Early plasma proteomic biomarkers and prediction model of acute respiratory distress syndrome after cardiopulmonary bypass: a prospective nested cohort study

Abstract

Background: Early recognition of the risk of acute respiratory distress syndrome (ARDS) after cardiopulmonary bypass (CPB) may improve clinical outcomes. The main objective of this study was to identify proteomic biomarkers and develop an early prediction model for CPB-ARDS.

Methods: The authors conducted three prospective nested cohort studies of all consecutive patients undergoing cardiac surgery with CPB at Union Hospital of Tongji Medical College Hospital. Plasma proteomic profiling was performed in ARDS patients and matched controls (Cohort 1, April 2021-July 2021) at multiple timepoints: before CPB (T1), at the end of CPB (T2), and 24 h after CPB (T3). Then, for Cohort 2 (August 2021-July 2022), biomarker expression was measured and verified in the plasma. Furthermore, lung ischemia/reperfusion injury (LIRI) models and sham-operation were established in 50 rats to explore the tissue-level expression of biomarkers identified in the aforementioned clinical cohort. Subsequently, a machine learning-based prediction model incorporating protein and clinical predictors from Cohort 2 for CPB-ARDS was developed and internally validated. Model performance was externally validated on Cohort 3 (January 2023-March 2023).

Results: A total of 709 proteins were identified, with 9, 29, and 35 altered proteins between ARDS cases and controls at T1, T2, and T3, respectively, in Cohort 1. Following quantitative verification of several predictive proteins in Cohort 2, higher levels of thioredoxin domain containing 5 (TXNDC5), cathepsin L (CTSL), and NPC intracellular cholesterol transporter 2 (NPC2) at T2 were observed in CPB-ARDS patients. A dynamic online predictive nomogram was developed based on three proteins (TXNDC5, CTSL, and NPC2) and two clinical risk factors (CPB time and massive blood transfusion), with excellent performance (precision: 83.33%, sensitivity: 93.33%, specificity: 61.16%, and F1 score: 85.05%). The mean area under the receiver operating characteristics curve (AUC) of the model after 10-fold cross-validation was 0.839 (95% CI: 0.824-0.855). Model discrimination and calibration were maintained during external validation dataset testing, with an AUC of 0.820 (95% CI: 0.685-0.955) and a Brier Score of 0.177 (95% CI: 0.147-0.206). Moreover, the considerably overexpressed TXNDC5 and CTSL proteins identified in the plasma of patients with CPB-ARDS, exhibited a significant upregulation in the lung tissue of LIRI rats.

Conclusions: This study identified several novel predictive biomarkers, developed and validated a practical prediction tool using biomarker and clinical factor combinations for individual prediction of CPB-ARDS risk. Assessing the plasma TXNDC5, CTSL, and NPC2 levels might identify patients who warrant closer follow-up and intensified therapy for ARDS prevention following major surgery.

Trial registration: ClinicalTrials.gov NCT04696172.

Figure 1

Study design and flow chart. AUC, area under the receiver operating characteristic curve; CC, calibration curve; CIC, clinical impact curve; CPB-ARDS, cardiopulmonary bypass induced acute respiratory distress syndrome; DCA, decision curve analysis; DIA, data-independent acquisition; DEPs, differentially expressed proteins; ELISA, enzyme-linked immunosorbent assay; LIRI, lung ischemia/reperfusion injury; LC–MS/MS, liquid chromatography–mass spectrometry; SLR, stepwise logistic regression algorithm.

Figure 2

The DEPs among CPB-ARDS and Non-ARDS patients in Cohort 1. The volcano plot of protein expression in CPB-ARDS patients compared to Non-ARDS patients at T1 (A), T2 (B), and T3 (C); The top three GO and KEGG enriched items at T2 (D), and T3 (G); The PPI networks from Genemania database at T2 (E), and T3 (H); The PPI networks from String database at T2 (F), and T3 (I).

Figure 3

The machine learning-based inference of biomarkers strongly altered at the end of CPB in Cohort 1. The XGBoost results for protein features at T2 (A); Receiver operating characteristic curve (B); Confusion matrix (C); The relative intensity of 11 protein features among Non-ARDS and CPB-ARDS groups at T2 (D–N). ROC, receiver operating characteristic curve.

Figure 4

ELISA analyses for the targeted proteins and the correlation of the protein concentration with values of a panel of clinical parameters in Cohort 2.The targeted protein concentration among CPB-ARDS and Non-ARDS patients for TXNDC5 (A), CTSL (B), NPC2 (C), and CD56 (D); The results of spearman correlation analyses of concentration of TXNDC5 (E), CTSL (F), NPC2 (G) and intubation retention time; The results of spearman correlation analyses of concentration of TXNDC5 (H), CTSL (I), NPC2 (J) and minimum PF ratio. PF ratio, PaO₂/FiO_2.

Figure 5

Nomogram-based CAPS prediction model for CPB-ARDS. The nomogram of CAPS composed of clinical and protein factors at T2 in Cohort 2 (A). Draw a vertical line from the corresponding axis of each factor to the points axis to acquire the point of this factor. Make a summation of the points for each factor to yield a total score, and the probability of CPB-ARDS could be estimated by projecting the total score to the lower probability axis. The blue wavy line on each axis represented the data distribution of this factor; Receiver operating characteristic curve for the CAPS model and clinical factor model in Cohort 2 (B); Calibration plot of the CAPS model with a 1000 repetition bootstrap in Cohort 2 (C); Decision curve of the two prediction models showed the net benefit for the CAPS model was larger than that for the traditional clinical model (D); Clinical impact analysis curve of CAPS visually showed the number of people classified as positive (high-risk) by the model and the number of true positive people under each threshold probability (E); Receiver operating characteristic curve for the external validation of CAPS model in Cohort 3 (F); Calibration plot for the external validation of CAPS model in Cohort 3 (G). CPB time: cardiopulmonary bypass time; MRBC: massive transfusion of red blood cells; CAPS: the combined CPB-ARDS prediction score; ROC: receiver operating characteristic curve; 95% CI: 95% confidence interval.

Figure 6

The expression and function of the targeted protein predictors in LIRI rat model. The degree of lung injury after 2, 6, and 24 h of reperfusion, including the hematoxylin-eosin staining (A), lung injury score (B), the amount of inflammatory cells (C), and the expression of MPO (D); The immunoblot of Occludin and immunofluorescence of ZO-1 (E); The volcano plot of altered protein expression in lung tissue of LIRI rats (F); The GO and KEGG enriched items (G, H); The Immunoblot (I) and quantification of the signal of the protein expression of TXNDC5 (J), and CTSL (K) in the lung tissues; The immunofluorescence of the protein expression of TXNDC5 and CTSL (L) in the lung tissues. Each bar represents the Mean±SD; ns: P≥0.05; *P<0.05; **P<0.01; ***P<0.001.