Zhong QH, Huang JS, Guo FL, Wu JY, Yuan MX, Zhu JF, Lin WW, Chen S, Zhang ZY, Lin JB. Prediction and stratification for the surgical adverse events after minimally invasive esophagectomy: A two-center retrospective study. World J Gastroenterol 2025; 31(3): 101041 [DOI: 10.3748/wjg.v31.i3.101041]
Corresponding Author of This Article
Jiang-Bo Lin, PhD, Professor, Department of Thoracic Surgery, Fujian Medical University Union Hospital, No. 29 Xinquan Road, Fuzhou 350001, Fujian Province, China. jiangbolin99@163.com
Research Domain of This Article
Surgery
Article-Type of This Article
Retrospective Study
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Qi-Hong Zhong, Jiang-Shan Huang, Fei-Long Guo, Jing-Yu Wu, Jia-Fu Zhu, Wen-Wei Lin, Sui Chen, Zhen-Yang Zhang, Jiang-Bo Lin, Department of Thoracic Surgery, Fujian Medical University Union Hospital, Fuzhou 350001, Fujian Province, China
Qi-Hong Zhong, Mao-Xiu Yuan, The Graduate School, Fujian Medical University, Fuzhou 350001, Fujian Province, China
Mao-Xiu Yuan, Department of Thoracic Surgery, Affiliated Hospital of Jinggangshan University, Ji’an 343000, Jiangxi Province, China
Co-first authors: Qi-Hong Zhong and Jiang-Shan Huang.
Co-corresponding authors: Zhen-Yang Zhang and Jiang-Bo Lin.
Author contributions: Zhong QH and Huang JS designed and performed the research and drafted the manuscript, they are co-first authors of this manuscript; Guo FL and Lin JB designed the research and supervised and reviewed the report; Lin JB and Zhang ZY supervised the report and provided funding acquisition, and they as co-corresponding authors; Wu JY and Zhong QH designed the research and contributed to the analysis; Zhu JF, Chen S, Lin WW, and Yuan MX collected the data and provided the methodology.
Supported by Joint Funds for the Innovation of Science and Technology, Fujian Province, No. 2023Y9187 and No. 2021Y9057.
Institutional review board statement: Our study was approved by the Institutional Review Board of Fujian Medical University Union Hospital (Approval No. 2024KY037) and the Affiliated Hospital of Jinggangshan University (Approval No. 2024115).
Informed consent statement: Patients were not required to give informed consent to the study because the analysis used anonymous clinical data that were obtained after each patient agreed to treatment by written consent.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Data sharing statement: The original anonymous dataset is available on request from the corresponding author at jiangbolin99@163.com.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Jiang-Bo Lin, PhD, Professor, Department of Thoracic Surgery, Fujian Medical University Union Hospital, No. 29 Xinquan Road, Fuzhou 350001, Fujian Province, China. jiangbolin99@163.com
Received: September 2, 2024 Revised: October 5, 2024 Accepted: November 25, 2024 Published online: January 21, 2025 Processing time: 108 Days and 13.2 Hours
Abstract
BACKGROUND
Minimally invasive esophagectomy (MIE) is a widely accepted treatment for esophageal cancer, yet it is associated with a significant risk of surgical adverse events (SAEs), which can compromise patient recovery and long-term survival. Accurate preoperative identification of high-risk patients is critical for improving outcomes.
AIM
To establish and validate a risk prediction and stratification model for the risk of SAEs in patients with MIE.
METHODS
This retrospective study included 747 patients who underwent MIE at two centers from January 2019 to February 2024. Patients were separated into a train set (n = 549) and a validation set (n = 198). After screening by least absolute shrinkage and selection operator regression, multivariate logistic regression analyzed clinical and intraoperative variables to identify independent risk factors for SAEs. A risk stratification model was constructed and validated to predict the probability of SAEs.
RESULTS
SAEs occurred in 10.2% of patients in train set and 13.6% in the validation set. Patients with SAE had significantly higher complication rate and a longer hospital stay after surgery. The key independent risk factors identified included chronic obstructive pulmonary disease, a history of alcohol consumption, low forced expiratory volume in the first second, and low albumin levels. The stratification model has excellent prediction accuracy, with an area under the curve of 0.889 for the training set and an area under the curve of 0.793 for the validation set.
CONCLUSION
The developed risk stratification model effectively predicts the risk of SAEs in patients undergoing MIE, facilitating targeted preoperative interventions and improving perioperative management.
