Background

The amount of same-day surgery has increased markedly worldwide in recent decades, but there remains limited evidence on chronic postsurgical pain in this setting.

Methods

This study assessed pain 90 days after ambulatory surgery in an international, multicenter prospective cohort study of patients at least 45 yr old with comorbidities or at least 65 yr old. Pain was assessed using the Brief Pain Inventory. Chronic postsurgical pain was defined as a change of more than 1 point in self-rated average pain at the surgical site between baseline and 90 days, and moderate to severe chronic postsurgical pain was defined as a score greater than 4 in self-rated average pain at the surgical site at 90 days. Risk factors for chronic postsurgical pain were identified using multivariable logistic regression.

Results

Between November 2021 and January 2023, a total of 2,054 participants were included, and chronic postsurgical pain occurred in 12% of participants, of whom 93.1% had new chronic pain at the surgical site (i.e., participants without pain before surgery). Moderate to severe chronic postsurgical pain occurred in 9% of overall participants. Factors associated with chronic postsurgical pain were active smoking (odds ratio, 1.82; 95% CI, 1.20 to 2.76), orthopedic surgery (odds ratio, 4.7; 95% CI, 2.24 to 9.7), plastic surgery (odds ratio, 4.3; 95% CI, 1.97 to 9.2), breast surgery (odds ratio, 2.74; 95% CI, 1.29 to 5.8), vascular surgery (odds ratio, 2.71; 95% CI, 1.09 to 6.7), and ethnicity (i.e., for Hispanic/Latino ethnicity, odds ratio, 3.41; 95% CI, 1.68 to 6.9 and for First Nations/native persons, odds ratio, 4.0; 95% CI, 1.05 to 15.4).

Conclusions

Persistent postsurgical pain after same-day surgery is common, is usually moderate to severe in nature, and occurs mostly in patients without chronic pain before surgery.

Editor’s Perspective
What We Already Know about This Topic
  • Chronic postsurgical pain, defined as pain persisting more than 3 months after surgical injury, may occur after a variety of procedures, with greater surgical extent and individual patient-level biopsychosocial characteristics conferring greater risk

  • Same-day surgery has markedly increased in recent decades, potentially jeopardizing the ability to identify individuals who will struggle with managing their postsurgical pain, thus raising the stakes for accurate preoperative prediction

What This Article Tells Us That Is New
  • This international, multicenter, prospective, longitudinal cohort study of more than 2,000 patients undergoing a variety of outpatient surgical procedures revealed that 9% of patients reported moderate to severe surgical area pain at 3 months after surgery

  • Factors that were independently associated with higher odds of chronic postsurgical pain in this cohort were preoperative pain, Hispanic or First Nations ethnicity, active smoking, and certain surgical types including orthopedic, plastic, breast, or vascular surgery

Chronic postsurgical pain is defined by the International Association for the Study of Pain (Washington, D.C.) as a new or worsened pain in the surgical area, persisting at least 3 months after the surgical intervention.1  Chronic postsurgical pain is an undesirable and potentially avoidable adverse event that can have a negative impact on postoperative quality of life and function, and this may be especially true in the elderly.2,3  Mood, sleep, and enjoyment of life, as well as patient’s physical, psychologic, and social well-being, can all be affected for months or years due to chronic postsurgical pain.4  Chronic postsurgical pain may also contribute to persistent opioid use after surgery.5,6  Chronic pain can also have economic impacts; the cost of chronic pain in Canada has been estimated around 40 billion Canadian dollars through direct medical costs, as well as indirect costs such as productivity loss.7 

Reported incidences of chronic postsurgical pain vary in the literature, ranging from 10 to 50%, depending on the type of surgery.8  Most studies reporting on chronic postsurgical pain are in the setting of inpatient surgery, with limited contemporary evidence in same-day surgery. A systematic review of prediction models for chronic postsurgical pain in adults published in 2021 identified 15 studies reporting on chronic postsurgical pain prediction models.9  Studies included in the systematic review were not focusing on same-day surgery and were limited to a single type or a few types of surgery.

Same-day surgery consists of a surgical intervention for which patients are discharged on the same day of their procedure without admission to a hospital ward. Managing postoperative pain in this setting can pose particular challenges.10,11  In contrast to inpatient surgery, during which patients are regularly monitored and assessed for postoperative pain on surgical wards, patients after same-day surgery must self-manage their pain medication, without previous titration of therapy and often with limited education on pain management.12  Studies have suggested that some patients may underuse their pain medications despite experiencing pain because of side effects, such as nausea and/or vomiting, or fear of addiction.12  In contrast, a small study in same-day surgery found that 43% of patients required analgesia in addition to what was originally prescribed at discharge.11 

It is now estimated that more than 140 million procedures are performed annually globally as same-day surgery in high-income countries and that this number will continue to rise.13  Despite the growing number of same-day surgeries performed worldwide, there is limited evidence on chronic postsurgical pain in this setting. Most of the studies in same-day surgery only report on acute postsurgical pain.10,14–17  Moreover, not only have same-day procedures grown in number, but patients undergoing same-day surgery nowadays commonly include older patients and more complex procedures.18,19  Considering the growing number and changing population of same-day surgery, as well as the challenges of pain management inherent to this context, our study aims to provide contemporary data on the incidence and risk factors of chronic postsurgical pain after same-day surgery.

