Improving Dengue Diagnostics and Management Through Innovative Technology

Mapping patient pathways and understanding clinical decision-making in dengue management to inform the development of digital health tools

BACKGROUND

Dengue is a common viral illness and severe disease results in life-threatening complications. Healthcare services in low- and middle-income countries treat the majority of dengue cases worldwide. However, the clinical decision-making processes which result in effective treatment are poorly characterised within this setting. In order to improve clinical care through interventions relating to digital clinical decision-support systems (CDSS), we set out to establish a framework for clinical decision-making in dengue management to inform implementation.

METHODS

We utilised process mapping and task analysis methods to characterise existing dengue management at the Hospital for Tropical Diseases, Ho Chi Minh City, Vietnam. This is a tertiary referral hospital which manages approximately 30,000 patients with dengue each year, accepting referrals from Ho Chi Minh city and the surrounding catchment area. Initial findings were expanded through semi-structured interviews with clinicians in order to understand clinical reasoning and cognitive factors in detail. A grounded theory was used for coding and emergent themes were developed through iterative discussions with clinician-researchers.

RESULTS

Key clinical decision-making points were identified: (i) at the initial patient evaluation for dengue diagnosis to decide on hospital admission and the provision of fluid/blood product therapy, (ii) in those patients who develop severe disease or other complications, (iii) at the point of recurrent shock in balancing the need for fluid therapy with complications of volume overload. From interviews the following themes were identified: prioritising clinical diagnosis and evaluation over existing diagnostics, the role of dengue guidelines published by the Ministry of Health, the impact of seasonality and caseload on decision-making strategies, and the potential role of digital decision-support and disease scoring tools.

CONCLUSIONS

The study highlights the contemporary priorities in delivering clinical care to patients with dengue in an endemic setting. Key decision-making processes and the sources of information that were of the greatest utility were identified. These findings serve as a foundation for future clinical interventions and improvements in healthcare. Understanding the decision-making process in greater detail also allows for development and implementation of CDSS which are suited to the local context.

A performance comparison between fluorescent immunoassay and immunochromatography for rapid dengue detection in clinical specimens

Dengue virus (DENV 1-4) infection has been a global health threat where no specific treatment is currently available. Therefore, a rapid and accurate diagnosis is critical for an appropriate management as it could reduce the burden of severe clinical manifestation. Currently, dengue immunochromatography (IC) is commonly used to primarily differentiate acute febrile illnesses. Fluorescent immunoassay (FIA) utilized a highly sensitive detection system and claimed 70-100% sensitivity and 83.5-91.7% specificity for dengue infection in a preliminary report. This report recruited samples with acute febrile illnesses sent for dengue screening and tested IC and FIA in parallel. The performance of both tests was verified by a definitive diagnosis retrieved from combinatorial reverse transcription-quantitative polymerase chain reaction and enzyme-linked immunosorbent assay (ELISA) for IgM and IgG confirmation tests. Results showed that the viral nonstructural protein (NS1) performance of FIA was slightly higher than IC with the sensitivity, specificity, PPV, NPV, agreement, kappa, and its standard error at 79.11, 92.28, 86.81, 87.31, 352 (87.13%), 0.725 ± 0.035, respectively; whereas those of the IC were at 76.58, 92.28, 86.43, 85.98, 348 (86.14%), 0.703 ± 0.037, respectively. Moreover, the IgM and IgG performance of FIA had higher specificity, PPV, and agreement than the IgM IC performance, suggesting that the FIA was more specific but less sensitive for antibody detection. No correlation was observed in IgM and IgG levels of ELISA and FIA assays. In conclusion, the FIA and IC were highly sensitive, specific, and substantially agreed in NS1 detection but moderately agreed in IgM and IgG detection.

