Quantized reduction as a tensor product

Symplectic reduction is reinterpreted as the composition of arrows in the category of integrable Poisson manifolds, whose arrows are isomorphism classes of dual pairs, with symplectic groupoids as units. Morita equivalence of Poisson manifolds amounts to isomorphism of objects in this category. This description paves the way for the quantization of the classical reduction procedure, which is based on the formal analogy between dual pairs of Poisson manifolds and Hilbert bimodules over C*-algebras, as well as with correspondences between von Neumann algebras. Further analogies are drawn with categories of groupoids (of algebraic, measured, Lie, and symplectic type). In all cases, the arrows are isomorphism classes of appropriate bimodules, and their composition may be seen as a tensor product. Hence in suitable categories reduction is simply composition of arrows, and Morita equivalence is isomorphism of objects.Comment: 44 pages, categorical interpretation adde

Predicting Ambulance Patient Wait Times : A Multicenter Derivation and Validation Study

AbstractStudy objective: To derive and internally and externally validate machine-learning models to predict emergency ambulance patient door–to–off-stretcher wait times that are applicable to a wide variety of emergency departments.Methods: Nine emergency departments provided 3 years (2017 to 2019) of retrospective administrative data from Australia. Descriptive and exploratory analyses were undertaken on the datasets. Statistical and machine-learning models were developed to predict wait times at each site and were internally and externally validated.Results: There were 421,894 episodes analyzed, and median site off-load times varied from 13 (interquartile range [IQR], 9 to 20) to 29 (IQR, 16 to 48) minutes. The global site prediction model median absolute errors were 11.7 minutes (95% confidence interval [CI], 11.7 to 11.8) using linear regression and 12.8 minutes (95% CI, 12.7 to 12.9) using elastic net. The individual site model prediction median absolute errors varied from the most accurate at 6.3 minutes (95% CI, 6.2 to 6.4) to the least accurate at 16.1 minutes (95% CI, 15.8 to 16.3). The model technique performance was the same for linear regression, random forests, elastic net, and rolling average. The important variables were the last k-patient average waits, triage category, and patient age. The global model performed at the lower end of the accuracy range compared with models for the individual sites but was within tolerable limits.Conclusion: Electronic emergency demographic and flow information can be used to estimate emergency ambulance patient off-stretcher times. Models can be built with reasonable accuracy for multiple hospitals using a small number of point-of-care variables.Abstract Study objective: To derive and internally and externally validate machine-learning models to predict emergency ambulance patient door–to–off-stretcher wait times that are applicable to a wide variety of emergency departments. Methods: Nine emergency departments provided 3 years (2017 to 2019) of retrospective administrative data from Australia. Descriptive and exploratory analyses were undertaken on the datasets. Statistical and machine-learning models were developed to predict wait times at each site and were internally and externally validated. Results: There were 421,894 episodes analyzed, and median site off-load times varied from 13 (interquartile range [IQR], 9 to 20) to 29 (IQR, 16 to 48) minutes. The global site prediction model median absolute errors were 11.7 minutes (95% confidence interval [CI], 11.7 to 11.8) using linear regression and 12.8 minutes (95% CI, 12.7 to 12.9) using elastic net. The individual site model prediction median absolute errors varied from the most accurate at 6.3 minutes (95% CI, 6.2 to 6.4) to the least accurate at 16.1 minutes (95% CI, 15.8 to 16.3). The model technique performance was the same for linear regression, random forests, elastic net, and rolling average. The important variables were the last k-patient average waits, triage category, and patient age. The global model performed at the lower end of the accuracy range compared with models for the individual sites but was within tolerable limits. Conclusion: Electronic emergency demographic and flow information can be used to estimate emergency ambulance patient off-stretcher times. Models can be built with reasonable accuracy for multiple hospitals using a small number of point-of-care variables

Emergency medicine patient wait time multivariable prediction models : a multicentre derivation and validation study

