Long-term mortality following complications after elective surgery: a secondary analysis of pooled data from two prospective cohort studies
Bibliographic record
Abstract
BACKGROUND: Complications after surgery affect survival and quality of life. We aimed to confirm the relationship between postoperative complications and death within 1 yr after surgery. METHODS: We conducted a secondary analysis of pooled data from two prospective cohort studies of patients undergoing surgery in five high-income countries between 2012 and 2014. Exposure was any complication within 30 days after surgery. Primary outcome was death within 1 yr after surgery, ascertained by direct follow-up or linkage to national registers. We adjusted for clinically important covariates using a mixed-effect multivariable Cox proportional hazards regression model. We conducted a planned subgroup analysis by type of complication. Data are presented as mean with standard deviation (sd), n (%), and adjusted hazard ratios (aHRs) with 95% confidence intervals (CIs). RESULTS: The pooled cohort included 10 132 patients. After excluding 399 (3.9%) patients with missing data or incomplete follow-up, 9733 patients were analysed. The mean age was 59 [sd 16.8] yr, and 5362 (55.1%) were female. Of 9733 patients, 1841 (18.9%) had complications within 30 days after surgery, and 319 (3.3%) died within 1 yr after surgery. Of 1841 patients with complications, 138 (7.5%) died within 1 yr after surgery compared with 181 (2.3%) of 7892 patients without complications (aHR 1.94 [95% CI: 1.53-2.46]). Respiratory failure was associated with the highest risk of death, resulting in six deaths amongst 28 patients (21.4%). CONCLUSIONS: Postoperative complications are associated with increased mortality at 1 yr. Further research is needed to identify patients at risk of complications and to reduce mortality.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".