Skip to main content
  • Loading metrics

Predictive markers for the early prognosis of dengue severity: A systematic review and meta-analysis


27 Jan 2022: Thach TQ, Eisa HG, Hmeda AB, Faraj H, Thuan TM, et al. (2022) Correction: Predictive markers for the early prognosis of dengue severity: A systematic review and meta-analysis. PLOS Neglected Tropical Diseases 16(1): e0010164. View correction



Predictive markers represent a solution for the proactive management of severe dengue. Despite the low mortality rate resulting from severe cases, dengue requires constant examination and round-the-clock nursing care due to the unpredictable progression of complications, posing a burden on clinical triage and material resources. Accordingly, identifying markers that allow for predicting disease prognosis from the initial diagnosis is needed. Given the improved pathogenesis understanding, myriad candidates have been proposed to be associated with severe dengue progression. Thus, we aim to review the relationship between the available biomarkers and severe dengue.


We performed a systematic review and meta-analysis to compare the differences in host data collected within 72 hours of fever onset amongst the different disease severity levels. We searched nine bibliographic databases without restrictive criteria of language and publication date. We assessed risk of bias and graded robustness of evidence using NHLBI quality assessments and GRADE, respectively. This study protocol is registered in PROSPERO (CRD42018104495).

Principal findings

Of 4000 records found, 40 studies for qualitative synthesis, 19 for meta-analysis. We identified 108 host and viral markers collected within 72 hours of fever onset from 6160 laboratory-confirmed dengue cases, including hematopoietic parameters, biochemical substances, clinical symptoms, immune mediators, viral particles, and host genes. Overall, inconsistent case classifications explained substantial heterogeneity, and meta-analyses lacked statistical power. Still, moderate-certainty evidence indicated significantly lower platelet counts (SMD -0.65, 95% CI -0.97 to -0.32) and higher AST levels (SMD 0.87, 95% CI 0.36 to 1.38) in severe cases when compared to non-severe dengue during this time window.


The findings suggest that alterations of platelet count and AST level—in the first 72 hours of fever onset—are independent markers predicting the development of severe dengue.

Author summary

The major concern in dengue fever is the abrupt occurrence of severe complications, for which only close monitoring of patients is the treatment scheme. Thus, the markers managing to predict the subsequent progression of complications—in the early stage of disease course—could alleviate the clinical management burden. Ideally, the predictors foretell the outcomes before the severe complications occur—usually on days 4–7 following fever onset. In this study, therefore, we reviewed the available markers collected during the first 3 days of fever onset. We found robust evidence of significantly lower platelet counts and higher AST levels in those who subsequently developed severe dengue than those who did not. In this regard, platelet count could serve as an independent warning sign rather than combining with hematocrit—as seen in the current classification—which remains unaltered during this time window. Also, abdominal pain and vomiting could predict the outcomes, but using these signs is arduous when their manifestations vary as per the patient without cutoffs. Hepatomegaly rate is substantially higher in severe dengue, but likely yields a high false-negative prediction rate. There is a need for larger studies to confirm the relatedness of hyaluronan in severe dengue.


Dengue fever is an acute mosquito-borne viral disease caused by infection of any of four dengue virus serotypes (DENV1–4) that predominantly circulates in tropics and subtropics, subjecting over 3 billion individuals to the risk of infection [1]. DENV accounts for an annual occurrence of ~ 400 million cases across 129 countries [2], though only ¼ is symptomatic [3].

Dengue clinical manifestation varies greatly from self-limiting febrile illness to fatal outcomes without clear-cut hallmarks to assist diagnosis. These life-threatening complications occur relatively late during the disease course—often day 4 of fever onset or around the critical phase [4,5]. At present, no therapeutics are available for dengue except supportive care as an off-label approach. Furthermore, dengue vaccine has acquired specific achievements, but on the horns of a dilemma, restricting the vaccinations only to those with dengue-infection history. Therefore, dengue management continues to rely upon constant examination and round-the-clock nursing care, imposing a burden on clinical triage and economy in resource-limited settings [1].

Representing a potential breakthrough in the proactive management of dengue, the effort to some extent has shifted to the search for means that can foretell the outcomes at the inception of disease. In 2009, the World Health Organization (WHO) revised dengue case classification in light of multi-centre study findings known as “DENCO” (DENgue COntrol), which was proved more sensitive to predicting severe cases [6,7]. The adapted classification, although improved, has demonstrated limited performance in the early prognosis of severe complications [8,9]. Given the betterment of pathogenesis understanding, accumulating evidence has associated numerous predictive candidates with severe dengue progression [10,11].

Nevertheless, the reported evidence is conflicting [1214]. These conflicts arise from insufficient study power or inconsistency in the timeframe during which markers are recorded [15,16], often late in the disease course [15,16]. Additionally, the combination of dengue hemorrhagic fever (DHF) and dengue shock syndrome (DSS) as a severe form is frequently seen [14,1719], which could mislead clinical triage given that management strategies differ between these two groups [20,21].

Previous systematic reviews offer insight as to predictive methods for severe dengue. Notwithstanding, the conclusions went hand in hand with the study limitations that formed the body of evidence, say the combination of DHF and DSS or late measurement [12,14,19,22,23]. Besides, studies have focused concern on immunogenetic markers that benefit pathophysiology rather than medical case management [18,2427]. Here we advance the findings of prior work to illustrate the association of available markers and severe dengue in a different context.


Protocol registration

We previously developed and registered the systematic review protocol in PROSPERO (CRD42018104495; S1 Protocol). We followed the PRISMA reporting guidelines (S1 Table).

Eligibility criteria

We included observational studies that reported on the association of host markers and dengue severities categorized by WHO classifications. The markers were measured within 72 hours of fever onset and before the occurrence of any severe complication, including shock, bleeding, and organ impairment.

Search strategy

On 7th June 2018, we searched for articles using nine databases, including Cochrane Library,, EMBASE, Google Scholar, POPLINE, PubMed, Scopus, SIGLE, and Web of Science (ISI). We updated the systematic search before the inception of data analysis (on 17th December 2019; without POPLINE database as no longer available) and before the submission for peer review (on 20th September 2020) by using the identical search terms (S1 Text). We manually searched preprint sites (bioRxiv and medRxiv), in-text citations from eligible articles, and previous reviews on the related topics. Concerning articles with insufficient information, we contacted the authors.

Study selection

We screened titles and abstracts for the relevance of the content that was continued by reviewing full texts. Three reviewers independently worked on tasks with an agreement reached through a discussion amongst the reviewers, and in case of discrepancy, we consulted the empirical authors (KH, NTH, SPD). We used the Kappa statistic to appraise the inter-rater reliability amongst the reviewers.

Data extraction

We piloted the in-house data collection tool for its applicability before the official extraction by three reviewers working independently (S1 Data). We collected data for study characteristics, population baseline characteristics, measurement times, pre-admission treatments, dengue case classifications, dengue serotypes, serostatus (primary or secondary infection), and host marker data collected in the pre-specified time window.

