Nomogram for the prediction of postoperative hypoxemia in patients with acute aortic dissection

Postoperative hypoxemia is quite common in patients with acute aortic dissection (AAD) and is associated with poor clinical outcomes. However, there is no method to predict this potentially life-threatening complication. The study aimed to develop a regression model in patients with AAD to predict postoperative hypoxemia, and to validate it in an independent dataset.

All patients diagnosed with AAD from December 2012 to December 2017 were retrospectively screened for potential eligibility. Preoperative and intraoperative variables were included for analysis. Logistic regression model was fit by using purposeful selection procedure. The original dataset was split into training and validating datasets by 4:1 ratio. Discrimination and calibration of the model was assessed in the validating dataset. A nomogram was drawn for clinical utility.

A total of 211 patients, involving 168 in non-hypoxemia and 43 in hypoxemia group, were included during the study period (incidence: 20.4%). Duration of mechanical ventilation (MV) was significantly longer in the hypoxemia than non-hypoxemia group (41(10.5140) vs. 12(3.75,70.25) hours; p = 0.002). There was no difference in the hospital mortality rate between the two groups. The purposeful selection procedure identified 8 variables including hematocrit (odds ratio [OR]: 0.89, 95% confidence interval [CI]: 0.80 to 0.98, p = 0.011), PaO₂/FiO₂ ratio (OR: 0.99, 95% CI: 0.99 to 1.00, p = 0.011), white blood cell count (OR: 1.21, 95% CI: 1.06 to 1.40, p = 0.008), body mass index (OR: 1.32, 95% CI: 1.15 to 1.54; p = 0.000), Stanford type (OR: 0.22, 95% CI: 0.06 to 0.66; p = 0.011), pH (OR: 0.0002, 95% CI: 2*10^− 8 to 0.74; p = 0.048), cardiopulmonary bypass time (OR: 0.99, 95% CI: 0.98 to 1.00; p = 0.031) and age (OR: 1.03, 95% CI: 0.99 to 1.08; p = 0.128) to be included in the model. In an independent dataset, the area under curve (AUC) of the prediction model was 0.869 (95% CI: 0.802 to 0.936). The calibration was good by visual inspection.

The study developed a model for the prediction of postoperative hypoxemia in patients undergoing operation for AAD. The model showed good discrimination and calibration in an independent dataset that was not used for model training.