Prediction Model For Postoperative Severe Acute Lung Injury In Patients
Undergoing Acute Type A Aortic Dissection Surgery
Abstract
Objective: This study aimed to establish a risk
assessment model to predict postoperative severe acute lung injury (ALI)
risk in patients with acute type A aortic dissection (ATAAD).
Methods: Consecutive patients with ATAAD admitted to our
hospital were included in this retrospective assessment and placed in
the postoperative severe ALI and non-severe ALI groups based on the
presence or absence of ALI within 72 h postoperatively (oxygen index
(OI) ≤100 mmHg). Patients were then randomly divided into training and
validation groups in a ratio of 8:2. Logistic regression analyses were
used to statistically assess data and establish the prediction model.
The prediction model’s effectiveness was evaluated via tenfold
cross-validation of the validation group to facilitate construction of a
nomogram. Results: After screening, 479 patients were
included in the study: 132 (27.5%) in the postoperative severe ALI
group and 347 (72.5%) in the postoperative non-severe ALI group. Based
on logistics regression analyses, the following variables were included
in the model: coronary heart disease (CHD), cardiopulmonary bypass (CPB)
≥257.5 min, left atrium (LA) diameter ≥35.5 mm, hemoglobin ≤139.5 g/L,
preCPB OI ≤100 mmHg, intensive care unit (ICU) OI ≤100 mmHg, left
ventricular posterior wall thickness (LVPWT) ≥10.5 mm, and neutrophilic
granulocyte percentage (NEUT) ≥0.824. The area under the receiver
operating characteristic (ROC) curve of the modeling group was 0.805,
and differences between observed and predicted values were not deemed
statistically significant via the Hosmer–Lemeshow test (χ2=6.037, df=8,
P=0.643). For the validation group, the area under the ROC curve was
0.778, and observed and predicted value differences were insignificant
when assessed using the Hosmer–Lemeshow test (χ
2=3.3782, df=7; P=0.848). The average tenfold
cross-validation score was 0.756. Conclusions: This
study established a prediction model and developed a nomogram to
determine the risk of postoperative severe ALI after ATAAD. Variables
used in the model were easy to obtain clinically and the effectiveness
of the model was good.