Pattern Of Events Research Articles

Group of longitudinal adverse event patterns after the fourth dose of COVID-19 vaccination with a latent class analysis.

Vaccination has been implemented as a useful measure to combat the COVID-19 pandemic. However, there is a tendency for individuals to avoid vaccination due to the possibility of adverse events, making it important to investigate the relationship between COVID-19 vaccines and their adverse events. This study explored longitudinal adverse event patterns and factors that influence adverse events following the second to fourth doses of the COVID-19 vaccine through a latent class analysis. Participants were recruited from the Fukushima Prefecture and included individuals who had completed four doses of the COVID-19 mRNA vaccine. This study utilized data from questionnaire surveys and blood collection conducted between September 2021 and November 2022. In the questionnaire, factors such as sex, age, medical history, medication, type of vaccine administered, and adverse events following vaccination were recorded. Additionally, in the blood data, serological tests [IgG(S)] and cellular immune responses (T-spot) were measured. Descriptive statistics, latent class analysis, multivariable logistic regression, and multiple regression analyses were performed to identify the longitudinal adverse event patterns and influencing factors. By analyzing adverse events over time, we identified two distinct groups: those less prone to experiencing adverse events (Group 1) and those more susceptible (Group 2) to latent class analysis. A total of 1,175 participants were included after excluding those without any adverse events. The median age of the participants in Group 1 was 70 years, and in Group 2 it was 51 years. The proportion of female participants was 298 in Group 1 and 353 in Group 2. Patients in Group 2 were significantly younger (p < 0.001) and more likely to be female (p < 0.001) than those in Group 1. Furthermore, the median IgG(S) value after the fourth vaccination was 3,233 AU/mL in Group 1 and 4,059.39 AU/mL in Group 2. The median T-spot value was 15.4 in Group 1 and 28.5 in Group 2. Group 2 showed significantly higher IgG(S) and T-spot values after the fourth vaccination (p < 0.001). Our findings suggest that factors other than age, particularly sex and a history of allergies, significantly influence the likelihood of experiencing adverse events. Groups categorized by latent class analysis for longitudinal adverse events are expected to be valuable for optimizing vaccination strategies and formulating public health measures.

Harmonic Wavelet Neural Network for Discovering Neuropathological Propagation Patterns in Alzheimer's Disease.

Emerging research indicates that the degenerative biomarkers associated with Alzheimer's disease (AD) exhibit a non-random distribution within the cerebral cortex, instead following the structural brain network. The alterations in brain networks occur much earlier than the onset of clinical symptoms, thereby affecting the progression of brain disease. In this context, the utilization of computational methods to ascertain the propagation patterns of neuropathological events would contribute to the comprehension of the pathophysiological mechanism involved in the evolution of AD. Despite the encouraging findings achieved by existing graph-based deep learning approaches in analyzing irregular graph data, their applications in identifying the spreading pathway of neuropathology are limited due to two disadvantages. They include (1) lack of a common brain network as an unbiased reference basis for group comparison, and (2) lack of an appropriate mechanism for the identification of propagation patterns. To this end, we propose a proof-of-concept harmonic wavelet neural network (HWNN) to predict the early stage of AD and localize disease-related significant wavelets, which can be used to characterize the spreading pathways of neuropathological events across the brain network. The extensive experiments constructed on both synthetic and real datasets demonstrate that our proposed method achieves superior performance in classification accuracy and statistical power of identifying propagation patterns, compared with other representative approaches.

Spatial patterns of Mental Health Act apprehensions in Toronto, Canada, 2014-2022.

This research examines the temporal rates and spatial patterns of police-involved Mental Health Act (MHA) apprehensions in Toronto, Canada, from 2014 to 2022. Building from the work of Toronto Police Services (TPS), the goal of this research is to deepen our understanding of MHA apprehensions and provide insights which may be used to guide local mental health reform efforts. Using data collected by TPS, an exploratory, descriptive analysis of spatial patterns of MHA apprehension events between 2014 and 2022 at the neighbourhood level was conducted. To identify high-rate clusters, we used a discrete Poisson model in SaTScan, with a circular moving window. Primary and secondary clusters with a relative risk of 1.5 or greater are reported. From 2014 to 2022, 93,932 MHA apprehensions occurred in Toronto. Apprehension rates were found to vary substantially between neighbourhoods, with approximately 91% of Toronto's 140 neighbourhoods experiencing a net positive increase in rates during the study period after adjusting for population growth. Repeated spatial cluster analysis for each year revealed a range of 4 to 7 clusters annually, with the downtown core consistently emerging as an area of elevated risk (RR range 1.58-1.99). Rising MHA apprehensions within Toronto highlight the pressing need to confront the city's intensifying mental health needs. These findings offer valuable insights into the patterns and nature of police-involved MHA apprehensions, outline reproducible analysis methods that can be used by others, and support targeted evaluation, expansion, and implementation of downstream initiatives to improve mental health responses.

