AIM:A prior non-randomized study suggests that potassium supplementation may improve survival among furosemide initiators, and a randomized trial suggests that salt substitutes containing potassium might lower stroke risk. We conducted a retrospective cohort study using health-care data to confirm or refute these associations among new users of furosemide. METHODS:The exposure of interest was empiric potassium dispensing (yes/no) concurrent with furosemide initiation. Outcomes were all-cause mortality, sudden cardiac arrest/ventricular arrhythmia (SCA/VA), and stroke. Primary as-started and secondary as-treated analyses were performed with Cox proportional hazards regression. We used inverse probability of treatment weighting (IPTW) to control for confounding, with weights calculated from high-dimensional propensity scores. RESULTS:We identified 511 462 and 320 703 initiators of furosemide <40 and ≥40 mg/day with 21.5% and 35.3%, respectively, starting empiric potassium supplementation. In initiators of furosemide <40 mg/day with (vs. without) empiric potassium, as-started IPTW-hazard ratios (HRs) were 1.02 (95%CI 1.01-1.04) for death, 1.00 (0.94-1.04) for SCA/VA, and 1.03 (1.00-1.06) for stroke. Similarly, in initiators of furosemide ≥40 mg/day with (vs. without) empiric potassium, as-started IPTW-HRs were 1.02 (1.00-1.03) for death, 0.98 (0.94-1.03) for SCA/VA, and 1.01 (0.98-1.04) for stroke. CONCLUSION:We did not observe associations suggesting a clinically meaningful effect of empiric potassium supplementation among furosemide users. However, given the high prevalence of furosemide use among highly heterogeneous patient populations, a large pragmatic trial may be warranted to more definitively evaluate the potential benefits and harms of empiric potassium supplementation among furosemide initiators.

Background:People living with HIV (PLWH) make up a significant proportion of the population in sub-Saharan Africa, and there exists a significant gap in research on the burden and associated risk factors for extended-spectrum cephalosporin-resistant Enterobacterales (ESCrE) in this population. We describe the risk factors associated with ESCrE colonization in nonhospitalized PLWH. Methods:This is a secondary data analysis of nonhospitalized adult PLWH included in a regional surveillance cohort study describing colonization with ESCrE in Botswana. Participants underwent rectal swab sampling to identify ESCrE. Bivariate and multivariable analysis was used to determine risk factors associated with ESCrE colonization. Results:A total of 546 adult PLWH were recruited over 3 districts and were included in this analysis. The mean (standard deviation) age was 42 (10.4) years, and 448 (82.1%) were women. Our findings demonstrated that 27.3% (149/546) of participants screened positive for ESCrE rectal colonization. Independent risk factors {adjusted odds ratio (aOR) [95% CI]} for ESCrE colonization included recent hospitalization (3.37 [1.13-9.98]), at least 1 household member with ESCrE colonization (1.74 [1.01-3.00]), and recruitment before the countrywide COVID-19 lockdown (2.01 [1.33-3.04]). Recent antibiotic use had an elevated OR for ESCrE colonization that did not achieve statistical significance in the adjusted analysis (aOR: 1.84 [.92-3.68]). Conclusions:Hospitalization and colonization of other household members with ESCrE are important factors associated with colonization with ESCrE, as seen in other populations. The prevalence of ESCrE following the COVID-19 lockdown was significantly lower, suggesting the presence of factors that were protective against colonization. It is unclear how long these effects lasted.

