# Pain Scores and Opioid Consumption after Robotic-assisted Abdominal Surgery: A Single Centre Historical Cohort Study

Maziar M Nourian, Theodora Wingert, Amélie Delaporte, Tristan Grogan, Brenton Alexander, Nancy M Boulos, S Rahman, Natale Naim, Zarah D Antongiorgi, J Moon, et al.

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Maziar M Nourian, Theodora Wingert, Amélie Delaporte, Tristan Grogan, Brenton Alexander, et al. Pain Scores and Opioid Consumption after Robotic-assisted Abdominal Surgery: A Single Centre Historical Cohort Study. Anaesthesia Critical Care & Pain Medicine, 2025, pp.101708. 10.1016/j.accpm.2025.101708. hal-05424147

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Pain Scores and Opioid Consumption after Robotic-assisted Abdominal Surgery: A Single Centre Historical Cohort Study

Maziar M. Nourian, Theodora Wingert, Amelie Delaporte, Tristan Grogan, Brenton Alexander, Nancy M. Boulos, Siamak Rahman, Natale Naim, Zarah D Antongiorgi, Jane Moon, Kevork Kazanjian, Valentina Rodriguez-Triana, Karim Chamie, Maxime Cannesson, Helene Beloeil, Alexandre Joosten

<figure><img src="image_2.png" /></figure>

PII: S2352-5568(25)00240-1

DOI: https://doi.org/10.1016/j.accpm.2025.101708

Reference: ACCPM 101708

To appear in: Anaesthesia Critical Care & Pain Medicine

Accepted Date: 18 November 2025

Please cite this article as: Nourian MM, Wingert T, Delaporte A, Grogan T, Alexander B, Boulos NM, Rahman S, Naim N, Antongiorgi ZD, Moon J, Kazanjian K, Rodriguez-Triana V, Chamie K, Cannesson M, Beloeil H, Joosten A, Pain Scores and Opioid Consumption after Robotic-assisted Abdominal Surgery: A Single Centre Historical Cohort Study, Anaesthesia Critical Care and Pain Medicine (2025), doi: https://doi.org/10.1016/j.accpm.2025.101708
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© 2025 Published by Elsevier Masson SAS on behalf of Société française d'anesthésie et de réanimation (SFAR).
# Pain Scores and Opioid Consumption after Robotic-assisted Abdominal Surgery:

## A Single Centre Historical Cohort Study

*Running Title:* Pain Scores and Opioid consumption in robotic abdominal surgery

Maziar M. Nourian¹ MD, Theodora Wingert¹ MD, Amelie Delaporte¹ MD, Tristan Grogan² MS, Brenton Alexander³ MD, Nancy M. Boulos¹ MD, Siamak Rahman¹ MD, Natale Naim¹ MD, Zarah. D Antongiorgi¹ MD, Jane Moon¹ MD, Kevork Kasanjian⁴ MD, Valentina Rodriguez-Triana⁵ MD, Karim Chamie⁶ MD, Maxime Cannesson¹ MD PhD, Helene Beloeil⁷ MD PhD, Alexandre Joosten¹ MD PhD

**Authors Institutional Affiliations:**

1. Department of Anesthesiology & Perioperative Medicine, David Geffen School of Medicine at UCLA, University of California Los Angeles, California, USA.

2. Department of Medicine Statistics Core, David Geffen School of Medicine, University of California Los Angeles, California, USA

3. Department of Anesthesiology & Perioperative Medicine, University of California San Diego, California, USA

4. Department of Surgery, University of California, Los Angeles (UCLA) David Geffen School of Medicine, Los Angeles, California.

5. Department of Obstetrics and Gynecology, David Geffen School of Medicine, University of California, Los Angeles

6. Department of Urology, UCLA Medical Center, Los Angeles.

7. CHU Rennes, Univ Rennes, Inserm, OSS 1242, CIC 1414, Anesthesia and Intensive Care Department, F-35000 Rennes, France
**Correspondence to:** Alexandre Joosten, Department of Anesthesiology & Perioperative Medicine, David Geffen School of Medicine, University of California Los Angeles, 757 Westwood Plaza, Los Angeles, California 90095, USA

Email: ajoosten@mednet.ucla.edu Phone: +1 310 962 7337

**Clinical Trial Registry and Number:** Not Applicable

# Abstract

# Background

Effective postoperative pain control is essential for recovery. Pain has long been considered the “fifth vital sign” in the United States, making its assessment routinely documented. However, data on pain and opioid consumption following robotic-assisted surgeries remain limited despite their widespread adoption. This study evaluated pain scores and opioid consumption in patients undergoing intermediate-risk robotic-assisted abdominal, urological, or gynecological procedures. We hypothesized that pain would be minimal (numerical rating scale (NRS < 4)) and opioid use low (< 5 morphine milligram equivalents (MME) within 24 h post-surgery).

