Timing Of Events Research Articles

Visual Dyslexia

Abstract Purpose To revive the idea which was previously generally accepted, that learning to sequence written text visually is the crucial first step in learning to read. But in the last few years the phonological theory of dyslexia (PT) has become dominant. It asserts that DD is due to a psycho-linguistic deficit causing difficulty with grasping the concept of phonemes. This ignores a century of research showing that the development of a network of visual magnocellular neurons (M- cells) that signal the timing of visual events precedes and is essential for the development of phonological awareness. Recent Findings We review the cerebral structures with which the visual M- system is associated in reading alphabetic and other scripts and show in each case how their development is impaired in DD. The simplest way to improve M- cell function involves viewing text through yellow or blue filters; we discuss how these may work. Even simpler is improving children’s nutrition; and we discuss some aspects of this. The genes which are associated with the M- system also play an important role in regulating the immune system; hence we discuss how DD is associated with autoimmune conditions such as asthma and eczema and disordered control of inflammation. Summary There is now a very great weight of evidence demonstrating that visual sequencing of text mediated by the visual magnocellular timing system is the first essential for learning to read, and that impaired development of this system is a major cause of Developmental Dyslexia.

Prior Anticoagulation and Risk of Hemorrhagic Transformation in Acute Stroke: A Post Hoc Analysis of the PRODAST Study.

Oral anticoagulation is highly effective in preventing ischemic events in patients with atrial fibrillation. Still, a considerable number of patients have an acute ischemic stroke or transient ischemic attack despite anticoagulation. In this study, we investigated the association of prior antithrombotic regimens with stroke severity, volume, and hemorrhagic transformation. This is a post hoc analysis of the prospective, multicenter, observational PRODAST (Prospective Record of the Use of Dabigatran in Patients With Acute Stroke or TIA) study, which was conducted in 86 stroke units in Germany between July 2015 and November 2020. In 9030 patients with atrial fibrillation who had an acute ischemic stroke or transient ischemic attack within 7 days before enrollment, we analyzed the association of anticoagulants in comparison to lack of prevalent antithrombotic treatment with clinical stroke severity, infarct size, and risk for hemorrhagic transformation. A total of 4479 patients had prior anticoagulation at the time of index event. After adjustment for confounders (arterial hypertension, diabetes, heart failure, age, and sex), patients with prior anticoagulation had less severe strokes (-2.5 National Institutes of Health Stroke Scale points [95% CI, -2.8 to -2.2]), smaller infarct sizes (-23 mL [95% CI, -44 mL to -2 mL], n=4041), and reduced odds for hemorrhagic transformation (5% versus 10%; odds ratio, 0.48 [95% CI, 0.40-0.57]) compared with patients without antithrombotic treatment. These findings were confirmed using sensitivity analyses accounting for thrombolysis and mechanical thrombectomy, as well as timing of brain imaging. Antiplatelet therapy had hardly any associationwith the end points compared with no antithrombotic pretreatment. Prior anticoagulation was not only associated with less severe stroke and smaller infarct size but also with a reduced risk of hemorrhagic transformation compared with no antithrombotic pretreatment. URL: https://www.clinicaltrials.gov; Unique identifier: NCT02507856.

Analysis Method of Real-World Digital Biomarkers for Clinical Impact in Cancer Patients

Introduction: Wearable technologies can enhance measurements completed from home by participants in decentralized clinical trials. These measurements have shown promise in monitoring patient wellness outside the clinical setting. However, there are challenges in handling data and its interpretation when using consumer wearables, requiring input from statisticians and data scientists. This paper describes three methods to estimate daily steps to address gaps in data from the Apple Watch in cancer patients and uses one of these methods in an analysis of the association between daily step count estimates and clinical events for these patients. Methods: A cohort of 50 cancer patients used the DigiBioMarC app integrated with an Apple Watch for 28 days. We identified different gap types in watch data based on their length and context to estimate daily steps. Cox proportional hazards regression models were used to determine the association between step count and time to death or time to first clinical event. Decision tree modeling and participant clustering were also employed to identify digital biomarkers of physical activity that were predictive of clinical event occurrence and hazard ratio to clinical events, respectively. Results: Among the three methods explored to address missing steps, the method that identified different step data gap types according to their duration and context provided the most reasonable estimate of daily steps. Ten hours of waking time was used to differentiate between sufficient and insufficient measurement days. Daily step count on sufficient days was the most promising predictor of time to first clinical event (p = 0.068). This finding was consistent in participant cluster and decision tree analyses where the participant cluster that emerged naturally from the different levels of daily steps was the group with the highest steps on sufficient days and lowest hazard probability of mortality and clinical events. Additionally, daily steps on sufficient days can also be used as a predictor of whether a participant will have clinical events with an accuracy of 83.3%. Conclusion: We have developed an effective way to estimate daily steps from consumer wearable data containing unknown data gaps. Daily step counts on days with sufficient sampling is a strong predictor of the timing and occurrence of clinical events, with individuals exhibiting higher daily step counts having reduced hazard of death or clinical events.

