Pain is a subjective and multidimensional experience that is often inadequately managed in clinical practice. Effective control of postoperative pain is important after anesthesia and surgery. A systematic review was conducted to identify the independent predictive factors for postoperative pain and analgesic consumption. The authors identified 48 eligible studies with 23,037 patients included in the final analysis. Preoperative pain, anxiety, age, and type of surgery were four significant predictors for postoperative pain. Type of surgery, age, and psychological distress were the significant predictors for analgesic consumption. Gender was not found to be a consistent predictor as traditionally believed. Early identification of the predictors in patients at risk of postoperative pain will allow more effective intervention and better management. The coefficient of determination of the predictive models was less than 54%. More vigorous studies with robust statistics and validated designs are needed to investigate this field of interest.
PAIN is a multifaceted and highly personal experience, as McCaffery described “pain is whatever the experiencing person says it is and exists whenever he/she says it does”.1It causes significant distress to patients and has adverse effects on the endocrine and immune function,2which can affect wound healing3and cardiopulmonary and thromboembolic diseases.4–6
Given that postoperative pain is one of the most frequently reported postoperative symptoms,7identification of the predictive factors for postoperative pain would facilitate early intervention and better pain management if the predictive factors for postoperative pain can be identified. To date, a review of the published literature indicates that there is no systematic review in this area. The purpose of this systematic review was to identify preoperative predictive factors for acute postoperative pain and analgesic consumption.
Materials and Methods
The purpose of the systematic review was to identify the risk factors determined by multivariate analyses for postoperative pain and analgesic consumption. We carried out separate analyses for pain intensity and analgesic consumption because these are two independent variables, with pain intensity being a subjective experience and analgesic consumption, which is also influenced by pharmacokinetics, together with health beliefs.
We searched the databases MEDLINE (January 1950 to October 2008), EMBASE (January 1980 to October 2008), CINAHL (January 1982 to October 2008), Psychological Abstracts (PsycINFO 1806–2008) for all studies investigating the risk factors for acute postoperative pain using both univariate and multivariate analyses. The following search terms used were: “pain, postoperative,”“pain: after surgery,”“pain: follow:operation,”“incision pain,”“analgesic follow surgery,”“risk factors,”“risk assessment,”“predict,”“univariate analysis,”“multivariate analysis,”“regression analysis,”“regression model,”“logistic regression,”“diagnostic model,”“analysis of variance.” The search was limited to adults over the age of 17 and to English language publications. The search strategy (see appendix) yielded 5,357 abstracts for initial consideration. All records were converted into the Reference Manager database. In addition, we hand-searched the reference lists of the relevant literature to identify additional references. Studies that were not in the public domain were not sought. Studies generated by the search were checked for relevance. Potentially relevant papers were retrieved in full and assessed by two independent reviewers (Drs. Ip and Abrishami) to minimize the risk of introducing bias to the results reviewed. Disagreements between the authors were resolved by the third reviewer (Dr. Chung).
This review was limited to publications in English, and retrospective studies were not included due to potential bias. Our inclusion population was the adult population of age 18 yr or above. Any study identifying one or more potential risk factors or predictive factors for acute postoperative pain or analgesic requirement was included. The potential risk factor or predictive factor had to be identified preoperatively. The postoperative period was defined as the period between arrival of the patient in recovery to 7 days after surgery, with day 1 being 24 h after surgery. Postoperative pain was measured via continuous scale of pain intensity or by categorical definition of moderate to severe pain. Also, pain had to be clearly measured and defined. All studies reporting multivariate analyses were included. Statistical importance of predictors was expressed as P value, regression coefficient, or odds ratio.
In this systematic review, surgeries performed under local anesthetics were excluded. This resulted from the fact that surgeries performed under local anesthetics tended to be less painful, the patient’s experience intraoperatively and postoperatively would be different, together with the analgesic consumption. For those studies, only reporting univariate analysis was excluded because of the possible introduction of confounding factors. In addition, studies with risk factors found incidentally or those studies examining the pain intensity, analgesic consumption, or recovery were excluded. Only studies with original data were included. Also, review articles were excluded, but the bibliographies of the review articles were searched for additional references.
