ArticleBMC anesthesiology2023
The development and validation of a nomogram to determine neurological outcomes in cardiac arrest patients.
Article in BMC anesthesiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Development and validation of a prediction model of hospital mortality for patients with cardiac arrest survived 24 hours after cardiopulmonary resuscitation.Frontiers in cardiovascular medicine · 2025Article
- Development and evaluation of the model for acute kidney injury in patients with cardiac arrest after successful resuscitation.BMC cardiovascular disorders · 2024Article
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Authors and funding
4 authors.
Funding
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Abstract
objectivesThis study aimed to investigate the variables that influence neurological functional restoration in cardiac arrest patients and construct a nomogram to predict neurofunctional prognosis. PATIENTS AND
methodsWe extracted the data from the Dryad database. Associations between patient variables and neurological outcomes were examined by logistic regression models. On the basis of these predictors, a prognostic nomogram was constructed. The identification and calibration of the prognostic nomogram were evaluated through the receiver operating characteristic (ROC) curve, the calibration curve, and the concordance index (C-index).
resultsA total of 374 cardiac arrest individuals were recruited in the research. Sixty percent of the participants had an adverse neurological result. The multivariable logistic regression analysis for poor neurological recovery, which showed patient age ≥ 65 years, previous neurological disease, witnessed arrest, bystander cardio-pulmonary resuscitation(CPR), cardiac arrest presenting with a non-shockable rhythm, total epinephrine dose ≥ 2.5 mg at the time of resuscitation and acute kidney injury(AKI) remained independent predictors for neurological outcomes.
conclusionsThe novel nomogram based on clinical characteristics is an efficient tool to predict neurological outcomes in cardiac arrest patients, which may help clinicians identifying high-risk patients and tailoring personalized treatment regimens.
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