18
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Dengue score as a diagnostic predictor for pleural effusion and/or ascites: external validation and clinical application

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Background

          The Dengue Score is a model for predicting pleural effusion and/or ascites and uses the hematocrit (Hct), albumin concentration, platelet count and aspartate aminotransferase (AST) ratio as independent variables. As this metric has not been validated, we conducted a study to validate the Dengue Score and assess its clinical application.

          Methods

          A retrospective study was performed at a private hospital in Jakarta, Indonesia. Patients with dengue infection hospitalized from January 2011 through March 2016 were included. The Dengue Score was calculated using four parameters: Hct increase≥15.1%, serum albumin≤3.49 mg/dL, platelet count≤49,500/μL and AST ratio ≥ 2.51. Each parameter was scored as 1 if present and 0 if absent. To validate the Dengue Score, goodness-of-fit was used to assess calibration, and the area under the receiver operating characteristic curve (AROC) was used to assess discrimination. Associations between clinical parameters and Dengue Score groups were determined by bivariate analysis.

          Results

          A total of 207 patients were included in this study. The calibration of the Dengue Score was acceptable (Hosmer-Lemeshow test, p = 0.11), and the score’s discriminative ability was good (AROC = 0.88 (95% CI: 0.83–0.92)). At a cutoff of ≥2, the Dengue Score had a positive predictive value (PPV) of 79.03% and a negative predictive value (NPV) of 90.36% for the diagnostic prediction of pleural effusion and/or ascites. Compared with the Dengue Score ≤ 1 group, the Dengue Score = 2 group was significantly associated with hemoconcentration> 20% ( p = 0.029), severe thrombocytopenia (p = 0.029), and increased length of hospital stay ( p = 0.003). Compared with the Dengue Score = 2 group, the Dengue Score ≥ 3 group was significantly associated with hemoconcentration> 20% ( p = 0.001), severe thrombocytopenia ( p = 0.024), severe dengue ( p = 0.039), and increased length of hospital stay ( p = 0.011).

          Conclusion

          The Dengue Score performed well and can be used in daily practice to help clinicians identify patients who have plasma leakage associated with severe dengue.

          Related collections

          Most cited references22

          • Record: found
          • Abstract: found
          • Article: not found

          Screening tests: a review with examples

          Screening tests are widely used in medicine to assess the likelihood that members of a defined population have a particular disease. This article presents an overview of such tests including the definitions of key technical (sensitivity and specificity) and population characteristics necessary to assess the benefits and limitations of such tests. Several examples are used to illustrate calculations, including the characteristics of low dose computed tomography as a lung cancer screen, choice of an optimal PSA cutoff and selection of the population to undergo mammography. The importance of careful consideration of the consequences of both false positives and negatives is highlighted. Receiver operating characteristic curves are explained as is the need to carefully select the population group to be tested.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: found
            Is Open Access

            Factors Associated with Dengue Shock Syndrome: A Systematic Review and Meta-Analysis

