The times are changing: A primer on novel clinical trial designs and endpoints in critical care research.

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Comparative Risk of Injury with Concurrent Use of Opioids and Skeletal Muscle Relaxants.

Concurrent use of skeletal muscle relaxants (SMRs) and opioids has been linked to an increased risk of injury. However, it remains unclear whether the injury risks differ by specific SMR when combined with opioids. We conducted nine retrospective cohort studies within a US Medicaid population. Each cohort consisted exclusively of person-time exposed to both an SMR and one of the three most dispensed opioids-hydrocodone, oxycodone, and tramadol. Opioid users were further divided into three cohorts based on the initiation order of SMRs and opioids-synchronically triggered, opioid-triggered, and SMR-triggered. Within each cohort, we used Cox proportional hazard models to compare the injury rates for different SMRs compared to methocarbamol, adjusting for covariates. We identified 349,543, 139,458, and 218,967 concurrent users of SMRs with hydrocodone, oxycodone, and tramadol, respectively. In the oxycodone-SMR-triggered cohort, the adjusted hazard ratios (HRs) were 1.86 (95% CI, 1.23-2.82) for carisoprodol and 1.73 (1.09-2.73) for tizanidine. In the tramadol-synchronically triggered cohort, the adjusted HRs were 0.69 (0.49-0.97) for metaxalone and 0.62 (0.42-0.90) for tizanidine. In the tramadol-SMR-triggered cohort, the adjusted HRs were 1.51 (1.01-2.26) for baclofen and 1.48 (1.03-2.11) for cyclobenzaprine. All other HRs were statistically nonsignificant. In conclusion, the relative injury rate associated with different SMRs used concurrently with the three most dispensed opioids appears to vary depending on the specific opioid and the order of combination initiation. If confirmed by future studies, clinicians should consider the varying injury rates when prescribing SMRs to individuals using hydrocodone, oxycodone, and tramadol.

Toward Equitable Kidney Function Estimation in Critical Care Practice: Guidance From the Society of Critical Care Medicine's Diversity, Equity, and Inclusion in Renal Clinical Practice Task Force.

OBJECTIVES: Accurate glomerular filtration rate (GFR) assessment is essential in critically ill patients. GFR is often estimated using creatinine-based equations, which require surrogates for muscle mass such as age and sex. Race has also been included in GFR equations, based on the assumption that Black individuals have genetically determined higher muscle mass. However, race-based GFR estimation has been questioned with the recognition that race is a poor surrogate for genetic ancestry, and racial health disparities are driven largely by socioeconomic factors. The American Society of Nephrology and the National Kidney Foundation (ASN/NKF) recommend widespread adoption of new "race-free" creatinine equations, and increased use of cystatin C as a race-agnostic GFR biomarker. DATA SOURCES: Literature review and expert consensus. STUDY SELECTION: English language publications evaluating GFR assessment and racial disparities. DATA EXTRACTION: We provide an overview of the ASN/NKF recommendations. We then apply an Implementation science methodology to identify facilitators and barriers to implementation of the ASN/NKF recommendations into critical care settings and identify evidence-based implementation strategies. Last, we highlight research priorities for advancing GFR estimation in critically ill patients. DATA SYNTHESIS: Implementation of the new creatinine-based GFR equation is facilitated by low cost and relative ease of incorporation into electronic health records. The key barrier to implementation is a lack of direct evidence in critically ill patients. Additional barriers to implementing cystatin C-based GFR estimation include higher cost and lack of test availability in most laboratories. Further, cystatin C concentrations are influenced by inflammation, which complicates interpretation. CONCLUSIONS: The lack of direct evidence in critically ill patients is a key barrier to broad implementation of newly developed "race-free" GFR equations. Additional research evaluating GFR equations in critically ill patients and novel approaches to dynamic kidney function estimation is required to advance equitable GFR assessment in this vulnerable population.

