Clinical prognostic scores for dengue fever: A systematic review

Abstract

BACKGROUND

Clinical predictors of dengue fever are crucial for guiding timely management and avoiding life-threatening complications. While prognostic scores are available, a systematic evaluation of these tools is lacking.

AIM

To evaluate the performance and accuracy of various proposed dengue clinical prognostic scores.

METHODS

Three databases, PubMed, EMBASE and Cochrane, were searched for peer-reviewed studies published from inception to 4 September 2023. Studies either developing or validating a prognostic model relevant to dengue fever were included. A total of 29 studies (n = 17910) were included.

RESULTS

Most commonly studied outcomes were severe dengue (15 models) and mortality (8 models). For the paediatric population, Bedside Dengue Severity Score by Gayathri et al (specificity = 0.98) and the nomogram model by Nguyen et al (sensitivity = 0.87) performed better. For the adult population, the most specific model was reported by Leo et al (specificity = 0.98). The most sensitive score is shared between Warning Signs for Severe Dengue as reported by Leo et al and Model 2 by Lee et al (sensitivity = 1.00).

CONCLUSION

While several models demonstrated precision and reliability in predicting severe dengue and mortality, broader application across diverse geographic settings is needed to assess their external validity.

Key Words: Dengue; Severe dengue; Systematic review; Prognostic scores; Clinical prognostic scores

Core Tip: This is a comprehensive systematic review to evaluate and compare the accuracy of prognostic models in both adult and paediatric populations with dengue fever. Out of 29 included studies comprising over 17000 patients, we highlight models that demonstrated high specificity and sensitivity. Notably, the Bedside Dengue Severity Score by Gayathri et al and the nomogram by Nguyen et al performed best among paediatric models, while Leo et al’s and Lee et al’s models showed outstanding performance in adult populations. This nuanced breakdown by demographic and model performance offers actionable insights not previously synthesized in the literature.

INTRODUCTION

Dengue incidence has been on the rise globally with both geographic expansion and a shift from urban to rural settings, with a burden of disease from eight countries studied in the period 2001-2005 estimated at USD 440 million[1]. Dengue infection has varied clinical manifestations from asymptomatic, mild fever, to fatal disease[2] and is often unpredictable in its evolution[1]. The predictability of the critical phase in patients remains especially elusive[3]. The hallmarks of severe dengue include severe plasma leakage, organ impairment, or bleeding[4], with several individual risk factors identified by World Health Organization (WHO) as warning signs[1], but utility of each sign may vary across different populations[5]. Besides the severity of dengue, it may also be important to consider predictors of clinical outcomes, such as mortality and length of stay, to better provide timely and tailored management.

While there have been many recent studies identifying the multitude of clinical predictors of dengue severity[6], few synthesise simple prognostication models or systems that allow for efficient decision-making regarding admission, monitoring, and therapy intensity. Additionally, given how dengue is hyperendemic in some of the poorest regions of the world[1,2], this is also helpful in the just allocation of scarce resources.

Therefore, this systematic review aims to evaluate different prognostic clinical scores for dengue by assessing their performance and accuracy on patients diagnosed with dengue fever. Moreover, a detailed evaluation of the studies’ characteristics will be informative towards commenting on their generalisability, thereby allowing this review to identify the most robust and applicable tool in dengue prognostication in varied contexts.

MATERIALS AND METHODS

A protocol for the study was published on PROSPERO (registration number CRD42024547027).

Literature search

We systematically searched PubMed, EMBASE and Cochrane from inception to 4 September 2023 to obtain studies either developing or validating prognostic models relevant to dengue fever.

Eligibility criteria

We included all studies that reported the development and/or validation of clinical scoring models for prognostic outcomes of patients diagnosed with dengue fever who were admitted and received adequate clinical treatment. The inclusion and exclusion criteria of this review are shown in Table 1.

Table 1 Summary of study design with inclusion and exclusion criteria.

	Inclusion criteria	Exclusion criteria
Population/problem	Patients primarily diagnosed with dengue fever through: Serum non-structural protein 1 antigen positivity or IgM and IgG antibodies to dengue virus or dengue virus RNA by real-time reverse transcriptase polymerase chain reaction	Patients diagnosed with other febrile illnesses
Intervention/exposure	Prognostic clinical scoring systems for dengue fever. Predicted outcomes of the models include any possible clinical endpoints of Dengue Fever such as dengue severity, critical outcomes, probability of intensive care unit outcomes or mortality rates	Scores for purposes other than prognostication e.g. diagnosis etc. Scores that are not specific to dengue fever. Non-clinical scoring systems
Control/comparison	Nil	Nil
Outcome	Accuracy of prognostic scoring	Nil
Study design	Articles in English/translated into English Observational studies (prospective and retrospective cohort). Randomised controlled trials	Articles not written in English/no available English translation Systematic reviews and meta-analyses. Review articles. Case series, case reports

Data extraction

A standardised data extraction was independently conducted by at least two of the first four authors for each included article. The following information was extracted: Authors, year of publication, title, journal, study type, paediatric only, sample size of patients diagnosed with dengue fever, age in years (mean ± SD), number and proportion of males, day of illness, symptoms at presentation, method of dengue diagnosis, 1997 WHO classification and 2009 WHO classification, duration of hospitalisation (days), mortality (%), intensive care unit (ICU) admissions (%), prognostic score characteristics (such as score type, score components, detailed descriptions of score and predicted/prognostic outcomes), and prognostic score performance markers (such as sensitivity, specificity, area under the receiver operating characteristic curve (AUC), positive predictive value, negative predictive value.

