ANNALS EXPRESS: The economic impact of poor sample · PDF file1 The economic impact of poor sample quality in clinical chemistry laboratories: results from a global survey Erik P

The economic impact of poor sample quality in clinical

chemistry laboratories: results from a global survey

Journal: Annals of Clinical Biochemistry

Manuscript ID ACB-16-015.R2

Manuscript Type: Research Article

Date Submitted by the Author: 22-Apr-2016

Complete List of Authors: Erdal, Erik; Becton Dickinson and Company, Mitra, Debanjali; Pfizer, Inc. Khangulov, Victor; Boston Strategic Partners, Inc. Church, Stephen; Becton Dickinson and Company Plokhoy, Elizabeth; Becton Dickinson and Company

Keywords: Laboratory management < Laboratory methods

Annals of Clinical Biochemistry

DOI: 10.1177/0004563216651647

Ann Clin Biochem OnlineFirst, published on May 10, 2016 as doi:10.1177/0004563216651647

at UNIV OF PITTSBURGH on May 14, 2016acb.sagepub.comDownloaded from

http://acb.sagepub.com/

1

The economic impact of poor sample quality in clinical chemistry laboratories: results from

a global survey

Erik P. Erdal ¹, Debanjali Mitra 2,a, Victor S. Khangulov, 3, Stephen Church, 4,Elizabeth Plokhoy

1

1 Becton Dickinson and Company, Franklin Lakes, NJ, USA 07417; 2 Pfizer, Inc., New York, NY

10017; 3 Boston Strategic Partners, Inc., Boston, MA, USA 02118; 4 Becton Dickinson and

Company, Oxford, UK OX4 4DQ

a D. Mitra was an employee of Becton Dickinson and Company, Franklin Lakes, NJ, USA, at the time that the manuscript was written

Corresponding author

Erik P. Erdal

1 Becton Drive, Franklin Lakes, NJ, 07417

Telephone: 201.847.6354

Fax: 201.847.5621

Email: [email protected]

Competing interests: EPE, SC, and EP are employees of Becton Dickinson and Company. DM

was previously an employee of Becton Dickinson and Company, and is now an employee of

Pfizer, Inc. VSK is an employee of Boston Strategic Partners, Inc., funded by Becton Dickinson

for the conduct of the study.

Funding: This study was funded by Becton Dickinson and Company, Franklin Lakes, NJ, USA.

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Ethical approval: Not applicable.

Guarantor: EPE

Contributorship: DM and VSK contributed to study design and execution, data interpretation,

and development, review, and approval of the manuscript. EPE, SC, and EP contributed to data

interpretation and development, review, and approval of the manuscript.

Acknowledgements: The authors acknowledge Alissa Kerner, Scott Paluszkiewicz, and

Matthew O’Hara of Boston Strategic Partners, Inc. funded by Becton Dickinson and Company

for editorial support.

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ABSTRACT

Background: Despite advances in clinical chemistry testing, poor blood sample quality

continues to impact laboratory operations and the quality of results. While previous studies have

identified pre-analytical causes of lower sample quality, few studies have examined the

economic impact of poor sample quality on the laboratory. Specifically, the costs associated with

workarounds related to fibrin and gel contaminants remain largely unexplored.

Methods: A quantitative survey of clinical chemistry laboratory stakeholders across 10

international regions, including countries in North America, Europe, and Oceania, was

conducted to examine current blood sample testing practices, sample quality issues, and practices

to remediate poor sample quality. Survey data were used to estimate costs incurred by

laboratories to mitigate sample quality issues.

Results: Responses from 164 participants were included in the analysis, which was focused on

three specific issues: fibrin strands, fibrin masses, and gel globules. Fibrin strands were the most

commonly reported issue, with an overall incidence rate of ~3%. Further, 65% of respondents

indicated that these issues contribute to analyzer probe clogging, and the majority of laboratories

had visual inspection and manual remediation practices in place to address fibrin- and gel-related

quality problems (55% and 70%, respectively). Probe maintenance/replacement, visual

inspection, and manual remediation were estimated to carry significant costs for the laboratories

surveyed. Annual cost associated with lower sample quality and remediation related to fibrin

and/or gel globules for an average US laboratory was estimated to be $100,247.

Conclusions: Measures to improve blood sample quality present an important step toward

improved laboratory operations.

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Keywords: laboratory management

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INTRODUCTION

Clinical laboratory results are a critical component of the patient management process,

affecting treatment decisions in as many as 70% of medical cases.1-3 Delayed or incorrect

laboratory results caused by poor sample quality or other pre-analytical factors have the potential

to lead to delayed or erroneous diagnoses or treatments, which can impact patient outcomes,4-6

compromise patient safety,3, 7 and lead to unintended economic burdens on the laboratory and

hospital.1, 8 Illustrating the clinical importance of accuracy in testing, a review of laboratory

incident reports by Astion et al. demonstrated that 95% of the laboratory errors analyzed had the

potential to cause adverse events (AEs) and 5% resulted in actual AEs.5 Furthermore, a 2013

study estimated an average added cost of $208 USD per pre-analytical error (defined as a defect

that occurred during the pre-analytical phase and negatively impacted the quality of the

laboratory result).8 These errors were estimated to make up 0.23-1.2% of hospital operating

costs, which for a 650-bed hospital translated to ~$1.2 million USD per year.8 Still, the total

costs associated with pre-analytical errors are likely to vary between hospitals.

Although clinical chemistry laboratories have made quality control improvements, poor

sample quality is still a concern.6, 9, 10 Studies have demonstrated that erroneous or delayed

laboratory results can result from sample quality issues that originate during sample collection

and/or processing.1, 8 Many pre-analytical steps, including collection and processing, occur

outside of the laboratory, and as a result these steps may be affected by variables that are difficult

for the laboratory to control.1, 11, 12 These external variables affecting the testing process may

result in poor sample quality,12 which can be difficult to resolve once samples have reached the

laboratory.5, 8 Pre-analytical factors can therefore play an important role in determining the

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accuracy and timing of clinical laboratory results, which can influence diagnostic or therapeutic

decisions.

Laboratories have made considerable efforts to address sample quality issues during the

analytical phase by way of automation, standardization, and advances in instrument technology,1,

13, 14 with less attention given to improving sample quality during pre-analytical steps. The pre-

analytical phase of testing, however, is an important consideration in this respect. For example,

one of the key components of blood collection, the collection tube, can impact sample quality

and therefore test results,11 as some collection tube materials, such as surfactants, anti-

coagulants, or separator gel particles may interfere with certain clinical assays.15-17 The type of

sample collected (plasma versus serum) may also affect the likelihood of encountering specific

sample quality issues. For example, serum samples may be prone to fibrin strand formation as a

result of insufficient clotting time or inappropriate centrifugation parameters,18 whereas plasma

samples are more likely to have higher residual cell counts.19

Previous studies have identified hemolysis, short draw, and clots in anti-coagulated

samples as common blood sample quality issues,1, 11 indicating that these factors may lead to

erroneous test results if left un-remediated.8 However, neither the incidences nor downstream

effects of other relevant issues such as fibrin strands, fibrin masses, and gel globules (separator

gel particles) have been sufficiently examined, though these have been identified as potential

sources of lower sample quality and delayed or incorrect test results.12, 18, 20-22 For example, fibrin

strands or masses (fibrin particles unincorporated into the clot) and gel globules can interfere

with sensitive immunoassay binding 15, 18, 22 and may also clog analyzer probes, depending on the

size of the particles.15, 22 In order to provide a more in-depth understanding of the incidence of

these important blood sample quality issues, as well as the methods used to remediate them, we

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conducted a quantitative survey of clinical chemistry laboratory stakeholders across 10

international regions (including countries in North America, Europe, and Oceania). In order to

assess the impact that these often over-looked problems can have on the laboratory, survey data

were used to estimate associated costs. It was hypothesized that fibrin strands, fibrin masses, and

gel globules have relevant impacts on laboratory costs.

METHODS

Survey respondents

A 63-question web-based survey was deployed using Qualtrics (Provo, UT) survey

software, and was open June 1-July 15, 2013. Requests to participate were made via telephone

and e-mail in the United States (US), Canada, United Kingdom (UK), Germany, France, Italy,

“Benelux” (Belgium, Netherlands, Luxembourg), “Nordic” (Finland and Sweden), Turkey, and

Australia, with a target of >10 respondents per region. Invitations to participate were not based

on any defined criteria, and were sent to a de-identified panel of laboratory professionals.

