Background

The primary goal of this study was to evaluate patterns in acute postoperative pain in a mixed surgical patient cohort with the hypothesis that there would be heterogeneity in these patterns.

Methods

This study included 360 patients from a mixed surgical cohort whose pain was measured across postoperative days 1 through 7. Pain was characterized using the Brief Pain Inventory. Primary analysis used group-based trajectory modeling to estimate trajectories/patterns of postoperative pain. Secondary analysis examined associations between sociodemographic, clinical, and behavioral patient factors and pain trajectories.

Results

Five distinct postoperative pain trajectories were identified. Many patients (167 of 360, 46%) were in the moderate-to-high pain group, followed by the moderate-to-low (88 of 360, 24%), high (58 of 360, 17%), low (25 of 360, 7%), and decreasing (21 of 360, 6%) pain groups. Lower age (odds ratio, 0.94; 95% CI, 0.91 to 0.99), female sex (odds ratio, 6.5; 95% CI, 1.49 to 15.6), higher anxiety (odds ratio, 1.08; 95% CI, 1.01 to 1.14), and more pain behaviors (odds ratio, 1.10; 95% CI, 1.02 to 1.18) were related to increased likelihood of being in the high pain trajectory in multivariable analysis. Preoperative and intraoperative opioids were not associated with postoperative pain trajectories. Pain trajectory group was, however, associated with postoperative opioid use (P < 0.001), with the high pain group (249.5 oral morphine milligram equivalents) requiring four times more opioids than the low pain group (60.0 oral morphine milligram equivalents).

Conclusions

There are multiple distinct acute postoperative pain intensity trajectories, with 63% of patients reporting stable and sustained high or moderate-to-high pain over the first 7 days after surgery. These postoperative pain trajectories were predominantly defined by patient factors and not surgical factors.

Editor’s Perspective
What We Already Know about This Topic
  • The resolution of pain after surgery is highly variable, and the factors contributing to these differences are poorly described

  • Identifying groups of patients sharing similar pain trajectories may help us predict and optimize recovery from surgery

What This Article Tells Us That Is New
  • Monitoring postoperative pain for 7 days in 360 patients recovering from surgery allowed the identification of five distinct pain trajectories

  • Patient-specific factors such as age, sex, and psychologic features were the predominant determinants of trajectory group membership

Of the 100 million patients who undergo surgery worldwide each year, more than 60% will experience moderate-to-severe postoperative pain.1  Increased acute postoperative pain intensity is associated with the development of persistent postsurgical pain, which is defined by the International Classification of Diseases-11 as pain persisting more than 3 months after surgery.2–4  Depending on the type of surgery, 10 to 56% of surgical patients will develop persistent postsurgical pain.5–7  Epidemiologic work by Fletcher et al.8  suggests that for every 10% increase in the patient estimate of the percentage of time spent in severe postoperative pain, there is a 24% increase in pain intensity at 6 months after surgery. This suggests that a better understanding of postoperative pain trajectories may not just influence acute postoperative suffering but also lead to preventative therapies for persistent postsurgical pain.

To personalize postoperative analgesia to these anticipated temporal profiles of acute and persistent postoperative pain, we must first develop better models of postoperative pain trajectories.9–12  Foundational work by Lavand’homme, Althaus, and others have characterized heterogeneity in the trajectory of postoperative pain and have linked these trajectories to the risk of persistent postsurgical pain.13–16  More recently, Althaus et al.17  applied growth mixture modeling analyses using postoperative day 1 through 5 average pain intensity ratings to assign individual patients into three classes described as high initial pain–high resolution (29.3% of subjects), low initial pain–moderate resolution (56.9%), and high initial pain–flat slope (13.4%).

Although the growth mixture models Althaus et al.17  established an empirical typology of acute postoperative pain trajectories, such typologies do not include details on sociodemographics, behavioral factors, or surgery type. These details are critical, given the role of age, sex, anxiety, catastrophizing, surgical procedure, and myriad additional factors previously shown to relate to both acute and persistent postsurgical pain outcomes.18  In addition, given the increasingly recognized problem of rebound pain after surgery, exploring growth mixture modeling with nonmonotonic functions (e.g., lines that both increase and decrease over time) may unmask late “bumps” in pain intensity that may be otherwise smoothed over in prior trajectory-modeling strategies.12,19 

The primary goal of this study was to use group-based trajectory modeling to characterize unique groups of postoperative pain trajectories for postoperative days 1 through 7 across a mixed surgical cohort. We hypothesized that we would identify more than two groups of pain trajectories. The secondary goal of this study was to examine sociodemographic, clinical, and behavioral factors in relation to pain trajectories; we hypothesized that these factors would differ across the identified trajectory groups.

This study was a prospective cohort using a mixed surgical sample that aimed to investigate how group-based trajectory modeling was associated with acute postoperative pain trajectories. The study protocol (institutional review board approval No. 201500153) was approved by the University of Florida Institutional Review Board (Gainesville, Florida; No. UF IRB-01) and was registered at ClinicalTrials.gov (NCT02407743; principal investigator: Dr. Tighe; April 3, 2015) before study initiation. All patients provided written informed consent for participation in this study and were generally recruited and enrolled more than 24 h before their surgery to avoid undue time pressure. This article adheres to the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) guidelines.

Study Participants

The study included patients undergoing elective, major orthopedic, urologic, colorectal, pancreatic/biliary, thoracic, or spine surgery with anticipated postoperative admission of at least 48 h. Inclusion criteria was age greater than 18 yr, anticipated length of stay 72 h or longer, and expected survival of longer than 6 months after surgery. Exclusion criteria included anticipated need for postoperative intubation greater than 24 h, urgent or emergent surgery, or the inability to understand or participate in data collection instruments. All subjects received surgery at University of Florida Health Shands Hospital (Gainesville, Florida) between November 2015 and September 2018. The convenience sample of patients was screened and recruited by trained research coordinators within the presurgical clinic, generally 1 to 3 weeks before surgery. All research participants were compensated for initial enrollment and for study completion. All eligible patients were screened by appropriate clinical staff on all weekdays, at all hours, to minimize the risk of selection bias. Figure 1 depicts the study flow diagram for the analytical sample of the current paper. Given that the cohort consisted of multiple surgical services, it was not possible to consider a universal analgesic strategy. Appendix 1 provides additional details on the perioperative analgesic strategy for surgical patients.

