Risk prediction scores for postoperative mortality after esophagectomy: validation of different models

Preoperative risk modelling for oesophagectomy: A systematic review

BACKGROUND

Oesophageal cancer is a frequently observed and lethal malignancy worldwide. Surgical resection remains a realistic option for curative intent in the early stages of the disease. However, the decision to undertake oesophagectomy is significant as it exposes the patient to a substantial risk of morbidity and mortality. Therefore, appropriate patient selection, counselling and resource allocation is important. Many tools have been developed to aid surgeons in appropriate decision-making.

AIM

To examine all multivariate risk models that use preoperative and intraoperative information and establish which have the most clinical utility.

METHODS

A systematic review of the MEDLINE, EMBASE and Cochrane databases was conducted from 2000-2020. The search terms applied were ((Oesophagectomy) AND (Risk OR predict OR model OR score) AND (Outcomes OR complications OR morbidity OR mortality OR length of stay OR anastomotic leak)). The applied inclusion criteria were articles assessing multivariate based tools using exclusively preoperatively available data to predict perioperative patient outcomes following oesophagectomy. The exclusion criteria were publications that described models requiring intra-operative or post-operative data and articles appraising only univariate predictors such as American Society of Anesthesiologists score, cardiopulmonary fitness or pre-operative sarcopenia. Articles that exclusively assessed distant outcomes such as long-term survival were excluded as were publications using cohorts mixed with other surgical procedures. The articles generated from each search were collated, processed and then reported in accordance with PRISMA guidelines. All risk models were appraised for clinical credibility, methodological quality, performance, validation, and clinical effectiveness.

RESULTS

The initial search of composite databases yielded 8715 articles which reduced to 5827 following the deduplication process. After title and abstract screening, 197 potentially relevant texts were retrieved for detailed review. Twenty-seven published studies were ultimately included which examined twenty-one multivariate risk models utilising exclusively preoperative data. Most models examined were clinically credible and were constructed with sound methodological quality, but model performance was often insufficient to prognosticate patient outcomes. Three risk models were identified as being promising in predicting perioperative mortality, including the National Quality Improvement Project surgical risk calculator, revised STS score and the Takeuchi model. Two studies predicted perioperative major morbidity, including the predicting postoperative complications score and prognostic nutritional index-multivariate models. Many of these models require external validation and demonstration of clinical effectiveness.

CONCLUSION

Whilst there are several promising models in predicting perioperative oesophagectomy outcomes, more research is needed to confirm their validity and demonstrate improved clinical outcomes with the adoption of these models.

Risk prediction nomogram for major morbidity related to primary resection for esophageal squamous cancer

BACKGROUND AND OBJECTIVES

Postoperative major complications after esophageal cancer resection vary and may significantly impact long-term outcomes. This study aimed to build an individualized nomogram to predict post-esophagectomy major morbidity.

METHODS

This retrospective study included 599 consecutive patients treated at a single center between January 2017 and April 2019. Of them, 420 and 179 were assigned to the model development and validation cohorts, respectively. Major morbidity predictors were identified using multiple logistic regression. Model discrimination and calibration were evaluated by validation. Regarding clinical usefulness, we examined the net benefit using decision curve analysis.

RESULTS

The mean age was 64 years; 79% of the patients were male. The most common comorbidities were hypertension, diabetes mellitus, and stroke history. The 30-day postoperative major morbidity rate was 24%. Multivariate logistic regression analysis showed that age, smoking history, coronary heart disease, dysphagia, body mass index, operation time, and tumor size were independent risk factors for surgery-associated major morbidity. Areas under the receiver-operating characteristic curves of the development and validation groups were 0.775 (95% confidence interval, 0.721-0.829) and 0.792 (95% confidence interval, 0.709-0.874), respectively. In the validation cohort, the nomogram showed good calibration. Decision curve analysis demonstrated that the prediction nomogram was clinically useful.

CONCLUSION

Morbidity models and nomograms incorporating clinical and surgical data can be used to predict operative risk for esophagectomy and provide appropriate resources for the postoperative management of high-risk patients.

Post-treatment mortality after definitive chemoradiotherapy versus resection for esophageal cancer

In efforts to better characterize incidence and predictors of 30- and 90-day mortality following operative versus nonoperative approaches for locally advanced esophageal cancer (EC), we conducted a novel investigation of a large, contemporary US database. The National Cancer Database was queried for newly-diagnosed T1-3N0-1 squamous cell or adenocarcinoma receiving surgical-based therapy (esophagectomy alone or preceded by chemotherapy and/or radiotherapy) versus definitive chemoradiotherapy (dCRT). Statistics included graphing cumulative incidences of mortality before and following propensity score matching (PSM), based on age-based intervals. Cox regression determined factors independently predictive of 30- and 90-day mortality. Of 15,585 patients, 9,278 (59.5%) received surgical-based therapy and 6,307 (40.5%) underwent dCRT. In the unadjusted population, despite nonsignificant differences at 30 days (3.3% dCRT, 3.6% surgical-based), the dCRT cohort experienced higher 90-day mortality (11.0% vs. 7.5%, P < 0.001). Following PSM, however, dCRT patients experienced significantly lower 30-day mortality (P < 0.001), with nonsignificant differences at 90 days (P = 0.092). Surgical-based management yielded similar (or better) mortality as dCRT in ≤70-year-old patients; however, dCRT was associated with reduced mortality in subjects > 70 years old. In addition to the intervention group, factors predictive for 30- and 90-day mortality included age, gender, insurance status, facility type, comorbidity index, tumor location, histology, and T/N classification. In summary, surgical-based therapy for EC is associated with higher 30-day mortality, which becomes statistically similar to dCRT by 90 days. Differences between surgery and dCRT were most pronounced in patients > 70 years of age. These data may better inform shared decision-making between multidisciplinary providers and patients.

