Next Article in Journal
Myocarditis, Myositis, and Myasthenia Gravis Overlap Syndrome Associated with Immune Checkpoint Inhibitors: A Systematic Review
Next Article in Special Issue
Machine Learning for Patient-Based Real-Time Quality Control (PBRTQC), Analytical and Preanalytical Error Detection in Clinical Laboratory
Previous Article in Journal
Morphological Analysis of the Anatomical Mandibular Lingual Concavity Using Cone Beam Computed Tomography Scans in East Asian Population—A Retrospective Study
Previous Article in Special Issue
Community Point of Care Testing in Diagnosing and Managing Chronic Kidney Disease
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Comparative Assessment of Risk and Turn-Around Time between Sequence-Based Typing and Next-Generation Sequencing for HLA Typing

1
Department of Laboratory Medicine, Korea University Anam Hospital, Seoul 02841, Republic of Korea
2
Department of Laboratory Medicine, Konkuk University School of Medicine, Seoul 05030, Republic of Korea
3
Department of Laboratory Medicine, Korea University College of Medicine, Seoul 02841, Republic of Korea
*
Author to whom correspondence should be addressed.
Diagnostics 2024, 14(16), 1793; https://doi.org/10.3390/diagnostics14161793
Submission received: 3 July 2024 / Revised: 14 August 2024 / Accepted: 15 August 2024 / Published: 16 August 2024
(This article belongs to the Special Issue Laboratory Medicine: Extended Roles in Healthcare Delivery)

Abstract

:
This study compared laboratory risk and turn-around time (TAT) between sequence-based typing (SBT) and next-generation sequencing (NGS) for human leukocyte antigen (HLA) typing. For risk assessment, we utilized the risk priority number (RPN) score based on failure mode and effect analysis (FMEA) and a risk acceptability matrix (RAM) according to the Clinical Laboratory Standards Institute (CLSI) guidelines (EP23-A). Total TAT was documented for the analytical phase, and hands-on time was defined as manual processes conducted by medical technicians. NGS showed a significantly higher total RPN score than SBT (1169 vs. 465). NGS indicated a higher mean RPN score, indicating elevated severity and detectability scores in comparison to SBT (RPN 23 vs. 12, p = 0.001; severity 5 vs. 3, p = 0.005; detectability 5 vs. 4, p < 0.001, respectively). NGS required a greater number of steps than SBT (44 vs. 25 steps), all of which were acceptable for the RAM. NGS showed a longer total TAT, total hands-on time, and hands-on time per step than SBT (26:47:20 vs. 12:32:06, 03:59:35 vs. 00:47:39, 00:05:13 vs. 00:01:54 hh:mm:ss, respectively). Transitioning from SBT to NGS for HLA typing involves increased risk and an extended TAT. This study underscored the importance of evaluating these factors to optimize laboratory efficiency in HLA typing.

1. Introduction

Human leukocyte antigen (HLA) typing plays a pivotal role in determining compatibility between donor and recipients in transplantation and providing insights into the genetic basis of autoimmune diseases [1,2,3,4]. HLA typing can be classified into three resolution levels: low resolution (serological-level typing), medium resolution (allelic group typing), and high resolution (allelic-level typing) [1]. Serological-level typing indicated lower sensitivity and specificity, along with reported inconsistencies, prompting the introduction of DNA-based testing [5]. DNA-based testing is considered a reliable alternative, identifying two alleles of major HLA loci—HLA-A, -B, -C, -DRB1, -DQB1, and -DPB1 [6].
Sequence-based typing (SBT) is considered the gold standard at the allele level for determining the full nucleotide sequence of the alleles present [7]. However, SBT can result in genotype ambiguity, allele ambiguity, and phase ambiguity. A growing list of ambiguities required additional testing, leading to a time-consuming process, additional costs, and delayed results [7]. Next-generation sequencing (NGS) has recently been actively introduced for HLA typing, utilizing the massive parallel sequencing of clonal DNA molecules [8,9]. This approach significantly increased genomic coverage and effectively improved ambiguity resolution [10].
Clinical laboratories are consistently under pressure to provide qualified and reliable results within a limited time, without compromising laboratory efficiency [11]. Risk assessment contributes to enhancing patient safety and satisfaction, ensuring the delivery of high-quality laboratory results [12]. Laboratory efficiency can be achieved by decreased risk and turn-around time (TAT) [13]. A shorter TAT facilitates early diagnosis and treatment, reduces hospital stays, and improves patient satisfaction [14,15].
According to the International Organization for Standardization (ISO) 22367:2020 and Clinical Laboratory Standards Institute (CLSI) guidelines (EP18-A2), it was recommended to conduct a failure mode and effect analysis (FMEA) as part of the risk assessment when introducing new test methods or equipment into a clinical laboratory [16,17]. FMEA systematically identifies and evaluates potential failures and their causes across the entire process by a top-down approach [17]. The risk associated with each step was determined by calculating a risk priority number (RPN) score, derived from the multiplication of severity, occurrence, and detectability scores [18]. In addition, ISO 14971:2007 and CLSI guidelines EP23-A have introduced a risk acceptability matrix (RAM) for qualitative risk evaluation [19,20]. This matrix determined the risk based on the combination of two factors: the severity and occurrence of risk.
Various fields, such as clinical chemistry, diagnostic hematology, and transfusion medicine, have actively assessed the risks associated with testing methods to improve their laboratory efficiency [21,22]. However, to our knowledge, there have been no studies evaluating FMEA and RAM for HLA typing methods. Thus, this study aimed to evaluate the relative risk of SBT, considered the gold standard for high-resolution HLA typing compared to the newly introduced NGS, using FMEA and RAM. A comparative analysis of the TAT was also conducted to gain insights into the multifaceted aspects of SBT and NGS.

