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Development and external validation of a nomogram to predict prolonged postoperative mechanical ventilation in
Qi Yue1,2, Jianhao Hu1,2, Xin Li3
1Department of Cardiovascular Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China.
Background:
Prolonged mechanical ventilation (PMV) significantly affects outcomes in patients undergoing acute type A aortic dissection (ATAAD) surgery. This study aimed to develop a nomogram to predict the risk of PMV (defined as mechanical ventilation >48 h) to help clinicians identify high-risk patients and improve outcomes.
Methods:
This retrospective study, designed and reported in accordance with the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement, included 479 ATAAD patients from the First Affiliated Hospital of Anhui Medical University (training set) and 120 patients from Beijing Anzhen Hospital of Capital Medical University (validation set). Potential predictors were selected using Least Absolute Shrinkage and Selection Operator (LASSO) regression and multivariate logistic regression. A nomogram was then developed using the retained predictors. Its performance was evaluated using receiver operating characteristic curves, calibration curves, decision curve analysis, and clinical impact curves.
Results:
Ten predictors were retained in the final model: age [odds ratio (OR) 1.036, 95% confidence interval (CI) 1.016-1.058, P < 0.001], preoperative serum albumin (OR 0.942, 95% CI 0.903-0.984, P < 0.01), fibrinogen (OR 0.777, 95% CI 0.643-0.938, P < 0.01), standard bicarbonate (OR 0.891, 95% CI 0.798-0.996, P < 0.05), red cell distribution width (OR 1.325, 95% CI 1.112-1.578, P < 0.01), serum creatinine (Cr) (OR 1.005, 95% CI 1.000-1.011, P = 0.056), uric acid (OR 1.002, 95% CI 1.000-1.004, P < 0.05), isolated ascending aortic replacement (OR 0.578, 95% CI 0.348-0.960, P < 0.05), total arch replacement with frozen elephant trunk (TAR-FET) (OR 1.999, 95% CI 1.250-3.198, P < 0.01), and aortic cross-clamp time (OR 1.010, 95% CI 1.003-1.017, P < 0.01). The nomogram demonstrated good discriminatory ability (training AUC 0.796, 95% CI 0.756-0.835; validation AUC 0.765, 95% CI 0.678-0.852), excellent calibration (Hosmer-Lemeshow test: P = 0.229 for the training cohort, P = 0.855 for the validation cohort), and good clinical applicability. At the optimal Youden index cutoff (predicted probability = 0.484 in the training cohort, 0.536 in the validation cohort), the nomogram achieved a sensitivity of 78.6%, specificity of 66.7%, positive predictive value of 74.6%, and negative predictive value of 71.4% in the training cohort and the corresponding values were 76.0%, 64.4%, 78.1%, and 61.7% in the validation cohort.
Conclusions:
The nomogram effectively predicts the risk of PMV after ATAAD surgery, providing valuable insights for perioperative management and potentially improving patient outcomes.
Insights
A new nomogram predicts prolonged mechanical ventilation (PMV) risk in acute type A aortic dissection (ATAAD) patients. This tool aids clinicians in identifying high-risk individuals for better perioperative management and improved patient outcomes.
Area of Science:
- Cardiovascular Surgery
- Thoracic Surgery
- Medical Prediction Modeling
Background:
- Prolonged mechanical ventilation (PMV) is a significant complication impacting patient outcomes after acute type A aortic dissection (ATAAD) surgery.
- Identifying patients at high risk for PMV is crucial for optimizing perioperative care and improving surgical results.
Purpose of the Study:
- To develop and validate a predictive nomogram for prolonged mechanical ventilation (PMV) in patients undergoing acute type A aortic dissection (ATAAD) surgery.
- To provide clinicians with a tool to identify patients at higher risk of requiring mechanical ventilation for more than 48 hours post-surgery.
Main Methods:
- A retrospective study involving 479 ATAAD patients (training set) and 120 patients (validation set).
- Predictors for PMV were identified using LASSO regression and multivariate logistic regression.
- A nomogram was constructed and its performance evaluated using ROC curves, calibration, and decision curve analysis.
Main Results:
- Ten predictors were identified: age, preoperative serum albumin, fibrinogen, standard bicarbonate, red cell distribution width, serum creatinine, uric acid, isolated ascending aortic replacement, total arch replacement with frozen elephant trunk (TAR-FET), and aortic cross-clamp time.
- The nomogram demonstrated good discriminatory ability (training AUC 0.796, validation AUC 0.765) and excellent calibration.
- The model achieved good sensitivity and specificity in both training and validation cohorts for predicting PMV risk.
Conclusions:
- The developed nomogram is an effective tool for predicting the risk of prolonged mechanical ventilation (PMV) in patients undergoing ATAAD surgery.
- This predictive model offers valuable insights for perioperative management strategies.
- Utilizing this nomogram can potentially lead to improved patient outcomes following ATAAD surgery.