Quantitative detection of dengue serotypes using a smartphone-connected handheld lab-on-chip platform

Dengue is one of the most prevalent infectious diseases in the world. Rapid, accurate and scalable diagnostics are key to patient management and epidemiological surveillance of the dengue virus (DENV), however current technologies do not match required clinical sensitivity and specificity or rely on large laboratory equipment. In this work, we report the translation of our smartphone-connected handheld Lab-on-Chip (LoC) platform for the quantitative detection of two dengue serotypes. At its core, the approach relies on the combination of Complementary Metal-Oxide-Semiconductor (CMOS) microchip technology to integrate an array of 78 × 56 potentiometric sensors, and a label-free reverse-transcriptase loop mediated isothermal amplification (RT-LAMP) assay. The platform communicates to a smartphone app which synchronises results in real time with a secure cloud server hosted by Amazon Web Services (AWS) for epidemiological surveillance. The assay on our LoC platform (RT-eLAMP) was shown to match performance on a gold-standard fluorescence-based real-time instrument (RT-qLAMP) with synthetic DENV-1 and DENV-2 RNA and extracted RNA from 9 DENV-2 clinical isolates, achieving quantitative detection in under 15 min. To validate the portability of the platform and the geo-tagging capabilities, we led our study in the laboratories at Imperial College London, UK, and Kaohsiung Medical Hospital, Taiwan. This approach carries high potential for application in low resource settings at the point of care (PoC).

The compensatory reserve index predicts recurrent shock in patients with severe dengue

BACKGROUND

Dengue shock syndrome (DSS) is one of the major clinical phenotypes of severe dengue. It is defined by significant plasma leak, leading to intravascular volume depletion and eventually cardiovascular collapse. The compensatory reserve Index (CRI) is a new physiological parameter, derived from feature analysis of the pulse arterial waveform that tracks real-time changes in central volume. We investigated the utility of CRI to predict recurrent shock in severe dengue patients admitted to the ICU.

METHODS

We performed a prospective observational study in the pediatric and adult intensive care units at the Hospital for Tropical Diseases, Ho Chi Minh City, Vietnam. Patients were monitored with hourly clinical parameters and vital signs, in addition to continuous recording of the arterial waveform using pulse oximetry. The waveform data was wirelessly transmitted to a laptop where it was synchronized with the patient's clinical data.

RESULTS

One hundred three patients with suspected severe dengue were recruited to this study. Sixty-three patients had the minimum required dataset for analysis. Median age was 11 years (IQR 8-14 years). CRI had a negative correlation with heart rate and moderate negative association with blood pressure. CRI was found to predict recurrent shock within 12 h of being measured (OR 2.24, 95% CI 1.54-3.26), P < 0.001). The median duration from CRI measurement to the first recurrent shock was 5.4 h (IQR 2.9-6.8). A CRI cutoff of 0.4 provided the best combination of sensitivity and specificity for predicting recurrent shock (0.66 [95% CI 0.47-0.85] and 0.86 [95% CI 0.80-0.92] respectively).

CONCLUSION

CRI is a useful non-invasive method for monitoring intravascular volume status in patients with severe dengue.

The Diagnosis of Dengue in Patients Presenting With Acute Febrile Illness Using Supervised Machine Learning and Impact of Seasonality

Background

Symptomatic dengue infection can result in a life-threatening shock syndrome and timely diagnosis is essential. Point-of-care tests for non-structural protein 1 and IgM are used widely but performance can be limited. We developed a supervised machine learning model to predict whether patients with acute febrile illnesses had a diagnosis of dengue or other febrile illnesses (OFI). The impact of seasonality on model performance over time was examined.

Methods

We analysed data from a prospective observational clinical study in Vietnam. Enrolled patients presented with an acute febrile illness of <72 h duration. A gradient boosting model (XGBoost) was used to predict final diagnosis using age, sex, haematocrit, platelet, white cell, and lymphocyte count collected on enrolment. Data was randomly split 80/20% into a training and hold-out set, respectively, with the latter not used in model development. Cross-validation and hold out set testing was used, with performance over time evaluated through a rolling window approach.

Results

We included 8,100 patients recruited between 16th October 2010 and 10th December 2014. In total 2,240 (27.7%) patients were diagnosed with dengue infection. The optimised model from training data had an overall median area under the receiver operator curve (AUROC) of 0.86 (interquartile range 0.84-0.86), specificity of 0.92, sensitivity of 0.56, positive predictive value of 0.73, negative predictive value (NPV) of 0.84, and Brier score of 0.13 in predicting the final diagnosis, with similar performances in hold-out set testing (AUROC of 0.86). Model performances varied significantly over time as a function of seasonality and other factors. Incorporation of a dynamic threshold which continuously learns from recent cases resulted in a more consistent performance throughout the year (NPV >90%).

