Modeling Intracerebral Hemorrhage Growth and Response to Anticoagulation

The mechanism for hemorrhage enlargement in the brain, a key determinant of patient outcome following hemorrhagic stroke, is unknown. We performed computer-based stochastic simulation of one proposed mechanism, in which hemorrhages grow in “domino” fashion via secondary shearing of neighboring vessel segments. Hemorrhages were simulated by creating an initial site of primary bleeding and an associated risk of secondary rupture at adjacent sites that decayed over time. Under particular combinations of parameters for likelihood of secondary rupture and time-dependent decay, a subset of lesions expanded, creating a bimodal distribution of microbleeds and macrobleeds. Systematic variation of the model to simulate anticoagulation yielded increases in both macrobleed occurrence (26.9%, 53.2%, and 70.0% of all hemorrhagic events under conditions simulating no, low-level, and high-level anticoagulation) and final hemorrhage size (median volumes 111, 276, and 412 under the same three conditions), consistent with data from patients with anticoagulant-related brain hemorrhages. Reversal from simulated high-level anticoagulation to normal coagulation was able to reduce final hemorrhage size only if applied relatively early in the course of hemorrhage expansion. These findings suggest that a model based on a secondary shearing mechanism can account for some of the clinically observed properties of intracerebral hemorrhage, including the bimodal distribution of volumes and the enhanced hemorrhage growth seen with anticoagulation. Future iterations of this model may be useful for elucidating the effects of hemorrhage growth of factors related to secondary shearing (such as small vessel pathology) or time-dependent decay (such as hemostatic agents)

Accurate and Interpretable Machine Learning for Transparent Pricing of Health Insurance Plans

Health insurance companies cover half of the United States population through commercial employer-sponsored health plans and pay 1.2 trillion US dollars every year to cover medical expenses for their members. The actuary and underwriter roles at a health insurance company serve to assess which risks to take on and how to price those risks to ensure profitability of the organization. While Bayesian hierarchical models are the current standard in the industry to estimate risk, interest in machine learning as a way to improve upon these existing methods is increasing. Lumiata, a healthcare analytics company, ran a study with a large health insurance company in the United States. We evaluated the ability of machine learning models to predict the per member per month cost of employer groups in their next renewal period, especially those groups who will cost less than 95\% of what an actuarial model predicts (groups with "concession opportunities"). We developed a sequence of two models, an individual patient-level and an employer-group-level model, to predict the annual per member per month allowed amount for employer groups, based on a population of 14 million patients. Our models performed 20\% better than the insurance carrier's existing pricing model, and identified 84\% of the concession opportunities. This study demonstrates the application of a machine learning system to compute an accurate and fair price for health insurance products and analyzes how explainable machine learning models can exceed actuarial models' predictive accuracy while maintaining interpretability.Comment: Accepted for publication in The Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-21), in the Innovative Applications of Artificial Intelligence track. This is the extended version with some stylistic fixes from the first posting and complete author lis

Probing Lorentz and CPT violation with space-based experiments

Space-based experiments offer sensitivity to numerous unmeasured effects involving Lorentz and CPT violation. We provide a classification of clock sensitivities and present explicit expressions for time variations arising in such experiments from nonzero coefficients in the Lorentz- and CPT-violating Standard-Model Extension.Comment: 15 page

Swine flu: lessons we need to learn from our global experience

There are important lessons to be learnt from the recent ‘Swine Flu’ pandemic. Before we call it a pandemic, we need to have appropriate trigger points that involve not only the spread of the virus but also its level of virulence. This was not done for H1N1 (swine flu). We need to ensure that we improve the techniques used in trying to decrease the spread of infection—both in the community and within our hospitals. This means improved infection control and hygiene, and the use of masks, alcohol hand rubs and so on. We also need to have a different approach to vaccines. Effective vaccines were produced only after the epidemic had passed and therefore had relatively little impact in preventing many infections. Mass population strategies involving vaccines and antivirals also misused large amounts of scarce medical resources

A Randomized Comparison of Aripiprazole and Risperidone for the Acute Treatment of First-Episode Schizophrenia and Related Disorders: 3-Month Outcomes