Risk of bias assessment

Two researchers, Basker G and Chong CS, independently conducted the assessment of risk of bias of the prognostic models using the Newcastle-Ottawa Scale (NOS) (Supplementary Table 1)[7]. An adaptation of the NOS was used for assessment of cross-sectional studies (Supplementary Table 2). Disagreements were resolved with the last author.

Patient and public involvement

No patients or the public were involved in the formulation or execution of this research study.

RESULTS

The PRISMA flow diagram outlining study selection is presented in Figure 1[8]. Literature search of three databases (PubMed, EMBASE, Cochrane) retrieved a total of 2067 studies, of which 254 duplicate articles were removed. The remaining 1813 articles underwent a first sieve based on study titles and abstracts. 81 studies were shortlisted and we were unable to find full text of 6 articles, despite contacting the corresponding authors of these papers. 75 articles were retrieved and sieved further based on full-text screening. 46 studies were excluded due to reasons including wrong study type, non-prognostic models, wrong outcomes, or a lack of clinical scoring system. The resulting 29 articles were included in this systematic review.

Figure 1 Flowchart of literature search for prognostic models.

Risk of bias assessment

The risk of bias assessment is reported in Supplementary Tables 3 and 4[9]. Overall, no studies presented a significant risk of bias.

Characteristics of included studies

A total of 17910 participants across the 29 studies published from 2010 to 2023 were included in this systematic review. Of the 29 included studies, there were 13 prospective cohort studies (44.8%)[10-22], 12 retrospective cohort studies (41.4%)[23-34], 2 case control studies (6.9%)[35,36], and 2 cross-sectional studies (6.9%)[37,38]. Table 2 summarises the characteristics and demographics of the included studies.

Table 2 Characteristics of included studies.