            Introduction Dengue infection is a major health problem in tropical and subtropical countries. Each year, more than 250,000 cases of DHF/DSS are reported from an estimated 50 million dengue infections [1]. Dengue disease ranges from asymptomatic or self-limiting dengue fever (DF) to severe dengue characterized by plasma leakage (dengue hemorrhagic fever [DHF], grades 1 and 2) that can lead to a life-threatening syndrome (dengue shock syndrome [DSS], grades 3 and 4) [2]. Recently, severe dengue was also defined by severe bleeding and/or severe organ impairment [3]. Fatal cases of dengue infection mostly occur in patients with DSS, and the mortality of DSS is reportedly 50 times higher than that of dengue patients without DSS [4]. There are no licensed vaccines or antiviral drugs against the disease, although some potential solutions are currently being studied [5]. Early appropriate treatment, vector control, and educational program are the only current methods to reduce mortality and global disease burden [3], [6], [7], [8]. Therefore, the World Health Organization (WHO) encourages research based around markers of severity to develop new tools and methods that can reduce the healthcare burden of dengue infection in endemic countries. Several factors associated with DSS have been reported in individual studies [9], [10], [11]; however, the associations for some factors are not observed consistently across studies [12], [13], [14], [15]. Therefore, we conducted a systematic review and meta-analysis of relevant studies to assess all reported factors associated with DSS. Methods Search strategy and study selection Our study was performed according to the recommendations of the PRISMA statement [16], which is available in supporting information (Checklist S1). We had developed a protocol of methods from June to August 2010, and our protocol can be assessed in our homepage at: http://www.tm.nagasaki-u.ac.jp/hiraken/member/file/n_tien_huy/protocol_of%20systemic_review_for_dengue3.pdf. In September 2010, PubMed, Scopus, EMBASE, LILACS via Virtual Health Library, Google Scholar, WHO Dengue bulletin, Cochrane Library, and a manual search of reference lists of articles were searched for suitable studies. The search terms used for PubMed, EMBASE and Scopus were as follows: “dengue AND (shock OR DSS OR severity OR severe OR “grade IV” OR “grade III”)”. We used “dengue” to search in LILACS and Cochrane Library. For the “Advanced Scholar Search”, we used “dengue” to fill in the field “with all of the words”, “shock OR DSS OR severity OR severe OR “grade IV” OR “grade III”” to fill in the field “with at least one of the words”, and “where my words occur” in the field “title of article”. Two independent reviewers (NTH, TVG) initially scanned primary titles and abstracts (when available) to select potential full text articles for further scrutiny according to the inclusion and exclusion criteria. The inclusion criteria were as follows: articles with reported epidemiology, clinical signs, and laboratory parameters for dengue-infected patients with shock compared with DHF. Since genetic markers are rarely reported for DSS groups [17], [18], we did not include these markers in this study. We used broad criteria made by original studies' authors for definition of dengue infection, DSS, and DHF to increase the number of studies included in our analysis. A subgroup analysis was used to investigate the effect of this variation on the pooled results. No restrictions were applied with respect to language, gender, patient age (children or adult), or study design. Non-English reports were translated into English by authors with the help of native international students in Nagasaki University. The exclusion criteria are shown in Figure 1. 10.1371/journal.pntd.0002412.g001 Figure 1 Flow diagram of the search and review process. When the title and abstract were not rejected by either reviewer, the full text of the article was obtained via Nagasaki University Library and carefully reviewed for inclusion by the two reviewers (NTH, TVG). Inclusion or exclusion of each study was determined by discussion and consensus between the two reviewers. When disagreement occurred, a consensus decision was made following discussion with a third reviewer (DHDT). We further supplemented these searches with a manual search of articles in the WHO Dengue Bulletin, reference lists, and citation lists using the Scopus databases. For each identified factor, we performed additional factor-specific searches by adding the factor terms beside “dengue”. Data extraction Data were extracted by one of two investigators (NTH, TVG), and were checked by at least two of three reviewers (NTH, TVG, DHDT). Disagreement was resolved via discussion and a consensus reached between the three authors. A data extraction form in an Excel file was developed by two authors (NTH, TVG) based on a pilot review, extraction, and