摘要
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).
