[PDF] Lumbar Imaging with Reporting of Epidemiology (LIRE): Primary



Previous PDF Next PDF







Lumbar Imaging with Reporting of Epidemiology (LIRE): Primary

A LIRE provider is any provider who ordered an index lumbar spine image for one or more participants in the LIRE trial A non-LIRE provider is any other provider Any provider includes both LIRE and non-LIRE providers



An Interview with Dr Jerry Jarvik

That was the spark of the LIRE trial, a pragmatic trial to answer this question: Does inserting prevalence information decrease downstream spine-related utilization or opioid prescribing rates by primary care physicians? Design LIRE is a cluster randomized trial with a stepped-wedge crossover design The primary unit of randomization is the



Blood flow restriction training Manual

exercise (LIRE) that when applied has demonstrated enhanced muscle growth, muscle strength, oxygen delivery and utilization (VO2Max) Currently two methods of BFR training exist, one of which is known as practical blood flow restriction (PBFR), with the second being pneumatic controlled BFR



Lire avec fluidité - La classe de Mallory

Lire avec fluidité Lire avec fluidité Entraîne-toi à lire ces virelangues Dans la gendarmerie, quand un gendarme rit, tous les gendarmes rient dans la gendarmerie saucisses, la cuisine est sale Des blancs pains, des bancs peints, des bains pleins -toi à lire ces virelangues Si six cents couteaux-scies scient, en six, six cent six



Lire, écrire et représenter les fractions

Lire, écrire et représenter les fractions 1- Relie la fraction et son nom $ 2- Écris la fraction représentée par la partie grisée 3- Colorie la fraction demandée " 4- Colorie la fraction demandée " " #" &’ (" #) 5- Écris sous la forme de fractions les longueurs suivantes 6- Écris ces fractions en chiffres



À LIRE - regionreunion

À lire partout dans l’Île 4 saint-paul saint-pierre saint-denis 4 saint-andre 1 salazie 7 3 sainte-suzanne 2 2 le port 2 la possession saint-joseph 1 petite-ile



(J AIME LIRE)

(J’AIME_LIRE) N°117 – Graine de monstre 1 Qui raconte l’histoire? Un monstre Un père de famille L’un des enfants de la famille 2 Retrouve la description la plus exacte du monstre : C’est un monstrosa abominaplus qui mord dès qu’on l’approche



Eugène Ionesco RHINOCÉROS

Eugène Ionesco RHINOCÉROS Pièce en trois actes Et quatre tableaux Éditions Gallimard, 1959 À Geneviève Serreau et au docteur T Fraenkel PERSONNAGES par ordre d’entrée en scène :



Le tour du monde en 80 jours - Ebooks gratuits

lire les journaux et de jouer au whist À ce jeu du silence, si bien approprié à sa nature, il gagnait souvent, mais ses gains n’entraient jamais dans sa bourse et figuraient pour une somme importante à son budget de charité D’ailleurs, il faut le remarquer, Mr Fogg jouait évidemment pour jouer, non pour gagner Le jeu était pour



La Princesse de Clèves - Ebooks gratuits

et élevée, et une égale capacité pour la guerre et pour les affaires Le cardinal de Lorraine, son frère, était né avec une ambition démesurée, avec un esprit vif et une éloquence admirable, et il avait acquis une science

[PDF] qui nous lit en copie ou qui nous lie en copie

[PDF] je lis

[PDF] a tout point m d'affixe z on associe le point m' d'affixe z' telle que z'=2z^2-3iz

[PDF] le plan est rapporté au repère orthonormé o i j

[PDF] on observe la vie ? travers un filtre magenta

[PDF] drapeau belge

[PDF] lazzara victor hugo analyse

[PDF] le poète au calife victor hugo analyse

[PDF] lazzara victor hugo resume

[PDF] a une passante commentaire composé introduction

[PDF] a une passante ouverture

[PDF] la rue assourdissante autour de moi hurlait figure de style

[PDF] a une passante baudelaire texte

[PDF] a une passante baudelaire analyse

[PDF] a une passante oral français

UW Medicine/ UNIVERSITY ofWASHINGTON

Jeffrey (Jerry) Jarvik, MD MPH

Departments of Radiology, Neurological Surgery, Health Services Comparative Effectiveness, Cost and Outcomes Research Center