Data Collection

Sociodemographics and Clinical Measures

Sociodemo-graphic variables, including age, sex, race, ethnicity, and preoperative body mass index, were extracted from the electronic health record using the values listed at the time of the surgical encounter. The use of a preoperative nerve block or intraoperative ketamine was noted and collected from the electronic health record. Type of pain medication was recoded preoperatively as part of the Brief Pain Inventory. Intraoperative opioid administration, calculated as oral morphine milligram equivalents, was also recorded along with use and dosage of intraoperative ketamine and lidocaine. Postoperative oral morphine milligram equivalents were recorded and analyzed as the sum of daily oral morphine milligram equivalents from postoperative days 1 to 7. Patient-controlled analgesia use was extremely low because of shortages and updated protocols, which deemphasized patient-controlled analgesia use as a standard treatment. At our institution, preoperative gabapentin and intraoperative magnesium and dexmedetomidine were not included in a general perioperative multimodal analgesic strategy and were instead used very rarely because of concerns over sedation, safety, and/or cost.

Pain Assessment

The perioperative pain experience was characterized using the average pain severity item from the Brief Pain Inventory, which asks subjects to indicate the number (0, which indicates “no pain,” to 10, which indicates “pain as bad as you can imagine”) that best describes their pain on average, as well as worst and least pain in the last 24 h.20  Patients were assessed with the Brief Pain Inventory before surgery and then each day after surgery through postoperative day 7. The postoperative Brief Pain Inventory was administered by trained research coordinators in the patient’s hospital room; in the event that a patient was discharged before postoperative day 7, the research coordinator contacted the subject via telephone to complete the assessment.

Inpatient and phone (after hospital discharge) Brief Pain Inventory assessments were completed from 9:00 to 11:00 am, with follow-up at 1:00 to 3:30 pm if the patient was unavailable during the first interview attempt. In the event that a patient preferred to receive follow-up assessments via an emailed link to the REDCap assessment interface for postdischarge time points, the email was sent at 7:00 am each postoperative day, and participants completed the surveys at their convenience.

Preoperative Mental Health and Behavioral Factors

Several assessments related to mental and behavioral health were conducted before the patients underwent surgery. Three measures were developed and validated as a part of the Patient-Reported Outcomes Measurement Information System (PROMIS), including adult measures of PROMIS Anxiety, PROMIS Depression, and PROMIS Pain Behavior (i.e., behaviors that would indicate to others that an individual is experience pain, such as grimacing or sighing).21  Patients also completed the Pain Catastrophizing Scale.22,23  The Pain Catastrophizing Scale has three subscales: magnification, rumination, and helplessness. These subscales assess the degree to which a patient views his/her pain experience in extreme terms (e.g., “I become afraid that the pain will get worse”); worries about their pain (e.g., “I keep thinking about how much it hurts”); and feels helpless to control their pain (e.g., “It’s terrible and I think it’s never going to get better”).

Statistical Analysis

Analyses were performed in JMP Pro 14 and SAS 9.3 (SAS Institute Inc., USA). Continuous measures were summarized with means and standard deviations (or medians and interquartile range), and categorical measures were summarized with percentages. Normality of the primary measurement of average pain was examined graphically, using histogram and normal quantile–quantile plots, stratified by postoperative day. Distributions were normal within each day, with no outliers detected.

In primary analysis to identify clusters or subgroups of patients with similar progressions (i.e., trajectories) of pain after surgery, group-based trajectory modeling, using maximum likelihood estimation, was implemented with PROC TRAJ in SAS software (SAS Institute).24  Each individual was clustered into the trajectory group to which they had the highest posterior probability of membership.25  Group-based trajectory modeling was used to determine both the number of distinct trajectory groups and the shape of each trajectory (i.e., order of polynomial). Model fitting followed a two-stage iterative process. First, the number of groups was determined by modeling each trajectory group as a higher-order shape (i.e., cubic), then by comparing models with different numbers of groups, starting with one group (no separate trajectories). After the number of groups was determined, the models were run to determine the shape of each trajectory. Bayesian information criteria were used to identify the most parsimonious, best-fitting model, i.e., the model that has the best fit using the fewest number of trajectories.26  To compare fits, the Bayes factor was calculated, which is approximately two times the difference in Bayesian information criteria between two models (2 × [Bayesian information criteria more complex model – Bayesian information criteria simpler model]).27  A Bayes factor of more than 2 suggests positive evidence to support meaningful change in Bayesian information criteria for a more complex model, with a Bayes factor of 10 or more providing very strong evidence.27  In addition, it is suggested that any given model should comprise at least 1% of the total sample.28  Because group-based trajectory modeling uses full information maximum likelihood estimation, patients with missing values were still included in the models.

The relationship between several preoperative factors and these trajectory groups were examined as our secondary analyses. Chi-square test or Fisher’s exact test analysis was used for categorical preoperative factors, and one-way ANOVA was used for continuous preoperative factors. For parametric tests, normal quantile–quantile plots were used to evaluate normality assumption; if violated, a nonparametric Kruskal–Wallis test was used. Levene’s test was used to evaluate equal variance assumptions, with Welch’s correction employed if this assumption was violated. Factors were entered into a multivariable multinomial logistic regression with pain trajectory as the outcome. Variable selection was based on univariate criteria of P < 0.25. Collinearity was assessed using variable inflation factor and univariate analyses between factors. Estimates are reported as odds ratios and 95% CIs. Internal validation was performed for our multivariable multinomial model using the bootstrapping approach (n = 500 samples) with the frequency with which variables were selected to the model estimated.29  Additionally, bootstrapped CIs for odds ratios were calculated to compare with those from the original sample. P < 0.05 (two-tailed) was considered statistically significant; P values for individual univariate analyses (n = 16 tests) were corrected with a false discovery rate approach.30  Results from univariate analyses were reported using both uncorrected (raw) and corrected (FDR) P values.

