Please use this identifier to cite or link to this item: https://doi.org/10.3389/fonc.2021.736265
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dc.titleStatistical Process Control Charts for Monitoring Next-Generation Sequencing and Bioinformatics Turnaround in Precision Medicine Initiatives
dc.contributor.authorJain, Sneha Rajiv
dc.contributor.authorSim, Wilson
dc.contributor.authorNg, Cheng Han
dc.contributor.authorChin, Yip Han
dc.contributor.authorLim, Wen Hui
dc.contributor.authorSyn, Nicholas L.
dc.contributor.authorKamal, Nur Haidah Bte Ahmad
dc.contributor.authorGupta, Mehek
dc.contributor.authorHeong, Valerie
dc.contributor.authorLee, Xiao Wen
dc.contributor.authorSapari, Nur Sabrina
dc.contributor.authorKoh, Xue Qing
dc.contributor.authorIsa, Zul Fazreen Adam
dc.contributor.authorHo, Lucius
dc.contributor.authorO’Hara, Caitlin
dc.contributor.authorUlagapan, Arvindh
dc.contributor.authorGu, Shi Yu
dc.contributor.authorShroff, Kashyap
dc.contributor.authorWeng, Rei Chern
dc.contributor.authorLim, Joey S. Y.
dc.contributor.authorLim, Diana
dc.contributor.authorPang, Brendan
dc.contributor.authorNg, Lai Kuan
dc.contributor.authorWong, Andrea
dc.contributor.authorSoo, Ross Andrew
dc.contributor.authorYong, Wei Peng
dc.contributor.authorChee, Cheng Ean
dc.contributor.authorLee, Soo-Chin
dc.contributor.authorGoh, Boon-Cher
dc.contributor.authorSoong, Richie
dc.contributor.authorTan, David S. P.
dc.date.accessioned2022-10-12T08:02:05Z
dc.date.available2022-10-12T08:02:05Z
dc.date.issued2021-09-24
dc.identifier.citationJain, Sneha Rajiv, Sim, Wilson, Ng, Cheng Han, Chin, Yip Han, Lim, Wen Hui, Syn, Nicholas L., Kamal, Nur Haidah Bte Ahmad, Gupta, Mehek, Heong, Valerie, Lee, Xiao Wen, Sapari, Nur Sabrina, Koh, Xue Qing, Isa, Zul Fazreen Adam, Ho, Lucius, O’Hara, Caitlin, Ulagapan, Arvindh, Gu, Shi Yu, Shroff, Kashyap, Weng, Rei Chern, Lim, Joey S. Y., Lim, Diana, Pang, Brendan, Ng, Lai Kuan, Wong, Andrea, Soo, Ross Andrew, Yong, Wei Peng, Chee, Cheng Ean, Lee, Soo-Chin, Goh, Boon-Cher, Soong, Richie, Tan, David S. P. (2021-09-24). Statistical Process Control Charts for Monitoring Next-Generation Sequencing and Bioinformatics Turnaround in Precision Medicine Initiatives. Frontiers in Oncology 11 : 736265. ScholarBank@NUS Repository. https://doi.org/10.3389/fonc.2021.736265
dc.identifier.issn2234-943X
dc.identifier.urihttps://scholarbank.nus.edu.sg/handle/10635/232399
dc.description.abstractPurpose: Precision oncology, such as next generation sequencing (NGS) molecular analysis and bioinformatics are used to guide targeted therapies. The laboratory turnaround time (TAT) is a key performance indicator of laboratory performance. This study aims to formally apply statistical process control (SPC) methods such as CUSUM and EWMA to a precision medicine programme to analyze the learning curves of NGS and bioinformatics processes. Patients and Methods: Trends in NGS and bioinformatics TAT were analyzed using simple regression models with TAT as the dependent variable and chronologically-ordered case number as the independent variable. The M-estimator “robust” regression and negative binomial regression were chosen to serve as sensitivity analyses to each other. Next, two popular statistical process control (SPC) approaches which are CUSUM and EWMA were utilized and the CUSUM log-likelihood ratio (LLR) charts were also generated. All statistical analyses were done in Stata version 16.0 (StataCorp), and nominal P < 0.05 was considered to be statistically significant. Results: A total of 365 patients underwent successful molecular profiling. Both the robust linear model and negative binomial model showed statistically significant reductions in TAT with accumulating experience. The EWMA and CUSUM charts of overall TAT largely corresponded except that the EWMA chart consistently decreased while the CUSUM analyses indicated improvement only after a nadir at the 82nd case. CUSUM analysis found that the bioinformatics team took a lower number of cases (54 cases) to overcome the learning curve compared to the NGS team (85 cases). Conclusion: As NGS and bioinformatics lead precision oncology into the forefront of cancer management, characterizing the TAT of NGS and bioinformatics processes improves the timeliness of data output by potentially spotlighting problems early for rectification, thereby improving care delivery. © Copyright © 2021 Jain, Sim, Ng, Chin, Lim, Syn, Kamal, Gupta, Heong, Lee, Sapari, Koh, Isa, Ho, O’Hara, Ulagapan, Gu, Shroff, Weng, Lim, Lim, Pang, Ng, Wong, Soo, Yong, Chee, Lee, Goh, Soong and Tan.
dc.publisherFrontiers Media S.A.
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.sourceScopus OA2021
dc.subjectbioinformatics
dc.subjectcomputational biology
dc.subjectnext generation sequencing
dc.subjectprecision medicine
dc.subjectprecision oncology
dc.typeArticle
dc.contributor.departmentDUKE-NUS MEDICAL SCHOOL
dc.contributor.departmentDEPT OF PATHOLOGY
dc.contributor.departmentDEPT OF MEDICINE
dc.description.doi10.3389/fonc.2021.736265
dc.description.sourcetitleFrontiers in Oncology
dc.description.volume11
dc.description.page736265
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