Prescreening and treatment of aortic dissection through an analysis of infinite-dimension data

Fluid balance and clinical outcomes in patients with aortic dissection: a retrospective case-control study based on ICU databases

OBJECTIVES

Aortic dissection (AD) is a life-threatening condition that requires intensive care and management. This paper explores the role of fluid management in the clinical care of AD patients, which has been unclear despite the substantial existing research that has been conducted on the treatment of AD.

DESIGN

A retrospective case-control study using data for AD patients from public databases.

SETTING

Two public intensive care unit (ICU) databases with hospital courses from the USA, Medical Information Mart for Intensive Care (MIMIC)-IV critical care dataset and the eICU Collaborative Research Database, with data from 2008 to 2019.

PARTICIPANTS

A total of 751 adult AD patients with detailed fluid management records from two databases were included.

INTERVENTIONS

The mean 24-hour intake and output were calculated by dividing the total amount of intake and output by the number of days in the ICU, respectively. The mean 24-hour fluid balance was generated by subtracting the output from the intake.

OUTCOME MEASURES

The relationship between the mean 24-hour fluid management and all-cause in-hospital death was assessed through univariate and multivariable regression analyses.

RESULTS

A positive correlation was found between mean 24-hour fluid intake and in-hospital mortality among AD patients (OR 1.029, 95% CI (1.018, 1.041), p<0.001), whereas a negative correlation was revealed between mean 24-hour fluid output and in-hospital mortality (OR 0.941, 95% CI (0.914, 0.968), p<0.001). A similar result was found for mean 24-hour fluid balance (OR 1.030, 95% CI (1.019, 1.042), p<0.001), and the cut-off was selected to be 5.12 dL (AUC=0.778, OR 3.066, 95% CI (1.634, 5.753), p<0.001).

CONCLUSIONS

This study stresses the importance of fluid balance in the clinical care of AD patients and provides new insights for optimising fluid management and monitoring strategies beyond the conventional focus on blood pressure and heart rate management.

Machine learning-based prognostic model for in-hospital mortality of aortic dissection: Insights from an intensive care medicine perspective

Objective

Aortic dissection (AD) is a severe emergency with high morbidity and mortality, necessitating strict monitoring and management. This retrospective study aimed to identify prognostic factors and establish predictive models for in-hospital mortality among AD patients in the intensive care unit (ICU).

Methods

We retrieved ICU admission records of AD patients from the Medical Information Mart for Intensive Care (MIMIC)-IV critical care data set and the eICU Collaborative Research Database. Functional data analysis was further applied to estimate continuous vital sign processes, and variables associated with in-hospital mortality were identified through univariate analyses. Subsequently, we employed multivariable logistic regression and machine learning techniques, including simple decision tree, random forest (RF), and eXtreme Gradient Boosting (XGBoost) to develop prognostic models for in-hospital mortality.

Results

Given 643 ICU admissions from MIMIC-IV and 501 admissions from eICU, 29 and 28 prognostic factors were identified from each database through univariate analyses, respectively. For prognostic model construction, 507 MIMIC-IV admissions were divided into 406 (80%) for training and 101 (20%) for internal validation, and 87 eICU admissions were included as an external validation group. Of the four models tested, the RF consistently exhibited the best performance among different variable subsets, boasting area under the receiver operating characteristic curves of 0.870 and 0.850. The models highlighted the mean 24-h fluid intake as the most potent prognostic factor.

Conclusions

The current prognostic models effectively forecasted in-hospital mortality among AD patients, and they pinpointed noteworthy prognostic factors, including initial blood pressure upon ICU admission and mean 24-h fluid intake.

Prediction of preoperative in-hospital mortality rate in patients with acute aortic dissection by machine learning: a two-centre, retrospective cohort study

OBJECTIVES

To conduct a comprehensive analysis of demographic information, medical history, and blood pressure (BP) and heart rate (HR) variability during hospitalisation so as to establish a predictive model for preoperative in-hospital mortality of patients with acute aortic dissection (AD) by using machine learning techniques.

DESIGN

Retrospective cohort study.

SETTING

Data were collected from the electronic records and the databases of Shanghai Ninth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine and the First Affiliated Hospital of Anhui Medical University between 2004 and 2018.

PARTICIPANTS

380 inpatients diagnosed with acute AD were included in the study.

PRIMARY OUTCOME

Preoperative in-hospital mortality rate.

