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Predicting worsening heart failure hospitalizations in patients with implantable cardioverter defibrillators: is it all about alerts? A pooled analysis of nine trials

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EUROPACE 2024年第2期26卷 euae032页

作者： Botto, Giovanni Luca Sinagra, Gianfranco Bulava, Alan Gargaro, Alessio Timmel, Tobias Giacopelli, Daniele D'Onofrio, Antonio Guedon-Moreau, Laurence ASST Rhodense UO Electrophysiol Viale Carlo Forlanini 95 I-20024 Garbagnate Milanese MI Italy ASUGI Cattinara Hosp Cardiothoracovasc Dept Trieste Italy Univ Trieste Trieste Italy Univ South Bohemia Ceske Budejovice Ceske Budejovice Hosp Fac Hlth & Social Sci Ceske Budejovice Czech Republic Biotronik Italia SPA Clin Unit Cologno Monzese MI Italy Biotron SE & Co KG Ctr Clin Res Berlin Germany Monaldi Hosp Unita Operativa Elettrofisiol Studio & Terapia Ari Naples Italy Univ Lille Lille Univ Hosp Ctr CHU Lille Lille Hauts De France France

Aims To predict worsening heart failure hospitalizations (WHFHs) in patients with implantable defibrillators and remote monitoring, the HeartInsight algorithm (Biotronik, Berlin, Germany) calculates a heart failure (HF) score combining seven physiologic parameters: 24 h heart rate (HR), nocturnal HR, HR variability, atrial tachyarrhythmia, ventricular extrasystoles, patient activity, and thoracic impedance. We compared temporal trends of the HF score and its components 12 weeks before a WHFH with 12-week trends in patients without WHFH, to assess whether trends indicate deteriorating HF regardless of alert status. Methods and results Data from nine clinical trials were pooled, including 2050 patients with a defibrillator capable of atrial sensing, ejection fraction <= 35%, NYHA class II/III, no long-standing atrial fibrillation, and 369 WHFH from 259 patients. The mean HF score was higher in the WHFH group than in the no WHFH group (42.3 +/- 26.1 vs. 30.7 +/- 20.6, P < 0.001) already at the beginning of 12 weeks. The mean HF score further increased to 51.6 +/- 26.8 until WHFH (+22% vs. no WHFH group, P = 0.003). As compared to the no WHFH group, the algorithm components either were already higher 12 weeks before WHFH (24 h HR, HR variability, thoracic impedance) or significantly increased until WHFH (nocturnal HR, atrial tachyarrhythmia, ventricular extrasystoles, patient activity). Conclusion The HF score was significantly higher at, and further increased during 12 weeks before WHFH, as compared to the no WHFH group, with seven components showing different behaviour and contribution. Temporal trends of HF score may serve as a quantitative estimate of HF condition and evolution prior to WHFH. [GRAPHICS] .

关键词： Worsening heart failure hospitalization predictive algorithm Heart failure score Implantable defibrillators Remote monitoring Heart rate

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A Driving-Force Predictor for Estimating Pathway Activation in Patient-Specific Models of Deep Brain Stimulation

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NEUROMODULATION 2019年第4期22卷 403-415页

作者： Howell, Bryan Gunalan, Kabilar McIntyre, Cameron C. Case Western Reserve Univ Dept Biomed Engn Wolstein Res BldgRoom 62242103 Cornell Rd Cleveland OH 44106 USA Emory Univ Dept Psychiat & Behav Sci Atlanta GA 30322 USA

