False Alarms in Consumer Genomics Add to Public Fear and Potential Health Care Burden.
Ontology highlight
ABSTRACT:
SUBMITTER: Liu X
PROVIDER: S-EPMC7712761 | biostudies-literature | 2020 Oct
REPOSITORIES: biostudies-literature
Publications
False Alarms in Consumer Genomics Add to Public Fear and Potential Health Care Burden.
Liu Xiaoming X Cragun Deborah D Pang Jinyong J Adapa Swamy R SR Fonseca Renee R Jiang Rays H Y RHY
Journal of personalized medicine 20201023 4
We have entered an era of direct-to-consumer (DTC) genomics. Patients have relayed many success stories of DTC genomics about finding causal mutations of genetic diseases before showing any symptoms and taking precautions. However, consumers may also take unnecessary medical actions based on false alarms of "pathogenic alleles". The severity of this problem is not well known. Using publicly available data, we compared DTC microarray genotyping data with deep-sequencing data of 5 individuals and ...[more]
Similar Datasets
Project description:Solar energetic particle (SEP) events are known to occur following solar flares and coronal mass ejections (CMEs). However, some high-energy solar events do not result in SEPs being detected at Earth, and it is these types of event which may be termed "false alarms". We define two simple SEP forecasting algorithms based upon the occurrence of a magnetically well-connected CME with a speed in excess of 1500kms-1 (a "fast" CME) or a well-connected X-class flare and analyse them with respect to historical datasets. We compare the parameters of those solar events which produced an enhancement of >40MeV protons at Earth (an "SEP event") and the parameters of false alarms. We find that an SEP forecasting algorithm based solely upon the occurrence of a well-connected fast CME produces fewer false alarms (28.8%) than an algorithm which is based solely upon a well-connected X-class flare (50.6%). Both algorithms fail to forecast a relatively high percentage of SEP events (53.2% and 50.6%, respectively). Our analysis of the historical datasets shows that false-alarm X-class flares were either not associated with any CME, or were associated with a CME slower than 500kms-1 ; false-alarm fast CMEs tended to be associated with flare classes lower than M3. A better approach to forecasting would be an algorithm which takes as its base the occurrence of both CMEs and flares. We define a new forecasting algorithm which uses a combination of CME and flare parameters, and we show that the false-alarm ratio is similar to that for the algorithm based upon fast CMEs (29.6%), but the percentage of SEP events not forecast is reduced to 32.4%. Lists of the solar events which gave rise to >40MeV protons and the false alarms have been derived and are made available to aid further study. Electronic Supplementary Material:The online version of this article (doi:10.1007/s11207-017-1196-y) contains supplementary material, which is available to authorized users.
| S-EPMC6953974 | biostudies-literature
Project description:This work attempts to reduce the number of false alarms generated by bedside monitors in the intensive care unit (ICU), as a majority of current alarms are false. In this study, we applied methods that can be categorized into three stages: signal processing, feature extraction, and optimized machine learning. At the stage of signal processing, we ensured that the heartbeats were properly annotated. During feature extraction, besides extracting features that are relevant to the arrhythmic alarms, we also extracted a set of signal quality indices (SQIs), which we used to distinguish noise/artifact from normal physiological signals. When applying a machine learning algorithm (Random Forest), we performed feature selection in order to reduce the complexity of the models and improve the efficiency of the algorithm. The dataset used is from Reducing False Arrhythmia Alarms in the ICU: the PhysioNet/Computing in Cardiology Challenge 2015. Using the performance metric "score" from the Challenge, we achieved a score of 83.08 in the real-time category on the hidden test set, which is the highest in all published work.
