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.
| Original language | English |
|---|---|
| Pages (from-to) | 532-540 |
| Number of pages | 9 |
| Journal | Journal of Infection |
| Volume | 75 |
| Issue number | 6 |
| DOIs | |
| Publication status | Published - 2017 Dec 1 |
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All Science Journal Classification (ASJC) codes
- Microbiology (medical)
- Infectious Diseases
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Dengue fever mortality score : A novel decision rule to predict death from dengue fever. / Huang, Chien Cheng; Hsu, Chien Chin; Guo, How-Ran; Su, Shih Bin; Lin, Hung Jung.
In: Journal of Infection, Vol. 75, No. 6, 01.12.2017, p. 532-540.Research output: Contribution to journal › Article
TY - JOUR
T1 - Dengue fever mortality score
T2 - A novel decision rule to predict death from dengue fever
AU - Huang, Chien Cheng
AU - Hsu, Chien Chin
AU - Guo, How-Ran
AU - Su, Shih Bin
AU - Lin, Hung Jung
PY - 2017/12/1
Y1 - 2017/12/1
N2 - 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.
AB - 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.
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U2 - 10.1016/j.jinf.2017.09.014
DO - 10.1016/j.jinf.2017.09.014
M3 - Article
VL - 75
SP - 532
EP - 540
JO - Journal of Infection
JF - Journal of Infection
SN - 0163-4453
IS - 6
ER -