Participation was voluntary and confidential, and respondents were informed that data would be

de-identified and analyzed in aggregate. Survey data were self-reported by qualified respondents

and no independent observations or verifications were made, however responses were reviewed

for internal consistency (see Data Analysis below). In order to reliably assess current laboratory

practices, the survey was open to individuals who self-identified as the director, manager, or

other healthcare professional directly involved in the operations of a hospital-based or

commercial clinical chemistry laboratory with a monthly blood sample number ≥1,000 tubes.

Respondents were not selected based on any current equipment or consumables manufacturer

criteria. Overall, 181 respondents across the target regions qualified and completed the survey.

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Survey themes

The first part of the survey examined general laboratory characteristics including

source(s) of blood samples (e.g. emergency department, intensive care units, outpatient

departments, outreach), routine versus STAT (urgent) testing, and level of front-end automation.

The second part of the survey covered blood sample quality issues encountered in the laboratory,

frequency of analyzer probe maintenance and perceived contribution of poor sample quality,

visual inspection and manual remediation, reasons for remediation, and finally, practices related

to the mitigation of erroneous results for high-sensitivity assays. In this survey, “high-sensitivity

assays” referred to sensitive assays performed on plasma samples and identified as potentially

being more susceptible to erroneous results due to fibrin clots or cellular contamination (human

chorionic gonadotropin (hCG), creatine kinase MB isoenzyme (CK-MB), digoxin,

triiodothyronine (T3), thyroxine (T4), troponin, phenytoin, thyroid-stimulating hormone (TSH))

High-sensitivity troponin assays, for example, have recently been reported to be associated with

the potential for false positive results.23 The key aspects of laboratory workflow covered in the

survey, including sample transportation, preparation, and remediation, are presented in Figure 1.

Data analysis

Because the survey data were self-reported and could not be verified, outliers likely to

have occurred due to user error, keystroke error, or miscomprehension were identified and

excluded from the analysis. Overall, data from 17 respondents were excluded based on the

presence of outliers for responses related to probe maintenance and replacement or frequency of

sample quality issues, or internal validity concerns (e.g. number of high-sensitivity assay tubes

exceeding total number of tubes). Only completed surveys were included in the analysis, and

results are therefore not likely to be confounded by missing data.

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Annual numbers of blood samples were derived from monthly sample numbers provided

by respondents. Cost estimates were based on the following respondent-reported values: number

of blood collection tubes, incidence of erroneous results and problems with sample quality,

remediation practices, and frequency of probe maintenance and replacement. For example, when

considering the costs of visual inspection, the following calculations were applied: (1) number of

sample tubes currently inspected = (% tubes inspected) x (# of tubes), (2) total time for manual

inspection = (# of tubes inspected) x (time to inspect each tube), and (3) total cost of manual

inspection = (total time for manual inspection) x (lab technician or receiving staff salary).

Detailed assumptions related to the cost of supplies, employee wages, and time requirements for

each workflow process step, as well as economic calculations are provided in the Supplementary

Material. All costs calculations were based on US labor and consumable costs, and data are

presented in US dollars ($) and standardized as cost per 10,000 sample tubes received.

RESULTS

Demographics and laboratory characteristics

Data from 164 respondents were included in the analysis. Of these respondents, the

majority were laboratory directors (n=40) or laboratory managers (n=65). Other respondents

were laboratory technicians (n=32), pathologists (n=13), and “other” (e.g. biologist, biochemist,

chief chemist, n=14). In total, 95% of respondents (n=156) worked in hospital-based

laboratories, while the remaining respondents worked in commercial reference laboratories (n=7)

or in a “community laboratory” (n=1). Laboratory characteristics are summarized in Table 1 and

Supplementary Tables 1 and 2. Overall, 46% of laboratories processed mainly serum tubes

(≥80% of blood tubes received were serum), while 42% of laboratories processed a

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plasma/serum mix (neither type accounted for ≥80% of samples). The percentage of laboratories

performing high-sensitivity assays on plasma samples ranged from 67% (Benelux and Nordic,

Italy) to 100% in (Germany, Australia) (84% overall). Laboratories used tubes, analyzers, and

centrifuges from various manufacturers (Supplementary Tables 1 and 2).

Blood sample quality issues

With respect to fibrin strands, fibrin masses, and gel globules, the most commonly

encountered issue was fibrin strands (2.8 ± 4.1% of blood samples for all respondents; Table 2

and Supplementary Table 3). Mean incidences of fibrin masses and gel globules were 1.4 ± 2.7%

and 0.9 ± 2.9% of blood sample tubes, respectively. Overall, 38% of respondents (n=62)

believed that sample transportation affected sample quality. Fibrin strands, fibrin masses and/or

gel globules in samples were perceived to contribute to analyzer probe clogging by 65% (n=107)

of respondents (range: 39% (UK) to 88% (Germany)), suggesting that these quality issues have

the potential to impact laboratory workflow.

Visual inspection and manual remediation

In total, 76% (n=124) of respondents’ laboratories performed visual inspection of blood

samples, while n=90 laboratories inspected samples for fibrin strands or masses and/or gel

globules (55% (n=90/164) of laboratories overall and 73% (n=90/124) of laboratories performing

visual inspection; Table 3). Other respondents inspected samples for hemolysis only. Overall,

66% of respondents (n=109) reported manually remediating fibrin strands and masses while 24%

(n=40) reported manually remediating gel globules. Manual remediation was generally more

common in regions where the sample quality issues in question were highest (US, Italy,

Germany, Canada, Turkey). In some regions, remediation of these issues was reported more

commonly than visual inspection. For example, 53% of Italian respondents (n=8) reported

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inspecting for fibrin or gel globules, however 87% (n=13) performed remediation on fibrin

strands and masses. Respondents in this country also reported relatively high incidences of fibrin

strands, masses, and gel globules (Table 2), suggesting that a lack of upfront inspection may

result in greater downstream remediation needs.

Analyzer probe maintenance and replacement

The mean frequency of unscheduled analyzer probe maintenance varied from 11.1 ± 18.0

times per year (US) to 2.5 ± 2.8 times per year (Australia) (Table 3). Responses suggest a

possible relationship between unscheduled probe maintenance frequency and the incidence

sample quality issues. Respondents in the US and Italy, who reported high frequencies of fibrin-

and gel-related sample quality issues, also reported relatively high rates of unscheduled probe

maintenance. Laboratories processing mostly plasma samples reported more unscheduled

analyzer probe maintenance and replacement than mostly serum laboratories (Supplementary

Table 4).

High-sensitivity assays

High-sensitivity assays may be more susceptible to erroneous results arising from issues

related to sample quality (micro-clots, cellular contamination). Overall, 84% of respondents

(n=138) reported performing high-sensitivity assays (Table 1), but only 59% (n=97) reported

having preventive practices in place for mitigating potentially erroneous results (Table 3).

Reported incidences of erroneous results with high-sensitivity assays were ≤1% for most

respondents (Supplementary Figure 1). The most frequently cited remediation practice for high-

sensitivity assays was testing a sample, and then re-testing a second sample from the tube if the

first result was out of range (Supplementary Table 5). Depending on the assay, 85-88% of

respondents whose laboratory performed the assay indicated that this was the remediation

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practice used (Supplementary Figure 2). Overall, 55% (n=90) of respondents reported the need to

re-test individual plasma tubes when performing high-sensitivity assays. In the case of high-

sensitivity assays requiring re-testing, the most common practice was to “re-centrifuge the

primary tube and then run through the analyzer” (n=54, 33% of all respondents) (Supplementary

Figure 2).

Financial impact of poor sample quality

Estimates of the impacts of poor sample quality, based on sample numbers, sample

quality issue incidences, and remediation practices employed, indicate that, on average,

laboratories spend considerable time on measures to reduce the impact of or remediate sample

quality issues (Table 4). Laboratories were estimated to spend an average of 151 ± 116 (Canada)

to 611 ± 804 hours per year (Germany) on routine and unscheduled probe maintenance and

replacement alone. Median annual time spent on probe maintenance ranged from 140 to 397

hours/year (Table 4; Supplementary Table 6). For manual remediation of fibrin strands or masses

and gel globules prior to testing, estimated mean annual time spent by laboratory staff ranged

from 9 ± 14 hours (Benelux and Nordic) and 9 ± 18 hours (France) to 47 ± 99 hours (Turkey).