ABSTRACT:Current US Food and Drug Administration (FDA) guidance recommends the sum of pain intensity differences (SPID) as the primary efficacy endpoint for acute pain trials, which lacks validated thresholds for clinically meaningful change and requires imputation of pain scores after rescue medication use. We introduce the responder outcome over time (ROOT), defined as the proportion of study time a participant experiences clinically important improvement in pain intensity without recent rescue medication use or early discontinuation. The ROOT combines the clinical interpretability of a responder-based outcome with the statistical efficiency of a continuous measure, without requiring imputation of pain scores after rescue. We reanalyzed data from phase 2 and 3 analgesic trials submitted to the FDA to assess concordance between SPID and ROOT. For SPID, pain scores after rescue were imputed using the established practice in FDA submissions, windowed last-observation-carried-forward (LOCF) for 6 hours. For ROOT, meaningful improvement was defined as ≥50% reduction in pain intensity; participants were classified as nonresponders for 6 hours after rescue. In 34 trials (N = 11,028), SPID and ROOT reached statistically concordant conclusions for 92.2% of the 204 treatment comparisons. Calculation of SPID required LOCF imputation of pain scores after rescue medication for a median of 27% of study time in active drug groups and 46% in placebo groups. Reanalyses showed that LOCF distorted the temporal pattern of treatment effects during periods of high rescue use, producing apparent treatment differences not corroborated with ROOT. These results show that ROOT provides a clinically interpretable, patient-centered alternative to SPID with similar ability to detect statistically significant treatment effects.

The American Heart Association's PREVENT equations are now embedded in three major AHA/ACC guidelines governing hypertension, dyslipidemia, and cardiovascular-kidney-metabolic syndrome, including a specific risk threshold that gates initiation of GLP-1-based therapy and SGLT2 inhibitors. This rapid guideline uptake has outpaced a persistent limitation: PREVENT requires laboratory values, chiefly lipids and estimated glomerular filtration rate, that are frequently missing in the populations where risk prediction matters most, including patients seen in community health centers, emergency departments, and substance use treatment programs. Real-world validation studies across multiple health systems show that PREVENT underestimates cardiovascular risk most severely in these same subgroups, with the largest underestimation among Medicaid-insured, uninsured, and Black patients. Imputation, whether simple substitution or multiple imputation, does not resolve this problem because the probability that a laboratory value is missing is itself associated with the underlying risk the model is trying to estimate, a form of informative missingness that standard imputation methods are not designed to address. We argue that the field should invest in developing and validating a standalone cardiovascular risk prediction model that does not require laboratory measures. Such a model would function as a complement to PREVENT, not a replacement, providing risk estimates for patients without laboratory data and helping identify those who warrant expedited evaluation. As PREVENT becomes the gatekeeper for guideline-directed therapy across three clinical domains simultaneously, closing this coverage gap is no longer a methodological refinement but a clinical implementation imperative.

The American Heart Association's PREVENT equations represent a major advance in cardiovascular risk prediction, in part because they account for the possibility that patients may die from non-cardiovascular causes before experiencing a cardiovascular event. Despite this advance, recent external validations have found that PREVENT systematically underestimates cardiovascular risk in Black patients, socioeconomically disadvantaged groups, veterans, and those with Medicaid or no insurance.We propose that one underrecognized contributor is how PREVENT handles non-cardiovascular death: by treating all such deaths from cancer, overdose, suicide, injury, and other causes as a single combined category. This aggregation is a problem because the mix of competing causes differs across populations. The direct consequence is that the model's competing-risk adjustment reflects the cause-of-death composition of its derivation cohort rather than that of the population being evaluated, and standard recalibration methods cannot correct this mismatch. The direction of the resulting error is predictable: populations with disproportionately high overdose, suicide, and injury mortality relative to the cancer-dominant pattern of general-population derivation cohorts will have their cardiovascular risk systematically underestimated.This problem is further obscured by a common methodological choice in validation studies: using Kaplan-Meier survival estimates rather than cause-specific competing-risk methods to measure observed event rates. This approach inflates apparent event rates in exactly the subgroups most affected, making miscalibration harder to interpret and potentially masking its true direction.Future cardiovascular risk prediction models should separate non-cardiovascular deaths into cause-specific competing processes. External validations should use cause-specific competing-risk estimation. Health systems considering adoption of PREVENT should validate it locally before implementation, with particular attention to calibration within subgroups defined by competing-mortality burden.

