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Abstract

  1. Top of page
  2. Abstract
  3. Databases
  4. What does this study add?
  5. 1. Introduction
  6. 2. Methods
  7. 3. Results
  8. 4. Discussion
  9. 5. Conclusions
  10. Author contributions
  11. References

The role of quantitative sensory testing (QST) in prediction of analgesic effect in humans is scarcely investigated. This updated review assesses the effectiveness in predicting analgesic effects in healthy volunteers, surgical patients and patients with chronic pain. A systematic review of English written, peer-reviewed articles was conducted using PubMed and Embase (1980–2013). Additional studies were identified by chain searching. Search terms included ‘quantitative sensory testing’, ‘sensory testing’ and ‘analgesics’. Studies on the relationship between QST and response to analgesic treatment in human adults were included. Appraisal of the methodological quality of the included studies was based on evaluative criteria for prognostic studies. Fourteen studies (including 720 individuals) met the inclusion criteria. Significant correlations were observed between responses to analgesics and several QST parameters including (1) heat pain threshold in experimental human pain, (2) electrical and heat pain thresholds, pressure pain tolerance and suprathreshold heat pain in surgical patients, and (3) electrical and heat pain threshold and conditioned pain modulation in patients with chronic pain. Heterogeneity among studies was observed especially with regard to application of QST and type and use of analgesics. Although promising, the current evidence is not sufficiently robust to recommend the use of any specific QST parameter in predicting analgesic response. Future studies should focus on a range of different experimental pain modalities rather than a single static pain stimulation paradigm.


Databases

  1. Top of page
  2. Abstract
  3. Databases
  4. What does this study add?
  5. 1. Introduction
  6. 2. Methods
  7. 3. Results
  8. 4. Discussion
  9. 5. Conclusions
  10. Author contributions
  11. References
  • The PubMed and Embase databases were used to electronically search and screen literature to answer the question ‘Can quantitative sensory testing predict responses to analgesic treatment?’
  • Fourteen of 63 generated articles met the inclusion criteria providing data on 720 individuals.

What does this study add?

  1. Top of page
  2. Abstract
  3. Databases
  4. What does this study add?
  5. 1. Introduction
  6. 2. Methods
  7. 3. Results
  8. 4. Discussion
  9. 5. Conclusions
  10. Author contributions
  11. References
  • Current evidence is not sufficiently robust to recommend the use of any specific quantitative sensory testing parameter in predicting analgesic response.
  • Findings are promising and call for future well-designed and sufficiently powered studies, focusing on different modalities or experimental pain modulation rather than a single static pain measure.

1. Introduction

  1. Top of page
  2. Abstract
  3. Databases
  4. What does this study add?
  5. 1. Introduction
  6. 2. Methods
  7. 3. Results
  8. 4. Discussion
  9. 5. Conclusions
  10. Author contributions
  11. References

Pain is a major problem for a large proportion of the adult population (Breivik et al., 2006). The wide variability in treatment outcomes and the high prevalence of adverse events associated with analgesic use makes it important to identify responders (Turk, 2002; Eisenberg et al., 2005). Unfortunately, biomarkers to guide individualized pain treatment are lacking in clinical practice. Consequently, the type of pain treatment in a given patient is based on doctors' experience and the primary diagnosis of the pain syndrome, rather than patients' individual characteristics. This trial-and-error principle often leads to insufficient and unsatisfactory treatment, side-effects and suffering (Noble et al., 2010). Hence, a tool to predict and distinguish a patient's responsiveness to analgesic treatment is warranted.

Quantitative sensory testing (QST) where the evoked pain intensity, timing and modality can be controlled has successfully been used to encompass the problems with individual variability in pain (Edwards et al., 2005; Arendt-Nielsen and Yarnitsky, 2009). QST refers to a broad range of psychophysical methods of stimulation to assess and quantify sensory function (Shy et al., 2003; Backonja et al., 2009). Most sensory stimulation protocols have been developed for cutaneous application, but QST has also been successfully applied to muscle and viscera (Arendt-Nielsen and Yarnitsky, 2009). QST is frequently applied in experimental laboratory pain research but also used in clinical practice mainly as part of the diagnostic work-up of neuropathic pain disorders (Arendt-Nielsen and Yarnitsky, 2009; Backonja et al., 2009; Haanpaa et al., 2011).

Previous studies have explored associations between QST responses and reports of daily pain symptoms among healthy adults (Edwards and Fillingim, 1999; Fillingim et al., 1999; Edwards et al., 2003). QST has also been used to characterize mechanisms underlying somatic and neuropathic pain disorders, including postoperative (Gottrup et al., 2000; Kosek and Ordeberg, 2000b), visceral (Olesen et al., 2012b), chronic and neuropathic pain syndromes (Geber et al., 2011), and to predict (Schiff and Eisenberg, 2003) and measure responses to interventions (Kosek and Ordeberg, 2000a). Moreover, an inverse association between nociceptive sensitivity and analgesic efficacy has been demonstrated in preclinical research (Mogil et al., 1995; Elmer et al., 1998). Recent reviews have exclusively investigated the correlation between responses to preoperatively applied experimental pain stimuli and clinical postoperative pain (Ip et al., 2009; Werner et al., 2010; Abrishami et al., 2011), analgesic consumption (Ip et al., 2009; Abrishami et al., 2011) and persistent postsurgical pain (Abrishami et al., 2011). Due to the complexity and heterogeneity of previous studies, results have varied leading to uncertainty whether QST may predict analgesic treatment response to clinical pain over a range of conditions.

The aim of this updated review was to assess whether QST can predict analgesic effects by evaluating experimental, postoperative and clinical studies in human adults. Interpretation and limitations of the findings and their implication for current clinical practice and future research will be discussed.

2. Methods

  1. Top of page
  2. Abstract
  3. Databases
  4. What does this study add?
  5. 1. Introduction
  6. 2. Methods
  7. 3. Results
  8. 4. Discussion
  9. 5. Conclusions
  10. Author contributions
  11. References

2.1 Search strategy and study selection

A literature search was conducted in the databases PubMed and Embase (January 1980 to March 2013) using the following MeSH terms and/or free text in combinations: ‘quantitative sensory testing’, ‘sensory testing’ and ‘analgesics’. IWDF conducted the initial database search, assisted by an experienced research librarian (Fig. 1). Search generated citations (title and abstract) were then independently screened for relevance by two reviewers (IWDF and KG). Any disagreement was solved by consensus and relevant full-text articles were retrieved. Reference lists of the relevant full-text articles were also searched.

figure

Figure 1. Flow chart of the literature search process in a systematic review on quantitative sensory testing measures as predictors of analgesic treatment responses.

