Evidence map›Paper›PMID 36229950›Full record

Observational studyJournal of evaluation in clinical practice2023

Evaluation of a fast-and-frugal clinical decision algorithm ('pathways') on clinical outcomes in hospitalised patients with COVID-19 treated with anticoagulants.

Benjamin Djulbegovic, Iztok Hozo, David Lizarraga, Joseph Thomas, Michael Barbee, Nupur Shah, Tyler Rubeor, Jordan Dale, Jochen Reiser, Gordon Guyatt

Open access · bronzeAbstract readObservational Study
In one paragraph

Observational study in Journal of evaluation in clinical practice, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
2.5field-weighted citation impact, top 10% 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

5 citing papers in PubMed, 9 citations in OpenAlex.

  1. Article
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  5. Observational
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

10 authors at 5 institutions in 2 countries.

Benjamin DjulbegovicDepartment of Computational & Quantitative Medicine, City of Hope, Beckman Research Institute, Duarte, California, USA.ORCID 0000-0003-0671-1447
Iztok HozoDepartment of Mathematics, Indiana University, Gary, Indiana, USA.
David LizarragaDepartment of Computational & Quantitative Medicine, City of Hope, Beckman Research Institute, Duarte, California, USA.
Joseph ThomasRush University Medical Center (RUMC), Chicago, Illinois, USA.
Michael BarbeeRush University Medical Center (RUMC), Chicago, Illinois, USA.
Nupur ShahRush University Medical Center (RUMC), Chicago, Illinois, USA.
Tyler RubeorRush University Medical Center (RUMC), Chicago, Illinois, USA.
Jordan DaleHouston Methodist Academic Institute, Houston, Texas, USA.
Jochen ReiserRush University Medical Center (RUMC), Chicago, Illinois, USA.
Gordon GuyattDepartment of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada.
Rush University Medical Center · USCity Of Hope National Medical Center · USHouston Methodist · USImpact · CAIndiana University Northwest · US

Funding

PA-20-070 "Development of evidence-based decision support for the management of COVID19"R01HS024917 · AHRQ · UNIVERSITY OF SOUTH FLORIDA · PI DJULBEGOVIC, BENJAMIN · 2016 to 2021
$2.3M
Agency for Healthcare Research and QualityAHRQ HHS R01 HS024917
6 · The paper itself

Abstract

RATIONALE, AIMS AND

objectivesCritics have charged that evidence-based medicine (EBM) overemphasises algorithmic rules over unstructured clinical experience and intuition, but the role of structured decision support systems in improving health outcomes remains uncertain. We aim to assess if delivery of anticoagulant prophylaxis in hospitalised patients with COVID-19 according to an algorithm based on evidence-based clinical practice guideline (CPG) improved clinical outcomes compared with administration of anticoagulant treatment given at individual practitioners' discretion.

methodsAn observational design consisting of the analysis of all acutely ill, consecutive patients (n = 1783) with confirmed COVID-19 diagnosis admitted between 10 March 2020 to 11 January 2022 to an US academic center. American Society of Haematology CPG for anticoagulant prophylaxis in hospitalised patients with COVID-19 was converted into a clinical pathway and translated into fast-and-frugal decision (FFT) tree ('algorithm'). We compared delivery of anticoagulant prophylaxis in hospitalised patients with COVID-19 according to the FFT algorithm with administration of anticoagulant treatment given at individual practitioners' discretion.

resultsIn an adjusted analysis, using combination of Lasso (least absolute shrinkage and selection operator) and propensity score based weighting [augmented inverse-probability weighting] statistical techniques controlling for cluster data, the algorithm did not reduce death, venous thromboembolism, or major bleeding, but helped avoid longer hospital stay [number of patients needed to be treated (NNT) = 40 (95% CI: 23-143), indicating that for every 40 patients (23-143) managed on FFT algorithm, one avoided staying in hospital longer than 10 days] and averted admission to intensive-care unit (ICU) [NNT = 19 (95% CI: 13-40)]. All model's selected covariates were well balanced. The results remained robust to sensitivity analyses used to test the stability of the findings.

conclusionsWhen delivered using a structured FFT algorithm, CPG shortened the hospital stay and help avoided admission to ICU, but it did not affect other relevant outcomes.

Indexed as

COVID-19AnticoagulantsCOVID-19 TestingEvidence-Based MedicineHospitalizationHumansAnticoagulantsclinical decision makingclinical pathwaysdecision supportevidence based medicinefast-and-frugal treespractice guidelines

Identifiers

PMID36229950
PMCPMC9840687
OpenAlexW4306158043

What OpenQuestion holds

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LicenceCC BY-NC
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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.