Core Tip: In this study, a predictive model was developed to stratify the risk of surgical adverse events following minimally invasive esophagectomy for esophageal cancer. By identifying key risk factors, including chronic obstructive pulmonary disease, low forced expiratory volume in the first second, and hypoalbuminemia, the model enhances preoperative assessment, allowing for targeted interventions. The model’s high predictive accuracy underscores its potential for integration into clinical practice to improve patient outcomes and reduce postoperative complications after minimally invasive esophagectomy. This approach represents a significant advancement in personalized surgical management for esophageal cancer patients.
Citation: Zhong QH, Huang JS, Guo FL, Wu JY, Yuan MX, Zhu JF, Lin WW, Chen S, Zhang ZY, Lin JB. Prediction and stratification for the surgical adverse events after minimally invasive esophagectomy: A two-center retrospective study. World J Gastroenterol 2025; 31(3): 101041
Esophageal cancer is the seventh most common cause of cancer-related deaths worldwide[1]. At present, comprehensive treatment based on minimally invasive esophagectomy (MIE) is still the most effective way to cure esophageal cancer. However, the incidence of postoperative complications after MIE is as high as 36.2%-54%, which significantly affects the perioperative and long-term survival rates of patients with esophageal cancer[2-5]. Surgical adverse events (SAEs) are a threat to patient survival and quality of life, with a mortality rate of 3.3%-3.6%, and are still the main cause of perioperative death[6-9]. Accordingly, in clinical practice, identifying patients with SAEs in time is necessary for preoperative prevention and postoperative detection measures to improve the prognosis of patients.
Clavien-Dindo (CD) grading system published in 2004 has changed the way postoperative complications are reported[10]. With the increasing application of the CD scoring system, the CD score has become the standard for judging SAEs in many surgical studies[11-13]. However, CD scores can be determined only after complications occur, which means that most SAEs can be identified only when patients are discharged from the hospital, which greatly delays treatment. Perioperative management has a significant effect on serious postoperative complications and recovery in patients with esophageal cancer. Timely preoperative intervention and postoperative monitoring for patients at different risk levels are beneficial for reducing perioperative mortality and SAEs incidence and promoting postoperative recovery[14-16]. Therefore, surgeons need to identify high-risk patients accurately and promptly and provide timely intervention at different risk levels so that patients can receive better perioperative management, which is positively correlated with length of stay, resource utilization, and complications[17,18].
To date, the prediction models for postoperative complications in previous studies still lack accuracy and pertinence[19-21]. There is still no clear standard for objectively and effectively stratifying patients with different risk levels. How to identify the risk group for SAEs before surgery is an urgent problem to be solved, and more efforts need to be invested in the construction of a targeted prognostic system. In our study, we established a model that can accurately stratify patients before surgery so that high-risk patients can be preventively managed before surgery to reduce the chance of SAEs occurring after surgery.
MATERIALS AND METHODS
Patient population
A total of 747 patients who were diagnosed with esophageal cancer and underwent MIE at Fujian Medical University Union Hospital and Affiliated Hospital of Jinggangshan University from January 2019 to February 2024 were included in this retrospective study. Patients from January 2019 to October 2023 were included in the training set (n = 549), and patients from October 2023 to February 2024 were used as the validation set (n = 198) (Figure 1). MIE surgery at Fujian Medical University Union Hospital was performed by three teams. MIE surgery at Jinggangshan University Affiliated Hospital is performed by Dr. Yuan’s team. All the teams had extensive experience in MIE surgery and management. The inclusion criteria were as follows: (1) Esophageal cancer was confirmed by preoperative gastroscopy; and (2) No distant metastasis was detected in either the preoperative assessments or postoperative pathology. The exclusion criteria were: (1) A prior history of cancer or malignant tumors in other parts of the body; (2) History of serious chronic illness; and (3) Incomplete data.
Figure 1 Work flow of research.
MIE: Minimally invasive esophagectomy; SAE: Severe adverse event.
Outcome definition
The complication rate and 30-day mortality rate of the patients were recorded after 30 days postoperatively. Postoperative complications were identified in the European definition of perioperative outcomes as any deviation from the normal course of postoperative recovery during esophagectomy treatment[22]. Postoperative short-term outcome data, including pulmonary infection, anastomotic fistula, blood transfusion, intensive care unit (ICU) management, anastomotic leakage, and length of stay, were collected from the patients’ medical records and course notes. All postoperative complications were diagnosed by experienced physicians. The severity of complications was assessed using the CD system that classifies complications based on the intensity of treatment required (Supplementary Table 1)[10]. CD I to II grades were identified as non-SAE and CD III to V grades were identified as SAE.