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2.1.1 Inclusion criteria

Original English written articles published in peer reviewed scientific journals were eligible for inclusion. Review articles, letters-to-the-editor and abstracts were excluded following manual search of their reference lists for additional relevant studies.

The inclusion criteria were:

  • Participants

    Adults (≥18 years of age), including healthy volunteers, surgical patients or patients with chronic pain

  • Intervention

    Any experimental or clinical application or combination of applications of QST parameters targeting analgesic response

  • Outcome

    Any response to analgesic treatment, including subjective assessment of analgesic effect and use of analgesics

2.1.2 Data extraction

Data extraction was performed by a single reviewer (KG) using a study-specific data extraction sheet. Information on study design, population, experimental protocol, predictors and outcomes was extracted from each article. Subsequently, two reviewers (IWDF and AEO) independently validated the extracted data. The reviewers were not blinded to study authors. Any disagreement was solved by consensus.

2.2 Assessment of methodological quality

Two reviewers (KG and AEO) independently assessed the methodological quality of the selected studies using six evaluative criteria for prognostic studies (Hayden et al., 2006). Accordingly, the risk of selection bias, attrition bias, measurement bias, bias due to confounding, and bias related to the statistical analysis and presentation of results was rated for each study using a 4-point categorical scale (Yes, Partly, No, Unsure) (Table 2). A ‘Yes’ response indicates that the study is designed and conducted to minimize the risk of bias for that specific item. An ‘Unsure’ response may arise when the answer to an item is not reported or is not reported clearly. Any disagreement was solved by consensus.

3. Results

  1. Top of page
  2. Abstract
  3. Databases
  4. What does this study add?
  5. 1. Introduction
  6. 2. Methods
  7. 3. Results
  8. 4. Discussion
  9. 5. Conclusions
  10. Author contributions
  11. References

3.1 Selection of articles

Fourteen of 63 generated articles met the inclusion criteria providing data on 720 individuals (Wilder-Smith et al., 2003; Attal et al., 2004; Hsu et al., 2005; Edwards et al., 2006; Pan et al., 2006; Martinez et al., 2007; Nielsen et al., 2007; Aasvang et al., 2008; Rudin et al., 2008; Eisenberg et al., 2010; Buhagiar et al., 2011; Yarnitsky et al., 2012; Olesen et al., 2013; Pedersen et al., 2013) (Table 1). Sample size of the included studies ranged from 20 to 162. A priori, studies were divided according to study population; (1) healthy volunteers (Eisenberg et al., 2010), (2) surgical patients (Wilder-Smith et al., 2003; Hsu et al., 2005; Pan et al., 2006; Martinez et al., 2007; Nielsen et al., 2007; Aasvang et al., 2008; Rudin et al., 2008; Buhagiar et al., 2011; Pedersen et al., 2013), and (3) patients with chronic pain (Attal et al., 2004; Edwards et al., 2006; Yarnitsky et al., 2012; Olesen et al., 2013).

Table 1. Studies included in a systematic review on quantitative sensory testing measures as predictors of analgesic treatment responses
StudyDesignConditionNPredictor(s)Outcome(s)Main finding(s)
  1. a

    Experimental pain stimulation was performed before and after the administration of PO oxycodone, 0.3 mg/kg or placebo; PO, per os; Cold pressor test, hand immersion in painful ice water (1 °C/cut off 180 s).

  2. b

    Pressure pain thresholds were recorded before and after administration IV phentanyl, 2 μg/kg.

  3. c

    Treatment consisted of IV lidocaine, 5 mg/kg; IV saline (placebo) followed by PO mexiletine, 200 mg/d (open basis: weekly titration upwards to maximal effective dose).

  4. d

    Treatment consisted of PO morphine, 15–240 mg (8 weeks); PO nortriptyline, 10–160 mg (8 weeks); or placebo (8 weeks).

  5. e

    To obtain an index of sensitivity between stimulation areas (i.e., pancreatic area vs. control area), the relation between thresholds was determined as pressure pain detection threshold (pPDT) ratio (pPDT pancreas/pPDT control) and electrical pain detection threshold (ePDT) ratio (ePDT pancreas/ePDT control).

  6. f

    Conditioned pain modulation, relative change (%) in pressure pain tolerance thresholds applied before and after hand immersion in a painful ice water bath (2 °C/180 s).

  7. g

    Treatment consisted of escalating doses of pregabalin (300 to 600 mg/day) or placebo (3 weeks).

  8. h

    Responders to treatment were defined as patients with a reduction in daily average pain score of 30% or more after 3 weeks of study treatment compared to baseline recordings.

  9. i

    Conditioned pain modulation, difference in average pain scores of two ‘pain-60’ heat stimuli (Pain-60 defined as the temperature that induced pain ratings of 60 on a numerical ranking scale of 0 to 100) applied before and during hand immersion in a painful hot water bath (46.5 °C/60 s).

  10. j

    Temporal summation, exposure to a single stimulus (suprathreshold mechanical pinprick pain) and repetitive stimuli (inter-stimulus interval of 1 s) within an area of 1 cm in diameter were rated on a numerical ranking scale (0–100) and the difference between the two measurements was calculated as the mechanical temporal summation score.

  11. k

    Treatment consisted of PO duloxetine, 30 mg/day (1 week) followed by PO duloxetine, 60 mg/day (4 weeks).

  12. DNIC-like effect, difference in heat pain intensity before and after cold pressor test (12 °C/30 s); IV, intravenous; IV-PCA, intravenous patient-controlled analgesia; N, no. of participants, included/evaluated; POD, postoperative day; RCT, randomized controlled trial; Temporal summation, difference in pain intensity after one and five painful heat stimulations; Ref., reference number; VAS, visual analogue scale.