The sample size was based on available data from patients participating in the current study (see “Study Participants”). No statistical power calculations were performed before this analysis.

Patient Characteristics

There were 363 patients included in this sample (fig. 1). Table 1 summarizes patient demographics and other preoperative clinical measures. The average age of patients was nearly 60 yr, with an even representation of men and women, and 14% (51 of 362, 14%) of the sample was nonwhite, and 4% (14 of 363) was Hispanic. Colorectal surgery was the most common surgical service, followed by thoracic/cardiovascular surgery and urologic surgery (table 1).

Daily Pain and Group-based Trajectory Analysis

For group-based trajectory analysis, n = 3 did not have enough postoperative pain data, with subsequent analyses having n = 360 patients (fig. 1). For this entire sample, average daily pain for the first day after surgery was moderate (5.1 ± 2.6), with a modest decrease across the 7 days after surgery (3.5 ± 2.4; fig. 2). Missingness at each time point (postoperative days 1 to 7) ranged from 6 to 18%, although 91% (n = 326) had complete pain data for at least 5 postoperative days. Table 2 reports model fitting for group-based trajectory analysis. The best-fitting model included five trajectory groups, with a combination of linear and quadratic trajectory groups. Figure 3 graphically represents these trajectory groups, with figure 4 displaying individual plots for patients within each trajectory. Data and SAS code for trajectory analysis are presented in Supplemental Digital Content 1 (http://links.lww.com/ALN/C535).

Nearly one-half of patients (167 of 360, 46%) were in the moderate-to-high pain group. One-quarter of patients (88 of 360, 24%) were in the moderate-to-low pain group. In total, 7% (25 of 360) of patients were in the low pain group, whereas 16.9% (58 of 360) of patients were in the high pain group. There was also a group comprising 6% (22 of 360) of patients that had steep decreases in pain across the 7 days after surgery.

Table 3 shows patient characteristics by trajectory group, and figure 4 depicts a mosaic plot between surgical service and trajectory groups. Below is summary of key demographic and clinical characteristics of each pain trajectory group:

High

This was the youngest group (54 ± 12 yr), and two-thirds of the patients were female. This group also had the largest proportion of Hispanics (4 of 58, 7%). Patients in this group were distributed relatively evenly across surgical services. These patients had higher opioid use across the 7-day postoperative period and greater opioid requirement intraoperatively.

Moderate-to-High.

Patients in this group had an average age of 58 ± 12 yr, and more than 50% (89 of 167) of the patients were female. More than 10% (20 of 167) of the patients in this group were Black, and 4% (6 of 167) were Hispanic. Nearly one-third (46 of 167) of this group received colorectal service. This group also had higher need for opioids later in postoperative period.

Moderate-to-Low

The average age of this group was 61 ± 13 yr, and more than one half of the patients were male. More than 90% (81 of 88) of the patients in this group were white, and 5% (4 of 88) reported being Hispanic. Nearly all patients in this group were evenly distributed across four surgical services: colorectal, orthopedics, thoracic/cardiovascular, and urology.

Low

The patients in this groups were the oldest (66 ± 13 yr) compared to other pain trajectory groups. Men comprised three-quarters (19 of 25) of the patients in this group, and all patients reported that they were non-Hispanic. Nearly 40% (9 of 25) of the patients in this group received colorectal surgical service. The low pain group also had the lowest opioid requirement during the 7-day postoperative period.

Decreasing

This group was similar to the low pain group, with an average age of 63 ± 10 yr. Nearly 70% (15 of 22) of the patients in this group were male, and no one reported being Hispanic. Nearly one-third (6 of 22) of the patients in this group were in the urology service.

Trajectory groups were statistically significantly associated with age (F(4,355) = 6.02, Praw < 0.001; PFDR < 0.001), sex (χ2 = 17.6, df = 4, Praw = 0.002; PFDR = 0.005), and postoperative opioid requirement (χ2 = 57.8, df = 4, Praw and PFDR < 0.001; table 3). After adjustment for multiple comparisons, the trajectory group was not statistically significantly associated with race, ethnicity, body mass index, intraoperative medications, preoperative block, or preoperative opioid use (table 3). Association between service and pain trajectory group did not reach statistical significance (χ2 = 16.0, df = 12, Praw = 0.173; PFDR = 0.308; fig. 5). Similar results were found for surgical procedure (χ2 = 14.6, df = 16, Praw = 0.552).

Pain Trajectory Groups and Preoperative Mental Health

Trajectory groups were statistically significantly associated with each preoperative mental health and behavior measure (fig. 6). Higher patient anxiety (F(4,352) = 13.7, Praw and PFDR < 0.001), depression (F(4,349) = 8.9, Praw and PFDR < 0.001), pain behaviors (F(4,349) = 9.3, Praw and PFDR < 0.001), and pain catastrophizing (F(4,342) = 10.8, Praw and PFDR < 0.001) were associated with trajectories with higher pain. Interestingly, for all measures, patients in the decreasing pain group had similar scores to patients in the low and moderate-to-low groups.

