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

Nomogram for predicting the development of pneumonia after colorectal cancer surgery

The aim of this study was to analyze the factors contributing to the development of pneumonia after colorectal cancer (CRC) surgery and to develop a validated nomogram to predict the risk. We retrospectively collected information on patients who underwent radical CRC resection at a single clinical center from January 2011 to December 2021. The information was then randomly assigned to a training cohort and a validation cohort in a 7:3 ratio. Univariate and multivariate logistic regression analysis were performed on the training cohort to identify independent risk factors for the development of pneumonias, which were then included in the nomogram. Validation was performed in a validation cohort, area under the curve (AUC) and calibration curves were used to determine the predictive accuracy and discriminative power of the graphs, and decision curve analysis (DCA) was used to further substantiate the clinical efficacy of the nomogram. A total of 7130 patients were included in the study. Based on multivariate logistic regression analysis of the training cohort, age, sex, preoperative albumin, surgical methods, and surgical time were identified as independent risk factors for the development of pneumonia after CRC surgery, and a nomogram prediction model was established using the above five variables. The AUC was 0.745 in training cohort and 0.773 in validation cohort. This study established a nomogram that is a good predictor of the risk of developing pneumonia after CRC surgery and provided surgeons with a reference for personalized management of patients in the perioperative period.

Development of a prognostic nomogram model for predicting outcomes in benign esophagogastric anastomotic stenosis treated with fluoroscopic balloon dilation

OBJECTIVE

This study aims to evaluate the clinical utility and effectiveness of a nomogram model in predicting outcomes for patients with benign esophagogastric anastomotic stenosis (BES) undergoing fluoroscopic balloon dilation (FBD).

METHODS

The clinical data of 428 patients with BES who received FBD treatment at our hospital between January 2013 and June 2023 were retrospectively analyzed. The patients were divided into training and validation cohorts in a 7:3 ratio. Relevant risk factors influencing patient prognosis were identified, and a nomogram model was developed to predict stenosis-free survival rates at 3, 6, 12, and 24 months. The model's accuracy was assessed by calculating the area under the receiver operating characteristic curve (AUC), and its predictive performance was validated in the test group.

RESULTS

The baseline data comparison between the training and validation groups revealed no significant differences, ensuring the comparability of the groups. Cox regression analysis identified that age, history of fistula or not, stenosis severity, and balloon diameter were independent risk factors influencing stenosis-free survival in patients with BES. The area under AUC for the nomogram prediction model of stenosis-free survival at 3, 6, 12, and 24 months was 0.77, 0.81, 0.85, and 0.83, respectively, in the training group, and 0.74, 0.80, 0.84, and 0.83, respectively, in the validation group.

CONCLUSIONS

Age, history of fistula, stenosis severity, and balloon diameter were identified as independent risk factors influencing the prognosis of BES. The nomogram model developed in this study demonstrates strong discriminatory power and holds significant clinical value for prognostic assessment.

A nomogram for postoperative pulmonary infections in esophageal cancer patients: a two-center retrospective clinical study

BACKGROUND

Postoperative pulmonary infections (POPIs) occur in approximately 13-38% of patients who undergo surgery for esophageal cancer, negatively impacting patient outcomes and prolonging hospital stays. This study aims to develop a novel clinical prediction model to identify patients at risk for POPIs early, thereby enabling timely intervention by clinicians.

METHODS

This study included 910 patients from two hospitals. Of these, 795 patients from one hospital were randomly assigned to the training cohort (n = 556) and the validation cohort (n = 239) at a 7:3 ratio. The external test cohort consisted of 115 patients from the second hospital. A nomogram was developed via logistic regression to predict the incidence of POPIs. The model's discrimination, precision and clinical benefit were evaluated by constructing a receiver operating characteristic (ROC) curve, calculating the area under the ROC curve (AUC), performing a calibration plot, conducting decision curve analysis (DCA) and clinical impact curves (CIC).

RESULTS

Multivariate logistic regression revealed that age, anemia, neoadjuvant therapy, T stage, thoracic adhesions and duration of surgery were independent risk factors for POPIs. The AUC for the training cohort was 0.8095 (95% CI: 0.7664-0.8527), that for the validation cohort was 0.8039 (95% CI: 0.7436-0.8643), and that for the external test cohort was 0.7174 (95% CI: 0.6145-0.8204). Calibration plots demonstrated good agreement between the predicted and observed probabilities, while DCA and CIC demonstrated good clinical applicability of the model in three cohorts.

CONCLUSION

The nomogram, which incorporates six key factors, effectively predicts the risk of POPIs and can serve as a valuable tool for clinicians in identifying high-risk patients.

A novel nomogram for predicting postoperative pneumonia risk in patients with localized bronchiectasis

BACKGROUND

Pneumonia is one of the most common complications after lung resection. However, there are currently no reports of postoperative pneumonia in patients with bronchiectasis.