calibration of 20 randomly selected studies. The data extracted included the first author, year of publication, year of patient recruitment (the midpoint of the study's time period), study design (all case or case-control), data collection (prospective or retrospective), assignment of patients (consecutive or random), country and city of origin, hospital where the patients were recruited, characteristics of the patient population (infant, children, adult), criteria of dengue infection (confirmed or clinical diagnosis), criteria of DSS and DHF, number of included individuals (DSS and control DHF), including DF patients in the DHF group, description of blinded interpretation of factors, gender, and age at examination of included individuals. Several types of data input for factors were extracted if available and are fully described in the supplemental method (Method S1). Papers published by the same research group and studying the same factors were checked for potential duplicate data based on the year of patient recruitment and hospital where the patients were recruited. When duplications were noted, the largest data set was used for our meta-analysis. When several types of data or several methods were presented for one particular factor, we extracted all data but used the one with the least significant association (the nearest odds ratio [OR] to one) if that factor was significantly associated with DSS after meta-analysis. Otherwise, data with lowest and highest ORs were pooled separately to get minimal and maximal odd ratios, respectively. When data were available on different days of the disease course, values at day 4, 3, 5, 2, and 6 were favored in that order for analysis, because shock frequently occurs at day 4 and we emphasize the importance of the transition from DHF to DSS. Quality assessment Quality assessment was independently performed by two authors (NTH, TVG). The quality of selected studies was assessed using the combined criteria suggested by Pai et al. [19] and Wells et al. [20], because these criteria can affect the accuracy of the pooled effect size. The quality of each study included in the meta-analysis was determined across nine metrics: study design, full description of characteristic of patient population (infant, children, adult), data collection (prospective or retrospective), assignment of the patient (consecutive or random), inclusion criteria, exclusion criteria, method quality (description and same method for DSS and DHF groups), blinded interpretation of factors, and full description of dengue diagnosis. The score system is available in Table S1. Quality assessment was also performed by discussion and consensus after the independent review of each study by two authors (NTH, TVG). Meta-analyses Meta-analyses for particular factors were performed using Comprehensive Meta-analysis software version 2·0 (Biostat, NJ, USA) where there was more than one study. Dichotomous and continuous variables were analyzed to compute pooled odds ratio (OR) and standardized mean difference, respectively when there were two groups of DSS and DHF. The standardized mean difference was then converted to OR according to the method of Borenstein et al [21]. Both dichotomous and continuous variables were combined to compute pooled odds ratio (OR) as previously suggested [22] to increase the number of studies included in our analysis. Therefore, the unit of continuous variables and the cut-off values of dichotomous and continuous variables were not required in the pooling result. Higher frequency of dichotomous variables and/or higher value of continuous variables in DSS compared to DHF groups resulted in a positive association of the particular variables with DSS. The event rate was pooled for the proportion of DSS among DHF/DSS cases. Only cross-sectional studies were included and DF patients in the DHF groups were excluded for this analysis of DSS prevalence. The corresponding 95% confidence intervals (95% CI) of pooled effect size were also calculated using a fixed-effects or random-effects model with weighting of the studies [23]. A fixed-effects model with weighting of the studies was used when there was a lack of significant heterogeneity (p>0.10), while a random-effects model with weighting of the studies was used when there was heterogeneity between studies (p≤0.10) [23]. Heterogeneity between studies was evaluated using the Q statistic and I 2-test. Heterogeneity was considered statistically significant if the p-value was 25%, 50%, or 75% were considered to represent low, moderate, or high heterogeneity, respectively [25]. To study the effect of covariates including total quality score, each parameter of quality score system, area of studies, and differences in definition of factors between studies (category/continuous variables, diagnosis, and unit of measurement) on the pooled effect size and the heterogeneity across studies, meta-regression