Quality Assessment of the Studies
Two independent reviewers (Drs. Ip and Abrishami) assessed quality by using the criteria shown in table 1, and any disagreements were resolved by discussion. If a resolution could not be reached, the opinion of the third reviewer was sought. The guideline for appraising the studies was adopted from systematic reviews on predictors and prognosis.8–11The assessment was based on four categories: sampling technique, predictive factors, statistical analysis, and follow-up (table 1). We did not adopt a scoring system like some systematic reviews9–11because it is not necessarily a scientific approach.8We evaluated each of the categories separately in every study. Each category was composed of different questions that could be answered as “Yes,”“No,” or “Unclear.” If all the questions in the category were answered as “Yes,” the category was considered as fully met. If the category had more than half the questions answered as “Yes,” the study was considered as partly met, and if less than half of the questions were answered as “Yes,” the category was considered as unsure. Finally, the category was considered as not met if all the related questions were answered as “No.” The final conclusion was presented on the basis of studies with low risk for bias associated with each quality category of the quality assessment.8Therefore, studies with not met in any of the quality assessment categories were excluded when drawing conclusions (tables 2 and 3).8
Data Extraction, Data Analysis, and Conclusion Synthesis
Data extraction was performed by two reviewers (Drs. Ip and Abrishami). The following data were extracted from the study: sample size, type of surgery, study design, time course, measures of predictive factors, outcome measures (i.e. , postoperative pain score or analgesic consumption), statistical methods, number of predictor variables, coefficient of regression (B) and its standard error (SE). Data were verified for consistency and accuracy by the second author (Dr. Abrishami). Meta-analysis of the regression coefficients was carried out only for gender and anxiety factors because they are adequately reported among the studies in a consistent way. The analysis was done with MIX version 1.0 (Leon Bax, Kitasato Clinical Research Center, Kanagawa, Japan12,13), a meta-analysis software, by using a random-effect model with weighting according to the inverse of SE of the coefficients for each factor.14The I 2statistic was used to measure inconsistency among the study results. I 2=[(Q − df)/Q ]× 100%, where Q is the χ2statistic and df is its degrees of freedom.15A value greater than 50% may be considered substantial heterogeneity. The range of regression coefficients was also reported in the text of the review for the above-mentioned factors. For age and type of surgery, the range of regression coefficient was not presented in the review, nor was a meta-analysis performed because these factors were defined or treated differently among the studies. For example, age was entered into the regression models in different ways (e.g. , age groups, continuous data, or categorical data) and type of surgery had different reference procedure (e.g. , gynecology, or ophthalmology, etc .) among the studies. The regression coefficients of other factors were not adequately reported in the included studies. To draw conclusions for each variable, we considered the following issues: a variable was considered to have significant correlation with postoperative pain outcomes if the respective statistical significance was P < 0.05. Also, the statistical power of each study with multiple regression was assessed by calculating the probability of type II error (β) using the number of variables, and total sample size (F test, multiple regression, G* power version 3.0.10; Franz Faul, Universitat Kiet, Germany). The effect size for power analysis is calculated by the software based on the squared multiple correlation (R2) of the regression model. To minimize type II error, the power of a regression analysis is considered to be sufficient if it is at least 0.9.16,17Therefore, studies with statistical power less than 0.9 were considered statistically insufficient to show that there is no correlation between a variable and the study outcomes.
Literature Search and Study Characteristics
The search strategy resulted in an initial yield of 5,357 references, of which 1,218 were duplicates. Therefore, a total of 4,139 references were generated by the electronic search. The title and abstracts were reviewed, and 111 articles were found to be of relevance. The full texts of 111 articles were retrieved and examined. Reference lists of relevant studies meeting inclusion criteria further identified eight articles not identified in the electronic search.3,18–24We excluded 71 articles as explained in figure 1. Finally, 48 articles were analyzed in this systematic review. This included a total of 23,037 patients.
The characteristics of all the studies are shown in table 4. The studies were heterogeneous in terms of sample size, type of surgery, variables examined, and instruments used for measuring variables. The three main groups of surgery with sufficient number of studies were mixed surgery, gastrointestinal, obstetrics, and gynecology surgery.
Methodological Quality of the Studies.
