Percutaneous tracheostomy: Comparison of three different methods with respect to tracheal cartilage injury in cadavers-Randomized controlled study.

Background: Performing tracheostomy improves patient comfort and success rate of weaning from prolonged invasive mechanical ventilation. Data suggest that patients have more benefit of percutaneous technique than the surgical procedure, however, there is no consensus on the percutaneous method of choice regarding severe complications such as late tracheal stenosis. Aim of this study was comparing incidences of cartilage injury caused by different percutaneous dilatation techniques (PDT), including Single Dilator, Griggs' and modified (bidirectional) Griggs' method. Materials and methods: Randomized observational study was conducted on 150 cadavers underwent post-mortem percutaneous tracheostomy. Data of cadavers including age, gender and time elapsed from death until the intervention (more or less than 72 h) were collected and recorded. Primary and secondary outcomes were: rate of cartilage injury and cannula malposition respectively. Results: Statistical analysis revealed that method of intervention was significantly associated with occurrence of cartilage injury, as comparing either standard Griggs' with Single Dilator (p = 0.002; OR: 4.903; 95% CI: 1.834-13.105) or modified Griggs' with Single Dilator (p < 0.001; OR: 6.559; 95% CI: 2.472-17.404), however, no statistical difference was observed between standard and modified Griggs' techniques (p = 0.583; OR: 0.748; 95% CI: 0.347-1.610). We found no statistical difference in the occurrence of cartilage injury between the early- and late post-mortem group (p = 0.630). Neither gender (p = 0.913), nor age (p = 0.529) influenced the rate of cartilage fracture. There was no statistical difference between the applied PDT techniques regarding the cannula misplacement/malposition. Conclusion: In this cadaver study both standard and modified Griggs' forceps dilatational methods were safer than Single dilator in respect of cartilage injury.

Self-Supervised Contrastive Learning for Medical Time Series: A Systematic Review.

Medical time series are sequential data collected over time that measures health-related signals, such as electroencephalography (EEG), electrocardiography (ECG), and intensive care unit (ICU) readings. Analyzing medical time series and identifying the latent patterns and trends that lead to uncovering highly valuable insights for enhancing diagnosis, treatment, risk assessment, and disease progression. However, data mining in medical time series is heavily limited by the sample annotation which is time-consuming and labor-intensive, and expert-depending. To mitigate this challenge, the emerging self-supervised contrastive learning, which has shown great success since 2020, is a promising solution. Contrastive learning aims to learn representative embeddings by contrasting positive and negative samples without the requirement for explicit labels. Here, we conducted a systematic review of how contrastive learning alleviates the label scarcity in medical time series based on PRISMA standards. We searched the studies in five scientific databases (IEEE, ACM, Scopus, Google Scholar, and PubMed) and retrieved 1908 papers based on the inclusion criteria. After applying excluding criteria, and screening at title, abstract, and full text levels, we carefully reviewed 43 papers in this area. Specifically, this paper outlines the pipeline of contrastive learning, including pre-training, fine-tuning, and testing. We provide a comprehensive summary of the various augmentations applied to medical time series data, the architectures of pre-training encoders, the types of fine-tuning classifiers and clusters, and the popular contrastive loss functions. Moreover, we present an overview of the different data types used in medical time series, highlight the medical applications of interest, and provide a comprehensive table of 51 public datasets that have been utilized in this field. In addition, this paper will provide a discussion on the promising future scopes such as providing guidance for effective augmentation design, developing a unified framework for analyzing hierarchical time series, and investigating methods for processing multimodal data. Despite being in its early stages, self-supervised contrastive learning has shown great potential in overcoming the need for expert-created annotations in the research of medical time series.

Practice Pattern Variation in Adoption of New and Evolving Percutaneous Coronary Intervention Procedures.

