Evidence map›Paper›PMID 39169349›Full record

ArticleBMC musculoskeletal disorders2024

The strategic use of Big Data - A study protocol for a multicenter clinical trial testing if the use of the Swespine Dialogue Support alter outcomes in degenerative spine surgery.

Eric Brisby Enger, Ludvig Valentin-Askman, Olle Hägg, Peter Fritzell, Catharina Parai

Registry-linked trialAbstract readClinical Trial Protocol
In one paragraph

Article in BMC musculoskeletal disorders, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05817747 (Strategic Use of Big Data in Spine Surgery - Testing a Digital Prediction of Outcome Tool in Clinical Praxis), which is not on this map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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.

NCT05817747 narecruitingnot on this map

Strategic Use of Big Data in Spine Surgery - Testing a Digital Prediction of Outcome Tool in Clinical Praxis

TypeinterventionalSponsorSahlgrenska University HospitalRan2022 to 2029Enrolled900ConditionsConstriction, Pathologic, Spine Degeneration, Surgery, RadiculopathyArmsDialogue support tool
3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. 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

5 authors.

Eric Brisby EngerHallands Sjukhus Varberg, Varberg, Sweden. ericbrisbyenger@gmail.com.
Ludvig Valentin-AskmanDepartment of Surgery and Orthopaedics, Alingsås Lasarett, Alingsås, Sweden.
Olle HäggCapio Spine Center Göteborg, Gruvgatan 6, Västra Frölunda, 421 30, Sweden.
Peter FritzellFuturum Academy for Health and Care, Hus B4, Lanssjukhuset Ryhov, Jonkoping, 553 05, Sweden.
Catharina ParaiDepartment of Orthopedics, Sahlgrenska University Hospital, Gothenburg, Sweden.ORCID http://orcid.org/0000-0002-8332-0426

Funding

AFA Försäkring 210194
6 · The paper itself

Abstract

backgroundPatients surgically treated for lumbar spinal stenosis or cervical radiculopathy report improvement in approximately two out of three cases. Advancements in Machine Learning and the utility of large datasets have enabled the development of prognostic prediction models within spine surgery. This trial investigates if the use of the postoperative outcome prediction model, the Dialogue Support, can alter patient-reported outcome and satisfaction compared to current practice.

methodsThis is a prospective, multicenter clinical trial. Patients referred to a spine clinic with cervical radiculopathy or lumbar spinal stenosis will be screened for eligibility. Participants will be assessed at baseline upon recruitment and at 12 months follow-up. The Dialogue Support will be used on all participants, and they will thereafter be placed into either a surgical or a non-surgical treatment arm, depending on the decision made between patient and surgeon. The surgical treatment group will be studied separately based on diagnosis of either cervical radiculopathy or lumbar spinal stenosis. Both the surgical and the non-surgical group will be compared to a retrospective matched control group retrieved from the Swespine register, on which the Dialogue Support has not been used. The primary outcome measure is global assessment regarding leg/arm pain in the surgical treatment group. Secondary outcome measures include patient satisfaction, Oswestry Disability Index (ODI), EQ-5D, and Numeric Rating Scales (NRS) for pain. In the non-surgical treatment group primary outcome measures are EQ-5D and mortality, as part of a selection bias analysis. DISCUSSION: The findings of this study may provide evidence on whether the use of an advanced digital decision tool can alter patient-reported outcomes after surgery.

trial registrationThe trial was retrospectively registered at ClinicalTrials.gov on April 17th, 2023, NCT05817747. PROTOCOL VERSION: 1. TRIAL

designClinical multicenter trial.

Indexed as

Big DataLumbar VertebraePatient Reported Outcome MeasuresRadiculopathySpinal StenosisCervical VertebraeFemaleHumansMaleMulticenter Studies as TopicPain MeasurementPatient SatisfactionProspective StudiesTreatment OutcomeDecision makingPrediction modellingPrediction of OutcomeSpine

Identifiers

PMID39169349
PMCPMC11340116

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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.