Efficiency And Quality In Post-Surgical Pain Therapy After Discharge
Phase: NA
Conditions: Opioid Use, Opioid Dependence, Opioid Misuse, Post-Surgical Complication
Sponsor: University of Nebraska

Efficiency and Quality In Post- Surgical Pain Therapy After Discharge- EQUIPPED 
NCT05221866 
January 25, 2022 
 
 

Methods 
This trial was conducted at a Midwestern academic medical center and community hospital using a 
modified 2-by-2 randomized factorial design in which participants who underwent major inpatient 
surgery were randomly assigned to use a participant-facing consumer health informatics phone-based 
app with education versus an app for data collection only. Secondarily, a clinician-facing real-time opioid 
prescription decision support tool embedded into electronic health records was activated halfway 
through enrollment. The study design followed Pragmatic Explanatory Continuum Indicator Summary 
guidelines to maximize broad applicability and is reported according to Consolidated Standards of 
Reporting Trials Extension for Factorial Randomized Trials guidelines (Supplemental Digital Content 1).  
 
Study design and participants 
Participants were eligible for enrollment if they were 19-89 years old, had access to a smartphone, and 
had inpatient surgery requiring at least an overnight hospitalization with anticipated discharge to home. 
Individuals re-hospitalized within 30 days, pregnant, unable to read the English language, discharged to a 
post- acute care facility, with contraindications to opioids, acetaminophen, or nonsteroidal anti-
inflammatory agents, or meeting the Agency for Healthcare Research and Quality (AHRQ) definition of 
long-term opioid therapy (opioid use on most days > 3 months) prior to surgery were excluded. All study 
data were deidentified.  
 
Randomization and masking 
Participants were randomized 1:1 to the consumer health informatics smartphone-based intervention 
app or a control app with only data collection. Permuted block randomization (pairs) was performed 
using a computer-generated code within a secure central online data management system, Research 
Electronic Data Capture (REDCap), which was used to store and manage all participant data. No 
randomization was instituted for the clinician-facing intervention; instead, the decision support tool was 
embedded into the electronic health record to facilitate optimized opioid prescription upon discharge 
based on recorded inpatient use. This was implemented after half of the participants were enrolled. The 
principal investigator, coinvestigators, clinical coordinating center staff, participants, and statisticians 
remained masked to treatment assignment until the database was locked for analysis. 
 
Sample Size Justification 
To estimate power and sample size for the primary outcome, self-reported cumulative opioid use in the 
four weeks after discharge, investigators combined data from previous observational studies on opiates 
taken at one week from discharge, where variability in opioid consumption is greatest, and summed 
across four weeks from discharge and then log-transformed values appropriately. Investigators utilized 
SAS Proc Power (version 15.2, SAS Institute Inc., Cary, NC, USA) software and specified significance level 
alpha = 0.05 two-tailed. Across decision support tool activation status and surgical subspecialties, 300 
participants per app group provided 85% power to detect minimum effect sizes (ES) of 0.23 in opioids 
consumed at one week and of 0.245 in opioids consumed across four weeks. Data was similarly 

combined for pain intensity scores from prior studies and could detect a minimum effect size of 0.244 in 
pain intensity t-score at week one following discharge. Within decision support tool type and across 
specialty (i.e., the three-way interaction among app, decision support tool, and time), 150 participants 
per group provided 85% power to detect minimum effect sizes of 0.35 in opioids consumed at one week, 
0.35 in opioids consumed across four weeks, and 0.35 in pain intensity t-score at one week after 
discharge. 
 
