The association between the Charlson Comorbidity Index and prognosis in patients with supratentorial spontaneous intracerebral hemorrhage following hematoma evacuation

Introduction

Spontaneous, nontraumatic intracerebral hemorrhage (ICH) is the most common and severe hemorrhagic stroke. A significant amount of mortality and morbidity is associated with ICH (1). Acute lobar ICH exhibits distinct clinical profiles and a poorer early prognosis compared to deep subcortical ICH (2). Additionally, non-hypertensive mechanisms predominate in the lobar location. In academic research, the Charlson Comorbidity Index (CCI) has been found to be associated with a range of critical outcomes, including mortality, disability, readmission rates, the duration of hospital stays, post-injury mortality, and the potential risk of postoperative adverse events. In studies that focus on patient outcomes and mortality, the CCI serves as a valuable tool by generating a composite score that considers multiple comorbid conditions, with each condition being weighted according to its specific presence and severity. Two independent retrospective studies demonstrated the prognostic utility of the CCI in ICH outcomes. Bar et al. (n=243) identified CCI as an independent predictor of 12-month functional outcomes and 3-month mortality (3). Similarly, Zhang and colleagues’ 2022 investigation in elderly ICH patients (n=248, age >70 years) established CCI as a significant predictor of both in-hospital mortality and 30-day prognosis (4). Recently, the CCI has been evaluated for its application in instances of ICH and ischemic stroke (3). However, its effectiveness in predicting clinical outcomes for patients with supratentorial spontaneous ICH following hematoma evacuation remains understudied. This is primarily due to the fact that comorbidities can impact the outcome of supratentorial spontaneous ICH subsequent to hematoma evacuation. Furthermore, critical unanswered questions persist regarding two key aspects: (I) the potential relationship between CCI stratification and both ICH severity (ICH score) and rebleeding risk in high-risk cohorts; and (II) whether the comparative prognostic value of CCI versus ICH Score remains valid in this surgical context. Therefore, these unresolved issues warrant further investigation through well-designed prospective cohort studies or randomized controlled trials (RCTs).

This study aims to explore the correlation between the CCI score and prognostic outcomes in patients who have undergone hematoma evacuation for supratentorial ICH. We hypothesize that a higher degree of comorbidity will correlate with unfavorable outcomes for these postoperative patients. The findings can be valuable in guiding optimal clinical treatment strategy. We present this article in accordance with the STROBE reporting checklist (available at https://qims.amegroups.com/article/view/10.21037/qims-2024-2789/rc).

Methods

Patients selection & inclusion and exclusion criteria

This retrospective study analyzed data from 515 postoperative cerebral hemorrhage patients at Lishui Municipal Central Hospital [2014–2021]. After rigorous screening, 488 patients with surgically treated spontaneous supratentorial ICH were initially selected, excluding cases with aneurysms, arteriovenous malformations, or other vascular abnormalities through computed tomography (CT) angiography/magnetic resonance imaging (MRI) verification. Further exclusions comprised 95 cases receiving only external ventricular drainage, 7 withdrawals, and 5 lost to follow-up, resulting in 381 final participants (Figure 1). Inclusion criteria required: (I) supratentorial spontaneous ICH diagnosis with hematoma evacuation (craniotomy, endoscopy, or stereotactic aspiration); (II) hypertension-confirmed etiology without other ICH causes on imaging. Exclusion criteria eliminated: (I) alternative ICH etiologies (trauma, vascular malformations, moyamoya disease, etc.); (II) sole ventricular drainage treatment; (III) incomplete follow-up data. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Ethics Committee of Lishui Municipal Central Hospital (IRB No. 2024-96) and individual consent for this retrospective analysis was waived.

Figure 1 Randomization enrollment flow chart of study patients. ICH, intracerebral hemorrhage.

Outcomes assessment

The primary outcome was recorded using the modified Rankin Scale (mRS) after 6 months. The mRS is a medical scale used to assess the degree of disability in patients after a stroke. It is an ordinal scale commonly used in clinical trials and research to measure the impact of stroke or other cerebrovascular events on patients’ ability to perform daily activities. The scoring range of the mRS spans from 0 to 6, where 0 signifies the absence of any symptoms, while 6 denotes death. Scores falling within the range of 0 to 3 are indicative of “favorable” outcomes, whereas scores exceeding 3 are indicative of “unfavorable” outcomes.

