1.Epidemiology and etiology of shoulder pain based on health statistics data from Healthcare Bigdata Hub in Korea
Journal of the Korean Medical Association 2022;65(11):687-698
The study investigated the current epidemiologic and etiologic trends of shoulder pain over the past 10 years in South Korea.Current Concepts: From 2011 to 2020, nationwide health statistics data of the following diseases and soft tissue damage codes related to shoulder pain were extracted from the Healthcare Bigdata Hub—M75, S43, and S46. The annual changes in total medical cost and the number of patients with the three codes were extracted. The crude and age-standardized prevalence rates, and the annual percentage change were analyzed to characterize trends in prevalence rates over time. Changes in the proportion of medical cost by age, hospital type, and outpatient/inpatient distribution were also analyzed. Among the three codes, a significant increase in total medical cost, crude and age-standardized prevalence was observed only for the code M75. Additionally, in the distribution of total medical cost for the code M75 by age, the increase in the number of patients of the age group of 60 to 69 years was remarkable. The total medical cost gradually decreased at the clinic level and showed a remarkably increasing trend at the hospital level. This pattern is consistent with those of rotator cuff disease (M751), a representative disease of shoulder lesions.Discussion and Conclusion: The age of patients with shoulder pain appears to be increasing. Considering this trend of change in health statistics on shoulder pain in Korea, socioeconomic support and improvement of health policy regarding the distribution of medical expenses and resources for shoulder pain will be more necessary in the future.
2.Long-Term Follow-Up of Interstitial Lung Abnormalities in Low-Dose Chest CT in Health Screening: Exploring the Predictors of Clinically Significant Interstitial Lung Diseases Using Artificial Intelligence-Based Quantitative CT Analysis
Won Jong JEONG ; Bo Da NAM ; Jung Hwa HWANG ; Chang Hyun LEE ; Hee-Young YOON ; Eun Ji LEE ; Eunsun OH ; Jewon JEONG ; Sung Hwan BAE
Journal of the Korean Society of Radiology 2024;85(6):1141-1156
Purpose:
This study examined longitudinal changes in interstitial lung abnormalities (ILAs) and predictors of clinically significant interstitial lung diseases (ILDs) in a screening population with ILAs.
Materials and Methods:
We retrieved 36891 low-dose chest CT records from screenings between January 2003 and May 2021. After identifying 101 patients with ILAs, the clinical findings, spirometry results, and initial and follow-up CT findings, including visual and artificial intelligence-based quantitative analyses, were compared between patients diagnosed with ILD (n = 23, 23%) and those who were not (n = 78, 77%). Logistic regression analysis was used to identify significant parameters for the clinical diagnosis of ILD.
Results:
Twenty-three patients (n = 23, 23%) were subsequently diagnosed with clinically significant ILDs at follow-up (mean, 8.7 years). Subpleural fibrotic ILAs on initial CT and signs of progression on follow-up CT were common in the ILD group (both p < 0.05). Logistic regression analysis revealed that emerging respiratory symptoms (odds ratio [OR], 5.56; 95% confidence interval [CI], 1.28–24.21; p = 0.022) and progression of ILAs at follow-up chest CT (OR, 4.07; 95% CI, 1.00–16.54; p = 0.050) were significant parameters for clinical diagnosis of ILD.
Conclusion
Clinically significant ILD was subsequently diagnosed in approximately one-quarter of the screened population with ILAs. Emerging respiratory symptoms and progression of ILAs at followup chest CT can be predictors of clinically significant ILDs.
3.Long-Term Follow-Up of Interstitial Lung Abnormalities in Low-Dose Chest CT in Health Screening: Exploring the Predictors of Clinically Significant Interstitial Lung Diseases Using Artificial Intelligence-Based Quantitative CT Analysis
Won Jong JEONG ; Bo Da NAM ; Jung Hwa HWANG ; Chang Hyun LEE ; Hee-Young YOON ; Eun Ji LEE ; Eunsun OH ; Jewon JEONG ; Sung Hwan BAE
Journal of the Korean Society of Radiology 2024;85(6):1141-1156
Purpose:
This study examined longitudinal changes in interstitial lung abnormalities (ILAs) and predictors of clinically significant interstitial lung diseases (ILDs) in a screening population with ILAs.
Materials and Methods:
We retrieved 36891 low-dose chest CT records from screenings between January 2003 and May 2021. After identifying 101 patients with ILAs, the clinical findings, spirometry results, and initial and follow-up CT findings, including visual and artificial intelligence-based quantitative analyses, were compared between patients diagnosed with ILD (n = 23, 23%) and those who were not (n = 78, 77%). Logistic regression analysis was used to identify significant parameters for the clinical diagnosis of ILD.
Results:
Twenty-three patients (n = 23, 23%) were subsequently diagnosed with clinically significant ILDs at follow-up (mean, 8.7 years). Subpleural fibrotic ILAs on initial CT and signs of progression on follow-up CT were common in the ILD group (both p < 0.05). Logistic regression analysis revealed that emerging respiratory symptoms (odds ratio [OR], 5.56; 95% confidence interval [CI], 1.28–24.21; p = 0.022) and progression of ILAs at follow-up chest CT (OR, 4.07; 95% CI, 1.00–16.54; p = 0.050) were significant parameters for clinical diagnosis of ILD.
Conclusion
Clinically significant ILD was subsequently diagnosed in approximately one-quarter of the screened population with ILAs. Emerging respiratory symptoms and progression of ILAs at followup chest CT can be predictors of clinically significant ILDs.
4.Long-Term Follow-Up of Interstitial Lung Abnormalities in Low-Dose Chest CT in Health Screening: Exploring the Predictors of Clinically Significant Interstitial Lung Diseases Using Artificial Intelligence-Based Quantitative CT Analysis
Won Jong JEONG ; Bo Da NAM ; Jung Hwa HWANG ; Chang Hyun LEE ; Hee-Young YOON ; Eun Ji LEE ; Eunsun OH ; Jewon JEONG ; Sung Hwan BAE
Journal of the Korean Society of Radiology 2024;85(6):1141-1156
Purpose:
This study examined longitudinal changes in interstitial lung abnormalities (ILAs) and predictors of clinically significant interstitial lung diseases (ILDs) in a screening population with ILAs.
Materials and Methods:
We retrieved 36891 low-dose chest CT records from screenings between January 2003 and May 2021. After identifying 101 patients with ILAs, the clinical findings, spirometry results, and initial and follow-up CT findings, including visual and artificial intelligence-based quantitative analyses, were compared between patients diagnosed with ILD (n = 23, 23%) and those who were not (n = 78, 77%). Logistic regression analysis was used to identify significant parameters for the clinical diagnosis of ILD.
Results:
Twenty-three patients (n = 23, 23%) were subsequently diagnosed with clinically significant ILDs at follow-up (mean, 8.7 years). Subpleural fibrotic ILAs on initial CT and signs of progression on follow-up CT were common in the ILD group (both p < 0.05). Logistic regression analysis revealed that emerging respiratory symptoms (odds ratio [OR], 5.56; 95% confidence interval [CI], 1.28–24.21; p = 0.022) and progression of ILAs at follow-up chest CT (OR, 4.07; 95% CI, 1.00–16.54; p = 0.050) were significant parameters for clinical diagnosis of ILD.
Conclusion
Clinically significant ILD was subsequently diagnosed in approximately one-quarter of the screened population with ILAs. Emerging respiratory symptoms and progression of ILAs at followup chest CT can be predictors of clinically significant ILDs.