analysis and subgroup analysis of a combination one or more groups were performed where there were eight or more studies assessing a particular factor [26]. The effect of covariates on the pooled effect size was considered significant when the p-value was <0·05 or its 95%CI did not overlap with the original one. To evaluate the presence of publication bias, we performed Begg's funnel plot [27] and Egger's regression test [28], [29] when there were five or more studies assessing the association of a particular factors with DSS. Publication bias was considered significant when the p value was <0·1. If publication bias was found, the trim and fill method of Duvall and Tweedie was performed by adding studies that appeared to be missing [30], [31] to enhance the symmetry [26]. The adjusted pooled effect size and its 95% CI were computed after the addition of potential missing studies. We further performed a sensitivity analysis by removing each study from the meta-analysis to investigate the effect of each study on the association. Cumulative meta-analysis was also carried out to test the effect of a few of the largest or smaller studies on the effect size by repeatedly performing a meta-analysis each time a new study was added according to its sample size (reversed variance of logOR or log of rate of event). The p-value for multiple comparisons was not adjusted because it may increase the likelihood of type II errors [32], [33]. Instead, to reduce the false discovery rate, a confidence interval, meta-regression, subgroup analysis, sensitivity analysis, and interpretation of across studies were conducted to give complement information to p-value. Thus, statistical significance was defined as p-value was <0.05 (two-tailed test) or its 95%CI did not overlap with the original one. Analysis of factor-specific relationships Analysis of factor-specific relationships with DSS was also performed when there were three or more categories reported for a particular factor using the GraphPad Prism 5 (GraphPad Software, Inc., San Diego, CA, USA). This analysis was performed according to the dose-response relationship as previously reported [34], [35]. Briefly, the midpoint of each category for a particular factor was assigned to plot against the natural logarithm of OR (logOR) or rate of event. If no upper bound was available, we assumed it to be the same amplitude as the preceding category. When no lower bound was reported, we assigned it a value of zero. We used a mixed-models analysis [36] to test a potential nonlinear factor-specific relationship between factor and DSS by using polynomial, sine wave, and exponential regression models weighing on the sample study size. When the candidate models were nested, we used likelihood ratio tests (F-test) to examine whether the more complex model was a better fit. When comparing two non-nested models, we used the Akaike information criterion [37], which indicates twice the number of parameters of the model minus twice the maximized log-likelihood. The model with lowest Akaike information criterion value was chosen for fitting. Results Systematic review The initial screening of the databases for title and abstract yielded 5612 papers, of which 798 papers were chosen for full text reading. A total of 600 articles were excluded for one of the reasons listed in Figure 1. Finally, 198 studies were selected for final analysis with agreement between the two reviewers at 93% (Cohen's kappa = 0·810). Three selected studies separately reported data into two data sets of infants, children, and/or adult groups [38], [39], [40], while one study divided data into three data sets of infants, children, and adult groups [41]; hence a total of 203 data sets were included in the final meta-analysis. Characteristics of the included studies are outlined in Table S2. Most studies were performed in Asia (n = 182); only 12, four, and five studies were from the Caribbean, South American, and French Polynesia, respectively. More studies were prospective (n = 150) than retrospective or not mentioned (n = 53). A total of 88 studies were case-control assessments and 115 were cross-sectional studies. Eight studies included infants, 83 studies enrolled children, 22 studies recruited adults, 54 studies enrolled both infants and children, 18 studies reported both children and adults, 14 studies included all types of subject, and four studies did not provide this information. Clinical diagnosis was used as for dengue infection definition in 14 studies, while serology, PCR, and virus isolation were used for confirmation of all dengue-infected patients in 170 studies. Fourteen studies did not report the criteria for dengue infection. The classification of DSS and DHF varied across studies, but most studies used the WHO 1997 criteria (n = 168), 28 studies used the Nimmannitya criteria [42], while the other seven studies simply classified the diseases as shock versus non-shock