智能外科引流——外科引流病人引流液的数字化分析。

IF 2.6 3区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
PLoS ONE Pub Date : 2025-07-28 eCollection Date: 2025-01-01 DOI:10.1371/journal.pone.0325072
Anastasia Meckler, Sebastian Künert, Leonardo Poggi, Julia Jeske, Lukas Schipper, Thanusiah Selvamoorthy, Felix Nensa, Bernadette Hosters, Michael Fabian Berger, Ramsi Siaj, Mario Vincent Roser
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引用次数: 0

摘要

手术引流管在术后护理中至关重要,及时清除引流管对预防并发症至关重要。早期切除可能导致血肿和血肿,而延迟切除可能导致感染。传统的人工分析漏液输出既耗时又不可靠,因此需要转向数字方法。本研究使用小型光谱仪快速、无创地分析手术引流液输出。该光谱仪工作在340- 850nm范围内,具有288个离散检测通道。从年龄0 ~ 85岁的181例患者中共采集528份样本。分析了14项实验室参数,包括白蛋白、淀粉酶、胆红素、总蛋白、LDH、脂肪酶、红细胞、血红蛋白和甘油三酯。在几个参数中观察到显著的相关性。本研究采用相关分析、回归分析和分类分析等方法,探讨了排液输出中各种生化实验室参数与其特定波长吸收峰之间的关系。通过认证的中心实验室的标准程序获得的数据与使用微型光谱仪收集的数据进行了比较。发现了显著的相关性,特别是血红蛋白和红细胞在586 nm处(分别r = -0.67和r = -0.46)。血红蛋白也与514 nm (r = -0.62)和557 nm (r = -0.45)的波长相关。胆红素在582 nm (r = 0.56)和496 nm (r = -0.49)处出现峰值。纳入随机效应的回归和分类模型提供了增强的性能。该分类模型有效区分病理值和非病理值,血红蛋白曲线下面积(AUC)为0.947,平衡精度(BAC)为0.853。甘油三酯的AUC为0.941,BAC为0.789。LDH、胆红素和红细胞模型的AUC值也超过0.9,BAC值超过0.79。这项研究证明了集成到手术引流管中的微型光谱仪的潜力,可以改善术后引流管理,与传统方法相比,可能提供更快、更可靠的分析。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Intelligent surgical drainage - digitizing the analysis of drainage fluid in patients with surgical drains.

Surgical drains are essential in post-operative care, where timely removal is critical to prevent complications. Early removal can result in seromas and hematomas, while delayed removal may lead to infections. Traditional manual analysis of drain output is time-consuming and often unreliable, necessitating a shift towards digital methods. This study used a compact mini-spectrometer to analyze surgical drain output quickly and non-invasively. The spectrometer operates in the 340-850 nm range with 288 discrete detection channels. A total of 528 samples were collected from 181 patients aged 0-85 years. Fourteen laboratory parameters, including albumin, amylase, bilirubin, total protein, LDH, lipase, erythrocytes, hemoglobin, and triglycerides were analyzed. Notable correlations were observed for several parameters. This study employed correlation, regression and classification analyses to investigate the relationships between various biochemical laboratory parameters in drain output and their absorption peaks at specific wavelengths. The data obtained from standard procedures in a certified central laboratory were compared with data collected using the mini-spectrometer. Significant correlations were found, particularly for hemoglobin and erythrocytes at 586 nm (r = -0.67 and r = -0.46, respectively). Hemoglobin also correlated with wavelengths at 514 nm (r = -0.62) and 557 nm (r = -0.45). Bilirubin showed peaks at 582 nm (r = 0.56) and 496 nm (r = -0.49). Regression and classification models, incorporating random effects, provided enhanced performance. The classification models effectively differentiated between pathological and non-pathological values, with hemoglobin showing an area under the curve (AUC) of 0.947 and a Balanced Accuracy (BAC) of 0.853. Triglycerides had an AUC of 0.941 and a BAC of 0.789. Models for LDH, bilirubin, and erythrocytes also achieved AUC values over 0.9, with BAC values exceeding 0.79. This study demonstrates the potential of mini-spectrometers integrated into surgical drains to improve post-operative drainage management, potentially offering faster, more reliable analyses compared to traditional methods.

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来源期刊
PLoS ONE
PLoS ONE 生物-生物学
CiteScore
6.20
自引率
5.40%
发文量
14242
审稿时长
3.7 months
期刊介绍: PLOS ONE is an international, peer-reviewed, open-access, online publication. PLOS ONE welcomes reports on primary research from any scientific discipline. It provides: * Open-access—freely accessible online, authors retain copyright * Fast publication times * Peer review by expert, practicing researchers * Post-publication tools to indicate quality and impact * Community-based dialogue on articles * Worldwide media coverage
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