Dataset Information

Prediction of mortality in severe dengue cases.

ABSTRACT: BACKGROUND:Increasing incidence of dengue cases in Malaysia over the last few years has been paralleled by increased deaths. Mortality prediction models will therefore be useful in clinical management. The aim of this study is to identify factors at diagnosis of severe dengue that predicts mortality and assess predictive models based on these identified factors. METHOD:This is a retrospective cohort study of confirmed severe dengue patients that were admitted in 2014 to Hospital Kuala Lumpur. Data on baseline characteristics, clinical parameters, and laboratory findings at diagnosis of severe dengue were collected. The outcome of interest is death among patients diagnosed with severe dengue. RESULTS:There were 199 patients with severe dengue included in the study. Multivariate analysis found lethargy, OR 3.84 (95% CI 1.23-12.03); bleeding, OR 8.88 (95% CI 2.91-27.15); pulse rate, OR 1.04 (95% CI 1.01-1.07); serum bicarbonate, OR 0.79 (95% CI 0.70-0.89) and serum lactate OR 1.27 (95% CI 1.09-1.47), to be statistically significant predictors of death. The regression equation to our model with the highest AUROC, 83.5 (95% CI 72.4-94.6), is: Log odds of death amongst severe dengue cases?=?-?1.021 - 0.220(Serum bicarbonate)?+?0.001(ALT)?+?0.067(Age) - 0.190(Gender). CONCLUSION:This study showed that a large proportion of severe dengue occurred early, whilst patients were still febrile. The best prediction model to predict death at recognition of severe dengue is a model that incorporates serum bicarbonate and ALT levels.

SUBMITTER: Md-Sani SS

PROVIDER: S-EPMC5963083 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature

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Prediction of mortality in severe dengue cases.

Md-Sani Saiful Safuan SS Md-Noor Julina J Han Winn-Hui WH Gan Syang-Pyang SP Rani Nor-Salina NS Tan Hui-Loo HL Rathakrishnan Kanimoli K A-Shariffuddin Mohd Azizuddin MA Abd-Rahman Marzilawati M

BMC infectious diseases 20180521 1

<h4>Background</h4>Increasing incidence of dengue cases in Malaysia over the last few years has been paralleled by increased deaths. Mortality prediction models will therefore be useful in clinical management. The aim of this study is to identify factors at diagnosis of severe dengue that predicts mortality and assess predictive models based on these identified factors.<h4>Method</h4>This is a retrospective cohort study of confirmed severe dengue patients that were admitted in 2014 to Hospital K ...[more]

PMID: 29783955

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Project description:BackgroundPrevious studies have identified COVID-19 risk factors, such as age and chronic health conditions, linked to severe outcomes and mortality. However, accurately predicting severe illness in COVID-19 patients remains challenging, lacking precise methods.ObjectiveThis study aimed to leverage clinical real-world data and multiple machine-learning algorithms to formulate innovative predictive models for assessing the risk of severe outcomes or mortality in hospitalized patients with COVID-19.MethodsData were obtained from the Taipei Medical University Clinical Research Database (TMUCRD) including electronic health records from three Taiwanese hospitals in Taiwan. This study included patients admitted to the hospitals who received an initial diagnosis of COVID-19 between January 1, 2021, and May 31, 2022. The primary outcome was defined as the composite of severe infection, including ventilator use, intubation, ICU admission, and mortality. Secondary outcomes consisted of individual indicators. The dataset encompassed demographic data, health status, COVID-19 specifics, comorbidities, medications, and laboratory results. Two modes (full mode and simplified mode) are used; the former includes all features, and the latter only includes the 30 most important features selected based on the algorithm used by the best model in full mode. Seven machine learning was employed algorithms the performance of the models was evaluated using metrics such as the area under the receiver operating characteristic curve (AUROC), accuracy, sensitivity, and specificity.ResultsThe study encompassed 22,192 eligible in-patients diagnosed with COVID-19. In the full mode, the model using the light gradient boosting machine algorithm achieved the highest AUROC value (0.939), with an accuracy of 85.5%, a sensitivity of 0.897, and a specificity of 0.853. Age, vaccination status, neutrophil count, sodium levels, and platelet count were significant features. In the simplified mode, the extreme gradient boosting algorithm yielded an AUROC of 0.935, an accuracy of 89.9%, a sensitivity of 0.843, and a specificity of 0.902.ConclusionThis study illustrates the feasibility of constructing precise predictive models for severe outcomes or mortality in COVID-19 patients by leveraging significant predictors and advanced machine learning. These findings can aid healthcare practitioners in proactively predicting and monitoring severe outcomes or mortality among hospitalized COVID-19 patients, improving treatment and resource allocation.