Emerging psychoactive drugs and the common life events among adolescents in a Nigerian rehabilitation centre

ABSTRACT Adolescence is characterised by intense experimentation. This study examined the prevalence and pattern of psychoactive substance use and common life events among adolescents abusing psychoactive substances in a Nigerian Hospital. The study employed cross-sectional descriptive method of 385 patients admitted for drug rehabilitation in the ABUAD Multisystem Hospital Ado Ekiti, Ekiti State Nigeria. Smoking and eating Marijuana (47.6%), eating Datura stramonium leaf (32.2%) and drinking alcohol (11.4%) were psychoactive substances commonly used among the respondents. The predominant life events among respondents included: heavy load of study (34.3%), study pressure from family (26.5%), family member serious illness (23.5%), scolding by parents (21.1%), and tired of learning (20.8%). Datura stramonium leaf abuse is an emerging psychoactive substance among the respondents. Relevant stakeholders should be aware of the new trend in drug use among adolescents and coordinated effort should be made to stop the trend.

The role of citizen science in assessing the spatiotemporal pattern of rainfall events in urban areas: a case study in the city of Genoa, Italy

Abstract. Climate change in the Mediterranean region is manifesting itself as an increase in average air temperature and a change in the rainfall regime: the value of cumulative annual rainfall generally appears to be constant, but the intensity of annual rainfall maxima, between 1 and 24 h, is increasing, especially in the period between late summer and early autumn. The associated ground effects in urban areas consist of flash floods and pluvial floods, often in very small areas, depending on the physical-geographical layout of the region. In the context of global warming, it is therefore important to have an adequate monitoring network for rain events that are highly concentrated in space and time. This research analyses the meteo-hydrological features of the 27 and 28 August 2023 event that occurred in the city of Genoa, Italy, just 4 d after the record maximum air temperature was recorded: between 19:00 and 02:00 UTC almost 400 mm of rainfall was recorded in the eastern sector of the historic centre of Genoa, with significant ground effects such as flooding episodes and the overflowing of pressurised culverts. Rainfall observations and estimates were made using both official or “authoritative” networks (rain gauges and meteorological radar) and rain gauge networks inspired by citizen science principles. The combined analysis of observations from authoritative and citizen science networks reveals, for the event analysed, a spatial variability of the precipitation field at an hourly and a sub-hourly timescale that cannot be captured by the current spatial density of the authoritative measurement stations (which have one of the highest densities in Italy). Monthly total rainfall and short-duration annual maximum time series recorded by the authoritative rain gauge network of the Genoa area are then analysed. The results show significant variation even at distances of less than 2 km in the average rainfall depth accumulated over sub-hourly duration. Extreme weather monitoring activity is confirmed as one of the most important aspects in terms of flood prevention and protection in urban areas. The integration between authoritative and citizen science networks can prove to be a valid contribution to the monitoring of extreme events.

Automatic detection of sleep apnea from a single-lead ECG signal based on spiking neural network model

BackgroundSleep apnea (SLA) is a commonly encountered sleep disorder characterized by repetitive cessation of respiration while sleeping. In the past few years, researchers have focused on developing less complex and more cost-effective diagnostic approaches for identifying SLA recipients, in contrast to the cumbersome, complicated, and expensive conventional methods. MethodThis study presents a biologically plausible learning approach of spiking neural networks (SNN) with temporal coding and a tempotron learning model for diagnosing SLA disorder using single-lead electrocardiogram (ECG) data information. The proposed framework utilizes temporal encoding and the leaky integrate and fire model to transform the ECG signal into spikes for capturing the signal's dynamic pattern nature and to simulate input response behaviors. The tempoton learning technique, a spike-based algorithm, trains the SNN model to identify SLA event patterns from encoded output spike trains. This study utilized ECG data to extract heart rate variability (HRV) and ECG-derived respiration (EDR) signals from 1-min segment data of ECG records for input to SNN model. Thirty-five recordings of both released and withheld data from the Apnea-ECG databases from Physionet have been applied to train the SNN model and validate the model's efficacy in identifying SLA occurrences. ResultsThe proposed method demonstrated substantial improvements compared to other SLA detection techniques, achieving a significant accuracy of 94.63 % for per-segment detection, along with specificity, sensitivity, F1-score and AUC values of 96.21 %, 92.04 %, 0.9285, and 0.9851 respectively. The accuracy for per-recording detection achieved 100 %, with a correlation coefficient value of 0.986. Additionally, the experiment used UCD data for validation methods, achieving an accuracy of 84.573 %. ConclusionsThese results suggest the effectiveness and accessibility of the presented approach for accurately identifying SLA cases. The suggested model enhances the performance of SLA detection when contrasted with various techniques based on feature engineering and feature learning.