For data reported by days before fever subsided, we included data reported within 3 days before defervescence, depending on the availability of data and the similarity of timeframe varying from study to study, assuming that day 4 was the day fever subsided [4,5,10,28]. Accordingly, we chose data from day 1–3 when authors reported data corresponding to the day since fever onset. For data graphically presented, we requested data from authors or used a web-based software program, available at, to collect the summary information of the outcomes or individual participant data by which the latter was then checked for normal distribution and normalized before computing mean and standard deviation (SD). For missing SD, we outsourced data from other articles based upon the similarity of population, measurement time, and severe outcomes that patients developed [29]. If neither of these methods satisfied, we considered synthesizing the evidence by narrative review in tabular formats. Concerning the overlapping data, we chose outcomes with a larger sample size for meta-analysis.

Quality assessment

At the study level, we used NHLBI quality assessment and Q-genie scoring to rate the risk of bias of the component studies corresponding to their designs, case-control or cohort [30] or genetic association studies [31] and presented them in the characteristics of included studies table. Briefly, each study underwent a set of signalling questions about the potential bias that a study may present. We mainly based the appraisal on the resemblance of the population, including age structures, enrollment times, and locations alongside the clear sample size justification. When the reviewers could not provide sound judgement due to insufficient information, the study was of unclear risk of bias.

At the outcome level, we used the GRADE approach to grade the certainty of our findings for their clinical applicability [32] and to generate an evidence profile, including the judgements on the risk of bias, inconsistency, imprecision, and indirectness of the findings [3336]. We graded down the robustness of evidence when serious concern arose from any of the four domains.

Outcomes and definition of markers

Outcomes were the differences in host marker data between severe and non-severe dengue cases. To uniform the severity levels through the series of WHO dengue classifications, to those of the 1997 classification or earlier, we grouped dengue fever, dengue hemorrhage fever grade 1 and grade 2 into non-severe cases; grade 3 and grade 4 (dengue shock syndrome) were defined as severe cases. Similarly, the 2009 WHO classification or later, we combined dengue with and without warning signs to form a group of non-severe cases.

The markers were biochemical substances (e.g., liver enzymes, VEGF), hematopoietic parameters (e.g., leukocyte, neutrophil), immune mediators (e.g., chemokines, cytokines), and viral footprints (e.g., viral load, NS1 antigen detected in any of host tissue or biological fluid), or clinical symptoms or signs, which altered or occurred due to dengue infection.

Statistical analysis

To ensure the appropriateness of conducting a meta-analysis, we initially examined the similarity across the studies based upon three dimensions, including clinical traits, methodology and observed effects [37,38]. Next, we performed a meta-analysis of the relationship between patient-derived data and severe dengue development using the Sidik-Jonkman method for a random-effect model that bears an adequate error rate in estimating the between-study variance [39]. We used Hedges’s g—a standardized mean difference (SMD)—as the pooled estimates for continuous variables [40] and natural logarithm odd ratios (LORs) for binary outcomes [41], followed by 95% confidence intervals (CIs). For the articles that were of unmet similarity, we performed the narrative review in tabular formats.

As the rule of thumb, the statistical approaches of heterogeneity that we incorporated into the principal analysis (I2, τ2, and Q test) reflected the arithmetical variability in estimates and the overlapping in confidence intervals [33]—rather than either actual clinical or methodological differences; therefore, we did not mainly base the exploration of the inconsistency on these approaches but the examination of clinical key features by GRADE approach. However, we could not fully perform the subgroup analysis and meta-regression to see the impact of the a priori hypotheses such as age, gender, pre-admission treatments, dengue case classifications, serotypes, serostatus, and study limitations (risk of bias) due to sporadic reporting data. We estimated mean and SD using the methods published elsewhere [4244].


The systematic search identified 4000 records through the three different operations of search against time. After removing duplicates, 2666 records were included for the title and abstract screening. The manual search identified eight additional articles. We updated the systematic search and found two research articles. In total, 40 articles were utilized to generate the body of evidence. Of these, 19 articles underwent meta-analysis. For the remaining articles, we mainly focused on the central findings in tandem with the methodological flaws, as presented in Fig 1.

Kappa statistics showed that the strength of agreement between the reviewers at any cross-checked screening step was moderate, by which the index varied from 0.42 to 0.56, with 95% CI varied from 0.30 to 0.65.

The 40 studies involved approximate 6160 laboratory-confirmed dengue patients who met our pre-specified criteria from three endemic continents of dengue: Asia (especially South-East Asia countries), Latin America, and the Pacific Islands. Of these, 19 studies reported data on children; 11 included all age groups; eight included only adults; two did not describe the target population (Table 1).

At the study level, risk of bias varied from low to high. Of the 40 studies, 57.5% were of a high risk of bias, 30.0% and 12.5% had low and unclear risk, respectively. We performed an additional assessment concerning the genetic association study indicating a good study design (Table 1). The robustness of evidence was extremely low to moderate based on GRADE scoring (Table 2).

Overall, 14 studies assessed 15 hematopoietic parameters, the meta-analyses of four eligible parameters indicated significantly lower platelet counts in those who subsequently developed severe dengue than those who did not (n = 3671, SMD -0.65, 95% CI -0.97 to -0.32; Fig 2). By contrast, there were no differences in leukocyte, lymphocyte, and neutrophil counts between those with and without severe dengue (S1, S2, and S3 Figs).

Fig 2. Forest plot showing the relationship between platelet counts and severe dengue.

One study was an outlier [67]; the estimated effects remained unaltered after the sensitivity analysis. The red dashed line represented the overall effect size.

Thirteen studies examined 18 biochemical markers. However, only four markers were eligible for meta-analysis, which showed significantly higher AST levels in severe dengue than in non-severe dengue (n = 3610, SMD 0.87, 95% CI 0.36 to 1.38; Fig 3). No relationship was found between the alteration of ALT, albumin, and sodium levels with severe dengue (S4, S5, and S6 Figs).

Fig 3. Forest plot showing the relationship between AST levels and severe dengue.

One study was an outlier [67]; the estimated effects remained unaltered after the sensitivity analysis. The red dashed line represented the overall effect size.

Four studies monitored a total of four clinical signs in connection with the progression of severe dengue. The meta-analyses revealed the association of the presence of abdominal pain, vomiting, and liver enlargement with the increased risk of severe dengue (n = 3384, lnOR = 0.40, 95% CI 0.01 to 0.80; n = 3405, lnOR 1.12, 95% CI 0.37 to 1.87; n = 1314, lnOR = 2.54, 95% CI 1.11 to 3.96, respectively; Figs 4, 5, and 6). No relationship between mucosal bleeding and severe dengue risk was detected within this study (S7 Fig).

Fig 4. Forest plot showing the relationship between the presence of abdominal pain and severe dengue.

The red dashed line represented the overall effect size.

Fig 5. Forest plot showing the relationship between vomiting and severe dengue.

The red dashed line represented the overall effect size.

Fig 6. Forest plot showing the relationship between hepatomegaly (>2 cm) and severe dengue.

The red dashed line represented the overall effect size.

Seven studies proposed 13 host cell structure-associated markers. We could only estimate the effects of one marker, hyaluronan, which demonstrated significantly higher levels in those who subsequently progressed to severe dengue (n = 211, SMD 0.63, 95% CI 0.21 to 1.05; Fig 7).

Fig 7. Forest plot showing the relationship between hyaluronan levels and severe dengue.

The red dashed line represented the overall effect size.

Seventeen studies reported the alterations of 25 immune mediators. Four eligible biomarkers, CRP, TNF-α, IL-10, and IFN-γ, found no significant variation in marker levels when comparing severe and non-severe dengue (S8S11 Figs).

