BackgroundLung adenocarcinoma (LUAD) is the most common type of Non-small-cell lung cancer (NSCLC). Distant metastasis of lung adenocarcinoma reduces the survival rate. we aim to develop a nomogram in order to predict the survival of patients with metastatic lung adenocarcinoma.MethodsWe retrospectively collected patients who were initially diagnosed as metastatic LUAD from 2010 to 2015 from SEER database. Based on the multivariate and univariate Cox regression analysis of the training cohorts, independent prognostic factors were assessed. The nomogram prediction model was then constructed based on these prognostic factors to predict the overall survival at 12, 24 and 36 months after surgery. Nomogram were identified and calibrated by c-index, time-dependent receiver operating characteristic curve (time-dependent AUC) and calibration curve. Decision curve analysis (DCA) was used to quantify the net benefit of the nomogram at different threshold probabilities, and to better compare with the TNM staging system, we calculated the c-index of this nomogram as well as the net reclassification improvement (NRI) and the integrated discrimination improvement (IDI).ResultA total of 1102 patients with metastatic LUAD who met the requirements were included for analysis. They were randomly divided into 774 in the training cohorts and 328 in the validation cohorts. As can be seen from the calibration plots, the predicted nomogram and the actual observations in both of the training and validation cohorts were generally consistent. The time dependent AUC values of 12 months, 24 months and 36 months were 0.707, 0.674 and 0.686 in the training cohorts and 0.690, 0.680 and 0.688 in the verification cohorts, respectively. C-indexes for the training and validation cohorts were 0.653 (95%CI 0.626-0.68)and 0.663 (95%CI 0.626-1), respectively. NRI and IDI show that the model is more clinical applicable than the existing staging system. In addition, our risk scoring system based on Kaplan Meier (K-M) survival curve can accurately divide patients into three hierarchy risk groups.ConclusionThis has led to the development and validation of a prognostic nomogram to assist clinicians in determining the prognosis of patients with metastatic lung adenocarcinoma after primary site surgery.

Objectives: Numerous studies have been performed on frailty, but rarely do studies explore the integrated impact of socio-demographic, behavioural and social support factors on frailty. This study aims to establish a comprehensive frailty risk prediction model including multiple risk factors. Methods: The 2018 wave of the Chinese Longevity and Health Longitudinal Survey was used. Univariate and multivariate logistic regressions were performed to identify the relationship between frailty and multiple risk factors and establish the frailty risk prediction model. A nomogram was utilized to illustrate the prediction model. The area under the receiver operating characteristic curve (AUC), Hosmer–Lemeshow test and calibration curve were used to appraise the prediction model. Results: Variables from socio-demographic, social support and behavioural dimensions were included in the final frailty risk prediction model. Risk factors include older age, working as professionals and technicians before 60 years old, poor economic condition and poor oral hygiene. Protective factors include eating rice as a staple food, regular exercise, having a spouse as the first person to share thoughts with, doing physical examination once a year and not needing a caregiver when ill. The AUC (0.881), Hosmer–Lemeshow test (p = 0.618), and calibration curve showed that the risk prediction model was valid. Conclusion: Risk factors from socio-demographic, behavioural and social support dimensions had a comprehensive effect on frailty, further supporting that a comprehensive and individualized intervention is necessary to prevent frailty.

BackgroundCurrently, longitudinal studies on frailty are in an early stage, particularly in low- and middle-income countries. Only one study was conducted in Hong Kong to examine age-period-cohort effects on the prevalence of frailty among Chinese older adults.ObjectivesThis study aims to shed light on the prevalence trajectory of frailty among older adults in mainland China through the APC model and to analyze the effects of age, period, and cohort on the prevalence trajectory.MethodsThe sample for this study was older adults aged 65–109 years old from the 2002 to 2014 Chinese Longitudinal Healthy Longevity Survey (CLHLS). Frailty status was measured by Rockwood FI. An age-period-cohort model was used to describe the effects of age, period, and cohort on the prevalence trajectory of frailty.ResultsThe prevalence of frailty among Chinese older adults changed significantly with age, period, and cohort. Furthermore, the effect of age was much stronger than the effect of period and cohort. The prevalence of frailty in the 101–103 and 104–106 age groups was 8.998 (95% CI 13.667–5.924) and 8.699 (95% CI 13.037–5.805) times higher than the in the 65–67 age group, respectively. The sensitivity analysis based on Fried's frailty phenotype showed similar results, confirming the robustness of our findings.ConclusionAll of the age effect reflecting the individual aging process, period effect reflecting change in the social environment, and birth cohort effect reflecting different generations could influence the prevalence of frailty at the population level. In contrast, the age effect was the main effect.

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