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ObjectiveTo compare the annual and age trends of the age-standard incidence rate （ASIR） and the age-standard mortality rate （ASMR） of lung cancer in countries with different human development index （HDI） from 1990 to 2019. MethodsThe data were collected from the global burden of disease study and GLOBOCAN 2020. The average annual percentage change （AAPC） and age trends of ASIR and ASMR in lung cancer were analyzed by the Joinpoint regression model， and the comparison between the four groups was analyzed by Kruskale-Wallis analysis. ResultsIn 2020， the incidence and mortality of lung cancer gradually increased with age and HDI grade. From 1990 to 2019， the global ASIR and ASMR of lung cancer decreased， and the ASIR of lung cancer among male decreased， while the ASIR of lung cancer among female increased. The results showed that ASIR of lung cancer in female residents in countries with very high HDI increased significantly from 1996 to 2011， resulting in an overall upward trend in female ASIR， while the other groups showed a downward trend. It was found that ASIR and ASMR of lung cancer in China and India were on the rise， while ASIR and ASMR of lung cancer in Russia and the United States were on the decline. ConclusionAlthough very high/high HDI countries face a higher burden of lung cancer occurrence and death， the accumulation of lung cancer burden is completed in the transitioning period. Therefore， lung cancer prevention measures in countries in transition are critical for global lung cancer control.
ABSTRACT
Chronic diseases have become an important public health problem for people under 70 years of age worldwide， while also causing a great economic burden. The establishment of clinical prediction models can help to predict the risk of a disease or the prognostic effect of a study subject in advance by means of index testing at the early stage of chronic diseases， and plays an increasingly important role in clinical practice. This study introduces clinical diagnostic prediction models and clinical prognostic prediction models， and reviews clinical data processing， clinical prediction model building， visualization methods and model evaluation from the perspective of the application of clinical prediction models， which contribute to the correct and reasonable use of prediction models in clinical research.
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Background@#Diabetic peripheral neuropathy (DPN) is one of the most serious complications of type 2 diabetes mellitus (T2DM). DPN increases the risk of ulcers, foot infections, and noninvasive amputations, ultimately leading to long-term disability. @*Methods@#Seven hundred patients with T2DM were investigated from 2013 to 2017 in the Sanlin community by obtaining basic data from the electronic medical record system (EMRS). From September 2018 to July 2019, 681 patients (19 missing) were investigated using a questionnaire, physical examination, biochemical index test, and follow-up Toronto clinical scoring system (TCSS) test. Patients with a TCSS score ≥6 points were diagnosed with DPN. After removing missing values, 612 patients were divided into groups in a 3:1 ratio for external validation. Using different Lasso analyses (misclassification error, mean squared error, –2log-likelihood, and area under curve) and a logistic regression analysis of the training set, models A, B, C, and D were established. The receiver operating characteristic (ROC) curve, calibration plot, dynamic component analysis (DCA) measurements, net classification improvement (NRI) and integrated discrimination improvement (IDI) were used to validate discrimination and clinical practicality of the model. @*Results@#Through data analysis, model A (containing four factors), model B (containing five factors), model C (containing seven factors), and model D (containing seven factors) were built. After calibration, ROC curve, DCA, NRI and IDI, models C and D exhibited better accuracy and greater predictive power. @*Conclusion@#Four prediction models were established to assist with the early screening of DPN in patients with T2DM. The influencing factors in model C and D are more important factors for patients with T2DM diagnosed with DPN.
ABSTRACT
Background@#Diabetic peripheral neuropathy (DPN) is one of the most serious complications of type 2 diabetes mellitus (T2DM). DPN increases the risk of ulcers, foot infections, and noninvasive amputations, ultimately leading to long-term disability. @*Methods@#Seven hundred patients with T2DM were investigated from 2013 to 2017 in the Sanlin community by obtaining basic data from the electronic medical record system (EMRS). From September 2018 to July 2019, 681 patients (19 missing) were investigated using a questionnaire, physical examination, biochemical index test, and follow-up Toronto clinical scoring system (TCSS) test. Patients with a TCSS score ≥6 points were diagnosed with DPN. After removing missing values, 612 patients were divided into groups in a 3:1 ratio for external validation. Using different Lasso analyses (misclassification error, mean squared error, –2log-likelihood, and area under curve) and a logistic regression analysis of the training set, models A, B, C, and D were established. The receiver operating characteristic (ROC) curve, calibration plot, dynamic component analysis (DCA) measurements, net classification improvement (NRI) and integrated discrimination improvement (IDI) were used to validate discrimination and clinical practicality of the model. @*Results@#Through data analysis, model A (containing four factors), model B (containing five factors), model C (containing seven factors), and model D (containing seven factors) were built. After calibration, ROC curve, DCA, NRI and IDI, models C and D exhibited better accuracy and greater predictive power. @*Conclusion@#Four prediction models were established to assist with the early screening of DPN in patients with T2DM. The influencing factors in model C and D are more important factors for patients with T2DM diagnosed with DPN.