Practical management of adverse events in patients receiving tarlatamab, a delta-like ligand 3-targeted bispecific T-cell engager immunotherapy, for previously treated small cell lung cancer.

Tarlatamab is a bispecific T-cell engager immunotherapy targeting delta-like ligand 3 (DLL3) and the cluster of differentiation 3 (CD3) molecule. In the phase 2 DeLLphi-301 trial of tarlatamab for patients with previously treated small cell lung cancer, tarlatamab 10 mg every 2 weeks achieved durable responses and encouraging survival outcomes. Analyses of updated safety data from the DeLLphi-301 trial demonstrated that the most common treatment-emergent adverse events were cytokine release syndrome (53%), pyrexia (38%), decreased appetite (36%), dysgeusia (32%), and an emia (30%). Cytokine release syndrome was mostly grade 1 or 2 in severity, occurred primarily after the first or second tarlatamab dose, and was managed with supportive care, which included the administration of antipyretics (e.g., acetaminophen), intravenous hydration, and/or glucocorticoids. Other treatment-emergent adverse effects of interest included neutropenia (16%) and immune effector cell-associated neurotoxicity syndrome and associated neurologic events (10%). Given that tarlatamab is the first T-cell engager approved for the treatment of small cell lung cancer, raising awareness with regard to the monitoring and management of tarlatamab-associated adverse events is essential. Here, the authors describe the timing, occurrence, and duration of these adverse events and review the management and risk-mitigation strategies used by clinical investigators during the DeLLphi-301 trial.

A Longitudinal Exploration of CACNA1A-Related Hemiplegic Migraine in Children Using Electronic Medical Records.

Since the initial description of CACNA1A-related hemiplegic migraine (HM), the phenotypic spectrum has expanded from mild episodes in neurotypical individuals to potentially life-threatening events frequently seen in individuals with developmental and epileptic encephalopathies. However, the overall longitudinal course throughout childhood remains unknown. We analyzed HM and seizure history from electronic medical records in individuals with CACNA1A-related HM, delineating frequency and severity of events in monthly increments through a standardized approach. Combining these data with medication prescription information, we assessed the response of HM to different agents. Our cohort involved 15 individuals between 3 and 29 years (163 patient years) and included 11 unique and 2 recurrent variants (p.R1349Q and p.V1393M; both n = 2). The age of first confirmed HM ranged from 14 months to 13 years (average 3 years). 25% of all HM events were severe (lasting >3 days), and 73% of individuals had at least 1 severe occurrence. Spacing of HM events ranged from 1 month to 14 years, and changes in HM severity over time showed increases or decreases of >2 severity levels in 12 of 122 events. Eight individuals had epilepsy, but severity of epilepsy was only weakly correlated with frequency and severity of HM events. While levetiracetam (n = 6) and acetazolamide (n = 5) were the most frequently used medications, they did not show efficacy in HM prevention or severity reduction. However, verapamil (risk differences [RD] 0.10, CI 0.05-0.15) and valproate (RD 0.08, CI 0.04-0.12) were associated with a modest prevention of HM, but not reduction in severity. The longitudinal course of CACNA1A-related HM lacks recognizable patterns for timing and severity of HM events or strong correlation with seizure patterns. Our data underscore the unpredictability of CACNA1A-related HM, highlighting the need for close surveillance for reoccurring HM events even in individuals with symptom-free periods.

Matching plus regression adjustment for the estimation of the average treatment effect on survival outcomes: a case study with mosunetuzumab in relapsed/refractory follicular lymphoma

Background and objectivesThe National Institute for Health and Care Excellence (England’s health technology assessment body) recommend the use of the average treatment effect (ATE) as an estimand for economic evaluations. However there is limited literature on methods to estimate the ATE, particularly in the case of survival outcomes. Single-arm trials and real-world data are playing an increasing role in health technology assessments, particularly in oncology/rare diseases, generating a need for new ATE estimation methods. This study aimed to present the adaptation and utility of this methodology for survival outcomes.MethodsThe approach is based on a “doubly robust” method combining matching with regression adjustment (Austin 2020) using a Weibull model (lowest Akaike information criteria [AIC] specification) to estimate counterfactual event times. As a case study, we compared mosunetuzumab versus rituximab/bendamustine, as a proxy for rituximab/chemotherapy, in 3L+ relapsed/refractory follicular lymphoma. Individual patient data for mosunetuzumab (NCT02500407) and a combination of two rituximab/bendamustine 3L+ follicular lymphoma cohorts (NCT02187861/NCT02257567) were used. Endpoints included overall survival (OS) and progression-free survival (PFS). Sensitivity analyses were performed to test robustness to different distributional assumptions (log-normal, log-logistic and exponential) or model specifications (second, third and fourth lowest AIC) for event times.ResultsThe case study found improved PFS (hazard ratio [HR] 0.43 [95% confidence interval (CI): 0.13, 0.91]) and OS (HR 0.30 [95% CI: 0.05, 5.28]) for mosunetuzumab. Consistent findings (HR range 0.25–0.47 and 0.21–0.50 with all CIs excluding/including 1 for PFS/OS, respectively) were observed in sensitivity analyses.Discussion/conclusionsThe proposed adaptation expands the range of available approaches for the estimation of the (local) ATE for survival outcomes in health technology assessments using “doubly robust” methods. This approach appeared relatively robust to modelling decisions in our case study.

