Frontiers in Oncology (Sep 2021)

Statistical Process Control Charts for Monitoring Next-Generation Sequencing and Bioinformatics Turnaround in Precision Medicine Initiatives

  • Sneha Rajiv Jain,
  • Wilson Sim,
  • Cheng Han Ng,
  • Yip Han Chin,
  • Wen Hui Lim,
  • Nicholas L. Syn,
  • Nicholas L. Syn,
  • Nicholas L. Syn,
  • Nur Haidah Bte Ahmad Kamal,
  • Mehek Gupta,
  • Valerie Heong,
  • Valerie Heong,
  • Xiao Wen Lee,
  • Nur Sabrina Sapari,
  • Xue Qing Koh,
  • Zul Fazreen Adam Isa,
  • Lucius Ho,
  • Caitlin O’Hara,
  • Arvindh Ulagapan,
  • Shi Yu Gu,
  • Kashyap Shroff,
  • Rei Chern Weng,
  • Joey S. Y. Lim,
  • Diana Lim,
  • Diana Lim,
  • Brendan Pang,
  • Brendan Pang,
  • Brendan Pang,
  • Lai Kuan Ng,
  • Andrea Wong,
  • Andrea Wong,
  • Ross Andrew Soo,
  • Ross Andrew Soo,
  • Wei Peng Yong,
  • Wei Peng Yong,
  • Cheng Ean Chee,
  • Soo-Chin Lee,
  • Soo-Chin Lee,
  • Boon-Cher Goh,
  • Boon-Cher Goh,
  • Boon-Cher Goh,
  • Richie Soong,
  • Richie Soong,
  • Richie Soong,
  • Richie Soong,
  • David S.P. Tan,
  • David S.P. Tan,
  • David S.P. Tan

DOI
https://doi.org/10.3389/fonc.2021.736265
Journal volume & issue
Vol. 11

Abstract

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PurposePrecision 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 MethodsTrends 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.ResultsA 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).ConclusionAs 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.

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