Purpose: There are few evidence-based programs to support social functioning in autistic adults. We developed a novel, 17-week cognitive-behavioral and mindfulness-based program, TUNE In (Training to Understand & Navigate Emotions and Interactions), which targets several components of social functioning in adults, including social motivation, social anxiety, social cognition, and social skills. We collected data on the potential efficacy of TUNE In for improving social functioning in autistic adults. Methods: TUNE In comprises individual and group sessions focused on social motivation, social anxiety, social cognition, and social skills, as well as participation in a volunteer work team for skill generalization. We piloted TUNE In in two separate cohorts of autistic adults without intellectual disability. Based on the experiences of Cohort 1 (n = 7), we updated the TUNE In protocol prior to starting Cohort 2 (n = 11); updates included increasing the time spent on mindfulness practices, reducing video modeling of social skills, and increasing naturalistic conversation practice. Results: Cohort 2 showed improvement in social functioning as reflected by a significant reduction in Social Responsiveness Scale-2 Adult Form (SRS-2) scores (Sign Rank Test z = 2.76, p = .006, d = 0.44) following participation in TUNE In. Conclusion: These data suggest that TUNE In may improve social functioning in autistic adults. Further study of TUNE In in comparison to a control condition in a larger sample of adults is warranted.

BACKGROUND:The prevalence of cigarette use in people with HIV (PWH) is 2-3 times higher than the general population. Cigarette use increases risk of myocardial infarction (MI). Faster nicotine metabolism, quantified by the nicotine metabolite ratio (NMR) is associated with greater risk for nicotine dependence and lung cancer, but its association with MI is unknown. METHODS:We conducted a nested case-control study within the Center for AIDS Research Network of Integrated Clinical Systems (CNICS) cohort. Cases were PWH who reported cigarette use with incident adjudicated MI between 2003 and 2020 and available plasma samples; cigarette-smoking controls were selected by incidence density sampling, matched on age, race, birth sex, and plasma HIV RNA level. Conditional logistic regression was used to estimate odds ratios (OR) for the association of NMR and MI. RESULTS:We identified 135 cases with MI and 252 controls. Median (IQR) NMR was greater in cases [0.51 (0.36, 0.73)] than in controls [0.47 (0.30, 0.70)]. In conditional logistic regression, the odds of having a high NMR were 1.4 times greater among MI cases than controls, but not at a statistically significant level (OR: 1.4, 95% CI: 0.97-1.9). This estimate did not substantively change after further adjustment for statin use, hypertension, and diabetes (OR: 1.4, 95% CI: 0.92-2.0). CONCLUSIONS:There is a small-magnitude association between NMR and MI, which was not statistically significant. NMR remains a potential biomarker for MI risk among PWH. Further investigation is needed to estimate the precise effect of NMR on MI in PWH.

Benzodiazepines and sedative hypnotics such as zolpidem (“z-drugs”) are commonly prescribed for anxiety and sleep disorders. Epidemiologic evidence links their use to increased risk of venous thromboembolism. We investigated venous thromboembolism risk among concomitant users of individual benzodiazepines/z-drugs (examined separately) with other prescription medications to generate data-driven hypotheses about drug interactions resulting in clinically meaningful harm to inform future etiologic studies of specific drug combinations. We conducted a series of self-controlled case series studies within a 50