AbstractObjective: Patients, families and community members would like emergency department wait time visibility. This would improve patient journeys through emergency medicine. The study objective was to derive, internally and externally validate machine learning models to predict emergency patient wait times that are applicable to a wide variety of emergency departments.Methods: Twelve emergency departments provided 3 years of retrospective administrative data from Australia (2017–2019). Descriptive and exploratory analyses were undertaken on the datasets. Statistical and machine learning models were developed to predict wait times at each site and were internally and externally validated. Model performance was tested on COVID-19 period data (January to June 2020).Results: There were 1 930 609 patient episodes analysed and median site wait times varied from 24 to 54 min. Individual site model prediction median absolute errors varied from±22.6 min (95% CI 22.4 to 22.9) to ±44.0 min (95% CI 43.4 to 44.4). Global model prediction median absolute errors varied from ±33.9 min (95% CI 33.4 to 34.0) to ±43.8 min (95% CI 43.7 to 43.9). Random forest and linear regression models performed the best, rolling average models underestimated wait times. Important variables were triage category, last-k patient average wait time and arrival time. Wait time prediction models are not transferable across hospitals. Models performed well during the COVID-19 lockdown period.Conclusions: Electronic emergency demographic and flow information can be used to approximate emergency patient wait times. A general model is less accurate if applied without site-specific factors.Abstract Objective: Patients, families and community members would like emergency department wait time visibility. This would improve patient journeys through emergency medicine. The study objective was to derive, internally and externally validate machine learning models to predict emergency patient wait times that are applicable to a wide variety of emergency departments. Methods: Twelve emergency departments provided 3 years of retrospective administrative data from Australia (2017–2019). Descriptive and exploratory analyses were undertaken on the datasets. Statistical and machine learning models were developed to predict wait times at each site and were internally and externally validated. Model performance was tested on COVID-19 period data (January to June 2020). Results: There were 1 930 609 patient episodes analysed and median site wait times varied from 24 to 54 min. Individual site model prediction median absolute errors varied from±22.6 min (95% CI 22.4 to 22.9) to ±44.0 min (95% CI 43.4 to 44.4). Global model prediction median absolute errors varied from ±33.9 min (95% CI 33.4 to 34.0) to ±43.8 min (95% CI 43.7 to 43.9). Random forest and linear regression models performed the best, rolling average models underestimated wait times. Important variables were triage category, last-k patient average wait time and arrival time. Wait time prediction models are not transferable across hospitals. Models performed well during the COVID-19 lockdown period. Conclusions: Electronic emergency demographic and flow information can be used to approximate emergency patient wait times. A general model is less accurate if applied without site-specific factors

The Australasian Resuscitation In Sepsis Evaluation: FLUid or vasopressors In Emergency Department Sepsis, a multicentre observational study (ARISE FLUIDS observational study): Rationale, methods and analysis plan

There is uncertainty about the optimal i.v. fluid volume and timing of vasopressor commencement in the resuscitation of patients with sepsis and hypotension. We aim to study current resuscitation practices in EDs in Australia and New Zealand (the Australasian Resuscitation In Sepsis Evaluation: FLUid or vasopressors In Emergency Department Sepsis [ARISE FLUIDS] observational study).ARISE FLUIDS is a prospective, multicentre observational study in 71 hospitals in Australia and New Zealand. It will include adult patients presenting to the ED during a 30 day period with suspected sepsis and hypotension (systolic blood pressur

The Australasian Resuscitation In Sepsis Evaluation : fluids or vasopressors in emergency department sepsis (ARISE FLUIDS), a multi-centre observational study describing current practice in Australia and New Zealand

Objectives: To describe haemodynamic resuscitation practices in ED patients with suspected sepsis and hypotension. Methods: This was a prospective, multicentre, observational study conducted in 70 hospitals in Australia and New Zealand between September 2018 and January 2019. Consecutive adults presenting to the ED during a 30-day period at each site, with suspected sepsis and hypotension (systolic blood pressure <100 mmHg) despite at least 1000 mL fluid resuscitation, were eligible. Data included baseline demographics, clinical and laboratory variables and intravenous fluid volume administered, vasopressor administration at baseline and 6- and 24-h post-enrolment, time to antimicrobial administration, intensive care admission, organ support and in-hospital mortality. Results: A total of 4477 patients were screened and 591 were included with a mean (standard deviation) age of 62 (19) years, Acute Physiology and Chronic Health Evaluation II score 15.2 (6.6) and a median (interquartile range) systolic blood pressure of 94 mmHg (87–100). Median time to first intravenous antimicrobials was 77 min (42–148). A vasopressor infusion was commenced within 24 h in 177 (30.2%) patients, with noradrenaline the most frequently used (n = 138, 78%). A median of 2000 mL (1500–3000) of intravenous fluids was administered prior to commencing vasopressors. The total volume of fluid administered from pre-enrolment to 24 h was 4200 mL (3000–5661), with a range from 1000 to 12 200 mL. Two hundred and eighteen patients (37.1%) were admitted to an intensive care unit. Overall in-hospital mortality was 6.2% (95% confidence interval 4.4–8.5%). Conclusion: Current resuscitation practice in patients with sepsis and hypotension varies widely and occupies the spectrum between a restricted volume/earlier vasopressor and liberal fluid/later vasopressor strategy

