{"title":"自发性脑出血血肿扩张的临床和影像学预测因素:一种预后模型的建立。","authors":"Yi-Guang Mao, Jia-Yu Chen, Man-Li Wang, Ying-Jun Ma, Chen Jiang","doi":"10.2147/RMHP.S534564","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Identifying risk factors associated with hematoma expansion following spontaneous intracerebral hemorrhage (ICH) is essential for improving early intervention strategies. We hope to use this predictive model in the future to comprehensively score the risk factors of hospitalized patients with cerebral hemorrhage and evaluate the possibility of hematoma enlargement. Being able to identify high-risk patients with hematoma enlargement early and take intervention measures to save their lives.</p><p><strong>Methods: </strong>A retrospective analysis was conducted on clinical data from 226 individuals diagnosed with spontaneous ICH between December 29, 2023, and August 29, 2024. Multiple logistic regression analysis was performed to identify risk factors associated with hematoma expansion. Predictive performance of the model was assessed using ROC curve analysis and receiver operating characteristic curve analysis. Mortality rates were calculated for each group following a 7-day follow-up period.</p><p><strong>Results: </strong>Hematoma expansion was associated with diabetes mellitus, a low Glasgow Coma Scale (GCS) score at admission, elevated systolic blood pressure at admission, coagulation abnormalities, and specific computed tomography (CT) imaging findings, such as heterogeneous density, black hole sign, swirl sign, lobulation sign, and blend sign. A prognostic model incorporating these factors demonstrated robust predictive performance, achieving an area under the curve of 0.771 (95% CI: 0.628-0.915, <i>p</i> = 0.002). The model yielded a maximum Youden index of 0.489, with an optimal cutoff score of 38, a sensitivity of 54.5%, and a specificity of 94.4%. Mortality among individuals with coagulation abnormalities was 53.3%.</p><p><strong>Conclusion: </strong>Coagulation abnormalities, GCS score, systolic blood pressure at admission, CT imaging findings, and diabetes mellitus were identified as predictors of hematoma expansion in spontaneous ICH. Individuals with coagulopathy and elevated systolic blood pressure at admission exhibited the poorest prognoses.</p>","PeriodicalId":56009,"journal":{"name":"Risk Management and Healthcare Policy","volume":"18 ","pages":"2865-2874"},"PeriodicalIF":2.0000,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12415086/pdf/","citationCount":"0","resultStr":"{\"title\":\"Clinical and Imaging Predictors of Hematoma Expansion in Spontaneous Intracerebral Hemorrhage: Development of a Prognostic Model.\",\"authors\":\"Yi-Guang Mao, Jia-Yu Chen, Man-Li Wang, Ying-Jun Ma, Chen Jiang\",\"doi\":\"10.2147/RMHP.S534564\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Identifying risk factors associated with hematoma expansion following spontaneous intracerebral hemorrhage (ICH) is essential for improving early intervention strategies. We hope to use this predictive model in the future to comprehensively score the risk factors of hospitalized patients with cerebral hemorrhage and evaluate the possibility of hematoma enlargement. Being able to identify high-risk patients with hematoma enlargement early and take intervention measures to save their lives.</p><p><strong>Methods: </strong>A retrospective analysis was conducted on clinical data from 226 individuals diagnosed with spontaneous ICH between December 29, 2023, and August 29, 2024. Multiple logistic regression analysis was performed to identify risk factors associated with hematoma expansion. Predictive performance of the model was assessed using ROC curve analysis and receiver operating characteristic curve analysis. Mortality rates were calculated for each group following a 7-day follow-up period.</p><p><strong>Results: </strong>Hematoma expansion was associated with diabetes mellitus, a low Glasgow Coma Scale (GCS) score at admission, elevated systolic blood pressure at admission, coagulation abnormalities, and specific computed tomography (CT) imaging findings, such as heterogeneous density, black hole sign, swirl sign, lobulation sign, and blend sign. A prognostic model incorporating these factors demonstrated robust predictive performance, achieving an area under the curve of 0.771 (95% CI: 0.628-0.915, <i>p</i> = 0.002). The model yielded a maximum Youden index of 0.489, with an optimal cutoff score of 38, a sensitivity of 54.5%, and a specificity of 94.4%. Mortality among individuals with coagulation abnormalities was 53.3%.</p><p><strong>Conclusion: </strong>Coagulation abnormalities, GCS score, systolic blood pressure at admission, CT imaging findings, and diabetes mellitus were identified as predictors of hematoma expansion in spontaneous ICH. Individuals with coagulopathy and elevated systolic blood pressure at admission exhibited the poorest prognoses.