group (Table S2). In terms of the quality of included studies, agreement between the two reviewers was 91% (Cohen's kappa = 0·808). Two studies scored the maximal points (9); the range of total points of included studies was two to nine (Table S2). A total of 242 factors were reported in at least one study. More than half of them (n = 130) were available in only one study and then were not assessed by meta-analysis, but the association with DSS derived from the original study is shown in the Table S3. There were 112 factors that were reported in two or more studies, and the results of our meta-analysis including the references of included studies for each factor are shown in Table S4. Among 112 factors, 72 factors were investigated in less than eight studies and were not interpreted here because drawing a conclusion is limited when there is very few included studies [26]. There is no clear cut off point for the minimal number of studies included in a meta-analysis to draw a conclusion. Cox et al suggest a description of individual studies is better than a meta-analysis [43]. Several studies chose eight as a cut off number for studies included in a meta-analysis to perform a regression analysis [44] and assessment of publication bias [45]. Finally, 40 factors were fully analyzed and interpreted when there were eight or more studies assessing the particular factor. Of these interpreted factors, 23 factors were found to be significantly associated with DSS (Table 1). 10.1371/journal.pntd.0002412.t001 Table 1 Meta-analysis of the association between significant factors and the risk of DSS. Variable Number of study Total sample size (DSS/DHF) Heterogeneity Model Association with DSS Egger's 2-tailed bias p-value p-value I2 p-value Odds ratio (95% CI) Largest p-value after removing any single study Gender (female)# 37 1957/4258 0·063 28 Random <0·001 1·37(1·17 - 1·60) <0·001 0·63 Age (year)† 37 2927/6400 <0·001 90 Random <0·001 0·50(0·36 - 0·70)0·27(0·17-0·42)a <0·001 0·009 Malnutrition# 9 1689/3449 0·37 8 Fixed 0·05 1·19(1·00-1·41)1·37(1·18-1·59)a 0·863 0·03 Normal nutrition# 9 1616/3398 0·26 21 Fixed 0·03 0·87(0·77-0·99) 0·20 0·43 Neurological signs# 15 859/1891 <0·001 82 Random 0·003 4·66(1·70 - 12·8) <0·01 0·42 Vomiting/Nausea# 14 839/1391 0·42 3 Fixed 0·001 1·43(1·15 - 1·78) <0·01 0·82 Abdominal pain# 17 2340/4986 0·014 48 Random <0·001 2·26(1·76 - 2·89) <0·001 0·17 Gastrointestinal bleeding# 18 786/1317 0·52 0 Fixed <0·001 1·84(1·42 - 2·39) <0·001 0·58 Hemoconcentration* 38 2847/5214 <0·001 71 Random <0·001 2·61(2·02 - 3·37) <0·001 0·54 Pleural effusion* 18 1757/3860 <0·001 77 Random <0·001 10·4(5·47 - 19·6)15·8(7·95 - 31·6)a <0·001 0·07 Ascites# 12 373/763 <0·001 76 Random <0·001 5·92(5·42 - 14·5) <0·001 0·99 Hypoalbuminemia* 13 1662/3461 <0·001 81 Random <0·001 4·34(2·51 - 7·52) <0·001 0·34 Hypoproteinemia* 8 178/276 0·021 58 Random 0·009 2·45(1·25- 4·81) <0·05 0·35 Hepatomegaly* 28 4130/8906 <0·001 84 Random <0·001 3·10(2·18 - 4·41) <0·001 0·19 ALT* 26 2772/6281 <0·001 82 Random <0·001 2·15(1·47 - 3·15) <0·001 0·21 AST* 26 2772/6281 <0·001 89 Random <0·001 2·08(1·39 - 3·12) <0·005 0·13 Thrombocytopenia (Low platelet count)* 47 2801/7172 <0·001 79 Random <0·001 2·64(1·95 - 3·59) <0·001 0·15 Prothrombin time* 15 1661/3713 <0·001 68 Random <0·001 2·83(1·84 - 4·37) <0·001 0·96 activated partial thromboplastin time (APTT)* 13 1557/3678 <0·001 93 Random <0·001 6·81(2·83 - 16·4)5·18(2·19 - 12·2)a <0·001 0·017 Fibrinogen level* 9 185/456 <0.001 83 Random <0.001 0.13(0.05- 0.35) 0.001 0.53 DENV-2# 20 1008/2240 <0·001 62 Random 0·019 1·66(1·09 - 2·55) 0·064 0·91 Primary infection# 37 1251/2696 0·67 0 Fixed <0·001 0·47(0·37 - 0·60) <0·001 0·76 Secondary infection# 40 1731/2989 <0·001 57 Random 0·001 1·75(1·26 - 2·42) <0·005 0·84 Pooled ORs with corresponding 95% CIs of the published results were calculated where more than one study had investigated the marker. * Factor was presented as both dichotomous (frequency of higher values) and continuous (higher value) variables. # Factor was presented as a dichotomous (frequency of higher values) variable. † Factor was presented as a continuous variable. a adjusted odds ratio calculated after the addition of potential missing studies using the trim and fill method of Duvall and Tweedie. Hemoconcentration was defined as an increase of hematocrit and presented as both dichotomous (frequency of higher values) and continuous (higher value) variables. Neurological signs (any signs) were defined as patients had any signs of convulsion, decreased consciousness, drowsiness, and lethargy. A particular dengue serotype infection was defined as a dichotomous variable versus infection with another DENV serotype (e.g., DENV-2 vs. non-DENV-2). Only studies investigated all four strains were included for the analysis. Prevalence of DSS among DHF/DSS In 80 published studies of cross-sectional design, our pooled results showed that the