Platelet Function Monitoring in Patients With Coronary Artery Disease

Studies focused on patient responsiveness to antiplatelet therapies, particularly aspirin and clopidogrel, have increased in recent years. However, the relations of in vivo platelet function and adverse clinical events to results of ex vivo platelet function tests remain largely unknown. This article describes current methods of measuring platelet function in various clinical and research situations and their advantages and disadvantages, reviews evidence for antiplatelet response variability and resistance, discusses the potential pitfalls of monitoring platelet function, and demonstrates emerging data supporting the positive clinical and treatment implications of platelet function testing

Predicting Hemolytic Uremic Syndrome and Renal Replacement Therapy in Shiga Toxin-producing Escherichia coli-infected Children.

BACKGROUND: Shiga toxin-producing Escherichia coli (STEC) infections are leading causes of pediatric acute renal failure. Identifying hemolytic uremic syndrome (HUS) risk factors is needed to guide care. METHODS: We conducted a multicenter, historical cohort study to identify features associated with development of HUS (primary outcome) and need for renal replacement therapy (RRT) (secondary outcome) in STEC-infected children without HUS at initial presentation. Children agedeligible. RESULTS: Of 927 STEC-infected children, 41 (4.4%) had HUS at presentation; of the remaining 886, 126 (14.2%) developed HUS. Predictors (all shown as odds ratio [OR] with 95% confidence interval [CI]) of HUS included younger age (0.77 [.69-.85] per year), leukocyte count ≥13.0 × 103/μL (2.54 [1.42-4.54]), higher hematocrit (1.83 [1.21-2.77] per 5% increase) and serum creatinine (10.82 [1.49-78.69] per 1 mg/dL increase), platelet count \u3c250 \u3e× 103/μL (1.92 [1.02-3.60]), lower serum sodium (1.12 [1.02-1.23 per 1 mmol/L decrease), and intravenous fluid administration initiated ≥4 days following diarrhea onset (2.50 [1.14-5.46]). A longer interval from diarrhea onset to index visit was associated with reduced HUS risk (OR, 0.70 [95% CI, .54-.90]). RRT predictors (all shown as OR [95% CI]) included female sex (2.27 [1.14-4.50]), younger age (0.83 [.74-.92] per year), lower serum sodium (1.15 [1.04-1.27] per mmol/L decrease), higher leukocyte count ≥13.0 × 103/μL (2.35 [1.17-4.72]) and creatinine (7.75 [1.20-50.16] per 1 mg/dL increase) concentrations, and initial intravenous fluid administration ≥4 days following diarrhea onset (2.71 [1.18-6.21]). CONCLUSIONS: The complex nature of STEC infection renders predicting its course a challenge. Risk factors we identified highlight the importance of avoiding dehydration and performing close clinical and laboratory monitoring

A prospective study of symptoms, function, and medication use during acute illness in nursing home residents: design, rationale and cohort description

<p>Abstract</p> <p>Background</p> <p>Nursing home residents are at high risk for developing acute illnesses. Compared with community dwelling adults, nursing home residents are often more frail, prone to multiple medical problems and symptoms, and are at higher risk for adverse outcomes from acute illnesses. In addition, because of polypharmacy and the high burden of chronic disease, nursing home residents are particularly vulnerable to disruptions in transitions of care such as medication interruptions in the setting of acute illness. In order to better estimate the effect of acute illness on nursing home residents, we have initiated a prospective cohort which will allow us to observe patterns of acute illnesses and the consequence of acute illnesses, including symptoms and function, among nursing home residents. We also aim to examine the patterns of medication interruption, and identify patient, provider and environmental factors that influence continuity of medication prescribing at different points of care transition.</p> <p>Methods</p> <p>This is a prospective cohort of nursing home residents residing in two nursing homes in a metropolitan area. Baseline characteristics including age, gender, race, and comorbid conditions are recorded. Participants are followed longitudinally for a planned period of 3 years. We record acute illness incidence and characteristics, and measure symptoms including depression, pain, withdrawal symptoms, and function using standardized scales.</p> <p>Results</p> <p>76 nursing home residents have been followed for a median of 666 days to date. At baseline, mean age of residents was 74.4 (± 11.9); 32% were female; 59% were white. The most common chronic conditions were dementia (41%), depression (38%), congestive heart failure (25%) and chronic obstructive lung disease (27%). Mean pain score was 4.7 (± 3.6) on a scale of 0 to 10; Geriatric Depression Scale (GDS-15) score was 5.2 (± 4.4). During follow up, 138 acute illness episodes were identified, for an incidence of 1.5 (SD 2.0) episodes per resident per year; 74% were managed in the nursing home and 26% managed in the acute care setting.</p> <p>Conclusion</p> <p>In this report, we describe the conceptual model and methods of designing a longitudinal cohort to measure acute illness patterns and symptoms among nursing home residents, and describe the characteristics of our cohort at baseline. In our planned analysis, we will further estimate the effect of the use and interruption of medications on withdrawal and relapse symptoms and illness outcomes.</p

