Explore the vast range of titles available.

High-sensitivity cardiac troponin T (cTnT) assays detect small clinically important myocardial infarctions (MI) but also yield higher rates of false-positive results owing to increased concentrations sometimes present in patients without MI. Better understanding is needed of factors influencing the 99th percentile of cTnT concentrations across populations and the frequency of changes in cTnT concentrations >20% often used in combination with increased cTnT concentrations for diagnosis of MI. cTnT percentiles were determined by use of the Elecsys® hscTnT immunoassay (Modular® Analytics E170) in a random population sample, in emergency room (ER) patients, and in patients with non-ST-elevation MI (NSTEMI). Changes in cTnT concentrations were determined in hospitalized patients without MI. The 99th cTnT percentile in a random population sample (median age, 65 years) was 24 ng/L. In ER patients <65 years old without obvious conditions that increase cTnT, the 99th cTnT percentile was 12 ng/L with little age dependence, whereas in those >65 years old it was 82 ng/L and highly age dependent. In hospitalized patients without MI the 97.5th percentile for change in the cTnT concentration was 51%-67%. cTnT remained below the 99th percentile (12 ng/L) in 1% of patients with NSTEMI until 8.5 h after symptom onset and 6 h after ER arrival. Age >65 years was the dominant factor associated with increased cTnT in ER patients. This age association was more prominent in ER patients than in a random population sample. Changes in serial cTnT concentrations >20% were common in hospitalized patients without MI.

In this paper, we propose a new unbiased stochastic derivative estimator in a framework that can handle discontinuous sample performances with structural parameters. This work extends the three most popular unbiased stochastic derivative estimators: (1) infinitesimal perturbation analysis (IPA), (2) the likelihood ratio (LR) method, and (3) the weak derivative method, to a setting where they did not previously apply. Examples in probability constraints, control charts, and financial derivatives demonstrate the broad applicability of the proposed framework. The new estimator preserves the single-run efficiency of the classic IPA-LR estimators in applications, which is substantiated by numerical experiments. The online appendix is available at https://doi.org/10.1287/opre.2017.1674 .

Plasma BNP and NT-proBNP are often regarded as interchangeable parameters in assessing heart failure (HF) severity and prognosis. Renal failure results in disproportionate increases of NT-proBNP and an increased NT-proBNP/BNP ratio. Low kidney function is therefore considered particularly when NT-proBNP is used to assess HF. The purpose of this study was to identify other conditions affecting the NT-proBNP/BNP ratio. We examined the NT-proBNP/BNP ratio, 26 other lab parameters, and clinical factors in 218 patients admitted to the HF ward. In addition to renal function, we also found significant correlations between the NT-proBNP/BNP ratio and inflammation as measured by orosomucoid (r = 0.525, p < 0.0001), CRP (r = 0.333, p < 0.0001), haptoglobulin (r = 0.201, p = 0.02), and alpha1-antitrypsin (r = 0.223, p = 0.01). Reverse correlation was found with transferrin (r = −0.323, p < 0.0001), albumin (r = −0.251, p = 0.003), and S-Fe (r = −0.205, p = 0.02), parameters known to decrease during inflammation. Inflammation increased levels of NT-proBNP more than BNP, resulting in an increased NT-proBNP/BNP ratio. Our findings indicate that NT-proBNP should be evaluated concomitantly with inflammatory status to avoid overestimation of HF severity.

We obtain a closed-form solution for pricing European options under a general jump-diffusion model that can incorporate arbitrary discrete jump-size distributions, including nonparametric distributions such as an empirical distribution. The flexibility in the jump-size distribution allows the model to better capture leptokurtic features found in real-world data. The model uses a discrete-time framework and leads to a pricing formula that is provably convergent to the continuous-time price as the discretization is increased. The solution is easy to implement with fast convergence properties. Numerical results illustrate the efficiency and accuracy of the proposed model and highlight its robustness and flexibility. This paper was accepted by Noah Gans, stochastic models and simulation .