Objective Detailed biophysical modeling of deep brain stimulation (DBS) provides a theoretical approach to quantify the cellular response to the applied electric field. However, the most accurate models for performing such analyses, patient-specific field-cable (FC) pathway-activation models (PAMs), are so technically demanding to implement that their use in clinical research is greatly limited. predictive algorithms can simplify PAM calculations, but they generally fail to reproduce the output of FC models when evaluated over a wide range of clinically relevant stimulation parameters. Therefore, we set out to develop a novel driving-force (DF) predictive algorithm (DF-Howell), customized to the study of DBS, which can better match FC results. Methods We developed the DF-Howell algorithm and compared its predictions to FC PAM results, as well as to the DF-Peterson algorithm, which is currently the most accurate and generalizable DF-based method. Comparison of the various methods was quantified within the context of subthalamic DBS using activation thresholds of axons representing the internal capsule, hyperdirect pathway, and cerebellothalamic tract for various combinations of fiber diameters, stimulus pulse widths, and electrode configurations. Results The DF-Howell predictor estimated activation of the three axonal pathways with less than a 6.2% mean error with respect to the FC PAM for all 21 cases tested. In 15 of the 21 cases, DF-Howell outperformed DF-Peterson in estimating pathway activation, reducing mean-errors up to 22.5%. Conclusions DF-Howell represents an accurate predictor for estimating axonal pathway activation in patient-specific DBS models, but errors still exist relative to FC PAM calculations. Nonetheless, the tractability of DF algorithms helps to reduce the technical barriers for performing accurate biophysical modeling in clinical DBS research studies.

关键词： Computational modeling deep brain stimulation pathway activation predictive algorithm tractography

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iPREDICT: Characterization of Asthma Triggers and Selection of Digital Technology to Predict Changes in Disease Control

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JOURNAL OF ASTHMA AND ALLERGY 2024年 17卷 653-666页

作者： Castro, Mario Zavod, Merrill Rutgersson, Annika Jornten-Karlsson, Magnus Dutta, Bhaskar Hagger, Lynn Univ Kansas Div Pulm Crit Care & Sleep Med Sch Med 4000 Cambridge StMSN 3007 Kansas City KS 66160 USA AstraZeneca User Experience Wilmington DE USA AstraZeneca Digital Patient Prod Molndal Sweden AstraZeneca Digital Implementat Digital Hlth R&D Molndal Sweden AstraZeneca Patient Safety Ctr Excellence Gaithersburg MD USA AstraZeneca Content Strategy & Experience Design Digital Global Commercial Gaithersburg MD USA

Purpose: The iPREDICT program aimed to develop an integrated digital health solution capable of continuous data streaming, predicting changes in asthma control, and enabling early intervention. Patients and Methods: As part of the iPREDICT program, asthma triggers were characterized by surveying 221 patients (aged >18 years) with self-reported asthma for a risk-benefit analysis of parameters predictive of changes in disease control. Seventeen healthy volunteers (age 25-65 years) tested 13 devices to measure these parameters and assessed their usability attributes. Results: Patients identified irritants such as chemicals, allergens, weather changes, and physical activity as triggers that were the most relevant to deteriorating asthma control. Device testing in healthy volunteers revealed variable data formats/units and quality issues, such as missing data and low signal-to-noise ratio. Based on user preference and data capture validity, a spirometer, vital sign monitor, and sleep monitor formed the iPREDICT integrated system for continuous data streaming to develop a personalized/predictive algorithm for asthma control. Conclusion: These findings emphasize the need to systematically compare devices based on several parameters, including usability and data quality, to develop integrated digital technology programs for asthma care.

关键词： asthma devices digital predictive algorithm sensors

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Predictors of perioperative complications in paediatric cranial vault reconstruction surgery: a multicentre observational study from the Pediatric Craniofacial Collaborative Group

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BRITISH JOURNAL OF ANAESTHESIA 2019年第2期122卷 215-223页