Generation of subject-specific, dynamic, multisegment ankle and foot models to improve orthotic design: a feasibility study

ABSTRACT: BACKGROUND: Currently, custom foot and ankle orthosis prescription and design tend to be based on traditional techniques, which can result in devices which vary greatly between clinicians and repeat prescription. The use of computational models of the foot may give further insight in the biomechanical effects of these devices and allow a more standardised approach to be taken to their design, however due to the complexity of the foot the models must be highly detailed and dynamic. METHODS: Functional and anatomical datasets will be collected in a multicentre study from 10 healthy participants and 15 patients requiring orthotic devices. The patient group will include individuals with metarsalgia, flexible flat foot and drop foot. Each participant will undergo a clinical foot function assessment, 3D surface scans of the foot under different loading conditions, and detailed gait analysis including kinematic, kinetic, muscle activity and plantar pressure measurements in both barefoot and shod conditions. Following this each participant will undergo computed tomography (CT) imaging of their foot and ankle under a range of loads and positions while plantar pressures are recorded. A further subgroup of participants will undergo magnetic resonance imaging (MRI) of the foot and ankle. Imaging data will be segmented to derive the size of bones and orientation of the joint axes. Insertion points of muscles and ligaments will be determined from the MRI and CT-scans and soft tissue material properties computed from the loaded CT data in combination with the plantar pressure measurements. Gait analysis data will be used to drive the models and in combination with the 3D surface scans for scaling purposes. Predicted plantar pressures and muscle activation patterns predicted from the models will be compared to determine the validity of the models. DISCUSSION: This protocol will lead to the generation of unique datasets which will be used to develop linked inverse dynamic and forward dynamic biomechanical foot models. These models may be beneficial in predicting the effect of and thus improving the efficacy of orthotic devices for the foot and ankle

Measuring complications of serious pediatric emergencies using ICD‐10

ObjectiveTo create definitions for complications for 16 serious pediatric conditions using the International Classification of Diseases, 10th Revision, Clinical Modification or Procedure Coding System (ICD‐10‐CM/PCS), and to assess whether complication rates are similar to those measured with ICD‐9‐CM/PCS.Data SourcesThe Healthcare Cost and Utilization Project State Emergency Department and Inpatient Databases from five states between 2014 and 2017 were used to identify cases and assess complication rates. Incidences were calculated using population counts from the 5‐year American Community Survey.Data Collection/Extraction MethodsPatients were identified by the presence of a diagnosis code for one of the 16 serious conditions. Only the first encounter for a given condition by a patient was included. Encounters resulting in transfer were excluded as the presence of complications was unknown.Study DesignWe defined complications using data elements routinely available in administrative databases including ICD‐10‐CM/PCS codes. The definitions were adapted from ICD‐9‐CM/PCS using general equivalence mappings and refined using consensus opinion. We included 16 serious conditions: appendicitis, bacterial meningitis, compartment syndrome, new‐onset diabetic ketoacidosis (DKA), ectopic pregnancy, empyema, encephalitis, intussusception, mastoiditis, myocarditis, orbital cellulitis, ovarian torsion, sepsis, septic arthritis, stroke, and testicular torsion. Using data from children under 18 years, we compared incidences and complication rates across the ICD‐10‐CM/PCS transition for each condition using interrupted time series.Principal FindingsThere were 61 314 ED visits for a serious condition; the most common was appendicitis (n = 37 493). Incidence rates for each condition were not significantly different across the ICD‐10‐CM/PCS transition for 13/16 conditions. Three differed: empyema (increased 42%), orbital cellulitis (increased 60%), and sepsis (increased 26%). Complication rates were not significantly different for each condition across the ICD‐10‐CM/PCS transition, except appendicitis (odds ratio 0.62, 95% CI 0.57‐0.68), DKA (OR 3.79, 95% CI 1.92‐7.50), and orbital cellulitis (OR 0.53, 95% CI 0.30‐0.95).ConclusionsFor most conditions, incidences and complication rates were similar before and after the transition to ICD‐10‐CM/PCS codes, suggesting our system identifies complications of conditions in administrative data similarly using ICD‐9‐CM/PCS and ICD‐10‐CM/PCS codes. This system may be applied to screen for cases with complications and in health services research.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/167093/1/hesr13615-sup-0003-FigureS1.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/167093/2/hesr13615_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/167093/3/hesr13615-sup-0001-Authormatrix.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/167093/4/hesr13615.pd