2. Materials and Methods

2.1. Sample Collection

This comparative study was conducted between July 2022 and May 2023 at Korea University Medical Center (KUMC) in Seoul, South Korea. The Institutional Review Board of KUMC exempted approval for the study protocol (K2023-0107) and waived the requirement for informed consent, given the utilization of the remaining samples after testing.

2.2. HLA Typing Using SBT and NGS

We analyzed samples for HLA-A, -B, -DRB1, and -DQB1 typing, with genomic DNA extracted from peripheral blood collected during the study. The initial steps, from sample collection to DNA extraction, were excluded from the comparative assessment as the process was identical for both SBT and NGS. The evaluation of risk and TAT for SBT and NGS was conducted from DNA amplification to the reporting of results. SBT was performed using AVITA plusTM HLA SBT kits (Biowithus Inc., Seoul, Republic of Korea) and BigDye Terminator mixture (Biowithus Inc.) according to the manufacturer’s instructions. Polymerase chain reaction (PCR) amplification, PCR product clean-up, and sequencing reaction were performed using GeneAmp® PCR System 9700 Thermal Cycler (PE-Biosystems, Foster City, CA, USA). The sequenced results were analyzed using BIOWITHUS SBT Analyzer software ver. 2.7.5 (Biowithus Inc.). NGS was performed using NGSgo®-AmpX c2 HLA kit (GenDx, Utrecht, The Netherlands) and NGSgo® Library Full kit (GenDx, Utrecht, Holland) according to the manufacturer’s instructions. PCR amplification, fragmentation with adaptor ligation, and indexing PCR were performed using GeneAmp® PCR System 9700 Thermal Cycler (PE-Biosystems, Foster City, CA, USA). Library amplification and sequencing were performed on a Miseq platform (Illumina Inc., San Diego, CA, USA). Sample genotypes were assigned using NGSengine version 2.30.0.28772 from GenDx based on the IPD-IMGT/HLA Database version 3.51.0.

2.3. Assessment of Risk and TAT

Risk assessment followed a systematic six-step process, as follows: (1) determination of the analytical test, (2) assembly of a team, (3) risk identification, (4) risk analysis, (5) risk assessment, and (6) action for risk improvement. The risk assessment team comprised a director, a medical doctor, and three qualified medical technicians. The director, experienced with FMEA, trained the other team members to identify potential defects, preventive actions, and outcomes. Team members independently conducted their risk assessment by reviewing each step of SBT or NGS and identifying risks based on their experience and information in the literature. Any discrepancies were analyzed and discussed until a consensus was reached for objectivity. For the risk assessment and evaluation, two tools, FMEA and RAM, were used. Using the FMEA rating scale, severity, occurrence, and detectability scores were assigned for each step [16,17,23]. Each score was determined as the mean of the scores assigned by the team members. The overall RPN score was calculated by multiplying the severity, occurrence, and detectability scores. Using the RAM rating scale, severity and occurrence were assigned for each step. Severity was categorized into five levels: negligible (inconvenience or temporary discomfort); minor (temporary injury or impairment not requiring medical intervention); serious (injury or impairment requiring medical intervention); critical (permanent impairment or life-threatening injury); and catastrophic (patient death). Occurrence was categorized into five levels: frequent (once per week); probable (once per month); occasional (once per year); remote (once every few years); and improbable (once during system measurement). Each step was judged as acceptable or unacceptable according to the classification described in Table 1. After the risk assessment and evaluation for each step, team members suggested preventive actions for steps with high RPN scores or unacceptable RAM results.
The TATs for each step in both SBT and NGS were recorded using a digital camera and stopwatch. The recording process was repeated three times to ensure precision in the comparison. The mean value of the three measurements was determined as the TAT for each step. The total TAT was calculated as the sum of TATs for all steps, further categorized into hands-on time and machine running time. Hands-on time represented manual steps performed by medical technicians during the test, while machine running time represented the non-manual steps.

2.4. Statistical Analysis

A Mann–Whitney U test was utilized for the nonparametric unpaired comparison between SBT and NGS. Statistical analysis was conducted using IBM SPSS v.26 (IBM Corp., Armonk, NY, USA), MedCalc v.19.1.7 (MedCalc Software, Ostend, Belgium), and Microsoft Excel 2013 on Windows 10. p-values < 0.05 were considered indicative of statistical significance.

3. Results

In SBT, the most common action for a potential defect was a repeat of the step, with outcomes resulting in delays or incorrect results (Table 2). According to the FMEA rating scale, the severity score ranged from 2 to 8, occurrence score ranged from 1 to 2, and detectability score ranged from 1 to 6. The resulting RPN scores for each step ranged from 3 to 72. Based on the RAM rating scale, severity showed 1 minor and 24 negligible steps, and occurrence showed 1 occasional, 5 remote, and 19 improbable steps. All steps revealed varying levels of risk, but according to the RAM rating scale, the risks were clinically acceptable.
Similarly, in NGS, a repeat of the step and delay or incorrect results were the most common actions and outcomes (Table 3). According to the FMEA rating scale, the severity score ranged from 3 to 8, occurrence score ranged from 1 to 2, and detectability score ranged from 3 to 6. The resulting RPN scores for each step ranged from 12 to 102. Based on the RAM rating scale, severity showed 1 minor and 43 negligible steps, and occurrence showed 1 occasional, 11 remote, and 32 improbable steps. All steps revealed varying levels of risk, but according to the RAM rating scale, the risks were clinically acceptable.
In SBT, the shortest step for hands-on time and overall TAT were the PCR product clean-up step and the interpretation and report step (00:02:30, 00:11:28 hh:mm:ss, respectively) (Table 4). The longest hands-on time, machine running time, and overall TAT were purification for the dye removal step (00:19:32, 05:56:57, 06:16:29 hh:mm:ss, respectively).
In NGS, the shortest hands-on time and overall TAT were the library quantification step (both 00:09:04 hh:mm:ss) (Table 5). The longest hands-on time was the interpretation and reporting step, (01:01:40 hh:mm:ss) and the longest machine running time and overall TAT were the next-generation sequencing step (17:00:00 and 17:13:43 hh:mm:ss, respectively).
NGS had a higher number of total steps than SBT (44 vs. 25 steps) (Table 6). The risks at all steps in both SBT and NGS were clinically acceptable. NGS showed a higher total RPN score than SBT (1169 vs. 465 RPN). NGS showed a higher mean RPN score with higher mean severity and detectability scores than SBT (RPN 23 vs. 12, p = 0.001; S 5 vs. 3, p = 0.005; D 5 vs. 4, p < 0.001, respectively). NGS indicated longer total TAT, total hands-on time, and hands-on time/step than SBT (26:47:20 vs. 12:32:06, 03:59:35 vs. 00:47:39, 00:05:13 vs. 00:01:54 hh:mm:ss, respectively).

