
==== Front
PNAS Nexus
PNAS Nexus
pnasnexus
PNAS Nexus
2752-6542
Oxford University Press US

10.1093/pnasnexus/pgae371
pgae371
Biological, Health, and Medical Sciences
AcademicSubjects/MED00010
AcademicSubjects/SCI00010
AcademicSubjects/SOC00010
PNAS_Nexus/emerg-med
PNAS_Nexus/immun
Inflammatory signature-based theranostics for acute lung injury in acute type A aortic dissection
https://orcid.org/0000-0002-8143-0912
Liu Hong Department of Cardiovascular Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing 2100299, P.R. China

Diao Yi-fei Department of Cardiovascular Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing 2100299, P.R. China

Qian Si-chong Department of Cardiovascular Surgery, Beijing Anzhen Hospital, Capital Medical University, Beijing 100029, P.R. China

Shao Yong-feng Department of Cardiovascular Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing 2100299, P.R. China

Zhao Sheng Department of Cardiovascular Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing 2100299, P.R. China

Li Hai-yang Department of Cardiovascular Surgery, Beijing Anzhen Hospital, Capital Medical University, Beijing 100029, P.R. China

Zhang Hong-jia Department of Cardiovascular Surgery, Beijing Anzhen Hospital, Capital Medical University, Beijing 100029, P.R. China

Levine Bruce Editor
To whom correspondence should be addressed: Email: dr.hongliu@foxmail.com
Competing Interest: The authors declare no competing interests.

9 2024
27 8 2024
27 8 2024
3 9 pgae37108 4 2024
15 8 2024
04 9 2024
© The Author(s) 2024. Published by Oxford University Press on behalf of National Academy of Sciences.
2024
https://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.

Abstract

Acute lung injury (ALI) is a serious adverse event in the management of acute type A aortic dissection (ATAAD). Using a large-scale cohort, we applied artificial intelligence-driven approach to stratify patients with different outcomes and treatment responses. A total of 2,499 patients from China 5A study database (2016–2022) from 10 cardiovascular centers were divided into 70% for derivation cohort and 30% for validation cohort, in which extreme gradient boosting algorithm was used to develop ALI risk model. Logistic regression was used to assess the risk under anti-inflammatory strategies in different risk probability. Eight top features of importance (leukocyte, platelet, hemoglobin, base excess, age, creatinine, glucose, and left ventricular end-diastolic dimension) were used to develop and validate an ALI risk model, with adequate discrimination ability regarding area under the receiver operating characteristic curve of 0.844 and 0.799 in the derivation and validation cohort, respectively. By the individualized treatment effect prediction, ulinastatin use was significantly associated with significantly lower risk of developing ALI (odds ratio [OR] 0.623 [95% CI 0.456, 0.851]; P = 0.003) in patients with a predicted ALI risk of 32.5–73.0%, rather than in pooled patients with a risk of <32.5 and >73.0% (OR 0.929 [0.682, 1.267], P = 0.642) (Pinteraction = 0.075). An artificial intelligence-driven risk stratification of ALI following ATAAD surgery were developed and validated, and subgroup analysis showed the heterogeneity of anti-inflammatory pharmacotherapy, which suggested individualized anti-inflammatory strategies in different risk probability of ALI.

extreme gradient boosting
type A aortic dissection
acute lung injury
risk prediction
inflammation
Jiangsu Provincial Innovative & Entrepreneurial Talent Project Nanjing Medical University 10.13039/501100007289 JZ23349020230306
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pmcSignificance Statement

Acute lung injury (ALI) is a serious adverse event after acute type A aortic dissection surgery. We developed a computational method that can use these laboratory biomarkers to determine whether a person is at increased risk of developing an ALI. This could enable regular monitoring of these people and might enable ALI to be prevented in some individuals.

Introduction

Acute type A aortic dissection (ATAAD) is a severe cardiovascular disease associated with major morbidity and mortality (1, 2). Despite improvements in surgical techniques and perioperative management strategies, the extremely complex and multifaceted factors including the dissected aorta itself, contrast media for computed tomography angiography, massive blood transfusion, deep hypothermia, cardiopulmonary bypass, serious ischemia–reperfusion injury due to lower body circulatory arrest, and exogenous graft implantation, as well as surgical trauma, anesthesia, mechanical ventilation together initiates serious systemic inflammatory response (3–5), deteriorating acute lung injury (ALI), and likely progressing to multiple organ dysfunction syndrome and even mortality (6). Therefore, early identification of patients at high risk of developing ALI after ATAAD is highly important to facilitate early interventions and medical care.

Several strategies have been suggested for treatment of ALI in ATAAD, of which anti-inflammatory pharmacotherapeutics plays an important protective role (7). Ulinastatin, a glycoprotein acting as a urinary trypsin inhibitor, has been proven to have anti-inflammatory activity by inhibiting the release of pro-inflammatory cytokines and elastase from macrophages and neutrophils to suppress the systemic inflammatory response, resulting in attenuation of ALI (8, 9). Yet, translating group-level estimates of trials to individual patients is challenging, as average measures implicitly consider that all patients have an average risk and the same average response to treatment. Absolute treatment effects, however, can vary substantially among individuals. Individualized prediction of treatment effects provides a comprehensive approach to identify those patients who benefit most from Ulinastatin, enabling clinicians to make patient-tailored treatment decisions and better weigh treatment benefits against harms (10).

