Objectives

Inadequate postoperative pain control is common and is associated with poor clinical outcomes. This study aimed to identify preoperative predictors of poor postoperative pain control in adults undergoing inpatient surgery.

Design

Systematic review and meta-analysis

Data sources

MEDLINE, Embase, CINAHL and PsycINFO were searched through October 2017.

Eligibility criteria

Studies in any language were included if they evaluated postoperative pain using a validated instrument in adults (≥18 years) and reported a measure of association between poor postoperative pain control (defined by study authors) and at least one preoperative predictor during the hospital stay.

Data extraction and synthesis

Two reviewers screened articles, extracted data and assessed study quality. Measures of association for each preoperative predictor were pooled using random effects models.

Results

Thirty-three studies representing 53 362 patients were included in this review. Significant preoperative predictors of poor postoperative pain control included younger age (OR 1.18 [95% CI 1.05 to 1.32], number of studies, n=14), female sex (OR 1.29 [95% CI 1.17 to 1.43], n=20), smoking (OR 1.33 [95% CI 1.09 to 1.61], n=9), history of depressive symptoms (OR 1.71 [95% CI 1.32 to 2.22], n=8), history of anxiety symptoms (OR 1.22 [95% CI 1.09 to 1.36], n=10), sleep difficulties (OR 2.32 [95% CI 1.46 to 3.69], n=2), higher body mass index (OR 1.02 [95% CI 1.01 to 1.03], n=2), presence of preoperative pain (OR 1.21 [95% CI 1.10 to 1.32], n=13) and use of preoperative analgesia (OR 1.54 [95% CI 1.18 to 2.03], n=6). Pain catastrophising, American Society of Anesthesiologists status, chronic pain, marital status, socioeconomic status, education, surgical history, preoperative pressure pain tolerance and orthopaedic surgery (vs abdominal surgery) were not associated with increased odds of poor pain control. Study quality was generally high, although appropriate blinding of predictor during outcome ascertainment was often limited.

Conclusions

Nine predictors of poor postoperative pain control were identified. These should be recognised as potentially important factors when developing discipline-specific clinical care pathways to improve pain outcomes and to guide future surgical pain research.

PROSPERO registration number

CRD42017080682.

Keywords: postoperative pain, preoperative predictors, surgery, pain, pain scales, meta-analysis

Patient and public involvement

Patients and the public were not involved in the development of this systematic review.

Search strategy

A search strategy was developed using thePeer Review of Electronic Search Strategy²⁸ in consultation with two research librarians. We focused on the keywords ‘pain’, ‘pain measurement’, ‘surgery’ and ‘predictors’. We searched MEDLINE (1950 to 13 October 2017), Embase (1980 to 13 October 2017), CINAHL (1937 to 13 October 2017) and PsycINFO (1967 to 13 October 2017) (onlinesupplementary appendix S1, online supplemental information). To maximise sensitivity for studies of prognosis, search filters were not used, and no restrictions were placed on date or language of publication.^{29 30} Our search was repeated using Google and Google Scholar for the grey literature. Bibliographies of included studies were searched by hand for other relevant articles. A local pain specialist was also consulted to identify any potential ongoing studies or unpublished data.

Study inclusion

We included observational studies (cohort and cross-sectional) reporting on adults (≥18 years old) undergoing surgery and admitted for at least 24 hours following their procedure (eg, excluded ambulatory surgery/procedures, dental procedures, carpal tunnel release, and so on), and studies that assessed for the association between preoperative patient-level predictors and poor postoperative pain control (as defined by individual study authors). Only inpatient procedures were included to minimise the heterogeneity of the surgical population as well as providing more reliable pain outcomes. Perioperative predictors were not assessed because our primary aim was to inform clinicians evaluating patients in the preoperative clinical setting where perioperative risk factors may not be known or modifiable. No interventional studies were included.

Studies were required to report an assessment of pain during the inpatient period using a validated pain scale. Previous studies have demonstrated that the visual analogue scale (VAS), numeric rating scale (NRS) and verbal rating scale (VRS) for pain are highly correlated with each other, and thus, they were considered comparable in the present study.³¹ To facilitate pooling of data, we only included studies that reported a measure of association, such as an OR or relative risk (RR), as well as studies with raw data where an OR could be manually calculated. Conference abstracts, reviews, protocols and secondary publications (of studies already included in our review) were excluded. Two reviewers (MMHY and RLH) independently reviewed titles, abstracts and full-text articles of the retrieved studies in duplicate. Discrepancies were resolved by consensus. Inter-rater agreement was evaluated using Cohen’s κ statistic for the full-text review stage.

Data extraction

Study information, such as author, year and country of publication, sample size, pain scale used, the definition of poorly controlled postoperative pain, number of predictors adjusted for in a multivariable regression model (where applicable) and the average age of the sample population, were extracted. Both unadjusted and most adjusted effect estimates were recorded whenever multiple estimates were presented. For studies that reported their results in distinct strata (eg, young vs old age, or moderate vs severe pain), each stratum was treated as an independent study for the pooled analysis (no patients were analysed in duplicate).^{23 32–34} Non-English studies were data-extracted with the help of a translator.

