Pre-analytical validity of arterial blood gas samples: A prospective experimental study on changes derived from time delay and mechanical stress

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Our study aimed to investigate the effect of prolonged time before analysis and mechanical manipulation on pre-analytical stability of biomarkers and thus the validity of the results of blood gas analyses. Methods: We collected blood samples from 240 patients in a university surgical intensive care unit. These samples were immediately analyzed following the clinical standard operating procedures. Subsequently, the sample containers were allowed to rest for 60 min, then subjected to standardized mechanical forces, and analyzed again. We analyzed 13 typical blood gas biomarkers, comprising respiratory gases, electrolytes, and protein biomarkers. Bland–Altman plots were prepared to analyze the differences between the test runs. The differences between the test groups were compared against the official limits of accuracy specified in the German requirements for quality assurance of medical laboratory tests. Results: For hemoglobin, creatinine, glucose, and electrolytes (including calcium, sodium, chlorine, and bicarbonate), the agreement between the immediate and post-interference-treatment analyses was within the ranges specified in the official requirements. For pH and potassium, the deviations were outside the quality assurance ranges but within a clinically acceptable measurement accuracy. Only oxygen partial pressure and lactate levels were altered to such an extent that they can no longer be used for clinical purposes. Conclusion: Even after a 60 minutes time delay and excessive mechanical stress, selected blood gas analysis biomarkers such as Hemoglobin, Glucose, Sodium, Calcium, Chloride, and Bicarbonate could be considered valid. Potassium and pCO2 were altered but suitable for approximation purposes. Findings for pO2 and Lactate were generally incorrect. In the future, in selected settings, these findings can aid in reducing unnecessary blood sampling in vulnerable patients. Blood gas analysis time delay mechanical stress intensive care unit biomarkers oxygen partial pressure Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 Background In anesthesia and intensive care medicine, blood gas analysis is a basic diagnostic tool for at-risk and critically ill patients. Careful handling of the samples after collection and immediate analysis are accepted standards. Nevertheless, numerous situations occur in everyday clinical practice that could lead to unintentional delays in the analysis or external interference with samples. These situations could include lengthy transport routes and equipment downtime owing to maintenance, human negligence, or mechanical interference caused by mishandling. The current literature does not answer whether samples remain suitable for analysis after prolonged periods and/or mechanical interference. This uncertainty often prompts clinicians to obtain new blood samples, incurring costs, time, and potential patient discomfort and harm associated with renewed blood loss or repeated vascular punctures [ 1 , 2 ]. Existing literature has only focused on certain aspects of the pre-analytical stability of blood gas samples. For example, Knowles and Harsten detected changes in partial pressures of respiratory gases (pO 2 and pCO 2 ) 30 and 60 min post-collection. Moreover, Smeenk et al. [ 3 ] described prolonged stability of pO 2 when using glass syringes or ice-water storage. However, beyond respiratory gases, modern blood gas analyzers measure various clinically relevant analytes, of ionic or organic-small-molecular character (for example, c(sodium), c(potassium), c(calcium), c(glucose), and c(lactate)). Existing data on the possible pre-analytical influence of these numerous parameters are limited. In a recent study, we demonstrated that time delays and mechanical stress on venous blood samples in an out-of-hospital emergency medical service setting did not lead to any clinically relevant alterations in various electrolytes, small organic molecules, or protein biomarkers [ 4 ]. Therefore, this study aimed to investigate the effect of prolonged time before analysis and mechanical manipulation on pre-analytical stability and the validity of the results of blood gas analyses. We aimed to answer the following questions: First, whether changes are systematic or erratic, statistically significant, or clinically relevant. Second, whether in terms of data quality, results derived from delayed analysis or samples subjected to mechanical stress remain suitable for clinical purposes and decision-making under pertinent clinical circumstances. Material and methods Aim, design, and setting This study aimed to investigate the effect of prolonged time before analysis and mechanical manipulation on pre-analytical stability and the validity of the results of blood gas analyses. The Ethics Committee of the Friedrich-Alexander University, Erlangen-Nuremberg, Germany, reviewed and approved the study proposal (vote 22-124-B). Prospective participants or their legal representatives provided both verbal and written consent in advance and participated voluntarily in the study. The study population comprised patients from all surgical specialties who were treated at our interdisciplinary tertiary-level intensive care unit. Pregnant women and minors were excluded from participation. Blood samples The procedure for collecting blood samples was standardized, ensuring compliance with the syringe manufacturer's specified product manuals and in-house standard operating procedures regarding hygiene and workplace safety. Blood samples were collected from arterial catheters, using a "safe Pico Aspirator" from Radiometer Medical ApS, Brønshøj, Denmark. Subsequently, the samples were analyzed using blood gas analyzers (ABLflex800) (Radiometer Medical ApS, Brønshøj, Denmark) located within the intensive care unit. Table 1 provides a list of the measured parameters, including the type of analyzer, analytical methods, and legally required accuracy of analysis. Parameters are divided into two groups: those directly measured from the sample, such as pH, pCO 2 , pO 2 , ctHb, cK + , cNa, cCl, cCa 2+ , cGluc, cCrea, and cLac, and those derived via calculation, such as HCO 3 and SO 2 . The case number was not estimated in advance. Instead, it was derived based on local resources and general practicability. Table 1 Biomarkers and analyzers, analytical methods, and quality criteria. Biomarkers Analyzer model Manufacturer Test method Frequency of quality controls (three shifts per day) Acceptable Root Mean Standard Deviation as defined by Rili-Beak Range of intrest as defined by Rili- Baek Unit Calcium ABL800 Flex Radiometer Medical ApS, Brønshøj, Denmark Potentiometric method Once per 8 h shift 14% 7.5% 0.2 to ≤ 1 > 1–2.5 mmol/L Chlorides ABL800 Flex Radiometer Medical ApS, Brønshøj, Denmark Potentiometric method Once per 8 h shift 4.5% 70–150 mmol/L Creatinine ABL800 Flex Radiometer Medical ApS, Brønshøj, Denmark Potentiometric method Once per 8 h shift 11.5 0.5–10 mg/L Glucose ABL800 Flex Radiometer Medical ApS, Brønshøj, Denmark Potentiometric method Once per 8 h shift 11% 40–400 2.2–22 mg/dL mmol/L Hemoglobin ABL800 Flex Radiometer Medical ApS, Brønshøj, Denmark Potentiometric method Once per 8 h shift 4.0% 2–20 g/dL Potassium ABL800 Flex Radiometer Medical ApS, Brønshøj, Denmark Potentiometric method Once per 8 h shift 4.5% 2–8 mmol/L Lactate ABL800 Flex Radiometer Medical ApS, Brønshøj, Denmark Potentiometric method Once per 8 h shift 11.0% 1–10 mmol/L Sodium ABL800 Flex Radiometer Medical ApS, Brønshøj, Denmark Potentiometric method Once per 8 h shift 3.0% 110–180 mmol/L pCO 2 ABL800 Flex Radiometer Medical ApS, Brønshøj, Denmark Potentiometric method Once per 8 h shift 7.5% 6.5% ≤ 35 > 35 mmHg pH ABL800 Flex Radiometer Medical ApS, Brønshøj, Denmark Potentiometric method Once per 8 h shift 0.4% 6.75–7.80 pO 2 ABL800 Flex Radiometer Medical ApS, Brønshøj, Denmark Potentiometric method Once per 8 h shift 5.5% 7.0% 11.0% > 125–350 > 80 to ≤ 125 40 to ≤ 80 mmHg HCO 3 ABL800 Flex Radiometer Medical ApS, Brønshøj, Denmark calculated SO 2 ABL800 Flex Radiometer Medical ApS, Brønshøj, Denmark calculated For further detailed information on laboratory medical examination methods, we recommend consulting the manufacturer's user manual and the quality criteria prescribed by the German Medical Association available on the Rili-Baek homepage ( https://www.bundesaerztekammer.de/themen/aerzte/qualitaetssicherung/richtlinien-leitlinien-empfehlungen-stellungnahmen ) Test scenario Blood samples were sequentially tested for the first time immediately after collection and for the second time 60 min post-mechanical stress. First, blood samples were collected as a part of routine clinical practice. A first blood gas analysis was performed immediately after the samples were obtained [ 5 ]; the remaining quantity of blood was not discarded but preserved. For this purpose, the sample containers were emptied of residual air and stored on a tray next to the blood gas analyzer at room temperature during the waiting period between the first and second analyses. They were not shielded from sunlight. Before the second analysis, the tubes were subjected to a standardized mechanical stress scenario. Each sample was dropped 10 times from a height of 85 cm (table height) and shaken vigorously 10 more times. The same samples were analyzed the second time to compare the paired results 60 min after the first analysis. The results of the second series of measurements were used only for this study and not for any clinical decisions. Data analysis and statistics Bland–Altman plots were used to compare the combined effects of mechanical stress and time delay on arterial blood gas samples [ 6 ]. Values from blood samples that were directly analyzed were compared with those analyzed