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Apnoea in infants with bronchiolitis: Incidence and risk factors for a prediction model

Anales de Pediatría (English Edition) 2018年第3期88卷 160-166页

作者： Ramos-Fernández, José Miguel Moreno-Pérez, David Gutiérrez-Bedmar, Mario Ramírez-Álvarez, María Martínez García, Yasmina Artacho-González, Lourdes Urda-Cardona, Antonio Sección de Lactantes Unidad de Gestión Clínica de Pediatría Grupo de Investigación IBIMA Hospital Materno-Infantil Hospital Regional Universitario de Málaga Málaga Spain Infectología Pediátrica e Inmunodeficiencias Unidad de Gestión Clínica de Pediatría Hospital Materno-Infantil Hospital Regional Universitario de Málaga Grupo de Investigación IBIMA Departamento de Pediatría y Farmacología Facultad de Medicina de la Universidad de Málaga Málaga Spain Departamento de Salud Pública y Psiquiatría Facultad de Medicina Universidad de Málaga Málaga Spain Unidad de Gestión Clínica de Pediatría Hospital Materno-Infantil Hospital Regional Universitario de Málaga Málaga Spain

AbstractIntroductionThe presence of apnoea in acute bronchiolitis (AB) varies between 1.2% and 28.8%, depending on the series, and is one of its most fearsome complications. The aim of this study is to determine the incidence of apnoea in hospitalised patients diagnosed with AB, and to define their associated risk factors in order to construct a prediction model. Patients and methodA retrospective observational study of patients admitted to a tertiary hospital in the last 5 years with a diagnosis of AB, according to the classic criteria. Data was collected on the frequency of apnoea and related clinical variables to find risk factors in a binary logistic regression model for the prediction of apnoea. A ROC curve was developed with the model. ResultsApnoea was recorded during the admission of 53 (4.4%) patients out of a total of 1.197 cases found. The risk factors included in the equation were: female (OR 0.6, 95% CI: 0.27–1.37), caesarean delivery (OR: 3.44, 95% CI: 1.5–7.7), postmenstrual age ≤43 weeks (OR: 6.62, 95% CI: 2.38–18.7), fever (OR: 0.33, 95% CI: 0.09–1.97), low birth weight (OR: 5.93, 95% CI: 2.23–7.67), apnoea observed by caregivers before admission (OR: 5.93, 95% CI: 2.64–13.3), and severe bacterial infection (OR: 3.98, 95% CI: 1.68–9.46). The optimal sensitivity and specificity of the model in the ROC curve was 0.842 and 0.846, respectively ( P< .001). ConclusionsThe incidence of apnoea during admission was 4.4 per 100 admissions of AB and year. The estimated prediction model equation may be of help to the clinician in order to classify patients with increased risk of apnoea during admission due to AB.

关键词： Bronchiolitis Apnoea Infants Caesarean delivery logistic regression model Bronquiolitis Apnea Lactantes Cesárea modelo de regresión logística

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Extreme temperature events affecting the electricity distribution system of the metropolitan area of Buenos Aires (1971-2013)

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ENERGY POLICY 2017年 106卷 404-414页

作者： Santagata, Daniela M. Castesana, Paula Rossler, Cristina E. Gomez, Dario R. Comis Nacl Energia Atom Gerencia Quim Av Gral Paz 1499B1650KNA San Martin Buenos Aires Argentina Univ Belgrano Zabala 1837 Buenos Aires DF Argentina Univ Nacl San Martin Inst Invest & Ingn Ambiental Campus Miguelete25 Mayo & Francia RA-1650 San Martin Buenos Aires Argentina