The summary of the quality assessment of each study can be found in table 5. Only nine studies (18.7% of all the included studies) partially or fully met each category of the quality assessment.25–33The remaining studies had at least one category of the quality assessment considered as unsure. There were eight studies (16.6% of all the included studies) failing to meet at least one of categories of the quality assessment34–41; therefore, their findings were not included in drawing conclusions. Due to insufficient raw data from the included studies, it was not possible to perform the sensitivity analysis. The number of studies in each surgical group and their limitations in terms of the quality assessment are summarized in table 6. The most common limitation among all the included studies was in the analysis category. This included factors such as insufficient measures to avoid collinearity, overfitting, and the lack of external validation of the models.
Predictors of Postoperative Pain Intensity and/or Analgesic Consumption.
After identifying 8 poor quality studies, 32 and 21 studies evaluating the predictive factors of postoperative pain intensity (table 2) and analgesic consumption (table 3), respectively, were available for drawing conclusions. These factors can be classified into four major categories: demographics, psychological, preoperative pain, and surgery-related factors.
Age was commonly found to have negative correlation with both analgesic consumption (six studies28,42–46) and postoperative pain intensity (six studies27,30,45,47–49); however, the latter finding was less consistent among the included studies (tables 2 and 3). The negative correlation suggested that the younger the patients, the more postoperative pain or analgesic requirement. There was one study that showed positive correlation between age and postoperative pain.20There were five studies23,26,29,33,50failing to show any correlation between age and postoperative pain (fig. 2). Of them, three studies23,29,50had a sample size ranging from 47 to 82 patients and low statistical power (1 −β= 0.4 − 0.7) which was relatively insufficient to detect an existing correlation between age and postoperative pain.
There were conflicting findings regarding correlation between gender and postoperative pain outcomes. Females patients were found to have more postoperative pain in four studies19,29,45,49(table 2) (coefficient of regression range: 0.22 − 0.79) and less pain in one study26(coefficient of regression:−0.56). The pooled coefficient was 0.53 (95% CI 0.44–0.63; P < 0.001, I2= 93.6%). Two studies showed positive correlation,19,46and one showed negative correlation26between female gender and postoperative analgesic consumption (fig. 3). On the other hand, three studies failed to show a significant correlation between gender and postoperative pain30,33,48and one between gender and analgesia requirement51(figs. 2 and 3). They all had high statistical power (1 −β > 0.9). Other demographic factors, e.g. , body mass, weight, American Society of Anesthesiologists status, and education level were evaluated in only a few studies and were found to be related to postoperative pain and/or analgesic consumption only in isolated studies (figs. 2 and 3).
These factors can be divided into three subcategories: anxiety, psychological distress, and coping strategies:
Anxiety was the most common predictor for postoperative pain and was shown to have positive correlation with pain intensity in 15 studies (table 2). Of these, six studies were on gastrointestinal surgery,25,29,46,48,52,53five on obstetrics and gynecology surgery,23,50,54–56two on mixed surgical population,30,33one on breast surgery,57and one on thoracic surgery.58Three studies specified state anxiety,25,52,53whereas three found trait anxiety to be significant.46,48,50The coefficient of regression (β) of preoperative anxiety state ranged from 0.05 to 1.60. The pooled coefficient regression was 0.074 (95% CI 0.042–0.106, P < 0.001, I2= 87%); for instance, any change in preoperative anxiety score (e.g. , State-Trait Anxiety Inventory score) by 10 can increase the pain intensity score by 0.74. Anxiety was also found to have positive correlation with postoperative analgesic consumption in four studies.22,46,59,60This finding was not supported by another four studies but they all had insufficient statistical power (1 −β= 0.5 − 0.7).
Psychological distress (other than anxiety) mainly measured by evaluating the patient’s mood, affect or personality trait (e.g. , neuroticism, hostility, etc .) was found to have positive correlation with both postoperative pain29,33,46–48,58,60and analgesic consumption29,44,46,59,61(figs. 2 and 3). The regression coefficients were not adequately reported to carry out meta-analysis. On the other hand, three studies failed to show any significant relation between psychological distress and pain28,48,62and/or analgesic consumption.28Of these, only one study had a relatively large sample size of 346 patients and an appropriate statistical power (1 −β > 0.9). It showed no relation between preoperative diazepam use and postoperative pain intensity28(table 2).