Study quality assessment

We used a component-based approach to assess the quality of included studies.³⁵ The following variables were considered to be the most important quality indicators for studies of prognosis (definition of quality indicators are in onlinesupplementary table S1, online supplemental information)³⁵: description of population, non-biased selection, adequate follow-up (eg, postoperative pain measurements were recorded for at least 80% of study participants), predictor measurement, outcome measurement and ascertainment, adjustment for confounding variables (operationalised as adjusting for at least three potential confounders), precision of reported results (eg, reporting of CIs), as well as the use of an appropriate reference standard (eg, definition of poor postoperative pain control provided).^{29 35 36} Data extraction and assessment of study quality were performed in duplicate; discrepancies were resolved by consensus. If a study presented unclear data, the corresponding author was emailed with a follow-up email after 2 weeks if a response was not received.

Statistical analysis

We used ORs as the common measure of association. RRs were converted to OR using the formula, OR=RR/(1/[1/(1−P_o)]+P_o), where P_o is the incidence of the outcome of interest in the non-exposed group.³⁷ When raw data were presented, ORs were manually calculated. For the primary analysis, the most adjusted ORs were used to determine the pooled estimates. The analysis was then repeated using the least adjusted effect estimates. Pooled estimates, expressed as ORs (with 95% CIs), were determined for each preoperative predictor associated with poor postoperative pain control levels using the DerSimonian and Laird random effects model and visualised using forest plots. A random effects model was chosen due to the variability in surgical specialties, definitions of poor postoperative pain and the reported timing of postoperative pain assessment in the included studies. Meta-analysis was performed using the ‘metan’ command within STATA V.15. Level of significance was set at alpha=0.05.

Between-study heterogeneity was examined and quantified using the Cochran’s Q test and I² statistic.³⁸ Stratified analysis and meta-regression were performed to explore for potential sources of heterogeneity based on ana priori list of factors related to study quality and clinical prognosis. Stratification was conducted on the following variables: degree of statistical adjustment (eg, operationalised as adjustment for <3 vs ≥3 variables), definition of poor postoperative pain control (moderate vs severe pain; moderate pain: 3–6, severe pain: >6 on an 11-point scale; studies not using a numeric scale [eg, morphine requirements as the definition for poor pain control] were considered moderate pain), surgical discipline, blinding of predictors when assessing pain scores and location of pain assessment (eg, postanaesthetic care unit vs ward). Preoperative factors only reported in a single study could not be pooled and therefore, were not included in the final analyses. We did not assess for publication bias because conventional tools used to examine for publication bias, such as funnel plots, are intended to detect small study effects. Small study effects are challenging to interpret for meta-analyses of observational studies, such as ours, where multiple sources of heterogeneity may be present, such as those arising from true clinical differences (eg, different surgical disciplines/procedures) or bias inherent to individual studies (eg, residual confounding and lack of blinding).³⁰

Literature search and study characteristics

We identified 9753 articles through the electronic database and grey literature search (figure 1). Consultation with a pain expert and searching of the grey literature yielded 38 articles. After initial screening, 291 articles were included for full-text review. Full-text review resulted in the inclusion of 33 articles for data extraction with excellent inter-rater reliability (κ=0.83 [95% CI 0.71 to 0.91]). No unpublished studies were identified and included in the final analysis.

The 33 included studies represented 53 362 patients with publication dates ranging between 2002 and 2017 (study characteristics of included studies are intable 1).19 21–24 32–34 39–63 Twenty-six studies were prospective cohort studies (79%) and seven were retrospective cohort studies (21%). Most studies were conducted in Europe (17/33 studies, 51.5%), followed by Asia (8/33 studies, 24.2%). Studies involving a mixture of specialties (11/33 studies, 33.3%) and general surgery (10/33 studies, 30.3%) had the largest representation. A variety of thresholds were used to define poor pain control on a standard 11-point scale (0–10) across studies; the most common definition of significant postoperative pain was ≥4 out of 10 (13/33 studies, 39.4%) followed by > or ≥5 out of 10 (7/33 studies, 21.1%). NRS, VAS and VRS scales for pain were used in 57.6%, 42.4% and 3.0% of studies, respectively. The most common time-interval when postoperative pain was measured was between 24–48 hours (19/33 studies, 57.6%). The mean number of predictors (including preoperative and perioperative variables) explored per study was 10.0 (SD 5.73, range 1–19) (table 1). There was a lack of dedicated prognostic studies evaluating predictors of postoperative pain control in most surgical subspecialties including neurosurgery, spine surgery, otolaryngology and plastic surgery.

Assessment of study quality

The overall methodological quality of the included studies was generally high except for the use of a blinded outcome assessment (figure 2). In 25 studies (76%), there was either no blinding or no reporting on whether there was blinding of predictors during outcome ascertainment. The lack of blinding of predictors during outcome ascertainment in the majority of studies could lead to increased risk of misclassification bias. Twelve studies (36%) did not adjust for at least three potential confounders, five studies (15%) did not provide definitions of preoperative predictors and four studies (12%) did not define how their sample was selected.

