Evaluation of data quality in the Spanish EURECCA Esophagogastric Cancer Registry

Cero, Mariagiulia Dal; Rodriguez-Santiago, Joaquín; Miró, Mònica; Castro, Sandra; Miranda, Coro; Santamaría, Maite; Gobbini, Yanina; Garsot, Elisenda; Pujadas, Marcel; Luna, A. Bayés de; Momblán, Dulce; Balagué, C.; Aldeano, Aurora; Olona, Carles; Molinas, Joan; Pulido, Laura; Sánchez-Cano, J.J.; Güell, Mercè; Salazar, David; Gimeno, M.A.F.; Grande, Luís; Pera, Manuel; Pérez-Romero, Noelia; Osorio, Javier; Gantxegi, Amaia; Rebasa, Pere; Yarnoz, María C.; Galofré, Gonzalo; Artigau, Eva; García-Moriana, Elisabet; Turrado, Víctor; Hermoso, Judit; Feliu, Jaume Cruz i; Prieto, Rosa Isabel; Sánchez-Cordero, Sergi

doi:10.1016/j.ejso.2021.04.025

Cited by 9 publications

(10 citation statements)

References 26 publications

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“…The online data cover information about the whole care process and the short-term and long-term follow-up. Detailed definitions of comorbidities, postoperative complications,17,18 histopathology findings, and quality outcome measures are used since January 2014 and have been previously described 15. The SEEGCR database was recently audited (period 2014–2017) with completeness estimated at 97% and data accuracy at 95% 15…”

Section: Methodsmentioning

confidence: 99%

“…15 The SEEGCR database was recently audited (period 2014-2017) with completeness estimated at 97% and data accuracy at 95%. 15…”

Section: Source Of Datamentioning

confidence: 99%

“…13,14 The Spanish EURECCA Esophagogastric Cancer Registry (SEEGCR) is a large and audited multi-institutional population-based registry launched in 2013 that provides real-world data on outcomes and quality measures of esophagogastric cancer surgery. 15 The present study used information from this database and machine learning methods to develop a clinically useful risk prediction model of 90DM after gastric cancer resection with curative intent, followed by internal-external validation of the model.…”

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confidence: 99%

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Machine Learning Risk Prediction Model of 90-day Mortality After Gastrectomy for Cancer

et al. 2022

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Objective: To develop and validate a risk prediction model of 90-day mortality (90DM) using machine learning in a large multicenter cohort of patients undergoing gastric cancer resection with curative intent. Background: The 90DM rate after gastrectomy for cancer is a quality of care indicator in surgical oncology. There is a lack of well-validated instruments for personalized prognosis of gastric cancer. Methods: Consecutive patients with gastric adenocarcinoma who underwent potentially curative gastrectomy between 2014 and 2021 registered in the Spanish EURECCA Esophagogastric Cancer Registry database were included. The 90DM for all causes was the study outcome. Preoperative clinical characteristics were tested in four 90DM predictive models: Cross Validated Elastic regularized logistic regression method (cv-Enet), boosting linear regression (glmboost), random forest, and an ensemble model. Performance was evaluated using the area under the curve by 10-fold cross-validation. Results: A total of 3182 and 260 patients from 39 institutions in 6 regions were included in the development and validation cohorts, respectively. The 90DM rate was 5.6% and 6.2%, respectively. The random forest model showed the best discrimination capacity with a validated area under the curve of 0.844 [95% confidence interval (CI): 0.841-0.848] as compared with cv-Enet (0.796, 95% CI: 0.784-0.808), glmboost (0.797, 95% CI: 0.785-0.809), and ensemble model (0.847, 95% CI: 0.836-0.858) in the development cohort. Similar discriminative capacity was observed in the validation cohort. Conclusions: A robust clinical model for predicting the risk of 90DM after surgery of gastric cancer was developed. Its use may aid patients and surgeons in making informed decisions.

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Section: Methodsmentioning

confidence: 99%

“…15 The SEEGCR database was recently audited (period 2014-2017) with completeness estimated at 97% and data accuracy at 95%. 15…”

Section: Source Of Datamentioning

confidence: 99%

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confidence: 99%

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Machine Learning Risk Prediction Model of 90-day Mortality After Gastrectomy for Cancer

et al. 2022

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“…Ninety variables with detailed definitions were continuously collected from each patient by the reference surgeon at each institution. Validation of data registration (period 2014-2018) in the EURECCA dataset was recently performed, revealing 97% completeness and 95% accuracy rates [25].…”

Section: Data Collectionmentioning

confidence: 99%

“…a According to Charlson Comorbidity Index[26]; b According to 8th edition of the International Union Against Cancer tumor node metastasis staging system[27]; c Performance status according to Eastern Cooperative Oncology Group (ECOG)[25].…”

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confidence: 99%

Effect of Perioperative Blood Transfusions and Infectious Complications on Inflammatory Activation and Long-Term Survival Following Gastric Cancer Resection

Puértolas

Osorio

Jericó

et al. 2022

Cancers

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Background: The aim of this study was to evaluate the impact of perioperative blood transfusion and infectious complications on postoperative changes of inflammatory markers, as well as on disease-free survival (DFS) in patients undergoing curative gastric cancer resection. Methods: Multicenter cohort study in all patients undergoing gastric cancer resection with curative intent. Patients were classified into four groups based on their perioperative course: one, no blood transfusion and no infectious complication; two, blood transfusion; three, infectious complication; four, both transfusion and infectious complication. Neutrophil-to-lymphocyte ratio (NLR) was determined at diagnosis, immediately before surgery, and 10 days after surgery. A multivariate Cox regression model was used to analyze the relationship of perioperative group and dynamic changes of NLR with disease-free survival. Results: 282 patients were included, 181 in group one, 23 in group two, 55 in group three, and 23 in group four. Postoperative NLR changes showed progressive increase in the four groups. Univariate analysis showed that NLR change > 2.6 had a significant association with DFS (HR 1.55; 95% CI 1.06–2.26; p = 0.025), which was maintained in multivariate analysis (HR 1.67; 95% CI 1.14–2.46; p = 0.009). Perioperative classification was an independent predictor of DFS, with a progressive difference from group one: group two, HR 0.80 (95% CI: 0.40–1.61; p = 0.540); group three, HR 1.42 (95%CI: 0.88–2.30; p = 0.148), group four, HR 2.85 (95%CI: 1.64–4.95; p = 0.046). Conclusions: Combination of perioperative blood transfusion and infectious complications following gastric cancer surgery was related to greater NLR increase and poorer DFS. These findings suggest that perioperative blood transfusion and infectious complications may have a synergic effect creating a pro-inflammatory activation that favors tumor recurrence.

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Data quality validation of the Spanish Incisional Hernia Surgery Registry (EVEREG): pilot study

Olona

Rodríguez

Comas

et al. 2023

Hernia

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Evaluation of data quality in the Spanish EURECCA Esophagogastric Cancer Registry

Cited by 9 publications

References 26 publications

Machine Learning Risk Prediction Model of 90-day Mortality After Gastrectomy for Cancer

Machine Learning Risk Prediction Model of 90-day Mortality After Gastrectomy for Cancer

Effect of Perioperative Blood Transfusions and Infectious Complications on Inflammatory Activation and Long-Term Survival Following Gastric Cancer Resection

Data quality validation of the Spanish Incisional Hernia Surgery Registry (EVEREG): pilot study

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