In the present analyses, we aimed to develop and validate a model with patient characteristics, for individualized prediction of the effects of Ulinastatin on ALI after ATAAD surgery, to investigate whether distinct risk stratification groups respond differently to anti-inflammatory pharmacotherapy based on a large-scale cohort of the Chinese ATAAD population.

Methods

Study population

The 5A cohort study (Additive Anti-inflammatory Action for Aortopathy and Arteriopathy) is a national prospective registry involving patients with aortic dissection, who were consecutively enrolled at 10 cardiovascular centers in China (see Supplementary Method). Further details about the China 5A registry are available in our previous study protocol (11). This study focused on patients with ATAAD who underwent surgical repair from 2016 January 1 to 2022 June 30. These patients had documented biomarkers of interest within 6 h of hospital admission, as recorded in the 5A database (12). The study adhered to the Declaration of Helsinki and was registered with ClinicalTrials.gov (NCT04398992). The Institutional Review Board (IRB) of the Aortic Collaborative Institutions approved the study protocol (2021-SR-381), which waived the requirement for written patient consent because of the nature of the retrospective study. The patient cohort was randomly divided into a training set (N1 = 1,749; 70%) and a test set (N2 = 750; 30%). The study followed the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) Guidelines (13).

Data recourse

Data collections mainly included these characteristics regarding demography, clinical factors, dissection, circulation characteristics, biomarkers, procedural variables, and perioperative outcomes. In particular, clinically available biomarkers were collected within 6 h prior to surgery, including peripheral blood leukocyte (× 109/L), platelet (× 109/L), hemoglobin (g/L), creatine kinase-MB (ng/mL), lactic dehydrogenase (U/L), alanine transaminase (U/L), aspartate aminotransferase (U/L), albumin (g/L), blood urea nitrogen (mmol/L), creatinine (μmoI/L), activated partial thromboplastin time (s), international normalized ratio, arterial pH, PaCO2 (mmHg), base excess (mmol/L), glucose (mmol/L), and lactate (mmol/L). Surgery-related procedures were described as previously reported (14).

Anti-inflammatory pharmacotherapy

Ulinastatin (TECHPOOL Biopharma Co., Ltd., Guangzhou, China) was injected intravenously following institutional protocol starting right after the surgery until intensive care unit (ICU) discharge. Because this is a retrospective study, the actual ulinastatin usage and dosage mainly depended on medicine specification (100,000 U once every 8 h).

Outcomes

Considering the Berlin definition of acute respiratory distress syndrome (15) and American Thoracic Society workshop report (16) as well as the institutional protocol, the primary outcome ALI was defined as radiological evidence of bilateral infiltrates, evidence of physiologic dysfunction (hypoxemia, arterial oxygen tension/fraction inspired oxygen <200 mmHg), and the absence of left atrial hypertension, occurring within 72 h subsequent to the operation, regardless of mechanical ventilation status. Secondary outcomes included 30-day mortality, inhospital mortality, mechanical ventilation duration, ICU length of stay, and hospital length of stay.

Model derivation and validation

The final cohort was randomly divided into a derivation cohort (70%) and a validation cohort (30%). The eXtreme Gradient Boosting (XGBoost) algorithm was selected for model derivation (11, 17). To allow for interpretation of our model's predictions, we used SHapley additive explanation (SHAP) to evaluate key feature importance with identification of a predictor's relative contribution for each observation and averaged across observations to the final prediction (18). Discrimination performance was assessed via the area under the receiver operating characteristic curve (AUROC) and the area under precision-recall curve (AUPRC) (19). Calibration ability was assessed via the calibration plot. Clinical utility was assessed using decision curve analysis (20).

Subgroup analysis

Patients were stratified according to the presence or absence of ulinastatin use. Cubic spline curve analysis was applied to fit the functional relationship of the predicted risk probability as a continuous variable with the primary outcome. Subsequently, we divided patients into three subgroups on the basis of their risk probability (<32.5, 32.5–73.0, and >73.0%) and further tested whether there were interactions between anti-inflammatory pharmacotherapy (ulinastatin) and operative mortality across subgroups of these risk differences. Alluvial plots were created to visualize risk stratification of ALI (low, intermediate, and high risk), anti-inflammatory pharmacotherapy (the absence vs. presence of ulinastatin), and primary outcome (non-ALI vs. ALI), in which a thicker ribbon indicates that a greater number of subjects fell into a particular risk stratification or range. Risk–benefit assessment was performed in subgroups stratified according to surgical strategy based on the number needed to treat (NNT) or the number needed to harm (NNH) measures. Of note, to alleviate the effects of the potential confounding factors as soon as possible, multivariable analysis with adjustment for baseline, clinical procedural factors was employed to investigate the association between ulinastatin use and ALI.

Sample size and power calculation

For binary outcome measures, we proposed that at least 10 events (i.e. patients with the defined outcome) per variable are necessary to avoid overfitting. This effective sample size was achieved in both the derivation cohort, which had 576 events for eight variables, and the validation cohort, with 245 events for the same number of variables.

Statistical analysis

Continuous data are presented as medians with interquartile ranges (IQRs), while categorical data are reported as percentages. We used binary logistic regression to evaluate odds ratios (ORs) and 95% CIs. Features with more than 20% missing values were excluded from the analysis. To handle missing data, we used multiple imputations with chained equations. All statistical analyses were conducted using R version 3.6.1 and Python version 3.6.