Coping strategies were found to predict the intensity of postoperative pain28,30,52,63and the amount of postoperative analgesic requirement.28,57Self-distraction and pain catastrophizing were correlated with higher postoperative pain scores in three studies28,52,63and information seeking behavior with less pain.30Coping strategies such as emotional support, religious-based, or intrusive thought/avoidant behavior were found to have positive correlations with postoperative analgesic consumption. There were two studies that failed to show a significant correlation between pain catastrophizing and postoperative pain62and analgesic consumption52(tables 2 and 3), but they both had small sample size and insufficient statistical power (1 −β= 0.4 and 0.6).
This factor can be divided into three subcategories: preoperative pain/analgesic experience, patient’s perception regarding pain or analgesia, and pain threshold.
Preoperative pain experience was a common predictor of postoperative pain intensity. A positive correlation was found in six studies,23,30,33,46–48and negative correlation was found in one study20(table 2). Preoperative existing pain was also found to have positive correlation with postoperative analgesic consumption46,64(table 3). There were three studies failing to show preoperative pain as a significant predictor of postoperative pain intensity31,53,62(fig. 2), but they had insufficient statistical power (1 −β < 0.9). Inconsistent results were found regarding the correlation among preoperative analgesic experience such as preoperative analgesic use, previous surgery with patient-controlled analgesia, and postoperative pain and analgesic consumption (tables 2 and 3).
Preoperative pain tolerance was another important predictor of postoperative pain and/or analgesic consumption that was found to be significant in six studies22,23,47,54,63,65(tables 2 and 3). The relation between pain threshold and postoperative analgesic consumption was studied in only two studies where a significant correlation was shown.22,54This factor was mainly examined in obstetrics and gynecology surgery by using different techniques such as heat pain perception,23cold pressor pain,47suprathreshold pain stimulation.63,65
Another important predictor was the type of surgery as derived from the mixed surgical group. Abdominal surgery,26,27,30orthopedic surgery,27,30and thoracic surgery26were shown to be positively correlated with postoperative pain. Emergency,66major,66and abdominal40surgery were reported40,66to predict postoperative analgesic consumption (tables 2 and 3). Procedures involving cancer67and a long duration of surgery45,66were also found to be correlated to postoperative analgesic consumption (table 3). There were two studies failing to find any correlation between type of surgery and postoperative pain outcomes (1 −β > 0.9).26,33
In summary, preexisting pain, anxiety (or other psychological distress), age, and type of surgery are the four most common variables consistently found to be significant predictors for postoperative pain. For postoperative analgesic consumption, the most consistent predictors are type of surgery, age, and psychological distress (including anxiety). The available literature is conflicting regarding female gender as a predictive factor of postoperative pain or analgesic consumption.
Our systematic review found that preexisting pain, anxiety, age, and type of surgery are the four most significant predictive factors for the intensity of postoperative pain. The type of surgery, age, and psychological distress are the three most important predictive factors for postoperative analgesic consumption. Gender was not found as a consistent predictor for postoperative pain or analgesic consumption as traditionally believed.
The type of surgery is found to be a strong predictor for both postoperative pain and analgesic consumption. The most painful operations are orthopedics with major joints surgery, thoracic, and open abdominal surgery.26,27,30Surgeries found to have the highest analgesic consumption are emergency, major, and abdominal surgeries.45,51,66Different types of surgery have varying degrees of tissue damage, and bone injury is more painful than soft tissue injury, owing to the fact that the periosteum had the lowest pain threshold of the deep somatic structures.27
In a study of 10,008 patients undergoing ambulatory surgery, Chung et al. reported that urology, general surgery, and orthopedic surgery were at least 17 times more likely to produce pain than ophthalmology surgery.27Neurosurgery, gynecology, and plastic surgery were at least nine times more likely to produce pain than ophthalmology.27Our review also showed that patients undergoing abdominal and emergency surgeries require more analgesic.40,66It is possible that patients who had emergency operations may have less preoperative information66and time for psychological preparation resulting in the increased requirement of postoperative analgesia.
