Inflammatory risk stratification individualizes anti-inflammatory pharmacotherapy for acute type A aortic dissection

Abstract

The systemic benefits of anti-inflammatory pharmacotherapy vary across cardiovascular diseases in clinical practice. We aimed to evaluate the application of artificial intelligence to acute type A aortic dissection (ATAAD) patients to determine the optimal target population who would benefit from urinary trypsin inhibitor use (ulinastatin). Patient characteristics at admission in the Chinese multicenter 5A study database (2016-2022) were used to develop an inflammatory risk model to predict multiple organ dysfunction syndrome (MODS). The population (5,126 patients from 15 hospitals) was divided into a 60% sample for model derivation, with the remaining 40% used for model validation. Next, we trained an extreme gradient-boosting algorithm (XGBoost) to develop a parsimonious patient-level inflammatory risk model for predicting MODS. Finally, a top-six-feature tool consisting of estimated glomerular filtration rate, leukocyte count, platelet count, De Ritis ratio, hemoglobin, and albumin was built and showed adequate predictive performance regarding its discrimination, calibration, and clinical utility in derivation and validation cohorts. By individual risk probability and treatment effect, our analysis identified individuals with differential benefit from ulinastatin use (risk ratio [RR] for MODS of RR 0.802 [95% confidence interval (CI) 0.656, 0.981] for the predicted risk of 23.5%-41.6%; RR 1.196 [0.698-2.049] for the predicted risk of <23.5%; RR 0.922 [95% CI 0.816-1.042] for the predicted risk of >41.6%). By using artificial intelligence to define an individual's benefit based on the risk probability and treatment effect prediction, we found that individual differences in risk probability likely have important effects on ulinastatin treatment and outcome, which highlights the need for individualizing the selection of optimal anti-inflammatory treatment goals for ATAAD patients.

Graphical abstract

Figure 1

SHAP summary plot of the risk model SHAP values above 0 indicate that the outcome is made more likely because of the predictor value, and SHAP values below 0 indicate that the outcome is made less likely because of the predictor value. The y axis represents the features included in model development (in descending order of importance), and the x axis indicates the change in prediction. The gradient color denotes the original value for that variable, with each point representing an individual participant. eGFR, estimated glomerular filtration rate; BMI, body mass index; COPD, chronic obstructive pulmonary disease; CHD, coronary heart disease; RR, risk ratio; MODS, multiple organ dysfunction syndrome; SHAP, Shapley additive explanation.

Figure 2

Performance of risk models in the derivation and validation cohorts (A) AUROC of risk models. (B) AUPRC of risk models. (C) Calibration plots of risk models. (D) Decision curves of risk models. ∗, Full risk model in the derivation cohort; †, inflammatory risk model in the derivation cohort; ‡, inflammatory risk model in the validation cohorts. AUROC, area under the receiver operating characteristic curve; AUPRC, area under the precision recall curve.

Figure 3

Association between ulinastatin use and primary outcome in the validation cohort ∗, Multivariable logistic model with adjustment for baseline and clinical characteristics, laboratory profiles, and procedural factors; †, multivariable logistic model with the same covariates with additional adjustment for the propensity score; ‡, multivariable logistic model with the same covariates with inverse probability weighting according to the propensity score; §, multivariable logistic model with the same strata and covariates with matching according to the propensity score. CI, confidence interval.

Figure 4

Association of ulinastatin use with risk of MODS (A) Relationship between predicted and observed risk of MODS by absence vs. presence of ulinastatin. (B) Number needed to treat (NNT) or harm (NNH) of ulinastatin use among low-, middle-, and high-risk subgroups.