Discussion
respiratory virus infection, exacerbations, COPD exacerbations, crescendo, COPD
DISEASE PROGRESSION, VIRUS, RESPIRATORY VIRUS INFECTION, COPD EXACERBATION, COPD
We developed a model for predicting COPD exacerbation on a daily basis by merging clinical cohort data, 5 years of follow-up claims data, and information regarding air pollution and respiratory virus infection rates. Those with spirometry confirmed airflow limitation in the nationally representative database that contains 5 years of data regarding claims and external factors were used as validation cohort. We found similar overall favorable predictive performance especially the random forest, XGBoost, LGBM, and MERF models exhibited good predictive power. Our personalized model to predict exacerbation was created using data from patients with symptomatic COPD who were treated and followed-up in referral hospitals, but also has high predictive ability in patients with less symptomatic, mild, and undiagnosed COPD. This suggestive the possibility of universal application of our tool.In our previous study of 594 COPD patients in the KOCOSS cohort, the HIRA data from 2007 to 2012 were merged and the relationships of future exacerbation with various clinical and environmental factors were analyzedTo our knowledge, this is the first daily prediction model for exacerbation of COPD patients worldwide; our enhancement of predictive power via machine learning is novel. We used daily exacerbation data collected over several years; we also used daily air pollution, climate, and epidemic respiratory information. Korea’s unique health insurance system and readily accessible big data enable data collection. Few studies have sought to predict the risk of acute exacerbation, particularly on a daily basis, for several reasons. First, sufficient data have been lacking. We used information from the KOCOSS multicenter cohort; we validated the model using information from a nationwide health survey (KNHANES) and claims data. Therefore, our model is applicable to mild, asymptomatic, underdiagnosed, and symptomatic COPD patients. Importantly, it was possible to identify hospital visits caused by exacerbation. In 1998, South Korea implemented a National Health Insurance system that covers almost all of the populationThe airflow limitation group (FEVIn this study, we created a predictive model for exacerbation that considered both external and clinical factors. The use of machine learning to improve the predictive power is both novel and encouraging. An exacerbation episode is not occurred with clear time interval with sharp start and end. But rather than it is a gradual process with crescendo and decrescendo process. Our prediction model could be applied regardless of any distinct time interval in predicting the risk of near future. If patients can easily assess their own daily risk of exacerbation, it may enable preventive interventions such as educating patients to wear masks or hand hygiene during respiratory virus outbreaks. Additionally, when there is an overall increase in exacerbation risk, patients could reschedule their outpatient visits, allowing for timely preventive interventions. However, our work had some limitations. First, because exacerbations were identified using claims data, over- or underestimation was possible. However, we combined the ICD-10 code with the prescribed drug; this may have rendered the data robust. Second, the PM level was based on the patient address in the HIRA database; this address was presumably not always the location where the exacerbation occurred. An association between PM level and COPD exacerbation has been found in many epidemiological studies. However, South Korea is a small country (total area 100,210 kmIn conclusion, because COPD exacerbations negatively impact health and disease progression including further exacerbation, early detection of at-risk patients is helpful to response timely and appropriately. Exacerbation is triggered by combinations of various clinical and environmental factors. To our knowledge, we are the first group worldwide to develop a personalized, daily, predictive, COPD exacerbation model; we integrated various relevant internal and external factors including multicenter cohort and claims data, as well as big data concerning climate, air pollution, and epidemic respiratory virus status. We improved the model’s predictive power by using several machine learning methods. Our model will encourage patients to visit the hospital early when they aware an increased risk of exacerbation; timely and appropriate intervention will follow.