Safety and efficacy of fixed versus variable-dose prothrombin complex concentrate for emergent reversal of vitamin K antagonists: A systematic review and meta-analysis.

STUDY OBJECTIVE: Four-factor prothrombin complex concentrate (4F-PCC) is standard of care for emergent vitamin K antagonist (VKA) reversal but optimal dosing is uncertain. This meta-analysis estimated the proportion of patients treated with fixed dose (FD) 4F-PCC who achieved adequate reversal and compared safety and efficacy of FD versus weight-based dose (WB) strategies. METHODS: This review was conducted according to PRISMA guidelines. Medline and Scopus were searched and included studies evaluating FD regimens and comparing FD and WB for emergent VKA reversal. Data was pooled using random effects. Subgroup analyses examined heterogeneity. Risk of bias was assessed with Newcastle-Ottawa Scale and RoB2 score. RESULTS: Twenty-three studies (n = 2055) were included with twelve (n = 1143) comparing FD versus WB. The proportion of patients achieving goal INR with FD varied depending on the INR target, being significantly higher for INR <2 (90.9%, 95% Confidence Interval (CI) 87.2, 94.06) compared to INR <1.6 (70.97%, 95%CI 65.33, 76.31). Compared to WB, FD was less likely to achieve a goal INR <1.6 (Risk Difference (RD) -13%, 95% CI -21, -4) but achieved similar reversal for a goal INR <2.0, (RD -1%, 95%CI -7, 4). There was no difference in hospital mortality (RD 4%, 95%CI -2, 9) or thrombosis (RD 0.0%, 95%CI -3, 3). CONCLUSION: FD VKA reversal was associated with significantly lower attainment of goal INR compared to WB with lower INR targets. This did not translate to differences in hospital mortality, but these results should be interpreted cautiously in light of the observational nature of the included studies.

Development and validation of a population pharmacokinetic model to guide perioperative tacrolimus dosing after lung transplantation.

Background: Tacrolimus therapy is standard of care for immunosuppression after lung transplantation. However, tacrolimus exposure variability during the early postoperative period may contribute to poor outcomes in this population. Few studies have examined tacrolimus pharmacokinetics (PK) during this high-risk time period. Methods: We conducted a retrospective pharmacokinetic study in lung transplant recipients at the University of Pennsylvania who were enrolled in the Lung Transplant Outcomes Group (LTOG) cohort. We derived a model in 270 patients using NONMEM (version 7.5.1) and examined validity in a separate cohort of 114 patients. Covariates were examined with univariate analysis and multivariable analysis was developed using forward and backward stepwise selection. Performance of the final model in the validation cohort was examined with calculation of mean prediction error (PE). Results: We developed a one-compartment base model with a fixed rate absorption constant. Significant covariates in multivariable analysis were postoperative day, hematocrit, transplant type, CYP3A5 genotype, total body weight, and time-varying postoperative day, hematocrit, and CYP inhibitor drugs. The strongest predictor of tacrolimus clearance was postoperative day, with median predicted clearance increasing more than threefold over the 14 day study period. In the validation cohort, the final model showed a mean PE of 36.4% (95%CI 30.8%-41.9%) and a median PE of 7.2% (IQR -29.3%-70.53%). Conclusion: Postoperative day was the strongest predictor of tacrolimus exposure in the early post-lung transplant period. Future multicenter studies employing intensive sampling to examine a broad set of variables related to critical illness physiology are needed to understand determinants of clearance, volume of distribution and absorption in this population.

Exposure to ambient air pollutants and acute respiratory distress syndrome risk in sepsis.