In 2018, the orthotopic heart transplant (OHT) allocation policy in the United States changed to prioritize more critically ill candidates, including those supported with temporary mechanical circulatory support (tMCS). This study evaluated changes in posttransplant bloodstream infection (BSI) rates following the OHT allocation change. We conducted a retrospective cohort study of OHT recipients at 2 large United States transplant centers from January 1, 2015, to December 31, 2022. Recipients were followed 90 days post-OHT for BSIs. The association between policy era and BSIs was evaluated using multivariable Cox regression with mediation analysis to explore the effect of tMCS. There were 895 OHT recipients with a >2.5-fold increase in pretransplant tMCS after the policy change. Ninety-five recipients developed BSIs (10.6%). OHT in the postpolicy era was associated with a 2.3-fold increased hazard of BSI (P = .002). Mediation analysis showed tMCS explained some, but not all, of the increased BSI risk after change. One-year mortality and graft failure were similar across the 2 policy eras, although there was a significant decrease in 1-year rejection following the change (P < .001). Overall, post-OHT BSI risk doubled following the allocation change, which is not fully explained by tMCS. Additional studies are needed to further investigate factors contributing to BSI risk.

Background:Cardiovascular disease (CVD) risk prediction tools have not been developed or validated for adults who use nonmedical stimulants (cocaine or methamphetamine), despite substantially elevated event rates and pathophysiologically distinct risk profiles in this population. Accurate estimation of absolute risk is needed to support individualized clinical decision-making. Methods:We developed a 3-year CVD risk prediction model using a cohort of 6940 adults aged 18-79 with documented stimulant use, identified from electronic health record (EHR) data of a large academic hospital system spanning 2016-2024. Type of stimulants used was a candidate predictor, alongside demographic characteristics, cigarette smoking status, systolic blood pressure, and baseline cardiovascular medications. Variable selection used least absolute shrinkage and selection operator (LASSO) with 10-fold cross-validation, followed by unpenalized logistic regression on selected variables. Model performance was assessed using discrimination and calibration metrics; bootstrap internal validation estimated and corrected for optimism in apparent performance. Results:LASSO selected 8 predictors: age, sex, Black race, Hispanic ethnicity, current cigarette smoking, cocaine-only use, any cardiovascular medication use, and systolic blood pressure. Calibration was excellent. Observed and predicted 3-year event rates were 29.6% and 30.1%, respectively (observed/expected [O/E] ratio 0.985, calibration slope 1.000, integrated calibration index [ICI] 0.008). Performance was maintained after optimism correction: O/E ratio 0.988, calibration slope 0.992, ICI 0.010. The apparent C-statistic was 0.730 (95% CI 0.717-0.743); the optimism-corrected C-statistic was 0.728. For illustration, a 45-year-old Black adult with cocaine use, current cigarette smoking, and systolic blood pressure of 130 mmHg has an estimated 3-year CVD risk of approximately 27.7%; the same profile with methamphetamine use yields approximately 22.1%. Conclusions:A CVD risk prediction model tailored to individuals with stimulant use accurately estimated absolute 3-year CVD risk, with excellent calibration and clinically meaningful risk stratification. These findings establish proof of concept for a stimulant-specific CVD risk prediction tool. External validation and implementation studies are needed before clinical deployment.

