Evidence map›Paper›PMID 34631570›Full record

ArticleFrontiers in oncology2021

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, Nur Haidah Bte Ahmad Kamal, Mehek Gupta, Valerie Heong, Xiao Wen Lee and 21 more

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.9field-weighted citation impact, top 22% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 5 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

31 authors at 4 institutions in 1 country.

Sneha Rajiv JainYong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
Wilson SimYong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
Cheng Han NgYong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
Yip Han ChinYong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
Wen Hui LimYong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
Nicholas L SynYong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
Nur Haidah Bte Ahmad KamalYong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
Mehek GuptaYong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
Valerie HeongDepartment of Haematology-Oncology, National University Cancer Institute, Singapore, Singapore.
Xiao Wen LeeDepartment of Haematology-Oncology, National University Cancer Institute, Singapore, Singapore.
Nur Sabrina SapariCancer Science Institute of Singapore, National University of Singapore, Singapore, Singapore.
Xue Qing KohCancer Science Institute of Singapore, National University of Singapore, Singapore, Singapore.
Zul Fazreen Adam IsaCancer Science Institute of Singapore, National University of Singapore, Singapore, Singapore.
Lucius HoYong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
Caitlin O'HaraYong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
Arvindh UlagapanYong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
Shi Yu GuYong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
Kashyap ShroffYong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
Rei Chern WengYong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
Joey S Y LimCancer Science Institute of Singapore, National University of Singapore, Singapore, Singapore.
Diana LimDepartment of Pathology, Yong Loo Lin School of Medicine, National University Health System, Singapore, Singapore.
Brendan PangCancer Science Institute of Singapore, National University of Singapore, Singapore, Singapore.
Lai Kuan NgCancer Science Institute of Singapore, National University of Singapore, Singapore, Singapore.
Andrea WongDepartment of Haematology-Oncology, National University Cancer Institute, Singapore, Singapore.
Ross Andrew SooDepartment of Haematology-Oncology, National University Cancer Institute, Singapore, Singapore.
Wei Peng YongDepartment of Haematology-Oncology, National University Cancer Institute, Singapore, Singapore.
Cheng Ean CheeDepartment of Haematology-Oncology, National University Cancer Institute, Singapore, Singapore.
Soo-Chin LeeDepartment of Haematology-Oncology, National University Cancer Institute, Singapore, Singapore.
Boon-Cher GohDepartment of Haematology-Oncology, National University Cancer Institute, Singapore, Singapore.
Richie SoongCancer Science Institute of Singapore, National University of Singapore, Singapore, Singapore.
David S P TanDepartment of Haematology-Oncology, National University Cancer Institute, Singapore, Singapore.
National University of Singapore · SGNational University Cancer Institute, Singapore · SGNational University Health System · SGNational University Hospital · SG

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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 82

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.

Indexed as

bioinformaticscomputational biologynext generation sequencingprecision medicineprecision oncology

Identifiers

PMID34631570
PMCPMC8498582
OpenAlexW3203709363

What OpenQuestion holds

Textmetadata
LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.