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Assessing the Performance of National Surgical Quality Improvement Program Surgical Risk Calculator in Elective Spine Surgery: Insights from Patients Undergoing Single-Level Posterior Lumbar Fusion

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WORLD NEUROSURGERY 2019年 126卷 E323-E329页

作者： Sebastian, Arjun Goyal, Anshit Alvi, Mohammed Ali Wahood, Waseem Elminawy, Mohamed Habermann, Elizabeth B. Bydon, Mohamad Mayo Clin Dept Orthoped Surg Rochester MN 55905 USA Mayo Clin Neuroinformat Lab Rochester MN 55905 USA Mayo Clin Dept Neurol Surg Rochester MN 55905 USA Mayo Clin Robert D & Patricia E Kern Ctr Sci Hlth Care Deli Rochester MN 55905 USA

INTRODUCTION: The American College of Surgeons-National Surgical Quality Improvement Program Surgical Risk Calculator is a tool developed to use 21 individual patient characteristics to make predictions for occurrence of 13 general and 2 procedure-specific outcomes. The goal of this study was to evaluate the performance of the Surgical Risk Calculator in predicting outcomes in patients receiving posterior lumbar fusion. METHODS: American College of Surgeons-National Surgical Quality Improvement Program Participant Use File for 2015 was queried for patients with age >= 18 years undergoing single-level posterior lumbar fusion (PLF) surgery. Individual patient characteristics were entered into the online risk calculator interface to retrieve the predicted estimated risk for perioperative outcomes and complications. Following this, predictive performance was analyzed by computing Brier score, c-statistic, and sensitivity values for all observed outcomes. RESULTS: A total of 2808 patients undergoing single-level PLF were included in the analysis. Overall, a very low incidence of 30-day postoperative complications was observed with the procedure (0.9%-6.3%). Poor predictive performance was found for all outcomes, including read-missions (c-statistic = 0.63;sensitivity = 15.28%;Brier score = 0.048) and returns to operating room (c-statistic = 0.56;sensitivity = 21.05%;Brier score = 0.032). The best performance was observed for venous thromboembolism (c-statistic = 0.66: Brier score = 0.008), although sensitivity was poor (3.85%) on account of low incidence. predictive performance for length of stay revealed good agreement between observed and predicted values with the exception of prolonged predicted hospital stays (>3.5 days). CONCLUSIONS: This study assesses the performance of the risk calculator for a homogenous population of patients undergoing a single-level PLF. Although the calculator did not fare well in predicting most outcomes, results need to be interpreted in t

关键词： Lumbar fusion NSQIP predictive algorithm Risk calculator Spine surgery

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HapLogic: A predictive Human Leukocyte Antigen-Matching algorithm to Enhance Rapid Identification of the Optimal Unrelated Hematopoietic Stem Cell Sources for Transplantation

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BIOLOGY OF BLOOD AND MARROW TRANSPLANTATION 2016年第11期22卷 2038-2046页

作者： Dehn, Jason Setterholm, Michelle Buck, Kelly Kempenich, Jane Beduhn, Beth Gragert, Loren Madbouly, Abeer Fingerson, Stephanie Maiers, Martin Natl Marrow Donor Program Minneapolis MN USA Tulane Univ Sch Med Dept Pathol & Lab Med Tulane Canc Ctr 1430 Tulane Ave New Orleans LA 70112 USA

The search for a suitable human leukocyte antigen (HLA)-matched unrelated adult stem cell donor (URD) or umbilical cord blood unit (UCB) is a complex process. The National Marrow Donor Program (NMDP) developed a search algorithm known as HapLogic, which is currently provided within the NMDP Traxis application. The HapLogic algorithm has been in use since 2006 and has advanced URD/UCB HLA-matching technology. The algorithm has been shown to have high predictive accuracy, which can streamline URD/UCB selection and drive efficiencies in the search process to the benefit of the stem cell transplantation community. Here, we describe the fundamental components of the NMDP matching algorithm, output, validation, and future directions. (C) 2016 American Society for Blood and Marrow Transplantation.

