Rare Event Research Articles

Patient- and Institution-Level Factors Associated With Intraoperative Cardiac Arrest During Major Noncardiac Surgery.

Intraoperative cardiac arrest (IOCA) is a rare but catastrophic event with significant morbidity, mortality, and health care costs. This study aimed to characterize the frequency, risk factors, and outcomes of IOCA. Adults undergoing noncardiac surgery were identified in the 2016 to 2021 National Inpatient Sample. IOCA events were identified using the relevant International Classification of Diseases code. Multivariable regression models examined factors independently associated with IOCA and in-hospital mortality. The significance of temporal trends was calculated using Cuzick's nonparametric test. Among 2671,834 noncardiac surgical admissions, 1294 (0.05%) experienced IOCA. The incidence increased from 0.05% to 0.06% during the study period, coinciding with an increase in nonelective operations during the coronavirus disease-2019 (COVID-19) pandemic. IOCA was associated with a 39.3% in-hospital mortality rate and increases in length of stay and hospitalization costs. Key risk factors for IOCA included advanced age, male sex, Black race (adjusted odds ratio [AOR] 1.40, 95% CI, 1.20-1.65), low-income status (AOR 1.21, 95% CI, 1.02-1.43), treatment at government nonfederal hospitals (AOR 1.22, 95% CI, 1.08-1.50), high-risk surgical procedures, and significant comorbidities such as congestive heart failure, cardiac arrhythmias, and valvular disease. Despite the initial reduction in the incidence of IOCA, this study highlights a temporal increase coinciding with the COVID-19 pandemic and an increase in nonelective surgeries. Future research should explore more granular predictors of IOCA and its outcomes to develop targeted interventions for at-risk populations and tailor guidelines to manage emerging challenges in population health.

Pembrolizumab-induced pemphigoid nodularis manifesting as an erythema multiforme-like eruption: a case report

A 56-year-old woman who was being treated with pembrolizumab for a squamous carcinoma of the skin with retroperitoneal metastases developed erythema multiforme-like lesions that progressed to nodular eruptions with erosions and scaling. Histopathological examination of a skin biopsy showed acanthosis, hyperkeratosis, and lymphocytic infiltration. High serum titers of anti-BP180 antibodies (40.96 U/mL; normal < 9.0 U/mL) confirmed pemphigoid nodularis, a rare immune-related adverse event. Treatment with corticosteroids and adjuvant therapies led to complete resolution of the skin lesions while maintaining tumor control, highlighting the importance of early recognition and management of cutaneous immune-related adverse events in patients on immune checkpoint inhibitors.

Ensemble design for seasonal climate predictions: studying extreme Arctic sea ice lows with a rare event algorithm

Abstract. Initialized ensemble simulations can help identify the physical drivers and assess the probabilities of weather and climate extremes based on a given initial state. However, the significant computational burden of complex climate models makes it challenging to quantitatively investigate extreme events with probabilities below a few percent. A possible solution to overcome this problem is to use rare event algorithms, i.e. computational techniques originally developed in statistical physics that increase the sampling efficiency of rare events in numerical simulations. Here, we apply a rare event algorithm to ensemble simulations with the intermediate-complexity coupled climate model PlaSim-LSG to study extremes of pan-Arctic sea ice area reduction under pre-industrial greenhouse gas conditions. We construct four pairs of control and rare event algorithm ensemble simulations, each starting from four different initial winter sea ice states. The rare event simulations produce sea ice lows with probabilities of 2 orders of magnitude smaller than feasible with the control ensembles and drastically increase the number of extremes compared to direct sampling. We find that for a given probability level, the amplitude of negative late-summer sea ice area anomalies strongly depends on the baseline winter sea ice thickness but hardly on the baseline winter sea ice area. Finally, we investigate the physical processes in two trajectories leading to sea ice lows with conditional probabilities of less than 0.001 %. In both cases, negative late-summer pan-Arctic sea ice area anomalies are preceded by negative spring sea ice thickness anomalies. These are related to enhanced surface downward longwave radiative and sensible heat fluxes in an anomalously moist, cloudy and warm atmosphere. During summer, extreme sea ice area reduction is favoured by enhanced open-water-formation efficiency, anomalously strong downward solar radiation and the sea ice–albedo feedback. This work highlights that the most extreme summer sea ice conditions result from the combined effects of preconditioning and weather variability, emphasizing the need for thoughtful ensemble design when turning to real applications.

