STUDY DESIGN: Data on ID were retrieved from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 and estimated by age, sex, geographical region, and sociodemographic index (SDI).
METHODS: The estimated annual percentage change (EAPC) was calculated to evaluate the changing trend of age-standardized incidence rate (ASIR), age-standardized prevalence rate (ASPR), and age-standardized DALYs rate (ASDR) related to ID during the period 1990-2019.
RESULTS: In Asia, there were 126,983,965.8 cases with 5,466,213.1 new incidence and 1,765,995.5 DALYs of ID in 2019. Between 1999 and 2019, the EAPC in ASIR, ASPR and ASDR were -0.6 (95% confidence interval [CI], -0.8 to -0.4), -0.9 (95% CI, -1.2 to -0.7), and -1.6 (95% CI, -1.8 to -1.5), respectively. Malaysia charted the largest decrease in ASIR, ASPR, and ASDR (82.4%, 85.3%, and 80.9% separately), whereas the Philippines and Pakistan were the only two countries that witnessed an increase in ASIR and ASPR. ID burdens were more pronounced in women, countries located to the south of the Himalayas, and low-middle SDI regions.
CONCLUSIONS: The incidence, prevalence, and DALYs of ID in Asia substantially decreased from 1990 to 2019. Women and low-middle SDI countries have relatively high ID burdens. Governments need to pay constant attention to the implementation and monitoring of universal salt iodization.
OBJECTIVES: This study aimed to provide a personalized surgical recommendation that enables more confidence in advising patients to pursue surgical treatment.
METHODS: We enrolled 328 patients with uPA harboring KCNJ5 mutations (n = 158) or not (n = 170) who had undergone adrenalectomy. Eighty-seven features were collected, including demographics, various blood and urine test results, and clinical comorbidities. We designed 2 versions of the prediction model: one for institutes with complete blood tests (full version), and the other for institutes that may not be equipped with comprehensive testing facilities (condensed version).
RESULTS: The results show that in the full version, the Light Gradient Boosting Machine outperformed other classifiers, achieving area under the curve and accuracy values of 0.905 and 0.864, respectively. The Light Gradient Boosting Machine also showed excellent performance in the condensed version, achieving area under the curve and accuracy values of 0.867 and 0.803, respectively.
CONCLUSIONS: We simplified the preoperative diagnosis of KCNJ5 mutations successfully using machine learning. The proposed lightweight tool that requires only baseline characteristics and blood/urine test results can be widely applied and can aid personalized prediction during preoperative counseling for patients with uPA.