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OPG/TRAIL ratio as a predictive biomarker of mortality in patients with type A acute aortic dissection.

ABSTRACT: Following hospital discharge, patients with type A acute aortic dissection (TA-AAD) may present an increase in mortality risk. However, little is known about specific biomarkers associated with post-discharge survival, and there is a paucity of prognostic markers associated with TA-AAD. Here, we identify nine candidate proteins specific for patietns with TA-AAD in a cross-sectional dataset by unbiased protein screening and in-depth bioinformatic analyses. In addition, we explore their association with short-term and long-term mortality in a derivation cohort of patients with TA-AAD, including an internal (n = 300) and external (n = 236) dataset. An elevated osteoprotegerin (OPG)/tumour necrosis factor-related apoptosis-inducing ligand (TRAIL) ratio was the strongest predictor of overall, 30-day, post-30-day mortality in both datasets and was confirmed to be a strong predictor of mortality in an independent validation cohort (n = 400). Based on OPG/TRAIL ratio-guided risk stratification, patients at high risk (>33) had a higher 1-year mortality (55.6% vs. 4.3%; 68.2% vs. 2.6%) than patients at low risk (<4) in both cohorts. In Conclusion, we show that an elevated OPG/TRAIL ratio is associated with a significant increase in short-term and long-term mortality in patients with TA-AAD.

SUBMITTER: Lu J

PROVIDER: S-EPMC8185077 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Background: Although great progress has been made in surgery and perioperative care, stroke is still a fatal complication of acute type A aortic dissection (ATAAD). Serum biomarkers may help assess brain damage and predict patient's prognosis. Methods: From March, 2019 to January, 2020, a total of 88 patients underwent surgical treatment at the Department of Cardiovascular Surgery of Beijing Anzhen Hospital, China, and were enrolled in this study. Patients were divided into two groups according to whether they had suffered a stroke after the operation. Blood samples were collected at 8 time points within 3 days after surgery to determine the level of S100β, neuron-specific enolase (NSE) and neurofilament light chain protein (NFL). Receiver operating characteristic curves (ROC) were established to explore the biomarker predictive value in stroke. The area under the curve (AUC) was used to quantify the ROC curve. Results: The patient average age was 48.1 ± 11.0 years old and 70 (79.6%) patients were male. Fifteen (17.0%) patients suffered stroke after surgery. The NFL levels of patients in the stroke group at 12 and 24 h after surgery were significantly higher than those in the non-stroke group (all P < 0.001). However, the NSE and S100β levels did not differ significantly at any time point between the two groups. The predictive value of NFL was the highest at 12 and 24 h after surgery, and the AUC was 0.834 (95% CI, 0.723-0.951, P < 0.001) and 0.748 (95% CI, 0.603-0.894, P = 0.004), respectively. Its sensitivity and specificity at 12 h were 86.7 and 71.6%, respectively. The NFL cutoff value for the diagnosis of stroke at 12 h after surgery was 16.042 ng/ml. Conclusions: This study suggests that NFL is an early and sensitive serum marker for predicting post-operative neurological prognosis of ATAAD patients. Further studies, including large-scale prospective clinical trials, are necessary to test whether the NFL can be used as a biomarker for clinical decision-making.

Project description:ObjectivesPostoperative acute kidney injury (pAKI) is a serious complication of Stanford type A aortic dissection (TAAD) surgery, which is significantly associated with the inflammatory response. This study aimed to explore the relationship between blood count-derived inflammatory markers (BCDIMs) and pAKI and to construct a predictive model for pAKI.MethodsPatients who underwent TAAD surgery were obtained from our center and the Medical Information Mart for Intensive Care (MIMIC)-IV database. The differences in preoperative BCDIMs and clinical outcomes of patients with and without pAKI were analyzed. Logistic regression was used to construct predictive models based on preoperative BCDIMs or white cell counts (WCCs). The performance of the BCDIMs and WCCs models was evaluated and compared using the receiver operating characteristic (ROC) curve, area under the ROC curve (AUC), Hosmer-Lemeshow test, calibration plot, net reclassification index (NRI), integrated discrimination improvement index (IDI), and decision curve analysis (DCA). The Kaplan-Meier curves were applied to compare the survival rate between different groups.ResultsThe overall incidence of pAKI in patients who underwent TAAD surgery from our center was 48.63% (124/255). The presence of pAKI was associated with longer ventilation time, higher incidence of cerebral complications and postoperative hepatic dysfunction, and higher in-hospital mortality. The results of the logistic regression indicated that the monocyte-lymphocyte ratio (MLR) was an independent risk factor for pAKI. The BCDIMs model had good discriminating ability, predictive ability, and clinical utility. In addition, the performance of the BCDIMs model was significantly better than that of the WCCs model. Analysis of data from the MIMIC-IV database validated that MLR was an independent risk factor for pAKI and had predictive value for pAKI. Finally, data from the MIMIC-IV database demonstrated that patients with a high MLR had a significantly poor 28-day survival rate when compared to patients with a low MLR.ConclusionOur study suggested that the MLR is an independent risk factor for pAKI. A predictive model based on BCDIMs had good discriminating ability, predictive ability, and clinical utility. Moreover, the performance of the BCDIMs model was significantly better than that of the WCCs model. Finally, a high MLR was significantly associated with poor short-term survival of patients who underwent TAAD surgery.

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OPG/TRAIL ratio as a predictive biomarker of mortality in patients with type A acute aortic dissection.

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