{"title":"某医院急性脑出血继发胃肠道出血的影响因素（饮酒史）及Nomogram预测模型的构建","authors":"Peng Ye, Yeting Luo","doi":"10.2147/RMHP.S511692","DOIUrl":null,"url":null,"abstract":"<p><strong>Objective: </strong>To investigate the factors influencing acute cerebral hemorrhage (ACH) secondary to nosocomial gastrointestinal hemorrhage (GIH) and construct a nomogram prediction model.</p><p><strong>Methods: </strong>A total of 500 ACH patients admitted to our hospital from August 2022 to August 2024 were retrospectively analyzed and divided into a modeling group (350 cases) and a validation group (150 cases) in a 7:3 ratio. Patients in the modeling group were further divided into the GIH and non-GIH groups. Clinical data were collected, and multivariate logistic regression was used to analyze risk factors. A nomogram model was constructed using R software. The predictive performance was evaluated using the ROC curve, calibration curve, and decision curve analysis (DCA).</p><p><strong>Results: </strong>Among 500 patients, 78 (15.6%) developed GIH. In the modeling group (350 cases), 56 (16.0%) had GIH. There were significant differences in age, history of coronary heart disease, history of alcohol consumption, NIHSS score, systolic blood pressure, and hemorrhage volume between groups (P<0.05). Logistic regression analysis identified these factors as independent risk factors for secondary GIH (P<0.05). The Area Under Curve(AUC) was 0.798 in the modeling group and 0.978 in the validation group, with calibration curves showing good agreement between predicted and observed values (Hosmer-Lemeshow(H-L) test: modeling group, χ²=7.156, P=0.732; validation group, χ²=7.015, P=0.703). DCA indicated a high clinical application value when the probability ranged from 0.06 to 0.95.</p><p><strong>Conclusion: </strong>Age, history of coronary heart disease, history of alcohol consumption, NIHSS score, systolic blood pressure, and hemorrhage volume are key risk factors for secondary GIH in ACH patients. The nomogram model constructed based on these factors demonstrates good predictive performance and clinical application value. It can help clinicians prevent early onset and reduce the risk of bleeding in patients.</p>","PeriodicalId":56009,"journal":{"name":"Risk Management and Healthcare Policy","volume":"18 ","pages":"1557-1568"},"PeriodicalIF":2.7000,"publicationDate":"2025-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12080485/pdf/","citationCount":"0","resultStr":"{\"title\":\"Influencing Factors (History of Alcohol Consumption) and Construction of a Nomogram Prediction Model for In-Hospital Gastrointestinal Bleeding Secondary to Acute Cerebral Hemorrhage in a Certain Hospital.\",\"authors\":\"Peng Ye, Yeting Luo\",\"doi\":\"10.2147/RMHP.S511692\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Objective: </strong>To investigate the factors influencing acute cerebral hemorrhage (ACH) secondary to nosocomial gastrointestinal hemorrhage (GIH) and construct a nomogram prediction model.</p><p><strong>Methods: </strong>A total of 500 ACH patients admitted to our hospital from August 2022 to August 2024 were retrospectively analyzed and divided into a modeling group (350 cases) and a validation group (150 cases) in a 7:3 ratio. Patients in the modeling group were further divided into the GIH and non-GIH groups. Clinical data were collected, and multivariate logistic regression was used to analyze risk factors. A nomogram model was constructed using R software. The predictive performance was evaluated using the ROC curve, calibration curve, and decision curve analysis (DCA).</p><p><strong>Results: </strong>Among 500 patients, 78 (15.6%) developed GIH. In the modeling group (350 cases), 56 (16.0%) had GIH. There were significant differences in age, history of coronary heart disease, history of alcohol consumption, NIHSS score, systolic blood pressure, and hemorrhage volume between groups (P<0.05). Logistic regression analysis identified these factors as independent risk factors for secondary GIH (P<0.05). The Area Under Curve(AUC) was 0.798 in the modeling group and 0.978 in the validation group, with calibration curves showing good agreement between predicted and observed values (Hosmer-Lemeshow(H-L) test: modeling group, χ²=7.156, P=0.732; validation group, χ²=7.015, P=0.703). DCA indicated a high clinical application value when the probability ranged from 0.06 to 0.95.</p><p><strong>Conclusion: </strong>Age, history of coronary heart disease, history of alcohol consumption, NIHSS score, systolic blood pressure, and hemorrhage volume are key risk factors for secondary GIH in ACH patients. The nomogram model constructed based on these factors demonstrates good predictive performance and clinical application value. It can help clinicians prevent early onset and reduce the risk of bleeding in patients.</p>\",\"PeriodicalId\":56009,\"journal\":{\"name\":\"Risk Management and Healthcare Policy\",\"volume\":\"18 \",\"pages\":\"1557-1568\"},\"PeriodicalIF\":2.7000,\"publicationDate\":\"2025-05-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12080485/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Risk Management and Healthcare Policy\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.2147/RMHP.S511692\",\"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.S511692","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}
Influencing Factors (History of Alcohol Consumption) and Construction of a Nomogram Prediction Model for In-Hospital Gastrointestinal Bleeding Secondary to Acute Cerebral Hemorrhage in a Certain Hospital.
Objective: To investigate the factors influencing acute cerebral hemorrhage (ACH) secondary to nosocomial gastrointestinal hemorrhage (GIH) and construct a nomogram prediction model.
Methods: A total of 500 ACH patients admitted to our hospital from August 2022 to August 2024 were retrospectively analyzed and divided into a modeling group (350 cases) and a validation group (150 cases) in a 7:3 ratio. Patients in the modeling group were further divided into the GIH and non-GIH groups. Clinical data were collected, and multivariate logistic regression was used to analyze risk factors. A nomogram model was constructed using R software. The predictive performance was evaluated using the ROC curve, calibration curve, and decision curve analysis (DCA).
Results: Among 500 patients, 78 (15.6%) developed GIH. In the modeling group (350 cases), 56 (16.0%) had GIH. There were significant differences in age, history of coronary heart disease, history of alcohol consumption, NIHSS score, systolic blood pressure, and hemorrhage volume between groups (P<0.05). Logistic regression analysis identified these factors as independent risk factors for secondary GIH (P<0.05). The Area Under Curve(AUC) was 0.798 in the modeling group and 0.978 in the validation group, with calibration curves showing good agreement between predicted and observed values (Hosmer-Lemeshow(H-L) test: modeling group, χ²=7.156, P=0.732; validation group, χ²=7.015, P=0.703). DCA indicated a high clinical application value when the probability ranged from 0.06 to 0.95.
Conclusion: Age, history of coronary heart disease, history of alcohol consumption, NIHSS score, systolic blood pressure, and hemorrhage volume are key risk factors for secondary GIH in ACH patients. The nomogram model constructed based on these factors demonstrates good predictive performance and clinical application value. It can help clinicians prevent early onset and reduce the risk of bleeding in patients.
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.
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