The mean annual time commitment related to pre-analytical redraw/recollection ranged from 25

± 32 hours (Benelux and Nordic) to 215 ± 417 hours (Germany, Table 4).

Manual remediation of fibrin strands or masses and gel globules was estimated to impose

a significant cost burden on the laboratories surveyed (Table 5). Annual mean manual

remediation costs (per 10,000 tubes; including labor/materials) ranged from $32 ± $67 per year

(UK) to $294 ± $417 per year (Italy). For analyzer probe maintenance and replacement, mean

annual costs were estimated to range from $560 ± $332 (France) to $1,633 ± $2,057 (US). For

some regions, costs related to mitigation of erroneous results in high-sensitivity assays were also

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relatively high (e.g. Turkey: $608 ± $1,344; Germany: $502 ± $1,376 per 10,000 tubes). Using

the mean annual number of tubes for the US, 417,000, and assuming mean costs for each

category, the total annual financial burden for one average US lab would be $100,247 (probe

maintenance/replacement: $68,096; visual inspection: $5,463; manual remediation: $10,091,

other costs: $16,597).

DISCUSSION

The survey data demonstrate that blood sample quality issues related to fibrin strands,

fibrin masses, and gel globules occur in laboratories across the regions examined and highlight

interesting region-specific differences that may reflect disparities in sample collection, testing

practices, or awareness regarding specific sample quality issues. Reported differences between

surveyed laboratories regarding sample quality issue incidences and general practices could

likewise reflect differences with respect to factors including workflow, sample and test types,

and instrumentation. While the survey was not powered to examine differences based on these

and other related factors, these represent important considerations for all laboratories.

While a lack of visual inspection or manual remediation may lead to downstream

problems if fibrin or gel globules are present in the sample and left un-remediated before

reaching the analyzer, it should be noted that the timing of visual inspection and manual

remediation was not addressed in the survey. Further, it was not determined whether the fibrin

strands/masses or gel globules were the sole cause of the necessary probe maintenance or repair.

In addition, it is important to note that automation might contribute to a lack of pre-inspection; if

laboratory processes are automated, tubes might only be inspected after a sample is rejected. It is

also possible that additional unmeasured factors contributing to sample quality issues could

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impact results. For example, measurement of electrolytes with instruments using an ion selective

electrode (ISE) might be associated with an increased risk of aspirating fibrin and/or gel in

samples and affecting probe performance.24 Given these important considerations, future studies

(including follow-on surveys or observational laboratory-based studies) focused on evaluating

the comparative ability and corresponding cost-effectiveness of the various methods used to

detect sample quality issues (e.g. visual inspection, pre-analytics, detection by automated

analyzer, discovery of rejected results) are warranted.

This survey was intended to provide a broad view of current laboratory practices and the

prevalence and potential impacts of fibrin- and gel- related samples quality issues. The study

design did not specify the inclusion of minimum numbers of respondents from laboratories using

tubes or analytical platforms from particular manufacturers. Thus, the study was not powered to

statistically compare sample quality issue rates in light of such factors. Notably, some tube types

were more highly represented than others among survey respondents (e.g. 61.0% of respondents

reported using BD tubes), which may have contributed to a biased representation of tubes used

across all clinical chemistry laboratories, with potential impacts on the data. Additional research

is needed to more comprehensively understand the impacts of factors including sample tube and

analyzer type on the prevalence of specific sample quality issues, error rates, and the resulting

economic implications on the laboratory. Further, while data regarding inspection, remediation,

and recollection practices were collected in order to highlight their contribution to annual

laboratory costs, the relative success of these approaches requires more in-depth studies of

laboratory workflow.

Interestingly, fibrin and gel globules were more commonly reported in mostly serum

versus mostly plasma laboratories, which may be related to the different serum tube components

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(e.g. clot activators) and treatment of serum samples (sufficient clotting time requirement).18, 19 It

is important to note, however, that respondents were not asked directly about which sample types

were more prone to these specific sample quality issues. Thus a more detailed understanding of

sample- and type-specific issues requires further study. Additionally, the survey did not collect

data regarding all laboratory procedures, such as sample standing time or storage conditions,

which may contribute to sample quality issues. Interestingly, however, transportation damage

was identified as potentially impacting sample quality by a number of respondents (Table 2).

While this suggests storage during transport as a potential concern, this cannot be addressed

through the present survey and requires further study.

Due to the self-reported nature of the survey, no independent verification or observations

were made. Reported incidences of fibrin or gel globules may be higher than actual incidences

due to the disruptive nature of their presence, however we have no reason to believe that these

values were inflated. Although the results of the survey cannot be generalized to all clinical

chemistry laboratories in these regions, the data do represent a diverse set of laboratories with

varying capabilities across a variety of geographic regions.

Previous studies have shown that errors in the pre-analytical phase are often due to

sample quality problems related to collection and processing,1, 11 however, there have been few

studies investigating the incidence of sample quality issues related to fibrin and gel particles

specifically, and the methods employed by laboratories to remediate them. To the best of our

knowledge, this is the first in-depth study of the incidence and impacts of these specific issues

across multiple geographic regions.

Efforts related to pre-analytical and analytical erroneous result mitigation have largely

been focused on informatics and automating laboratory processes,25 and although these

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improvements have been helpful, significant time and capital are still spent remediating blood

sample quality issues in laboratories, as exhibited by the present study. As demonstrated by our

economic estimates, even sample defects that occur relatively infrequently can have important

impacts on laboratory workflow and costs. While this study was focused on the laboratory, the

impacts of these sample quality issues on patients (for example, time required to return for

redraw, pain resulting from repeated venipuncture) were not estimated. These are also important

downstream effects, however, that warrant attention from clinical chemistry laboratories. The

results of this study suggest that focusing on improving the quality of the blood sample itself, for

example by improving collection practices, transport, or blood collection tube technology, may

be the next logical step for further prevention and reduction of erroneous test results.

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REFERENCES

1. Lippi G, Guidi GC, Mattiuzzi C and Plebani M. Preanalytical variability: the dark side of

the moon in laboratory testing. Clin Chem Lab Med 2006; 44: 358-365.

2. Epner PL, Gans JE and Graber ML. When diagnostic testing leads to harm: a new

outcomes-based approach for laboratory medicine. BMJ Qual Saf 2013; 22: ii6-ii10.

3. Plebani M. Errors in clinical laboratories or errors in laboratory medicine? Clin Chem

Lab Med 2006; 44: 750-759.

4. Bonini P, Plebani P, Ceriotti F and Rubboli F. Errors in Laboratory Medicine. Clin Chem

2002; 48: 691-698.

5. Astion ML, Shojania KG, Hamill TR, Kim S and Ng VL. Classifying Laboratory

Incident Reports to Identify Problems That Jeopardize Patient Safety. Am J Clin Pathol 2003;

120: 18-26.

6. Carraro P and Plebani M. Errors in a Stat Laboratory: Types and Frequencies 10 Years

Later. Clin Chem 2007; 53: 1338-1342.

7. Valenstein PN, Raab SS and Walsh MK. Identification Errors Involving Clinical

Laboratories. Arch Pathol Lab Med 2006; 130.

8. Green SF. The cost of poor blood specimen quality and errors in preanalytical processes.

Clin Biochem 2013; 46: 1175-1179.

9. Carraro P, Zago T and Plebani M. Exploring the initial steps of the testing process:

frequency and nature of pre-preanalytic errors. Clin Chem 2012; 58: 638-642.

10. Kalra J. Medical errors: impact on clinical laboratories and other critical areas. Clin

Biochem 2004; 37: 1052-1062.

Page 17 of 42


123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

DOI: 10.1177/0004563216651647



18

11. Ashavaid TF, Dandekar SP, Khodaiji S, Ansari M and Singh AP. Influence of method of

specimen collection on various preanalytical sample quality indicators in EDTA blood collected

for cell counting. Ind J Clin Biochem 2009; 24: 356-360.

12. McCraw A, Hillarp A and Echenagucia M. Considerations in the laboratory assessment

of haemostasis. Haemophilia 2010; 16: 74-78.

