Abstract Background Delirium is a complex neuropsychiatric syndrome whose severe and lasting consequences in hospitalized patients are largely considered preventable. Prevention primarily relies on the early identification of high-risk patients prior to syndrome onset. However, this remains challenging in clinical practice due to the absence of a diagnostic gold standard and the considerable administrative burden associated with established screening tools such as the 4AT score. Objective The aim of this study is to compare the 4AT score with an established AI-supported prognostic tool in applied clinical practice. Our goal is to demonstrate that shifting from a purely diagnostic approach toward AI-supported delirium risk prediction enables more efficient clinical workflows, reduces misclassification rates, facilitates earlier and more targeted interventions, and ultimately improves long-term patient outcomes. Methods In this prospective study, a total of 2002 patients from a trauma ward were screened and assessed for delirium risk using the 4AT score and the AI-based tool at admission, on the evening of admission, and on the following day, with each assessment predicting the risk of delirium occurrence at any point during the remainder of the hospital stay. The AI tool based its predictions on clinical data automatically derived from electronic health records. The AI-based tool applied a previously developed and validated random forest model (not developed as part of this study). Results Of the 2002 processed patients, the AI prediction tool correctly predicted 87.3% (165/189) of patients with delirium onset as at-risk, compared to an early detection rate of 28.0% (5/189) achieved by the 4AT score. Moreover, the AI-based tool correctly classified 73.9% (1340/1813) patients without delirium as not-at-risk, compared to 96.3% (1746/1813) for the 4AT score. Therefore, the AI-based tool achieved very good delirium predictions, with an area under the receiver operating characteristic curve of 0.884 (95% CI 0.8634‐0.9039). Conclusions The AI-based delirium prognostic tool demonstrated superior early predictive capacity for delirium risk at admission, compared with the 4AT score. The AI-based tool enables proactive risk management and targeted risk reduction.
BACKGROUND Major depressive disorder is a leading cause of disability, and access to specialist psychiatric follow-up remains limited in many settings. Telepsychiatry may reduce logistical barriers to care, but data on video-based follow-up under routine outpatient conditions in Türkiye are scarce, and comparisons of delivery modalities are frequently limited by baseline imbalance between self-selected groups. OBJECTIVE The aim of the study is to describe symptom trajectories and attendance over 6 months of routine psychiatric follow-up delivered either face-to-face or online and to examine whether outcomes differed between modalities after adjustment for baseline severity. METHODS In this prospective, nonrandomized observational cohort study, 120 adults with Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) major depressive disorder were followed for 6 months at a university hospital psychiatry department in Ankara, Türkiye (September 2021 to May 2022). Participants entered face-to-face (n=60) or online (n=60) follow-up according to their own preference. Seven sessions were scheduled at approximately 4-week intervals; the Hamilton Depression Rating Scale (HAM-D; primary outcome), the Hamilton Anxiety Rating Scale, and DSM-5 Level 1 and Level 2 measures were administered at baseline and at sessions 1, 2, 4, and 6. Attendance was recorded at all sessions. Between-group comparisons used analysis of covariance adjusted for the corresponding baseline score and for prespecified covariates. Linear mixed models incorporating a group-by-time interaction were fitted to all available observations, and multiple imputation (m=20) was performed as a sensitivity analysis. RESULTS Depressive symptoms decreased substantially in both groups: mean HAM-D fell from 26.6 to 5.0 in the face-to-face group (81.2% reduction; Cohen d=4.86; P<.001; n=43) and from 24.1 to 3.3 in the online group (86.3% reduction; Cohen d=6.06; P<.001; n=49). Response rates were 41 of 43 (95.3%) and 48 of 49 (98%; P=.60), and remission rates were 32 of 43 (74.4%) and 43 of 49 (87.8%; P=.11). Unadjusted between-group differences at sessions 4 and 6 did not persist after adjustment for baseline severity (session 4: -0.47, 95% CI -1.71 to 0.77; P=.46; session 6: -0.79, 95% CI -2.22 to 0.65; P=.28), and the group-by-time interaction was not significant (χ23=1.9; P=.59). Anxiety scores did not differ at any time point. Findings were unchanged under multiple imputation, and prescribing patterns were comparable. Attendance across all 7 scheduled sessions was higher in the online group overall (373/420, 88.8% vs 319/420, 76%; P<.001), with differences at sessions 2 through 5. CONCLUSIONS Both modalities were followed by large reductions in depressive symptoms, and between-group differences did not persist after adjustment for baseline severity. The study was not designed as an equivalence trial and cannot establish equivalence. The most consistent difference favoring online follow-up was attendance, although its reasons could not be determined. These findings characterize online psychiatric follow-up as a feasible means of maintaining continuity of routine care rather than as a more effective one.
