Evidence map›Paper›PMID 35556235›Full record

ArticleOncology and therapy2022

Cost-Effectiveness of PD-L1 Testing in Non-Small Cell Lung Cancer (NSCLC) Using In Vitro Diagnostic (IVD) Versus Laboratory-Developed Test (LDT).

Jason T Hurwitz, Shannon Vaffis, Amy J Grizzle, Søren Nielsen, Andrew Dodson, Suzanne Parry

Registry-linked trialOpen access · goldAbstract read
In one paragraph

Article in Oncology and therapy, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07246759 (NEUTROFLOW), which is not on this map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.3field-weighted citation impact, top 42% 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.

NCT07246759 not yet recruitingnot on this mapstarted 2025, after this paper: background citation

NEUTROFLOW: Development and Validation of a Pan-cancer Neutrophil Biomarker Test for Predicting Clinical Benefit From Immunotherapy Based on Flow Cytometry Analysis of Blood Samples

TypeobservationalSponsorOncoHost Ltd.Ran2025 to 2028Enrolled600ConditionsNon Small Cell Lung Cancer (NSCLC), Melanoma (Skin Cancer), Triple Negative Breast Cancer (TNBC), RCC, Renal Cell CancerArmsBlood sample collection
3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 3 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

6 authors at 2 institutions in 2 countries.

Jason T HurwitzCenter for Health Outcomes and PharmacoEconomic Research, College of Pharmacy, University of Arizona, 1295 N Martin, P.O. Box 210202, Tucson, AZ, 85721-0202, USA.
Shannon VaffisCenter for Health Outcomes and PharmacoEconomic Research, College of Pharmacy, University of Arizona, 1295 N Martin, P.O. Box 210202, Tucson, AZ, 85721-0202, USA.
Amy J GrizzleCenter for Health Outcomes and PharmacoEconomic Research, College of Pharmacy, University of Arizona, 1295 N Martin, P.O. Box 210202, Tucson, AZ, 85721-0202, USA. grizzle@pharmacy.arizona.edu.ORCID http://orcid.org/0000-0003-4485-1459
Søren NielsenNordiQC, Institute of Pathology, Aalborg University Hospital, P.O. Box 561, 9100, Aalborg, Denmark.
Andrew DodsonUK National External Quality Assessment Scheme for Immunocytochemistry and In-Situ Hybridisation, 5 Coldbath Square, London, EC1R 5HL, UK.
Suzanne ParryUK National External Quality Assessment Scheme for Immunocytochemistry and In-Situ Hybridisation, 5 Coldbath Square, London, EC1R 5HL, UK.
University of Arizona · USAalborg University Hospital · DK

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionAccurate PD-L1 testing for non-small cell lung cancer (NSCLC) maximizes the benefits of immune checkpoint inhibitor (ICI) drugs like pembrolizumab. False negative test results deny ICI treatments to eligible patients, worsening clinical and economic outcomes, while false positives increase costs by using ICI treatments without their benefits. This study evaluates the cost-effectiveness of PD-L1 testing with an in vitro diagnostic (IVD) compared to a laboratory-developed test (LDT) for allocating patients with NSCLC to treatment with either pembrolizumab or chemotherapy using the German healthcare system as a model.

methodsWe developed a decision analytical model to evaluate the cost-effectiveness of PD-L1 testing with a regulatory body approved IVD compared to an LDT from the national German healthcare payer (statutory health insurance system) perspective. Accuracy of PD-L1 testing was based on data from two independent proficiency testing programs. The 1-year model was based on outcomes data from the KEYNOTE-024 clinical trial and treatment patterns reflecting current German practices.

resultsIVDs produced accurate PD-L1 testing results in 93% (752/811) of tested cases compared to 73% (492/672) with LDTs. Most misclassifications concerned false negatives, occurring in 21% of LDTs vs 7% of IVDs. Total costs of the IVD group (48,878 €) were 196 € higher than the LDT group (48,682 €). These costs incorporate testing, first- and second-line therapy, managing treatment-related grade 3+ adverse events (AEs), and end-of-life costs for those who died within the year. Total effectiveness (percentage of patients successfully diagnosed and prescribed the correct therapy per German treatment guidelines) was 19 percentage points higher for the IVD group (88%) compared to the LDT group (69%). These differences in costs and effects lead to an incremental cost-effectiveness ratio (ICER) of 1057 €.

conclusionCompared to LDT technology, on-label IVD use for PD-L1 testing is only slightly more costly and substantially more effective for aligning patients with PD-L1-positive NSCLC with ICI therapy according to German practice guidelines. Given these findings, changes to testing and reimbursement policies may be considered to maximize patient outcomes in NSCLC.

Indexed as

Advanced NSCLCCost-effectivenessDiagnosticGermanyPD-L1Pembrolizumab

Identifiers

PMID35556235
PMCPMC9681966
OpenAlexW4280537462

What OpenQuestion holds

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LicenceCC BY-NC
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Registered trials

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.