Elder Orphans Hiding in Plain Sight: A Growing Vulnerable Population

Adults are increasingly aging alone with multiple chronic diseases and are geographically distant from family or friends. It is challenging for clinicians to identify these individuals, often struggling with managing the growing difficulties and the complexities involved in delivering care to this population. Clinicians often may not recognize or know how to address the needs that these patients have in managing their own health. While many such patients function well at baseline, the slightest insult can initiate a cascade of avoidable negative events. We have resurrected the term elder orphan to describe individuals living alone with little to no support system. Using public data sets, including the US Census and University of Michigan’s Health and Retirement Study, we estimated the prevalence of adults 65 years and older to be around 22%. Thus, in this paper, we strive to describe and quantify this growing vulnerable population and offer practical approaches to identify and develop care plans that are consistent with each person’s goals of care. The complex medical and psychosocial issues for elder orphans significantly impact the individual person, communities, and health-care expenditures. We hope to encourage professionals across disciplines to work cooperatively to screen elders and implement policies to prevent elder orphans from hiding in plain sight

Microporous polysaccharide hemosphere absorbable hemostat use in cardiothoracic surgical procedures

BACKGROUND: Topical hemostatic agents are used to reduce bleeding and transfusion need during cardiothoracic surgery. We report our experience with Arista® AH Absorbable Hemostatic Particles (Arista® AH), a novel plant-based microporous polysaccharide hemostatic powder. METHODS: Data were retrospectively collected for patients (n = 240) that received cardiothoracic surgery at our institution from January 2009 to January 2013 with (n = 103) or without (n = 137) the use of Arista® AH. Endpoints included protamine to skin closure time (hemostasis time), cardiopulmonary bypass time, quantity of Arista® AH applied, intraoperative blood product usage, intraoperative blood loss, chest tube output 48 hours postoperatively, blood products required 48 hours postoperatively, length of stay in the intensive care unit, 30-day morbidity, and 30-day mortality. RESULTS: 240 patients (176 M: 64 F) underwent 240 cardiothoracic procedures including heart transplantation (n = 53), cardiac assist devices (n = 113), coronary artery bypass grafts (n = 20), valve procedures (n = 19), lung transplantation (n = 17), aortic dissection (n = 8), and other (n = 10). Application of Arista® AH led to significant reduction in hemostasis time versus the untreated control group (Arista® AH: 93.4 ± 41 min. vs. Control: 107.6 ± 56 min., p = 0.02). Postoperative chest tube output in the first 48 hours was also significantly reduced (Arista® AH: 1594 ± 949 mL vs. Control: 2112 ± 1437 mL, p < 0.001), as well as transfusion of packed red blood cells (Arista® AH: 2.4 ± 2.5 units vs. Control: 4.0 ± 5.1 units, p < 0.001). There was no significant difference in 30-day mortality or postoperative complications. CONCLUSION: Use of Arista® AH in complex cardiothoracic surgery resulted in a significant reduction in hemostasis time, postoperative chest tube output, and need for postoperative blood transfusion

Collectives of diagnostic biomarkers identify high-risk subpopulations of hematuria patients: exploiting heterogeneity in large-scale biomarker data

<p>Abstract</p> <p>Background</p> <p>Ineffective risk stratification can delay diagnosis of serious disease in patients with hematuria. We applied a systems biology approach to analyze clinical, demographic and biomarker measurements (n = 29) collected from 157 hematuric patients: 80 urothelial cancer (UC) and 77 controls with confounding pathologies.</p> <p>Methods</p> <p>On the basis of biomarkers, we conducted agglomerative hierarchical clustering to identify patient and biomarker clusters. We then explored the relationship between the patient clusters and clinical characteristics using Chi-square analyses. We determined classification errors and areas under the receiver operating curve of Random Forest Classifiers (RFC) for patient subpopulations using the biomarker clusters to reduce the dimensionality of the data.</p> <p>Results</p> <p>Agglomerative clustering identified five patient clusters and seven biomarker clusters. Final diagnoses categories were non-randomly distributed across the five patient clusters. In addition, two of the patient clusters were enriched with patients with 'low cancer-risk' characteristics. The biomarkers which contributed to the diagnostic classifiers for these two patient clusters were similar. In contrast, three of the patient clusters were significantly enriched with patients harboring 'high cancer-risk" characteristics including proteinuria, aggressive pathological stage and grade, and malignant cytology. Patients in these three clusters included controls, that is, patients with other serious disease and patients with cancers other than UC. Biomarkers which contributed to the diagnostic classifiers for the largest 'high cancer- risk' cluster were different than those contributing to the classifiers for the 'low cancer-risk' clusters. Biomarkers which contributed to subpopulations that were split according to smoking status, gender and medication were different.</p> <p>Conclusions</p> <p>The systems biology approach applied in this study allowed the hematuric patients to cluster naturally on the basis of the heterogeneity within their biomarker data, into five distinct risk subpopulations. Our findings highlight an approach with the promise to unlock the potential of biomarkers. This will be especially valuable in the field of diagnostic bladder cancer where biomarkers are urgently required. Clinicians could interpret risk classification scores in the context of clinical parameters at the time of triage. This could reduce cystoscopies and enable priority diagnosis of aggressive diseases, leading to improved patient outcomes at reduced costs.</p

A Method for Using Player Tracking Data in Basketball to Learn Player Skills and Predict Team Performance

Player tracking data represents a revolutionary new data source for basketball analysis, in which essentially every aspect of a player's performance is tracked and can be analyzed numerically. We suggest a way by which this data set, when coupled with a network-style model of the offense that relates players' skills to the team's success at running different plays, can be used to automatically learn players' skills and predict the performance of untested 5-man lineups in a way that accounts for the interaction between players' respective skill sets. After developing a general analysis procedure, we present as an example a specific implementation of our method using a simplified network model. While player tracking data is not yet available in the public domain, we evaluate our model using simulated data and show that player skills can be accurately inferred by a simple statistical inference scheme. Finally, we use the model to analyze games from the 2011 playoff series between the Memphis Grizzlies and the Oklahoma City Thunder and we show that, even with a very limited data set, the model can consistently describe a player's interactions with a given lineup based only on his performance with a different lineup