Postural fall in systolic blood pressure is an useful warning sign in Dengue fever
Data files
Apr 20, 2023 version files 36.70 KB
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Dengue_F1000.xlsx
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README.md
Apr 24, 2023 version files 41.51 KB
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Dengue_F1000.xlsx
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README.md
Abstract
Purpose: Capillary leak is the hallmark of development of severe dengue. A rise in hematocrit has been a major warning sign in WHO guidelines. Postural hypotension, which could reflect the intravascular volume reduction in capillary leak, has been noted as a warning sign in CDC and Pan American Health Organisation guidelines. We evaluated the diagnostic accuracy of Postural hypotension as a marker of development of severe dengue.
Methods: 150 patients admitted with dengue fever were recruited in this prospective observational study. Diagnostic accuracy of conventional warning signs (abdominal pain, persistent vomiting, fluid accumulation, mucosal bleeding, lethargy, liver enlargement, increasing hematocrit with decreasing platelets) and postural hypotension was evaluated.
Result: 23 (15.3%) subjects developed severe dengue. Multiple logistic regression analysis showed that Ascites/Pleural effusion and postural fall in systolic blood pressure of >10.33% had an odds ratio of 5.024(95%CI:1.11 – 22.75) and 11.369 (95% CI:2.27 – 56.87) respectively. Other parameters did not reach statistical significance. Sensitivity and specificity of Ascites/Pleural effusion were 82.6% and 88.2% for development of severe dengue, whereas postural fall in systolic blood pressure had sensitivity and specificity of 87% and 82.7%.
Conclusion: These findings present a strong case for including postural hypotension as a warning sign in patients with dengue fever, especially in resource-limited settings.
Methods
Patients admitted to hospital with Dengue fever as per WHO criteria were included in this prospective observational study. Patients with NS1 antigen-positive results or positive Dengue IgM ELISA reports were included. Patients having severe dengue on admission were excluded. Descriptive clinical data regarding atypical manifestations of Dengue among these patients was published earlier. Analytical data regarding the diagnostic performance of warning clinical signs and postural hypotension are presented in this paper. The rise in hematocrit was calculated by comparing the hematocrit after admission with population mean values. Mean baseline hematocrit for South Indian males was 44.3% and for females 36.4%. A rise of 20% compared to the baseline hematocrit was considered significant (53.2% for males and 43.7% for Females) Patients were treated as per the WHO 2009 Protocol for the management of Dengue. Patients were followed up until they recovered or succumbed to the illness. Patients who developed Severe dengue were identified as per the WHO criteria. Ethical committee clearance was obtained from the institutional ethics committee.
Data were analysed using the software SPSS version 25. Sensitivity, Specificity, Negative predictive value, and positive predictive value were calculated for all 7 warning signs and Postural fall in systolic blood pressure (SBP). Multiple logistic regression test was performed to find out the adjusted odds ratio of parameters between non-severe and severe dengue patients. Receiver operator characteristic curve analysis was done to find the optimal cut-off value of the continuous variables which would yield the best specificity and sensitivity for severe dengue fever. Based on the logistic regression analysis, parameters that had significant odds ratio were identified and decision tree analysis was performed using those parameters as independent variables and severe dengue as the dependent variable.
CHAID method of model development was used to develop the decision tree model with 70% of the sample for development of the model (Training sample) and 30% of the data (Test sample) for split sample validation of the model. Accuracy of the model for training and test sample was expressed as accuracy with a 95% confidence interval. Specificity, sensitivity, negative likelihood ratio, positive likelihood ratio, and diagnostic odds ratio were calculated for the entire dataset using the model developed by the decision tree.