Impact of congestive heart failure on patients undergoing lumbar spine fusion for adult spine deformity
Bibliographic record
Abstract
ABSTRACT Background: With the increasing amount of elective spine fusion patients presenting with cardiac disease and congestive heart failure, it is becoming difficult to assess when it is safe to proceed with surgery. Assessing the severity of heart failure (HF) through ejection fraction may provide insight into patients’ short- and long-term risks. Purpose: The purpose of this study was to assess the severity of HF on perioperative outcomes of spine fusion surgery patients. Study Design/Setting: This was a retrospective cohort study of the PearlDiver database. Patient Sample: We enrolled 670,526 patients undergoing spine fusion surgery. Outcome Measures: Thirty-day and 90-day complication rates, discharge destination, length of stay (LOS), physician reimbursement, and hospital costs. Methods: Patients undergoing elective spine fusion surgery were isolated and stratified by preoperative HF with preserved ejection fraction (P-EF) or reduced ejection fraction (R-EF) (International Classification of Diseases-9: 428.32 [chronic diastolic HF] and 428.22 [chronic systolic HF]). Means comparison tests (Chi-squared and independent samples t-tests, as appropriate) compared differences in demographics, diagnoses, comorbidities, procedural characteristics, LOS, 30-day and 90-day complication outcomes, and total hospital charges between those diagnosed with P-EF and those not R-EF. Binary logistic regression assessed the odds of complication associated with HF, controlling for levels fused (odds ratio [OR] [95% confidence interval]). Statistical significance was set at P < 0.05. Results: Totally 670,526 elective spine fusion patients were included. Four thousand and seventy-seven were diagnosed with P-EF and 2758 R-EF. Overall, P-EF patients presented with higher rates of morbid obesity, chronic kidney disease, chronic obstructive pulmonary disease, diabetes mellitus, and hypertension (all P < 0.001). In relation to No-HF, P-EF patients had higher rates of 30-day major complications including pulmonary embolism, pneumonia, cerebrovascular accident (CVA), myocardial infarctions (MI), sepsis, and death (all P < 0.001). Furthermore, P-EF was associated significantly with increased odds of pneumonia (OR: 2.07 [1.64–2.56], P < 0.001) and sepsis (OR: 2.09 [1.62–2.66], P < 0.001). Relative to No-HF, R-EF was associated with significantly higher odds of MI (OR: 3.66 [2.34–5.47]), CVA (OR: 2.70 [1.67–4.15]), and pneumonia (OR: 1.85 [1.40–2.40]) (all P < 0.001) postoperative within 30 days. Adjusting for prior history of MI, CAD, and the presence of a pacemaker R-EF was a significant predictor of an MI 30 days postoperatively (OR: 2.2 [1.14–4.32], P = 0.021). Further adjusting for history of CABG or stent placement, R-EF was associated with higher odds of CVA (OR: 2.11 [1.09–4.19], P = 0.028) and MI (OR: 2.27 [1.20–4.43], P = 0.013). Conclusions: When evaluating the severity of HF before spine surgery, R-EF was associated with a higher risk of major complications, especially the occurrence of a myocardial infarction 30 days postoperatively. During preoperative risk assessment, congestive HF should be considered thoroughly when thinking of postoperative outcomes with emphasis on R-EF.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".