Clinical characteristics, laboratory tests, and intraoperative factors
Baseline information and clinical characteristics included age, sex, body mass index, registration, American Society of Anesthesiologists score, tumor site, alcohol consumption, smoking history, and medical history. The laboratory test results of patients with esophageal cancer, including white blood cell counts, hemoglobin (Hb) levels, platelet counts, calcium (Ca) levels, neutrophil, lymphocyte, monocyte, carcinoembryonic antigen levels, and albumin levels, were recorded within 24 hours of admission. Pulmonary function tests, including forced expiratory volume in the first second (FEV1), maximal voluntary ventilation, and FEV1/forced vital capacity, were collected from pulmonary function test reports. Intraoperative data, including blood loss, operation time, and number of lymph nodes dissected, were collected from surgical records. We used EpiData to collect data for each patient. The data collectors were unaware of the patient groups during data collection and used a double-check system to ensure data accuracy. In cases of disputes, a third person is invited to make a judgment.
Statistical analysis
Intergroup differences were analyzed for categorical variables using the χ2 test or Fisher’s exact test. For normally distributed continuous variables, we applied the independent t-test. For continuous variables, the Mann-Whitney U test was used to analyze differences. The least absolute shrinkage and selection operator (LASSO) based on the “glmnet” package was used for variable screening when the λ value was the minimum mean square error. The selected factors were further analyzed by backward stepwise multifactor logistic regression to determine independent risk factors. Receiver operating characteristic curves and the area under the curve (AUC) were calculated to assess the accuracy of the predictive models. Statistical analyses were performed with R software (version 4.3.3).
RESULTS
General baseline
A total of 549 patients with MIE for esophageal cancer were included in the training set, 56 (10.2%) were found to have SAEs, whereas non-SAEs occurred in 493 (89.8%) patients. No marked differences in the baseline parameters, including sex, body mass index, diabetes status, high blood pressure, tumor location, coronary heart disease, neoadjuvant therapy, pT, pN, or history of smoking (all P values > 0.05), were observed between the groups. However, the SAE group was significantly older (SAE group vs non-SAE group = 65.77 years vs 61.11 years, P < 0.001) and had a significantly greater chronic obstructive pulmonary disease (COPD) rate (SAE group vs non-SAE group = 12.5% vs 2.2%, P < 0.001) (Table 1). Among the 198 patients in the validation set, 27 (13.6%) were SAE patients. The SAE patients in the validation group had a superior incidence of COPD (SAE group vs non-SAE group = 14.8% vs 1.8%, P = 0.007), whereas the other variables were not significantly different (P > 0.05).
Table 1 Compared different baseline information between non-surgical adverse events and surgical adverse events patients, n (%).
A comparison of the postoperative recovery of the SAE group and the non-SAE group revealed that the SAE group had a significantly greater incidence of pneumonia, blood transfusion, ICU management, anastomotic leakage, and reoperation. The SAE group also had a worse prognosis than the non-SAE group in respect of the duration of chest tube drainage and duration of ICU stay (all P values < 0.05, Table 2). However, no mortality occurred within 30 days in either group. The main complications in the SAE group were anastomotic leakage (80.4%), pneumonia (39.3%), and blood transfusion (21.4%). The median chest tube indwelling time and ICU management time were 13 days and 7 days, respectively. The Kaplan-Meier curve revealed that patients with SAE had significantly slower discharge than did those without SAE (P value < 0.001, Figure 2).
LASSO regression variable screening and univariate and multivariate analyses
To prevent the model from overfitting due to high-dimensional variables and to alleviate the problem of multicollinearity, we used LASSO regression to screen the 41 variables in the training cohort. Using lasso internal cross-validation (10 k-fold), the number of variables was reduced to 10 when log(λ) achieved the minimum mean square error and to 4 when the minimum distance standard error (onefold SE) was achieved (Figure 3). To include more variables to improve the model performance, we choose the minimum mean square error to screen the variables (λ value = 0.00841). LASSO regression was used to screen 10 optimal variables, including age, history of alcohol consumption, COPD, neoadjuvant chemotherapy, neoadjuvant radiotherapy, tumor size, and the FEV1, Ca, Hb, and albumin levels.
