Dataset Information

Genetic Polymorphisms in the Dopamine Receptor 2 Predict Acute Pain Severity After Motor Vehicle Collision.

ABSTRACT: OBJECTIVES:Dopaminergic signaling is implicated in nociceptive pathways. These effects are mediated largely through dopamine receptors and modulated in part by dopamine transporters. This study tested the hypothesis that genetic variants in the genes encoding dopamine receptor 2 (DRD2) and the dopamine active transporter (SLC6A3) influence acute pain severity after motor vehicle collision. MATERIALS AND METHODS:European Americans presenting to the emergency department after motor vehicle collision were recruited. Overall pain intensity in emergency department was assessed using a 0 to 10 numeric rating scale. DNA was extracted from blood samples and genotyping of single-nucleotide polymorphisms (SNPs) in the DRD2 and SLC6A3 gene was performed. RESULTS:A total of 948 patients completed evaluation. After correction for multiple comparisons, SNP rs6276 at DRD2 showed significant association with pain scores, with individuals with the A/A genotype reporting lower mean pain scores (5.3; 95% confidence interval [CI], 5.1-5.5) than those with A/G (5.9; 95% CI, 5.6-6.1) or G/G (5.7; 95% CI, 5.2-6.2) genotypes (P=0.0027). Secondary analyses revealed an interaction between sex and DRD2 SNPs rs4586205 and rs4648318 on pain scores: females with 2 minor alleles had increased pain intensity, whereas males with 2 minor alleles had less pain than individuals with a major allele (interaction P=0.0019). DISCUSSION:Genetic variants in DRD2 are associated with acute pain after a traumatic stressful event. These results suggest that dopaminergic agents may be useful for the treatment of individuals with acute posttraumatic pain as part of a multimodal opioid-sparing analgesic regimen.

SUBMITTER: Qadri YJ

PROVIDER: S-EPMC4417662 | biostudies-literature | 2015 Sep

REPOSITORIES: biostudies-literature

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Genetic Polymorphisms in the Dopamine Receptor 2 Predict Acute Pain Severity After Motor Vehicle Collision.

Qadri Yawar J YJ Bortsov Andrey V AV Orrey Danielle C DC Swor Robert A RA Peak David A DA Jones Jeffrey S JS Rathlev Niels K NK Lee David C DC Domeier Robert M RM Hendry Phyllis L PL Mclean Samuel A SA

The Clinical journal of pain 20150901 9

<h4>Objectives</h4>Dopaminergic signaling is implicated in nociceptive pathways. These effects are mediated largely through dopamine receptors and modulated in part by dopamine transporters. This study tested the hypothesis that genetic variants in the genes encoding dopamine receptor 2 (DRD2) and the dopamine active transporter (SLC6A3) influence acute pain severity after motor vehicle collision.<h4>Materials and methods</h4>European Americans presenting to the emergency department after motor ...[more]

PMID: 25370144

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Project description:ObjectiveTransporting severely injured pediatric patients to a trauma center has been shown to decrease mortality. A decision support tool to assist emergency medical services (EMS) providers with trauma triage would be both as parsimonious as possible and highly accurate. The objective of this study was to determine the minimum set of predictors required to accurately predict severe injury in pediatric patients.MethodsCrash data and patient injuries were obtained from the NASS and CISS databases. A baseline multivariable logistic model was developed to predict severe injury in pediatric patients using the following predictors: age, sex, seat row, restraint use, ejection, entrapment, posted speed limit, any airbag deployment, principal direction of force (PDOF), change in velocity (delta-V), single vs. multiple collisions, and non-rollover vs. rollover. The outcomes of interest were injury severity score (ISS) ≥16 and the Target Injury List (TIL). Accuracy was measured by the cross-validation mean of the receiver operator curve (ROC) area under the curve (AUC). We used Bayesian Model Averaging (BMA) based on all subsets regression to determine the importance of each variable separately for each outcome. The AUC of the highest performing model for each number of variables was compared to the baseline model to assess for a statistically significant difference (p < 0.05). A reduced variable set model was derived using this information.ResultsThe baseline models performed well (ISS ≥ 16: AUC 0.91 [95% CI: 0.86-0.95], TIL: AUC 0.90 [95% CI: 0.86-0.94]). Using BMA, the rank of the importance of the predictors was identical for both ISS ≥ 16 and TIL. There was no statistically significant decrease in accuracy until the models were reduced to fewer than five and six variables for predicting ISS ≥ 16 and TIL, respectively. A reduced variable set model developed using the top five variables (delta-V, entrapment, ejection, restraint use, and near-side collision) to predict ISS ≥ 16 had an AUC 0.90 [95% CI: 0.84-0.96]. Among the models that did not include delta-V, the highest AUC was 0.82 [95% CI: 0.77-0.87].ConclusionsA succinct logistic regression model can accurately predict severely injured pediatric patients, which could be used for prehospital trauma triage. However, there remains a critical need to obtain delta-V in real-time.