关键词： HLA matching predictive algorithm Donor selection Cord blood unit selection

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Predicting TMS-induced activation in human neocortex using concurrent TMS/PET, finite element analysis and computational modeling

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BIOMEDICAL PHYSICS & ENGINEERING EXPRESS 2019年第2期5卷

作者： Arabkheradmand, Ghazaleh Krieg, Todd D. Salinas, Felipe S. Fox, Peter T. Mogul, David J. IIT Biomed Engn Dept Chicago IL 60616 USA Univ Texas Hlth Sci Ctr San Antonio Res Imaging Inst San Antonio TX 78229 USA

Transcranial magnetic stimulation (TMS) is a powerful technique to noninvasively activate neurons in the brain. However, the relationship between TMS-generated electric fields (E-fields) and specific cortical responses is not well understood. The goal of this study was to investigate the relationship between induced E-fields and neocortical activation measured by metabolic responses. Human subject-specific detailed finite element models (FEM) of the head were constructed to calculate the distribution of induced cortical E-field vectors. Positron emission tomography (PET) recordings were made during concurrent TMS application as a measure of cortical activation. A functional model of local circuit connections was developed to study the relationship between applied magnetic fields and neocortical activation and was fitted to experimental data. Sensitivity of interneurons to induced tangential E-fields was over twice as strong as pyramidal neuron sensitivity to induced normal E-fields which may help explain why cortical electrophysiological responses to TMS have specific sensitivities to coil orientation. Furthermore, this study produced an algorithm for predicting electrophysiological responses in human neocortex with high accuracy (> 95%) that could provide an invaluable tool for planning of specific regional cortical activation critical in both research and clinical applications.

关键词： transcranial magnetic stimulation pet cortical activation predictive algorithm finite element modeling

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Discovery of novel antimicrobial peptides: A transcriptomic study of the sea anemone Cnidopus japonicus

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JOURNAL OF BIOINFORMATICS AND COMPUTATIONAL BIOLOGY 2018年第2期16卷 1840006页

作者： Grafskaia, Ekaterina N. Polina, Nadezhda F. Babenko, Vladislav V. Kharlampieva, Daria D. Bobrovsky, Pavel A. Manuvera, Valentin A. Farafonova, Tatyana E. Anikanov, Nikolay A. Lazarev, Vassili N. Moscow Inst Phys & Technol Dept Biol & Med Phys Inst Skii 9 Moscow 141700 Russia Fed Med Biol Agcy Fed Res & Clin Ctr Phys Chem Med 1A Malaya Pirogovskaya St Moscow 119435 Russia Russian Acad Sci Inst Biomed Chem Lab Syst Biol Dept Prote Res & Mass Spectrometry 10 Pogodinskaya St Moscow 119121 Russia Russian Acad Sci Dept Peptide & Prot Technol Lab Prote Shemyakin Ovchinnikov Inst Bioorgan Chem 16-10 Miklukho Maklaya St Moscow 117997 Russia

As essential conservative component of the innate immune systems of living organisms, antimicrobial peptides (AMPs) could complement pharmaceuticals that increasingly fail to combat various pathogens exhibiting increased resistance to microbial antibiotics. Among the properties of AMPs that suggest their potential as therapeutic agents, diverse peptides in the venoms of various predators demonstrate antimicrobial activity and kill a wide range of microorganisms. To identify potent AMPs, the study reported here involved a transcriptomic profiling of the tentacle secretion of the sea anemone Cnidopus japonicus. An in silico search algorithm designed to discover toxin-like proteins containing AMPs was developed based on the evaluation of the properties and structural peculiarities of amino acid sequences. The algorithm revealed new proteins of the anemone containing antimicrobial candidate sequences, and 10 AMPs verified using high-throughput proteomics were synthesized. The antimicrobial activity of the candidate molecules was experimentally estimated against Gram-positive and -negative bacteria. Ultimately, three peptides exhibited antimicrobial activity against bacterial strains, which suggests that the method can be applied to reveal new AMPs in the venoms of other predators as well.

关键词： Antimicrobial peptides sea anemone toxin transcriptome predictive algorithm

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algorithm for Predicting Mathematical Formulae from Linear Strings for Mathematical Inputs 1

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Conference on Applications of Computer Algebra (ACA)

作者： Fukui, Tetsuo Mukogawa Womens Univ Nishinomiya Hyogo Japan

ISBN: (数字)9783319569321

ISBN: (纸本)9783319569321;9783319569307

Recently, computer-aided assessment (CAA) systems have been used for mathematics education, with some CAA systems capable of assessing learners' answers using mathematical expressions. However, the standard input method for mathematics education systems is cumbersome for novice learners. In 2011, we proposed a new mathematical input method that allowed users to input mathematical expressions through an interactive conversion of mathematical expressions from colloquial-style linear strings in WYSIWYG. In this study, we propose a predictive algorithm to improve the input efficiency of this conversion process by using machine learning to determine the score parameters with a structured perceptron similar to natural language processing. In our experimental evaluation, with a training dataset comprising 700 formulae, the prediction accuracy was 96.2% for the top ten ranking by stable score parameter learning;this accuracy is sufficient for a mathematical input interface system.