Study Design

This is a prospective cohort, a preplanned substudy nested within the VAscuLar events In pAtieNts undergoing same-day nonCardiac surgery (VALIANCE) study. VALIANCE is an international, multicenter prospective cohort of patients undergoing mixed noncardiac same-day surgery (ClinicalTrial.gov NCT04973397). The study was approved by the research ethics board of the Center Hospitalier de l’Université de Montreal (Quebec, Canada) and at each of the participating center’s local research ethics board before study initiation (see Supplemental Digital Content 1 for participating centers, https://links.lww.com/ALN/D548). All participants provided written informed consent before enrolment in the study.

Study Population and Procedures

Participants enrolled in the VALIANCE cohort were 65 yr or older or between 45 and 64 yr of age with at least one risk factor and underwent noncardiac, nonophthalmologic same-day surgery with planned duration in the operating room of 60 min or longer (for detailed eligibility criteria, see Supplemental Digital Content 2, https://links.lww.com/ALN/D548). Patients were considered not eligible if the intervention did not require the presence of an anesthesiologist in the operating room. Centers were encouraged to approach all potentially eligible patients for informed consent, using consecutive sampling of patients. For this substudy, we included only participants who had provided information on pain status at baseline and 90 days.

Study personnel collected the following baseline characteristics before surgery, in person or by phone interview, as well as by chart review: age, biologic sex (i.e., sex assigned at birth), lived gender (i.e., the gender patient identifies with), past medical history, medications, type of surgery, and anxiety/depression symptomatology using the EQ-5D-5L validated questionnaire.20  Pain was assessed in our study using the Brief Pain Inventory. The Brief Pain Inventory is a validated questionnaire that assesses pain severity (i.e., worst, least, and average pain in the last 7 days and pain right now) and interference with functioning (i.e., general activity, mood, walking activity, normal work, relations with other people, sleep, and enjoyment of life) on a scale from 0 to 10.21  At baseline, participants were asked whether they had chronic pain and chronic pain specifically at the surgical site. Participants who reported chronic pain before surgery (i.e., pain in any area that persists or recurs for at least 3 months) were asked to complete the Brief Pain Inventory to assess the overall pain severity and pain interference with function and were asked about their baseline pain medication intake. Participants were contacted by phone at 90 days, and those who reported pain at the surgical site completed the Brief Pain Inventory. Pain severity and interference with activities related to the pain at the surgical site were assessed, and participants were asked about pain medication intake. Study data were entered in a secure online electronic database (REDCap) hosted at the Center Hospitalier de l’Université de Montréal.

The primary outcome of chronic postsurgical pain was defined according to the International Association for the Study of Pain, namely as persistent pain in the surgical area, with an increase on the self-rated average pain intensity on the Brief Pain Inventory of 1 out of 10 or higher, between baseline and 90-day follow-up.1  Secondary outcomes included moderate to severe chronic postsurgical pain, defined as a score of 4 out of 10 or higher for the self-rated average pain intensity reported on the Brief Pain Inventory at 90 days.22  New chronic postsurgical pain was defined as the presence of pain at the surgical site, with a score of 1 out of 10 or higher for the average intensity reported on the Brief Pain Inventory at 90-day follow-up, in the absence of pain at the surgical site reported at baseline. Worsened chronic postsurgical pain was defined as pain at the surgical site, with a change in score of 1 point or more for the self-rated average pain at the surgical site between baseline and 90-day follow-up, in a patient who reported having pain at the surgical site at baseline. New opioid consumption was defined as any consumption of opioids at the 90-day follow-up, in patients who did not report opioid consumption at baseline.

Statistical Analyses

Baseline characteristics are reported using descriptive statistics (i.e., number and percentages, mean and SD, and median and interquartile range, as appropriate). The results are reported with 95% CI and corresponding P values. Baseline and surgical characteristics were compared for patients with and without chronic postsurgical pain, using independent t tests for normally distributed continuous data or Mann–Whitney U tests for not normally distributed continuous data. Categorical data were compared using chi-square test or Fisher’s exact test, as appropriate.

We determined the incidence, severity, and interference of chronic postsurgical pain and moderate to severe chronic postsurgical pain. We also reported the incidence of new chronic postsurgical pain (i.e., participants without pain at the surgical site at baseline) and worsened chronic postsurgical pain (i.e., participants with a change in score of 1 point or more in pain severity at the surgical site). We also explored the patterns of pain change at the surgical site between baseline and 90 days and incidence of new opioid consumption.

We used a multivariable logistic regression to identify independent risk factors of chronic postsurgical pain. We identified risk factors a priori based on literature review, and we included the following variables in the multivariable model: age,9,23  biologic sex,9,24  body mass index,6,8  smoking status,8,23  baseline anxiety/depression,8,25  baseline opioid consumption,23,26  baseline chronic pain,9,27  active or recent cancer,28  type of surgery (i.e., orthopedic, breast, urology, gynecology, digestive/hepatobiliary/pancreatic, vascular, thoracic, neurosurgery, plastic, otorhinolaryngology, and other),6,27  and surgical approach (i.e., endoscopic/endovascular/robotic, laparoscopy, open).8,29  The Box–Tidwell test was used to check linearity assumptions for continuous variables. Lived gender could not be explored in a separate analysis in place of biologic sex given the high concordance (99.4%) between sex and gender in our cohort. We also planned to explore other potential risk factors that would have significant differences in baseline characteristics between patients with and without chronic postsurgical pain (P < 0.10). We assessed model discrimination using the c-statistic, corrected for optimism using bootstrapping (B = 1000). Calibration was assessed using the Brier score and a calibration curve with locally weighted scatterplot smoothing, with a bias-corrected curve. Sensitivity analyses were planned for the multivariable model to predict chronic postsurgical pain if variables with more than 5% missing data were included in the model.