CCI

The CCI is a tool employed within the medical domain, especially in the realm of clinical research. It serves to forecast a patient’s likelihood of mortality within a year by assessing the presence of comorbid conditions—that is, additional diseases the patient may have. The CCI assigns varying weight scores to these comorbidities, reflecting the severity of their influence on the patient’s prognosis. The aggregation of these scores yields a cumulative index, which is then utilized to evaluate the patient’s general health condition and estimate their survival risk. Patients were categorized into two groups based on their CCI score: low comorbidity group (CCI 0–1) and high comorbidity group (CCI ≥2). This cutoff was selected based on prior validation showing CCI ≥2 as a threshold for significantly elevated mortality risk in neurosurgical populations (5).

The following clinical conditions and scores are listed in Table 1: 1 each—congestive heart failure, myocardial infarction, dementia, cerebrovascular disease, peripheral vascular disease, diabetes without complications, connective tissue disease, chronic lung disease, peptic ulcer disease, mild chronic liver disease; 2 each—hemiplegia, diabetes with complications, moderate or severe kidney disease, leukemia, lymphoma, and tumor without metastases; 3 each—moderate or severe liver disease; and 6 each—metastatic solid tumor and acquired immunodeficiency syndrome (AIDS) (6).

Image evaluation

For all patients, a Siemens Somatom CT scanner (Siemens Healthineers AG, 80333 Erlangen, Germany) was used to obtain images with a slice thickness of 0.5 cm. The CT images were analyzed using commercially available volumetry software (Leonardo V; Siemens Healthcare, Erlangen, Germany). In this study, the perihematomal edema volume and the periventricular hemorrhage volume were semi-automatically calculated using computerized planimetric techniques. We performed CT volumetry manually by tracing the hemorrhage areas on each slice and summing the traced area [region of interest (ROI), cm²] and slice thickness (7). An assessment of the midline shift was performed at the septum pellucidum.

Statistical analysis

Statistics were analyzed using the SPSS 25.0 software program (SPSS Inc., Chicago, IL, USA). Continuous variables were expressed as mean ± standard deviation (SD) or as median (Q1, Q3), while categorical variables were reported as frequencies and percentages. Variables that followed a normal distribution were subjected to a Student’s t-test, whereas those that did not were analyzed using the Mann-Whitney U test. Categorical variables were presented as frequencies with corresponding percentages and were analyzed using the Chi-squared (χ²) test. Binary logistic regression was employed to analyze 6-month poor outcomes (mRS 4–6) between two groups across four models. Model 1: crude; Model 2: adjusted for sex, age, hypertension, diabetes mellitus (DM), smoke, alcohol, antiplatelet, anticoagulant, stroke, hemorrhage, heart disease, preoperative Glasgow Coma Scale (GCS) score; Model 3: adjusted for age, ICH score, preoperative hematoma volume, preoperative intraventricular hemorrhage (IVH) volume, lobar (vs. deep) origin, postoperative rehemorrhage; Model 4: adjusted for age, ICH score, surgery, hematoma volume, IVH volume, poor neurologic outcome, midline shift, lobar or deep location, residual ICH, residual IVH, rehemorrhage. Variables that showed a P value less than 0.05 in the univariate analysis were considered for inclusion in the multivariate analysis. A statistical significance threshold of 0.05 was considered significant.

Results

Baseline characteristics

This study included a total of 381 patients, who were divided into two groups based on their comorbidity status: the low-comorbidity group (n=340) and the high-comorbidity group (n=41), accounting for 89.2% and 10.8% of the total sample, respectively (Figure 2). Table 2 presents a comparison of the baseline characteristics between the high and low-comorbidity groups. The high-comorbidity group had a greater proportion of medical histories that included diabetes, stroke, hemorrhage, heart disease, and anticoagulant use compared to the low-comorbidity group, with statistically significant differences. Furthermore, the high-comorbidity group exhibited significantly greater preoperative and postoperative hematoma volume compared to the low-comorbidity group [47.64 (33.9–67.8) vs. 55.88 (44.5–69.3) mL, P=0.08; 9.81 (4.6–18.4) vs. 12.88 (6.0–24.1) mL, P=0.045]. The incidence of postoperative rehemorrhage [23 (6.8%) vs. 7 (17.1%), P=0.045] and 6-month poor outcomes (mRS score 4–6) [209 (61.5%) vs. 37 (90.2%), P<0.001] was also elevated in the high comorbidity group, with statistically significant differences.

