Effect of Preinjury Oral Anticoagulants on Outcomes Following Traumatic Brain Injury from Falls in Older Adults

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/156128/2/phar2435_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/156128/1/phar2435.pd

Factors associated with optimal patient outcomes after operative repair of isolated hip fractures in the elderly

Background: Increased time to operative intervention is associated with a greater risk of mortality and complications in adults with a hip fracture. This study sought to determine factors associated with timeliness of operation in elderly patients presenting with an isolated hip fracture and the influence of surgical delay on outcomes. Methods: Trauma quality collaborative data (July 2016 to June 2019) were analyzed. Inclusion criteria were patients ≥65 years with an injury mechanism of fall, Abbreviated Injury Scale (AIS) 2005 diagnosis of hip fracture, and AIS extremity ≤3. Exclusion criteria included AIS in other body regions >1 and non-operative management. We examined the association of demographic, hospital, injury presentation, and comorbidity factors on a surgical delay >48 hours and patient outcomes using multivariable regression analysis. Results: 10 182 patients fit our study criteria out of 212 620 patients. Mean age was 82.7±8.6 years and 68.7% were female. Delay in operation >48 hours occurred in 965 (9.5%) of patients. Factors that significantly increased mortality or discharge to hospice were increased age, male gender, emergency department hypotension, functionally dependent health status (FDHS), advanced directive, liver disease, angina, and congestive heart failure (CHF). Delay >48 hours was associated with increased mortality or discharge to hospice (OR 1.52; 95% CI 1.13 to 2.06; p48 hours were male gender, FDHS, CHF, chronic renal failure, and advanced directive. Admission to the orthopedic surgery service was associated with less incidence of delay >48 hours (OR 0.43; 95% CI 0.29 to 0.64; p<0.001). Discussion: Hospital verification level, admission service, and patient volume did not impact the outcome of mortality/discharge to hospice. Delay to operation >48 hours was associated with increased mortality. The only measured modifiable characteristic that reduced delay to operative intervention was admission to the orthopedic surgery service. Level of evidence: III

Stroke genetics informs drug discovery and risk prediction across ancestries

Previous genome-wide association studies (GWASs) of stroke — the second leading cause of death worldwide — were conducted predominantly in populations of European ancestry1,2. Here, in cross-ancestry GWAS meta-analyses of 110,182 patients who have had a stroke (five ancestries, 33% non-European) and 1,503,898 control individuals, we identify association signals for stroke and its subtypes at 89 (61 new) independent loci: 60 in primary inverse-variance-weighted analyses and 29 in secondary meta-regression and multitrait analyses. On the basis of internal cross-ancestry validation and an independent follow-up in 89,084 additional cases of stroke (30% non-European) and 1,013,843 control individuals, 87% of the primary stroke risk loci and 60% of the secondary stroke risk loci were replicated (P < 0.05). Effect sizes were highly correlated across ancestries. Cross-ancestry fine-mapping, in silico mutagenesis analysis3, and transcriptome-wide and proteome-wide association analyses revealed putative causal genes (such as SH3PXD2A and FURIN) and variants (such as at GRK5 and NOS3). Using a three-pronged approach4, we provide genetic evidence for putative drug effects, highlighting F11, KLKB1, PROC, GP1BA, LAMC2 and VCAM1 as possible targets, with drugs already under investigation for stroke for F11 and PROC. A polygenic score integrating cross-ancestry and ancestry-specific stroke GWASs with vascular-risk factor GWASs (integrative polygenic scores) strongly predicted ischaemic stroke in populations of European, East Asian and African ancestry5. Stroke genetic risk scores were predictive of ischaemic stroke independent of clinical risk factors in 52,600 clinical-trial participants with cardiometabolic disease. Our results provide insights to inform biology, reveal potential drug targets and derive genetic risk prediction tools across ancestries

Stroke genetics informs drug discovery and risk prediction across ancestries

Previous genome-wide association studies (GWASs) of stroke - the second leading cause of death worldwide - were conducted predominantly in populations of European ancestry(1,2). Here, in cross-ancestry GWAS meta-analyses of 110,182 patients who have had a stroke (five ancestries, 33% non-European) and 1,503,898 control individuals, we identify association signals for stroke and its subtypes at 89 (61 new) independent loci: 60 in primary inverse-variance-weighted analyses and 29 in secondary meta-regression and multitrait analyses. On the basis of internal cross-ancestry validation and an independent follow-up in 89,084 additional cases of stroke (30% non-European) and 1,013,843 control individuals, 87% of the primary stroke risk loci and 60% of the secondary stroke risk loci were replicated (P < 0.05). Effect sizes were highly correlated across ancestries. Cross-ancestry fine-mapping, in silico mutagenesis analysis(3), and transcriptome-wide and proteome-wide association analyses revealed putative causal genes (such as SH3PXD2A and FURIN) and variants (such as at GRK5 and NOS3). Using a three-pronged approach(4), we provide genetic evidence for putative drug effects, highlighting F11, KLKB1, PROC, GP1BA, LAMC2 and VCAM1 as possible targets, with drugs already under investigation for stroke for F11 and PROC. A polygenic score integrating cross-ancestry and ancestry-specific stroke GWASs with vascular-risk factor GWASs (integrative polygenic scores) strongly predicted ischaemic stroke in populations of European, East Asian and African ancestry(5). Stroke genetic risk scores were predictive of ischaemic stroke independent of clinical risk factors in 52,600 clinical-trial participants with cardiometabolic disease. Our results provide insights to inform biology, reveal potential drug targets and derive genetic risk prediction tools across ancestries.</p