We used the approach proposed by Riley et al.30  to determine the sample size requirement for developing a prediction model to predict chronic postsurgical pain. Based on a 15% incidence of chronic postsurgical pain, an estimated c-statistic of 0.74 from previous studies31,32  and inclusion of 10 variables using 20 degrees of freedom in the model, we estimated a minimum sample size of 1,803 patients.

P values of less than 0.05 were used to determine statistical significance, unless stated otherwise. The analysis and reporting of the results follow the TRIPOD statement, as well as the STROBE criteria for reporting of observational studies.33,34  A statistical analysis plan was written, dated, and recorded in the investigator’s files before the data were accessed and any statistical analysis was performed. Analyses were performed using R (version 4.1.0, R Foundation for Statistical Computing, Austria) and SPSS (IBM SPSS Statistics 27, USA).

A total of 2,200 participants were enrolled in the VALIANCE Study between November 17, 2021, and January 31, 2023, from 10 centers in three countries: Canada, the United States, and the Netherlands (fig. 1 shows the patient flowchart), of which 2,054 were eligible for this study. Only 0.7% of patients had missing pain data at 90 days and were excluded from this study. The mean age of participants was 65.9 ± 9.7 yr, and 53.2% (1,093 of 2,054) were female. Before surgery, a third (35.3%; 726 of 2,054) of participants reported experiencing chronic pain, and 20.1% (413 of 2,054) had pain in the surgical area specifically. Preoperative chronic opioid use was reported in 8.3% (171 of 2,054) of patients. The most common types of surgery were orthopedic (17.6%; 362 of 2,054), urology (17.2%; 354 of 2,054), otorhinolaryngology (11.9%; 244 of 2,054), and breast surgery (10.7%; 220 of 2,054). The participants’ baseline characteristics are presented in table 1. The baseline characteristics of patients with moderate to severe chronic postsurgical pain are reported in Supplemental Digital Content 3 (https://links.lww.com/ALN/D548).

Table 1.

Baseline and Operative Characteristics

Baseline and Operative Characteristics
Baseline and Operative Characteristics
Fig. 1.

Participant flow diagram. VALIANCE, VAscuLar events In pAtieNts undergoing same-day nonCardiac surgEry.

Fig. 1.

Participant flow diagram. VALIANCE, VAscuLar events In pAtieNts undergoing same-day nonCardiac surgEry.

Close modal

At 90 days, 12.0% (247 of 2,054) of participants met the chronic postsurgical pain definition (i.e., score change of greater than or equal to 1 point in self-reported average pain at the surgical site), of which 93.1% (230 of 247) was new chronic pain (i.e., participants without pain at the surgical site at baseline; table 2). Moderate to severe chronic postsurgical pain (i.e., average pain score greater than or equal to 4 at the surgical site) occurred in 9.0% (184 of 2,054) of participants. Among patients who had chronic postsurgical pain at 90 days, the least pain on average was 1.6 ± 2.0, the worst pain was 5.1 ± 2.3, and average pain was 3.5 ± 1.9 (table 3). The average interference was 2.1 ± 2.2. The average pain severity on all individual components and average interference was greater in patients with worsened chronic pain than those with new chronic pain. Within participants who already had pain in the surgical area preoperatively, 90.6% (374 of 413) saw their pain improved and 4.6% (19 of 413) worsened (Supplemental Digital Content 4, https://links.lww.com/ALN/D548). Supplemental Digital Content 5 (https://links.lww.com/ALN/D548) reports on pain intensity by score (0 to 10) on the Brief Pain Inventory in patients who reported pain at the surgical site at 90 days.

Table 2.

Outcome Incidence at 90 Days (n = 2,054)

Outcome Incidence at 90 Days (n = 2,054)
Outcome Incidence at 90 Days (n = 2,054)
Table 3.

Individual Component Brief Pain Inventory Scores at 90 Days for Patients with Chronic Postsurgical Pain

Individual Component Brief Pain Inventory Scores at 90 Days for Patients with Chronic Postsurgical Pain
Individual Component Brief Pain Inventory Scores at 90 Days for Patients with Chronic Postsurgical Pain

At 90 days, 137 (6.7%) of participants reported taking opioids, and of those, 48.2% (66 of 137) were new users of opioids (i.e., patients who did not report opioid intake at baseline). (table 4). In patients with chronic postsurgical pain and moderate to severe chronic postsurgical pain, the incidence of opioid use at 90 days was 13.4% (33 of 247) and 26.6% (49 of 184), respectively, compared to 5.8% (104 of 1,807) in patients without chronic postsurgical pain.

Table 4.