4. Discussion

We conducted a comparative analysis of risk and TAT between SBT and NGS for HLA typing. Our findings revealed that the total RPN score of NGS was higher than SBT (1169 vs. 465 RPN), primarily due to the increased number of processing steps (44 vs. 25 steps). The median RPN score in NGS was also higher than SBT (23 vs. 12 RPN). NGS showed higher median severity and detectability scores than SBT (severity 5 vs. 3; detectability 5 vs. 4). Despite the elevated risk of NGS compared to SBT, it remained acceptable according to the RAM rating scale. The total TAT, total hands-on time, and hands-on time/step of NGS were longer than SBT (26:47:20 vs. 12:32:06, 03:59:35 vs. 00:47:39, 00:05:13 vs. 00:01:54 hh:mm:ss, respectively).
Recently, there has been a growing interest in assessing laboratory efficiency in clinical laboratories to improve productivity and patient safety [11]. The assessments involved the evaluation of risk assessment, TAT, and costs. FMEA, initially used in the industrial field, has recently found application in laboratory risk assessment. In a previous study by Nam et al. [24], a risk assessment comparison using FMEA was conducted between the tube test and automated column agglutination technology for anti-A/B isoagglutinin titers. The automated column agglutination technology demonstrated lower RPN scores than the tube test (337 vs. 1843 RPN), with the manual serial dilution step of the tube test accompanying the highest RPN score of 1080. This finding highlighted that RPN scores significantly increased when a processing step involved manual procedures, required skilled techniques, or included repetitive and numerous steps. NGS involved numerous complex steps, such as DNA fragmentation, adapter ligation, library pooling, size selection, and library quantification. The critical steps in NGS required more manual procedures and skilled techniques compared to SBT, leading to increased mean severity, mean detectability, mean RPN, and total RPN score. RAM, another indicator of laboratory risk management suggested by CLSI EP23-A, was also evaluated in this study [19]. Although the risk of NGS was higher than SBT, NGS was still deemed acceptable for HLA typing. Since there is currently no research applying the RAM in laboratory efficiency, further research is needed.
Our novel findings involved a comparison between FMEA and RAM. FMEA demonstrated significant advantages over RAM in comparing two or more items by quantifying the risk through scores [17]. However, a disadvantage of FMEA was its inability to establish a clear standard for evaluating the true acceptability of each processing step [23]. There was no standardized or validated cutoff for RPN scores. Nevertheless, Han et al. [23] suggested a careful review of the steps exceeding a score of 300 RPN. In this study, none of the processing steps in SBT and NGS exceeded 300 RPN. Regarding RAM, the rating scale appeared to be a more suitable method than FMEA for performing conformity assessments at each step [19]. However, RAM was not suitable for performing comparisons between different methods. In this study, a risk comparison between SBT and NGS could not be performed using RAM due to the same proportion of acceptable steps. Each laboratory should consider the appropriate criteria for conducting a risk assessment or evaluation based on its purpose.
TAT, another critical indicator of laboratory efficiency, has been actively used in previous research. Kim et al. [25] measured the mean, 99th percentile, and CV% TAT to assess the laboratory efficiency of total laboratory automation. The mean TAT, representing the timeliness of result reporting, decreased by 6.1%; the 99th percentile TAT, representing the outlier rate, decreased by 13.3%; and the CV% TAT, representing predictability, decreased by 70.0%. Despite the effectiveness of TAT in assessing laboratory efficiency, there is no clear consensus on which period should be incorporated when establishing TAT for a specific test [26,27]. In this study, we did not analyze the pre-analytical phase and DNA extraction step due to performing the same process in SBT and NGS. TATs were assessed after DNA extraction until the final reporting. Similar to risk assessment, TAT increased when the test involved manual procedures, required skilled techniques, or included repetitive and numerous steps. Due to the complex and numerous steps involved in NGS, NGS indicated longer total TAT, total hands-on time, and particularly hands-on time/step.
According to several reports, an increase in hands-on times has been correlated with increasing medical costs. Shin et al. [28] reported labor costs into the total expense, considering the average technician salary and the hands-on time required for testing a single sample. In the case of unexpected antibody screening, the automated analyzer significantly reduced labor costs compared to manual methods (KRW 539 vs. 2961 Korean), subsequently reducing the total expense (KRW 5920 vs. 7157 Korean). In this study, the increased hands-on time in NGS could potentially elevate labor costs, consequently contributing to an overall increase in total expense. However, we did not comprehensively compare costs in this study; further research is needed to compare direct and indirect costs between NGS and SBT.
Recent studies have shown controversial results regarding the impact of laboratory automation in terms of laboratory efficiency. Chung et al. [21] revealed that the automated crossmatching method has a lower RPN score (229 vs. 1435 RPN), shorter TAT (19.1 vs. 23.3 min), shorter hands-on time (1.1 vs. 5.3 min), and lower costs/test (USD 1.44 vs. 2.70) than the manual crossmatching method. Nam et al. [24] found that automated column agglutination technology showed lower RPN scores (33,700 vs. 184,300 RPN) than manual tube tests for anti-A/B isoagglutinin titers. However, TAT and cost were similar in automated and manual methods (TAT, 15:23:00 vs. 14:26:40; cost, 1377.4 vs. 1312.4, respectively). Nam et al. [22] revealed that automated white blood cell counting in leukopenic samples showed an even longer TAT than manual counting. In this study, NGS showed an increased risk and TAT due to the increase in the number and complexity of steps. Despite the controversial results on laboratory efficiency for automation, we expect that the automation of several NGS processes can improve laboratory efficiency. In particular, the DNA clean-up step revealed the highest RPN score, and the DNA quantification step revealed a remarkably longer hands-on time. The automation of these steps can contribute to improving the laboratory efficiency of NGS.
This study has some limitations. It involved a comparison of risk and TAT for a single sample without the evaluation of multiple samples. Given that NGS utilized a flow cell for analyzing multiple samples simultaneously, there is potential for a significant reduction in risk and TAT. In addition, TAT might differ between qualified and unskilled technicians. Therefore, it will be necessary to minimize the possibility of variation by conducting multiple measurements of the performance of a qualified and skilled technician.

