ArticleBMC cardiovascular disorders2026
Construction of a predictive nomogram for ICU mortality in patients with cardiac arrest using the MIMIC-IV database and the eICU Collaborative Research Database.
Article in BMC cardiovascular disorders, 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
6 authors.
Funding
Abstract
backgroundCardiac arrest (CA) is a critical clinical event associated with extremely high mortality and long-term disability. This study aimed to identify risk factors associated with ICU mortality and develop a practical nomogram for risk prediction to support prognostic assessment and risk stratification among patients with CA.
methodsA retrospective analysis was conducted using two large public critical care databases. The MIMIC-IV database was used as the training cohort for model development, while the eICU-CRD database served as the external validation cohort. Variables were initially screened using least absolute shrinkage and selection operator (LASSO) regression, and a predictive nomogram was subsequently established using binary logistic regression analysis. The discriminative performance of the model was evaluated using the receiver operating characteristic (ROC) curve. Calibration was assessed using calibration curves and the Hosmer-Lemeshow goodness-of-fit test. Clinical applicability and net benefit were further analyzed using decision curve analysis (DCA).
resultsA total of 3,029 patients from the MIMIC-IV database and 2,805 patients from the eICU-CRD database were enrolled in this study. After variable selection by LASSO regression, eight predictors were included in the nomogram. The model was compared with conventional ICU scoring systems, including SAPS II, SOFA, and CCI. In the training cohort, the AUROC values were 0.802 for the nomogram, 0.725 for SAPS II, 0.709 for SOFA, and 0.509 for CCI. In the external validation cohort, the corresponding AUROC values were 0.733, 0.596, 0.701, and 0.517, respectively. The Hosmer-Lemeshow test yielded P-values of 0.976 in the training set and 0.905 in the validation set, indicating favorable goodness-of-fit and satisfactory calibration. Moreover, DCA demonstrated that the nomogram provided greater net benefit compared with traditional scoring systems.
conclusionThe established nomogram for predicting ICU mortality in patients with CA demonstrated acceptable predictive performance and calibration. As a quantitative risk stratification tool, it may provide clinicians with useful individualized prognostic information for this vulnerable patient population.
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.