Multivariable Analyses

Variables entered into multinomial logistic regression were, age, sex, PROMIS Anxiety, PROMIS Pain Behaviors, pain catastrophizing, service, and intraoperative oral morphine milligram equivalents. Additionally, although intraoperative ketamine and lidocaine did not reach the defined threshold for inclusion (P < 0.25), they were also included because of clinical relevance. For surgical service, spine and orthopedic were combined; pancreas, biliary, and transplant were combined; and colorectal and urology were combined. Low and decreasing pain trajectory were combined as reference group for multinomial logistic regression. No other variables were recoded for this analysis. Table 4 reports the full results from analysis, with bootstrapped estimates and 95% CIs; Appendix 2 reports estimates from original analyses compared to bootstrapped estimates. From this analysis, age, sex, PROMIS Anxiety, and PROMIS Pain Behavior emerged as independent predictors of pain trajectory. Specifically, lower age (odds ratio, 0.94; 95% CI, 0.91 to 0.99), female sex (odds ratio, 6.4; 95% CI, 1.49 to 15.6), higher anxiety (odds ratio, 1.08; 95% CI, 1.01 to 1.14), and more pain behaviors (odds ratio, 1.10; 95% CI, 1.02 to 1.18) were related to increased likelihood of being in high pain trajectory. Table 4 also includes the frequency of which variables were selected for model in bootstrap analysis (n = 500 samples).

This study used group-based trajectory analysis to identify five distinct pain trajectory groups in the first 7 days after surgery. Four trajectories identified patients with low, moderate-to-low, moderate-to-high, and high pain over time, with one trajectory identifying patients with drastically decreasing postoperative pain. Women and younger patients were more likely to be in the stable moderate-to-high and high pain groups. Patients in the stable high group also had higher anxiety and depression and greater pain behaviors and pain catastrophizing preoperatively. Additionally, patients in the moderate-to-high and high pain groups had greater opioid requirements postoperatively.

In the regional anesthesiology literature, heuristics on approaches to perioperative pain control largely center on surgical procedure type. This is a logical first step in regional anesthetic decisions because many regional anesthetics are predicated on anatomical considerations related to surgery type, and certain surgeries tend to result in more acute postoperative pain.31,32  However, once anatomical location is determined, anesthesiologists have numerous options regarding desired block kinetics, ranging from minutes to days, through local anesthetic selection, additives to local anesthetics, delayed-release local anesthetic formulations, and continuous catheter-based techniques.9,10,33–35  To aid patients in selecting a kinetically rational approach to regional anesthesia for their procedure, anesthesiologists must first understand the anticipated postoperative acute pain trajectory.9,11,12,19  Although procedure-based heuristics offer important information for population-based recommendations, consideration of patient sociodemographic, medical, and behavioral factors becomes necessary to enable personalized regional anesthetics that are optimized for the individual.36–38  This is increasingly important in the design of randomized controlled trials given accumulating evidence on the lack of true randomization of patients with relevant features.39,40 

Sociodemographic and behavioral factors have been strongly associated with acute postoperative pain intensity over the last few decades.41,42  However, much of this early work on preoperative predictors of acute postoperative pain used pain intensity assessments on a single day after surgery or aggregates over the first few days after surgery.43–45  Early work first posed outcomes directly related to postoperative pain changes over time, uncovering significant differences in the slopes of acute pain trajectories.14,16  Althaus et al. extended this work to demonstrate that the parameter estimates for certain behavioral risk factors for acute postoperative pain could invert when the outcome of interest was the rate of recovery rather than initial pain intensity.13  For instance, preoperative anxiety was associated with greater initial postoperative pain intensity but also a more rapid rate of resolution of acute postoperative pain. Recent work using growth mixture modeling in a mixed surgical cohort suggested three postoperative pain trajectories compared to the five identified here.17  However, this analysis did not describe the patient or procedural characteristics of the patient pain trajectory groups.

One of the key contributions of this analysis is the relative impact of patient sociodemographic and behavioral factors over procedural factors in assigning patients to a postoperative pain trajectory group. Despite work by Gerbershagen et al.31,32  showing the differences in acute postoperative pain intensity across a range of surgical procedures, procedure type was not a key determinant of trajectory group assignment in our results. This is noteworthy given that our cohort included several procedure categories such as spine, thoracic, and orthopedic surgeries, commonly associated with high, prolonged acute postoperative pain.31  This discrepancy also applied to regional anesthesia; although regional anesthesia is robustly associated with decreased postoperative pain intensity in many surgical procedures, using a preoperative nerve block was not associated with trajectory group assignment. Preoperative nerve block was confounded with surgical service and could not be included in multivariable analysis. This discrepancy in regional anesthetics could be related to the fact that 76% of patients received some type of preoperative nerve block, thus minimizing potential variability across the groups. Similarly, we did not identify any difference in the use of ketamine or lidocaine between different trajectory groups. To be clear, our results do not suggest that surgical procedure type, regional anesthesia, or multimodal analgesia are not associated with greater or lesser postoperative pain but rather that these factors were not key differentiators of trajectory group assignments for acute postoperative pain. Furthermore, given the availability of opioids for breakthrough pain and identification of differences in intraoperative and postoperative oral morphine milligram equivalents between trajectory groups, the effects of multimodal analgesics may be best borne out in weighted composite endpoints encompassing pain intensity, opioid reduction, and functional improvement.

Multivariable modeling showed that sex, anxiety, and pain behaviors were independently associated with group trajectory assignment. Female sex has previously shown a strong association with increased postoperative pain intensity across a variety of surgical procedures.46–48  By contrast, despite the strong associations reported between catastrophizing and postoperative pain intensity, catastrophizing was not associated with group trajectory assignment.23,49,50  A study in breast cancer surgery patients suggests that pain catastrophizing may be a full mediator between preoperative anxiety and acute postoperative pain assessed 48 h after surgery.51  This discrepancy in findings on catastrophizing may also be interpreted in a similar manner as surgical procedures, whereby trajectory distinction remains conceptually different than pain intensity itself. Additionally, our logistic regression considered each group in equipoise rather than in an ordinal fashion, given difficulties in quantifying the “worseness” of the different trajectories.