# Methods

This historical cohort study included consecutive adult patients who underwent intermediate-risk robotic-assisted abdominal, urological, or gynecological surgery between 2013 and 2024. Co-primary endpoints were the maximal NRS and total opioid consumption (MME) at the end of the day of surgery (POD 0). Secondary endpoints included maximal NRS and total MME at the end of postoperative day 1 (POD 1) and the incidence of nausea and vomiting (PONV) in the post-anesthesia care unit.

# Results

Among 9,978 cases (57% urological, 34% gynecological, 9% abdominal), median [Q1–Q3]
maximal NRS and total MME were 7 [5–8] and 10 mg [5–15] on POD 0, and 6 [4–7] and 8 mg [0–12] on POD 1. PONV occurred in 11% of patients

## Conclusions

Contrary to the initial hypothesis, patients undergoing intermediate-risk robotic-assisted procedures experienced higher-than-expected pain scores and moderate opioid consumption on the day of surgery, emphasizing the need to optimize multimodal analgesic strategies for robotic surgery in our center.

**Keywords:** Robotic surgery, opioid consumption, pain scores, nausea and vomiting, laparoscopic surgery

## Introduction

Postoperative pain is common and often underestimated despite its significant impact on recovery [¹, ²]. Severe pain after surgery is linked to lower patient satisfaction, delayed rehabilitation, longer hospital stays, and higher morbidity and mortality [³⁻⁵]. Risk factors vary by patient and procedure, yet few guidelines exist for surgery-specific pain management [⁵, ⁶].

To create a procedure-specific post-operative pain management protocol, pain should be studied and documented for each surgery type. Anecdotally, it has been reported that robotic-assisted surgery has advantages over traditional techniques in terms of improved pain and physical recovery [⁷, ⁸]. Unfortunately, there is limited data on postoperative pain following robotic-assisted surgeries despite the growing popularity of this modality [⁹, ¹⁰]. At the University of California, Los Angeles (UCLA), a high volume of intermediate risk robotic-assisted abdominal surgeries is performed per year. In this patient population, the standard of care is to have surgeons infiltrate local anesthesia near port sites and to leave opioid administration to the discretion of the anesthesiologist.
This retrospective study aims to evaluate pain scores and opioid use in patients undergoing intermediate-risk robotic-assisted abdominal surgery. We hypothesize that these patients experience minimal pain (Numerical rating scale (NRS < 4) and require low postoperative opioid doses (total MME < 5).

# Patients and Methods

## Ethics and trial design

This single-center retrospective study was conducted at UCLA and approved by the Office of the Human Research Protection Program (IRB-25-0203, approved February 18, 2025). Deidentified data were obtained from consecutive patients who underwent major non-cardiac surgery at Ronald Reagan UCLA Medical Center between 2013 and 2025. Data collection was sourced from electronic health records from the UCLA Perioperative Data Warehouse [¹¹]. This study adheres to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines.

## Patients

We studied consecutive adult patients who underwent intermediate risk robotic-assisted abdominal surgery (either abdominal, urological, and/or gynecologic) from 2013 to 2024 at Santa Monica and Ronald Regan UCLA Medical Centers. Patients were excluded if they underwent emergency surgery. Intermediate risk abdominal surgeries are defined in this
guidelines paper and include mainly hysterectomy, prostatectomy, nephrectomy, cholecystectomy, and colorectal surgery [¹²]. Additional demographic and clinical data collected included: age, sex, body mass index (BMI), ASA score, type of surgery, pre-operative opioid use, chronic pain history, and pre-operative opioid use. Anesthesia-specific data collected included the use of epidural or regional block, analgesics administered intraoperatively, including adjuvants, and fluid and hemodynamic management strategy.

All postoperative pain scores and opioid consumption information were collected in the postoperative anesthesia care unit (PACU), postoperative day 0 (day of the surgery), postoperative day 1 (POD 1), and postoperative day 2 (POD 2). For the purposes of this study, the day of the surgery is defined as up to midnight within the same day of the surgery; POD 1 is the following 24 hours after midnight of the day of surgery, and so on. Additional postoperative variables collected included: post-operative nausea and vomiting (PONV) occurrence in the PACU, length of stay in the PACU and in hospital, incidence of myocardial infarction, and in-hospital mortality rate at 30 days post-surgery.

Each patient underwent standard preoperative screening and received general endotracheal anesthesia with the induction and maintenance medications at the discretion of the anesthesia team. Any thoracic or epidural catheters were placed preoperatively, and any Transversus Abdominal Plane (TAP)/Quadratus Lumborum (QL) blocks were performed post-induction prior to incision. Standard ASA monitors were used for each case, and processed EEG was used at the discretion of the anesthesiologist. Nociception monitoring was never used. Preoperative anxiolysis with midazolam is classical in the United States, unless contraindicated (e.g., severe frailty, high delirium risk, or hemodynamic instability).
Postoperative pain management followed standard institutional protocols and was primarily nurse-driven. Intravenous hydromorphone was the main opioid used in the PACU and immediate postoperative period (POD 0), administered as nurse-titrated boluses based on serial pain assessments. Patient-controlled analgesia (PCA) is not routinely used and is at the discretion of the anesthesiologist in charge of the patient. Once oral intake was resumed (typically on POD 1), oral oxycodone (solution or tablet) became the preferred opioid, administered on demand by nursing staff according to standardized pain reassessment protocols.