Healthy volunteers      
Eisenberg et al. (2010)RCTHealthy volunteers40/40
  • Heat pain threshold
  • DNIC-like effect
  • Temporal summation
(1) Latency to pain onset, (2) pain intensity, and (3) tolerance to cold pressor test before and after administration of opioid analgesic treatmentaHeat pain threshold predicted the magnitude of pain reduction in response to oxycodone treatment and the magnitude of temporal summation predicted the effect of oxycodone on cold pain tolerance (r2 = 0.17, p = 0.027 and 0.005, respectively)
Surgical patients      
Aasvang et al. (2008)CohortGroin herniotomy165/162
  • Electrical sensory threshold
  • Electrical pain threshold
  • Electrical pain tolerance
Cumulative consumption of (1) paracetamol; (2) ibuprofen; and (3) tramadol at seven days postoperativelyNo correlation between preoperative electrical pain thresholds and analgesic consumption
Buhagiar et al. (2011)CohortCaesarean section65/65
  • Pressure pain threshold
  • Pressure pain tolerance
  • Electrical pain threshold
Cumulative consumption of paracetamol at 48 h postoperativelyCorrelation between electrical pain threshold and consumption of paracetamol (r = −0.33, p < 0.005)
Hsu et al. (2005)CohortHysterectomy40/40
  • Pressure pain thresholdb
Cumulative consumption of IV-PCA morphine at 24 h postoperativelyCorrelation between pressure pain tolerance after administration of phentanyl and consumption of morphine (r = −0.48, p < 0.002)
Martinez et al. (2007)CohortKnee arthroplasty20/20
  • Heat pain threshold
  • Cold pain threshold
  • Suprathreshold heat/cold pain
  • Punctuate pain threshold (von Frey filaments)
  • Tactile allodynia (brush)
Cumulative consumption of IV-PCA morphine at 24 and 48 h postoperativelyNo correlation between thermal and mechanical pain thresholds and analgesic consumption. Significant correlation between responses to preoperative suprathreshold pain (preoperative heat hyperalgesia) and cumulative morphine use at 24 h (Spearman's rho = 0.63, p = 0.01)
Nielsen et al. (2007)CohortCaesarean section45/39
  • Electrical sensory threshold
  • Electrical pain threshold
Cumulative consumption of supplemental morphine equivalents at 48 h postoperativelyNo correlation between preoperative electrical pain thresholds and supplemental morphine consumption
Pan et al. (2006)CohortCaesarean section34/34
  • Heat pain threshold
  • Supra-threshold heat pain
Cumulative consumption of morphine equivalents: (1) during surgery; (2) in the recovery room; (3) in the first 6h of IV-PCA; and (4) during the above mentioned time periods (1, 2, 3)Correlation between thermal pain thresholds and morphine consumption in the recovery room (Spearman's rho = −0.41 to −0.43, p < 0.05)
Pedersen et al. (2013)CohortPercutaneous nephrolithotomy55/44
  • Pressure pain tolerance
  • Electrical pain threshold
Cumulative consumption of morphine equivalents at 4 h postoperativelyCorrelation between preoperative electrical pain threshold on the control-side and postoperative consumption of morphine (single stimuli: rho = −0.43, p = 0.01 and repeated stimuli: rho = −0.45, p = 0.005).
Rudin et al. (2008)CohortLaparascopic tubal ligation86/59
  • Heat sensory threshold
  • Heat pain threshold
  • Supra-threshold heat pain
Cumulative consumption of paracetamol and ibuprofen at 10 days postoperativelyCorrelation between heat pain perception and ibuprofen consumption (r = 0.3, p = 0.02)
Wilder-Smith et al. (2003)RCTDisc herniation surgery45/41
  • Electrical sensory threshold
  • Electrical pain threshold
  • Electrical pain tolerance
Cumulative consumption of IV-PCA morphine at 24 h postoperativelyNo correlation between preoperative electrical pain thresholds and morphine consumption
Patients with chronic pain      
Attal et al. (2004)RCTPost-herpetic neuralgia and traumatic nerve injury24/22
  • Tactile allodynia (brush)
  • Punctuate pain threshold (von Frey filaments)
  • Heat/cold sensory threshold
  • Heat/cold pain threshold
  • Supra-threshold heat/cold pain
Change in pain intensity from baseline to the peak of treatment effectc(1) Pain intensity was more improved in patients with mechanical allodynia (F = 5.3, p < 0.05); (2) Correlation between the severity of mechanical allodynia and the effects of lidocaine on pain (p < 0.01); (3) Correlation between the magnitude of heat deficits and the effects of lidocaine and mexiletine on pain (p < 0.01 for lidocaine, p < 0.05 for mexiletine)
Edwards et al. (2006)RCTPost-herpetic neuralgia76/64
  • Heat pain threshold
(1) Percentage change in pain intensity and (2) pain relief from pretreatment to maintenancedHigher baseline heat pain thresholds were associated with a greater percentage change in pain intensity (r2 = 0.29, p = 0.002) and higher ratings of pain relief (r2 = 0.35, p = 0,001) during opioid treatment
Olesen et al. (2013)RCTChronic pancreatitis64/60
  • Pressure pain threshold ratioe
  • Electrical pain threshold ratioe
  • Conditioned pain modulationf
Percentage change in pain intensity after three weeks of study treatment compared to baselinegHypersensitivity to electric tetanic stimulation of the pancreatic area was observed in pregabalin respondersh compared to non-responders (p = 0.001).
Yarnitsky et al. (2012)RCTPainful diabetic neuropathy33/30
  • Conditioned pain modulationi
  • Temporal summationj
  • Heat/cold sensory threshold
  • Heat pain threshold
  • Supra-threshold heat pain
  • Punctuate sensory threshold (von Frey filaments)
  • Punctuate pain threshold (von Frey filaments)
(1) Analgesic efficacy and (2) change in pain intensity during last two weeks of treatmentk(1) Correlation between conditioned pain modulation efficiency and drug effect (r = 0.63, p < 0.001); (2) Correlation between conditioned pain modulation and the extent of pain reduction (r = 0.44, p = 0.023)

3.2 Healthy volunteers

3.2.1 Methods

The single experimental study in healthy volunteers selected for this review randomly assigned subjects to experimental pain sessions before and after double-blinded administration of oxycodone or placebo (Eisenberg et al., 2010).