Objective: Assess factors contributing to variation in the use of new and evolving diagnostic and interventional procedures for percutaneous coronary intervention (PCI). Background: Evidence-based practices for PCI have the potential to improve outcomes but are variably adopted. Finding possible drivers of PCI procedure-use variability is key for efforts aimed at establishing more uniform practice. Methods: Veterans Affairs Clinical Assessment, Reporting, and Tracking Program data were used to estimate a proportion of variation attributable to hospital-, operator-, and patient-level factors across (a) radial arterial access, (b) intravascular imaging/optical coherence tomography, and (c) atherectomy for PCI. We used random-effects models with hospital, operator, and patient random effects. Overlap between levels generated cumulative variability estimates greater than 100%. Results: A total of 445 operators performed 95,391 PCI procedures across 73 hospitals from 2011 to 2018. The rates of all procedures increased over this time. 24.45% of variability in the use of radial access was attributable to the hospital, 53.04% to the operator, and 57.83% to patient-level characteristics. 9.06% of the variability in intravascular imaging use was attributable to the hospital, 43.92% to the operator, and 21.20% to the patient. Lastly, 20.16% of the variability in use of atherectomy was attributed to the hospital, 34.63% to the operator, and 57.50% to the patient. Conclusions: The use of radial access, intracoronary imaging, and atherectomy is influenced by patient, operator, and hospital factors, but patient and operator-level effects predominate. Efforts to increase the use of evidence-based practices for PCI should consider interventions at these levels.

Agitated ants: regulation and self-organization of incipient nest excavation via collisional cues.

Ants are millimetres in scale yet collectively create metre-scale nests in diverse substrates. To discover principles by which ant collectives self-organize to excavate crowded, narrow tunnels, we studied incipient excavation in small groups of fire ants in quasi-two-dimensional arenas. Excavation rates displayed three stages: initially excavation occurred at a constant rate, followed by a rapid decay, and finally a slower decay scaling in time as t-1/2. We used a cellular automata model to understand such scaling and motivate how rate modulation emerges without global control. In the model, ants estimated their collision frequency with other ants, but otherwise did not communicate. To capture early excavation rates, we introduced the concept of 'agitation'-a tendency of individuals to avoid rest if collisions are frequent. The model reproduced the observed multi-stage excavation dynamics; analysis revealed how parameters affected features of multi-stage progression. Moreover, a scaling argument without ant-ant interactions captures tunnel growth power-law at long times. Our study demonstrates how individual ants may use local collisional cues to achieve functional global self-organization. Such contact-based decisions could be leveraged by other living and non-living collectives to perform tasks in confined and crowded environments.

A question of time? Deployments, dwell time, and work-life balance for military personnel in Scandinavia.

In recent years, interest in the different ways in which military employment affects individuals' work-life balance (WLB) has grown. At the same time, research on military organizations and personnel has increasingly included time-related factors such as deploy-to-dwell (D2D) ratios to help explain adverse health effects of overseas deployments. The aim of this article is to explore connections between organizational systems for regulating deployment frequency and dwell (or respite) time with a particular focus on potential consequences for work-life balance. We focus on personal and organizational factors that shape the nature and outcome of work-life balance, including stress, mental health problems, job satisfaction, and turnover intentions. To explore these links, we first provide an overview of research on the impact of deploy-to-dwell ratios on mental health and social relations. We then turn to the regulation and organization of deployment and dwell time in Scandinavia. Here, the ambition is to identify potential sources of work-life conflict and associated effects for deployed personnel. The results provide a basis for further research into time-related effects of military deployments.

Arrival time at a referral hospital and functional disability of people with stroke: a cohort study.

