利用心率和脉搏灌注变异指数构建宫颈癌患者脊髓硬膜外麻醉低血压预测模型

IF 3.6 3区 医学 Q2 ONCOLOGY
American journal of cancer research Pub Date : 2024-09-15 eCollection Date: 2024-01-01 DOI:10.62347/WPPP9827
Chunping Xing, Gaolin Ji, Dongbo Zhang, Xiao Qin, Li Zhang, Cuiyun Yan
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引用次数: 0

摘要

对接受脊髓硬膜外麻醉的宫颈癌患者采取的预防和治疗策略越来越侧重于早期筛查与潜在低血压相关的高危因素。我们对 312 名接受脊髓硬膜外麻醉的宫颈癌患者的一般情况和术前检查结果进行了分析,以确定低血压的独立风险因素,评估其预测效果,并构建一个提名图。本研究共纳入 312 名接受脊髓硬膜外麻醉的宫颈癌患者。其中,164 名患者在脊髓硬膜外麻醉子宫切除术后出现低血压,属于低血压组。通过单变量和多变量分析确定了脊髓硬膜外麻醉子宫切除术后低血压的重要风险预测因素,然后构建了临床提名图。预测准确性通过未调整的一致性指数(C-index)和校准图进行评估。单变量和多变量回归分析发现,基础 HR (≥95) (95% CI 0.831-0.900; P = 0.000) 和基础 PVI (95% CI 0.679-0.877; P = 0.000) 是脊柱硬膜外麻醉的宫颈癌患者出现低血压的独立风险因素。这些风险因素被用于构建临床预测提名图。基于上述因素的回归方程模型为 logit (P) = -6.820 + 0.216 * 基本 HR + 基本 PVI * 0.312。低血压风险校准曲线显示,预测提名图模型非常准确。决策曲线分析表明,当阈值概率为 20% 至 75% 时,预测模型可用于临床。我们推测,基础 HR 值和 PVI 值是宫颈癌患者脊髓硬膜外麻醉低血压的独立风险因素。建立提名图有利于预测这些患者的低血压风险。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Construction of nomogram prediction model using heart rate and pulse perfusion variability index as predictors for hypotension in cervical cancer patients with spinal epidural anesthesia.

The prevention and treatment strategies for cervical cancer patients undergoing spinal epidural anesthesia have increasingly focused on early screening for high-risk factors associated with potential hypotension. We analyze the general conditions and preoperative examination results of 312 cervical cancer patients who received spinal epidural anesthesia, in order to identify independent risk factors for hypotension, assess their predictive efficacy, and construct a nomogram. 312 patients with cervical cancer received spinal epidural anesthesia were included in this study. Among them, 164 patients with hypotension after hysterectomy with spinal epidural anesthesia were in a hypotension group. Important risk predictors of hypotension after hysterectomy with spinal epidural anesthesia were identified using univariate and multivariate analyses, then a clinical nomogram was constructed. The predictive accuracy was assessed by unadjusted concordance index (C-index) and calibration plot. Univariate and multivariate regression analysis identified basal HR (≥95) (95% CI 0.831-0.900; P = 0.000) and basal PVI (95% CI 0.679-0.877; P = 0.000) were the independent risk factors for hypotension in cervical cancer patients with spinal epidural anesthesia. Those risk factors were used to construct a clinical predictive nomogram. The regression equation model based on the above factors was logit (P) = -6.820 + 0.216 * basal HR + basic PVI * 0.312. The calibration curves for hypotension risk revealed excellent accuracy of the predictive nomogram model. Decision curve analysis showed that the predictive model could be applied clinically when the threshold probability was 20 to 75%. We surmised that the basal HR values and PVI values are the independent risk factors for hypotension in cervical cancer patients with spinal epidural anesthesia. The construction of nomograms is beneficial in predicting the risk of hypotension in these patients.

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来源期刊
自引率
3.80%
发文量
263
期刊介绍: The American Journal of Cancer Research (AJCR) (ISSN 2156-6976), is an independent open access, online only journal to facilitate rapid dissemination of novel discoveries in basic science and treatment of cancer. It was founded by a group of scientists for cancer research and clinical academic oncologists from around the world, who are devoted to the promotion and advancement of our understanding of the cancer and its treatment. The scope of AJCR is intended to encompass that of multi-disciplinary researchers from any scientific discipline where the primary focus of the research is to increase and integrate knowledge about etiology and molecular mechanisms of carcinogenesis with the ultimate aim of advancing the cure and prevention of this increasingly devastating disease. To achieve these aims AJCR will publish review articles, original articles and new techniques in cancer research and therapy. It will also publish hypothesis, case reports and letter to the editor. Unlike most other open access online journals, AJCR will keep most of the traditional features of paper print that we are all familiar with, such as continuous volume, issue numbers, as well as continuous page numbers to retain our comfortable familiarity towards an academic journal.
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