after 60 min delay and mechanical manipulation. Every Bland–Altman plot containes several lines. The central solid line in the graph represents the mean values of the differences in measured parameters. The two dashed lines above and below the solid line represent the upper and lower limits of agreement, respectively, which correspond to a maximum 1.96-fold deviation from the mean value upward and downwards. Typically, 95% of the measured values were within these limits. As an external criterion for determining negligible differences, we used the specified validity ranges of the respective biomarkers following the German Medical Association guidelines for the quality assurance of laboratory medical examinations (Rili-Baek) [ 7 ]. Rili-Baek specifies the basic requirements for the quality management and assurance of laboratory medical examinations in Germany. Among other factors, the validity of individual test results within their respective validity ranges is defined. Hence, we assumed that the results can be regarded as equivalent and deviations negligible if they lie within the error margins of the Rili-Baek [ 7 ]. The validity ranges are shown in the diagrams by spreading green lines. The analysis was performed using SPSS and Microsoft Excel (IBM SPSS Statistics Version: 28.0.1.1(15)/ Microsoft® Excel® 2019 MSO (16.0.14332.20563) 32-Bit). Results In total, 240 blood samples were collected. Thirteen biomarkers were analyzed, 11 of which were directly measured using the device, and two were derived via calculation. No missing data were reported. Each analyzed parameter was investigated for differences between the measurements using Bland–Altman plots. Figures 1 – 14 present the graphs for each parameter. Table 2 provides an overview of all the parameters, standard deviations, and confidence intervals. Table 2 Overview of analyzed biomarkers Biomarkers in alphabetical order Unit Number of measurements Mean of differences CI Mean Upper limit of agreement CI Upper limit of agreement Lower limit of agreement CI lower limit of agreement Calcium mmol/l 240 -0.0073 -0.0074 0.035 -0.0045 -0.049 -0.010 Chlorides mmol/l 240 0.104 0.1019 2.836 0.281 -2.628 -0.073 Creatinine mg/L 239 -0.002 -0.0054 0.162 0.009 -0.166 -0.013 Glucose mg/L 240 -4.675 -4.8704 4.401 -4.086 -13.751 -5.263 Hemoglobin g/dL 240 0.098 0.0806 0.518 0.1260 -0.321 0.072 Potassium mmol/L 240 0.2189 0.2067 0.619 0.245 -0.181 0.193 Lactate mmol/L 240 0.609 0.6047 1.074 0.639 0.145 0.579 Sodium mmol/L 240 -0.077 -0.963 2.465 0.088 -2.619 -0.242 pCO 2 mmHg 240 1.196 1.2208 1.499 1.372 -3.893 1.022 pH 240 -0.017 -0.0171 0.044 -0.015 -0.009 -0.019 pO 2 mmol/L 240 33.740 33.7935 80.456 36.771 -12.976 30.709 HCO 3 mmol/L 240 -0.373 -0.4005 0.748 -0.300 -1.494 -0.4461 SO 2 % 240 1.855 1.7199 6.184 2.136 -2.475 1.577 We discovered that the deviations in the results of all parameters except K+, lactate, pCO 2 , and pO 2 were within the specified limits of Rili-BaeK and the standard deviation of the Bland-Altman plots. The measured values for K+, lactate, PCO 2 , and PO 2 indicated that deviations in the results were within the standard deviation of the Bland–Altman plot. However, we observed a substantial deviation from the validity criteria specified by Rili-Baek. Even a 10% deviation corridor of the measured values from the original values, which we introduced based on possible clinical decision-making strategies, could often not be met. For potassium, 52.5% (126/240) of the values fell within the limits specified by Rili-Baek. Additionally, 88.3% (212/240) of the measured results were within the proposed 10% margin of deviation. Furthermore, 93.75% (225/240) of measurements showed an overall increase in potassium concentration. The measured values for lactate were rarely (1/240: 0.4%) within the validity range specified by Rili-Baek. Similarly, only 1 out of 240 values fell within an additional 10% corridor. Moreover, 99.6% (239/240) of the measured values showed an increase in lactate concentration. Regarding the measured values of pCO 2 , we discovered that 80.4% (202/240) were within the required measurement ranges of the Rili-Baek. Moreover, 97.9% (235/240) of the values fell within the 10% corridor. In total, 85.4% (205/240) showed an overall increase in partial pressure. The results for the measured pO 2 values revealed that 5.4% (13/240) were within the Rili-Baek limits, and 7.9% (19/240) were within the additional 10% corridor. In total, 95% (228/240) showed an overall increase in partial pressure. The results for the measured pH values showed that 84.5% (203/240) were within the Rili-Baek limits, with 100% (240/240) falling within the additional 10% corridor. In total, 81.6% (196/240) showed an overall decrease in the measured value. The results for the measured glucose values indicated that 98.75% (237/240) were within the margin of error specified by the Rili-Baek. Furthermore, 89.2% (214/240) showed a decrease in the measured value. Discussion Careful handling and immediate analysis of blood samples are medical standards. However, in the bustling and stressful environment of an intensive care unit, blood samples might unintentionally undergo rule violations. The novel findings of this study demonstrate that many biomarkers from an arterial blood gas sample are robust against pre-analytical mechanical stress or time delay before analysis. To that end, we compared the test results of an arterial blood gas analysis (BGA) after immediate analysis with those from the same sample after a combination of a defined time delay and simulated mechanical stress. We deliberately exaggerated both confounding variables (time and mechanical forces) to a level greater than expected under real-life conditions. The rationale for this experimental approach was that biomarkers proving stable against supramaximal scenarios would certainly also remain stable against stressors in everyday circumstances. In our study, the specifications for technical quality control of the analyzers, in accordance with Rili-Baek, were used to define what would constitute a negligible deviation. Deviations of the measured values that were within the methodological measurement inaccuracy were regarded as clinically equivalent. Therefore, a subjective interpretation of values, prone to bias, was initially unnecessary. The most common blood gas analysis parameters were included in the dataset. The results are interpreted in three different ways. 1. Dissolved gases undergo relevant diffusion processes across the walls of the sample containers. Based on the partial pressure differences between the blood and room air, a net inflow of oxygen from the room air into the sample and a net outflow of carbon dioxide from the sample into the room air occurs [8/9]. In most cases, the increase in pO 2 exceeded the margin specified in Rili–Baek. Substantial oxygen enrichment of the samples (mean + 38 mmHg) without any meaningful relation to the patient's condition was observed. Therefore, an interpretation of the oxygenation performance of the patients was categorically impossible in our test scenario. For CO 2 , we observed a slight average increase in the partial pressure in the sample vessel (mean +1.2 mmHg; upper limit of agreement < 4 mmHg). At first glance, this observation appears to contradict the net CO 2 loss from the containers we described earlier. However, as a result of the substantial increase in hemoglobin oxygenation in the sample vessel, the CO 2 previously bound to hemoglobin was released. This so-called Haldane effect obviously outweighs the diffusion loss during the investigation period, and the sum of these effects results in a slight increase in the pCO 2 of the sample [10]. These changes in pCO 2 were mostly within a 10% deviation margin that we set suggestively. We believe that such a +/- 10% corridor often is part of clinical decision-making patterns. As a consequence, a discussion is warranted whether the observed moderate changes in pCO 2 values should be viewed as numerically different but clinically similar and could therefore be used as an approximation of decarboxylation. 2. Some biomarkers of the erythrocyte metabolism (mean lactate +0.6 mmol/L; mean potassium +0.2 mmol/L) were also subject to changes outside the margin of error specified in the Rili-BAEK. While changes in lactate were far beyond a clinically acceptable margin of error, the majority of potassium measurements lay within the above-mentioned 10% corridor. Hence, we would like to spark a discussion, whether clinicians would still view these changes as an acceptable inaccuracy and should utilize those potassium values for therapeutic decisions. 