3.2.2 Participants

The study involved 40 paid male and female volunteers. Main inclusion criteria included healthy adults (between the ages of 18 and 45 years) with no clinical pain, substance abuse or medication use (except for oral contraceptives). (Eisenberg et al., 2010)

3.2.3 QST protocol

A computerized thermode was used to assess heat pain threshold. Heat stimulation was also used as the test pain (47 °C/4 s) in the assessment of conditioned pain modulation with cold stimulation acting as the conditioning stimulus (12 °C/30 s). The term conditioned pain modulation describes psychophysical paradigms in which a conditioning stimulus is used to affect a test stimulus (Yarnitsky, 2010; Yarnitsky et al., 2010). Finally, temporal summation of heat pain intensity was assessed using a numerical rating scale (NRS) to brief, repetitive suprathreshold heat pulses (Eisenberg et al., 2010). Temporal summation is a phenomenon attributed to sensory physiology. Gradual increase in pain intensity following repeated painful mechanical stimulation is due to central temporal integration in dorsal horn neurons (Staahl and Drewes, 2004; Arendt-Nielsen and Yarnitsky, 2009).

3.2.4 Outcomes

A cold pressor test apparatus (1 °C/180 s cut-off) was used to assess the efficacy of oxycodone on (1) latency to cold pain, (2) time until hand withdrawal, (3) pain intensity upon hand withdrawal (NRS), and (4) maximum pain intensity during immersion (NRS) (Eisenberg et al., 2010).

3.2.5 Results

Heat pain threshold significantly predicted the effect of oxycodone on magnitude of pain reduction on 15 s of cold pressor testing, and the magnitude of temporal summation significantly predicted the effect of oxycodone on cold pain tolerance (Eisenberg et al., 2010).

3.3 Studies in surgical patients

3.3.1 Methods

The nine studies in surgical patients were all observational cohort studies (Wilder-Smith et al., 2003; Hsu et al., 2005; Pan et al., 2006; Martinez et al., 2007; Nielsen et al., 2007; Aasvang et al., 2008; Rudin et al., 2008; Buhagiar et al., 2011; Pedersen et al., 2013) (Table 1). Groups of patients were followed before and after more or less standardized surgical procedures with postoperative pain management administered as part of daily clinical practice. Very different surgical models were used ranging from minimally invasive laparoscopic procedures over keyhole incisions to open surgical procedures. Similarly, different anaesthetic and postoperative pain management regimens were used. Protocols varied substantially with regard to the assessment of postoperative analgesic consumption, thus requirement of both opioid and non-opioid analgesics was used as study outcomes. Importantly, analgesic consumption was not defined as a primary endpoint in any of the included studies since most focused on exploring the relationship between QST and clinical postoperative pain.

3.3.2 Participants

The studies involved a total of 564 surgical patients. As a consequence of the surgical procedure in focus, gender and age distribution of participants varied between studies (Table 1). Most studies included only pain-free patients preoperatively, except the studies by Pedersen et al. (2013), Martinez et al. (2007) and Wilder-Smith et al. (2003). In fact, the two last mentioned studies were designed to recruit homogeneous groups of patients with significant preoperative pain.

3.3.3 QST protocol

Despite the use of various experimental protocols, there were similarities in preoperative QST parameters between studies (Table 1). Electrical stimulation was most frequent followed by thermal and pressure. All studies determined pain detection thresholds and the majority also assessed pain tolerance thresholds. In addition, four studies assessed sensory thresholds and three studies used responses to repetitive painful thermal stimulation.

3.3.4 Outcomes

The postoperative consumption of the following analgesics was recorded: paracetamol (Aasvang et al., 2008; Rudin et al., 2008; Buhagiar et al., 2011), ibuprofen (Aasvang et al., 2008; Rudin et al., 2008), tramadol (Aasvang et al., 2008), morphine (Wilder-Smith et al., 2003; Hsu et al., 2005; Martinez et al., 2007) or morphine equivalents (Pan et al., 2006; Nielsen et al., 2007; Pedersen et al., 2013) (Table 1). Routes of administration included oral and intravenous administration and the cumulative consumption of analgesics was most frequently assessed at 24 h or 48 h postoperatively. Two studies recorded long-term cumulative analgesic consumption over 7 and 10 postoperative days, respectively.

3.3.5 Results

Six studies found a relationship between QST measurements and subsequent postoperative analgesic consumption (Hsu et al., 2005; Pan et al., 2006; Martinez et al., 2007; Rudin et al., 2008; Buhagiar et al., 2011; Pedersen et al., 2013), whereas three studies did not (Wilder-Smith et al., 2003; Nielsen et al., 2007; Aasvang et al., 2008). Buhagiar et al. (2011) showed a correlation between electrical pain threshold and consumption of paracetamol at 48 h following caesarean section. Hsu et al. (2005) found that pressure pain tolerance scores predicted total morphine consumption within 24 h following hysterectomy. Martinez et al. (2007) reported a correlation between responses to preoperative suprathreshold heat stimulations and cumulative morphine use at 24 h following total knee arthroplasty. Pan et al. (2006) found correlations between pain reporting after thermal stimulation and consumption of morphine equivalents in the recovery room in patients undergoing caesarean section. Pedersen et al. (2013) showed a correlation between preoperative electrical pain threshold on the control side and postoperative consumption of morphine at 4 h following percutaneous nephrolithotomy, and Rudin et al. (2008) documented that sensitivity for heat stimulation predicted ibuprofen requirement within the first 10 postoperative days following laparoscopic tubal ligation. However, three studies were unable to find a relationship between electrical pain thresholds and subsequent postoperative analgesic consumption following caesarean section (Nielsen et al., 2007), disc herniation surgery (Wilder-Smith et al., 2003) and groin herniotomy (Aasvang et al., 2008).

3.4 Studies in patients with chronic pain

3.4.1 Methods

Four studies of patients with chronic pain were included in this review (Attal et al., 2004; Edwards et al., 2006; Yarnitsky et al., 2012; Olesen et al., 2013) (Table 1). All studies were part of clinical drug trials: lidocaine combined with oral mexiletine (Attal et al., 2004), morphine or nortriptyline (Edwards et al., 2006), duloxetine (Yarnitsky et al., 2012) and pregabalin (Olesen et al., 2013). All studies performed QST at baseline prior to treatment.

