JMIR Publications

ABSTRACT

Background:

Dengue fever (DF) is one of the most common arthropod-borne viral diseases worldwide, particularly in South East Asia, Africa, the Western Pacific, and the Americas. However, DF symptoms are usually assessed using a dichotomous (i.e., absent versus present) evaluation. There has been no published study that has reported using the specific sequence of symptoms to detect DF. An App is required to help patients/their family members or clinicians to identify DF at an earlier stage.

Objective:

We developed an App examining symptoms to effectively predict DF.

Methods:

We extracted statistically significant features from 17 DF-related clinical symptoms in 177 pediatric patients (69 diagnosed with DF) using (1) the unweighted summation score and (2) the non-parametric HT person fit statistic, which can jointly combine (3) the weighted score (yielded by logistic regression) to predict DF risk.

Results:

ix symptoms (Family History, Fever  39°C, Skin Rash, Petechiae, Abdominal Pain, and Weakness) significantly predicted DF. When a cutoff point of −1.03 (p = 0.26) suggested combining the weighted score and the HT coefficient, the sensitivity was 0.91, and the specificity was 0.76. The area under the ROC curve was 0.88, which was a better predictor: specificity was 5.56% higher than for the traditional logistic regression.

Conclusions:

Six simple symptoms analyzed using logistic regression were useful and valid for early detection of DF risk in children. A better predictive specificity increased after combining the non-parametric HT coefficient to the weighted regression score. A self-assessment using patient smartphones is available to discriminate DF and may eliminate the need for a costly and time-consuming dengue laboratory test. Clinical Trial: Not available