3. Hemoglobin, creatinine, glucose, and electrolytes that are not closely related to erythrocyte metabolism are within the specified accuracy limits except for a few individual observations. Therefore, the results can be regarded as equivalent, justifying the use of these parameters for clinical decision-making, even after time delay and mechanical disturbance. In summary, it is impossible to draw a uniform picture regarding the pre-analytical validity of our ill-treated blood samples. A differentiated interpretation of each biomarker based on its biochemical and physicochemical properties is required. Naturally, our study has some strengths and weaknesses. One strength of our interpretative approach lies in using the purely technically derived error margins of the Rili-Baek as a basis for deciding whether value deviations should be regarded as acceptable or un-acceptable. Parameters that were within this predefined margin of error, even after our intervention, can be regarded as reliable with a high degree of probability and can be used for clinical decision-making. However, some parameters fell into a gray area outside the interpretation certainty of the Rili-Baek margins but they remained within a clinically justified 10% error corridor. Consequently, the interpretation of these values remains debatable. One limitation of this study is the incomplete selection of only a few standardized confounding factors. Other environmental factors that were not part of our experimental setup, such as temperature fluctuations and ultraviolet radiation, might also affect the pre-analytical stability of blood samples to an unknown extent. Finally, it might be possible that the excessive degree of rule violations in sample treatment at the supra-everyday level was responsible for the large deviations in results for some biomarkers. One has to wonder, whether a milder form of treatment (for example, only a 30-min delay, only one drop) might have led to smaller changes, closer to Rili-Baek and thus to results that would have been clearer to interpret. Conclusion In our study, we demonstrated that time delay and mechanical stress before analysis of arterial blood gases had no relevant influence on the results for protein biomarkers (Hemoglobin, Creatinine), Glucose and certain electrolytes (Sodium, Calcium, Chloride, HCO3). pCO2 and Potassium are altered, but results might be considered for approximation purposes. Measurements of oxygenation (pO2) and lactate levels are generally incorrect after introducing time delay and mechanical stress. Based on our data, it is not necessary for selected clinical questions to repeat a BGA if there was time delay or mechanical irritation of the sample before analysis. Despite its limitations, our study makes a valuable contribution to reducing unnecessary blood sampling in patients in the intensive care unit. Future follow-up studies should consider the differential effects of time, mechanical force, or other factors on the pre-analytical stability of blood samples. Abbreviations Rili-BAEK, Guidelines of the German Medical Association for Quality Assurance in Medical Laboratory Testing Declarations Ethics approval and consent to participate The Ethics Committee of Friedrich-Alexander-University, Erlangen-Nuremberg, Germany, approved this study under reference Number 22-124-B. Written informed consent was obtained from all participants before the experiments. Consent for publication Not applicable Availability of data and materials The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding Not applicable. Authors' contributions MG conducted the experiments and performed statistical analyses. The present work was performed in fulfillment of the requirements for MG to obtain the academic degree "Dr. med." HI performed statistical analysis and critically revised the manuscript for intellectual content. FK and AM coordinated the study and critically revised the intellectual content of the manuscript. JP conceived the study and performed statistical analyses. All authors have read and approved the final manuscript. Acknowledgments We thank all volunteers for their contributions to our study. Author’s information 1 Anesthesia group practice Bernard and Weber, 91054 Erlangen, Germany 2 Department of Anesthesiology, Erlangen University Hospital, 91054 Erlangen, Germany. 3 Faculty of Medicine, Friedrich Alexander University, Erlangen-Nuremberg, Erlangen, Germany. 4 Department of Anesthesiology and Critical Care, Klagenfurt Hospital, 9020 Klagenfurt, Austria *Corresponding author References Neef V, Himmele C, Piekarski F, Blum LV, Hof L, Derwich W, Holubec T, Meybohm P, Choorapoikayil S. Effect of using smaller blood volume tubes and closed blood collection devices on total blood loss in patients undergoing major cardiac and vascular surgery. Can J Anaesth. 2024;71:213-23. Prof Patrick Meybohm, MD: Patient Blood Management. https://www.patientbloodmanagement.de/pbm-informationen-fuer-aerzte/. Assessed 29 February 2024. Smeenk FW, Janssen JD, Arends BJ, Harff GA, van den Bosch JA, Schönberger JP, Postmus PE. Effects of four different methods of sampling arterial blood and storage time on gas tensions and shunt calculation in the 100% oxygen test. Eur Respir J. 1997;10:910-3. Prottengeier J, Jess N, Harig F, Gall C, Schmidt J, Birkholz T. Can we rely on out-of-hospital blood samples? A prospective interventional study on the pre-analytical stability of blood samples under prehospital emergency medicine conditions. Scand J Trauma Resusc Emerg Med. 2017;25:24. Schinko H, Funk GC, Meschkat, M, Lamprecht B . Arterial blood gas analysis. Wien Klin. Wochenschr Educ . 2017;12: 115-30. Bland JM, Altman DG. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet. 1986;1:307-10. German Medical Association. Guideline of the German Medical Association for the quality assurance of laboratory medical examinations - Rili-BÄK. Dtsch Arztebl 2014;111:A1583-1618. Gruber MA, Felbermeir S, Lindner R, Kieninger M. Preanalytics: The (in-)stability of volatile POCT parameters and the homogeneity of blood in syringes at the market. Clin Chim Acta. 2016;457:18-23. Knowles TP, Mullin RA, Hunter JA, Douce FH. Effects of syringe material, sample storage time, and temperature on blood gases and oxygen saturation in arterialized human blood samples. Respir Care. 2006;51:732-6. Tyuma I. The Bohr effect and the Haldane effect in human hemoglobin. Jpn J Physiol. 1984;34:205-16. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4319836","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":301449271,"identity":"e3a05fc5-e2f7-4d2c-afe7-b39220e545f8","order_by":0,"name":"Max Gutermuth","email":"","orcid":"","institution":"Anesthesia group practice Bernard and Weber","correspondingAuthor":false,"prefix":"","firstName":"Max","middleName":"","lastName":"Gutermuth","suffix":""},{"id":301449272,"identity":"b6c8c1ad-331c-47f5-8638-7b0b11fb5b9f","order_by":1,"name":"Harald Ihmsen","email":"","orcid":"","institution":"Friedrich Alexander University","correspondingAuthor":false,"prefix":"","firstName":"Harald","middleName":"","lastName":"Ihmsen","suffix":""},{"id":301449274,"identity":"b6d49932-cbeb-4eb6-862f-e9eba3cead04","order_by":2,"name":"Frederick Krischke","email":"","orcid":"","institution":"Friedrich Alexander University","correspondingAuthor":false,"prefix":"","firstName":"Frederick","middleName":"","lastName":"Krischke","suffix":""},{"id":301449277,"identity":"bb6d667a-b5a4-4ce8-9065-f04a172a794e","order_by":3,"name":"Andreas Moritz","email":"","orcid":"","institution":"Friedrich Alexander University","correspondingAuthor":false,"prefix":"","firstName":"Andreas","middleName":"","lastName":"Moritz","suffix":""},{"id":301449278,"identity":"c8d492fc-71bc-4d44-85d9-b3897e863b7b","order_by":4,"name":"Johannes Prottengeier","email":"data:image/png;base64,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","orcid":"","institution":"Friedrich Alexander University","correspondingAuthor":true,"prefix":"","firstName":"Johannes","middleName":"","lastName":"Prottengeier","suffix":""}],"badges":[],"createdAt":"2024-04-24 17:55:56","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4319836/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4319836/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":56479754,"identity":"302928ee-0893-4c61-b450-8881c69661a2","added_by":"auto","created_at":"2024-05-14 18:07:27","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":218224,"visible":true,"origin":"","legend":"\u003cp\u003eGlucose mg/dL\u003c/p\u003e\n\u003cp\u003eBland–Altman plots for selected biomarkers. The difference in measurements is plotted against the average value of both associated measurements. The margins of accuracy, as specified in Guidelines of the German Medical Association for Quality Assurance in Medical Laboratory Testing (Rili-Baek), are drawn as sloping lines. The agreement is high, with random variations, and no systemic bias attributable to treatment was detected. Inaccuracy remains well within the limits outlined in Rili-Baek and the acceptance range for clinical interpretation.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4319836/v1/5834d3a0921bda88babfff75.jpg"},{"id":56479324,"identity":"bf49170f-f9da-4b08-afc6-a413c373715c","added_by":"auto","created_at":"2024-05-14 17:59:27","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":181874,"visible":true,"origin":"","legend":"\u003cp\u003eGlucose mmol/L\u003c/p\u003e\n\u003cp\u003eBland–Altman plots for selected biomarkers. The difference in measurements is plotted against the average value of both associated measurements. The margins of accuracy, as specified in Rili-Baek, are drawn as sloping lines. The agreement is high, with random variations, and no systemic bias attributable to treatment was detected. Inaccuracy remains well within the limits outlined in Rili-Baek and within tolerance of clinical interpretation.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4319836/v1/a452ca98dd58574343131346.jpg"},{"id":56479756,"identity":"558b482f-1cee-448b-b0fe-d4612ea7e152","added_by":"auto","created_at":"2024-05-14 18:07:28","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":182110,"visible":true,"origin":"","legend":"\u003cp\u003eHb\u003c/p\u003e\n\u003cp\u003eBland–Altman plots for selected biomarkers. The difference in measurements is plotted against the average value of both associated measurements. The margins of accuracy, as specified in Rili-Baek, are drawn as sloping lines. The agreement is high, with random variations, and no systemic bias attributable to treatment was detected. Inaccuracy remains well within the limits specified in Rili-Baek and the tolerance of clinical interpretation.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4319836/v1/a39a0fcbcbf46ae424a77be0.jpg"},{"id":56480094,"identity":"b0485a86-95ba-446d-a91b-51cb1c4a980d","added_by":"auto","created_at":"2024-05-14 18:15:27","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":215031,"visible":true,"origin":"","legend":"\u003cp\u003eCl\u003c/p\u003e\n\u003cp\u003eBland–Altman plots for selected biomarkers. The difference in measurements is plotted against the average value of both associated measurements. The margins of accuracy, as specified by Rili-Baek, are drawn as sloping lines. The agreement is high, with random variations, and no systemic bias attributable to treatment was detected. Inaccuracy remains well within the limits outlined by the Rili-Baek and tolerance of clinical interpretation.