Plasma metabolomics reveals the shared and distinct metabolic disturbances associated with cardiovascular events in coronary artery disease

Risk prediction for subsequent cardiovascular events remains an unmet clinical issue in patients with coronary artery disease. We aimed to investigate prognostic metabolic biomarkers by considering both shared and distinct metabolic disturbance associated with the composite and individual cardiovascular events. Here, we conducted an untargeted metabolomics analysis for 333 incident cardiovascular events and 333 matched controls. The cardiovascular events were designated as cardiovascular death, myocardial infarction/stroke and heart failure. A total of 23 shared differential metabolites were associated with the composite of cardiovascular events. The majority were middle and long chain acylcarnitines. Distinct metabolic patterns for individual events were revealed, and glycerophospholipids alteration was specific to heart failure. Notably, the addition of metabolites to clinical markers significantly improved heart failure risk prediction. This study highlights the potential significance of plasma metabolites on tailed risk assessment of cardiovascular events, and strengthens the understanding of the heterogenic mechanisms across different events.

MRI resting-state signature of the propensity to experience meaningful coincidences: a functional coupling analysis.

The propensity to experience meaningful patterns in random arrangements and unrelated events shows considerable interindividual differences. Reduced inhibitory control (over sensory processes) and decreased working memory capacities are associated with this trait, which implies that the activation of frontal as well as posterior brain regions may be altered during rest and working memory tasks. In addition, people experiencing more meaningful coincidences showed reduced gray matter of the left inferior frontal gyrus (IFG), which is linked to the inhibition of irrelevant information in working memory and the control and integration of multisensory information. To study deviations in the functional connectivity of the IFG with posterior associative areas, the present study investigated the fMRI resting state in a large sample of n = 101 participants. We applied seed-to-voxel analysis and found that people who perceive more meaningful coincidences showed negative functional connectivity of the left IFG (i.e. pars triangularis) with areas of the left posterior associative cortex (e.g. superior parietal cortex). A data-driven multivoxel pattern analysis further indicated that functional connectivity of a cluster located in the right cerebellum with a cluster including parts of the left middle frontal gyrus, left precentral gyrus, and the left IFG (pars opercularis) was associated with meaningful coincidences. These findings add evidence to the neurocognitive foundations of the propensity to experience meaningful coincidences, which strengthens the idea that deviations of working memory functions and inhibition of sensory and motor information explain why people experience more meaning in meaningless noise.

Investigating the spatial propagation patterns of meteorological drought events and underlying mechanisms using complex network theory: A case study of the Yangtze River Basin, China

The spatial propagation patterns of meteorological drought events (MDEs) and underlying mechanisms contribute to elucidating and forecasting drought evolution. In this study, gridded MDEs in the Yangtze River Basin (YRB) throughout the entire year, wet season and dry season were extracted from 3-month Standardized Precipitation Evapotranspiration Index (SPEI-3) series. Event synchronization (ES) and complex networks (CN) were employed to construct the MDE synchronization networks and MDE spatial propagation networks for various periods. The former were utilized to identify MDE synchronized subregions where MDEs co-occur and co-evolve in the YRB, while the latter were used to quantify the MDE spatial propagation patterns over both the basin and its subregions. The driving mechanisms behind MDE spatial propagation were further investigated by diagnosing the concomitant drought-inducing climate systems. The findings reveal the presence of four MDE synchronized subregions during the wet season and five subregions during the entire year and dry season. These subregions exhibited distinct spatial propagation patterns of MDEs, aligning with overall findings across the YRB. Notable differences were observed between wet and dry seasons, with various subregions exhibiting distinctive spatial propagation patterns during each season. These patterns are driven by variations in the controlling atmospheric circulation systems, leading to anomalies of wind patterns and moisture distribution, ultimately resulting in deficient moisture supply. The variations of tropical sea surface thermal conditions, influences of the Tibetan Plateau and MDE self-propagation triggered by land–atmosphere feedback are considered as three primary influencing factors for MDE spatial propagation in the YRB.

