Abstract
BackgroundAlthough postoperative pulmonary infection (POI) commonly occurs in patients with esophageal cancer after curative surgery, a patient-specific predictive model is still lacking. The main aim of this study is to construct and validate a nomogram for estimating the risk of POI by investigating how perioperative features contribute to POI.MethodsThis cohort study enrolled 637 patients with esophageal cancer. Perioperative information on participants was collected to develop and validate a nomogram for predicting postoperative pulmonary infection in esophageal cancer. Predictive accuracy, discriminatory capability, and clinical usefulness were evaluated by calibration curves, concordance index (C-index), and decision curve analysis (DCA).ResultsMultivariable logistic regression analysis indicated that length of stay, albumin, intraoperative bleeding, and perioperative blood transfusion were independent predictors of POI. The nomogram for assessing individual risk of POI indicated good predictive accuracy in the primary cohort (C-index, 0.802) and validation cohort (C-index, 0.763). Good consistency between predicted risk and observed actual risk was presented as the calibration curve. The nomogram for estimating POI of esophageal cancer had superior net benefit with a wide range of threshold probabilities (4–81%).ConclusionsThe present study provided a nomogram developed with perioperative features to assess the individual probability of infection may conducive to strengthen awareness of infection control and provide appropriate resources to manage patients at high risk following esophagectomy.
Highlights
Esophageal cancer (EC) was diagnosed with 572,034 new cases and results in 508,585 deaths around the world in 2018 [1]
Patients will be included in this study if they meet the following requirements: (1) aged 18 years or older (2) pathological section diagnosed as malignant esophageal cancer (3) underwent curative esophagectomy
Selected predictors Of 28 features, 4 potential predictors were selected on the basis of Least absolute shrinkage and selection operator (LASSO) regression analysis (Fig. 1)
Summary
Esophageal cancer (EC) was diagnosed with 572,034 new cases and results in 508,585 deaths around the world in 2018 [1]. The past few decades have seen a rapid increase in the incidence of esophageal cancer [2]. Li et al BMC Pulm Med (2021) 21:283 long-term survival [5,6,7]. It needs to, distinct and identify those patients at the greatest risk of POI, and promote early intervention to reduce its incidence or improve postoperative prognosis outcomes. A study observed that the increasing POI rate was connected with several risk factors such as age, smoking, preoperative comorbidity, lower hemoglobin, higher creatinine, postoperative dysphagia [8,9,10].
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