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OBJECTIVE: This study examined the prevalence of falls among older adults with generalized and localized osteoarthritis (OA) and identified the association between falls and both chronic diseases and medications. PATIENTS AND METHODS: A retrospective design using the Healthcare Enterprise Repository for Ontological Narration (HERON) database was used. A cohort of 760 patients aged ≥65 years with at least two diagnosis codes for either localized or generalized OA were included. The extracted data included demographics (age, sex, and race), body mass index (BMI), fall history, comorbid health conditions (i.e., type 2 diabetes, hypertension, dyslipidemia, neuropathy, cardiovascular diseases, depression, anxiety, and sleep disorders), and medications [i.e., pain medication (opioids, non-opioids), antidiabetics (insulin or hypoglycemic), antihypertensives, antilipemic, and antidepressants]. RESULTS: The prevalence rates of falls and recurrent falls were 27.77% and 9.88%, respectively. Individuals with generalized OA had a higher prevalence of falls (33.8%) than those with localized OA (24.2%). Multivariable logistic regression analysis showed that individuals with OA who had hypertension [odds ratio (OR): 1.86, 95% CI, (1.20, 2.89), p=0.006] and used antidepressants [OR: 1.72, 95% CI, (1.04, 2.84), p=0.035] were more likely to have a fall. Individuals with OA who had hypertension [OR: 2.69, 95% CI, (1.30, 5.60), p=0.008], neuropathy [OR: 4.95, 95% CI, (2.95, 11.68), p<0.001], and insulin [OR: 2.85, 95% CI, (1.12, 7.22), p=0.035] were more likely to have a recurrent fall (two or more falls). CONCLUSIONS: Falls are common in individuals with generalized OA. Comorbid health conditions including hypertension and neuropathy, need to be considered in the screening of the risk of fall. Fall risk needs to be considered when discussing medication prescriptions, especially antidepressants and insulin. [ABSTRACT FROM AUTHOR]

Objective: Some critical events in anesthesiology occur as seemingly preventable misadventures, their exact origins indeterminable. Inexperienced anesthetists, anesthesia machine malfunctions, lack of vigilance and human error inevitably initiate some incidents. Anesthesia training improves recognition and decision-making. Avoiding crisis initiation and amelioration of those that do occur is one role of the consultant anesthesiologist. Safe patient care requires medical and procedural knowledge, technical expertise, and control of resources in a complex milieu. Anesthesia simulators are clinical laboratories where anesthetists can sharpen both cognitive and manual skills. Dynamic scenarios allow opportunities for anesthetists to explore and experience crises as they develop and apply their knowledge while attempting to manage these events. Simulator-based scenarios are reproducible and large amounts of useful data can be collected and saved. The authors hypothesize these data can be utilized to compare performance of anesthetists and to measure improvement of individual anesthetists over time.Methods: We have designed "Stable Anesthesia," a prototypic scenario to test anesthetists' capabilities under the stress of performance guidelines. Three subjects performed anesthesia using the simulator and this protocol. Data from the simulator were archived by the system and analyzed by the authors.Results: A simple mathematical analysis gave good separation of data from three subjects of different training level.Conclusions: It is suggested that the use of the techniques mentioned here may be of value in the development of a standardized testing protocol for anesthetists. [ABSTRACT FROM AUTHOR]

Objective: Develop and test methods for representing and classifying breath sounds in an intensive care setting.Methods: Breath sounds were recorded over the bronchial regions of the chest. The breath sounds were represented by their averaged power spectral density, summed into feature vectors across the frequency spectrum from 0 to 800 Hertz. The sounds were segmented by individual breath and each breath was divided into inspiratory and expiratory segments. Sounds were classified as normal or abnormal. Different back-propagation neural network configurations were evaluated. The number of input features, hidden units, and hidden layers were varied.Results: 2127 individual breath sounds from the ICU patients and 321 breaths from training tapes were obtained. Best overall classification rate for the ICU breath sounds was 73% with 62% sensitivity and 85% specificity. Best overall classification rate for the training tapes was 91% with 87% sensitivity and 95% specificity.Conclusions: Long term monitoring of lung sounds is not feasible unless several barriers can be overcome. Several choices in signal representation and neural network design greatly improved the classification rates of breath sounds. The analysis of transmitted sounds from the trachea to the lung is suggested as an area for future study. [ABSTRACT FROM AUTHOR]

Medication reconciliation was developed to reduce medical mistakes and injuries through a process of creating and comparing a current medication list from independent patient information sources, and resolving discrepancies. The structure and clinician assignments of medication reconciliation varies between institutions, but usually includes physicians, nurses and pharmacists. The Joint Commission has recognized the value of medication reconciliation and mandated implementation in 2006; however, a variety of issues have prevented simple, easy, and universal implementation. This review references issues related to the development and the implementation of medication reconciliation including: - the need of a system or standard for accurate drug identification to create a definitive 'gold standard' patient medication list, - identifying stakeholders of medication reconciliation within the institution and contrasting staff interest and participation with institutional resources, - observations and opportunities of integrating medication reconciliation with the electronic patient health record, and - summarizing a series of institutions experiences developing and implementing medication reconciliation. Last, as medication reconciliation becomes a regular process within medical centers, key concepts for effective implementation are discussed.