RESULTS

A total of 55 patients (14.47%) died in the hospital before surgery. The results of the areas under the receiver operating characteristic curves, decision curve analysis and calibration curves indicated that the eXtreme Gradient Boosting (XGBoost) model had the highest accuracy and robustness. According to the SHapley Additive exPlanations analysis of the XGBoost model, Stanford type A, maximum aortic diameter >5.5 cm, high variability in HR, high variability in diastolic BP and involvement of the aortic arch had the greatest impact on the occurrence of in-hospital deaths before surgery. Moreover, the predictive model can accurately predict the preoperative in-hospital mortality rate at the individual level.

CONCLUSION

In the current study, we successfully constructed machine learning models to predict the preoperative in-hospital mortality of patients with acute AD, which can help identify high-risk patients and optimise the clinical decision-making. Further applications in clinical practice require the validation of these models using a large-sample, prospective database.

TRIAL REGISTRATION NUMBER

ChiCTR1900025818.

A stable and quantitative method for dimensionality reduction of aortic centerline

Aortic dissection (AD) is a fatal aortic disease with high mortality. Assessing the morphology of the aorta is critical for diagnostic and surgical decisions. Aortic centerline projection methods have been used to evaluate the morphology of the aorta. However, there is a big difference between the current model of primary plane projection (PPP) and the actual shape of individuals, which is not conducive to morphological statistical analysis. Finding a method to compress the three-dimensional information of the aorta into two dimensions is helpful to clinical decision-making. In this paper, the evaluation parameters, including contour length (CL), enclosure area, and the sum of absolute residuals (SAR), were introduced to objectively evaluate the optimal projection plane rather than artificial subjective judgment. Our results showed that the optimal projection plane could be objectively characterized by the three evaluation parameters. As the morphological criterion, SAR is optimal among the three parameters. Compared to the optimal projection plane selected by traditional PPP, our method has better AD discrimination in the analysis of aortic tortuosity, and is conducive to the clinical operation of AD. Thus, it has application prospects for the preprocessing techniques for the geometric morphology analysis of AD.

Calibration and Distraction Mining Network for Aortic True Lumen segmentation

Automatic segmentation of aortic true lumen based on deep learning can save the time for diagnosis of aortic dissection. However, fuzzy boundary, small true lumen region, and high similarity usually leads to inaccurate prediction. To make better use of the details supplemented by the encoder to restore boundaries, we decompose the recovery of detail features in the decoder into two sub-processes: calibration and distraction mining. And we propose a novel calibration and distraction mining (CDM) module. It utilizes deep features to calibrate shallow features so that features are concentrated in the main region. Then, it leverages the distraction mining procedure to extract false-negative features as a supplement to calibrated features and recover details of the segmentation object. We construct CDM-Net and verify its performance on the Aorta-CT dataset (private dataset), it achieves the Dice similarity coefficient of 96.94% and the Jaccard index coefficient of 94.08%, which is the best compared with 10 latest methods. Similarly, we explore its robustness on three more public datasets, including ISIC 2018 dataset (skin lesion segmentation), the 2018 data science bowl dataset (nucleus segmentation), LUNA dataset (lung segmentation). Experimental results prove that our method produces competitive results on all three data sets. Through quantitative and qualitative research, the proposed CDM-Net has good performance and can process aortic slices with complex semantic features, additional experiments show that it has good robustness, and it has the potential to be applied and expanded conveniently.

[Clinical Application of "Three-Low" Technique Combined with Artificial Intelligence Iterative Reconstruction Algorithm in Aortic CT Angiography]

Objective

To explore the application value of the "three-low" technique (low radiation dose, low contrast agent dosage and low contrast agent flow rate) combined with artificial intelligence iterative reconstruction (AIIR) in aortic CT angiography (CTA).

Methods

A total of 33 patients who underwent aortic CTA were prospectively enrolled. Based on the time of their follow-up examinations, the imaging data were divided into Group A and Group B, with Group A being the control group (100 kV, 0.8 mL/kg, 5 mL/s) and Group B being the "three-low" technique group (70 kV, 0.5 mL/kg, 3 mL/s). In group A, the images were reconstructed by Karl iterative algorithm. Group B was divided into B1 and B2 subgroups, with their images being reconstructed by Karl iterative algorithm and AIIR, respectively. The CT and SD values of the ascending aorta, descending aorta, abdominal aorta, left common iliac artery and right common iliac artery were measured, and the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated. The subjective scoring of image quality was performed. The radiation dose parameters were documented.