| S-EPMC6728371 | biostudies-literature
Project description:ObjectiveIn intensive care units (ICU), frequent false alarms from medical equipment can cause alarm fatigue among nurses, which might lead to delayed or missed responses and increased risk of adverse patient events. This review was conducted to evaluate the effectiveness of intelligent management interventions to reduce false alarms in ICU.MethodFollowing the framework of Whitmore and Knafl, the reviewers systematically searched six databases: PubMed, EMBASE, CINAHL, OVID, Cochrane Library, and Scopus, and studies included intelligent management of clinical alarms published in the English or Chinese language from the inception of each database to December 2022 were retrieved. The researchers used the PICOS framework to formulate the search strategy, developed keywords, screened literature, and assessed the studies' quality using the Joanna Briggs Institute-Meta-Analysis of Statistics, Assessment, and Review Instrument (JBI-MAStARI). The review was preregistered on PROSPERO (CRD42023411552).ResultsSeven studies met the inclusion criteria. The results showed that different interventions for intelligent management of alarms were beneficial in reducing the number of false alarms, the duration of alarms, the response time to important alarms for nurses, and the alarm fatigue levels among nurses. Positive results were found in practice after the application of the novel alarm management approaches.ConclusionIntelligent management intervention may be an effective way to reduce false alarms. The application of systems or tools for the intelligent management of clinical alarms is urgent in hospitals. To ensure more effective patient monitoring and less distress for nurses, more alarm management approaches combined with artificial intelligence will be needed in the future to enable accurate identification of critical alarms, ensure nurses are responding accurately to alarms, and make a real difference to alarm-ridden healthcare environments.
| S-EPMC10859571 | biostudies-literature
Project description:The hippocampus supports distinctive encoding, enabling discrimination of perceptions from similar memories. Here, an experimental and individual differences approach examined the role of encoding quality in the classification of similar lures. An object recognition task included thought probes during study and similar lures at test. On-task study reports were associated with lure discrimination in within-subject and between-subjects analyses. Within-subject on-task reports were also associated with false classifications of lures as studied objects. These results are compatible with the view that quality encoding supports memory-based rejection of lures but also engenders false alarms when perceptions and memories are inaccurately compared.
| S-EPMC10165994 | biostudies-literature
Project description:Prediction of sepsis using machine-learning approaches has recently gained traction. However, the lack of translation of these algorithms into clinical routine remains a major issue. Existing early sepsis detection methods are either based on the older definition of sepsis or do not accurately detect sepsis leading to the high frequency of false-positive alarms. This results in a well-known issue of clinicians' "alarm fatigue", leading to decreased responsiveness and identification, ultimately resulting in delayed clinical intervention. Hence, there is a fundamental, unmet need for a clinical decision system capable of accurate and timely sepsis diagnosis, running at the point of need. In this work, SepsisAI-a deep-learning algorithm based on long short-term memory (LSTM) networks was developed to predict the early onset of hospital-acquired sepsis in real-time for patients admitted to the ICU. The models are trained and validated with data from the PhysioNet Challenge, consisting of 40,336 patient data files from two healthcare systems: Beth Israel Deaconess Medical Center and Emory University Hospital. In the short term, the algorithm tracks frequently measured vital signs, sparsely available lab parameters, demographic features, and certain derived features for making predictions. A real-time alert system, which monitors the trajectory of the predictions, is developed on top of the deep-learning framework to minimize false alarms. On a balanced test dataset, the model achieves an AUROC, AUPRC, sensitivity, and specificity of 0.95, 0.96, 88.19%, and 96.75%, respectively at the patient level. In terms of lookahead time, the model issues a warning at a median of 6 hours (IQR 6 to 20 hours) and raises an alert at a median of 4 hours (IQR 2 to 5 hours) ahead of sepsis onset. Most importantly, the model achieves a false-alarm ratio of 3.18% for alerts, which is significantly less than other sepsis alarm systems. Additionally, on a disease prevalence-based test set, the algorithm reported similar outcomes with AUROC and AUPRC of 0.94 and 0.87, respectively, with sensitivity, and specificity of 97.05%, and 96.75%, respectively. The proposed algorithm might serve as a clinical decision support system to assist clinicians in the accurate and timely diagnosis of sepsis. With exceptionally high specificity and low false-alarm rate, this algorithm also helps mitigate the well-known issue of clinician alert fatigue arising from currently proposed sepsis alarm systems. Consequently, the algorithm partially addresses the challenges of successfully integrating machine-learning algorithms into routine clinical care.
