Prognostic value of initial hematoma area in patients with acute type B aortic intramural hematoma

Introduction

Aortic intramural hematoma (IMH) is an important subtype of acute aortic syndrome (AAS) (1). It is characterized by rupture of the vasa vasorum in the medial layer of the aortic wall, leading to blood accumulation in the media. Usually, it is hard to find an intimal tear or classic double-lumen. According to the involved aortic segment, IMH can be classified into type A and type B (2). Acute type B aortic intramural hematoma (type B IMH), which mainly involves the descending thoracic aorta, is the most common clinical presentation (3). Previous studies have shown that strict blood pressure control and close surveillance of patients with type B IMH can achieve long-term clinical stability, so some of the patients can be managed with medical therapy alone (4). However, during the period of follow-up, there is still a considerable proportion of patients who present progression of disease including hematoma enlargement, development of ulcer-like projection (ULP), exacerbation to classic aortic dissection, and even rupture leading to death (5,6). How to identify these high-risk patients early and optimize the timing and strategy of intervention is still a major challenge and a key research issue in the management of patients with acute type B IMH.

In recent years, imaging parameters have increasingly become important tools for predicting the risk of IMH progression (7). Conventional parameters such as maximum aortic diameter, maximum hematoma thickness (MHT), and the presence of ULP have all been reported to be related to adverse events (8). These parameters are helpful for identifying the “most critical point” of the lesion, but most studies mainly focus on point or linear measurements. Thus, they may not fully reflect the overall extent and burden of the hematoma, which limits their ability to support risk classification. Area-based parameters show the overall size of the hematoma. They can reflect disease severity and may serve as new imaging markers for risk stratification.

Earlier studies have pointed out that larger hematoma volume and more extensive vessel involvement increase local wall stress, and may lead to adverse outcomes such as ULPs, rupture, and even death (9). Therefore, it is essential to identify such high-risk patients early and carry out interventions, such as thoracic endovascular aortic repair (TEVAR). Although spontaneous intracerebral hemorrhage (sICH) is different from IMH in anatomy and disease process, research in sICH suggests that area-based measures can provide more information than a single length. In sICH, studies have shown that a larger relative surface area of the hematoma is linked to worse outcomes (10). This finding, to some degree, reflects the potential value of surface-area parameters in showing hematoma burden and prognosis. However, there is still a lack of systematic evidence about the independent prognostic value of imaging parameters such as area-based parameters in acute type B IMH, and their extra value beyond traditional indicators has not been fully investigated. Therefore, this study intended to assess the incremental predictive value of parameters such as area measured on initial computed tomography angiography (CTA) for the 1-year prognosis of patients with acute type B IMH, providing more accurate evidence for the early clinical identification of high-risk patients. We present this article in accordance with the TRIPOD reporting checklist (available at https://qims.amegroups.com/article/view/10.21037/qims-2025-1-2700/rc).

Methods

Study population and eligibility criteria

The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. This study was approved by the ethics committee of the General Hospital of Northern Theater Command [approval No. Y(2025)462]. Informed consent was waived in this retrospective study. During the whole study, we made sure that all medical records and personal health information were kept confidential.

Between January 2015 and September 2024, a total of 619 patients diagnosed with type B IMH by CTA were screened. The study was carried out in the Emergency Department and the Department of Cardiology of the General Hospital of Northern Theater Command, and all patients with CTA-confirmed IMH during this period were assessed for eligibility.

Inclusion criteria were: patients with an acute type B IMH confirmed by imaging. Exclusion criteria were: age <18 years, subacute or chronic IMH (more than 15 days), complicated type B IMH, previous TEVAR or open aortic surgery, IMH related to Marfan syndrome or other inherited connective tissue disorders, missing clinical or imaging data, or loss to follow-up. The complicated IMH was defined as the presence of any of the following clinical conditions at presentation: uncontrolled hypertension or refractory pain despite medical therapy, expansion of the hematoma, impending rupture, or end-organ damage.

