Derivation of the first clinical diagnostic models for dehydration severity in patients over five years with acute diarrhea (vol 15, e0009266, 2021)

Diarrheal diseases lead to an estimated 1.3 million deaths each year, with the majority of those deaths occurring in patients over five years of age. As the severity of diarrheal disease can vary widely, accurately assessing dehydration status remains the most critical step in acute diarrhea management. The objective of this study is to empirically derive clinical diagnostic models for assessing dehydration severity in patients over five years with acute diarrhea in low resource settings. We enrolled a random sample of patients over five years with acute diarrhea presenting to the icddr,b Dhaka Hospital. Two blinded nurses independently assessed patients for symptoms/signs of dehydration on arrival. Afterward, consecutive weights were obtained to determine the percent weight change with rehydration, our criterion standard for dehydration severity. Full and simplified ordinal logistic regression models were derived to predict the outcome of none (<3%), some (3-9%), or severe (>9%) dehydration. The reliability and accuracy of each model were assessed. Bootstrapping was used to correct for over-optimism and compare each model's performance to the current World Health Organization (WHO) algorithm. 2,172 patients were enrolled, of which 2,139 (98.5%) had complete data for analysis. The Inter-Class Correlation Coefficient (reliability) was 0.90 (95% CI = 0.87, 0.91) for the full model and 0.82 (95% CI = 0.77, 0.86) for the simplified model. The area under the Receiver-Operator Characteristic curve (accuracy) for severe dehydration was 0.79 (95% CI: 0.76-0.82) for the full model and 0.73 (95% CI: 0.70, 0.76) for the simplified model. The accuracy for both the full and simplified models were significantly better than the WHO algorithm (p<0.001). This is the first study to empirically derive clinical diagnostic models for dehydration severity in patients over five years. Once prospectively validated, the models may improve management of patients with acute diarrhea in low resource settings.Author summaryMore than a million adults and older children die each year from diarrhea, with the vast majority living in low- and middle-income countries. Accurately assessing hydration status remains the most critical step in caring for these patients. While most patients with diarrhea can be managed safely at home, a small proportion will have severe dehydration and require intravenous fluid in a hospital setting to prevent organ damage and death. Despite dramatic improvements in diarrhea care around the world over the past several decades, there are still no evidence-based tools that doctors and nurses in low resource settings can use to determine a patient's hydration status. Our study collected data on more than two thousand patients with acute diarrhea over the age of five years at Dhaka Hospital in Bangladesh. Using artificial intelligence techniques, we developed two new clinical diagnostic tools that can accurately and reliably predict the severity of dehydration in patients with diarrhea, which we will incorporate into a simple mobile phone application for use by doctors and nurses in low resource settings. Once validated, these new tools could improve care for patients with acute diarrhea worldwide.