| S-EPMC11318852 | biostudies-literature
Project description:Despite its widespread use in North America and many other parts of the world, the safety of etomidate as an induction agent for rapid sequence intubation in septic patients is still debated. In this article, we evaluate the current literature on etomidate, review its clinical history, and discuss the controversy regarding its use, especially in sepsis. We address eight questions: (i) When did concern over the safety of etomidate first arise? (ii) What is the mechanism by which etomidate is thought to affect the adrenal axis? (iii) How has adrenal insufficiency in relation to etomidate use been defined or identified in the literature? (iv) What is the evidence that single dose etomidate is associated with subsequent adrenal-cortisol dysfunction? (v) What is the clinical significance of adrenal insufficiency or dysfunction associated with single dose etomidate, and where are the data that support or refute the contention that single-dose etomidate is associated with increased mortality or important post emergency department (ED) clinical outcomes? (vi) How should etomidate's effects in septic patients best be measured? (vii) What are alternative induction agents and what are the advantages and disadvantages of these agents relative to etomidate? (viii) What future work is needed to further clarify the characteristics of etomidate as it is currently used in patients with sepsis? We conclude that the observational nature of almost all available data suggesting adverse outcomes from etomidate does not support abandoning its use for rapid sequence induction. However, because we see a need to balance theoretical harms and benefits in the presence of data supporting the non-inferiority of alternative agents without similar theoretical risks associated with them, we suggest that the burden of proof to support continued widespread use may rest with the proponents of etomidate. We further suggest that practitioners become familiar with the use of more than one agent while awaiting further definitive data.
| S-EPMC2908652 | biostudies-literature
Project description:ObjectiveTo estimate potential savings associated with the Consumer Reports Best Buy Drugs program, a national educational program that provides consumers with price and effectiveness information on prescription drugs.Data sourcesNational data on 2006 prescription sales and retail prices paid for angiotensin-converting enzyme inhibitors (ACEIs), β-blockers, calcium channel blockers, and 3-hydroxy-3-methylglutaryl coenzyme A (HMG-coA) reductase inhibitors (statins).Study designWe converted national data on aggregate unit sales of drugs in the four classes to defined daily doses (DDD) and estimated a range of potential savings from generic and therapeutic substitution.Principal findingsWe estimated that $2.76 billion, or 7.83 percent of sales, could be saved if use of the drugs recommended by the educational program was increased. The recommended drugs' prices were 15-65 percent lower per DDD than their therapeutic alternatives. The majority (57.4 percent) of potential savings would be achieved through therapeutic substitution.ConclusionsSubstantial savings can be achieved through greater use of comparatively effective and lower cost drugs recommended by a national consumer education program. However, barriers to dissemination of consumer-oriented drug information must be addressed before savings can be realized.
| S-EPMC2653882 | biostudies-literature
Project description:PURPOSE:There is increasing demand from the public for direct-to-consumer (DTC) genetic tests, and the US Food and Drug Administration limits the type of health-related claims DTC tests can market. Some DTC companies provide raw genotyping data to customers if requested, and these raw data may include variants occurring in genes recommended by the American College of Medical Genetics and Genomics to be reported as incidental/secondary findings. The purpose of this study was to review the outcome of requests for clinical confirmation of DTC results that were received by our laboratory and to analyze variant classification concordance. METHODS:We identified 49 patient samples received for further testing that had previously identified genetic variants reported in DTC raw data. For each case identified, information pertaining to the outcome of clinical confirmation testing as well as classification of the DTC variant was collected and analyzed. RESULTS:Our analyses indicated that 40% of variants in a variety of genes reported in DTC raw data were false positives. In addition, some variants designated with the "increased risk" classification in DTC raw data or by a third-party interpretation service were classified as benign at Ambry Genetics as well as several other clinical laboratories, and are noted to be common variants in publicly available population frequency databases. CONCLUSION:Our results demonstrate the importance of confirming DTC raw data variants in a clinical laboratory that is well versed in both complex variant detection and classification.