TransformerLSR: Attentive joint model of longitudinal data, survival, and recurrent events with concurrent latent structure.

In applications such as biomedical studies, epidemiology, and social sciences, recurrent events often co-occur with longitudinal measurements and a terminal event, such as death. Therefore, jointly modeling longitudinal measurements, recurrent events, and survival data while accounting for their dependencies is critical. While joint models for the three components exist in statistical literature, many of these approaches are limited by heavy parametric assumptions and scalability issues. Recently, incorporating deep learning techniques into joint modeling has shown promising results. However, current methods only address joint modeling of longitudinal measurements at regularly-spaced observation times and survival events, neglecting recurrent events. In this paper, we develop TransformerLSR, a flexible transformer-based deep modeling and inference framework to jointly model all three components simultaneously. TransformerLSR integrates deep temporal point processes into the joint modeling framework, treating recurrent and terminal events as two competing processes dependent on past longitudinal measurements and recurrent event times. Additionally, TransformerLSR introduces a novel trajectory representation and model architecture to potentially incorporate a priori knowledge of known latent structures among concurrent longitudinal variables. We demonstrate the effectiveness and necessity of TransformerLSR through simulation studies and analyzing a real-world medical dataset on patients after kidney transplantation.

Experience in setting up non-robotic minimally invasive direct coronary artery bypass grafting in a non-routine off-pump coronary artery bypass center

BackgroundThe safety of minimally invasive direct coronary artery bypass (MIDCAB) has been proven. Nevertheless, reports on clinical outcomes in MIDCAB and the learning curve of this challenging technique in a non-routine off-pump coronary artery bypass (OPCAB) center are still limited. Here, we introduce our clinical outcomes of non-robotic MIDCAB.MethodsBetween August 2022 and March 2024, 72 consecutive patients with a mean age of 67.4 ± 9.5 years underwent non-robotic MIDCAB (defined as off-pump bypass grafting of the left internal mammary artery to the left anterior descending artery through left-sided mini-thoracotomy). We analyzed operation time and incidence of major adverse cardiac and cerebrovascular events (MACCE). Further, subgroup analyses included body mass index (BMI) with a cut-off of 30 kg/m2 [BMI ≧ 30: n = 18 (25.0%)] and body surface area (BSA) with a cut-off of 2.0 m2 [BSA ≧ 2.0: n = 34 (47.2%)].ResultsAll patients survived, whereas MACCE occurred in 4 patients (5.6%). By correlation analyses, no learning curve for operation time was observed in all cases analysis (p = 0.79), but MACCE (n = 4, 5.6%) exclusively observed in the first 34 patients. Furthermore, BMI ≧ 30 or BSA ≧ 2.0 was not significantly related to longer operation time (p = 0.42 and p = 0.52, respectively) and MACCE (p = 0.26 and p = 0.35, respectively). In addition, body size had no effect on operation time according to multiple regression analysis (p = 0.36).ConclusionsOur study suggested that implementing non-robotic MIDCAB can be safely accomplished at a center with no previous routine in OPCAB surgery, even for patients with bigger body sizes. MACCE occurs more frequently in the early stages when adopting this surgical technique.

A new method for determining the onset times of solar energetic particles and their uncertainties: Poisson-CUSUM bootstrap hybrid method

Solar energetic particle (SEP) events are a type of space weather phenomena in which highly energetic charged particles are released from the Sun into interplanetary space by violent and eruptive phenomena, such as solar flares and coronal mass ejections. In order to assess the origin of SEPs, an accurate timing of their arrival at spacecraft is of utmost importance. Several methods for determining the starting time of an SEP event at an observer exist, but the uncertainty of this starting time is not assessed in a systematic way by the vast majority of studies. Employing a newly developed hybrid method of Poisson-CUSUM combined with bootstrapping, we show that the uncertainty related to the onset of an event in any particular energy range is often more than the mere time resolution of the measuring apparatus, and furthermore, it is not necessarily symmetric with respect to the past and future of the determined onset. In addition, we provide a software tool to the scientific community that applies the presented method and automates the determination of SEP event onset times and their related uncertainties, and it finally allows one to easily perform a velocity dispersion analysis. By applying the Poisson-CUSUM method coupled with statistical bootstrapping to SEP event observations, we demonstrate the effectiveness of the method on synthetic and real data, and we compare them to an analysis conducted using a classical approach in which the uncertainty is assumed based on the time resolution of the data. In the example case, the inferred SEP path length and injection time related to the event, acquired by the velocity dispersion analysis, differ from what is obtained without properly assessing the uncertainty related to the onset times in varying energies. We also present the software package, PyOnset, that automates many steps of the method along with providing powerful data-visualization methods and analysis tools. We release the code to the scientific community as open-source software.