\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4319836/v1/32e31f495c2480883f3f4cea.jpg"},{"id":56479321,"identity":"e8d14ac0-d46d-496d-9787-f3324c43cbac","added_by":"auto","created_at":"2024-05-14 17:59:27","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":207035,"visible":true,"origin":"","legend":"\u003cp\u003eCrea\u003c/p\u003e\n\u003cp\u003eBland–Altman plots for selected biomarkers. The difference in measurements is plotted against the average value of both associated measurements. The margins of accuracy, as specified in Rili-Baek, are drawn as sloping lines. The agreement is high, with random variations, and no systemic bias attributable to treatment was detected. Inaccuracy is well within the limits of the Rili-Baek and tolerance of clinical interpretation.\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4319836/v1/dd69743608e0fc56bc30a5be.jpg"},{"id":56479757,"identity":"10d032c8-0da1-4d80-a1ae-17325db802c3","added_by":"auto","created_at":"2024-05-14 18:07:28","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":188875,"visible":true,"origin":"","legend":"\u003cp\u003eCa\u003c/p\u003e\n\u003cp\u003eBland–Altman plots for selected biomarkers. The difference in measurements is plotted against the average value of both associated measurements. The margins of accuracy, as specified by the Rili-Baek, are drawn as sloping lines. The agreement is high, with random variations, and no systemic bias attributable to treatment was detected. Inaccuracy is well within the limits of the Rili-Baek and tolerance of clinical interpretation\u003c/p\u003e","description":"","filename":"Figure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4319836/v1/15cb232c458af2b741dffaa0.jpg"},{"id":56479326,"identity":"7632daff-7aa5-4388-bf07-c711b205a5e1","added_by":"auto","created_at":"2024-05-14 17:59:28","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":182207,"visible":true,"origin":"","legend":"\u003cp\u003epH\u003c/p\u003e\n\u003cp\u003eBland–Altman diagram for selected biomarkers. The difference between measurements is plotted against the average value of both associated measurements. The measurements are \u0026gt; 95% within the tolerance range specified by the Bland–Altman plot. The specified quality ranges in the Rili-Baek can only be partially adhered to. However, the value deviations are all within a 10% margin of error.\u003c/p\u003e","description":"","filename":"Figure7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4319836/v1/9c20a77dee6497da47786dfb.jpg"},{"id":56479330,"identity":"5de048b1-02bf-4d2f-94f5-3dc9aa4836f3","added_by":"auto","created_at":"2024-05-14 17:59:28","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":233254,"visible":true,"origin":"","legend":"\u003cp\u003eNa\u003c/p\u003e\n\u003cp\u003eBland–Altman plots for selected biomarkers. The difference in measurements is plotted against the average value of both associated measurements. The margins of accuracy, as specified in Rili-Baek, are drawn as sloping lines. The agreement is high, with random variations, and no systemic bias attributable to treatment was detected. Inaccuracy is well within the limits of the Rili-Baek and tolerance of clinical interpretation\u003c/p\u003e","description":"","filename":"Figure8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4319836/v1/f911ae3ddc49ec5d3757caab.jpg"},{"id":56479759,"identity":"7e5c1d5e-f07d-46b2-9906-ddef70298cb3","added_by":"auto","created_at":"2024-05-14 18:07:28","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":179671,"visible":true,"origin":"","legend":"\u003cp\u003eLac\u003c/p\u003e\n\u003cp\u003eBland–Altman diagram for selected biomarkers. The difference between the measurements is plotted against the average value of both associated measurements. The measurements are \u0026gt; 95% within the tolerance range specified by the Bland–Altman plot. The specified quality ranges of the Rili-Baek cannot be complied with.\u003c/p\u003e","description":"","filename":"Figure9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4319836/v1/37cf9a90d8515b6d68d44bfa.jpg"},{"id":56479758,"identity":"e6ef9bee-489c-4cce-9c02-04a03c9fca93","added_by":"auto","created_at":"2024-05-14 18:07:28","extension":"jpg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":175800,"visible":true,"origin":"","legend":"\u003cp\u003eC\u003c/p\u003e\n\u003cp\u003eBland–Altman diagram for selected biomarkers. The difference between the measurements is plotted against the average value of both associated measurements. The measurements are \u0026gt; 95% within the tolerance range specified by the Bland–Altman plot. The specified quality ranges of the Rili-Baek can only be partially adhered to. However, the value deviations are usually within a 10% margin of error.\u003c/p\u003e","description":"","filename":"Figure10.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4319836/v1/3ead41a7f915ff3a4546b92c.jpg"},{"id":56479333,"identity":"9dd1a2b7-4f9b-4413-bc53-9992f359726c","added_by":"auto","created_at":"2024-05-14 17:59:28","extension":"jpg","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":174935,"visible":true,"origin":"","legend":"\u003cp\u003ePO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n\u003cp\u003eBland–Altman diagram for selected biomarkers. The difference between the measurements is plotted against the average value of both associated measurements. The measurements are \u0026gt; 95% within the tolerance range specified by the Bland–Altman plot. The specified quality ranges of the Rili-Baek can usually not be adhered to. The value deviations are usually also outside a 10% margin of error.\u003c/p\u003e","description":"","filename":"Figure11.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4319836/v1/24345aecac5c6a91ee8af299.jpg"},{"id":56479329,"identity":"c1eaf32b-9625-4ec2-8006-6c4f5dc91a57","added_by":"auto","created_at":"2024-05-14 17:59:28","extension":"jpg","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":244844,"visible":true,"origin":"","legend":"\u003cp\u003ePCO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n\u003cp\u003eBland–Altman diagram for selected biomarkers. The difference between the measurements is plotted against the average value of both associated measurements. The measurements are \u0026gt; 95% within the tolerance range specified by the Bland–Altman plot. The specified quality ranges of the Rili-Baek cannot be adhered to in some cases. However, the value deviations are usually within a 10% margin of error.\u003c/p\u003e","description":"","filename":"Figure12.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4319836/v1/c43f72c094b7aca6100181df.jpg"},{"id":56479334,"identity":"fa4034f7-fec2-4b29-99fa-273126310473","added_by":"auto","created_at":"2024-05-14 17:59:28","extension":"jpg","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":173151,"visible":true,"origin":"","legend":"\u003cp\u003eHCO\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e\n\u003cp\u003eBland–Altman diagram for selected biomarkers. The difference between the measurements is plotted against the average value of both associated measurements. \u0026gt; 95% of the measurements are within the tolerance range specified in the Bland–Altman plot. With the exception of one outlier, the value deviations are within a 10% margin of error.\u003c/p\u003e","description":"","filename":"Figure13.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4319836/v1/ec01105494375c7f92013183.jpg"},{"id":56479332,"identity":"630db722-2c73-4284-93d7-3c56daa5e1d8","added_by":"auto","created_at":"2024-05-14 17:59:28","extension":"jpg","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":149341,"visible":true,"origin":"","legend":"\u003cp\u003eSO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n\u003cp\u003eBland–Altman diagram for selected biomarkers. The difference between the measurements is plotted against the average value of both associated measurements. The measurements are \u0026gt; 95% within the tolerance range specified by the Bland–Altman plot. With the exception of one outlier, the value deviations are within a 10% margin of error.\u003c/p\u003e","description":"","filename":"Figure14.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4319836/v1/c2d5b363fe4f3eb8c292618e.jpg"},{"id":60559826,"identity":"4fb26ca6-4a9a-45f8-9fc5-0209dd8bd3ad","added_by":"auto","created_at":"2024-07-18 07:18:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3393154,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4319836/v1/2786c98d-5750-4910-8cdd-13044b9848f1.