5. Conclusions

This is the first study to compare risk and TAT between NGS and SBT for HLA typing. NGS required more processing steps and a more significant manual workload than SBT, resulting in higher RPN scores and longer TATs. These results suggest that it is necessary to consider the high risk and long TAT in NGS when contemplating the transition from SBT to NGS for HLA typing. Furthermore, companies involved in NGS development need to optimize the testing workflow, reduce the manual workload, and increase the automation process.

Author Contributions

Conceptualization, M.H. and M.N. (Minjeong Nam); methodology, H.K. and J.C.; formal analysis, J.C.; investigation, M.N. (Minjeong Nam) and J.C.; resources, S.Y., M.N. (Myunghyun Nam) and Y.C.; writing—original draft preparation, M.N. and J.C.; writing—review and editing, M.H., H.K., J.C. and M.N. (Minjeong Nam). All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by a grant of Korea University Anam Hospital, Seoul, Republic of Korea (O2412281).

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board (Korea University Medical Center) (K2023-0107), who waived the requirement for informed consent, given the utilization of the remaining samples after testing.

Informed Consent Statement

Patient consent was waived for informed consent, given the utilization of the remaining samples after testing.

Data Availability Statement

All data analyzed during this study are included in this published article and the datasets generated during this study are available from the corresponding author on reasonable request.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Edgerly, C.H.; Weimer, E.T. The Past, Present, and Future of HLA Typing in Transplantation. Methods Mol. Biol. 2018, 1802, 1–10. [Google Scholar] [PubMed]
  2. Mayor, N.P.; Robinson, J.; McWhinnie, A.J.M.; Ranade, S.; Eng, K.; Midwinter, W.; Bultitude, W.P.; Chin, C.H.; Bowman, B.; Marks, P.; et al. HLA Typing for The Next Generation. PLoS ONE 2015, 10, e0127153. [Google Scholar] [CrossRef] [PubMed]
  3. Yoo, H.J.; Yi, Y.; Kang, Y.; Kim, S.J.; Yoon, Y.I.; Tran, P.H.; Kang, T.; Kim, M.K.; Han, J.; Tak, E.; et al. Reduced Ceramides Are Associated with Acute Rejection in Liver Transplant Patients and Skin Graft and Hepatocyte Transplant Mice, Reducing Tolerogenic Dendritic Cells. Mol. Cells 2023, 46, 688–699. [Google Scholar] [CrossRef] [PubMed]
  4. Kim, G.R.; Choi, J.M. Current Understanding of Cytotoxic T Lymphocyte Antigen-4 (CTLA-4) Signaling in T-Cell Biology and Disease Therapy. Mol. Cells 2022, 45, 513–521. [Google Scholar] [CrossRef] [PubMed]
  5. De’Ath, A.; Rees, M.T.; Pritchard, D. The History and Evolution of HLA Typing External Proficiency Testing Schemes in UK NEQAS for H&I. Front. Genet. 2023, 14, 1272618. [Google Scholar]
  6. Nunes, E.; Heslop, H.; Fernandez-Vina, M.; Taves, C.; Wagenknecht, D.R.; Eisenbrey, A.B.; Fischer, G.; Poulton, K.; Wacker, K.; Hurley, C.K.; et al. Harmonization of Histocompatibility Typing Terms Working Group Definitions of histocompatibility typing terms. Harmonization of Histocompatibility Typing Terms Working Group. Hum. Immunol. 2011, 72, 1214–1216. [Google Scholar] [CrossRef] [PubMed]
  7. Voorter, C.E.; Palusci, F.; Tilanus, M.G. Sequence-based Typing of HLA: An Improved Group-specific Full-length Gene Sequencing Approach. Methods Mol. Biol. 2014, 1109, 101–114. [Google Scholar] [PubMed]
  8. Yin, Y.; Butler, C.; Zhang, Q. Challenges in The Application of NGS in The Clinical Laboratory. Hum. Immunol. 2021, 82, 812–819. [Google Scholar] [CrossRef] [PubMed]
  9. Jung, S.; Lee, J.S. Single-Cell Genomics for Investigating Pathogenesis of Inflammatory Diseases. Mol. Cells 2023, 46, 120–129. [Google Scholar] [CrossRef]
  10. Hosomichi, K.; Shiina, T.; Tajima, A.; Inoue, I. The Impact of Next-generation Sequencing Technologies on HLA Research. J. Hum. Genet. 2015, 60, 665–673. [Google Scholar] [CrossRef]
  11. Chaudhry, A.S.; Inata, Y.; Nakagami-Yamaguchi, E. Quality Analysis of the Clinical Laboratory Literature and Its Effectiveness on Clinical Quality Improvement: A Systematic Review. J. Clin. Biochem. Nutr. 2023, 73, 108–115. [Google Scholar] [CrossRef] [PubMed]
  12. Njoroge, S.W.; Nichols, J.H. Risk Management in the Clinical Laboratory. Ann. Lab. Med. 2014, 34, 274–278. [Google Scholar] [CrossRef] [PubMed]