OBJECTIVES

Our study aims to construct a new nomogram to predict the risk of postoperative pneumonia in patients with localized bronchiectasis.

DESIGN

The clinical data of patients with localized bronchiectasis from April 2012 to August 2022 were retrospectively analyzed.

METHODS

Independent risk factors were identified through simple linear regression and multiple linear regression analysis, and a new nomogram was constructed based on independent risk factors. The validity of the nomogram was evaluated using the consistency index (C-index), receiver operating characteristic curve, calibration chart, and decision curve analysis chart.

RESULTS

The new nomogram prediction model included five independent risk factors: tuberculosis history, smoking history, platelet-lymphocyte ratio (PLR), diffusing capacity of the lung for carbon monoxide, and controlled nutritional status score. The area under the curve of the prediction model is 0.870 (95% CI: 0.750-0.892), showing good discrimination ability, and the probability threshold was set at 0.2013. In addition, the calibration curve shows that the nomogram has good calibration. In the decision curve, the nomogram model showed good clinical net benefit.

CONCLUSION

This study is the first to construct a nomogram prediction model for postoperative pneumonia of localized bronchiectasis, which can more accurately and directly assess the risk probability of postoperative pneumonia, and provide certain help for clinicians in prevention and treatment decisions.

Association Between Early Mobilization and Postoperative Pneumonia Following Robot-assisted Minimally Invasive Esophagectomy in Patients with Thoracic Esophageal Squamous Cell Carcinoma

OBJECTIVE

The objective of this study was to confirm that early mobilization (EM) could reduce pneumonia in patients undergoing robot-assisted minimally invasive esophagectomy (RAMIE) for thoracic esophageal squamous cell carcinoma (TESCC).

METHODS

Postoperative pneumonia was defined as physician-diagnosed pneumonia using the Esophagectomy Complications Consensus Group definition of pneumonia with a Clavien-Dindo classification grade II-V on postoperative day (POD) 3-5. EM was defined as achieving an ICU Mobility Scale (IMS) ≥7 by POD 2. Patients were divided into EM (n = 36) and non-EM (n = 35) groups. Barriers to EM included pain, orthostatic intolerance (OI), and orthostatic hypotension.

RESULTS

The overall incidence of postoperative pneumonia was 12.7%, with a significant difference between the EM (2.8%) and non-EM (22.9%) groups (P = 0.014). The odds ratio was 0.098 in the EM group compared to the non-EM group. A significant difference was found between the two groups in terms of the barriers to EM at POD 2 only for OI, with a higher incidence in the non-EM group. Multivariate logistic regression analysis showed that patients with OI were more likely to be unable to achieve EM than those without OI (odds ratio, 7.030; P = 0.006).

CONCLUSION

EM within POD 2 may reduce the incidence of postoperative pneumonia in patients undergoing RAMIE for TESCC. Furthermore, it was suggested that OI can have a negative impact on the EM after RAMIE.

Risk prediction model for postoperative pneumonia in esophageal cancer patients: A systematic review

Background

Numerous studies have developed or validated prediction models to estimate the likelihood of postoperative pneumonia (POP) in esophageal cancer (EC) patients. The quality of these models and the evaluation of their applicability to clinical practice and future research remains unknown. This study systematically evaluated the risk of bias and applicability of risk prediction models for developing POP in patients undergoing esophageal cancer surgery.

Methods

PubMed, Embase, Web of Science, Cochrane Library, Cumulative Index to Nursing and Allied Health Literature (CINAHL), China National Knowledge Infrastructure (CNKI), China Science and Technology Journal Database (VIP), WanFang Database and Chinese Biomedical Literature Database were searched from inception to March 12, 2024. Two investigators independently screened the literature and extracted data. The Prediction Model Risk of Bias Assessment Tool (PROBAST) checklist was employed to evaluate both the risk of bias and applicability.

Result

A total of 14 studies involving 23 models were included. These studies were mainly published between 2014 and 2023. The applicability of all studies was good. However, all studies exhibited a high risk of bias, primarily attributed to inappropriate data sources, insufficient sample size, irrational treatment of variables and missing data, and lack of model validation. The incidence of POP in patients undergoing esophageal cancer surgery ranged from 14.60% to 39.26%. The most frequently used predictors were smoking, age, chronic obstructive pulmonary disease(COPD), diabetes mellitus, and methods of thoracotomy. Inter-model discrimination ranged from 0.627 to 0.850, sensitivity ranged between 60.7% and 84.0%, and specificity ranged from 59.1% to 83.9%.

Conclusion

In all included studies, good discrimination was reported for risk prediction models for POP in patients undergoing esophageal cancer surgery, indicating stable model performance. However, according to the PROBAST checklist, all studies had a high risk of bias. Future studies should use the predictive model assessment tool to improve study design and develop new models with larger samples and multicenter external validation.

Systematic review registration

https://www.crd.york.ac.uk/prospero, identifier CRD42024527085.

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.