A predictive model for early death in elderly patients with gastric cancer: A population-based study

Abstract

BackgroundThe mean age of gastric cancer (GC) patients has increased due to the aging society. Elderly GC patients with poor physical status tend to develop complications during the treatment courses, which cause early death. This study aimed to identify risk factors and establish nomograms for predicting total early death and cancer-specific early death in elderly GC patients.MethodsData for elderly GC patients were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. These patients were randomly assigned to a training cohort and a validation cohort. The univariate logistic regression model and backward stepwise logistic regression model were used to identify independent risk factors for early death. Nomograms were constructed to predict the overall risk of early death and their performance was validated by receiver operating characteristic (ROC) curve, calibration curve, decision curve analyses (DCA), integrated discrimination improvement (IDI), and net reclassification improvement (NRI) in both training and validation cohorts.ResultsAmong the 3102 enrolled patients, 1114 patients died within three months from the first diagnosis and 956 of them died due to cancer-specific causes. Non-Asian or Pacific Islander (API) race, non-cardia/fundus or lesser/greater curvature, higher AJCC stage, no surgery and no chemotherapy were all related to a high risk of both all-cause early death and cancer-specific early death. Higher T stage and N0 stage were only positively related to total early mortality, while liver metastasis was only positively related to cancer-specific early mortality. Based on these identified factors, two nomograms were developed for predicting the risk of all-cause and cancer-specific early death, which showed good performance with the AUC of the nomograms were 0.775 and 0.766, respectively. The calibration curves, DCAs, NRI, and IDI also confirmed the value of these nomograms.ConclusionsThese nomogram models were considered a practical tool to identify the early death of elderly GC patients and help provide a more individualized treatment strategy.

A practical nomogram for predicting early death in elderly small cell lung cancer patients: A SEER-based study

“…[23,26] Thus, there is a necessity to identify prognostic factors associated with early death in elderly SCLC patients, which will facilitate the identification of patients at higher risk of early death and give them access to more individualized treatment plans. Previous studies have investigated early mortality in malignancies such as gastric, colorectal and ovarian cancers [27][28][29][30][31][32] and identified relevant risk factors. Prognostic factors in patients with metastatic SCLC have also been explored, [33][34][35] but fewer studies have been conducted on early death in elderly SCLC patients.…”

Prognostic factors and predictive nomogram models for early death in elderly patients with hepatocellular carcinoma: a population-based study

“…In the management of cancer in elderly patients, it is notable to take into account their increased susceptibility to postoperative complications compared to younger patients. This predisposition often results in a delayed initiation of subsequent oncological interventions such as radiotherapy and chemotherapy, thereby diminishing the resultant survival benefits for this particular demographic ( Liang et al, 2013 ; Yang et al, 2022 ). HCC is recognized as an aggressive form of carcinoma, with a significant proportion of patients (14.0%–36.7%) presenting with extrahepatic metastases ( Natsuizaka et al, 2005 ).…”

Prognostic value of the preoperative albumin‐bilirubin score among patients with stages I–III gastric cancer undergoing radical resection: A retrospective study

“…Therefore, identifying a simple and effective prognostic indicator for patients with GC would be highly desirable. [5][6][7] The albumin-bilirubin (ALBI) grade is a novel measure of evaluating liver function based on serum levels of these two proteins and can be derived more simply and objectively. 8 Previous studies have found the ALBI score to be useful for predicting the prognoses of patients with gastrointestinal tumors and liver metastases.…”

“…Early studies of models that predicted the postoperative survival of patients with GC included those that incorporated tumor indicators; however, the sensitivity and specificity of these models were limited and could not be readily applied in clinical practice. Therefore, identifying a simple and effective prognostic indicator for patients with GC would be highly desirable 5–7 …”