  13. Sciacovelli, L.; Lippi, G.; Sumarac, Z.; West, J.; Castro, I.G.D.P.; Vieira, K.F.; Ivanov, A.; Plebani, M.; Working Group of International Federation of Clinical Chemistry and Laboratory Medicine (IFCC). Quality Indicators in Laboratory Medicine: The Status of the Progress of IFCC Working Group “Laboratory Errors and Patient Safety” Project. Clin. Chem. Lab. Med. 2017, 55, 348–357. [Google Scholar] [CrossRef] [PubMed]
  14. Holland, L.L.; Smith, L.L.; Blick, K.E. Reducing Laboratory Turnaround Time Outliers Can Reduce Emergency Department Patient Length of Stay: An 11-hospital Study. Am. J. Clin. Pathol. 2005, 124, 672–674. [Google Scholar] [CrossRef]
  15. Dolci, A.; Giavarina, D.; Pasqualetti, S.; Szőke, D.; Panteghini, M. Total Laboratory Automation: Do Stat Tests Still Matter? Clin. Biochem. 2017, 50, 605–611. [Google Scholar] [CrossRef]
  16. ISO 22367:2020; Medical Laboratories—Application of Risk Management to Medical Laboratories. International Organization for Standardization: London, UK, 2020.
  17. EP18A2; Risk Management Techniques to Identify and Control Laboratory Error Sources; Approved Guideline. Clinical and Laboratory Standards Institute: Malvern, PA, USA, 2009.
  18. Chiozza, M.L.; Ponzetti, C. FMEA: A Model for Reducing Medical Errors. Clin. Chim. Acta 2009, 404, 75–78. [Google Scholar] [CrossRef] [PubMed]
  19. EP23-A; Laboratory Quality Control Based on Risk Management: Approved Guideline. Clinical and Laboratory Standards Institute: Malvern, PA, USA, 2011.
  20. ISO 14971:2019; Medical Devices-application of Risk Management to Medical Devices. The Association for the Advancement of Medical Instrumentation: Arlington, VA, USA; American National Standards Institute: Washington, DC, USA, 2019.
  21. Chung, H.-J.; Hur, M.; Choi, S.G.; Lee, H.-K.; Lee, S.; Kim, H.; Moon, H.-W.; Yun, Y.-M. Benefits of VISION Max Automated Cross-matching in Comparison with Manual Cross-matching: A Multidimensional Analysis. PLoS ONE. 2019, 14, e0226477. [Google Scholar] [CrossRef] [PubMed]
  22. Nam, M.; Yoon, S.; Hur, M.; Lee, G.H.; Kim, H.; Park, M.; Kim, H.N. Digital Morphology Analyzer Sysmex DI-60 vs. Manual Counting for White Blood Cell Differentials in Leukopenic Samples: A Comparative Assessment of Risk and Turnaround Time. Ann. Lab. Med. 2022, 42, 398–405. [Google Scholar] [CrossRef] [PubMed]
  23. Han, T.H.; Kim, M.J.; Kim, S.; Kim, H.O.; Lee, M.A.; Choi, J.S.; Hur, M.; John, A.S. The Role of Failure Modes and Effects Analysis in Showing the Benefits of Automation in the Blood Bank. Transfusion 2013, 53, 1077–1082. [Google Scholar] [CrossRef]
  24. Nam, M.; Hur, M.; Lee, H.; Kim, H.; Park, M.; Moon, H.-W.; Yun, Y.-M. Comparison between Tube Test and Automated Column Agglutination Technology on VISION Max for Anti-A/B Isoagglutinin Titres: A Multidimensional Analysis. Vox Sang. 2022, 117, 399–407. [Google Scholar] [CrossRef]
  25. Kim, K.; Lee, S.G.; Kim, T.H.; Lee, S.G. Economic Evaluation of Total Laboratory Automation in the Clinical Laboratory of a Tertiary Care Hospital. Ann. Lab. Med. 2022, 42, 89–95. [Google Scholar] [CrossRef]
  26. Hawkins, R.C. Laboratory Turnaround Time. Clin. Biochem. Rev. 2007, 28, 179–194. [Google Scholar] [PubMed]
  27. Pati, H.P.; Singh, G. Turnaround Time (TAT): Difference in Concept for Laboratory and Clinician. Indian J. Hematol. Blood Transfus. 2014, 30, 81–84. [Google Scholar] [CrossRef] [PubMed]
  28. Shin, K.H.; Kim, H.H.; Chang, C.L.; Lee, E.Y. Economic and Workflow Analysis of a Blood Bank Automated System. Ann. Lab. Med. 2013, 33, 268–273. [Google Scholar] [CrossRef] [PubMed]
Table 1. Rating scale of severity and occurrence by using * risk acceptability matrix.
Table 1. Rating scale of severity and occurrence by using * risk acceptability matrix.
Severity
NegligibleMinorSeriousCriticalCatastrophic
Occurrence FrequentUUUUU
ProbableAUUUU
OccasionalAAAUU
RemoteAAAAU
ImprobableAAAAA
* Risk acceptability matrixsourced from CLSI EP23-A [19].
Table 2. Risk assessment in SBT for HLA typing.
Table 2. Risk assessment in SBT for HLA typing.
Processing StepPotential DefectActionOutcomeFMEARAM
SODRPNSO
Amplification
 1. Add PCR mixture to PCR tubesIA, MARD, IR41520NI
 2. Add extracted DNA to PCR tubesIA, MARD, IR81544MI
 3. Run in PCR systemIncorrect conditionsRD, IR41310NI
Gel electrophoresis
 4. Add PCR product to agarose gelIA, MARD, IR21412NRm
 5. ElectrophoresisIncorrect conditionsRD, IR3125NI
 6. Interpret the bands with an analyzerIncorrect interpretationRIR3125NI
