Author:
ProCite
RefWorks
Reference Manager
BibTeX
Zotero
EndNote
-
1.
Wilder-Smith A, Ooi EE, Horstick O, Wills B, 2019. Dengue. Lancet 393: 350–363.
-
2.
Lee TH, Lee LK, Lye DC, Leo YS, 2017. Current management of severe dengue infection. Expert Rev Anti Infect Ther 15: 67–78.
-
3.
Huy BV, Toàn NV, 2022. Prognostic indicators associated with progresses of severe dengue. PLoS One 17: e0262096.
-
4.
Lee H, Hyun S, Park S, 2023. Comprehensive analysis of multivariable models for predicting severe dengue prognosis: Systematic review and meta-analysis. Trans R Soc Trop Med Hyg 117: 149–160.
-
5.
Snell KIE, Levis B, Damen JAA, Dhiman P, Debray TPA, Hooft L, Reitsma JB, Moons KGM, Collins GS, Riley RD, 2023. Transparent reporting of multivariable prediction models for individual prognosis or diagnosis: Checklist for systematic reviews and meta-analyses (TRIPOD-SRMA). BMJ 381: e073538.
-
6.
Moons KG, de Groot JA, Bouwmeester W, Vergouwe Y, Mallett S, Altman DG, Reitsma JB, Collins GS, 2014. Critical appraisal and data extraction for systematic reviews of prediction modelling studies: The CHARMS checklist. PLoS Med 11: e1001744.
-
7.
Wolff RF, Moons KGM, Riley RD, Whiting PF, Westwood M, Collins GS, Reitsma JB, Kleijnen J, Mallett S; PROBAST Group†, 2019. PROBAST: A tool to assess the risk of bias and applicability of prediction model studies. Ann Intern Med 170: 51–58.
-
8.
Bhaskar M, Mahalingam S, H MM, Achappa B, 2022. Predictive scoring system for risk of complications in pediatric dengue infection.F1000Res 11: 446.
-
9.
Carrasco LR, Leo YS, Cook AR, Lee VJ, Thein TL, Go CJ, Lye DC, 2014. Predictive tools for severe dengue conforming to World Health Organization 2009 criteria. PLoS Negl Trop Dis 8: e2972.
-
10.
Djossou F et al., 2016. A predictive score for hypotension in patients with confirmed dengue fever in Cayenne Hospital, French Guiana.Trans R Soc Trop Med Hyg 110: 705–713.
-
11.
Fernández E, Smieja M, Walter SD, Loeb M, 2017. A retrospective cohort study to predict severe dengue in Honduran patients. BMC Infect Dis 17: 676.
-
12.
Gayathri V, Lakshmi SV, Murugan SS, Poovazhagi V, Kalpana S, 2023. Development and validation of a bedside dengue severity score for predicting severe dengue in children. Indian Pediatr 60: 359–363.
-
13.
Gupta S, Mall P, Alam A, 2020. Combined score based on arterial lactate, aspartate transaminase and prolonged capillary refill time is a useful diagnostic criterion for identifying severe dengue. Trans R Soc Trop Med Hyg 114: 838–846.
-
14.
Hsieh CC, Cia CT, Lee JC, Sung JM, Lee NY, Chen PL, Kuo TH, Chao JY, Ko WC, 2017. A cohort study of adult patients with severe dengue in Taiwanese intensive care units: The elderly and APTT prolongation matter for prognosis. PLoS Negl Trop Dis 11: e0005270.
-
15.
Huang CC, Hsu CC, Guo HR, Su SB, Lin HJ, 2017. Dengue fever mortality score: A novel decision rule to predict death from dengue fever.J Infect 75: 532–540.
-
16.
Huang HS, Hsu CC, Ye JC, Su SB, Huang CC, Lin HJ, 2017. Predicting the mortality in geriatric patients with dengue fever. Medicine (Baltimore) 96: e7878.
-
17.