3.4.2 Participants

The studies involved 172 patients with chronic pain receiving analgesic treatment for post-herpetic neuralgia (Attal et al., 2004; Edwards et al., 2006), traumatic nerve injury (Attal et al., 2004), painful diabetic neuropathy (Yarnitsky et al., 2012) or chronic pancreatitis (Olesen et al., 2013). Main inclusion criteria generally included pain persisting for more than 3 to 6 months, and a NRS pain severity score of ≥4/10 or more specifically; chronic abdominal pain typical due to pancreatitis (epigastric pain more than 3 days a week for at least 3 months) (Olesen et al., 2013).

3.4.3 QST protocol

Pretreatment QST protocols were comparable regarding the application of thermal stimulation (Table 1). Two studies (Attal et al., 2004; Yarnitsky et al., 2012) additionally applied punctuate stimulation and one study (Attal et al., 2004) applied tactile stimulation using a paint brush. Olesen et al. (2013) performed pressure and electric tetanic threshold testing at two sites (i.e. the upper abdominal and the lower neck area), and subsequently used the ratio between thresholds obtained from these two sites in further analysis. In addition to static pain measures, two studies assessed inhibition of experimental pain using conditioned pain modulation paradigms (Yarnitsky et al., 2012; Olesen et al., 2013).

3.4.4 Outcomes

Difference in pain intensity scores from baseline defined treatment response in all studies. Additional endpoints included various types of responder analyses (i.e. the percentage of patients experiencing a clinically meaningful pain reduction (e.g. ≥30%) and the percentage of patients reporting different levels of response on ratings of overall improvement or treatment satisfaction) (Table 1).

3.4.5 Results

Edwards et al. (2006) found that baseline heat pain sensitivity predicted the effect of morphine but not the responses to nortriptylin or placebo. Attal et al. (2004) found a correlation between baseline heat pain magnitude and the effect of lidocaine and mexiletine on spontaneous pain. However, the correlation coefficient was not shown. Yarnitsky et al. (2012) suggested that patients with less efficient conditioned pain modulation are most likely to benefit from duloxetine. In the study by Attal et al. (2004), 65% of patients presenting with spontaneous pain and concomitant mechanical allodynia were found to be lidocaine responders, whereas none of the patients presenting with only spontaneous pain responded to lidocaine. This finding suggests that patients with mechanical allodynia may be better candidates for sodium-channel blockers (Attal et al., 2004). Finally, Olesen et al. (2013) found that the effect of pregabalin was associated with sensitivity to pretreatment electrical stimulation, suggesting that patients with lower pain thresholds in the pancreatic viscerotome compared to a control area (lower neck) were more likely to benefit from pregabalin treatment than patients with no difference across QST test sites.

3.5 Risk of bias (all studies)

Appraisal of the methodological quality of the included studies is depicted in Table 2. The reviewers initially agreed on 62 out of 84 items (74%). Consensus was reached on all items following discussion. Inclusion criteria were reported in all studies. However, strict inclusion criteria, such as age limit (e.g. 18–45 years old) and limited range of pain scores (e.g. ≥4/10), entail that some studies are distanced from the clinical reality. Subject recruitment was generally poorly described with only three studies (21%) giving the numbers of patients screened and reasons for exclusion in detail (Attal et al., 2004; Rudin et al., 2008; Pedersen et al., 2013). The number of secondary exclusions and their explanations (e.g. reoperation, incomplete follow-up data, refusal, contraindication) was clearly reported in 36% (n = 5) of studies (Attal et al., 2004; Nielsen et al., 2007; Aasvang et al., 2008; Rudin et al., 2008; Pedersen et al., 2013). Specifically, the studies by Edwards et al. (2006) and by Olesen et al. (2013) were secondary analyses of data from previously published clinical drug trials in post-herpetic neuralgia (Raja et al., 2002) and chronic pancreatitis (Olesen et al., 2011). Consequently, it was often unclear whether (all) consecutive participants were included or how many participants refused to participate, and accordingly, whether there were important differences between key characteristics and outcomes in participants who completed the individual study and those who did not. Ideally, studies should enrol a consecutive series of patients or a random sample and report accordingly. Prediction of more than one outcome was common in the included studies, apparently because of their exploratory aim. However, the finding of a statistical association in such studies is not necessarily evidence of a causal relationship and selective reporting of outcomes (and predictors) is often a risk (Kirkham et al., 2010). Consequently, only three studies (23%) calculated the sample size needed to demonstrate a pain tolerance threshold change of 20% (Wilder-Smith et al., 2003), a difference of 20 mm in visual analogue scale 0–100 (Nielsen et al., 2007), and a difference of three points in NRS 0–10 (Aasvang et al., 2008). In addition, the two last mentioned studies increased their sample sizes by 10% according to anticipated dropout rates. None of the studies clearly reported that they blinded the outcome measurement for predictor values and few accounted for the distribution of data. Knowledge of the predictors might influence outcome assessment, resulting in a biased estimation of the predictor effects for the outcome (Bouwmeester et al., 2012). Occurrence and handling of missing values was not described or was unclear in all studies. Most studies reported simple parametric or non-parametric measures of statistical dependence, including correlation coefficients (Hsu et al., 2005; Rudin et al., 2008; Eisenberg et al., 2010; Buhagiar et al., 2011; Yarnitsky et al., 2012); Spearman's rank correlations (Wilder-Smith et al., 2003; Pan et al., 2006; Martinez et al., 2007; Nielsen et al., 2007; Aasvang et al., 2008; Pedersen et al., 2013) or Kendall's rank correlations (tau) (Attal et al., 2004). Such crude associations maybe useful in initial explorative analyses, although analyses controlling for the effects of other variables are preferable. Of note, a regression coefficient with a confidence interval would have provided substantially more information than a correlation coefficient. More advanced statistical methods included linear regression (Yarnitsky et al., 2012; Pedersen et al., 2013), area under the curve (Aasvang et al., 2008), analysis of variance (Wilder-Smith et al., 2003; Attal et al., 2004; Martinez et al., 2007; Eisenberg et al., 2010; Buhagiar et al., 2011; Yarnitsky et al., 2012), principal component factor analysis (Pan et al., 2006) and a support vector machine based on machine learning (Olesen et al., 2013). The choice of statistical techniques reflects the number of independent and dependent variables, and often a mix of univariate and multivariate procedures are needed to answer the major research question. However, the use of multivariate analyses is essential in observational studies, where the control of confounding is a key issue in the analysis. Importantly, seven studies (50%) analysed more than one predictor and/or confounder variable simultaneously using multiple regression analyses (Hsu et al., 2005; Edwards et al., 2006; Pan et al., 2006; Rudin et al., 2008; Buhagiar et al., 2011; Yarnitsky et al., 2012; Pedersen et al., 2013). Regression models are generally the best approach when we wish to control for the effects of a number of confounding variables. However, the automated stepwise regression procedures used in most studies are unlikely to be appropriate in analyses where the aim is to estimate the effect of a particular variable. Only a single study employed a random subsampling cross validation test (Eisenberg et al., 2010) and another study used receiver operating curve characteristics to confirm the predictive performance of their respective models (Pan et al., 2006). The assessment of model performance is important for understanding how predictions from the model correspond to the observed outcomes (Bouwmeester et al., 2012).