Acetaminophen was administered systematically to > 90% of patients either pre-incision or before skin closure and then scheduled at regular intervals (every 6–8 hours) postoperatively.

NSAID use (ketorolac) was left to surgical discretion and was uncommon, reflecting variability in surgeon preference and perceived bleeding risk.

## Variables and data handling

All perioperative variables were extracted from the Epic-based UCLA Perioperative Data Warehouse (PDW), which aggregates structured fields from the anesthesia record, flowsheets, the medication administration record (MAR), and PACU/ward nursing documentation.

## Time windows.

* **PACU:** from PACU arrival to PACU discharge.

* **POD 0:** from PACU arrival to 23:59 on the day of surgery.

* **POD 1:** from 00:00 to 23:59 on postoperative day 1.

(These operational definitions are applied consistently across pain and opioid variables)
Pain intensity was assessed by licensed nursing staff using the 0–10 numerical rating scale (0 = no pain, 10 = worst pain) at standard intervals (PACU routine assessments and after analgesic administration; ward reassessments per protocol) and as clinically indicated. For each period (PACU, POD 0, POD 1), we derived the maximal NRS as the highest recorded value in that period. If a period contained no NRS documentation, that patient-period was treated as missing for NRS summaries and excluded from that period’s NRS analyses; no imputation was performed.

For each opioid administration, the EHR applies a route-specific morphine milligram equivalent (MME) multiplier from an institutional, pharmacy-validated conversion library (consistent with widely used conversion schemes) to generate oral morphine equivalents. We aggregated MMEs within each study period (PACU, POD 0, POD 1). Intraoperative administrations are not included in postoperative MME totals except those occurring after PACU arrival.

Postoperative nausea and vomiting (PONV) was defined as any documented episode of nausea or vomiting, or the administration of rescue antiemetics in the PACU flowsheet. This variable is routinely documented by PACU nurses in the EHR. As with pain scores, if no PONV documentation was available, the data for that period were treated as missing without imputation.

## Primary endpoints:

The co-primary endpoints were the maximal pain scores recorded using the numerical rating scale (NRS) and opioid consumption expressed in milligrams or morphine equivalent (MME) at the end of the day of surgery (from PACU arrival to 11:59 pm on the day of the surgery).

The NRS is a scale ranging from 0 to 10, where 0 means no pain and 10 equals the worst possible pain. MME conversion factors were automatically calculated within the electronic
medical record and follow the CONSORT classification of opioids for equivalent conversions
[¹³]. For interpretability, we pre-defined POD 0 opioid consumption *a priori* as low (<5 MME),
moderate (5–15 MME), high (15-50 MME), and very high (> 50 MME) based on prior
prospective cohorts-based multimodal analgesia protocols for minimally invasive procedures.
Expected 24-hour opioid requirements after minimally invasive intermediate-risk procedures
are generally low, typically in the range of 0–5 mg MME, when multimodal analgesia is
implemented, as reported in prospective postoperative pain cohort studies [¹, ¹⁰].

Secondary endpoints

Secondary endpoints included pain score and total MME at PACU and POD 1 (0:00 am the day after surgery till 23:59 pm). Additional secondary endpoints included: PONV in PACU, length of stay in PACU, incidence of MI, and mortality rates.

Exploratory endpoints

All perioperative data are also compared between the three main types of surgery (urological, gynecological, and abdominal) and shown for hypothesis-generating future studies.

Statistical Analysis

Our primary objective was descriptive: to characterize postoperative pain (NRS) and
opioid exposure (MME) after intermediate-risk robotic surgery. No formal hypothesis testing
was planned beyond exploratory group comparisons. Accordingly, the analysis prespecified
summary statistics (median [Q1–Q3] for continuous/ordinal variables; counts and percentages
for categorical variables) and used nonparametric tests as appropriate for the data scale and
skewed distributions.

Specifically, differences between surgical types (urological, gynecological, abdominal) were evaluated using the Kruskal-Wallis test for continuous variables and $\chi^2$ or Fisher's exact tests for categorical variables. No regression modeling or correlation analyses were performed.

This approach maintains the study's descriptive intent while allowing transparent exploration of potential differences between surgical groups, which may inform future hypothesis-driven studies. All statistical analyses were conducted using IBM SPSS Statistics for Windows, Version 29.0 (IBM Corp., Armonk, NY, USA). A *p*-value of < 0.05 was considered statistically significant.

