DEVELOPMENT AND VALIDATION OF A RISK CALCULATOR FOR MORTALITY AMONG GENERAL SURGERY PATIENTS AT THE TIME OF INTER-HOSPITAL TRANSFER

Background
An incisional hernia (IH) is one of the most frequent complications after a laparotomy, affecting between 10 -20% of patients. This incidence increases substantially in patients with comorbidities such as obesity, smoking, diabetes, immunosuppression and certain conditions such as diverticulitis or abdominal aortic aneurysm. The molecular basis for this variability is poorly understood but is now being addressed by many investigators. In this study, we looked at the role of molecular proteins called cytokines in the improper wound healing that leads to IH. We examined the cytokines present within muscle tissues of IH. We hypothesize that increased proinflammatory cytokines and decreased anti-inflammatory cytokines lead to impaired wound healing and the development of IH. Understanding the molecular microenvironment of muscle tissue in IH is important because these cytokines serve as potential therapeutic targets for improving the body’s repair response to ultimately decrease susceptibility to IH.

Experimental Design
Samples of muscle, adipose, and fascia were collected during IH repair. Muscle tissue was separated, processed, transversely sectioned onto glass slides, and stained using immunofluorescent tagged antibodies that bind leptin, adiponectin, TNF-α, and IL-6. The immunofluorescent findings were compared with the results of RTPCR quantification of the specific genes studied within the same tissues. The stained sections were scanned and analyzed using ImageJ software to quantify the expression of each cytokine in the samples compared to controls. Control muscle tissue was obtained from brain dead organ donors without prior abdominal surgery.

Results
Results of our immunofluorescence analysis demonstrate that proinflammatory cytokines leptin, TNF-α, and IL-6 were expressed in higher quantities while anti-inflammatory cytokine adiponectin was expressed in lower quantity in IH tissue compared to control tissue. The results of RTPCR analysis also showed significantly increased gene expression of leptin, TNF-α, and IL-6. However, adiponectin was significantly increased in patients compared to controls which was not consistent with our immunofluorescence analysis. This discrepancy may be due to a regulatory mechanism that affects the translation of mRNA (RTPCR) to protein (immunofluorescence) in a predominantly proinflammatory environment.

Conclusion
We found a significantly increased expression of proinflammatory cytokines leptin, TNF-α, and IL-6 in muscles tissue and decreased expression of anti-inflammatory cytokine adiponectin from IH patients when compared to controls. This supports the notion that an imbalance of cytokines with a predominance of proinflammatory cytokines impairs the wound healing process and may be a treatable target to prevent postoperative IH.

Background:

Inter-hospital transfers mark a critical decision point in the patient care continuum. Despite evidence that patients transferred between healthcare systems are more complex and experience greater morbidity and mortality, there are no available tools to correctly triage surgical patients based on their disease acuity. We hypothesized that readily available, low complexity patient parameters at the time of transfer could predict mortality after transfer.

Methods:

All patients transferred into general and colorectal surgery services at a quaternary care hospital between January-2016 and August-2022 were included. Demographics, laboratory values, vital signs, intensive care unit (ICU) admission, and vasopressor use were extracted from the medical record. Variables were chosen for easy availability, decreased variability, and feasible collection by trained non-physician transfer center personnel with nursing input from the transferring center. The primary outcome was admission-related mortality, defined as death during the admission or within 30 days post-discharge. Univariate differences were tested between the outcome groups. Logistic regression, penalized regression, gradient boosting regression, and deep neural network predictive models were trained on the training dataset and their performance was compared on the validation dataset.

Results:

A total of 4,664 adult transfers were included. Admission-related mortalities were 280 (6.0%): 142 (50.7%) occurred during the admission and 138 (49.3%) occurred in the 30-days after discharge. On univariate analysis, differences in all components of complete blood count, basic metabolic panel, and vital signs were statistically different between the two outcome groups. In addition, compared to survivors, patients who suffered mortality were more likely to be transferred from or into an ICU [153 (54.6%) vs. 758 (17.3%), P<0.001] and more likely to require vasopressor support [87 (31.1%) vs. 202 (4.6%), P<0.001]. While all variables were tested, only 12 commonly collected variables were included in the final model based on the penalized regression method. The model coefficients are numerated in Table 1. When validated, the final model achieved an area under the receiver operating characteristic curve, sensitivity, specificity, and accuracy of 0.846, 0.79, 0.72, and 0.72, respectively. After bias-correction, the Hosmer-Lemeshow C-statistic for the model was 8.22, P=0.412 indicating strong prediction and calibration.

Conclusion:

In an inter-hospital transfer setting, it is possible to predict the risk of mortality for general surgical patients based on readily available clinical parameters. Utilizing such a risk score could assist accepting hospitals in triaging patients to prioritize transfer acuity, improve resource allocation, and standardize care.