Huang SW, Tsai HP, Hung SJ, Ko WC, Wang JR, 2020. Assessing the risk of dengue severity using demographic information and laboratory test results with machine learning. PLoS Negl Trop Dis 14: e0008960.
-
18.
Huy NT et al., 2013. Development of clinical decision rules to predict recurrent shock in dengue.Crit Care 17: R280.
-
19.
Issop A et al.; Epidengue Cohort Investigation Team, 2023. Dengue clinical features and harbingers of severity in the diabetic patient: A retrospective cohort study on Reunion Island, 2019. Travel Med Infect Dis 54: 102586.
-
20.
Jain S et al., 2017. Predictors of dengue-related mortality and disease severity in a tertiary care center in north India. Open Forum Infect Dis 4: ofx056.
-
21.
Juneja D, Nasa P, Singh O, Javeri Y, Uniyal B, Dang R, 2011. Clinical profile, intensive care unit course, and outcome of patients admitted in intensive care unit with dengue.J Crit Care 26: 449–452.
-
22.
Lam PK et al., 2017. The value of daily platelet counts for predicting dengue shock syndrome: Results from a prospective observational study of 2301 Vietnamese children with dengue. PLoS Negl Trop Dis 11: e0005498.
-
23.
Lee IK, Huang CH, Huang WC, Chen YC, Tsai CY, Chang K, Chen YH, 2018. Prognostic factors in adult patients with dengue: Developing risk scoring models and emphasizing factors associated with death ≤7 days after illness onset and ≤3 days after presentation.J Clin Med 7: 396.
-
24.
Lee IK, Liu JW, Chen YH, Chen YC, Tsai CY, Huang SY, Lin CY, Huang CH, 2016. Development of a simple clinical risk score for early prediction of severe dengue in adult patients. PLoS ONE 11: e0154772.
-
25.
Lee VJ, Lye DC, Sun Y, Fernandez G, Ong A, Leo YS, 2008. Predictive value of simple clinical and laboratory variables for dengue hemorrhagic fever in adults. J Clin Virol 42: 34–39.
-
26.
Low GKK et al., 2018. The predictive and diagnostic accuracy of vascular endothelial growth factor and pentraxin-3 in severe dengue.Pathog Glob Health 112: 334–341.
-
27.
Marois I et al., 2021. Development of a bedside score to predict dengue severity. BMC Infect Dis 21: 470.
-
28.
Md-Sani SS, Md-Noor J, Han WH, Gan SP, Rani NS, Tan HL, Rathakrishnan K, As MA, Abd-Rahman M, 2018. Prediction of mortality in severe dengue cases. BMC Infect Dis 18: 232.
-
29.
Pang J et al., 2016. Discovery and validation of prognostic biomarker models to guide triage among adult dengue patients at early infection. PLoS ONE 11: e0155993.
-
30.
Pang J, Thein TL, Leo YS, Lye DC, 2014. Early clinical and laboratory risk factors of intensive care unit requirement during 2004–2008 dengue epidemics in Singapore: A matched case-control study. BMC Infect Dis 14: 649.
-
31.
Phakhounthong K et al., 2018. Predicting the severity of dengue fever in children on admission based on clinical features and laboratory indicators: Application of classification tree analysis. BMC Pediatr 18: 109.
-
32.
Pinto RC, de Castro DB, de Albuquerque BC, de Souza Sampaio V, Dos Passos RA, da Costa CF, Sadahiro M, Braga JU, 2016. Mortality predictors in patients with severe dengue in the state of Amazonas, Brazil. PLoS One 11: e0161884.
-
33.
Pongpan S, Patumanond J, Wisitwong A, Tawichasri C, Namwongprom S, 2014. Validation of dengue infection severity score. Risk Manag Healthc Policy 7: 45–49.
-
34.
Pongpan S, Wisitwong A, Tawichasri C, Patumanond J, Namwongprom S, 2013. Development of dengue infection severity score. ISRN Pediatr 2013: 845876.