Preoperative predictors of poor postoperative pain control

Of the 23 variables examined, nine statistically significant preoperative predictors of poor postoperative pain control were found: younger age (OR 1.18 [95% CI 1.05 to 1.32]), female sex (OR 1.29 [95% CI 1.17 to 1.43]), smoking (OR 1.33 [95% CI 1.09 to 1.61]), history of depressive symptoms (OR 1.71 [95% CI 1.32 to 2.22]), history of anxiety symptoms (OR 1.22 [95% CI 1.09 to 1.36]), sleep difficulties (OR 2.32 [95% CI 1.46 to 3.69]), higher body mass index (BMI) as a continuous variable (OR 1.02 [95% CI 1.01 to 1.03]), presence of preoperative pain (OR 1.21 [95% CI 1.10 to 1.32]) and use of preoperative analgesia (OR 1.54 [95% CI 1.18 to 2.03]). Pooled ORs and definition for each preoperative variable are given intable 2. Summary forest plots of significant preoperative predictors of poor postoperative pain control are shown infigure 3. Significant heterogeneity was detected in five of these predictors (female sex, younger age, the presence of preoperative pain, history of anxiety symptoms and smoking) with I² values ranging from 50.4% to 82.4% (table 2). Detailed forest plots for each significant preoperative predictor are shown in onlinesupplementary figures S1–S3.

Non-significant preoperative predictors of poor postoperative pain control

Fourteen predictors were not significant in the final analysis: Pain Catastrophizing Scale (exaggerated negative perception to painful stimuli) as a dichotomous variable, marital status, high BMI as a dichotomous variable, any previous surgical history, orthopaedic surgery compared with abdominal surgery, diabetes, pain catastrophising as a continuous variable, higher education, age as a continuous variable, chronic pain, American Society of Anesthesiologists physical status, alcohol use, preoperative pressure pain tolerance and low socioeconomic status (table 2). Detailed forest plots for each non-significant preoperative predictor are shown in onlinesupplementary figures S4–S8.

Preoperative variables reported in only one study (and hence were excluded from the meta-analyses) included: patient weight, surgeon’s anticipated pain level, self-assessment of good health, generalised self-efficacy scale, sedentary lifestyle, employment status, short portable mental status questionnaire, preoperative delirium (confusion assessment method), constipation, rectal volume, body image scale, history of cancer, hypertension, heart disease, preoperative anaemia, anticonvulsant medication, home sedatives, electrical pain threshold, heat pain threshold, von Frey pain intensity, blood type, preoperative 24 hours urinary cortisol level, thoracic surgery, spine surgery, head and neck surgery, and total knee replacement.

Stratified meta-analysis and meta-regression

Stratified meta-analyses (according to the level of statistical adjustment, the definition of poor pain, surgical discipline, blinding of predictors and location of pain assessment) showed no differences in the pooled estimates and therefore, did not explain the significant level of heterogeneity observed between studies. These results were corroborated by meta-regression. Repeating the analysis using least adjusted versus most adjusted models also found similar pooled results for each preoperative predictor.

Strengths and limitations

The strengths of our study are the comprehensive search of the literature, inclusion of 33 articles (resulting in data on more than 53 000 patients), and the ability to generate pooled estimates for a large number of prognostic factors. The inclusion and stratification by multiple surgical specialties and the diversity of geographic locations increase the generalisability of the findings. However, the findings from the present report should be interpreted in the context of the study design. First, the primary studies included in our systematic review and meta-analysis were observational in nature. As is inherent to all observational designs, residual confounding cannot be excluded. This was particularly the case for unadjusted estimates. Nonetheless, we found that the most adjusted models yielded broadly similar results to the least adjusted estimates. Further, we performed meta-analyses on studies that had appreciable heterogeneity as it pertains to definition of poor postoperative pain control (which was variably defined by individual study authors), surgical procedure/specialty, timing and instrument used for pain assessment and threshold used to categorise continuous preoperative predictors between studies (eg, young vs old). Outcome heterogeneity may have been a potential source of bias if, for example, a particular predictor was associated with an increased risk of postoperative pain with one instrument (or cut-off) and a decreased risk of pain using a different instrument (or cut-off). In such cases, a pooled analysis might fail to detect either finding. Although we do not believe this issue biased our findings, future studies should attempt to standardise definitions (common data elements) to facilitate comparisons between studies. For significant predictors that were evaluated by a limited number of studies (eg, sleep difficulty), future studies should be performed to ensure reproducibility. Finally, there was significant statistical heterogeneity between studies, which could not be explained by stratified analysis or meta-regression based on a variety of clinical and study design factors (and the results should be interpreted with caution for surgical discipline as there were limited number of studies in each group). This heterogeneity was likely a product of important clinical differences as the included studies differed widely in surgery type and case-mix. Additional research may further define the influence of specific types of surgery on pain control.

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