Pain Medication Consumption at 90 days after Surgery

Pain Medication Consumption at 90 days after Surgery
Pain Medication Consumption at 90 days after Surgery

In the multivariable model analysis, active smoking (adjusted odds ratio, 1.82; 95% CI, 1.20 to 2.76) and undergoing orthopedic (adjusted odds ratio, 4.7; 95% CI, 2.24 to 9.7), plastic (adjusted odds ratio, 4.3; 95% CI, 1.97 to 9.2), breast (adjusted odds ratio, 2.74; 95% CI, 1.29 to 5.8), or vascular surgery (adjusted odds ratio, 2.71; 95% CI, 1.09 to 6.7) were associated with chronic postsurgical pain at 90 days (table 5). We found meaningful differences in ethnicity between participants with and without chronic postsurgical pain, and thus we included ethnicity in the final model. Compared to white patients, patients with Hispanic/Latino ethnicity and First Nations/native persons had statistically significant higher risk of chronic postsurgical pain (adjusted odds ratio, 3.41; 95% CI, 1.68 to 6.9 and adjusted odds ratio, 4.0; 95% CI, 1.05 to 15.4, respectively). Ethnicity remained associated with chronic postsurgical pain despite exploratory analyses adjusting for language, center where patients were recruited, and country. Anxiety/depression was measured using a five-level question from the EQ-5D-5L questionnaire, which was dichotomized (presence/absence) to be included in the multivariable model. We performed exploratory analysis incorporating anxiety/depression as a five-level ranked variable and did not find an association with chronic postsurgical pain. Surgical approach was not included in the final model, given it was relevant to only certain types of surgery that allow for different approaches. The c-statistic for the final model was 0.70 (corrected for optimism 0.65), and the Brier score was 0.099. The calibration curve for this model can be found in Supplemental Digital Content 6 (https://links.lww.com/ALN/D548). No sensitivity analysis was performed because no variable included in the model had more than 5% missing data.

Table 5.

Multivariable Models to Predict Chronic Postsurgical Pain and Moderate to Severe Chronic Postsurgical Pain

Multivariable Models to Predict Chronic Postsurgical Pain and Moderate to Severe Chronic Postsurgical Pain
Multivariable Models to Predict Chronic Postsurgical Pain and Moderate to Severe Chronic Postsurgical Pain

We also assessed the variables in the chronic postsurgical pain model to predict moderate to severe chronic postsurgical pain (table 5). We found that preoperative chronic pain (adjusted odds ratio, 2.82; 95% CI, 1.95 to 4.1), preoperative opioid use (adjusted odds ratio, 1.69; 95% CI 1.08 to 2.66) and active smoking (adjusted odds ratio, 1.71; 95% CI, 1.08 to 2.72) were associated with moderate to severe chronic postsurgical pain. Compared to white patients, patients of non-Asian, nonwhite ethnicities were at a 2- to 5-fold increased risk of moderate to severe chronic postsurgical pain. Orthopedic, plastic, and neurosurgery were significantly associated with greater risk of moderate to severe chronic postsurgical pain. The c-statistic for the multivariable model to predict moderate to severe chronic postsurgical pain was 0.77 (corrected for optimism 0.71), and the Brier score was 0.075.

After mixed, noncardiac, and nonophthalmological same-day surgery, 12% of participants developed chronic postsurgical pain and 9.0% developed moderate to severe chronic postsurgical pain at 90 days. Opioid use was reported in 6.7% of patients at 90 days, and half was new opioid use. The majority (93.1%) of participants who had chronic postsurgical pain at 90 days had no pain at the surgical site before surgery. Active smoking and ethnicity were the baseline characteristics found to be significantly associated with an increased risk of chronic postsurgical pain at 90 days postoperative; in addition, preoperative chronic pain and opioid use were associated with moderate to severe chronic postsurgical pain. Orthopedic, breast, vascular, and plastic surgery had greater risks of chronic postsurgical pain.

Previous studies have reported various risk factors for chronic postsurgical pain, and although some are reported more commonly, there is significant heterogeneity in the risk factors identified. In a systematic review published in 2021 (n = 15 studies), risk factors of chronic postsurgical pain identified were: age, sex or gender, preoperative pain in the surgical area, and acute postsurgical pain intensity.9  A prospective cohort study of 908 patients undergoing mixed same-day surgery reported a chronic postsurgical pain incidence of 15.3 and 3.2% severe chronic postsurgical pain at 1 yr.27  The chronic postsurgical pain risk factors identified were surgical specialty, preoperative pain, preoperative analgesic use, acute postoperative pain intensity, surgical fear, lack of optimism, and poor preoperative quality of life.

Sex is a commonly reported risk factor for chronic postsurgical pain.9  In our study, differences in biologic sex were seen when comparing baseline characteristics of participants with and without chronic postsurgical pain, but sex was not significantly associated with chronic postsurgical pain in our multivariable model after adjusting for the type of surgery. This could be explained by the fact that some surgical procedures are almost exclusively performed in one of the biologic sexes and may drive the observed effect, because some types of surgery may differ on the risk of chronic postsurgical pain (e.g., breast surgery, gynecology, urology). Our statistical power was limited to further explore these findings in this cohort. Kuck et al.35  reported a similar finding in their multicenter cohort of 1,093 patients who underwent mixed noncardiac surgeries under regional or general anesthesia; in multivariable analyses, preoperative surgical site pain and orthopedic procedures were the only independent risk factors of chronic postsurgical pain at 3 months, whereas female sex, preoperative opioid use, anxiety, and depression were not significantly associated. Lived gender and gender roles have also been reported to affect pain experience.36  We could not, however, explore whether lived gender would have been associated with chronic postsurgical pain differently than biologic sex because of the near complete concordance (99.4%) with biologic sex in our cohort study.36 

We found active smoking to be associated with chronic postsurgical pain but not smoking history. This is consistent with studies that have showed active smoking to be associated with greater pain intensity after surgery.37,38  Several mechanisms have been suggested explaining the association between nicotine and pain, including changes in the endogenous opioid system mediated by exposition to nicotine.39  Preoperative chronic pain is also a common risk factor for chronic postsurgical pain reported in the literature.8,23,25,27,29  However, in our cohort, chronic preoperative pain at the surgical site was not statistically significantly associated with chronic postsurgical pain (defined as an increase of 1 point or more in the Brief Pain Inventory score) but was associated with moderate to severe chronic postsurgical pain (defined as a score greater than or equal to 4 out of 10). In our cohort, a large portion (90.6%) of patients with chronic preoperative pain saw their pain improve after surgery. One potential explanation is that a portion of patients had milder preoperative pain that was resolved by the surgery, whereas those who did not have an improvement saw their pain worsened, resulting in moderate to severe chronic postsurgical pain. We did not have sufficient statistical power to explore models for subtypes of surgeries or procedures.

