PMID- 35963491
OWN - NLM
STAT- MEDLINE
DCOM- 20221207
LR  - 20231202
IS  - 1528-8447 (Electronic)
IS  - 1526-5900 (Print)
IS  - 1526-5900 (Linking)
VI  - 23
IP  - 12
DP  - 2022 Dec
TI  - Applying the Rapid OPPERA Algorithm to Predict Persistent Pain Outcomes Among a 
      Cohort of Women Undergoing Breast Cancer Surgery.
PG  - 2003-2012
LID - S1526-5900(22)00371-6 [pii]
LID - 10.1016/j.jpain.2022.07.012 [doi]
AB  - Persistent postmastectomy pain after breast surgery is variable in duration and 
      severity across patients, due in part to interindividual variability in pain 
      processing. The Rapid OPPERA Algorithm (ROPA) empirically identified 3 clusters 
      of patients with different risk of chronic pain based on 4 key psychophysical and 
      psychosocial characteristics. We aimed to test this type of group-based 
      clustering within in a perioperative cohort undergoing breast surgery to 
      investigate differences in postsurgical pain outcomes. Women (N = 228) scheduled 
      for breast cancer surgery were prospectively enrolled in a longitudinal 
      observational study. Pressure pain threshold (PPT), anxiety, depression, and 
      somatization were assessed preoperatively. At 2-weeks, 3, 6, and 12-months after 
      surgery, patients reported surgical area pain severity, impact of pain on 
      cognitive/emotional and physical functioning, and pain catastrophizing. The ROPA 
      clustering, which used patients' preoperative anxiety, depression, somatization, 
      and PPT scores, assigned patients to 3 groups: Adaptive (low psychosocial scores, 
      high PPT), Pain Sensitive (moderate psychosocial scores, low PPT), and Global 
      Symptoms (high psychosocial scores, moderate PPT). The Global Symptoms cluster, 
      compared to other clusters, reported significantly worse persistent pain outcomes 
      following surgery. Findings suggest that patient characteristic-based clustering 
      algorithms, like ROPA, may generalize across diverse diagnoses and clinical 
      settings, indicating the importance of "person type" in understanding pain 
      variability. PERSPECTIVE: This article presents the practical translation of a 
      previously developed patient clustering solution, based within a chronic pain 
      cohort, to a perioperative cohort of women undergoing breast cancer surgery. Such 
      preoperative characterization could potentially help clinicians apply 
      personalized interventions based on predictions concerning postsurgical pain.
CI  - Copyright (c) 2022 United States Association for the Study of Pain, Inc. Published 
      by Elsevier Inc. All rights reserved.
FAU - Wilson, Jenna M
AU  - Wilson JM
AD  - Department of Anesthesiology, Perioperative, and Pain Medicine, Brigham & Women's 
      Hospital, Harvard Medical School, Boston, Massachusetts. Electronic address: 
      jwilson47@bwh.harvard.edu.
FAU - Colebaugh, Carin A
AU  - Colebaugh CA
AD  - Department of Anesthesiology, Perioperative, and Pain Medicine, Brigham & Women's 
      Hospital, Harvard Medical School, Boston, Massachusetts.
FAU - Flowers, K Mikayla
AU  - Flowers KM
AD  - Department of Anesthesiology, Perioperative, and Pain Medicine, Brigham & Women's 
      Hospital, Harvard Medical School, Boston, Massachusetts.
FAU - Overstreet, Demario
AU  - Overstreet D
AD  - Department of Anesthesiology, Perioperative, and Pain Medicine, Brigham & Women's 
      Hospital, Harvard Medical School, Boston, Massachusetts.
FAU - Edwards, Robert R
AU  - Edwards RR
AD  - Department of Anesthesiology, Perioperative, and Pain Medicine, Brigham & Women's 
      Hospital, Harvard Medical School, Boston, Massachusetts.
FAU - Maixner, William
AU  - Maixner W
AD  - Department of Anesthesiology, Duke University, Durham, North Carolina.
FAU - Smith, Shad B
AU  - Smith SB
AD  - Department of Anesthesiology, Duke University, Durham, North Carolina.
FAU - Schreiber, Kristin L
AU  - Schreiber KL
AD  - Department of Anesthesiology, Perioperative, and Pain Medicine, Brigham & Women's 
      Hospital, Harvard Medical School, Boston, Massachusetts.
LA  - eng
GR  - K23 GM110540/GM/NIGMS NIH HHS/United States
GR  - R35 GM128691/GM/NIGMS NIH HHS/United States
PT  - Journal Article
PT  - Observational Study
PT  - Randomized Controlled Trial
PT  - Research Support, N.I.H., Extramural
DEP - 20220810
PL  - United States
TA  - J Pain
JT  - The journal of pain
JID - 100898657
SB  - IM
MH  - Humans
MH  - Female
MH  - Mastectomy/adverse effects
MH  - *Breast Neoplasms/surgery/psychology
MH  - *Chronic Pain/diagnosis/etiology
MH  - Pain, Postoperative/diagnosis/etiology/psychology
MH  - Algorithms
PMC - PMC9729400
MID - NIHMS1829275
OTO - NOTNLM
OT  - Clustering
OT  - Pain
OT  - Postsurgical
OT  - Psychophysical
OT  - Psychosocial
COIS- The authors declare no conflict of interest.
EDAT- 2022/08/14 06:00
MHDA- 2022/12/10 06:00
PMCR- 2023/12/01
CRDT- 2022/08/13 19:35
PHST- 2022/03/02 00:00 [received]
PHST- 2022/07/27 00:00 [revised]
PHST- 2022/07/31 00:00 [accepted]
PHST- 2022/08/14 06:00 [pubmed]
PHST- 2022/12/10 06:00 [medline]
PHST- 2022/08/13 19:35 [entrez]
PHST- 2023/12/01 00:00 [pmc-release]
AID - S1526-5900(22)00371-6 [pii]
AID - 10.1016/j.jpain.2022.07.012 [doi]
PST - ppublish
SO  - J Pain. 2022 Dec;23(12):2003-2012. doi: 10.1016/j.jpain.2022.07.012. Epub 2022 
      Aug 10.