Clinical Features and Outcomes Differ between Skeletal and Extraskeletal Osteosarcoma.

Background. Extraskeletal osteosarcoma (ESOS) is a rare subtype of osteosarcoma. We investigated patient characteristics, overall survival, and prognostic factors in ESOS. Methods. We identified cases of high-grade osteosarcoma with known tissue of origin in the Surveillance, Epidemiology, and End Results database from 1973 to 2009. Demographics were compared using univariate tests. Overall survival was compared with log-rank tests and multivariate analysis using Cox proportional hazards methods. Results. 256/4,173 (6%) patients with high-grade osteosarcoma had ESOS. Patients with ESOS were older, were more likely to have an axial tumor and regional lymph node involvement, and were female. Multivariate analysis showed ESOS to be favorable after controlling for stage, age, tumor site, gender, and year of diagnosis [hazard ratio 0.75 (95% CI 0.62 to 0.90); p = 0.002]. There was an interaction between age and tissue of origin such that older patients with ESOS had superior outcomes compared to older patients with skeletal osteosarcoma. Adverse prognostic factors in ESOS included metastatic disease, larger tumor size, older age, and axial tumor site. Conclusion. Patients with ESOS have distinct clinical features but similar prognostic factors compared to skeletal osteosarcoma. Older patients with ESOS have superior outcomes compared to older patients with skeletal osteosarcoma

Estimating the Probability of Events that Have Never Occurred: When is Your Vote Decisive?

Researchers sometimes argue that statisticians have little to contribute when few realizations of the process being estimated are observed. We show that this argument is incorrect even in the extreme situation of estimating the probabilities of events so rare that they have never occurred. We show how statistical forecasting models allow us to use empirical data to improve inferences about the probabilities of these events. Our application is estimating the probability that your vote will be decisive in a U.S. presidential election, a problem that has been studied by political scientists for more than two decades. The exact value of this probability is of only minor interest, but the number has important implications for understanding the optimal allocation of campaign resources, whether states and voter groups receive their fair share of attention from prospective presidents, and how formal "rational choice" models of voter behavior might be able to explain why people vote at all. We show how the probability of a decisive vote can be estimated empirically from state-level forecasts of the presidential election and illustrate with the example of 1992. Based on generalizations of standard political science forecasting models, we estimate the (prospective) probability of a single vote being decisive as about 1 in 10 million for close national elections such as 1992, varying by about a factor of 10 among states. Our results support the argument that subjective probabilities of many types are best obtained through empirically based statistical prediction models rather than solely through mathematical reasoning. We discuss the implications of our findings for the types of decision analyses used in public choice studies

Development and Validation of eRADAR: A Tool Using EHR Data to Detect Unrecognized Dementia.

ObjectivesEarly recognition of dementia would allow patients and their families to receive care earlier in the disease process, potentially improving care management and patient outcomes, yet nearly half of patients with dementia are undiagnosed. Our aim was to develop and validate an electronic health record (EHR)-based tool to help detect patients with unrecognized dementia (EHR Risk of Alzheimer's and Dementia Assessment Rule [eRADAR]).DesignRetrospective cohort study.SettingKaiser Permanente Washington (KPWA), an integrated healthcare delivery system.ParticipantsA total of 16 665 visits among 4330 participants in the Adult Changes in Thought (ACT) study, who undergo a comprehensive process to detect and diagnose dementia every 2 years and have linked KPWA EHR data, divided into development (70%) and validation (30%) samples.MeasurementsEHR predictors included demographics, medical diagnoses, vital signs, healthcare utilization, and medications within the previous 2 years. Unrecognized dementia was defined as detection in ACT before documentation in the KPWA EHR (ie, lack of dementia or memory loss diagnosis codes or dementia medication fills).ResultsOverall, 1015 ACT visits resulted in a diagnosis of incident dementia, of which 498 (49%) were unrecognized in the KPWA EHR. The final 31-predictor model included markers of dementia-related symptoms (eg, psychosis diagnoses, antidepressant fills), healthcare utilization pattern (eg, emergency department visits), and dementia risk factors (eg, cerebrovascular disease, diabetes). Discrimination was good in the development (C statistic = .78; 95% confidence interval [CI] = .76-.81) and validation (C statistic = .81; 95% CI = .78-.84) samples, and calibration was good based on plots of predicted vs observed risk. If patients with scores in the top 5% were flagged for additional evaluation, we estimate that 1 in 6 would have dementia.ConclusionThe eRADAR tool uses existing EHR data to detect patients with good accuracy who may have unrecognized dementia. J Am Geriatr Soc 68:103-111, 2019

