scholarly journals Prevalence Comparisons of Somatic and Psychiatric Symptoms Between Community Nonpatients Without Pain, Acute Pain Patients, and Chronic Pain Patients

Pain Medicine ◽  
2015 ◽  
Vol 16 (1) ◽  
pp. 37-50 ◽  
Author(s):  
David A. Fishbain ◽  
Jinrun Gao ◽  
John E. Lewis ◽  
Daniel Bruns ◽  
Laura J. Meyer ◽  
...  
Keyword(s):  
Chronic Pain ◽  
Acute Pain ◽  
Psychiatric Symptoms ◽  
Chronic Pain Patients ◽  
Pain Patients

scholarly journals The Effect of Social Observational Learning, Empathy and Catastrophizing in Chronic Pain Patients During Acute Pain Induction

Pain Medicine ◽  
2016 ◽  
pp. pnw186
Author(s):  
Dmitry Y. Yakunchikov ◽  
Camille J. Olechowski ◽  
Mark K. Simmonds ◽  
Michelle J. Verrier ◽  
Saifudin Rashiq ◽  
...  
Keyword(s):  
Chronic Pain ◽  
Acute Pain ◽  
Observational Learning ◽  
Chronic Pain Patients ◽  
Pain Patients

Postoperative Pain Trajectories in Chronic Pain Patients Undergoing Surgery: The Effects of Chronic Opioid Pharmacotherapy on Acute Pain

2011 ◽  
Vol 12 (12) ◽  
pp. 1240-1246 ◽  
Author(s):  
C. Richard Chapman ◽  
Jennifer Davis ◽  
Gary W. Donaldson ◽  
Justin Naylor ◽  
Daniel Winchester
Keyword(s):  
Chronic Pain ◽  
Postoperative Pain ◽  
Acute Pain ◽  
Chronic Pain Patients ◽  
Pain Trajectories ◽  
Pain Patients

scholarly journals Examination of Symptom Clusters in Acute and Chronic Pain Patients

2014 ◽  
Vol 3;17 (3;5) ◽  
pp. E349-E357
Author(s):  
David Fishbain
Keyword(s):  
Chronic Pain ◽  
Acute Pain ◽  
Severe Pain ◽  
Somatic Symptoms ◽  
The United States ◽  
Symptom Clusters ◽  
Pain Level ◽  
Cluster Number ◽  
Chronic Pain Patients ◽  
Pain Patients

Background: Symptom clusters have not been previously explored in acute pain patients (APPs) and chronic pain patients (CPPs) with non-cancer pain. Objectives: The objectives of this study were to determine in CPPs and APPs which somatic and non-somatic symptoms cluster with each other, the number of clusters, and if cluster number and cluster symptom makeup differ by pain level. Study Design: Study sample was 326 APPs and 341 CPPs who had completed a pool of questions that had included current symptom questions other than pain. Symptom cluster analyses were performed on 15 somatic and non-somatic symptoms for APPs and CPPs and for 2 CPP subgroups with moderate and severe pain. Setting: APPs and CPPs were from rehabilitation facilities located in 30 states in all geographical regions of the United States. Results: APPs had 4 symptom clusters and CPPs had 5. For CPPs, the clusters represented memory, neurological, behavioral, somatic, and autonomic problems. CPPs with moderate and severe pain had 3 and 4 symptom clusters, respectively, and differed in cluster symptom constitution. Limitations: Patients selected themselves for study inclusion and were paid for their participation. This could have affected random selection. Lastly, we used the current time definitions of acute pain versus chronic pain (90 days) to separate our patients into these groups. Currently, no consensus exists regarding the optimal time duration to divide acute from chronic. Conclusions: APPs and CPPs are characterized by symptom comorbidities that form clusters. In CPPs, cluster number and cluster symptom makeup are affected by pain level. This has implications for clinical practice and future research. Key words: Comorbidity, somatic symptoms, comorbid symptoms, chronic pain patients, acute pain patients, community patients without pain, clusters, symptom clusters

scholarly journals Risk for Five Forms of Suicidality in Acute Pain Patients and Chronic Pain Patients vs Pain-Free Community Controls

Pain Medicine ◽  
2009 ◽  
Vol 10 (6) ◽  
pp. 1095-1105 ◽  
Author(s):  
David A. Fishbain ◽  
Daniel Bruns ◽  
John Mark Disorbio ◽  
John E. Lewis
Keyword(s):  
Chronic Pain ◽  
Acute Pain ◽  
Chronic Pain Patients ◽  
Community Controls ◽  
Pain Patients

Measuring Pain in Outpatient Surgical Patients: Variation Resulting from Instrument Choice

2012 ◽  
Vol 78 (11) ◽  
pp. 1292-1296 ◽  
Author(s):  
David Goodyear ◽  
Vic Velanovich
Keyword(s):  
Chronic Pain ◽  
Acute Pain ◽  
Linear Regression Analysis ◽  
Bodily Pain ◽  
Pain Scale ◽  
Surgical Patients ◽  
P Value ◽  
Sf 36 ◽  
Chronic Pain Patients ◽  
Pain Patients