Figure 2 The distribution of the CCI sum scores. CCI, Charlson Comorbidity Index.

Risk factors for poor prognosis

In the univariate and multivariate analyses (Tables S1,S2), all variables with a P value less than 0.05 were considered for the multivariate analysis. The multivariate forest plot results indicated that the risk factors associated with poor outcomes included: age [odds ratio (OR) 1.06; 95% confidence interval (CI): 1.04–1.09; P<0.001], ICH score (OR 1.82; 95% CI: 1.27–2.60; P=0.001), lobar (vs. deep) origin (OR 5.08; 95% CI: 2.59–9.95; P<0.001), preoperative hematoma volume (OR 1.03; 95% CI: 1.01–1.04; P=0.001), and preoperative IVH volume (OR 1.04; 95% CI: 1.01–1.07; P=0.015). Of particular note, a high CCI score emerged as the most significant determinant of poor prognosis (OR 10.68; 95% CI: 2.76–41.30; P=0.001) (Figure 3). According to binary logistic regression (Table 3), a high CCI score was independently associated with poor outcomes in Model 1 (Model 1, OR 5.80; 95% CI: 2.02–16.64; P=0.001). After adjusting for clinical preset variables in Model 2, the difference remained statistically significant (Model 2, OR 7.48; 95% CI: 2.15–25.96; P=0.002). Following adjustments for baseline differences and clinical preset variables, the results remained consistent (Model 3, OR 10.68; 95% CI: 2.76–41.30; P<0.001; Model 4, OR 10.89; 95% CI: 2.75–43.05; P<0.001). Figure 4 shows the 6-month mRS scores for low and high CCI groups post-craniotomy for supratentorial ICH. The low CCI group (38.8%, 131/340) had a better prognosis (mRS ≤3) than the high CCI group (9.8%, 4/41).

Figure 3 The multivariate forest plot. CCI, Charlson Comorbidity Index; CI, confidence interval; ICH, intracerebral hemorrhage; IVH, intraventricular hemorrhage volume; OR, odds ratio.

Figure 4 Bar chart showing the distribution of mRS outcomes at 6-month in low CCI and high CCI groups. The black dotted line indicates the difference in favorable outcomes between the groups. CCI, Charlson Comorbidity Index; mRS, modified Rankin Scale.

Discussion

In our study, a retrospective cohort comprising 381 patients who underwent craniotomy for spontaneous supratentorial ICH was investigated. Our findings revealed that the high comorbidity group exhibited a significantly greater incidence of diabetes, stroke, hemorrhage, heart disease, and histories of anticoagulant use. Furthermore, these patients presented with larger preoperative and postoperative hematoma volumes, as well as a notably higher incidence of postoperative rehemorrhage and poor outcomes (mRS 4–6) at six months. Notably, high CCI scores were independently associated with unfavourable outcomes across all models. Specifically, the high CCI group faced a ninefold increased risk of unfavorable outcomes, which highlights the significance of CCI in directing clinical treatment decisions and prognosis assessments.

While extensive research has established the CCI as a significant predictor of clinical outcomes in cerebrovascular disorders (3,8-10), its specific prognostic significance in patients undergoing surgical treatment for supratentorial spontaneous ICH remains to be determined. Notably, although prior studies have established the CCI as an independent predictor of 12-month functional outcomes in spontaneous supratentorial ICH (3) and demonstrated its prognostic utility for survival in general ICH populations (4,6), this investigation specifically addresses the critical knowledge gap in post-operative prognostication following hematoma evacuation.

The novel contribution of our study emerges from multivariate logistic regression analysis, which reveals that CCI maintains independent predictive value for surgical outcomes even after rigorous adjustment for hematoma volume, ICH score, and operative parameters (Model 4: OR 10.89, 95% CI: 2.75–43.05, P<0.001). These findings elucidate two clinically significant observations: first, comorbidity burden quantified by CCI directly impacts functional recovery following hematoma evacuation, independent of traditional hemorrhage severity markers; second, the index serves as a critical risk stratification tool for surgical decision-making in supratentorial ICH patients with multiple comorbidities. Importantly, these results are particularly relevant given that hematoma evacuation patients inherently present with more severe hemorrhages, where traditional ICH scores may reach their predictive upper limits while the strong correlation with CCI persists.