Conclusion

Supervised machine learning models are able to discriminate between dengue and OFI diagnoses in patients presenting with an early undifferentiated febrile illness. These models could be of clinical utility in supporting healthcare decision-making and provide passive surveillance across dengue endemic regions. Effects of seasonality and changing disease prevalence must however be taken into account-this is of significant importance given unpredictable effects of human-induced climate change and the impact on health.

Novel Clinical Monitoring Approaches for Reemergence of Diphtheria Myocarditis, Vietnam

Diphtheria is a life-threatening, vaccine-preventable disease caused by toxigenic Corynebacterium bacterial species that continues to cause substantial disease and death worldwide, particularly in vulnerable populations. Further outbreaks of vaccine-preventable diseases are forecast because of health service disruptions caused by the coronavirus disease pandemic. Diphtheria causes a spectrum of clinical disease, ranging from cutaneous forms to severe respiratory infections with systemic complications, including cardiac and neurologic. In this synopsis, we describe a case of oropharyngeal diphtheria in a 7-year-old boy in Vietnam who experienced severe myocarditis complications. We also review the cardiac complications of diphtheria and discuss how noninvasive bedside imaging technologies to monitor myocardial function and hemodynamic parameters can help improve the management of this neglected infectious disease.

A 3D Microfluidic ELISA for the Detection of Severe Dengue: Sensitivity Improvement and Vroman Effect Amelioration by EDC-NHS Surface Modification

Serum is commonly used as a specimen in immunoassays but the presence of heterophilic antibodies can potentially interfere with the test results. Previously, we have developed a microfluidic device called: 3D Stack for enzyme-linked immunosorbent assay (ELISA). However, its evaluation was limited to detection from a single protein solution. Here, we investigated the sensitivity of the 3D Stack in detecting a severe dengue biomarker—soluble CD163 (sCD163)—within the serum matrix. To determine potential interactions with serum matrix, a spike-and-recovery assay was performed, using 3D Stacks with and without surface modification by an EDC–NHS (N-ethyl-N′-(3-(dimethylamino)propyl)carbodiimide/N-hydroxysuccinimide) coupling. Without surface modification, a reduced analyte recovery in proportion to serum concentration was observed because of the Vroman effect, which resulted in competitive displacement of coated capture antibodies by serum proteins with stronger binding affinities. However, EDC–NHS coupling prevented antibody desorption and improved the sensitivity. Subsequent comparison of sCD163 detection using a 3D Stack with EDC–NHS coupling and conventional ELISA in dengue patients’ sera revealed a high correlation (R = 0.9298, p < 0.0001) between the two detection platforms. Bland–Altman analysis further revealed insignificant systematic error between the mean differences of the two methods. These data suggest the potentials of the 3D Stack for further development as a detection platform.

Risk predictors of progression to severe disease during the febrile phase of dengue: a systematic review and meta-analysis

BACKGROUND

The ability to accurately predict early progression of dengue to severe disease is crucial for patient triage and clinical management. Previous systematic reviews and meta-analyses have found significant heterogeneity in predictors of severe disease due to large variation in these factors during the time course of the illness. We aimed to identify factors associated with progression to severe dengue disease that are detectable specifically in the febrile phase.

METHODS

We did a systematic review and meta-analysis to identify predictors identifiable during the febrile phase associated with progression to severe disease defined according to WHO criteria. Eight medical databases were searched for studies published from Jan 1, 1997, to Jan 31, 2020. Original clinical studies in English assessing the association of factors detected during the febrile phase with progression to severe dengue were selected and assessed by three reviewers, with discrepancies resolved by consensus. Meta-analyses were done using random-effects models to estimate pooled effect sizes. Only predictors reported in at least four studies were included in the meta-analyses. Heterogeneity was assessed using the Cochrane Q and I2 statistics, and publication bias was assessed by Egger's test. We did subgroup analyses of studies with children and adults. The study is registered with PROSPERO, CRD42018093363.