-
35.
Potts JA, Gibbons RV, Rothman AL, Srikiatkhachorn A, Thomas SJ, Supradish PO, Lemon SC, Libraty DH, Green S, Kalayanarooj S, 2010. Prediction of dengue disease severity among pediatric Thai patients using early clinical laboratory indicators. PLoS Negl Trop Dis 4: e769.
-
36.
Sreenivasan P, Geetha S, Sasikala K, 2018. Development of a prognostic prediction model to determine severe dengue in children. Indian J Pediatr 85: 433–439.
-
37.
Srisuphanunt M, Puttaruk P, Kooltheat N, Katzenmeier G, Wilairatana P, 2022. Prognostic indicators for the early prediction of severe dengue infection: A retrospective study in a university hospital in Thailand.Trop Med Infect Dis 7: 162.
-
38.
Tamibmaniam J, Hussin N, Cheah WK, Ng KS, Muninathan P, 2016. Proposal of a clinical decision tree algorithm using factors associated with severe dengue infection. PLoS One 11: e0161696.
-
39.
Tanner L et al., 2008. Decision tree algorithms predict the diagnosis and outcome of dengue fever in the early phase of illness. PLoS Negl Trop Dis 2: e196.
-
40.
Tuan NM et al., 2017. An evidence-based algorithm for early prognosis of severe dengue in the outpatient setting. Clin Infect Dis 64: 656–663.
-
41.
Vuong NL et al., 2021. Combination of inflammatory and vascular markers in the febrile phase of dengue is associated with more severe outcomes. eLife 10: e67460.
-
42.
Yang J, Mosabbir AA, Raheem E, Hu W, Hossain MS, 2023. Demographic characteristics, clinical symptoms, biochemical markers and probability of occurrence of severe dengue: A multicenter hospital-based study in Bangladesh. PLoS Negl Trop Dis 17: e0011161.
-
43.
Steyerberg EW, 2019. Clinical Prediction Models: A Practical Approach to Development, Validation, and Updating, 2nd ed. Cham, Switzerland: Springer Cham.
-
44.
Kok BH, Lim HT, Lim CP, Lai NS, Leow CY, Leow CH, 2023. Dengue virus infection - a review of pathogenesis, vaccines, diagnosis and therapy. Virus Res 324: 199018.
-
45.
Horstick O, Martinez E, Guzman MG, Martin JL, Ranzinger SR, 2015. WHO dengue case classification 2009 and its usefulness in practice: An expert consensus in the Americas. Pathog Glob Health 109: 19–25.
-
46.
Guzman MG, Gubler DJ, Izquierdo A, Martinez E, Halstead SB, 2016. Dengue infection. Nat Rev Dis Primers 2: 16055.
-
47.
Leung XY, Islam RM, Adhami M, Ilic D, McDonald L, Palawaththa S, Diug B, Munshi SU, Karim MN, 2023. A systematic review of dengue outbreak prediction models: Current scenario and future directions. PLoS Negl Trop Dis 17: e0010631.
-
48.
Sangkaew S, Ming D, Boonyasiri A, Honeyford K, Kalayanarooj S, Yacoub S, Dorigatti I, Holmes A, 2021. Risk predictors of progression to severe disease during the febrile phase of dengue: A systematic review and meta-analysis. Lancet Infect Dis 21: 1014–1026.
-
49.
Efthimiou O, Seo M, Chalkou K, Debray T, Egger M, Salanti G, 2024. Developing clinical prediction models: A step-by-step guide. BMJ 386: e078276.
-
50.
Riley RD, Ensor J, Snell KI, Harrell FE, Martin GP, Reitsma JB, Moons KG, Collins G, Van Smeden M, 2020. Calculating the sample size required for developing a clinical prediction model. BMJ 368: m441.
-
51.
