Development of a bedside score to predict dengue severity
BACKGROUND: In 2017, New Caledonia experienced an outbreak of severe dengue causing high hospital burden (4379 cases, 416 hospital admissions, 15 deaths). We decided to build a local operational model predictive of dengue severity, which was needed to ease the healthcare circuit.
METHODS: We retrospectively analyzed clinical and biological parameters associated with severe dengue in the cohort of patients hospitalized at the Territorial Hospital between January and July 2017 with confirmed dengue, in order to elaborate a comprehensive patient's score. Patients were compared in univariate and multivariate analyses. Predictive models for severity were built using a descending step-wise method.
RESULTS: Out of 383 included patients, 130 (34%) developed severe dengue and 13 (3.4%) died. Major risk factors identified in univariate analysis were: age, comorbidities, presence of at least one alert sign, platelets count < 30 × 109/L, prothrombin time < 60%, AST and/or ALT > 10 N, and previous dengue infection. Severity was not influenced by the infecting dengue serotype nor by previous Zika infection. Two models to predict dengue severity were built according to sex. Best models for females and males had respectively a median Area Under the Curve = 0.80 and 0.88, a sensitivity = 84.5 and 84.5%, a specificity = 78.6 and 95.5%, a positive predictive value = 63.3 and 92.9%, a negative predictive value = 92.8 and 91.3%. Models were secondarily validated on 130 patients hospitalized for dengue in 2018.
CONCLUSION: We built robust and efficient models to calculate a bedside score able to predict dengue severity in our setting. We propose the spreadsheet for dengue severity score calculations to health practitioners facing dengue outbreaks of enhanced severity in order to improve patients' medical management and hospitalization flow.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2021 |
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| Erschienen: |
2021 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:21 |
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| Enthalten in: |
BMC infectious diseases - 21(2021), 1 vom: 24. Mai, Seite 470 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Marois, Ingrid [VerfasserIn] |
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| Links: |
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| Themen: |
Arboviruses |
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| Anmerkungen: |
Date Completed 02.06.2021 Date Revised 02.06.2021 published: Electronic Citation Status MEDLINE |
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| doi: |
10.1186/s12879-021-06146-z |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM325798745 |
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| 245 | 1 | 0 | |a Development of a bedside score to predict dengue severity |
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| 500 | |a Citation Status MEDLINE | ||
| 520 | |a BACKGROUND: In 2017, New Caledonia experienced an outbreak of severe dengue causing high hospital burden (4379 cases, 416 hospital admissions, 15 deaths). We decided to build a local operational model predictive of dengue severity, which was needed to ease the healthcare circuit | ||
| 520 | |a METHODS: We retrospectively analyzed clinical and biological parameters associated with severe dengue in the cohort of patients hospitalized at the Territorial Hospital between January and July 2017 with confirmed dengue, in order to elaborate a comprehensive patient's score. Patients were compared in univariate and multivariate analyses. Predictive models for severity were built using a descending step-wise method | ||
| 520 | |a RESULTS: Out of 383 included patients, 130 (34%) developed severe dengue and 13 (3.4%) died. Major risk factors identified in univariate analysis were: age, comorbidities, presence of at least one alert sign, platelets count < 30 × 109/L, prothrombin time < 60%, AST and/or ALT > 10 N, and previous dengue infection. Severity was not influenced by the infecting dengue serotype nor by previous Zika infection. Two models to predict dengue severity were built according to sex. Best models for females and males had respectively a median Area Under the Curve = 0.80 and 0.88, a sensitivity = 84.5 and 84.5%, a specificity = 78.6 and 95.5%, a positive predictive value = 63.3 and 92.9%, a negative predictive value = 92.8 and 91.3%. Models were secondarily validated on 130 patients hospitalized for dengue in 2018 | ||
| 520 | |a CONCLUSION: We built robust and efficient models to calculate a bedside score able to predict dengue severity in our setting. We propose the spreadsheet for dengue severity score calculations to health practitioners facing dengue outbreaks of enhanced severity in order to improve patients' medical management and hospitalization flow | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Arboviruses | |
| 650 | 4 | |a Dengue | |
| 650 | 4 | |a Hospital triage | |
| 650 | 4 | |a Operational tool | |
| 650 | 4 | |a Pacific | |
| 650 | 4 | |a Severity score | |
| 700 | 1 | |a Forfait, Carole |e verfasserin |4 aut | |
| 700 | 1 | |a Inizan, Catherine |e verfasserin |4 aut | |
| 700 | 1 | |a Klement-Frutos, Elise |e verfasserin |4 aut | |
| 700 | 1 | |a Valiame, Anabelle |e verfasserin |4 aut | |
| 700 | 1 | |a Aubert, Daina |e verfasserin |4 aut | |
| 700 | 1 | |a Gourinat, Ann-Claire |e verfasserin |4 aut | |
| 700 | 1 | |a Laumond, Sylvie |e verfasserin |4 aut | |
| 700 | 1 | |a Barsac, Emilie |e verfasserin |4 aut | |
| 700 | 1 | |a Grangeon, Jean-Paul |e verfasserin |4 aut | |
| 700 | 1 | |a Cazorla, Cécile |e verfasserin |4 aut | |
| 700 | 1 | |a Merlet, Audrey |e verfasserin |4 aut | |
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| 700 | 1 | |a Dupont-Rouzeyrol, Myrielle |e verfasserin |4 aut | |
| 700 | 1 | |a Descloux, Elodie |e verfasserin |4 aut | |
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