PCR product clean-up
 7. Add clean solution and run in the
PCR system
Incomplete cleaningRD, IR41418NI
Sequencing reaction
 8. Dilute the PCR productsIncorrect conditionsRD, IR41526NRm
 9. Add primer and dye mixture IA, MARD, IR62548NRm
 10. Add diluted PCR products IA, MARD, IR72572NOc
 11. Run in PCR system Incorrect conditionsRD, IR41210NI
Purification for dye removal
 12. Add sodium acetate/EDTA buffer IA, MARD, IR31521NRm
 13. Add ethanol to the plateIA, MARD, IR31513NI
 14. Centrifuge (30 min at 2000× g)Spill, mechanical errorRD3113NI
 15. Remove supernatantInsufficient removalRD21511NI
 16. Add 80% ethanolIA, MARD, IR31412NI
 17. Centrifuge (5 min at 2000× g)Spill, mechanical errorRD3113NI
 18. Remove supernatantInsufficient removalRD21511NI
 19. Dry 3 min at 65 °CIncorrect conditionsRD2125NI
 20. Add high-deionized formamideIA, MARD, IR41416NI
 21. Dry 3 min at 95 °CIncorrect conditionsRD2124NI
 22. Incubate for 3 min at 0 °CIncorrect conditionsRD, IR2124NI
 23. Run in genetic analyzerInsufficient volumeRetestD51314NI
Interpretation and report
 24. Interpret the resultsIncorrect interpretationRIR61645NRm
 25. Input the result on LIS manuallyClerical errorCorrectIR51633NI
Total 465
Abbreviations: SBT, sequence-based typing; HLA, human leukocyte antigen; FMEA, failure mode and effect analysis; RAM, risk acceptability matrix; S, severity; O, occurrence; D, detectability; RPN, risk priority number; PCR, polymerase chain reaction; IA, incorrect amounts; MA, missed addition; R, repeat; D, delay; IR, incorrect result; N, negligible; I, improbable; DNA, deoxyribonucleic acid; M, minor; temp., temperature; Rm, remote; Oc, occasion; EDTA, ethylenediamine tetraacetic acid; LIS, laboratory information system.
Table 3. Risk assessment and evaluation in NGS for HLA typing.
Table 3. Risk assessment and evaluation in NGS for HLA typing.
Processing StepPotential DefectActionOutcomeFMEARAM
SODRPNSO
Amplification
 1. Add nuclease-free water to PCR tubeIA, MARD, IR41415NI
 2. Add polymerase, buffer, and dNTPsIA, MARD, IR51523NI
 3. Identify and add primer pelletIA, MARD, IR52544NRm
 4. Add extracted DNA to reaction mixIA, MARD, IR81542MI
 5. Run in PCR systemIncorrect conditionsRD, IR51313NI
DNA quantification
 6. Add Qubit reagent to Qubit tubeIA, MARD, IR41518NI
 7. Add Qubit buffer to Qubit tubeIA, MARD, IR41518NI
 8. Add DNA to Qubit tubeIA, MARD, IR31517NI
 9. Measure amplicons conc. at
fluorometer
Incorrect interpretationRIR42320NRm
 10. Calculate the required volume of
each gene and pool amplicons
Incorrect calculationRIR42536NRm
Fragmentation and adapter ligation
 11. Prepare fragmentation master mixContaminationRD41520NI
 12. Add master mix to plate wellIA, MARD, IR51525NI
 13. Add amplicon to plate wellIA, MARD, IR51527NI
 14. Incubation in a PCR system at 25 °C for 20 min and 70 °C for 10 minIncorrect conditionsRD, IR41312NI
 15. Prepare adaptor ligation master mixIA, MARD, IR51527NI
 16. Add ligation master mix to DNA
fragmentation
IA, MARD, IR51525NI
 17. Incubation in PCR system at 20 °C for 15 min Incorrect conditionsRD, IR51313NI
DNA clean-up
 18. Add magnetic beads and incubate
for 5 min
IA, MARD, IR41522
NI
 19. Place the plate on a magnetic stand
and remove supernatant
Insufficient removalRD42316NRm
 20. * Add 80% ethanol, incubate for 30 s, and remove the supernatantIA, MARD, IR425102NRm
 21. Remove ethanol and dry 3~5 minExcessive drying, missed removalRD, IR42540NOc
 22. Add elution buffer and incubate for
2 min at RT on shaker
IA, MARD, IR41313NI
 23. Place the plate on a magnetic stand
and transfer eluate to a new plate
IA, contaminationRD41520NI
Indexing PCR
 24. Prepare (thaw and centrifuge) for
reagent
Insufficient thawing, contaminationRD41520NI
 25. Add HiFi PCR Mix to IndX plateIA, MARD, IR51527NI
 26. Add eluate to IndX plateIA, MARD, IR51528NI
 27. Run in PCR systemIncorrect conditionsRD, IR51313NI
Library pooling, DNA clean-up, and size selection
 28. Transfer DNA to new tube and mixIA, contaminationRD51525NI
 29. Add magnetic beads and incubate
for 5 min
IA, MARD, IR51523NI
 30. Place the plate on a magnetic stand
and remove the supernatant
Insufficient removalRD42531NRm
 31. Add 80% ethanol, incubate for 30
s, and remove supernatant
IA, MARD, IR52536NRm
 32. Remove ethanol and dry 3~5 minExcessive drying, missed removalRD, IR42531NRm
 33. Add elution buffer and incubate for