Patient and public involvement

No patients or members of the public were involved in determining the research question, outcome measures, or interpreting the results as this was a doctoral student project without funding to support patient and public involvement. The results of this study will be summarized for the public in a blog post by the first authors on publication, disseminated on the Chinese 5A Alliance websites to their relevant audiences, and publicized on social media.

Results

Patient characteristics and outcomes

There were 2,499 ATAAD patients included for final analysis: 1,749 (70%) in the derivation cohort and 750 (30%) in the validation cohort (Fig. S1). Among overall patients, the median age was 51 (IQR 41–59) years, 1,874 (75.0%) were male, and the median body mass index was 25.4 (IQR 23.0–27.8) kg/m2. Of these patients, 560 (32.0%) presented with one of the following conditions: coronary malperfusion, renal malperfusion, cerebral malperfusion, intestinal malperfusion, or any pulse deficit/limb ischemia. Baseline, clinical, laboratory, and procedural features of derivation, validation, and overall cohort are reported in Table 1. There were no significant differences in baseline, clinical, laboratory, and procedural features between two cohorts. In derivation cohort, 576 patients developed ALI who were older, obese, higher percentage of hypertension and arrhythmias, higher percentage of malperfusion compared with those without ALI (Table 2).

Table 1. Baseline and clinical characteristics and perioperative outcomes of two cohorts.

