Kap Su Han1, Su Jin Kim1, Eui Jung Lee1, Joong Ho Shin1, Ji Sung Lee2, Sung Woo Lee3. 1. Department of Emergency Medicine, College of Medicine, Korea University, Goryeodae-ro 73, Seongbuk-gu, Seoul, 02841, Republic of Korea. 2. Clinical Research Center, Asan Institute for Life Sciences, Asan Medical Center, 88 Olympic-ro 43gil, Songpa-gu, Seoul, 05505, Republic of Korea. 3. Department of Emergency Medicine, College of Medicine, Korea University, Goryeodae-ro 73, Seongbuk-gu, Seoul, 02841, Republic of Korea. kuedlee@korea.ac.kr.
Abstract
BACKGROUND: A prediction model of mortality for patients with acute poisoning has to consider both poisoning-related characteristics and patients' physiological conditions; moreover, it must be applicable to patients of all ages. This study aimed to develop a scoring system for predicting in-hospital mortality of patients with acute poisoning at the emergency department (ED). METHODS: This was a retrospective analysis of the Injury Surveillance Cohort generated by the Korea Center for Disease Control and Prevention (KCDC) during 2011-2018. We developed the new-Poisoning Mortality Scoring system (new-PMS) to generate a prediction model using the derivation group (2011-2017 KCDC cohort). Points were computed for categories of each variable. The sum of these points was the new-PMS. The validation group (2018 KCDC cohort) was subjected to external temporal validation. The performance of new-PMS in predicting mortality was evaluated using area under the receiver operating characteristic curve (AUROC) for both the groups. RESULTS: Of 57,326 poisoning cases, 42,568 were selected. Of these, 34,352 (80.7%) and 8216 (19.3%) were enrolled in the derivation and validation groups, respectively. The new-PMS was the sum of the points for each category of 10 predictors. The possible range of the new-PMS was 0-137 points. Hosmer-Lemeshow goodness-of-fit test showed adequate calibration for the new-PMS with p values of 0.093 and 0.768 in the derivation and validation groups, respectively. AUROCs of the new-PMS were 0.941 (95% CI 0.934-0.949, p < 0.001) and 0.946 (95% CI 0.929-0.964, p < 0.001) in the derivation and validation groups, respectively. The sensitivity, specificity, and accuracy of the new-PMS (cutoff value: 49 points) were 86.4%, 87.2%, and 87.2% and 85.9%, 89.5%, and 89.4% in the derivation and validation groups, respectively. CONCLUSIONS: We developed a new-PMS system based on demographic, poisoning-related variables, and vital signs observed among patients at the ED. The new-PMS showed good performance for predicting in-hospital mortality in both the derivation and validation groups. The probability of death increased according to the increase in the new-PMS. The new-PMS accurately predicted the probability of death for patients with acute poisoning. This could contribute to clinical decision making for patients with acute poisoning at the ED.
BACKGROUND: A prediction model of mortality for patients with acute poisoning has to consider both poisoning-related characteristics and patients' physiological conditions; moreover, it must be applicable to patients of all ages. This study aimed to develop a scoring system for predicting in-hospital mortality of patients with acute poisoning at the emergency department (ED). METHODS: This was a retrospective analysis of the Injury Surveillance Cohort generated by the Korea Center for Disease Control and Prevention (KCDC) during 2011-2018. We developed the new-Poisoning Mortality Scoring system (new-PMS) to generate a prediction model using the derivation group (2011-2017 KCDC cohort). Points were computed for categories of each variable. The sum of these points was the new-PMS. The validation group (2018 KCDC cohort) was subjected to external temporal validation. The performance of new-PMS in predicting mortality was evaluated using area under the receiver operating characteristic curve (AUROC) for both the groups. RESULTS: Of 57,326 poisoning cases, 42,568 were selected. Of these, 34,352 (80.7%) and 8216 (19.3%) were enrolled in the derivation and validation groups, respectively. The new-PMS was the sum of the points for each category of 10 predictors. The possible range of the new-PMS was 0-137 points. Hosmer-Lemeshow goodness-of-fit test showed adequate calibration for the new-PMS with p values of 0.093 and 0.768 in the derivation and validation groups, respectively. AUROCs of the new-PMS were 0.941 (95% CI 0.934-0.949, p < 0.001) and 0.946 (95% CI 0.929-0.964, p < 0.001) in the derivation and validation groups, respectively. The sensitivity, specificity, and accuracy of the new-PMS (cutoff value: 49 points) were 86.4%, 87.2%, and 87.2% and 85.9%, 89.5%, and 89.4% in the derivation and validation groups, respectively. CONCLUSIONS: We developed a new-PMS system based on demographic, poisoning-related variables, and vital signs observed among patients at the ED. The new-PMS showed good performance for predicting in-hospital mortality in both the derivation and validation groups. The probability of death increased according to the increase in the new-PMS. The new-PMS accurately predicted the probability of death for patients with acute poisoning. This could contribute to clinical decision making for patients with acute poisoning at the ED.