DOI: 10.5061/dryad.jwstqjqfc
title: Postural fall in systolic blood pressure is an useful warning sign in Dengue fever

description: 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 &gt;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.
description: 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.