The impact of indirect notification of a cancer diagnosis and a risk model based on it to predict the prognosis of postoperative stage T3 esophageal cancer patients

Abstract

Abstract Chinese doctors are required to inform patients’ direct relatives of a cancer diagnosis rather than the patients themselves. The disease may be hidden from patients by their family members, which could result in severe outcomes. We selected postoperative T3 esophageal cancer (EsC) patients hospitalized from June 2015 to December 2016 as research subjects. The patients were divided into a direct-notification group and an indirect-notification group. Several variables were used to evaluate both groups’ 36-month progress-free survival (PFS). Statistically significant results based on univariate analysis were incorporated into a Cox regression analysis. A risk prediction model of prognosis based on the risk score was established, which was assessed using the area under the curve (AUC) of the receiver operating characteristic curve. Internal validation was performed using bootstrapping. Seventy-two patients were enrolled in the study. Cox multivariate regression analysis revealed that late stage, poor pathological differentiation, and indirect notification were independent worse risk factors for postoperative T3 stage EsC patients at 36-month PFS (hazard ratio (HR) = 1.829, 95% confidence interval (CI): 1.027–3.258, P = 0.040; HR = 0.457, 95% CI: 0.259–0.805, P = 0.007; HR = 1.993, 95% CI: 1.117–3.556, P = 0.020, respectively). The risk score was calculated as follows: risk score = 0.690 × cancer notification (indirect = 1, direct = 0) + 0.604 × stage (IIIB = 1, IIA + IIB = 0) – 0.784 × pathological differentiation (moderately + well = 1, poorly = 0). The AUC of the model that included the type of notification was 0.727 (95% CI: 0.606–0.848), with a sensitivity of 70.0% and specificity of 75.0%, and the stability was better through internal verification. Indirect notification of a cancer diagnosis was an important negative predictor of postoperative EsC patients' PFS. The model that combined stage, pathological differentiation, and the type of notification of a cancer diagnosis displayed good accuracy and internal stability in the prediction of risk for cancer progression.