What is the clinical evidence from this study?

Study design: Systematic Review. Population: Esophageal cancer undergoing esophagectomy (n=108208). Intervention: Preoperative prediction models. Primary outcome: Prognostic accuracy (AUC) for morbidity and mortality.

What does this research mean for the field?

Preoperative prediction models for morbidity and mortality after esophagectomy demonstrate prognostic accuracies ranging from 0.51 to 0.85, but none are currently rigorous enough for clinical implementation. Novelty: ClaimNovelty.SYNTHESIS. Consensus alignment: ConsensusAlignment.NEUTRAL.

February 21, 2024Open Access

Prediction of Morbidity and Mortality After Esophagectomy: A Systematic Review

MAM. P. van Nieuw AmerongenAmsterdam Neuroscience HGHarm‐Jan de GroothUniversity Medical Center Utrecht GVGigi VeermanAmsterdam University Medical Centers

Key Result

Preoperative prediction models for morbidity and mortality after esophagectomy demonstrated prognostic accuracies (AUC) ranging from 0.51 to 0.85, though none are currently ready for clinical implementation.

Study Design

Type

Systematic Review (n=108,208)

Structured PICO

What are the best prediction models for morbidity and mortality after esophagectomy in patients with esophageal cancer?

Population

108,208 patients from 22 studies undergoing esophagectomy for esophageal cancer, evaluated to assess the prognostic accuracy of preoperative prediction models for morbidity and mortality.

Exposure

Prediction models for morbidity and mortality

Outcome

Morbidity and mortality after esophagectomyhard clinical

Current prediction models for morbidity and mortality after esophagectomy have variable accuracy and high risk of bias, meaning none are currently ready for clinical implementation.

Limitations

High risk of bias in many included studies, mainly due to insufficient event-to-variable rates or insufficient overall event rates.
None of the 18 newly developed models were externally validated by independent research groups.
Generalizability issues due to the low event rate of post-esophagectomy mortality.
Lengthy study periods required to obtain workable sample sizes mean some data may be outdated regarding current surgical techniques and protocols.
Pretreatment details were often not clearly stated or varied significantly across studies.

Abstract

BACKGROUND: Esophagectomy for esophageal cancer has a complication rate of up to 60%. Prediction models could be helpful to preoperatively estimate which patients are at increased risk of morbidity and mortality. The objective of this study was to determine the best prediction models for morbidity and mortality after esophagectomy and to identify commonalities among the models. PATIENTS AND METHODS: A systematic review was performed in accordance to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement and was prospectively registered in PROSPERO ( https://www.crd.york.ac.uk/prospero/ , study ID CRD42022350846). Pubmed, Embase, and Clarivate Analytics/Web of Science Core Collection were searched for studies published between 2010 and August 2022. The Prediction model Risk of Bias Assessment Tool was used to assess the risk of bias. Extracted data were tabulated and a narrative synthesis was performed. RESULTS: Of the 15,011 articles identified, 22 studies were included using data from tens of thousands of patients. This systematic review included 33 different models, of which 18 models were newly developed. Many studies showed a high risk of bias. The prognostic accuracy of models differed between 0.51 and 0.85. For most models, variables are readily available. Two models for mortality and one model for pulmonary complications have the potential to be developed further. CONCLUSIONS: The availability of rigorous prediction models is limited. Several models are promising but need to be further developed. Some models provide information about risk factors for the development of complications. Performance status is a potential modifiable risk factor. None are ready for clinical implementation.

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