13. Plebani M. Harmonization in laboratory medicine: the complete picture. Clin Chem Lab

Med 2013; 51: 741-751.

14. Plebani M and Lippi G. Hemolysis index: quality indicator or criterion for sample

rejection? Clin Chem Lab Med 2009; 47: 899-902.

15. Bowen RAR, Hortin GL, Csako G, Otanez OH and Remaley AT. Impact of blood

collection devices on clinical chemistry assays. Clin Biochem 2010; 43: 4-25.

16. Spiritus T, Zaman Z and Desmet W. Iodinated contrast media interfere with gel barrier

formation in plasma and serum separator tubes. Clin Chem 2003; 49: 1187-1189.

17. Daves M, Lippi G, Cosio G, et al. An unusual case of a primary blood collection tube

with floating separator gel. J Clin Lab Anal 2012; 26: 246-247.

18. Phlebotomy Order of Draw: WHO, WHAT, WHEN, WHERE, WHY, & HOW. National

Center for Competency Testing 7.0: (2013).

19. Beckman Coulter. Technical Bulletin: The Role of Preanalytical Factors in

Immunoassays,

https://www.beckmancoulter.com/ucm/idc/groups/public/documents/webasset/glb_bci_150910.p

df (2006 (updated 2014) accessed 15 February 2016).

20. Lippi G, Salvagno GL, Montagnana M, Franchini M and Guidi GC. Phlebotomy issues

and quality improvement in results of laboratory testing. Clinical Laboratory 2006; 52: 217-230.

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21. Wilde C. Subject preparation, sample collection, and handling. In: Wild D, (ed.). The

immunoassay handbook. 3rd ed. NY: Elsevier, 2005, pp. 443-455.

22. Çuhadar S. Preanalytical variables and factors that interfere with the biochemical

parameters: a review. OA Biotechnology 2013; 2: 19.

23. Ferraro S and Panteghini M. Laboratory medicine as the science that underpins medicine:

the “high-sensitivity” troponin paradigm. Clin Chem Lab Med 2015; 53: 653-664.

24. Cervellin G, Aloe R and Lippi G. A case of factitious hyponatremia and hypokalemia due

to the presence of fibrin gel in serum. Diagnosis 2015; 2: 73-74.

25. Lippi G. Governance of preanalytical variability: travelling the right path to the bright

side of the moon? Clin Chim Acta 2009; 404: 32-36.

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Table 1. Respondent laboratory characteristics.

United

States

(n=36)

Canada

(n=18)

United

Kingdom

(n=18)

Germany

(n=17)

France

(n=18)

Italy

(n=15)

Benelux

and

Nordica

(n=15)

Turkey

(n=13)

Australia

(n=14)

All

respondents

(n=164)

Annual number of blood collection

tubes, n

Mean 417,000 655,270 395,333 824,471 232,200 358,000 410,880 378,462 341,143 447,242

Maximum 1,920,000 7,200,000 1,440,000 6,000,000 720,000 936,000 1,200,000 720,000 1,200,000 7,200,000

Minimum 14,400 36,000 60,000 30,000 42,000 19,200 10,800 48,000 72,000 10,800

Blood sample type, n (%)b

Mostly plasma 9 (25%) 4 (22%) 0 (0%) 2 (12%) 2 (11%) 0 (0%) 3 (20%) 0 (0%) 0 (0%) 20 (12%)

Mostly serum 9 (25%) 5 (28%) 16 (89%) 3 (18%) 3 (17%) 10 (67%) 5 (33%) 13 (100%) 12 (86%) 76 (46%)

Plasma / serum mix 18 (50%) 9 (50%) 2 (11%) 12 (70%) 13 (72%) 5 (33%) 7 (47%) 0 (0%) 2 (14%) 68 (42%)

Blood tubes received from within

hospital, mean % of total (SD) 61% (31%) 67% (32%) 74% (16%) 92% (9%) 94% (6%) 65% (31%) 77% (20%) 83% (27%) 79% (13%) 75% (26%)

Other laboratory characteristics, n (%)

Laboratories with > 20% of testing STAT

16 (44%) 6 (33%) 8 (44%) 4 (24%) 5 (28%) 3 (20%) 4 (27%) 7 (54%) 14 (100%) 67 (41%)

Laboratories with ≥1 automated workflow step

28 (78%) 12 (67%) 16 (89%) 16 (94%) 10 (56%) 11 (73%) 7 (47%) 10 (77%) 12 (86%) 122 (74%)

Laboratories performing high-

sensitivity assaysc

33 (92%) 14 (78%) 16 (89%) 17 (100%) 14 (78%) 10 (67%) 10 (67%) 10 (77%) 14 (100%) 138 (84%)

aBenelux includes Belgium, Netherlands, and Luxembourg; Nordic includes Finland and Sweden

b Characterized as “mostly plasma” or “mostly serum” if ≥80% of tubes received were plasma or serum, respectively, and as “plasma/serum mix” if neither type accounted for ≥80% of sample tubes

received

c Human chorionic gonadotropin (hCG), creatine kinase MB isoenzyme (CK-MB), digoxin, triiodothyronine (T3), thyroxine (T4), troponin, phenytoin, thyroid-stimulating hormone (TSH).

All percentages rounded to the nearest unit and represent percent of all respondents unless otherwise noted; SD = standard deviation

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Table 2. Prevalence of blood sample quality issues in respondent laboratories

United

States

(n=36)

Canada

(n=18)

United

Kingdom

(n=18)

Germany

(n=17)

France

(n=18) Italy (n=15)

Benelux and

Nordic a

(n=15)

Turkey

(n=13)

Australia

(n=14)

All

respondents

(n=164)

Sample quality issues b

Fibrin strand in sample, mean % of tubes (SD)

4.1% (3.9%) 3.3% (3.4%) 0.6% (1.2%) 2.2% (3.5%) 1.8% (3.4%) 4.3% (3.9%) 2.0% (5.0%) 4.8% (7.0%) 0.7% (1.3%) 2.8% (4.1%)

Mostly plasma, mean % of tubes (SD)

3.7% (3.0%) 0.9% (0.7%) --- 0.5% (0.5%) 0.5% (0.5%) --- 0.3% (0.5%) --- --- 2.0% (2.6%)

Mostly serum, mean % of tubes (SD)

3.2% (3.8%) 3.6% (3.6%) 0.5% (1.2%) 0.8% (0.2%) 6.0% (6.4%) 4.8% (3.9%) 5.0% (7.7%) 4.8% (7.0%) 0.8% (1.4%) 2.9% (4.8%)

Fibrin mass in sample, mean % of tubes (SD)

2.4% (3.6%) 0.9% (1.6%) 0.4% (0.7%) 1.3% (2.5%) 1.2% (2.5%) 1.8% (3.4%) 1.1% (1.7%) 1.8% (2.9%) 0.1% (0.3%) 1.4% (2.7%)


1.0% (1.6%) 0.3% (0.4%) --- 2.0% (1.0%) 0.0% (0.0%) --- 0.0% (0.0%) --- --- 0.7% (1.3%)


3.0% (5.2%) 1.2% (1.9%) 0.3% (0.6%) 0.3% (0.5%) 4.0% (4.3%) 2.5% (4.0%) 2.6% (2.2%) 1.8% (2.9%) 0.1% (0.3%) 1.5% (3.1%)

Gel globule in sample, mean % of tubes (SD)

1.5% (2.4%) 0.7% (1.3%) 0.5% (1.2%) 0.3% (0.7%) 0.1% (0.2%) 3.7% (7.6%) 0.2% (0.5%) 1.0% (2.7%) 0.1% (0.3%) 0.9% (2.9%)


0.8% (1.6%) 0.3% (0.4%) --- 0.1% (0.1%) 0.0% (0.0%) --- 0.0% (0.0%) --- --- 0.4% (1.2%)


1.1% (2.1%) 0.4% (0.8%) 0.2% (0.4%) 0.0% (0.0%) 0.0% (0.0%) 5.5% (8.8%) 0.6% (0.8%) 1.0% (2.7%) 0.1% (0.3%) 1.2% (3.9%)

Respondents perceiving sample

transportation to impact quality, n

(%)

16 (44%) 7 (39%) 4 (22%) 6 (35%) 7 (39%) 1 (7%) 6 (40%) 8 (62%) 7 (50%) 62 (38%)

