Key result
A multivariable risk prediction model incorporating comorbidities and preoperative laboratory variables accurately predicted 30-day death or major cardiac events after non-cardiac surgery in people with kidney failure (optimism-adjusted c-statistic 0.814).
Why the study?
Existing postoperative risk prediction tools excluded patients with kidney failure or perform poorly in this population despite their worse surgical outcomes.
Cohort (n=8,997)
Effect estimate: c-statistic 0.814 (95% CI 0.803-0.826)
Novel risk prediction models incorporating comorbidities and laboratory variables accurately predict 30-day major cardiac events or death in patients with kidney failure undergoing non-cardiac surgery.
May aid preoperative risk assessment in kidney failure; leaves open external validation and outcome impact.
BACKGROUND: People with kidney failure often require surgery and experience worse postoperative outcomes compared to the general population, but existing risk prediction tools have excluded those with kidney failure during development or exhibit poor performance. Our objective was to derive, internally validate, and estimate the clinical utility of risk prediction models for people with kidney failure undergoing non-cardiac surgery. DESIGN, SETTING, PARTICIPANTS, AND MEASURES: or receipt of maintenance dialysis) undergoing non-cardiac surgery between 2005-2019. Three nested prognostic risk prediction models were assembled using clinical and logistical rationale. Model 1 included age, sex, dialysis modality, surgery type and setting. Model 2 added comorbidities, and Model 3 added preoperative hemoglobin and albumin. Death or major cardiac events (acute myocardial infarction or nonfatal ventricular arrhythmia) within 30 days after surgery were modelled using logistic regression models. RESULTS: The development cohort included 38,541 surgeries, with 1,204 outcomes (after 3.1% of surgeries); 61% were performed in males, the median age was 64 years (interquartile range [IQR]: 53, 73), and 61% were receiving hemodialysis at the time of surgery. All three internally validated models performed well, with c-statistics ranging from 0.783 (95% Confidence Interval [CI]: 0.770, 0.797) for Model 1 to 0.818 (95%CI: 0.803, 0.826) for Model 3. Calibration slopes and intercepts were excellent for all models, though Models 2 and 3 demonstrated improvement in net reclassification. Decision curve analysis estimated that use of any model to guide perioperative interventions such as cardiac monitoring would result in potential net benefit over default strategies. CONCLUSIONS: We developed and internally validated three novel models to predict major clinical events for people with kidney failure having surgery. Models including comorbidities and laboratory variables showed improved accuracy of risk stratification and provided the greatest potential net benefit for guiding perioperative decisions. Once externally validated, these models may inform perioperative shared decision making and risk-guided strategies for this population.
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Harrison et al. (2023) conducted a cohort in Kidney failure undergoing non-cardiac surgery (n=8,997). Risk prediction models (clinical and laboratory variables) was evaluated on Composite of all-cause mortality or major cardiac events (acute myocardial infarction or nonfatal ventricular arrhythmia) within 30 days after surgery (c-statistic 0.814, 95% CI 0.803-0.826). A multivariable risk prediction model incorporating comorbidities and preoperative laboratory variables accurately predicted 30-day death or major cardiac events after non-cardiac surgery in people with kidney failure (optimism-adjusted c-statistic 0.814).
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