Patients were categorized into exacerbation and stable groups. The exacerbation group included patients who, during follow-up, developed aortic-related adverse events, defined as aortic-related or sudden death, progression to classic aortic dissection, development or progression of penetrating atherosclerotic ulcer (PAU) or other complex aortic pathology requiring invasive treatment (open surgery or endovascular repair), or rapid hematoma progression with an increase in thickness of >10 mm. The stable group included patients who had no aortic-related adverse events during follow-up and whose hematoma showed complete or partial resolution on imaging.

The primary endpoint was defined as IMH progression or need for invasive aortic intervention (open surgery or endovascular repair). Follow-up time was measured from symptom onset to the occurrence of the primary endpoint or the date of the last follow-up CTA scan.

Treatment and management

All patients were treated with optimized medical therapy (OMT), including sedation, analgesia, blood pressure and heart rate control, and continuous electrocardiographic monitoring (11). The primary therapeutic goals were relief of pain and stabilization of hemodynamics. Antihypertensive drugs were adjusted to maintain a target systolic blood pressure of 100–120 mmHg and diastolic blood pressure of 70–80 mmHg (1 mmHg =0.133 kPa), with the heart rate controlled between 60 and 70 beats per minute. Blood pressure was mainly managed with oral antihypertensive drugs, which were selected according to individual clinical status, and β-blockers were used for heart rate control. When the heart rate fell below 55 beats per minute, dose reduction or temporary discontinuation of β-blockers was considered (12).

CTA measurement

All cases of IMH were evaluated and diagnosed using thoracoabdominal aortic CTA with three-dimensional reconstruction. The scan range extended from the lower neck to the inferior margin of the pelvis, covering the entire thoracoabdominal aorta. All CTA datasets were retrieved from the institutional picture archiving and communication system (PACS).

Imaging assessment included the extent of aortic involvement, morphological feature details, and quantitative measurements of the IMH. The extent of involvement was recorded for each aortic segment, including the aortic arch, descending thoracic aorta, abdominal aorta, diaphragmatic segment, and iliac arteries. Besides, it was recorded if the hematoma crossed the diaphragm. Morphologic features included the shape of the hematoma (circular or crescent-shaped), the presence of ULPs, and atherosclerotic plaque. The presence or absence of pleural effusion and pericardial effusion was also recorded.

Quantitative measurements included maximal ascending aortic diameter (MAAD), maximal descending aortic diameter (MDAD), maximal descending hematoma thickness (MDHT), maximal descending aortic area (MDAA), and maximal descending hematoma area (MDHA) (Figure 1). In addition, the hematoma diameter ratio and hematoma area ratio were calculated to reflect the degree of luminal involvement.

Figure 1 Representative CTA images of aortic intramural hematoma. Shown are axial contrast-enhanced CTA images from patient 1 (A) and patient 2 (B) with acute type B aortic intramural hematoma. The area circled in yellow represents the MDAA. The area circled in blue represents the corresponding descending aortic area, and the gray-shaded area represents the MDHA. CTA, computed tomography angiography; MDAA, maximal descending aortic area; MDHA, maximal descending hematoma area.

According to the imaging follow-up protocol, after the baseline CTA confirmed the diagnosis, repeated CTA was scheduled at 1 week, 1 month, 3 months, 6 months, and 12 months after symptom onset. When follow-up imaging showed progression of IMH to aortic dissection, PAU, or other high-risk features, such as marked hematoma progression or signs of impending rupture, invasive intervention was considered to prevent aortic rupture or death. All CTA images of IMH patients were assessed by at least two experienced clinicians.

Data collection and definitions

Baseline clinical characteristics, laboratory results, and follow-up outcomes were obtained from an institutional clinical database. Follow-up information was updated through telephone contact and review of outpatient and inpatient medical records, including overall clinical status, imaging examinations, and biochemical tests. The primary objective of this study was to assess the prognostic value of area-based imaging parameters for 1-year outcomes in patients with acute type B IMH.

Imaging parameters were defined as follows. PAU was defined as an atherosclerotic ulceration where an atherosclerotic plaque penetrates through the intima into the media, forming a contrast-filled crater-like pocket in the aortic wall and it may be associated with a localized IMH (13). ULP was defined as a focal contrast enhancement that communicates with the aortic lumen through an intimal defect >3 mm. Unlike PAU, it is often seen without obvious adjacent atherosclerotic plaque, which helps distinguish it from PAU.