P-1275. Quasi-experimental Approaches for Estimating Epidemiological Efficacy of Non-randomised Field Trials: Applications to Wolbachia Interventions

Abstract Background Wolbachia symbiosis in Aedes aegypti is an emerging biocontrol measure against dengue. However, assessing its real-world efficacy is challenging due to the non-randomised, field-based nature of most intervention studies. This research re-evaluates the spatial-temporal impact of Wolbachia interventions on dengue incidence using a large battery of quasi-experimental methods and assesses each method’s validity. Aggregated intervention efficacies by event time for each study site Aggregated intervention efficacies for (a) Selangor, (b) Niterói, and (c) Singapore based on event time. Each efficacy assessment method is represented by a specific color: red for Pre-post, yellow for RDD (Regression Discontinuity Design), green for 2x2 DiD (Difference-in-Differences), blue for SCM (Synthetic Control Method), and purple for cSCM (count Synthetic Control Method). The intervention efficacy (IE) reported in the original paper is depicted on the right side of each diagram, represented by the colour black. The dot represents the IE point estimate for each method, while the line indicates the corresponding confidence intervals. Extreme confidence interval values and point estimates are denoted by arrows, with their respective values displayed next to the arrow. Methods A systematic search for Wolbachia intervention data was conducted via PUBMED. Efficacy was reassessed using commonly-used quasi-experimental approaches with extensive robustness checks, including geospatial placebo tests and a simulation study. Intervention efficacies across multiple study sites were computed using high-resolution aggregations to examine heterogeneities across sites and study periods. We further designed a stochastic simulation framework to assess the methods’ ability to estimate intervention efficacies (IE). Aggregate intervention efficacy (IE) estimates (%) of Wolbachia releases on total dengue incidence rates across all intervention sitesTable 2:Aggregate intervention efficacy (IE) estimates (%) of Wolbachia releases on total dengue incidence rates across all intervention sites. The estimates are reported for each method and are accompanied by the corresponding confidence intervals (in parentheses). The IE estimate reported in the respective original papers, highlighted in blue, is provided for comparison. Numbers in parenthesis represent lower and upper bounds for 95% confidence intervals, estimated using the bootstrapping procedure. # indicates that the estimate passed in-time placebo checks while * indicates that it passed in-space placebo check. Bolded figures represent significant IE estimates which also passed in-space and in-time placebo checks. Results Wolbachia interventions in Singapore, Malaysia, and Brazil significantly decreased dengue incidence, with reductions ranging from 48.17% to 69.19%. IEs varied with location and duration. Selangor showed increasing efficacy over time, while Niterói exhibited initial success with subsequent decline, hinting at operational challenges. Singapore's strategy was highly effective despite partial saturation. Simulations identified Synthetic Control Methods (SCM) and its variant, count Synthetic Control Method (cSCM), as superior in precision, with the smallest percentage errors in efficacy estimation. These methods also demonstrated robustness in placebo tests. Conclusion Wolbachia interventions exhibit consistent protective effects against dengue. SCM and cSCM provided the most precise and robust estimates of IEs, validated across simulated and real-world settings. Disclosures All Authors: No reported disclosures

P-2119. Development of a Machine-Learning-Based Prediction Model for Candidemia Diagnosis