Users of escalators and moving walkways with sufficient space to accommodate two lanes of users often follow an implied etiquette for the two lanes: one for walking and one for standing (in the US, China, and many countries, the convention is “walk left, stand right”). When there is high volume, e.g., when exiting a subway or train station or at the conclusion of an athletic event, the escalators often experience bottleneck congestion that constitutes the primary source of delay. It has been suggested that during such high-congestion periods, it would be more efficient if everyone just stood in both lanes, with empirical evidence used to support this counterintuitive finding. Simple deterministic queueing models are used to show under what conditions such results hold and also to provide further insights regarding tradeoffs between performance metrics as a function of the distribution of walkers and standers, which could inform practical implementation policies to increase efficiency.

Estimating the latent dynamics underlying biological processes is a central problem in computational biology. State-space models with Gaussian statistics are widely used for estimation of such latent dynamics and have been successfully utilized in the analysis of biological data. Gaussian statistics, however, fail to capture several key features of the dynamics of biological processes (e.g., brain dynamics) such as abrupt state changes and exogenous processes that affect the states in a structured fashion. Although Gaussian mixture process noise models have been considered as an alternative to capture such effects, data-driven inference of their parameters is not well-established in the literature. The objective of this paper is to develop efficient algorithms for inferring the parameters of a general class of Gaussian mixture process noise models from noisy and limited observations, and to utilize them in extracting the neural dynamics that underlie auditory processing from magnetoencephalography (MEG) data in a cocktail party setting. We develop an algorithm based on Expectation-Maximization to estimate the process noise parameters from state-space observations. We apply our algorithm to simulated and experimentally-recorded MEG data from auditory experiments in the cocktail party paradigm to estimate the underlying dynamic Temporal Response Functions (TRFs). Our simulation results show that the richer representation of the process noise as a Gaussian mixture significantly improves state estimation and capturing the heterogeneity of the TRF dynamics. Application to MEG data reveals improvements over existing TRF estimation techniques, and provides a reliable alternative to current approaches for probing neural dynamics in a cocktail party scenario, as well as attention decoding in emerging applications such as smart hearing aids. Our proposed methodology provides a framework for efficient inference of Gaussian mixture process noise models, with application to a wide range of biological data with underlying heterogeneous and latent dynamics.

To evaluate the effects of person-centred support via telephone in two chronically ill patient groups, chronic obstructive pulmonary disease (COPD) and/or chronic heart failure (CHF). 221 patients ≥ 50 years with COPD and/or CHF were randomized to usual care vs. usual care plus a person-centred telephone-support intervention and followed for six months. Patients in the intervention group were telephoned by a registered nurse initially to co-create a person-centred health plan with the patient and subsequently to discuss and evaluate the plan. The primary outcome measure was a composite score comprising General Self-Efficacy (GSE), re-hospitalization and death. Patients were classified as deteriorated if GSE had decreased by ≥ 5 points, or if they had been re-admitted to hospital for unscheduled reasons related to COPD and/or CHF or if they had died. At six-month follow-up no difference in the composite score was found between the two study groups (57.6%, n = 68 vs. 46.6%, n = 48; OR = 1.6, 95% CI: 0.9-2.7; P = 0.102) in the intention-to-treat analysis (n = 221); however, significantly more patients in the control group showed a clinically important decrease in GSE (≥ 5 units) (22.9%, n = 27 vs. 9.7%, n = 10; OR = 2.8, 95% CI: 1.3-6.0; P = 0.011). There were 49 clinical events (14 deaths, 35 re-admissions) in the control group and 41 in the intervention group (9 deaths, 32 re-admissions). Per-protocol analysis (n = 202) of the composite score showed that more patients deteriorated in the control group than in the intervention group (57.6%, n = 68 vs. 42.9%, n = 36; OR = 1.8, 95% CI 1.0-3.2; P = 0.039). Person-centred support via telephone mitigates worsening self-efficacy without increasing the risk of clinical events in chronically ill patients with CHF and/or COPD. This indicates that a patient-healthcare professional partnership may be established without the need for face-to-face consultations, even in vulnerable patient groups. ISRCTN.com ISRCTN55562827.