MDAD was the largest inner diameter of the aorta at the segment involved by the hematoma. MDHT was the greatest thickness of the hematoma on the axial slice where it appeared most prominent. The hematoma thickness ratio was calculated as MDHT divided by MDAD and reflected the relative thickness of the hematoma in relation to the aortic lumen. MDAA was defined as the largest cross-sectional area of the descending aorta measured on the slice corresponding to the MHT. On the same slice, MDHA was manually outlined as the cross-sectional area of the hematoma, which typically reflected the smallest true lumen area and the maximal extent of hematoma involvement. The hematoma area ratio was then calculated as MDHA divided by MDAA on that cross-section.

Statistical analysis

Statistical analyses were carried out using SPSS 26.0 (IBM Corp., Armonk, NY, USA) and R version 4.5.1 (RStudio, PBC, Boston, MA, USA). All continuous variables were first checked for normality with the Shapiro-Wilk test. Variables were reported as mean ± standard deviation (mean ± SD) if they followed a normal distribution, and the means of groups were compared using independent-samples t-test (for two groups) or one-way analysis of variance (ANOVA) (for more than two groups). Variables that did not follow a normal distribution were described as median (P25, P75), and comparisons between groups were made using the Mann-Whitney U test (for two groups) or the Kruskal-Wallis H test (for more than two groups).

We put all candidate variables into a least absolute shrinkage and selection operator (LASSO) regression model. The optimal penalty parameter λ was determined by 10-fold cross-validation combined with the one standard error (1-SE) rule, aiming to select the most prognostically relevant features. The variables selected by LASSO were then entered into a multivariable logistic regression model to identify independent predictors of adverse outcomes in acute type B IMH.

Then we developed a base model and an enhanced model. The rms package was used to develop nomograms and calibration curves to assess model visualization and goodness of fit. The pROC package was used to plot receiver operating characteristic (ROC) curves and to calculate the area under the curve (AUC), thereby evaluating model discrimination.

The added predictive contribution of hematoma area-related parameters was assessed using the net reclassification improvement (NRI). Decision curve analysis (DCA) was further used to compare the clinical net benefit of the base and enhanced models across a range of risk thresholds. To evaluate the robustness of the models, internal validation was carried out with 1,000 bootstrap resamples, and the concordance index (C-index), calibration slope, Emax, and Nagelkerke R² of the two models were compared. All statistical tests were two-sided, and a P<0.05 was considered statistically significant.

Results

Baseline characteristics

In total, 290 patients with acute uncomplicated type B IMH were included in this study (Figure 2). Among 290 patients with acute uncomplicated type B IMH, 110 (37.9%) developed exacerbation within 1 year. Among the 110 patients in the exacerbation group, 28 died, 9 progressed to classic aortic dissection, 60 developed new or progressive PAUs, and 32 showed progressive dilatation of the affected aortic segment (some patients experienced multiple events).

Figure 2 Flowchart of patient selection. IMH, intramural hematoma; TEVAR, thoracic endovascular aortic repair.

Baseline clinical characteristics of the stable and exacerbation groups are shown in Table 1. Baseline clinical characteristics were mostly comparable between the two groups, except for diabetes, which was more common in the stable group than in the exacerbation group (12.8% vs. 5.5%, P=0.044).

The distribution of presenting symptoms is summarized in Table 2. There were no significant differences between the two groups in presenting symptoms (all P>0.05).

Medication use is presented in Table 3. Prescription rates for all major drug classes were similar between the two groups (all P>0.05).

Laboratory test results are listed in Table 4. The median white blood cell (WBC) of the exacerbation group was significantly higher than that of the stable group [9.7 (8.0, 13.5) vs. 9.0 (6.9, 12.2) ×10⁹/L, P=0.013]. Similarly, D-dimer levels were markedly elevated in the exacerbation group compared with the stable group [2.1 (1.1, 4.7) vs. 1.5 (0.6, 3.0) mg/L, P=0.007]. However, no significant differences were observed between the two groups for other laboratory tests (all P>0.05).