Table 2. Quality appraisal of studies included in a systematic review on quantitative sensory testing measures as predictors of analgesic treatment responsesa
StudyThe study sample represents the population of interest on key characteristics?Proportion of study sample completing the study and providing outcome data is adequate?The prognostic factor of interest is adequately measured in study subjects?The outcome of interest is adequately measured in study subjects?Important potential confounders are appropriately accounted for?The statistical analysis is appropriate for the design of the study?
  1. a

    The criteria used in quality assessment of primary studies related to quantitative sensory testing parameter(s) as predictor(s) of analgesic treatment response(s) was based on criteria proposed by Hayden et al., (2006). Each of the six potential biases (Study participation, Study attribution, Prognostic factor measurement, Outcome measurement, Confounding measurement and account, and Analysis) was judged on a 4-point categorical scale (Yes, Partly, No, Unsure). A ‘Yes’ response indicates that the study has been designed and conducted in such a way as to sufficiently limit potential bias for that item. An ‘Unsure’ response to a question may arise when the answer to an item is not reported or is not reported clearly.

Eisenberg et al. (2010)YesPartlyYesYesNoYes
Aasvang et al. (2008)YesYesYesPartlyNoNo
Buhagiar et al. (2011)YesYesPartlyPartlyYesYes
Hsu et al. (2005)PartlyYesYesYesYesYes
Martinez et al. (2007)PartlyYesUnsurePartlyNoPartly
Nielsen et al. (2007)YesYesYesPartlyNoPartly
Pan et al. (2006)YesYesYesYesYesYes
Pedersen et al. (2013)YesPartlyPartlyYesPartlyYes
Rudin et al. (2008)YesYesPartlyPartlyPartlyPartly
Wilder-Smith et al. (2003)PartlyUnsurePartlyPartlyNoPartly
Attal et al. (2004)YesYesYesYesPartlyYes
Edwards et al. (2006)PartlyPartlyYesYesPartlyYes
Olesen et al. (2013)YesPartlyYesYesNoYes
Yarnitsky et al. (2012)PartlyPartlyYesYesPartlyPartly

4. Discussion

  1. Top of page
  2. Abstract
  3. Databases
  4. What does this study add?
  5. 1. Introduction
  6. 2. Methods
  7. 3. Results
  8. 4. Discussion
  9. 5. Conclusions
  10. Author contributions
  11. References

The current review suggests that the following quantitative sensory measures can predict analgesic effects:

  • Heat pain threshold in healthy volunteers
  • Electrical and heat pain thresholds, pressure pain tolerance and suprathreshold heat pain in surgical patients
  • Electrical and heat pain thresholds and conditioned pain modulation in patients with chronic pain.

In addition, mechanical allodynia (chronic pain patients) and preoperative hyperalgesia (surgical patients) correlated with the effect and cumulative consumption of analgesics. Conflicting findings on the predictive effect of electrical pain threshold were obtained with three studies in surgical patients producing insignificant results.

4.1 Interpretation and applicability of results

We primarily base our knowledge about human pain on clinical studies and the term QST may give the impression that it is a reliable and easily reproducible test that quantifies sensory function (Siao and Cros, 2003). To the best of our knowledge, this is the first review that combines data across experimental, postoperative and clinical studies in order to assess whether QST can predict analgesic effects. Two previously published systematic reviews focusing on the correlation between preoperative pain sensitivity and postoperative pain intensity, and a review on the clinical relevance of experimental pain assessment suggested that heat stimuli of suprathreshold intensity produced a better predictive ability than heat pain threshold, particularly in women (Edwards et al., 2005; Werner et al., 2010; Abrishami et al., 2011). This was not evident in our review. Although suprathreshold heat pain was found to be a significant predictor of analgesic effect in studies of postoperative pain, heat pain threshold was identified as the principal predictor in studies of experimental and chronic pain. Furthermore, Werner et al. (2010) suggested that electrical pain thresholds have a much greater predictive potential than the mechanical or thermal pain thresholds. Werner et al. (2010) in part based their observation on the study by Nielsen et al. (2007), also included in our review, in which it was demonstrated that preoperative electrical pain threshold significantly correlated with post-caesarean section pain scores at rest and upon movement, but not with postoperative use of supplemental morphine. These apparently conflicting results regarding electrical stimulation may be related to several factors. The advantage of electrical stimulation is that it allows the administration of standardized stimuli of predictable intensity. Timing of the stimuli can also be accurately programmed. Despite these advantages and easy clinical application, electrical stimulation bypasses the peripheral receptors and activates all nerve fibre types (i.e., Aβ-, Aδ- and C-fibres) directly and therefore the method is not a specific activation of the nociceptors (Staahl and Drewes, 2004). Because of the non-specific nature of the stimulus, subjects may find it difficult to reliably assess its intensity (Drewes et al., 2003). Type of surgery, age and psychological distress have, in a recent systematic review, been identified as the three most important predictive factors for postoperative analgesic consumption (Ip et al., 2009). Surgery with low-intensity noxious stimuli combined with an outcome such as consumption of (supplemental) opioid analgesics may thus not generate enough difference between study patients. Hence, the choice of analgesic outcome measure (e.g., cumulative paracetamol vs. morphine consumption) may add to the conflicting results. Unfortunately, heterogeneity in the studies included in our review does not allow for comparisons of predictive values, and age and gender effects across studies.

