Background: Our study aimed to establish a clinically practical and reliable prognostic nomogram based on the important prognostic factors to predict the prognosis of patients with lung metastatic renal cell carcinoma (RCC).Methods: Clinical data of patients with lung metastatic RCC between 2010 and 2015 were collected from the SEER database. Prognostic nomogram was established using R software to predict the OS and CSS probability for individual patients. Consistency index (C-index), calibration curve and decision curve analysis (DCA) were used to assess the predictive performance of the nomogram, and to calibrated the nomogram for 1-, 2-, and 3-year cancer-specific survival (CSS) and overall survival (OS).Results: 1,563 patients were enrolled in this study. All patients were randomly divided into the primary cohort (937) and the validation cohort (626). Multivariate Cox regression showed that age, histology, N-stage, T-stage, surgery and radiotherapy were independent risk factor of OS, and histology, N-stage, T-stage, surgery were CSS related factors in patients with lung metastatic RCC in the primary cohort. The C-index of the nomogram OS was 0.662 and the C-index of CSS was 0.658 in the primary cohort. In the validation cohort, the C-index of the nomogram CSS and OS were 0.685 and 0.694, respectively. Moreover, the calibration curves showed good consistency between nomogram predictions and actual 1-, 2-, and 3-year OS and CSS rates in the primary and external verification cohorts. Conclusions:The prognostic nomogram constructed in this study can provide an individualized treatment and risk assessment for survival in patients with lung metastatic RCC.

Purpose: This study aimed to determine whether insomnia is associated with hypertension (HBP) and coronary artery disease (CAD) in a hospital-based sample of patients with type 2 diabetes mellitus (T2DM).Methods: Our present study included 354 patients with T2DM. According to the diagnostic criteria of insomnia, the participants were assigned to three groups based on the duration of T2DM and insomnia diagnosis. Patients with T2DM alone were placed in group A; patients with T2DM longer than insomnia were placed in group B; and patients with insomnia longer than T2DM were placed in group C. Medical history was collected from all the patients in detail. Besides, the participants underwent thorough physical examinations and laboratory measurements. Propensity score matching (PSM) was applied to evaluate the associations of insomnia with HBP and CAD. The univariate and multivariate logistic regression analysis was used to explore whether insomnia was a risk factor for HBP and CAD in patients with T2DM.Results: Of 354 patients, 225 patients were included in group A, 62 patients were included in group B, and 67 patients were included in group C. Compared with groups B and C, group A showed a lower prevalence of HBP and CAD (p < 0.05). In addition, compared with group B, group C showed no difference in the prevalence of HBP and CAD (p > 0.05). After PSM was performed, groups B and C had a higher prevalence of HBP and CAD (p < 0.05) than group A with no significant difference between groups B and C (p > 0.05). In the univariate and multivariate logistic regression analysis, insomnia was a risk factor for HBP [univariate: odds ratio (OR) = 3.376, 95% CI 2.290–6.093, p < 0.001; multivariate: OR = 2.832, 95% CI 1.373–5.841, p = 0.005] and CAD (univariate: OR = 5.019, 95% CI 3.148–8.001, p < 0.001; multivariate: OR = 5.289, 95% CI 2.579–10.850, p < 0.001).Conclusion: T2DM combined with insomnia was related to HBP and CAD and insomnia was a risk factor for HBP and CAD in patients with T2DM. However, larger, prospective studies are required to confirm our findings.