Our finding regarding the role of pain behaviors, as assessed using the Patient-Reported Outcomes Measurement Information System Pain Behavior item bank, was surprising in its ability to distinguish among the group trajectories in a multivariable model. Pain behaviors are those physical or verbal, voluntary or involuntary, external manifestations of pain assessed using self-report.21  To the best of our knowledge, prior reports examining postoperative trajectories have not included pain behaviors alongside assessments of catastrophizing. One possible explanation for the lack of significance regarding catastrophizing in multivariable analysis could be that the information contained in the pain catastrophizing measure that is relevant to pain trajectory group assignment is better captured within the assessment of pain behavior. Further work is necessary to more fully explore the relationships between catastrophizing and pain behaviors in the surgical population.

In our analysis, group assignment was performed only using postoperative pain intensity time series data, with post hoc analysis of intergroup differences, rather than inclusion of patient and procedural factors in the group classification itself. This approach allowed us to focus on the postoperative pain experience of patients in a manner similar to a clinical decision framework. Our analyses also examined a range of relationships including linear and polynomial functions. These polynomial functions proved illustrative given the nonlinear shapes of the stable low and decreasing categories. Notably, across all groups except the decreasing group, there was minimal to absent overlap in the CI of each postoperative day between each group. In total, this interpretation suggests face validity in addition to the Bayes factor optimization steps.

Overall, our results contain many similarities to that of the survey of postoperative pain experience by Gan et al.1  In their survey of 146 patients who received an inpatient surgical procedure within the prior 14 months, 47% reported moderate pain, 20.2% reported severe pain, and 11.9% reported extreme pain after surgery. In comparison, our results had 46% in the moderate-to-high pain group and 16.9% in the sustained high pain group. Only 7% of patients in our cohort were in a sustained low pain group, which is less than the 20.9% of patients reporting only slight postoperative pain from Gan et al. but similar to the 8.2% who did not report postoperative pain. Similar work by Buvanendran et al.52  examining pain intensity on the day of discharge after inpatient surgery found that 12% of patients reported “severe to extreme” pain and 54% “moderate to extreme pain.” Notably, Gan et al.1  reported that 90% of surveyed patients reported being “somewhat” or “very” satisfied with their pain management. In our sample, 37% of patients still reported moderate to severe pain (pain rating, 5 to 10) at discharge. Although significant differences in methodologies make direct comparisons difficult and fail to consider related differences to functional outcomes and opioid requirements, our findings suggest a lack of overall progress in pain intensity reduction.

Our study had several limitations. Although our prospective study design permitted collection of extraclinical variables such as preoperative behavioral factors and postdischarge pain assessments, this limited the number of subjects, constraining the number of factors considered for multivariable modeling. Additionally, we examined only acute postoperative pain intensity; thus, these results cannot yet inform extrapolations to pain beyond the first 7 days after surgery. Our selection of a multisurgical cohort presented several tradeoffs. The overall goal was to examine both inter- and intraprocedural differences in postoperative pain trajectories; thus, capturing multiple surgical procedures permitted such comparisons across multiple patterns of tissue injury. However, within each type of surgery, there remains the potential for considerable heterogeneity as well. For instance, a “revision total hip arthroplasty” can yield blood losses ranging from less than hundreds of milliliters to multiple liters, depending on the clinical and procedural circumstances of the revision. Furthermore, although these results point toward methodologies that can enable kinetically rationale block selection to minimize postoperative opioid requirements for the duration of anticipated maximal pain intensities, we have yet to examine trajectories that incorporate pain interference with function or to test the responsiveness of trajectory group assignments to improvements in postoperative functioning and pain.

In conclusion, our results demonstrate the existence of at least five categories of acute postoperative pain trajectories defined predominantly by patient factors rather than type of surgery and intraoperative medications. Further work is necessary to better specify the patient and analgesic requirements across these groups, as well as to understand the role of such group assignments in the risk of persistent postsurgical pain.

Acknowledgments

The authors thank Elizabeth Thomas, D.O., Lei Zhang, M.S., and Atif Iqbal, M.D., for their assistance with study management (University of Florida College of Medicine, Gainesville, Florida); Corey Astrom, E.L.S., and Leah Buletti, B.A., for editorial assistance (University of Florida College of Medicine); Trevor Pogue, M.A., Brian Holloway, C.C.R.P., Andrea Castro Caro, R.N., and Amy Gunnett, R.N., C.C.R.C., for assistance with data collection and coordination (University of Florida College of Medicine); and Ron Ison, M.S., and John Zhang, M.S., for assistance with data management (University of Florida College of Medicine).

Research Support

Supported by National Institutes of Health grant No. R01 GM114290 (Bethesda, Maryland; to Dr. Tighe) and the Donn M. Dennis, M.D., Professorship in Anesthetic Innovation, University of Florida (Gainesville, Florida; to Dr. Tighe).

Competing Interests

Dr. Tighe is a Director at Large for the American Academy of Pain Medicine (Chicago, Illinois), serves on the editorial board for Pain Medicine, and is the principal investigator of the federal grant (National Institutes of Health [Bethesda, Maryland] grant No. 5R01GM114290) that funded the study. Dr. Wallace serves as a consultant for the Children’s Tumor Foundation (New York, New York) on a gene therapy program and as an external advisor for a cancer basic science project at Boston Children’s Hospital (Boston, Massachusetts). The remaining authors declare no conflicts of interest.