PP01.20 Hospitalization patterns for immune-related adverse events in patients with NSCLC treated with immunotherapy vs chemo-immunotherapy

PP01.20 Hospitalization patterns for immune-related adverse events in patients with NSCLC treated with immunotherapy vs chemo-immunotherapy

Identification of dust sources inside and outside of Iran affecting air quality in the Tehran region

Introduction: Recent climate changes and droughts have exacerbated the impact of dust sources globally, necessitating a thorough understanding of their influence on air quality. In Tehran, the interaction between internal dust sources from Iran and external sources from neighboring countries significantly affects air quality. Materials and methods: This study spans a 20-year period (2003-2022), utilizing satellite data and advanced algorithms to analyze dust event trends and patterns. The material and methods section outlines the use of Aqua satellite MODIS sensor data alongside two algorithms to identify dust sources, focusing on Aerosol Optical Depth (AOD) data during peak dust event seasons. Statistical analysis of dust events in 2022 supplements the investigation. Results: The analysis reveals distinct patterns in dust particle origin and transport, highlighting the predominant contribution of internal dust sources from Iran's southern Semnan province and significant input from external sources in Iraq and northern Saudi Arabia. Examining meteorological conditions during severe dust events highlights the role of synoptic conditions, particularly the presence of a tropospheric trough, in facilitating dust transport. Increased convective motions and downburst occurrences in specific regions further exacerbate the spread of dust particles, particularly those originating from external countries and provinces. Conclusion: The conclusion emphasizes the complex interplay between internal and external dust sources and the necessity of understanding dust emission dynamics for effective mitigation strategies. It calls for proactive measures involving local and regional cooperation to mitigate dust pollution's adverse effects on air quality in Tehran and similar urban centers.

Leveraging complex event processing for monitoring and automatically detecting anomalies in Ethereum-based blockchain networks

Blockchain is a relatively recent technology that provides immutability, traceability and transparency of information, thus building trust in the digital society. Blockchain networks generate a large amount of logs which capture and describe data flowing through the network in the form of transactions, blocks and events. Monitoring these blockchain data from the off-chain world is needed to detect anomalies with the aim of mitigating the risks that may arise as a result of using blockchain technology. However, the real-time monitoring of these logs by off-chain systems has become a challenge from the beginning of 2018 when the blockchain networks reached a high number of daily transactions. In this paper, we propose a portable, maintainable and easily configurable architecture integrating blockchain and complex event processing technologies that allows for both the real-time monitoring of logs generated in Ethereum Virtual Machine (EVM)-compatible blockchain networks and the automatic detection of anomalies in these networks by matching event patterns. This architecture was tested by using vast amounts of blockchain data already publicly registered in Ethereum and Polygon networks. The results demonstrate that the proposed architecture is able to automatically detect anomalies which occur in different blockchain networks, making analytics of blockchain data possible by off-chain systems.

Multiscale Interactions Driving Summer Extreme Precipitation in Central Asia

AbstractThis study identified four patterns of regional extreme precipitation events (REPEs) in Central Asia (CA) and their crucial synoptic systems and multiscale interactions. Four patterns with distinct spatial distributions were identified in: northern Kazakhstan, southern Xinjiang, western CA, and the Tianshan Mountains. Focusing on the three most frequent REPEs, the kinetic energy (KE) cross‐scale transfer from the basic‐to synoptic‐scale windows exhibited a zonal dipole, resulting in the development and enhancement of REPEs in northern Kazakhstan. The available potential energy (APE) cross‐scale transfer exhibited opposing patterns between the upper and lower troposphere, indicating baroclinic instability in the lower troposphere and barotropic instability of the basic flow in the upper troposphere. Both mechanisms enhanced the Central Asian vortices (CAVs) in southern Xinjiang and induced REPEs. Conversely, the energy budgets exhibited baroclinic instability of the basic flow throughout the entire region when the Tianshan Mountains REPEs occurred, providing energy for prevalent CAVs.

The relationship between event boundary strength and pattern shifts across the cortical hierarchy during naturalistic movie-viewing.