INTRODUCTION:In May 2023, the US Food and Drug Administration (FDA) initially approved an AS01E-adjuvanted respiratory syncytial virus (RSV) prefusion F protein-based vaccine (adjuvanted RSVPreF3) for adults aged ≥60 years. The approval was expanded in June 2024 to include adults 50-59 years of age at increased risk for RSV-associated lower respiratory tract disease. In this paper, we describe the protocol of a postmarketing safety study evaluating the association between adjuvanted RSVPreF3 and new-onset Guillain-Barré syndrome (GBS), acute disseminated encephalomyelitis (ADEM) and atrial fibrillation (AF) among adults ≥50 years of age in the USA and provide our rationale for key methodological decisions. METHODS AND ANALYSIS:The potential associations between adjuvanted RSVPreF3 and GBS, ADEM and AF will be evaluated using secondary healthcare data and the self-controlled risk interval (SCRI) design. Data from five research partners in the USA spanning August 2023 through June 2030 will be used for the conduct of yearly monitoring queries and, sample size permitting, SCRI analyses. Claims-based definitions for new-onset outcomes (first diagnosis in 365 days) are: ≥1 inpatient diagnosis for GBS and ADEM; ≥1 inpatient or ≥2 ambulatory/emergency diagnoses for AF. The primary risk and control windows are 1-42 and 43-84 days, respectively, for GBS and ADEM; and 1-8 and 9-16 days for AF. SCRI analyses for GBS and ADEM will include chart-confirmed cases. SCRI analyses for AF will adjust for the positive predictive value obtained from validation against charts. Conditional Poisson regression will be used to calculate incidence rate ratios. ETHICS AND DISSEMINATION:This study was approved by the Institutional Review Boards (IRB) of Harvard Pilgrim Health Care Institute; WIRB-Copernicus Group, Inc and its affiliates (collectively, 'WCG'); WCG IRB, Inc; and Sterling IRB, with Federal Wide Assurance (FWA) numbers FWA00000100, FWA00033319 and FWA00025632, respectively, for all participating research partners. Study results will be shared with the US FDA and publicly disseminated through national or international clinical or scientific conferences and peer-reviewed publications. REGISTRATION:This protocol has been registered in the Heads of Medicines Agencies-European Medicines Agency Real World Data Catalogues (EUPAS1000000486).

Abstract Background The epidemiology of colonization with ESCrE in the community in low- and middle-income countries (LMICs) is largely uncharacterized. In the community, the household is of particular importance as it is the setting in which individuals spend a majority of their time. Identifying risk factors for household ESCrE colonization is critical to inform antibiotic resistance reduction strategies in LMICs. Methods Participants were enrolled from 1/1/20-9/4/20, with a pause 4/2/20-5/21/20 due to a country-wide COVID lockdown. This study was conducted in 6 clinics located in 3 regions in Botswana. In each clinic, we surveyed a random sample of outpatients presenting for care. We also invited each enrolled subject to refer household members. Only those subjects who referred at least one other household member were included. All participants had rectal swabs collected for identification of ESCrE. Data were collected on demographics, comorbidities, antibiotic use, healthcare exposures, travel, and farm/animal contact. Households were considered exposed if any member had the exposure of interest. Households with ESCrE colonization (cases) were compared to non-colonized households (controls). Bivariable and multivariable analyses were conducted to identify risk factors for household ESCrE colonization. Results 327 households were enrolled. The median (IQR) number of people enrolled per household was 3 (2-4) ranging from 2-10. Among enrolled households, the median proportion of household residents participating was 0.71 (0.5 – 1). The median (IQR) age of subjects was 18 years (5-34) and 304 (93%) households included at least one child. Of 327 households, 176 (54%) had at least one household member colonized with ESCrE. Independent risk factors for household colonization are noted in the table. Conclusion ESCrE household colonization was common with evidence of geographic variability in ESCrE epidemiology as well as a possible role of animal exposure. The role of yogurt exposure requires additional study as we did not distinguish source (commercial, homemade). Further prospective studies of household ESCrE colonization with longitudinal assessments of exposures are required to identify effective prevention strategies. Disclosures Robert Gross, MD, MSCE, Pfizer Inc: DSMB member for drug unrelated to study

