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.
All Science Journal Classification (ASJC) codes
- Microbiology (medical)
- Infectious Diseases