A novel nomogram predicting the risk of postoperative pneumonia for esophageal cancer patients after minimally invasive esophagectomy

The impact of minimally-invasive esophagectomy operative duration on post-operative outcomes

Background

Esophagectomy, an esophageal cancer treatment mainstay, is a highly morbid procedure. Prolonged operative time, only partially predetermined by case complexity, may be uniquely harmful to minimally-invasive esophagectomy (MIE) patients for numerous reasons, including anastomotic leak, tenuous conduit perfusion and protracted single-lung ventilation, but the impact is unknown. This multi-center retrospective cohort study sought to characterize the relationship between MIE operative time and post-operative outcomes.

Methods

We abstracted multi-center data on esophageal cancer patients who underwent MIE from 2010 to 2021. Predictor variables included age, sex, comorbidities, body mass index, prior cardiothoracic surgery, stage, and neoadjuvant therapy. Outcomes included complications, readmissions, and mortality. Association analysis evaluated the relationship between predictor variables and operative time. Multivariate logistic regression characterized the influence of potential predictor variables and operative time on post-operative outcomes. Subgroup analysis evaluated the association between MIE >4 h vs. ≤4 h and complications, readmissions and survival.

Results

For the 297 esophageal cancer patients who underwent MIE between 2010 and 2021, the median operative duration was 4.8 h [IQR: 3.7-6.3]. For patients with anastomotic leak (5.1%) and 1-year mortality, operative duration was elevated above the median at 6.3 h [IQR: 4.8-8.6], p = 0.008) and 5.3 h [IQR: 4.4-6.8], p = 0.04), respectively. In multivariate logistic regression, each additional hour of operative time increased the odds of anastomotic leak and 1-year mortality by 39% and 19%, respectively.

Conclusions

Esophageal cancer is a poor prognosis disease, even with optimal treatment. Operative efficiency, a modifiable surgical variable, may be an important target to improve MIE patient outcomes.

Complication Prediction after Esophagectomy with Machine Learning

Esophageal cancer can be treated effectively with esophagectomy; however, the postoperative complication rate is high. In this paper, we study to what extent machine learning methods can predict anastomotic leakage and pneumonia up to two days in advance. We use a dataset with 417 patients who underwent esophagectomy between 2011 and 2021. The dataset contains multimodal temporal information, specifically, laboratory results, vital signs, thorax images, and preoperative patient characteristics. The best models scored mean test set AUROCs of 0.87 and 0.82 for leakage 1 and 2 days ahead, respectively. For pneumonia, this was 0.74 and 0.61 for 1 and 2 days ahead, respectively. We conclude that machine learning models can effectively predict anastomotic leakage and pneumonia after esophagectomy.

Development and external validation of a clinical-radiomics nomogram for preoperative prediction of LVSI status in patients with endometrial carcinoma

PURPOSE

To develop and validate a model that incorporates radiomics based on MRI scans and clinical characteristics to predict lymphovascular invasion (LVSI) in endometrial cancer (EC) patients.

METHODS

There were 332 patients with EC enrolled retrospectively in this multicenter study. Radiomics score (Radscore) were computed using the valuable radiomics features. The independent predictors of LVSI were identified by univariate logistic analysis. Multivariate logistic regression was used to develop a clinical-radiomics predictive model. Based on the model, a nomogram was developed and validated internally and externally. The nomogram was evaluated with discrimination, calibration, decision curve analysis (DCA), and clinical impact curves (CIC).

RESULTS

Three predictive models were constructed based on clinicopathological features, radiomic factors and a combination of them, and that the clinic-radiomic model performed best among the three models. Four independent factors comprised the clinical-radiomics model: dynamic contrast enhancement rate of late arterial phase (DCE2), deep myometrium invasion (DMI), lymph node metastasis (LNM), and Radscore. Clinical-radiomics model performance was 0.901 (95% CI 0.84-0.96) in the training cohort, 0.80 (95% CI 0.68-0.92) in the internal validation cohort, and 0.81 (95% CI 0.73-0.9) in the external validation cohort for identifying patients with LVSI, respectively. The model is used to develop a nomogram for clinical use.

CONCLUSIONS

The MRI-based radiomics nomogram could serve as a noninvasive tool to predict LVSI in EC patients.

C-Reactive Protein as Predictor for Infectious Complications after Robotic and Open Esophagectomies

Introduction: The value of C-reactive protein (CRP) as a predictor of anastomotic leakage (AL) after esophagectomy has been addressed by numerous studies. Despite its increasing application, robotic esophagectomy (RAMIE) has not been considered separately yet in this context. We, therefore, aimed to evaluate the predictive value of CRP in RAMIE. Material and Methods: Patients undergoing RAMIE or completely open esophagectomy (OE) at our University Center were included. Clinical data, CRP- and Procalcitonin (PCT)-values were retrieved from a prospectively maintained database and evaluated for their predictive value for subsequent postoperative infectious complications (PIC) (AL, gastric conduit leakage or necrosis, pneumonia, empyema). Results: Three hundred and five patients (RAMIE: 160, OE: 145) were analyzed. PIC were noted in 91 patients on postoperative day (POD) 10 and 123 patients on POD 30, respectively. Median POD of diagnosis of PIC was POD 8. Post-operative CRP-values in the robotic-group peaked one and two days later, respectively, and converged from POD 5 onward compared to the open-group. In the group with PIC, CRP-levels in the robotic-group were initially lower and started to differ significantly from POD 3 onward. In the open-group, increases were already noticed from POD 3 on. Procalcitonin levels did not differ. Best Receiver operating curve (ROC)-results were on POD 4, highest negative predictive values at POD 5 (RAMIE) and POD 4 (OE) with cut-off values of 70 mg/L and 88.3 mg/L, respectively. Conclusion: Post-operative CRP is a good negative predictor for PIC, after both RAMIE and OE. After RAMIE, CRP peaks later with a lower cut-off value.