{Reference Type}: Journal Article
{Title}: Modified National Early Warning Score (MNEWS) in predicting the mortality of intensive care unit patients.
{Author}: Wang S;Tian Y;Yao Y;Zhou J;Chen H;Diao X;
{Journal}: Postgrad Med J
{Volume}: 99
{Issue}: 1174
{Year}: Jul 2023 21
{Factor}: 4.973
{DOI}: 10.1093/postmj/qgad014
{Abstract}: OBJECTIVE: This study aims to develop an accurate and simplified scoring system based on the national early warning score (NEWS) to predict the mortality of intensive care unit (ICU) patients.
METHODS: The information of patients was retrieved from the Medical Information Mart for Intensive Care (MIMIC)-III and -IV databases. The Modified National Early Warning Score (MNEWS) of the patients was calculated. The discrimination ability of the MNEWS, acute physiology and chronic health scoring system II (APACHE II), and original NEWS systems in predicting patients' mortality was evaluated using area under the receiver operating characteristic (AUROC) analysis. The DeLong test was used to estimate the receiver operating characteristic curve. The Hosmer-Lemeshow goodness-of-fit test was then applied to evaluate the calibration of the MNEWS.
RESULTS: In total, 7275 ICU patients from the MIMIC-III and -IV databases were included in the derivation cohort and 1507 ICU patients from Xi'an Medical University were included in the validation cohort. In the derivation cohort, the nonsurvivors had significantly higher MNEWSs than the survivors (12.5 ± 3.4 vs 8.8 ± 3.4, P < 0.05). MNEWS and APACHE II both had a better performance than the NEWS in predicting hospital mortality and 90-day mortality. The optimal cutoff of MNEWS was 11. Patients with an MNEWS ≥ 11 had significantly shorter survival than those having an MNEWS of <11. Furthermore, MNEWS had a high calibration ability in predicting hospital mortality of ICU patients (χ2 = 6.534 and P = 0.588) by the Hosmer-Lemeshow test. This finding was confirmed in the validation cohort.
CONCLUSIONS: MNEWS is a simple and accurate scoring system for evaluating the severity and predicting the outcomes of ICU patients.