Intelligent surgical drainage - digitizing the analysis of drainage fluid in patients with surgical drains

Anastasia Meckler, Sebastian Künert, Leonardo Poggi, Julia Jeske, Lukas Schipper, Thanusiah Selvamoorthy, Felix Nensa, Bernadette Hosters, Michael Fabian Berger, Ramsi Siaj and Mario Vincent Roser

PLOS ONE, 2025, vol. 20, issue 7, 1-17

Abstract: 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.

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0325072

DOI: 10.1371/journal.pone.0325072

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