1.
Gan
TJ
,
Habib
AS
,
Miller
TE
,
White
W
,
Apfelbaum
JL
:
Incidence, patient satisfaction, and perceptions of post-surgical pain: Results from a US national survey.
Curr Med Res Opin
.
2014
;
30
:
149
60
2.
Gilron
I
,
Vandenkerkhof
E
,
Katz
J
,
Kehlet
H
,
Carley
M
:
Evaluating the association between acute and chronic pain after surgery: Impact of pain measurement methods.
Clin J Pain
.
2017
;
33
:
588
94
3.
Richebé
P
,
Capdevila
X
,
Rivat
C
:
Persistent postsurgical pain: Pathophysiology and preventative pharmacologic considerations.
Anesthesiology
.
2018
;
129
:
590
607
4.
Treede
RD
,
Rief
W
,
Barke
A
,
Aziz
Q
,
Bennett
MI
,
Benoliel
R
,
Cohen
M
,
Evers
S
,
Finnerup
NB
,
First
MB
,
Giamberardino
MA
,
Kaasa
S
,
Kosek
E
,
Lavand’homme
P
,
Nicholas
M
,
Perrot
S
,
Scholz
J
,
Schug
S
,
Smith
BH
,
Svensson
P
,
Vlaeyen
JW
,
Wang
SJ
:
A classification of chronic pain for ICD-11.
Pain
.
2015
;
156
:
1003
7
5.
Macrae
WA
:
Chronic post-surgical pain: 10 years on.
Br J Anaesth
.
2008
;
101
:
77
86
6.
Montes
A
,
Roca
G
,
Sabate
S
,
Lao
JI
,
Navarro
A
,
Cantillo
J
,
Canet
J
;
GENDOLCAT Study Group
:
Genetic and clinical factors associated with chronic postsurgical pain after hernia repair, hysterectomy, and thoracotomy: A two-year multicenter cohort study.
Anesthesiology
.
2015
;
122
:
1123
41
7.
Kehlet
H
,
Jensen
TS
,
Woolf
CJ
:
Persistent postsurgical pain: Risk factors and prevention.
Lancet
.
2006
;
367
:
1618
25
8.
Fletcher
D
,
Stamer
UM
,
Pogatzki-Zahn
E
,
Zaslansky
R
,
Tanase
NV
,
Perruchoud
C
,
Kranke
P
,
Komann
M
,
Lehman
T
,
Meissner
W
;
euCPSP group for the Clinical Trial Network group of the European Society of Anaesthesiology
:
Chronic postsurgical pain in Europe: An observational study.
Eur J Anaesthesiol
.
2015
;
32
:
725
34
9.
Bugada
D
,
Ghisi
D
,
Mariano
ER
:
Continuous regional anesthesia: A review of perioperative outcome benefits.
Minerva Anestesiol
.
2017
;
83
:
1089
100
10.
Ilfeld
BM
:
Continuous peripheral nerve blocks: An update of the published evidence and comparison with novel, alternative analgesic modalities.
Anesth Analg
.
2017
;
124
:
308
35
11.
Williams
BA
,
Ibinson
JW
,
Mangione
MP
,
Modrak
RT
,
Tonarelli
EJ
,
Rakesh
H
,
Kmatz
AM
,
Cohen
PZ
:
Research priorities regarding multimodal peripheral nerve blocks for postoperative analgesia and anesthesia based on hospital quality data extracted from over 1,300 cases (2011–2014).
Pain Med
.
2015
;
16
:
7
12
12.
Abdallah
FW
,
Halpern
SH
,
Aoyama
K
,
Brull
R
:
Will the real benefits of single-shot interscalene block please stand up? A systematic review and meta-analysis.
Anesth Analg
.
2015
;
120
:
1114
29
13.
Althaus
A
,
Arránz Becker
O
,
Neugebauer
E
:
Distinguishing between pain intensity and pain resolution: Using acute post-surgical pain trajectories to predict chronic post-surgical pain.
Eur J Pain
.
2014
;
18
:
513
21
14.
Lavand’homme
PM
,
Grosu
I
,
France
MN
,
Thienpont
E
:
Pain trajectories identify patients at risk of persistent pain after knee arthroplasty: An observational study.
Clin Orthop Relat Res
.
2014
;
472
:
1409
15
15.
Chapman
CR
,
Zaslansky
R
,
Donaldson
GW
,
Shinfeld
A
:
Postoperative pain trajectories in cardiac surgery patients.
Pain Res Treat
.
2012
;
2012
:
608359
16.
Chapman
CR
,
Donaldson
GW
,
Davis
JJ
,
Bradshaw
DH
:
Improving individual measurement of postoperative pain: The pain trajectory.
J Pain
.
2011
;
12
:
257
62
17.
Althaus
A
,
Arránz Becker
O
,
Moser
KH
,
Lux
EA
,
Weber
F
,
Neugebauer
E
,
Simanski
C
:
Postoperative pain trajectories and pain chronification: An empirical typology of pain patients.
Pain Med
.
2018
;
19
:
2536
45
18.
Chapman
CR
,
Vierck
CJ
:
The transition of acute postoperative pain to chronic pain: An integrative overview of research on mechanisms.
J Pain
.
2017
;
18
:
359.e1
8
19.
Lavand’homme
P
:
Rebound pain after regional anesthesia in the ambulatory patient.
Curr Opin Anaesthesiol
.
2018
;
31
:
679
84
20.
Cleeland
CS
,
Ryan
KM
:
Pain assessment: Global use of the Brief Pain Inventory.
Ann Acad Med Singap
.
1994
;
23
:
129
38
21.
Askew
RL
,
Cook
KF
,
Revicki
DA
,
Cella
D
,
Amtmann
D
:
Evidence from diverse clinical populations supported clinical validity of PROMIS pain interference and pain behavior.
J Clin Epidemiol
.
2016
;
73
:
103
11
22.
Osman
A
,
Barrios
FX
,
Gutierrez
PM
,
Kopper
BA
,
Merrifield
T
,
Grittmann
L
:
The Pain Catastrophizing Scale: Further psychometric evaluation with adult samples.
J Behav Med
.
2000
;
23
:
351
65
23.
Sullivan
MJL
,
Bishop
SR
,
Pivik
J
:
The Pain Catastrophizing Scale: Development and validation.
Psychological Assessment
.
1995
;
7
:
524
32
24.
Jones
BL
,
Nagin
DS
:
Advances in group-based trajectory modeling and an SAS procedure for estimating them.
Sociological Methods & Research
.
2007
;
35
:
542
71
25.
Andruff
H
,
Carraro
N
,
Thompson
A
,
Gaudreau
P
,
Louvet
B
:
Latent class growth modelling: A tutorial.
Tutorials in Quantitative Methods for Psychology
.
2009
;
5
:
11
24
26.
Vittinghoff
E
,
Glidden
DV
,
Shiboski
SC
,
McCulloch
CE
:
Regression Methods in Biostatistics: Linear, Logistic, Survival, and Repeated Measures Models
,
New York
,
Springer
,
2011
27.
Jones
BL
,
Nagin
DS
,
Roeder
K
:
A SAS procedure based on mixture models for estimating developmental trajectories.
Sociological Methods & Research
.
2001
;
29
:
374
93
28.
Jung
T
,
Wickrama
KA
:
An introduction to latent class growth analysis and growth mixture modeling.
Social and Personality Psychology Compass
