ArticleBMJ open2024
Predictors of mortality in hospitalised patients with COVID-19: a 1-year case-control study.
Article in BMJ open, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed, 4 citations in OpenAlex.
- Serum osmolarity as a predictor of mortality in ICU COVID-19 patients: a retrospective analysis.PeerJ · 2026Article
- Article
- Predictors of 2-year mortality in geriatric patients hospitalized with COVID-19 in Türkiye: a retrospective cohort study.Biomarkers in medicine · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
18 authors at 5 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundA paucity of predictive models assessing risk factors for COVID-19 mortality that extend beyond age and gender in Latino population is evident in the current academic literature.
objectivesTo determine the associated factors with mortality, in addition to age and sex during the first year of the pandemic.
designA case-control study with retrospective revision of clinical and paraclinical variables by systematic revision of clinical records was conducted. Multiple imputations by chained equation were implemented to account for missing variables. Classification and regression trees (CART) were estimated to evaluate the interaction of associated factors on admission and their role in predicting mortality during hospitalisation. No intervention was performed.
settingHigh-complexity centre above 2640 m above sea level (masl) in Colombia.
participantsA population sample of 564 patients admitted to the hospital with confirmed COVID-19 by PCR. Deceased patients (n=282) and a control group (n=282), matched by age, sex and month of admission, were included.
main outcome measureMortality during hospitalisation. MAIN
resultsAfter the imputation of datasets, CART analysis estimated 11 clinical profiles based on respiratory distress, haemoglobin, lactate dehydrogenase, partial pressure of oxygen to inspired partial pressure of oxygen ratio, chronic kidney disease, ferritin, creatinine and leucocytes on admission. The accuracy model for prediction was 80.4% (95% CI 71.8% to 87.3%), with an area under the curve of 78.8% (95% CI 69.63% to 87.93%).
conclusionsThis study discloses new interactions between clinical and paraclinical features beyond age and sex influencing mortality in COVID-19 patients. Furthermore, the predictive model could offer new clues for the personalised management of this condition in clinical settings.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.