Our hypothesis is that the type of instrument will affect variation in pain assessment. A sample of 269 patients administered the visual analog pain scale (VAS) and the generic quality-of-life instrument, and the SF-36 were evaluated for gender, age, the VAS score and the bodily pain domain of the SF-36 (BP-SF-36) score, primary surgical diagnosis, preoperative or postoperative status, and type of operation. Patients were grouped into preoperative (Preop) and postoperative (postop) status and those with chronic pain (CP) conditions and acute/no pain (AP) conditions. Linear regression analysis showed statistically significant (all P value ≤ 0.0006) correlations between the VAS and BP-SF-36 scores all patients, preoperative patients, postoperative patients, acute pain patients, and chronic pain patients. However, the strength of these correlations were moderate (r values between 0.51 and 0.61). Preoperative had more pain compared with postoperative patients as measured by both the VAS and BP-SF-36 ( P = 0.05). Similarly, chronic pain patients had more pain compared with acute pain patients as measured by both scales ( P < 0.0001). Although there are statistically significant associations between the BP-SF-36 and VAS, the correlations are moderate. Different instruments may measure different aspects of pain and the precision with which pain is measured in surgical patients.

scholarly journals Chronic Opioid Therapy Utilization Following an Acute Pain Prescription Supply Restriction Law: An Interrupted Time Series Analysis

2021 ◽  
Vol 24 (6) ◽  
pp. 417-424
Keyword(s):  
Chronic Pain ◽  
Time Series ◽  
Acute Pain ◽  
Interrupted Time Series ◽  
Opioid Therapy ◽  
Interrupted Time Series Analysis ◽  
Chronic Opioid Therapy ◽  
Chronic Pain Patients ◽  
Time Series Analyses ◽  
Pain Patients

BACKGROUND: Florida House Bill 21 (HB21) was implemented in July 2018 to limit Schedule II opioids prescriptions for patients with acute pain to a 3-day supply. Little is known about the potential unintended effects that such opioid restriction policies may have on chronic pain patients, who are exempt from the law. OBJECTIVE: We aimed to evaluate the effect of HB21 on opioid utilization measures among a cohort of chronic opioid therapy (COT) patients. STUDY DESIGN: A quasi-experimental design with interrupted time series analyses. SETTING: Pharmacy claims from January 1, 2015 to June 31, 2019 from a large employer-based health plan in Florida. METHODS: COT patients were those who received a >= 70 days’ supply of opioids in the prior 90 days, representing 15,310 patients. Interrupted time series analyses were conducted to compare the following monthly measures among COT patients before and after HB21 implementation: 1) number of COT patients, 2) daily Morphine Milligram Equivalents [MMEs], 3) days’ supply of prescriptions. RESULTS: There was a significant 25% reduction in the trend (pre-HB21 RR: 0.95, 95% CI: 0.93, 0.96 versus post-HB21 RR: 0.70, 95% CI: 0.65, 0.76) and an 8% immediate decrease (RR: 0.92, 95% CI: 0.88, 0.97) in the monthly prevalence of COT patients after HB21 implementation. However, no significant change was observed in trends for monthly number of days supplied per prescription, monthly MMEs per COT patient-day, or total MMEs per prescription. LIMITATIONS: Our study used data from employer-based private health insurance and did not include a longer post-policy period to adjust for implementation lag. CONCLUSION: Fewer patients received COT after HB21; however, patients who continued to receive COT experienced no significant changes in their regimen. The study did not assess whether COT patients were appropriately tapered or if therapeutic alternatives were initiated for new chronic pain patients. KEY WORDS: Prescription opioids, health policy evaluation, chronic opioid therapy, drug utilization

scholarly journals Do Acute and Chronic Pain Patients Differ on Affirmation of One Aspect of Pain Acceptance? Acknowledgement that a Cure Is Unlikely

2015 ◽  
Vol 18;4 (4;18) ◽  
pp. E597-E604
Author(s):  
David Fishbain
Keyword(s):  
Chronic Pain ◽  
Acute Pain ◽  
Prediction Models ◽  
Behavior Pattern ◽  
Face Validity ◽  
Health Improvement ◽  
Pain Acceptance ◽  
Disability Status ◽  
Chronic Pain Patients ◽  
Pain Patients

Background: Many chronic pain patients (CPPs) cannot be cured of their pain, but can learn to manage it. This has led to research on pain “acceptance” which is defined as a behavior pattern with awareness of pain but not directed at changing pain. Objective: CPPs who have accepted their pain generally acknowledge that a cure is unlikely. Time with pain may be necessary to reach such an acknowledgment. It was therefore hypothesized that fewer acute pain patients (APPs) than CPPs should affirm that a cure is unlikely and that other described aspects of acceptance such as denial of disability status should be associated with cure is unlikely in both APPs and CPPs. Study Design: APPs and CPPs were compared for frequency of endorsement of 2 items/questions with face validity for cure is unlikely: little hope of getting better from pain (LH) and physical problem (pain) can’t be cured (CBC). Demographic variables and variables reported associated with acceptance were utilized in logistic prediction models for the above items in APPs and CPPs. Setting: Rehabilitation programs/offices. Results: CPPs were statistically more likely than APPs to affirm both LH and CBC. In both APPs and CPPs, items reported associated with acceptance, e.g., denial of disability status, predicted LH and CBC. Limitations: Information gathered from CPP self-reports. Conclusions: APPs versus CPPs differ on their affirmation on acknowledgement that a cure is unlikely. Key Words: Acceptance, pain acceptance, chronic pain, acute pain, chronic pain patients, acute pain patients, Battery of Health Improvement (BHI 2), cure disability, illness uncertainty

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