Ethnicity was significantly associated with chronic postsurgical pain, in particular nonwhite, non-Asian ethnicities, although the number of patients was small, which is a limitation of this study. This finding is, however, consistent with differences in pain intensity among ethnicities that have been reported in the literature.40–42  In a systematic review exploring race differences in postoperative pain and pain management, Black/African American patients experienced greater postoperative pain intensity than white individuals.41  Opioids were more commonly prescribed for pain management to white individuals compared to individuals from Black, Hispanic, and Asian ethnicity.41  This could also be related to socioeconomical status, but socioeconomic data were not collected as part of this study.

In a 2021 systematic review of prediction models for chronic postsurgical pain, all 15 included studies were evaluated to be at high risk of bias.9  According to the Prediction model Risk Of Bias ASsessment Tool (PROBAST) assessment tool, risk of bias was mainly due to lack of model performance evaluation, model overfitting (i.e., high number of events per variable), missing data, and absence of model validation.9,43  The strengths of our results include a large sample from prospectively collected data at multiple centers and mixed noncardiac surgeries. Our centers used consecutive sampling to ensure representativity, and only a small proportion of patients (6.6%) were excluded due to missing pain status. All variables included in our model had less than 5% missing data. We calculated our sample size requirement using a method specifically designed for prediction models to avoid overfitting that is preferrable to using events per variable, which can lead to biased estimates.30,44  Our model performance assessment yielded good discrimination and calibration, and we performed internal validation by bootstrapping to assess for optimism.45 

Our study also has limitations. First, we did not have information available on acute postoperative pain and in our cohort. Acute postoperative pain is common after ambulatory surgery, and poorly controlled acute postsurgical pain may lead to chronic postsurgical pain.9,27  It is also possible that events that occur in the early postoperative course may influence and play a meaningful role in the occurrence of chronic postsurgical pain. Another limitation is that our data did not allow us, at baseline, to distinguish between the intensity of the pain at the surgical site and at other sites in patients with multisite pain, in contrast to pain at 90 days, when only surgical pain was assessed. Pain severity and interference was assessed overall at baseline and therefore could have led to an underestimation of the incidence of worsened persistent pain.

Further, anxiety and depression, which have been identified as risk factors of postoperative pain,8,29,46  were not associated with chronic postsurgical pain in our population. Anxiety/depression was measured using a question from the EQ-5D-5L questionnaire, in which anxiety and depression are assessed in a combined question (i.e., “anxious or depressed”) and the severity is ranked across five levels ranging from “no” to “extremely.” This limited distinction between anxiety and depression is a limitation of this study, and it remains uncertain whether using a validated questionnaire designed specifically for anxiety and/or depression would have yielded different results. However, previous studies in various settings have demonstrated good responsiveness and validity of the EQ-5D-5L for anxiety and depression when compared to questionnaires specific for anxiety and depression.47–51  Another limitation of this study is the absence of data on pain catastrophizing, which has also been reported as a risk factor of chronic postsurgical pain.52 

In conclusion, in our multicenter prospective cohort of patients who underwent mixed noncardiac same-day surgery, chronic postsurgical pain was common at 90 days, with more than 1 in 10 patients developing new chronic postsurgical pain, and 1 in 11 developing moderate to severe chronic postsurgical pain. Opioid consumption persisted at 90 days in a meaningful number of patients. This study provides contemporary data from an international, prospective cohort on chronic postsurgical pain in the setting of same-day surgery. Our findings would benefit from external validation, and a larger sample would also allow for subgroup analyses specific to subtypes of surgery. The VALIANCE cohort is an ongoing large international prospective cohort that will allow to inform further on postoperative pain after same-day surgery. Findings from this study provide valuable information to inform future analyses in VALIANCE and other studies on the important topic of chronic postsurgical pain in same-day surgery.

Research Support

The VALIANCE study was funded by Canadian Institutes of Health Research (Ottawa, Ontario, Canada) grant No. PJT-175108 and Fonds de Recherche du Québec–Santé (Montreal, Quebec, Canada) grant No. 309849 (to Dr. Duceppe).