Ramspek CL, Jager KJ, Dekker FW, Zoccali C, van Diepen M, 2021. External validation of prognostic models: What, why, how, when and where? Clin Kidney J 14: 49–58.
| Past two years | Past Year | Past 30 Days | |
|---|---|---|---|
| Abstract Views | 1285 | 1285 | 153 |
| Full Text Views | 28 | 28 | 5 |
| PDF Downloads | 31 | 31 | 6 |
Prognostic Models in Patients with Dengue: A Systematic Review
Carlos Diaz-Arocutipa
cdiazar@usil.edu.pe
Carlos Diaz-Arocutipa
Unidad de Revisiones Sistemáticas y Meta-análisis (URSIGET), Vicerrectorado de Investigación, Universidad San Ignacio de Loyola, Lima, Peru;
Instituto de Evaluación de Tecnologías en Salud e Investigación – EsSalud, Lima, Peru;
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Instituto de Evaluación de Tecnologías en Salud e Investigación – EsSalud, Lima, Peru;
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María Chumbiauca
María Chumbiauca
Facultad de Ciencias de la Salud, Universidad San Ignacio de Loyola, Lima, Peru
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Percy Soto-Becerra
Percy Soto-Becerra
Instituto de Evaluación de Tecnologías en Salud e Investigación – EsSalud, Lima, Peru;
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ABSTRACT.
There is uncertainty regarding the usefulness of predictive models for dengue prognosis. We performed a systematic review to identify and evaluate prognostic models in patients with dengue. We conducted a literature search in PubMed, Embase, and Literatura Latinoamericana y del Caribe en Ciencias de la Salud (LILACS) up to May 24, 2023. We included case–control and cohort studies that developed or validated multivariable prognostic models related to severity, hospitalization, intensive care unit (ICU) admission, or mortality in patients of any age with a laboratory-based diagnosis of dengue. A narrative synthesis of the performance measures of the prognostic models evaluated in each study was performed. Of the 4,211 articles, a total of 35 studies reporting information on 43 prognostic models were included. Among these, 35 were developmental and 8 were for external validation. Most models were designed to predict severity (n = 30), followed by mortality (n = 10), hospitalization (n = 2), and ICU admission (n = 1). The reported C-statistic in the models ranged from 0.70 to 0.95 for severity, 0.83 to 0.99 for mortality, 0.87 for hospitalization, and 0.92 for ICU admission. Calibration measures were poorly reported in the vast majority of models. According to the Prediction Study Risk of Bias Assessment Tool, the risk of bias was considered high for all included models, and applicability was of low concern for most models. Our study identified multiple prognostic models, particularly for predicting severity and mortality in patients with dengue. Although most models demonstrated acceptable discriminative ability, calibration measures were poorly reported, and the overall methodological design was poor.
- Supplemental Materials (PDF 1626.2 KB)
Author Notes
Current contact information: Carlos Diaz-Arocutipa, Unidad de Revisiones Sistemáticas y Meta-análisis (URSIGET), Vicerrectorado de Investigación, Universidad San Ignacio de Loyola, Lima, Peru, and Instituto de Evaluación de Tecnologías en Salud e Investigación – EsSalud, Lima, Peru, E-mail: cdiazar@usil.edu.pe. María Chumbiauca, Facultad de Ciencias de la Salud, Universidad San Ignacio de Loyola, Lima, Peru, E-mail: maria.chumbiauca@usil.pe. Percy Soto-Becerra, Instituto de Evaluación de Tecnologías en Salud e Investigación – EsSalud, Lima, Peru, E-mail: percys1991@gmail.com.
ProCite
RefWorks
Reference Manager
BibTeX
Zotero
EndNote
-
1.
Wilder-Smith A, Ooi EE, Horstick O, Wills B, 2019. Dengue. Lancet 393: 350–363.
-
2.
Lee TH, Lee LK, Lye DC, Leo YS, 2017. Current management of severe dengue infection. Expert Rev Anti Infect Ther 15: 67–78.