Single cell dissection of plasma cell heterogeneity in symptomatic and asymptomatic myeloma

Multiple myeloma, a plasma cell malignancy, is the second most common blood cancer. Despite extensive research, disease heterogeneity is poorly characterized, hampering efforts for early diagnosis and improved treatments. Here, we apply single cell RNA sequencing to study the heterogeneity of 40 individuals along the multiple myeloma progression spectrum, including 11 healthy controls, demonstrating high interindividual variability that can be explained by expression of known multiple myeloma drivers and additional putative factors. We identify extensive subclonal structures for 10 of 29 individuals with multiple myeloma. In asymptomatic individuals with early disease and in those with minimal residual disease post-treatment, we detect rare tumor plasma cells with molecular characteristics similar to those of active myeloma, with possible implications for personalized therapies. Single cell analysis of rare circulating tumor cells allows for accurate liquid biopsy and detection of malignant plasma cells, which reflect bone marrow disease. Our work establishes single cell RNA sequencing for dissecting blood malignancies and devising detailed molecular characterization of tumor cells in symptomatic and asymptomatic patients

Post-vasectomy semen analysis: Optimizing laboratory procedures and test interpretation through a clinical audit and global survey of practices

Purpose: The success of vasectomy is determined by the outcome of a post-vasectomy semen analysis (PVSA). This article describes a step-by-step procedure to perform PVSA accurately, report data from patients who underwent post vasectomy semen analysis between 2015 and 2021 experience, along with results from an international online survey on clinical practice. Materials and Methods: We present a detailed step-by-step protocol for performing and interpretating PVSA testing, along with recommendations for proficiency testing, competency assessment for performing PVSA, and clinical and laboratory scenarios. Moreover, we conducted an analysis of 1,114 PVSA performed at the Cleveland Clinic’s Andrology Laboratory and an online survey to understand clinician responses to the PVSA results in various countries. Results: Results from our clinical experience showed that 92.1% of patients passed PVSA, with 7.9% being further tested. A total of 78 experts from 19 countries participated in the survey, and the majority reported to use time from vasectomy rather than the number of ejaculations as criterion to request PVSA. A high percentage of responders reported permitting unprotected intercourse only if PVSA samples show azoospermia while, in the presence of few non-motile sperm, the majority of responders suggested using alternative contraception, followed by another PVSA. In the presence of motile sperm, the majority of participants asked for further PVSA testing. Repeat vasectomy was mainly recommended if motile sperm were observed after multiple PVSA’s. A large percentage reported to recommend a second PVSA due to the possibility of legal actions. Conclusions: Our results highlighted varying clinical practices around the globe, with controversy over the significance of non-motile sperm in the PVSA sample. Our data suggest that less stringent AUA guidelines would help improve test compliance. A large longitudinal multi-center study would clarify various doubts related to timing and interpretation of PVSA and would also help us to understand, and perhaps predict, recanalization and the potential for future failure of a vasectomy.American Center for Reproductive Medicin

Diagnosing DVT in the Emergency Department: Combining Clinical Predictors, D-dimer and Bedside Ultrasound

I assessed the accuracy of two clinical prediction rules, the d-dimer blood test and point of care ultrasound for diagnosing lower limb deep vein thrombosis. Emergency physicians were trained in ultrasound and prospectively scanned emergency department patients with suspected deep vein thrombosis. Accuracy of the Wells and AMUSE rules and the ultrasound result was compared to radiology-performed ultrasound and a 90-day clinical outcome. Univariate and multivariate analyses were performed assessing which factors were associated with the outcome. The sensitivity and specificity of the Wells score for the clinical outcome was 85.7% and 68.5%; the AMUSE score 85.7% and 54.4%. Ultrasound had a sensitivity of 91.7% and specificity of 91.7% for radiology-diagnosed thrombus and 78.6% and 95.0% for clinical outcome. The odds ratio of a positive outcome with a positive ultrasound was 65.1. After receiving the ultrasound training program, emergency physicians were unable to demonstrate sufficient accuracy to replace current diagnostic strategies.