Research findings are particularly important for medication choice for first-episode patients as individual prior medication response to guide treatment decisions is unavailable. We describe the first large-scale double-masked randomized comparison with first-episode patients of aripiprazole and risperidone, 2 commonly used first-episode treatment agents. One hundred ninety-eight participants aged 15-40 years with schizophrenia, schizophreniform disorder, schizoaffective disorder or psychotic disorder Not Otherwise Specified, and who had been treated in their lifetime with antipsychotics for 2 weeks or less were randomly assigned to double-masked aripiprazole (5-30mg/d) or risperidone (1-6mg/d) and followed for 12 weeks. Positive symptom response rates did not differ (62.8% vs 56.8%) nor did time to response. Aripiprazole-treated participants had better negative symptom outcomes but experienced more akathisia. Body mass index change did not differ between treatments but advantages were found for aripiprazole treatment for total and low-density lipoprotein cholesterol, fasting glucose, and prolactin levels. Post hoc analyses suggested advantages for aripiprazole on depressed mood. Overall, if the potential for akathisia is a concern, low-dose risperidone as used in this trial maybe a preferred choice over aripiprazole. Otherwise, aripiprazole would be the preferred choice over risperidone in most situations based upon metabolic outcome advantages and some symptom advantages within the context of similar positive symptom response between medications

Cancer-Associated Fibroblasts Neutralize the Anti-tumor Effect of CSF1 Receptor Blockade by Inducing PMN-MDSC Infiltration of Tumors.

Tumor-associated macrophages (TAM) contribute to all aspects of tumor progression. Use of CSF1R inhibitors to target TAM is therapeutically appealing, but has had very limited anti-tumor effects. Here, we have identified the mechanism that limited the effect of CSF1R targeted therapy. We demonstrated that carcinoma-associated fibroblasts (CAF) are major sources of chemokines that recruit granulocytes to tumors. CSF1 produced by tumor cells caused HDAC2-mediated downregulation of granulocyte-specific chemokine expression in CAF, which limited migration of these cells to tumors. Treatment with CSF1R inhibitors disrupted this crosstalk and triggered a profound increase in granulocyte recruitment to tumors. Combining CSF1R inhibitor with a CXCR2 antagonist blocked granulocyte infiltration of tumors and showed strong anti-tumor effects

Frequency of GP communication addressing the patient's resources and coping strategies in medical interviews: a video-based observational study

<p>Abstract</p> <p>Background</p> <p>There is increasing focus on patient-centred communicative approaches in medical consultations, but few studies have shown the extent to which patients' positive coping strategies and psychological assets are addressed by general practitioners (GPs) on a regular day at the office. This study measures the frequency of GPs' use of questions and comments addressing their patients' coping strategies or resources.</p> <p>Methods</p> <p>Twenty-four GPs were video-recorded in 145 consultations. The consultations were coded using a modified version of the Roter Interaction Analysis System. In this study, we also developed four additional coding categories based on cognitive therapy and solution-focused therapy: attribution, resources, coping, and solution-focused techniques.</p> <p>The reliability between coders was established, a factor analysis was applied to test the relationship between the communication categories, and a tentative validating exercise was performed by reversed coding.</p> <p>Results</p> <p>Cohen's kappa was 0.52 between coders. Only 2% of the utterances could be categorized as resource or coping oriented. Six GPs contributed 59% of these utterances. The factor analysis identified two factors, one task oriented and one patient oriented.</p> <p>Conclusion</p> <p>The frequency of communication about coping and resources was very low. Communication skills training for GPs in this field is required. Further validating studies of this kind of measurement tool are warranted.</p

Phase I/pharmacokinetic study of CCI-779 in patients with recurrent malignant glioma on enzyme-inducing antiepileptic drugs

Objectives : CCI-779 is an ester of the immunosuppressive agent sirolimus (rapamycin) that causes cell-cycle arrest at G1 via inhibition of key signaling pathways resulting in inhibition of RNA translation. Antitumor activity has been demonstrated using cell lines and animal models of malignant glioma. Patients receiving enzyme-inducing anti-epileptic drugs (EIAEDs) can have altered metabolism of drugs like CCI-779 that are metabolized through the hepatic cytochrome P450 enzyme system. The objectives of this study were to determine the pharmacokinetic profile and the maximum tolerated dose of CCI-779 in patients with recurrent malignant gliioma taking EIAEDs. Study design: The starting dose of CCI-779 was 250 mg intravenously (IV) administered weekly on a continuous basis. Standard dose escalation was performed until the maximum tolerated dose was established. Toxicity was assessed using the National Cancer Institute common toxicity criteria. Results : Two of 6 patients treated at the second dose level of 330 mg sustained a dose-limiting toxicity: grade III stomatitis, grade 3 hypercholesterolemia, or grade 4 hypertriglyceridemia. The maximum tolerated dose was reached at 250 mg IV. Pharmacokinetic profiles were similar to those previously described, but the area under the whole blood concentration-time curve of rapamycin was 1.6 fold lower for patients on EIAEDs. Conclusions : The recommended phase II dose of CCI 779 for patients on enzyme-inducing antiepileptic drugs is 250 mg IV weekly. A phase II study is ongoing to determine the efficacy of this agent.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/45250/1/10637_2004_Article_5273867.pd