作者： Goobie, S. M. Zurakowski, D. Isaac, K. V. Taicher, B. M. Fernandez, P. G. Derderian, C. K. Hetmaniuk, M. Stricker, P. A. Abruzzese, Christopher Apuya, Jesus Beethe, Amy Benzon, Hubert Binstock, Wendy Brzenskim, Alyssa Budac, Stefan Busso, Veronica Chhabada, Surendrasingh Chiao, Franklin Cladis, Franklyn Claypool, Danielle Collins, Michael Dabek, Rachel Dalesio, Nicholas Falconl, Ricardo Fernandez, Allison Fernandez, Patrick Fiadjoe, John Gangadharan, Meera Gentry, Katherine Glover, Chris Goobie, Susan M. Gosman, Amanda Grap, Shannon Gries, Heike Griffin, Allison Haberkern, Charles Hajduk, John Hall, Rebecca Hansen, Jennifer Hetmaniuk, Mali Hsieh, Vincent Huang, Henry Ingelmo, Pablo Ivanova, Iskra Jain, Ranu Kars, Michelle Kowalczyk-Derderian, Courtney Kugler, Jane Labovsky, Kristen Lakheeram, Indrani Lee, Andrew Martinez, Jose Luis Masel, Brian Medellin, Eduardo Meier, Petra Levy, Heather Mitzel Muhly, Wallis T. Muldowney, Bridget Nelson, Jonathon Nicholson, Julie Nguyen, Kim-Phuong Nguyen, Thanh Olutuye, Olutoyin Owens-Stubblefield, Margaret Parekh, Uma Ramesh Petersen, Timothy Pohl, Vanessa Post, Julian Poteet-Schwartz, Kim Prozesky, Jansie Reddy, Srijaya Reid, Russell Ricketts, Karene Rubens, Daniel Ryan, Laura Skitt, Rochelle Soneru, Codruta Spitznagel, Rachel Stricker, Paul Singh, Davinder Singhal, Neil R. Sorial, Emad Staudt, Susan Stubbeman, Bobbie Sung, Wai Syed, Tariq Szmuk, Peter Taicher, Brad M. Thompson, Douglas Tretault, Lisa Ungar-Kastner, Galit Watts, Rheana Wieser, John Wong, Karen Zamora, Lillian Harvard Med Sch Boston MA 02115 USA Boston Childrens Hosp Dept Anesthesiol Crit Care & Pain Med Boston MA 02115 USA Univ British Columbia Div Plast & Reconstruct Surg Vancouver BC Canada Duke Univ Med Ctr Durham NC USA Duke Childrens Hosp Dept Anesthesiol Durham NC USA Univ Colorado Sch Med Dept Anesthesiol Aurora CO USA Childrens Hosp Colorado Aurora CO USA Childrens Med Ctr Dallas Dept Anesthesiol & Crit Care Dallas TX USA Univ Texas Southwestern Dallas TX USA Univ Washington Seattle WA 98195 USA Seattle Childrens Hosp Dept Anesthesiol & Pain Management Seattle WA USA Childrens Hosp Philadelphia Philadelphia PA 19104 USA Univ Penn Perelman Sch Med Dept Anesthesiol & Crit Care Philadelphia PA 19104 USA Univ Wisconsin Madison Amer Family Childrens Hosp Madison WI USA Ann & Robert H Lurie Childrens Hosp Chicago Chicago IL 60611 USA Arkansas Childrens Hosp 800 Marshall St Little Rock AR 72202 USA Boston Childrens Hosp Boston MA USA Charlotte R Bloomberg Johns Hopkins Childrens Ctr Baltimore MD USA Egleston Childrens Healthcare Atlanta Atlanta GA USA Childrens Hosp & Med Ctr Omaha NE USA Childrens Hosp Colorado Denver CO USA Childrens Hosp Philadelphia Philadelphia PA 19104 USA Childrens Hosp Wisconsin Milwaukee WI 53201 USA Childrens Natl Hlth Syst Washington DC USA CHU St Justine Montreal PQ Canada Cincinnati Childrens Hosp Med Ctr Cincinnati OH 45229 USA Cleveland Clin Childrens Cleveland OH USA Driscoll Childrens Hosp Corpus Christi TX USA Duke Childrens Hosp & Hlth Ctr Durham NC USA Johns Hopkins All Childrens Hosp St Petersburg FL USA Vanderbilt Univ Med Ctr Monroe Carell Jr Childrens Hosp Vanderbilt Nashville TN USA Montreal Childrens Hosp Montreal PQ Canada New York Presbyterian Hosp Weill Cornell Med Coll New York NY USA Oregon Hlth & Sci Univ Doernbecher Childrens Hosp Portland OR 97201 USA Penn State Hershey Med Ctr Hershey PA USA Phoenix Childrens Hosp Phoenix AZ USA Seattle Childrens Hosp Seattle WA USA Texas C

