Evidence map›Paper›PMID 31384618›Full record

ArticleOrthopaedic journal of sports medicine2019

Predicting the Need for Surgical Intervention Prior to First Encounter for Individuals With Knee Complaints: A Novel Approach.

José F Vega, Gregory J Strnad, James Bena, Kurt P Spindler

Abstract read
In one paragraph

Article in Orthopaedic journal of sports medicine, 2019. 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
–field-weighted citation impact
1 · What the graph read from it

What it found

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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.

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3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

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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

4 authors.

José F VegaCleveland Clinic Lerner College of Medicine of Case Western Reserve University, Cleveland, Ohio, USA.
Gregory J StrnadCleveland Clinic Orthopaedics Department, Cleveland, Ohio, USA.
James BenaCleveland Clinic Quantitative Health Sciences, Cleveland, Ohio, USA.
Kurt P SpindlerCleveland Clinic Orthopaedic Sports Health, Cleveland, Ohio, USA.

Funding

Prognosis and Predictors of ACL Reconstruction: A Multicenter Cohort StudyR01AR053684 · NIAMS · VANDERBILT UNIVERSITY · PI SPINDLER, KURT PAUL · 2006 to 2020
$8.3M
NIAMS NIH HHS R01 AR053684
6 · The paper itself

Abstract

backgroundOrthopaedic complaints, particularly those relating to the knee, are some of the most common conditions that bring patients to the hospital. Many patients bypass their primary care physician to seek the care of an orthopaedic surgeon without referral, leaving the surgeon to manage an increasingly large number of patients, many of whom will never require surgery. PURPOSE: To develop a brief questionnaire that can be administered via phone/web at the time of appointment request to predict an individual patient's probability of requiring surgical intervention. STUDY

designCase-control study; Level of evidence, 3.

methodsAll patients (N = 1307) seeking an appointment for a new knee-related complaint completed a branching-logic questionnaire. A retrospective chart review was conducted following the conclusion of each patient's episode of care to determine whether surgery was recommended. Logistic regression models were used to predict the risk of surgery based on triage question responses, basic demographics (age, sex), and laterality (unilateral vs bilateral). The ability of the models to discriminate between those who did and did not receive a surgical recommendation was measured with a concordance index.

resultsThe model provided a high level of discrimination between surgical and nonsurgical cases (concordance index, 0.69). Recent injury with inability to walk and no recent injury with no pain were both associated with an increased probability of receiving a recommendation of surgical intervention as compared with patients who reported pain without recent injury (odds ratio [OR]: 3.51 [

conclusionThe current model, which utilizes demographic questions and portions of a routine history alone, was able to accurately identify individuals who are most likely (up to 65% probability) and least likely (<5% probability) to need knee surgery. This model can quickly and easily conduct triage at the time of appointment request to ensure that patients with the highest likelihood of receiving a recommendation for surgical intervention are seen by surgical providers, while those who are unlikely to receive such a recommendation can be seen by nonsurgical providers.

Indexed as

kneepredictive modelingschedulingsurgical risktriage

Identifiers

PMID31384618
PMCPMC6659191

What OpenQuestion holds

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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.