2 min at RT on shaker
IA, MARD, IR51314NI
 34. Place the plate on a magnetic stand
and transfer eluate to a new tube
IA, contaminationRD41521NI
Library quantification
 35. Prepare Qubit working solution
with library sample
IA, MARD, IR51523NI
 36. Measure library conc. at a
fluorometer and calculate Qubit
results to nanomolar conc.
IA, MARD, IR52326NRm
Next-generation sequencing
 37. Thaw and wash for reagentContaminationRD41520NI
 38. Dilute library to a conc. of 4 nMIncorrect conditionsRD, IR62550NRm
 39. Add 0.2N NaOHIA, MARD, IR51523NI
 40. Incubation the tubeIncorrect conditionsRD, IR31517NI
 41. Add HT1 buffer IA, MARD, IR51523NI
 42. Run paired-end sequencing in
Miseq analyzer
Insufficient volumeRetestD51312NI
Interpretation and report
 43. Interpret the resultsIncorrect interpretationRIR71662NRm
 44. Input the result on LIS manuallyClerical errorCorrectIR61636NI
Total 1169
* Step 20 was repeated 3 times and the RPN score was multiplied by 3. Abbreviations: NGS, next-generation sequencing; HLA, human leukocyte antigen; FMEA, failure mode and effect analysis; RAM, risk acceptability matrix; S, severity; O, occurrence; D, detectability; RPN, risk priority number; PCR, polymerase chain reaction; IA, incorrect amounts; MA, missed addition; R, repeat; D, delay; IR, incorrect result; N, negligible; I, improbable; dNTPs, deoxynucleotide triphosphates; Rm, Remote; M, minor; temp., temperature; conc., concentration; Oc, occasion; RT, room temperature; HT, hybridization; LIS, laboratory information system.
Table 4. TAT assessment and evaluation in SBT for HLA typing.
Table 4. TAT assessment and evaluation in SBT for HLA typing.
Processing StepTATHands-On Time Machine Running Time Overall TAT
Amplification
 1. Add PCR mixture to PCR tube00:02:3400:04:4602:22:4802:27:34
 2. Add extracted DNA to PCR tube00:02:10
 3. Run in PCR system 02:22:48
Gel electrophoresis
 4. Add PCR product to agarose gel00:05:4300:07:4900:27:1900:35:08
 5. Electrophoresis00:27:19
 6. Interpret the bands with an analyzer00:02:06
PCR product clean-up
 7. Add clean solution and run in the PCR system00:24:3000:02:3000:22:0000:24:30
Sequencing reaction
 8. Dilute the PCR products00:03:0200:03:0202:33:5702:36:59
 9. Add primer and dye mixture to plate
 10. Add diluted PCR products to plate
 11. Run in PCR system 02:33:57
Purification for dye removal
 12. Add sodium acetate/EDTA buffer 00:01:3600:19:3205:56:5706:16:29
 13. Add ethanol to plate00:01:36
 14. Centrifuge (30 min at 2000× g)00:31:10
 15. Invert to a paper towel and remove supernatant00:00:47
 16. Add 80% ethanol00:01:35
 17. Centrifuge (5 min at 2000× g)00:05:49
 18. Invert to a paper towel and remove supernatant00:00:49
 19. Dry 3 min at 65 °C00:03:15
 20. Add high-deionized formamide00:02:41
 21. Dry 3 min at 95 °C00:03:12
 22. Incubate for 3 min at 0 °C00:03:12
 23. Run in genetic analyzer05:20:47
Interpretation and report
 24. Interpret the results00:11:2800:10:0000:01:2800:11:28
 25. Input the result on LIS manually
Total12:32:0600:47:3911:44:2912:32:06
Abbreviations: TAT, turn-around time; SBT, sequence-based typing; HLA, human leukocyte antigen; PCR, polymerase chain reaction; DNA, deoxyribonucleic acid; EDTA, ethylenediamine tetraacetic acid; LIS, laboratory information system.
Table 5. TAT assessment in NGS for HLA typing.
Table 5. TAT assessment in NGS for HLA typing.
Processing StepTATHands-On TimeMachine
Running Time
Overall
TAT
Amplification
 1. Add nuclease-free water to PCR tube00:01:2100:14:1204:40:0004:54:12
 2. Add polymerase, buffer, and dNTPs 00:04:08
 3. Identify and add primer pellet 00:08:43
 4. Add extracted DNA to reaction mix00:09:19
 5. Run in PCR system04:40:00
DNA quantification
 6. Add Qubit reagent to Qubit tube00:20:2200:47:4600:00:0000:47:46
 7. Add Qubit buffer to Qubit tube
 8. Add DNA to Qubit tube00:10:23
 9. Measure amplicons conc. at fluorometer00:06:35
 10. Calculate the required volume of each gene and
pool amplicons
00:10:26
Fragmentation and adapter ligation
 11. Prepare fragmentation master mix00:08:2300:14:1600:45:4501:00:01
 12. Add master mix to plate well
 13. Add amplicon to plate well00:01:32
 14. Incubation in a PCR system at 25 °C for 20 min and 70 °C for 10 min00:30:45
 15. Prepare adaptor ligation master mix00:04:21
 16. Add ligation master mix to DNA
fragmentation
 17. Incubation in PCR system at 20 °C for 15 min00:15:00
DNA clean-up
