Bhagra, Sumit

Abstract Background We compared long-term mortality and readmission rates after COVID-19 hospitalization based on rural-urban status and assessed the impact of COVID-19 vaccination introduction on clinical outcomes by rurality. Methods The study comprised adults hospitalized for COVID-19 at 17 hospitals in 4 US states between March 2020 and July 2022, followed until May 2023. The main analysis included all patients, whereas a sensitivity analysis focused on residents from 4 states containing 17 hospitals. Additional analyses compared the pre- and postvaccination periods. Results The main analysis involved 9325 COVID-19 hospitalized patients: 31% were from 187 rural counties in 31 states; 69% from 234 urban counties in 44 states; the mean age was 65 years (rural, 66 years; urban, 64 years); 3894 women (rural, 41%; urban, 42%); 8007 Whites (rural, 87%; urban, 83%); 1738 deaths (rural, 21%; urban, 17%); and 2729 readmissions (rural, 30%; urban, 29%). During a median follow-up of 602 days, rural residence was associated with a 22% higher all-cause mortality (log-rank, P < .001; hazard ratio, 1.22; 95% confidence interval, 1.10-1.34, P < .001), and a trend toward a higher readmission rate (log-rank, P = .038; hazard ratio, 1.06; 95% confidence interval, .98-1.15; P = .130). The results remained consistent in the sensitivity analysis and in both pre- and postvaccination time periods. Conclusions and Relevance Patients from rural counties experienced higher mortality and tended to be readmitted more frequently following COVID-19 hospitalization over the long term compared with those from urban counties, a difference that remained even after the introduction of COVID-19 vaccines.

Abstract Aims We developed new machine learning (ML) models and externally validated existing statistical models [ischaemic stroke predictive risk score (iScore) and totalled health risks in vascular events (THRIVE) scores] for predicting the composite of recurrent stroke or all-cause mortality at 90 days and at 3 years after hospitalization for first acute ischaemic stroke (AIS). Methods and results In adults hospitalized with AIS from January 2005 to November 2016, with follow-up until November 2019, we developed three ML models [random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGBOOST)] and externally validated the iScore and THRIVE scores for predicting the composite outcomes after AIS hospitalization, using data from 721 patients and 90 potential predictor variables. At 90 days and 3 years, 11 and 34% of patients, respectively, reached the composite outcome. For the 90-day prediction, the area under the receiver operating characteristic curve (AUC) was 0.779 for RF, 0.771 for SVM, 0.772 for XGBOOST, 0.720 for iScore, and 0.664 for THRIVE. For 3-year prediction, the AUC was 0.743 for RF, 0.777 for SVM, 0.773 for XGBOOST, 0.710 for iScore, and 0.675 for THRIVE. Conclusion The study provided three ML-based predictive models that achieved good discrimination and clinical usefulness in outcome prediction after AIS and broadened the application of the iScore and THRIVE scoring system for long-term outcome prediction. Our findings warrant comparative analyses of ML and existing statistical method–based risk prediction tools for outcome prediction after AIS in new data sets.

AbstractIntroductionCoronavirus disease 2019 (COVID‐19) is associated with high rates of morbidity and mortality. Primary hypothyroidism is a common comorbid condition, but little is known about its association with COVID‐19 severity and outcomes. This study aims to identify the frequency of hypothyroidism in hospitalized patients with COVID‐19 as well as describe the differences in outcomes between patients with and without pre‐existing hypothyroidism using an observational, multinational registry.MethodsIn an observational cohort study we enrolled patients 18 years or older, with laboratory‐confirmed severe acute respiratory syndrome coronavirus‐2 infection between March 2020 and February 2021. The primary outcomes were (1) the disease severity defined as per the World Health Organization Scale for Clinical Improvement, which is an ordinal outcome corresponding with the highest severity level recorded during a patient's index COVID‐19 hospitalization, (2) in‐hospital mortality and (3) hospital‐free days. Secondary outcomes were the rate of intensive care unit (ICU) admission and ICU mortality.ResultsAmong the 20,366 adult patients included in the study, pre‐existing hypothyroidism was identified in 1616 (7.9%). The median age for the Hypothyroidism group was 70 (interquartile range: 59–80) years, and 65% were female and 67% were White. The most common comorbidities were hypertension (68%), diabetes (42%), dyslipidemia (37%) and obesity (28%). After adjusting for age, body mass index, sex, admission date in the quarter year since March 2020, race, smoking history and other comorbid conditions (coronary artery disease, hypertension, diabetes and dyslipidemia), pre‐existing hypothyroidism was not associated with higher odds of severe disease using the World Health Organization disease severity index (odds ratio [OR]: 1.02; 95% confidence interval [CI]: 0.92, 1.13; p = .69), in‐hospital mortality (OR: 1.03; 95% CI: 0.92, 1.15; p = .58) or differences in hospital‐free days (estimated difference 0.01 days; 95% CI: −0.45, 0.47; p = .97). Pre‐existing hypothyroidism was not associated with ICU admission or ICU mortality in unadjusted as well as in adjusted analysis.ConclusionsIn an international registry, hypothyroidism was identified in around 1 of every 12 adult hospitalized patients with COVID‐19. Pre‐existing hypothyroidism in hospitalized patients with COVID‐19 was not associated with higher disease severity or increased risk of mortality or ICU admissions. However, more research on the possible effects of COVID‐19 on the thyroid gland and its function is needed in the future.

