Context:The capacity completeness are one of the serious problems in the bed's managements of the critical care units in a crisis and disaster situation. Reverse triage can help to hospital surge capacity in this situations.Aims:The aim of this study was to develop a reverse triage system based on Modified Sequential Organ Failure Assessment (MSOFA) for increasing critical care surge capacity.Settings and Design:This study was a prospective design that performed on the medical patients in critical care unit.Subjects and Methods:The MSOFA score for each patient was calculated in admission time and be continued until discharging time from critical care unit.Statistical Analysis Used:The Cox regression method was used to determine the relative risk values. At last, the patients were divided into three levels for reverse triage.Results:Four hundred and twenty patients were participated in this study. The mean of patients' MSOFA scores in the 1st day of admission in Critical Care was 5.40 ± 3.8. The relative risk of internal patients discharge from critical care was (8.2%). Death relative risks were <25%, higher than 70% and between 25.1% and 69.9% for three color level of green, black, and red, respectively.Conclusion:The MSOFA scores can contribute to the design a leveling system for discharging patients from critical care unit. Based on this system, the members of the caring team can predict the final health status of the patient.
Background: Nine equivalents of nursing manpower use score (NEMS) is one of the scoring systems used in some studies to determine the severity of the status of patients in the ICU. The present study was conducted to investigate the application of NEMS to identify patients at the end stages of life. Methods: This study was a prospective design where 420 patients were selected from the intensive care unit (ICU) of a referral hospital. Data collection tools were demographic, disease-related, and NEMS questionnaires. The last calculated NEMS score for patients was used to detect their need for end-of-life care. After completing the sampling, the data were analyzed by descriptive statistics and Cox regression at 95% confidence interval.
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