Comparison of the Performance of 24 Early Warning Scores with the Updated National Early Warning Score (NEWS2) for Predicting Unplanned Intensive Care Unit (ICU) Admission in Postoperative Brain Tumor Patients: A Retrospective Study at a Single Center
Lingli Peng, Zhen Luo, Lingling Liang, Mingming Liu, Lingyao Meng, Jianwen Tan, Lili Song, Yan Zhang, Lixiang Wu
Xiangya School of Public Health, Central South University, Changsha, Hunan, China (mainland)
Med Sci Monit 2021; 27:e929168
Available online: 2021-01-12
There have been few studies to evaluate early warning score (EWS) systems, or track and trigger systems (TTS), to identify early clinical deterioration in patients following brain tumor surgery who are admitted to the Intensive Care Unit (ICU). The National Early Warning Score (NEWS2) is an established method used in the U.K. National Health Service to improve care for in-hospital patients. This retrospective study from a single center aimed to compare the performance of NEWS2 with 24 other types of EWS to evaluate unplanned ICU admissions within 72 h after brain tumor surgery.
MATERIAL AND METHODS: A total of 326 patients with brain tumors were included in the study. Patients who experienced unplanned ICU transfer after surgery (69 cases) were diagnostically matched with patients who did not require intensive care (257 controls). We collected the physiological variables to calculate the area under the receiver operator characteristic curve (AUROC), sensitivity, specificity, Youden index values, cutoff values, positive predictive values, and negative predictive values.
RESULTS: The NEWS2 identified postoperative brain tumor patients with AUROC (0.860, p=0.000). The Patient-At-Risk (PAR) score was higher than NEWS2 in terms of AUROC value (0.870, P=0.000), Youden index (0.589 vs 0.542).
CONCLUSIONS: The findings showed that although the NEWS 2 performed well when used to evaluate unplanned ICU admissions within 72 h of postoperative brain tumor patients, the PAR score was also an accurate EWS.
Keywords: Brain Neoplasms, Decision Support Techniques, Intensive Care Units