Eight studies correlated either the quantity of virus or NS1 antigen in the bloodstream with severity levels. The estimated effect pooled from three studies found no association between viral load in the early stage and the subsequent progression of severe dengue (S12 Fig).

Although statistical evidence implied the variation amongst the studies involving AST levels and platelet counts, the substantial heterogeneity ensued from the differences between small and large study effects rather than direction, which was apparent when we removed the most extensive study (Nguyen et al., 2016)—as was the outlier here—from the estimates (S13 and S14 Figs). On the other hand, other possible inconsistencies were not serious to compromise the estimated effects. In contrast, abdominal pain, vomiting, and enlarged liver showed low statistical heterogeneity despite the marked differences in the definition of severe outcomes.


This review found 40 studies comparing 108 host and viral markers amongst patients with varying dengue severity, published from 1989 to September 2020. Our findings suggested that the alterations of platelet counts and AST levels within 72 hours of fever onset were associated with severe dengue development. Similarly, the presence of abdominal pain, vomiting, liver enlargement and altered hyaluronan level were suggestive of the higher risk of severe dengue progression, but with exceptionally low robustness of the evidence.

Thrombocytopenia is commonly seen in dengue patients [84]. For this reason, platelet counts have long been used as a parameter to keep track of dengue progression. Our finding revealed an association between platelet counts and severe dengue, consistent with the previous systematic reviews [19,22], although we restricted the assessment to the first 72 hours following fever onset. The platelet decline occurs due to the massive activation of itself, apoptosis, and bone marrow hypoplasia, on which DENV initially has a direct or indirect impact [8587]. In addition, the hyper-activated platelets, per se, could induce the extravasation by local secretion of pro-inflammatory mediators such as serotonin and VEGF [88,89].

As the findings have shown, AST level was significantly higher in severe than in non-severe patients during the early stage. Our analysis supported existing evidence of AST elevation in complicated dengue regardless of the time window [19,22,90,91]. Moreover, AST elevation alone was more indicative of systemic inflammation than hepatic injury, although DENV highly infects hepatocytes in the context of dengue tropism [92]. Generally, the elevation rate of AST is greater than that of ALT in dengue infection [9397]. Wang et al., 2016 reported that 52% and 54% of mild and complicated dengue, respectively, demonstrated elevated ALT. When considering AST, these proportions increased to 75% and 80% [91]. The temporal change of liver transaminases begins early in the illness course, and elevations are significantly higher in severe dengue. Still, with moderate prediction power, especially in the case of ALT [62]. Instead, the combined index, such as AST2/ALT, could improve discriminative performance [98]. On this point, our finding was inconsistent with most previous systematic reviews in which the ALT level was significantly higher in severe dengue [12,19,22]. This could be attributed to the time window that we used—the first 3 days versus 4 days or later in these studies—which could be premature for the hepatocellular damage to be noted.

We observed the association between abdominal pain, vomiting, liver enlargement, and altered serum hyaluronan level with severe dengue progression. However, our findings were unable to firmly confer their benefits in clinical practice due to weak evidence. Alternatively, our study puts forward several points that medical care may find helpful.

The major inconsistency in our findings regarding clinical signs was the existence of different case classifications. Given that the updated WHO classification, which includes broader clinical outcomes, is more sensitive to detecting severe cases than the 1997 guideline [6,99], the estimated effect of the markers defined was larger. This was apparent when comparing the effects of abdominal pain between two large cohorts, Nguyen et al., 2016 versus Lam et al., 2017, corresponding to the 2009 versus 1997 WHO classification (Fig 4). In the same way, vomiting was assigned more weights in Nguyen et al., 2016 study than that by Lam et al., 2017, but as yet, the difference was weaker (Fig 5).

Further, at the outcome level, bias and inconsistency may arise from the measurements of abdominal pain and vomiting. The effects could vary in terms of a “dose-response” relationship—referring to the resulting progressions of different clinical manifestations. Regarding vomiting, Vuong et al., 2016 suggested two episodes per day to predict severe dengue in general [80]; another study proposed three times per day associated with plasma leak [100]. Next, many causes explain the acute abdominal pain in dengue, from non-specific to the more specific causes such as hepatitis, acalculous cholecystitis, pancreatitis, or several unusual causes [101104], still having been merely referred to as “abdominal pain”. It is clear that individuals who have a greater number of vomiting episodes are more likely to experience complicated dengue, and different clinical manifestations could speak to the different progressions. As such, vomiting and abdominal pain fulfill their prognostic tasks, but not optimally relax case-management pressure when using these signs—which ignores the beneficial cutoffs or hallmarks—goes with the umbrella admission. It also underscores the need to properly report clinical signs, featuring how the symptoms manifest—rather than whether they do present—in association with severe dengue.

There was evidence that hepatomegaly is more prevalent in complicated dengue [14,22,105107]. Liver enlargement occurred in 1.0–34.6% of dengue infected adults [78,97,108112]. The rate was even higher in children, 43.0–97.4% [107,110,113119]. Nevertheless, the hepatomegaly rate was lower than expected in our study, despite the vast majority of participants being under 15 years old. One ultrasound study reported that 21.8% of children had an enlarged liver in the first 3 days of fever onset [120]. Based on this scenario, the optimal information size is approximately 300 events in total sample size, at the power of 80% and confidence level of 95%, to capture the real effects [34]. In comparison, the number of events in our analysis was shorter than the required size to provoke a precise point estimate. Despite this, the finding was consistent, underpinning the unclear liver involvement at the early stage of dengue infection—as no evidence of the substantial ALT and albumin differences between the clinical severities during this period.

The modest detection rate of hepatomegaly may require a re-evaluation of ultrasound benefits in the early stage, despite its proficiency in identifying sophisticated disturbances undetectable by physical examinations. Only few patients who subsequently developed complicated dengue exhibited fluid accumulation in the pleural cavity and peritoneal recesses (rectovesical pouch or pouch of Douglas), were reported during the first 72 hours of fever onset [121]. Gallbladder wall abnormalities became detectable on days 3–8 of the disease course [120125]. Overall, the relationship between plasma leak signs—detected by ultrasound—and complicated dengue is undeniable. However, the sonographic hallmarks allow for reliable prediction mostly around the critical phase or later [121]. The plausible explanation, supported by Srikiatkhachorn et al., 2007, is that ultrasound requires a significant fluid accumulation to detect the differences. For this reason, although the differences between severe and non-severe dengue were detected during the first 3 days, a high false-negative prediction rate may occur. This explained the relatively low event rates in our study and the previous ones. To the best of our knowledge and as observed throughout this project, no evidence ascertains the performance of individual plasma leak signs by ultrasound during the first 72 hours of fever onset. The recent systematic review provided the broad landscape demonstrating a trade-off between sensitivity and specificity alongside the late presence of sonography signs or unclear measurement time [126].

Hyaluronan is the structural component maintaining the integrity of the extracellular matrix in connective tissues [127]. Hyaluronan increases during the inflammatory responses, reflecting the de novo synthesis and perturbed degradation that leads to its accumulation in the circulation [128]. However, few studies advance hyaluronan to explain dengue infection pathogenesis. Honsawek et al., 2007 first demonstrated the significantly increased hyaluronan level in children with DSS during the acute stage defined as days 3–7 [129]. Other studies noticed no differences in hyaluronan level between DHF and DF on day 3 of fever onset [77,130]. The different time windows and insufficient sample size appeared to render the inharmonious conclusions. Thus, further studies with a larger size are needed to explore this association.