	Derivation cohort (N1 = 1,749)	Validation cohort (N2 = 750)	Overall (N = 2,499)	P value	
Demographic characteristics					
Age (years)	51 (41–59)	50 (42–58)	51 (41–59)	0.468	
Sex (male)	1,299 (74.3%)	575 (76.7%)	1,874 (75.0%)	0.205	
Height (cm)	171 (165–175)	172 (167–176)	171 (165–176)	0.074	
Weight (kg)	75 (65–84)	75 (65–85)	75 (65–85)	0.640	
Body mass index (kg/m2)	25.4 (23.1–27.8)	25.4 (22.8–27.8)	25.4 (23.0–27.8)	0.504	
Clinical characteristics					
Time onset to operation (days)	1 (1–2)	1 (1–2)	1 (1–2)	0.876	
Heart rate (bpm)	80 (76–88)	80 (75–88)	80 (76–88)	0.271	
Diastolic blood pressure (mmHg)	75 (68–80)	73 (65–80)	75 (67–80)	0.026	
Systolic blood pressure (mmHg)	130 (120–140)	130 (120–140)	130 (120–140)	0.986	
Mean blood pressure (mmHg)	93 (85–100)	93 (85–98)	93 (85–100)	0.155	
Smoking n (%)	738 (42.8%)	334 (45.5%)	1,072 (43.6%)	0.209	
Drinking n (%)	344 (20.4%)	173 (24.1%)	517 (21.5%)	0.044	
Chronic lung disease n (%)	37 (2.1%)	23 (3.1%)	60 (2.4%)	0.154	
Coronary heart disease n (%)	164 (9.4%)	80 (10.7%)	244 (9.8%)	0.317	
Hypertension n (%)	1,249 (71.9%)	526 (70.5%)	1,775 (71.5%)	0.493	
Diabetes n (%)	90 (5.1%)	41 (5.5%)	131 (5.2%)	0.742	
Arrhythmias n (%)	46 (2.6%)	17 (2.3%)	63 (2.5%)	0.593	
Congestive heart failure n (%)	4 (0.2%)	6 (0.8%)	10 (0.4%)	0.038	
Marfan syndrome n (%)	26 (1.5%)	6 (0.8%)	32 (1.3%)	0.161	
Home pharmacological treatments					
 Statins n (%)	254 (14.5%)	113 (15.1%)	367 (14.7%)	0.771	
 Beta-blockers n (%)	281 (16.6%)	98 (13.0%)	379 (15.2%)	0.063	
 Metformin n (%)	42 (2.4%)	21 (2.8%)	63 (2.5%)	0.657	
 Aspirin n (%)	77 (4.4%)	41 (5.4%)	118 (4.7%)	0.295	
Dissection characteristics					
Malperfusiona  n (%)	560 (32.0%)	215 (28.7%)	775 (31.0%)	0.097	
Circulation characteristics					
Aortic regurgitation n (%)				0.801	
 Mild	553 (33.6%)	230 (32.3%)	783 (33.2%)		
 Moderate	215 (13.1%)	96 (13.5%)	311 (13.2%)		
 Severe	339 (20.6%)	158 (22.2%)	497 (21.1%)		
Pericardial effusion n (%)				0.670	
 Mild	161 (9.3%)	61 (8.2%)	222 (8.9%)		
 Moderate	30 (1.7%)	17 (2.3%)	47 (1.9%)		
 Severe	14 (0.8%)	6 (0.8%)	20 (0.8%)		
Pleural effusion n (%)				0.927	
 Minor	65 (3.7%)	30 (4.0%)	95 (3.8%)		
 Major	27 (1.5%)	11 (1.5%)	38 (1.5%)		
LVEDD (mm)	50 (46–55)	50 (46–55)	50 (46–55)	0.836	
LVEF (%)	62 (59–66)	62 (59–66)	62 (59–66)	0.899	
LVESD (mm)	33 (30–37)	33 (30–37)	33 (30–37)	0.309	
Biomarkers					
Leukocyte (× 109/L)	8.4 (6.1–12.0)	8.6 (6.2–11.7)	8.5 (6.2–11.9)	0.927	
Platelet (× 109/L)	194 (157–239)	192 (157–232)	193 (157–236)	0.470	
Hemoglobin (g/L)	138 (125–149)	139 (126–151)	139 (126–150)	0.050	
Creatine kinase-MB (ng/mL)	1.3 (0.8–2.2)	1.3 (0.8–2.2)	1.3 (0.8–2.2)	0.484	
Lactic dehydrogenase (U/L)	195 (165–237)	197 (164–245)	196 (165–239)	0.244	
Alanine transaminase (U/L)	19 (14–30)	19 (14–30)	19 (14–30)	0.819	
Aspartate aminotransferase (U/L)	20 (16–26)	20 (16.0–26.0)	20 (16–26)	0.632	
Albumin (g/L)	40.4 (37.1–43.2)	40.5 (37.4–43.3)	40.4 (37.2–43.2)	0.557	
Blood urea nitrogen (mmol/L)	5.8 (4.7–7.4)	6.0 (4.7–7.4)	5.9 (4.7–7.4)	0.594	
Creatinine (μmoI/L)	74.3 (61.9–89.8)	75.9 (63.5–91.5)	74.9 (62.4–90.5)	0.106	
INR	1.07 (1.01–1.15)	1.06 (1.00–1.15)	1.07 (1.01–1.15	0.167	
APPT (s)	30.4 (28.2–32.9)	30.6 (28.2–33.0)	30.5 (28.2–32.9)	0.635)	
PH	7.42 (7.40–7.44)	7.42 (7.40–7.44)	7.42 (7.40–7.44)	0.325	
PaCO2 (mmHg)	35.1 (32.1–38.0)	35.1 (32.1–38.3)	35.1 (32.1–38.1)	0.666	
Base excess (mmol/L)	−0.7 (−2.0 to 0.8)	−0.7 (−1.9 to 1.0)	−0.7 (−2.0 to 0.8)	0.400	
Glucose (mmol/L)	6.0 (5.0–7.4)	5.9 (5.0–7.4)	6.0 (5.0–7.4)	0.930	
Lactate (mmol/L)	1.4 (1.0–1.9)	1.3 (1.0–1.9)	1.4 (1.0–1.9)	0.830	
Procedural variables					
Root procedure				0.070	
 Aortic root repair (%)	36 (2.1%)	12 (1.6%)	48 (1.9%)		
 Aortic valve replacement (%)	73 (4.2%)	18 (2.4%)	91 (3.6%)		
 Aortic root replacement (%)	671 (38.4%)	315 (42.0%)	986 (39.5%)		
  Bentall (%)	628 (35.9%)	287 (38.3%)	915 (36.6%)	0.262	
  David (%)	23 (1.3%)	13 (1.7%)	36 (1.4%)	0.419	
Total arch replacement + FET implantation (%)	851 (48.7%)	344 (45.9%)	1,195 (47.8%)	0.201	
Hemi-arch replacement (%)	220 (12.6%)	84 (11.2%)	304 (12.2%)	0.196	
Total arch replacement (%)	864 (49.4%)	353 (47.1%)	1,217 (48.7%)	0.285	
Inclusion technique (%)	1,256 (71.8%)	521 (69.5%)	1,777 (71.1%)	0.236	
Concomitant CABG (%)	134 (7.7%)	62 (8.3%)	196 (7.8%)	0.606	
Concomitant valve surgery (%)	77 (4.4%)	37 (4.9%)	114 (4.6%)	0.560	
Cardiopulmonary bypass time (min)	172 (136–206)	168 (134–201)	171 (136–205)	0.252	
Aortic cross-clamp time (min)	99 (77–123)	98 (77–121)	98 (77–123)	0.633	
Circulatory arrest time (min)	23 (18–29)	23 (18–30)	23 (18–30)	0.276	
Anti-inflammatory pharmacotherapy					
Ulinastatin therapy	675 (38.6%)	310 (41.3%)	985 (39.4%)	0.199	
Perioperative outcomes					
 Acute lung injury (%)	576 (32.9%)	245 (32.7%)	821 (32.9%)	0.897	
 30-day mortality (%)	71 (4.1%)	26 (3.5%)	97 (3.9%)	0.482	
 Inhospital mortality (%)	78 (4.5%)	31 (4.1%)	109 (4.4%)	0.714	
 Mechanical ventilation time (h)	18 (14–38)	18 (14–36)	18 (14–37)	0.664	
 ICU stay (h)	29 (19–63)	28 (18–65)	29 (19–64)	0.767	
 Hospital stay (days)	16 (12–22)	15 (11–21)	16 (11–21)	0.308	
Data are n (%) or median (IQR), unless otherwise specified.

APTT, activated partial thromboplastin time; LVEDD, left ventricular end-diastolic dimension; LVESD, left ventricular end-systolic dimension; LVEF, left ventricular ejection fraction; INR, international normalized ratio; APTT, activated partial prothrombin time; FET, frozen elephant trunk; CABG, coronary artery bypass grafting; ICU, intensive care unit.

aDefined as one of the following conditions: coronary malperfusion, renal malperfusion, cerebral perfusion, spinal/lumbar, and intestinal and limb ischemia.