## Results

During the study period, a total of 10,006 surgeries were performed. Twenty-eight emergency surgeries were excluded (Figure 1), resulting in 9,978 patients included in the analysis. Baseline demographic characteristics are presented in Table 1. The median patient age was 60 [49-68] years, and 49% of the patients were female. Among the robotic-assisted procedures, 57% were urological, 34% gynecological, and 9% abdominal. The overall incidence of preoperative chronic pain in the study cohort was 6.7%, and 2% of patients were on opioids preoperatively. Most patients (74%) received midazolam before induction of anesthesia.

Regional anesthesia (TAP/QL blocks, epidurals) techniques were utilized in 4.7% of patients. Roughly 17% of patients did not receive any opioids intraoperatively (opioid free anesthesia), and at least two continuous adjuvants, among lidocaine, ketamine, magnesium, or dexmedetomidine, were used in 25% of patients (multimodal analgesia). All patients received at least one antiemetic drug intraoperatively. PONV was documented in 11% of patients in the
PACU, based on nursing documentation of nausea, emesis, or rescue antiemetic administration. All patients received acetaminophen (either preoperative or before skin closure), while only 20% of patients received intraoperative ketorolac. Fentanyl and hydromorphone were the two most commonly used opioids intraoperatively (83% and 68%, respectively). Patient-controlled analgesia with hydromorphone was used for 116 patients (1.2%) (Table 2).

The maximum pain score (median [Q1-Q3]) on the day of surgery was 7 [5-8], and the total morphine milligram equivalents (MME) administered was 10 mg [5-15]. No patients required more than 50 MME in the post-anesthesia care unit (PACU), while 150 patients (2%) received more than 50 MME by the end of the day of surgery. The maximum pain score in the PACU was 6 [3-7], and the total MME in the PACU was 8 mg [0-10] (Table 3). Maximum pain scores and MME across all time periods are shown in Figures 2A-2B, with data stratified by surgical type in Figures 3A-3B.

Given the 10-year study duration, we compared analgesia practices and outcomes between an early (2013–2018) and a recent (2019–2024) period. Over time, total POD 0 opioid exposure decreased modestly (median [Q1–Q3] 11 [5–18] vs. 8 [5–13], *p* < 0.001), driven by increased oral oxycodone use and slightly lower iv hydromorphone doses. Despite these changes, pain intensity remained similar: POD 0 maximal NRS 7 [5–8] vs. 6 [5–8] (*p* = 0.305), POD 1 maximal NRS 6 [3–7] vs. 6 [4–7] (*p* = 0.621). Regional anesthesia, NSAID use, and PACU PONV rates were unchanged. A full summary of these results is provided in **Supplemental Table 1**.

**Supplemental Table 2** describes pain scores and opioids used during POD 1 (as defined by the period from midnight on the first postoperative day to 11:59 pm the same day. During POD1, the maximal NRS was 6 [4-7], and total MME was 8 mg [0-12], and 546 (6%) of patients had MME above 50.
In Supplemental **Table 3-5**, all variables are shown per type of surgery for exploring potential clinical differences among them and for hypothesis-generating future trials.

Lastly, myocardial infarction and inpatient mortality were extremely rare in this patient population (0.4% and 0.1%, respectively).

Discussion

This single-center retrospective analysis of postoperative pain and opioid use after
intermediate-risk robotic-assisted abdominal surgery highlights three key findings. First, pain
scores and opioid consumption were higher than expected. Second, regional or multimodal
analgesia was rarely used, despite support in the literature [¹⁴]. Third, despite substantial
intraoperative opioid use, PACU nausea and vomiting rates were low - likely due to effective
prophylaxis. Overall, this manuscript helps characterize the expected postoperative pain
intensity and opioid use trajectory of a variety of intermediate-risk robotic-assisted abdominal
surgeries in a single-center US academic center. In this perspective, our findings contribute to
the growing body of literature on postoperative pain and opioid utilization following robotic-
assisted abdominal surgery [¹⁰].

