Development and validation of a novel clinical risk score to predict hypoxemia in children with pneumonia using the WHO PREPARE dataset
Authors: Tan Rainer , Chandna Arjun , Colbourn Tim , Hooli Shubhada , King Carina , Lufesi Norman , McCollum Eric D , Mwansambo Charles , Mathew Joseph L. , Cutland Clare , Madhi Shabir Ahmed , Nunes Marta , Basnet Sudha , Strand Tor A. , O’Grady Kerry-Ann , Gessner Brad , Addo-Yobo Emmanuel , Chisaka Noel , Hibberd Patricia L. , Jeena Prakash , Lozano Juan M. , MacLeod William B. , Patel Archana , Thea Donald M. , Nguyen Ngoc Tuong Vy , Lucero Marilla , Zaman Syed Mohammad Akram uz , Bhatnagar Shinjini , Wadhwa Nitya , Lodha Rakesh , Aneja Satinder , Santosham Mathuram , Awasthi Shally , Bavdekar Ashish , Chou Monidarin , Nymadawa Pagbajabyn , Pape Jean-William , Paranhos-Baccala Glaucia , Picot Valentina S. , Rakoto-Andrianarivelo Mala , Rouzier Vanessa , Russomando Graciela , Sylla Mariam , Vanhems Philippe , Wang Jianwei , Libster Romina , Clara Alexey W. , Beynon Fenella , Levine Gillian , Rees Chris A , Neuman Mark I , Qazi Shamim A. , Nisar Yasir Bin 
Published: August 2024

ABSTRACTBackgroundHypoxemia predicts mortality at all levels of care, and appropriate management can reduce preventable deaths. However, pulse oximetry and oxygen therapy remain inaccessible in many primary care health facilities. We aimed to develop and validate a simple risk score comprising commonly evaluated clinical features to predict hypoxemia in 2-59-month-old children with pneumonia.MethodsData from 7 studies conducted in 5 countries from the Pneumonia Research Partnership to Assess WHO Recommendations (PREPARE) dataset were included. Readily available clinical features and demographic variables were used to develop a multivariable logistic regression model to predict hypoxemia (SpO2<90%) at presentation to care. The adjusted log coefficients were transformed to derive the PREPARE hypoxemia risk score and its diagnostic value was assessed in a held-out, temporal validation dataset.ResultsWe included 14,509 children in the analysis; 9.8% (n=2,515) were hypoxemic at presentation. The multivariable regression model to predict hypoxemia included age, sex, respiratory distress (nasal flaring, grunting and/or head nodding), lower chest indrawing, respiratory rate, body temperature and weight-for-age z-score. The model showed fair discrimination (area under the curve 0.70, 95% CI 0.67 to 0.73) and calibration in the validation dataset. The simplified PREPARE hypoxemia risk score includes 5 variables: age, respiratory distress, lower chest indrawing, respiratory rate and weight-for-age z-score.ConclusionThe PREPARE hypoxemia risk score, comprising five easily available characteristics, can be used to identify hypoxemia in children with pneumonia with a fair degree of certainty for use in health facilities without pulse oximetry. Its implementation would require careful consideration to limit inappropriate referrals on patients and the health system. Further external validation in community settings in low-and middle-income countries is required.KEY MESSAGESWhat is already known on this topicPulse oximetry is unavailable or underutilized in many resource-limited settings in low- and middle-income countries.Hypoxemia is a good predictor of mortality and its early identification and further management can reduce mortality.What this study addsThe PREPARE hypoxemia risk score was developed using one of the largest and most geographically diverse datasets on childhood pneumonia to date.Using age, lower chest indrawing, respiratory rate, respiratory distress and weight-for-age z-score to calculate the PREPARE hypoxemia risk score could help identify children with hypoxemia in settings without pulse oximeters.How this study might affect research, practice or policyThis study contributes to the important discussion on how best to identify hypoxemic children in the absence of pulse oximetry.Further research is warranted to validate the findings in community settingsOperationalizing and integrating the score within existing clinical management pathways must be tailored to the setting of implementation.