چکیده مقاله
Background: Coronaviruses COVID 19 are highly contagious viruses which cause symptoms ranging from the common flu to severe respiratory symptoms that cause critical illnesses and death This study aimed to develop a predictive mortality scoring system for inpatients with COVID 19 based on Demographic, clinical, and laboratory characteristics Methods: This study accessed data from open cohort of Isfahan, Khorshid COVID Cohort KCC study 134 death cases were reported and we randomly selected 270 cases from discharged patients Clinical, and laboratory characteristics were used in logistic regression modeling to detect significant risk factors and develop the scoring system ROC curve and AUC criteria was performed to evaluate the efficiency of the risk score developed at identifying patients with high risk COVID 19 related mortality Results: Six variables in the final logistic regression model were associated with outcome discharged & death in this cohort These variables included Age > 65 vs ≤ 55 years , SatO2 7 for identification of COVID 19 cases at high risk of mortality Conclusions: Using this scoring system in COVID 19 patients, quickly and easily predicting COVID 19 mortality can be identified
کلیدواژهها
نویسندگان
شیوه ارجاع
karimi, Raheleh and Sami, Ramin and Maracy, Mohammad Reza and Mansourian, Marjan,1402,Persian risk scoring system for predicting hospital- based mortality between covid-19 patients,The 13th International Conference on Health, Treatment and Health Promotion
ارائهشده در
مجموعه مقالات سیزدهمین کنفرانس بین المللی بهداشت، درمان و ارتقای سلامت12 خرداد 1402