Generation of representative meteorological years through anomaly-based detection of extreme events

Typical Meteorological Years (TMYs) have long supported the building sector by integrating local climate into building design for energy, thermal comfort, and peak load assessments. As climates shift, past heat waves and cold spells signal future conditions requiring greater adaptability. This study proposes a new file generation method that preserves TMY properties while embedding extreme events. We combine three anomaly-detection methods—temperature thresholds, Graph Neural Networks (GNNs), and Extreme Value Theory (EVT)—to capture climatic deviations, detect anomalies, and model statistical extremes. An integrated hierarchical method forms the new Representative Meteorological Year (RMY) file. RMY files for six ASHRAE climate-zones consistently capture past extremes, producing worst-case scenarios for key metrics, including peak loads, indoor thermal stress, natural ventilation and outdoor comfort. The largest deviation between the TMY and RMY was a doubling of indoor thermal stress hours across all climates, while average energy use remained aligned, with a deviation of 6%.

A Stationary Mean-Field Equilibrium Model of Irreversible Investment in a Two-Regime Economy

We study firms size distribution in a mean-field model of Cournot competition in a commodity market, where price follows an inverse power demand function. Firms face irreversible investment decisions and constant depreciation of production capacity. Output is affected by Gaussian productivity shocks, whose volatility and the price function can shift due to rare macroeconomic events modeled by a two-state Markov chain. Firms aim to maximize expected discounted profits, net of investment and operating costs, based on the long-run stationary price. We establish existence and uniqueness of a stationary mean-field equilibrium and characterize it through a barrier-type investment strategy with endogenous thresholds for each economic regime. A quasi-closed form for the stationary distribution of firms’ states is provided. The model generates Pareto-distributed firm sizes, consistent with empirical industry data. It also shows that downturns raise market concentration and that firm performance depends on depreciation rates and the persistence of economic fluctuations.

Exploring the Relationship Between Muon Detection Rates and Atmospheric Pressure Using Cosmic Watch Detectors

This study explores how atmospheric pressure affects muon detection rates. By analyzing data from six Cosmic Watch detectors, we organized the collected information, examined the correlation between atmospheric pressure and muon detection rates, and addressed the timing discrepancies across detectors. Our findings show that muon detection rates decrease as atmospheric pressure rises. The study also improves time difference correction methods, providing new strategies and tools for detecting rare coincidence events recorded by multiple detectors.

Everything everywhere all at once: a probability-based enhanced sampling approach to rare events.

The problem of studying rare events is central to many areas of computer simulations. We recently proposed an approach to solving this problem that involves computing the committor function, showing how it can be iteratively computed in a variational way while efficiently sampling the transition state ensemble. Here we greatly improve this procedure by combining it with a metadynamics-like enhanced sampling approach in which a logarithmic function of the committor is used as a collective variable. This procedure leads to an accurate sampling of the free energy surface in which transition states and metastable basins are studied with the same thoroughness. We show that our approach can be used in cases with the possibility of competing reactive paths and metastable intermediates. In addition, we demonstrate how physical insights can be obtained from the optimized committor model and the sampled data, thus providing a full characterization of the rare event under study.

What Controls the Evolution of Pacific Coastal Niño Events in the CESM Large Ensemble?

AbstractCoastal Niños, warm events in the far eastern tropical Pacific without corresponding strong anomalies in the central equatorial Pacific, strongly influence local rainfall and fisheries. However, individual events strongly differ from each other. They can develop into a basin‐wide El Niño (as in 2023) or stay confined to the South American coast (as in 2017). The large event‐to‐event variability in combination with the rarity of events in the observational record limits our understanding of Coastal Niños. The Community Earth System Model Large Ensemble Project (CESM‐LE) simulates Coastal Niños realistically, including their phase‐locking to boreal spring and variable evolution. Conditions in the subtropical Pacific, equatorial wave dynamics and western tropical Pacific wind anomalies are identified as factors important for the evolution of Coastal Niños in the CESM‐LE. Coastal Niños in turn increase the probability of a basin‐wide El Niño in the following months.