Our continuous experience is spontaneously segmented by the brain into discrete events. However, the beginning of a new event (an event boundary) is not always sharply identifiable: phenomenologically, event boundaries vary in salience. How are the response profiles of cortical areas at event boundaries modulated by boundary strength during complex, naturalistic movie-viewing? Do cortical responses scale in a graded manner with boundary strength, or do they merely detect boundaries in a binary fashion? We measured "cortical boundary shifts" as transient changes in multi-voxel patterns at event boundaries with different strengths (weak, moderate, and strong), determined by across-subject agreement. Cortical regions with different processing timescales were examined. In auditory areas, which have short timescales, cortical boundary shifts exhibited a clearly graded profile both in group-level and individual-level analyses. In cortical areas with long timescales, including the default mode network, boundary strength modulated pattern shift magnitude at the individual subject level. We also observed a positive relationship between boundary strength and the extent of temporal alignment of boundary shifts across different levels of the cortical hierarchy. Additionally, hippocampal activity was highest at event boundaries for which cortical boundary shifts were most aligned across hierarchical levels. Overall, we found that event boundary strength modulated cortical pattern shifts strongly in sensory areas and more weakly in higher-level areas, and that stronger boundaries were associated with greater alignment of these shifts across the cortical hierarchy.

Multi‐Decadal Skill Variability in Predicting the Spatial Patterns of ENSO Events

AbstractSeasonal hindcasts have previously been demonstrated to show multi‐decadal variability in skill across the twentieth century in indices describing El‐Niño Southern Oscillation (ENSO), which drives global seasonal predictability. Here, we analyze the skill of predicting ENSO events' magnitude and spatial pattern, in the CSF‐20C coupled seasonal hindcasts in 1901–2010. We find minima in the skill of predicting the first (in 1930–1950) and second (in 1940–1960) principal components of sea‐surface temperature (SST) in the tropical Pacific. This minimum is also present in the spatial correlation of SSTs, in 1930–1960. The skill reduction is explained by lower ENSO magnitude and variance in 1930–1960, as well as decreased SST persistence. The SST skill minima project onto surface winds, leading to worse predictions in coupled hindcasts compared to hindcasts using prescribed SSTs. Questions remain about the offset between the first and second principal components' skill minima, and how the skill minima impact the extra‐tropics.

Comprehensive evaluation of extreme hydrometeorological events coincidence and their interrelationships in the Hanjiang River Basin, China

Extreme hydrometeorological events are often highly destructive and can cause huge damage to the natural environment and social economy. As climate change and human activities intensify, the frequency and intensity of such extreme events continue to increase. This phenomenon is also shown in the Hanjiang River Basin (HRB) in recent decades. In this study, we focus on the coincidence occurrence patterns of extreme hydrometeorological events. First, we used the standardized index method to extract the annual maximum and minimum values of runoff, precipitation, maximum and minimum temperatures from 1961 to 2019 in the control area of the Baihe (BH) hydrological station, with time scales from 1 month to 12 months. Then through the Copula and nested structure Copula functions, the joint occurrence probability of bivariate and multivariate were analyzed. The results show that too high a maximum temperature will inhibit runoff, while an increase in minimum temperature will promote an increase in runoff to a certain extent. Both the maximum and minimum temperatures in the basin shown a significant increasing trend, and the intensity of hydrological drought also shows an increasing trend. An increase in maximum temperature will aggravate hydrological drought, as the combined joint occurrence probability of them is the greatest. Analysis through the nested structure Copula functions show that when extreme hydrometeorological events both occurred at the median of their intervals, their coincidence occurrence probability is the maximum, and when the time scale is eight months, their coincidence occurrence probability is the highest. This study can provide a reference for joint probability analysis of extreme hydrometeorological events.

Myeloproliferative neoplasms in the adolescent and young adult population: A comprehensive review of the literature.

Myeloproliferative neoplasms (MPN) are characterized by a clonal proliferation of myeloid lineage cells within the bone marrow. The classical BCR-ABL negative MPNs are comprised of polycythaemia vera, essential thrombocythaemia and primary myelofibrosis. Historically, the majority of MPNs are diagnosed in adults older than 60 years of age; however, in recent years, there has been recognition of MPNs in the adolescent and young adult (AYA) population. AYAs with MPN, typically defined as between the ages of 15 and 39 years old, may comprise up to 20% of patients diagnosed with MPN. They demonstrate unique patterns of driver mutations and thrombotic events and remain at risk for progression to more aggressive disease states. Given the likely long length of time they will live with their disease, there is a significant unmet need in identifying well-tolerated and effective treatment options for these patients, particularly with the advent of disease modification. In this review, we provide a comprehensive overview of the clinical features, disease course and management of AYA patients with MPN and, in doing so, highlight key characteristics that distinguish them from their older counterparts.