Imaging characteristics are shown in Table 5. Regarding the extent of aortic involvement, the exacerbation group had significantly higher proportions of lesions involving the descending thoracic aorta (93.6% vs. 83.9%, P=0.015), abdominal aorta (90.0% vs. 74.4%, P=0.001), and the segment crossing the diaphragm (86.4% vs. 72.2%, P=0.005) compared with the stable group. ULPs (22.7% vs. 11.7%, P=0.012) and atherosclerotic plaques (48.2% vs. 32.8%, P=0.009) were also more frequent in the exacerbation group. In addition, the exacerbation group exhibited a larger MDAD (35.3±4.7 vs. 34.0±4.6 mm, P=0.022), greater MDHT (8.3 vs. 7.9 mm, P=0.039), larger maximal aortic cross-sectional area (MDAA: 1,028.5 vs. 927.4 mm², P<0.001), larger maximal hematoma area (MDHA: 438.5 vs. 338.1 mm², P<0.001), and a higher hematoma area ratio (0.43 vs. 0.37, P=0.001). No significant differences were observed for other imaging features (all P>0.05).

LASSO regression and multivariable logistic regression

To enhance the clinical applicability and simplicity of the final prediction model, the continuous variables MDAA, MDHA, and hematoma area ratio were dichotomized. The optimal cut-off values were determined by ROC curve analysis (Figure S1) using the maximum Youden index (MDAA: 980 mm², MDHA: 390.4 mm², hematoma area ratio: 0.37), so that they could be conveniently applied in subsequent multivariable logistic regression and risk scoring. The corresponding cut-off values and discriminative performance are summarized in Table 6. All candidate variables were then entered into a LASSO regression for feature selection (Figure 3A). As the penalty parameter λ increased, the coefficients of some variables shrank gradually toward zero, and only predictors with greater contribution to the model were retained. The optimal λ was determined by 10-fold cross-validation (Figure 3B), and 11 potential predictors were selected under the 1-SE criterion. These variables were ranked by the absolute value of their regression coefficients (Figure 3C). To determine whether these 11 variables were independent risk factors for progression of acute uncomplicated type B IMH, we used multivariable logistic regression adjusting for other confounders. We plotted the odds ratios (ORs) on a log scale, and the horizontal lines showed the 95% confidence intervals (CIs) (Figure 4). Involvement of the abdominal aorta (OR 3.32, 95% CI: 1.51–7.84, P=0.004), MDAA >980 mm² (OR 2.52, 95% CI: 1.29–4.98, P=0.007), presence of ULP (OR 2.51, 95% CI: 1.20–5.39, P=0.016), involvement of the aortic arch (OR 2.07, 95% CI: 1.15–3.81, P=0.017), WBC count (OR 1.07, 95% CI: 1.01–1.14, P=0.025), and hematoma area ratio >37.5% (OR 2.26, 95% CI: 1.06–4.82, P=0.034) were identified as independent risk factors for disease progression. In contrast, diabetes, arterial plaque, diastolic blood pressure, systolic blood pressure, and MDHA >390.4 mm² were not significantly associated with disease progression in the multivariable model (all P>0.05).

Figure 3 LASSO regression for variable selection. (A) Coefficient paths of all candidate predictors across log(λ). (B) Ten-fold cross-validation curve for selecting the optimal penalty λ; vertical dashed lines mark the λ values chosen by the 1-SE rule. (C) Variable-importance plot of the LASSO logistic model under the 1-SE rule, with predictors ranked by the absolute value of their coefficients. DBP, diastolic blood pressure; LASSO, least absolute shrinkage and selection operator; MDAA, maximal descending aortic area; MDHA, maximal descending hematoma area; SBP, systolic blood pressure; SE, standard error; ULP, ulcer-like projection; WBC, white blood cell.

Figure 4 Development of the multivariable regression model. The plot shows ORs with 95% CIs from the multivariable logistic regression analysis. CI, confidence interval; DBP, diastolic blood pressure; MDAA, maximal descending aortic area; MDHA, maximal descending hematoma area; OR, odds ratio; SBP, systolic blood pressure; ULP, ulcer-like projection; WBC, white blood cell.