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Pre-analytical validity of arterial blood gas samples: A prospective experimental study on changes derived from time delay and mechanical stress","fulltext":[{"header":"Background","content":"\u003cp\u003eIn anesthesia and intensive care medicine, blood gas analysis is a basic diagnostic tool for at-risk and critically ill patients. Careful handling of the samples after collection and immediate analysis are accepted standards. Nevertheless, numerous situations occur in everyday clinical practice that could lead to unintentional delays in the analysis or external interference with samples. These situations could include lengthy transport routes and equipment downtime owing to maintenance, human negligence, or mechanical interference caused by mishandling. The current literature does not answer whether samples remain suitable for analysis after prolonged periods and/or mechanical interference. This uncertainty often prompts clinicians to obtain new blood samples, incurring costs, time, and potential patient discomfort and harm associated with renewed blood loss or repeated vascular punctures [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eExisting literature has only focused on certain aspects of the pre-analytical stability of blood gas samples. For example, Knowles and Harsten detected changes in partial pressures of respiratory gases (pO\u003csub\u003e2\u003c/sub\u003e and pCO\u003csub\u003e2\u003c/sub\u003e) 30 and 60 min post-collection. Moreover, Smeenk et al. [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] described prolonged stability of pO\u003csub\u003e2\u003c/sub\u003e when using glass syringes or ice-water storage. However, beyond respiratory gases, modern blood gas analyzers measure various clinically relevant analytes, of ionic or organic-small-molecular character (for example, c(sodium), c(potassium), c(calcium), c(glucose), and c(lactate)). Existing data on the possible pre-analytical influence of these numerous parameters are limited. In a recent study, we demonstrated that time delays and mechanical stress on venous blood samples in an out-of-hospital emergency medical service setting did not lead to any clinically relevant alterations in various electrolytes, small organic molecules, or protein biomarkers [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTherefore, this study aimed to investigate the effect of prolonged time before analysis and mechanical manipulation on pre-analytical stability and the validity of the results of blood gas analyses.\u003c/p\u003e \u003cp\u003eWe aimed to answer the following questions:\u003c/p\u003e \u003cp\u003eFirst, whether changes are systematic or erratic, statistically significant, or clinically relevant. Second, whether in terms of data quality, results derived from delayed analysis or samples subjected to mechanical stress remain suitable for clinical purposes and decision-making under pertinent clinical circumstances.\u003c/p\u003e"},{"header":"Material and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eAim, design, and setting\u003c/h2\u003e \u003cp\u003eThis study aimed to investigate the effect of prolonged time before analysis and mechanical manipulation on pre-analytical stability and the validity of the results of blood gas analyses. The Ethics Committee of the Friedrich-Alexander University, Erlangen-Nuremberg, Germany, reviewed and approved the study proposal (vote 22-124-B). Prospective participants or their legal representatives provided both verbal and written consent in advance and participated voluntarily in the study. The study population comprised patients from all surgical specialties who were treated at our interdisciplinary tertiary-level intensive care unit. Pregnant women and minors were excluded from participation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eBlood samples\u003c/h2\u003e \u003cp\u003eThe procedure for collecting blood samples was standardized, ensuring compliance with the syringe manufacturer's specified product manuals and in-house standard operating procedures regarding hygiene and workplace safety.\u003c/p\u003e \u003cp\u003eBlood samples were collected from arterial catheters, using a \"safe Pico Aspirator\" from Radiometer Medical ApS, Br\u0026oslash;nsh\u0026oslash;j, Denmark. Subsequently, the samples were analyzed using blood gas analyzers (ABLflex800) (Radiometer Medical ApS, Br\u0026oslash;nsh\u0026oslash;j, Denmark) located within the intensive care unit.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e provides a list of the measured parameters, including the type of analyzer, analytical methods, and legally required accuracy of analysis. Parameters are divided into two groups: those directly measured from the sample, such as pH, pCO\u003csub\u003e2\u003c/sub\u003e, pO\u003csub\u003e2\u003c/sub\u003e, ctHb, cK\u003csup\u003e+\u003c/sup\u003e, cNa, cCl, cCa\u003csup\u003e2+\u003c/sup\u003e, cGluc, cCrea, and cLac, and those derived via calculation, such as HCO\u003csub\u003e3\u003c/sub\u003e and SO\u003csub\u003e2\u003c/sub\u003e. The case number was not estimated in advance. Instead, it was derived based on local resources and general practicability.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBiomarkers and analyzers, analytical methods, and quality criteria.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBiomarkers\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnalyzer model\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eManufacturer\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTest method\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFrequency of quality controls\u003c/p\u003e \u003cp\u003e(three shifts per day)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAcceptable Root Mean Standard Deviation as defined by Rili-Beak\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRange of intrest as defined by Rili- Baek\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eUnit\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCalcium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eABL800 Flex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRadiometer Medical ApS, Br\u0026oslash;nsh\u0026oslash;j, Denmark\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePotentiometric\u003c/p\u003e \u003cp\u003emethod\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOnce per 8 h shift\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14%\u003c/p\u003e \u003cp\u003e7.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.2 to \u0026le;\u0026thinsp;1\u003c/p\u003e \u003cp\u003e\u0026gt;\u0026thinsp;1\u0026ndash;2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003emmol/L\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChlorides\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eABL800 Flex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRadiometer Medical ApS, Br\u0026oslash;nsh\u0026oslash;j, Denmark\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePotentiometric\u003c/p\u003e \u003cp\u003emethod\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOnce per 8 h shift\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e70\u0026ndash;150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003emmol/L\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eABL800 Flex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRadiometer Medical ApS, Br\u0026oslash;nsh\u0026oslash;j, Denmark\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePotentiometric\u003c/p\u003e \u003cp\u003emethod\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOnce per 8 h shift\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.5\u0026ndash;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003emg/L\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlucose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eABL800 Flex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRadiometer Medical ApS, Br\u0026oslash;nsh\u0026oslash;j, Denmark\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePotentiometric\u003c/p\u003e \u003cp\u003emethod\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOnce per 8 h shift\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e40\u0026ndash;400\u003c/p\u003e \u003cp\u003e2.2\u0026ndash;22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003emg/dL\u003c/p\u003e \u003cp\u003emmol/L\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eABL800 Flex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRadiometer Medical ApS, Br\u0026oslash;nsh\u0026oslash;j, Denmark\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePotentiometric\u003c/p\u003e \u003cp\u003emethod\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOnce per 8 h shift\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2\u0026ndash;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eg/dL\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePotassium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eABL800 Flex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRadiometer Medical ApS, Br\u0026oslash;nsh\u0026oslash;j, Denmark\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePotentiometric\u003c/p\u003e \u003cp\u003emethod\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOnce per 8 h shift\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2\u0026ndash;8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003emmol/L\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLactate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eABL800 Flex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRadiometer Medical ApS, Br\u0026oslash;nsh\u0026oslash;j, Denmark\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePotentiometric\u003c/p\u003e \u003cp\u003emethod\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOnce per 8 h shift\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u0026ndash;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003emmol/L\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSodium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eABL800 Flex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRadiometer Medical ApS, Br\u0026oslash;nsh\u0026oslash;j, Denmark\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePotentiometric\u003c/p\u003e \u003cp\u003emethod\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOnce per 8 h shift\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e110\u0026ndash;180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003emmol/L\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epCO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eABL800 Flex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRadiometer Medical ApS, Br\u0026oslash;nsh\u0026oslash;j, Denmark\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePotentiometric\u003c/p\u003e \u003cp\u003emethod\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOnce per 8 h shift\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.5%\u003c/p\u003e \u003cp\u003e6.