Auxora vs. placebo for the treatment of patients with severe COVID-19 pneumonia: A randomized-controlled clinical trial

Background: Calcium release-activated calcium (CRAC) channel inhibitors block proinflammatory cytokine release, preserve endothelial integrity and may effectively treat patients with severe COVID-19 pneumonia. Methods: CARDEA was a phase 2, randomized, double-blind, placebo-controlled trial evaluating the addition of Auxora, a CRAC channel inhibitor, to corticosteroids and standard of care in adults with severe COVID-19 pneumonia. Eligible patients were adults with ≥ 1 symptom consistent with COVID-19 infection, a diagnosis of COVID-19 confirmed by laboratory testing using polymerase chain reaction or other assay, and pneumonia documented by chest imaging. Patients were also required to be receiving oxygen therapy using either a high flow or low flow nasal cannula at the time of enrolment and have at the time of enrollment a baseline imputed PaO2/FiO2 ratio \u3e 75 and ≤ 300. The PaO2/FiO2 was imputed from a SpO2/FiO2 determine by pulse oximetry using a non-linear equation. Patients could not be receiving either non-invasive or invasive mechanical ventilation at the time of enrolment. The primary endpoint was time to recovery through Day 60, with secondary endpoints of all-cause mortality at Day 60 and Day 30. Due to declining rates of COVID-19 hospitalizations and utilization of standard of care medications prohibited by regulatory guidance, the trial was stopped early. Results: The pre-specified efficacy set consisted of the 261 patients with a baseline imputed PaO2/FiO2≤ 200 with 130 and 131 in the Auxora and placebo groups, respectively. Time to recovery was 7 vs. 10 days (P = 0.0979) for patients who received Auxora vs. placebo, respectively. The all-cause mortality rate at Day 60 was 13.8% with Auxora vs. 20.6% with placebo (P = 0.1449); Day 30 all-cause mortality was 7.7% and 17.6%, respectively (P = 0.0165). Similar trends were noted in all randomized patients, patients on high flow nasal cannula at baseline or those with a baseline imputed PaO2/FiO2 ≤ 100. Serious adverse events (SAEs) were less frequent in patients treated with Auxora vs. placebo and occurred in 34 patients (24.1%) receiving Auxora and 49 (35.0%) receiving placebo (P = 0.0616). The most common SAEs were respiratory failure, acute respiratory distress syndrome, and pneumonia. Conclusions: Auxora was safe and well tolerated with strong signals in both time to recovery and all-cause mortality through Day 60 in patients with severe COVID-19 pneumonia. Further studies of Auxora in patients with severe COVID-19 pneumonia are warranted. Trial registration NCT04345614

Optimal channel switching for average capacity maximization in the presence of switching delays

In this study, the optimal channel switching problem is investigated for average capacity maximization in the presence of channel switching delays. First, the optimal strategy is obtained and the corresponding average capacity is derived when channel switching is performed among a given number of channels. Then, it is proved that channel switching among more than two different channels is not optimal. Also, the maximum average capacity achieved by the optimal channel switching strategy is expressed as a function of the channel switching delay parameter and the average and peak power limits. Then, scenarios in which the optimal strategy corresponds to the use of a single channel or to channel switching between two channels are described. Numerical examples are presented for showing the effects of channel switching delays. © 2016 IEEE