Despite the minimally invasive nature of these robotic procedures, patients in our study reported relatively high pain intensity in the immediate postoperative period, with a median maximal NRS score of 6 in the PACU, increasing slightly to 7 on the day of surgery, and decreasing slightly on POD 1. These pain scores are notably higher than those reported in previous studies of similar robotic procedures, where NRS values typically remain below 5 within the first 24 hours [¹⁰, ¹⁵⁻¹⁸].
Opioid consumption mirrored the pain trajectories, with a median MME of approximately 10 mg on POD 0 and 8 mg on POD 1. Although total opioid consumption may appear low in absolute terms, it actually exceeds typical benchmarks for intermediate-risk minimally invasive surgery, where expected 24-hour opioid requirements are generally < 5 MME under guideline-based multimodal analgesia. In our cohort, median opioid consumption was approximately double this threshold, suggesting a moderate level of opioid use despite conservative administration protocols. The apparent mismatch between high maximal NRS scores and moderate opioid use likely reflects a combination of nurse-driven protocols, limited PCA use, and possibly a degree of undertreatment in the context of opioid stewardship efforts. These values suggest a relatively high reliance on opioids during the early postoperative period, which may reflect both patient-reported pain intensity and prevailing institutional prescribing practices. Comparisons with published data show wide variability in opioid use following robotic surgery, with reported 24-hour MME values ranging from as low as 2-3 for hysterectomy to as high as 28 for colorectal procedures [¹⁷]. The opioid use observed in our cohort aligns more closely with the latter, potentially highlighting differences in surgical complexity, patient populations, or perioperative pain management protocols. However, in our study cohort, the type of robotic surgery does not seem to influence pain score and opioid consumption. Several factors may contribute to the observed patterns. Robotic-assisted abdominal surgery, while less invasive than open techniques, still involves significant tissue manipulation, pneumoperitoneum, and port-site trauma—factors known to influence postoperative pain. Moreover, variations in intraoperative analgesic techniques, use of multimodal analgesia, and lack of standardized protocols may further account for differences in opioid requirements. Interestingly, few patients in our study required very high opioid doses (>50 MME), and pain scores declined progressively after POD 1, indicating that the immediate postoperative period represents a critical window for effective pain control. These findings
underscore the importance of targeted analgesic strategies during the first 24 - 48 hours, such as scheduled multimodal non-opioid analgesics, regional blocks, and individualized opioid titration [¹⁴].

It is also worth noting that opioid consumption may be influenced by institutional culture and broader healthcare trends. In the U.S., emphasis on pain as the “fifth vital sign” may have inadvertently contributed to more liberal opioid prescribing, particularly in the perioperative setting [¹⁹]. In contrast, studies from European centers often report lower opioid use, potentially reflecting more conservative pain management approaches, differences in patient expectations, or differences in intraoperative pain management [¹⁰]. Indeed, in a similar cohort of urologic robotic cases conducted in Europe, regional anesthesia was used in 12% of cases, and most patients (60%) received at least two intraoperative analgesic adjuvants [¹⁰]. The use of multimodal analgesia and increasing protocolized perioperative pain practices may be effective in reducing opioid usage [¹, ¹⁴, ²⁰⁻²⁶]. The reference to the ‘fifth vital sign’ culture in the U.S. was intended to contextualize practice patterns rather than to imply excessive opioid administration in this cohort. While total opioid consumption in our study was moderate in absolute terms, it exceeded expected benchmarks for minimally invasive procedures (< 5 MME) and thus reflects a relatively liberal use compared with ERAS and PROSPECT recommendations. At the same time, postoperative opioid administration was largely nurse-driven and bolus-based, with minimal PCA use, likely limiting escalation despite high pain scores. This pattern reflects a transitional state in perioperative practice, where the cultural emphasis on aggressive pain control persists, but is increasingly tempered by opioid stewardship efforts. The continued predominance of oral oxycodone in our postoperative protocols also warrants mention. Although widely prescribed in the US, oxycodone carries a higher abuse liability than several alternative short-acting opioids and has been central to national concerns surrounding prescription opioid misuse. Its persistent use in our institution
likely reflects entrenched prescribing habits rather than evidence-based preference. This further emphasizes the need for strengthened opioid stewardship efforts, alongside broader adoption of multimodal non-opioid analgesia.

Postoperative NSAID use was extremely low in our cohort, reflecting a surgeon-driven restriction based on concerns about bleeding, renal effects, and anastomotic complications. This practice pattern likely contributed to the high postoperative pain scores and moderate opioid consumption observed, highlighting a gap between current practice and guideline-based multimodal analgesia recommendations. Importantly, these analgesic practices differ substantially from ERAS or PROSPECT guideline pathways, in which NSAIDs and other non-opioid adjuvants are routinely administered as part of standardized multimodal protocols. Consequently, while our findings accurately reflect contemporary U.S. institutional practice, their generalizability to centers applying guideline-based multimodal analgesia is limited. In such settings, both pain scores and opioid consumption would likely be lower, as shown in prior prospective cohorts.

The incidence of PONV in the PACU was 11% based on documented episodes of nausea, vomiting, or antiemetic administration. While this incidence appears numerically lower than some reports in the literature [¹⁰, ²⁷, ²⁸], it is likely attributable to the systematic use of effective intraoperative prophylactic antiemetic measures [¹⁴] and to the low precision inherent to nurse-driven documentation in the electronic health record, which more reliably captures vomiting and rescue treatment than nausea. As such, the reported incidence probably reflects episodes of vomiting rather than the true combined rate of nausea and vomiting.