关键词： Mathematical input interface predictive algorithm Machine learning Mathematical formula editor

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CADD score has limited clinical validity for the identification of pathogenic variants in noncoding regions in a hereditary cancer panel

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GENETICS IN MEDICINE 2016年第12期18卷 1269-1275页

作者： Mather, Cheryl A. Mooney, Sean D. Salipante, Stephen J. Scroggins, Sheena Wu, David Pritchard, Colin C. Shirts, Brian H. Univ Washington Dept Lab Med Seattle WA 98195 USA Univ Washington Dept Biomed Informat & Med Educ Seattle WA 98195 USA Univ Calif San Francisco Dept Pathol San Francisco CA 94140 USA

Purpose: Several in silico tools have been shown to have reasonable research sehsitivity and specificity for classifying sequence variants in coding regions. The recently developed combined annotation dependent depletion (CADD) method generates predictive scores for single-nucleotide variants (SNVs) in all areas of the genome, including noncoding regions. We sought for non-coding variants to determine the clinical validity of common CADD scores. Methods: We evaluated 12,391 unique SNVs in 624 patient samples submitted for germ-line mutation testing in a cancer-related gene panel. Stratifying by genomic region, we compared the distributions of CADD scores of rare SNVs, SNVs common in our patient population, and the null distribution of all possible SNVs. Results: The median CADD scores of intronic and nonsynonymous variants were significantly different between rare and common SNVs (P < 0.0001). Despite these different distributions, no individual variants could be identified as plausibly causative among the rare intronic variants with the highest scores. The receiver-operating characteristics (ROC) area under the curve (AUC) for noncoding variants is modest, and the positive predictive value of CADD for intronic variants in panel testing was found to be 0.088. Conclusion: Focused in silico scoring systems with much higher predictive value will be necessary for clinical genomic applications.

关键词： combined annotation-dependent depletion CADD score in silico predictor noncoding sequences predictive algorithm

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Predicting Taxi Demand at High Spatial Resolution: Approaching the Limit of Predictability 4

Predicting Taxi Demand at High Spatial Resolution: Approachi...

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4th IEEE International Conference on Big Data (Big Data)

作者： Zhao, Kai Khryashchev, Denis Freire, Juliana Silva, Claudio Vo, Huy NYU Ctr Urban Sci & Progress New York NY 10003 USA NYU Dept Comp Sci & Engn New York NY 10003 USA CUNY Dept Comp Sci New York NY USA

ISBN: (纸本)9781467390057

In big cities, taxi service is imbalanced. In some areas, passengers wait too long for a taxi, while in others, many taxis roam without passengers. Knowledge of where a taxi will become available can help us solve the taxi demand imbalance problem. In this paper, we employ a holistic approach to predict taxi demand at high spatial resolution. We showcase our techniques using two real-world data sets, yellow cabs and Uber trips in New York City, and perform an evaluation over 9,940 building blocks in Manhattan. Our approach consists of two key steps. First, we use entropy and the temporal correlation of human mobility to measure the demand uncertainty at the building block level. Second, to identify which predictive algorithm can approach the theoretical maximum predictability, we implement and compare three predictors: the Markov predictor (a probability-based predictive algorithm), the Lempel-Ziv-Welch predictor (a sequence-based predictive algorithm), and the Neural Network predictor (a predictive algorithm that uses machine learning). The results show that predictability varies by building block and, on average, the theoretical maximum predictability can be as high as 83%. The performance of the predictors also vary: the Neural Network predictor provides better accuracy for blocks with low predictability, and the Markov predictor provides better accuracy for blocks with high predictability. In blocks with high maximum predictability, the Markov predictor is able to predict the taxi demand with an 89% accuracy, 11% better than the Neural Network predictor, while requiring only 0.03% computation time. These findings indicate that the maximum predictability can be a good metric for selecting prediction algorithms.