Construction of the nomogram model

Using LASSO regression and multivariable logistic analysis, six independent predictive factors were finally identified. Based on these variables, two nomogram prediction models were developed: a base model (without area-based imaging parameters: MDAA >980 mm², hematoma area ratio >37.5%) and an enhanced model (including these two area-based imaging parameters) (Figure 5).

Figure 5 Nomogram for predicting 1-year disease progression. (A) Enhanced model incorporating six variables: abdominal aorta involvement, MDAA >980 mm², WBC, presence of ULP, aortic arch involvement, and hematoma area ratio >37.5%. (B) Base model including four predictors without area-based imaging parameters: abdominal aorta involvement, WBC, presence of ULP, and aortic arch involvement. MDAA, maximal descending aortic area; ULP, ulcer-like projection; WBC, white blood cell.

Model performance evaluation

We evaluated the discriminative ability of the two models using ROC analysis (Figure 6). The enhanced model achieved an AUC of 0.745 (95% CI: 0.687–0.803), which was higher than that of the base model (AUC 0.664; 95% CI: 0.601–0.727). The DeLong test showed that this difference was statistically significant (P=0.0045), indicating that adding hematoma area-related parameters improved the ability to identify high-risk patients. Calibration was assessed with the Hosmer-Lemeshow goodness-of-fit test (Figure 7), which showed good calibration for both the enhanced model (P=0.296) and the base model (P=0.485), with no evidence of lack of fit (both P>0.05).

Figure 6 Comparison of ROC curves between the base and enhanced models. The enhanced model shows a higher AUC (0.745; 95% CI, 0.687–0.803) than the base model (0.664; 95% CI, 0.601–0.727), and the difference is statistically significant by DeLong’s test (P=0.0045). AUC, area under the curve; CI, confidence interval; ROC, receiver operating characteristic.

Figure 7 Calibration of two models. (A) Calibration curve of the enhanced model with 1,000 bootstrap bias correction (Hosmer-Lemeshow P=0.296). (B) Calibration curve of the base model (Hosmer-Lemeshow P=0.485). “Apparent” shows the original model fit, and “Bias-corrected” shows the estimates after bootstrap correction.

In order to further quantify the incremental predictive value of area-related imaging parameters, we performed an NRI analysis, which demonstrated an overall NRI of 0.543 (95% CI: 0.378–0.712, P<0.001) (Figure 8). Using the base model as the reference, predicted probabilities were grouped into risk categories of 0.10, 0.30, 0.50, 0.70, and 0.90. Compared with the base model, the enhanced model reclassified 40.0% of the 110 patients with events into higher risk categories, while only 14.5% were incorrectly moved to lower risk categories, giving a positive NRI (NRI⁺) of 0.255. Among the 180 patients without events, 47.8% were correctly reclassified into lower risk categories and only 18.9% were incorrectly moved to higher risk categories, resulting in a negative NRI (NRI⁻) of 0.289. The overall NRI was 0.543 (Table S1), indicating that adding area-related parameters greatly improved risk reclassification. This is consistent with the scatter plots, where predicted risk tends to increase in patients with events and decrease in those without events.

Figure 8 Scatter plot of category-based NRI comparing the enhanced model with the base (reference) model. The overall NRI was 0.543 (95% CI: 0.378–0.712, P<0.001), with 40.0% of cases correctly reclassified into higher risk categories and 47.8% of controls correctly reclassified into lower risk categories. CI, confidence interval; NRI, net reclassification improvement.

Finally, we used DCA to compare the clinical net benefit of the two models across a range of threshold probabilities (Figure 9). Between threshold probabilities of 0.20 and 0.60, the enhanced model (including area-related parameters) showed a higher net benefit than the base model, indicating that the addition of area-related parameters has greater clinical usefulness.

Figure 9 DCA of the enhanced and base models. Within the threshold range of approximately 0.20–0.60, the enhanced model consistently shows a higher net benefit. DCA, decision curve analysis.

We then performed internal validation with 1,000 bootstrap resamples (Figure 10), which further supported the advantage of the enhanced model. The optimism-corrected C-index of the enhanced model was 0.724, higher than 0.647 for the base model. For calibration, the enhanced model had a slightly higher calibration slope (0.891 vs. 0.883) and a lower Emax (0.032 vs. 0.036), indicating better agreement between predicted and observed risk. In addition, the Nagelkerke R² of the enhanced model was higher (0.189 vs. 0.072), showing stronger explanatory power. Overall, adding area-related parameters clearly improved discrimination, calibration stability, and model interpretability.