Results

Differences in the CT value, SD value, SNR and CNR of the three groups were statistically significant ( P<0.001). The CT value, SNR and CNR of group B2 were significantly higher than those of group B1, while the SD value of group B2 was significantly lower than that of group B1 ( P<0.017). There was no significant difference between the CT values of group A and those of group B2 ( P>0.017). The SD values, SNR and CNR in group B2 were better than those in group A ( P>0.017). There was significant difference in the subjective evaluation of image quality among the three groups ( P<0.05), but there was no significant difference between group A and group B2 ( P>0.017). The radiation dose and contrast medium dosage in group B decreased 84.14% and 37.08%, respectively, compared with those of group A.

Conclusion

With the "three-low" technique combined with AIIR algorithm, the image quality of aortic CTA obtained is comparable to that of conventional dose scanning, while the radiation dose, contrast agent dosage and contrast agent flow rate of patients are significantly reduced.

Prognostic Impact of Blood Pressure Change Patterns on Patients With Aortic Dissection After Admission

Objectives

Hypertension is a predominant risk factor for aortic dissection (AD), and blood pressure (BP) control plays a vital role in the management of AD. However, the correlation between BP change and the prognosis for AD remains unclear. This study aims to demonstrate the impact of BP change patterns on AD prognosis.

Methods

This retrospective study included AD patients at two institutions (Shanghai Ninth People's Hospital Affiliated with Shanghai Jiao Tong University School of Medicine and the Vascular Department of the First Affiliated Hospital of Anhui Medical University) between 2004 and 2018. The systolic BP (SBP) change patterns of these patients were analyzed by functional data analysis (FDA). The relationship between BP change patterns and the risk of adverse events (AEs) was assessed using survival analysis.

Results

A total of 458 patients with AD were eligible for analysis. The logistic regression analysis indicated that compared with that in patients with low SBP variation (SBPV), the incidence of AEs in patients with high SBPV was significantly higher (35.84 vs. 20.35%, OR 2.19, P &lt; 0.001). The patients were divided into four categories (accelerating rise, accelerating drop, decelerating rise, and decelerating drop) based on their SBP patterns after FDA fitting. The results of Kaplan–Meier analysis showed that at the 15- and 20-min time points, the incidence of AEs in the decelerating-drop group was significantly lower than that in the accelerating-rise group (OR 0.19, P = 0.031 and OR 0.23, P = 0.050). However, at the 25- and 30-min time points, the difference between these four groups was not significant (OR 0.26, P = 0.08 and OR 0.29, P = 0.10).

Conclusions

This study classified AD patients into four groups according to the SBP change patterns the first 30 min following admission, of which those with accelerating rises in SBP are at the highest risk of AEs, while those with decelerating drops have the best prognosis in the first 24 h after admission. Clinical practitioners may benefit from analyzing patterns of in-hospital SBP.

Potential Clinical Value of Biomarker-Guided Emergency Triage for Thoracic Aortic Dissection

Aim: Thoracic aortic dissection (TAD) is a high-risk vascular disease. The mortality rate of untreated TADs in 24 h was as high as 50%. Thus, rapid diagnosis of TAD in the emergency department would get patients to the right treatments to save their lives. Methods: We profiled the proteome of aortic tissues from TAD patients using a label-free quantification proteomics method. The differentially expressed proteins were screened and subjected to bioinformatics analysis. Candidate biomarkers were selected and validated in independent serum samples using enzyme-linked immunosorbent assays (ELISAs). The diagnostic values were further predicted via receiver operating characteristic (ROC) curve analysis. Results: A total of 1,141 differentially expressed proteins were identified in aortic tissues from 17 TAD patients and eight myocardial infarction (MI) patients. Six proteins were selected as candidate biomarkers for ELISAs in an independent training set of 20 serum samples (TAD = 10, MI = 10). Of these proteins, four with a P-value < 0.01 were further validated in another independent set of 64 serum samples (TAD = 32, MI = 32) via ELISAs. ITGA2, COL2A1, and MIF had P-values < 0.0001, and their areas under the curve (AUCs) were 0.801 (95% CI: 0.691-0.911), 0.773 (95% CI: 0.660-0.887), and 0.701 (95% CI: 0.574-0.828), respectively. Conclusion: ITGA2, COL2A1, and MIF were identified as promising biomarkers for discriminating TAD from emergency patients with severe chest pain. Biomarker-guided emergency triage could further shorten the time for patients to get more effective treatments.