FINDINGS

Of 6643 studies identified, 150 articles were included in the systematic review, and 122 articles comprising 25 potential predictors were included in the meta-analyses. Female patients had a higher risk of severe dengue than male patients in the main analysis (2674 [16·2%] of 16 481 vs 3052 [10·5%] of 29 142; odds ratio [OR] 1·13 [95% CI 1·01-1·26) but not in the subgroup analysis of studies with children. Pre-existing comorbidities associated with severe disease were diabetes (135 [31·3%] of 431 with vs 868 [16·0%] of 5421 without; crude OR 4·38 [2·58-7·43]), hypertension (240 [35·0%] of 685 vs 763 [20·6%] of 3695; 2·19 [1·36-3·53]), renal disease (44 [45·8%] of 96 vs 271 [16·0%] of 1690; 4·67 [2·21-9·88]), and cardiovascular disease (nine [23·1%] of 39 vs 155 [8·6%] of 1793; 2·79 [1·04-7·50]). Clinical features during the febrile phase associated with progression to severe disease were vomiting (329 [13·5%] of 2432 with vs 258 [6·8%] of 3797 without; 2·25 [1·87-2·71]), abdominal pain and tenderness (321 [17·7%] of 1814 vs 435 [8·1%] of 5357; 1·92 [1·35-2·74]), spontaneous or mucosal bleeding (147 [17·9%] of 822 vs 676 [10·8%] of 6235; 1·57 [1·13-2·19]), and the presence of clinical fluid accumulation (40 [42·1%] of 95 vs 212 [14·9%] of 1425; 4·61 [2·29-9·26]). During the first 4 days of illness, platelet count was lower (standardised mean difference -0·34 [95% CI -0·54 to -0·15]), serum albumin was lower (-0·5 [-0·86 to -0·15]), and aminotransferase concentrations were higher (aspartate aminotransferase [AST] 1·06 [0·54 to 1·57] and alanine aminotransferase [ALT] 0·73 [0·36 to 1·09]) among individuals who progressed to severe disease. Dengue virus serotype 2 was associated with severe disease in children. Secondary infections (vs primary infections) were also associated with severe disease (1682 [11·8%] of 14 252 with vs 507 [5·2%] of 9660 without; OR 2·26 [95% CI 1·65-3·09]). Although the included studies had a moderate to high risk of bias in terms of study confounding, the risk of bias was low to moderate in other domains. Heterogeneity of the pooled results varied from low to high on different factors.

INTERPRETATION

This analysis supports monitoring of the warning signs described in the 2009 WHO guidelines on dengue. In addition, testing for infecting serotype and monitoring platelet count and serum albumin, AST, and ALT concentrations during the febrile phase of illness could improve the early prediction of severe dengue.

FUNDING

Wellcome Trust, National Institute for Health Research, Collaborative Project to Increase Production of Rural Doctors, and Royal Thai Government.

Combination of inflammatory and vascular markers in the febrile phase of dengue is associated with more severe outcomes

Background

Early identification of severe dengue patients is important regarding patient management and resource allocation. We investigated the association of 10 biomarkers (VCAM-1, SDC-1, Ang-2, IL-8, IP-10, IL-1RA, sCD163, sTREM-1, ferritin, CRP) with the development of severe/moderate dengue (S/MD).

Methods

We performed a nested case-control study from a multi-country study. A total of 281 S/MD and 556 uncomplicated dengue cases were included.

Results

On days 1-3 from symptom onset, higher levels of any biomarker increased the risk of developing S/MD. When assessing together, SDC-1 and IL-1RA were stable, while IP-10 changed the association from positive to negative; others showed weaker associations. The best combinations associated with S/MD comprised IL-1RA, Ang-2, IL-8, ferritin, IP-10, and SDC-1 for children, and SDC-1, IL-8, ferritin, sTREM-1, IL-1RA, IP-10, and sCD163 for adults.

Conclusions

Our findings assist the development of biomarker panels for clinical use and could improve triage and risk prediction in dengue patients.

Funding

This study was supported by the EU's Seventh Framework Programme (FP7-281803 IDAMS), the WHO, and the Bill and Melinda Gates Foundation.

Arbo-Score: A Rapid Score for Early Identification of Patients with Imported Arbovirosis Caused by Dengue, Chikungunya and Zika Virus

BACKGROUND

Chikungunya (CHIKV), Dengue (DENV), and Zika (ZIKV) viruses present significant clinical and epidemiological overlap, making an accurate and rapid diagnosis challenging. Timely activation of preventive vector control measures is crucial to avoid outbreaks in non-endemic settings. Diagnosis is based on combination of serological and molecular assays which could be time consuming and sometimes disappointing.