.
2008
;
2
:
302
17
29.
Mannan
HR
:
A practical application of a simple bootstrapping method for assessing predictors selected for epidemiologic risk models using automated variable selection.
Int J Stat Appl
.
2017
;
7
:
239
49
30.
Vasilopoulos
T
,
Morey
TE
,
Dhatariya
K
,
Rice
MJ
:
Limitations of significance testing in clinical research: A review of multiple comparison corrections and effect size calculations with correlated measures.
Anesth Analg
.
2016
;
122
:
825
30
31.
Gerbershagen
HJ
,
Aduckathil
S
,
van Wijck
AJ
,
Peelen
LM
,
Kalkman
CJ
,
Meissner
W
:
Pain intensity on the first day after surgery: A prospective cohort study comparing 179 surgical procedures.
Anesthesiology
.
2013
;
118
:
934
44
32.
Gerbershagen
HJ
,
Pogatzki-Zahn
E
,
Aduckathil
S
,
Peelen
LM
,
Kappen
TH
,
van Wijck
AJ
,
Kalkman
CJ
,
Meissner
W
:
Procedure-specific risk factor analysis for the development of severe postoperative pain.
Anesthesiology
.
2014
;
120
:
1237
45
33.
Kirkham
KR
,
Jacot-Guillarmod
A
,
Albrecht
E
:
Optimal dose of perineural dexamethasone to prolong analgesia after brachial plexus blockade: A systematic review and meta-analysis.
Anesth Analg
.
2018
;
126
:
270
9
34.
Shah
J
,
Votta-Velis
EG
,
Borgeat
A
:
New local anesthetics.
Best Pract Res Clin Anaesthesiol
.
2018
;
32
:
179
85
35.
Vorobeichik
L
,
Brull
R
,
Abdallah
FW
:
Evidence basis for using perineural dexmedetomidine to enhance the quality of brachial plexus nerve blocks: A systematic review and meta-analysis of randomized controlled trials.
Br J Anaesth
.
2017
;
118
:
167
81
36.
Kopp
SL
,
Børglum
J
,
Buvanendran
A
,
Horlocker
TT
,
Ilfeld
BM
,
Memtsoudis
SG
,
Neal
JM
,
Rawal
N
,
Wegener
JT
:
Anesthesia and analgesia practice pathway options for total knee arthroplasty: An evidence-based review by the American and European Societies of Regional Anesthesia and Pain Medicine.
Reg Anesth Pain Med
.
2017
;
42
:
683
97
37.
Warrender
WJ
,
Syed
UAM
,
Hammoud
S
,
Emper
W
,
Ciccotti
MG
,
Abboud
JA
,
Freedman
KB
:
Pain management after outpatient shoulder arthroscopy: A systematic review of randomized controlled trials.
Am J Sports Med
.
2017
;
45
:
1676
86
38.
Gustafsson
UO
,
Scott
MJ
,
Hubner
M
,
Nygren
J
,
Demartines
N
,
Francis
N
,
Rockall
TA
,
Young-Fadok
TM
,
Hill
AG
,
Soop
M
,
de Boer
HD
,
Urman
RD
,
Chang
GJ
,
Fichera
A
,
Kessler
H
,
Grass
F
,
Whang
EE
,
Fawcett
WJ
,
Carli
F
,
Lobo
DN
,
Rollins
KE
,
Balfour
A
,
Baldini
G
,
Riedel
B
,
Ljungqvist
O
:
Guidelines for perioperative care in elective colorectal surgery: Enhanced Recovery After Surgery (ERAS®) Society recommendations: 2018.
World J Surg
.
2019
;
43
:
659
95
39.
Frieden
TR
:
Evidence for health decision making: Beyond randomized, controlled trials.
N Engl J Med
.
2017
;
377
:
465
75
40.
Deaton
A
,
Cartwright
N
:
Understanding and misunderstanding randomized controlled trials.
Soc Sci Med
.
2018
;
210
:
2
21
41.
Yang
MMH
,
Hartley
RL
,
Leung
AA
,
Ronksley
PE
,
Jetté
N
,
Casha
S
,
Riva-Cambrin
J
:
Preoperative predictors of poor acute postoperative pain control: A systematic review and meta-analysis.
BMJ Open
.
2019
;
9
:
e025091
42.
Ip
HY
,
Abrishami
A
,
Peng
PW
,
Wong
J
,
Chung
F
:
Predictors of postoperative pain and analgesic consumption: A qualitative systematic review.
Anesthesiology
.
2009
;
111
:
657
77
43.
Werner
MU
,
Mjöbo
HN
,
Nielsen
PR
,
Rudin
A
:
Prediction of postoperative pain: A systematic review of predictive experimental pain studies.
Anesthesiology
.
2010
;
112
:
1494
502
44.
Sommer
M
,
de Rijke
JM
,
van Kleef
M
,
Kessels
AG
,
Peters
ML
,
Geurts
JW
,
Patijn
J
,
Gramke
HF
,
Marcus
MA
:
Predictors of acute postoperative pain after elective surgery.
Clin J Pain
.
2010
;
26
:
87
94
45.
Gramke
HF
,
de Rijke
JM
,
van Kleef
M
,
Kessels
AG
,
Peters
ML
,
Sommer
M
,
Marcus
MA
:
Predictive factors of postoperative pain after day-case surgery.
Clin J Pain
.
2009
;
25
:
455
60
46.
Tighe
PJ
,
Riley
JL
3rd
,
Fillingim
RB
:
Sex differences in the incidence of severe pain events following surgery: A review of 333,000 pain scores.
Pain Med
.
2014
;
15
:
1390
404
47.
Fillingim
RB
,
King
CD
,
Ribeiro-Dasilva
MC
,
Rahim-Williams
B
,
Riley
JL
3rd
:
Sex, gender, and pain: A review of recent clinical and experimental findings.
J Pain
.
2009
;
10
:
447
85
48.
Bartley
EJ
,
Fillingim
RB
:
Sex differences in pain: A brief review of clinical and experimental findings.
Br J Anaesth
.
2013
;
111
:
52
8
49.
Khan
RS
,
Ahmed
K
,
Blakeway
E
,
Skapinakis
P
,
Nihoyannopoulos
L
,
Macleod
K
,
Sevdalis
N
,
Ashrafian
H
,
Platt
M
,
Darzi
A
,
Athanasiou
T
:
Catastrophizing: A predictive factor for postoperative pain.
Am J Surg
.
2011
;
201
:
122
31
50.
Dunn
LK
,
Durieux
ME
,
Fernández
LG
,
Tsang
S
,
Smith-Straesser
EE
,
Jhaveri
HF
,
Spanos
SP
,
Thames
MR
,
Spencer
CD
,
Lloyd
A
,
Stuart
R
,
Ye
F
,
Bray
JP
,
Nemergut
EC
,
Naik
BI
:
Influence of catastrophizing, anxiety, and depression on in-hospital opioid consumption, pain, and quality of recovery after adult spine surgery.
J Neurosurg Spine
.
2018
;
28
:
119
26
51.
Pinto
PR
,
McIntyre
T
,
Almeida
A
,
Araújo-Soares
V
:
The mediating role of pain catastrophizing in the relationship between presurgical anxiety and acute postsurgical pain after hysterectomy.
Pain
.
2012
;
153
:
218
26
52.
Buvanendran
A
,
Fiala
J
,
Patel
KA
,
Golden
AD
,
Moric
M
,
Kroin
JS
:
The incidence and severity of postoperative pain following inpatient surgery.
Pain Med
.
2015
;
16
:
2277
83