Objective To characterise the potential association of hyperlipidaemia (HLP) versus no HLP with all-cause mortality among patients hospitalised for pneumonia. Design Propensity score matched retrospective study. Participants The study cohort consisted of consecutive 8553 adults hospitalised at a large academic centre with a discharge diagnosis of pneumonia from 1996 through 2015, followed until death or end of the study period, 17 August 2017. Outcomes The outcome was HR for mortality at 28 days and in the long term in patients with pneumonia with concurrent HLP compared with those with no HLP. We first constructed multivariable Cox proportional regression models to estimate the association between concurrent HLP versus no HLP and mortality after pneumonia hospitalisation for the entire cohort. We then identified 1879 patients with pneumonia with concurrent HLP and propensity score matched in a 1:1 ratio to 1879 patients with no HLP to minimise the imbalance from measured covariates for further analysis. Results Among 8553 unmatched patients with pneumonia, concurrent HLP versus no HLP was independently associated with lower mortality at 28 days (HR 0.52, 95% CI 0.41 to 0.66) and at a median follow-up of 3.9 years (HR 0.75, 95% CI 0.70 to 0.80). The risk difference in mortality was consistent between 1879 propensity score matched pairs both at 28 days (HR 0.65, 95% CI 0.49 to 0.86) and at a median follow-up of 4 years (HR 0.88, 95% CI 0.81 to 0.96). In the subgroup of patients with clinically measured low-density lipoprotein cholesterol (LDL-C), graded inverse associations between LDL-C levels and mortality were found in both unmatched and matched cohorts. Conclusions Among hospitalised patients with pneumonia, a diagnosis of HLP is protective against both short-term and long-term risk of death after adjustment for other major contributors to mortality in both unmatched and propensity score matched cohorts. These findings should be further investigated.

OBJECTIVE—We sought to systematically ascertain the quality of randomized controlled trials (RCTs) in diabetes. RESEARCH DESIGN AND METHODS—We identified the 10 most recently published trials as of 31 October 2003 in each of six general medical, five diabetes, and five metabolism and nutrition journals and further enriched our sample with 10 additional RCTs from each of five journals that published the most eligible RCTs in a year. We explored the association between trial characteristics and reporting quality using univariate analyses and a preplanned multivariate regression model. RESULTS—After excluding redundant reports of included trials and one trial that measured outcomes on the health system and not on patients, we included 199 RCTs: 119 assessed physiological and other laboratory outcomes, 42 assessed patient-important outcomes (e.g., morbidity and mortality, quality of life), and 38 assessed surrogate outcomes (e.g., disease progression or regression, HbA1c, cholesterol). Fifty-three percent were of low methodological quality, as were one-third (36–40%) of trials reporting patient-important or surrogate outcomes and two-thirds (64%) of laboratory investigations. Independent predictors of low quality were nonprofit funding source (odds ratio 3.1 [95% CI 1.5–6.2]), measure of physiological and laboratory outcomes (2.3 [1.2–4.4]), and cross-over design (2.3 [1.1–4.8]), all characteristics of laboratory clinical investigations. CONCLUSIONS—There is ample room for improving the quality of diabetes trials. To enhance the practice of evidence-based diabetes care, trialists need to pay closer attention to the rigorous implementation and reporting of important methodological safeguards against bias in randomized trials.