Plasma leakage in dengue: a systematic review of prospective observational studies

Abstract

Plasma leakage is a precursor to life-threatening complications of dengue, but this group is poorly defined and not often reported in literature. Patients with Dengue haemorrhagic fever (DHF) as defined in the 1997 World Health Organization classification are often reported, and they all have plasma leakage, but some patients with plasma leakage do not meet the definition of DHF. The study aims to estimate the frequency of plasma leakage and DHF (as a surrogate of plasma leakage) in dengue and its variations based on virus serotype, geography, patient gender and pre-existing immunity to dengue. PUBMED, Scopus, EMBASE, CINAHL and Web of Science were searched for prospective observational studies reporting on plasma leakage or DHF. Quality of data was assessed using the NIH quality assessment tool for cohort studies. Forty-three studies that recruited 15,794 confirmed dengue patients were eligible. Cumulative frequency of plasma leakage was 36.8% (15 studies, 1642/4462, 95% CI 35.4–38.2%), but surprisingly the estimated cumulative frequency of DHF was higher (45.7%, 32 studies, 4758/10417, 95% CI 44.7–46.6%), indicating that current medical literature over-reports DHF or under-reports plasma leakage. Therefore, a reliable estimate for the proportion of dengue patients developing plasma leakage cannot be derived from existing medical literature even after applying rigorous inclusion criteria to select homogenous studies. Plasma leakage is an important marker of “at-risk” dengue patients and standardizing its definition, diagnosis and reporting should be a priority in research and global policy.

Demographic characteristics, clinical symptoms, biochemical markers and probability of occurrence of severe dengue: A multicenter hospital-based study in Bangladesh

“…In addition, the definition and development of criteria for diagnosing plasma leakage have long been neglected, resulting in challenging and underreporting of plasma leakage. It is recommended that standardization of diagnosis and reporting of plasma leakage should be a research priority for dengue [ 31 ]. Moreover, we found that patients with hemorrhage were more likely to develop severe dengue than those without hemorrhage.…”

Development of a machine learning model for early prediction of plasma leakage in suspected dengue patients

“…There were no deaths in our cohorts to replicate these findings. Death is a rare outcome in dengue (<1%), even amongst hospitalised patients who have more severe disease [ 6 ]. Other studies used ML to predict adverse outcomes in dengue using genomics and transcriptomics data [ 39 , 40 ], the findings however are of limited value to clinicians managing patients for bed-side risk prediction.…”

“…Considering all these factors we selected plasma leakage as the optimal outcome for risk prediction since it is a single criterion that can be objectively measured, and complications of dengue mostly (except for rare instances of abnormal bleeding) occur in patients who have plasma leakage. Given the dependence on WHO based classifications most dengue studies do not report incidence plasma leakage as a stand-alone figure but estimates from available literature suggests that 36.8% (95% CI: 35.4–38.2%) of all dengue patients have this outcome [ 6 ]. Thus, predicting this subgroup accurately can be a cost saver by reducing duration of hospital admissions.…”

“…The estimated portion of subgroup of dengue patients who develop plasma leakage varies between 37–46% of total infections. Identifying this group will capture almost all of the patients at risk of life-threatening complications for monitoring purposes [ 6 ]. Plasma leakage is typically observed after day four of fever and therefore a system aiming to predict this outcome should ideally only consider data obtained up to that point.…”

“…However, these examples have either included laboratory generated data that are not typically available to clinicians in resource limited settings in real-time (e.g., anti-dengue IgG/IgM, viral load, host genetic polymorphisms, single cell RNAseq data [ 13 ]), or used an outcome such as “severe dengue” which has limited clinical value because of its subjective definition and low event rate [ 12 , 14 ]. While prediction of the severity of dengue infection has been relatively well documented in either statistical or ML approaches [ 3 , 4 , 12 – 17 ], prediction of plasma leakage in comparison, is not [ 6 ]. This study addresses the prediction of plasma leakage where the data and the study design are strictly limited to circumstances which on-the-ground clinicians can observe and implement in real-time.…”