Objective:To develop and validate clinical predictive models for identifying poor short-term response to recombinant human growth hormone(rhGH) treatment in children with short stature.Methods:A retrospective analysis was conducted on 118 children diagnosed with growth hormone deficiency or idiopathic short stature who were treated at the First Affiliated Hospital of Zhengzhou University and two other hospitals between January 1, 2020, and January 1, 2024. A poor response to rhGH was defined as a height increase of less than 0.2 standard deviation score(SDS) after 6 months of rhGH treatment. LASSO regression was used to identify predictive variables from baseline and follow-up data. Two logistic regression models were conducted: Model A(incorporating baseline variables only) and model B(incorporating both baseline and follow-up variables), and nomograms were created for visualization. External data and internal resampling were used for dual validation of the models, and their performance was compared.Results:A total of 118 children with short stature were included. Six baseline predictive variables(diagnosis, initial height SDS, bone age, bone age-chronological age difference, rhGH dose, and gender) and one follow-up variable(height SDS after 3 months of rhGH treatment) were identified. Area under the curve values for Model A and Model B were 0.753(95% CI 0.696-0.811) and 0.930(95% CI 0.891-0.975), respectively. Calibration curves, decision curve analysis, and other evaluation metrics demonstrated good discrimination and clinical utility for both models. Model B, incorporating the 3-month follow-up variable, showed superior predictive performance compared to Model A. Conclusions:The clinical prediction models developed in this study(Model A and Model B) are practical and reliable tools for quantitatively, conveniently, and intuitively identifying children with short stature at risk of poor response to rhGH treatment.

Objective:To develop and validate clinical predictive models for identifying poor short-term response to recombinant human growth hormone(rhGH) treatment in children with short stature.Methods:A retrospective analysis was conducted on 118 children diagnosed with growth hormone deficiency or idiopathic short stature who were treated at the First Affiliated Hospital of Zhengzhou University and two other hospitals between January 1, 2020, and January 1, 2024. A poor response to rhGH was defined as a height increase of less than 0.2 standard deviation score(SDS) after 6 months of rhGH treatment. LASSO regression was used to identify predictive variables from baseline and follow-up data. Two logistic regression models were conducted: Model A(incorporating baseline variables only) and model B(incorporating both baseline and follow-up variables), and nomograms were created for visualization. External data and internal resampling were used for dual validation of the models, and their performance was compared.Results:A total of 118 children with short stature were included. Six baseline predictive variables(diagnosis, initial height SDS, bone age, bone age-chronological age difference, rhGH dose, and gender) and one follow-up variable(height SDS after 3 months of rhGH treatment) were identified. Area under the curve values for Model A and Model B were 0.753(95% CI 0.696-0.811) and 0.930(95% CI 0.891-0.975), respectively. Calibration curves, decision curve analysis, and other evaluation metrics demonstrated good discrimination and clinical utility for both models. Model B, incorporating the 3-month follow-up variable, showed superior predictive performance compared to Model A. Conclusions:The clinical prediction models developed in this study(Model A and Model B) are practical and reliable tools for quantitatively, conveniently, and intuitively identifying children with short stature at risk of poor response to rhGH treatment.

Objective:To explore the clinical characteristics and risk factors of abnormal urinary albumin/creatinine ratio(UACR) in obese population.Methods:Baseline data from 2011 to 2012 in Henan Sub-center of"Risk Evaluation of cAncers in Chinese diabeTic Individuals: A lONgitudinal(REACTION) study"were utilized and those of body mass index≥28 kg/m 2 were screened. The patients were divided into UACR normal group and UACR abnormal group(101 pairs) upon being matched on a 1∶1 basis by age and gender. Multivariate logistic regression analysis, receiver operating characteristic(ROC) curve, and restricted cubic spline(RCS)analysis were performed to explore the risk factors for abnormal UACR. Results:Compared with the normal UACR group, the UACR abnormal group had a higher number of alcohol consumers, a higher prevalence of hypertension, elevated systolic blood pressure, and triglyceride(all P<0.05). Multivariate logistic regression analysis showed that alcohol consumption( P=0.008), systolic blood pressure( P<0.001), triglyceride( P=0.049), and homeostasis model assessment for insulin resistance(HOMA-IR, P=0.033) were independent risk factors for abnormal UACR in obese people. The ROC curve analysis indicated that systolic blood pressure had the strongest diagnostic performance as a single factor(ROC curve area=0.801), and there was no significant difference in diagnostic performance compared to multiple factors combination. RCS analysis results showed that the probability of abnormal UACR increased monotonically with the increase of systolic blood pressure when the systolic blood pressure was between 130 and 158 mmHg(1 mmHg=0.133 kPa). When systolic blood pressure was not in the interval, the probability of abnormal UACR did not change significantly. The results of regression analysis of triglyceride subgroup showed that when triglyceride level was greater than or equal to 5.6 mmol/L, the risk of abnormal UACR level was significantly increased( P=0.029). Conclusion:Systolic blood pressure, triglyceride, HOMA-IR, and alcohol drinking history are independent risk factors for abnormal UACR in obese people. When systolic blood pressure is≥130 mmHg or triglyceride is≥5.6 mmol/L, the risk of abnormal UACR is significantly increased.