Colorectal Surgery Outcomes in Patients With Connective Tissue Disease: Insights From a National Database Analysis
Bibliographic record
Abstract
Background: Colorectal surgery poses significant risks, especially in individuals who smoke or have pre-existing conditions such as heart disease, diabetes, or obesity. However, there is a notable gap in understanding the inpatient outcomes for individuals with comorbid connective tissue disease (CTD) undergoing colorectal surgery. This study aimed to address this knowledge gap by analyzing data from the National Inpatient Sample (NIS) to evaluate these outcomes. Methods: Utilizing the extensive NIS database covering 2016 to 2020, we conducted a retrospective analysis of colorectal surgery patients with CTD, identified through the International Classification of Diseases, 10th Revision. Primary outcomes included in-hospital mortality, while secondary outcomes encompassed postoperative abdominal complications, acute kidney injury (AKI), acute respiratory failure (ARF), sepsis, pneumonia, blood transfusion, mechanical ventilator use, and healthcare utilization. Employing STATA software, we utilized multivariate logistic and linear regression analyses to adjust for confounders. Results: Among 19,274 colorectal surgery patients with CTD, the average age was 67.1 years, with a higher proportion of females. Patients with CTD exhibited a higher in-hospital mortality rate (6.31% vs. 4.14%) with an adjusted odds ratio of 1.55 (P ¡Ü 0.001). Patients with CTD had a higher likelihood of developing complications such as postoperative abdominal complications, ARF, AKI, sepsis, mechanical ventilator use, and blood transfusions. Conclusion: Patients with CTD undergoing colorectal surgery face an elevated risk of in-hospital mortality and postoperative complications compared to those without CTD. This underscores the critical importance of integrating CTD into preoperative risk assessment and care planning for individuals undergoing colorectal surgery. J Curr Surg. 2024;14(1):5-10 doi: https://doi.org/10.14740/jcs476
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| 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".