Acta anaesthesiologica Scandinavica · 2026
Prognostic Factors for Postoperative Complications. An Aggregate Protocol for 10 Observational Studies From the Danish TRIPLE-A Cohort of 1.2 Million Surgeries.
СтатьяНазвание и аннотация: Перевод в очереди
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Karlsen Anders Peder Højer, Prescott Asta, Folkersen Caroline, Laigaard Jens, Saito Atena, Klammer Caroline Lando, Wetterslev Mik, Ørskov Emil Ipsen, Fahrendorff Mathilde, Kiær Rebecca, Laigaard Helene Holm, Hovmand Nichlas, Lundstrøm Lars Hyldborg, Rasmussen Sebastian Buhl, Nørskov Anders Kehlet, Olsen Markus Harboe
Аннотация
BACKGROUND: Postoperative complications substantially increase morbidity, mortality and healthcare costs. Understanding prognostic factors is essential for risk stratification, targeted prevention strategies, and development of prediction models.
OBJECTIVES: To identify prognostic factors for 18 major postoperative complications in 10 studied domains using the Danish TRIPLE-A database: (1) acute postoperative pain, nausea and vomiting, and need for rescue opioids; (2) persistent opioid use; (3) acute kidney injury; (4) venous thromboembolism; (5) staphylococcal surgical site infection and infections requiring antibiotic treatment; (6) three airway management complications; (7) transfusion requirements; (8) delirium; (9) new-onset postoperative atrial fibrillation; and (10) readmission, reoperation and unplanned ICU admission.
METHODS: We will conduct 10 retrospective cohort studies using electronic health record data from 1.2 million surgical procedures performed in the Capital and Zealand Regions of Denmark from 2017 to 2025. Each individual complication will be identified via validated combinations of ICD-10 codes, medication administration, laboratory values, radiology examinations, and free-text mining of clinical notes. Candidate prognostic factors will be selected a priori based on literature review and clinical expertise, and will encompass patient demographics, comorbidities, surgical characteristics, anaesthetic management, vital parameters and biomarkers. We will estimate adjusted associations between candidate prognostic factors and complications using multivariable logistic regression with LASSO penalisation to reduce overfitting. Effect estimates will be reported as odds ratios with 95% confidence intervals, and the relative contribution of predictors will be assessed by changes in model discrimination (Δ-AUC). The approach for surgical procedure, calendar year, and hospital site will be determined for each study (adjustment, stratification, or inclusion as candidate predictors).
CONCLUSION: These studies aim to validate established prognostic factors and identify novel ones for major postoperative complications, providing a foundation for the development of individualised perioperative risk stratification models.
OBJECTIVES: To identify prognostic factors for 18 major postoperative complications in 10 studied domains using the Danish TRIPLE-A database: (1) acute postoperative pain, nausea and vomiting, and need for rescue opioids; (2) persistent opioid use; (3) acute kidney injury; (4) venous thromboembolism; (5) staphylococcal surgical site infection and infections requiring antibiotic treatment; (6) three airway management complications; (7) transfusion requirements; (8) delirium; (9) new-onset postoperative atrial fibrillation; and (10) readmission, reoperation and unplanned ICU admission.
METHODS: We will conduct 10 retrospective cohort studies using electronic health record data from 1.2 million surgical procedures performed in the Capital and Zealand Regions of Denmark from 2017 to 2025. Each individual complication will be identified via validated combinations of ICD-10 codes, medication administration, laboratory values, radiology examinations, and free-text mining of clinical notes. Candidate prognostic factors will be selected a priori based on literature review and clinical expertise, and will encompass patient demographics, comorbidities, surgical characteristics, anaesthetic management, vital parameters and biomarkers. We will estimate adjusted associations between candidate prognostic factors and complications using multivariable logistic regression with LASSO penalisation to reduce overfitting. Effect estimates will be reported as odds ratios with 95% confidence intervals, and the relative contribution of predictors will be assessed by changes in model discrimination (Δ-AUC). The approach for surgical procedure, calendar year, and hospital site will be determined for each study (adjustment, stratification, or inclusion as candidate predictors).
CONCLUSION: These studies aim to validate established prognostic factors and identify novel ones for major postoperative complications, providing a foundation for the development of individualised perioperative risk stratification models.