Current status of perioperative temporary mechanical circulatory support during cardiac surgery
Bibliographic record
Abstract
OBJECTIVES: We sought to determine utilization and outcomes of perioperative temporary mechanical circulatory support (tMCS) in the current practice of cardiac surgery. BACKGROUND: tMCS is an evolving adjunct to cardiac surgery not fully characterized in contemporary practice. METHODS: Using the nationwide inpatient sample we retrospectively analyzed hospital discharge data between January 1, 2016 and December 31, 2019. ICD-10-CM procedure codes were used to identify and divide patient hospitalizations into those who had preoperative tMCS (pre-tMCS) versus tMCS instituted the day of surgery or afterwards (sd/post-tMCS). RESULTS: In all, 1,383,520 hospitalizations met inclusion criteria. 86,445 (6.25%) had tMCS. tMCS was utilized in 8.74% of coronary artery bypass grafting (CABG), 2.58% of isolated valve, and 9.71% of valve/CABG; operations. 29,325 (33.9%) had pre-tMCS while 57,120 (66.1%) had sd/post-tMCS. The use of tMCS was associated with greater inpatient mortality (15.66% vs. 1.53%, p < .001), longer length of stay (LOS) (14.4 vs. 8.5 days, p < .001), and higher mean inflation-adjusted costs ($93,040 ± 1038 vs. $51,358 ± 296, p < .001) compared to no use. Inpatient mortality (5.98% vs. 20.63%, p < .001), LOS (13.87 vs. 14.68, p < .001), and cost ($82,621 ± 1152 SEM vs. $98,381 ± 1242) were all significantly lower with pre-tMCS compared to sd/post tMCS. When analyzed separately, mortality was higher with later utilization of tMCS (5.98% pre, 17.1% sd, and 49.05% postsurgical date insertion, p < .001). CONCLUSIONS: Perioperative tMCS is utilized in 6.25% of modern cardiac surgery, with two-thirds of cases instituted on the day of surgery or afterwards. The use of tMCS is associated with significantly higher mortality, longer LOS, and higher costs. Among patients undergoing tMCS, earlier utilization is associated with better outcomes.
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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 | 0.002 | 0.010 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".