This study also has several limitations. First, the restrictive time window and “severe outcome” definitions yielded few studies as well as participants, which impacted in the capturing of the markers. Second, lacking data from Latin America and Africa—the frequent or continuous dengue risk areas—diminished our conclusions on the markers for these sites. Third, given that dengue patients reach the clinical outcomes do so by the multifactorial interactions, immune status and viral factors probably introduced noise into the findings. Nevertheless, such information was not always explicitly described for examining its potential impact on the inferences.

In conclusion, our review highlights the topics which merit further consideration. First, although the early alterations of platelets and AST levels indicate a higher risk of severe dengue development, these indicators require establishing quantitative diagnostic values and additional validation through prospective studies. Finally, decreased platelet counts in the first 72 hours could serve as an independent warning sign, instead of combining with elevated hematocrit detectable when plasma leak has implicitly occurred, often on day 3 or around the critical phase [61,74,131133].

Supporting information

S1 Fig. Forest plot showing the relationship between leukocyte counts and severe dengue.

The red dashed line represented the overall effect size.


S2 Fig. Forest plot showing the relationship between relative lymphocyte counts (RLCs) and severe dengue.

The red dashed line represented the overall effect size.


S3 Fig. Forest plot showing the relationship between absolute neutrophil counts (ANCs) and severe dengue.

The red dashed line represented the overall effect size.


S4 Fig. Forest plot showing the relationship between ALT levels and severe dengue.

The red dashed line represented the overall effect size.


S5 Fig. Forest plot showing the relationship between albumin levels and severe dengue.

The red dashed line represented the overall effect size.


S6 Fig. Forest plot showing the relationship between serum sodium and severe dengue.

The red dashed line represented the overall effect size.


S7 Fig. Forest plot showing the relationship between the presence of mucosal bleeding and severe dengue.

The red dashed line represented the overall effect size.


S8 Fig. Forest plot showing the relationship between CRP levels and severe dengue.

The red dashed line represented the overall effect size.


S9 Fig. Forest plot showing the relationship between TNF-α levels and severe dengue.

The red dashed line represented the overall effect size.


S10 Fig. Forest plot showing the relationship between IL-10 levels and severe dengue.

The red dashed line represented the overall effect size.


S11 Fig. Forest plot showing the relationship between IFN-γ levels and severe dengue.

The red dashed line represented the overall effect size.


S12 Fig. Forest plot showing the relationship between viral load and severe dengue.

The red dashed line represented the overall effect size.


S13 Fig. Sensitivity analysis showing the estimated effects of platelet counts.

The estimated effects remained unchanged by excluding seven studies; the heterogeneity considerably reduced by removing an outlier [67].


S14 Fig. Sensitivity analysis showing the estimated effects of AST levels.

The estimated effects remained unchanged by excluding seven studies; the heterogeneity considerably reduced by removing an outlier [67].



I want to express my gratitude to all members of the Immunogenetic Department of Nagasaki University for the wholehearted advice at any study stage, in that data meetings were vital for me to keep this research on its right track. Further, I am thankful to the Online Research Club members (ORC) for the comprehensive remarks, especially Ms Simmies Ta, for her helpful conversations considering the language revision before submitting this research.