Abstract Background Early recognition and prompt initiation of antifungal therapy for candidemia substantially reduce morbidity and mortality. However, differentiating candidemia from bacteremia remains a clinical challenge and often results in delays in antifungal treatment. This study aimed to develop a machine-learning-based prediction model for candidemia.Figure 1.Receiver Operating Characteristic (ROC) Curves for Machine-Learning-Based Prediction Models for Candidemia Methods We conducted a single-center retrospective cohort study involving adult patients diagnosed with candidemia from 2014 to 2021, who were included as cases. Control patients, defined as adults with bacteremia, were randomly selected during the same timeframe. Patients with concurrent bacterial and candida infections were excluded. The event time was defined as the moment of blood culture collection for diagnosing each case of candidemia and bacteremia. Clinical data collected at the event time included vital signs (systolic blood pressure and heart rate), epidemiologic, and laboratory data. In total, 55 types of clinical variables were collected. Nine different machine learning algorithms—XGBoost, Random Forest, Logistic Regression, Support Vector Machine, Multinomial Naive Bayes, K-Nearest Neighbors, AdaBoost, CatBoost, and an ensemble incorporating CatBoost and AdaBoost—were utilized to develop a machine-learning-based prediction model for candidemia. The model demonstrating the highest area under the receiver operating characteristic curve (AUROC) was selected for further analysis. Model performance was assessed based on accuracy, precision, recall, and F1 score. Results The cohort included 261 patients with candidemia and 522 with bacteremia, with a median age of 68 years. The AUROC for the models varied from 0.439 to 0.895. The ensemble model achieved the highest AUROC of 0.895 (Figure 1). For this ensemble model, the F1 score was 0.853, recall was 0.849, precision was 0.862, and accuracy was 0.849. Conclusion A machine-learning-based prediction model could be a useful tool for identifying high-risk patients for the early diagnosis of candidemia. Disclosures All Authors: No reported disclosures

P-741. Dalbavancin Rationale and Clinical Outcomes in Patients Hospitalized with Serious Bacterial Infections at an Urban Safety Net Hospital: an Observational Cohort

Abstract Background Dalbavancin is an emerging therapeutic option for gram positive bacterial infections in patients for whom standard IV or PO therapies are less desirable due to housing instability, substance use, or psychiatric conditions. Data on success rates of dalbavancin, rationale for use, and as step down therapy in at-risk populations are limited. In a large urban cohort of hospitalized patients receiving dalbavancin, we assessed rationale for therapy and clinical outcomes following administration. Patient demographic and clinical characteristics and rationale for dalbavancin among inpatients receiving therapy (N=107) *Patients may have more than one A: Substance use included: amphetamines including methamphetamine, cocaine, opioids, and other non-prescribed drugs. B: Housing instability included: any unhoused status including staying on street, in shelter, with friends/family, in vehicle, etc. C: Other infections included: pneumonia, lymphadenitis, septic thrombophlebitis, septic emboli, septic bursitis, mycotic aneurysm, myositis, TMJ septic arthritis, diabetic foot infection, and unknown. D: Source control was considered achieved if bacteremia was cleared, incision and drainage was performed, or hardware/prosthetic line/device was removed. Methods All adult inpatients who received ≥ 1 dalbavancin doses between 12/2021-3/2024 at Zuckerberg San Francisco General Hospital were evaluated. We assessed patients’ demographic features, provider rationale for dalbavancin use, and treatment completion rate. Time-to-event analysis assessed timing of clinical events. Primary outcome was treatment success (lack of recurrence/nonresolution at 90 days post-dalbavancin by in-depth chart review). Secondary outcomes were 90-day hospital re-admission, dalbavancin completion, and adverse effects. Results Overall, 107 patients received dalbavancin, and 48% of treatments were in a context of patient self-discharge from hospital. Co-morbidities were prevalent and included HIV (19%; 11/20 [55%] had viral suppression), substance use (84%), psychiatric conditions (59%), and housing instability (64%). Dalbavancin was used to treat skin/skin structure infections (55/107; 51%), bacteremia (46/107; 43%), bone/joint infection (61/107; 57%), and endocarditis (6/107; 6%). Treatment success was seen in 85% of patients, and 88% with Staphylococcus aureus. Time-to-event analysis shows clinical failures occurred evenly throughout the 90 days post dalbavancin. Ninety-day hospital readmission was 17%. Adverse events were rare (6%). Dalbavancin dose completion for those prescribed > 1 dalbavancin was 77%. Incomplete therapy had a trend towards lower treatment success (5/13, 39%, P=0.09). Conclusion Dalbavancin achieved high treatment success and completion among patients with high prevalence of housing instability, substance use, and medical/psychiatric co-morbidities. Dalbavancin has potential as a patient centered alternative to standard antibiotic therapies in at-risk patients. Disclosures All Authors: No reported disclosures

In-Cylinder Imaging and Emissions Measurements of Cold-Start Split Injection Strategies

Abstract Strategies to mitigate cold-start emissions are critical to meet stringent standards for particulate and regulated gaseous emissions from direct injection spark ignition (DISI) engines. The objective of the current work was to establish an experimental protocol for cold-start studies and to identify the in-cylinder phenomena important during engine warming in terms of engine power and stability and engine-out particulates, NOx, and unburned hydrocarbon emissions. Metal-engine experiments were conducted with ambient intake air temperature and coolant air temperature of 20 °C to quantify the sensitivity of the performance of a single-cylinder engine to timing of a strategy using two injection events per power cycle and late spark ignition. The results showed strong sensitivity to the timing of the second injection event and weak sensitivity to the timing of the first injection. Complementary high-speed imaging experiments were conducted with the same engine where the fuel spray and combustion characteristics were visualized during cold-start split-injection operation. The imaging data showed the sources of particulates were due to fuel impingement on combustion chamber surfaces and due to fuel rich regions, in particular regions where the fuel recondensed due to the conditions associated with late ignition timing. The combination of engine-out particulate and imaging measurements indicated smaller particles (10–20 nm) were associated with fuel impingement on surfaces, and larger particles (20–200 nm) were associated with fuel rich “pockets”.

