Sharma, Umesh M.

Background Patients with substance use disorder (SUD) have high rates of hospitalization, readmission, and prolonged lengths of stay. Objectives To conduct a systematic review and meta-analysis of the impact of addiction consult services in the inpatient setting for patients with SUD. Methods Five databases were systematically searched from their earliest available records through January 2025. Two authors independently screened articles, extracted data, and assessed the quality of each study with the Newcastle-Ottawa Scale for observational studies and the Cochrane risk-of-bias tool for randomized clinical trials. Pooled odds ratios (ORs) with 95% CIs were calculated for dichotomous outcomes, and mean differences were calculated for continuous outcomes. Results The search retrieved 20 studies that met inclusion criteria. The intervention for all studies was an addiction medicine consult, but the definition of this consult varied. The meta-analysis did not detect statistically significant differences in hospital length of stay, hospital readmission, or emergency department visits at 1 year among patients who received the consult compared with those who did not; however, many of these outcomes were likely underpowered. However, addiction medicine consults significantly improved the odds of inpatient initiation of medication for SUD (OR, 7.30; 95% CI, 3.30-16.13; P <.001) and postdischarge SUD treatment linkage and adherence (OR, 3.31; 95% CI, 2.06-5.32; P <.001). Conclusion Inpatient addiction consult services are associated with increased inpatient initiation of medication for SUD treatment and postdischarge treatment linkage and adherence. No clear association was identified between the consult and readmissions, emergency department utilization, or length of stay.

Sudden cardiac arrest (CA) is a leading cause of death in the United States, with coexisting chronic obstructive pulmonary disease (COPD) recognized as an independent risk factor. National trends in mortality among individuals with both conditions remain unclear. Using Centers for Disease Control and Prevention’s Wide-ranging Online Data for Epidemiologic Research (multiple cause-of-death data, we conducted a retrospective cross-sectional analysis of deaths with International Classification of Diseases, Tenth Revision codes for COPD (J41–J44) and CA (I46). Age-adjusted mortality rates (AAMRs) were calculated and stratified by race, sex, age group, census region, urbanization level, and place of death. Temporal trends were assessed using Joinpoint regression, reporting annual percent change (APC), and average annual percent change. From 1999 to 2023, a total of 853,773 deaths were recorded, with AAMRs declining from 35.48 to 22.77. The decline was steepest from 1999 to 2006 (APC: –2.77), followed by a more gradual reduction through 2021 (APC: –0.84), and a marked acceleration between 2021 and 2023 (APC: –5.52). Males consistently had higher AAMRs than females (35.41 vs 23.32). Racial disparities persisted, with non-Hispanic (NH) White (29.5) and NH Black individuals (28.16) experiencing the highest rates, followed by NH American Indians (26.17), Hispanics (22.2), and NH Asian/Pacific Islanders (17.82). Mortality was also highest in nonmetropolitan areas (31.37) and the Western region (39.68). Notably, home deaths increased following the coronavirus disease 2019 pandemic. Overall, CA-related mortality in individuals with COPD has declined over the past 25 years, with sharper reductions observed in the post-coronavirus disease period. Despite these improvements, persistent and widening disparities underscore the need for targeted interventions in high-risk populations.

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.

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.