Little is known about the effectiveness of BMMNC therapy in hypertensive heart disease. In the current study, we show that delivery of BMMNCs from hypertension protected SS-13 BN /MCWi donor rats, but not BMMNC from hypertension susceptible SS/MCWi donor rats, resulted in 57.2 and 83.4% reductions in perivascular and interstitial fibrosis, respectively, as well as a 60% increase in capillary-to-myocyte count in the left ventricles (LV) of hypertensive SS/MCWi recipients. These histological changes were associated with improvements in LV compliance and relaxation (103 and 46.4% improvements, respectively). Furthermore, improved diastolic function in hypertensive SS/MCWi rats receiving SS-13 BN /MCWi derived BMMNCs was associated with lower clinical indicators of heart failure, including reductions in end diastolic pressure (65%) and serum brain natriuretic peptide levels (49.9%) with no improvements observed in rats receiving SS/MCWi BMMNCs. SS/MCWi rats had a lower percentage of endothelial progenitor cells in their bone marrow relative to SS-13 BN /MCWi rats. These results suggest that administration of BMMNCs can prevent or reverse pathological remodeling in hypertensive heart disease, which contributes to ameliorating diastolic dysfunction and associated symptomology. Furthermore, the health and hypertension susceptibility of the BMMNC donor are important factors influencing therapeutic efficacy, possibly via differences in the cellular composition of bone marrow.
ObjectivesThe diagnostic process is a vital component of safe and effective emergency department (ED) care. There are no standardized methods for identifying or reliably monitoring diagnostic errors in the ED, impeding efforts to enhance diagnostic safety. We sought to identify trigger concepts to screen ED records for diagnostic errors and describe how they can be used as a measurement strategy to identify and reduce preventable diagnostic harm.MethodsWe conducted a literature review and surveyed ED directors to compile a list of potential electronic health record (EHR) trigger (e-triggers) and non-EHR based concepts. We convened a multidisciplinary expert panel to build consensus on trigger concepts to identify and reduce preventable diagnostic harm in the ED.ResultsSix e-trigger and five non-EHR based concepts were selected by the expert panel. E-trigger concepts included: unscheduled ED return to ED resulting in hospital admission, death following ED visit, care escalation, high-risk conditions based on symptom-disease dyads, return visits with new diagnostic/therapeutic interventions, and change of treating service after admission. Non-EHR based signals included: cases from mortality/morbidity conferences, risk management/safety office referrals, ED medical director case referrals, patient complaints, and radiology/laboratory misreads and callbacks. The panel suggested further refinements to aid future research in defining diagnostic error epidemiology in ED settings.ConclusionsWe identified a set of e-trigger concepts and non-EHR based signals that could be developed further to screen ED visits for diagnostic safety events. With additional evaluation, trigger-based methods can be used as tools to monitor and improve ED diagnostic performance.
Background Diagnostic decision making, especially in emergency departments, is a highly complex cognitive process that involves uncertainty and susceptibility to errors. A combination of factors, including patient factors (eg, history, behaviors, complexity, and comorbidity), provider-care team factors (eg, cognitive load and information gathering and synthesis), and system factors (eg, health information technology, crowding, shift-based work, and interruptions) may contribute to diagnostic errors. Using electronic triggers to identify records of patients with certain patterns of care, such as escalation of care, has been useful to screen for diagnostic errors. Once errors are identified, sophisticated data analytics and machine learning techniques can be applied to existing electronic health record (EHR) data sets to shed light on potential risk factors influencing diagnostic decision making. Objective This study aims to identify variables associated with diagnostic errors in emergency departments using large-scale EHR data and machine learning techniques. Methods This study plans to use trigger algorithms within EHR data repositories to generate a large data set of records that are labeled trigger-positive or trigger-negative, depending on whether they meet certain criteria. Samples from both data sets will be validated using medical record reviews, upon which we expect to find a higher number of diagnostic safety events in the trigger-positive subset. Machine learning will be used to evaluate relationships between certain patient factors, provider-care team factors, and system-level risk factors and diagnostic safety signals in the statistically matched groups of trigger-positive and trigger-negative charts. Results This federally funded study was approved by the institutional review board of 2 academic medical centers with affiliated community hospitals. Trigger queries are being developed at both organizations, and sample cohorts will be labeled using the triggers. Machine learning techniques such as association rule mining, chi-square automated interaction detection, and classification and regression trees will be used to discover important variables that could be incorporated within future clinical decision support systems to help identify and reduce risks that contribute to diagnostic errors. Conclusions The use of large EHR data sets and machine learning to investigate risk factors (related to the patient, provider-care team, and system-level) in the diagnostic process may help create future mechanisms for monitoring diagnostic safety. International Registered Report Identifier (IRRID) DERR1-10.2196/24642
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