Project description:ImportanceA substantial proportion of the 40 million people in the US who present to emergency departments (EDs) each year after traumatic events develop posttraumatic stress disorder (PTSD) or major depressive episode (MDE). Accurately identifying patients at high risk in the ED would facilitate the targeting of preventive interventions.ObjectivesTo develop and validate a prediction tool based on ED reports after a motor vehicle collision to predict PTSD or MDE 3 months later.Design, setting, and participantsThe Advancing Understanding of Recovery After Trauma (AURORA) study is a longitudinal study that examined adverse posttraumatic neuropsychiatric sequalae among patients who presented to 28 US urban EDs in the immediate aftermath of a traumatic experience. Enrollment began on September 25, 2017. The 1003 patients considered in this diagnostic/prognostic report completed 3-month assessments by January 31, 2020. Each patient received a baseline ED assessment along with follow-up self-report surveys 2 weeks, 8 weeks, and 3 months later. An ensemble machine learning method was used to predict 3-month PTSD or MDE from baseline information. Data analysis was performed from November 1, 2020, to May 31, 2021.Main outcomes and measuresThe PTSD Checklist for DSM-5 was used to assess PTSD and the Patient Reported Outcomes Measurement Information System Depression Short-Form 8b to assess MDE.ResultsA total of 1003 patients (median [interquartile range] age, 34.5 [24-43] years; 715 [weighted 67.9%] female; 100 [weighted 10.7%] Hispanic, 537 [weighted 52.7%] non-Hispanic Black, 324 [weighted 32.2%] non-Hispanic White, and 42 [weighted 4.4%] of non-Hispanic other race or ethnicity were included in this study. A total of 274 patients (weighted 26.6%) met criteria for 3-month PTSD or MDE. An ensemble machine learning model restricted to 30 predictors estimated in a training sample (patients from the Northeast or Midwest) had good prediction accuracy (mean [SE] area under the curve [AUC], 0.815 [0.031]) and calibration (mean [SE] integrated calibration index, 0.040 [0.002]; mean [SE] expected calibration error, 0.039 [0.002]) in an independent test sample (patients from the South). Patients in the top 30% of predicted risk accounted for 65% of all 3-month PTSD or MDE, with a mean (SE) positive predictive value of 58.2% (6.4%) among these patients at high risk. The model had good consistency across regions of the country in terms of both AUC (mean [SE], 0.789 [0.025] using the Northeast as the test sample and 0.809 [0.023] using the Midwest as the test sample) and calibration (mean [SE] integrated calibration index, 0.048 [0.003] using the Northeast as the test sample and 0.024 [0.001] using the Midwest as the test sample; mean [SE] expected calibration error, 0.034 [0.003] using the Northeast as the test sample and 0.025 [0.001] using the Midwest as the test sample). The most important predictors in terms of Shapley Additive Explanations values were symptoms of anxiety sensitivity and depressive disposition, psychological distress in the 30 days before motor vehicle collision, and peritraumatic psychosomatic symptoms.Conclusions and relevanceThe results of this study suggest that a short set of questions feasible to administer in an ED can predict 3-month PTSD or MDE with good AUC, calibration, and geographic consistency. Patients at high risk can be identified in the ED for targeting if cost-effective preventive interventions are developed.

Dataset Information

Genetic Polymorphisms in the Dopamine Receptor 2 Predict Acute Pain Severity After Motor Vehicle Collision.

Publications

Genetic Polymorphisms in the Dopamine Receptor 2 Predict Acute Pain Severity After Motor Vehicle Collision.

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