 18. Add magnetic beads and incubate for 5 min00:07:5200:27:3500:00:0000:27:35
 19. Place the plate on a magnetic stand and remove supernatant 00:05:26
 20. Add 80% ethanol, incubate for 30 s, and
remove the supernatant. Repeat 3 times
00:04:13
 21. Remove ethanol and dry 3~5 min00:01:37
 22. Add elution buffer and incubate for 2 min at
RT on shaker
00:03:12
 23. Place the plate on a magnetic stand and transfer
eluate to a new plate
00:05:15
Indexing PCR
 24. Prepare (thaw and centrifuge) for reagent00:06:3300:09:3000:22:0000:31:30
 25. Add HiFi PCR Mix to IndX plate00:01:13
 26. Add eluate to IndX plate00:01:44
 27. Run in PCR system00:22:00
Library pooling, DNA clean-up, and size selection
 28. Transfer DNA to new tube and mix00:01:5200:22:3000:00:0000:22:30
 29. Add magnetic beads and incubate for 5 min00:05:41
 30. Place the plate on a magnetic stand and remove
the supernatant
00:05:26
 31. Add 80% ethanol, incubate for 30 s, and
remove supernatant
00:01:26
 32. Remove ethanol and dry 3~5 min00:03:09
 33. Add elution buffer and incubate for 2 min at
RT on shaker
00:02:27
 34. Place the plate on a magnetic stand and transfer
eluate to a new tube
00:02:29
Library quantification
 35. Prepare Qubit working solution with library
sample
00:06:0900:09:0400:00:0000:09:04
 36. Measure library conc. at a fluorometer and
calculate Qubit results to nanomolar conc.
00:02:55
Next-generation sequencing
 37. Prepare (thaw and wash) for reagent00:07:1300:13:4317:00:0017:13:43
 38. Dilute library to a conc. of 4 nM
 39. Add 0.2N NaOH
 40. Incubation the tube00:05:00
 41. Add HT1 buffer 00:01:30
 42. Run paired-end sequencing in Miseq analyzer17:00:00
Interpretation and report
 43. Interpret the results01:01:4001:01:4000:00:0001:01:40
 44. Input the result on LIS manually
Total26:47:2003:59:3522:47:4526:47:20
Abbreviations: TAT, turn-around time; NGS, next-generation sequencing; HLA, human leukocyte antigen; PCR, polymerase chain reaction; dNTPs, deoxynucleotide triphosphates; conc., concentration; RT, room temperature; HT, hybridization; LIS, laboratory information system.
Table 6. Comparison of risk and TAT between SBT and NGS.
Table 6. Comparison of risk and TAT between SBT and NGS.
SBTNGSp Value
Risk
 Total steps, N2544NA
  Acceptable steps, N (%)25 (100)44 (100)NA
  Unacceptable steps, N (%)0 (0)0 (0)NA
 Total RPN4651169NA
 * RPN, median (range)12 (3–72)23 (12–62)0.001
 Severity, median (range)3 (2–8)5 (3–8)0.005
 Occurrence, median (range)1 (1–2)1 (1–2)0.446
 Detectability, median (range)4 (1–6)5 (3–6)<0.001
TAT
 Total TAT, hh:mm:ss12:32:0626:47:20NA
 Total hands-on time, hh:mm:ss00:47:3903:59:35NA
 Hands on time/step, hh:mm:ss00:01:5400:05:13NA
* The median values of RPN, severity, occurrence, and detectability for SBT and NGS were compared using a Kruskal–Wallis test. The severity, occurrence, and detectability scores were calculated using FMEA, and acceptable and unacceptable steps were accessed using the RAM rating scale. Abbreviations: SBT, sequence-based typing; NGS, next-generation sequencing; RPN, risk priority number; TAT, turn-around time; NA, not available; RAM, risk acceptability matrix; FMEA, failure mode and effect analysis.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Cha, J.; Hur, M.; Kim, H.; Yun, S.; Nam, M.; Cho, Y.; Nam, M. Comparative Assessment of Risk and Turn-Around Time between Sequence-Based Typing and Next-Generation Sequencing for HLA Typing. Diagnostics 2024, 14, 1793. https://doi.org/10.3390/diagnostics14161793

AMA Style

Cha J, Hur M, Kim H, Yun S, Nam M, Cho Y, Nam M. Comparative Assessment of Risk and Turn-Around Time between Sequence-Based Typing and Next-Generation Sequencing for HLA Typing. Diagnostics. 2024; 14(16):1793. https://doi.org/10.3390/diagnostics14161793

Chicago/Turabian Style

Cha, Jaehyun, Mina Hur, Hanah Kim, Seunggyu Yun, Myunghyun Nam, Yunjung Cho, and Minjeong Nam. 2024. "Comparative Assessment of Risk and Turn-Around Time between Sequence-Based Typing and Next-Generation Sequencing for HLA Typing" Diagnostics 14, no. 16: 1793. https://doi.org/10.3390/diagnostics14161793

APA Style

Cha, J., Hur, M., Kim, H., Yun, S., Nam, M., Cho, Y., & Nam, M. (2024). Comparative Assessment of Risk and Turn-Around Time between Sequence-Based Typing and Next-Generation Sequencing for HLA Typing. Diagnostics, 14(16), 1793. https://doi.org/10.3390/diagnostics14161793

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop