Home The treatment lottery of chronic back pain? A case series at a multidisciplinary pain centre
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The treatment lottery of chronic back pain? A case series at a multidisciplinary pain centre

  • Anna Mattsson , Nazdar Ghafouri and Emmanuel Bäckryd EMAIL logo
Published/Copyright: December 14, 2022
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Abstract

Objectives

Despite the number of people affected by chronic back pain, and the many available treatment options, even the best modalities provide limited pain reduction on a group level, often without simultaneous improvements in functioning or health-related quality of life. The objective was to provide an overview of the treatment of chronic back pain in clinical practice at a multidisciplinary pain centre, and to study patient and pain characteristics in different treatment groups.

Methods

104 chronic back pain patients (primary ICD-10-SE-diagnosis M53.0–M54.9 excluding M54.1 and M54.3), referred to the Pain and Rehabilitation Centre, University Hospital, Linköping in 2015, were studied using data from the Swedish Quality Registry for Pain Rehabilitation, self-reported medication data, and a retrospective medical record review.

Results

The following treatment groups were identified: rehabilitation (n=21), analgesics (n=33), invasive intervention (n=14), and no treatment (n=35). Significant differences between groups were found with regards to age, sick leave, education level, persisting pain duration, punishing responses by significant other, previous invasive intervention, receiving sub-clinic, physician speciality and referring care level.

Conclusions

Overall, patient demographics were associated with treatment strategy to a higher degree than patient-reported outcome measures. Moreover, physician speciality and organisational factors seemed to play a role in treatment choice.

Introduction

Defined as pain exceeding three months [1], chronic pain is associated with several somatic and psychiatric comorbidities, which in turn affect pain experience and complicate treatment [2], [3], [4], [5]. Behaviors such as fear-avoidance, catastrophizing, and low self-efficacy, as well as work-related factors such as poor job satisfaction, are indicative of worse prognosis in chronic pain patients [2, 6], [7], [8]. In both Europe and United States, approximately 20% of the adult population is affected by chronic pain [9, 10]. Moreover, 40% of European chronic pain sufferers were unsatisfied with the treatment they were receiving [10]. Low back pain and neck pain are ranked 1 and 4, respectively, among the leading causes of years lived with disability globally [11]. Chronic, disabling, low back pain is more prevalent in people with low income and short education [2], indicating the need for a biopsychosocial approach [12, 13].

The Pain and Rehabilitation Centre (PRC), Linköping, is an academic, tertiary, specialist pain center for treating patients with analgesics [14], invasive interventions such as for instance spinal cord stimulation or non-neurodestructive pulsed radiofrequency (RF) treatment [15], or interdisciplinary pain rehabilitation [16], as appropriate. When treating chronic (back) pain, analgesics alone rarely lead to adequate pain relief [17], [18], [19]. The only group of analgesics currently recommended by the English National Institute for Health and Care Excellence (NICE) is non-steroidal anti-inflammatory drugs (NSAIDs) [20].

The existence of a “facet syndrome”, that would in theory benefit from RF treatment, is generally accepted [21]. However, the Mint study showed that RF treatment had no clinically important, additional effect to physiotherapy for pain originating from the facet joints, sacroiliac joints, or intervertebral discs [22]. A smaller randomized controlled trial, comparing RF treatment to placebo-RF for facet joint pain, showed similar results [23]. RF treatment is currently recommended by NICE for certain treatment-refractory back pain conditions, only after a positive diagnostic test block [20], although the predictive value of such a test block is poor [15, 23].

The interdisciplinary pain rehabilitation programs offered at PRC are intense group-based treatments that include pain education, exercise therapy, cognitive behavioral therapy (CBT), and work-related interventions, as described by Ringqvist et al. [24]. Such programs show moderate long-term effect sizes for musculoskeletal conditions on pain intensity, interference in daily life, and overall health, and small effect sizes for many other outcome measures [24]. Evidence suggests that patients with worse self-reported disability and suffering yield better outcomes of interdisciplinary pain rehabilitation [3, 24].

For patients with “unspecific” chronic back pain without a clear component of neuropathic pain, there are currently no guidelines at PRC as to which patients should be offered which treatment. Rather, it is up to the individual physician to choose the most suitable treatment, also considering the patient’s wishes. The present study aimed to provide an overview of which treatment chronic back pain patients are being provided in clinical practice at this tertiary pain center. More precisely, this real-life study aimed at answering the following research questions:

  1. What is the distribution of the three main forms of treatment available for chronic back pain in clinical practice?

  2. Do the three treatment groups differ, with regards to

    1. Patient demographics?

    2. Pain characteristics?

    3. Patient-reported outcome measures?

    4. Medication use?

    5. Previous treatment?

    6. Organisational factors?

Methods

Study population

As of today, the Swedish version of International Classification of Diseases version 10 is used in Sweden (ICD-10-SE). All patients with a primary chronic back pain diagnosis, defined as ICD-10-SE-diagnosis M53.0–M54.9 but excluding M54.1 and M54.3, were extracted from the new visits to the PRC, University Hospital, Linköping, Sweden, in 2015, provided that the patient had answered the Swedish Quality Registry for Pain Rehabilitation (SQRP) questionnaires. Two patients were excluded during the medical record review, one due to many blocked notes and the other because all visits and treatment focused on pain other than back pain. A third patient was excluded after the analysis to determine treatment groups, as the initial treatment strategy was impossible to categorize (Figure 1).

Figure 1: 
            Flowchart of referral requests leading to analysis of 103 patients. SQRP, Swedish Quality Registry for pain Rehabilitation. Diagnoses do not include M54.1 and M54.3.
Figure 1:

Flowchart of referral requests leading to analysis of 103 patients. SQRP, Swedish Quality Registry for pain Rehabilitation. Diagnoses do not include M54.1 and M54.3.

Swedish quality registry for pain rehabilitation (SQRP)

SQRP is a register of chronic pain patients incorporating 95% of the Swedish specialized pain centers. It contains demographic data, pain characteristics, and patient-reported outcome measures (PROMs) [3, 24]. The variables used for this study, and described below, were extracted from the questionnaires completed before admittance to the PRC. The validated Swedish versions were used for all questionnaires.

Demographic data

Age, gender, birth country, completed university or college (HighEdu), number of days since last in work or studies (DaysNotWork), waiting time between referral and first visit (RefWait), number of physician visits within the last year (NbDrVisits) were extracted from SQRP.

Pain characteristics

Pain intensity during the previous week (NRS7d), days since start of pain (PainDur), days with persistent pain (PainDurPer), and number of pain locations (NbPainReg) were extracted from SQRP. NbPainReg is measured using 36 predefined anatomical areas (18 on the front and 18 on the back of the body), patients marking on a drawing the anatomical regions where they experienced pain: 1) head/face, 2) neck, 3) shoulder, 4) upper arm, 5) elbow, 6) forearm, 7) hand, 8) anterior aspect of chest, 9) lateral aspect of chest, 10) belly, 11) sexual organs, 12) upper back, 13) low back, 14) hip/gluteal area, 15) thigh, 16) knee, 17) shank, and 18) foot [25]. Hence the range of NbPainReg values is 0–36.

Hospital anxiety and depression scale (HADS)

The Hospital Anxiety and Depression Scale is a self-assessment questionnaire designed to assess depression and anxiety symptoms outside of psychiatric care. HADS comprises a total of 14 items, seven each for the anxiety-subscale (HAD-A) and the depression-subscale (HAD-D). Each item gives a maximum of three points, leading to a total of 21 points per subscale. Seven points or less indicates a non-case, 8–10 points a possible case, 11–14 points a probable case, and 15–21 points a severe case [26].

Multidimensional pain inventory (MPI)

The Multidimensional Pain Inventory is a self-assessment questionnaire of psychosocial and behavioral consequences of chronic pain. It includes a total of 61 items, each with a seven-point scale. The items are divided into three parts and a total of eight subscales: pain severity (MPI-PainSev), pain interference (MPI-Interf), life control (MPI-Contr), affective distress (MPI-AffDis), social support (MPI-SocSup), punishing responses (MPI-Pun), solicitous responses (MPI-Solic), distracting responses (MPI-Distra), household chores, outdoor work, and leisure activities [27]. The three latter combined make up the subscale general activity (MPI-GAI), which was used, as suggested by Bergström [27].

European quality of life instrument (EQ-5D)

The European Quality of Life Instrument is designed to measure a patient’s experience of health and is often used in health economic calculations. It contains a health barometer ranging from 0–100 (EQ-5D-VAS), as well as five questions concerning physical and psychological functioning and activity: mobility, self-care, usual activities, pain/discomfort, and anxiety/depression. These questions can be weighted to provide an overall “quality of life-index” (EQ-5D-VAS-index) where 1=full health, 0=dead, and negative figures indicate health “worse than death”. The mean score for a general Swedish population is 0.84 [28].

Short form health survey (SF-36)

The Short Form Health Survey is an instrument for measuring health-related quality of life in any population. It contains questions within eight domains: physical function, physical role function, pain, general health, vitality, social function, emotional role function, and psychological wellbeing. Each domain contains 2–10 questions, and each domain gives values between 0 and 100. These can be divided into two subscales: a physical component score (SF36-PCS) and a mental component score (SF36-MCS). The norm values in a general population vary between domains [29].

Chronic pain acceptance questionnaire (CPAQ-8)

The Chronic Pain Acceptance Questionnaire contains eight questions across two subscales, which together capture the acceptance of chronic pain, defined as the awareness of pain, giving up ineffective attempts to control pain, and the ability to live a fulfilling life despite pain. Each question has a scale of 0–6 points, giving a maximum of 24 points per subscale. One subscale measures engagement in activity (CPAQ-AE), and the other willingness to feel pain (CPAQ-PW) [30].

Tampa scale for kinesophobia (TSK-17)

The Tampa Scale for Kinesophobia measures kinesophobia, defined as the fear of movement, activity, and injury. It contains 17 questions, each giving 1–4 points (1=completely disagree, 4=completely agree). This gives a maximum of 68 points, where 37 is sometimes considered a cut-off-score for clinically significant kinesophobia [7].

Life satisfaction questionnaire (LiSat-11)

The Life Satisfaction Questionnaire measures overall satisfaction of life, as well as ten specific areas of life: vocation (LiSat-Voc), economy (LiSat-Eco), leisure (LiSat-Leis), social life (LiSat-Soc), sexual life (LiSat-Sex), activities of daily living (LiSat-ADL), family life (LiSat-Fam), partner relationship (LiSat-Part), physical health (LiSat-Phys), and mental health (LiSat-Ment). Each item gives 1–6 points (1=very unsatisfied, 6=very satisfied). The median values for a general Swedish population vary from 4–6 between domains [31].

Pain catastrophising scale (PCS-total)

The Pain Catastrophising Scale measures the level of pain-related catastrophising. It contains 13 items with five answer levels (0=not at all, 4=all the time), giving a possible score of 0–52 points. 30 points is considered a cut-off level for clinically significant catastrophising, equivalent to the most catastrophising quarter of the population [8].

Medication data

Upon admittance, all patients were asked to fill in a medication data form, together with the SQRP forms. All current medication, including dose, and whether it was taken continuously (_cont) or as needed (_an) was recorded. Medication was organized according to the Anatomic Therapeutic Chemical (ATC) classification system [32]. All medication groups reported by five or more patients were analyzed for differences between groups.

Medical record review

The electronic medical records from the PRC, including relevant referral text, were reviewed from the first visit in 2015, up until February 2021. In the case of a previously known patient returning to the same physician, notes before 2015 were read for background data, replacing information that would normally be found in referral text. In cases of conflicting information between the referral text and the first visit, the first visit records were used.

Initial treatment strategy

A distinction was made between four separate groups: analgesics, rehabilitation, invasive intervention, and no treatment. When several treatment strategies implemented simultaneously, analgesics were subordinate to rehabilitation and invasive intervention. Only treatments administered and followed up by the PRC were included. Recommendations of treatment to referring physician were recorded as no treatment.

Sick leave

The rate of sick leave at the referral date was stated by the referring physician. No difference was made between sickness benefit and sickness compensation. Parental leave, studies, and unemployment were registered as 0% sick leave. Retirement was registered separately.

Previous treatment

Previous back surgery (PrevSurgery), interdisciplinary pain rehabilitation (PrevRehab), and/or invasive intervention (PrevInvasive) for the current condition were recorded.

Organizational data

The studied PRC has three sub-clinics: one rehabilitation unit (RU) and two hospital-based units (HU-L and HU-N) which specialize in analgesics and invasive interventions. After an initial assessment, the patient can be referred between the sub-clinics. The sub-clinic, referring care level (RefLevel) and the specialty of the physician (PhysSpec) at the first visit were recorded. No difference was made between specialists and specialists in training.

Ethical considerations

Ethical approval for use of SQRP and patient-reported medication data was granted by the Regional Ethics Committee, Linköping, Sweden (Dnr 2015/108-51). The medical record review was part of a quality improvement project which was approved by the head of the PRC, in accordance with Swedish health care law (SFS 1982:763) which stipulates that healthcare providers are required to systematically improve the quality of care.

Statistical analysis

IBM SPSS statistics version 28 was used. Data are presented as the median and interquartile range (IQR) or percentages. Kruskal Wallis test or Pearson’s χ2 test (or Fisher’s exact test) were used as appropriate, with a significance level of 0.05 and a false discovery rate (FDR) of 25% for multiple omnibus tests using the Benjamini-Hochberg procedure. Post-hoc pairwise tests were performed with a Bonferroni correction. Effect sizes are presented by Cohen’s d, when applicable. Cohen’s d of 0.2 was considered a small effect size, 0.5 a medium effect size, and 0.8 a large effect size [33].

Multivariate data analysis (MVDA) was performed using SIMCA-P+ version 16. Principal component analysis (PCA) and partial least squares – discriminant analysis (PLS-DA) [5, 34] were used to explore the relationship between SQRP data and demographics and treatment strategy. PCA is a form of multivariate correlation analysis that is well suited for handling missing data, many intercorrelated variables, and a low subject-to-variable ratio. PCA summarizes the most important variation in the data and expresses it as latent variables called principal components (PC). The R2 value describes the goodness of fit and the Q2-value describes the goodness of prediction. PLS-DA is a supervised analysis in which group membership is regressed in order to find the key variables responsible for group discrimination. Further details concerning MVDA have been described elsewhere in previous open access papers by our group, e.g. [3, 5, 24].

Results

Diagnoses

A total of 103 patients were analyzed, and ICD-10-SE diagnoses are listed in Table 1.

Table 1:

Diagnoses of included patients, according to ICD-10-SE. Total number of patients (bold values) with each diagnosis as well as distribution of diagnoses across treatment groups.

ICD-10-SE-code Diagnosis Number of patients
Total No treatment Rehabilitation Analgesics Invasive intervention
M54.5 Lumbago 35 8 5 14 8
M53.1 Cervicobrachial syndrome 21 10 3 7 1
M54.2 Cervicalgia 11 6 2 2 1
M53.0 Cervicocranial syndrome 10 5 3 2 0
M54.6 Pain in thoracic spine 8 3 3 1 1
M54.9 Dorsalgia 7 2 2 3 0
M54.4 Lumbago with sciatica 7 0 3 3 1
M53.3 Sacralgia 3 0 0 1 2
M53.9 Dorsopathy 1 1 0 0 0

Treatment groups

The medical records review confirmed that it was possible to classify treatment strategies into three main groups: interdisciplinary pain rehabilitation (hereafter referred to as rehabilitation, n=21), invasive intervention (n=14), and changes in analgesic medication (hereafter referred to as analgesics, n=33). However, a fourth category for no treatment was added (n=35); these patients were not considered suitable for tertiary level treatment and a treatment recommendation was instead made to the referring physician. In 11 patients, analgesic changes and rehabilitation were conducted at the same time, and three patients received analgesics and an invasive intervention at the same time – these were registered as rehabilitation and invasive intervention, respectively. The distribution of pain diagnoses did not differ across treatment groups (Table 1, p=0.434). PROMs and other background data for the four patient groups are reported in Table 2, and PRC organizational data are reported in Table 3. The main findings from these two tables will be summarized below.

Table 2:

Characteristics of the treatment groups. Data are expressed as median (25th–75th quartile range), or as percentages.

No treatment Rehabilitation Analgesics Invasive intervention Statistics

Gender (% male) 23% 38% 36% 50% p=0.287
Age, year 51 44–62 38 30–45 50 36–61 57 49–67 p=0.001b
Birth country (% Sweden) 71% 75% 79% 71% p=0.907a
HighEdu (% university/college) 8.6% 25% 18% 43% p=0.093a
SickLeave (% yes including part time) 66% 86% 45% 54% p=0.184a
DaysNotWork 951 (315–3,230) 196 (105–450) 1685 (313–4,612) 1541 (719–6,394) p=0.016b
PainDur 2654 (1,293–5,457) 1214 (443–2,184) 1595 (420–3,070) 820 (344–4,017) p=0.052
PainDurPer 2604 (1,016–5,451) 908 (150–1724) 682 (211–1903) 1255 (404–3,183) p=0.018b
NRS7d 7.0 (6–8) 7.0 (6–8) 8.0 (6–9) 8.0 (7–9) p=0.224
NbPainReg 10.0 (5–16) 8.0 (6–16) 8.0 (5–16) 7.0 (5–10) p=0.593
HAD_A 9.0 (5.0–14.0) 8.5 (6.3–12.0) 8.0 (4.0–11.0) 9.0 (5.5–11.5) p=0.683
HAD_D 10.0 (6.0–11.0) 7.5 (5.3–10.0) 8.0 (3.0–12.5) 10.0 (4.5–15.0) p=0.674
MPI_PainSev 4.3 (3.3–5.3) 4.8 (4.0–5.3) 4.7 (3.8–5.6) 5.0 (4.0–5.5) p=0.466
MPI_Interf 4.6 (3.6–5.4) 4.3 (3.5–4.9) 4.2 (3.4–5.2) 4.6 (3.5–5.0) p=0.863
MPI_Contr 3.3 (2.0–3.5) 2.8 (1.8–3.3) 2.8 (1.5–3.3) 3.1 (1.2–3.5) p=0.707
MPI_AffDis 3.7 (2.7–4.3) 3.8 (2.3–4.9) 3.3 (2.3–4.6) 3.2 (1.2–4.1) p=0.648
MPI_SocSup 4.0 (3.0–4.7) 4.3 (3.3–5.3) 4.2 (3.1–5.3) 5.0 (4.0–5.4) p=0.120
MPI_Pun 2.3 (1.4–3.0) 1.5 (0.8–2.8) 1.3 (0.8–2.6) 1.0 (0.1–1.9) p=0.035b
MPI_Solic 2.4 (1.5–3.4) 2.3 (1.0–4.8) 3.0 (1.7–4.4) 3.0 (2.0–4.8) p=0.452
MPI_Distra 2.6 (1.5–3.5) 2.5 (1.8–3.5) 2.3 (1.5–3.3) 2.8 (1.8–3.4) p=0.957
MPI_GAI 2.5 (1.8–3.4) 2.6 (1.7–3.2) 2.4 (1.8–2.9) 2.1 (1.5–2.9) p=0.613
EQ5D_Index 0.19 (0.06–0.64) 0.10 (0.03–0.19) 0.12 (−0.08–0.53) 0.09 (−0.05–0.22) p=0.360
EQ5D_VAS 40 (25–50) 30 (20–42) 45 (28–60) 30 (20–46) p=0.279
SF36_PCS 29 (22.6–35.3) 29 (21.1–34.5) 28 (20.0–35.2) 26 (20.4–32.9) p=0.803
SF36_MCS 38 (26.8–45.2) 39 (26.5–48.0) 37 (26.0–49.4) 36 (15.8–45.1) p=0.813
CPAQ_AE 25 (16–32) 32 (13–41) 27 (18–35) 26 (17–36) p=0.510
CPAQ_PW 21 (15–26) 19 (17–26) 19 (16–26) 20 (13–26) p=0.943
TSK 43 (37–50) 41 (33–54) 42 (37–47) 42 (35–52) p=0.965
LiSat_Life 3 (2–4) 4 (3–4) 3 (2–4) 3 (3–4) p=0.567
LiSat_Voc 2 (1–4) 1 (1–5) 2 (1–4) 2 (1–4) p=0.685
LiSat_Eco 4 (2–5) 3 (2–4) 4 (3–5) 3 (2–6) p=0.431
LiSat_Leis 3 (2–4) 4 (2–4) 3 (2–4) 3 (1–4) p=0.865
LiSat_Cont 3 (2–5) 4 (3–5) 4 (3–4) 4 (3–5) p=0.458
LiSat_Sex 2 (1–4) 3 (1–5) 3 (1–5) 3 (2–5) p=0.771
LiSat_ADL 5 (4–6) 5 (3–6) 4 (3–5) 5 (4–6) p=0.418
LiSat_Fam 5 (3–5) 5 (3–5) 5 (4–6) 5 (4–6) p=0.607
LiSat_Part 5 (4–6) 5 (3–6) 5 (4–6) 5 (4–6) p=0.939
LiSat_Phys 2 (2–3) 2 (1–3) 2 (2–3) 2 (1–3) p=0.434
LiSat_Ment 4 (2–5) 4 (3–5) 4 (2–5) 2 (1–4) p=0.327
PCS_Total 24 (12–33) 21 (12–32) 28 (20–39) 30 (17–41) p=0.398
Nb_medicines_cont 1 (0–3) 1 (1–1) 2.5 (1–4) 3 (2–3) p=0.172
On any medication (% yes 70% 88% 77% 100% p=0.209
NSAID_cont 15% 25% 32% 20% p=0.629
NSAID_an 15% 19% 14% 10% p=0.939
Musclerelax_cont 15% 0% 9% 0% p=0.275
Opioids_cont 25% 25% 41% 60% p=0.200
Opioids_an 15% 31% 5% 10% p=0.140
Paracetamol_cont 40% 56% 64% 40% p=0.387
Paracetamol_an 25% 25% 14% 0% p=0.293
Gabapentinoids 5% 0% 18% 20% p=0.176
Sleepmed_all 15% 0% 14% 30% p=0.176
Antidepressives_all 35% 38% 27% 40% p=0.873
PrevRehab (yes %) 6% 15% 6% 0% p=0.628
PrevInvasive (yes %) 14% 5% 21% 71% p=0.001b
PrevSurgery (yes %) 29% 24% 18% 57% p=0.054
  1. aP-value refers to omnibus testing. bStatistically significant (p≤0.05) for omnibus test. NSAID, Non-steroidal antinflammatory drugs; Suffix _an, as needed; Suffix _cont, continuous medication every day. For other abbreviations, see text.

Table 3:

Organizational data in the four treatment groups.

No treatment Rehabilitation Analgesics Invasive intervention Statistics
Subclinic p<0.001a
RU (n=84) 38% 25% 31% 6%
HU-L (n=12) 8.3% 0.0% 33.3% 58.3%
HU-N (n=7) 28.5% 0.0% 43% 28.5%
PhysSpec p<0.001a
Rehabilitation (n=65) 34% 25% 35% 6%
Anesthesiology (n=20) 20% 0% 35% 45%
General Medicine (n=14) 43% 36% 14% 7%
Psychiatry (n=2) 100% 0% 0% 0%
RefLevel p=0.007a
Primary/Occupational (n=66) 39% 26% 29% 6%
Specialist (n=36) 25% 11% 36% 28%
  1. Data are expressed as percentage of cases in each row. RU, rehabilitation unit; HU-L, hospital-based pain unit L; HU-N, hospital-based pain unit N; PhysSpec, speciality of physician at first visit; RefLevel, referring care level. asignificant differences (p≤0.05).

Summary of main findings of Table 2

For most of the 55 variables listed in Table 2, omnibus testing revealed no differences between groups. For instance pain intensity, psychosocial or psychological distress, or medication did not differ between groups. However, we found that 5 of the 55 variables were statistically significant at 0.05 level, and 4 out of 5 variables remained significant at FDR 25%. The 4 variables were age, DaysNotWork, PainDurPer, and PrevInvasive:

  1. Age: As shown in Figure 2, the rehabilitation group was younger than both the invasive intervention group (p=0.008, Bonferroni-corrected) and the no treatment group (p=0.002, Bonferroni-corrected), with large effect sizes (d=1.3 and d=1.2, respectively).

  2. DaysNotWork: As shown in Figure 3, the rehabilitation group reported much fewer days since last in work or studies, compared to the invasive intervention group (p=0.025, Bonferroni-corrected), with a large effect size (d=1.3).

  3. PainDurPer: As shown in Figure 4, the no treatment group had been in persistent pain for much longer than the analgesics group (p=0.029, Bonferroni-corrected), with a medium effect size (d=0.6).

  4. PrevInvasive: As shown in Figure 5, the invasive intervention group had been treated much more often with an invasive intervention previously than all 3 other groups (Bonferroni-corrected p-values were 0.001, <0.001 and 0.006 compared to the no treatment, rehabilitation, and analgesics groups, respectively).

Figure 2: 
            Age of the patients in the four treatment groups. After Bonferroni correction, p-values between the no treatment group and the rehabilitation group, and between the rehabilitation group and invasive intervention group, remained significant (0.002 and 0.008, respectively).
Figure 2:

Age of the patients in the four treatment groups. After Bonferroni correction, p-values between the no treatment group and the rehabilitation group, and between the rehabilitation group and invasive intervention group, remained significant (0.002 and 0.008, respectively).

Figure 3: 
            Number of days out of work or studies in the four treatment groups. After Bonferroni correction, the p-value between the rehabilitation group and the invasive intervention group remained significant (0.025).
Figure 3:

Number of days out of work or studies in the four treatment groups. After Bonferroni correction, the p-value between the rehabilitation group and the invasive intervention group remained significant (0.025).

Figure 4: 
            Duration of persistent pain (PainDurPer) in the four treatment groups, measured in days. After Bonferroni-correction, the p-value between the no treatment group and the analgesics group remained significant (0.029).
Figure 4:

Duration of persistent pain (PainDurPer) in the four treatment groups, measured in days. After Bonferroni-correction, the p-value between the no treatment group and the analgesics group remained significant (0.029).

Figure 5: 
            Percentages of patients previously treated with an invasive intervention. After Bonferroni-correction, the p-values for comparisons between the invasive intervention group and the other 3 groups remained highly significant, see text.
Figure 5:

Percentages of patients previously treated with an invasive intervention. After Bonferroni-correction, the p-values for comparisons between the invasive intervention group and the other 3 groups remained highly significant, see text.

Summary of main findings of Table 3

Omnibus testing of the 3 organizational variables listed in Table 3 showed that all of them were statistically significant:

  1. Sub-clinics: Treatment strategies varied widely and significantly between the sub-clinics (p<0.001). Notably, 58.3% of the patients assessed at HU-L underwent an invasive intervention compared to 6% of the RU patients, and no patients at either HU-L or HU-N underwent rehabilitation.

  2. PhysSpec: Treatment strategies varied significantly with the specialty of the assessing physician (p<0.001). For example, anesthesiologists chose invasive interventions in 45% of the patients they met and never opted for rehabilitation, whereas the corresponding numbers for rehabilitation physicians were 6 and 25%.

  3. RefLevel: Treatment strategies varied significantly depending on what level of care the patient was referred from (p=0.007). Notably, 28% of patients referred from specialist care underwent an invasive intervention, whereas the corresponding number was 6% for primary or occupational care.

Multivariate analysis

With the intention of performing a subsequent PLS-DA-analysis, demographic and SQRP-data according to Table 2 were overviewed using unsupervised PCA. The PCA model had 2 PC, R2=0.36 and Q2=0.23. No multivariate outliers were identified. However, it was not possible to regress treatment group belonging (i.e., treatment group as Y-variable) by PLS-DA-analysis using patient demographics and SQRP-data as predictors (i.e., as X-variables).

Discussion

This real-life study illustrates the wide range of treatment strategies available for chronic back pain [17]. Overall, the rehabilitation group was younger, had fewer number of days out of work or studies, and had shorter duration of persistent pain – factors that can obviously be inter-related, at least to some extent. Most variables listed in Table 2, however, did not differ between groups. Moreover, organizational factors such as for instance the specialty of the physician were associated with choice of treatment (Table 3). The question therefore arises: Was the choice of chronic back pain treatment essentially a “lottery” for the individual patient? In other words, was the treatment of chronic back pain more a reflection of where and by whom the patient was assessed, rather than the condition itself? Even if, due to the nature of the present study, it is not possible to give a definite answer, it is nonetheless important to reflect upon these important questions.

Each referral request was assessed beforehand by a senior physician, and we therefore think that there was substantial selection bias involved. Patients fitting general principles for rehabilitation at PRC (e.g. fewer days since last in work, and indirectly hence lower age) would to a greater extent be assigned to a rehabilitation physician, whereas other patients (and especially those who had previously been treated by invasive methods) would to a greater extent be assigned to an anesthesiologist. Hence, the statistically significant group differences in Tables 2 and 3 probably reflect (at least in part) a selection bias. However, there is a clear limit to the amount of information a referral letter can contain, and it is difficult to believe that especially the clear differences found in Table 3 would solely be the result of a well-informed pre-visit selection based on medical facts found in the referral letter.

Altogether, we do think that it is fair to ask the “lottery” question, and we also think that it is sensible to postulate that the situation at PRC is a reflection of the treatment of chronic back pain in general, i.e., that patients are often treated more according to the individual physician’s “armamentarium” and local treatment availability rather than according to objective evidence-based criteria, amounting to a gap between evidence and practice [17, 35, 36]. Simply put, even in a large multidisciplinary pain center with a wide range of possible treatment options, it seems that physicians tend to use the tools they are themselves accustomed to from their previous professional experience. As in pain medicine in general, there is a need for more precision medicine in the context of chronic back pain. Even if this vision is only partially possible to attain today (more research is indeed needed concerning the mechanisms of chronic back pain and how to assess them with high validity and reliability), it is still very important that pain centers create a comprehensive and structured way of caring for chronic back pain patients – not least in order to avoid what we have described as a “lottery”.

The evidence for RF treatment is poor [15, 22, 23], and one of the research questions defined by UK-based NICE [37] is: “What is the clinical and cost effectiveness of radiofrequency denervation for chronic low back pain in the long term?” According to NICE clinical recommendations [37], RF treatment can be considered for chronic low back pain patients when:

  1. non-surgical treatment has not worked for them and

  2. the main source of pain is thought to come from structures supplied by the medial branch nerve and

  3. they have moderate or severe levels of localised back pain (rated as 5 or more on a visual analogue scale, or equivalent) at the time of referral.

NICE also stresses the importance to only treat patients with RF after a positive response to a diagnostic medial branch block [37]. Hence, if one views the problem from a primary care perspective, RF treatment should be viewed more or less as a last resort. But if one views the problem from a pain center point of view, the judicious use of rehabilitation vs. RF treatment is a more complicated question. For instance, should RF in that setting really be viewed as a last resort, or could one conceptually view diagnostic medial branch blocks for facet joint pain [21] as an integral part of the diagnostic workflow? Is it possible to integrate the best from the world of the anesthesiologist with the best from the world of the rehabilitation physician? As already mentioned, one option could perhaps be to use diagnostic medial branch blocks as one tool in a multimodal assessment of the patient. Once again, what is needed is a “reunion” of the two worlds of the anesthesiologist and the rehabilitation physician in both assessment and treatment. Concerning the analgesic treatment group, it is important to stress that the evidence for analgesics in chronic back pain is generally low, not least concerning opioids.

It may seem strange and even perhaps ethically worrying that there was a “no treatment” group. However, it is important to understand what this means. In this context, “no treatment” almost always meant that the pain physician gave the referring physician pharmacological advice, or advice about unimodal rehabilitative strategies available in primary care – such as physiotherapy or contact with a primary care psychologist. In other cases, the patient was referred to another specialist such as neurologist, addiction specialist, etc. In a few cases, advice to the referring physician even focused on the importance of tackling obesity issues. The latter shows that the physicians generally took a holistic approach. All in all, “no treatment” should not be understood as physician passivity or dismissiveness.

A few additional issues are worth discussing. Although this was not statistically significant by omnibus testing, there was a tendency for higher educated patients to rarely be part of the no treatment group (Table 2). Our study is relatively small, implying low statistical power to detect real differences, but it would indeed be ethically worrying if it turned out to be the case that patients with lower education level are treated at a multidisciplinary tertiary pain center to a lesser extent than others just because of their lower education level. There are, however, several possible confounders, one being that education level is associated with occupation, which in turn often affects back pain aetiology and therefore treatment options [2]. On the same note, it is reassuring that there does not seem to be any discrimination based on gender or country of birth but once again, the relatively low power of this study is a caveat.

Although the hypothesized treatment classification proved useful in this study, the medical record review itself can at best be regarded as semi-objective. Medical record reviews, while important tools for gaining an overview of current medical practice and patient safety [38], have questionable inter-rater reliability [39]. The internal validity of the medical records review also depends upon correct and comprehensive reporting by each physician. The external validity of this study is also limited, the study population coming from a single multidisciplinary pain center (tertiary care) at a university hospital, implying a selected group of the most complicated pain patients. None of the results of the present study can therefore easily be generalized to other populations of chronic back pain patients. Another obvious methodological limitation is the cross-sectional and observational nature of the study. Correlation does not imply causation, and one should therefore be cautious when interpreting the results of this study.

To conclude, our findings study are congruent with the described need for a “redesign of clinical pathways” [17] for chronic back pain patients. This is clinically highly relevant, not least because of the fact that back pain is one of the leading causes of years lived with disability globally [11]. Treatment guidelines are often based on the results of randomized controlled trials (RCTs), resulting in a gap between trial efficacy and real-world effectiveness [40]. This study provides insight into factors affecting choice of treatment for patients with chronic back pain at a tertiary pain clinic. Better implementation of existent evidence-based guidelines as well as further “precision medicine” research are needed for this very large group of patients, the aim being a personalized assessment that takes into consideration relevant biomedical and psychosocial factors (i.e., the biopsychosocial model), leading to a judicious use of the different tools available to help this large and heterogenous group of patients. The results also highlight the need of better integration of treatments aiming to decrease nociceptive transmission vs. treatments primarily directed at changing the perception and/or consequences of chronic pain.


Corresponding author: Emmanuel Bäckryd, Pain and Rehabilitation Center, and Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden, Phone: +46-(0)10-103 4441, E-mail:

  1. Research funding: ALF Research Grants, Region Östergötland (Emmanuel Bäckryd) and NEURO Sweden (Emmanuel Bäckryd).

  2. Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission. The specific contributions of each author are as follows: inception and design of the study (EB); drafting the protocol (EB and AM), data acquisition (AM), data analyses (EB and AM), data interpretation (NG, EB, AM), and drafting of the manuscript (AM), critical revisions of the manuscript (NG and EB).

  3. Competing interests: Authors state no conflict of interest.

  4. Informed consent: Informed consent was obtained from all individuals included in this study.

  5. Ethical approval: Research involving human subjects complied with all relevant national regulations, institutional policies and is in accordance with the tenets of the Helsinki Declaration (as amended in 2013). Ethical approval was granted by the Regional Ethics Committee, Linköping, Sweden (Dnr 2015/108-51).

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Received: 2022-09-13
Accepted: 2022-11-19
Published Online: 2022-12-14
Published in Print: 2023-04-25

© 2022 Walter de Gruyter GmbH, Berlin/Boston

Articles in the same Issue

  1. Frontmatter
  2. Editorial Comment
  3. Chronic pain and health inequalities: why we need to act
  4. Systematic Reviews
  5. Resilience as a protective factor in face of pain symptomatology, disability and psychological outcomes in adult chronic pain populations: a scoping review
  6. Is intravenous magnesium sulphate a suitable adjuvant in postoperative pain management? – A critical and systematic review of methodology in randomized controlled trials
  7. Topical Review
  8. Pain assessment 3 × 3: a clinical reasoning framework for healthcare professionals
  9. Clinical Pain Researches
  10. The treatment lottery of chronic back pain? A case series at a multidisciplinary pain centre
  11. Parameters of anger as related to sensory-affective components of pain
  12. Loneliness in patients with somatic symptom disorder
  13. The development and measurement properties of the Dutch version of the fear-avoidance components scale (FACS-D) in persons with chronic musculoskeletal pain
  14. Observational Studies
  15. Can interoceptive sensitivity provide information on the difference in the perceptual mechanisms of recurrent and chronic pain? Part I. A retrospective clinical study related to multidimensional pain assessment
  16. Distress intolerance and pain catastrophizing as mediating variables in PTSD and chronic noncancer pain comorbidity
  17. Stress-induced headache in the general working population is moderated by the NRCAM rs2300043 genotype
  18. Does poor sleep quality lead to increased low back pain the following day?
  19. “I had already tried that before going to the doctor” – exploring adolescents’ with knee pain perspectives on ‘wait and see’ as a management strategy in primary care; a study with brief semi-structured qualitative interviews
  20. Problematic opioid use among osteoarthritis patients with chronic post-operative pain after joint replacement: analyses from the BISCUITS study
  21. Worst pain intensity and opioid intake during the early postoperative period were not associated with moderate-severe pain 12 months after total knee arthroplasty – a longitudinal study
  22. Original Experimentals
  23. How gender affects the decoding of facial expressions of pain
  24. A simple, bed-side tool to assess evoked pressure pain intensity
  25. Effects of psychosocial stress and performance feedback on pain processing and its correlation with subjective and neuroendocrine parameters
  26. Participatory research: a Priority Setting Partnership for chronic musculoskeletal pain in Denmark
  27. Educational Case Report
  28. Hypophosphatasia as a plausible cause of vitamin B6 associated mouth pain: a case-report
  29. Short Communications
  30. Pain “chronification”: what is the problem with this model?
  31. Korsakoff syndrome and altered pain perception: a search of underlying neural mechanisms
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