血液恶性肿瘤患者中性粒细胞减少性败血症的预测:一项回顾性病例对照研究。

IF 1.7 4区 医学 Q2 NURSING
Clinical Nursing Research Pub Date : 2024-11-01 Epub Date: 2024-09-08 DOI:10.1177/10547738241273862
Jiwon Lee, Hee-Ju Kim
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引用次数: 0

摘要

中性粒细胞脓毒症(NS)是导致血液系统恶性肿瘤患者死亡的主要原因之一。确定其预测因素是早期发现的基础。很少有研究评估与微生物感染确认有关的预测因素,而微生物感染确认对于启动败血症治疗具有重要的临床意义。本研究旨在确定选定的生物标志物(即体温、C 反应蛋白、白蛋白、降钙素原)、治疗相关特征(即诊断、中性粒细胞减少持续时间、治疗方式)和感染相关特征(即感染源、致病菌)是否能预测血液系统恶性肿瘤患者的脓毒症。我们还旨在确定这些参数的最佳预测截断点。这项回顾性病例对照研究共使用了 163 例患者(败血症组 58 例,非败血症组 105 例)的数据。我们收集的数据以标本采集日为准,微生物感染在标本采集日得到确认。多重逻辑回归用于确定预测风险因素和最佳预测截断点的接收者操作特征曲线下面积(AUC)。NS的独立预测因素是发热时的平均体温和降钙素原水平。平均体温每升高1°C,发生NS的几率上升9.97倍(95% 置信区间,CI [1.33,75.05]);降钙素原水平每升高1纳克/毫升,发生NS的几率上升2.09倍(95% 置信区间,CI [1.08,4.04])。平均体温(AUC = 0.77,95% CI [0.68,0.87])和降钙素原水平(AUC = 0.71,95% CI [0.59,0.84])预测 NS 的准确性尚可,最佳临界点分别为 37.9°C 和 0.55 纳克/毫升。本研究发现,发热时的平均体温和降钙素原对预测 NS 有帮助。因此,护士应仔细监测血液恶性肿瘤患者的体温和降钙素原水平,以检测 NS 的发生。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Predicting Neutropenic Sepsis in Patients with Hematologic Malignancy: A Retrospective Case-Control Study.

Neutropenic sepsis (NS) is one of the leading causes of death among patients with hematologic malignancies. Identifying its predictive factors is fundamental for early detection. Few studies have evaluated the predictive factors in relation to microbial infection confirmation, which is clinically important for initiating sepsis treatment. This study aimed to determine whether selected biomarkers (i.e., body temperature, C-reactive protein, albumin, procalcitonin), treatment-related characteristics (i.e., diagnosis, duration of neutropenia, treatment modality), and infection-related characteristics (i.e., infection source, causative organisms) can predict NS in patients with hematologic malignancies. We also aimed to identify the optimal predictive cutoff points for these parameters. This retrospective case-control study used the data from a total of 163 patients (58 in the sepsis group and 105 in the non-sepsis group). We collected data with reference to the day of specimen collection, with which microbial infection was confirmed. Multiple logistic regression was used to determine predictive risk factors and the area under the curve (AUC) of the receiver operating characteristic for the optimal predictive cutoff points. The independent predictors of NS were average body temperature during a fever episode and procalcitonin level. The odds for NS rose by 9.97 times with every 1°C rise in average body temperature (95% confidence interval, CI [1.33, 75.05]) and by 2.09 times with every 1 ng/mL rise in the procalcitonin level (95% CI [1.08, 4.04]). Average body temperature (AUC = 0.77, 95% CI [0.68, 0.87]) and procalcitonin levels (AUC = 0.71, 95% CI [0.59, 0.84]) have fair accuracy for predicting NS, with the optimal cutoff points of 37.9°C and 0.55 ng/mL, respectively. This study found that average body temperature during a fever episode and procalcitonin are useful in predicting NS. Thus, nurses should carefully monitor body temperature and procalcitonin levels in patients with hematologic malignancies to detect the onset of NS.

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来源期刊
CiteScore
3.40
自引率
5.90%
发文量
107
审稿时长
>12 weeks
期刊介绍: Clinical Nursing Research (CNR) is a peer-reviewed quarterly journal that addresses issues of clinical research that are meaningful to practicing nurses, providing an international forum to encourage discussion among clinical practitioners, enhance clinical practice by pinpointing potential clinical applications of the latest scholarly research, and disseminate research findings of particular interest to practicing nurses. This journal is a member of the Committee on Publication Ethics (COPE).
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