Kei Teramoto

Screening of anticancer drugs to detect drug‐induced interstitial pneumonia using the accumulated data in the electronic medical record

Because drug‐induced interstitial pneumonia (DIP) is a serious adverse drug reaction, its quantitative risk with individual medications should be taken into due consideration when selecting a medicine. We developed an algorithm to detect DIP using medical record data accumulated in a hospital. Chest computed tomography (CT) is mainly used for the diagnosis of IP, and chest X‐ray reports, KL‐6, and SP‐D values are used to support the diagnosis. The presence of IP in the reports was assessed by a method using natural language‐processing, in which IP was estimated according to the product of the likelihood ratio of characteristic keywords in each report. The sensitivity and the specificity of the method for chest CT reports were 0.92 and 0.97, while those for chest X‐ray reports were 0.83 and 1, respectively. The occurrence of DIP was estimated by the patterns of presence of IP before, during, and after the administration of the target medicine. The occurrence rate of DIP in cases administered Gefitinib; Methotrexate (MTX); Tegafur, Gimeracil, and Oteracil potassium (TS‐1); and Tegafur and Uracil (UTF) was 6.0%, 2.3%, 1.4%, and 0.7%, respectively. The estimated DIP cases were checked by having the medical records independently reviewed by medical doctors. By chart review, the positive predictive values of DIP against Gefitinib, MTX, TS‐1, and UFT were 69.2%, 44.4%, 58.6%, and 77.8%, respectively. Although the cases extracted by this method included some that did not have DIP, this method can estimate the relative risk of DIP between medicines.

Method for Detecting Drug-Induced Interstitial Pneumonia from Accumulated Medical Record Data at a Hospital

Drug-induced interstitial pneumonia (DIP) is a serious adverse drug reaction. The occurrence rete of DIP was evaluated by clinical trial before available in the market. However, due to limited number of cases in clinical trials, it may be inapplicable to the real market. We aimed to seek a method to evaluate the occurrence rate of DIP using clinical data warehouse at a hospital. Initially we developed a method that assesses whether presence of IP was written in reports by natural language processing. Next we detected DIP by estimating IP before, during and after the drug administration. Presence of IP was determined according to the reports of CT if CT was performed, otherwise it was determined based on the changes in the results of chest X-ray, level of KL-6 or SP-D. DIP was determined according to the pattern of presence of IP in each phase. In this study we chose amiodarone as a target drug. The number of patients who suffered from IP caused by amiodarone was 16 (3.9 %), including one definitively diagnosed and 15 strong doubt cases. Most of them could be validated by medical record chart. Using this method, we were able to successfully detect occurrence of DIP from accumulated data in a hospital information system.