Genetic risk factors for postoperative atrial fibrillation—a nationwide genome-wide association study (GWAS)

BackgroundAtrial fibrillation (AF) is a major cause of morbidity with a high prevalence among the elderly and has an established genetic disposition. Surgery is a well-known risk factor for AF; however, it is currently not recognized how much common genetic variants influence the postoperative risk. The purpose of this study was to identify Single Nucleotide Polymorphisms associated with postoperative AF.MethodsThe UK Biobank was utilized to conduct a Genome-Wide Association Study (GWAS) to identify variants associated with AF after surgery. An initial discovery GWAS was performed in patients that had undergone surgery with subsequent replication in a unique non-surgical cohort. In the surgical cohort, cases were defined as newly diagnosed AF within 30 days after surgery. The threshold for significance was set at 5 × 10−8.ResultsAfter quality control, 144,196 surgical patients with 254,068 SNPs were left for analysis. Two variants (rs17042171 (p = 4.86 × 10−15) and rs17042081 (p = 7.12 × 10−15)) near the PITX2-gene reached statistical significance. These variants were replicated in the non-surgical cohort (1.39 × 10−101 and 1.27 × 10−93, respectively). Several other loci were significantly associated with AF in the non-surgical cohort.ConclusionIn this GWAS-analysis of a large national biobank, we identified 2 variants that were significantly associated with postoperative AF. These variants were subsequently replicated in a unique non-surgical cohort. These findings bring new insight in the genetics of postoperative AF and may help identify at-risk patients and guide management

Resuscitation speed affects brain injury in a large animal model of traumatic brain injury and shock

Background: Optimal fluid resuscitation strategy following combined traumatic brain injury (TBI) and hemorrhagic shock (HS) remain controversial and the effect of resuscitation infusion speed on outcome is not well known. We have previously reported that bolus infusion of fresh frozen plasma (FFP) protects the brain compared with bolus infusion of 0.9% normal saline (NS). We now hypothesize reducing resuscitation infusion speed through a stepwise infusion speed increment protocol using either FFP or NS would provide neuroprotection compared with a high speed resuscitation protocol. Methods: 23 Yorkshire swine underwent a protocol of computer controlled TBI and 40% hemorrhage. Animals were left in shock (mean arterial pressure of 35 mmHg) for two hours prior to resuscitation with bolus FFP (n = 5, 50 ml/min) or stepwise infusion speed increment FFP (n = 6), bolus NS (n = 5, 165 ml/min) or stepwise infusion speed increment NS (n = 7). Hemodynamic variables over a 6-hour observation phase were recorded. Following euthanasia, brains were harvested and lesion size as well as brain swelling was measured. Results: Bolus FFP resuscitation resulted in greater brain swelling (22.36 ± 1.03% vs. 15.58 ± 2.52%, p = 0.04), but similar lesion size compared with stepwise resuscitation. This was associated with a lower cardiac output (CO: 4.81 ± 1.50 l/min vs. 5.45 ± 1.14 l/min, p = 0.03). In the NS groups, bolus infusion resulted in both increased brain swelling (37.24 ± 1.63% vs. 26.74 ± 1.33%, p = 0.05) as well as lesion size (3285.44 ± 130.81 mm3 vs. 2509.41 ± 297.44 mm3, p = 0.04). This was also associated with decreased cardiac output (NS: 4.37 ± 0.12 l/min vs. 6.35 ± 0.10 l/min, p < 0.01). Conclusions: In this clinically relevant model of combined TBI and HS, stepwise resuscitation protected the brain compared with bolus resuscitation

Assessing optimal methods for transferring machine learning models to low-volume and imbalanced clinical datasets: experiences from predicting outcomes of Danish trauma patients