ABSTRACT:The validity of enriched enrollment randomized withdrawal (EERW) trials to evaluate the efficacy of opioids for the treatment of chronic pain has been questioned. Enriched enrollment randomized withdrawal trials include an open-label titration phase to identify treatment responders who tolerate the drug, followed by a double-blind randomized phase in which responders either continue the drug or switch to placebo. A key concern is that the apparent efficacy of opioids in EERW trials may be attributable to induction of withdrawal symptoms among participants switched to placebo. We used individual participant data from 13 EERW trials (N = 5070) submitted to the US Food and Drug Administration (FDA) to estimate the extent to which withdrawal symptoms mediated the treatment effect of opioids on pain. The primary mediator was the maximum Subjective Opioid Withdrawal Scale score during the randomized phase. The primary outcome was the change in pain intensity (numeric rating scale) from randomization baseline to week 12. The pooled average treatment effect was -0.71 (95% confidence interval [CI], -0.87 to -0.55) on the numeric rating scale. Withdrawal symptoms did not significantly mediate the effect of opioids on pain overall, accounting for 2% of the pooled treatment effect (95% CI, -1% to 4%). However, significant mediation was observed in 3 individual trials (range, 8% to 28% of treatment effect mediated). Although withdrawal symptoms did not systematically bias efficacy findings in EERW trials of opioids submitted to the FDA, they contributed to overestimation in some cases. These findings support incorporating mediation analyses in future EERW trials to ensure accurate interpretation of study results.

This cross-sectional study aims to quantify methadone use for opioid use disorder in the Medicaid program and assesses buprenorphine use as a possible shift between medications for opioid use disorder.

1 Responder analyses for the evaluation of randomized clinical trial (RCT) data have become more common in the recent past, since they can provide the medical community with results that are more directly applicable to clinical care. For pain studies, the predominant responder analysis compares the change in the individual participants’ pain level at baseline to their value at the end of the study period and uses a predetermined clinically important change cut-off value to define a response. While useful, this method substantially reduces the efficiency of the RCT by dichotomizing the results and is limited to comparing the baseline to the end of the study only. In this paper, we introduce a novel approach to the patient response over time with a focus on single dose post-operative studies. This technique provides graphical presentations and statistical approaches to understand the onset of any specified level of response, the maximum proportion of patients with a response at any point in time, and the duration of that response over time. In addition, each outcome can be summarized to examine the result across all possible cut-off points for clinically important differences (CID). We accomplish this by introducing three interrelated, longitudinal efficacy statistics: ROOT, GRO, and GROOT. The response outcome over time (ROOT) estimates the total proportion of a study period an individual patient spends as a responder. The group response outcome (GRO) estimates the instantaneous proportion of responders at all time points across the study period. The group response outcome over time (GROOT) summarizes total efficacy in a cohort, and can be calculated as the area under the GRO curve, or as the mean ROOT; they are identical. This novel method provides a clinically interpretable responder analysis over the full period of the study and, by using every data point across time, mitigates the loss of statistical power typically associated with dichotomized responder outcomes. Group response analysis is based upon repeated assessments of categorical or continuous measures categorizing each participant’s status as a treatment responder or non-responder at every timepoint based on the prespecified clinically important difference. Both the visual and statistical comparison of any two or more curves provide a comparison of the overall efficacy, which can be statistically tested using a standard asymptotic hypothesis test (such as Wald (Johnson & Romer, 2016)). The method allows for an integrated evaluation of three main components of drug efficacy: the proportion of participants achieving a CID over time (effect), the time to achieve that response (onset), and the length of the response (duration). In this paper, we present the group response analysis methodology and then illustrate it using data from a placebo-controlled randomized clinical trial (RCT) for postoperative pain after third molar extraction treated with meloxicam and ibuprofen as an active comparator (Christensen et al., 2018). Our approach yields similar effect sizes as the sum of pain intensity differences (SPID) commonly used for pain study analyses while providing superior clinical interpretability and a more complete evaluation of drug therapies beyond just efficacy. We propose that this method can be used as a primary or secondary analysis of pain RCTs to answer the question of the patient response to treatment and provide suitable data to compare efficacies across treatment groups.

