An esophagectomy surgical Apgar score-based nomogram-a risk-based postoperative triage system: a phase II randomised trial

Background

The esophagectomy surgical Apgar score (eSAS) has been found to be a predictor of postoperative complications in esophagectomy. In our previous study, we built a graphic nomogram based on eSAS and demonstrated that it can effectively predict the risk of major morbidity after esophagectomy. In this study, we aimed to assess the benefits of using an eSAS-based nomogram model as a postoperative risk-based triage system for patients undergoing esophagectomy.

Methods

We enrolled 119 patients diagnosed with esophageal carcinoma and randomly assigned them to a nomogram group (NG) or control group (CG) from January 2019 to December 2020. Patients in the NG were assigned to a low-risk group and high-risk group based on the nomogram. Patients in the high-risk group were admitted to the intensive care unit (ICU) after esophagectomy. Risk estimation in the CG patients was based on the surgeon's clinical experience. Thirty-day major complications, postoperative hospital stay, hospital costs, and quality of life (QOL) during the follow-up were compared between the two groups.

Results

Baseline clinicopathological characteristics were comparable between the NG (n=58) and CG (n=61). All patients underwent esophagectomy. Postoperative complications were significantly higher in the CG (30, 49.2%) than in the NG (14, 24.1%) (P=0.008), with pneumonia being the most common (CG: 23, 37.7%; NG: 12, 20.7%; P=0.042). There was no significant difference in anastomotic leakage (NG: 1, 1.7%; CG: 6, 9.8%; P=0.12). Postoperative median hospital stay was shorter in the NG (14 days) than in the CG (16 days) (P=0.041). Hospital costs (NG: ¥60,045.1; CG: ¥63,961.5; P=0.21) and postoperative QOL did not differ significantly between groups.

Conclusions

An eSAS-based nomogram as a triage system can reduce the overall occurrence of postoperative complications and shorten postoperative hospital stay without increasing hospital costs.

Trial Registration

Chinese Clinical Trial Registry ChiCTR1900021636.

A Quest for Optimization of Postoperative Triage After Major Surgery

INTRODUCTION

Innovative strategies to reduce costs while maintaining patient satisfaction and improving delivery of care are greatly needed in the setting of rapidly rising health care expenditure. Intensive care units (ICUs) represent a significant proportion of health care costs due to their high resources utilization. Currently, the decision to admit a patient to the ICU lacks standardization because of the lack of evidence-based admission criteria. The objective of our research is to develop a prediction model that can help the physician in the clinical decision-making of postoperative triage.

MATERIALS AND METHODS

Our group identified a list of index events that commonly grants admission to the ICU independently of the hospital system. We analyzed correlation among 200 quantitative and semiquantitative variables for each patient in the study using a decision tree modeling (DTM). In addition, we validated the DTM against explanatory models, such as bivariate analysis, multiple logistic regression, and least absolute shrinkage and selection operator.

RESULTS

Unlike explanatory modeling, DTM has several unique strengths: tree models are easy to interpret, the analysis can examine hundreds of variables at once, and offer insight into variable relative importance. In a retrospective analysis, we found that DTM was more accurate at predicting need for intensive care compared with current clinical practice.

DISCUSSION

DTM and predictive modeling may enhance postoperative triage decision-making. Future areas of research include larger retrospective analyses and prospective observational studies that can lead to an improved clinical practice and better resources utilization.