4.2 Limitations of the original studies

A shortcoming of this review is that the included studies had significant heterogeneity and were generally of poor or moderate quality according to the criteria proposed by Hayden et al. (2006). Accordingly, several factors may limit firm conclusions. There are two obvious explanations why data were inconclusive: (1) the QST measure in focus is not predictive, and an inadequate study design produces false positive results or (2) the QST measure in focus is predictive, and an inadequate study design permits false negative results. In brief, two types of factors can affect predictive performance: factors that influence the measurement of the predictor and those that affect the outcome. The rationale for the use of QST in analgesic treatment prediction is that QST (when tested in a standardized and reproducible fashion) can detect and quantify excitatory or inhibitory alterations of peripheral and central nervous system function (i.e., nociceptive neuroplasticity) (Coderre et al., 1993). The neurophysiological mechanisms that underlie individual pain perceptions are also important in determining the degree of response to analgesic treatment. Nevertheless, QST assessment is still dependent on the participant and not ‘objective’ in the sense of being independent of perception (Backonja et al., 2009). In mechanism-based pain diagnosis and treatment, the assessment tools should be sufficiently sensitive and advanced to deliver valid and credible information (Woolf et al., 1998; Woolf and Max, 2001). The term surrogate pain model has been defined as an indirect measure that predicts the efficacy of a drug in a clinical patient population (Arendt-Nielsen and Hoeck, 2011). Obviously, an acute experimental pain model does not fully simulate the drug action in a complex clinical condition involving multiple mechanisms (Arendt-Nielsen and Yarnitsky, 2009; Staahl et al., 2009a,b; Olesen et al., 2012a). However, the pain biomarker approach has several advantages in human pain research and QST may be used to link symptoms and clinical signs with mechanisms and thus improve treatment strategies for neuropathic pain (Jensen and Baron, 2003; Cruccu et al., 2010). Hence, the individual pattern of sensory symptoms in neuropathic pain most likely closely reflects the underlying pain-generating mechanisms and might also determine the reason for differential and individual treatment responses (Baron et al., 2010). Of note, using drugs themselves as a probe of mechanism have potential advantages over sensory tests. Unfortunately, a recent systematic review demonstrated that intravenous analgesic tests have limited overall clinical utility in selecting patients for long-term treatment with specific oral analgesic agents (Cohen et al., 2009). On the other hand, there are probably more ways to stimulate or block sites of pain processing with selective drugs than with bedside examining tools (Woolf and Max, 2001) and better studies with more appropriate designs are highly warranted. Importantly, the reliability of the individual stimulation measures and/or paradigms was not evaluated in any of the included studies. In addition, QST and outcome measurements were evaluated at very different time-points, particularly in surgical populations, limiting firm conclusions concerning optimal timing of the QST assessments. However, the included stimulation parameters are all widely accepted methods in experimental pain assessment and most studies managed to adequately measure the predictor of interest sufficiently to limit potential bias. Additionally, the methodology, reliability, reproducibility, limitations and potential clinical applications of QST have previously been addressed in a number of reviews (Yarnitsky, 1997; Shy et al., 2003; Siao and Cros, 2003; Chong and Cros, 2004; Hansson et al., 2007; Arendt-Nielsen and Yarnitsky, 2009; Backonja et al., 2009; Oono et al., 2011a,b; Olesen et al., 2012b; Nahman-Averbuch et al., 2013). The German Research Network on Neuropathic Pain has also published extensive validation data for all somatosensory submodalities (Rolke et al., 2006a,b). There are several other important considerations to make when interpreting the type of information obtained from QST, including patient characteristics, instructions, examiner, the tissue and organs tested and the type and method of application of the stimulus (Backonja et al., 2009; Staahl et al., 2009a).

Regarding the validity of the dependent variable, response to analgesic treatment was inconsistently defined and most frequently reported as a secondary outcome. Analgesic consumption is influenced by both pharmacokinetics and health convictions, whereas pain intensity and relief are subjective measures. There is large between-patient variability in analgesic consumption and this measure may become problematic when used as a surrogate marker for pain in populations or groups, particularly in studies with small sample sizes (Moore et al., 2011). The validity of analgesic consumption as an outcome measure remains unclear; however, several factors have been proposed as potential confounding factors in analgesic consumption (McQuay et al., 2008). A basic assumption must be that patients will consume analgesic until pain intensity is reduced and they reach a state of comfort. Obviously, this entails similar pain scores across patients. Thus, titration must have failed if pain scores are different. Indeed, we can only test the null hypothesis (i.e., no difference in analgesic consumption between groups) when pain scores and consumption are similar. Furthermore, analgesic consumption may also be affected by the pharmacology of the drug(s) in focus. Opioid-induced side effects may thus interfere with analgesic consumption. For example, sedation could lead to reduced consumption, and subsequently be mistaken for analgesia. Similarly, if increasing intake of an opioid analgesic corresponds to nausea and vomiting, patients will likely limit their analgesic intake accordingly. Additionally, fear of addiction and acceptance of pain may also reduce and thereby affect analgesic consumption. Unfortunately, none of the included studies were sufficiently powered to detect differences in adverse events. Studies using consumption of non-opioid analgesics (e.g., paracetamol and nonsteroidal anti-inflammatory drugs) as an outcome measure may additionally be hampered by a dose-limiting factor and an analgesic ceiling effect associated with these drugs. Consequently, patients experiencing moderate to severe pain may be unable to reduce their pain scores as much as preferred, whereas patients with no or mild pain would experience superior pain relief receiving the same amount of drug.