This study has several limitations. First, as a single-center retrospective analysis, generalizability may be limited, although pain management practices are likely similar across many U.S. institutions. Second, anesthesia and analgesia protocols were not standardized, and
data on discharge opioid prescriptions and conversion to open surgery were not collected.
However, the incidence of conversion is likely low, given that 40% of patients were discharged
the same day. Third, intraoperative variables such as port site number, local anesthetic use, and
pneumoperitoneum pressure were also unavailable despite the effective benefit on pain scores
[²⁹]. Fourth, MME calculations ended at 11:59 p.m. daily, potentially biasing results toward
higher opioid use in morning surgeries. Fifth, the 10-year study period may have introduced
temporal heterogeneity. Although iv opioid use modestly declined in recent years, likely
reflecting growing emphasis on opioid stewardship, there was no parallel improvement in
postoperative pain or PONV rates. This suggests that reducing opioid doses alone, without
broader adoption of structured multimodal or regional analgesia protocols, may be insufficient
to meaningfully enhance recovery. Finally, we did not perform adjusted analyses for patient-
level covariates, as the study's primary objective was descriptive, to characterize typical
postoperative pain intensity and opioid use patterns after robotic-assisted intermediate-risk
procedures rather than identify their predictors.

## Conclusion

Contrary to our initial hypothesis, patients undergoing intermediate-risk robotic-assisted
procedures experienced higher-than-expected postoperative pain scores and moderate opioid
consumption during the first 24 hours after surgery. These findings highlight important gaps
between current practice and evidence-based multimodal analgesia, particularly given the very
limited use of NSAIDs and regional anesthesia techniques in our institution. The continued
predominance of oral oxycodone as the postoperative opioid is also noteworthy. Although widely
used in the US, oxycodone carries a higher abuse liability than several alternatives and has been
central to national concerns regarding prescription opioid misuse. Its persistent use likely reflects
longstanding prescribing habits rather than evidence-based preference, further underscoring the
need for strengthened opioid stewardship. Overall, this work identifies several major opportunities
for improvement in perioperative analgesic care at our center. A coordinated, multidisciplinary
quality-improvement initiative involving both surgeons and anesthesiologists is warranted to
expand multimodal analgesia, standardize NSAID use when safe, incorporate regional techniques
more routinely, and rationalize opioid selection. The present data provide a robust baseline from
which such institutional changes can be implemented, measured, and refined. Without proactive
local efforts, meaningful improvements in analgesia and opioid stewardship are unlikely to occur.

**Author's Contribution:**

Study design/conception: MN, TW, AD, BA, NB, SR, NN, ZA, JM, KK, VT, KC, MC, HB, AJ

Study conduct: MN, TW, AD, BA, NB, SR, NN, ZA, JM, KK, VT, KC, MC, HB, AJ

Data analysis: TW, TG

Writing paper: MN, A.J

Editing paper: All authors

Revising paper: All authors

**Funding Disclosure:** This work was supported by *departmental sources*

**Authors' declarations of interests:** The other authors declare that they have no conflicts of interest concerning this article.

**Acknowledgments:** We wish to thank all the anesthesiology and the surgical teams of the Ronald Reagan and Santa Monica UCLA medical centers for their help in this study.

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18 Gereta S, Ancha N, Eldin M, Blankenship E, Wolf JS Jr, Osterberg EC, et al. Decreased Opioid Use and Equivalent Pain Score Outcomes After Robotic Urologic Surgery Using a Simplified Opioid Minimization Protocol: A Safety-Net Hospital Experience. *Urol Pract* 2024; **11**: 283-92

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**Figure Legends**

<caption>**Figure 1:** Consort flowchart</caption>

<figure><img src="image_3.png" /><figcaption>**Figure 2 A:** Boxplots representing the distribution of maximal pain score at PACU, POD 0, and POD 1</figcaption></figure>

<caption>**Figure 2 B:** Boxplots representing the distribution of opioid consumption reported as morphine milligrams equivalent (MME) at PACU, POD 0, and POD 1.</caption>

Legend for Figures 2 A & 2 B: PACU: post-anesthesia care unit, POD 0: postoperative day 0, POD 1: postoperative day 1, MME: morphine milligram equivalents

The solid line represents the median value, the box edges represent the first and third quartiles, the whiskers represent the min-max values, and the dots or crosses represent the more extreme value (outliers)
<figure><img src="image_4.png" /><figcaption>Figure 2A</figcaption></figure>

<figure><img src="image_5.png" /><figcaption>Figure 2B</figcaption></figure>

**Figure 3A:** Boxplots representing the distribution of maximal pain score at PACU, POD 0, and POD 1 between the three types of surgery (urological-gynecological and abdominal) at PACU, POD 0, and POD 1.

**Figure 3B:** Boxplots representing the distribution of opioid consumption reported as milligrams morphine equivalent (MME) at PACU, POD 0 and POD 1 between the three types of surgery (urological-gynecological and abdominal) at PACU, POD 0 and POD 1 (log scale)

Legend for Figures 3 A & 3 B: PACU: post-anesthesia care unit, POD 0: postoperative day 0, POD 1: postoperative day 1, MME: morphine milligram equivalents.