Figure 10 The performance of the base and enhanced models after bootstrap corrected. Compared with the base model, the enhanced model demonstrates better discrimination (C-index: 0.724 vs. 0.647), calibration (calibration slope: 0.891 vs. 0.883; lower Emax: 0.032 vs. 0.036), and explanatory power (Nagelkerke R²: 0.189 vs. 0.072). C-index, concordance index.

Discussion

Previous studies have shown that, in patients with acute type B IMH, CTA-based assessment of hematoma volume is better than the single axial measurements of maximal aortic diameter or hematoma thickness, and can more accurately predict disease progression (14). In intracerebral hemorrhage and other hemorrhagic conditions, hematoma volume has been regarded as one of the strongest imaging predictors of clinical outcome (15,16). These findings emphasize that overall hematoma burden, rather than any single linear dimension, is a key factor of poor prognosis.

In this study, we could not obtain routine three-dimensional volumetric analysis. Therefore, we used the maximum descending aortic area and the hematoma area ratio as two-dimensional markers of cross-sectional hematoma burden. The results showed that these area-based parameters had significant incremental prognostic value beyond traditional measures. We then integrated these features into an enhanced model and developed a nomogram. Compared with the base model, this model could more accurately identify one-year disease progression and aortic adverse events in patients with acute uncomplicated type B IMH. In our study, about 38% of patients showed disease progression during follow-up, which aligns with rates reported in recent cohort studies and observational series (17). This suggests that our study population is representative and further highlights the importance of early risk stratification and close follow-up.

The prevalence of diabetes was higher in the stable group, and diabetes did not emerge as a risk factor for IMH progression in multivariable analysis. This finding is consistent with the so-called “diabetes paradox” reported in some aortic disease cohorts, whereby patients with type 2 diabetes appear to have a lower risk of developing aortic dissection and a reduced risk of aortic complications and mortality after TEVAR. This phenomenon has been hypothesized to relate to long-standing hyperglycemia-induced medial collagen deposition and vascular wall fibrosis, which may increase the “anti-tear” resistance of the aortic wall (18). Given the relatively low prevalence of diabetes in our cohort and the fact that our study population consisted of patients with acute uncomplicated type B IMH initially managed with optimal medical therapy, this observation should be interpreted with caution and warrants confirmation in larger, multicenter prospective studies.

In addition, patients in the exacerbation group had significantly higher WBC counts, among which WBC remained an independent risk factor in multivariable analysis, supporting its role as a simple marker of inflammatory burden and disease instability (19).

Previous studies have shown that, in patients with type B IMH, the maximum aortic diameter and maximum aortic wall thickness on initial computed tomography (CT) imaging can predict progression (20). For type B IMH with a maximum aortic diameter ≥40 mm or a maximum aortic wall thickness ≥10 mm, careful treatment and management are necessary (21). In our study of 290 patients with acute uncomplicated type B IMH, we systematically evaluated the prognostic value of area-based imaging parameters (22,23). Compared with patients in the stable group, those in the exacerbation group had a clearly heavier overall disease burden, with more extensive aortic involvement, a larger MDAA, and a higher hematoma area ratio. These findings suggest that progression of type B IMH is influenced not only by the circumferential wall thickness of the hematoma, but also by the involved aortic segments and the cross-sectional burden of the hematoma. Larger MDAA values and higher hematoma area ratios suggest more severe luminal compression and involvement of a larger portion of the aortic circumference, which may further increase local wall stress, reducing the blood supply from the vasa vasorum and leading to ongoing damage of the aortic media (24,25). These findings suggest that adding quantitative area-based measures can show overall disease severity more clearly and help improve risk stratification beyond traditional diameter or wall-thickness criteria.