Figure 3 LASSO regression curves.
A: The curve of the regression coefficient vs log (λ); B: The curve of mean squared error vs log (λ).
Univariate and multivariate analyses were performed on the screened variables associated with SAEs. Univariate analysis revealed that age, history of alcohol consumption, COPD, FEV1, Ca, Hb, and albumin were significant factors for increasing SAEs (all P < 0.05). Variables with P < 0.1 in the univariate analysis were included in the multivariate analysis. Multivariate analysis revealed that a history of alcohol consumption [odds ratio (OR) = 4.69, 95% confidence interval (CI): 2.32-9.51, P < 0.001], COPD (OR = 4.82, 95%CI: 1.28-18.18, P = 0.020), a lower FEV1 (OR = 0.13, 95%CI: 0.06-0.27, P < 0.001), and a lower albumin concentration (OR = 0.83, 95%CI: 0.76-0.90, P < 0.001) were independent risk factors for SAEs (P < 0.05, Table 3 and Figure 4). In addition, neoadjuvant radiotherapy (OR = 3.69, 95%CI: 1.00-13.71, P = 0.051) was also associated with the occurrence of SAEs, but the difference was not statistically significant.
According to the regression coefficients of each factor in multivariate logistic regression, a risk stratification model was established to identify patients at high risk of SAEs: Risk score = 0.026 × age + 1.546 × history of alcohol consumption + 1.573 × COPD + 1.307 × neoadjuvant radiotherapy - 2.044 × FEV1 - 2.824 × Ca - 0.013 × Hb - 0.190 × albumin. And a nomogram was developed to precisely calculate the odds of a poor prognosis (Figure 5). In addition, we constructed a checklist to promote the application of the model (Table 4). The AUC of the stratification model in the validation set was 0.889 (95%CI: 0.853-0.926; Figure 6A). In the validation set, the AUC of this stratification model was 0.793 (95%CI: 0.701-0.884; Figure 6B). The stratification model score used the maximum Youden index to determine the optimal cutoff value and divided the patients into a low-risk group (risk score ≤ -16.98, n = 393) and a high-risk group (risk score > -16.98, n = 156) (Figure 7A). We categorized the validation set into high and low risk groups based on the cutoff values obtained. And we found that almost no SAE patients were in the low-risk group (4.4%), while the proportion of SAE patients in the high risk-group (33.9%) greatly increased (Figure 7B).
Figure 6 Receiver operating characteristic curves of prediction model in train set and validation set.
A: Receiver operating characteristic curves of train set; the area under the curve of prediction model was 0.889 (95% confidence interval: 0.853-0.926); B: Receiver operating characteristic curves of validation set; the area under the curve of prediction model was 0.793 (95% confidence interval: 0.701-0.884).
Figure 7 Optimal cut-point stratification validation for risk stratification model.
A: The best cut-point values are 16.98; B: Validation of risk stratification model in validation set. SAE: Severe adverse event.
The prevention and management of SAEs after MIE have always been challenging for surgeons. The occurrence of SAEs can significantly delay patient recovery, leading to a perioperative mortality rate exceeding 3%, and is associated with a significant decrease in long-term survival rates post-surgery due to complications that can lead to further health deterioration[8,23,24]. Timely identification of patients at high risk for SAEs and improved perioperative management can significantly improve patient prognosis. Consequently, clinicians continue to face challenges in rapidly and accurately identifying SAEs[25-27].
The incidence of SAEs in MIE is lower than that in traditional open surgery, but the risk of SAEs still exists. In this study, the incidence of SAEs (10.2%) was lower than Jung et al[19] in Ivor Lewis esophagectomy (52.9%). However, compared with studies that also used MIE (10.2%-15.3%), there was no significant difference in the incidence of SAEs[28,29]. We found that compared with patients without SAEs, patients with SAEs had significantly worse postoperative complications and recovery, which may lead to prolonged hospitalization and, in turn, may lead to the occurrence of more severe complications. SAEs also lead to poor quality of life and long-term symptoms after surgery, such as fatigue, nausea and vomiting, dyspnea, and problems with eating or swallowing[30]. For patients with advanced esophageal cancer who develop SAEs, postoperative complications can also influence the choice of treatment options for follow-up. Therefore, our study revealed a strong correlation between SAEs and prolonged hospital stays and poor long-term survival.