To contextualize these findings, ICH accounts for 10–20% of all strokes and carries higher mortality and morbidity than ischemic strokes (11). Although surgical intervention for spontaneous supratentorial ICH remains controversial, our retrospective study identified critical predictors of prognosis. Specifically, advanced age (≥65 years) was identified as an independent risk factor for adverse postoperative outcomes, with risk increasing by 6.7% per additional year (12,13). Furthermore, concurrent aging-related comorbidities (e.g., diabetes, myocardial infarction) further amplify this risk (14). Equally critical, impaired consciousness on admission (quantified by lower GCS scores) strongly predicted elevated 30-day mortality and unfavorable short-term prognosis (14,15). Additionally, hematoma characteristics critically influenced prognosis: specifically, the intraventricular extension of a hematoma is an important predictor of poor early outcome (16). Moreover, deep-seated location independently predicted poor recovery in both univariate (P<0.001) and multivariate analyses (OR 5.209, 95% CI: 2.541–10.680, P<0.001), a finding consistent with cohort studies associating deep ICH with impaired functional recovery (15,17). Collectively, these findings underscore the synergistic impact of age, neurological status, and hematoma topography on ICH prognosis.

Building upon these insights, our study highlights the pivotal clinical value of CCI in postoperative management following spontaneous supratentorial ICH. Three key implications warrant emphasis: first, CCI functions not only as an independent prognostic predictor distinct from hematoma volume and traditional hemorrhage severity scores (e.g., ICH score) but also establishes a valuable risk stratification framework for surgical candidates with significant comorbidity burdens. Second, it addresses the limitations of conventional metrics in evaluating functional recovery after hematoma evacuation. Third, the synergistic effects of advanced age, impaired consciousness on admission, and deep hematoma location underscore the necessity of establishing multidimensional assessment frameworks in clinical practice. In light of these findings, we emphasize the imperative to systematically integrate comorbidity assessments with neurological evaluations in both surgical decision-making and prognosis management. Such an integrated approach could ultimately refine individualized therapeutic strategies and enhance clinical management efficacy for critically ill ICH patients, particularly by leveraging CCI’s independent predictive value even after adjusting for established prognostic markers. Therefore, future research should validate these findings in large-scale, multi-center prospective cohort studies. Additionally, studies should specifically investigate the causes of death, differentiating between neurogenic and non-neurogenic etiologies. Collectively, these efforts will strengthen the robustness of the conclusions.

Limitations

In this study, there are several limitations. Firstly, this was a single-center study; the surgical level of patients in different hospitals might have impacted their patients’ prognosis. Secondly, due to the retrospective nature of this study, the results may be susceptible to selection bias. Thirdly, due to the CCI’s sum score nature, the contribution of individual comorbidities to the patient outcome cannot be assessed because not all medical comorbidities are represented. Additionally, the single-center real-world data design inherently resulted in substantial imbalance between the low CCI group (n=340, 89.2%) and high CCI group (n=41, 10.8%). This skewed group distribution may compromise the validity of statistical inferences, particularly in multivariate modeling approaches. Finally, this study had a small sample size and should be increased in future research.

Conclusions

We found that high CCI scores could significantly increase the risk of adverse prognosis in patients who experienced supratentorial ICH surgery. It may be useful to select a plan for clinical treatment and evaluate a patients’ prognosis after surgery based on this finding. It could facilitate the implementation of new treatments and provide clinical outcome information to patients. The use of the CCI score in clinical practice can help improve the accuracy of prognostic predictions and guide the development of more effective treatment strategies, ultimately leading to better patient outcomes and improved quality of life.

Acknowledgments

We would like to thank all the study participants and the Steering Committee Members of this study.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://qims.amegroups.com/article/view/10.21037/qims-2024-2789/rc

Data Sharing Statement: Available at https://qims.amegroups.com/article/view/10.21037/qims-2024-2789/dss

Funding: This work was supported by grants from the Innovative Talent Support Program of Zhejiang Provincial Medical and Health Science and Technology Project (Nos. 2022RC301 and 2022KY1432).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-2024-2789/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. The study was approved by the Ethics Committee of Lishui Municipal Central Hospital (IRB No. 2024-96) and individual consent for this retrospective analysis was waived.

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/.