Competing Interests

Dr. Khanna consults for Medtronic (Dublin, Ireland), Edwards Life Sciences (Irvine, California), Philips Research North America (Cambridge, Massachusetts), Baxter (Deerfield, Illinois), GE Healthcare (Chicago, Illinois), Potrero Medical (Hayward, California), Retia Medical (White Plains, New York), Trevena Pharmaceuticals (Chesterbrook, Pennsylvania), Renibus Therapeutics (Southlake, Texas), Pharmazz Inc. (Willowbrook, Illinois), Fifth Eye Inc. (Ann Arbor, Michigan), and Caretaker Medical (Charlottesville, Virginia). He is also funded with a Clinical and Translational Science Institute–National Institutes of Health/NCTAS KL2 TR001421 award for a trial on continuous postoperative hemodynamic and saturation monitoring. The Department of Anesthesiology at Wake Forest School of Medicine (Winston-Salem, North Carolina) is supported by Edwards Lifesciences under a master clinical trial agreement and receives grant funding from Masimo (Irvine, California) and Medtronic. In addition, Dr. Khanna is on the research committee of the Surviving Sepsis Campaign, co-chairs a joint task force for norepinephrine dosing and formulations for the Society of Critical Care Medicine, and is a founding partner for the BrainX group (Newbury Park, California) with interests in advancements of artificial intelligence–based technology. Dr. d’Aragon receives a clinician–researcher salary award from the Fonds de Recherche du Québec–Santé (Montreal, Quebec, Canada). Dr. Spence is supported by a Clinician Scientist Award from the Canadian Anesthesia Research Foundation (Toronto, Ontario, Canada) and a Mentored Research Award from the International Anesthesia Research Society (San Francisco, California). Dr. Spence has received honoraria from Trimedic Pharmaceuticals (Toronto, Ontario, Canada) and PhaseBio (Devault, Pennsylvania) and research support from AOP Global Health (Vienna, Austria). Dr. Conen received research grants from the Canadian Institutes of Health Research (Ottawa, Ontario, Canada), speaker fees from Servier (Suresnes, France), and advisory board fees from Roche Diagnostics (Indianapolis, Indiana) and Trimedics, all outside of the current study. Dr. Wijeysundera is supported in part by a merit award from the Department of Anesthesiology and Pain Medicine at the University of Toronto (Toronto, Ontario, Canada) and the Endowed Chair in Translational Anesthesiology Research at St. Michael’s Hospital and the University of Toronto. Dr. Choinière received grants from the Canadian Institutes of Health Research. Dr. Carrier is a recipient of a clinician–researcher salary award from the Fonds de Recherche du Québec–Santé and is the holder of the Chaire de Recherche en Médecine Transfusionnelle Fondation Héma-Québec-Bayer de l’Université de Montréal. Dr. Koopman received an in-kind contribution to unrelated scientific research by Werfen B.V. (Barcelona, Spain). Dr. Bhojani receives a clinician–researcher salary award from the Fonds de Recherche du Québec–Santé. Dr. Durand receives a clinician–researcher salary award from the Fonds de Recherche du Québec–Santé. Dr. Pagé received honorarium from Canopy Growth (2020), received a research grant from Pfizer UCL Canada (ended 2021; Quebec, Canada) for work unrelated to the current article, and receives a researcher salary award from the Fonds de Recherche du Québec–Santé. Dr. Devereaux has received grants from Abbott Diagnostics (Green Oaks, Illinois), AOP, AstraZeneca (Wilmington, Delaware), Bayer (Berkeley, California), Boehringer Ingelheim (Ingelheim am Rhein, Germany), Bristol Myers Squibb (New York, New York), Cloud DX (Kitchener, Canada), Coviden, Octapharma (Lachen, Switzerland), Philips Healthcare, Roche Diagnostics, Roche, Siemens (Munich, Germany), Stryker (Portage, Michigan), and Trimedic. He has also participated in advisory board meetings for GlaxoSmithKline (Brentford, United Kingdom), Bayer, Quidel Canada (Ontario, Canada), Trimedic, expert panel meetings with AstraZeneca, Boehringer Ingelheim, and Roche, and international meetings with AOP. Dr. Duceppe received grants from Roche Laboratories and Abbott Diagnostics, outside of the submitted work, and receives a clinician–researcher salary award from the Fonds de Recherche du Québec–Santé. The other authors declare no competing interests.

Supplemental material, https://links.lww.com/ALN/D548

Supplemental Digital Content 1. List of collaborators

Supplemental Digital Content 2. VALIANCE study eligibility criteria

Supplemental Digital Content 3. Baseline characteristics - moderate/severe pain

Supplemental Digital Content 4. Pain trajectories at 90 days

Supplemental Digital Content 5. Pain intensity histograms

Supplemental Digital Content 6. Calibration curve

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Appendix: VALIANCE Study Group

Montreal, Quebec, Canada

Roberta Daila Carling, M.Sc.: Centre Hospitalier de l’Université de Montréal, Montreal, Quebec, Canada – provided central coordination of the study

Catherine Lapointe, M.Sc.: Centre Hospitalier de l’Université de Montréal, Montreal, Quebec, Canada – collected data

Diane Baptiste, B.Sc.: Centre Hospitalier de l’Université de Montréal, Montreal, Quebec, Canada – collected data

Imène Chergui, M.D.: Centre Hospitalier de l’Université de Montréal, Montreal, Quebec, Canada – collected data

Elliott Deligne, D.E.C.: Centre Hospitalier de l’Université de Montréal, Montreal, Quebec, Canada – collected data

Sheherazade Jannat, M.Sc.: Centre Hospitalier de l’Université de Montréal, Montreal, Quebec, Canada – collected data

Myriam Hamtiaux, B.Sc.: Centre Hospitalier de l’Université de Montréal, Montreal, Quebec, Canada – collected data

Juliette Lalonde, M.D.: Centre Hospitalier de l’Université de Montréal, Montreal, Quebec, Canada – collected data

Hamilton, Ontario, Canada

Jacqueline Hare: Juravinski Cancer Centre, Hamilton General Hospital, Hamilton, Ontario, Canada – collected data

Kelly Lawrence, B.A.Sc.: Juravinski Cancer Centre, Hamilton, Ontario, Canada – collected data

Emily Gregus-Juriansz, P.S.: Juravinski Cancer Centre, Hamilton General Hospital, Hamilton, Ontario, Canada – collected data