Proper preoperative assessment of high-risk patients to avoid the occurrence of SAEs should be a priority for surgeons. In this study, both the patient’s clinical history and laboratory test results influenced the occurrence of SAEs, including factors such as age, history of alcohol consumption, COPD, neoadjuvant radiotherapy, FEV1, Hb, and albumin. Elderly patients often present with more comorbidities and poorer overall preoperative health status, although individual differences exist. As a result, while age appeared to be a significant risk factor in the univariate regression analysis, it did not retain statistical significance when adjusted for other variables in the multivariate regression. Chronic alcohol consumption can impair the normal defense mechanisms of the liver, disrupt the intestinal barrier, and compromise mucosal immune function, leading to reduced nutrient absorption. This makes a history of alcohol consumption an independent risk factor for SAEs[31]. Since MIE involves intrathoracic surgery, it has a certain impact on lung function. Patients with COPD and low FEV1 have poor lung function and are often unable to cough effectively after surgery, which increases the risk of lung infection and other complications and severely slows recovery[32-34]. Like previous studies, our study confirmed that COPD and lower FEV1 were independent risk factors for SAEs[33,35]. The dense esophageal fibrosis and edema caused by neoadjuvant radiotherapy make surgical operations more difficult[36]. In addition, radiation-induced microvascular damage after radiotherapy can slow tissue healing in the irradiated field and increase the risk of anastomotic leakage[37]. Similarly, low albumin and low Hb caused by malnutrition lead to tissue edema, lower immune levels, and repair difficulty[34,38,39], which was also confirmed to be an independent risk factor for SAEs in our study. Giving more attention to the above factors that cause SAEs in the clinic may help healthcare providers further improve the quality of postoperative management and medical care and speed up patients’ recovery.
Establishing a stratified model for determining SAE risk before surgery can more accurately identify high-risk patients. Recently, some studies have focused on the prediction of severe complications after esophagectomy, but the accuracy and clinical applicability of these models are still lacking[19,40-42]. However, in our study, we used LASSO regression for variable screening to prevent the model from overfitting due to high-dimensional variables and alleviate the problem of multicollinearity[43]. For further regression analyses, independent risk factors were identified and risk stratification models were developed to identify high-risk patients. The model’s AUC reached 0.889 (95%CI: 0.853-0.926) in the train set and 0.793 (95%CI: 0.701-0.884) in the validation set, indicating high accuracy. The comparison of SAE incidence rates between the high-risk and the low-risk group in the validation set also confirmed the accuracy of the model. For patients classified into the high-risk group, nutritional intervention and more detailed preoperative preparation should be given during the perioperative period. If necessary, they should be sent to the ICU for timely management to reduce the probability of more serious complications. Patients in the low-risk group also need continuous and dynamic observation. In the context of enhanced recovery after surgery, these patients can actively implement rapid recovery strategies[16,26]. The application of a risk stratification model to personalize the treatment of MIE patients can promote the precise development of perioperative management and postoperative care.
The limitations of this study are as follows. First, data for this study were collected retrospectively at two centers, and there may inevitably have been possible human intervention that affected the results. Second, due to the low incidence of SAEs, some parameters may not be recognized. Third, since the two centers selected for this study are both hospitals with good diagnosis and treatment levels, the generalizability may be limited to a certain extent. In the future, we will design further prospective multicenter studies to confirm these findings, establish causal relationships, and further explore the mechanisms underlying these risk factors.
CONCLUSION
The risk stratification model predicts SAEs and provides a comprehensive assessment of the recovery of patients undergoing MIE after treatment, which helps to carry out preventive management and postoperative care for high-risk patients during the perioperative period and reduces the chance of postoperative SAEs.
ACKNOWLEDGEMENTS
The authors thank the staff of Prof. Lin and the Department of Thoracic Surgery of Fujian Medical University Union Hospital, for their guidance and support.
Footnotes
Provenance and peer review: Unsolicited article; Externally peer reviewed.
Peer-review model: Single blind
Specialty type: Gastroenterology and hepatology
Country of origin: China
Peer-review report’s classification
Scientific Quality: Grade B, Grade C
Novelty: Grade B, Grade B
Creativity or Innovation: Grade B, Grade B
Scientific Significance: Grade B, Grade B
P-Reviewer: Miao HW; Pan D S-Editor: Wang JJ L-Editor: A P-Editor: Zhao S
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