-
3.
Huy BV, Toàn NV, 2022. Prognostic indicators associated with progresses of severe dengue. PLoS One 17: e0262096.
-
4.
Lee H, Hyun S, Park S, 2023. Comprehensive analysis of multivariable models for predicting severe dengue prognosis: Systematic review and meta-analysis. Trans R Soc Trop Med Hyg 117: 149–160.
-
5.
Snell KIE, Levis B, Damen JAA, Dhiman P, Debray TPA, Hooft L, Reitsma JB, Moons KGM, Collins GS, Riley RD, 2023. Transparent reporting of multivariable prediction models for individual prognosis or diagnosis: Checklist for systematic reviews and meta-analyses (TRIPOD-SRMA). BMJ 381: e073538.
-
6.
Moons KG, de Groot JA, Bouwmeester W, Vergouwe Y, Mallett S, Altman DG, Reitsma JB, Collins GS, 2014. Critical appraisal and data extraction for systematic reviews of prediction modelling studies: The CHARMS checklist. PLoS Med 11: e1001744.
-
7.
Wolff RF, Moons KGM, Riley RD, Whiting PF, Westwood M, Collins GS, Reitsma JB, Kleijnen J, Mallett S; PROBAST Group†, 2019. PROBAST: A tool to assess the risk of bias and applicability of prediction model studies. Ann Intern Med 170: 51–58.
-
8.
Bhaskar M, Mahalingam S, H MM, Achappa B, 2022. Predictive scoring system for risk of complications in pediatric dengue infection.F1000Res 11: 446.
-
9.
Carrasco LR, Leo YS, Cook AR, Lee VJ, Thein TL, Go CJ, Lye DC, 2014. Predictive tools for severe dengue conforming to World Health Organization 2009 criteria. PLoS Negl Trop Dis 8: e2972.
-
10.
Djossou F et al., 2016. A predictive score for hypotension in patients with confirmed dengue fever in Cayenne Hospital, French Guiana.Trans R Soc Trop Med Hyg 110: 705–713.
-
11.
Fernández E, Smieja M, Walter SD, Loeb M, 2017. A retrospective cohort study to predict severe dengue in Honduran patients. BMC Infect Dis 17: 676.
-
12.
Gayathri V, Lakshmi SV, Murugan SS, Poovazhagi V, Kalpana S, 2023. Development and validation of a bedside dengue severity score for predicting severe dengue in children. Indian Pediatr 60: 359–363.
-
13.
Gupta S, Mall P, Alam A, 2020. Combined score based on arterial lactate, aspartate transaminase and prolonged capillary refill time is a useful diagnostic criterion for identifying severe dengue. Trans R Soc Trop Med Hyg 114: 838–846.
-
14.
Hsieh CC, Cia CT, Lee JC, Sung JM, Lee NY, Chen PL, Kuo TH, Chao JY, Ko WC, 2017. A cohort study of adult patients with severe dengue in Taiwanese intensive care units: The elderly and APTT prolongation matter for prognosis. PLoS Negl Trop Dis 11: e0005270.
-
15.
Huang CC, Hsu CC, Guo HR, Su SB, Lin HJ, 2017. Dengue fever mortality score: A novel decision rule to predict death from dengue fever.J Infect 75: 532–540.
-
16.
Huang HS, Hsu CC, Ye JC, Su SB, Huang CC, Lin HJ, 2017. Predicting the mortality in geriatric patients with dengue fever. Medicine (Baltimore) 96: e7878.
-
17.
Huang SW, Tsai HP, Hung SJ, Ko WC, Wang JR, 2020. Assessing the risk of dengue severity using demographic information and laboratory test results with machine learning. PLoS Negl Trop Dis 14: e0008960.
-
18.
Huy NT et al., 2013. Development of clinical decision rules to predict recurrent shock in dengue.Crit Care 17: R280.
-
19.