BACKGROUND: Stroke is a major cause of death and functional disability worldwide. Knowledge of the associated factors is essential for defining education, management, and healthcare strategies. OBJECTIVE: To analyze the association between arrival time at a neurology referral hospital (ATRH) and functional disability in patients with ischemic stroke 90 days after the event. DESIGN AND SETTING: Prospective cohort study conducted at a public institution of higher education in Brazil. METHODS: This study included 241 people aged ≥ 18 years who presented ischemic stroke. The exclusion criteria were death, inability to communicate without companions who could answer the research questions, and > 10 days since ictus. Disability was assessed using the Rankin score (mR). Variables for which associations showed a P value ≤ 0.20 in bivariate analysis were tested as modifiers between ATRH and disability. Significant interaction terms were used for multivariate analysis. Multivariate logistic regression analysis was performed with all variables, arriving at the complete model and adjusted beta measures. The confounding variables were included in the robust logistic regression model, and Akaike's Information Criterion was adopted to choose the final model. The Poisson model assumes a statistical significance of 5% and risk correction. RESULTS: Most participants (56.0%) arrived at the hospital within 4.5 hours of symptom onset, and 51.7% presented with mRs of 3 to 5 after 90 days of ictus. In the multivariate model, ATRH ≥ 4.5 hours and females were associated with more significant disability. CONCLUSIONS: Arrival at the referral hospital 4.5 hours after the onset of symptoms or wake-up stroke was an independent predictor of a high degree of functional disability.

AI-Driven sleep staging from actigraphy and heart rate.

Sleep is an important indicator of a person's health, and its accurate and cost-effective quantification is of great value in healthcare. The gold standard for sleep assessment and the clinical diagnosis of sleep disorders is polysomnography (PSG). However, PSG requires an overnight clinic visit and trained technicians to score the obtained multimodality data. Wrist-worn consumer devices, such as smartwatches, are a promising alternative to PSG because of their small form factor, continuous monitoring capability, and popularity. Unlike PSG, however, wearables-derived data are noisier and far less information-rich because of the fewer number of modalities and less accurate measurements due to their small form factor. Given these challenges, most consumer devices perform two-stage (i.e., sleep-wake) classification, which is inadequate for deep insights into a person's sleep health. The challenging multi-class (three, four, or five-class) staging of sleep using data from wrist-worn wearables remains unresolved. The difference in the data quality between consumer-grade wearables and lab-grade clinical equipment is the motivation behind this study. In this paper, we present an artificial intelligence (AI) technique termed sequence-to-sequence LSTM for automated mobile sleep staging (SLAMSS), which can perform three-class (wake, NREM, REM) and four-class (wake, light, deep, REM) sleep classification from activity (i.e., wrist-accelerometry-derived locomotion) and two coarse heart rate measures-both of which can be reliably obtained from a consumer-grade wrist-wearable device. Our method relies on raw time-series datasets and obviates the need for manual feature selection. We validated our model using actigraphy and coarse heart rate data from two independent study populations: the Multi-Ethnic Study of Atherosclerosis (MESA; N = 808) cohort and the Osteoporotic Fractures in Men (MrOS; N = 817) cohort. SLAMSS achieves an overall accuracy of 79%, weighted F1 score of 0.80, 77% sensitivity, and 89% specificity for three-class sleep staging and an overall accuracy of 70-72%, weighted F1 score of 0.72-0.73, 64-66% sensitivity, and 89-90% specificity for four-class sleep staging in the MESA cohort. It yielded an overall accuracy of 77%, weighted F1 score of 0.77, 74% sensitivity, and 88% specificity for three-class sleep staging and an overall accuracy of 68-69%, weighted F1 score of 0.68-0.69, 60-63% sensitivity, and 88-89% specificity for four-class sleep staging in the MrOS cohort. These results were achieved with feature-poor inputs with a low temporal resolution. In addition, we extended our three-class staging model to an unrelated Apple Watch dataset. Importantly, SLAMSS predicts the duration of each sleep stage with high accuracy. This is especially significant for four-class sleep staging, where deep sleep is severely underrepresented. We show that, by appropriately choosing the loss function to address the inherent class imbalance, our method can accurately estimate deep sleep time (SLAMSS/MESA: 0.61±0.69 hours, PSG/MESA ground truth: 0.60±0.60 hours; SLAMSS/MrOS: 0.53±0.66 hours, PSG/MrOS ground truth: 0.55±0.57 hours;). Deep sleep quality and quantity are vital metrics and early indicators for a number of diseases. Our method, which enables accurate deep sleep estimation from wearables-derived data, is therefore promising for a variety of clinical applications requiring long-term deep sleep monitoring.