Background: The current incidence of major complications in paediatric craniofacial surgery in North America has not been accurately defined. In this report, the Pediatric Craniofacial Collaborative Group evaluates the incidence and determines the independent predictors of major perioperative complications using a multicentre database. Methods: The Pediatric Craniofacial Surgery Perioperative Registry was queried for subjects undergoing complex cranial vault reconstruction surgery over a 5-year period. Major perioperative complications were identified through a structured a priori consensus process. Logistic regression was applied to identify predictors of a major perioperative complication with bootstrapping to evaluate discrimination accuracy and provide internal validity of the multivariable model. Results: A total of 1814 patients from 33 institutions in the US and Canada were analysed;15% were reported to have a major perioperative complication. Multivariable predictors included ASA physical status 3 or 4 (P = 0.005), craniofacial syndrome (P = 0.008), antifibrinolytic administered (P = 0.003), blood product transfusion >50 ml kg(-1) (P<0.001), and surgery duration over 5 h (P<0.001). Bootstrapping indicated that the predictive algorithm had good internal validity and excellent discrimination and model performance. A perioperative complication was estimated to increase the hospital length of stay by an average of 3 days (P<0.001). Conclusions: The predictive algorithm can be used as a prognostic tool to risk stratify patients and thereby potentially reduce morbidity and mortality. Craniofacial teams can utilise these predictors of complications to identify high-risk patients. Based on this information, further prospective quality improvement initiatives may decrease complications, and reduce morbidity and mortality.

关键词： predictive algorithm perioperative complications perioperative outcome craniosynostosis multivariable model paediatrics risk assessment

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Estimating Time-Variant Variables' Effects on a Micro Scale for Time-Based Crime Prediction 7

Estimating Time-Variant Variables' Effects on a Micro Scale ...

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7th International Conference on Business and Industrial Research (ICBIR)

作者： Ohyama, Tomoya Tohoku Univ Ctr Data Driven Sci & Artificial Intelligence Sendai Miyagi Japan

ISBN: (纸本)9781665494755

Crime prediction research has been biased toward spatial factors, and insufficient research has detailed predictions regarding temporal factors. To build an industrially practical crime prediction system, it is necessary to develop a method that considers both temporal and spatial risk factors. This study estimates the effect of time-variant variables such as the day of the week, season, weather, and events for a fundamental analysis of time-based crime prediction. However, because crime data are often imbalanced data with extremely few positive classes, subdividing the data spatially and temporally makes it difficult to estimate the parameters. Therefore, we attempted to solve this problem by dividing the city into clusters with high spatial homogeneity and analyzing each cluster. We observed different effects of time-varying factors for different types of crime.

关键词： crime prediction predictive algorithm time-variant variables spatial clustering generalized linear mixed model

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PA-SPS: A predictive adaptive approach for an elastic stream processing system

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JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING 2024年 192卷

作者： Wladdimiro, Daniel Arantes, Luciana Sens, Pierre Hidalgo, Nicolas Sorbonne Univ CNRS LIP6 4 Pl Jussieu F-75005 Paris France Univ Diego Portales Ave Manuel Rodriguez 415 Santiago Chile

Stream Processing Systems (SPSs) dynamically process input events. Since the input is usually not a constant flow, presenting rate fluctuations, many works in the literature propose to dynamically replicate SPS operators, aiming at reducing the processing bottleneck induced by such fluctuations. However, these SPSs do not consider the problem of load balancing of the replicas or the cost involved in reconfiguring the system whenever the number of replicas changes. We present in this paper a predictive model which, based on input rate variation, execution time of operators, and queued events, dynamically defines the necessary current number of replicas of each operator. A predictor, composed of different models (i.e., mathematical and Machine Learning ones), predicts the input rate. We also propose a Storm -based SPS, named PA-SPS, which uses such a predictive model, not requiring reboot reconfiguration when the number of operators replica change. PA-SPS also implements a load balancer that distributes incoming events evenly among replicas of an operator. We have conducted experiments on Google Cloud Platform (GCP) for evaluation PA-SPS using real traffic traces of different applications and also compared it with Storm and other existing SPSs.

