in 1 s (FEV1%) and per cent predicted forced vital capacity (FVC%). Statistical comparisons were performed using multivariable linear regression. The analysis was stratified based on baseline smoking status and the presence of obstructive pattern (FEV1/FVC <0.7).ResultsAmong participants who underwent baseline and follow-up spirometry, we identified 233 patients with RA and 37 735 non-RA comparators. Among never-smoking participants without an obstructive pattern, RA was significantly associated with more FEV1% decline (β=−0.49, p=0.04). However, in ever smokers with ≥10 pack-years, those with RA exhibited significantly less FEV1% decline than non-RA comparators (β=0.50, p=0.02). This difference was more pronounced among those with an obstructive pattern at baseline (β=1.12, p=0.01). Results were similar for FEV1/FVC decline. No difference was observed in the annual FVC% change in RA versus non-RA.ConclusionsSmokers with RA, especially those with baseline obstructive spirometric patterns, experienced lower FEV1% and FEV1/FVC decline than non-RA comparators. Conversely, never smokers with RA had more FEV1% decline than non-RA comparators. Future studies should investigate potential treatments and the pathogenesis of obstructive lung diseases in smokers with RA.

Determine the associations of sleep disturbances with hospitalization risk among older women. One thousand eight hundred and twenty-seven women (mean age 83.6 years) participating in Study of Osteoporotic Fractures Year 16 (Y16) examination (2002-2004) linked with Medicare and/or HMO claims. At Y16 examination, sleep/wake parameters were measured by actigraphy (total sleep time [TST], sleep efficiency [SE], sleep latency [SL], and wake after sleep onset [WASO]) and subjective sleep measures (sleep quality [Pittsburgh Sleep Quality Index] and daytime sleepiness [Epworth Sleepiness Scale]) were assessed by questionnaire. Measures except TST were dichotomized based on clinical thresholds. Incident hospitalizations were determined from claims data. Nine hundred and seventy-six women (53%) had ≥1 hospitalization in the 3 years after the Year 16 examination. Reduced SE (odds ratio [OR] = 2.39, 95% confidence interval [CI] 1.69-3.39), prolonged SL (OR = 1.41, 95% CI 1.11-1.78), greater WASO (OR = 1.57, 95% CI 1.28-1.93), shorter TST (OR = 1.98, 95% CI 1.42-2.77) and poorer sleep quality (OR = 1.33, 95% CI 1.07-1.65) were each associated with a higher age and site-adjusted odds of hospitalization; associations were attenuated after multivariable adjustment for traditional prognostic factors with the OR for reduced SE (OR = 1.60, 95% CI 1.08-2.38) and shorter TST (OR = 1.63, 95% CI 1.12-2.37) remaining significant. Among women who were hospitalized, greater WASO (rate ratio [RR] = 1.20, 95% CI 1.04-1.37) and poorer sleep quality (RR = 1.18, 95% CI 1.02-1.35) were each associated with a greater age and site-adjusted RR of inpatient days, but associations did not persist after multivariate adjustment. Older women with sleep disturbances have an increased risk of hospitalization partially attributable to demographics, poorer health status, and comorbidities.