Analysis of the interval between submission and publication in genetics journals.

One of the main factors that attracts authors to choose a journal is the time interval between submission and publication, which varies between journals and subject matter. Here, we evaluated the time intervals between submission and publication according to journal impact factor and continent of author's affiliation, considering articles with authors from single or multiple continents. Altogether, 72 journals indexed in the Web of Science database within the subject matter "Genetics and Heredity", divided by impact factor into four quartiles and randomly selected were analyzed for time intervals from article submission to publication. Data from a total of 46,349 articles published from 2016 to 2020 were collected and analyzed considering the following time intervals: submission to acceptance (SA), acceptance to publication (AP) and submission to publication (SP). The median of the quartiles for the SP interval was 166 (IQR [118-225]) days for Q1, 147 (IQR [103-206]) days for Q2, 161 (IQR [116-226]) days for Q3 and 137 (IQR [69-264]) days for Q4, showing a significant difference among quartiles (p < 0.001). In Q4, median interval of time was shorter in interval SA but longer in interval AP, and overall, articles in Q4 had the shortest interval of time in SP. A potential association of the median time interval and authors' continent was analysed and no significant difference was observed between articles with authors from single versus multiple continents or between continents in articles with authors from only one continent. However, in journals from Q4, time from submission to publication was longer for articles with authors from North America and Europe than from other continents, although the difference was not significant. Finally, articles of authors from the African continent had the smallest representation in journals from Q1-Q3 and articles from Oceania were underrepresented in group Q4. The study provides a global analysis of the total time required for submission, acceptance and publication in journals in the field of genetics and heredity. Our results may contribute in the development of strategies to expedite the process of scientific publishing in the field, and to promote equity in knowledge production and dissemination for researchers from all continents.

An integer GARCH model for a Poisson process with time-varying zero-inflation.

A serially dependent Poisson process with time-varying zero-inflation is proposed. Such formulations have the potential to model count data time series arising from phenomena such as infectious diseases that ebb and flow over time. The model assumes that the intensity of the Poisson process evolves according to a generalized autoregressive conditional heteroscedastic (GARCH) formulation and allows the zero-inflation parameter to vary over time and be governed by a deterministic function or by an exogenous variable. Both the expectation maximization (EM) and the maximum likelihood estimation (MLE) approaches are presented as possible estimation methods. A simulation study shows that both parameter estimation methods provide good estimates. Applications to two real-life data sets on infant deaths due to influenza show that the proposed integer-valued GARCH (INGARCH) model provides a better fit in general than existing zero-inflated INGARCH models. We also extended a non-linear INGARCH model to include zero-inflation and an exogenous input. This extended model performed as well as our proposed model with respect to some criteria, but not with respect to all.

Quantitative Study of a Regional Patient Portal Usage in the Pandemic Period.

In 2013 using a Public Procurement of Innovation procedure the Region of Galicia developed a patient portal called "E-Saúde", that went live in 2015. COVID situation in 2019 produced a high demand of e-health services, scaling by 10x the number of users in 2021. OBJECTIVE: In this study a quantitative description of patient portal usage from 2018 to 2022 is made to show the behaviour of usage trends of a patient portal before, during and after COVID pandemic. METHODS: Two main data sets were obtained from patient portal logs to obtain: 1) Enrolment of new users and number of sessions opened in the portal. 2) Detailed usage of relevant functionalities. Descriptive statistical methods were applied to show the usage of the portal in a biannual time series description. RESULTS: Prior to the pandemic, the portal was gradually being introduced to citizens. During pandemics, more than 1 million users were registered and a peak of 15x usage could be observed. After COVID, the level of usage of portal services decreased, but kept a sustained trend five times higher than in Pre-COVID situation. CONCLUSION: There is limited information available on metrics, functionalities and acceptability for general purpose patient portals, but the analysis performed on usage levels shows that after a high peak reached during COVID period, explained by the need of direct access to clinical information, the level of usage of the patient portal remains five times higher than in pre-pandemic situation for all functionalities of the patient portal.