  1. 1. Wilder-Smith A, Ooi E-E, Horstick O, Wills B. Dengue. The Lancet. 2019;393(10169):350–63.
  2. 2. WHO. Dengue and severe dengue World Health Organization; 2020 [Available from: pmid:32794470
  3. 3. CDC. About Dengue: What You Need to Know USA: CDC; 2019 [Available from:
  4. 4. WHO. Dengue guidelines for diagnosis, treatment, prevention and control: new edition. Geneva: World Health Organization; 2009.
  5. 5. Malavige GN, Ogg GS. Pathogenesis of vascular leak in dengue virus infection. Immunology. 2017;151(3):261–9. pmid:28437586
  6. 6. Hadinegoro SR. The revised WHO dengue case classification: does the system need to be modified? Paediatr Int Child Health. 2012;32 Suppl 1(s1):33–8. pmid:22668448
  7. 7. Alexander N, Balmaseda A, Coelho IC, Dimaano E, Hien TT, Hung NT, et al. Multicentre prospective study on dengue classification in four South-east Asian and three Latin American countries. Trop Med Int Health. 2011;16(8):936–48. pmid:21624014
  8. 8. Robinson M, Sweeney TE, Barouch-Bentov R, Sahoo MK, Kalesinskas L, Vallania F, et al. A 20-Gene Set Predictive of Progression to Severe Dengue. Cell Rep. 2019;26(5):1104–11 e4. pmid:30699342
  9. 9. Ajlan BA, Alafif MM, Alawi MM, Akbar NA, Aldigs EK, Madani TA. Assessment of the new World Health Organization’s dengue classification for predicting severity of illness and level of healthcare required. PLoS Negl Trop Dis. 2019;13(8):e0007144. pmid:31430283
  10. 10. Yacoub S, Wills B. Predicting outcome from dengue. BMC Med. 2014;12(1):147. pmid:25259615
  11. 11. Pawitan JA. Dengue virus infection: predictors for severe dengue. Acta Med Indones. 2011;43(2):129–35. pmid:21785176
  12. 12. Htun TP, Xiong Z, Pang J. Clinical signs and symptoms associated with WHO severe dengue classification: a systematic review and meta-analysis. Emerg Microbes Infect. 2021;10(1):1116–28. pmid:34036893
  13. 13. Rathore AP, Farouk FS, St John AL. Risk factors and biomarkers of severe dengue. Curr Opin Virol. 2020;43:1–8. pmid:32688269
  14. 14. Zhang H, Zhou YP, Peng HJ, Zhang XH, Zhou FY, Liu ZH, et al. Predictive symptoms and signs of severe dengue disease for patients with dengue fever: a meta-analysis. Biomed Res Int. 2014;2014:359308. pmid:25097856
  15. 15. Chen CC, Lee IK, Liu JW, Huang SY, Wang L. Utility of C-Reactive Protein Levels for Early Prediction of Dengue Severity in Adults. Biomed Res Int. 2015;2015 pmid:26247033
  16. 16. Biswas HH, Ortega O, Gordon A, Standish K, Balmaseda A, Kuan G, et al. Early clinical features of dengue virus infection in nicaraguan children: a longitudinal analysis. PLoS Negl Trop Dis. 2012;6(3):e1562. pmid:22413033
  17. 17. Morsy S, Hashan MR, Hieu TH, Mohammed AT, Elawady SS, Ghosh P, et al. The association between dengue viremia kinetics and dengue severity: A systemic review and meta-analysis. Rev Med Virol. 2020;30(6):1–10. pmid:32856357
  18. 18. Soo KM, Khalid B, Ching SM, Tham CL, Basir R, Chee HY. Meta-analysis of biomarkers for severe dengue infections. PeerJ. 2017;5:e3589. pmid:28929009
  19. 19. Sangkaew S, Ming D, Boonyasiri A, Honeyford K, Kalayanarooj S, Yacoub S, et al. Risk predictors of progression to severe disease during the febrile phase of dengue: a systematic review and meta-analysis. Lancet Infect Dis. 2021;21(7):1014–26. pmid:33640077
  20. 20. Kalayanarooj S. Clinical Manifestations and Management of Dengue/DHF/DSS. Trop Med Health. 2011;39(4 Suppl):83–7. pmid:22500140
  21. 21. Chuansumrit A, Tangnararatchakit K. Pathophysiology and management of dengue hemorrhagic fever. Transfusion Alternatives in Transfusion Medicine. 2006;8(s1):3–11.
  22. 22. Huy NT, Van Giang T, Thuy DH, Kikuchi M, Hien TT, Zamora J, et al. Factors associated with dengue shock syndrome: a systematic review and meta-analysis. PLoS Negl Trop Dis. 2013;7(9):e2412. pmid:24086778
  23. 23. Lima WG, Souza NA, Fernandes SOA, Cardoso VN, Godoi IP. Serum lipid profile as a predictor of dengue severity: A systematic review and meta-analysis. Rev Med Virol. 2019;29(5):e2056. pmid:31172625
  24. 24. Kien ND, El-Qushayri AE, Ahmed AM, Safi A, Mageed SA, Mehyar SM, et al. Association of Allergic Symptoms with Dengue Infection and Severity: A Systematic Review and Meta-analysis. Virol Sin. 2020;35(1):83–92. Epub 2019 Oct 21. pmid:31637633
  25. 25. Sherif NA, Zayan AH, Elkady AH, Ghozy S, Ahmed AR, Omran ES, et al. Mast cell mediators in relation to dengue severity: A systematic review and meta-analysis. Rev Med Virol. 2020;30(1):e2084. pmid:31709696
  26. 26. Chen Y, Liao Y, Yuan K, Wu A, Liu L. HLA-A, -B, -DRB1 Alleles as Genetic Predictive Factors for Dengue Disease: A Systematic Review and Meta-Analysis. Viral Immunol. 2019;32(3):121–30. pmid:30835646
  27. 27. Naing C, Htet NH, Siew Tung W, Basavaraj AK, Mak JW. Association of tumour necrosis factor-alpha (TNF-alpha) gene polymorphisms (-308 G>A and -238 G>A) and the risk of severe dengue: A meta-analysis and trial sequential analysis. PLoS One. 2018;13(10):e0205413. pmid:30300401
  28. 28. Lum L, Ng CJ, Khoo EM. Managing dengue fever in primary care: A practical approach. Malays Fam Physician. 2014;9(2):2–10. pmid:25893065
  29. 29. Furukawa TA, Barbui C, Cipriani A, Brambilla P, Watanabe N. Imputing missing standard deviations in meta-analyses can provide accurate results. J Clin Epidemiol. 2006;59(1):7–10. pmid:16360555
  30. 30. Study Quality Assessment Tools: National Heart, Lung, and Blood Institute; 2014 [Available from:
  31. 31. Q-Genie tool: McMaster University; 2015 [Available from:
  32. 32. Zhang Y, Akl EA, Schunemann HJ. Using systematic reviews in guideline development: the GRADE approach. Research synthesis methods. 2018.
  33. 33. Schünemann H BJ, Guyatt G, Oxman A, editors. GRADE handbook for grading quality of evidence and strength of recommendations. McMaster University: The GRADE Working Group; 2013.
  34. 34. Guyatt GH, Oxman AD, Kunz R, Brozek J, Alonso-Coello P, Rind D, et al. GRADE guidelines 6. Rating the quality of evidence—imprecision. J Clin Epidemiol. 2011;64(12):1283–93. pmid:21839614
  35. 35. Guyatt GH, Oxman AD, Vist G, Kunz R, Brozek J, Alonso-Coello P, et al. GRADE guidelines: 4. Rating the quality of evidence—study limitations (risk of bias). J Clin Epidemiol. 2011;64(4):407–15. pmid:21247734
  36. 36. Guyatt GH, Oxman AD, Kunz R, Woodcock J, Brozek J, Helfand M, et al. GRADE guidelines: 7. Rating the quality of evidence—inconsistency. J Clin Epidemiol. 2011;64(12):1294–302. pmid:21803546
  37. 37. Methods Guide for Effectiveness and Comparative Effectiveness Reviews: Agency for Healthcare Research and Quality; 2015 [Available from:
  38. 38. Deeks J, Higgins J, Altman D. Analysing data and undertaking meta-analyses. In: Higgins J, Green S, editors. Cochrane handbook for systematic reviews of interventions Chichester, UK: John Wiley & Sons; 2008.