Krysten Gregus, R.P.N.: Juravinski Cancer Centre, Hamilton General Hospital, Hamilton, Ontario, Canada – collected data

Kristen Lombardo, B.Sc.: Hamilton General Hospital, Hamilton, Ontario, Canada – collected data

Lisa Trombetta: Hamilton General Hospital, Hamilton, Ontario, Canada – collected data

Muammar Abdulrahman: Juravinski Cancer Centre; Hamilton General Hospital, Hamilton, Ontario, Canada – collected data

Antonella Tidy, H.B.Sc.: McMaster University Medical Centre, Hamilton, Ontario, Canada – collected data

Adriana Baranov, M.D.: McMaster University Medical Centre, Hamilton, Ontario, Canada – collected data

Anne Clarke, R.N.: McMaster University Medical Centre, Hamilton, Ontario, Canada – collected data

Sathurthika Selvanayagam, H.B.Sc.: McMaster University Medical Centre, Hamilton, Ontario, Canada – collected data

Sherbrooke, Quebec, Canada

Maxime Tissot-Therrien, M.D.: Centre Hospitalier de l’Université de Sherbrooke, Sherbrooke, Quebec, Canada – served as site coinvestigator

Marie-Pier Bouchard, D.E.C.: Centre Hospitalier de l’Université de Sherbrooke, Sherbrooke, Quebec, Canada – collected data

Julie Belisle, D.E.C.: Centre Hospitalier de l’Université de Sherbrooke, Sherbrooke, Quebec, Canada – collected data

Élaine Carbonneau, D.E.C.: Centre Hospitalier de l’Université de Sherbrooke, Sherbrooke, Quebec, Canada – local site coordination

Dominique Pellerin, M.Sc.: Centre Hospitalier de l’Université de Sherbrooke, Sherbrooke, Quebec, Canada – collected data

Ève-Marie Casavant, D.E.C.: Centre Hospitalier de l’Université de Sherbrooke, Sherbrooke, Quebec, Canada – collected data

Line Côté, D.E.C.: Centre Hospitalier de l’Université de Sherbrooke, Sherbrooke, Quebec, Canada – collected data

Ilyona d’Hervé, D.E.C.: Centre Hospitalier de l’Université de Sherbrooke, Sherbrooke, Quebec, Canada – collected data

Justine Grégoire, D.E.C.: Centre Hospitalier de l’Université de Sherbrooke, Sherbrooke, Quebec, Canada – collected data

Félix Lamontagne, D.E.C.: Centre Hospitalier de l’Université de Sherbrooke, Sherbrooke, Quebec, Canada – collected data

Toronto, Ontario, Canada

Karim S. Ladha, M.D., M.Sc.: Unity Health Toronto–St. Michael’s Hospital, Ontario, Canada – served as site coinvestigator

Janneth Pazmino-Canizares, B.Sc., M.Sc.: Unity Health Toronto–St. Michael’s Hospital, Toronto, Ontario, Canada – collected data

Maya Lota, B.Sc.: Unity Health Toronto–St. Michael’s Hospital, Toronto, Ontario, Canada – collected data

Gabriella Mattina, B.H.Sc., Ph.D.: Unity Health Toronto–St. Michael’s Hospital, Toronto, Ontario, Canada – collected data

Sandra Drozdz, B.A.Sc.: Unity Health Toronto–St. Michael’s Hospital, Toronto, Ontario, Canada – collected data

Tedros Mokonnen, B.Kin.: Unity Health Toronto–St. Michael’s Hospital, Toronto, Ontario, Canada – collected data

Roshni Nayar, B.Sc.: Unity Health Toronto–St. Michael’s Hospital, Toronto, Ontario, Canada – collected data

Zaaria Thomas, B.A.: Unity Health Toronto–St. Michael’s Hospital, Toronto, Ontario, Canada – collected data

Winston-Salem, North Carolina

Jonathan Douglas Jaffe, D.O.: Atrium Wake Forest Baptist Medical Centre, Winston-Salem, North Carolina – served as site coinvestigator

Amelia Eaton, R.N.: Atrium Wake Forest Baptist Medical Centre, Winston-Salem, North Carolina – local site coordination

Lynnette Harris, B.S.N.: Atrium Wake Forest Baptist Medical Centre, Winston-Salem, North Carolina – local site coordination

Evan Youshock, B.S.: Atrium Wake Forest Baptist Medical Centre, Winston-Salem, North Carolina – local site coordination

Sheetal Autade, M.D.: Atrium Wake Forest Baptist Medical Centre, Winston-Salem, North Carolina – collected data

Carter Bell, B.S.: Atrium Wake Forest Baptist Medical Centre, Winston-Salem, North Carolina – collected data

Bethany Bouldin, B.S.: Atrium Wake Forest Baptist Medical Centre, Winston-Salem, North Carolina – collected data

Alexandra Coffield, B.S.: Atrium Wake Forest Baptist Medical Centre, Winston-Salem, North Carolina – collected data

Emily Deschler, B.S.: Atrium Wake Forest Baptist Medical Centre, Winston-Salem, North Carolina – collected data

Nataya Disher, B.S.: Atrium Wake Forest Baptist Medical Centre, Winston-Salem, North Carolina – collected data

Jaclyn Eberting, B.A.: Atrium Wake Forest Baptist Medical Centre, Winston-Salem, North Carolina – collected data

Seth Eller, B.S.: Atrium Wake Forest Baptist Medical Centre, Winston-Salem, North Carolina – collected data

