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Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
Assessing the prognosis mortality in patients with cutaneous verrucous carcinoma using Lasso-cox regression model: a
Santosh Chokkakula1, Siomui Chong2,3, Yu-Yen Yang4
1Department of Microbiology, Chungbuk National University College of Medicine and Medical Research Institute Cheongju, Chungbuk, 28644, South Korea.
Abstract:
Elucidating risk factors and prognostic indicators for cutaneous verrucous carcinoma (CVC) is crucial for rapid medical intervention. This study examined CVC incidence risk and prognostic factors, emphasizing sex disparities. Utilizing SEER Database records, we analyzed patients diagnosed with primary CVC from 2004 to 2015. Multivariate logistic regression identified risk factors for the incidence model, while multivariate Cox regression developed the mortality prognosis model. Lasso regression and lasso Cox models determined key factors for respective models. Restricted cubic spline (RCS) models measured age-related risk associated with CVC presence and survival. The study included 1,125 CVC patients (668 males, 59.4%; 457 females, 40.6%) at the time of diagnosis. Lasso regression identified independent risk factors including age, sex, race, marital status, AJCC Stage, Combined Summary Stage, radiation, surgery, tumor size, chemotherapy, and regional lymph node involvement. The Age, sex, marital status, AJCC stage, combined summary stage, and surgery were independently associated with overall survival (OS) and statistically significant. Kaplan-Meier analysis revealed significantly shorter OS in female CVC patients compared to males (P < 0.05). The RCS model demonstrated a U-shaped pattern, indicating a significant nonlinear relationship between age and CVC incidence. The current study uncovered sex-related variations in incident risk and mortality prognostic factors, prediction of complications among CVC patients, offering valued insights for healthcare professionals in clinical assessments and interventions.
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