PURPOSE: Exposures to ambient air pollutants may prime the lung enhancing risk of acute respiratory distress syndrome (ARDS) in sepsis. Our objective was to determine the association of short-, medium-, and long-term pollutant exposures and ARDS risk in critically ill sepsis patients. METHODS: We analyzed a prospective cohort of 1858 critically ill patients with sepsis, and estimated short- (3 days), medium- (6 weeks), and long- (5 years) term exposures to ozone, nitrogen dioxide (NO2), sulfur dioxide (SO2), carbon monoxide (CO), particulate matter < 2.5 µm (PM2.5), and PM < 10 µm (PM10) using weighted averages of daily levels from monitors within 50 km of subjects' residences. Subjects were followed for 6 days for ARDS by the Berlin Criteria. The association between each pollutant and ARDS was determined using multivariable logistic regression adjusting for preselected confounders. In 764 subjects, we measured plasma concentrations of inflammatory proteins at presentation and tested for an association between pollutant exposure and protein concentration via linear regression. RESULTS: ARDS developed in 754 (41%) subjects. Short- and long-term exposures to SO2, NO2, and PM2.5 were associated with ARDS risk (SO2: odds ratio (OR) for the comparison of the 75-25th long-term exposure percentile 1.43 (95% confidence interval (CI) 1.16, 1.77); p < 0.01; NO2: 1.36 (1.06, 1.74); p = 0.04, PM2.5: 1.21 (1.04, 1.41); p = 0.03). Long-term exposures to these three pollutants were also associated with plasma interleukin-1 receptor antagonist and soluble tumor necrosis factor receptor-1 concentrations. CONCLUSION: Short and long-term exposures to ambient SO2, PM2.5, and NO2 are associated with increased ARDS risk in sepsis, representing potentially modifiable environmental risk factors for sepsis-associated ARDS.

Robust causal inference of drug-drug interactions.

There is growing interest in developing causal inference methods for multi-valued treatments with a focus on pairwise average treatment effects. Here we focus on a clinically important, yet less-studied estimand: causal drug-drug interactions (DDIs), which quantifies the degree to which the causal effect of drug A is altered by the presence versus the absence of drug B. Confounding adjustment when studying the effects of DDIs can be accomplished via inverse probability of treatment weighting (IPTW), a standard approach originally developed for binary treatments and later generalized to multi-valued treatments. However, this approach generally results in biased results when the propensity score model is misspecified. Motivated by the need for more robust techniques, we propose two empirical likelihood-based weighting approaches that allow for specifying a set of propensity score models, with the second method balancing user-specified covariates directly, by incorporating additional, nonparametric constraints. The resulting estimators from both methods are consistent when the postulated set of propensity score models contains a correct one; this property has been termed multiple robustness. In this paper, we derive two multiply-robust estimators of the causal DDI, and develop inference procedures. We then evaluate their finite sample performance through simulation. The results demonstrate that the proposed estimators outperform the standard IPTW method in terms of both robustness and efficiency. Finally, we apply the proposed methods to evaluate the impact of renin-angiotensin system inhibitors (RAS-I) on the comparative nephrotoxicity of nonsteroidal anti-inflammatory drugs (NSAID) and opioids, using data derived from electronic medical records from a large multi-hospital health system.

Validating a Proteomic Signature of Severe COVID-19.

COVID-19 is a heterogenous disease. Biomarker-based approaches may identify patients at risk for severe disease, who may be more likely to benefit from specific therapies. Our objective was to identify and validate a plasma protein signature for severe COVID-19. DESIGN: Prospective observational cohort study. SETTING: Two hospitals in the United States. PATIENTS: One hundred sixty-seven hospitalized adults with COVID-19. INTERVENTION: None. MEASUREMENTS AND MAIN RESULTS: We measured 713 plasma proteins in 167 hospitalized patients with COVID-19 using a high-throughput platform. We classified patients as nonsevere versus severe COVID-19, defined as the need for high-flow nasal cannula, mechanical ventilation, extracorporeal membrane oxygenation, or death, at study entry and in 7-day intervals thereafter. We compared proteins measured at baseline between these two groups by logistic regression adjusting for age, sex, symptom duration, and comorbidities. We used lead proteins from dysregulated pathways as inputs for elastic net logistic regression to identify a parsimonious signature of severe disease and validated this signature in an external COVID-19 dataset. We tested whether the association between corticosteroid use and mortality varied by protein signature. One hundred ninety-four proteins were associated with severe COVID-19 at the time of hospital admission. Pathway analysis identified multiple pathways associated with inflammatory response and tissue repair programs. Elastic net logistic regression yielded a 14-protein signature that discriminated 90-day mortality in an external cohort with an area under the receiver-operator characteristic curve of 0.92 (95% CI, 0.88-0.95). Classifying patients based on the predicted risk from the signature identified a heterogeneous response to treatment with corticosteroids (p = 0.006). CONCLUSIONS: Inpatients with COVID-19 express heterogeneous patterns of plasma proteins. We propose a 14-protein signature of disease severity that may have value in developing precision medicine approaches for COVID-19 pneumonia.