An Exceptionally Rare Predation on a Chameleon Species (Squamata: Chamaeleonidae) by a Eurasian Eagle-Owl (Aves: Strigidae)

The Eurasian eagle-owl (Bubo bubo) is a large, generalist predator widely distributed across Eurasia, including mainland Greece, where it occupies a broad range of habitats. Its diet is known to consist primarily of mammals and birds, with reptiles occasionally included. However, to date, chameleons have not been documented among its prey. Here, we report the first confirmed case of B. bubo preying on an African chameleon (Chamaeleo africanus) in the western Peloponnese of southern Greece—a region where the two species occur in limited sympatry. C. africanus, native to North Africa and introduced to Greece since antiquity, is a diurnal, slow-moving, arboreal lizard with highly cryptic behavior and specialized anti-predator strategies. Despite these traits, our observation highlights its potential vulnerability to nocturnal avian predators. This rare predation event offers novel insights into the trophic ecology of B. bubo and raises important considerations for the conservation of C. africanus, a species listed as Critically Endangered in Greece and increasingly threatened by habitat loss, illegal collection, and road traffic collisions.

Real-time crash risk estimation with autonomous vehicle data: a comparative analysis of extreme value models

In the era of autonomous vehicles (AVs), accurately predicting extreme traffic conflicts is vital for improving road safety. This study leverages Extreme Value Theory (EVT) to analyse AV-generated conflict data, focusing on Modified Time-to-Collision (MTTC) and Post-Encroachment Time (PET) indicators. We compare univariate and bivariate EVT models using Peak-Over-Threshold (POT) and Block Maxima (BM) methods, addressing data’s spatiotemporal gaps. A novel model validation criterion is introduced, applicable across modeling approaches and sample sizes, independent of crash records. Results show bivariate POT models outperform univariate models by up to 20% lower Mean Absolute Error (MAE) and offer greater temporal stability. Univariate BM models are reliable only for short intervals (∼5–7 min), while POT models maintain or improve accuracy over time. Covariate selection significantly impacts model performance, varying by structure. Overall, bivariate POT models prove most effective, offering practical, adaptive tools for AV-based traffic conflict analysis in diverse urban environments.

Feasibility Analysis of Monitoring Contact Wire Rupture in High-Speed Catenary Systems

The rupture of the contact wire (CW) of a railway overhead contact line (OCL or catenary) is expected to be a rare event. However, when it occurs, and a pantograph transits under the already broken section of the CW, this can have catastrophic consequences for the pantograph which in turn can cause a further extension of the damaged portion on the OCL with a consequent disruption in the service and cause there to be a long time before the operating condition can be restored. Therefore, the prevention of such events through effective catenary monitoring is gaining significant attention. The purpose of this work is to investigate the feasibility of a monitoring system that can be installed at each end of an OCL section which is able to detect the occurrence of a broken CW event, sending an alert to the management traffic system, so as to stop the train traffic before the damaged catenary is reached by other trains. A nonlinear dynamic analysis is employed to model the OCL’s response following a simulated CW rupture and identify a set of variables that can be measured at the line’s extremities related to the occurrence of breakage in the CW. Several locations of the rupture of a CW section along the line are simulated to investigate the influence on the time pattern of the measured variables and consequently on the extraction of a signature. Finally, a proposed measurement setup is presented, combining accelerometers and displacement transducers, instead of the direct measurement of the axial load of the OCL conductors.