Performance of a cognitive load inventory during simulated handoffs: Evidence for validity.

BackgroundAdvancing patient safety during handoffs remains a public health priority. The application of cognitive load theory offers promise, but is currently limited by the inability to measure cognitive load types.ObjectiveTo develop and collect validity evidence for a revised self-report inventory that measures cognitive load types during a handoff.MethodsBased on prior published work, input from experts in cognitive load theory and handoffs, and a think-aloud exercise with residents, a revised Cognitive Load Inventory for Handoffs was developed. The Cognitive Load Inventory for Handoffs has items for intrinsic, extraneous, and germane load. Students who were second- and sixth-year students recruited from a Dutch medical school participated in four simulated handoffs (two simple and two complex cases). At the end of each handoff, study participants completed the Cognitive Load Inventory for Handoffs, Paas' Cognitive Load Scale, and one global rating item for intrinsic load, extraneous load, and germane load, respectively. Factor and correlational analyses were performed to collect evidence for validity.ResultsConfirmatory factor analysis yielded a single factor that combined intrinsic and germane loads. The extraneous load items performed poorly and were removed from the model. The score from the combined intrinsic and germane load items associated, as predicted by cognitive load theory, with a commonly used measure of overall cognitive load (Pearson's r = 0.83, p < 0.001), case complexity (beta = 0.74, p < 0.001), level of experience (beta = -0.96, p < 0.001), and handoff accuracy (r = -0.34, p < 0.001).ConclusionThese results offer encouragement that intrinsic load during handoffs may be measured via a self-report measure. Additional work is required to develop an adequate measure of extraneous load

Coronary Risk Assessment by Point-Based vs. Equation-Based Framingham Models: Significant Implications for Clinical Care

US cholesterol guidelines use original and simplified versions of the Framingham model to estimate future coronary risk and thereby classify patients into risk groups with different treatment strategies. We sought to compare risk estimates and risk group classification generated by the original, complex Framingham model and the simplified, point-based version. We assessed 2,543 subjects age 20–79 from the 2001–2006 National Health and Nutrition Examination Surveys (NHANES) for whom Adult Treatment Panel III (ATP-III) guidelines recommend formal risk stratification. For each subject, we calculated the 10-year risk of major coronary events using the original and point-based Framingham models, and then compared differences in these risk estimates and whether these differences would place subjects into different ATP-III risk groups (<10% risk, 10–20% risk, or >20% risk). Using standard procedures, all analyses were adjusted for survey weights, clustering, and stratification to make our results nationally representative. Among 39 million eligible adults, the original Framingham model categorized 71% of subjects as having “moderate” risk (<10% risk of a major coronary event in the next 10 years), 22% as having “moderately high” (10–20%) risk, and 7% as having “high” (>20%) risk. Estimates of coronary risk by the original and point-based models often differed substantially. The point-based system classified 15% of adults (5.7 million) into different risk groups than the original model, with 10% (3.9 million) misclassified into higher risk groups and 5% (1.8 million) into lower risk groups, for a net impact of classifying 2.1 million adults into higher risk groups. These risk group misclassifications would impact guideline-recommended drug treatment strategies for 25–46% of affected subjects. Patterns of misclassifications varied significantly by gender, age, and underlying CHD risk. Compared to the original Framingham model, the point-based version misclassifies millions of Americans into risk groups for which guidelines recommend different treatment strategies

