Qadeer, Abdul

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.

Systemic sclerosis-associated pulmonary arterial hypertension (SSc-PAH) is a life-threatening vascular complication of SSc, marked by high morbidity and mortality. Early diagnosis remains a major challenge due to nonspecific symptoms and the limitations of conventional tools such as echocardiography (ECHO), pulmonary function tests (PFTs), and serum biomarkers. This review evaluates the emerging role of artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), in improving the diagnostic landscape of SSc-PAH. A comprehensive literature search was conducted across PubMed, Scopus, IEEE Xplore, Embase and Google Scholar to identify studies involving AI applications in SSc, pulmonary arterial hypertension (PAH), and their intersection. Evidence indicates that AI models can assist interpretation across modalities, including heart sounds, ECGs, chest X-rays (CXRs), ECHOs, CT pulmonary angiography (CTPA), and omics-based biomarkers. While several models show encouraging diagnostic performance, their accuracy varies by dataset and modality, and most require external validation against right heart catheterization (RHC)-confirmed cohorts. Integrating multimodal data through AI frameworks may enhance early recognition and individualized risk stratification; however, these tools remain exploratory. Future work should emphasize harmonized hemodynamic definitions, transparent validation protocols, and SSc-specific datasets to ensure clinical applicability and reproducibility.