Ref.	Period of data collection	Location	Study type	Model	Validation	Sample size
Chi et al[38], 2023	1 July 2015 to 30 November 2015	Tainan, Taiwan	Retrospective cross-sectional	Multivariate binary logistic regression with significant coefficient transformed to scores by inverse odds ratio	External validation (separate region and time)	701
Yang et al[37], 2023	August 15, 2019 and September 30, 2019	Dhaka, Bangladesh	Cross-sectional	CART model used on univariate and multivariate logistic regression models	Internal validation via split-sample with random assignment (80% training sample, 20% hold-out sample)	1090
Gayathri et al[10], 2023	Model: October 2019; Validation: September 2019 to January 2021	Chennai, India	Prospective cohort	Binary logistic regression to develop prediction severity model with forward stepwise method in 3 steps to identify 3 significant variables and Nagelkerke square to quantify influence of variables	Temporal validation on 2021 data (n = 312)	312
McBride et al[11], 2022	June 2019 to June 2021	Ho Chi Minh city, Vietnam	Prospective observational cohort	mSOFA score and delta excluding bilirubin calculated from day 0 and 2. Brier score rescaled from 0 to 1	Internal validation via bootstrap procedure with 500 resamples with replacement	124
Bhaskar et al[12], 2022	January 2016 to December 2020	Manipal, India	Prospective case cohort	Logistic regression model of significant variables	No validation	303
Srisuphanunt et al[23], 2022	2017 to 2019	Bangkok, Thailand	Retrospective cohort	Potential predictor tested for trend with nonparametric method	Internal validation (method not mentioned)	302
Sachdev et al[13], 2021	July 1, 2016 to December 31, 2019	New Deli, India	Prospective cohort	Multivariate logistic regression model to identify independent risk factors, stepwise entry of new terms into model	No validation	78
Marois et al[24], 2021	January 1, 2017 to July 31, 2017	New Caledonia	Retrospective cohort	Predictive model built using multiple logistic regression and descending stepwise analysis	Internal validation via k-fold cross-validation (k = 10)	383
Devarbhavi et al[25], 2020	January 2014 to December 2017	Bangalore, India	Retrospective cohort	MELD score, arterial pH, lactate used to generate ROC with C-statistics	No validation	36
Tangnararatchakit et al[26], 2020	2004 to 2018	Bangkok, Thailand	Retrospective cohort	Daily Dengue severity score created in Phase I (n = 191)	Temporal validation on Phase II (n = 51)	242
Lee et al[27], 2018	Kaohsiung Chang Gung Memorial Hospital: 2022 to 2015; Kaohsiung Medical University Hospital[2]: 2009 to 2013	Kaohsiung, Taiwan	Retrospective cohort	Multivariate logistic regression model and assigning points by dividing its regression coefficient by smallest coefficient in model (rounded to nearest whole number)	No validation	1068
Phakhounthong et al[28], 2018	October 12, 2009 to October 12, 2010	Siem Reap, Cambodia	Retrospective cohort	CART tree constructed with J48 algorithm to generate decision tree	Internal validation via 10-fold cross-validation by Weka sed to estimate out-of-sample accuracy (split data into 10, 9 for training, 1 for testing). Multiple rounds of cross-validation performed using different partitions	198
Park et al[14], 2018	Queen Sirikit National Institute of Child Health[3]: 1994 to 1997, 1999 to 2002, 2004 to 2007; Kamphaeng Phet Provincial Hospital[4]: 1994 to 1997	Bangkok, Thailand; Nai Mueang, Thailand	Prospective cohort	SEM using data from n = 257 with complete data	Internal validation via multiple imputation via Markov-chain Monte Carlo method to create 50 imputed datasets without missing data on n = 1244 to assess Sn	744
Md-Sani et al[29], 2018	September 8, 2022 to November 18, 2022	Kuala Lumpur, Malaysia	Retrospective cohort	Variable selection via 5-fold cross-validated Lasso regression used to build logistic regression model	Internal validation via cross-validation	199
Suwarto et al[30], 2018	January 2011 to March 2016	Jarkarta, Indonesia	Retrospective cohort	Dengue Score (Suwarto et al[18], 2016)	External validation	207
Hsieh et al[15], 2017	July 1, 2015 to December 31, 2015	Tainan, Taiwan	Prospective cohort	Univariate and multivariate with binary variables Cox model to identify predictive factors for mortality with cut-off values selected using Youden index	No validation	625
Huang et al[35], 2017	September 1, 2015 to December 31, 2015	Tainan, Taiwan	Case control	Univariate analysis and Multivariate logistic regression analysis to investigate independent predictors for 30-day mortality. Novel prediction score developed by assigning a score of 1 to each independent variable	Internal validation via bootstrapping method by generating 1000 hypothetical study population using random sampling from study sample	2358
Fernández et al[31], 2017	2009 to 2010	Tegucigalpa and San Pedro Sula, Honduras	Retrospective cohort	Univariable analysis and multivariable logistic regression analysis using forward stepwise selection to construct a predictive model for severe dengue	Internal validation via bootstrap technique (sampling with replacement using 320 individuals sampling 1000 times)	320
Nguyen et al[16], 2017	October 1, 2010 to December 31, 2013	Southern Vietnam	Prospective cohort	Logistic regression to develop prognostic model	Internal validation via "leave-one-site-out cross validation" (develop algorithm on all but 1 study site and validate using that study site) and Temporal validation	2060
Djossou et al[17], 2016	March 17, 2013 to September 30, 2013	Cayenne, French Guiana	Prospective cohort	Final model include variables with significant association in single covariable analysis	Internal validation via bootstrapping 1000 replications	806
Lee et al[32], 2016	Kaohsiung Chang Gung Memorial Hospital: July 1, 2002 to May 31, 2015; Kaohsiung Medical University Hospital[6]: 2009 to 2011	Kaohsiung, Taiwan	Retrospective cohort	Significant variables in univariate analysis entered into multivariate logistic regression and point assignment calculated by dividing regression coefficient by smallest coefficient in model	Temporal validation (model set before 31 Jul 2014 n = 1063, validation set after Aug 1 2014 n = 190)	1253
Suwarto et al[18], 2016	March 2010 to August 2015	Jarkarta, Indonesia	Prospective cohort	Variables entered into multiple regression analysis using backward selection algorithm to estimate coefficient and independent diagnostic predictors and converted into simplified risk score system	Validation published separately[30]	172
Lam et al[19], 2015	2003 to 2009	Ho Chi Minh City, Vietnam	Prospective cohort	Univariate and multivariate analysis via logistic regression and model simplified using stepwise backwards model selection based on Akaike Information Criterion	Temporal validation (model from n = 939 enrolled before 2009 and validated on 268 enrolled during 2009) and internal validation via repeated 10-fold cross-validation	1207
Pang et al[36], 2014	January 1, 2004 to December 31, 2008	Singapore	Case control	Univariate and multivariate conditional logistic regression performed to assess association	No validation	135
Pongpan et al[33], 2014	2007 to 2010	Phrae, Thailand; Lamphun, Thailand; Chiang Mai, Thailand	Retrospective cohort	Scoring system (Pongpan et al[34], 2013)	External validation	400
Pongpan et al[34], 2013	2007 to 2010	Nakorn Sawan, Thailand; Kampaeng Phet, Thailand; Uttaradit, Thailand	Retrospective cohort	Scoring system analysed by multivariable ordinal logistic regression and assigned item scores derived from coefficient transformation	Validation published separately[33]	777
Leo et al[20], 2013	January 2010 to September 2012	Singapore	Prospective cohort	Variables selected from World Health Organization[7] Warning Signs	External validation	499
Diaz-Quijano et al[21], 2010	Not reported	Bucaramanga, Colombia	Prospective cohort	Risk score based on independent predictors and risk group formed	No validation	729
Potts et al[22], 2010	Queen Sirikit National Institute of Child Health: 1994 to 1997, 1999 to 2002, 2004 to 2007; Kamphaeng Phet Provincial Hospital: 1994 to 1997	Bangkok, Thailand; Kamphaeng Phet, Thailand	Prospective cohort	CART analysis with age, gender, and clinical laboratory data to establish a diagnostic decision tree	Internal validation via k-fold cross validation method (k = 5) of each tree	582

CART: Classification and Regression Tree; MELD: Model for end-stage liver disease; mSOFA: Modified sequential organ failure assessment; ROC: Receiver operating characteristic.

