Factors Predicting Cardiac Arrest in the Intensive Care Unit: A Matched Case-Control Study

Abstract Background: Early detection and intervention of disease deterioration are the keys to reducing the incidence of preventable intensive care unit cardiac arrest (ICU-CA). We aimed to investigate the ICU-CA predictive factors, including vital signs and laboratory indicators, and to analyze the performance of trends value of those factors on predicting ICU-CA. Methods: We conducted a matched case-control study at Qilu Hospital of Shandong University. Data on adult patients in ICU who suffered a cardiac arrest (CA) were retrospectively collected from 2016 to 2019, including vital signs and laboratory indicators at 48, 36, 24, 12, and 8 hours before ICU-CA. These cases were matched (ward, sex, and admission data) with controls (no ICU-CA) at a 1:2 ratio. Univariable logistic regression was used for statistical comparisons between cases and controls, and multivariate logistic regression was used to investigate the independent associations of indicators and their tendency with ICU-CA at given time points. The area under receiver operating characteristic (AUROC) was used to evaluate the predictive performance on ICU-CA.Results: Of 6164 ICU patients, 1042 patients suffered an ICU-CA during the 3 years. After careful screening, a total of 427 patients were included as the cases in the study, and 790 patients were included as controls. The vital signs and laboratory indicators at 8h before cardiac arrest, such as heart rate (HR), respiratory rate (RR), systolic blood pressure (SBP), oxygen saturation (SaO2), hemoglobin (HGB), potassium (K+), sodium (Na+), lactic acid (Lac), and pH all can predict the ICU-CA. The mean value, maximum value, minimum value, and range of these indicators were related to the occurrence of ICU-CA, and the trend values were more accurate than the current value for the variability in laboratory indicators. Conclusions: The ability of trends value of laboratory indicators for predicting ICU-CA was more accurate than the value at given time points for the variability in laboratory indicators. Adding trends of laboratory indicators may increase the accuracy of models designed to detect critical illness in ICU. Trial registration: ClinicalTrials.gov Identifier: NCT04670458.