Perceived causes of analyzer probe

clogging, n (%)

Fibrin and/or gel globules 28 (77%) 12 (66%) 7 (39%) 15 (88%) 8 (44%) 9 (60%) 10 (67%) 11 (85%) 7 (50%) 107 (65%)

Fibrin only 12 (33%) 8 (44%) 2 (11%) 14 (82%) 6 (33%) 4 (27%) 10 (67%) 6 (46%) 4 (29%) 66 (40%)

Gel globules only 8 (22%) 2 (11%) 1 (6%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 1 (8%) 0 (0%) 12 (7%)

Both fibrin and gel globules 8 (22%) 2 (11%) 4 (22%) 1 (6%) 2 (11%) 5 (33%) 0 (0%) 4 (31%) 3 (21%) 29 (18%)

a Benelux includes Belgium, Netherlands, and Luxembourg; Nordic includes Finland and Sweden


received

All percentages rounded to the nearest unit and represent percent of all respondents unless otherwise noted; SD = standard deviation

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Table 3. Blood sample remediation, erroneous result mitigation, and analyzer probe maintenance in respondent laboratories

United

States

(n=36)

Canada

(n=18)

United

Kingdom

(n=18)

Germany

(n=17)

France

(n=18)

Italy

(n=15)

Benelux

and

Nordic a

(n=15)

Turkey

(n=13)

Australia

(n=14)

All

respondents

(n=164)

Visual inspection practices, n (%)

Visually inspect blood collection tubes 28 (78%) 15 (83%) 18 (100%) 11 (65%) 12 (67%) 8 (53%) 10 (67%) 10 (77%) 12 (86%) 124 (76%)

Inspect for fibrin strands and/or gel globules 26 (72%) 11 (61%) 7 (39%) 8 (47%) 7 (39%) 8 (53%) 7 (47%) 9 (69%) 7 (50%) 90 (55%)

Samples from remote locationsb 25 (69%) 10 (56%) 7 (39%) 3 (18%) 5 (28%) 3 (20%) 6 (40%) 7 (54%) 7 (50%) 73 (45%)

Samples from within hospitalb 22 (61%) 11 (61%) 5 (28%) 8 (47%) 7 (39%) 8 (53%) 6 (40%) 7 (54%) 7 (50%) 81 (49%)

Manual remediation, n (%)

Remediate fibrin strands and masses 30 (83%) 14 (78%) 4 (22%) 16 (94%) 11 (61%) 13 (87%) 7 (47%) 9 (69%) 5 (36%) 109 (66%)

Remediate gel globules 13 (36%) 6 (33%) 5 (28%) 4 (24%) 1 (6%) 5 (33%) 2 (13%) 2 (15%) 2 (14%) 40 (24%)

Analyzer probe maintenance and replacement,

times/year

Unscheduled maintenance, mean (SD) 11.1 (18.0) 3.5 (2.7) 5.7 (15.8) 5.4 (9.1) 4.6 (3.9) 6.6 (11.8) 6.1 (5.0) 3.8 (3.6) 2.5 (2.8) 6.2 (11.6)

Unscheduled maintenance, median (range) 5 (0-100) 3 (0-12) 1 (0-70) 2 (0-40) 4 (1-15) 3 (1-50) 5 (0-20) 3 (0-13) 1 (0-10) 3 (0-100)

Probe replacement, mean (SD) 4.0 (4.6) 1.3 (0.8) 2.0 (2.8) 7.1 (12.7) 1.3 (0.7) 2.5 (3.4) 2.0 (1.6) 1.6 (1.3) 1.4 (1.0) 2.8 (5.2)

Probe replacement, median (range) 3 (0-25) 1 (0-3) 1 (0-12) 3 (0-50) 1 (0-2) 2 (0-15) 1 (0-5) 1 (0-5) 1 (1-5) 2 (0-50)

Laboratories with erroneous/delayed result

mitigation practices for high-sensitivity assays, n (%) 28 (78%) 13 (72%) 5 (28%) 17 (100%) 10 (56%) 6 (40%) 5 (33%) 5 (39%) 8 (57%) 97 (59%)


b Inspecting from remote locations vs. within hospital are non-mutually exclusive practices

All percentages rounded to the nearest unit and represent percent of all respondents unless otherwise noted

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Table 4. Annual time costs of blood sample remediation and erroneous result mitigation

United

States

(n=36)

Canada

(n=18)

United

Kingdom

(n=18)

Germany

(n=17)

France

(n=18) Italy (n=15)

Benelux and

Nordica

(n=15)

Turkey

(n=13)

Australia

(n=14)

All sample remediation and erroneous result mitigation practices

Visual inspection (h)

Mean (SD) 158 (240) 42 (51) 18 (43) 165 (222) 54 (82) 99 (193) 53 (112) 60 (113) 50 (108)

Median (range) 39 (0-1063) 26 (0-200) 0 (0-180) 0 (0-700) 0 (0-250) 10 (0-750) 0 (0-410) 10 (0-428) 4 (0-360)

Manual remediation (h)

Mean (SD) 46 (76) 29 (45) 14 (43) 12 (27) 9 (18) 36 (56) 9 (14) 47 (99) 8 (19)

Median (range) 16.2 (0-

370) 9.4 (0-187) 0 (0-189) 1.2 (0-113) 0.6 (0-65) 11.6 (0-210) 0 (0-45) 3.5 (0-304) 0 (0-73)

Analyzer probe maintenance and

replacement (h)

Mean (SD) 277 (254) 151 (116) 534 (791) 611 (804) 302 (289) 279 (324) 525 (651) 395 (331) 387 (250)

Median (range) 156 (8-833) 140 (28-539) 279 (114-

3,343) 375 (70-3,612)

161 (3-989) 203 (11-1,385)

391 (31-2,731) 397 (51-1,343)

322 (116-1,166)

Pre-analytic redraw/recollection (h)

Mean (SD) 116 (175) 50 (70) 66 (176) 215 (417) 31 (48) 97 (139) 25 (32) 193 (295) 28 (79)

Median (range) 47 (0-868) 21 (0-282) 0 (0-670) 28 (2-1,395) 9 (0-186) 42 (0-434) 13 (0-99) 47 (0-1023) 0 (0-310)


SD = standard deviation

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Table 5. Annual costs of blood sample remediation and erroneous result mitigation, per 10,000 tubes

United States

(n=36)

Canada

(n=18)

United

Kingdom

(n=18)

Germany

(n=17) France (n=18) Italy (n=15)

Benelux and

Nordica (n=15)

Turkey

(n=13)

Australia

(n=14)

Transport damage

Total cost, mean (SD) $69 ($127) $228 ($413) $28 ($71) $05 ($12) $7 ($20) $51 ($82) $21 ($36) $48 ($140) $6 ($10)

Median (range) $0 ($0-$536) $2 ($0-$1445)

$0 ($0-$306) $0 ($0-$49) $0 ($0-$85) $0 ($0-$265) $0 ($0-$121) $0 ($0-$527) $0 ($0-$36)

Visual inspection


Median (range) $184 ($0-$233) $70 ($0-

$233) $0 ($0-$233) $0 ($0-$233) $0 ($0-$233)

$152 ($0-$233)

$0 ($0-$233) $12 ($0-

$233) $5 ($0-$233)

Manual remediation


Median (range) $121 ($0-

$1312) $65 ($0-

$777) $0 ($0-$247) $53 ($14-$385)

$27 ($0-$1028)

$99 ($0-$1376)

$0 ($0-$1203) $69 ($0-$1233)

$0 ($0-$195)

Analyzer probe maintenance

and replacement (h)


Median (range) $699 ($36-

$7593) $516 ($18-

$1533) $626 ($47-

$1408) $568 ($132-

$4221) $486 ($44-

$1148) $332 ($4-

$3219) $484 ($155-

$11071) $439 ($122-

$3791) $457 ($121-

$1646)

Pre-analytic

redraw/recollection


Median (range) $77 ($0-$464) $54 ($0-

$263) $46 ($0-$464) $31 ($8-$386) $15 ($0-$386) $77 ($0-$541) $15 ($0-$386) $46 ($0-$464) $0 ($0-$77)

High-sensitivity assay re-

testing


Median (range) $31 ($0-$2745) $13 ($0-

$502) $54 ($0-$135) $110 ($6-$5965)

$42 ($0-$1621)

$1 ($0-$117) $2 ($0-$107) $14 ($0-$4297)

$19 ($7-$210)


All costs are annual and in 2013 USD; SD = standard deviation

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FIGURE LEGEND

Figure 1. Blood sample collection and testing workflow, including pre-analytical and

analytical steps.