Table 2. Baseline and clinical characteristics and perioperative outcomes of ALI vs. non-ALI patients in derivation cohort.

	Non-ALI (N1 = 1,173)	ALI (N2 = 576)	P value	
Demographic characteristics				
Age (years)	50 (40–58)	52 (44–61)	<0.001	
Sex (male)	886 (75.5%)	413 (71.7%)	0.085	
 Height (cm)	172 (166–176)	170 (165–175)	<0.001	
 Weight (kg)	75 (65–83)	75 (65–85)	0.835	
Body mass index (kg/m2)	25.4 (22.9–27.8)	25.6 (23.5–28.5)	0.021	
Clinical characteristics				
Time onset to operation (days)	1 (1–2)	1 (1–2)	0.532	
Heart rate (bpm)	80 (75–85)	80 (76–88)	<0.001	
Diastolic blood pressure (mmHg)	75 (68–80)	75 (67–80)	0.789	
Systolic blood pressure (mmHg)	129 (120–139)	130 (119–140)	0.623	
Mean blood pressure (mmHg)	93 (85–99)	93 (84–100)	0.941	
Smoking n (%)	499 (43.2%)	239 (41.8%)	0.564	
Drinking n (%)	226 (20.0%)	118 (21.1%)	0.594	
Chronic lung disease n (%)	25 (2.1%)	12 (2.1%)	0.940	
Coronary heart disease n (%)	116 (9.9%)	48 (8.3%)	0.292	
Hypertension n (%)	800 (68.8%)	449 (78.1%)	<0.001	
Diabetes n (%)	61 (5.2%)	29 (5.0%)	0.883	
Arrhythmias n (%)	24 (2.1%)	22 (3.8%)	0.030	
Congestive heart failure n (%)	3 (0.3%)	1 (0.2%)	0.735	
Marfan syndrome n (%)	22 (1.9%)	4 (0.7%)	0.055	
Dissection characteristics				
Malperfusiona  n (%)	326 (27.8%)	234 (40.6%)	<0.001	
Circulation characteristics				
Aortic regurgitation n (%)			<0.001	
 Mild	356 (31.6%)	197 (38.0%)		
 Moderate	132 (11.7%)	83 (16.0%)		
 Severe	241 (21.4%)	98 (18.9%)		
Pericardial effusion n (%)			0.006	
 Mild	103 (8.9%)	58 (10.1%)		
 Moderate	13 (1.1%)	17 (3.0%)		
 Severe	6 (0.5%)	8 (1.4%)		
Pleural effusion n (%)			0.121	
 Minor	42 (3.6%)	23 (4.0%)		
 Major	23 (2.0%)	4 (0.7%)		
LVEDD (mm)	51 (47–56)	49 (45–54)	<0.001	
LVEF (%)	62 (59–66)	62 (58–65)	0.191	
LVESD (mm)	33 (30–37)	32 (28–36)	<0.001	
Biomarkers				
Leukocyte (× 109/L)	7.8 (5.9–11.0)	10.3 (7.0–13.4)	<0.001	
Platelet (× 109/L)	201 (162–246)	181 (144–223)	<0.001	
Hemoglobin (g/L)	139.0 (127.0–150.0)	137.0 (122.0–147.0)	0.003	
Creatine kinase-MB (ng/mL)	1.1 (0.7–2.0)	1.5 (0.9–3.0)	<0.001	
Lactic dehydrogenase (U/L)	187 (161–228)	209 (173–255)	<0.001	
Alanine transaminase (U/L)	19 (13–28)	20 (14.0–32)	0.010	
Aspartate aminotransferase (u/L)	19 (15–25)	21 (17–32)	<0.001	
Albumin (g/L)	40.8 (37.7–43.6)	39.5 (36.1–42.2)	<0.001	
Blood urea nitrogen (mmol/L)	5.5 (4.6–7.1)	6.5 (5.0–8.3)	<0.001	
Creatinine (μmoI/L)	72.2 (60.6–84.6)	78.8 (64.6–102.6)	<0.001	
INR	1.07 (1.01–1.14)	1.08 (1.02–1.17)	0.003	
APPT (s)	30.6 (28.3–33.2)	30.0 (28.0–32.4)	0.005	
PH	7.42 (7.40–7.44)	7.42 (7.39–7.45)	0.989	
PaCO2 (mmHg)	35.2 (32.3–38.2)	34.7 (31.4–37.5)	0.009	
Base excess (mmol/L)	−0.6 (−1.9 to 0.9)	−1.0 (−2.4 to 0.6)	<0.001	
Glucose (mmol/L)	5.6 (4.9–7.0)	6.8 (5.5–8.0)	<0.001	
Lactate (mmol/L)	1.3 (1.0–1.8)	1.4 (1.0–2.1)	0.006	
Procedural variables				
Root procedure			0.269	
Aortic root repair (%)	22 (1.9%)	14 (2.4%)		
Aortic valve replacement (%)	55 (4.7%)	18 (3.1%)		
Aortic root replacement (%)	458 (39.0%)	213 (37.0%)		
Bentall (%)	424 (36.1%)	204 (35.4%)	0.765	
David (%)	23 (2.0%)	0 (0.0%)	<0.001	
Total arch replacement + FET implantation (%)	483 (41.2%)	368 (63.9%)	<0.001	
Hemi-arch replacement (%)	144 (12.3%)	76 (13.2%)	<0.001	
Total arch replacement (%)	494 (42.1%)	370 (64.2%)		
Inclusion technique (%)	781 (66.6%)	475 (82.5%)	<0.001	
Concomitant CABG (%)	79 (6.7%)	55 (9.5%)	0.038	
Concomitant valve surgery (%)	53 (4.5%)	24 (4.2%)	0.736	
Cardiopulmonary bypass time (min)	160 (129–195)	192 (158–224)	<0.001	
Aortic cross-clamp time (min)	94 (72–118)	107 (88–133)	<0.001	
Circulatory arrest time (min)	23 (18–30)	23 (18–29)	0.597	
Anti-inflammatory therapeutics				
Ulinastatin	474 (40.4%)	201 (34.9%)	0.026	
Perioperative outcomes				
30-day mortality (%)	20 (1.7%)	51 (8.9%)	<0.001	
Inhospital mortality (%)	21 (1.8%)	57 (9.9%)	<0.001	
Mechanical ventilation time (h)	16 (12–18)	51 (37–111)	<0.001	
ICU stay (h)	20 (17–35)	84 (45–156)	<0.001	
Hospital stay (days)	15 (11–21)	16 (12–23)	0.037	
Data are n (%) or median (IQR), unless otherwise specified.