Functional Status After Colon Cancer Surgery in Elderly Nursing Home Residents

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/91352/1/jgs3915.pd

Preimplantation Mouse Embryo Selection Guided by Light-Induced Dielectrophoresis

Selection of optimal quality embryos for in vitro fertilization (IVF) transfer is critical to successful live birth outcomes. Currently, embryos are chosen based on subjective assessment of morphologic developmental maturity. A non-invasive means to quantitatively measure an embryo's developmental maturity would reduce the variability introduced by the current standard. We present a method that exploits the scaling electrical properties of pre-transfer embryos to quantitatively discern embryo developmental maturity using light-induced dielectrophoresis (DEP). We show that an embryo's DEP response is highly correlated with its developmental stage. Uniquely, this technique allows one to select, in sequence and under blinded conditions, the most developmentally mature embryos among a mixed cohort of morphologically indistinguishable embryos cultured in optimized and sub-optimal culture media. Following assay, embryos continue to develop normally in vitro. Light-induced dielectrophoresis provides a non-invasive, quantitative, and reproducible means to select embryos for applications including IVF transfer and embryonic stem cell harvest

Is It Time to Rethink How Neuropsychological Tests Are Used to Diagnose Mild Forms of HIV-Associated Neurocognitive Disorders? Impact of False-Positive Rates on Prevalence and Power

BACKGROUND: Between 0–48% of normal HIV-uninfected individuals score below threshold neuropsychological test scores for HIV-associated neurocognitive disorders (HAND), or are false-positives. There has been little effort to understand the effect of varied interpretations of research criteria for HAND on false-positive frequencies, prevalence and analytic estimates. METHODS: The proportion of normal individuals scoring below Z-score thresholds drawn from research criteria for HAND, or false-positive frequencies, was estimated in a normal Kenyan population and a simulated normal population using varied interpretations of research criteria for HAND. We calculated the impact of false-positive frequencies on prevalence estimates and statistical power. RESULTS: False-positive frequencies of 2–74% were observed for Asymptomatic Neurocognitive Impairment/Mild Neurocognitive Disorder and 0–8% for HIV-associated Dementia. False-positive frequencies depended on definition of an abnormal cognitive domain, Z-score thresholds, and neuropsychological battery size. Misclassification led to clinically important overestimation of prevalence and dramatic decreases in power. CONCLUSIONS: Minimizing false-positive frequencies is critical to decrease bias in prevalence estimates and minimize reductions in power in studies of association, particularly for mild forms of HAND. We recommend changing the Z-score threshold to ≤−1.5 for mild impairment, limiting analysis to 3–5 cognitive domains, and using the average Z-score to define an abnormal domain

Clinical Features and Outcomes Differ between Skeletal and Extraskeletal Osteosarcoma

Background. Extraskeletal osteosarcoma (ESOS) is a rare subtype of osteosarcoma. We investigated patient characteristics, overall survival, and prognostic factors in ESOS. Methods. We identified cases of high-grade osteosarcoma with known tissue of origin in the Surveillance, Epidemiology, and End Results database from 1973 to 2009. Demographics were compared using univariate tests. Overall survival was compared with log-rank tests and multivariate analysis using Cox proportional hazards methods. Results. 256/4,173 (6%) patients with high-grade osteosarcoma had ESOS. Patients with ESOS were older, were more likely to have an axial tumor and regional lymph node involvement, and were female. Multivariate analysis showed ESOS to be favorable after controlling for stage, age, tumor site, gender, and year of diagnosis [hazard ratio 0.75 (95% CI 0.62 to 0.90); p=0.002]. There was an interaction between age and tissue of origin such that older patients with ESOS had superior outcomes compared to older patients with skeletal osteosarcoma. Adverse prognostic factors in ESOS included metastatic disease, larger tumor size, older age, and axial tumor site. Conclusion. Patients with ESOS have distinct clinical features but similar prognostic factors compared to skeletal osteosarcoma. Older patients with ESOS have superior outcomes compared to older patients with skeletal osteosarcoma