The solid line represents the median value, the box edges represent the first and third quartiles, the whiskers represent the min-max values, the dots or crosses represent the more extreme value (outliers)

<figure><img src="image_6.png" /><figcaption>Figure 3A</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>Figure 3B</figcaption></figure>
<table><caption>Table 1: Demographic characteristics of the study cohort</caption><thead><tr><th>Variables</th><th>N = 9978</th></tr></thead><tbody><tr><td>Age, years</td><td>60 [49-68]</td></tr><tr><td>Sex, female</td><td>4925 (49%)</td></tr><tr><td>American Society of Anesthesiologist (ASA), N (%)</td><td></td></tr><tr><td>■ ASA 1 / 2</td><td>427 (4%) / 5477 (55%)</td></tr><tr><td>■ ASA 3 / 4</td><td>3949 (40%) / 125 (1%)</td></tr><tr><td>Body mass index (kg.m<sup>-2</sup>)</td><td>27 [24-30]</td></tr><tr><td>Ethnicity, N (%)</td><td></td></tr><tr><td>■ White, Caucasian</td><td>7567 (76%)</td></tr><tr><td>■ Mexican, Hispanic or Latino</td><td>1017 (10%)</td></tr><tr><td>■ Choose not to answer</td><td>1394 (14%)</td></tr><tr><td>Chronic arterial hypertension, N (%)</td><td>4211 (42%)</td></tr><tr><td>Illicit drug use, N (%)</td><td>384 (3.8%)</td></tr><tr><td>Alcohol use, N (%)</td><td>232 (2.3%)</td></tr><tr><td>Chronic pain, N (%)</td><td>673 (6.7%)</td></tr><tr><td>Numerical rating scale (NRS) before surgery</td><td>0 [0-0]</td></tr><tr><td>Patients with NRS &gt; 3, N (%)</td><td>409 (4%)</td></tr><tr><td>Preoperative opioid use, N (%)</td><td>193 (2%)</td></tr><tr><td>Preoperative scopolamine patch, N (%)</td><td>1588 (16%)</td></tr><tr><td>Preoperative midazolam, N (%)</td><td>7141 (72%)</td></tr><tr><td>Classification of Robotic surgery, N (%)</td><td></td></tr><tr><td>■ Urology <sup>§</sup></td><td>5703 (57%)</td></tr><tr><td>■ Gynecology <sup>#</sup></td><td>3360 (34%)</td></tr><tr><td>■ Abdominal and other <sup>&amp;</sup></td><td>915 (9%)</td></tr></tbody></table>

Data are expressed in median [Q1-Q3] or number (%)

ASA: American society of anesthesiologists

$ includes mainly prostatectomy, nephrectomy, ureterectomy, pyeloplasty

# includes mainly hysterectomy, salpingectomy, myomectomy.

& includes mainly colorectal, cholecystectomy, hiatal hernia repair
<table><caption>Table 2: Intraoperative data of the study cohort</caption><thead><tr><th>Variables</th><th>N = 9978</th></tr></thead><tbody><tr><td>Duration of surgery (min)</td><td>253 [209-306]</td></tr><tr><td>General anesthesia, N (%)</td><td>9978 (100%)</td></tr><tr><td>■ Sevoflurane for maintenance</td><td>8235 (83%)</td></tr><tr><td>■ Propofol for maintenance</td><td>1743 (18%)</td></tr><tr><td>Regional anesthesia</td><td></td></tr><tr><td>■ TAP or Quadratus Lumborum block, N (%)</td><td>459 (5%)</td></tr><tr><td>■ Lumbar epidural, N (%)</td><td>6 (0%)</td></tr><tr><td>Opioid free analgesia, N (%)</td><td>1718 (17%)</td></tr><tr><td>I.V. adjuvants, N (%)</td><td>2497 (25%)</td></tr><tr><td>■ Lidocaine infusion</td><td>74 (0.7%)</td></tr><tr><td>■ Ketamine infusion/bolus</td><td>1739 (17%)</td></tr><tr><td>■ Magnesium sulfate infusion</td><td>39 (0.4%)</td></tr><tr><td>■ Dexmedetomidine infusion</td><td>679 (7%)</td></tr><tr><td>At least two IV analgesic adjuvants <sup>s</sup>, N (%)</td><td>2269 (23%)</td></tr><tr><td>Nausea and vomiting prevention, N (%)</td><td></td></tr><tr><td>■ Dexamethasone</td><td>8847 (89%)</td></tr><tr><td>■ Ondansetron</td><td>9469 (95%)</td></tr><tr><td>■ Droperidol</td><td>1595 (16%)</td></tr><tr><td>■ Any anti-emetic drug</td><td>9886 (99%)</td></tr><tr><td>Opioids & Analgesics, N (%)</td><td></td></tr><tr><td>■ Fentanyl</td><td>8243 (83%)</td></tr><tr><td>■ Remifentanil</td><td>2913 (29%)</td></tr><tr><td>■ Hydromorphone</td><td>6799 (68%)</td></tr><tr><td>■ Methadone</td><td>66 (0.7%)</td></tr><tr><td>■ Acetaminophen <sup>#</sup></td><td>9978 (100%)</td></tr><tr><td>■ Ketorolac</td><td>1959 (20%)</td></tr><tr><td>Hemodynamic and fluid management</td><td></td></tr><tr><td>■ Heart rate (beat.min<sup>-1</sup>)</td><td>70 [64-77]</td></tr><tr><td>■ Mean arterial pressure (MAP) (mmHg)</td><td>84 [78-90]</td></tr><tr><td>■ Patients with MAP < 65 mmHg, N (%)</td><td>2870 (29%)</td></tr><tr><td>■ Ephedrine, N (%)</td><td>4343 (44%)</td></tr><tr><td>■ Phenylephrine, N (%)</td><td>4579 (46%)</td></tr><tr><td>■ Norepinephrine, N (%)</td><td>84 (0.8%)</td></tr><tr><td>■ Any vasopressor, N (%)</td><td>6570 (66%)</td></tr><tr><td>■ Any anti hypertension drugs, N (%)</td><td>2359 (24%)</td></tr><tr><td>■ Crystalloid (ml)</td><td>1000 [1000-1000]</td></tr><tr><td>■ Use of albumin 5%, N (%)</td><td>546 (6%)</td></tr><tr><td>■ Urine output (ml)</td><td>150 [0-345]</td></tr><tr><td>■ Estimated blood loss (ml)</td><td>50 [50-100]</td></tr><tr><td>■ Fluid balance (ml)</td><td>800 [550-950]</td></tr></tbody></table>