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;35\u003c/p\u003e \u003cp\u003e\u0026gt;\u0026thinsp;35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003emmHg\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eABL800 Flex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRadiometer Medical ApS, Br\u0026oslash;nsh\u0026oslash;j, Denmark\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePotentiometric\u003c/p\u003e \u003cp\u003emethod\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOnce per 8 h shift\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.75\u0026ndash;7.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eABL800 Flex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRadiometer Medical ApS, Br\u0026oslash;nsh\u0026oslash;j, Denmark\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePotentiometric\u003c/p\u003e \u003cp\u003emethod\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOnce per 8 h shift\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.5%\u003c/p\u003e \u003cp\u003e7.0%\u003c/p\u003e \u003cp\u003e11.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;125\u0026ndash;350\u003c/p\u003e \u003cp\u003e\u0026gt;\u0026thinsp;80 to \u0026le;\u0026thinsp;125\u003c/p\u003e \u003cp\u003e40 to \u0026le;\u0026thinsp;80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003emmHg\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHCO\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eABL800 Flex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRadiometer Medical ApS, Br\u0026oslash;nsh\u0026oslash;j, Denmark\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ecalculated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eABL800 Flex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRadiometer Medical ApS, Br\u0026oslash;nsh\u0026oslash;j, Denmark\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ecalculated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eFor further detailed information on laboratory medical examination methods, we recommend consulting the manufacturer's user manual and the quality criteria prescribed by the German Medical Association available on the Rili-Baek homepage (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.bundesaerztekammer.de/themen/aerzte/qualitaetssicherung/richtlinien-leitlinien-empfehlungen-stellungnahmen\u003c/span\u003e\u003cspan address=\"https://www.bundesaerztekammer.de/themen/aerzte/qualitaetssicherung/richtlinien-leitlinien-empfehlungen-stellungnahmen\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eTest scenario\u003c/h2\u003e \u003cp\u003eBlood samples were sequentially tested for the first time immediately after collection and for the second time 60 min post-mechanical stress. First, blood samples were collected as a part of routine clinical practice. A first blood gas analysis was performed immediately after the samples were obtained [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]; the remaining quantity of blood was not discarded but preserved. For this purpose, the sample containers were emptied of residual air and stored on a tray next to the blood gas analyzer at room temperature during the waiting period between the first and second analyses. They were not shielded from sunlight. Before the second analysis, the tubes were subjected to a standardized mechanical stress scenario. Each sample was dropped 10 times from a height of 85 cm (table height) and shaken vigorously 10 more times. The same samples were analyzed the second time to compare the paired results 60 min after the first analysis. The results of the second series of measurements were used only for this study and not for any clinical decisions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eData analysis and statistics\u003c/h2\u003e \u003cp\u003eBland\u0026ndash;Altman plots were used to compare the combined effects of mechanical stress and time delay on arterial blood gas samples [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Values from blood samples that were directly analyzed were compared with those analyzed after 60 min delay and mechanical manipulation.\u003c/p\u003e \u003cp\u003eEvery Bland\u0026ndash;Altman plot containes several lines. The central solid line in the graph represents the mean values of the differences in measured parameters. The two dashed lines above and below the solid line represent the upper and lower limits of agreement, respectively, which correspond to a maximum 1.96-fold deviation from the mean value upward and downwards. Typically, 95% of the measured values were within these limits.\u003c/p\u003e \u003cp\u003eAs an external criterion for determining negligible differences, we used the specified validity ranges of the respective biomarkers following the German Medical Association guidelines for the quality assurance of laboratory medical examinations (Rili-Baek) [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRili-Baek specifies the basic requirements for the quality management and assurance of laboratory medical examinations in Germany. Among other factors, the validity of individual test results within their respective validity ranges is defined. Hence, we assumed that the results can be regarded as equivalent and deviations negligible if they lie within the error margins of the Rili-Baek [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The validity ranges are shown in the diagrams by spreading green lines. The analysis was performed using SPSS and Microsoft Excel (IBM SPSS Statistics Version: 28.0.1.1(15)/ Microsoft\u0026reg; Excel\u0026reg; 2019 MSO (16.0.14332.20563) 32-Bit).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eIn total, 240 blood samples were collected. Thirteen biomarkers were analyzed, 11 of which were directly measured using the device, and two were derived via calculation. No missing data were reported. Each analyzed parameter was investigated for differences between the measurements using Bland\u0026ndash;Altman plots. Figures \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan class=\"InternalRef\"\u003e14\u003c/span\u003e present the graphs for each parameter. Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e provides an overview of all the parameters, standard deviations, and confidence intervals.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eOverview of analyzed biomarkers\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"9\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBiomarkers in alphabetical order\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eUnit\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNumber of measurements\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean of differences\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCI Mean\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eUpper limit of agreement\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCI Upper limit of agreement\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLower limit of agreement\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCI lower limit of agreement\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCalcium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emmol/l\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e240\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.0073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.0074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.0045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChlorides\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emmol/l\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e240\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.1019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.836\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.281\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.628\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.073\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCreatinine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e239\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.0054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.162\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGlucose\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e240\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-4.675\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-4.8704\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.401\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-4.086\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-13.751\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-5.263\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHemoglobin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e240\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.098\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0806\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.518\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.1260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.321\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.072\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePotassium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e240\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2189\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.619\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.245\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.181\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.193\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLactate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e240\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.609\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.6047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.639\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.579\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSodium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e240\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.963\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.465\