A Nomogram with the Keloid Activity Assessment Scale for Predicting the Recurrence of Chest Keloid after Surgery and Radiotherapy.

BACKGROUND: Patients with chest keloids undergoing surgery and adjuvant radiotherapy still have a high recurrence rate, which is a critical problem. The level of keloid activity has not been studied, and a nomogram model for predicting keloid recurrence has not been established in previous studies. METHODS: A total of 145 patients with chest keloids who underwent surgery and radiotherapy between January 2015 and January 2019 at Peking Union Medical College Hospital were included in our study. Demographic and clinical features and the score of KAAS were analyzed. We compared the area under the curve (AUC) and decision curve analysis (DCA) between KAAS and the Vancouver scar scale (VSS) and established a nomogram model for predicting the risk of recurrence. We used bootstrap and calibration plots to evaluate the performance of the nomogram. RESULTS: The KAAS can predict recurrence in patients with chest keloids after surgery and radiotherapy. Areas under the curve (AUCs) of KAAS and VSS were 0.858 and 0.711, respectively (p < 0.001). Decision curve analysis (DCA) demonstrated that the KAAS was better than the VSS. Complications after treatment may be risk factors for keloid recurrence. We created a nomogram by using complications and KAAS. The AUC was 0.871 (95% CI 0.812-0.930). The ROC of the model's bootstrap was 0.865 and was well calibrated. CONCLUSIONS: The KAAS can be used to predict the recurrence and we developed a nomogram for predicting the recurrence of chest keloids after surgery and adjuvant radiotherapy. LEVEL OF EVIDENCE IV: This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

Systematic estimation of BMI: A novel insight into predicting overweight/obesity in undergraduates.

The prevalence of overweight-obesity has increased sharply among undergraduates worldwide. In 2016, approximately 52% of adults were overweight-obese. This cross-sectional study aimed to investigate the prevalence of overweight-obesity and explore in depth the connection between eating habits and overweight-obesity among Chinese undergraduates.The study population included 536 undergraduates recruited in Shijiazhuang, China, in 2017. They were administered questionnaires for assessing demographic and daily lifestyle characteristics, including sex, region, eating speed, number of meals per day, and sweetmeat habit. Anthropometric status was assessed by calculating the body mass index (BMI). The determinants of overweight-obesity were investigated by the Pearson χ test, Spearman rho test, multivariable linear regression, univariate/multivariate logistic regression, and receiver operating characteristic curve analysis.The prevalence of undergraduate overweight-obesity was 13.6%. Sex [male vs female, odds ratio (OR): 1.903; 95% confidence interval (95% CI): 1.147-3.156], region (urban vs rural, OR: 1.953; 95% CI: 1.178-3.240), number of meals per day (3 vs 2, OR: 0.290; 95% CI: 0.137-0.612), and sweetmeat habit (every day vs never, OR: 4.167; 95% CI: 1.090-15.933) were significantly associated with overweight-obesity. Eating very fast was positively associated with overweight-obesity and showed the highest OR (vs very slow/slow, OR: 5.486; 95% CI: 1.622-18.553). However, the results of multivariate logistic regression analysis indicated that only higher eating speed is a significant independent risk factor for overweight/obesity (OR: 17.392; 95% CI, 1.614-187.363; P = .019).Scoremeng = 1.402 × scoresex + 1.269 × scoreregion + 19.004 × scoreeatin speed + 2.546 × scorenumber of meals per day + 1.626 × scoresweetmeat habit and BMI = 0.253 × Scoremeng + 18.592. These 2 formulas can help estimate the weight status of undergraduates and predict whether they will be overweight or obese.