BPASS stellar evolution models incorporating α-enhanced composition – I. Single star models from 0.1 to 316 M⊙

Abstract Stellar evolution modelling is fundamental to many areas of astrophysics including stellar populations in both nearby and distant galaxies. It is heavily influenced by chemical composition. Observations of distant galaxies and nucleosynthesis calculations show that α-process elements are enriched faster than iron group elements. We present a dense grid of single-star models calculated using the bpass stellar evolution code and covering masses (0.1 ≤ M/M⊙≤316), metallicity mass fractions (10−5 ≤ Z ≤ 0.04) and α-to-iron abundance ratios (−0.2 ≤$[\alpha /\rm {Fe}]$≤+0.6). By comparing Solar-scaled models to ones enriched in α-process elements, we find that stellar radii, surface temperatures, Main Sequence lifetimes, supernova progenitor properties and supernova rates are all sensitive to changes in $[\alpha /\rm {Fe}]$. Lifetimes of low-mass stars differ by up to 0.4 dex, while surface temperatures of massive stars at the end of the Main Sequence also differ by around 0.4 dex. Inferred supernova rates when $[\rm {Fe}/\rm {H}]$ is unknown can be highly uncertain. Models with different $[\alpha /\rm {Fe}]$ but comparable iron abundances show smaller variations, indicating that while iron primarily defines the course of evolution; α-enhancement nonetheless has an impact of up to 0.1 dex on stellar properties. Such changes are small for individual stars, but have a large cumulative effect when considering an entire stellar population as demonstrated by isochrone fitting to nearby clusters. Changes in radii and lifetimes have further consequences for a stellar population including binary stars, as they influence the timing, nature and occurrence rate of mass transfer events.

Performance of algorithms using wrist temperature for retrospective ovulation day estimate and next menses start day prediction: a prospective cohort study.

Can algorithms using wrist temperature, available on compatible models of iPhone and Apple Watch, retrospectively estimate the day of ovulation and predict the next menses start day? Algorithms using wrist temperature can provide retrospective ovulation estimates and next menses start day predictions for individuals with typical or atypical cycle lengths. Wrist skin temperature is affected by hormonal changes associated with the menstrual cycle and can be used to estimate the timing of cycle events. We conducted a prospective cohort study of 262 menstruating females (899 menstrual cycles) aged 14 and older who logged their menses, performed urine LH testing to define day of ovulation, recorded daily basal body temperature (BBT), and collected overnight wrist temperature. Participants contributed between 2 and 13 menstrual cycles. Algorithm performance was evaluated for three algorithms: one for retrospective ovulation day estimate in ongoing cycles (Algorithm 1), one for retrospective ovulation day estimate in completed cycles (Algorithm 2), and one for prediction of next menses start day (Algorithm 3). Each algorithm's performance was evaluated under multiple scenarios, including for participants with all typical cycle lengths (23-35 days) and those with some atypical cycle lengths (<23, >35 days), in cycles with the temperature change of ≥0.2°C typically associated with ovulation, and with any temperature change included. Two hundred and sixty participants provided 889 cycles. Algorithm 1 provided a retrospective ovulation day estimate in 80.5% of ongoing menstrual cycles of all cycle lengths with ≥0.2°C wrist temperature signal with a mean absolute error (MAE) of 1.59 days (95% CI 1.45, 1.74), with 80.0% of estimates being within ±2 days of ovulation. Retrospective ovulation day in an ongoing cycle (Algorithm 1) was estimated in 81.9% (MAE 1.53 days, 95% CI 1.35, 1.70) of cycles for participants with all typical cycle lengths and 77.7% (MAE 1.71 days, 95% CI 1.42, 2.01) of cycles for participants with atypical cycle lengths. Algorithm 2 provided a retrospective ovulation day estimate in 80.8% of completed menstrual cycles with ≥0.2°C wrist temperature signal with an MAE of 1.22 days (95% CI 1.11, 1.33), with 89.0% of estimates being within ±2 days of ovulation. Wrist temperature provided the next menses start day prediction (Algorithm 3) at the time of ovulation estimate (89.4% within ±3 days of menses start) with an MAE of 1.65 (95% CI 1.52, 1.79) days in cycles with ≥0.2°C wrist temperature signal. There are several limitations, including reliance on LH testing to identify ovulation, which may mislabel some cycles. Additionally, the potential for false retrospective ovulation estimates when no ovulation occurred reinforces the idea that this estimate should not be used in isolation. Algorithms using wrist temperature can provide retrospective ovulation estimates and next menses start day predictions for individuals with typical or atypical cycle lengths. Apple is the funding source for this manuscript. Y.W., C.Y.Z., J.P., S.Z., and C.L.C. own Apple stock and are employed by Apple. S.M. has research funding from Apple for a separate study, the Apple Women's Health Study, including meeting and travel support to present research findings related to that separate study. A.M.Z.J., D.D.B., B.A.C., and J.P. had no conflicts of interest. NCT05852951.

