Development and Validation of a Prognostic Model to Predict the Risk of In-hospital Death in Patients With Acute Kidney Injury Undergoing Continuous Renal Replacement Therapy After Acute Type a Aortic Dissection

The creation and validation of predictive models to assess the risk of unfavorable outcomes following hybrid total arch repair for Stanford type A aortic dissection

BACKGROUND

The objective of this study was to develop and validate a nomogram for the individualized prediction of adverse events in patients with Stanford type A aortic dissection (TAAD) undergoing hybrid total aortic arch repair.

METHODS

From April 2019 to April 2022, we conducted a comprehensive review of the medical records of Stanford type A aortic dissection patients who underwent hybrid total aortic arch repair surgery at our hospital. Patients were separated into two groups based on whether or not a composite adverse event occurred following surgery. Using univariate and multivariate analyses of logistic regression, the prediction model was created. Construct risk prediction models utilizing nomograms and evaluate their precision, discrimination, and clinical utility.

RESULTS

Age, platelets, serum blood urea nitrogen, and ascending aortic diameter were the variables included in the nomogram by univariate and multivariate analysis. The risk model performed well in internal validation, with an area under the curve (AUC) of 0.829. The calibration curve demonstrated good agreement between predicted and actual probabilities (Hosmer-Lemeshow test, P = 0.22). Clinical decision analysis curves demonstrate predictive nomograms' clinical utility.

CONCLUSION

This study created and validated a nomogram for predicting the risk of composite endpoint events in TAAD patients undergoing hybrid total aortic arch repair. The nomogram can help determine the severity of a patient's condition and provide a more personalized diagnosis and treatment.

Postoperative nomogram and risk calculator of acute renal failure for Stanford type A aortic dissection surgery

BACKGROUND

This study aimed to explore the risk factors of acute renal failure (ARF) after Stanford type A aortic dissection (AAD) surgery, establish a nomogram prediction model and calculate the risk of ARF.

MATERIAL AND METHODS

241 AAD patients who received aortic surgery in the department of cardiovascular surgery, Zhongnan Hospital of Wuhan University were enrolled in this study. All enrolled patients were divided into the ARF group and non-ARF group. The clinical data of the two groups were collected and compared. The independent risk factors of ARF after aortic surgery were analyzed by univariate and multivariate logistic regression analyses. Moreover, a nomogram prediction model was generated. The calibration curve, ROC curve and independent external validation were performed to evaluate the nomogram prediction model.

RESULTS

67 patients were diagnosed with ARF within 48 h after the operation. Univariate and multivariate logistic regression analyses showed that hypertension, preoperative renal artery involvement, CPB time extension and postoperative decreased platelet lymphocyte ratio were the independent risk factors of ARF after AAD surgery. The nomogram model could predict the risk of ARF with a sensitivity of 81.3% and a specificity of 78.6%. The calibration curve displayed good agreement of the predicted probability with the actual observed probability. AUC of the ROC curve was 0.839. External data validation was performed with a sensitivity of 79.2% and a specificity of 79.8%.

CONCLUSIONS

Hypertension, preoperative renal artery involvement, CPB time extension and postoperative decreased platelet lymphocyte ratio could predict the risk of ARF after AAD surgery.

Ischemia modified albumin can act as an independent predictor of inhospital mortality in patients with acute aortic dissection

Acute aortic dissection (AAD) is a serious disease characterized by high mortality. However, there are no accurate indicators to predict in-hospital mortality. The objective of this study was to identify the potential value of ischemia modified albumin (IMA) in prediction of in-hospital mortality of AAD patients. This was a single-center, prospective study involved 314 patients undergoing AAD, including 197 males and 117 females, aged 26-87 (57.14 ± 21.71) years old, 116 cases of TAAD and 198 cases of TBAD (37 cases of complicated, 114 cases of high risk, and 47 cases of uncomplicated), 228 cases were underwent surgery/intervention treatment (77 cases of TAAD,151 cases of TBAD) and 86 cases were underwent conservative therapy (39 cases of TAAD, 47 cases of TBAD). The basic data, on-admission IMA level, and the all-cause in-hospital mortality was recorded. IMA in the non-survivor group and TAAD group was found to be significantly higher than that in the survivor group and TBAD group (P < 0.001). Multivariate logistic regression analysis results revealed that age (OR = 1.923, 95%CI: 1.102-4.481, P = 0.020), conservative therapy (OR = 17.892, 95%CI: 7.641-24.748, P < 0.001), D-dimer level (OR = 3.517, 95%CI: 1.874-7.667, P = 0.011) and IMA level (OR = 5.406, 95%CI: 2.951-10.395, P = 0.004) served as independent risk factors for in-hospital mortality of TAAD patients. And D-dimer level (OR = 2.241, 95%CI: 1.475-5.663, P = 0.018), IMA level (OR = 3.115, 95%CI: 1.792-6.925, P = 0.009) also served as independent risk factors for in-hospital mortality of TBAD patients, whereas surgery (OR = 0.110, 95%CI: 0.075-0.269, P < 0.001) was the protective factor of in-hospital mortality of TAAD patients. After IMA prediction, the AUC, optimal cut-off value, sensitivity, and the specificity of in-hospital mortality of AAD patients were observed to be 0.801 (95%CI: 0.744-0.858), 86.55 U/mL, 79.1%, and 73.2%, respectively. In addition, it was found that AUC was 0.799 (95%CI: 0.719-0.880) in TAAD and 0.753 (95% CI: 0.641-0.866) in TBAD. Overall, it was concluded that on-admission IMA level acted as an independent prediction index for in-hospital mortality of AAD patients.