The lack of established minimum clinically important differences in acute pain has made it challenging to interpret efficacy in analgesic trials. We performed a patient-level re-analysis of double-blind, placebo-controlled trials submitted to the US Food and Drug Administration to estimate minimum clinically important differences in acute postoperative pain. Trials were categorized by acute surgical pain model: dental extraction, bunionectomy, orthopedic surgery, and soft tissue surgery. Pain intensity was assessed using the 0 to 10 numeric rating scale (NRS) or 0 to 100 visual analog scale, with visual analog scale scores converted to NRS for analysis. To avoid misclassification from arbitrary thresholds on global impression of change or pain relief scales, meaningful pain relief was determined using the double-stopwatch technique, where patients actively indicated the times they experienced perceptible and meaningful relief. Across 29 trials, 9047 patients with moderate-to-severe baseline pain were included. Patients with severe baseline pain (NRS ≥7) reported meaningful relief at a higher absolute NRS and required larger absolute reductions in pain intensity than those with moderate baseline pain (NRS 4-<7). However, the percent reduction in pain at meaningful relief remained stable across baseline pain levels, suggesting patients assess meaningful relief in relative rather than absolute terms. No appreciable differences in the changes in pain at meaningful relief were observed by age, sex, drug, or route of administration. Receiver operating characteristic curve analysis identified a 50% reduction in pain intensity as a consistent and clinically meaningful threshold across surgical pain models, supporting its use as a standardized patient-centric metric for evaluating analgesic efficacy.

Introduction:No prediction models exist for the success for buprenorphine initiation in opioid-naïve patients or in transition from other opioids in patients treated for chronic pain. Objectives:To create a prediction model for the successful use of buprenorphine to treat chronic pain. Methods:Stepwise Akaike information criterion prediction modeling procedures were applied to a harmonized participant-level data set of 10 enriched enrollment randomized withdrawal clinical trials of buprenorphine submitted to the Food and Drug Administration. Available baseline factors and nine patient-reported outcomes were considered to predict success with the titration (10 studies) and maintenance of benefit after randomization (5 studies). Patient-reported outcomes were modeled separately given inconsistent use across studies. Results:No prediction model reached an area under the receiver operator curve ≥0.70, the threshold for clinical usefulness. Successful initiation or transition of buprenorphine was accomplished in 3541 of 6052 (58.7%) participants, and 614 of 877 (70.0%) completed the 12-week maintenance phase with no difference between opioid-experienced and opioid-naïve participants. Only a medical history of obesity and baseline pain were retained in the overall titration model and only baseline pain in the maintenance model. Only brief pain inventory and subject opioid withdrawal scores were retained in the titration subsets containing those measures. Conclusion:No clinically useful prediction models of clinical benefit were identified, but a few covariates may be of interest in future studies of the initiation of buprenorphine in opioid-naïve patients or of transition from other opioids to buprenorphine. The lack of a predictor supports considering a trial of buprenorphine in clinically relevant scenarios for patients without known opioid use disorder, including careful monitoring and an a priori plan to deal with any problems that may occur.

This study aims to identify predictors of success in treating chronic pain patients with full agonist opioids by analyzing harmonized individual patient data from 5594 participants in 9 enriched enrollment randomized withdrawal clinical trials available in the Food and Drug Administration data repository. We analyzed both the participants' success with titration and continued success in the 84-day maintenance phases after randomization for those maintained on the drug. We used the full data set to assess participant demographics and subsets of data containing participant reported outcomes at baseline. Participants had an average age of 51, with 55% female participants and 66% non-Hispanic white. No clinically relevant differences were observed between participants who failed titration or those who continued on full agonists through the maintenance phase. Prediction models were developed using mixed effects logistic regression and generalized linear mixed models, with the study as a random effect to account for inter-study differences. Despite large numbers, the analysis did not reveal clinically useful prediction models for either the titration or maintenance phase; however, higher initial pain scores were modest predictors of poorer outcomes. No patient-reported outcome measures were predictive of responses to therapy. The study's limitations include its volunteer-based sample and the exclusion criteria, although excluding patients with opioid use disorder or serious psychological conditions are similar to those used in clinical care. As no strong predictive factors for successful treatment were identified, the decision to use opioids to treat chronic pain requires careful clinical judgment and close monitoring.