IntroductionAccurately predicting patient outcomes is crucial for improving healthcare delivery, but large-scale risk prediction models are often developed and tested on specific datasets where clinical parameters and outcomes may not fully reflect local clinical settings. Where this is the case, whether to opt for de-novo training of prediction models on local datasets, direct porting of externally trained models, or a transfer learning approach is not well studied, and constitutes the focus of this study. Using the clinical challenge of predicting mortality and hospital length of stay on a Danish trauma dataset, we hypothesized that a transfer learning approach of models trained on large external datasets would provide optimal prediction results compared to de-novo training on sparse but local datasets or directly porting externally trained models.MethodsUsing an external dataset of trauma patients from the US Trauma Quality Improvement Program (TQIP) and a local dataset aggregated from the Danish Trauma Database (DTD) enriched with Electronic Health Record data, we tested a range of model-level approaches focused on predicting trauma mortality and hospital length of stay on DTD data. Modeling approaches included de-novo training of models on DTD data, direct porting of models trained on TQIP data to the DTD, and a transfer learning approach by training a model on TQIP data with subsequent transfer and retraining on DTD data. Furthermore, data-level approaches, including mixed dataset training and methods countering imbalanced outcomes (e.g., low mortality rates), were also tested.ResultsUsing a neural network trained on a mixed dataset consisting of a subset of TQIP and DTD, with class weighting and transfer learning (retraining on DTD), we achieved excellent results in predicting mortality, with a ROC-AUC of 0.988 and an F2-score of 0.866. The best-performing models for predicting long-term hospitalization were trained only on local data, achieving an ROC-AUC of 0.890 and an F1-score of 0.897, although only marginally better than alternative approaches.ConclusionOur results suggest that when assessing the optimal modeling approach, it is important to have domain knowledge of how incidence rates and workflows compare between hospital systems and datasets where models are trained. Including data from other health-care systems is particularly beneficial when outcomes are suffering from class imbalance and low incidence. Scenarios where outcomes are not directly comparable are best addressed through either de-novo local training or a transfer learning approach

Resuscitation with Valproic Acid Alters Inflammatory Genes in a Porcine Model of Combined Traumatic Brain Injury and Hemorrhagic Shock

Traumatic brain injury and hemorrhagic shock (TBI+HS) elicit a complex inflammatory response that contributes to secondary brain injury. There is currently no proven pharmacologic treatment for TBI+HS, but modulation of the epigenome has been shown to be a promising strategy. The aim of this study was to investigate whether valproic acid (VPA), a histone deacetylase inhibitor, modulates the expression of cerebral inflammatory gene profiles in a large animal model of TBI+HS. Ten Yorkshire swine were subjected to computer-controlled TBI+HS (40% blood volume). After 2?h of shock, animals were resuscitated with Hextend (HEX) or HEX+VPA (300?mg/kg, n?=?5/group). Six hours after resuscitation, brains were harvested, RNA was isolated, and gene expression profiles were measured using a porcine microarray. Ingenuity Pathway Analysis? (IPA), gene ontology (GO), Parametric Gene Set Enrichment Analysis (PGSEA), and DAVID (Database for Annotation, Visualization, and Integrated Discovery) were used for pathway analysis. Key microarray findings were verified using real-time polymerase chain reaction (PCR). IPA analysis revealed that VPA significantly down-regulated the complement system (p?Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/140171/1/neu.2015.4163.pd

Assessment of post-trauma complications in eight million trauma cases over a decade in the USA

Background Trauma is associated with a significant risk of post-trauma complications (PTCs). These include thromboembolic events, strokes, infections, and failure of organ systems (eg, kidney failure). Although care of the trauma patient has evolved during the last decade, whether this has resulted in a reduction in specific PTCs is unknown. We hypothesize that the incidence of PTCs has been decreasing during a 10-year period from 2007 to 2017.Methods This is a descriptive study of trauma patients originating from level 1, 2, 3, and 4 trauma centers in the USA, obtained via the Trauma Quality Improvement Program (TQIP) database from 2007 to 2017. PTCs documented throughout the time frame were extracted along with demographic variables. Multiple regression modeling was used to associate admission year with PTCs, while controlling for age, gender, Glasgow Coma Scale score, and Injury Severity Score.Results Data from 8 720 026 trauma patients were extracted from the TQIP database. A total of 366 768 patients experienced one or more PTCs. There was a general decrease in the incidence of PTCs during the study period, with the overall incidence dropping from 7.0% in 2007 to 2.8% in 2017. Multiple regression identified a slight decrease in incidence in all PTCs, although deep surgical site infection (SSI), deep venous thrombosis (DVT), and stroke incidences increased when controlled for confounders.Discussion Overall the incidence of PTCs dropped during the 10-year study period, although deep SSI, DVT, stroke, and cardiac arrest increased during the study period. Better risk prediction tools, enabling a precision medicine approach, are warranted to identify at-risk patients.Level of evidence III