Psychosocial and behavioral factors are often neglected in the management of postoperative pain.68In our systematic review, anxiety was found to be an important predictor for postoperative pain, especially in gastrointestinal, obstetrical, and gynecological surgery. An anxious state has been advocated as a factor in lowering pain threshold,69facilitating overestimation of pain intensity,70and activation in the entorhinal cortex of the hippocampal formation.71The state-trait anxiety theory predicts individuals with high trait-anxiety are generally hypersensitive to stimuli and psychologically more reactive,72albeit state anxiety in response to the environment is also an important predictor. Good patient communication, development of rapport, reassurance, preoperative anxiolytics if not contraindicated are just a few measures that could be implemented to reduce the preoperative anxiety in an attempt to decrease postoperative pain.
Anxiety also predicts postoperative analgesic consumption, especially in obstetrics and gynecology surgery. There are studies showing no relation between anxiety and postoperative analgesic consumption, but they are studies with small sample size that add to the type II error in the regression analysis that predominantly detects a correlation.
Psychological distress such as depressive mood, and negative affect can increase postoperative analgesic consumption. Normally, mild level of depressive symptoms had not been identified or recognized as having clinical repercussions, especially in patients without psychiatric diagnosis.73However, the negative effect of depressed mood on postoperative pain immediately after surgery has been described, such as a transient suppression of the immune function,74higher mortality, and a longer convalescence.75The relationship between depressed mood and the development of postoperative chronic pain has also been suggested.76Therefore, it is imperative that this aspect be taken into account to improve postoperative pain management and possibly also disrupt the processes responsible for the transition to chronicity of pain.48
Other significant psychosocial or behavioral factors are coping strategies, including pain catastrophization. This may be the result of an increased expression of pain or focus of pain.77However, diverting attention may not be an effective strategy for people who catastrophize about pain,78,79and catastrophizing may need to be reduced before distraction can be effective.80,81Pain catastrophization was also found to have a positive correlation with postoperative pain in two studies.63,65The correlation between pain catastrophization and pain has been described previously.82,83The mechanisms are poorly understood but some suggested that thought intrusion may be interpreted as signals of coping failure, thereby increasing the threat value of the pain stimulus. Furthermore, catastrophizers may have come to expect that their pain experience will be high regardless of variations in thought intrusions.83Cognitive behavioral strategies may be helpful in dealing with thought process and pain-related ideation.
Preexisting pain, chronic pain, and low preoperative pain threshold22,23,47,54,63,65are also significant predicting factors for postoperative pain. Intense influx of pain signal from tissue trauma after surgery can lead to enhancement of excitability and responsiveness of dorsal horn neurons to pain transmission.84Their excitability can be further sustained by transcriptional changes, such as induction of genes cyclooxygenase-2 (COX-2) inhibitors leading to prostaglandin E2(PGE2) production. As a result, the pain may persist beyond apparent tissue healing, leading to chronic postsurgical pain syndromes.85A growing body of evidence suggests that improvement in postoperative pain management might disrupt the transition to chronicity of pain.48,57,85–88
Genetic polymorphisms also contributed to a number of specific pain phenotypes. Mogil et al. provided evidence for at least five fundamental types of nociception and hypersensitivity: baseline thermal nociception, spontaneous responses to noxious chemical stimuli, thermal hypersensitivity, baseline mechanical sensitivity, and afferent input-dependent hypersensitivity.89,90In addition, there is positive evidence for the correlation between genetic polymorphisms and altered pain perception and processing, ranging from the μ-opioid receptor gene to specialized pain transducing receptors expressed in primary afferent neurons, such as the heat/capsaicin sensing vanilloid receptor ionophore-1 through to interleukin-1 proinflammatory cytokine.91–93
It was recently demonstrated that a mutation of the μ-opioid receptor gene increased binding affinity to β-endorphin, resulting in a reduction in pain sensitivity in healthy adults.94There has also been suggestion that the catechol-o-methyltransferase (COMT) genotype is associated with pain reports and pain-induced brain opioid receptor binding such that individuals with a particular genotype had a higher sensory and affective pain rating.95Another COMT genotype has been reported to be less pain-sensitive.96Furthermore, it has recently been demonstrated that the interleukin-1 receptor antagonist (IL-1Ra) polymorphism plays a role in predicting postoperative morphine consumption.97
In most individuals, the experience and susceptibility of pain results from a complex interaction among several genetic variants involved in different steps of neuronal processing of nociceptive information with additional contributions of other genetic or psychosocial factors, sociocultural environment, and prior learning. Furthermore, different genes may be involved in different kinds of pain, their sensory and affective dimensions, all intertwining with another, resulting in highly variable responses across individuals.91,93,98This field is still in its infancy, and much research is needed to explore the variety of polymorphisms and their interactions.
