Exhaustive mathematical analysis of simple clinical measurements for childhood pneumonia diagnosis

Kosasih, Keegan and Abeyratne, Udantha (2017) Exhaustive mathematical analysis of simple clinical measurements for childhood pneumonia diagnosis. World Journal of Pediatrics, 13 5: 446-456. doi:10.1007/s12519-017-0019-4


Author Kosasih, Keegan
Abeyratne, Udantha
Title Exhaustive mathematical analysis of simple clinical measurements for childhood pneumonia diagnosis
Journal name World Journal of Pediatrics   Check publisher's open access policy
ISSN 1867-0687
1708-8569
Publication date 2017-03-22
Year available 2017
Sub-type Article (original research)
DOI 10.1007/s12519-017-0019-4
Open Access Status Not yet assessed
Volume 13
Issue 5
Start page 446
End page 456
Total pages 12
Place of publication Hangzhou, China
Publisher Zhejiang University * School of Medicine Children's Hospital
Language eng
Abstract Background: 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.
Formatted abstract
Background: 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.

Methods: 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.

Results: 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).

Conclusions: 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).
Keyword Developing countries
Diagnosis
Logistic regression modelling
Pneumonia
Q-Index Code C1
Q-Index Status Provisional Code
Grant ID OPP1008199 GCE
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: HERDC Pre-Audit
School of Information Technology and Electrical Engineering Publications
 
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