Aspects of workflow that can be automated include: ‘Centrifugation’, ‘Aliquot’, ‘Short

draw handling’, ‘Fibrin/gel handling’, ‘Send to autosorter’. Steps that may not be

performed in all laboratories are marked with an asterisk (*). Specifically, not all

laboratories may perform “visual inspection” or will “review flags” manually before

testing.

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Figure 1. Blood sample collection and testing workflow, including pre-analytical and analytical steps. Aspects of workflow that can be automated are highlighted in green. Steps that may not be performed in all

laboratories are marked with an asterisk (*). Specifically, not all laboratories may perform “visual

inspection” or will “review flags” manually before testing. 304x136mm (300 x 300 DPI)

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SUPPLEMENTARY MATERIAL

Detailed cost calculations and assumptions

These assumptions were used for cost calculations; however it is important to note that

costs will vary between laboratories.

General assumptions: hourly wages

Personnel Average hourly wage1

Laboratory technician $28

Pathologist $100

Receiving Staff $15

Nurse $35

Phlebotomist $17

Manual remediation: assumptions

Remediation step Time or cost2

Re-centrifugation

Centrifuge loading and unloading (min) 6

Number of samples per re-spin 10

De-capping and rimming out the tube

Time to de-cap & find new tube (sec) 2

Time to rim tube with pipette (sec) 7

Aliquoting

Time to aliquot sample to transfer cup (sec) 4

Cost of transfer cup $0.05

Cost of pipette $0.05

• Re-centrifugation, de-capping, and aliquoting times/costs applied only when the respondent indicated that they perform these respective steps; otherwise, they were zero

• 100% of tubes with visual quality issue are remediated • Rimming out tube defined as using an applicator to remove artifacts from sample

Manual remediation: inputs

• Annual tube volume, % of tubes that are serum, % of tubes that are plasma

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• Incidence of sample quality issues (% of tubes) • Steps performed to remediate (i.e. re-centrifugation, etc.)

Manual remediation: calculations

• Cost of re-centrifugation per tube = (Time to load/unload centrifuge) x (laboratory technician wage) / (# of samples per re-spin)

• Cost of de-capping per tube = (time to de-cap + time to rim) x (laboratory technician wage)

• Cost of aliquoting = (time to aliquot) x (laboratory technician wage) + (cost of transfer cup) + (cost of pipette)

• Number of tubes requiring remediation = (rate of quality issues) x (total number of tubes) x (% of damaged tubes that require remediation)

• Total cost of manual remediation = (# of tubes requiring remediation) x (cost of re-centrifugation) + (# of tubes requiring remediation) x (cost of de-capping and rimming out) + (# of tubes requiring remediation) x (cost of aliquoting)

Pre-analytic re-draw and re-collection: assumptions

Cost of phlebotomy supplies Cost per unit2 units

Gloves $0.10 1

Needle with holder $0.52 1

Tourniquet $0.10 1

Alcohol Wipe $0.03 1

Gauze (or Cotton) $0.04 1

Bandage (or Tape) $0.01 1

Sharps Container $0.06 1

Tubes $0.12 1

Tube barcode label $0.03 1

Tube waste removal $0.01 1

Total cost of phlebotomy supplies per unit $1.01

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Total time Nurse time (50%) Phlebotomist time (50%)

Time to re-collect (minutes/tube) 15 7.5 7.5

Call for re-draw 0.3 0.15 0.15

Inversion 0.1 0.05 0.05

Labeling 0.1 0.05 0.05

Hourly wage1 $35.00 $17.00

• Note: It was assumed that 100% of tubes that require re-collection are re-collected. While in some cases, tubes requiring re-collection might not be recollected, the assumption is that all tubes that require re-collection are re-collected and analyzed, as this is the only way that the laboratory could obtain results.

Pre-analytic re-draw and re-collection: inputs

• Annual tube volume • Incidence of damaged tubes needing re-draw/re-collect

Pre-analytic re-draw and re-collection: calculations

• Total # of tubes requiring re-draw/re-collect = (total # of tubes with fibrin or gel issues) x (% of damaged tubes requiring recollect)

• Total time spent by nurse/phlebotomist = ((time to collect) + (time to call for re-draw) + (time to invert tube) + (time to label tube)) x (total # of tubes requiring re-draw/recollect) x 50%

• Total labor cost = (total # of tubes requiring re-draw/re-collect) x (time spent by a nurse) x (nurse's hourly wage) + (total # of tubes requiring re-draw/recollect) x (time spent by a phlebotomist) x (phlebotomist's hourly wage)

• Total supplies cost = (total # of tubes requiring re-draw/re-collect) x (cost of phlebotomy supplies)

• Total cost of re-draw/re-collect = total labor cost + total supplies cost

High-sensitivity assay re-testing: assumptions

Analyte Average cost of reagent3,4

Human chorionic gonadotropin (hCG) $1.63

Creatine kinase MB isoenzyme (CK-MB) $1.91

Digoxin $1.30

Triiodothyronine (T3) $1.89

Thyroxine (T4) $1.75

Phenytoin (Dilantin) $0.99

Troponin $3.97

Thyroid-stimulating hormone (TSH) $1.49

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High-sensitivity assay re-testing: inputs

• Assays performed • Rate of false positives • Number of tubes per assay

High-sensitivity assay re-testing: calculations

• Cost of re-testing positives = (rate of all positives) x (cost of reagents) • Cost of re-testing false positives = (rate of false positives) x (cost of reagents) • Total cost of re-testing = (cost of re-testing positives) + (cost of re-testing false

positives)

Analyzer probe maintenance and replacement: Assumptions

Time and cost components Time or cost2

Time component

Time to run autoprocessor (min) 30 min

Time to run instrument analyzer (min) 10 min

Cost component

Cost of cleaning supplies $1.00

Cost of probe/cuvette $900

• Cost of supplies and labor is zero when the maintenance is performed by the technician from the instrument company

Analyzer probe maintenance and replacement: inputs

• Number of analyzers • Personnel performing routine cleaning/maintenance, unscheduled

cleaning/maintenance, and probe replacement • Incidence of routine probe cleaning, time for routine probe cleaning • Annual frequency of unscheduled probe maintenance, time for unscheduled

maintenance • Annual frequency of probe replacement, time for probe replacement

Analyzer probe maintenance and replacement: calculations

• Cost of supplies = (# of annual probe issues) x (cost of supplies per event) • Cost of labor = (# of annual probe issues) x (time to perform maintenance) x

(hourly wage)

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Transport damage: assumptions

Material Cost per tube

Transfer Tube $0.12

Pipette $0.05

Transport damage: inputs

• Annual tube volume • Percentage of tubes drawn from within hospital and from remote locations • Percentage of tubes centrifuged and/or aliquoted prior to transport

Transport damage: calculations

• Number of tubes centrifuged and aliquoted prior to transport = (% tubes centrifuged and aliquoted prior to transport) x (# of tubes)

• Cost of aliquoting = (cost of transfer tube) + (cost of pipette) • Cost of aliquoting prior to transport = (# of tubes centrifuged and aliquoted) x (cost

of aliquoting)

Visual inspection: assumptions

Step Time per tube

(sec)

Visual inspection 3

Personnel Average hourly wage1

Lab technicians $28

Receiving staff $15

Visual inspection: inputs

• Percentage of tubes from within the hospital and from remote sites that are visually inspected

• Personnel conducting the visual inspection

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Visual inspection: calculations

• Number of tubes currently inspected = (% tubes inspected) x (# of tubes) • Total time for manual inspection = (# of tubes inspected) x (time to inspect each

tube) • Total cost of manual inspection = (total time for manual inspection) x (lab

technician or receiving staff salary)

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Supplementary Table 1. Identified primary tube and instrument manufacturers in respondent laboratories

United States

(n=36)

Canada

(n=18)

United

Kingdom

(n=18)

Germany

(n=17)

France

(n=18)

Italy

(n=15) a

Benelux and

Nordicb

(n=15)

Turkey

(n=13)

Australia

(n=14)

All

respondents

(n=164)

