Meckler Anastasia, Künert Sebastian, Poggi Leonardo, Jeske Julia, Schipper Lukas, Selvamoorthy Thanusiah, Nensa Felix, Hosters Bernadette, Berger Michael Fabian, Siaj Ramsi, Roser Mario Vincent
Department of Pediatric Surgery, University Hospital Essen, Germany.
Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Germany.
PLoS One. 2025 Jul 28;20(7):e0325072. doi: 10.1371/journal.pone.0325072. eCollection 2025.
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.
手术引流管在术后护理中至关重要，及时拔除对于预防并发症至关重要。过早拔除可能导致血清肿和血肿，而延迟拔除可能导致感染。传统的手动分析引流液输出既耗时又往往不可靠，因此需要转向数字方法。本研究使用紧凑型微型光谱仪快速且无创地分析手术引流液输出。该光谱仪在340 - 850纳米范围内运行，有288个离散检测通道。共从181名年龄在0至85岁的患者中收集了528个样本。分析了包括白蛋白、淀粉酶、胆红素、总蛋白、乳酸脱氢酶、脂肪酶、红细胞、血红蛋白和甘油三酯在内的14项实验室参数。观察到几个参数之间存在显著相关性。本研究采用相关性、回归和分类分析来研究引流液输出中各种生化实验室参数与其在特定波长下的吸收峰之间的关系。将在经过认证的中央实验室通过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。发现了显著相关性，特别是血红蛋白和红细胞在586纳米处（r分别为 - 0.67和 - 0.46）。血红蛋白在514纳米（r = - 0.62）和557纳米（r = - 0.45）处也与波长相关。胆红素在582纳米（r = 0.56）和496纳米（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更好的性能。分类模型有效地区分了病理值和非病理值，血红蛋白的曲线下面积（AUC）为0.947，平衡准确率（BAC）为0.853。甘油三酯的AUC为0.941，BAC为0.789。乳酸脱氢酶、胆红素和红细胞的模型也实现了AUC值超过0.9，BAC值超过0.79。本研究证明了集成到手术引流管中的微型光谱仪在改善术后引流管理方面的潜力，与传统方法相比，可能提供更快、更可靠的分析。