| S-EPMC6301953 | biostudies-literature
Project description:BACKGROUND:Next-generation sequencing (NGS) is increasingly being translated into routine public health practice, affecting the surveillance and control of many pathogens. The purpose of this scoping review is to identify and characterize the recent literature concerning the application of bacterial pathogen genomics for public health practice and to assess the added value, challenges, and needs related to its implementation from an epidemiologist's perspective. METHODS:In this scoping review, a systematic PubMed search with forward and backward snowballing was performed to identify manuscripts in English published between January 2015 and September 2018. Included studies had to describe the application of NGS on bacterial isolates within a public health setting. The studied pathogen, year of publication, country, number of isolates, sampling fraction, setting, public health application, study aim, level of implementation, time orientation of the NGS analyses, and key findings were extracted from each study. Due to a large heterogeneity of settings, applications, pathogens, and study measurements, a descriptive narrative synthesis of the eligible studies was performed. RESULTS:Out of the 275 included articles, 164 were outbreak investigations, 70 focused on strategy-oriented surveillance, and 41 on control-oriented surveillance. Main applications included the use of whole-genome sequencing (WGS) data for (1) source tracing, (2) early outbreak detection, (3) unraveling transmission dynamics, (4) monitoring drug resistance, (5) detecting cross-border transmission events, (6) identifying the emergence of strains with enhanced virulence or zoonotic potential, and (7) assessing the impact of prevention and control programs. The superior resolution over conventional typing methods to infer transmission routes was reported as an added value, as well as the ability to simultaneously characterize the resistome and virulome of the studied pathogen. However, the full potential of pathogen genomics can only be reached through its integration with high-quality contextual data. CONCLUSIONS:For several pathogens, it is time for a shift from proof-of-concept studies to routine use of WGS during outbreak investigations and surveillance activities. However, some implementation challenges from the epidemiologist's perspective remain, such as data integration, quality of contextual data, sampling strategies, and meaningful interpretations. Interdisciplinary, inter-sectoral, and international collaborations are key for an appropriate genomics-informed surveillance.
| S-EPMC6692930 | biostudies-literature
Project description:OBJECTIVE:To characterize the rate of monitoring alarms by alarm priority, signal type, and developmental age in a Level-IIIB Neonatal Intensive Care Unit (NICU) population. STUDY DESIGN:Retrospective analysis of 2,294,687 alarm messages from Philips monitors in a convenience sample of 917 NICU patients, covering 12,001 patient-days. We stratified alarm rates by alarm priority, signal type, postmenstrual age (PMA) and birth weight (BW), and reviewed and adjudicated over 21,000 critical alarms. RESULTS:Of all alarms, 3.6% were critical alarms, 55.0% were advisory alarms, and 41.4% were device alerts. Over 60% of alarms related to oxygenation monitoring. The average alarm rate (±SEM) was 177.1 ± 4.9 [median: 135.9; IQR: 89.2-213.3] alarms/patient-day; the medians varied significantly with PMA and BW (p < 0.001) in U-shaped patterns, with higher rates at lower and higher PMA and BW. Based on waveform reviews, over 99% of critical arrhythmia alarms were deemed technically false. CONCLUSIONS:The alarm burden in this NICU population is very significant; the average alarm rate significantly underrepresents alarm rates at low and high PMA and BW. Virtually all critical arrhythmia alarms were artifactual.
| S-EPMC6092211 | biostudies-literature