A Retrospective Cohort Study Evaluating the Use of the Modified Early Warning Score to Improve Outcome Prediction in Neurosurgical Patients.

Introduction The modified early warning score (mEWS) has been used to identify decompensating patients in critical care settings, potentially leading to better outcomes and safer, more cost-effective patient care. We examined whether the admission or maximum mEWS of neurosurgical patients was associated with outcomes and total patient costs across neurosurgical procedures. Methods This retrospective cohort study included all patients hospitalized at a quaternary care hospital for neurosurgery procedures during 2019. mEWS were automatically generated during a patient's hospitalization from data available in the electronic medical record. Primary and secondary outcome measures were the first mEWS at admission, maximum mEWS during hospitalization, length of stay (LOS), discharge disposition, mortality, cost of hospitalization, and patient biomarkers (i.e., white blood cell count, erythrocyte sedimentation rate, C-reactive protein, and procalcitonin). Results In 1,408 patients evaluated, a mean first mEWS of 0.5 ± 0.9 (median: 0) and maximum mEWS of 2.6 ± 1.4 (median: 2) were observed. The maximum mEWS was achieved on average one day (median = 0 days) after admission and correlated with other biomarkers (p < 0.0001). Scores correlated with continuous outcomes (i.e., LOS and cost) distinctly based on disease types. Multivariate analysis showed that the maximum mEWS was associated with longer stay (OR = 1.8; 95% CI = 1.6-1.96, p = 0.0001), worse disposition (OR = 0.82, 95% CI = 0.71-0.95, p = 0.0001), higher mortality (OR = 1.7; 95% CI = 1.3-2.1, p = 0.0001), and greater cost (OR = 1.2, 95% CI = 1.1-1.3, p = 0.001). Machine learning algorithms suggested that logistic regression, naïve Bayes, and neural networks were most predictive of outcomes. Conclusion mEWS was associated with outcomes in neurosurgical patients and may be clinically useful. The composite score could be integrated with other clinical factors and was associated with LOS, discharge disposition, mortality, and patient cost. mEWS also could be used early during a patient's admission to stratify risk. Increase in mEWS scores correlated with the outcome to a different degree in distinct patient/disease types. These results show the potential of the mEWS to predict outcomes in neurosurgical patients and suggest that it could be incorporated into clinical decision-making and/or monitoring of neurosurgical patients during admission. However, further studies and refinement of mEWS are needed to better integrate it into patient care.