Manufacturer of primary chemistry/immunohistochemistry tubes used

Becton Dickinson 91.7% 77.8% 44.4% 11.8% 61.1% 40.0% 73.3% 69.2% 42.9% 61.0%

Greiner 8.3% 16.7% 27.8% 0.0% 27.8% 46.7% 20.0% 15.4% 21.4% 18.3%

SARSTEDT 0.0% 0.0% 5.6% 88.2% 5.6% 6.7% 0.0% 0.0% 7.1% 12.2%

Terumo 0.0% 0.0% 11.1% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 1.8% Other/don’t know 0.0% 5.6% 11.1% 0.0% 5.6% 6.7% 6.7% 15.4% 28.6% 6.7%

Manufacturer of primary analyzer used for routine chemistry testing

Abbott 5.6% 5.6% 11.1% 0.0% 11.1% 0.0% 20.0% 7.7% 7.1% 7.3%

Beckman Coulter 30.6% 16.7% 0.0% 17.6% 16.7% 7.1% 0.0% 30.8% 14.3% 16.5%


Bio-Rad 0.0% 0.0% 11.1% 0.0% 0.0% 0.0% 0.0% 7.7% 7.1% 2.4%

Ortho Diagnostics 8.3% 27.8% 0.0% 0.0% 0.0% 0.0% 6.7% 0.0% 0.0% 5.5%

Roche 13.9% 16.7% 33.3% 5.9% 38.9% 42.9% 73.3% 23.1% 21.4% 27.4%

Siemens 36.1% 27.8% 11.1% 58.8% 16.7% 14.3% 0.0% 7.7% 0.0% 22.0%

Sysmex 0.0% 0.0% 0.0% 5.9% 0.0% 0.0% 0.0% 0.0% 0.0% 0.6%

Other/don’t know 2.8% 0.0% 33.4% 0.0% 5.6% 35.7% 0.0% 15.4% 35.7% 12.2%

Manufacturer of primary centrifuge used for chemistry testing

Abbott 0.0% 0.0% 5.6% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.6%

Beckman Coulter 16.7% 27.8% 11.1% 17.6% 16.7% 0.0% 0.0% 15.4% 14.3% 14.0%


Bio-Rad 2.8% 0.0% 5.6% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 1.2%

Drucker 2.8% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.6%

Eppendorf 5.6% 11.1% 22.2% 5.9% 0.0% 0.0% 0.0% 23.1% 35.7% 10.4%

Hettich 25.0% 16.7% 5.6% 17.6% 5.6% 0.0% 26.7% 15.4% 21.4% 15.9%

Iris 2.8% 5.6% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 1.2%

Roche 2.8% 11.1% 16.7% 5.9% 16.7% 33.3% 20.0% 15.4% 0.0% 12.2%

Siemens 5.6% 0.0% 5.6% 35.3% 5.6% 6.7% 0.0% 0.0% 0.0% 6.7%

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Sigma 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 26.7% 0.0% 0.0% 2.4%

Sysmex 0.0% 0.0% 0.0% 5.9% 0.0% 0.0% 0.0% 0.0% 0.0% 0.6%

Thermo Fisher Scientific 8.3% 22.2% 11.1% 0.0% 27.8% 6.7% 26.7% 0.0% 0.0% 11.6%

Other/don’t know 25.0% 5.6% 16.7% 11.8% 22.2% 53.4% 0.0% 30.8% 14.3% 20.1%

a n=14 for analyzer data

b Benelux includes Belgium, Netherlands, and Luxembourg; Nordic includes Finland and Sweden

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Supplementary Table 2. Summary of respondent laboratory sample centrifugation times and speeds

United States Canada United

Kingdom Germany France Italy

Benelux and

Nordica Turkey Australia Grand Total

Sample centrifugation time, min

Routine samples n=36 n=18 n=18 n=17 n=18 n=15 n=15 n=13 n=14 n=164

Mean (SD) 7.7 (2.6) 8.3 (3.4) 6.3 (2.2) 9.1 (2.4) 12.3 (2.7) 11.1 (3.5) 9.3 (2.3) 7.5 (2.8) 8.4 (2.8) 8.8 (3.2)

Median (range) 7 (3-15) 10 (3-15) 5 (3-10) 10 (5-15) 10 (9-17) 10 (5-15) 10 (5-15) 10 (3-10) 9 (5-15) 10 (3-17)

STAT samples n=36 n=18 n=18 n=17 n=18 n=15 n=15 n=13 n=14 n=164

Mean (SD) 5.9 (2.5) 6.5 (3.1) 6.0 (1.9) 8.8 (2.6) 10.5 (3.1) 10.7 (4.0) 9.3 (2.3) 6.3 (2.0) 8.4 (2.8) 7.7 (3.3)

Median (range) 5 (3-15) 5 (3-15) 5 (3-10) 10 (5-15) 10 (3-15) 10 (5-15) 10 (5-15) 5.5 (3-10) 9 (5-15) 7 (3-15)

Sample centrifugation speed, RCF or RPM

Typical chemistry

(RCF) n=6 n=5 -- n=5 n=1 n=2 n=11 n=3 -- n=33

Mean (SD) 1597 (1132) 1841 (1817) -- 2440 (625) 2200 (0) 5120 (1120) 2157 (410) 2833 (943) -- 2292 (1283)

Median (range) 1200

(1-3180) 1200

(1.3-4000) --

2000 (200-3600)

2200 (2200-2200)

5120 (4000-6240)

2000 (1800-3000)

3500 (1500-3500)

--

2000 (1-6240)

Typical chemistry

(RPM) n=27 n=11 n=4 n=13 n=12 n=11 n=4 n=7 n=5 n=94

Mean (SD) 3454 (962) 2791 (1140) 3500 (866) 5138 (2697) 3375 (740) 4455 (1421) 3225 (228) 3371 (1035) 3100 (583) 3684 (1543)

Median (range) 3200

(2000-6200) 3000

(2.9-4000) 3000

(3000-5000) 4000

(3000-14000) 3500

(2000-5000) 3500

(3000-6000) 3200

(3000-3500) 4000

(1000-4100) 3000

(2500-4000) 3500

(2.9-14000)

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Supplementary Table 3. Prevalence of blood sample quality issues in respondent laboratories

United

States

(n=36)

Canada

(n=18)

United

Kingdom

(n=18)

Germany

(n=17)

France

(n=18) Italy (n=15)

Benelux and

Nordic a

(n=15)

Turkey

(n=13)

Australia

(n=14)

All

respondents

(n=164)

Sample quality issues b

Fibrin strands in sample, median % of tubes (range)

2.5% (0-15%)

2% (0-15%) 0% (0-5%) 1% (0-15%) 1% (0-15%) 5% (0-10%) 0% (0-20%) 1% (0-20%) 0% (0-5%) 1% (0-20%)

Median % of tubes (range) 5% (0-10%) 0.8% (0-

2%) ---

0.5% (0-1%)

0.5% (0-1%)

--- 0% (0-1%) --- --- 1% (0-10%)

Median % of tubes (range) 2% (0-10%) 2% (0-10%) 0% (0-5%) 1% (0.5-

1%) 2% (1-15%) 5% (0-10%) 0% (0-20%) 1% (0-20%) 0% (0-5%) 1% (0-20%)

Median % of tubes (range) 1% (0-15%) 0% (0-10%) 0% (0-2%) 0% (0-10%) 0% (0-10%) 0% (0-10%) 0% (0-5%) 0% (0-10%) 0% (0-1%) 0% (0-15%)

Median % of tubes (range) 0% (0-5%) 0% (0-1%) --- 2% (1-3%) 0% (0-0%) --- 0% (0-0%) --- --- 0% (0-%5)



Median % of tubes (range) 0% (0-5%) 0% (0-1%) --- 0.1% (0-

0.1%) 0% (0-0%) --- 0% (0-0%) --- --- 0% (0-5%)

Median % of tubes (range) 0% (0-5%) 0% (0-2%) 0% (0-1%) 0% (0-0%) 0% (0-0%) 0% (0-25%) 0% (0-2%) 0% (0-10%) 0% (0-1%) 0 (0-25)


b Characterized as “mostly plasma” or “mostly serum” if ≥80% of tubes received were plasma or serum, respectively, and as “plasma/serum mix” if neither type accounted for ≥80% of sample

tubes received

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Supplementary Table 4. Frequency of unscheduled probe maintenance and replacement, by predominant blood sample type