As a result of the abovementioned shortcomings, a number of alternative clinical endpoints have been suggested including simple multiplication of raw pain scores and hourly analgesic consumption (Lehmann et al., 1986), dichotomous classification of analgesic consumption at 750 μg fentanyl (equivalent to 75 mg morphine) over 48 h (Moore et al., 2011), and integrated assessment of pain scores and analgesic consumption based on a percentage difference from the mean rank of the specific variable (Silverman et al., 1993). Notably, none of these measures have gained widespread acceptance and therefore their validity remains unknown. In addition, the use of a combined endpoint may cause problems if the predictor effect is in opposite directions for different outcomes included in the composite endpoint (Bouwmeester et al., 2012).

Measurement of pain intensity may be reliable for multiple measurements within a single individual. However, there is also high variability in pain, particularly among chronic pain patients, and adding to the complexity, pain scales are also typically influenced by clinical co-morbidities (Price et al., 2008; Farrar, 2010). Furthermore, the improvement in pain within an individual patient, expressed as a percent change, is highly correlated with his or her overall condition (Farrar, 2010). Likewise, there is no linear relationship between nociception and clinical pain and particularly chronic pain can be influenced by psychosocial factors, such as catastrophizing, depression and anxiety in up to 50% of patients (Vase et al., 2011; Mao, 2012). Interestingly, the study by Eisenberg et al. (2010) in healthy volunteers was the only study that used changes in QST measures during a cold pressor test (120 s hand immersion in 1 °C ice water). Unfortunately, almost half of their study population withdrew their hand within 15 s of hand immersion precluding firm conclusions on the prediction of the magnitude of opioid analgesia. Finally, analgesic treatment responses may be driven by variables not analysed in the included studies. Thus, recent research indicates that individual responses to analgesic treatment are partly genetically controlled (Zwisler et al., 2010).

4.3 Limitations of the review

Despite widespread and increasing use of QST in pain research, only a few studies of the predictive effects on analgesic treatment response were identified. As discussed earlier, the risk of bias was high or unclear for many of the included studies, leading to uncertainty regarding the internal validity of the findings. A variety of predictors and outcomes was used at different points in time across studies making it difficult to arrive at any firm conclusion about the best strategy to use QST in predicting analgesic response. In many cases, the reporting of study details was inadequate and therefore, the size of the predictive effect remains uncertain. The focus on a limited number of QST parameters in the current review provides no information on other tests that may be used in clinical or experimental practice. Likewise, we have focused on selected outcomes ignoring other potentially relevant endpoints such as physical and emotional functioning, sleep, safety, tolerability and patient adherence to treatment (Turk et al., 2003; Dworkin et al., 2009).

4.4 Implications for current practice and future research

Experimental models using acute stimuli may activate the nervous system in different ways compared with tonic pain generated from ongoing inflammation (Olesen et al., 2012a). Other symptoms also often influence patients' pain perception confounding a clear evaluation (Olesen et al., 2012a). Likewise, drug response can be modulated by a number of non-genetic factors, especially by co-medications and the presence of concurrent diseases (Stamer and Stuber, 2007). Existing analgesic treatments are inefficient or poorly tolerated in approximately half of patients with chronic pain and, in those who do respond, it is relatively rare for pain to be completely relieved or even reduced to mild severity (Dworkin et al., 2009). In addition, treatment of chronic pain is challenging because of much more influential co-morbidities, as discussed earlier. The goal remains to identify a true clinical profile and to use such a profile to formulate individualized medicine (Mao, 2012).

The current findings, particularly the most recent, are indeed interesting and suggest that QST may have some predictive value regarding analgesic treatment response. However, our review does not yet suggest that it is reasonable that clinicians use any specific QST measure to (1) discriminate between patients who are likely to respond to analgesic treatment or (2) to identify patients at higher risk for severe postoperative pain and high analgesic requirement outside the context of experimental or clinical studies. On the other hand, this does not imply that clinicians should discontinue clinically driven sensory testing, as individual differences in pain sensitivity that can be assessed by QST may still be relevant for treatment decisions. As previously suggested, we need to recognize that clinical pain cannot be addressed by only understanding limited dimensions of the pain process (Mao, 2012). Studies including deeper, tonic pain stimulations as well as methods evoking hyperalgesia are therefore warranted in future prediction studies (Olesen et al., 2012a). In our opinion, experimental pain models should aim for translation into clinical practice. Importantly, the use of QST to follow patients over time requires that the test is reproducible in test-retest conditions. It is important to keep in mind that the sensitivity of a given experimental model for detecting analgesia is also affected by the method used to measure specific pain conditions (Staahl et al., 2009a). In addition to clinical outcomes, such as pain intensity and consumption of analgesics, a composite score that takes both these measures into account and perhaps also includes other relevant outcomes may also be beneficial to examine the effectiveness of any given QST parameter. However, large prospective studies are needed to establish the clinical value of such approaches.

5. Conclusions

  1. Top of page
  2. Abstract
  3. Databases
  4. What does this study add?
  5. 1. Introduction
  6. 2. Methods
  7. 3. Results
  8. 4. Discussion
  9. 5. Conclusions
  10. Author contributions
  11. References

Eight of the 14 studies included in this review found QST parameters capable of predicting analgesic responses across a wide range of conditions. Consequently, the evidence is not yet sufficiently robust to determine the effectiveness of these QST parameters as predictors of analgesic treatment response. However, results are promising and call for future well-designed and sufficiently powered studies focusing on different modalities or experimental pain modulation rather than a single static pain paradigm.

Author contributions

  1. Top of page
  2. Abstract
  3. Databases
  4. What does this study add?
  5. 1. Introduction
  6. 2. Methods
  7. 3. Results
  8. 4. Discussion
  9. 5. Conclusions
  10. Author contributions
  11. References

All authors contributed to the conception of the review. A.M.D. oversaw its implementation. K.G., I.W.D.F. and A.E.O. coordinated the review activities, including literature searches, study selection and data extraction. K.G. and A.E.O. appraised the methodological quality of the included studies. All authors contributed to the interpretation of data. KG drafted the article. I.W.D.F., A.E.O. and A.M.D. revised the article critically and contributed to the writing of the final version. All authors reviewed and approved the final version of the manuscript.

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  2. Abstract
  3. Databases
  4. What does this study add?
  5. 1. Introduction
  6. 2. Methods
  7. 3. Results
  8. 4. Discussion
  9. 5. Conclusions
  10. Author contributions
  11. References
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