Development and Validation of a New Integrative Score Based on Various Systemic Inflammatory and Nutritional Indicators in Predicting Prognosis in Patients With Resectable Esophageal Squamous Cell Carcinoma: A Retrospective Cohort Study

Abstract

Background Recent studies revealed that various inflammatory and nutritional indexes were associated with prognosis in esophageal cancer (EC). However, these studies only evaluated one or two indexes, and the prognostic value of these indexes individually or in combination is unclear. This study aimed to construct an integrative score based on various inflammatory and nutritional indexes for prognosis in resectable esophageal squamous cell carcinoma (ESCC). Methods A total of 421 consecutive patients were randomly divided into either a training or validation cohort at a ratio of 7:3 for retrospective analysis. Using logic regression analyses, independent risk factors from peripheral blood indexes were screened to construct an integrative score. The associations regarding the integrative score, clinical characteristics, cancer-specific survival (CSS), and overall survival (OS) were analyzed. Results Out of 20 indexes, hemoglobin (HB), C-reactive protein to albumin ratio (CAR), and platelet to lymphocyte ratio (PLR) were independent risk factors based on logical regression analyses. Then, an integrative score with the optimal cut-off value of .67 was established according to the Combination Of HB, CAR, and PLR (COHCP). The area under the curve (AUC) indicated higher predictive ability of COHCP on prognosis than other indicators. Multivariate analyses revealed that COHCP serves as an independent prognostic score. Patients with COHCP low group (≤.67) had better 5-year CSS (57.3% vs 13.5%, P < .001) and OS (51.1% vs 12.3%, P < .001) than those with high group, respectively. Finally, the nomogram based on COHCP was established and validated regarding CSS and OS, which can accurately and effectively predict individual survival in resected ESCC. Conclusion The COHCP was a novel, simple, and useful predictor in resectable ESCC. The COHCP-based nomogram may accurately and effectively predict survival.

Laboratory blood parameters and machine learning for the prognosis of esophageal squamous cell carcinoma

“…Although age (Figure 7K) is controversial for the prognosis of patients with ESCC, it is a good predictor in this experiment. Studies have shown that the nutritional status of patients is also closely related to the prognosis of tumors (10), and MCH, Na, FIB, DBIL, CL (Figures 7H, I, L-N) and other factors also show high predictive ability in this experiment. In addition, the prognosis of patients with ESCC varies with different treatments, and treatment (Figure 7D) (24) is also included in this model.…”

“…Compared with some previous studies, the prognostic ability of our model (three-year AUC 0.761, five-year AUC 0.771) also showed some advantages. In Feng’s experiment ( 10 ), they used laboratory data such as HB and CAR to construct a COHCP model to evaluate the prognosis of ESCC, and its AUC (0.771 for continuous and 0.744 for categorical) showed good predictive ability. In another study, Jayaprakasam’s team ( 22 ) constructed a prognostic model of ESCC using PET and CT, with an AUC of 0.…”

“…Previous research has found that age, sex, tumor size, lymph node (LN) metastasis, tumor location, tumor invasion depth level, angiolymphatic invasion, pT stage and pN stage were known independent predictors of ESCC outcomes (6)(7)(8)(9). Moreover, numerous of laboratory blood parameters, including mean platelet volume(MPV), hemoglobin (HB), C-reactive protein to albumin ratio (CAR), neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), monocyte-lymphocyte ratio (MLR), systemic immune-inflammation index (SII) and prognostic nutritional index (PNI), have been shown to be related to tumor progression and unfavorable prognosis of ESCC (10)(11)(12)(13). Despite the availability of these factors, it remains uncertain whether they should be collectively considered to establish and apply a survival prediction model for ESCC.…”