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.088\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.619\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.242\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epCO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e240\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.196\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.2208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.499\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.372\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-3.893\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e240\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.0171\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e240\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e33.740\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e33.7935\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e80.456\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e36.771\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-12.976\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30.709\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHCO\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e240\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.373\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.4005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.748\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.494\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.4461\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e240\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.855\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.7199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.184\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.475\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.577\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eWe discovered that the deviations in the results of all parameters except K+, lactate, pCO\u003csub\u003e2\u003c/sub\u003e, and pO\u003csub\u003e2\u003c/sub\u003e were within the specified limits of Rili-BaeK and the standard deviation of the Bland-Altman plots.\u003c/p\u003e\n\u003cp\u003eThe measured values for K+, lactate, PCO\u003csub\u003e2\u003c/sub\u003e, and PO\u003csub\u003e2\u003c/sub\u003e indicated that deviations in the results were within the standard deviation of the Bland\u0026ndash;Altman plot. However, we observed a substantial deviation from the validity criteria specified by Rili-Baek. Even a 10% deviation corridor of the measured values from the original values, which we introduced based on possible clinical decision-making strategies, could often not be met.\u003c/p\u003e\n\u003cp\u003eFor potassium, 52.5% (126/240) of the values fell within the limits specified by Rili-Baek. Additionally, 88.3% (212/240) of the measured results were within the proposed 10% margin of deviation. Furthermore, 93.75% (225/240) of measurements showed an overall increase in potassium concentration.\u003c/p\u003e\n\u003cp\u003eThe measured values for lactate were rarely (1/240: 0.4%) within the validity range specified by Rili-Baek. Similarly, only 1 out of 240 values fell within an additional 10% corridor. Moreover, 99.6% (239/240) of the measured values showed an increase in lactate concentration.\u003c/p\u003e\n\u003cp\u003eRegarding the measured values of pCO\u003csub\u003e2\u003c/sub\u003e, we discovered that 80.4% (202/240) were within the required measurement ranges of the Rili-Baek. Moreover, 97.9% (235/240) of the values fell within the 10% corridor. In total, 85.4% (205/240) showed an overall increase in partial pressure.\u003c/p\u003e\n\u003cp\u003eThe results for the measured pO\u003csub\u003e2\u003c/sub\u003e values revealed that 5.4% (13/240) were within the Rili-Baek limits, and 7.9% (19/240) were within the additional 10% corridor. In total, 95% (228/240) showed an overall increase in partial pressure.\u003c/p\u003e\n\u003cp\u003eThe results for the measured pH values showed that 84.5% (203/240) were within the Rili-Baek limits, with 100% (240/240) falling within the additional 10% corridor. In total, 81.6% (196/240) showed an overall decrease in the measured value.\u003c/p\u003e\n\u003cp\u003eThe results for the measured glucose values indicated that 98.75% (237/240) were within the margin of error specified by the Rili-Baek. Furthermore, 89.2% (214/240) showed a decrease in the measured value.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eCareful handling and immediate analysis of blood samples are\u0026nbsp;medical standards. However, in the\u0026nbsp;bustling and stressful environment of an intensive care unit, blood samples might unintentionally undergo rule violations. The novel findings of this study demonstrate that many biomarkers from an arterial blood gas sample are robust against pre-analytical mechanical stress or time delay before analysis.\u003c/p\u003e\n\u003cp\u003eTo that end, we compared the test results of an arterial blood gas analysis (BGA) after immediate analysis\u0026nbsp;with\u0026nbsp;those from the same sample\u0026nbsp;after a combination of\u0026nbsp;a defined\u0026nbsp;time delay and simulated mechanical stress. We deliberately exaggerated both confounding variables (time and mechanical forces) to a level greater than expected under real-life conditions. The rationale for this experimental approach was that biomarkers proving stable against supramaximal scenarios\u0026nbsp;would certainly also remain stable against stressors in everyday circumstances.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn our study, the specifications for technical quality control of the analyzers,\u0026nbsp;in accordance with Rili-Baek, were used to define what would constitute a negligible deviation. Deviations of the measured values that were within the methodological measurement inaccuracy were regarded as clinically equivalent. Therefore, a subjective interpretation of values, prone to bias, was initially\u0026nbsp;unnecessary.\u003c/p\u003e\n\u003cp\u003eThe most common blood gas analysis parameters were included in the dataset. The results are interpreted in three different ways.\u003c/p\u003e\n\u003cp\u003e1. Dissolved gases undergo\u0026nbsp;relevant diffusion processes\u0026nbsp;across the walls of the sample containers. Based on the partial pressure differences between\u0026nbsp;the blood and room air, a net inflow of oxygen from the room air into the sample and a net outflow of carbon dioxide from the sample into the room air occurs\u0026nbsp;[8/9].\u003c/p\u003e\n\u003cp\u003eIn most cases, the increase in pO\u003csub\u003e2\u003c/sub\u003e exceeded the margin specified in Rili–Baek. Substantial oxygen enrichment of the samples (mean + 38\u0026nbsp;mmHg) without any meaningful relation to the patient's condition was observed.\u0026nbsp;Therefore, an interpretation of the oxygenation performance of the patients was categorically impossible in our test scenario.\u003c/p\u003e\n\u003cp\u003eFor CO\u003csub\u003e2\u003c/sub\u003e, we observed a slight average increase in the partial pressure in the sample vessel\u0026nbsp;(mean +1.2\u0026nbsp;mmHg; upper limit of agreement\u0026nbsp;\u0026lt; 4\u0026nbsp;mmHg). At first glance, this observation appears to contradict the net\u0026nbsp;CO\u003csub\u003e2\u003c/sub\u003e loss\u0026nbsp;from the containers we described earlier. However, as a result of the substantial increase in hemoglobin\u0026nbsp;oxygenation\u0026nbsp;in the sample vessel,\u0026nbsp;the CO\u003csub\u003e2\u003c/sub\u003e previously bound to hemoglobin was released. This so-called Haldane effect obviously outweighs the diffusion loss during the investigation period, and the sum of these effects results in a slight\u0026nbsp;increase in the pCO\u003csub\u003e2\u0026nbsp;\u003c/sub\u003eof the sample [10].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThese changes in pCO\u003csub\u003e2\u003c/sub\u003e were mostly within a 10% deviation margin that we set suggestively. We believe that such a +/- 10% corridor often is part of clinical decision-making patterns. As a consequence, a discussion is warranted whether the observed moderate changes in pCO\u003csub\u003e2\u003c/sub\u003e values should be viewed as numerically different but clinically similar and could therefore be used as an approximation of decarboxylation.\u003c/p\u003e\n\u003cp\u003e2. Some biomarkers of the erythrocyte metabolism (mean lactate +0.6\u0026nbsp;mmol/L;\u0026nbsp;mean potassium +0.2\u0026nbsp;mmol/L) were also subject to changes outside the margin of error specified in the Rili-BAEK. While changes in lactate were far beyond a clinically acceptable margin of error, the majority of potassium measurements lay within the above-mentioned 10% corridor. Hence, we would like to spark a discussion, whether clinicians would still view these changes\u0026nbsp;as an acceptable inaccuracy and should utilize those potassium values for therapeutic decisions.\u003c/p\u003e\n\u003cp\u003e3. Hemoglobin, creatinine, glucose,\u0026nbsp;and electrolytes that\u0026nbsp;are not\u0026nbsp;closely related to erythrocyte metabolism are within the specified accuracy limits except for a few individual observations. Therefore, the results can be regarded as equivalent, justifying the use of these parameters for clinical decision-making, even after\u0026nbsp;time delay and mechanical disturbance.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn summary, it is impossible to draw a uniform picture regarding the pre-analytical validity of our ill-treated blood samples. A differentiated interpretation of each biomarker based on its biochemical and physicochemical properties is required.