Background:Although congenital hypothyroidism(CH)has been widely studied in Western countries,CH incidence at different administrative levels in China during the past decade remains unknown.This study aimed to update ...Background:Although congenital hypothyroidism(CH)has been widely studied in Western countries,CH incidence at different administrative levels in China during the past decade remains unknown.This study aimed to update the incidence and revealed the spatial pattern of CH incidence in the mainland of China,which could be helpful in the planning and implementation of preventative measures.Methods:The data used in our study were derived from 245 newborns screening centers that cover 30 provinces of the Chinese Newborn Screening Information System.Spatial auto-correlation was analyzed by Global Moran I and Getis-Ord Gi statistics at the provincial level.Kriging interpolation methods were applied to estimate a further detailed spatial distribution of CH incidence at city level throughout the mainland of China,and Kulldorff space scanning statistical methods were used to identify the spatial clusters of CH cases at the city level.Results:A total of 91,921,334 neonates were screened from 2013 to 2018 and 42,861 cases of primary CH were identified,yielding an incidence of 4.66 per 10,000 newborns screened(95%confidence interval[CI]:4.62–4.71).Neonates in central(risk ratio[RR]=0.84,95%CI:0.82–0.85)and western districts(RR=0.71,95%CI:0.69–0.73)had lower probability of CH cases compared with the eastern region.The CH incidence indicated a moderate positive global spatial autocorrelation(Global Moran I value=0.394,P<0.05),and the CH cases were significantly clustered in spatial distribution.A most likely city-cluster(log-likelihood ratio[LLR]=588.82,RR=2.36,P<0.01)and 25 secondary city-clusters of high incidence were scanned.The incidence of each province and each city in the mainland of China was estimated by kriging interpolation,revealing the most affected province and city to be Zhejiang Province and Hangzhou city,respectively.Conclusion:This study offers an insight into the space clustering of CH incidence at provincial and city scales.Future work on environmental factors need to focus on the effects of CH occurren展开更多
The aim of this study is to evaluate the ability of the random forest algorithm that combines data on transrectal ultrasound findings, age, and serum levels of prostate-specific antigen to predict prostate carcinoma. ...The aim of this study is to evaluate the ability of the random forest algorithm that combines data on transrectal ultrasound findings, age, and serum levels of prostate-specific antigen to predict prostate carcinoma. Clinico-demographic data were analyzed for 941 patients with prostate diseases treated at our hospital, including age, serum prostate-specific antigen levels, transrectal ultrasound findings, and pathology diagnosis based on ultrasound-guided needle biopsy of the prostate. These data were compared between patients with and without prostate cancer using the Chi-square test, and then entered into the random forest model to predict diagnosis. Patients with and without prostate cancer differed significantly in age and serum prostate-specific antigen levels （P 〈 0.001）, as well as in all transrectal ultrasound characteristics （P 〈 0.05） except uneven echo （P = 0.609）. The random forest model based on age, prostate-specific antigen and ultrasound predicted prostate cancer with an accuracy of 83.10%, sensitivity of 65.64%, and specificity of 93.83%. Positive predictive value was 86.72%, and negative predictive value was 81.64%. By integrating age, prostate-specific antigen levels and transrectal ultrasound findings, the random forest algorithm shows better diagnostic performance for prostate cancer than either diagnostic indicator on its own. This algorithm may help improve diagnosis of the disease by identifying patients at high risk for biopsy.展开更多
基金supported by a grant from the National Key Research and Development Program of China(No.2017YFC1001700).
文摘Background:Although congenital hypothyroidism(CH)has been widely studied in Western countries,CH incidence at different administrative levels in China during the past decade remains unknown.This study aimed to update the incidence and revealed the spatial pattern of CH incidence in the mainland of China,which could be helpful in the planning and implementation of preventative measures.Methods:The data used in our study were derived from 245 newborns screening centers that cover 30 provinces of the Chinese Newborn Screening Information System.Spatial auto-correlation was analyzed by Global Moran I and Getis-Ord Gi statistics at the provincial level.Kriging interpolation methods were applied to estimate a further detailed spatial distribution of CH incidence at city level throughout the mainland of China,and Kulldorff space scanning statistical methods were used to identify the spatial clusters of CH cases at the city level.Results:A total of 91,921,334 neonates were screened from 2013 to 2018 and 42,861 cases of primary CH were identified,yielding an incidence of 4.66 per 10,000 newborns screened(95%confidence interval[CI]:4.62–4.71).Neonates in central(risk ratio[RR]=0.84,95%CI:0.82–0.85)and western districts(RR=0.71,95%CI:0.69–0.73)had lower probability of CH cases compared with the eastern region.The CH incidence indicated a moderate positive global spatial autocorrelation(Global Moran I value=0.394,P<0.05),and the CH cases were significantly clustered in spatial distribution.A most likely city-cluster(log-likelihood ratio[LLR]=588.82,RR=2.36,P<0.01)and 25 secondary city-clusters of high incidence were scanned.The incidence of each province and each city in the mainland of China was estimated by kriging interpolation,revealing the most affected province and city to be Zhejiang Province and Hangzhou city,respectively.Conclusion:This study offers an insight into the space clustering of CH incidence at provincial and city scales.Future work on environmental factors need to focus on the effects of CH occurren
文摘The aim of this study is to evaluate the ability of the random forest algorithm that combines data on transrectal ultrasound findings, age, and serum levels of prostate-specific antigen to predict prostate carcinoma. Clinico-demographic data were analyzed for 941 patients with prostate diseases treated at our hospital, including age, serum prostate-specific antigen levels, transrectal ultrasound findings, and pathology diagnosis based on ultrasound-guided needle biopsy of the prostate. These data were compared between patients with and without prostate cancer using the Chi-square test, and then entered into the random forest model to predict diagnosis. Patients with and without prostate cancer differed significantly in age and serum prostate-specific antigen levels （P 〈 0.001）, as well as in all transrectal ultrasound characteristics （P 〈 0.05） except uneven echo （P = 0.609）. The random forest model based on age, prostate-specific antigen and ultrasound predicted prostate cancer with an accuracy of 83.10%, sensitivity of 65.64%, and specificity of 93.83%. Positive predictive value was 86.72%, and negative predictive value was 81.64%. By integrating age, prostate-specific antigen levels and transrectal ultrasound findings, the random forest algorithm shows better diagnostic performance for prostate cancer than either diagnostic indicator on its own. This algorithm may help improve diagnosis of the disease by identifying patients at high risk for biopsy.
