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Title: Statistical Process Control Charts for Monitoring Next-Generation Sequencing and Bioinformatics Turnaround in Precision Medicine Initiatives
Authors: Jain, 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. 
Keywords: bioinformatics
computational biology
next generation sequencing
precision medicine
precision oncology
Issue Date: 24-Sep-2021
Publisher: Frontiers Media S.A.
Citation: Jain, 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.
Rights: Attribution 4.0 International
Abstract: Purpose: 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.
Source Title: Frontiers in Oncology
ISSN: 2234-943X
DOI: 10.3389/fonc.2021.736265
Rights: Attribution 4.0 International
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