Health science reports · 2026
Developing a Mortality Risk Score in Intensive Care Units of Referral Hospitals in Bahir Dar City, Ethiopia: A Prognostic Study.
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Wubie Yihun Miskir, Abdu Asiya Mohammed, Anteneh Zelalem Alamrew
Аннотация
OBJECTIVE: The aim of this study was to develop a mortality risk score in the intensive care units of referral hospitals in Bahir Dar City.
METHODS: The study included 852 participants who were admitted from January 1, 2019 to December 31, 2021. We used EpiData version 3.1 for data entry and R-software for analysis. The mortality rate among participants was 35.9%. Multivariable logistic regression was employed to identify the independent prognostic determinants. Using beta-coefficients, we developed and validated a prognostic model. Then a mortality risk score was determined based on the value of each prognostic determinant variable.
RESULTS: Age, sex, health insurance user status, respiratory rate, temperature, mean arterial pressure, Glasgow Coma Scale, WBC count, sepsis, ARDS, organ-insufficiency, mechanical ventilation, and vasopressor were independent prognostic determinants. Based on the prognostic determinants, we developed an easily applicable mortality risk score model. The model had a discrimination performance of AUC 0.90 (95% confidence interval of 0.88-0.92) and a calibration p value of 0.69.
CONCLUSION: The prognostic determinants identified in this study are easily accessible and easy to capture in routine clinical settings. As a result, the developed model has the potential to be effectively applied in low-income countries where resources may be limited.
IMPLICATION FOR CLINICAL PRACTICE: The model can help healthcare providers in low-income settings to identify high-risk patients and develop appropriate interventions to improve patient outcomes.
METHODS: The study included 852 participants who were admitted from January 1, 2019 to December 31, 2021. We used EpiData version 3.1 for data entry and R-software for analysis. The mortality rate among participants was 35.9%. Multivariable logistic regression was employed to identify the independent prognostic determinants. Using beta-coefficients, we developed and validated a prognostic model. Then a mortality risk score was determined based on the value of each prognostic determinant variable.
RESULTS: Age, sex, health insurance user status, respiratory rate, temperature, mean arterial pressure, Glasgow Coma Scale, WBC count, sepsis, ARDS, organ-insufficiency, mechanical ventilation, and vasopressor were independent prognostic determinants. Based on the prognostic determinants, we developed an easily applicable mortality risk score model. The model had a discrimination performance of AUC 0.90 (95% confidence interval of 0.88-0.92) and a calibration p value of 0.69.
CONCLUSION: The prognostic determinants identified in this study are easily accessible and easy to capture in routine clinical settings. As a result, the developed model has the potential to be effectively applied in low-income countries where resources may be limited.
IMPLICATION FOR CLINICAL PRACTICE: The model can help healthcare providers in low-income settings to identify high-risk patients and develop appropriate interventions to improve patient outcomes.