Issop A et al.; Epidengue Cohort Investigation Team, 2023. Dengue clinical features and harbingers of severity in the diabetic patient: A retrospective cohort study on Reunion Island, 2019. Travel Med Infect Dis 54: 102586.
-
20.
Jain S et al., 2017. Predictors of dengue-related mortality and disease severity in a tertiary care center in north India. Open Forum Infect Dis 4: ofx056.
-
21.
Juneja D, Nasa P, Singh O, Javeri Y, Uniyal B, Dang R, 2011. Clinical profile, intensive care unit course, and outcome of patients admitted in intensive care unit with dengue.J Crit Care 26: 449–452.
-
22.
Lam PK et al., 2017. The value of daily platelet counts for predicting dengue shock syndrome: Results from a prospective observational study of 2301 Vietnamese children with dengue. PLoS Negl Trop Dis 11: e0005498.
-
23.
Lee IK, Huang CH, Huang WC, Chen YC, Tsai CY, Chang K, Chen YH, 2018. Prognostic factors in adult patients with dengue: Developing risk scoring models and emphasizing factors associated with death ≤7 days after illness onset and ≤3 days after presentation.J Clin Med 7: 396.
-
24.
Lee IK, Liu JW, Chen YH, Chen YC, Tsai CY, Huang SY, Lin CY, Huang CH, 2016. Development of a simple clinical risk score for early prediction of severe dengue in adult patients. PLoS ONE 11: e0154772.
-
25.
Lee VJ, Lye DC, Sun Y, Fernandez G, Ong A, Leo YS, 2008. Predictive value of simple clinical and laboratory variables for dengue hemorrhagic fever in adults. J Clin Virol 42: 34–39.
-
26.
Low GKK et al., 2018. The predictive and diagnostic accuracy of vascular endothelial growth factor and pentraxin-3 in severe dengue.Pathog Glob Health 112: 334–341.
-
27.
Marois I et al., 2021. Development of a bedside score to predict dengue severity. BMC Infect Dis 21: 470.
-
28.
Md-Sani SS, Md-Noor J, Han WH, Gan SP, Rani NS, Tan HL, Rathakrishnan K, As MA, Abd-Rahman M, 2018. Prediction of mortality in severe dengue cases. BMC Infect Dis 18: 232.
-
29.
Pang J et al., 2016. Discovery and validation of prognostic biomarker models to guide triage among adult dengue patients at early infection. PLoS ONE 11: e0155993.
-
30.
Pang J, Thein TL, Leo YS, Lye DC, 2014. Early clinical and laboratory risk factors of intensive care unit requirement during 2004–2008 dengue epidemics in Singapore: A matched case-control study. BMC Infect Dis 14: 649.
-
31.
Phakhounthong K et al., 2018. Predicting the severity of dengue fever in children on admission based on clinical features and laboratory indicators: Application of classification tree analysis. BMC Pediatr 18: 109.
-
32.
Pinto RC, de Castro DB, de Albuquerque BC, de Souza Sampaio V, Dos Passos RA, da Costa CF, Sadahiro M, Braga JU, 2016. Mortality predictors in patients with severe dengue in the state of Amazonas, Brazil. PLoS One 11: e0161884.
-
33.
Pongpan S, Patumanond J, Wisitwong A, Tawichasri C, Namwongprom S, 2014. Validation of dengue infection severity score. Risk Manag Healthc Policy 7: 45–49.
-
34.
Pongpan S, Wisitwong A, Tawichasri C, Patumanond J, Namwongprom S, 2013. Development of dengue infection severity score. ISRN Pediatr 2013: 845876.
-
35.
Potts JA, Gibbons RV, Rothman AL, Srikiatkhachorn A, Thomas SJ, Supradish PO, Lemon SC, Libraty DH, Green S, Kalayanarooj S, 2010. Prediction of dengue disease severity among pediatric Thai patients using early clinical laboratory indicators. PLoS Negl Trop Dis 4: e769.
