Risk factor analysis and creation of an externally-validated prediction model for perioperative stroke following non-cardiac surgery: A multi-center retrospective and modeling study.

IF 15.8 1区 医学 Q1 Medicine
PLoS Medicine Pub Date : 2025-03-21 eCollection Date: 2025-03-01 DOI:10.1371/journal.pmed.1004539
Yulong Ma, Siyuan Liu, Faqiang Zhang, Xuhui Cong, Bingcheng Zhao, Miao Sun, Huikai Yang, Min Liu, Peng Li, Yuxiang Song, Jiangbei Cao, Yingfu Li, Wei Zhang, Kexuan Liu, Jiaqiang Zhang, Weidong Mi
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引用次数: 0

Abstract

Background: Perioperative stroke is a serious and potentially fatal complication following non-cardiac surgery. Thus, it is important to identify the risk factors and develop an effective prognostic model to predict the incidence of perioperative stroke following non-cardiac surgery.

Methods and findings: We identified potential risk factors and built a model to predict the incidence of perioperative stroke using logistic regression derived from hospital registry data of adult patients that underwent non-cardiac surgery from 2008 to 2019 at The First Medical Center of Chinese PLA General Hospital. Our model was then validated using the records of two additional hospitals to demonstrate its clinical applicability. In our hospital cohorts, 223,415 patients undergoing non-cardiac surgery were included in this study with 525 (0.23%) patients experiencing a perioperative stroke. Thirty-three indicators including several intraoperative variables had been identified as potential risk factors. After multi-variate analysis and stepwise elimination (P < 0.05), 13 variables including age, American Society of Anesthesiologists (ASA) classification, hypertension, previous stroke, valvular heart disease, preoperative steroid hormones, preoperative β-blockers, preoperative mean arterial pressure, preoperative fibrinogen to albumin ratio, preoperative fasting plasma glucose, emergency surgery, surgery type and surgery length were screened as independent risk factors and incorporated to construct the final prediction model. Areas under the curve were 0.893 (95% confidence interval (CI) [0.879, 0.908]; P < 0.001) and 0.878 (95% CI [0.848, 0.909]; P < 0.001) in the development and internal validation cohorts. In the external validation cohorts derived from two other independent hospitals, the areas under the curve were 0.897 and 0.895. In addition, our model outperformed currently available prediction tools in discriminative power and positive net benefits. To increase the accessibility of our predictive model to doctors and patients evaluating perioperative stroke, we published an online prognostic software platform, 301 Perioperative Stroke Risk Calculator (301PSRC). The main limitations of this study included that we excluded surgical patients with an operation duration of less than one hour and that the construction and external validation of our model were from three independent retrospective databases without validation from prospective databases and non-Chinese databases.

Conclusions: In this work, we identified 13 independent risk factors for perioperative stroke and constructed an effective prediction model with well-supported external validation in Chinese patients undergoing non-cardiac surgery. The model may provide potential intervention targets and help to screen high-risk patients for perioperative stroke prevention.

背景:围手术期中风是非心脏手术后严重且可能致命的并发症。因此,确定风险因素并建立有效的预后模型来预测非心脏手术围手术期中风的发生率非常重要:我们从中国人民解放军总医院第一医学中心 2008 年至 2019 年接受非心脏手术的成年患者的医院登记数据中识别了潜在的风险因素,并利用逻辑回归建立了一个预测围手术期脑卒中发生率的模型。随后,我们使用另外两家医院的记录对模型进行了验证,以证明其临床适用性。在我们的医院队列中,有 223415 名接受非心脏手术的患者被纳入本研究,其中有 525 名(0.23%)患者在围手术期发生卒中。包括多个术中变量在内的 33 个指标被确定为潜在风险因素。经过多变量分析和逐步剔除(P 结论),我们发现了 13 个独立的风险因素:在这项工作中,我们确定了 13 个围术期卒中的独立危险因素,并构建了一个有效的预测模型,该模型在中国非心脏手术患者中得到了充分的外部验证。该模型可提供潜在的干预目标,有助于筛查高危患者,预防围术期卒中的发生。
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来源期刊
PLoS Medicine
PLoS Medicine MEDICINE, GENERAL & INTERNAL-
CiteScore
17.60
自引率
0.60%
发文量
227
审稿时长
4-8 weeks
期刊介绍: PLOS Medicine is a prominent platform for discussing and researching global health challenges. The journal covers a wide range of topics, including biomedical, environmental, social, and political factors affecting health. It prioritizes articles that contribute to clinical practice, health policy, or a better understanding of pathophysiology, ultimately aiming to improve health outcomes across different settings. The journal is unwavering in its commitment to uphold the highest ethical standards in medical publishing. This includes actively managing and disclosing any conflicts of interest related to reporting, reviewing, and publishing. PLOS Medicine promotes transparency in the entire review and publication process. The journal also encourages data sharing and encourages the reuse of published work. Additionally, authors retain copyright for their work, and the publication is made accessible through Open Access with no restrictions on availability and dissemination. PLOS Medicine takes measures to avoid conflicts of interest associated with advertising drugs and medical devices or engaging in the exclusive sale of reprints.
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