Feature Selection Based on a Genetic Algorithm for Optimizing Weaning Success.

Finding the right time for weaning from ventilator is a difficult clinical decision. Several systems based on machine or deep learning are reported in literature. However, the results of these applications are not completely satisfactory and may be improved. An important aspect is represented by the features used as input of these systems. In this paper we present the results of the application of genetic algorithms to perform feature selection on a dataset containing 13688 patients under mechanical ventilation characterizing by 58 variables, extracted from the MIMIC III database. The results show that all features are important, but four of them are essential: 'Sedation_days', 'Mean_Airway_Pressure', 'PaO2', and 'Chloride'. This is only the initial step to obtain a tool to be added to the other clinical indices for minimize the risk of extubation failure.

The evolution of the labor curve and its implications for clinical practice: the relationship between cervical dilation, station, and time during labor.

The assessment of labor progress is germane to every woman in labor. Two labor disorders-arrest of dilation and arrest of descent-are the primary indications for surgery in close to 50% of all intrapartum cesarean deliveries and are often contributing indications for cesarean deliveries for fetal heart rate abnormalities. Beginning in 1954, the assessment of labor progress was transformed by Friedman. He published a series of seminal works describing the relationship between cervical dilation, station of the presenting part, and time. He proposed nomenclature for the classification of labor disorders. Generations of obstetricians used this terminology and normal labor curves to determine expected rates of dilation and fetal descent and to decide when intervention was required. The analysis of labor progress presents many mathematical challenges. Clinical measurements of dilation and station are imprecise and prone to variation, especially for inexperienced observers. Many interrelated factors influence how the cervix dilates and how the fetus descends. There is substantial variability in when data collection begins and in the frequency of examinations. Statistical methods to account for these issues have advanced considerably in recent decades. In parallel, there is growing recognition among clinicians of the limitations of using time alone to assess progress in cervical dilation in labor. There is wide variation in the patterns of dilation over time and most labors do not follow an average dilation curve. Reliable assessment of labor progression is important because uncertainty leads to both over-use and under-use of cesarean delivery and neither of these extremes are desirable. This review traces the evolution of labor curves, describes how limitations are being addressed to reduce uncertainty and to improve the assessment of labor progression using modern statistical techniques and multi-dimensional data, and discusses the implications for obstetrical practice.

Temporal relation between human mobility, climate, and COVID-19 disease.

Using the example of the city of São Paulo (Brazil), in this paper, we analyze the temporal relation between human mobility and meteorological variables with the number of infected individuals by the COVID-19 disease. For the temporal relation, we use the significant values of distance correlation t0(DC), which is a recently proposed quantity capable of detecting nonlinear correlations between time series. The analyzed period was from February 26, 2020 to June 28, 2020. Fewer movements in recreation and transit stations and the increase in the maximal temperature have strong correlations with the number of newly infected cases occurring 17 days after. Furthermore, more significant changes in grocery and pharmacy, parks, and recreation and sudden changes in the maximal pressure occurring 10 and 11 days before the disease begins are also correlated with it. Scanning the whole period of the data, not only the early stage of the disease, we observe that changes in human mobility also primarily affect the disease for 0-19 days after. In other words, our results demonstrate the crucial role of the municipal decree declaring an emergency in the city to influence the number of infected individuals.