proportion of DSS among DHF/DSS was 28·5% (95% CI: 24·7 - 32·6) with high heterogeneity (p-value for heterogeneity <0·001, I2  = 95). Sub-analysis further demonstrated that DSS prevalence in adults (17·7%; 95% CI: 10·1 - 29·4; in 11 studies recruited only adults) was significantly lower than in children (37·4%; 95% CI: 29·6 - 45·9; in 26 studies recruited only children). Meta-regression analysis showed a trend in the proportion of DSS among DHF/DSS cases that gradually decreased over a period of 40 years, but the trend was not statically significant (p = 0·089, Figure 2A). However, after excluding three studies in South America, the decreased trend became significant (p = 0·040, Figure 2B). The decreased trend was significant for 49 studies in Southeast Asia (p = 0·045, Figure 2C) and particularly steep for 23 studies in Thailand (p = 0·004, Figure 2F). The reduced trend was also observed for 16 studies in South Asia but was not statistically significant (p = 0·6, Figure 2D), probably due to the small number and shorter duration of studies. The proportion of DSS among DHF/DSS was low in Caribbean countries (pooled prevalence: 19·7%; 95% CI: 15·1 - 25·4; n = 7), explaining the non-significant reduction in prevalence over a period of 25 years (p = 0·5, Figure 2E). Other covariates including quality score, sample size, area, country, study design, data collection, different inclusion criteria for DHF, different inclusion criteria for DSS, assignment of the patient (consecutive or random), blinded interpretation of factors, and confirmation of dengue diagnosis had no effect on the pooled prevalence and heterogeneity across studies in Southeast Asia and Thailand, separately. No evidence of publication bias was found using Begg's funnel plot [27] and Egger's regression test [28], [29]. 10.1371/journal.pntd.0002412.g002 Figure 2 Meta-regression analysis between the proportion of DSS among DHF/DSS cases and year of recruitment. All studies (A). Sub-regression analysis of all studies except three studies in South America (B), 49 studies in Southeast Asia (C), 16 studies in South Asia (D), seven studies in Caribbean countries (E), and 23 studies in Thailand (F). The logit event rate was calculated as follow: logit event rate = ln[event rate/(1 − event rate)]. The Y-axis on the right shows the proportion of DSS patients amongst DHF/DSS patients. Each circle represents a data set in the meta-analysis, and the size of the circle is proportional to study weighting. Gender difference Meta-analysis of 37 studies for gender difference showed a significant association with DSS (OR: 1·37, 95% CI: 1·17-1·60). Removing any study among selected studies had little effect on the pooled OR (Table 1). Cumulative meta-analysis by repeated meta-analyses each time a new study was added according to sample size demonstrated that this significant association was established without the 17 largest studies. This finding suggested a strong association between female gender and DSS. Significant heterogeneity was found among studies of females; however, removing three studies in the Caribbean area lowered the heterogeneity degree (p value for heterogeneity = 0·075, I2  = 27). Further, removing one study from Colombia made the data homologous (p value for heterogeneity = 0·489, I2  =  = 0), but did not significantly affect the summary effect size. Meta-regression and sub-analysis for several co-variables including quality of study, year of publication, year of patient recruitment, area/country of the study, study design (all case or case-control), study that included DF patients in the DHF group, data collection (prospective or retrospective), assignment of the patient (consecutive or random), confirmed diagnosis of dengue, different criteria of DSS and DHF, and characteristic of patient population (infant, children, adult) were performed to evaluate the effect of these co-variables on the summarized effect size and heterogeneity (Table S5). The homogeneity was present when pooling 16 studies in children with a positive correlation between female and DSS (OR: 1·23; 95% CI: 1·03-1·51) and five studies in adults with a significant association between female and DSS (OR: 1·32, 95% CI: 0·94-1·87). Moreover, subgroup analysis of 24 prospective studies showed an identical pooled OR 1·36 (95% CI: 1·17-1·59) with a homogenous characteristic (p value for heterogeneity = 0·175, I2  = 21). Other co-variables including quality of study, year of publication, year of patient recruitment, area/country of the study, study design (all case or case-control), study that included DF patients in the DHF group, assignment of the patient (consecutive or random), confirmed diagnosis of dengue, and different criteria of DSS and DHF did not affect the summarized effect size and the heterogeneity. No evidence of publication bias was found for female gender as a factor (Table 1). Age factor Pooled odds ratio showed that age was