Use of structural equation models to predict dengue illness phenotype

A simple nomogram to predict dengue shock syndrome: A study of 4522 south east Asian children

Dengue shock syndrome (DSS) substantially worsens the prognosis of children with dengue infection. This study aimed to develop a simple clinical tool to predict the risk of DSS. A cohort of 2221 Thai children with a confirmed dengue infection who were admitted to King Chulalongkorn Memorial Hospital between 1987 and 2007 was conducted. Another data set from a previous publication comprising 2,301 Vietnamese children with dengue infection was employed to create a pooled data set, which was randomly split into training (n = 3182), testing (n = 697) and validating (n = 643) datasets. Logistic regression was compared to alternative machine learning algorithms to derive the most predictive model for DSS. 4522 children, including 899 DSS cases (758 Thai and 143 Vietnamese children) with a mean age of 9.8 ± 3.4 years, were analyzed. Among the 12 candidate clinical parameters, the Bayesian Model Averaging algorithm retained the most predictive subset of five covariates, including body weight, history of vomiting, liver size, hematocrit levels, and platelet counts. At an Area Under the Curve (AUC) value of 0.85 (95% CI: 0.81-0.90) in testing data set, logistic regression outperformed random forest, XGBoost and support vector machine algorithms, with AUC values being 0.82 (0.77-0.88), 0.82 (0.76-0.88), and 0.848 (0.81-0.89), respectively. At its optimal threshold, this model had a sensitivity of 0.71 (0.62-0.80), a specificity of 0.84 (0.81-0.88), and an accuracy of 0.82 (0.78-0.85) on validating data set with consistent performance across subgroup analyses by age and gender. A logistic regression-based nomogram was developed to facilitate the application of this model. This work introduces a simple and robust clinical model for DSS prediction that is well-tailored for children in resource-limited settings.

Comprehensive analysis of multivariable models for predicting severe dengue prognosis: systematic review and meta-analysis

Dengue fever has often been confused with other febrile diseases, with deterioration occurring in the later state. Many predictive models for disease progression have been developed, but there is no definite statistical model for clinical use yet. We retrieved relevant articles through Global Health, EMBASE, MEDLINE and CINAHL Plus. The Prediction Model Risk of Bias Assessment Tool was adopted to assess potential bias and applicability. Statistical analysis was performed using Meta-DiSc software (version 1.4). Of 3184 research studies, 22 were included for the systematic review, of which 17 were selected for further meta-analysis. The pooled data of predictive accuracy was as follows: the sensitivity was 0.88 (95% CI 0.86 to 0.89), the specificity was 0.60 (95% CI 0.59 to 0.60), the positive likelihood ratio was 2.83 (95% CI 2.38 to 3.37), the negative likelihood ratio was 0.20 (95% CI 0.14 to 0.0.29) and the diagnostic OR was 16.31 (95% CI 10.25 to 25.94). The area under the summary receiver operating characteristic curve value was 0.86 (SE=0.02) with 0.79 (SE=0.02) of the Cochran Q test value. The overall predictive power of models in this study was relatively high. With careful adaption and standardization, the implementation of predictive models for severe dengue could be practical in actual clinical settings.

An autonomous cycle of data analysis tasks for the clinical management of dengue

Dengue is the most widespread vector-borne disease worldwide. Timely diagnosis and treatment of dengue is the main objective of medical professionals to decrease mortality rates. In this paper, we propose an autonomous cycle that integrates data analysis tasks to support decision-making in the clinical management of dengue. Particularly, the autonomous cycle supports dengue diagnosis and treatment. The proposed system was built using machine learning techniques for classification tasks (artificial neural networks and support vector machines) and evolutionary techniques (a genetic algorithm) for prescription tasks (treatment). The system was quantitatively evaluated using dengue-patient datasets reported by healthcare institutions. Our system was compared with previous works using qualitative criteria. The proposed system has the ability to classify a patient's clinical picture and recommend the best treatment option. In particular, the classification of dengue was done with 98% accuracy and a genetic algorithm recommends treatment options for particular patients. Finally, our system is flexible and easily adaptable, which will allow the addition of new tasks for dengue analysis.

A clinical decision-support system for dengue based on fuzzy cognitive maps

Dengue is a viral infection widely distributed in tropical and subtropical regions of the world. Dengue is characterized by high fatality rates when the diagnosis is not made promptly and effectively. To aid in the diagnosis of dengue, we propose a clinical decision-support system that classifies the clinical picture based on its severity, and using causal relationships evaluates the behavior of the clinical and laboratory variables that describe the signs and symptoms related to dengue. The system is based on a fuzzy cognitive map that is defined by the signs, symptoms and laboratory tests used in the conventional diagnosis of dengue. The evaluation of the model was performed on datasets of patients diagnosed with dengue to compare the model with other approaches. The developed model showed a good classification performance with 89.4% accuracy and could evaluate the behaviour of clinical and laboratory variables related to dengue severity (it is an explainable method). This model serves as a diagnostic aid for dengue that can be used by medical professionals in clinical settings.

