Kim Peterson1, Johanna Anderson2, Donald Bourne2, Katherine Mackey2, Mark Helfand2. 1. Department of Veterans Affairs, VA Portland Health Care System, Evidence-based Synthesis Program (ESP) Coordinating Center, Portland, OR, USA. Kimberly.Peterson4@va.gov. 2. Department of Veterans Affairs, VA Portland Health Care System, Evidence-based Synthesis Program (ESP) Coordinating Center, Portland, OR, USA.
Abstract
BACKGROUND: Primary care providers (PCPs) face many system- and patient-level challenges in providing multimodal care for patients with complex chronic pain as recommended in some pain management guidelines. Several models have been developed to improve the delivery of multimodal chronic pain care. These models vary in their key components, and work is needed to identify which have the strongest evidence of clinically-important improvements in pain and function. Our objective was to determine which primary care-based multimodal chronic pain care models provide clinically relevant benefits, define key elements of these models, and identify patients who are most likely to benefit. METHODS: To identify studies, we searched MEDLINE® (1996 to October 2016), CINAHL, reference lists, and numerous other sources and consulted with experts. We used predefined criteria for study selection, data abstraction, internal validity assessment, and strength of evidence grading. RESULTS: We identified nine models, evaluated in mostly randomized controlled trials (RCTs). The RCTs included 3816 individuals primarily from the USA. The most common pain location was the back. Five models primarily coupling a decision-support component-most commonly algorithm-guided treatment and/or stepped care-with proactive ongoing treatment monitoring have the best evidence of providing clinically relevant improvement in pain intensity and pain-related function over 9 to 12 months (NNT range, 4 to 13) and variable improvement in quality of life, depression, anxiety, and sleep. The strength of the evidence was generally low, as each model was only supported by a single RCT with imprecise findings. DISCUSSION: Multimodal chronic pain care delivery models coupling decision support with proactive treatment monitoring consistently provide clinically relevant improvement in pain and function. Wider implementation of these models should be accompanied by further evaluation of clinical and implementation effectiveness.
BACKGROUND: Primary care providers (PCPs) face many system- and patient-level challenges in providing multimodal care for patients with complex chronic pain as recommended in some pain management guidelines. Several models have been developed to improve the delivery of multimodal chronic pain care. These models vary in their key components, and work is needed to identify which have the strongest evidence of clinically-important improvements in pain and function. Our objective was to determine which primary care-based multimodal chronic pain care models provide clinically relevant benefits, define key elements of these models, and identify patients who are most likely to benefit. METHODS: To identify studies, we searched MEDLINE® (1996 to October 2016), CINAHL, reference lists, and numerous other sources and consulted with experts. We used predefined criteria for study selection, data abstraction, internal validity assessment, and strength of evidence grading. RESULTS: We identified nine models, evaluated in mostly randomized controlled trials (RCTs). The RCTs included 3816 individuals primarily from the USA. The most common pain location was the back. Five models primarily coupling a decision-support component-most commonly algorithm-guided treatment and/or stepped care-with proactive ongoing treatment monitoring have the best evidence of providing clinically relevant improvement in pain intensity and pain-related function over 9 to 12 months (NNT range, 4 to 13) and variable improvement in quality of life, depression, anxiety, and sleep. The strength of the evidence was generally low, as each model was only supported by a single RCT with imprecise findings. DISCUSSION: Multimodal chronic pain care delivery models coupling decision support with proactive treatment monitoring consistently provide clinically relevant improvement in pain and function. Wider implementation of these models should be accompanied by further evaluation of clinical and implementation effectiveness.
Authors: Marianne S Matthias; Amy L Parpart; Kathryn A Nyland; Monica A Huffman; Dawana L Stubbs; Christy Sargent; Matthew J Bair Journal: Pain Med Date: 2010-11 Impact factor: 3.750
Authors: Dan Cherkin; Benjamin Balderson; Georgie Brewer; Andrea Cook; Katherine Talbert Estlin; Sarah C Evers; Nadine E Foster; Jonathan C Hill; Rene Hawkes; Clarissa Hsu; Mark Jensen; Anne-Marie LaPorte; Martin D Levine; Diane Piekara; Pam Rock; Karen Sherman; Gail Sowden; Rob Wellman; John Yeoman Journal: BMC Musculoskelet Disord Date: 2016-08-24 Impact factor: 2.362
Authors: Natalie B Connell; Pallavi Prathivadi; Karl A Lorenz; Sophia N Zupanc; Sara J Singer; Erin E Krebs; Elizabeth M Yano; Hong-Nei Wong; Karleen F Giannitrapani Journal: J Gen Intern Med Date: 2022-03-03 Impact factor: 6.473
Authors: Kristin Mattocks; Marc I Rosen; John Sellinger; Tu Ngo; Brad Brummett; Diana M Higgins; Thomas E Reznik; Paul Holtzheimer; Alicia M Semiatin; Todd Stapley; Steve Martino Journal: Pain Med Date: 2020-05-01 Impact factor: 3.750
Authors: John J Sellinger; Steve Martino; Christina Lazar; Kristin Mattocks; Kenneth Rando; Kristin Serowik; Karen Ablondi; Brenda Fenton; Kathryn Gilstad-Hayden; Bradley Brummett; Paul E Holtzheimer; Diana Higgins; Thomas E Reznik; Alicia M Semiatin; Todd Stapley; Tu Ngo; Marc I Rosen Journal: Pain Pract Date: 2021-06-26 Impact factor: 3.079
Authors: Simon Deslauriers; Jean-Sébastien Roy; Sasha Bernatsky; Debbie E Feldman; Anne Marie Pinard; François Desmeules; Mary-Ann Fitzcharles; Kadija Perreault Journal: J Pain Res Date: 2019-07-30 Impact factor: 3.133
Authors: Steve Martino; Christina Lazar; John Sellinger; Kathryn Gilstad-Hayden; Brenda Fenton; Paul G Barnett; Brad R Brummett; Diana M Higgins; Paul Holtzheimer; Kristin Mattocks; Tu Ngo; Thomas E Reznik; Alicia M Semiatin; Todd Stapley; Marc I Rosen Journal: Pain Med Date: 2020-12-12 Impact factor: 3.750