Population-based screening to detect benzodiazepine drug-drug-drug interaction signals associated with unintentional traumatic injury.

Drug interactions involving benzodiazepines and related drugs (BZDs) are increasingly recognized as a contributor to increased risk of unintentional traumatic injury. Yet, it remains unknown to what extent drug interaction triads (3DIs) may amplify BZDs' inherent injury risk. We identified BZD 3DI signals associated with increased injury rates by conducting high-throughput pharmacoepidemiologic screening of 2000-2019 Optum's health insurance data. Using self-controlled case series design, we included patients aged ≥ 16 years with an injury while using a BZD + co-dispensed medication (i.e., base pair). During base pair-exposed observation time, we identified other co-dispensed medications as candidate interacting precipitants. Within each patient, we compared injury rates during time exposed to the drug triad versus to the base pair only using conditional Poisson regression, adjusting for time-varying covariates. We calculated rate ratios (RRs) with 95% confidence intervals (CIs) and accounted for multiple estimation via semi-Bayes shrinkage. Among the 65,123 BZD triads examined, 79 (0.1%) were associated with increased injury rates and considered 3DI signals. Adjusted RRs for signals ranged from 3.01 (95% CI = 1.53-5.94) for clonazepam + atorvastatin with cefuroxime to 1.42 (95% CI = 1.00-2.02, p = 0.049) for alprazolam + hydrocodone with tizanidine. These signals may help researchers prioritize future etiologic studies to investigate higher-order BZD interactions.

Association of vancomycin plus piperacillin-tazobactam with early changes in creatinine versus cystatin C in critically ill adults: a prospective cohort study.

PURPOSE: Although dozens of studies have associated vancomycin + piperacillin-tazobactam with increased acute kidney injury (AKI) risk, it is unclear whether the association represents true injury or a pseudotoxicity characterized by isolated effects on creatinine secretion. We tested this hypothesis by contrasting changes in creatinine concentration after antibiotic initiation with changes in cystatin C concentration, a kidney biomarker unaffected by tubular secretion. METHODS: We included patients enrolled in the Molecular Epidemiology of SepsiS in the ICU (MESSI) prospective cohort who were treated for ≥ 48 h with vancomycin + piperacillin-tazobactam or vancomycin + cefepime. Kidney function biomarkers [creatinine, cystatin C, and blood urea nitrogen (BUN)] were measured before antibiotic treatment and at day two after initiation. Creatinine-defined AKI and dialysis were examined through day-14, and mortality through day-30. Inverse probability of treatment weighting was used to adjust for confounding. Multiple imputation was used to impute missing baseline covariates. RESULTS: The study included 739 patients (vancomycin + piperacillin-tazobactam n = 297, vancomycin + cefepime n = 442), of whom 192 had cystatin C measurements. Vancomycin + piperacillin-tazobactam was associated with a higher percentage increase of creatinine at day-two 8.04% (95% CI 1.21, 15.34) and higher incidence of creatinine-defined AKI: rate ratio (RR) 1.34 (95% CI 1.01, 1.78). In contrast, vancomycin + piperacillin-tazobactam was not associated with change in alternative biomarkers: cystatin C: - 5.63% (95% CI - 18.19, 8.86); BUN: - 4.51% (95% CI - 12.83, 4.59); or clinical outcomes: dialysis: RR 0.63 (95% CI 0.31, 1.29); mortality: RR 1.05 (95%CI 0.79, 1.41). CONCLUSIONS: Vancomycin + piperacillin-tazobactam was associated with creatinine-defined AKI, but not changes in alternative kidney biomarkers, dialysis, or mortality, supporting the hypothesis that vancomycin + piperacillin-tazobactam effects on creatinine represent pseudotoxicity.