Spencer Faircloth, B.S.: Atrium Wake Forest Baptist Medical Centre, Winston-Salem, North Carolina – collected data

Justin Holbrook, B.S.: Atrium Wake Forest Baptist Medical Centre, Winston-Salem, North Carolina – collected data

Aidan Keleghan, B.S.: Atrium Wake Forest Baptist Medical Centre, Winston-Salem, North Carolina – collected data

Tae Kyong Kim, M.D.: Atrium Wake Forest Baptist Medical Centre, Winston-Salem, North Carolina – assisted with data entry

Tanner Lydic, B.S.: Atrium Wake Forest Baptist Medical Centre, Winston-Salem, North Carolina – collected data

Lakyn Mathis, B.S.: Atrium Wake Forest Baptist Medical Centre, Winston-Salem, North Carolina – collected data

Raleigh McCabe, B.S.: Atrium Wake Forest Baptist Medical Centre, Winston-Salem, North Carolina – collected data

Vida Motamedi, B.S.: Atrium Wake Forest Baptist Medical Centre, Winston-Salem, North Carolina – collected data

Tiye Rahmah, B.S.: Atrium Wake Forest Baptist Medical Centre, Winston-Salem, North Carolina – collected data

Jessica Reeves, R.N.: Atrium Wake Forest Baptist Medical Centre, Winston-Salem, North Carolina – data entry

Abigail Reynolds, B.S.: Atrium Wake Forest Baptist Medical Centre, Winston-Salem, North Carolina – collected data

Rishika Sahajpal, M.D.: Atrium Wake Forest Baptist Medical Centre, Winston-Salem, North Carolina – collected data

Anusha Samant, B.S.: Atrium Wake Forest Baptist Medical Centre, Winston-Salem, North Carolina – collected data

Michael Schellenberg, B.S.: Atrium Wake Forest Baptist Medical Centre, Winston-Salem, North Carolina – collected data

Tanmay Sura, M.B.B.S.: Atrium Wake Forest Baptist Medical Centre, Winston-Salem, North Carolina – collected data

Lily Sykes, B.S.: Atrium Wake Forest Baptist Medical Centre, Winston-Salem, North Carolina – collected data

Rabeya Tahir, B.S.: Atrium Wake Forest Baptist Medical Centre, Winston-Salem, North Carolina – collected data

Spencer Tingey, B.S.: Atrium Wake Forest Baptist Medical Centre, Winston-Salem, North Carolina – collected data

Mert Tore, M.D.: Atrium Wake Forest Baptist Medical Centre, Winston-Salem, North Carolina – collected data

Robert Treadway, B.S.: Atrium Wake Forest Baptist Medical Centre, Winston-Salem, North Carolina – collected data

Sydney Ward, B.S.: Atrium Wake Forest Baptist Medical Centre, Winston-Salem, North Carolina – collected data

Cleveland, Ohio

Richard L. Hofstra, B.A.: Outcomes Research Consortium, Department of Anesthesiology, Cleveland Clinic, Cleveland, Ohio – collected data

Jorge Araujo-Duran, M.D.: Outcomes Research Consortium, Department of Anesthesiology, Cleveland Clinic, Cleveland, Ohio – collected data

Leonardo Marquez-Roa, M.D.: Outcomes Research Consortium, Department of Anesthesiology, Cleveland Clinic, Cleveland, Ohio – collected data

Jose L. Diz Ferre, M.D.: Outcomes Research Consortium, Department of Anesthesiology, Cleveland Clinic, Cleveland, Ohio – collected data

Joshua E. Insler, M.D.: Outcomes Research Consortium, Department of Anesthesiology, Cleveland Clinic, Cleveland, Ohio – collected data

Orkun Kopac, M.D.: Outcomes Research Consortium, Department of Anesthesiology, Cleveland Clinic, Cleveland, Ohio – collected data

Aram Abbas, M.D.: Outcomes Research Consortium, Department of Anesthesiology, Cleveland Clinic, Cleveland, Ohio – collected data

Maria J Corrales-Martinez, M.D.: Outcomes Research Consortium, Department of Anesthesiology, Cleveland Clinic, Cleveland, Ohio – collected data

Catalina Dussan, M.D.: Outcomes Research Consortium, Department of Anesthesiology, Cleveland Clinic, Cleveland, Ohio – collected data

Elyad Ekrami, M.D.: Outcomes Research Consortium, Department of Anesthesiology, Cleveland Clinic, Cleveland, Ohio – collected data

Annie Cipriani, B.A.: Outcomes Research Consortium, Department of Anesthesiology, Cleveland Clinic, Cleveland, Ohio – collected data

Maeve Slife, B.A.: Outcomes Research Consortium, Department of Anesthesiology, Cleveland Clinic, Cleveland, Ohio – collected data

Fabio Rodriguez, M.D.: Outcomes Research Consortium, Department of Anesthesiology, Cleveland Clinic, Cleveland, Ohio – collected data

Michael Mosqueda, B.S.: Outcomes Research Consortium, Department of Anesthesiology, Cleveland Clinic, Cleveland, Ohio – collected data

Richard Gatt, B.A.: Outcomes Research Consortium, Department of Anesthesiology, Cleveland Clinic, Cleveland, Ohio – collected data

Rotterdam, The Netherlands

Conny Reimelink, M.Sc.: Maasstad Ziekenhuis Hospital, Rotterdam, The Netherlands – collected data

Ankie Koopman, M.D., Ph.D.: Maasstad Ziekenhuis Hospital, Rotterdam, The Netherlands – collected data