An esophagectomy Surgical Apgar Score (eSAS)-based nomogram for predicting major morbidity in patients with esophageal carcinoma

Background

Performing an esophagectomy for a malignancy presents an operation with an elevated risk of complications. The esophagectomy Surgical Apgar Score (eSAS) has been confirmed to be a strong predictor of major postoperative morbidity. The purpose of this study was to construct and establish an eSAS-based nomogram for predicting major morbidity after esophagectomy for esophageal carcinoma.

Methods

A total of 194 patients underwent radical esophagectomy for the malignant disease was analyzed by internal validation, and the clinical value was calculated on external validation (n=135). The 30-day major morbidity was recorded as the outcome. Univariable and multivariable logistic regression analysis analyzed the preoperative and intraoperative variables. An eSAS-based nomogram was constructed to predict the risk of major postoperative morbidity. The verification curves for the performance were drawn.

Results

Major morbidity occurred in 34.04% (n=66) of cases. Based on the final regression analysis, we proved that the eSAS had a highly linear association with major morbidity after esophagectomy. We further constructed a nomogram integrating the eSAS and clinical predictors [body mass index (BMI), American Society of Anesthesiologists (ASA) classification, and diabetes mellitus] to predict the probability of major postoperative morbidity. The performance of the eSAS-based nomogram was assessed and proven to be clinically useful by internal and external validation.

Conclusions

We constructed an eSAS-based nomogram that can effectively predict the risk of major morbidity after esophagectomy in patients with esophageal carcinoma. With a highly exact and exceedingly simple model, clinicians could more precisely ease the individual perioperative management for decreasing the postoperative complication.

Predictive value of intraoperative factors for complications after oesophagectomy

OBJECTIVES

Oesophagectomy for malignancy is a highly complex and difficult procedure associated with considerable postoperative complications. In this study, we aimed to identify the ability of an intraoperative factor (IPFs)-based classifier to predict complications after oesophagectomy.

METHODS

This retrospective review included 251 patients who underwent radical oesophagectomy from October 2015 to December 2017. Using the least absolute shrinkage and selection operator regression model, we extracted IPFs that were associated with postoperative morbidity and then built a classifier. Preoperative variables and the IPF-based classifier were analysed using univariable and multivariable logistic regression analysis. A nomogram to predict the risk of postoperative morbidity was constructed and validated using bootstrap resampling.

RESULTS

Following the least absolute shrinkage and selection operator regression analysis, we discovered that those 4 IPF (surgical approach, lowest heart rate, lowest mean arterial blood pressure and estimated blood loss) were associated with postoperative morbidity. After stratification into low-and high-risk groups with the IPF-based classifier, the differences in 30-day morbidity (7.2% vs 70.1%, P < 0.001, respectively) and mortality (0% vs 4.7%, P = 0.029, respectively) were found to be statistically significant. The multivariable analysis demonstrated that the IPF-based classifier was an independent risk factor for predicting postoperative morbidity for patients with oesophageal cancer. The performance of the nomogram was evaluated and proven to be clinically useful.

CONCLUSIONS

We demonstrated that an IPF-based nomogram could reliably predict the risk of postoperative morbidity. It has the potential to facilitate the individual perioperative management of patients with oesophageal cancer.

Individual risk modelling for esophagectomy: a systematic review

INTRODUCTION

A number of models have been applied to predict outcomes from esophagectomy. This systematic review aimed to compare their clinical credibility, methodological quality and performance.

METHODS

A systematic review of the PubMed, EMBASE and Cochrane databases was performed in October 2012. Model and study quality were appraised using the framework of Minne et al.

RESULTS

Twenty studies were included in total; these were heterogeneous, retrospective and conducted over a number of years; all models were generated via logistic regression. Overall mortality was high, and consequently not representative of current practice. Clinical credibility and methodological quality were variable, with frequent failure to perform internal validation and variable presentation of calibration and discrimination metrics. P-POSSUM demonstrated the best calibration and discrimination for predicting mortality. Other than the Southampton score (which has yet to be externally validated) and the Amsterdam score, no studies had utility in predicting complications.