Non-invasive assessment of arterial elastance using three-dimensional echocardiography: an accurate alternative to invasive methods

Abstract Introduction Assessing arterial elastance (Ea) is crucial for understanding vascular properties and managing conditions such as hypertension, heart failure, and valvular heart disease. While invasive pressure-volume (PV) analysis provides precise measurements, the procedures involved pose significant risks and limitations. Three-dimensional (3D) echocardiography, combined with non-invasive LV pressure curve estimation, however, offers a highly accurate method for generating non-invasive PV-loops. Thus, this approach has the potential to provide a safe and widely accessible means of assessing Ea. Aim Validate non-invasive Ea by PV analysis using 3D echocardiography, comparing it to gold-standard Ea derived from invasive measurements. Methods In 10 open-chest canines, invasive pressure was measured using a micromanometer, and volume determined from subendocardial piezoelectric crystals, using a three-axis ellipsoid model (Figure 1). Echocardiography provided non-invasive 3D LV volumes and valve event timings. The LV pressure curve was estimated by adjusting a reference curve to the valve events and measured peak LV pressure, which in clinical practice would be approximated as systolic brachial cuff pressure. Arterial elastance (Ea) was defined as the absolute slope of the line from volume axis interception at end-diastolic volume to the point on the PV-loop in the upper left quadrant with the longest normalized distance from the loop center. Recordings were performed at baseline and during aortic constriction. Results Panel A in Figure 2 illustrates how tightly non-invasive (dotted lines) followed invasive (solid lines) pressure-volume curves both during baseline (left) and aortic constriction (right) in a representative experiment. As can be seen, this resulted in very similar Ea. Panel B shows pooled data from all experiments, demonstrating that indeed, the non-invasive method captured similar changes in Ea during aortic constriction compared to the invasive method. Furthermore, as shown in panel C, there was an excellent correlation (r = 0.91, p &amp;lt; 0.0001) and good agreement (mean difference 0.3, SD 0.3) between the non-invasive and invasive Ea. Conclusions Three-dimensional echocardiography provides an accurate and non-invasive method for assessing arterial elastance, with excellent agreement with gold standard invasive method. Ultrasound crystals placement Invasive vs non-invasive Ea

Myocardial work indices and global longitudinal strain: equivalent prognostic value in chronic HFrEF

Abstract Background Non-invasive myocardial work (MW) indices can be estimated from pressure-strain loops obtained by integrating left ventricular global longitudinal strain (LVGLS) with brachial systolic blood pressure (SBP) and cardiac event timings (opening and closure of the aortic and mitral valve). Compared to LV GLS, MW indices offer the possibility to correct myocardial performance for LV afterload and dyssynchrony. Purpose To assess whether MW indices could provide additional prognostic value in a contemporary cohort of stable outpatients with chronic heart failure with reduced ejection fraction (HFrEF) on optimal medical and device therapy, with controlled afterload and no dyssynchrony. Methods We retrospectively analyzed 307 patients (mean age 65.6±12.7 years, 78% males) with reduced LV ejection fraction (LVEF&amp;lt;50%). Liner regressions were performed to assess the relationships between MW indices and LVGLS, SBP, and parameters of inter- (i.e. inter-ventricular mechanical delay&amp;gt;40 msec) and intra-ventricular (i.e. mechanical dispersion&amp;gt;100 msec) dyssynchrony. The study endpoint was a composite of all-cause death/HF-hospitalization. Multivariable models including each MW indices once at a time [global work index (GWI), global constructive work (GCW), global wasted work (GWW) and global work efficiency (GWE)] were compared with the model including LVGLS by C-statistics. Results Mean LVEF was 32±9%, mean LVGLS was -9±3, mean GWI was 800±378 mmHg% and median NT-proBNP was 1.800 pg/ml (IQR: 731–4110). The majority of patients (84%) had SBP&amp;lt; 130 mmHg (mean 117±16 mmHg), and had no inter- (71%) and (68%) intra-ventricular dyssynchrony. Over a median follow-up of 20 months (IQR: 11-34), 82 patients reached the study endpoint. After multivariable adjustment for MAGGIC score, natriuretic peptide levels, LV end-diastolic volume index, left atrial volume index, E/e’ ratio, and TAPSE/PASP ratio, both GWI and GCW were independently associated with adverse outcomes, with identical goodness of fit compared to the model including LVGLS (C-index 0.75 for all). On the contrary, both GWW and GWE were not independently associated with outcomes. While GWI and GCW were strongly correlated with LVGLS (B coefficient&amp;lt;-0.85; Figure), their association with SBP was modest and there was no significant association between them and parameters of intra- and inter-ventricular dyssynchrony. Conclusions In a cohort of stable chronic HFrEF patients on optimal medical and device therapy, with controlled afterload and without dyssynchrony, MW indices showed similar prognostic value to LVGLS. Association between LVGLS and MW indices