In general, age and gender are traditionally believed to be predictors for postoperative pain and analgesic consumption. We found that the results from the different studies were conflicting, especially for these two predictors. It could be due to the difference in sampling population; for example, Chia et al. examined only the Chinese population.26Other studies had small sample size,23,29,50which was relatively insufficient to detect an existing correlation between age and postoperative pain. The statistical methods were of variable standards. Many studies did not state whether collinearity was avoided.29,30,33,48Others might have studied many covariates for the sample size, creating an overfitting of the data.23,33To minimize bias in our final results, the studies were critically appraised, and the conclusion was drawn from studies of sound quality and of sufficient power. In our review, age was found to be a significant predictor for both postoperative pain and even more so for analgesic consumption.
Age has been suggested to have blunted the peripheral nociceptive function, decreasing pain in some contexts and reduced morphine requirements.35However, the presence of persistent or recurrent clinical pain may have a greater effect on the psychological, social, and physical function of older adults.99There could also be potential confounding factors such as the confusion of elderly people with patient-controlled analgesia and the underreporting and exclusion of patients over the age of 70 yr from studies.26Elderly patients have been noted to be more susceptive to the effects of opioid analgesia than young patients,100–102and some phases of pharmacokinetics are affected in aging, such as distribution,103metabolism,104,105and elimination.105,106Some studies showed that analgesic use declined with advancing age,43,45whereas others were unable to show a relationship.26,64In older patients, fewer opioids were prescribed and consumed, but pain in the elderly population can induce postoperative cardiopulmonary complications, ileus, nausea, and vomiting,107and each patient should be considered on an individual basis. However, for those aged greater than 75 yr, Aubrun et al. did not observe any difference in analgesic consumption.108There is also evidence that advancing age appears to reduce the influence of specific genes on the experience of pain.93
Our review showed a conflicting result for gender as a predictor for either postoperative pain or analgesic consumption. Gender differences in pain perception and analgesic consumption remain tentative, and age may be a confounding factor. The mechanism for gender differences is still elusive. There is some evidence that genetics plays a part in influencing interindividual variation in clinical and experimental pain responses.109It can also be attributed to a different socialization processes for men and women that influence bodily experience and the willingness to communicate distress.110Hormone variations,111neurotransmitters that can influence patient perception of pain, and pharmacokinetic variations may also occur.112Nevertheless, the difference could reflect pain reporting bias, patient belief in analgesic requirement and unwarranted psychogenic attributions made by health care providers.113,114On the other hand, Chia et al. reported reduced morphine consumption by Chinese women in the first 3 postoperative days compared to men. It should be noted that two-thirds of the patients were female in that study. It is also possible that cultural, ethnic, or genetic factors may account for the differing findings in the Chinese study.84
The knowledge of the important predictive factors for postoperative pain and analgesic consumption will enable early recognition of the at-risk patients. This will help in formulating an appropriate plan for effective pain management postoperatively and to attend to the pain considering the four predictors of pain, namely, preexisting pain, anxiety, age, and type of surgery.
Type of surgery is often interpreted only as a different subspecialty. Our review demonstrates that we should have a high suspicion for patients undergoing orthopedic, thoracic, and abdominal surgery or major and emergency surgery. We also need to be aware of those patients suffering from preexisting acute or chronic pain. They may have a higher postoperative pain and analgesic requirement. Furthermore, we must not forget the potential impact of psychological factors on the postoperative pain and analgesic requirement. We can discuss and educate our patients regarding concerns related to anxiety and coping strategies and provide anxiolytics or other medication as clinically indicated.115This review also raises questions regarding whether gender is predictive of postoperative pain and analgesic consumption as traditionally believed. Nonetheless, our systematic review provides a better insight into the predictors of postoperative pain such that future studies should take this into consideration in study design since these predictors are dependent variables.