Identifying Clinically Relevant Drug-Drug Interactions With Methadone and Buprenorphine: A Translational Approach to Signal Detection.

Methadone and buprenorphine have pharmacologic properties that are concerning for a high risk of drug-drug interactions (DDIs). We performed high-throughput screening for clinically relevant DDIs with methadone or buprenorphine by combining pharmacoepidemiologic and pharmacokinetic approaches. We conducted pharmacoepidemiologic screening via a series of self-controlled case series studies (SCCS) in Optum claims data from 2000 to 2019. We included persons 18 years or older who experienced an outcome of interest during target drug treatment. Exposures were all overlapping medications (i.e., the candidate precipitants) during target drug treatment. Outcomes were opioid overdose, non-overdose adverse effects, and cardiac arrest. We used conditional Poisson regression to calculate rate ratios, accounting for multiple comparisons with semi-Bayes shrinkage. We explored the impact of key study design choices in analyses that varied the exposure definitions of the target drugs and the candidate precipitant drugs. Pharmacokinetic screening was conducted by incorporating published data on CYP enzyme metabolism into an equation-based static model. In SCCS analysis, 1,432 events were included from 248,069 new users of methadone or buprenorphine. In the primary analysis, statistically significant DDIs included gabapentinoids with either methadone or buprenorphine; baclofen with methadone; and benzodiazepines with methadone. In sensitivity analysis, additional statistically significant DDIs included methocarbamol, quetiapine, or simvastatin with methadone. Pharmacokinetic screening identified two moderate-to-strong potential DDIs (clonidine and fluconazole with buprenorphine). The combination of clonidine and buprenorphine was also associated with a significantly increased risk of opioid overdose in pharmacoepidemiologic screening. These DDI signals may be the most important targets for future confirmation studies.

Systematic review and meta-analysis of the correlation between bispectral index (BIS) and clinical sedation scales: Toward defining the role of BIS in critically ill patients.

INTRODUCTION: The bispectral index (BIS) is an attractive approach for monitoring level of consciousness in critically ill patients, particularly during paralysis, when commonly used sedation scales cannot be used. OBJECTIVES: As a first step toward establishing the utility of BIS during paralysis, this review examines the strength of correlation between BIS and clinical sedation scales in a broad population of non-paralyzed, critically ill adults. METHODS: We included studies evaluating the strength of correlation between concurrent assessments of BIS and Richmond Agitation Sedation Scale (RASS), Ramsay Sedation Scale (RSS), or Sedation Agitation Scale (SAS) in critically ill adult patients. Studies involving assessment of depth sedation periperative or procedural time periods, and those reporting BIS and sedation scale assessments conducted >5 min apart or while neuromuscular blocking agents (NMBA) were administered, were excluded. Data were abstracted on sedation scale, correlation coefficients, setting, patient characteristics, and BIS assessment characteristics that could impact the quality of the studies. RESULTS: Twenty-four studies which enrolled 1235 patients met inclusion criteria. The correlation between BIS and RASS, RSS, and SAS overall was 0.68 (95% confidence interval, 0.61-0.74, Ƭ2  = 0.06 I2  = 71.26%). Subgroup analysis by sedation scale indicated that the correlation between BIS and RASS, RSS, and SAS were 0.66 (95% confidence interval 0.58-0.73, Ƭ2  = 0.01 I2  = 30.20%), 0.76 (95% confidence interval 0.69-0.82, Ƭ2  = 0.04 I2  = 67.15%), and 0.53 (95% confidence interval 0.42-0.63, Ƭ2  = 0.01 I2  = 26.59%), respectively. Factors associated with significant heterogeneity included comparator clinical sedation scale, neurologic injury, and the type of intensive care unit (ICU) population. CONCLUSIONS: BIS demonstrated moderate to strong correlation with clinical sedation scales in adult ICU patients, providing preliminary evidence for the validity of BIS as a measure of sedation intensity when clinical scales cannot be used. Future studies should determine whether BIS monitoring is safe and effective in improving outcomes in patients receiving NMBA treatment.

