ArticleBMC infectious diseases2026
Artificial intelligence-based chest X-ray (AI-CXR) and hematology parameters to predict mortality and intubation events In COVID-19 patients at the second-referral hospital in Indonesia.
Article in BMC infectious diseases, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
19 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundCOVID-19 can cause acute respiratory distress syndrome (ARDS), and artificial intelligence (AI) algorithms for chest X-ray (CXR) analysis have been developed to assess pulmonary damage in COVID-19 patients. We evaluated the combination of AI-CXR and hematological parameters to predict mortality and intubation events for COVID-19 patients.
methodsThis study is a retrospective cohort study of COVID-19 patients for which we have collected data during the 2020–2022 period at Airlangga University Hospital, Surabaya, Indonesia.
resultsThe total number of patients involved in this study was 312. The results of the scoring evaluation using a combination of AI-CXR, hematology parameters, respiratory rate (RR), and SaO2 showed ROC 0.854; p = 0.000; sensitivity: 74.2%; specificity: 89% in predicting 30-day mortality events, and ROC 0.846; p = 0.000; sensitivity: 80.3%; specificity: 80.9% in predicting 30-day intubation events. Meanwhile, without using AI-CXR, it shows ROC 0.822; p = 0.000; sensitivity: 80.3%; specificity: 81.7% in predicting 30-day mortality events, and ROC 0.815; p = 0.000; sensitivity: 77.3%; specificity: 81.7% in predicting 30-day intubation events.
conclusionThe combination of hematology parameters with or without AI-CXR has excellent sensitivity and specificity in predicting the incidence of mortality and intubation in COVID-19 patients. CLINICAL TRIAL: Not applicable.
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.