References

1. Balasa A, Ghiga D, Andone RS, Zahan AE, Florian IA, Chinezu R. Effects of Surgery on the 30-Day Survival Rate in Spontaneous Supratentorial Intracerebral Hemorrhage. Brain Sci 2020;11:5. [Crossref] [PubMed]
2. Mendiola JMF, Arboix A, García-Eroles L, Sánchez-López MJ. Acute Spontaneous Lobar Cerebral Hemorrhages Present a Different Clinical Profile and a More Severe Early Prognosis than Deep Subcortical Intracerebral Hemorrhages-A Hospital-Based Stroke Registry Study. Biomedicines 2023;11:223. [Crossref] [PubMed]
3. Bar B, Hemphill JC 3rd. Charlson comorbidity index adjustment in intracerebral hemorrhage. Stroke 2011;42:2944-6. [Crossref] [PubMed]
4. Zhang T, Chen R, Wen D, Wang X, Ma L. The prognostic value of the Charlson comorbidity index in aged patients with intracerebral hemorrhage. BMC Neurol 2022;22:443. [Crossref] [PubMed]
5. Wirdefeldt K, Adami HO, Cole P, Trichopoulos D, Mandel J. Epidemiology and etiology of Parkinson's disease: a review of the evidence. Eur J Epidemiol 2011;26:S1-58. [Crossref] [PubMed]
6. Jiménez Caballero PE, López Espuela F, Portilla Cuenca JC, Ramírez Moreno JM, Pedrera Zamorano JD, Casado Naranjo I. Charlson comorbidity index in ischemic stroke and intracerebral hemorrhage as predictor of mortality and functional outcome after 6 months. J Stroke Cerebrovasc Dis 2013;22:e214-8. [Crossref] [PubMed]
7. Lv XN, Cheng J, Liu XY, Liu JC, Deng L, Li ZQ, Pu MJ, Chen C, Li Q. Ultraearly Intraventricular Hemorrhage Growth Predicts Early Neurologic Deterioration and Poor Functional Outcome After Acute Intracerebral Hemorrhage. J Am Heart Assoc 2023;12:e031214. [Crossref] [PubMed]
8. Goldstein LB, Samsa GP, Matchar DB, Horner RD. Charlson Index comorbidity adjustment for ischemic stroke outcome studies. Stroke 2004;35:1941-5. [Crossref] [PubMed]
9. Tessier A, Finch L, Daskalopoulou SS, Mayo NE. Validation of the Charlson Comorbidity Index for predicting functional outcome of stroke. Arch Phys Med Rehabil 2008;89:1276-83. [Crossref] [PubMed]
10. Liu M, Domen K, Chino N. Comorbidity measures for stroke outcome research: a preliminary study. Arch Phys Med Rehabil 1997;78:166-72. [Crossref] [PubMed]
11. An SJ, Kim TJ, Yoon BW. Epidemiology, Risk Factors, and Clinical Features of Intracerebral Hemorrhage: An Update. J Stroke 2017;19:3-10. [Crossref] [PubMed]
12. Zhang K, Wei L, Zhou X, Yang B, Meng J, Wang P. Risk factors for poor outcomes of spontaneous supratentorial cerebral hemorrhage after surgery. J Neurol 2022;269:3015-25. [Crossref] [PubMed]
13. Rådholm K, Arima H, Lindley RI, Wang J, Tzourio C, Robinson T, Heeley E, Anderson CS, Chalmers J. INTERACT2 Investigators. Older age is a strong predictor for poor outcome in intracerebral haemorrhage: the INTERACT2 study. Age Ageing 2015;44:422-7. [Crossref] [PubMed]
14. Schmidt M, Jacobsen JB, Johnsen SP, Bøtker HE, Sørensen HT. Eighteen-year trends in stroke mortality and the prognostic influence of comorbidity. Neurology 2014;82:340-50. [Crossref] [PubMed]
15. Kuohn LR, Witsch J, Steiner T, Sheth KN, Kamel H, Navi BB, Merkler AE, Murthy SB, Mayer SA. Early Deterioration, Hematoma Expansion, and Outcomes in Deep Versus Lobar Intracerebral Hemorrhage: The FAST Trial. Stroke 2022;53:2441-8. [Crossref] [PubMed]
16. Arboix A, Rodríguez-Aguilar R, Oliveres M, Comes E, García-Eroles L, Massons J. Thalamic haemorrhage vs internal capsule-basal ganglia haemorrhage: clinical profile and predictors of in-hospital mortality. BMC Neurol 2007;7:32. [Crossref] [PubMed]
17. Rajan Ponnaam Perumal S, Narayanan R, Palanisamy B. Clinical Profile of Intracerebral Haemorrhage. Journal of Evolution of Medical and Dental Sciences 2016;5:4650-4.