  39. 39. IntHout J, Ioannidis JP, Borm GF. The Hartung-Knapp-Sidik-Jonkman method for random effects meta-analysis is straightforward and considerably outperforms the standard DerSimonian-Laird method. BMC Med Res Methodol. 2014;14(1):25. pmid:24548571
  40. 40. Lin L, Aloe AM. Evaluation of various estimators for standardized mean difference in meta-analysis. Stat Med. 2021;40(2):403–26. pmid:33180373
  41. 41. Higgins JP, White IR, Anzures-Cabrera J. Meta-analysis of skewed data: combining results reported on log-transformed or raw scales. Stat Med. 2008;27(29):6072–92. pmid:18800342
  42. 42. Luo Y, Stephens DA, Buckeridge DL. Estimating prevalence using indirect information and Bayesian evidence synthesis. Canadian Journal of Statistics. 2018;46(4):673–89.
  43. 43. Wan X, Wang W, Liu J, Tong T. Estimating the sample mean and standard deviation from the sample size, median, range and/or interquartile range. BMC Med Res Methodol. 2014;14(1):135. pmid:25524443
  44. 44. Shi J, Luo D, Weng H, Zeng XT, Lin L, Chu H, et al. Optimally estimating the sample standard deviation from the five-number summary. Research synthesis methods. 2020;11(5):641–54. pmid:32562361
  45. 45. Pozo-Aguilar JO, Monroy-Martinez V, Diaz D, Barrios-Palacios J, Ramos C, Ulloa-Garcia A, et al. Evaluation of host and viral factors associated with severe dengue based on the 2009 WHO classification. Parasit Vectors. 2014;7:590. pmid:25500154
  46. 46. Avirutnan P, Punyadee N, Noisakran S, Komoltri C, Thiemmeca S, Auethavornanan K, et al. Vascular leakage in severe dengue virus infections: a potential role for the nonstructural viral protein NS1 and complement. J Infect Dis. 2006;193(8):1078–88. pmid:16544248
  47. 47. Biswas HH, Gordon A, Nunez A, Perez MA, Balmaseda A, Harris E. Lower Low-Density Lipoprotein Cholesterol Levels Are Associated with Severe Dengue Outcome. PLoS Negl Trop Dis. 2015;9(9):e0003904. pmid:26334914
  48. 48. Butthep P, Chunhakan S, Tangnararatchakit K, Yoksan S, Pattanapanyasat K, Chuansumrit A. Elevated Soluble Thrombomodulin in the Febrile Stage Related to Patients at Risk for Dengue Shock Syndrome. The Pediatric Infectious Disease Journal. 2006;25(10). pmid:17006283
  49. 49. Butthep P, Chunhakan S, Yoksan S, Tangnararatchakit K, Chuansumrit A. Alteration of cytokines and chemokines during febrile episodes associated with endothelial cell damage and plasma leakage in dengue hemorrhagic fever. Pediatr Infect Dis J. 2012;31(12):e232–8. pmid:22926216
  50. 50. Chaiyaratana W, Chuansumrit A, Atamasirikul K, Tangnararatchakit K. Serum ferritin levels in children with dengue infection. Southeast Asian J Trop Med Public Health. 2008;39(5):832–6. pmid:19058577
  51. 51. Chandrashekhar C, Balaji K, Vasudev PH, Panachiyil GM, Babu T. Estimation of serum neopterin level as an early marker for detecting severe dengue infection. Int J Pediatr Adolesc Med. 2019;6(4):151–4. pmid:31890841
  52. 52. Chunhakan S, Butthep P, Yoksan S, Tangnararatchakit K, Chuansumrit A. Vascular leakage in dengue hemorrhagic Fever is associated with dengue infected monocytes, monocyte activation/exhaustion, and cytokines production. Int J Vasc Med. 2015;2015:917143. pmid:25722892
  53. 53. Fernando S, Wijewickrama A, Gomes L, Punchihewa CT, Madusanka SD, Dissanayake H, et al. Patterns and causes of liver involvement in acute dengue infection. BMC Infect Dis. 2016;16 319. pmid:27391896
  54. 54. Hapugaswatta H, Amarasena P, Premaratna R, Seneviratne KN, Jayathilaka N. Differential expression of microRNA, miR-150 and enhancer of zeste homolog 2 (EZH2) in peripheral blood cells as early prognostic markers of severe forms of dengue. J Biomed Sci. 2020;27(1):25. pmid:31954402
  55. 55. Hoang LT, Lynn DJ, Henn M, Birren BW, Lennon NJ, Le PT, et al. The early whole-blood transcriptional signature of dengue virus and features associated with progression to dengue shock syndrome in Vietnamese children and young adults. J Virol. 2010;84(24):12982–94. pmid:20943967
  56. 56. Hober D, Poli L, Roblin B, Gestas P, Chungue E, Granic G, et al. Serum levels of tumor necrosis factor-alpha (TNF-alpha), interleukin-6 (IL-6), and interleukin-1 beta (IL-1 beta) in dengue-infected patients. Am J Trop Med Hyg. 1993;48(3):324–31. pmid:8470771
  57. 57. Hober D, Delannoy AS, Benyoucef S, De Groote D, Wattre P. High levels of sTNFR p75 and TNF alpha in dengue-infected patients. Microbiol Immunol. 1996;40(8):569–73. pmid:8887351
  58. 58. Koraka P, Murgue B, Deparis X, Van Gorp EC, Setiati TE, Osterhaus AD, et al. Elevation of soluble VCAM-1 plasma levels in children with acute dengue virus infection of varying severity. J Med Virol. 2004;72(3):445–50. pmid:14748068
  59. 59. Kurane I, Innis BL, Nimmannitya S, Nisalak A, Meager A, Janus J, et al. Activation of T lymphocytes in dengue virus infections. High levels of soluble interleukin 2 receptor, soluble CD4, soluble CD8, interleukin 2, and interferon-gamma in sera of children with dengue. J Clin Invest. 1991;88(5):1473–80. pmid:1939640
  60. 60. Kurane I, Innis BL, Nimmannitya S, Nisalak A, Meager A, Ennis FA. High levels of interferon alpha in the sera of children with dengue virus infection. Am J Trop Med Hyg. 1993;48(2):222–9. pmid:8447527
  61. 61. Lam PK, Ngoc TV, Thu Thuy TT, Hong Van NT, Nhu Thuy TT, Hoai Tam DT, et al. 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. 2017;11(4):e0005498. pmid:28448490
  62. 62. Lee LK, Gan VC, Lee VJ, Tan AS, Leo YS, Lye DC. Clinical relevance and discriminatory value of elevated liver aminotransferase levels for dengue severity. PLoS Negl Trop Dis. 2012;6(6):e1676. pmid:22679523
  63. 63. Liao B, Tang Y, Hu F, Zhou W, Yao X, Hong W, et al. Serum levels of soluble vascular cell adhesion molecules may correlate with the severity of dengue virus-1 infection in adults. Emerg Microbes Infect. 2015;4(4):e24. pmid:26421267
  64. 64. Lin CY, Kolliopoulos C, Huang CH, Tenhunen J, Heldin CH, Chen YH, et al. High levels of serum hyaluronan is an early predictor of dengue warning signs and perturbs vascular integrity. EBioMedicine. 2019;48:425–41. pmid:31526718
  65. 65. Low GKK, Gan SC, Zainal N, Naidu KD, Amin-Nordin S, Khoo CS, et al. The predictive and diagnostic accuracy of vascular endothelial growth factor and pentraxin-3 in severe dengue. Pathog Glob Health. 2018;112(6):334–41. pmid:30246621
  66. 66. Mekmullica J, Suwanphatra A, Thienpaitoon H, Chansongsakul T, Cherdkiatkul T, Pancharoen C, et al. Serum and urine sodium levels in dengue patients. Southeast Asian J Trop Med Public Health. 2005;36(1):197–9. pmid:15906667