Adopt or Abandon? Surgeon-Specific Trends in Robotic Bariatric Surgery Utilization Between 2010 and 2019

Background: It is unknown if surgeons are more likely to adopt or abandon robotic techniques given that bariatric procedures are already performed by surgeons with advanced laparoscopic skills. Methods: We used a statewide bariatric-specific data registry to evaluate surgeon-specific volumes of robotic bariatric cases between 2010 and 2019. Operative volume, procedure type, and patient characteristics were compared between the highest utilizers of robotic bariatric procedures (adopters) and surgeons who stopped performing robotic cases, despite demonstrating prior use (abandoners). Results: A total of 44 surgeons performed 3149 robotic bariatric procedures in Michigan between 2010 and 2019. Robotic utilization peaked in 2019, representing 7.24% of all bariatric cases. We identified 7 surgeons (16%) who performed 95% of the total number of robotic cases (adopters) and 12 surgeons (27%) who stopped performing bariatric cases during the study period (abandoners). Adopters performed a higher proportion of gastric bypass both robotically (22.9% versus 3.1%, P \u3c .001) and laparoscopically (27.5% versus 15.1%, P \u3c .001), when compared with abandoners. Surgeon experience (no. of years in practice), type of practice (teaching versus nonteaching hospital), and patient populations were similar between groups. Conclusions: Robotic bariatric utilization increased during the study period. The majority of robotic cases were performed by a small number of surgeons who were more likely to perform more complex cases such as gastric bypass in their own practice. Robotic adoption may be influenced by surgeon-specific preferences based upon procedure-specific volumes and may play a greater role in performing more complex surgical procedures in the future

Complications and resource utilization in trauma patients with diabetes.

Diabetes is associated with poor outcomes in critically ill populations. The goal of this study was to determine if diabetic patients suffer poorer outcomes following trauma. Collaborative trauma patient data from 2012-2018 was analyzed. Patients with no signs-of-life, Injury Severity Score (ISS) <5, age <16 years, and hospitalization <1 day were excluded. Multivariable logistic and linear regression were used to compare patients with and without diabetes for selected outcomes. Risk-adjustment variables included demographics, physiology, comorbidities, and injury scoring. Of 106,141 trauma patients, 14,150 (13%) had diabetes. On admission, diabetes was associated with significantly increased risk of any, serious, infectious, urinary tract, sepsis, pneumonia, and cardiac complications. Diabetes was also associated with increased ventilator days (7.5 vs. 6.6 days, p = 0.003), intensive care unit days (5.8 vs. 5.3 days, p<0.001), and hospital length of stay (5.7 vs. 5.3 days, p<0.001). Subgroup analysis revealed the least injured diabetic category (ISS 5-15) suffered higher odds of hospital mortality and any, serious, infectious and cardiac complications. The association between diabetes, hospital mortality and complication rates in mild traumatic injury is independent of age. Trauma patients with diabetes experience higher rates of complications and resource utilization. The largest cohort of diabetics experience the least severe injuries and suffer the greatest in hospital mortality and complication rates. A better understanding of the physiologic derangements associated with diabetes is necessary to develop novel approaches to reduce excess trauma morbidity, mortality and resource consumption

Predicting Early Weight Loss Failure Using a Bariatric Surgery Outcomes Calculator and Weight Loss Curves

CONTEXT: Weight loss after bariatric surgery can be accurately predicted using an outcomes calculator; however, outliers exist that do not meet the 1 year post-surgery weight projections. OBJECTIVE: Our goal was to determine how soon after surgery these outliers can be identified. DESIGN: We conducted a retrospective cohort study. SETTING, PATIENTS, AND INTERVENTION: Using a bariatric surgery outcomes calculator formulated by the Michigan Bariatric Surgery Collaborative (MBSC), predicted weight loss at 1 year post-surgery was calculated on all patients who underwent primary bariatric surgery at a single-center academic institution between 2006 and 2015 who also had a documented 1-year follow-up weight (n = 1050). MAIN OUTCOME MEASURES: Weight loss curves were compared between high, low, and non-outliers as defined by their observed-to-expected (O:E) weight loss ratio based on total body weight loss (TBWL) %. RESULTS: Mean predicted weight loss for the study group was 39.1 ± 9.9 kg, while mean actual weight loss was 39.7 ± 17.1 kg resulting in a mean O:E 1.01 (± 0.35). Based on analysis of the O:E ratios at 1 year post-surgery, the study group was sub-classified. Low outliers (n = 188, O:E 0.51) had significantly lower weight loss at 2 months (13.1% vs 15.6% and 16.5% TBWL, p \u3c 0. 001) and at 6 months (19% vs 26% and 30% TBWL, p \u3c 0.001) when compared to non-outliers (n = 638, O:E 1.00) and high outliers (n = 224, O:E 1.46), respectively. CONCLUSIONS: Weight loss curves based on individually calculated outcomes can help identify low outliers for additional interventions as early as 2 months after bariatric surgery