Algorithms Identifying Patients With Acute Exacerbation of Interstitial Pneumonia and Acute Interstitial Lung Diseases Developed Using Japanese Administrative Data.

ABSTRACT

BACKGROUND: We aimed to develop algorithms to identify patients with acute exacerbation of interstitial pneumonia and acute interstitial lung diseases using Japanese administrative data. METHODS: This single-center validation study examined diagnostic algorithm accuracies. We included patients >18 years old with at least one claim that was a candidate for acute exacerbation of interstitial pneumonia, acute interstitial lung diseases, and pulmonary alveolar hemorrhage who were admitted to our hospital between January 2016 and December 2021. Diagnoses of these conditions were confirmed by at least two respiratory physicians through a chart review. The positive predictive value was calculated for the created algorithms. RESULTS: Of the 1,109 hospitalizations analyzed, 285 and 243 were for acute exacerbation of interstitial pneumonia and acute interstitial lung diseases, respectively. As there were only five cases of pulmonary alveolar hemorrhage, we decided not to develop an algorithm for it. For acute exacerbation of interstitial pneumonia, acute interstitial lung diseases, and acute exacerbation of interstitial pneumonia or acute interstitial lung diseases, algorithms with high positive predictive value (0.82, 95% confidence interval 0.76-0.86; 0.82, 0.74-0.88; and 0.89, 0.85-0.92, respectively) and algorithms with slightly inferior positive predictive value but more true positives (0.81, 0.75-0.85; 0.77, 0.71-0.83; and 0.85, 0.82-0.88, respectively) were developed. CONCLUSION: We developed algorithms with high positive predictive value for identifying patients with acute exacerbation of interstitial pneumonia and acute interstitial lung diseases, useful for future database studies on such patients using Japanese administrative data.