Matsunaga Nobuaki, Kamata Keisuke, Asai Yusuke, Tsuzuki Shinya, Sakamoto Yasuaki, Ijichi Shinpei, Akiyama Takayuki, Yu Jiefu, Yamada Gen, Terada Mari, Suzuki Setsuko, Suzuki Kumiko, Saito Sho, Hayakawa Kayoko, Ohmagari Norio
AMR Clinical Reference Center, National Center for Global Health and Medicine, Tokyo, Japan.
DataRobot Inc., Tokyo, Japan.
Infect Dis Model. 2022 Sep;7(3):526-534. doi: 10.1016/j.idm.2022.07.006. Epub 2022 Aug 5.
With the rapid increase in the number of COVID-19 patients in Japan, the number of patients receiving oxygen at home has also increased rapidly, and some of these patients have died. An efficient approach to identify high-risk patients with slowly progressing and rapidly worsening COVID-19, and to avoid missing the timing of therapeutic intervention will improve patient prognosis and prevent medical complications. Patients admitted to medical institutions in Japan from November 14, 2020 to April 11, 2021 and registered in the COVID-19 Registry Japan were included. Risk factors for patients with High Flow Nasal Cannula invasive respiratory management or higher were comprehensively explored using machine learning. Age-specific cohorts were created, and severity prediction was performed for the patient surge period. We were able to obtain a model that was able to predict severe disease with a sensitivity of 57% when the specificity was set at 90% for those aged 40-59 years, and with a specificity of 50% and 43% when the sensitivity was set at 90% for those aged 60-79 years and 80 years and older, respectively. We were able to identify lactate dehydrogenase level (LDH) as an important factor in predicting the severity of illness in all age groups. Using machine learning, we were able to identify risk factors with high accuracy, and predict the severity of the disease. We plan to develop a tool that will be useful in determining the indications for hospitalisation for patients undergoing home care and early hospitalisation.
随着日本新冠肺炎患者数量的迅速增加，在家接受吸氧的患者数量也迅速上升，其中一些患者已经死亡。一种有效的方法是识别病情进展缓慢和迅速恶化的新冠肺炎高危患者，并避免错过治疗干预时机，这将改善患者预后并预防医疗并发症。纳入了2020年11月14日至2021年4月11日期间入住日本医疗机构并登记在日本新冠肺炎登记处的患者。使用机器学习全面探索了接受高流量鼻导管有创呼吸管理或更高级别治疗的患者的危险因素。创建了特定年龄队列，并对患者激增期进行了严重程度预测。我们能够获得一个模型，对于40-59岁的人群，当特异性设定为90%时，该模型能够以57%的灵敏度预测重症疾病；对于60-79岁和80岁及以上的人群，当灵敏度设定为90%时，该模型的特异性分别为50%和43%。我们能够确定乳酸脱氢酶水平（LDH）是预测所有年龄组疾病严重程度的一个重要因素。通过机器学习，我们能够高精度地识别危险因素，并预测疾病的严重程度。我们计划开发一种工具，该工具将有助于确定接受家庭护理和早期住院治疗患者的住院指征。