ALI, acute lung injury; LVEDD, left ventricular end-diastolic dimension; LVESD, left ventricular end-systolic dimension; LVEF, left ventricular ejection fraction; INR, international normalized ratio; APTT, activated partial prothrombin time; FET, frozen elephant trunk; CABG, coronary artery bypass grafting; ICU, intensive care unit.

aDefined as one of the following conditions: coronary malperfusion, renal malperfusion, cerebral perfusion, spinal/lumbar, and intestinal and limb ischemia.

The incidence of ALI was 32.9, 32.9, and 32.7% in derivation, validation, and overall cohort, respectively. The incidence of 30-day and operative mortality among overall patients was 3.9 and 4.4%, respectively (Table 1). Among the derivation cohort, ALI patients had higher percentage of 30-day and operative mortality, and longer mechanical ventilation time, ICU length of stay, and hospital length of stay than those without ALI (Table 2). Univariate analysis of ALI in overall population was showed in Table S1.

Model characteristics: discrimination, calibration, and clinical use

The SHAP analysis showed the candidate predictor's relative contribution, either positively or negatively, to the prediction of ALI (Fig. 1A–C), which we used to develop a full model to predict ALI, with an AUROC of 0.971 and AUPRC of 0.945 as well as good calibration and clinical utility (Fig. 2 and Table S2). To improve the model's practical application, we selected features that were most strongly associated with the systemic effects of the anti-inflammatory strategy based on a SHAP feature importance of 0.030 or higher, which identified the top eight features: leukocyte, platelet, hemoglobin, base excess, age, creatinine, glucose, and left ventricular end-diastolic dimension (Figs. 1 and S1). By entering these eight indicators, clinicians can easily obtain the appropriate risk probability for a single patient to support decision-making based on an online browser accessible version available for external use (http://www.empowerstats.net/pmodel/? m=7473_ALI) (Fig. 1D).

Fig. 1. Characteristics and schematics of simplified ALI risk model. A) SHAP plot of each predictor influencing non-ALI prediction, B) SHAP plot of each predictor influencing ALI prediction, C) summary plot of each predictor influencing ALI prediction, and D) the screenshot of the simplified model online calculator. LVEDD, left ventricular end-diastolic dimension.

Fig. 2. AUROC and AUPRC of the ALI risk model in the derivation and validation cohorts. A, D) AUROC and AUPRC of the full ALI risk model in the derivation cohort; B, E) AUROC and AUPRC of the simplified ALI risk model in the derivation cohort; C, F) AUROC and AUPRC of the simplified ALI risk model in the validation cohort; G, I) calibration curve and decision curve of the full ALI risk model in the derivation cohort; H, J) calibration curve and decision curve of the simplified ALI risk model in the derivation cohort; I, L) calibration curve and decision curve of the simplified ALI risk model in the validation cohort. AUROC, area under the receiver operating characteristic curve; AUPRC, area under the precision-recall curve.

The selected ALI risk model had high discrimination in development and validation populations, with AUROCs of 0.844 and 0.799, respectively. This inflammatory risk model had adequate accuracy in both the derivation and validation populations, with AUPRC values of 0.764 and 0.696 (Figs. 2 and 3), respectively. There was good calibration and clinical utility in the derivation and validation cohort, respectively (Fig. 2). The other performances of these three risk models were showed in Tables S3 and S4.

Fig. 3. Relationship between risk stratification and ulinastatin use in the derivation cohort. A) Alluvial plot showing distribution of risk stratifications across ulinastatin use and ALI; B) cubic splines of the predicted and observed risk of ALI by absence vs. presence of ulinastatin use; C) NNT/NNH of the absence vs. presence of ulinastatin use overall and in each risk stratification.