</p>\",\"PeriodicalId\":56009,\"journal\":{\"name\":\"Risk Management and Healthcare Policy\",\"volume\":\"18 \",\"pages\":\"2865-2874\"},\"PeriodicalIF\":2.0000,\"publicationDate\":\"2025-09-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12415086/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Risk Management and Healthcare Policy\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.2147/RMHP.S534564\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2025/1/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q2\",\"JCRName\":\"HEALTH CARE SCIENCES & SERVICES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Risk Management and Healthcare Policy","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.2147/RMHP.S534564","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/1/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"HEALTH CARE SCIENCES & SERVICES","Score":null,"Total":0}
引用次数: 0
摘要
背景:确定自发性脑出血(ICH)后血肿扩张相关的危险因素对于改善早期干预策略至关重要。我们希望将来利用该预测模型对住院脑出血患者的危险因素进行综合评分,评估血肿扩大的可能性。能够及早发现血肿扩大高危患者,并采取干预措施挽救生命。方法:回顾性分析2023年12月29日至2024年8月29日226例自发性脑出血患者的临床资料。进行多元logistic回归分析以确定与血肿扩张相关的危险因素。采用ROC曲线分析和受试者工作特征曲线分析评估模型的预测性能。在7天的随访期后计算每组的死亡率。结果:血肿扩张与糖尿病、入院时格拉斯哥昏迷评分(GCS)较低、入院时收缩压升高、凝血异常和特定的计算机断层扫描(CT)影像学表现相关,如非均匀密度、黑洞征、漩涡征、分叶征和混合征。纳入这些因素的预后模型显示出稳健的预测性能,曲线下面积为0.771 (95% CI: 0.628-0.915, p = 0.002)。该模型的最大约登指数为0.489,最佳临界值为38,敏感性为54.5%,特异性为94.4%。凝血功能异常者死亡率为53.3%。结论:凝血异常、GCS评分、入院时收缩压、CT影像学表现和糖尿病可作为自发性脑出血血肿扩张的预测因素。入院时有凝血功能障碍和收缩压升高的患者预后最差。
Clinical and Imaging Predictors of Hematoma Expansion in Spontaneous Intracerebral Hemorrhage: Development of a Prognostic Model.
Background: Identifying risk factors associated with hematoma expansion following spontaneous intracerebral hemorrhage (ICH) is essential for improving early intervention strategies. We hope to use this predictive model in the future to comprehensively score the risk factors of hospitalized patients with cerebral hemorrhage and evaluate the possibility of hematoma enlargement. Being able to identify high-risk patients with hematoma enlargement early and take intervention measures to save their lives.
Methods: A retrospective analysis was conducted on clinical data from 226 individuals diagnosed with spontaneous ICH between December 29, 2023, and August 29, 2024. Multiple logistic regression analysis was performed to identify risk factors associated with hematoma expansion. Predictive performance of the model was assessed using ROC curve analysis and receiver operating characteristic curve analysis. Mortality rates were calculated for each group following a 7-day follow-up period.
Results: Hematoma expansion was associated with diabetes mellitus, a low Glasgow Coma Scale (GCS) score at admission, elevated systolic blood pressure at admission, coagulation abnormalities, and specific computed tomography (CT) imaging findings, such as heterogeneous density, black hole sign, swirl sign, lobulation sign, and blend sign. A prognostic model incorporating these factors demonstrated robust predictive performance, achieving an area under the curve of 0.771 (95% CI: 0.628-0.915, p = 0.002). The model yielded a maximum Youden index of 0.489, with an optimal cutoff score of 38, a sensitivity of 54.5%, and a specificity of 94.4%. Mortality among individuals with coagulation abnormalities was 53.3%.
Conclusion: Coagulation abnormalities, GCS score, systolic blood pressure at admission, CT imaging findings, and diabetes mellitus were identified as predictors of hematoma expansion in spontaneous ICH. Individuals with coagulopathy and elevated systolic blood pressure at admission exhibited the poorest prognoses.
期刊介绍:
Risk Management and Healthcare Policy is an international, peer-reviewed, open access journal focusing on all aspects of public health, policy and preventative measures to promote good health and improve morbidity and mortality in the population. Specific topics covered in the journal include:
Public and community health
Policy and law
Preventative and predictive healthcare
Risk and hazard management
Epidemiology, detection and screening
Lifestyle and diet modification
Vaccination and disease transmission/modification programs
Health and safety and occupational health
Healthcare services provision
Health literacy and education
Advertising and promotion of health issues
Health economic evaluations and resource management
Risk Management and Healthcare Policy focuses on human interventional and observational research. The journal welcomes submitted papers covering original research, clinical and epidemiological studies, reviews and evaluations, guidelines, expert opinion and commentary, and extended reports. Case reports will only be considered if they make a valuable and original contribution to the literature. The journal does not accept study protocols, animal-based or cell line-based studies.