This systematic review has several limitations that may explain the lack of consistency across the studies. The first limitation of this review is that in studies of prognostic models, none of the criteria of quality assessment have been widely accepted. The main problem with quality scores was to determine the weight that each item should provide to the overall score and the cutoffs for high-quality studies and poor-quality studies.116A number of studies have questionable quality, and others are limited by methodology problems; for example, the absence of standardized measuring instruments for the psychological variables may explain some of the inconsistent findings for postoperative pain. Therefore, we have excluded the poor quality studies in our final conclusion. The explanation for conflicting results in some predictors could be secondary to a lack of sufficient studies examining the factors in question. For regression analysis, the variables showing no correlation are not generally reported, and type II error can be introduced, which is affected by the sample size. We are unable to pool the data in this systematic review because it is impractical to obtain the raw data from every study that spans over a few decades. To minimize this problem, we discounted those studies with small sample size whose variables did not show any correlation when drawing the final conclusion. Different studies also looked at pain intensity and analgesic consumption at different time period postoperatively, together with the different analgesic regimes making comparison between studies difficult. Furthermore, pain itself is difficult to define because the measure of pain intensity is a subjective entity, and the quality of pain was not usually measured, for example, via McGill Pain Questionnaire; therefore, the use of visual analogue score may not reflect the actual pain experienced.
Uncontrolled anesthetic management, the nonhomogenous patient populations, different follow-up time period, and uncontrolled surgical procedure variables were all possible confounders. The age range of the studies is not large enough to show a significant difference, and not all studies examined age as a predictive factor. There could also be potential confounding factors such as the confusion of elderly people with patient-controlled analgesia and underreporting and exclusion of patients over the age of 70 yr from some studies.26It is possible that older patients are less inclined to complain to medical and nursing staff, resulting in reporting bias.50
The R2is a statistical value expressed in percentage that quantifies the extent to which a variable can be predicted by a given logistic regression model. The higher the percentage, the greater contribution the variable in question has on a particular independent variable. For the majority of the predictors, it was below 54%, leaving about half of the variability not explained by the measured variables. Furthermore, different studies measured different variables, and very few studies analyzed all the demographic, psychosocial, and surgical predictors simultaneously as independent factors into the models of regression analysis. Further research on the predictors of postoperative pain is needed.
In conclusion, preexisting pain, anxiety, age, and type of surgery are the four most significant predictors for postoperative pain. Type of surgery, age, and psychological distress were those for postoperative analgesic consumption. Gender was not found to be a significant predictor as traditionally believed. Early identification of the predictors in patients at risk of postoperative pain and an increased awareness of the importance of the psychological factors will allow more effective intervention and better pain management. There is a need for further studies to investigate a wide range of variables using sound instruments and clear definitions of pain intensities or pain control with analgesic consumption. This is especially true in orthopedic and breast surgery, where very painful procedures are performed with a limited amount of studies. Studies should be of a large-scale and ideally homogenous in terms of demographics, medical and psychological history, underlying pathology such as malignancy, and surgical procedure.
Appendix: Assessment Tools for Perioperative Variables and Outcome Measures
The following outlines the various instruments commonly used in the studies for assessing the perioperative risk factors or predictors and outcomes.
Mental Health inventory 120: An 18-item scale measuring symptoms of psychological distress and wellbeing along the five dimensions – anxiety, depression, loss of behavioral/emotional control, positive affect, and interpersonal ties.
26-Item Stress Scale 121,122: For assessing negative affect and is reliable to measure acute distress. Each item was rated on a five-point scale ranging from “not at all” to “extremely.”
Hospital Anxiety and Depression Scale 123: A self-assessment scale for detecting symptoms of anxiety and depression in nonpsychiatric patients from a medical outpatients department. It contains two seven-item scales: one for anxiety and one for depression with a score ranging from 0–21.
Self-Rating Questionnaire for Depression (SRQ-D) 124: Examines depressive conditions. The questionnaire consists of 18 items (4 somatic and 8 cognitive). Patients have a choice of four answers to each item: seldom or never (0), some of the time (1), quite often (2), and almost always (3).
Montgomery-Asberg Depression Rating Scale (MADRS) 125: A tool for measuring depressive symptoms: moderate to intense (>13) or mildly depressive symptoms (≤13).
State-Trait Anxiety Inventory 72: Comprises two self-report scales to measure state anxiety and trait anxiety. Each consists of 20 statements that are used to describe a person’s feelings or disposition, a transitory emotional state induced by a particular situation.
Hamilton Depression and Anxiety Rating Scales (HDARS) 126,127: Completed by a trained clinical psychologist or nurse practitioner who administered structured interviews specifically developed for rating these two scales.
