Postoperative non-fatal outcomes in people receiving chronic dialysis: a systematic review and meta-analysis of 42 studies and 78,805 patients
Bibliographic record
Abstract
INTRODUCTION: People with end-stage kidney disease (ESKD) receiving chronic dialysis frequently undergo major surgery, but their absolute and relative risks of postoperative complications compared to non-dialysis patients are unclear. As a result, graded perioperative risk assessment and counselling remain difficult for chronic dialysis treatment for ESKD. The aim of this study was to estimate the risks of non-fatal postoperative outcomes in patients on chronic dialysis undergoing non-transplant surgery. METHODS: Two authors performed a systematic review of observational studies indexed in Embase and MEDLINE up to October 2018 that reported postoperative outcomes in chronic dialysis and non-dialysis patients undergoing major, non-transplant surgery. Risk of bias was assessed with the Newcastle-Ottawa Scale. Summary level data on study characteristics, type of surgical procedure, patient demographics and comorbidities were extracted. Outcomes recorded included myocardial infarction, stroke, surgical site infection and sepsis. Random effects meta-analysis was performed to derive summary risk estimates and meta-regression was performed to explore heterogeneity. RESULTS: The systematic review included 42 studies involving 78,805 chronic dialysis and 9,984,469 non-dialysis patients undergoing orthopaedic, vascular, cardiothoracic, general and urological procedures. Cohort selection and outcome ascertainment were of good quality but comparability was poor. Summary, unadjusted risk estimates showed that, compared with people not on dialysis, those receiving chronic dialysis experienced increased risks of postoperative myocardial infarction (OR 3.4, 95%CI 2.4-4.8, I280%), stroke (OR 2.2, 95% CI 1.6-3.2, I291%), surgical site infection (OR 2.3, 95% CI 1.7-3.1, I294%) and sepsis (OR 3.5, 95% CI 2.4-5.0, I296%), as well as longer length of hospital stay (weighted mean difference 2.1 days, 95%CI 2.00-2.02) irrespective of type of surgery. When the meta-analysis was restricted to include only those studies that adjusted for age and comorbidities, there was an attenuation of the observed risks of postoperative myocardial infarction (OR 1.7, 95% CI 1.3-2.2 I297%), stroke (OR 1.1, 95% CI 1.0-1.2, I283%), surgical site infection (OR 1.3, 95% CI 1.2-1.5, I274%) and sepsis (OR 2.4, 95% CI 2.1-2.7, I277%). Weighted univariate meta-regression showed significant inverse linear relationships between study-level mean age and the excess risks of both myocardial infarction (slope -0.06, p=0.029) and stroke (slope -0.07, p= 0.031) for people on chronic dialysis. A similar relationship was observed between study level prevalence of ischemic heart disease and excess stroke risk (slope -0.02, p = 0.001), which was maintained in multivariable meta-regression (slope -0.02, p=0.006). Meta-regression did not demonstrate a significant variation in excess stroke risk with age difference between dialysis and non-dialysis study cohorts, highlighting the inherent heightened stroke risk in patients with ESKD on dialysis. No factors were found to be significantly related to excess risks of sepsis or surgical site infections. CONCLUSIONS: People receiving chronic dialysis have substantially increased risks of non-fatal postoperative complications across all surgical disciplines. This heightened risk may in part be explained by their older age and higher comorbid illness burden.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.029 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".