Chan-Wa Cheong1, Chien-Lin Chen1,2, Chih-Huang Li1, Chen-June Seak1,2, Hsiao-Jung Tseng3, Kuang-Hung Hsu1,4, Chip-Jin Ng1, Cheng-Yu Chien5,6,7. 1. Department of Emergency Medicine, Chang Gung Memorial Hospital, Linkou and College of Medicine, Chang Gung University, Taoyuan, Taiwan. 2. Department of Emergency Medicine, New Taipei Municipal Tucheng Hospital, New Taipei City, Taiwan. 3. Biostatistical Unit, Clinical Trial Center, Chang Gung Memorial Hospital, Linkou, Taiwan. 4. Laboratory for Epidemiology, Chang Gung University, Kwei-Shan, Taiwan. 5. Department of Emergency Medicine, Chang Gung Memorial Hospital, Linkou and College of Medicine, Chang Gung University, Taoyuan, Taiwan. rainccy217@gmail.com. 6. Department of Emergency Medicine, Ton-Yen General Hospital, Zhubei, Taiwan. rainccy217@gmail.com. 7. Graduate Institute of Business and Management, Chang Gung University, Kwei-Shan, Taiwan. rainccy217@gmail.com.
Abstract
BACKGROUND: Infleunza is a challenging issue in public health. The mortality and morbidity associated with epidemic and pandemic influenza puts a heavy burden on health care system. Most patients with influenza can be treated on an outpatient basis but some required critical care. It is crucial for frontline physicians to stratify influenza patients by level of risk. Therefore, this study aimed to create a prediction model for critical care and in-hospital mortality. METHODS: This retrospective cohort study extracted data from the Chang Gung Research Database. This study included the patients who were diagnosed with influenza between 2010 and 2016. The primary outcome of this study was critical illness. The secondary analysis was to predict in-hospital mortality. A two-stage-modeling method was developed to predict hospital mortality. We constructed a multiple logistic regression model to predict the outcome of critical illness in the first stage, then S1 score were calculated. In the second stage, we used the S1 score and other data to construct a backward multiple logistic regression model. The area under the receiver operating curve was used to assess the predictive value of the model. RESULTS: In the present study, 1680 patients met the inclusion criteria. The overall ICU admission and in-hospital mortality was 10.36% (174 patients) and 4.29% (72 patients), respectively. In stage I analysis, hypothermia (OR = 1.92), tachypnea (OR = 4.94), lower systolic blood pressure (OR = 2.35), diabetes mellitus (OR = 1.87), leukocytosis (OR = 2.22), leukopenia (OR = 2.70), and a high percentage of segmented neutrophils (OR = 2.10) were associated with ICU admission. Bandemia had the highest odds ratio in the Stage I model (OR = 5.43). In stage II analysis, C-reactive protein (OR = 1.01), blood urea nitrogen (OR = 1.02) and stage I model's S1 score were assocaited with in-hospital mortality. The area under the curve for the stage I and II model was 0.889 and 0.766, respectively. CONCLUSIONS: The two-stage model is a efficient risk-stratification tool for predicting critical illness and mortailty. The model may be an optional tool other than qSOFA and SIRS criteria.
BACKGROUND: Infleunza is a challenging issue in public health. The mortality and morbidity associated with epidemic and pandemic influenza puts a heavy burden on health care system. Most patients with influenza can be treated on an outpatient basis but some required critical care. It is crucial for frontline physicians to stratify influenzapatients by level of risk. Therefore, this study aimed to create a prediction model for critical care and in-hospital mortality. METHODS: This retrospective cohort study extracted data from the Chang Gung Research Database. This study included the patients who were diagnosed with influenza between 2010 and 2016. The primary outcome of this study was critical illness. The secondary analysis was to predict in-hospital mortality. A two-stage-modeling method was developed to predict hospital mortality. We constructed a multiple logistic regression model to predict the outcome of critical illness in the first stage, then S1 score were calculated. In the second stage, we used the S1 score and other data to construct a backward multiple logistic regression model. The area under the receiver operating curve was used to assess the predictive value of the model. RESULTS: In the present study, 1680 patients met the inclusion criteria. The overall ICU admission and in-hospital mortality was 10.36% (174 patients) and 4.29% (72 patients), respectively. In stage I analysis, hypothermia (OR = 1.92), tachypnea (OR = 4.94), lower systolic blood pressure (OR = 2.35), diabetes mellitus (OR = 1.87), leukocytosis (OR = 2.22), leukopenia (OR = 2.70), and a high percentage of segmented neutrophils (OR = 2.10) were associated with ICU admission. Bandemia had the highest odds ratio in the Stage I model (OR = 5.43). In stage II analysis, C-reactive protein (OR = 1.01), blood ureanitrogen (OR = 1.02) and stage I model's S1 score were assocaited with in-hospital mortality. The area under the curve for the stage I and II model was 0.889 and 0.766, respectively. CONCLUSIONS: The two-stage model is a efficient risk-stratification tool for predicting critical illness and mortailty. The model may be an optional tool other than qSOFA and SIRS criteria.
Authors: A Danielle Iuliano; Katherine M Roguski; Howard H Chang; David J Muscatello; Rakhee Palekar; Stefano Tempia; Cheryl Cohen; Jon Michael Gran; Dena Schanzer; Benjamin J Cowling; Peng Wu; Jan Kyncl; Li Wei Ang; Minah Park; Monika Redlberger-Fritz; Hongjie Yu; Laura Espenhain; Anand Krishnan; Gideon Emukule; Liselotte van Asten; Susana Pereira da Silva; Suchunya Aungkulanon; Udo Buchholz; Marc-Alain Widdowson; Joseph S Bresee Journal: Lancet Date: 2017-12-14 Impact factor: 79.321
Authors: Lone Simonsen; Peter Spreeuwenberg; Roger Lustig; Robert J Taylor; Douglas M Fleming; Madelon Kroneman; Maria D Van Kerkhove; Anthony W Mounts; W John Paget Journal: PLoS Med Date: 2013-11-26 Impact factor: 11.069
Authors: Won Suk Choi; Ji Hyeon Baek; Yu Bin Seo; Sae Yoon Kee; Hye Won Jeong; Hee Young Lee; Byung Wook Eun; Eun Ju Choo; Jacob Lee; Young Keun Kim; Joon Young Song; Seong-Heon Wie; Jin Soo Lee; Hee Jin Cheong; Woo Joo Kim Journal: Korean J Intern Med Date: 2014-01-02 Impact factor: 2.884