Evidence map›Paper›PMID 38449385›Full record

ArticleMedical decision making : an international journal of the Society for Medical Decision Making2024

A Generalizable Decision-Making Framework for Selecting Onsite versus Send-out Clinical Laboratory Testing.

Lee F Schroeder, Paul Rebman, Parastu Kasaie, Ernest Kenu, Jon Zelner, David W Dowdy

Abstract read
In one paragraph

Article in Medical decision making : an international journal of the Society for Medical Decision Making, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
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

1 citing paper in PubMed.

  1. 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.

Lee F SchroederDepartment of Pathology, University of Michigan School of Medicine, Ann Arbor, MI, USA.ORCID 0000-0002-2787-3608
Paul RebmanDepartment of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
Parastu KasaieDepartment of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.ORCID 0000-0002-3744-9501
Ernest KenuSchool of Public Health, University of Ghana, Ghana.
Jon ZelnerDepartment of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI, USA.
David W DowdyDepartment of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.

Funding

Understanding and Improving the Effectiveness of Public Health Laboratory Networks for Infectious Diseases in GhanaR01AI136977 · NIAID · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI DOWDY, DAVID WESLEY, SCHROEDER, LEE FREDERICK · 2019 to 2023
$3.4M
Integrating data for causal inference in behavioral healthT32MH122357 · NIMH · JOHNS HOPKINS UNIVERSITY · PI Rashelle Jean Musci, Elizabeth A. Stuart · 2020 to 2026
$1.8M
Projecting the Epidemiologic and Economic Impact of HIV Prevention Efforts in the Context of Other Sexually Transmitted InfectionsK01AI138853 · NIAID · JOHNS HOPKINS UNIVERSITY · PI KASAIE, PARASTU · 2019 to 2023
$591k
NIAID NIH HHS K01 AI138853NIAID NIH HHS R01 AI136977NIMH NIH HHS T32 MH122357
6 · The paper itself

Abstract

backgroundLaboratory networks provide services through onsite testing or through specimen transport to higher-tier laboratories. This decision is based on the interplay of testing characteristics, treatment characteristics, and epidemiological characteristics.

objectivesOur objective was to develop a generalizable model using the threshold approach to medical decision making to inform test placement decisions.

methodsWe developed a decision model to compare the incremental utility of onsite versus send-out testing for clinical purposes. We then performed Monte Carlo simulations to identify the settings under which each strategy would be preferred. Tuberculosis was modeled as an exemplar.

resultsThe most important determinants of the decision to test onsite versus send-out were the clinical utility lost due to send-out testing delays and the accuracy decrement with onsite testing. When the sensitivity decrements of onsite testing were minimal, onsite testing tended to be preferred when send-out delays reduced clinical utility by >20%. By contrast, when onsite testing incurred large reductions in sensitivity, onsite testing tended to be preferred when utility lost due to delays was >50%. The relative cost of onsite versus send-out testing affected these thresholds, particularly when testing costs were >10% of treatment costs.

conclusionsDecision makers can select onsite versus send-out testing in an evidence-based fashion using estimates of the percentage of clinical utility lost due to send-out delays and the relative accuracy of onsite versus send-out testing. This model is designed to be generalizable to a wide variety of use cases. HIGHLIGHTS: The design of laboratory networks, including the decision to place diagnostic instruments at the point-of-care or at higher tiers as accessed through specimen transport, can be informed using the threshold approach to medical decision making.The most important determinants of the decision to test onsite versus send-out were the clinical utility lost due to send-out testing delays and the accuracy decrement with onsite testing.The threshold approach to medical decision making can be used to compare point-of-care testing accuracy decrements with the lost utility of treatment due to send-out testing delays.The relative cost of onsite versus send-out testing affected these thresholds, particularly when testing costs were >10% of treatment costs.

Indexed as

Clinical Laboratory TechniquesTuberculosisCost-Benefit AnalysisHealth Care CostsHumansPoint-of-Care Systemscost-effectivenessCP generaldecision analysisdiagnostic networkglobal healthlaboratory networkmanagement/administrationmodelspecimen transportthreshold approachutility

Identifiers

PMID38449385
PMCPMC10987262

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Registered trials

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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.