Pressure-volume analysis by three-dimensional echocardiography: an accurate non-invasive alternative for assessing left ventricular contractility

Abstract Introduction Accurately assessing left ventricular (LV) contractility is vital for diagnosing and managing cardiac conditions like heart failure, cardiomyopathies, and ischemic heart disease. LV contractility assessment by traditional invasive pressure-volume (PV) analysis necessitates invasive measurements, introducing significant risks and limitations. However, three-dimensional (3D) echocardiography and non-invasive LV pressure curve estimation enables accurate PV-loop estimation, presenting a harmless and widely available alternative for LV contractility assessment. Aim Validate indices of LV contractility by PV analysis using 3D echocardiography by comparing them to gold-standard invasive measurements. Methods In 10 open-chest canines, invasive LV pressure was measured by micromanometer and volume by subendocardial piezoelectric crystals. Echocardiography provided non-invasive 3D LV volumes and valve event timings. The pressure curve was estimated by adjusting a reference curve to the valve events and measured peak LV pressure. Invasive LV end-systolic elastance (Ees) was obtained from multiple PV -loops during preload reduction through least square regression of the points on the PV-loops in the upper left corner with longest normalized distance from loop center. Non-invasive Ees was estimated using a simplified one-beat approach as the slope of a line drawn from this PV-loop point to the origin (Figure 1A). Invasive and non-invasive Ees, along with contractility indices peak LV pressure (Pmax)/end-diastolic volume (EDV) and LV pressure at end-systole (Pes)/EDV, were compared during baseline and during dobutamine infusion. Results As shown in the representative experiment illustrated in Figure 1A, non-invasive Ees captured changes in LV contractility in a similar manner as compared to invasive Ees. Indeed, this was consistent across all experiments, resulting in significant increase in non-invasive Ees in parallel with invasive Ees during dobutamine infusion (p &amp;lt; 0.001 for both) (Figure 1B). Consequently, there was a strong correlation and good agreement between the two methods (r = 0.63, p = 0.003; mean difference – 1.5, SD 2.4) (Figure 1C). For LV contractility indices Pmax/EDV and Pes/EDV, results were comparable: both increased significantly during dobutamine infusion, similar to invasive Ees. This was consistent across all experiments, without exceptions (Figure 2A). Consequently, there were strong correlations between Pmax/EDV and Pes/EDV and invasive Ees (r = 0.77, p = 0.0001 and r = 0.79, p &amp;lt; 0.0001, respectively) (Figure 2B). Conclusions 3D echocardiography provides a reliable and non-invasive method for assessing LV contractility, with good agreement with traditional invasive gold standard Ees. These findings support the use of 3D echocardiography as an accurate and easily accessible alternative for evaluating LV contractility in clinical settings. Invasive vs non-invasive LV Ees Pmax/EDV and Pes/EDV

Nationwide evaluation of medical training in psychiatry and psychotherapy in Germany

To date there has been no evaluation of further training and working conditions in medical specialist training in psychiatry and psychotherapy in Germany. In order to counteract the lack of young talent and ensure long-term care for patients, quality assurance is essential. The evaluation aims to identify strengths and weaknesses of the further training and to derive measures for improvement. For abetter classification, acomparison is also made with other disciplines with medical specialist further training. The online-based survey took place from 15 May 2023 to 30 June 2023. The results refer to 315 questionnaires from 202 physicians in further training and 113 medical specialists (specialist title for less than 3 years). Of those surveyed 64% were satisfied with the quality and implementation of the general psychiatry part, only 11% were considering changing their specialty and 18% were considering moving their job abroad due to the training conditions. There is aneed for improvement in induction and in the organization of the training. In addition, significant time and financial burdens for mandatory external training events as well as the lack of compatibility between training and research, teaching and care work responsibilities were criticized. Overall, it appears that the working conditions are better than in other disciplines. With respect to the potential for improvement, the creation of financing for further training is essential; certificates for high quality training and the publication of positive examples can also contribute to quality assurance and improvement.

Factors associated with high-intensity physical activity and sudden cardiac death in hypertrophic cardiomyopathy

BackgroundHigh-intensity physical activity has traditionally been discouraged in patients with hypertrophic cardiomyopathy due to concerns about triggering sudden cardiac death. However, current guidelines adopt a more liberal stance, and evidence...