Optimal active unsupervised fault detection in cascaded h-bridge inverters based on machine learning

Multi-Level Inverters (MLIs) are commonly used in high-voltage, high-power industrial applications. In this regard, their reliability, and health optimal performance are in the first priority. However, as the number of switches in a multilevel inverter increases, it comes so common to occur faults within the system. Ensuring the reliability of MLI is an important concern in power industries, making effective fault detection methods essential. Developing precise physics-based, model-based, and hardware-based models for fault detection is challenging, largely due to unknown parameters and incomplete understanding of the physical processes within the system. At this end, the proposed paper presents a highly efficient hyper-tuned machine learning (ML) model known as Isolation Forest (IF). This algorithm is an unsupervised method used for anomaly detection, which isolates outliers by recursively partitioning data points, as an effective way for identifying faults or rare events in large datasets with minimal computational complexity of the MLI system. To test this algorithm, a 17-level Cascaded H-Bridge (CHB) inverter is simulated with several faults, the proposed IF model is tested. In the next phase, the proposed model compared to the others, based on the performance indicators of F1-Score, Precision, Recall, and Accuracy, which the highest results retained for IF to have an accurate unsupervised fault detection model, that smoothens the way for a fully automated, and self-healing industrial application system.

Identifying stable pitting pathways in 316 L stainless steel via fractal-inspired PCA-based clustering

This study introduces a fractal-inspired PCA-based framework to distinguish stable pitting pathways in 316 L stainless steel in chloride media. By transforming potentiodynamic polarisation data into a Mandelbrot space, the approach reveals two distinct scenarios of stable pitting growth: directly following passivity breakdown (case I) and preceded by metastable activity (case II). Clustering identifies critical pitting potentials (E_pit and E_sp) with high accuracy, effectively capturing rare metastability-driven events often overlooked in traditional analyses. The method demonstrates robust performance across varying chloride concentrations, with classification metrics highlighting its ability to detect low-frequency E_sp events. Results show that metastability-driven stable pitting (case II) occurs at higher activity levels and potentially at lower potentials. This work advances the understanding of the probabilistic nature of pitting and provides a scalable, data-driven strategy for predicting stable growth regimes.

Occurrence of papillary renal cell carcinoma and clear cell renal carcinoma in a patient: A unique case report.

Renal cancer is one of the most common neoplasms in both males and females. Precise diagnosis, grading, and staging are very important for the outcome and the prognosis of the malignant process. Renal carcinoma disorders are presented by kidney tumors usually of the same histological type. The presence of various tumor histological types is an extremely rare event. Two different histological types of tumors were found in the left kidney of a 74-year-old man. The diagnosis obtained was papillary renal cell carcinoma type 1 and clear cell renal carcinoma with pathologic stage T2N0M1. After abdominal ultrasound and computer tomography, consultation with an anesthesiologist, and a cardiologist, the patient underwent radical left nephrectomy. Pathologists must be aware of the possibility of the presence of more than one histological type of renal carcinoma due to genomic alterations. Further genetic investigations must be conducted to identify the specific type and thus the treatment will be most precise.

Leveraging interest-growth differentials: hidden effects of government financial assets in the European Union

PurposeGiven that government financial assets represent a large proportion of gross debt accumulation, this study examines their impact on debt leveraging and potential returns on the gap between interest rates and economic growth (r-g).Design/methodology/approachThis research focuses on the co-movements of r-g differentials, government financial assets and the primary deficit through a channel of gross debt, investment, external balance and ratings, using a sample of 27 European Union economies from 2000 to 2022. The following co-integration methods were estimated: (1) for the aggregate, panel quantile autoregressive distributed lags (QARDL), ARDL- pooled mean group (PMG) for panel data, implemented with a (PMG) and (2) ARDL-error correction (EC) for individual countries at a granular level.FindingsWhile government financial assets drive short- and long-run debt trajectories, granular country heterogeneities reveal differentiated results for financial assets leveraging potential returns on the differential between interest rates and output growth (r-g). Government financial assets may enhance r-g, but may risk even undermining gains from primary deficit consolidation efforts. By comparing aggregate estimations with country granular approaches, outliers from non-statistically significant estimations reveal the epistemological limits of aggregation, statistics and probability theory, warning against overconfidence in such mere guidance tools, which are not safeguarding guarantees.Research limitations/implicationsStatistical asymptotics and instability of non-independent and identical distributions may underestimate variance. Furthermore, skewness and leptokurtosis may benefit from extreme value theory. In addition, technological changes, policy regimes, geopolitical events and economic crises can change in-built long-run relationships.Practical implicationsHeterogeneity of government financial assets effects depend on socio and macrofinance conditions, advocating the principle of subsidiarity. Financial assets, such as sovereign wealth funds linked to natural resources, oil in Norway, copper in Chile, may benefit from financial assets assessments. The strengthening of democratic accountability calls for transparency about financial assets contribution to debt trajectories, r-g effects and risks of potential undermining primary deficit consolidations. Accounting reporting should appropriately disclose changes in assets value from exposition to market volatility, accumulation of holding costs due to constraints to asset liquidation, due to non-active secondary markets, or long investment horizons.Social implicationsTo strengthen democratic accountability, there should be transparency about their contribution to debt trajectories, r-g effects and risks to potential undermining primary deficit consolidation. Their performance depends on financial markets and socio- and macro-finance conditions, calling for the principle of subsidiarity.Originality/valueRather than the traditional emphasis on government debt, this study examines the leverage effect on the gap between interest rates and economic growth (r-g differential). While the literature primarily addresses stock-flow adjustments (SFAs), the focus is narrowed to financial assets underlying government interventions on the supply side of the economy. Evidence is provided on the risks of financial assets undermining primary deficit consolidation efforts. While the literature highlights the short and medium terms, estimates are divided into short-term dynamics and hypothetical in-built long-run cointegrations. Panel aggregation is compared with granular estimates, uncovering heterogeneities and supporting governance subsidiarity. Support for statistical pluralism is provided by comparing results and methodological limitations.