  67. 67. Nguyen MT, Ho TN, Nguyen VV, Nguyen TH, Ha MT, Ta VT, et al. An Evidence-Based Algorithm for Early Prognosis of Severe Dengue in the Outpatient Setting. Clin Infect Dis. 2017;64(5):656–63. pmid:28034883
  68. 68. Pandey N, Jain A, Garg RK, Kumar R, Agrawal OP, Lakshmana Rao PV. Serum levels of IL-8, IFNγ, IL-10, and TGF β and their gene expression levels in severe and non-severe cases of dengue virus infection. Arch Virol. 2015;160(6):1463–75. Epub 2015 Apr 10. pmid:25860648
  69. 69. Park S, Srikiatkhachorn A, Kalayanarooj S, Macareo L, Green S, Friedman JF, et al. Use of structural equation models to predict dengue illness phenotype. PLoS Negl Trop Dis. 2018;12(10):e0006799. pmid:30273334
  70. 70. Patil R, Bajpai S, Ghosh K, Shetty S. Microparticles as prognostic biomarkers in dengue virus infection. Acta Trop. 2018;181:21–4. pmid:29378163
  71. 71. Phuong NTN, Manh DH, Dumre SP, Mizukami S, Weiss LN, Van Thuong N, et al. Plasma cell-free DNA: a potential biomarker for early prediction of severe dengue. Ann Clin Microbiol Antimicrob. 2019;18(1):10. pmid:30871553
  72. 72. Prasad D, Bhriguvanshi A. Clinical Profile, Liver Dysfunction and Outcome of Dengue Infection in Children: A Prospective Observational Study. Pediatr Infect Dis J. 2020;39(2):97–101. pmid:31815826
  73. 73. Sehrawat P, Biswas A, Kumar P, Singla P, Wig N, Dar L, et al. Role of Cytokines as Molecular Marker of Dengue Severity. Mediterr J Hematol Infect Dis. 2018;10(1):e2018023. pmid:29755701
  74. 74. Sigera PC, Amarasekara R, Rodrigo C, Rajapakse S, Weeratunga P, De Silva NL, et al. Risk prediction for severe disease and better diagnostic accuracy in early dengue infection; the Colombo dengue study. BMC Infect Dis. 2019;19(1):680. pmid:31370795
  75. 75. Soundravally R, Sankar P, Bobby Z, Hoti SL. Oxidative stress in severe dengue viral infection: association of thrombocytopenia with lipid peroxidation. Platelets. 2008;19(6):447–54. pmid:18925513
  76. 76. Srichaikul T, Nimmannitya S, Sripaisarn T, Kamolsilpa M, Pulgate C. Platelet function during the acute phase of dengue hemorrhagic fever. Southeast Asian J Trop Med Public Health. 1989;20(1):19–25. pmid:2772702
  77. 77. Suwarto S, Sasmono RT, Sinto R, Ibrahim E, Suryamin M. Association of Endothelial Glycocalyx and Tight and Adherens Junctions With Severity of Plasma Leakage in Dengue Infection. J Infect Dis. 2017;215(6):992–9. pmid:28453844
  78. 78. Trung DT, Thao le TT, Hien TT, Hung NT, Vinh NN, Hien PT, et al. Liver involvement associated with dengue infection in adults in Vietnam. Am J Trop Med Hyg. 2010;83(4):774–80. pmid:20889864
  79. 79. Vaughn DW, Green S, Kalayanarooj S, Innis BL, Nimmannitya S, Suntayakorn S, et al. Dengue viremia titer, antibody response pattern, and virus serotype correlate with disease severity. J Infect Dis. 2000;181(1):2–9. pmid:10608744
  80. 80. Vuong NL, Manh DH, Mai NT, Phuc le H, Luong VT, Quan VD, et al. Criteria of "persistent vomiting" in the WHO 2009 warning signs for dengue case classification. Trop Med Health. 2016;44:14. pmid:27433133
  81. 81. Vuong NL, Le Duyen HT, Lam PK, Tam DTH, Vinh Chau NV, Van Kinh N, et al. C-reactive protein as a potential biomarker for disease progression in dengue: a multi-country observational study. BMC Med. 2020;18(1):35. pmid:32063229
  82. 82. Wills B, Tran VN, Nguyen TH, Truong TT, Tran TN, Nguyen MD, et al. Hemostatic changes in Vietnamese children with mild dengue correlate with the severity of vascular leakage rather than bleeding. Am J Trop Med Hyg. 2009;81(4):638–44. pmid:19815879
  83. 83. Zhao H, Qiu S, Hong WX, Song KY, Wang J, Yang HQ, et al. Dengue Specific Immunoglobulin A Antibody is Present in Urine and Associated with Disease Severity. Sci Rep. 2016;6:27298.
  84. 84. Chao CH, Wu WC, Lai YC, Tsai PJ, Perng GC, Lin YS, et al. Dengue virus nonstructural protein 1 activates platelets via Toll-like receptor 4, leading to thrombocytopenia and hemorrhage. PLoS Pathog. 2019;15(4):e1007625. pmid:31009511
  85. 85. de Azeredo EL, Monteiro RQ, de-Oliveira Pinto LM. Thrombocytopenia in Dengue: Interrelationship between Virus and the Imbalance between Coagulation and Fibrinolysis and Inflammatory Mediators. Mediators Inflamm. 2015;2015:313842. pmid:25999666
  86. 86. Hottz ED, Oliveira MF, Nunes PC, Nogueira RM, Valls-de-Souza R, Da Poian AT, et al. Dengue induces platelet activation, mitochondrial dysfunction and cell death through mechanisms that involve DC-SIGN and caspases. J Thromb Haemost. 2013;11(5):951–62. pmid:23433144
  87. 87. Vogt MB, Lahon A, Arya RP, Spencer Clinton JL, Rico-Hesse R. Dengue viruses infect human megakaryocytes, with probable clinical consequences. PLoS Negl Trop Dis. 2019;13(11):e0007837. pmid:31765380
  88. 88. Masri MFB, Mantri CK, Rathore APS, John ALS. Peripheral serotonin causes dengue virus-induced thrombocytopenia through 5HT2 receptors. Blood. 2019;133(21):2325–37. pmid:30755421
  89. 89. Singh A, Bisht P, Bhattacharya S, Guchhait P. Role of Platelet Cytokines in Dengue Virus Infection. Front Cell Infect Microbiol. 2020;10:561366. pmid:33102253
  90. 90. John DV, Lin YS, Perng GC. Biomarkers of severe dengue disease—a review. J Biomed Sci. 2015;22:83. pmid:26462910
  91. 91. Wang XJ, Wei HX, Jiang SC, He C, Xu XJ, Peng HJ. Evaluation of aminotransferase abnormality in dengue patients: A meta analysis. Acta Trop. 2016;156(1873–6254 (Electronic)):130–6. pmid:26739659
  92. 92. Begum F, Das S, Mukherjee D, Mal S, Ray U. Insight into the Tropism of Dengue Virus in Humans. Viruses. 2019;11(12):1136. pmid:31835302
  93. 93. Samanta J, Sharma V. Dengue and its effects on liver. World J Clin Cases. 2015;3(2):125–31. pmid:25685758
  94. 94. Nguyen TL, Nguyen TH, Tieu NT. The impact of dengue haemorrhagic fever on liver function. Res Virol. 1997;148(4):273–7. pmid:9272578
  95. 95. Umar S, Ahmad B, Rana MY, Khan Khattak JZ, Shabbir MI. Hepatic dysfunction in patients infected with dengue virus. J Coll Physicians Surg Pak. 2014;24(1):74. pmid:24411552
  96. 96. Kalayanarooj S, Vaughn DW, Nimmannitya S, Green S, Suntayakorn S, Kunentrasai N, et al. Early clinical and laboratory indicators of acute dengue illness. J Infect Dis. 1997;176(2):313–21. pmid:9237695
  97. 97. Kittitrakul C, Silachamroon U, Phumratanaprapin W, Krudsood S, Wilairatana P, Treeprasertsuk S. Liver function tests abnormality and clinical severity of dengue infection in adult patients. J Med Assoc Thai. 2015;98 Suppl 1(Suppl 1):S1–8. pmid:25764606
  98. 98. Md Sani SS, Han WH, Bujang MA, Ding HJ, Ng KL, Amir Shariffuddin MA. Evaluation of creatine kinase and liver enzymes in identification of severe dengue. BMC Infect Dis. 2017;17(1):505. pmid:28732476
  99. 99. Dussart P, Duong V, Bleakley K, Fortas C, Lorn Try P, Kim KS, et al. Comparison of dengue case classification schemes and evaluation of biological changes in different dengue clinical patterns in a longitudinal follow-up of hospitalized children in Cambodia. PLoS Negl Trop Dis. 2020;14(9):e0008603. pmid:32925941