Using LASSO regression followed by multivariable logistic regression, we identified abdominal aortic involvement, MDAA ≥980 mm², the presence of ULPs, and a hematoma area ratio ≥37.5% as independent risk factors for adverse outcomes (26). These variables collectively capture both the aortic segment involvement (e.g., distal abdominal aortic involvement) and the cross-sectional/luminal burden (e.g., larger MDAA and higher hematoma area ratio), indicating more advanced structural injury of the aortic wall. In particular, distal extension into the abdominal aorta may represent a more diffuse pathological process involving longer segments of the thoracoabdominal aorta and potentially affecting major visceral branches. The presence of ULP has long been recognized as a marker of instability and a potential “entry point” for blood to penetrate deeper into the media (27). By quantifying MDAA and the hematoma area ratio, our model incorporates not only “where” the lesion is located, but also “how much” of the lumen and wall are involved at a given level.

The nomogram developed in this study combines abdominal aortic involvement, aortic arch involvement, WBC, ULP, and area-based hematoma indices to provide an individualized prediction of 1-year IMH progression (28,29). By putting clinical, laboratory, and imaging information into a single visual tool, the nomogram gives clinicians a practical way to estimate risk at the bedside or when interpreting routine CTA scans.

Incorporating these parameters into the enhanced model significantly improved its performance in terms of discrimination (AUC 0.745 vs. 0.664), reclassification (NRI =0.543), clinical net benefit, C-index (0.724 vs. 0.647), calibration slope (0.891 vs. 0.883), Emax (0.032 vs. 0.036) and Nagelkerke R² (0.189 vs. 0.072) (30,31). Taken together, these findings suggest that integrating area-related parameters with conventional markers can improve risk stratification and help identify patients who may benefit from closer follow-up or earlier preventive intervention. In practice, patients whose hematoma area or area ratio is above the threshold may need more careful management and treatment (such as stricter control of blood pressure and heart rate), closer imaging follow-up, and early consideration of TEVAR if there are surgical indications (32). Other patients can continue standard follow-up and conservative management. In the future, larger multicenter studies are needed to confirm the cut-off values, test them across different patient populations, and find out whether using area-based parameters improves patient outcomes (33).

This study has several limitations. First, we used ROC-derived cut-off values to dichotomize MDAA and MDHA. This approach improves interpretability and supports clinical decision-making. However, because these cut-offs are data-driven, they may cause loss of information and potential overfitting. Importantly, ROC-derived cut-offs should not be viewed as strict biological thresholds and require external validation. Second, the area-based parameters were measured manually on axial CTA images by experienced doctors, so there may still be some differences between observers. Third, as a single-center retrospective study, this study may be influenced by local practice patterns and the specific characteristics of the study population, which could limit how well these findings apply to other hospitals or populations. What’s more, under the constraints that thin-slice CTA data were not available for all patients, we didn’t perform volumetric [three-dimensional (3D)] hematoma measurements. Thus, our area-based indices only reflect cross-sectional burden rather than true hematoma volume. Further studies are needed to validate the relationship between two-dimensional (2D) indices and volumetric measurements.

Conclusions

In this study, we systematically evaluated the imaging characteristics of acute uncomplicated type B IMH. We found that area-related parameters, particularly the MDAA and hematoma area ratio, are strong predictors of adverse outcomes. Incorporating these variables significantly improved model discrimination, calibration, risk reclassification, and clinical net benefit compared with the traditional model. These findings suggest that the related area parameters provide incremental prognostic value for risk stratification in acute type B IMH, enhancing the identification of patients at high-risk and to guide more timely clinical decisions. More multicenter prospective studies are needed to confirm this model and to improve individualized management strategies based on hematoma burden.

Acknowledgments

The authors thank the clinicians and imaging specialists of the General Hospital of Northern Theater Command for their assistance in data collection and patient management.

Footnote

Reporting Checklist: The authors have completed the TRIPOD reporting checklist. Available at https://qims.amegroups.com/article/view/10.21037/qims-2025-1-2700/rc

Data Sharing Statement: Available at https://qims.amegroups.com/article/view/10.21037/qims-2025-1-2700/dss

Funding: This work was supported by the Science and Technology Plan Joint Plan Project of Liaoning Province (No. 2025JH2/101800031).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-2025-1-2700/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. This study was approved by the ethics committee of the General Hospital of Northern Theater Command [Approval No. Y(2025)462]. Informed consent was waived in this retrospective study.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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