Investigate trends in myocarditis incidence and prognosis in Sweden during 2000–2014. Little data exist concerning population-trends in incidence of hospitalizations for myocarditis and subsequent prognosis. Linking Swedish National Patient and Cause of Death Registers, we identified individuals ≥ 16 years with first-time diagnosis of myocarditis during 2000–2014. Reference population, matched for age and birth year (n = 16,622) was selected from Swedish Total Population Register. Among the 8 679 cases (75% men, 64% < 50 years), incidence rate/100,000 inhabitants rose from 6.3 to 8.6 per 100,000, mostly in men and those < 50 years. Incident heart failure/dilated cardiomyopathy occurred in 6.2% within 1 year after index hospitalization and in 10.2% during 2000–2014, predominantly in those ≥ 50 years (12.1% within 1 year, 20.8% during 2000–2014). In all 8.1% died within 1 year, 0.9% (< 50 years) and 20.8% (≥ 50 years). Hazard ratios (adjusted for age, sex) for 1-year mortality comparing cases and controls were 4.00 (95% confidence interval 1.37–11.70), 4.48 (2.57–7.82), 4.57 (3.31–6.31) and 3.93 (3.39–4.57) for individuals aged < 30, 30 to < 50, 50 to < 70, and ≥ 70 years, respectively. The incidence of myocarditis during 2000–2014 increased, predominantly in men < 50 years. One-year mortality was low, but fourfold higher compared with reference population.

Aims This study aims to investigate hospital readmissions and timing, as well as risk factors in a real world heart failure (HF) population. Methods and results All patients discharged alive in 2016 from Sahlgrenska University Hospital/Östra, Gothenburg, Sweden, with a primary diagnosis of HF were consecutively included. Patient characteristics, type of HF, treatment, and follow‐up were registered. Time to first all‐cause or HF readmission, as well as number of 1 year readmissions from discharge were recorded. In total, 448 patients were included: 273 patients (mean age 78 ± 11.8 years) were readmitted for any cause within 1 year (readmission rate of 60.9%), and 175 patients (mean age 76.6 ± 13.7) were never readmitted. Among readmissions, 60.1% occurred during the first quarter after index hospitalization, giving a 3 month all‐cause readmission rate of 36.6%. HF‐related 1 year readmission rate was 38.4%. Patients who were readmitted had significantly more renal dysfunction (52.4% vs. 36.6%, P = 0.001), pulmonary disease (25.6% vs. 15.4%, P = 0.010), and psychiatric illness (24.9% vs. 12.0%, P = 0.001). Number of co‐morbidities and readmissions were significantly associated (P < 0.001 for all cause readmission rate and P = 0.012 for 1 year HF readmission rate). Worsening HF constituted 63% of all‐cause readmissions. Psychiatric disease was an independent risk factor for 1 month and 1 year all‐cause readmissions. Poor compliance to medication was an independent risk factor for 1 month and 1 year HF readmission. Conclusions In our real world cohort of HF patients, frequent hospital readmissions occurred in the early post‐discharge period and were mainly driven by worsening HF. Co‐morbidity was one of the most important factors for readmission.

Hong (2009) [Hong LJ (2009) Estimating quantile sensitivities. Oper. Res. 57(1):118-130.] introduced a general framework based on probability sensitivities and a conditional expectation relationship for estimating quantile sensitivities by infinitesimal perturbation analysis (IPA). We present an alternative more direct derivation of the IPA estimators that leads to simplified proofs for strong consistency and convergence rate of the unbatched estimator, and strong consistency and a central limit theorem for the batched estimator.