关键词： Big data Distributed stream processing Self-adaptive system Dynamic traffic predictive algorithm Google cloud platform

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Rational design of peptide drugs: avoiding aggregation

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DRUG DISCOVERY TODAY 2005年第18期10卷 1208-1209页

作者： Bradbury, J

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Comparative Analysis of Prediction algorithms for Diabetes

Comparative Analysis of Prediction Algorithms for Diabetes

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International Conference on Computer, Communication and Computational Sciences (IC4S)

作者： Karun, Shweta Raj, Aishwarya Attigeri, Girija Manipal Univ Manipal Inst Technol Dept ICT Manipal Karnataka India

ISBN: (纸本)9789811303418;9789811303401

Machine learning is a widely growing field which helps in better learning from data and its analysis without any human intervention. It is being popularly used in the field of healthcare for analyzing and detecting serious and complex conditions. Diabetes is one such condition that heavily affects the entire system. In this paper, application of intelligent machine learning algorithms like logistic regression, naive Bayes, support vector machine, decision tree, k-nearest neighbors, neural network, and random decision forest are used along with feature extraction. The accuracy of each algorithm, with and without feature extraction, leads to a comparative study of these predictive models. Therefore, a list of algorithms that works better with feature extraction and another that works better without it is obtained. These results can be used further for better prediction and diagnosis of diabetes.

关键词： Machine learning predictive algorithm Feature extraction Diabetes prediction Classification Ensemble

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System Architecture of the Sustainable Energy System for Performance Buildings

System Architecture of the Sustainable Energy System for Per...

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IEEE Electrical Power and Energy Conference (EPEC)

作者： Grebenyuk, Vladimir Ascent Syst Technol Sun Peaks BC V0R 5N0 Canada

ISBN: (纸本)9781479901067

Every system, however big or small, is always a part of a bigger system and in turn consists of a number of smaller subsystems. This is true for both natural and technical systems. According to Systems Architecture, in order to be successful, any complex technical system has to take into account its place in the hierarchy of other systems, as well as interdependency between the components of the system on each level. The principles of Systems Architecture are universal, but there is hardly a more important area where they need to be applied than the area of sustainability. An important conclusion of applying the principles of Systems Architecture to sustainable development is that the system itself must be sustainable. This paper presents an approach to designing sustainable system architecture for buildings based on a predictive algorithm, using a specific example of a building-integrated renewable energy control system.

关键词： sustainable system architecture predictive algorithm integrated energy control system high-performance building

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Application of Prediction algorithm of Photovoltaic Power in Distributed Photovoltaic Volatility Control

Application of Prediction Algorithm of Photovoltaic Power in...

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2nd International Conference on Energy and Environmental Protection (ICEEP 2013)

作者： Xiao, Hongxia Chen, Hong Mo, Hue Jiangxi Elect Power Corp Training Ctr Nanchang Peoples R China

ISBN: (纸本)9783037857410

For the uncertain characteristics of output power of distributed Photovoltaic (PV), the energy storage device is used to make up the error, between the predicted output and the actual output, to achieve the volatility control of PV power system. This paper presents that the application of support vector machine (SVM) in the power output forecasting of PV volatility control. Based on the complex and non-linear of the factor affecting the PV output, it points the non-linear relationship between the influencing factors and predicted values, and it establishes the SVM regression model of the PV system. Meanwhile, combined with the historical data of the PV operation, it gives the predictive values. Both theoretical analyses and calculation examples show that this method is simple, with high precision, and it is more suitable to the PV output volatility control.

关键词： Photovoltaic Generation Fluctuation Support Vector Machines predictive algorithm

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