Patient baseline characteristics

The participant baseline characteristics are outlined in Table 3. There were a total of 7051 males (51.1%) (summarised from 25 studies[10-13,15,17-20,23-38]) consisting of participants from both adults and paediatric studies, although the mean ages cannot be reliably summarised due to incomplete data.

Table 3 Baseline patient characteristics, n (%)/mean ± SD.

Ref.	Paediatrics only	n	Age	Gender (male)	Ethnicity	Social demographics	Onset	Presentation
Chi et al[38], 2023	No	701	54.1 ± 19.2	363 (51.8)	Nil	Nil	Nil	Fever, nausea, vomit, bleeding, fatigue, hyporexia, abdominal pain (data not available)
Yang et al[37], 2023	No	1090	< 18 years: 318 (29.2). 18-39 years: 553 (50.7). ≥ 40 years: 219 (20.1)	652 (59.8)	Nil	Uneducated: 28 (26.1). Primary education: 339 (31.1). Secondary education: 306 (28.1). Tertiary education: 112 (10.3). Missing education data: 49 (4.5). Low income (< 15000 BDT per month): 34 (31.4). Low-mid income (15000-25000 BDT per month): 404 (37.1). High-mid income (25000-50000 BDT per month): 206 (18.9). High income (≥ 50000 BDT per month): 71 (6.5). Missing income data: 67 (6.1). Slum: 384 (35.2). Flat: 540 (49.5). House: 125 (11.5). Missing residence data: 41 (3.8)	Nil	Fever, myalgia, vomit, headache, abdominal pain
Gayathri et al[10], 2023	Yes	312	6.4 ± 3.44	196 (62.8)	Nil	Nil	Nil	Fever, bleeding, vomit, fatigue, abdominal pain
McBride et al[11], 2022	No	124	24.5, IQR: 20-32	63 (50.8)	Nil	Nil	Median 5 days (range 3-7)	Nil
Bhaskar et al[12], 2022	Yes	303	≤ 6 years: 60 (19.8). > 6 years: 243 (80.2)	161 (53.1)	Nil	Nil	Nil	Headache, myalgia, abdominal pain, rash, vomit, dyspnoea
Srisuphanunt et al[23], 2022	No	302	24.9 ± 17.3	154 (50.1)	Nil	Nil	Nil	Nil
Sachdev et al[13], 2021	Yes	78	10, IQR: 6.2-12	49 (62.8)	Nil	Nil	4.44 ± 2.15	Nil
Marois et al[24], 2021	No	383	32, IQR: 34	174 (45.4)	Melanesian: 141 (36.7). European: 86 (22.5). Polynesian: 68 (17.8). Others: 63 (17.4)	Tobacco: 105 (27.4). Cannabis: 19 (4.9). Kava: 15 (3.9). Alcohol (> 3 units/day): 9 (2.3)	Median 4 days, IQR: 3	Fever, arthralgia, myalgia, eye pain, headache, diarrhoea, nausea, vomit, rash, third spacing, fatigue, hepatomegaly, abdominal pain
Devarbhavi et al[25], 2020	No	36	32.31 ± 17.04	20 (55.6)	Nil	Nil	Range 3 to 7 days	Nil
Tangnararatchakit et al[26], 2020	Yes	242	10.6 ± 3.9	137 (56.6)	Nil	Nil	Nil	Nil
Lee et al[27], 2018	No	1068	52, IQR: 18-91	513 (47.2)	Nil	Nil	Median 3 days (range 1-10)	Fever, myalgia, arthralgia, eye pain, rash, headache, cough, diarrhoea, vomit, fatigue, abdominal pain
Phakhounthong et al[28], 2018	Yes	198	1 month-< 1 year: 56 (28.2). 1 year < 5 year: 59 (29.8). ≥ 5 years: 83 (41.9)	107 (54.0)	Nil	Nil	< 2 days	Fever, vomit, bleeding, dyspnoea, hepatomegaly, headache, rash, altered mental state
Park et al[14], 2018	Yes	744	Validation set not reported)	Nil	Nil	Nil	< 3 days	Fever
Md-Sani et al[29], 2018	No	199	30.8, IQR: 24.7-41.3	127 (63.8)	Nil	Nil	Nil	Fever, vomit, bleeding, fatigue, hepatomegaly, third spacing
Suwarto et al[30], 2018	No	207	33, IQR: 23-46	91 (44)	Nil	Nil	Nil	Fever
Hsieh et al[15], 2017	No	625	72.3 ± 9.3	46 (61.3)	Nil	Nil	Nil	Nil
Huang et al[35], 2017	No	2358	47.8 ± 21.9	1197 (50.8)	Nil	Stay with family: 2296 (97.4). Stay alone: 53 (2.2). Long-term care: 9 (0.4). Tobacco: 47 (2). Alcoholism: 34 (1.4)	Nil	Fever, arthralgia, myalgia, eye pain, headache, nausea, vomit, bleeding, rash, hyporexia, diarrhoea, fatigue, cough, dizzy, altered mental state, dyspnoea, chest pain, abdominal pain
Fernández et al[31], 2017	No	320	22.4 (missing SD)	181 (56.6)	Nil	Nil	≥ 6 days	Fever, headache, eye pain, arthralgia, myalgia, rash, vomit, hyporexia
Nguyen et al[16], 2017	Yes	2060	Given as 2 cohorts in median IQR	Nil	Nil	Nil	< 3 days	Fever
Djossou et al[17], 2016	No	806	< 1 year: 23 (2.9). 1-15 year: 294 (36.5). 16-65 year: 480 (59.6). > 65 years: 15 (1.9)	408 (50.2)	Nil	Nil	Median 2 days	Myalgia, arthralgia, bleeding, rash, vomit, abdominal pain (data not available)
Lee et al[32], 2016	No	1253	Given as 2 cohorts in median IQR	595 (47.5)	Nil	Nil	Derivation cohort Median 4 days, range 1-15. Validation cohort. Median 4 days, range 1-13	Nil
Suwarto et al[18], 2016	No	172	22, IQR: 11-33	89 (51.7)	Nil	Nil	3 days	Fever
Lam et al[19], 2015	Yes	1207	10, IQR: 7-12	645 (53)	Nil	Nil	Median 5 days (IQR: 5-6)	Fever, bleeding, third spacing, abdominal pain
Pang et al[36], 2014	No	135	Given as 2 cohorts in median IQR	88 (65.2)	Chinese: 98 (72.6). Malay: 7 (5.2). Indian: 17 (12.6). Others: 13 (9.6)	Nil	Cases: 3 days (IQR: 3-5). Control: 5 days (IQR: 4-5)	Nil
Pongpan et al[33], 2014	Yes	400	10.3 ± 3.4	223 (55.8)	Nil	Nil	Nil	Vomit, cough, bleeding, hepatomegaly, headache, myalgia, rash, third spacing, abdominal pain
Pongpan et al[34], 2013	Yes	777	9.6 ± 3.3	376 (48.4)	Nil	Nil	Nil	Hepatomegaly, headache, myalgia, vomit, cough, rash, third spacing, bleeding, abdominal pain
Leo et al[20], 2013	No	499	Given as 2 cohorts in median IQR	396 (79.4)	Nil	Nil	ED cohort. Median 6 days (5%-95% 3-8). Outpatient cohort. Median 6 days (5%-95% 3-8)	Nil
Diaz-Quijano et al[21], 2010	No	729	25.8 ± 15.9	Nil	Nil	Nil	Median: 7 days; range: 4-10	Fever, headache, eye pain, myalgia, arthralgia, hyporexia, cough, rash, vomit, diarrhoea, bleeding, abdo pain
Potts et al[22], 2010	Yes	582	8.7 ± 0.5	Nil	Nil	Nil	Mean 2.15 days	Fever