United States Canada United

Kingdom Germany France Italy

Benelux and

Nordica Turkey Australia

All

respondents

Unscheduled probe maintenance (times/year)

Mostly plasma

n 9 4 --- 2 2 --- 3 --- --- 20

Mean (SD) 5.6 (7.4) 6.0 (3.7) --- 20.0 (20.0) 2.5 (1.5) --- 7.0 (2.2) --- --- 7.0 (9.4)

Median (range) 2 (0-25) 4.5 (3-12) --- 20 (0-40) 2.5 (1-4) --- 6 (5-10) --- --- 4 (0-40)

Mostly serum

n 9 5 16 3 3 10 5 13 12 76

Mean (SD) 12.8 (14.6) 1.2 (0.7) 1.9 (2.3) 1.7 (0.5) 3.0 (1.4) 7.7 (14.3) 4.8 (2.8) 3.8 (3.6) 2.7 (3.0) 4.6 (8.4)

Median (range) 8 (2-50) 1 (0-2) 1 (0-10) 2 (1-2) 2 (2-5) 2 (1-50) 4 (2-10) 2 (0-13) 1.5 (1-10) 2 (0-50)

Plasma / serum mix

n 18 9 2 12 13 5 7 --- 2 68

Mean (SD) 12.9 (22.3) 3.7 (1.5) 35.5 (34.5) 3.8 (3.1) 5.3 (4.3) 4.4 (1.6) 6.7 (6.7) --- 1.5 (0.5) 7.7 (14.7)

Median (range) 5 (2-100) 4 (1-6) 35.5 (1-70) 2 (1-10) 5 (1-15) 5 (2-6) 5 (0-20) --- 1.5 (1-2) 4 (0-100)

Unscheduled probe replacement (times/year)

Mostly plasma

n 9 4 --- 2 2 --- 3 --- --- 20

Mean (SD) 2.7 (2.8) 1.8 (1.1) --- 16.5 (13.5) 1.0 (0.0) --- 3.0 (2.2) --- --- 3.8 (6.4)

Median (range) 2 (0-10) 2 (0-3) --- 16.5 (3-30) 1 (n/a) --- 4 (0-5) --- --- 2 (0-30)

Mostly serum

n 9 5 16 3 3 10 5 13 12 76

Mean (SD) 2.4 (0.8) 0.8 (0.4) 1.4 (1.6) 1.3 (1.2) 1.3 (0.5) 2.8 (4.1) 2.0 (1.1) 1.6 (1.3) 1.4 (1.1) 1.8 (2.0)

Median (range) 2 (1-4) 1 (0-1) 1 (0-6) 1 (0-3) 1 (1-2) 1.5 (0-15) 2 (1-4) 1 (0-5) 1 (1-5) 1 (0-15)

Plasma / serum mix

n 18 9 2 12 13 5 7 --- 2 68

Mean (SD) 5.4 (5.9) 1.3 (0.7) 6.5 (5.5) 7.0 (13.2) 1.3 (0.7) 1.8 (0.4) 1.6 (1.5) --- 1.0 (0.0) 3.6 (6.9)

Median (range) 3.5 (0-25) 1 (0-2) 6.5 (1-12) 2 (0.5-50) 1 (0-2) 2 (1-2) 1 (0-5) --- 1 (n/a) 2 (0-50)



received


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Supplementary Table 5. Plasma tube re-testing for high-sensitivity assays

United

States

(n=36)

Canada

(n=18)

United

Kingdom

(n=18)

Germany

(n=17)

France

(n=18) Italy (n=15)

Benelux and

Nordic a

(n=15)

Turkey

(n=13)

Australia

(n=14)

All

respondents

(n=164)

Plasma tube re-testing for high-

sensitivity assays, n (%)a

Laboratories re-testing single plasma tubes

27 (75%) 13 (72%) 5 (28%) 16 (94%) 8 (44%) 6 (40%) 5 (33%) 2 (15%) 8 (57%) 90 (55%)

Re-centrifuge the primary tube then run through the analyzer

13 (36%) 7 (39%) 3 (17%) 13 (76%) 5 (28%) 3 (20%) 2 (13%) 0 (0%) 8 (57%) 54 (33%)

Aliquot out of the primary tube to another tube which is then run through the analyzer

14 (39%) 6 (33%) 3 (17%) 3 (18%) 5 (28%) 5 (33%) 3 (20%) 1 (8%) 7 (50%) 47 (29%)

Run the primary tube through the analyzer again without further handling

15 (42%) 7 (39%) 5 (28%) 3 (18%) 5 (28%) 1 (7%) 1 (7%) 1 (8%) 5 (36%) 43 (26%)

a Answers were non-mutually exclusive

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Supplementary Table 6. Estimated time spent on analyzer probe maintenance and replacement, routine or unscheduled, by

region

United

States

(n=36)

Canada

(n=18)

United

Kingdom

(n=18)

Germany

(n=17)

France

(n=18) Italy (n=15)

Benelux and

Nordica

(n=15)

Turkey

(n=13)

Australia

(n=14)

Analyzer probe maintenance and replacement – routine or unscheduled

Routine probe cleaning (h)

Mean (SD) 109 (140) 96 (112) 350 (421) 267 (229) 161 (190) 108 (129) 300 (395) 227 (216) 268 (149)

Median (range) 1 (3-683) 59 (0-455) 228 (26-1911) 228 (33-956) 65 (0-637) 78 (0-546) 182 (0-1,638) 182 (6-637) 205 (52-546)

Unscheduled probe cleaning (h)

Mean (SD) 150 (184) 47 (34) 163 (496) 265 (577) 129 (147) 151 (266) 204 (283) 148 (181) 102 (134)

Median (range) 61 (0-750) 38 (0-113) 41 (0-2,205) 90 (0-2,520) 45 (2-540) 68 (9-1125) 105 (0-1,080) 63 (0-630) 59 (0-540)

Probe replacement (h)

Mean (SD) 18 (22) 7 (6) 21 (40) 79 (133) 12 (10) 20 (38) 21 (24) 20 (19) 17 (18)

Median (range) 12 (0-108) 7 (0-22) 11 (0-182) 27 (0-455) 10 (0-37) 13 (0-163) 15 (0-76) 15 (0-76) 640 (8-80)



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Supplementary Figure 1. Incidence of erroneous results in high-sensitivity assays.

Percentages are based on the number of laboratories performing each given assay.

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

<1% 1% ≥2%

Percentage of respondets who perfrom assay

Incidence of erroneous results

Human chorionic gonadotropin (hCG)

Creatine kinase MB isoenzyme (CK-MB)

Digoxin

Triiodothyronine (T3)

Thyroxine (T4)

Phenytoin (Dilantin)

Troponin

Thyroid-stimulating hormone (TSH)

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Supplementary Figure 2. Current handling practices for high-sensitivity assays from

plasma tubes to prevent erroneous results. For each respondent, reported prevention practices

were the same for all high-sensitivity assays performed.

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Human chorionic gonadotropin (hCG)

Creatine kinase MB isoenzyme (CK-MB)

Digoxin

Triiodothyronine (T3)

Thyroxine (T4)

Phenytoin (Dilantin)

Troponin

Thyroid-stimulating hormone (TSH )

Percentage of respondents who perform assay

Draw one tube, process the same tube twice, sequentially

Draw one tube, process the tube once, if positive result/out of range, run the same

tube a second time

Draw two tubes, process both tubes simultaneously

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REFERENCES

1. US Bureau of Labor Statistics. NAICS 621500 Medical and diagnostic laboratories,

http://www.bls.gov/oes/current/naics4_621500.htm (accessed 10 March 2014).

2. Data on File. Becton Dickinson and Company. Franklin Lakes, NJ, USA. 2013.

3. Steimer W, Müller C, Eber B. Digoxin assays: Frequent, substantial, and potentially

dangerous interference by spironolactone, canrenone, and other steroids. Clin Chem

2002; 48: 507-516.

4. Department of Veterans Affairs Federal Supply Service. Authorized Federal Supply

Schedule Price List, 2007-2012,

http://www.va.gov/nac/forms/Ortho_Clinical_Diagnostics_V797P_7126A_OCD_CPT.pd

f (accessed 22 November 2014).

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