Association between ulinastatin use and ALI

Among the 1,749 patients in the derivation cohort, patients with ulinastatin use were less likely to develop ALI than patients without ulinastatin use (375/1,074 [34.9%] vs. 201/675 [29.8%]), Risk difference 0.11 (95% CI 0.01, 0.21), crude OR 0.790 (95% CI 0.642, 0.972, P = 0.026) (Table 3). The multivariable analysis confirmed the significant association between ulinastatin use and ALI (adjusted OR 0.666; 95% CI 0.530, 0.836, P= 0.0005) with adjustment for procedural factors (root procedure, arch procedure, and concomitant procedure, as well as cardiopulmonary bypass time, and aortic cross-clamp time, and circulatory arrest time). However, there were no significant differences in 30-day and operative mortality between ulinastatin use and no use (all P > 0.05) (Table 3).

Table 3. Comparison of outcome of interest between ulinastatin use or not use in derivation cohort.

	No ulinastatin	Ulinastatin	Risk difference (95% CI)	OR (95% CI)	P value	Adjusted ORa (95% CI)	P valuea	
Acute lung injury								
 Overall, N = 1,749	375/1,074 (34.9%)	201/675 (29.8%)	0.11 (0.01, 0.21)	0.790 (0.642, 0.972)	0.026	0.666 (0.530, 0.836)	0.0005	
 Low and high-risk subgroup, n1 = 1,076	134/662 (20.2%)	79/414 (19.1%)	0.03 (−0.09, 0.15)	0.929 (0.682, 1.267)	0.642	0.736 (0.520, 1.041)	0.083	
 Intermediate risk subgroup, n2 = 673	241/412 (58.5%)	122/261 (46.7%)	0.24 (0.08, 0.39)	0.623 (0.456, 0.851)	0.003	0.578 (0.417, 0.801)	0.001	
30-day mortality								
 Overall, N = 1,749	41/1,074 (3.8%)	30/675 (4.4%)	0.03 (−0.06, 0.13)	1.172 (0.724, 1.896)	0.518	0.936 (0.551, 1.590)	0.807	
 Low and high-risk subgroup, n1 = 1,076	11/662 (1.7%)	7/414 (1.7%)	0.00 (−0.12, 0.13)	1.018 (0.391, 2.647)	0.971	0.545 (0.173, 1.715)	0.299	
 Intermediate risk subgroup, n2 = 673	30/412 (7.3%)	23/261 (8.8%)	0.06 (−0.10, 0.21)	1.231 (0.698, 2.169)	0.473	1.151 (0.625, 2.118)	0.652	
Inhospital mortality								
 Overall, N = 1,749	46/1,074 (4.3%)	32/675 (4.7%)	0.02 (−0.07, 0.12)	1.112 (0.701, 1.765)	0.651	0.924 (0.550, 1.553)	0.766	
 Low and high-risk subgroup, n1 = 1,076	15/662 (2.3%)	9/414 (2.2%)	0.01 (−0.12, 0.13)	0.959 (0.416, 2.211)	0.920	0.604 (0.208, 1.752)	0.353	
Intermediate risk subgroup, n2 = 673	31/412 (7.5%)	23/261 (8.8%)	0.05 (−0.11, 0.20)	1.188 (0.676, 2.086)	0.549	1.106 (0.603, 2.026)	0.745	
aAdjustment for baseline, clinical procedural factors.

Subgroup analysis

Alluvial plot showed distribution of risk stratification of ALI (low, intermediate, and high risk) across ulinastatin use and ALI in derivation data (Fig. 3A). The risk model was then used to predict ALI risk for the derivation cohort and plotted against observed risk (Fig. 3B), in which the spline curves for the effect of ulinastatin use vs. no use on the occurrence of ALI mainly across 32.5 and 73.0% from the perspective of clinical significance. By analysis of individual risk probability and treatment effect, ulinastatin use was significantly associated with lower risk of developing ALI (241/1,074 [58.5%] vs. 122/675 [46.7%]; risk difference 0.24 [95% CI 0.08, 0.39]; OR 0.623 [95% CI 0.456, 0.851]; P = 0.003) in patients with a risk of 32.5–73.0%, rather than in patients with an ALI risk probability of <32.5% and >73.0% (134/1,074 [20.2%] vs. 79/675 [19.1%]; risk difference 0.03 [95% CI −0.09, 0.15]; OR 0.929 [0.682, 1.267], P = 0.642) (Pinteraction = 0.075) (Table 3). The multivariable analysis confirmed the significant association between ulinastatin use and ALI (adjusted OR 0.666; 95% CI 0.530, 0.836, P = 0.0005) (Pinteraction = 0.133) with adjustment for baseline, clinical procedural factors.

In derivation cohort, the estimated NNT was 19 (95% CI 11, 162) showing one patient being prevented from developing ALI in every 19 patients who have been treated with ulinastatin compared with those without ulinastatin. The estimated NNT was 9 (95% CI 5, 25) showing one patient being prevented from developing ALI in every nine patients who have been treated with ulinastatin among patients with a predicted risk of 32.5–73.0% compared with those without ulinastatin, however, no significant have no statistical differences between absence and presence of ulinastatin in patients with a risk probability of <32.5 or >73.0% (Fig. 3C).