IQR: Interquartile range.

Dengue diagnosis was confirmed via serology for immunoglobulin against dengue virus in 21 studies (75.9%)[10,12-17,19,21-23,25-29,31,32,35,36,38], NS1 antigen testing in 19 studies (65.5%)[10,12,13,15-18,20,23,25-30,32,35,37,38], reverse transcription-polymerase chain reaction in 14 studies (44.8%)[13-20,22,24,27,32,36,38], and viral isolation in 4 studies (13.8%)[14,21,22,31], while 2 studies (6.9%)[33,34] extracted the diagnosis via electronic medical records. Recruited patients presented with a range of symptoms, in descending order from the most reported: Fever (85.6%), vomiting in 14 studies (43.3%), abdominal pain in 12 studies (42.3%), headache reported in 10 studies (47.5%), and myalgia reported in 10 studies (48.7%).

Comorbidities of the participants reported included diabetes mellitus (n = 752 of 6722, 11.2%, from 8 studies[15,24,27,29,32,35,36,38]), hypertension (n = 1150 of 6021, 19.1%, from 7 studies[15,24,27,29,32,35,36]), and chronic kidney disease/end-stage renal failure (n = 165 of 6388, 2.58%, from 6 studies[15,24,27,32,35,38]).

Prognostic outcome

The most common prognostic outcomes were severe dengue (15 models[10,14,16,17,19,20,22-24,26,28,32-34,37]) and mortality (8 models[11,13,15,25,27,29,35,38]). Severe dengue was often defined according to the 2009 WHO Classification, or as dengue haemorrhagic fever or dengue shock syndrome following the older 1997 WHO Classification.

ICU outcomes were predicted by 2 models[11,36]. Pang et al’s model predicted the probability of patients requiring ICU admission[36], while the model by McBride et al[11] was able to prognosticate multiple outcomes in relation to ICU admission, including duration of admission, organ support requirements and duration of intravenous fluid therapy.

The remaining models were designed to predict specific clinical outcomes. Hypotension, including threatened and profound shock, was covered by a few studies and was the main predicted outcome in the model by Djossou et al[17]. The score developed by Diaz-Quijano et al[21] focused on predicting bleeding manifestations, and was able to do so for acute febrile syndrome in dengue and non-dengue diagnoses. Finally, Suwarto et al[30] created a Dengue Score which could predict plasma leakage manifestations such as pleural effusion and ascites in patients with dengue.

Prognostic model type

The majority of studies utilised univariate and multivariate regression models to identify factors independently associated with their respective outcomes in order to generate prognostic models. These models were then converted into assigned integer scores or nomograms which are easily utilised in the clinical setting. 3 studies[22,28,37] developed classification and regression tree (CART) prognostic models for dengue fever. Predictor variables included both baseline clinical characteristics and laboratory test results, and the predicted outcome for all 3 CART models was severe dengue.

