Diagnostic accuracy and predictive value in differentiating the severity of dengue infection

Development and Validation of a Prediction Score for Critical Admission in Children With Dengue

OBJECTIVES

This study aimed to develop and validate a clinical score for the prediction of critical care entrance in children with dengue.

METHODS

We conducted a retrospective cohort study using admissions from January 2019 to August 2021, at Hospital Infantil Napoleón Franco Pareja, in Cartagena, Colombia. We included all children 18 years or younger, with a positive immunoglobulin M or nonstructural protein 1 laboratory test and admitted for follow-up at the emergency department. We selected variables retrospectively collected on emergency admission for feature selection. We assessed discrimination and calibration in the development dataset, using 1000 bootstrap replications for internal validation. Data from 2019 to 2020 were used for development and 2021 for temporal validation. We report the c -statistic for discrimination with 95% confidence intervals (CIs), as well as the calibration intercept and slope.

RESULTS

One thousand three hundred eighty-five patients were included for development and internal validation. In temporal validation with 519 additional patients, the c -statistic was 0.82 (95% CI: 0.77-0.87), with a calibration slope of 0.98 (95% CI: 0.77-1.18). We selected the 50 th percentile of the distribution of predicted probability of critical care entrance (5%) as a threshold value for increased alert at emergency admission, missing 10% of all cases that need to enter critical care (sensitivity of 90% with 95% CI of 82-95, and specificity of 48% with 95% CI of 41-50).

CONCLUSIONS

Our validated model can be useful to predict critical care entrance in children with dengue. We recommend the validation and potential recalibration of our score in other clinical settings.

A predictive score for severity in patients with confirmed dengue fever in a tertiary care hospital in Kerala, India

BACKGROUND

The study aimed to identify predictors of severe dengue during the 2017 epidemic and to develop and validate a simple predictive score for severity.

METHODS

A retrospective analytical study was conducted using clinical and laboratory data from adult dengue patients with a confirmed microbiological diagnosis. The study included patients who presented to a tertiary care centre in Kerala, India, during the febrile phase (≤4 d) between June 2017 and February 2019. Using appropriate statistical tests, we derived predictors of severe disease and computed a risk score model.

RESULTS

Of the 153 patients (mean age 50±17 y; 64% males), 31 (20%) had severe dengue and 4 (3%) died. Petechial lesions, hypoalbuminemia (<3.5 g/dl), elevated alanine aminotransferase (>40 IU/l) and urea >40 IU/l were significant predictors. Our scoring system (cut-off: 2) showed excellent performance, with an area under the receiver operating characteristics curve of 0.9741, sensitivity of 100%, specificity of 96% and accuracy of 98%. The risk score was secondarily validated on 48 patients hospitalized from March 2019 to June 2019.

CONCLUSION

Our scoring system is easy to implement and will help primary healthcare practitioners in promptly identifying severe dengue cases upon hospital presentation.

Improving dengue diagnosis and case confirmation in children by combining rapid diagnostic tests, clinical, and laboratory variables

Background

Dengue is the most widely distributed arboviral disease in tropical and subtropical countries. Most suspected cases are diagnosed according to the clinical criteria, and early diagnosis is difficult. Moreover, in underdeveloped countries, several factors continue to challenge the diagnosis and surveillance of dengue cases. This study aimed to design a diagnostic algorithm using rapid diagnostic tests (RDTs), ELISA tests, and clinical and hematological variables to confirm dengue cases in febrile patients in Colombia.

Methods

Altogether, 505 samples were collected. Serum samples were evaluated by RDTs (IgM and IgG antibodies and NS1 antigen), capture IgM and IgG ELISAs, and endpoint hemi-nested RT-PCR assay (qualitative). We statistically analyzed the performance of individual tests to determine the most useful ones to confirm dengue cases accurately.

Results

Individual results for IgM, IgG, and NS1 RDTs yielded lower sensitivity and specificity values than the reference standard. High sensitivity and specificity were obtained after combining IgM and NS1 ELISA results (96.3% and 96.4%) and NS1 RDT plus IgM ELISA results (90.3% and 96.2%), respectively. Adjusted odds ratios (aORs) were calculated for clinical variables and laboratory tests to differentiate dengue from other febrile illnesses (OFI). This approach showed that myalgia, abdominal tenderness, and platelet count were identified with higher sensitivity to confirm dengue cases. IgM RDT and NS1 RDT differentiated dengue cases from OFI. A positive IgM RDT or a positive NS1 RDT combined with specific signs or symptoms confirmed 81.6% of dengue cases. A combination of clinical findings and a positive NS1 RDT or positive ELISA IgM confirmed 90.6% of the cases.

Conclusion

Our findings showed that clinical diagnoses in pediatric population alone cannot confirm true dengue cases and needs to be complemented by laboratory diagnostic tests. We also demonstrate the usefulness of combining clinical criteria with RDTs, suggesting that their implementation with the IgM ELISA test improves dengue case confirmation.

Vector-borne diseases in pregnancy

Vector-borne infections cause a significant proportion of world-wide morbidity and mortality and many are increasing in incidence. This is due to a combination of factors, primarily environmental change, encroachment of human habitats from urban to peri-urban areas and rural to previously uninhabited areas, persistence of poverty, malnutrition and resource limitation in geographical areas where these diseases are endemic. Pregnant women represent the single largest ‘at risk’ group, due to immune-modulation and a unique physiological state. Many of these diseases have not benefitted from the same level of drug development as other infectious and medical domains, a factor attributing to the ‘neglected tropical disease’ title many vector-borne diseases hold. Pregnancy compounds this issue as data for safety and efficacy for many drugs is practically non-existent, precluding exposure in pregnancy to many first-line therapeutic agents for ‘fear of the unknown’ or overstated adverse pregnancy-foetal outcomes. In this review, major vector-borne diseases, their impact on pregnancy outcomes, current treatment, vaccination and short-comings of current medical practice for pregnant women will be discussed.