\u003c/p\u003e\n\u003cp\u003eNaturally, our study has some strengths and weaknesses. One strength of our interpretative approach lies in using the purely technically derived error margins of the Rili-Baek as a basis for deciding whether value deviations should be regarded as acceptable or un-acceptable. Parameters that were within this predefined margin of error, even after our intervention, can be regarded as reliable with a high degree of probability and can be used\u0026nbsp;for clinical decision-making.\u003c/p\u003e\n\u003cp\u003eHowever, some parameters fell into a gray area outside the interpretation certainty of the Rili-Baek margins but they remained within a clinically justified 10% error corridor. Consequently, the interpretation of these values remains debatable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOne limitation of this study is the incomplete selection of only a few standardized confounding factors. Other environmental factors that were not part of our experimental setup, such as\u0026nbsp;temperature fluctuations and ultraviolet radiation, might also affect the pre-analytical stability of blood samples to an unknown extent.\u003c/p\u003e\n\u003cp\u003eFinally, it might be possible that the excessive degree of rule violations in sample treatment at the supra-everyday level was responsible for the large deviations in results for some biomarkers. One has to wonder, whether a milder form of treatment (for example, only a 30-min delay, only one drop) might have led to smaller changes, closer to Rili-Baek and thus to results that would have been clearer to interpret.\u0026nbsp;\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn our study, we demonstrated that time delay and mechanical stress before analysis of arterial blood gases had no relevant influence on the results for protein biomarkers (Hemoglobin, Creatinine), Glucose and certain electrolytes (Sodium, Calcium, Chloride, HCO3). pCO2 and Potassium are altered, but results might be considered for approximation purposes. Measurements of oxygenation (pO2) and lactate levels are generally incorrect after introducing time delay and mechanical stress.\u003c/p\u003e \u003cp\u003eBased on our data, it is not necessary for selected clinical questions to repeat a BGA if there was time delay or mechanical irritation of the sample before analysis. Despite its limitations, our study makes a valuable contribution to reducing unnecessary blood sampling in patients in the intensive care unit.\u003c/p\u003e \u003cp\u003eFuture follow-up studies should consider the differential effects of time, mechanical force, or other factors on the pre-analytical stability of blood samples.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eRili-BAEK, Guidelines of the German Medical Association for Quality Assurance in Medical Laboratory Testing\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Ethics Committee of Friedrich-Alexander-University, Erlangen-Nuremberg, Germany, approved this study under reference Number 22-124-B. Written informed consent was obtained from all participants before the experiments.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;MG conducted the experiments and performed statistical analyses. The present work was performed in fulfillment of the requirements for MG to obtain the academic degree \"Dr. med.\" HI performed statistical analysis and\u0026nbsp;critically revised the manuscript\u0026nbsp;for intellectual content. FK and AM coordinated the study and critically revised the intellectual content\u0026nbsp;of the manuscript. JP conceived\u0026nbsp;the study and performed statistical analyses. All\u0026nbsp;authors have read and approved\u0026nbsp;the final manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank all volunteers for their contributions to our study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor’s information\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e1 Anesthesia group practice \u003cem\u003eBernard\u0026nbsp;\u003c/em\u003eand \u003cem\u003eWeber,\u0026nbsp;\u003c/em\u003e91054 Erlangen, Germany\u003c/p\u003e\n\u003cp\u003e2 Department of Anesthesiology, Erlangen University Hospital, 91054 Erlangen, Germany.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e3 Faculty of Medicine, Friedrich Alexander University, Erlangen-Nuremberg, Erlangen, Germany.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e4 Department of Anesthesiology and Critical Care, Klagenfurt Hospital, 9020 Klagenfurt, Austria\u003c/p\u003e\n\u003cp\u003e*Corresponding author\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eNeef V, Himmele C, Piekarski F, Blum LV, Hof L, Derwich W, Holubec T, Meybohm P, Choorapoikayil S. Effect of using smaller blood volume tubes and closed blood collection devices on total blood loss in patients undergoing major cardiac and vascular surgery. Can J Anaesth. 2024;71:213-23.\u003c/li\u003e\n\u003cli\u003eProf Patrick Meybohm, MD: Patient Blood Management. https://www.patientbloodmanagement.de/pbm-informationen-fuer-aerzte/. Assessed 29 February 2024. \u003c/li\u003e\n\u003cli\u003eSmeenk FW, Janssen JD, Arends BJ, Harff GA, van den Bosch JA, Sch\u0026ouml;nberger JP, Postmus PE. Effects of four different methods of sampling arterial blood and storage time on gas tensions and shunt calculation in the 100% oxygen test. Eur Respir J. 1997;10:910-3.\u003c/li\u003e\n\u003cli\u003eProttengeier J, Jess N, Harig F, Gall C, Schmidt J, Birkholz T. Can we rely on out-of-hospital blood samples? A prospective interventional study on the pre-analytical stability of blood samples under prehospital emergency medicine conditions. Scand J Trauma Resusc Emerg Med. 2017;25:24.\u003c/li\u003e\n\u003cli\u003eSchinko H, Funk GC, Meschkat, M, Lamprecht B\u003cem\u003e.\u003c/em\u003e\u003cem\u003e \u003c/em\u003eArterial blood gas analysis. \u003cem\u003eWien\u003c/em\u003e\u003cem\u003e Klin. Wochenschr Educ\u003c/em\u003e\u003cem\u003e. \u003c/em\u003e2017;12: 115-30. \u003c/li\u003e\n\u003cli\u003eBland JM, Altman DG. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet. 1986;1:307-10.\u003c/li\u003e\n\u003cli\u003eGerman Medical Association. Guideline of the German Medical Association for the quality assurance of laboratory medical examinations - Rili-B\u0026Auml;K. Dtsch Arztebl 2014;111:A1583-1618.\u003c/li\u003e\n\u003cli\u003eGruber MA, Felbermeir S, Lindner R, Kieninger M. Preanalytics: The (in-)stability of volatile POCT parameters and the homogeneity of blood in syringes at the market. Clin Chim Acta. 2016;457:18-23.\u003c/li\u003e\n\u003cli\u003eKnowles TP, Mullin RA, Hunter JA, Douce FH. Effects of syringe material, sample storage time, and temperature on blood gases and oxygen saturation in arterialized human blood samples. Respir Care. 2006;51:732-6.\u003c/li\u003e\n\u003cli\u003eTyuma I. The Bohr effect and the Haldane effect in human hemoglobin. Jpn J Physiol. 1984;34:205-16.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Blood, gas, analysis, time delay, mechanical stress, intensive care unit, biomarkers, oxygen partial pressure","lastPublishedDoi":"10.21203/rs.3.rs-4319836/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4319836/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eTime delays and mechanical stress of samples obtained for Point of Care (POC) blood gas analyses are common; however, their influence on the results of these analyses has not been systematically investigated. Our study aimed to investigate the effect of prolonged time before analysis and mechanical manipulation on pre-analytical stability of biomarkers and thus the validity of the results of blood gas analyses.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eWe collected blood samples from 240 patients in a university surgical intensive care unit. These samples were immediately analyzed following the clinical standard operating procedures. Subsequently, the sample containers were allowed to rest for 60 min, then subjected to standardized mechanical forces, and analyzed again. We analyzed 13 typical blood gas biomarkers, comprising respiratory gases, electrolytes, and protein biomarkers. Bland–Altman plots were prepared to analyze the differences between the test runs. The differences between the test groups were compared against the official limits of accuracy specified in the German requirements for quality assurance of medical laboratory tests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eFor hemoglobin, creatinine, glucose, and electrolytes (including calcium, sodium, chlorine, and bicarbonate), the agreement between the immediate and post-interference-treatment analyses was within the ranges specified in the official requirements. For pH and potassium, the deviations were outside the quality assurance ranges but within a clinically acceptable measurement accuracy. Only oxygen partial pressure and lactate levels were altered to such an extent that they can no longer be used for clinical purposes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eEven after a 60 minutes time delay and excessive mechanical stress, selected blood gas analysis biomarkers such as Hemoglobin, Glucose, Sodium, Calcium, Chloride, and Bicarbonate could be considered valid. Potassium and pCO2 were altered but suitable for approximation purposes. Findings for pO2 and Lactate were generally incorrect. In the future, in selected settings, these findings can aid in reducing unnecessary blood sampling in vulnerable patients.\u003c/p\u003e","manuscriptTitle":"Pre-analytical validity of arterial blood gas samples: A prospective experimental study on changes derived from time delay and mechanical stress","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-14 17:59:23","doi":"10.21203/rs.3.rs-4319836/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5af25e87-9003-4930-a928-b814beaa5981","owner":[],"postedDate":"May 14th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-08-12T04:12:45+00:00","versionOfRecord":[],"versionCreatedAt":"2024-05-14 17:59:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4319836","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4319836","identity":"rs-4319836","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}