Background Existing risk scores for nonvariceal upper gastrointestinal bleeding (NVUGIB) may not adequately represent the heterogeneity of coexisting chronic conditions. Routinely collected hospital discharge records may enable data-driven characterization of clinically meaningful comorbidity profiles. Objective This formative study aimed to identify reproducible comorbidity profiles among patients hospitalized with NVUGIB and examine their associations with in-hospital outcomes and health care use. Methods We conducted a retrospective cohort study of 3193 adults hospitalized with NVUGIB at a tertiary hospital in China between 2015 and 2025. Of 55 prespecified chronic conditions, 33 with a prevalence of at least 1% were included as binary clustering variables. Patients were randomly divided into an 80% training subset and a 20% held-out validation subset. Comorbidity profiles were identified in the training subset using a self-organizing map and Ward hierarchical clustering and then assigned to the validation subset using the fixed clustering solution. Profile reproducibility was evaluated by comparing condition prevalence vectors between subsets. Multivariable regression models assessed associations with severe in-hospital outcomes, intensive care unit admission, in-hospital death, length of stay, and hospital costs. Results At least 1 selected comorbidity was recorded in 89.7% (2864/3193) of patients. Six profiles were identified: low-burden heterogeneous (cluster 1), non-upper gastrointestinal neoplastic-metastatic (cluster 2), circulatory-renal (cluster 3), cardiometabolic-renal (cluster 4), atherosclerotic-vascular (cluster 5), and rheumatologic-neurological (cluster 6). Profile composition was broadly reproducible between the training and validation subsets (weighted mean Spearman correlation=0.891, SD 0.04; P=.001). The circulatory-renal profile had the strongest associations with severe in-hospital outcomes (adjusted odds ratio [aOR] 8.57, 95% CI 5.39-14.01), intensive care unit admission (aOR 10.38, 95% CI 4.87-25.76), and in-hospital death (aOR 4.66, 95% CI 1.97-13.75). The rheumatologic-neurological and cardiometabolic-renal profiles were also associated with higher odds of these outcomes. All 6 profiles were associated with longer hospital stays and higher hospital costs. Conclusions Using routinely collected hospital discharge records, we identified clinically interpretable comorbidity profiles among patients with NVUGIB. The profiles were broadly reproducible within the study population and showed different associations with clinical outcomes and resource use. Profile-based information may complement established risk assessment and could be examined further as an electronic health record-based approach to clinical management and resource planning.
BACKGROUND Tuberculosis (TB) remains a major global health challenge, with 10.6 million new cases and 1.3 million deaths reported in 2022. Artisanal miners in sub‑Saharan Africa are particularly vulnerable due to poverty, overcrowded living conditions, and high HIV prevalence. In the Democratic Republic of Congo (DRC), TB prevalence among miners is high, yet determinants remain insufficiently studied. OBJECTIVE This study aimed to identify sociodemographic, behavioral, and clinical factors associated with TB among artisanal miners in the Mulongo health zone, Haut‑Lomami Province, DRC. METHODS We conducted an unmatched case-control study among miners aged ≥15 years. Data were collected using a validated questionnaire covering socioeconomic, behavioral, and biomedical variables. Interviewers were blinded to TB status to minimize bias. Determinants were assessed using bivariate and multivariable logistic regression. RESULTS Poverty was strongly associated with TB (adjusted odds ratio [aOR] 3.16, 95% CI 1.95-5.11; P<.001). Primary education was associated with increased TB risk (aOR 2.78, 95% CI 1.04-7.44; P=.04), while secondary education showed a borderline association (aOR 2.53, 95% CI 0.93-6.89; P=.07). Higher education was protective (aOR 0.62, 95% CI 0.40-0.95; P=.03). Poor knowledge of TB transmission (aOR 2.11, 95% CI 1.16-3.83; P=.02), cause (aOR 1.79, 95% CI 1.00-3.20; P=.049), and diagnosis (aOR 2.49, 95% CI 1.48-4.17; P=.001) were consistently linked to higher vulnerability. HIV infection was significantly associated with TB (aOR 4.65, 95% CI 1.00-21.74; P=.049). Overcrowding was more frequent among TB cases, but in adjusted models, the association was borderline (aOR 1.65, 95% CI 0.95-2.85; P=.07). Tobacco use (aOR 1.47, 95% CI 0.83-2.61; P=.18), alcohol consumption (aOR 1.18, 95% CI 0.70-1.98; P=.54), and nutritional status (aOR 1.36, 95% CI 0.78-2.37; P=.28) were not significantly associated with TB. CONCLUSIONS TB among artisanal miners appears to be driven primarily by poverty, limited education, poor knowledge, and HIV infection. Overcrowding showed a suggestive but nonsignificant association. Integrated interventions should prioritize poverty alleviation, improved housing, tailored health education, and strengthened TB and HIV services, while further studies are needed to clarify the role of overcrowding and other contextual factors.
