RESUMEN
Background: Medical resource management can be improved by assessing the likelihood of prolonged length of stay (LOS) for head and neck cancer surgery patients. The objective of this study was to develop predictive models that could be used to determine whether a patient's LOS after cancer surgery falls within the normal range of the cohort. Methods: We conducted a retrospective analysis of a dataset consisting of 300 consecutive patients who underwent head and neck cancer surgery between 2017 and 2022 at a single university medical center. Prolonged LOS was defined as LOS exceeding the 75th percentile of the cohort. Feature importance analysis was performed to evaluate the most important predictors for prolonged LOS. We then constructed 7 machine learning and deep learning algorithms for the prediction modeling of prolonged LOS. Results: The algorithms reached accuracy values of 75.40 (radial basis function neural network) to 97.92 (Random Trees) for the training set and 64.90 (multilayer perceptron neural network) to 84.14 (Random Trees) for the testing set. The leading parameters predicting prolonged LOS were operation time, ischemia time, the graft used, the ASA score, the intensive care stay, and the pathological stages. The results revealed that patients who had a higher number of harvested lymph nodes (LN) had a lower probability of recurrence but also a greater LOS. However, patients with prolonged LOS were also at greater risk of recurrence, particularly when fewer (LN) were extracted. Further, LOS was more strongly correlated with the overall number of extracted lymph nodes than with the number of positive lymph nodes or the ratio of positive to overall extracted lymph nodes, indicating that particularly unnecessary lymph node extraction might be associated with prolonged LOS. Conclusions: The results emphasize the need for a closer follow-up of patients who experience prolonged LOS. Prospective trials are warranted to validate the present results.
RESUMEN
Previous data provided evidence for a critical role of desmosomes to stabilize intestinal epithelial barrier (IEB) function. These studies suggest that desmosomes not only contribute to intercellular adhesion but also play a role as signaling hubs. The contribution of desmosomal plaque proteins plakophilins (PKP) in the intestinal epithelium remains unexplored. The intestinal expression of PKP2 and PKP3 was verified in human gut specimens, human intestinal organoids as well as in Caco2 cells whereas PKP1 was not detected. Knock-down of PKP2 using siRNA in Caco2 cells resulted in loss of intercellular adhesion and attenuated epithelial barrier. This was paralleled by changes of the whole desmosomal complex, including loss of desmoglein2, desmocollin2, plakoglobin and desmoplakin. In addition, tight junction proteins claudin1 and claudin4 were reduced following the loss of PKP2. Interestingly, siRNA-induced loss of PKP3 did not change intercellular adhesion and barrier function in Caco2 cells, while siRNA-induced loss of both PKP2 and PKP3 augmented the changes observed for reduced PKP2 alone. Moreover, loss of PKP2 and PKP2/3, but not PKP3, resulted in reduced activity levels of protein kinase C (PKC). Restoration of PKC activity using Phorbol 12-myristate 13-acetate (PMA) rescued loss of intestinal barrier function and attenuated the reduced expression patterns of claudin1 and claudin4. Immunostaining, proximity ligation assays and co-immunoprecipitation revealed a direct interaction between PKP2 and PKC. In summary, our in vitro data suggest that PKP2 plays a critical role for intestinal barrier function by providing a signaling hub for PKC-mediated expression of tight junction proteins claudin1 and claudin4.