CONCLUSION

Whilst a number of models have been developed, adapted or trialled, due to numerous limitations, larger and more contemporary studies are required to develop and validate models further. The role of alternative techniques such as decision tree analysis and artificial neural networks is not known.

The Impact of Age on Morbidity and Mortality following Esophagectomy for Esophageal Cancer

Background

In patients with esophageal cancer, treatment decisions often involve a balance between a high-risk procedure and the chance for long-term benefit. The decision can be additionally challenging for elderly patients since some studies have reported an increased incidence of morbidity and mortality in this age group, and data are not clear on the overall benefit of multimodality therapy.

Methods

To investigate the management and outcomes associated with esophagectomy in elderly patients with esophageal cancer, we performed a review of the literature as well as an analysis of our own institutional data, with a focus on the impact of age on surgical outcomes. We examined type of surgery, neoadjuvant and adjuvant therapy, postoperative complications, length of hospitalization, and mortality as variables in elderly patients with esophageal cancer.

Results

When assessing the impact of age on the success of esophagectomy, several studies have concluded that advanced age itself is not a predictor of outcomes as much as associated comorbidities are. Our own experience suggests that age is not associated with adverse outcomes when controlling for patient comorbidities. This finding is similar to data reported elsewhere.

Conclusions

When considering treatment for patients of advanced age, the risks of treatment should be compared with the survival benefits of the therapy prescribed, taking into account additional factors such as poor performance status, existing comorbidities, and residual tumor following neoadjuvant therapy. Many reports, as well as our own experience, have concluded that when adjusted for comorbidities, patient age does not significantly affect outcomes.

Assessment of a scoring system for predicting complications after esophagectomy

Complications occur frequently after esophagectomy. Identifying the risk of complications preoperatively may help in patient selection and postoperative management. We performed a retrospective review of patients who underwent esophagectomy between 1980 and 2009. A previously reported scoring system was used to estimate risk, and its ability to predict complications was assessed. A total of 514 patients (382 men; 74%) with a mean age of 59.0 ± 12.5 years underwent esophagectomy for cancer (398; 77%) or benign disease. Minor complications occurred in 224 patients (44%) and severe complications occurred in 134 patients (26%). The calculated risk score was based on weighted values for age, coronary artery disease, cerebrovascular disease, type of operation, and forced expiratory volume in the first second expressed as a percent of predicted (FEV1%). Increasing risk score was associated with a linear increase in the incidence of complications (P < 0.001 for either severe complications or any complications). The scoring system predicted severe complications with an accuracy of 65.3% (P < 0.001). Score groups identified an incremental risk of severe complications (0 to 6 = 12%; 7 to 13 = 18%; 14 to 20 = 28%; 21 to 27 = 36%; >27 = 52%; P < 0.001). Complications are frequent after esophagectomy and can be predicted using a previously reported scoring system. This scoring system may assist in patient selection for esophagectomy and in providing appropriate resources for postoperative management of higher risk patients.

Prediction of major pulmonary complications after esophagectomy

BACKGROUND

Pulmonary complications are the most frequent morbid event after esophagectomy. Understanding factors that are associated with pulmonary complications may help in patient selection and postoperative management.

METHODS

We performed a retrospective review of patients who underwent esophagectomy between 1980 and 2009. Univariate analysis was used to identify potential covariates for the development of major pulmonary complications. Multivariable logistic regression analysis was used to identify predictors of complications. A scoring system was developed, and its ability to predict complications was assessed.

RESULTS

A total of 516 patients (382 men [74%]) with a mean age of 59.0±12.5 years underwent esophagectomy for cancer (398 [77%]) or benign disease. Major pulmonary complications occurred in 197 patients (38%) and were associated with a 10-fold increase in operative mortality (2.5% vs 28%; p<0.001). Independent predictors included patient age, forced expiratory volume in 1 second (% predicted), diffusion capacity of the lung for carbon monoxide (% predicted), performance status, serum creatinine, current cigarette use, and transthoracic resection. The scoring system (based on weighted scores for the first 4 covariates listed above) predicted pulmonary complications with an accuracy of 70.8% (p<0.001). Score groups identified an incremental risk of complications of 0 to 2, 12%; 3 to 4, 18%; 5 to 6, 46%; 7 to 8, 52%; and 9 to 13, 60% (p<0.001).

CONCLUSIONS

Major pulmonary complications are frequent after esophagectomy and can be predicted using commonly available clinical information. A scoring system identifying the risk of such complications may assist in patient selection and in providing appropriate resources for postoperative management of higher-risk patients.

Preoperative risk assessment and prevention of complications in patients with esophageal cancer

In this review the preoperative risk assessment and prevention of complications in patients undergoing esophagectomy for cancer is discussed. Age, pulmonary and cardiovascular condition, nutritional status, and neoadjuvant chemo(radio)therapy are known predictive factors. None of these factors is a valid exclusion criterion for esophagectomy, but may help in careful patient selection. Both anesthetists and surgeons play an important role in intraoperative risk reduction by means of appropriate fluid management and application of optimal surgical techniques.