Assessing spatiotemporal variation in dry-wet abrupt alternation using an improved process-based approach

Study regionThe Pearl River Basin (PRB), located in the south of China, has a tropical to subtropical climate. Study focusIn this study, we present an improved process-based approach to assess the intensity of the Dry-Wet Abrupt Alternation (DWAA) events, considering both the transition gap (alternation) and the transition rate (urgency) between dry and wet states. Using daily precipitation data from 1961 to 2020 across 75 meteorological stations in the PRB, we analyze the temporal and spatial evolution patterns of DWAA events. New hydrological insights for the regionThe findings reveal that: (1) DWAA events in the western region generally occur less frequently but with higher intensity. (2) Dry-to-wet (D-W) events have an urgency intensity of 1.35, almost twice that of wet-to-dry (W-D) events at 0.69. However, the alternation intensity of W-D is only slightly higher by 0.17 compared to D-W. (3) The alternation and urgency changes in D-W events are asynchronous, with alternation intensity initially decreasing from 9.7 to 9.27, then increasing to 9.58, while urgency decreases continuously from 1.66 to 1.07. (4) The alternation and urgency changes in W-D events are synchronous. The alternation intensity initially decreases from 9.7 to 9.06, then rises to 10.1, while the urgency intensity decreases from 0.68 to 0.58 before subsequently climbing to 0.8.

Temporal Patterns of Angular Displacement of Endosomes: Insights into Motor Protein Exchange Dynamics.

The material transport system, facilitated by motor proteins, plays a vital role in maintaining a non-equilibrium cellular state. However, understanding the temporal coordination of motor protein activity requires an advanced imaging technique capable of measuring 3D angular displacement in real-time. In this study, a Fourier transform-based plasmonic dark-field microscope has been developed using anisotropic nanoparticles, enabling the prolonged and simultaneous observation of endosomal lateral and rotational motion. A sequence of discontinuous 3D angular displacements has been observed during the pause and run phases of transport. Notably, a serially correlated temporal pattern in the intermittent rotational events has been demonstrated during the tug-of-war mechanism, indicating Markovian switching between the exploitational and explorational modes of motor protein exchange prior to resuming movement. Alterations in transition frequency and the exploitation-to-exploration ratio upon dynein inhibitor treatment highlight the relationship between disrupted motor coordination and reduced endosomal transport efficiency. Collectively, these results suggest the importance of orchestrated temporal motor protein patterns for efficient cellular transport.

Pattern of Medical Device Adverse Events in a Tertiary Care Hospital in Northern India: An Ambispective Study.

Knowledge about the pattern of adverse events caused by medical devices (MDs) is limited in India. We aimed to assess the pattern of MD adverse events (MDAEs) in a tertiary hospital in Northern India. This descriptive study was conducted ambispectively at various clinical departments of PGIMER, Chandigarh. We followed the guidelines edged by the Materiovigilance Program of India (MvPI) to conduct this study. The prospective study (PS) was done from January to December 2020, with a concurrent retrospective study (RS) proceeding to 3 years to learn more about the reporting culture, demographics, notification status, risk class of defective devices, and the type of adverse events. We received 224 MDAE in the PS and identified 413 MDAE in the RS. Reporting of adverse events to the national MvPI was negligible in the RS. In the PS, nurses reported the majority of MDAEs (65%), followed by doctors (30%). The occurrence of MDAE was higher in males (PS; 52%, RS; 57%) and age groups between 21 and 30 years (PS; 19.1%, RS; 23.2%) in both studies. MDAEs were frequent in low- to moderate-risk devices (class B: 66%) in the PS, while it was documented only for high-risk devices (class C: 51% and class D: 49%) in the RS. Most of the serious adverse events (SAEs) were reported among moderate to high-risk devices, and an increased frequency of SAE (60.4%) was observed among nonnotified MDs. The overall incidence of near-miss events was 14%. Knowledge of MDAEs and reporting of defective devices to regulatory authorities is essential to prevent further incidence. Adverse events caused by MDs are ubiquitous irrespective of their risk classification, notification status, and patient demographic factors. Accelerated reporting of MDAE by all cadre of healthcare professionals is urgently required to safeguard the health of Indians.