Three studies[11,15,25] utilised existing clinical scores to predict outcomes in patients with dengue fever. McBride et al[11] developed a modified Sequential Organ Failure Assessment (SOFA) score for dengue fever which was made more specific to dengue by adding pulse pressure to the cardiovascular assessment to reflect physiological compensation during profound plasma leakage, and changing the PaO₂/FiO₂ ratio to SpO₂/FiO₂ given that obtaining arterial blood gas is often avoided in dengue patients due to thrombocytopenia. Hsieh et al[15] utilised both the SOFA and Acute Physiology and Chronic Health Evaluation (APACHE) II scores, calculated within the first 24 hours of ICU admission, in their study. They showed that either APACHE II score > 24 or SOFA score > 15 was associated with in-hospital fatality, and linked to acute respiratory failure, acute kidney injury and cardiac arrest. Lastly, Devarbhavi et al[25] found that in patients with dengue hepatitis presenting with acute liver failure, the model for end-stage liver disease (MELD) score on admission was a significant predictor of mortality.

Of the total 27 scores proposed, 11 (40.7%) received only internal validations, 3 (11.1%) with only temporal validations, 2 (7.4%) with both internal and temporal validation, 4 (14.8%) with only external validations, and 7 (25.9%) had no validations mentioned.

Paediatric model characteristics

Of the 29 included studies, 11 studies (37.9%)[10,12-14,16,19,22,26,28,33,34] focused solely on dengue fever in the paediatric population, defined as participants under 18 years of age in this paper. This accounts for 6661 (37.2%) participants out of the total 17910 patients. Fever was the most common presenting symptom along with vomiting, abdominal pain and bleeding manifestations.

Nine out of the 10 paediatric models created were designed to predict severe or complicated dengue in children, including manifestations such as profound or recurrent shock, dengue hemorrhagic fever or dengue shock syndrome. Only one model created by Sachdev et al[13] predicted mortality in children with dengue fever, although the study only included children admitted to the paediatric ICU.

Model performance

The results of the prognostic score performance have been summarised in Table 4. Models developed by Chi et al[38] for critical outcomes, McBride et al[11] for duration of ICU, organ support requirement, IV therapy duration, Mortality, Lee et al[27] for mortality at 3, 7 days and overall (Supplementary Figure 1), Park et al[14] for severity, Md-Sani et al[29] for mortality, and Pang et al[36] for ICU requirement attained an AUC value of above 0.9.

Table 4 Results of prognostic score performance.