Patrick Heagerty, PhD

Professor, Department of Biostatistics

Director, Center for Biomedical Statistics

NIH Health Systems Collaboratory Grand Rounds 11/8/19

Lumbar Imaging with Reporting

of Epidemiology (LIRE): Primary

Results and Lessons Learned

Disclosures (Jarvik)

Wolters Kluwer/UpToDate: Royalties as a topic contributor Springer Publishing: Royalties as a co-editor for Evidence

Based Neuroimaging Diagnosis and Treatment

GE-AUR Radiology Research Academic Fellowship: Travel reimbursement to academic advisory board meeting

NIH: UH2 AT007766-01; UH3 AT007766; P30 AR072572

Acknowledgements

Talk Outline

Brief review of study goals/design

Main results

Next steps and some lessons

learned

LIRE (pronounced leer)

Background and Rationale

Lumbar spine imaging frequently

reveals incidental findings

These findings may have an adverse

effect on:

Subsequent healthcare utilization

Patient health related quality of life

Disc Degeneration in Asx

Results: Subsequent Narcotic Rx

Within 1 Yr(retrospective pilot)

p=0.01

OR*=0.29

5/71

37/166

* Adjusted for imaging severity

Primary Hypothesis

For patients referred from primary care,

inserting prevalence benchmark data in lumbar spine imaging reports will reduce overall spine-related healthcare utilization as measured by spine-related relative value units (RVUs)

Secondary Hypotheses

We also hypothesized that the

intervention would decrease:

Subsequent cross-sectional imaging

(MR/CT)

Opioid prescriptions

Spinal injections

Surgery

Intervention Text

The following findings are so common in normal,

pain-free volunteers, that while we report their presence, they must be interpreted with caution and in the context of the clinical situation. Among people between the age of 40 and 60 years, who do nothave back pain, a plain film x-ray will find that about:

8 in 10 have disk degeneration

6 in 10 have disk height loss

Note that even 3 in 10 means that the finding is

quite common in people without back pain.

Randomization

Cluster (clinic)

Stepped wedge (one way crossover)

Stepped Wedge RCT

Clinics in

Clinics in

Clinics in

Clinics in

Clinics in

Analytic Approach-RVUs

Primary

Linear mixed effects models or

generalized linear mixed models

Log transformation of RVU to address

right skew

Random effects for clinic, TX, provider

Robust standard errors

All analyses used intention to treat

Analytic Approach-Opioids

Similar to RVU approach except used

logistic models for binary outcome

Post hoc sensitivity analyses

alternative modeling

LIRE vs. non-LIRE providers

Talk Outline

Brief review of study goals/design

Main results

Next steps and some lessons

learned

Stepped Wedge Consort

Randomization Waves

# Primary Care

Clinics

Randomized

# Patients

Randomized/Analyzed

Control

# Patients

Randomized/Analyzed

Intervention

Wave 1

clinics 1910,63041,558

Wave 2

clinics2015,60531,611

Wave 3

clinics2029,62830,157

Wave 4

clinics1821,97010,277

Wave 5

clinics2139,6227,828

Total98117,455121,431

X-over784 (1%) intervention15,888 (13%) no intervention

Baseline

ControlIntervention

Site

A6,950 (6)7,388 (6)

B96,275 (82)100,729 (83)

C7,486 (7)7,726 (6)

D6,384 (5)5,588 (5)

Age

18-3921,237 (18)22,105 (18)

40-6045,032 (38)44,995 (37)

>6051,186 (44)54,331 (45) Race

Asian13,311 (11)13,197 (11)

Black or African Amer11,919 (10)11,649 (10)

Other2,170 (2)2,306 (1)

White76,431 (65)79,142 (65)

Unknown13,624 (12)15,308 (13)

Baseline

ControlIntervention

Ethnicity

Hispanic or Latino17,754 (15)18,475 (15)