-
36.
Sreenivasan P, Geetha S, Sasikala K, 2018. Development of a prognostic prediction model to determine severe dengue in children. Indian J Pediatr 85: 433–439.
-
37.
Srisuphanunt M, Puttaruk P, Kooltheat N, Katzenmeier G, Wilairatana P, 2022. Prognostic indicators for the early prediction of severe dengue infection: A retrospective study in a university hospital in Thailand.Trop Med Infect Dis 7: 162.
-
38.
Tamibmaniam J, Hussin N, Cheah WK, Ng KS, Muninathan P, 2016. Proposal of a clinical decision tree algorithm using factors associated with severe dengue infection. PLoS One 11: e0161696.
-
39.
Tanner L et al., 2008. Decision tree algorithms predict the diagnosis and outcome of dengue fever in the early phase of illness. PLoS Negl Trop Dis 2: e196.
-
40.
Tuan NM et al., 2017. An evidence-based algorithm for early prognosis of severe dengue in the outpatient setting. Clin Infect Dis 64: 656–663.
-
41.
Vuong NL et al., 2021. Combination of inflammatory and vascular markers in the febrile phase of dengue is associated with more severe outcomes. eLife 10: e67460.
-
42.
Yang J, Mosabbir AA, Raheem E, Hu W, Hossain MS, 2023. Demographic characteristics, clinical symptoms, biochemical markers and probability of occurrence of severe dengue: A multicenter hospital-based study in Bangladesh. PLoS Negl Trop Dis 17: e0011161.
-
43.
Steyerberg EW, 2019. Clinical Prediction Models: A Practical Approach to Development, Validation, and Updating, 2nd ed. Cham, Switzerland: Springer Cham.
-
44.
Kok BH, Lim HT, Lim CP, Lai NS, Leow CY, Leow CH, 2023. Dengue virus infection - a review of pathogenesis, vaccines, diagnosis and therapy. Virus Res 324: 199018.
-
45.
Horstick O, Martinez E, Guzman MG, Martin JL, Ranzinger SR, 2015. WHO dengue case classification 2009 and its usefulness in practice: An expert consensus in the Americas. Pathog Glob Health 109: 19–25.
-
46.
Guzman MG, Gubler DJ, Izquierdo A, Martinez E, Halstead SB, 2016. Dengue infection. Nat Rev Dis Primers 2: 16055.
-
47.
Leung XY, Islam RM, Adhami M, Ilic D, McDonald L, Palawaththa S, Diug B, Munshi SU, Karim MN, 2023. A systematic review of dengue outbreak prediction models: Current scenario and future directions. PLoS Negl Trop Dis 17: e0010631.
-
48.
Sangkaew S, Ming D, Boonyasiri A, Honeyford K, Kalayanarooj S, Yacoub S, Dorigatti I, Holmes A, 2021. Risk predictors of progression to severe disease during the febrile phase of dengue: A systematic review and meta-analysis. Lancet Infect Dis 21: 1014–1026.
-
49.
Efthimiou O, Seo M, Chalkou K, Debray T, Egger M, Salanti G, 2024. Developing clinical prediction models: A step-by-step guide. BMJ 386: e078276.
-
50.
Riley RD, Ensor J, Snell KI, Harrell FE, Martin GP, Reitsma JB, Moons KG, Collins G, Van Smeden M, 2020. Calculating the sample size required for developing a clinical prediction model. BMJ 368: m441.
-
51.
Ramspek CL, Jager KJ, Dekker FW, Zoccali C, van Diepen M, 2021. External validation of prognostic models: What, why, how, when and where? Clin Kidney J 14: 49–58.
| Past two years | Past Year | Past 30 Days | |
|---|---|---|---|
| Abstract Views | 1285 | 1285 | 153 |
| Full Text Views | 28 | 28 | 5 |
| PDF Downloads | 31 | 31 | 6 |