Ref.	Model	Score components	Predicted outcomes	Threshold	Sensitivity (%)	Specificity (%)
Leo et al[20], 2013	Number of warning signs	Abdominal pain; Persistent vomiting; Clinical fluid accumulation; Mucosal bleeding; Hepatomegaly (> 2 cm); ↑ in hematocrit; rapid ↓ of platelet	DHF I-IV and severe dengue	Nil	1 warning sign: DHF I-IV 79%. DHF II-IV 100%. Severe dengue 100%. 2 warning signs: DHF I-IV 33%, DHF II-IV 47%, Severe dengue 46%. 3 warning signs: DHF I-IV 6%, DHF II-IV 9%, Severe dengue 8%	1 warning sign: DHF I-IV 52%, DHF II-IV 52%, Severe dengue 48%. 2 warning signs: DHF I-IV 88%, DHF II-IV 88%, Severe dengue 85%. 3 warning signs: DHF I-IV 99%, DHF II-IV 99%, Severe dengue 98%
Chi et al[38], 2023	Multivariable binary logistic regression	Clinical presentations; age; chronic comorbidities, such as DM, CKD, chronic heart failure, and neoplasms; and abnormal laboratory finding	Critical outcomes early identification and treatment	4	95.7%	76.8%
Lee et al[27], 2018	Regression equation	Serum bicarbonate; ALT; age; gender	Mortality	2	94.9%	85.2%
Suwarto et al[30], 2018	Nomogram	Hct; Serum Albumin; Platelet count; AST ratio	Pleural effusion and/or ascites	≥ 2	92.45	74.26
Huang et al[35], 2017	Nomogram	Elderly age (≥ 65 years); Hypotension (systolic blood pressure < 90 mmHg); hemoptysis; DM; chronic bedridden	Mortality	1 and 3	Score ≥ 1: 91.2%	Score ≥ 3: 99.7%
Sachdev et al[13], 2021	Outcome predictor variables	SGPT; S lactate; PRISM 12 (paediatric risk of mortality at 12 hours admission); VIS (vasopressor inotrope score); FB (fluid balance % at 24 hours)	Mortality	S lactate: 2.73 mmol/L, VIS: 22.5	S lactate: 0.90	VIS: 0.948
Pang et al[36], 2014	Prognostic index (equation)	Neutrophil proportion; ALT; serum urea level	ICU requirement	P = −1.4	88.2	88.9
Nguyen et al[16], 2017	Nomogram	Vomiting; PLT; n × AST ULN; NS1 +ve	Severe Dengue	Nil	0.87	0.88
Gayathri et al[10], 2023	Binary logistic regression	Bedside dengue severity score = -1.297 + 4.234 (narrow pulse pressure) + 1.284 (mucosal bleed) + 0.489 (third space fluid loss)	Severe dengue	Nil	86.75%	98.25%
Tangnararatchakit et al[26], 2020	Nomogram	Age ≤ 1 year; aspirin or nonsteroidal drug ingestion; underlying disease such as hemolytic anaemia and congenital heart disease; additional vital signs; urine output; bleeding sites; amounts of the required crystalloid; colloid and blood components; inotropic drug administration; respiratory support and invasive procedures	Subsequent threatened shock and profound shock	≥ 12	86.21	84.26
Marois et al[24], 2021	Sex-specific multivariable predictive model	Female model: Age class; Medical history; Hypertension (treated/untreated); Symptoms-Mucosal bleeding, clinical liquid accumulation, skin rash (except purpura); last biological results-Platelets < 30 g/L, ALT > 10 N. Male model: Age class; Risky behaviour; Alcohol abuse > 3 u/day; symptoms mucosal bleeding; last biological results-Platelets < 30 g/L, ALT > 10N	Severe dengue	Nil	Female model: 84.5%. Male model: 84.5%	Female model: 78.6%. Male model: 95.5%
Bhaskar et al[12], 2022	Multivariable binary regression model	PCV; Platelet count; ALT; Highest WBC; Hypotension	Complicated dengue in paediatric patients	2	84.1	72.5
Suwarto et al[18], 2016	Nomogram	Hct; Serum Albumin; Platelet count; AST ratio	Pleural effusion/or ascites	≥ 2	82.47	70.42
Devarbhavi et al[25], 2020	MELD score	Admission lactate	Mortality	Nil	81%	74%
Park et al[14], 2018	Structural equation modelling	Any dengue illness; AST, WBC; %lymphocytes; PLT; tourniquet test at fever day -3 and -1	DF, DHF vs DSS	0.587	80.4	80.4
Fernández et al[31], 2017	Univariable and multivariable logistic regression	Headache; petechiae; ascites; platelets < 50000 platelets/mm3 at baseline	Plasma leakage	7%	76.4	70.3
Lee et al[32], 2016	Multivariable model based on disease duration	Model 1 age (≥ 65 years vs < 65 years); minor gastrointestinal bleeding (present vs absent); leukocytosis WBC > 10 × 109 cells/L (present vs absent); Platelet count ≥ 100 × 109 cells/L (present vs absent)	Severe dengue	1	70.3%	90.6%
Phakhounthong et al[28], 2018	CART (classification and regression tree)	HCT; GCS; urine protein; Cr; PLT	Severe dengue	0.5	0.605	0.65
Djossou et al[17], 2016	Logistic regression model	Hematocrit increase; protein concentration; sodium concentration; lymphocyte count; age; aches; extensive purpura; Rash; serous effusion; bleeding	Shock	Nil	48.2	94.2
Yang et al[37], 2023	CART and random forest model	Age; dyspnoea; plasma leakage; lowest platelet	Severe dengue	Nil	Nil	Nil
McBride et al[11], 2022	Nomogram	SpO2/FiO2; platelet count; Bilirubin level; MAP/PP; Adrenergic agents; GCS score; Creatinine and urine output	Duration of ICU admission Requirement for organ support (mechanical ventilation, vasopressors, renal replacement therapy). Duration of intravenous fluid therapy. Death	Nil	Nil	Nil
Srisuphanunt et al[23], 2022	Nomogram	Albumin; AST; ALT; PLT; PTT; DENV IgM	Severe dengue	Nil	Nil	Nil
Md-Sani et al[29], 2018	Regression equation	Serum bicarbonate; ALT; age; gender	Mortality	Nil	Nil	Nil
Hsieh et al[15], 2017	Multivariate Cox model	APTT; SOFA; APACHE II scores	Mortality	Nil	Nil	Nil
Lam et al[19], 2015	Nomogram	Age; day of illness; pulse rate; temperature; hematocrit; hemodynamic index	Profound DSS, Recurrent shock	Nil	Nil	Nil
Pongpan et al[33], 2014	Nomogram	Age; Hepatomegaly; SBP; WBC; PLT	DF, DHF vs DSS	Nil	Nil	Nil
Pongpan et al[34], 2013	Nomogram	Age; Hepatomegaly; HCT; SBP; WBC; PLT	DF, DHF vs DSS	Nil	Nil	Nil
Diaz-Quijano et al[21], 2010	Binomial regression	Age between 12 and 45 years, rash; vomiting; temperature > 38 °C; leukocyte count < 4500/L; platelet count < 90.000/L	Bleeding	Nil	Nil	Nil
Potts et al[22], 2010	CART	(1) WBC; %monocytes; PLT; HCT; and (2) WBC; AST; %neutrophil; PLT; Age	Severe Dengue (DSS vs DHF Grade 3/4, or PEI > 15)	Nil	Nil	Nil

ALT: Alanine transaminase; AST: Aspartate aminotransferase; DF: Dengue fever; DHF: Dengue haemorrhagic fever; DSS: Dengue shock syndrome; GCS: Glasgow coma scale; HCT: Haemotocrit; MAP: Mean arterial pressure; PLT: Platelet; PP: Pulse pressure; SGPT: Former alanine transaminase; SOFA: Sequential organ failure assessment; ULN: Upper limit of normal; WBC: White blood cell; PEI: Pleural effusion index.

