Statistical Analysis of Mortality Rates of Coronavirus Disease 2019 (COVID-19) Patients in Japan Across the 4C Mortality Score Risk Groups, Age Groups, and Epidemiological Waves: A Report From the Nationwide COVID-19 Cohort.

Background: The mortality rates of coronavirus disease 2019 (COVID-19) have been changed across the epidemiological waves. The aim was to investigate the differences in mortality rates of COVID-19 patients in Japan across the 6 epidemiological waves stratified by age group and Coronavirus Clinical Characterisation Consortium (4C) mortality score risk group. Methods: A total of 56 986 COVID-19 patients in the COVID-19 Registry Japan from 2 March 2020 to 1 February 2022 were enrolled. These patients were categorized into 4 risk groups based on their 4C mortality score. Mortality rates of each risk group were calculated separately for different age groups: 18-64, 65-74, 75-89, and ≥90 years. In addition, mortality rates across the wave periods were calculated separately in 2 age groups: <75 and ≥75 years. All calculated mortality rates were compared with reported data from the United Kingdom (UK) during the early epidemic. Results: The mortality rates of patients in Japan were significantly lower than in the UK across the board, with the exception of patients aged ≥90 years at very high risk. The mortality rates of patients aged ≥75 years at very high risk in the fourth and fifth wave periods showed no significant differences from those in the UK, whereas those in the sixth wave period were significantly lower in all age groups and in all risk groups. Conclusions: The present analysis showed that COVID-19 patients had a lower mortality rate in the most recent sixth wave period, even among patients ≥75 years old at very high risk.

Prediction of Change in Pelvic Tilt After Total Hip Arthroplasty Using Machine Learning.

BACKGROUND: A postoperative change in pelvic flexion following total hip arthroplasty (THA) is considered to be one of the causes of dislocation. This study aimed to predict the change of pelvic flexion after THA integrating preoperative and postoperative information with artificial intelligence. METHODS: This study involved 415 hips which underwent primary THA. Pelvic flexion angle (PFA) is defined as the angle created by the anterior pelvic plane and the horizontal/vertical planes in the supine/standing positions, respectively. Changes in PFA from preoperative supine position to standing position at 5 years after THA were recorded and which were defined as a 5-year change in PFA. Machine learning analysis was performed to predict 5-year change in PFA less than -20° using demographic, blood biochemical, and radiographic data as explanatory variables. Decision trees were constructed based on the important predictors for 5-year change in PFA that can be handled by humans in clinical practice. RESULTS: Among several machine learning models, random forest showed the highest accuracy (area under the curve = 0.852). Lumbo-lordotic angle, femoral anteversion angle, body mass index, pelvic tilt, and sacral slope were most important random forest predictors. By integrating these preoperative predictors with those obtained 1 year after the surgery, we developed a clinically applicable decision tree model that can predict 5-year change in PFA with area under the curve = 0.914. CONCLUSION: A machine learning model to predict 5-year change in PFA after THA has been developed by integrating preoperative and postoperative patient information, which may have capabilities for preoperative planning of THA.