The observed ALI rates varied substantially across risk groups: 180/1,043 (17.3%), 363/673 (53.9%), and 33/33 (100.0%) in the low, intermediate, and high-risk group, respectively (P for trend <0.001). With reference to the low-risk group, the intermediate group conferred significantly higher risk of ALI (crude OR 5.614 [95% CI 4.502, 7.002]; P < 0.00001; adjusted OR 4.890 [95% CI 3.841, 6.227]; P < 0.001).

Discussion

In this cohort of ATAAD patients from China, we have developed and confirmed a risk scoring model that predicted the risk of developing ALI after ATAAD surgery. This model exhibits satisfactory performance in terms of discrimination, calibration, and clinical usefulness in both the development and validation groups. Subgroup analysis showed that ulinastatin use was associated with a significantly lower risk of developing ALI in patients with a risk probability of 32.5–73.0%, but with similar risk of developing ALI in patients with a risk probability of <32.5 or >73.0%. These findings underline the importance of risk stratification to provide better individualized anti-inflammatory treatment for patients with ATAAD.

The mechanism by which ALI forms after ATAAD has not been completely elucidated; however, it is generally believed that inflammation plays an important role in the process (21, 22). High inflammatory biomarker such as C-reactive protein (CRP), interleukin-6, and leukocyte has been observed in patients as soon as the onset of the syndrome, indicating the early initiation of the inflammatory cascade at the very beginning of the development of aortic dissection, which have been confirmed to be related to perioperative mortality among patients with ATAAD (23–25).

However, the association between leukocyte count and ALI in patients with ATAAD remain unclear. In this study, we investigated the role of leukocyte in the development of ALI following ATAAD surgery. Unfortunately, CRP and IL-6 levels were not statistically analyzed due to their high missing rates. Based on SHAP analysis, we found that leukocyte was the top feature of importance among all the baseline and clinical covariates in predicting ALI after ATAAD surgery. In addition, an elevation in peripheral leukocyte count was associated with a higher risk of developing ALI. Given that systemic inflammatory reactions played a vital role in initiation and development of ALI along with the onset and treatment of ATAAD (3–5, 26), it highlighted the importance and necessity of anti-inflammatory in the treatment and prevention of ALI in management of ATAAD (27).

Our findings showed that patients with a low and high risk of ALI (defined risk probability of <32.5 or >73.0%) will not benefit from ulinastatin use while patients with an intermediate risk of ALI (defined risk probability of 32.5–73.0%) will benefit from ulinastatin use, which indicates patients with a predicted ALI risk of 32.5–73.0% were likely to be the potential benefited population who had less risk probability of developing ALI after ATAAD surgery. These findings showed the significant heterogeneity of anti-inflammatory pharmacotherapy, which suggested individualized anti-inflammatory strategies in different risk probability of ALI. The clinical implications of our study hold significant importance. It is probable that individuals with varying risk probabilities exhibited diverse responses to anti-inflammatory pharmacotherapy, which could indicate variations in patient-specific risk profiles (10, 11). However, decision-making regarding ATAAD is complex in practice and requires weighing the benefits and risks of ulinastatin administration at the patient level. In addition, emerging immune-inflammatory properties including interleukins, noncoding RNA, and next-generation nanotechnology are being investigated and translated into medical therapies, which may indicate further advances in the pathologies of this catastrophic disease to improve treatment across in this area (28–30).

Strengths and limitations

This study benefits from a large sample size and multicenter nature. However, there are notable limitations worth discussing. First, one key limitation is the completeness of the datasets derived from clinical practice. Of note, the lack of information regarding genetic mutations especially for patients with aortopathies might compromise deep insight into the molecular mechanisms related to aortic dissection. Additionally, we did not analyze the relationship between the dose of ulinastatin and risk of ALI. Last, the study population was quite homogeneous, which may not reflect the diversity found in other countries and regions, which is likely to restrict the applicability of our findings to other healthcare settings.

Conclusion

In this study, we developed a data-driven machine learning-based risk scoring model to evaluate the treatment effect of anti-inflammatory pharmacotherapy (ulinastatin) in Chinese ATAAD patients. Our finding suggested that ATAAD patients with intermediate risk of ALI were likely to benefit from ulinastatin use, however, ATAAD patients with low and high risk of ALI were not likely to benefit from ulinastatin use. These discoveries emphasized the significance of risk stratification in more personalized treatment for individuals with ATAAD. The interpretation of our results is associated with a few limitations, which require further investigation in future studies.

Supplementary Material

pgae371_Supplementary_Data

Supplementary Material

Supplementary material is available at PNAS Nexus online.

Funding

This work was supported by Jiangsu Provincial Innovative & Entrepreneurial Talent Project, Outstanding Young Talent Support Plan Project of Jiangsu Provincial Hospital, and Public Welfare Project of Nanjing Medical University Alliance for Specific Diseases (JZ23349020230306).

Author Contributions

H.L. and S.Z. had the idea of the study, conceptualized the research aims; H.L. design the study and take responsibility for the integrity of the data and the accuracy of the data analysis, doing the statistical analysis, and wrote the first draft of the article; H.L., Y.F.D., S.C.Q., S.Z., H.Y.L., Y.F.S., and H.J.Z. contributed to the acquisition of data; and other authors provided comments and approved the final manuscript.

Data Availability

All data supporting the findings of this study are included in the main text and Supplementary material.

Ethics Approval

Patient written consent for the publication of the study data was waived by the IRB due to this retrospectively observational study.
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