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

BACKGROUND

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.

METHODOLOGY

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.

CONCLUSION

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.

Dengue models based on machine learning techniques: A systematic literature review

BACKGROUND

Dengue modeling is a research topic that has increased in recent years. Early prediction and decision-making are key factors to control dengue. This Systematic Literature Review (SLR) analyzes three modeling approaches of dengue: diagnostic, epidemic, intervention. These approaches require models of prediction, prescription and optimization. This SLR establishes the state-of-the-art in dengue modeling, using machine learning, in the last years.

METHODS

Several databases were selected to search the articles. The selection was made based on Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology. Sixty-four articles were obtained and analyzed to describe their strengths and limitations. Finally, challenges and opportunities for research on machine-learning for dengue modeling were identified.

RESULTS

Logistic regression was the most used modeling approach for the diagnosis of dengue (59.1%). The analysis of the epidemic approach showed that linear regression (17.4%) is the most used technique within the spatial analysis. Finally, the most used intervention modeling is General Linear Model with 70%.

CONCLUSIONS

We conclude that cause-effect models may improve diagnosis and understanding of dengue. Models that manage uncertainty can also be helpful, because of low data-quality in healthcare. Finally, decentralization of data, using federated learning, may decrease computational costs and allow model building without compromising data security.

Comparing machine learning with case-control models to identify confirmed dengue cases

In recent decades, the global incidence of dengue has increased. Affected countries have responded with more effective surveillance strategies to detect outbreaks early, monitor the trends, and implement prevention and control measures. We have applied newly developed machine learning approaches to identify laboratory-confirmed dengue cases from 4,894 emergency department patients with dengue-like illness (DLI) who received laboratory tests. Among them, 60.11% (2942 cases) were confirmed to have dengue. Using just four input variables [age, body temperature, white blood cells counts (WBCs) and platelets], not only the state-of-the-art deep neural network (DNN) prediction models but also the conventional decision tree (DT) and logistic regression (LR) models delivered performances with receiver operating characteristic (ROC) curves areas under curves (AUCs) of the ranging from 83.75% to 85.87% [for DT, DNN and LR: 84.60% ± 0.03%, 85.87% ± 0.54%, 83.75% ± 0.17%, respectively]. Subgroup analyses found all the models were very sensitive particularly in the pre-epidemic period. Pre-peak sensitivities (<35 weeks) were 92.6%, 92.9%, and 93.1% in DT, DNN, and LR respectively. Adjusted odds ratios examined with LR for low WBCs [≤ 3.2 (x103/μL)], fever (≥38°C), low platelet counts [< 100 (x103/μL)], and elderly (≥ 65 years) were 5.17 [95% confidence interval (CI): 3.96-6.76], 3.17 [95%CI: 2.74-3.66], 3.10 [95%CI: 2.44-3.94], and 1.77 [95%CI: 1.50-2.10], respectively. Our prediction models can readily be used in resource-poor countries where viral/serologic tests are inconvenient and can also be applied for real-time syndromic surveillance to monitor trends of dengue cases and even be integrated with mosquito/environment surveillance for early warning and immediate prevention/control measures. In other words, a local community hospital/clinic with an instrument of complete blood counts (including platelets) can provide a sentinel screening during outbreaks. In conclusion, the machine learning approach can facilitate medical and public health efforts to minimize the health threat of dengue epidemics. However, laboratory confirmation remains the primary goal of surveillance and outbreak investigation.

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

OBJECTIVE

To review the diagnostic test accuracy and predictive value of statistical models in differentiating the severity of dengue infection.

METHODS

Electronic searches were conducted in the Cochrane Database of Systematic Reviews, Cochrane Central Register of Controlled Trials, MEDLINE (complete), PubMed and Scopus. Eligible studies to be included in this review were cohort studies with participants confirmed by laboratory test for dengue infection and comparison among the different severity of dengue infection by using statistical models. The methodological quality of the paper was assessed by independent reviewers using QUADAS-2.

RESULTS

Twenty-six studies published from 1994 to 2017 were included. Most diagnostic models produced an accuracy of 75% to 80% except one with 86%. Two models predicting severe dengue according to the WHO 2009 classification have 86% accuracy. Both of these logistic regression models were applied during the first three days of illness, and their sensitivity and specificity were 91-100% and 79.3-86%, respectively. Another model which evaluated the 30-day mortality of dengue infection had an accuracy of 98.5%.

CONCLUSION

Although there are several potential predictive or diagnostic models for dengue infection, their limitations could affect their validity. It is recommended that these models be revalidated in other clinical settings and their methods be improved and standardised in future.