Functional Assessment of Cancer Treatment-Emotion Scale (FACT-E) 128: Designed to assess mood and anxiety in patients with cancer.
Somatosensory Amplification Scale 129: A measure of sensitivity to and amplification of unpleasant bodily sensations that may also reflect somatic anxiety.
Illness Behavior Questionnaire Disease Conviction Scale 130: A measure of symptom preoccupation, rejection of physician reassurance, and affirmation of physical disease that has been found to be a risk factor for the development of postherpetic neuralgia. 131
Minnesota Multiphasic Personality Inventory (MMPI) : A 174-item questionnaire identifying the personality of the subjects.
Eysenck Personality Questionnaire (EPQ) 132: Used to measure different personality traits such as neuroticism. A typical neurotic patient scoring highly on the EPQ is an anxious, worrying individual who is moody and frequently depressed (range 0–23).
Multidimensional Health Locus of Control Scale (MHLC) 133: An 18-item questionnaire with three scales. Each scale contains six items using six response options ranging from strongly agree to strongly disagree.
Impact Event Scale 134: Fifteen-item self-report scale that assesses two categories of cognitive responses to stressful events: intrusion (intrusively experienced ideas, images, feelings, or bad dreams) and avoidance (consciously recognized avoidance of certain ideas, feelings, or situations).
Brief COPE (Coping scale) 135: Used to measure coping tendency or style. It measures a set of conceptually distinct coping subscales that include active coping, use of social support, acceptance, venting, humor, religious-based coping, and avoidant coping.
Pain Catastrophizing Scale 136: A questionnaire that includes 13 items that assess three components: rumination, magnification, and helplessness.
Pain Threshold Measures
Sensory, Mechanical Pain Threshold and Heat Pain Threshold and Perception 131: A first-degree burn injury was induced with a thermode (7 min at 47 degrees). Patients rated pain intensity at the start and every minute during the burn. The area of secondary hyperalgesia developing around the burn injury was assessed by a rigid von Frey monofilament that would be the mechanical pain threshold and mechanical pain perception in terms of the visual analog scale (VAS). Heat pain threshold was assessed with a contact thermode using a baseline temperature of 32 degrees. Heat pain perception in terms of the VAS was evaluated with a 10-s 45°C heat stimulus in the burn area.
Electronic Pressure Algometer : Used to determine pain threshold and pain tolerance pressure. A probe is applied to the pulp of the finger, and the pressure is increased at a speed of 30 kPa/s. Patients are asked to press a button on a patient-operated switch when they start to feel pain (pain threshold) and when they can no longer stand the pain (pain tolerance). The algometer records the pressure at each point.54
Suprathreshold Pain : A magnitude estimation of suprathreshold noxious stimulation assessment performed by applying phasic heat stimuli at four different temperatures: 45°C, 46°C, 47°C, and 48°C. The subjects are asked to report the level of perceived pain intensity by means of a VAS immediately after each stimulus.63
Pain Outcome Measures
Mcgill Pain Questionnaire : An instrument developed by Melzack and Torgerson (1971) and based on the Gate control theory of pain103composed of four major parts:
Part 1 is the pain rating (PRI) composed of 78 descriptive words that are scaled on intensity and qualitative dimensions.
Part 2 is a present pain intensity (PPI) item that rates the subject’s choice of weighted terms to depict the amount of pain felt at the moment.
Part 3 consists of front and back views of the body, and subjects are asked to mark the areas where they are having pain.
Part 4 of the tool is used to record specific symptoms that accompany the pain.
Visual Analogues Scale : An ungraduated 10-cm-long scale, scored on the left extremity with either “no pain at all” or “very effective treatment” and on the right extremity with either “unbearable pain” or “ineffective treatment.”
Numerical Rating Scores/Numerical Pain Score : An example of this scoring system is the 11-point numerical rating score of 1–10, where 0 is no pain and 10 is the worst imaginable pain.
Brief Pain Inventory 137: A method to measure pain intensity and the extent to which pain interferes with life.
The authors thank Marina Englesakis, B.A. (Hons), M.L.I.S. Information Specialist, Librarian, Toronto Western Hospital, Toronto, Ontario, Canada, for her help with the literature search.