In-hospital mortality among hospitalized COVID-19 patients in a tertiary care hospital in Dhaka City: a retrospective cohort study
Bibliographic record
Abstract
Background: In-hospital mortality during COVID-19 treatment is a crucial metric used to assess the severity of the disease and the effectiveness of medical interventions. By identifying mortality risk factors, we aim to inform policy decisions, optimize resource allocation, and improve preparedness for future pandemics. Methods: This retrospective cohort study was conducted at a tertiary hospital in Dhaka City, Bangladesh. Data were gathered from the hospital's electronic medical records between July 2021 and September 2021. After applying specific inclusion and exclusion criteria, 218 patients with complete medical records were selected for the analysis. The independent variables examined included demographic characteristics, comorbidities, and clinical features. To assess in-hospital mortality, relative risks (RR) and 95% confidence intervals were calculated using multivariable logistic regression analysis employing the Delta method. Results: The study included 218 hospitalized COVID-19 patients, primarily male (51.4%) with an average age of 56.4 years (standard deviation of 15 years). The overall in-hospital mortality rate was 18.3%. Older age (≥60) (RR:3.10, 95% CI: 1.16-8.29), long-standing hypertension (≥5 years) (RR:2.78, 95% CI:1.54-5.02), and chronic kidney disease (CKD) (RR:4.43, 95% CI:2.93-6.70) were significant risk factors for mortality. Patients with diabetes (≥3 years) had a moderately increased risk (RR:1.68, 95% CI: 1.01-2.83). Notably, shorter hospital stays (≤7 days) were associated with higher mortality, potentially due to delayed treatment initiation. Moreover, Vaccinated patients have a significantly lower risk of death (RR: 0.07) compared to unvaccinated patients, highlighting the protective effect of vaccination. Greater lung involvement (especially in lower lobes) and higher Total Severity Scores (TSS ≥ 14) strongly predict COVID-19 mortality, with non-survivors exhibiting significantly worse radiographic damage. Conclusion: Age, particularly when combined with chronic conditions like hypertension or chronic kidney disease, is a key predictor of in-hospital COVID-19 mortality. While gender is not an independent risk factor, males tend to have higher mortality rates. Delayed treatment, reflected by shorter hospital stays, also increases risk. Vaccination markedly lowers mortality. In resource-limited settings, lower lobe involvement >50% and TSS ≥ 14 can serve as early triage markers to guide ICU admission or intensified care. These indicators should inform risk assessment tools, resource allocation, and targeted interventions to reduce pandemic-related mortality.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".