negatively associated with DSS (OR: 0·50, 95% CI: 0·36 - 0·70). However, pooling all studies gave high heterogeneity and publication bias (p<0·001), probably due to large variation of population age in the studies (children/adults). Adding 13 missing studies on the left to enhance the symmetry using the trim and fill method of Duvall and Tweedie (random effect) gave a stronger association with DSS (adjusted OR: 0·27, 95% CI: 0·17-0·42). Because only five and two studies investigated adults and infants, respectively, we could not analyze the age factor in these sub-groups. Pooling 26 studies of children gave a negative association with DSS (OR: 0·67, 95% CI: 0·54 - 0·84). We further investigated the average age of children in DSS and DHF groups in South East Asia. The difference in average age was slightly wider over a period of 40 years, but the trend was not statistically significant (p = 0·37, Figure 3A). There was strong evidence of increasing average age of children in both DSS and DHF groups in this area (p<0·05, Figure 3B–C), agreeing with a previous study [46]. The age increase of DHF children (slope = 0·084) was slightly faster than that of DSS (slope = 0·067). 10.1371/journal.pntd.0002412.g003 Figure 3 Meta-regression analysis between children's age and DSS association over year of recruitment in South East Asia. (A) Difference in mean age between DSS and DHF groups; (B) Average age of DSS children over year of recruitment in South East Asia; (C) Average age of DHF children over year of recruitment in South East Asia. Each circle represents a data set in the meta-analysis, and the size of the circle is proportional to study weighting. Nutritional status Upon pooling nine studies, malnutrition was positively associated with DSS (OR: 1·19, 95% CI: 1·00-1·41, Figure 4A). No evidence of heterogeneity was found for malnutrition (p = 0·37, I2  = 8), but publication bias was observed by an asymmetric funnel plot (figure 4B) and Egger's test (p = 0·03, Table 1). The definition of malnutrition differed between studies: three studies did not provide definitions [47], [48], [49]; one study used weight-for-height [50], while other five [15], [51], [52], [53], [54] used weight-for-age to assess this factor. Removing any subgroups of no definition and weight-for-height did not affect the association. Furthermore, sub-analysis of five studies using the weight-for-age also gave a positive association with DSS (OR: 1·29, 95% CI: 1·05-1·58) without any evidence of publication bias. However, removal of the largest study [15] eliminated the association of malnutrition and DSS (OR: 0·97, 95% CI: 0·76-1·25, Figure 4C) and publication bias (p = 0·12, Figure 4D). 10.1371/journal.pntd.0002412.g004 Figure 4 Association of nutritional factors and DSS. (A) Meta-analysis forest plot showing the pooled ORs of malnutrition for association of DSS with 95% CIs using fixed effect models. (B) Funnel plots of publication bias for malnutrition. Each blue circle represents each study in the meta-analysis, forming an asymmetric funnel plot with a pooled log OR (blue rhombus). Five missing studies (red symbols) were added in the right site to make the graph more symmetric and gave an adjusted log OR (red rhombus), which was higher than the original one. (C) Meta-analysis forest plot of malnutrition after removing the largest study. (D) The asymmetric funnel plot of malnutrition became symmetric after removing the largest study. (E) Sensitivity analysis by removing each study showing the pooled association of normal nutrition with DSS without any particular removed study to investigate the effect of each study on the association. Normal nutrition was inversely linked with DSS in nine studies (OR: 0·87, 95% CI: 0·77-0·99) without evidence of heterogeneity (p = 0·26, I2  = 21) or publication bias (p = 0·43). No report significantly demonstrated a positive association of this factor with DSS, while one study showed a negative association with DSS [50]. Two studies did not give definition of normal nutrition [47], [49]; two study used weight-for-height [50], [55], while other five [15], [51], [52], [53], [54] used weight-for-age to assess this factor. Removing the subgroup of no definition [47], [49] gave a significant association with DSS (OR: 0·86, 95% CI: 0·76-0·98) with very low heterogeneity (p = 0·42, I2  = 0) and without publication bias (p = 0·83). However, thought sub-analysis of five studies using the weight-for-age still gave an OR <1 but the statistical significant association was lost (OR: 0·92, 95% CI: 0·80-1·05). Furthermore, a sensitivity analysis showed that removing any of three studies of Junia et al [50], Kalayanarooj et al [15], or Pham et al [55] resulted in a loss of statistical association but the ORs were less than one (0·05
              Bookmark
              • Record: found
              • Abstract: found
              • Article: found
              Is Open Access

