Development and Internal Validation of a Predictive Model Including Pulse Oximetry for Hospitalization of Under-Five Children in Bangladesh

Abstract

Background: The reduction in the deaths of millions of children who die from infectious diseases requires early initiation of treatment and improved access to care available in health facilities. A major challenge is the lack of objective evidence to guide front line health workers in the community to recognize critical illness in children earlier in their course.

Methods: We undertook a prospective observational study of children less than 5 years of age presenting at the outpatient or emergency department of a rural tertiary care hospital between October 2012 and April 2013. Study physicians collected clinical signs and symptoms from the facility records, and with a mobile application performed recordings of oxygen saturation, heart rate and respiratory rate. Facility physicians decided the need for hospital admission without knowledge of the oxygen saturation. Multiple logistic predictive models were tested.

Findings: Twenty-five percent of the 3374 assessed children, with a median (interquartile range) age of 1.02 (0.42-2.24), were admitted to hospital. We were unable to contact 20% of subjects after their visit. A logistic regression model using continuous oxygen saturation, respiratory rate, temperature and age combined with dichotomous signs of chest indrawing, lethargy, irritability and symptoms of cough, diarrhea and fast or difficult breathing predicted admission to hospital with an area under the receiver operating characteristic curve of 0.89 (95% confidence interval -CI: 0.87 to 0.90). At a risk threshold of 25% for admission, the sensitivity was 77% (95% CI: 74% to 80%), specificity was 87% (95% CI: 86% to 88%), positive predictive value was 70% (95% CI: 67% to 73%) and negative predictive value was 91% (95% CI: 90% to 92%).

Conclusion: A model using oxygen saturation, respiratory rate and temperature in combination with readily obtained clinical signs and symptoms predicted the need for hospitalization of critically ill children. External validation of this model in a community setting will be required before adoption into clinical practice.

Fig 1. Flowchart of study population and distribution of outcomes.

Fig 2. Receiver operating characteristic curve of the final model in the study cohort.

AUC ROC = area under the curve of the receiver operating characteristic. Sens = sensitivity. Spec = specificity. PPV = positive predictive value. NPV = negative predictive value.

Fig 3. Calibration plot of the final 10-predictor model applied to the 3263 cases excluding subjects who were unconscious or who had experienced convulsions (Hosmer-Lemeshow goodness-of-fit p-value = 0.53).

The 45 degree straight line corresponds to the line of perfect calibration on which model predicted risks coincide with the observed risks.

Fig 4. Weighted classification score for the full range of thresholds using different trade-offs between false negative and false positive cases.