Mechanical ventilation is a widely used intervention for critically ill patients in intensive care units. However, it is associated with various complications. The most up-to-date method for monitoring ventilation-related complications is through ventilator-associated events (VAE). The objective of this study was to evaluate the incidence of VAE and their impact on major clinical outcomes in patients admitted to the intensive care units. This was a single-center retrospective cohort study that all the adult patients who underwent invasive mechanical ventilation were included. The VAE identification was performed using an automated algorithm based on criteria established by the Centers for Disease Control and Prevention. Clinical, demographic, and outcome data were extracted from electronic medical records. A total of 709 patients were analyzed, of whom 39 (5.6%) developed VAE, resulting in a rate of 5.99 per 1,000 ventilator-days. Patients with VAE had significantly longer durations of mechanical ventilation (19.7 ± 27.4 vs 4.7 ± 7.9 days; P = .002), intensive care units stay (25.1 ± 24.7 vs 8.7 ± 11.1 days; P < .001), and hospital stay (72.7 ± 73.5 vs 46 ± 98 days; P < .001). The occurrence of VAE was associated with significantly worse clinical outcomes. The adoption of algorithm-based automated surveillance may enhance early VAE detection, representing the first step toward implementing targeted prevention strategies.

(1) Background: The dynamics of hand tremors involve nonrandom and short-term motor patterns (STMPs). This study aimed to (i) identify STMPs in Parkinson’s disease (PD) and physiological resting tremor and (ii) characterize STMPs by amplitude, persistence, and regularity. (2) Methods: This study included healthy (N = 12, 60.1 ± 5.9 years old) and PD (N = 14, 65 ± 11.54 years old) participants. The signals were collected using a triaxial gyroscope on the dorsal side of the hand during a resting condition. Data were preprocessed and seven features were extracted from each 1 s window with 50% overlap. The STMPs were identified using the clustering technique k-means applied to the data in the two-dimensional space given by t-Distributed Stochastic Neighbor Embedding (t-SNE). The frequency, transition probability, and duration of the STMPs for each group were assessed. All STMP features were averaged across groups. (3) Results: Three STMPs were identified in tremor signals (p < 0.05). STMP 1 was prevalent in the healthy control (HC) subjects, STMP 2 in both groups, and STMP3 in PD. Only the coefficient of variation and complexity differed significantly between groups. (4) Conclusion: These results can help professionals characterize and evaluate tremor severity and treatment efficacy.

Clinical diagnosis of Parkinson’s disease (PD) motor symptoms remains a problem. Most of the current studies focus on objective evaluations to make the evaluation more reliable. Most of these systems are based on the use of inertial and electromyographic sensors that require contact with the body part being assessed. Contact sensors restrict natural movement, may be uncomfortable and may require preparation of the body, which may cause irritation. As an alternative to contact sensors for the study of hand motor tasks performed by subjects with and without PD, electrical potential sensing technology is used in this research. A custom hardware has been designed to enable data collection by hand movement. A micro-machine system validated the developed system, and a relationship model was established between hand displacement and non-contact capacitive (NCC) sensor response. An experiment was conducted, including 57 subjects, 30 with PD (experimental group) and 27 healthy control group, followed by an analysis of statistical features extracted from the instantaneous mean frequency (IMNF) of NCC sensor. These results were compared with those obtained from gyroscope signals that are considered in the field to be the gold standard. As a result, NCC responses were correlated linearly with hand displacement (R² = 0.7692 and $${\text{R}}_{\text{adj}}^{2}$$R adj 2 = 0.7631). The statistical evaluation of IMNF features showed, that both, contact and non-contact sensors, were able to discriminate movement patterns of the control group from the experimental one. The results confirm statistical similarity between features extracted from NCC and gyroscope signals.

Background: Out-of-hospital cardiac arrests (OHCAs) predominantly occur in residential settings, often witnessed by children who could act as first responders. The World Health Organization (WHO) supports the Kids Save Lives (KSL) initiative, recommending basic life support (BLS) training for children aged ≥11 years. However, disparities in BLS education persist globally, particularly in low-resource regions where socioeconomic barriers, such as school type, malnutrition, and limited infrastructure, hinder implementation. Younger children (aged <11 years) face additional challenges due to physical limitations (eg, height, weight, and grip strength), which may compromise their ability to achieve adequate chest compression depth. While gamified learning has shown promise in improving BLS engagement and skill acquisition, its efficacy across diverse socioeconomic groups remains understudied. Objective: This study aimed to compare cardiopulmonary resuscitation (CPR) performance (compression depth, rate, and recoil) between public and private school students following a game-based BLS intervention and evaluate the feasibility of a serious game (Kids Save Hearts) in improving BLS knowledge and irrespective of socioeconomic background. Methods: We conducted an observational cohort study with 336 students aged 7‐17 years from 10 public and 10 private schools in Brazil (April to November 2022). Participants received 40 minutes of video-based CPR training (American Heart Association CPR in Schools) followed by 10 minutes of gamified training using the Children Save Hearts serious game (SG). CPR quality was assessed via quality of cardiopulmonary resuscitation (QCPR) scores (Laerdal QCPR manikin), measuring compression depth (mm), compression rate (per minute), and chest recoil. Anthropometric data (height, weight, and grip strength) and socioeconomic indicators (school type) were collected. Nonparametric tests (Mann-Whitney U and chi-square tests ) and multivariate regression (SPSS v27.0; IBM Corp) were used to analyze associations between demographics, physical characteristics, and CPR performance. Results: Older students (11‐17 years) outperformed younger peers (7‐10 years) in median compression depth (48 mm vs 37 mm; P <.001) and overall QCPR scores (84 vs 42, P <.001). Private school students had higher grip strength (24.92 vs 21.48 g/cm²; P =.001), but school type did not significantly affect CPR quality. Postintervention SG scores improved universally ( P <.001), with no age or socioeconomic disparities. Multivariate analysis identified age ( P <.001), height ( P <.001), and grip strength ( P <.001) as independent predictors of high QCPR scores (≥70). Conclusions: Age and physical development were stronger determinants of CPR quality than socioeconomic factors. The game-based intervention effectively improved BLS knowledge and skills across all participants, demonstrating its potential as an equitable training tool. These findings support the scalability of gamified BLS programs in resource-limited settings.