Impact of Hypoalbuminemia on Outcomes Following Hepatic Resection: A NSQIP Retrospective Cohort Analysis of 26,394 Patients
Bibliographic record
Abstract
Background/Objectives: Efforts to preoperatively risk stratify and optimize patients before liver resection allow for improvements in postoperative outcomes, with hypoalbuminemia being increasingly researched as a surrogate for nutrition, overall health and functional status. Given the paucity of studies examining the relationship between hypoalbuminemia and liver resection, this study aims to determine the impact of hypoalbuminemia on outcomes following liver resections using a large multicenter database. Methods: The American College of Surgeons–National Surgical Quality Improvement Program (2017–2021) database was used to extract the data of patients who underwent a hepatic resection. Two cohorts were defined; those with hypoalbuminemia (HA; <3.0 g/L) and those with normal albumin levels (≥3.0 g/L). Both baseline characteristics and 30-day postoperative complication rates were compared between the two cohorts. Multivariable logistic regression models were used to assess the independent effect of HA on various outcomes. Area under curve–receiver operating characteristic (AUC-ROC) curves were used to identify optimal albumin thresholds for both serious complications and mortality. Results: We evaluated 26,394 patients who underwent liver resections, with 1347 (5.1%) having preoperative HA. The HA patients were older (62.3 vs. 59.8; p < 0.001) and more likely to be of an ASA class ≥ 4 (13.0% vs. 6.5%; p < 0.001). The patients with HA had significantly more complications such as an increased length of stay, readmission, reoperation, sepsis, surgical site infection, bile leak, and need for transfusion. After controlling for demographics and comorbidities, HA remained a significant independent predictor associated with both 30-day serious complication rates (aOR 2.93 [CI 95% 2.36–3.65, p < 0.001]) and mortality (aOR 2.15 [CI 95% 1.38–3.36, p = 0.001]). The optimal cut-off for albumin with respect to predicting serious complications was 4.0 g/dL (sensitivity 59.1%, specificity 56.8%, AUC-ROC 0.61) and 3.8 g/dL (sensitivity 56.6%, specificity 68.3%, AUC-ROC 0.67) for mortality. Conclusions: In this large, retrospective database analysis, preoperative HA was significantly associated with 30-day morbidity and mortality rates following hepatic resection. Preoperative albumin may serve as a useful marker for risk stratification in conjunction with pre-existing calculators. Future studies evaluating the risk mitigation impact of nutrition and exercise prehabilitation in these patients and its capacity to modify hypoalbuminemia would be beneficial.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".