SAT-316 Sustained HBsAg loss in HIV/HBV-coinfected patients is not a rare event under HBV-active ART even if a residual risk of viral reactivation and liver disease progression still persists over time

SAT-316 Sustained HBsAg loss in HIV/HBV-coinfected patients is not a rare event under HBV-active ART even if a residual risk of viral reactivation and liver disease progression still persists over time

Bowel Perforation Caused by Biliary Stent Migration After ERCP: A Systematic Review.

This systematic review aims to evaluate the risk factors, clinical features, and outcomes of bowel perforation caused by stent migration after endoscopic retrograde cholangiopancreatography (ERCP). Distal migration of biliary stents can occur after ERCP. Upon migration, most stents pass through the intestine without adverse events; however, bowel perforation has been reported. A comprehensive literature search of PubMed, EMBASE, and Cochrane databases was conducted through October 2023 for articles that reported bowel perforation because of stent migration. Cases of incomplete stent migration and proximal stent migration were excluded. We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines to identify full-length articles in English reporting. Of 2041 articles retrieved on the initial search, 92 met the inclusion criteria. A total of 132 cases of bowel perforation occurred due to stent migration after ERCP (56.1% female; average age: 66y). The median time from initial ERCP to perforation was 44.5 days (IQR 12.5-125.5). Most cases of perforation occurred in the small bowel (64.4%) compared with the colon (34.8%). Stents were mostly plastic (87.1%) with a median diameter of 10 Fr (IQR 8.5-10) and median length of 10.3 cm (IQR 715). Surgical management was pursued in 52.3% and endoscopic management in 42.4%. Bowel resection was required for 25.8% of patients. The overall mortality rate was 17.4%. In summary, this study demonstrates that bowel perforation after ERCP stent migration primarily occurs within 44.5 days and most frequently with a 10 Fr plastic biliary stent. The overall mortality rate was 17.4%. It is important for endoscopists to be mindful of this rare but serious adverse event.

AXL and SRC in clear cell renal cell carcinoma: absence of mutations, rare alternative splicing events, but association of protein expression with poor prognosis.