Appendix 1

In general, all patients except spine surgery patients received neuraxial or perineural catheters before surgery according to procedure-specific institutional guidelines customized as necessary for individual patients. Total knee replacement patients received femoral nerve catheters (10- to 20-ml bolus of 0.2% ropivacaine followed by a 5 ml/h infusion with a 5-ml bolus available once per 60 min) along with single-injection sciatic nerve block using approximately 20 ml of 0.2% ropivacaine. Catheters were maintained for 1 to 3 days after surgery, including daily trials off beginning on postoperative day 1 or 2, depending on patient progression with physical therapy, comorbidity status, and anticipated length of stay. Ambulatory catheters were typically offered to candidate patients departing the hospital before postoperative day 3. Total hip arthroplasty patients received either a lumbar epidural (0.2% ropivacaine at 4 ml/h with a 4-ml bolus available once per 60 min) or a combination of femoral nerve catheter (10- to 20-ml bolus of 0.2% ropivacaine followed by a 5 ml/h infusion with a 5-ml bolus available once per 60 min) and proximal obturator nerve single-injection nerve block (10- to 20-mL of 0.2% ropivacaine). Shoulder surgery patients generally received cervical paravertebral catheters (10- to 20-ml bolus of 0.2% ropivacaine followed by a 5 ml/h infusion with a 5-ml bolus available once per 60 min) maintained for 1 to 5 days and included the option of ambulatory catheters for eligible candidates. Orthopedic oncology patients received single-injection nerve blocks and perineural or neuraxial catheters, depending on the nature of the surgery. Thoracic surgery, pancreatic/biliary surgery, colorectal surgery, and urologic oncologic surgery patients received thoracic epidural catheters (thoracic, T4 to T8; pancreatic/biliary, T7 to T8; colorectal surgery, T9 to T12; urologic procedures, T8 to T12; ranges are provided to account for variations in individual surgical procedures). Eligible patients generally received nonsteroidal anti-inflammatory drugs and intravenous acetaminophen at the conclusion of surgery; no protocols required the consistent use of gabapentinoids, ketamine, or lidocaine infusion, although these were applied in select circumstances.

All patients were seen by the perioperative pain service in the recovery room for titration of regional anesthetic infusions and then twice on each postoperative day until catheter removal. Ambulatory perineural catheter replacement patients were interviewed by telephone using a standard protocol at least once per day until catheter removal.

Overall, patients received general anesthetics across all surgeries; there were no protocols in place to standardize neuraxial anesthetics for any particular surgery. Preoperative sedation for nerve block placement generally consisted of 1 to 5 mg of midazolam and up to 1,000 μg of alfentanil, titrated ideally to anxiolysis. Select candidates for orthopedic surgery received neuraxial anesthetics with sedation maintained with propofol for lower extremity procedures, and distal upper extremity procedures were commonly performed using a nerve block with propofol sedation.

Appendix 2