              Liver Involvement Associated with Dengue Infection in Adults in Vietnam

              Globally, the number of adults hospitalized with dengue has increased markedly in recent years. It has been suggested that hepatic dysfunction is more significant in this group than among children. We describe the spectrum and evolution of disease manifestations among 644 adults with dengue who were prospectively recruited on admission to a major infectious disease hospital in southern Vietnam and compare them with a group of patients with similar illnesses not caused by dengue. Transaminase levels increased in virtually all dengue patients and correlated with other markers of disease severity. However, peak enzyme values usually occurred later than other complications. Clinically severe liver involvement was infrequent and idiosyncratic, but usually resulted in severe bleeding. Chronic co-infection with hepatitis B was associated with modestly but significantly increased levels of alanine aminotransferase, but did not otherwise impact the clinical picture.
                Bookmark

                Author and article information

                Contributors
                suhendro@ui.ac.id
                Journal
                BMC Infect Dis
                BMC Infect. Dis
                BMC Infectious Diseases
                BioMed Central (London )
                1471-2334
                23 February 2018
                23 February 2018
                2018
                : 18
                : 90
                Affiliations
                [1 ]Tropical and Infectious Diseases Consultant, Pondok Indah Hospital, Jakarta, Indonesia
                [2 ]ISNI 0000000120191471, GRID grid.9581.5, Division of Tropical and Infectious Diseases, Department of Internal Medicine, , Faculty of Medicine Universitas Indonesia, Cipto Mangunkusumo National Hospital, ; Jakarta, Indonesia
                [3 ]Department of Radiology, Pondok Indah Hospital, Jakarta, Indonesia
                [4 ]Department of Clinical Pathology, Pondok Indah Hospital, Jakarta, Indonesia
                Author information
                http://orcid.org/0000-0001-6394-0591
                Article
                2996
                10.1186/s12879-018-2996-x
                5824608
                29471786
                46ed0d6a-e71d-43ef-b4e0-ddfb0c417fa1
                © The Author(s). 2018

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 21 March 2017
                : 15 February 2018
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2018

                Infectious disease & Microbiology
                clinical application,dengue score,external validation
                Infectious disease & Microbiology
                clinical application, dengue score, external validation

                Comments

                Comment on this article