Respiratory rate and pulse oximetry derived information as predictors of hospital admission in young children in Bangladesh: a prospective observational study

Ainara Garde, Guohai Zhou, Shahreen Raihana, Dustin Dunsmuir, Walter Karlen, Parastoo Dekhordi, Tanvir Huda, Shams El Arifeen, Charles Larson, Niranjan Kissoon, Guy A Dumont, J Mark Ansermino, Ainara Garde, Guohai Zhou, Shahreen Raihana, Dustin Dunsmuir, Walter Karlen, Parastoo Dekhordi, Tanvir Huda, Shams El Arifeen, Charles Larson, Niranjan Kissoon, Guy A Dumont, J Mark Ansermino

Abstract

Objective: Hypoxaemia is a strong predictor of mortality in children. Early detection of deteriorating condition is vital to timely intervention. We hypothesise that measures of pulse oximetry dynamics may identify children requiring hospitalisation. Our aim was to develop a predictive tool using only objective data derived from pulse oximetry and observed respiratory rate to identify children at increased risk of hospital admission.

Setting: Tertiary-level hospital emergency department in Bangladesh.

Participants: Children under 5 years (n=3374) presenting at the facility (October 2012-April 2013) without documented chronic diseases were recruited. 1-minute segments of pulse oximetry (photoplethysmogram (PPG), blood oxygen saturation (SpO2) and heart rate (HR)) and respiratory rate were collected with a mobile app.

Primary outcome: The need for hospitalisation based on expert physician review and follow-up.

Methods: Pulse rate variability (PRV) using pulse peak intervals of the PPG signal and features extracted from the SpO2 signal, all derived from pulse oximetry recordings, were studied. A univariate age-adjusted logistic regression was applied to evaluate differences between admitted and non-admitted children. A multivariate logistic regression model was developed using a stepwise selection of predictors and was internally validated using bootstrapping.

Results: Children admitted to hospital showed significantly (p<0.01) decreased PRV and higher SpO2 variability compared to non-admitted children. The strongest predictors of hospitalisation were reduced PRV-power in the low frequency band (OR associated with a 0.01 unit increase, 0.93; 95% CI 0.89 to 0.98), greater time spent below an SpO2 of 98% and 94% (OR associated with 10 s increase, 1.4; 95% CI 1.3 to 1.4 and 1.5; 95% CI 1.4 to 1.6, respectively), high respiratory rate, high HR, low SpO2, young age and male sex. These variables provided a bootstrap-corrected AUC of the receiver operating characteristic of 0.76.

Conclusions: Objective measurements, easily obtained using a mobile device in low-resource settings, can predict the need for hospitalisation. External validation will be required before clinical adoption.

Keywords: Global health; Heart rate variability; Mobile health; Paediatric infectious disease; Pulse oximetry.

Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

Figures

Figure 1
Figure 1
(A) The pulse oximetry page of PhoneOxR2 showing a completed 1 min good quality (mostly green) recording with the median blood oxygen saturation and HR values from the recording, (B) an in progress recording in the blinded version of the app, used in the study.
Figure 2
Figure 2
The area under the curve of the receiver operating characteristic of (A) the ‘mobile model’, which uses objective information recorded by the phone and derived from pulse oximetry and (B) the ‘baseline model’, which uses respiratory rate and median blood oxygen saturation value.
Figure 3
Figure 3
Weighted classification score calculated using the (A) ‘mobile model’ and (B) ‘baseline model’, for the full range of thresholds using three different trade-offs between false negative and false positive cases (1:3, 1:5 and 1:10).
Figure 4
Figure 4
Admitted and non-admitted children classification results using ‘baseline’ and reclassification results using the ‘mobile’ model. The height is proportional to the percentage of each classification or reclassification (correct reclassification is represented in green and incorrect reclassification in red).

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