Dengue fever mortality score

A novel decision rule to predict death from dengue fever

Chien Cheng Huang, Chien Chin Hsu, How-Ran Guo, Shih Bin Su, Hung Jung Lin

Research output: Contribution to journalArticle

6 Citations (Scopus)

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 languageEnglish
Pages (from-to)532-540
Number of pages9
JournalJournal of Infection
Volume75
Issue number6
DOIs
Publication statusPublished - 2017 Dec 1

Fingerprint

Dengue
Mortality
Confidence Intervals
Disease Outbreaks
Decision Making
Odds Ratio
Blood Pressure
Hemoptysis
Vital Signs
Hypotension
Signs and Symptoms
Area Under Curve
Case-Control Studies
Comorbidity
Diabetes Mellitus
Multivariate Analysis
Public Health
Logistic Models
Regression Analysis
Demography

All Science Journal Classification (ASJC) codes

  • Microbiology (medical)
  • Infectious Diseases

Cite this

Huang, Chien Cheng ; Hsu, Chien Chin ; Guo, How-Ran ; Su, Shih Bin ; Lin, Hung Jung. / Dengue fever mortality score : A novel decision rule to predict death from dengue fever. In: Journal of Infection. 2017 ; Vol. 75, No. 6. pp. 532-540.
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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.",
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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 journalArticle

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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

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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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