  100. 100. Binh PT, Matheus S, Huong VT, Deparis X, Marechal V. Early clinical and biological features of severe clinical manifestations of dengue in Vietnamese adults. J Clin Virol. 2009;45(4):276–80. pmid:19451025
  101. 101. Dhannur PK, Pn S, Kr C, editors. Acute abdomen presentation in dengue haemorrhagic fever in South Indian population: An observational study2019.
  102. 102. Gupta B, Nehara H, Parmar S, Meena S, Gajraj S, Gupta J. Acute abdomen presentation in dengue fever during recent outbreak. Journal of Acute Disease. 2017;6(5):198–204.
  103. 103. Khanna S, Vij J, Kumar A, Singal D, Tandon R. Etiology of Abdominal Pain in Dengue Fever. Dengue Bulletin. 2005;29.
  104. 104. Waseem T, Latif H, Shabbir B. An unusual cause of acute abdominal pain in dengue fever. Am J Emerg Med. 2014;32(7):819 e3–4. pmid:24560393
  105. 105. Shah GS, Islam S, Das BK. Clinical and laboratory profile of dengue infection in children. Kathmandu Univ Med J (KUMJ). 2006;4(1):40–3. pmid:18603866
  106. 106. Pongpan S, Wisitwong A, Tawichasri C, Patumanond J. Prognostic Indicators for Dengue Infection Severity. International Journal of Clinical Pediatrics; Vol 2, No 1, Jun 2013. 2013.
  107. 107. Roy A, Sarkar D, Chakraborty S, Chaudhuri J, Ghosh P, Chakraborty S. Profile of hepatic involvement by dengue virus in dengue infected children. N Am J Med Sci. 2013;5(8):480–5. pmid:24083224
  108. 108. Ooi ET, Ganesananthan S, Anil R, Kwok FY, Sinniah M. Gastrointestinal manifestations of dengue infection in adults. Med J Malaysia. 2008;63(5):401–5. pmid:19803300
  109. 109. Tripathi BK, Gupta B, Sinha RS, Prasad S, Sharma DK. Experience in adult population in dengue outbreak in Delhi. J Assoc Physicians India. 1998;46(3):273–6. pmid:11273345
  110. 110. Wichmann O, Hongsiriwon S, Bowonwatanuwong C, Chotivanich K, Sukthana Y, Pukrittayakamee S. Risk factors and clinical features associated with severe dengue infection in adults and children during the 2001 epidemic in Chonburi, Thailand. Trop Med Int Health. 2004;9(9):1022–9. pmid:15361117
  111. 111. Sharma S, Sharma SK, Mohan A, Wadhwa J, Dar L, Thulkar S, et al. Clinical Profile of Dengue Haemorrhagic Fever in Adults during 1996- Outbreak in Delhi, India. 1998.
  112. 112. Rowe EK, Leo YS, Wong JG, Thein TL, Gan VC, Lee LK, et al. Challenges in dengue fever in the elderly: atypical presentation and risk of severe dengue and hospital-acquired infection [corrected]. PLoS Negl Trop Dis. 2014;8(4):e2777. pmid:24699282
  113. 113. Jagadishkumar K, Jain P, Manjunath VG, Umesh L. Hepatic involvement in dengue Fever in children. Iran J Pediatr. 2012;22(2):231–6. pmid:23056891
  114. 114. Mohan B, Patwari A, Anand V. Brief report. Hepatic dysfunction in childhood dengue infection. Journal of tropical pediatrics. 2000;46(1):40–3. pmid:10730040
  115. 115. Narayanan M, Aravind M, Thilothammal N, Prema R, Sargunam CR, Ramamurty N. Dengue fever epidemic in Chennai-a study of clinical profile and outcome. Indian pediatrics. 2002;39(11):1027–33. pmid:12466573
  116. 116. Joshi R, Baid V. Profile of dengue patients admitted to a tertiary care hospital in Mumbai. Turk J Pediatr. 2011;53(6):626–31. pmid:22389984
  117. 117. Shah I, Deshpande GC, Tardeja PN. Outbreak of dengue in Mumbai and predictive markers for dengue shock syndrome. J Trop Pediatr. 2004;50(5):301–5. pmid:15510763
  118. 118. Mishra S, Ramanathan R, Agarwalla SK. Clinical Profile of Dengue Fever in Children: A Study from Southern Odisha, India. Scientifica (Cairo). 2016;2016:6391594. pmid:27213083
  119. 119. Setiawan MW, Samsi TK, Wulur H, Sugianto D, Pool TN. Dengue haemorrhagic fever: ultrasound as an aid to predict the severity of the disease. Pediatr Radiol. 1998;28(1):1–4. pmid:9426264
  120. 120. Venkata Sai PM, Dev B, Krishnan R. Role of ultrasound in dengue fever. Br J Radiol. 2005;78(929):416–8. pmid:15845934
  121. 121. Srikiatkhachorn A, Krautrachue A, Ratanaprakarn W, Wongtapradit L, Nithipanya N, Kalayanarooj S, et al. Natural history of plasma leakage in dengue hemorrhagic fever: a serial ultrasonographic study. Pediatr Infect Dis J. 2007;26(4):283–90; discussion 91–2. pmid:17414388
  122. 122. Tavares MA, Joao GAP, Bastos MS, Gimaque JBL, Almeida ACG, Ngo TT, et al. Clinical relevance of gallbladder wall thickening for dengue severity: A cross-sectional study. PLoS One. 2019;14(8):e0218939. pmid:31469845
  123. 123. Parmar JP, Mohan C, Vora M. Patterns of Gall Bladder Wall Thickening in Dengue Fever: A Mirror of the Severity of Disease. Ultrasound Int Open. 2017;3(2):E76–E81. pmid:28603785
  124. 124. Setiawan MW, Samsi TK, Pool TN, Sugianto D, Wulur H. Gallbladder wall thickening in dengue hemorrhagic fever: an ultrasonographic study. J Clin Ultrasound. 1995;23(6):357–62. pmid:7673451
  125. 125. Shah S, Rolfe R, Henostroza G, Seas C. Ultrasound Findings of Plasma Leakage in Dengue Fever. Am J Trop Med Hyg. 2018;99(6):1362–3. pmid:30851024
  126. 126. Low GK, Looi SY, Yong MH, Sharma D. Predictive and diagnostic test accuracy of ultrasonography in differentiating severe dengue from nonsevere dengue. J Vector Borne Dis. 2018;55(2):79–88. pmid:30280705
  127. 127. Laurent TC, Fraser JR. Hyaluronan. FASEB J. 1992;6(7):2397–404. pmid:1563592
  128. 128. Fraser JR, Laurent TC, Laurent UB. Hyaluronan: its nature, distribution, functions and turnover. J Intern Med. 1997;242(1):27–33. pmid:9260563
  129. 129. Honsawek S, Kongtawelert P, Pothacharoen P, Khongphatthanayothin A, Chongsrisawat V, Poovorawan Y. Increased levels of serum hyaluronan in patients with dengue infection. J Infect. 2007;54(3):225–9. pmid:16876870
  130. 130. Tang TH, Alonso S, Ng LF, Thein TL, Pang VJ, Leo YS, et al. Increased Serum Hyaluronic Acid and Heparan Sulfate in Dengue Fever: Association with Plasma Leakage and Disease Severity. Sci Rep. 2017;7:46191. pmid:28393899
  131. 131. Sirivichayakul C, Limkittikul K, Chanthavanich P, Jiwariyavej V, Chokejindachai W, Pengsaa K, et al. Dengue infection in children in Ratchaburi, Thailand: a cohort study. II. Clinical manifestations. PLoS Negl Trop Dis. 2012;6(2):e1520. pmid:22389735
  132. 132. Ferreira RAX, Kubelka CF, Velarde LGC, Matos JPS, Ferreira LC, Reid MM, et al. Predictive factors of dengue severity in hospitalized children and adolescents in Rio de Janeiro, Brazil. Rev Soc Bras Med Trop. 2018;51(6):753–60. pmid:30517528
  133. 133. Michels M, Sumardi U, de Mast Q, Jusuf H, Puspita M, Dewi IM, et al. The predictive diagnostic value of serial daily bedside ultrasonography for severe dengue in Indonesian adults. PLoS Negl Trop Dis. 2013;7(6):e2277. pmid:23785539