Procedures 
Investigators primarily tested a participant-facing app to reduce opioid intake which was designed and 
implemented through a user-centered design approach. The intervention app provided education on 
elements of pain management that could be modified by patients, including: 1) post-operative pain 
expectations, 2) pharmacologic alternatives to opioids, 3) opioid safety and appropriate disposal, and 4) 
non-pharmacological therapy, such as meditation. The control app featured the same interface but only 
collected patient data. The app was deployed to research team members and pilot participants for beta 
testing before finalization.  
Baseline information was obtained from the participant in-person during the index hospitalization. 
During this visit, patients familiarized themselves with the format, asked questions, and received 
explanations regarding the apps’ functionality. Following discharge, a weekly notification was sent to the 
participant, reminding them to complete standardized surveys and custom questionnaires during the 
first four weeks following discharge, a time when most opioids are consumed and when patients have 
shown to report consumption accurately. The day of discharge was considered day 0. If no responses 
were obtained via the app, participants were followed up by telephone. 
In addition to the participant-facing app intervention, a clinician-facing decision support tool was 
operationalized for the second half of the study period for participants not requiring opioids 24 hours 
prior to discharge. Previous work in three diverse samples of surgical procedures found that among 
available predictor variables that could be incorporated into an electronic decision support tool, 24-hour 
pre-discharge opioid intake was most strongly associated with patient-reported post-discharge opioid 
intake. 
 
Outcomes 
The primary outcome was self-reported cumulative oral morphine milligram equivalents (MME) in the 
first four weeks after discharge. The secondary outcomes were amount of opioid prescription at 
discharge, supplementary opioid prescriptions within four weeks after discharge, disposal of leftover 
opioids within four weeks of discharge, pain intensity scores during the four weeks after discharge, and 
pain interference scores during the four weeks after discharge. Non-opioid prescriptions within four 
weeks of discharge were an exploratory outcome. Pain intensity and interference were both quantified 
using the National Institutes of Health Patient-Reported Outcomes Measurement Information System 
(PROMIS ) four-item pain intensity scale at four weeks and the four-item scale pain interference score at 
four weeks. All PROMIS™ scores were analyzed as standardized T- scores (mean 50, standard deviation 
10). 

Independent variables included sociodemographic characteristics obtained from the electronic health 
record and from a questionnaire provided to participants during their hospitalization: age, sex, race, 
ethnicity, body mass index (kg m2-1), and insurance type. Health history variables were obtained via 
surveys and electronic health records as necessary and included history of substance use disorder, 
PROMIS™ Sleep Disturbance Score, Patient Health Questionnaire-(PHQ-8) score, pre-operative opioid 
use, preoperative benzodiazepine use, preoperative gabapentinoid use, and preoperative antidepressant 
use. Clinical characteristics included the primary surgical subspecialty for the requisite hospital stay, 
multiple procedures performed during admission, total in-hospital opioid intake, pain score at discharge, 
and length of hospital stay. 
Adverse events were monitored. For this study, an adverse event was considered as one requiring more 
than two additional opioid prescriptions within two weeks of discharge and/or access of confidential 
information by a non-authorized person. We considered a serious adverse event as all-cause mortality 
and/or re-hospitalization for any reason. 
 
Statistical analysis 
Baseline balance on potential confounding characteristics was assessed using absolute standardized 
difference (ASD), calculated as the difference in means or proportions divided by the pooled standard 
deviation. A variable was considered imbalanced if the ASD was greater than 0.16. 
For the primary analysis, the effect of the intervention app on self-reported cumulative opioid use in the 
four weeks after discharge was modeled by log-transforming the outcome variable and then fitting a 
linear regression model with the app treatment group as the covariate. The treatment effects were 
estimated as ratio of geometric means. A sensitivity analysis was conducted using quantile regression to 
test the robustness of results to outliers. A model was fit with an interaction between app and decision 
support tool groups to evaluate potential multiplicative effects between the groups. Analysis was on a 
modified intention-to-treat basis, excluding participants that did not engage with the intervention or 
answer survey questions. There were no partial responses to the opioid-related outcomes. Missing data 
from pain-related survey responses were imputed from the mixed effects model. 
For the secondary analysis, the effect of the intervention app on opioids prescribed at discharge was 
modeled by log-transforming the outcome and fitting a linear regression model with the app treatment 
group as the covariate. The effect of the intervention app on supplementary opioid requirements in the 
four weeks after discharge, opioid disposal, and non- opioid prescriptions in the four weeks after 
discharge was evaluated using a logistic regression model. The effect of the app on pain intensity and 
interference t-scores was evaluated by fitting linear mixed models with fixed covariates for app 
treatment group and time (categorical), and random effects for participant identification code. No 
interim analyses were planned because the population studied was not considered high risk for adverse 
events, the interventions were generally considered not harmful  and to prevent alpha dilution.