Not Hispanic or Latino19,867 (17)19,276 (16)

Not available279,834 (68)83,680 (69)

Charlson ComorbIndex

075,106 (64)77,973 (64)

120,675 (18)21,193 (17)

211,451 (10)11,760 (10)

3+10,223 (9)10,505 (9)

Primary Insurance at Index

Medicare44,362 (38)46,479 (38)

Medicaid/state-subsidized5,546 (5)6,510 (5)

Commercial65,375 (56)66,368 (55)

Other2,172 (1)2,131 (2)

0 20000
40000
60000
80000

100000

XrayMRCT

Index Test Modality

ControlIntervention

82%80%

18%20%449

(1%) 494
(<1%) 0 20000
40000
60000
80000

100000

Likely Clin ImpFinding Not

Likely Clin Imp

Neither

Finding on Index Test

ControlIntervention

14%15%63%61%23%24%

0

No prior opioids1 or more prior Rx

Opioid Prescriptions Prior to Index

ControlIntervention

76%73%

24%27%

Index Provider

ControlIntervention

Type

MD105,359 (90)108,165 (89)

DO8,131 (7)9,157 (8)

NP/PA3,965 (3)4,109 (3)

Specialty

Family Medicine56,795 (48)60,277 (50)

Internal Medicine59,684 (51)60,158 (50)

Other976 (1)996 (1)

Gender

Female62,840 (54)62,680 (52)

Age

Mean age (SD)49 (9)49 (9)

Primary Outcome: Spine-related RVUs

Pre-Specified Secondary Outcome: Opioid

Prescriptions

Sensitivity Analyses for Opioid Prescriptions

A LIRE provideris any provider who ordered an index lumbar spine image for one or more participants in the LIRE trial. A non-LIRE provideris any other provider. Any provider includes both LIRE and non-LIRE providers.

Safety Outcomes: ED Admissions and Death

Analyses in Progress

Exploration of potential differences in

group getting CT Index test

Cost analysis

Injections and surgeries as outcomes

Characterization of imaging findings

in cohort

Talk Outline

Brief review of study goals/design

Main results

Next steps and lessons learned

Next Steps

Publish primary results

Continue discussions with sites

re implementation

Efforts at wider dissemination

Lessons

Learned

Some Key Lessons Learned

Prior

Keep intervention as simple as possible

Minimize burden on system partners

Big data sets are complex

Understanding complexities iterative process that

takes time

Current

Pragmatic interventions often weak

Pre-specified subgroup and secondary outcomes are

critical

Conclusions

Intervention did not decrease spine-

related RVUs for overall cohort

Subgroup that had CT for index exam

did show a drop in spine-related RVUs

Intervention reduced opioid

prescriptions-small but potentially important effect

No evidence that the intervention

caused harm

Key People

Katie James, PA, MPH, Director

Brian Bresnahan, PhD-Health Econ

Bryan Comstock, MS-Biostats

Janna Friedly, MD-Rehab

Laurie Gold, PhD-Radiology

Patrick Heagerty, PhD-Biostats

Larry Kessler, PhD-HSR

Danielle Lavallee, Pharm D, PhD

Eric Meier, MS-Biostats

Nancy Organ, BA-Statistics

Kari Stephens, PhD-Informatics

Judy Turner, PhD-Psychol/Psych

Sean Rundell, DPT, PhD

Zachary Marcum, PharmD, PhD

Katherine Tan, PhD Candidate, Biostats

Rick Deyo, MD, MPH-OHSU

Dan Cherkin, PhD-KPWA

Karen Sherman, PhD-KPWA

Heidi Berthoud, KPWA

Brent Griffith, MD-HFHS

Dave Nerenz, PhD-HFHS

Dave Kallmes, MD-Mayo

Patrick Luetmer, MD-Mayo

Andy Avins, MD, MPH-KPNC

Why Pragmatic Trials Are Important

What Are Spine-Related

RVUs?

Sensitivity Analyses for Opioid Prescriptions

quotesdbs_dbs13.pdfusesText_19