A nomogram predictive model for long-term survival in spontaneous intracerebral hemorrhage patients without cerebral herniation at admission

医学 列线图 队列 比例危险模型 脑出血 危险系数 一致性 试验预测值 前瞻性队列研究 生存分析 内科学 外科 蛛网膜下腔出血 置信区间
作者
Fuxin Lin,Qiu He,Lingyun Zhuo,Mingpei Zhao,Gengzhao Ye,Zhuyu Gao,Wei Huang,Lveming Cai,Fangyu Wang,Huang-Cheng Shang‐Guan,Wenhua Fang,Yuanxiang Lin,Dengliang Wang,Dezhi Kang
出处
期刊:Scientific Reports [Nature Portfolio]
卷期号:13 (1)
标识
DOI:10.1038/s41598-022-26176-0
摘要

Abstract Stratification of spontaneous intracerebral hemorrhage (sICH) patients without cerebral herniation at admission, to determine the subgroups may be suffered from poor outcomes or benefit from surgery, is important for following treatment decision. The aim of this study was to establish and verify a de novo nomogram predictive model for long-term survival in sICH patients without cerebral herniation at admission. This study recruited sICH patients from our prospectively maintained ICH patient database (RIS-MIS-ICH, ClinicalTrials.gov Identifier: NCT03862729) between January 2015 and October 2019. All eligible patients were randomly classified into a training cohort and a validation cohort according to the ratio of 7:3. The baseline variables and long-term survival outcomes were collected. And the long-term survival information of all the enrolled sICH patients, including the occurrence of death and overall survival. Follow-up time was defined as the time from the onset to death of the patient or the last clinical visit. The nomogram predictive model was established based on the independent risk factors at admission for long-term survival after hemorrhage. The concordance index (C-index) and ROC curve were used to evaluate the accuracy of the predictive model. Discrimination and calibration were used to validate the nomogram in both the training cohort and the validation cohort. A total of 692 eligible sICH patients were enrolled. During the average follow-up time of 41.77 ± 0.85 months, a total of 178 (25.7%) patients died. The Cox Proportional Hazard Models showed that age (HR 1.055, 95% CI 1.038–1.071, P < 0.001), Glasgow Coma Scale (GCS) at admission (HR 2.496, 95% CI 2.014–3.093, P < 0.001) and hydrocephalus caused by intraventricular hemorrhage (IVH) (HR 1.955, 95% CI 1.362–2.806, P < 0.001) were independent risk factors. The C index of the admission model was 0.76 and 0.78 in the training cohort and validation cohort, respectively. In the ROC analysis, the AUC was 0.80 (95% CI 0.75–0.85) in the training cohort and was 0.80 (95% CI 0.72–0.88) in the validation cohort. SICH patients with admission nomogram scores greater than 87.75 were at high risk of short survival time. For sICH patients without cerebral herniation at admission, our de novo nomogram model based on age, GCS and hydrocephalus on CT may be useful to stratify the long-term survival outcomes and provide suggestions for treatment decision-making.

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
刚刚
Bit完成签到,获得积分10
1秒前
时尚溪流完成签到,获得积分10
1秒前
shanshan完成签到,获得积分10
1秒前
华仔应助老刘采纳,获得10
1秒前
1秒前
Helen完成签到,获得积分10
2秒前
大爱仙尊发布了新的文献求助10
2秒前
2秒前
lebangzhanshi发布了新的文献求助10
2秒前
健壮的凝冬完成签到 ,获得积分10
2秒前
Amanda柏完成签到,获得积分10
3秒前
mito完成签到,获得积分10
3秒前
moooj完成签到,获得积分10
3秒前
mmmm完成签到,获得积分10
3秒前
朴素的幻然完成签到,获得积分10
3秒前
3秒前
曹中明发布了新的文献求助100
3秒前
3秒前
3秒前
4秒前
戴维少尉完成签到,获得积分10
4秒前
归海一刀完成签到 ,获得积分10
4秒前
HHHH完成签到,获得积分10
4秒前
二兄完成签到,获得积分20
4秒前
4秒前
Jiaxixi完成签到,获得积分10
4秒前
515完成签到,获得积分10
4秒前
5秒前
0verloader完成签到,获得积分10
5秒前
潘宋发布了新的文献求助20
5秒前
居居子完成签到,获得积分10
6秒前
kaka1981sdu完成签到,获得积分0
7秒前
GQ完成签到,获得积分10
8秒前
lebangzhanshi发布了新的文献求助10
9秒前
xu关注了科研通微信公众号
9秒前
lebangzhanshi发布了新的文献求助10
9秒前
9秒前
棟糖完成签到,获得积分10
9秒前
laola完成签到,获得积分10
9秒前
高分求助中
Principles of Economics, 11th Edition 10000
University Physics with Modern Physics, 16th edition 10000
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
48V Low-voltage Power Distribution Network (PDN) Architecture Industry Report, 2024 800
ズームレンズの光学設計に関する研究 800
Fundamentals of Pharmaceutical and Biologics Regulations: A Global Perspective, Second Edition 700
Matrix Methods in Data Mining and Pattern Recognition Second Edition 610
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 内科学 物理 复合材料 催化作用 细胞生物学 无机化学 光电子学 物理化学 电极 基因
热门帖子
关注 科研通微信公众号,转发送积分 7298539
求助须知:如何正确求助?哪些是违规求助? 8916989
关于积分的说明 18880573
捐赠科研通 6963638
什么是DOI,文献DOI怎么找? 3210680
关于科研通互助平台的介绍 2380000
邀请新用户注册赠送积分活动 2187188