Skeletal muscle relaxant drug-drug-drug interactions and unintentional traumatic injury: Screening to detect three-way drug interaction signals.

AIM: The aim of this study was to identify skeletal muscle relaxant (SMR) drug-drug-drug interaction (3DI) signals associated with increased rates of unintentional traumatic injury. METHODS: We conducted automated high-throughput pharmacoepidemiologic screening of 2000-2019 healthcare data for members of United States commercial and Medicare Advantage health plans. We performed a self-controlled case series study for each drug triad consisting of an SMR base-pair (i.e., concomitant use of an SMR with another medication), and a co-dispensed medication (i.e., candidate interacting precipitant) taken during ongoing use of the base-pair. We included patients aged ≥16 years with an injury occurring during base-pair-exposed observation time. We used conditional Poisson regression to calculate adjusted rate ratios (RRs) with 95% confidence intervals (CIs) for injury with each SMR base-pair + candidate interacting precipitant (i.e., triad) versus the SMR-containing base-pair alone. RESULTS: Among 58 478 triads, 29 were significantly positively associated with injury; confounder-adjusted RRs ranged from 1.39 (95% CI = 1.01-1.91) for tizanidine + omeprazole with gabapentin to 2.23 (95% CI = 1.02-4.87) for tizanidine + diclofenac with alprazolam. Most identified 3DI signals are new and have not been formally investigated. CONCLUSION: We identified 29 SMR 3DI signals associated with increased rates of injury. Future aetiologic studies should confirm or refute these SMR 3DI signals.

Opioid Drug-Drug-Drug Interactions and Unintentional Traumatic Injury: Screening to Detect Three-Way Drug Interaction Signals.

Growing evidence suggests that drug interactions may be responsible for much of the known association between opioid use and unintentional traumatic injury. While prior research has focused on pairwise drug interactions, the role of higher-order (i.e., drug-drug-drug) interactions (3DIs) has not been examined. We aimed to identify signals of opioid 3DIs with commonly co-dispensed medications leading to unintentional traumatic injury, using semi-automated high-throughput screening of US commercial health insurance data. We conducted bi-directional, self-controlled case series studies using 2000-2015 Optum Data Mart database. Rates of unintentional traumatic injury were examined in individuals dispensed opioid-precipitant base pairs during time exposed vs unexposed to a candidate interacting precipitant. Underlying cohorts consisted of 16-90-year-olds with new use of opioid-precipitant base pairs and ≥1 injury during observation periods. We used conditional Poisson regression to estimate rate ratios adjusted for time-varying confounders, and semi-Bayes shrinkage to address multiple estimation. For hydrocodone, tramadol, and oxycodone (the most commonly used opioids), we examined 16,024, 8185, and 9330 drug triplets, respectively. Among these, 75 (0.5%; hydrocodone), 57 (0.7%; tramadol), and 42 (0.5%; oxycodone) were significantly positively associated with unintentional traumatic injury (50 unique base precipitants, 34 unique candidate precipitants) and therefore deemed potential 3DI signals. The signals found in this study provide valuable foundations for future research into opioid 3DIs, generating hypotheses to motivate crucially needed etiologic investigations. Further, this study applies a novel approach for 3DI signal detection using pharmacoepidemiologic screening of health insurance data, which could have broad applicability across drug classes and databases.