Data are expressed in median [Q1-Q3] or number (%); Transversus abdominal plane block (TAP);

# received either just before induction or intraoperatively

s at least 2 infusions among the four adjuvant drugs presented (multimodal analgesia)
<table><caption>Table 3: Postoperative Variables</caption><thead><tr><th>Variables</th><th>N = 9978</th></tr></thead><tbody><tr><td><strong>Post-anesthesia care unit</strong></td><td></td></tr><tr><td>■ Length of stay (min)</td><td>135 [98-193]</td></tr><tr><td>■ Numerical rating scale at PACU arrival</td><td>0 [0-6]</td></tr><tr><td>■ Numerical rating scale at PACU discharge</td><td>3 [0-4]</td></tr><tr><td>■ Maximal numerical rating scale</td><td>6 [3-7]</td></tr><tr><td>■ Postoperative nausea and vomiting, N (%)</td><td>1068 (11%)</td></tr><tr><td>■ Any opioid received, N (%)</td><td>7032 (71%)</td></tr><tr><td>&nbsp;&nbsp;&nbsp;&nbsp;o Fentanyl</td><td>2909 (29%)</td></tr><tr><td>&nbsp;&nbsp;&nbsp;&nbsp;o Hydromorphone</td><td>5943 (60%)</td></tr><tr><td>&nbsp;&nbsp;&nbsp;&nbsp;o Oxycodone</td><td>1743 (18%)</td></tr><tr><td>&nbsp;&nbsp;&nbsp;&nbsp;o Methadone</td><td>4 (0.04%)</td></tr><tr><td>&nbsp;&nbsp;&nbsp;&nbsp;o Tramadol</td><td>77 (1%)</td></tr><tr><td>■ Total morphine milligram equivalents (MME)</td><td>8 [0-10]</td></tr><tr><td>■ Patient with MME &gt; 50, N (%)</td><td>0 (0%)</td></tr><tr><td>■ Acetaminophen, N (%)</td><td>1660 (17%)</td></tr><tr><td>■ Ketorolac, N (%)</td><td>50 (1%)</td></tr><tr><td><strong>Postoperative Day 0 (POD 0) #</strong></td><td></td></tr><tr><td>■ Maximal numerical rating scale</td><td>7 [5-8]</td></tr><tr><td>■ Any Opioids received, N (%)</td><td>9859 (99%)</td></tr><tr><td>&nbsp;&nbsp;&nbsp;&nbsp;o Hydromorphone</td><td>8722 (87%)</td></tr><tr><td>&nbsp;&nbsp;&nbsp;&nbsp;o Oxycodone</td><td>3117 (31%)</td></tr><tr><td>&nbsp;&nbsp;&nbsp;&nbsp;o Tramadol</td><td>876 (9%)</td></tr><tr><td>■ Total MME</td><td>10 [5-15]</td></tr><tr><td>■ Patient with MME &gt; 50, N (%)</td><td>150 (1.5%)</td></tr><tr><td>■ Acetaminophen, N (%)</td><td>9025 (90%)</td></tr><tr><td>■ Ketorolac, N (%)</td><td>1600 (16%)</td></tr></tbody></table>

Data are expressed in median [Q1-Q3] or number (%);

# POD 0 is from PACU arrival to 11:59 pm the day of the surgery.