METHODS

We report the results of a retrospective case-control study carried out at a tertiary teaching hospital in Italy, including all febrile subjects returning from tropical countries during the period 2014-2019. Controls were travelers with other febrile illnesses who tested negative in laboratory analysis for CHIKV, DENV, ZIKV arbovirosis. A score weighted on the regression coefficients for the independent predictors was generated.

RESULTS

Ninety patients were identified: 34 cases (22 DENV, 4 CHIKV, and 8 ZIKV) and 56 controls. According to our results, myalgia, cutaneous rash, absence of respiratory symptoms, leukopenia, and hypertransaminasemia showed the strongest association with arbovirosis. Combining these variables, we generated a scoring model that showed an excellent performance (AUC 0.93). The best cut-off (>=2) presented a sensitivity of 82.35% and specificity of 96.43%.

CONCLUSION

A handy and simple score, based on three clinical data (myalgia, cutaneous rash and absence of respiratory symptoms) and two laboratory results (leukopenia and hypertransaminasemia), provides a useful tool to help diagnose arboviral infections and appropriately activate vector control measures in order to avoid local transmission.

Development and Performance of Dengue Diagnostic Clinical Algorithms in Colombia

Diagnosing dengue in endemic areas remains problematic because of the low specificity of the symptoms and lack of accurate diagnostic tests. This study aimed to develop and prospectively validate, under routine care, dengue diagnostic clinical algorithms. The study was carried out in two phases. First, diagnostic algorithms were developed using a database of 1,130 dengue and 918 non-dengue patients, expert opinion, and literature review. Algorithms with > 70% sensitivity were prospectively validated in a single-group quasi-experimental trial with an adaptive Bayesian design. In the first phase, the algorithms that were developed with the continuous Bayes formula and included leukocytes and platelet counts, in addition to selected signs and symptoms, showed the highest sensitivities (> 80%). In the second phase, the algorithms were applied on admission to 1,039 consecutive febrile subjects in three endemic areas in Colombia of whom 25 were laboratory-confirmed dengue, 307 non-dengue, 514 probable dengue, and 193 undetermined. Including parameters of the hemogram consistently improved specificity without affecting sensitivity. In the final analysis, considering only confirmed dengue and non-dengue cases, an algorithm with a sensitivity and specificity of 65.4% (95% credibility interval 50-83) and 40.1% (34.7-45.7) was identified. All tested algorithms had likelihood ratios close to 1, and hence, they are not useful to confirm or rule out dengue in endemic areas. The findings support the use of hemograms to aid dengue diagnosis and highlight the challenges of clinical diagnosis of dengue.

Continuous physiological monitoring using wearable technology to inform individual management of infectious diseases, public health and outbreak responses

Optimal management of infectious diseases is guided by up-to-date information at the individual and public health levels. For infections of global importance, including emerging pandemics such as COVID-19 or prevalent endemic diseases such as dengue, identifying patients at risk of severe disease and clinical deterioration can be challenging, considering that the majority present with a mild illness. In our article, we describe the use of wearable technology for continuous physiological monitoring in healthcare settings. Deployment of wearables in hospital settings for the management of infectious diseases, or in the community to support syndromic surveillance during outbreaks, could provide significant, cost-effective advantages and improve healthcare delivery. We highlight a range of promising technologies employed by wearable devices and discuss the technical and ethical issues relating to implementation in the clinic, focusing on low- and middle- income countries. Finally, we propose a set of essential criteria for the rollout of wearable technology for clinical use.