Population-based signals of benzodiazepine drug interactions associated with unintentional traumatic injury.

Benzodiazepine receptor agonists and related medications, such as Z-drugs and dual orexin receptor antagonists (BZDs), have been associated with unintentional traumatic injury due to their central nervous system (CNS)-depressant effects. Drug-drug interactions (DDIs) may contribute to the known relationship between BZD use and unintentional traumatic injury, yet evidence is still lacking. We conducted high-throughput pharmacoepidemiologic screening using the self-controlled case series design in a large US commercial health insurance database to identify potentially clinically relevant DDI signals among new users of BZDs. We used conditional Poisson regression to estimate rate ratios (RRs) between each co-exposure (vs. not) and unintentional traumatic injury (primary outcome), typical hip fracture (secondary outcome), and motor vehicle crash (secondary outcome). We identified 48 potential DDI signals (1.1%, involving 39 unique co-dispensed drugs), i.e., with statistically significant elevated adjusted RRs for injury. Signals were strongest for DDI pairs involving zolpidem, lorazepam, temazepam, alprazolam, eszopiclone, triazolam, and clonazepam. We also identified four potential DDI signals for typical hip fracture, but none for motor vehicle crash. Many signals have biologically plausible explanations through additive or synergistic pharmacodynamic effects of co-dispensed antidepressants, opioids, or muscle relaxants on CNS depression, impaired psychomotor and cognitive function, and/or somnolence. While other signals that lack an obvious mechanism may represent true associations that place patients at risk of injury, it is also prudent to consider the roles of chance, reverse causation, and/or confounding by indication, which merit further exploration. Given the high-throughput nature of our investigation, findings should be interpreted as hypothesis generating.

Signals of Muscle Relaxant Drug Interactions Associated with Unintentional Traumatic Injury: A Population-Based Screening Study.

BACKGROUND: Use of muscle relaxants is rapidly increasing in the USA. Little is understood about the role of drug interactions in the known association between muscle relaxants and unintentional traumatic injury, a clinically important endpoint causing substantial morbidity, disability, and death. OBJECTIVE: We examined potential associations between concomitant drugs (i.e., precipitants) taken with muscle relaxants (affected drugs, i.e., objects) and hospital presentation for unintentional traumatic injury. METHODS: In a series of self-controlled case series studies, we screened to identify drug interaction signals for muscle relaxant + precipitant pairs and unintentional traumatic injury. We used Optum's de-identified Clinformatics® Data Mart Database, 2000-2019. We included new users of a muscle relaxant, aged 16-90 years, who were dispensed at least one precipitant drug and experienced an unintentional traumatic injury during the observation period. We classified each observation day as precipitant exposed or precipitant unexposed. The outcome was an emergency department or inpatient discharge diagnosis for unintentional traumatic injury. We used conditional Poisson regression to estimate rate ratios adjusting for time-varying confounders and then accounted for multiple estimation via semi-Bayes shrinkage. RESULTS: We identified 74,657 people who initiated muscle relaxants and experienced an unintentional traumatic injury, in whom we studied concomitant use of 2543 muscle relaxant + precipitant pairs. After adjusting for time-varying confounders, 16 (0.6%) pairs were statistically significantly and positively associated with injury, and therefore deemed signals of a potential drug interaction. Among signals, semi-Bayes shrunk, confounder-adjusted rate ratios ranged from 1.29 (95% confidence interval 1.04-1.62) for baclofen + sertraline to 2.28 (95% confidence interval 1.14-4.55) for methocarbamol + lamotrigine. CONCLUSIONS: Using real-world data, we identified several new signals of potential muscle relaxant drug interactions associated with unintentional traumatic injury. Only one among 16 signals is currently reported in a major drug interaction knowledge base. Future studies should seek to confirm or refute these signals.