DISCUSSION

For the paediatric population

Outcomes analysed by the proposed models included severity of disease including complications of shock[14,16,19,22,26,28,33,34], and mortality[13].

The model with the highest specificity for severe dengue was by Gayathri et al[10] (specificity = 0.9825, 95%CI: 0.9556-0.9952) (Supplementary Figure 2). Aside from pulse pressure, other components were consistent with WHO Dengue warning signs[1]. This included third-spacing and mucosal bleeding, although other warning signs were more specific and also differed for other paediatric ages[5]. Meanwhile, the most sensitive model for dengue severity was by Nguyen et al[16] (sensitivity = 0.87) (Supplementary Figure 3). The variables similarly included WHO Dengue warning signs[1] of vomiting, and also platelet count which were separately reported to have high negative predictive values[5]. Additionally, raised aspartate transaminase (AST) was associated with dengue severity in children as a marker of liver injury[39] and was found to rise before alanine transaminase (ALT)[40]. Meanwhile NS1 status correlated with severity for primary infections[41] which were also more likely in children. These scores further refine the implementation of WHO warning signs in evaluating for Dengue severity and progression.

On the other hand, mortality prediction was only evaluated by the Vasopressor Inotrope Score (VIS) (Supplementary Figure 4) with a specificity of 0.948. This is consistent with VIS being a measure of haemodynamic support[42], and is used in the prediction of mortality across other causes and ages, including cardiovascular disease such as Cardiac Arterial Bypass Graft in adults (specificity = 0.88)[43], Extracorporeal Membrane Oxygenation in paediatrics (specificity = 0.82)[44], and for sepsis (specificity = 0.83)[45]. Moreover, the most sensitive model as reported by Sachdev et al[13] and children[46,47]. This is because lactate in both young and old is indicative of not only the degree of anaerobic respiration as with tissue hypoxia (i.e. shock), but may also contribute to severe lactic acidosis[48].

For the adult population

For disease severity and complications, the most specific model was reported by Leo et al[20] with a specificity of 98% for severe dengue (Supplementary Figure 5). As with the paediatric population, the WHO warning signs, while nonspecific when assessed individually, are closely associated with dengue severity[1,49], although a combination may improve their specificity[20]. In contrast, the most sensitive scores were the warning signs for severe dengue as reported by Leo et al[20], where all Warning Signs except lethargy were included (Supplementary Figure 6) as well as Model 2 by Lee et al[20], with a sensitivity of 100% (Supplementary Figure 7). For the latter, older adults were at higher risk[50] possibly because of the higher possibility of secondary infection[51], while leukocytosis may relate to severity from its apparent link with bacterial superinfection or dyserythropoiesis[40].

With regards to mortality, the most specific model was a quantitative integer scoring as proposed by Huang et al[35] (Supplementary Figure 8), and is linked with poor morbidity and mortality especially in the elderly with comorbidities[52,53]. Simultaneously, hypotension may be a manifestation of distributive shock[54]. Therefore, there is clear preceding evidence which supports these predictive variables in measuring the mortality risk. Alternatively, the most sensitive was Model 3 by Lee et al[27,32] with leukocytosis especially identified as a red flag[55,56] while gastrointestinal bleeding may be precipitated by and thus an indicator of severe thrombocytopenia[57]. Furthermore, as these measures are already routine in dengue, it is also simpler to integrate them for use in the clinical context.

Lastly, specificity and sensitivity of the predictive model relating to ICU stay were only reported in one study by Pang et al[36] (sensitivity 88.2%, specificity 88.9%) (Supplementary Figure 9). Neutrophils are a significant contributor to dengue pathophysiology, including viral clearance and mediating dengue severity via vascular permeability or cardiac complications[58]. Likewise, ALT is associated with bleeding and hepatic impairment[40,59], while serum urea has been implicated in increased agglutination[60] and may suggest involvement of acute kidney impairment[61]. Therefore, these explain the relevance of neutrophils, ALT, and serum urea in determining the need for closer monitoring and timely interventions in the ICU.

In examining the variables used in dengue prognostication, there were several common factors including clinical features of gastrointestinal bleeding and hypotension, as well as laboratory results such as platelet count and evidence of acute kidney injury or liver impairment, in keeping with previous reviews identifying individual factors. However, paediatric ages, gender, secondary infection, other WHO warning signs, and biomarkers like Serum albumin, AST, or C-reactive protein[6,62,63] were missing. Therefore, it may be pertinent that future research incorporates these variables to ascertain their efficacy in prognosticating dengue infections.

Limitations

Firstly, few prognostic scores rely solely on clinical information. This thus limits the utility of such prognostication, especially in rural or primary care settings with scarce resources. Secondly, decision-analytic evaluations of prognostic models were absent. It is important to additionally measure the net benefit and compare against reference strategies to guide protocol and algorithm generation[64]. This is because while these scores may perform well in correlating to clinical outcomes, they may not sufficiently inform clinicians on the most appropriate management to then carry out.

CONCLUSION

In conclusion, despite the increasing prevalence and severity of dengue, there appears to be a paucity in well-validated instruments for dengue prognostication. This paper evaluates different proposed prognostic clinical scores via assessing their prognostic performance and accuracy. While several models were found to be accurate in predicting severe dengue and mortality, they have to be applied in wider contexts and different geographical locations to better evaluate their external validity.