Project description:BackgroundA major challenge in dengue management in resource limited settings is the confirmation of diagnosis. Clinical features of dengue often overlap with other infections and molecular diagnostic tools are not readily accessible to clinicians at hospitals. In addition, the prediction of plasma leakage in dengue is also difficult. Hematocrit level and ultrasound scans (combined with clinical parameters) are helpful to detect plasma leakage once it has happened, not before.MethodsColombo Dengue Study (CDS) is a prospective cohort study of clinically suspected adult dengue patients recruited from the National hospital of Sri Lanka (within the first 3?days of fever) that aimed to a) identify clinical and basic laboratory test parameters to differentiate dengue from non-dengue fever, b) evaluate the comparative efficacy of loop-mediated isothermal amplification (LAMP) for dengue diagnosis (vs. NS1 antigen test and RT-qPCR) and c) identify early associations that are predictive of plasma leakage or severe dengue. The basic laboratory tests considered here included hematological parameters, serum biochemistry and inflammatory markers.ResultsOnly 70% of clinically suspected patients were confirmed as having dengue by either the NS1 antigen test or RT-qPCR. On a Bayesian latent class model which assumes no "gold standard", LAMP performed equally or better than RT-qPCR and NS1 antigen test respectively. When confirmed dengue patients were compared with others, the earlier group had significantly lower lymphocyte counts and higher aspartate aminotransferase levels (AST) within the first 3?days of fever. Confirmed dengue patients with plasma leakage had a lower mean age and a higher median baseline AST level compared to those without plasma leakage (p?<?0.05).ConclusionClinical suspicion overestimates the true number of dengue patients. RT-LAMP is a potentially useful low-cost diagnostic tool for dengue diagnosis. Confirmed dengue patients had significantly higher AST levels and lower lymphocyte counts in early disease compared to others. In confirmed dengue patients, younger age and a higher AST level in early infection were associated with subsequent plasma leakage.

Project description:BackgroundEach year 3-6 million people develop life-threatening severe dengue (SD). Clinical warning signs for SD manifest late in the disease course and are nonspecific, leading to missed cases and excess hospital burden. Better SD prognostics are urgently needed.MethodsWe integrated 11 public datasets profiling the blood transcriptome of 365 dengue patients of all ages and from seven countries, encompassing biological, clinical, and technical heterogeneity. We performed an iterative multi-cohort analysis to identify differentially expressed genes (DEGs) between non-severe patients and SD progressors. Using only these DEGs, we trained an XGBoost machine learning model on public data to predict progression to SD. All model parameters were "locked" prior to validation in an independent, prospectively enrolled cohort of 377 dengue patients in Colombia. We measured expression of the DEGs in whole blood samples collected upon presentation, prior to SD progression. We then compared the accuracy of the locked XGBoost model and clinical warning signs in predicting SD.ResultsWe identified eight SD-associated DEGs in the public datasets and built an 8-gene XGBoost model that accurately predicted SD progression in the independent validation cohort with 86.4% (95% CI 68.2-100) sensitivity and 79.7% (95% CI 75.5-83.9) specificity. Given the 5.8% proportion of SD cases in this cohort, the 8-gene model had a positive and negative predictive value (PPV and NPV) of 20.9% (95% CI 16.7-25.6) and 99.0% (95% CI 97.7-100.0), respectively. Compared to clinical warning signs at presentation, which had 77.3% (95% CI 58.3-94.1) sensitivity and 39.7% (95% CI 34.7-44.9) specificity, the 8-gene model led to an 80% reduction in the number needed to predict (NNP) from 25.4 to 5.0. Importantly, the 8-gene model accurately predicted subsequent SD in the first three days post-fever onset and up to three days prior to SD progression.ConclusionsThe 8-gene XGBoost model, trained on heterogeneous public datasets, accurately predicted progression to SD in a large, independent, prospective cohort, including during the early febrile stage when SD prediction remains clinically difficult. The model has potential to be translated to a point-of-care prognostic assay to reduce dengue morbidity and mortality without overwhelming limited healthcare resources.

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Prediction of mortality in severe dengue cases.

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Prediction of mortality in severe dengue cases.

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