Chien-Cheng Huang1, Chien-Chin Hsu2, How-Ran Guo3, Shih-Bin Su4, Hung-Jung Lin5. 1. Department of Emergency Medicine, Chi-Mei Medical Center, Tainan, Taiwan, ROC; Department of Environmental and Occupational Health, College of Medicine, National Cheng Kung University, Tainan, Taiwan, ROC; Bachelor Program of Senior Service, Southern Taiwan University of Science and Technology, Tainan, Taiwan; Department of Geriatrics and Gerontology, Chi-Mei Medical Center, Tainan, Taiwan, ROC; Department of Occupational Medicine, Chi-Mei Medical Center, Tainan, Taiwan, ROC. 2. Department of Emergency Medicine, Chi-Mei Medical Center, Tainan, Taiwan, ROC; Department of Biotechnology, Southern Taiwan University of Science and Technology, Tainan, Taiwan, ROC. 3. Department of Environmental and Occupational Health, College of Medicine, National Cheng Kung University, Tainan, Taiwan, ROC; Department of Occupational and Environmental Medicine, National Cheng Kung University Hospital, Tainan, Taiwan, ROC. 4. Department of Occupational Medicine, Chi-Mei Medical Center, Tainan, Taiwan, ROC; Department of Leisure, Recreation and Tourism Management, Southern Taiwan University of Science and Technology, Tainan, Taiwan, ROC; Department of Medical Research, Chi-Mei Medical Center, Liouying, Tainan, Taiwan, ROC. 5. Department of Emergency Medicine, Chi-Mei Medical Center, Tainan, Taiwan, ROC; Department of Biotechnology, Southern Taiwan University of Science and Technology, Tainan, Taiwan, ROC; Department of Emergency Medicine, Taipei Medical University, Taipei, Taiwan, ROC. Electronic address: hjlin52@gmail.com.
Abstract
OBJECTIVES: Dengue fever (DF) is still a major challenge for public health, especially during massive outbreaks. We developed a novel prediction score to help decision making, which has not been performed till date. METHODS: We conducted a retrospective case-control study to recruit all the DF patients who visited a medical center during the 2015 DF outbreak. Demographic data, vital signs, symptoms/signs, chronic comorbidities, laboratory data, and 30-day mortality rates were included in the study. Univariate analysis and multivariate logistic regression analysis were used to identify the independent mortality predictors, which further formed the components of a DF mortality (DFM) score. Bootstrapping method was used to validate the DFM score. RESULTS: In total, a sample of 2358 DF patients was included in this study, which also consisted of 34 deaths (1.44%). Five independent mortality predictors were identified: elderly age (≥65 years), hypotension (systolic blood pressure <90 mmHg), hemoptysis, diabetes mellitus, and chronic bedridden. After assigning each predictor a score of "1", we developed a DFM score (range: 0-5), which showed that the mortality risk ratios for scores 0, 1, 2, and ≥3 were 0.2%, 2.3%, 6.0%, and 45.5%, respectively. The area under the curve was 0.849 (95% confidence interval [CI]: 0.785-0.914), and Hosmer-Lemeshow goodness-of-fit was 0.642. Compared with score 0, the odds ratios for mortality were 12.73 (95% CI: 3.58-45.30) for score 1, 34.21 (95% CI: 9.75-119.99) for score 2, and 443.89 (95% CI: 86.06-2289.60) for score ≥3, with significant differences (all p values <0.001). The score ≥1 had a sensitivity of 91.2% for mortality and score ≥3 had a specificity of 99.7% for mortality. CONCLUSIONS: DFM score was a simple and easy method to help decision making, especially in the massive outbreak. Further studies in other hospitals or nations are warranted to validate this score.
OBJECTIVES:Dengue fever (DF) is still a major challenge for public health, especially during massive outbreaks. We developed a novel prediction score to help decision making, which has not been performed till date. METHODS: We conducted a retrospective case-control study to recruit all the DF patients who visited a medical center during the 2015 DF outbreak. Demographic data, vital signs, symptoms/signs, chronic comorbidities, laboratory data, and 30-day mortality rates were included in the study. Univariate analysis and multivariate logistic regression analysis were used to identify the independent mortality predictors, which further formed the components of a DF mortality (DFM) score. Bootstrapping method was used to validate the DFM score. RESULTS: In total, a sample of 2358 DF patients was included in this study, which also consisted of 34 deaths (1.44%). Five independent mortality predictors were identified: elderly age (≥65 years), hypotension (systolic blood pressure <90 mmHg), hemoptysis, diabetes mellitus, and chronic bedridden. After assigning each predictor a score of "1", we developed a DFM score (range: 0-5), which showed that the mortality risk ratios for scores 0, 1, 2, and ≥3 were 0.2%, 2.3%, 6.0%, and 45.5%, respectively. The area under the curve was 0.849 (95% confidence interval [CI]: 0.785-0.914), and Hosmer-Lemeshow goodness-of-fit was 0.642. Compared with score 0, the odds ratios for mortality were 12.73 (95% CI: 3.58-45.30) for score 1, 34.21 (95% CI: 9.75-119.99) for score 2, and 443.89 (95% CI: 86.06-2289.60) for score ≥3, with significant differences (all p values <0.001). The score ≥1 had a sensitivity of 91.2% for mortality and score ≥3 had a specificity of 99.7% for mortality. CONCLUSIONS: DFM score was a simple and easy method to help decision making, especially in the massive outbreak. Further studies in other hospitals or nations are warranted to validate this score.
Authors: Ting-Wu Chuang; Ka-Chon Ng; Thi Luong Nguyen; Luis Fernando Chaves Journal: Int J Environ Res Public Health Date: 2018-02-26 Impact factor: 3.390