关键词： human mobility taxi demand prediction spatio-temporal data limit of predictability predictive algorithm

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Chronic obstructive pulmonary disease and coronary disease: COPDCoRi, a simple and effective algorithm for predicting the risk of coronary artery disease in COPD patients

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RESPIRATORY MEDICINE 2015年第8期109卷 1019-1025页

作者： Cazzola, Mario Calzetta, Luigino Matera, Maria Gabriella Muscoli, Saverio Rogliani, Paola Romeo, Francesco Univ Roma Tor Vergata Dept Syst Med Rome Italy Univ Naples 2 Dept Expt Med Naples Italy

Background: Chronic obstructive pulmonary disease (COPD) is often associated with cardiovascular artery disease (CAD), representing a potential and independent risk factor for cardiovascular morbidity. Therefore, the aim of this study was to identify an algorithm for predicting the risk of CAD in COPD patients. Methods: We analyzed data of patients afferent to the Cardiology ward and the Respiratory Diseases outpatient clinic of Tor Vergata University (2010-2012, 1596 records). The study population was clustered as training population (COPD patients undergoing coronary arteriography), control population (non-COPD patients undergoing coronary arteriography), test population (COPD patients whose records reported information on the coronary status). The predicting model was built via causal relationship between variables, stepwise binary logistic regression and Hosmer-Lemeshow analysis. The algorithm was validated via split-sample validation method and receiver operating characteristics (ROC) curve analysis. The diagnostic accuracy was assessed. Results: In training population the variables gender (men/women OR: 1.7, 95%Cl: 1.237-2.5, P < 0.05), dyslipidemia (OR: 1.8, 95%Cl: 1.2-2.5, P < 0.01) and smoking habit (OR: 1.5, 95%CI: 1.2-1.9, P < 0.001) were significantly associated with CAD in COPD patients, whereas in control population also age and diabetes were correlated. The stepwise binary logistic regressions permitted to build a well fitting predictive model for training population but not for control population. The predictive algorithm shown a diagnostic accuracy of 81.5% (95%Cl: 77.78-84.71) and an AUC of 0.81 (95%Cl: 0.78-0.85) for the validation set. Conclusions: The proposed algorithm is effective for predicting the risk of CAD in COPD patients via a rapid, inexpensive and non-invasive approach. 2015 Elsevier Ltd. All rights reserved.

关键词： COPD Coronary artery disease Comorbidities predictive algorithm

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Data Sources for Signature Discovery in Toxicology

Data Sources for Signature Discovery in Toxicology

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作者： Dragana Mitic Potkrajac Branka Rakic Gordana Apic Robert B. Russell

From a systems biology point of view, signature of a chemical can be defined as a collection of data or a measure of cellular response to a certain chemical, where biomarkers are often the best characteristics objectively measured and evaluated as an indicator of the biological processes. Chemical profiles (signatures) related to chemical-induced gene expression and gene changes coupled to particular pathologies require additional interpretation and processing to identify true biomarker candidates and together with the mechanistic hypothesis underlying biological effects of that chemical enable comprehensive knowledge about chemical toxic effect. Knowledge bases such as ToxWiz capture a broad spectrum of mechanistic hypothesis and pathways for toxic effects derived from precise expert analysis of millions of scientific articles, establishing connections between mechanism of disease, pathology, and toxic endpoints, and representing them in a form of biological pathways for underlying toxic endpoints. A unique module connected to ToxWiz knowledge base represents a hand-curated database of gene expression signatures. This module contains a good size collection of 1000 unique toxicity signatures related to chemical-induced toxicities in human, rat, mouse, and several nonmammalian species covering studies with 297 compounds, known to induce major toxicities in liver, kidney, and most other major organ systems. Implemented software tools based on systems biology principles allow analysis of novel compounds for toxic effects and allows the analysis of your own data in order to identify new biomarker candidates by interrogating your -omics data with gene signatures module. By describing biological pathways underlying the toxic effects, and discovering and exploring related biomarkers, these tools promise to help design safer chemicals. Furthermore, we describe here an example exercise on annotated public data, using ToxWiz knowledge base and tools, which confirm and expand

关键词： Systems biology Biomarkers Molecular mechanism Manual curation Data extraction ToxWiz Toxic endpoint Chemical signature predictive algorithm Gene expression data

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A Single-Phase Power Factor Correction with Current Control

A Single-Phase Power Factor Correction with Current Control

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2012 International Conference on Power Electronics Engineering and Computer Technology(PEECT 2012)

作者： Zezhong Xia Xuebei Ren Rongde Yang Automation College Wuhan University of Technology

In this paper a new current control strategy based on DSP for power factor correction is presented. Boost converter controlled by DSC can achieve sinusoidal current, but we have to compromise between the switching frequency and the executing speed. With the help of predictive algorithm, DSP can efficiently compute the duty cycle needed for the next period, and less instruction cycles is needed to complete the current calculation. A 400 W experiment prototype is built using the ds PIC33 F digital signal controller. Experiment and simulation show that the predictive current control algorithm can achieve high power factor and low ripple of output voltage. And the algorithm can produce less THD of input current and has better dynamic response, compared with the traditional current PI control strategy.

关键词： PFC Digital control predictive algorithm

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