Novel treatment options for metastatic renal cell carcinomas (RCC) include specific MET inhibitors, GAS6/AXL inhibitors, and SRC inhibitors. The interplay between c-MET, SRC, AXL expression, and their gene mutation patterns in different renal carcinoma subtypes is unclear. To improve the understanding of these signaling pathways, we analyzed c-MET, AXL, and SRC expression in 590 clear cell RCC (ccRCC) and 127 papillary RCC (pRCC) by immunohistochemistry and integrated sequencing data to investigate the frequency of MET, AXL, and SRC gene mutations, their expression levels, and the presence of splice variants. In TCGA and in Foundation Medicine, Inc. (FMI) datasets, AXL and SRC gene alterations were extremely rare (<2%) or absent in ccRCC (n = 531 and 2,781, respectively) and pRCC (n = 290 and 566, respectively). On the other hand, MET mutations or amplifications were found in 9.7% (TCGA) and 10.2% (FMI) of pRCC. We show that strong SRC staining intensity by immunohistochemistry is associated with high tumor stage, high grade, and shorter survival in ccRCC (p < 0.001 each). AXL expression correlates with high stage and grade in ccRCC (p < 0.001 each). Both SRC and AXL expression were independent prognostic parameters in multivariate analysis (p < 0.05). MET expression is associated with longer survival in pRCC (p < 0.05). Our TCGA data analysis aligns with SRC immunohistochemistry findings on tumor stage and shorter survival in ccRCC. TCGA expression data showed a moderate positive correlation between AXL and c-MET in pRCC. In addition, we identified alternative splicing events reported for AXL in pRCC, and MET and SRC in ccRCC, across various alternative splicing databases. In conclusion, we identified high SRC expression as a biomarker for poor prognosis of ccRCC. Our data demonstrate c-MET, AXL, and SRC signaling pathway interactions independent of c-MET, AXL, and SRC mutations in ccRCC.

Uterine Rupture Trends in Patients Pursuing Trial of Labor After Cesarean in the United States from 2010 to 2022.

National rates of trial of labor after cesarean (TOLAC) have increased, but the risk of uterine rupture remains. Clinical practice- and patient-level changes also have occurred in the past decade, and an evaluation of contemporary uterine rupture rates is needed. Using natality files available in the National Vital Statistics System from 2010 to 2022, we conducted a repeated cross-sectional analysis to evaluate U.S. trends in uterine rupture and perinatal outcomes among individuals undertaking TOLAC. Temporal trends were characterized using joinpoint regression, with average annual percent change (AAPC) and 95% CI reported. There were 2,888 uterine ruptures (0.28%) among 1,016,073 included deliveries. The rate of uterine rupture increased over time, from 0.20% in 2010 to 0.37% in 2022 (AAPC 6.2, 95% CI, 4.3-8.7). Although rates of uterine rupture increased over time, it remains a rare event with a low absolute risk.

Out-of-hospital births and the experiences of emergency ambulance clinicians and birthing parents: a scoping review of the literature.

Emergency ambulance services attend a wide array of medical and trauma patients. Infrequently, this includes imminent or out-of-hospital births (OOHBs). This scoping review explores emergency ambulance clinician involvement with OOHBs, and patient and clinician experiences with birthing in the out-of-hospital setting. Scoping review using the Joanna Briggs Institute framework and 'participant, concept, context' criteria. CINAHL, Embase, Medline, Web of Science and Wiley Online were searched until 20 February 2024. Articles discussing an unplanned OOHB, or a planned home birth with complications where an emergency ambulance was required, were included. Two reviewers independently determined inclusion using Preferred Reporting Items for Systematic Reviews and Meta-Analysis extension for Scoping Reviews guidelines. A data extraction tool summarised findings for descriptive synthesis. 63 articles were included. 36 articles involved retrospective research. 38 articles were published since 2015, with USA (n=17) and Australia (n=13) the highest contributors. Risk factors for OOHBs were varied, with maternal age or being multigravida/multiparous often cited. 99 complications were described, ranging from relatively minor ailments (ie, nausea and vomiting) to life-threatening situations such as maternal or neonatal cardiac arrest. Common management/interventions reported were assisting with birth, maternal intravenous cannulation and medication administration.Birth parents, partners and clinicians all describe OOHBs as anxiety-provoking but joyous when a healthy neonate is born. The OOHB experience is enhanced for patients when clinicians communicate well, while those who appeared inexperienced increased patient anxiety.OOHBs experience many challenges to optimal care, categorised as 'emergency ambulance clinicians desiring additional education and training', 'communication and collaboration difficulties', 'environmental issues', 'technology and aids' and 'other' limitations. OOHBs are rare events requiring expert assistance to optimise patient outcomes. There remain significant challenges to unplanned OOHBs; ongoing training and skill competency is required to improve patient safety and clinician confidence. Further research investigating patient outcomes and experiences is recommended.