Exhaustive mathematical analysis of simple clinical measurements for childhood pneumonia diagnosis
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Pneumonia is the leading cause of mortality for children below 5 years of age. The majority of these occur in poor countries with limited access to diagnosis. The World Health Organization (WHO) criterion for pneumonia is the de facto method for diagnosis. It is designed targeting a high sensitivity and uses easy to measure parameters. The WHO criterion has poor specificity.
We propose a method using common measurements (including the WHO parameters) to diagnose pneumonia at high sensitivity and specificity. Seventeen clinical features obtained from 134 subjects were used to create a series of logistic regression models. We started with one feature at a time, and continued building models with increasing number of features until we exhausted all possible combinations. We used a k-fold cross validation method to measure the performance of the models.
The sensitivity of our method was comparable to that of the WHO criterion but the specificity was 84%-655% higher. In the 2-11 month age group, the WHO criteria had a sensitivity and specificity of 92.0%±11.6% and 38.1%±18.5%, respectively. Our best model (using the existence of a runny nose, the number of days with runny nose, breathing rate and temperature) performed at a sensitivity of 91.3%±13.0% and specificity of 70.2%±22.80%. In the 12-60 month age group, the WHO algorithm gave a sensitivity of 95.7%±7.6% at a specificity of 9.8%±13.1%, while our corresponding sensitivity and specificity were 94.0%±12.1% and 74.0%±23.3%, respectively (using fever, number of days with cough, heart rate and chest in-drawing).
The WHO algorithm can be improved through mathematical analysis of clinical observations and measurements routinely made in the field. The method is simple and easy to implement on a mobile phone. Our method allows the freedom to pick the best model in any arbitrary field scenario (e.g., when an oximeter is not available).
Key wordsdeveloping countries diagnosis logistic regression modelling pneumonia
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- 1.Fullman N, Lim S, Dieleman J, Greenslade L, Graves C, Huynh C, et al. Pushing the pace: progress and challenges in fighting childhood pneumonia. Seattle, WA: IHME, 2014.Google Scholar
- 3.We can end poverty: millenium development goals and beyond 2015, 2015. http://www.un.org/millenniumgoals/childhealth.shtml (accessed December 28, 2016).Google Scholar
- 5.UN. The millenium development goals report 2014. New York: UN, 2014.Google Scholar
- 10.WHO. Antimicrobial resistance: global report on surveillance 2014. Geneva: World Health Organization, 2014.Google Scholar
- 15.WHO. Consultative meeting to review evidence and research priorities in the management of ARI. Geneva: World Health Organization, 2004.Google Scholar
- 16.Scott JAG, Wonodi C, Moïsi JC, Deloria-Knoll M, DeLuca AN, Karron RA, et al. The definition of pneumonia, the assessment of severity, and clinical standardization in the Pneumonia Etiology Research for Child Health study. Clin Infect Dis 2012;54 Suppl 2:S109-S116.Google Scholar
- 27.The Acute Respiratory Ifection Diagnostic Aid (ARIDA) Project. http://www.unicef.org/innovation/innovation_81722.html (accessed December 22, 2016).Google Scholar
- 28.World’s first pneumonia innovations summit unveils next generation prevention, diagnostic and treatment innovations. http://www.malariaconsortium.org/news-centre/worlds-firstpneumonia-innovations-summit-unveils-next-generationprevention-diagnostic-and-treatment-innovations.htm (accessed November 12,, 2015).Google Scholar
- 29.Abeyratne UR. Emerging tools for pneumonia diagnosis in resource poor regions. http://www.malariaconsortium.org/userfiles/file/Dr%20Abeyratne_slides.pdf (accessed January 27, 2017).Google Scholar
- 37.Naydenova E, Tsanas A, Casals-Pascual C, De Vos M. Smart diagnostic algorithms for automated detection of childhood pneumonia in resource-constrained settings. IEEE Global Humanitarian Technology Conference, 2015:377–384.Google Scholar