Predicting Clinical Outcome in Dengue Fever: A Retrospective Cohort Modeling Study

Dengue fever demonstrates a broad spectrum of clinical severity, ranging from self-limiting illness to life-threatening complications. Early identification of patients at risk of progression remains a clinical challenge, particularly in high-burden settings. Although platelet count and World Health Organization (WHO) dengue classification are routinely used in clinical practice, reliance on individual parameters may not adequately capture the multifactorial nature of disease severity. Development of multivariable predictive models integrating clinical and laboratory markers available at admission may improve early risk stratification and support evidence-based triage decisions.

This retrospective predictive analysis included 667 consecutively hospitalized patients with laboratory-confirmed dengue infection. This retrospective cohort study was conducted in a tertiary care teaching hospital in Western Maharashtra over a 1.5-year period. Data were extracted from hospital records of patients evaluated during their admission period. An ordinal logistic regression model was developed to predict clinical outcome. Internal validation was performed using bootstrap resampling.

Of 667 patients, 585 (87.7%) had uncomplicated recovery, 69 (10.3%) had complicated recovery, and 13 (1.9%) had adverse outcomes. WHO classification, platelet count, neutrophil-to-lymphocyte ratio (NLR), and warning sign count at admission independently predicted clinical outcome, with an optimism-corrected C-index of 0.820.

Admission WHO classification, platelet count, NLR, and warning signs enable accurate early prediction of dengue severity and may assist in triage and monitoring decisions.