Inapparent dengue in a community living among dengue-positive Aedes mosquitoes and in a hospital in Klang Valley, Malaysia

The public health burden of dengue is most likely under reported. Current dengue control measures only considered symptomatic dengue transmission. Hence, there is a paucity of information on the epidemiology of inapparent dengue. This study reports that many people have been unknowingly exposed to dengue infection. Almost 10% and 70% of individuals without any history of dengue infection and living in a dengue hotspot, in Selangor, Malaysia, were dengue IgM and IgG positive respectively. When dengue-positive mosquitoes were detected in the hotspot, 11 (6.3%) of the 174 individuals tested were found to have dengue viremia, of which 10 were asymptomatic. Besides, upon detection of a dengue-infected mosquito, transmission was already widespread. In a clinical setting, it appears that people living with dengue patients have been exposed to dengue, whether asymptomatic or symptomatic. They can either have circulating viral RNA and/or presence of NS1 antigen. It is also possible that they are dengue seropositive. Collectively, the results indicate that actions taken to control dengue transmission after the first report of dengue cases may be already too late. The current study also revealed challenges in diagnosing clinically inapparent dengue in hyperendemic settings. There is no one best method for diagnosing inapparent dengue. This study demonstrates empirical evidence of inapparent dengue in different settings. Early dengue surveillance in the mosquito population and active serological/virological surveillance in humans can go hand in hand. More studies are required to investigate the epidemiology, seroprevalence, diagnostics, and control of inapparent dengue. It is also crucial to educate the public, health staff and medical professionals on asymptomatic dengue and to propagate awareness, which is important for controlling transmission.

Framework for DNA Quantification and Outlier Detection Using Multidimensional Standard Curves

Real-time PCR is a highly sensitive and powerful technology for the quantification of DNA and has become the method of choice in microbiology, bioengineering, and molecular biology. Currently, the analysis of real-time PCR data is hampered by only considering a single feature of the amplification profile to generate a standard curve. The current “gold standard” is the cycle-threshold (C_t) method which is known to provide poor quantification under inconsistent reaction efficiencies. Multiple single-feature methods have been developed to overcome the limitations of the C_t method; however, there is an unexplored area of combining multiple features in order to benefit from their joint information. Here, we propose a novel framework that combines existing standard curve methods into a multidimensional standard curve. This is achieved by considering multiple features together such that each amplification curve is viewed as a point in a multidimensional space. Contrary to only considering a single-feature, in the multidimensional space, data points do not fall exactly on the standard curve, which enables a similarity measure between amplification curves based on distances between data points. We show that this framework expands the capabilities of standard curves in order to optimize quantification performance, provide a measure of how suitable an amplification curve is for a standard, and thus automatically detect outliers and increase the reliability of quantification. Our aim is to provide an affordable solution to enhance existing diagnostic settings through maximizing the amount of information extracted from conventional instruments.

Evaluation of a new rapid fluorescence immunoassay for the diagnosis of dengue and Zika virus infection

BACKGROUND

Dengue (DENV) and Zika virus (ZIKV) are important mosquito-transmitted viruses.

OBJECTIVES

To investigate the performance of Standard F, Fluorescence Immunoassay (FIA, SD Biosensor Inc., Suwon, South Korea) providing results in 15 min to detect DENV IgG, IgM and NS1Ag, and ZIKV IgG, IgM, and Ag.

STUDY DESIGN

A well-characterized panel of patient samples (11 acute DENV, 11 acute ZIKV, 10 past DENV, 10 past ZIKV infection, 36 with other conditions) were tested with the FIA test.

RESULTS

In acute DENV infection, the combination of FIA-NS1Ag and/or IgM positivity showed a sensitivity of 100%. In past DENV, FIA-IgG test showed a sensitivity of 70%. Specificity of FIA-DENV NS1Ag, IgG, and IgM was 87.5%, 83.5%, and 91.7%, respectively. The sensitivity of FIA-ZIKV IgM and FIA-ZIKV Ag, in confirmed acute infection, was 72.7% and 9.1%, respectively. FIA-ZIKV Ag did not improve the sensitivity in detecting acute ZIKV infection, being positive only in one IgM positive sample. In past ZIKV infection (32-183 days after symptom onset), FIA-ZIKV IgG and IgM showed a sensitivity of 40% and 80% respectively, generating an overall 90% sensitivity. Specificity of FIA-ZIKV Ag, IgM, and IgG was 92.6%, 100%, and 97%, respectively.

CONCLUSION

FIA test, a rapid and easy to perform assay, showed high sensitivity to detect acute DENV infection, but lower in acute ZIKV infection. In past ZIKV infections, the best performance of FIA test is obtained by combining detection of IgG and IgM.