We studied the role of cold waves and heat waves on major power outages in the metropolitan area of Buenos Aires. Impacts of events occurring in the tails of distributions were assessed estimating deviations of minimum temperature, maximum temperature and hourly electricity consumption with respect to statistically derived thresholds and using three sets of data: temperature observations (1911-2013);major power outages reported in a disaster database (1971-2013) and hourly electricity consumption (2006-2013). These deviations (exceedances) proved to be adequate indicators of the stress posed by extreme temperature events to the electricity distribution system leading to major blackouts. Based on these indicators, we found that the electricity distribution system was under similar stress during cold waves or heat waves, but it was much more vulnerable to heat waves (three blackouts under cold waves against 20 under heat waves between 2006 and 2013). For heat waves, the results of a binomial regression logistic model provided an adequate description of the probability of disastrous supply interruptions in terms of exceedances in extreme temperatures and electricity consumption stress. This approach may be of use for other cities wishing to evaluate the effects of extreme temperature events on the electricity distribution infrastructure.

关键词： Extreme weather events Heat waves Cold waves Electricity distribution Power outages logistic regression model

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Late-onset moderate to severe acute respiratory distress syndrome is associated with shorter survival and higher mortality: a two-stage association study

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INTENSIVE CARE MEDICINE 2017年第3期43卷 399-407页

作者： Zhang, Ruyang Wang, Zhaoxi Tejera, Paula Frank, Angela J. Wei, Yongyue Su, Li Zhu, Zhaozhong Guo, Yichen Chen, Feng Bajwa, Ednan K. Thompson, B. Taylor Christiani, David C. Harvard Sch Publ Hlth Massachusetts Gen Hosp Pulm & Crit Care Div Dept Environm HlthDept Med 665 Hunting AveBldg 1 Room 1401 Boston MA 02115 USA Harvard Med Sch 665 Hunting AveBldg 1 Room 1401 Boston MA 02115 USA Nanjing Med Univ Sch Publ Hlth Dept Biostat Minist EducKey Lab Modern Toxicol Nanjing Jiangsu Peoples R China Nanjing Med Univ Joint Lab Hlth & Environm Risk Assessment HERA Sch Publ Hlth Harvard Sch Publ Hlth Nanjing Jiangsu Peoples R China Massachusetts Gen Hosp Dept Med Pulm & Crit Care Div Boston MA 02114 USA

To evaluate the association between acute respiratory distress syndrome (ARDS) onset time and prognosis. Patients with moderate to severe ARDS (N = 876) were randomly assigned into derivation (N = 520) and validation (N = 356) datasets. Both 28-day and 60-day survival times after ARDS onset were analyzed. A data-driven cutoff point between early- and late-onset ARDS was determined on the basis of mortality risk effects of onset times. We estimated the hazard ratio (HR) and odds ratio (OR) of late-onset ARDS using a multivariate Cox proportional hazards model of survival time and a multivariate logistic regression model of mortality rate, respectively. Late-onset ARDS, defined as onset over 48 h after intensive care unit (ICU) admission (N = 273, 31%), was associated with shorter 28-day survival time: HR = 2.24, 95% CI 1.48-3.39, P = 1.24 x 10(-4) (derivation);HR = 2.16, 95% CI 1.33-3.51, P = 1.95 x 10(-3) (validation);and HR = 2.00, 95% CI 1.47-2.72, P = 1.10 x 10(-5) (combined dataset). Late-onset ARDS was also associated with shorter 60-day survival time: HR = 1.70, 95% CI 1.16-2.48, P = 6.62 x 10(-3) (derivation);HR = 1.78, 95% CI 1.15-2.75, P = 9.80 x 10(-3) (validation);and HR = 1.59, 95% CI 1.20-2.10, P = 1.22 x 10(-3) (combined dataset). Meanwhile, late-onset ARDS was associated with higher 28-day mortality rate (OR = 1.46, 95% CI 1.04-2.06, P = 0.0305) and 60-day mortality rate (OR = 1.44, 95% CI 1.03-2.02, P = 0.0313). Late-onset moderate to severe ARDS patients had both shorter survival time and higher mortality rate in 28-day and 60-day observations.

关键词： ARDS Early and late onset Overall survival time Mortality rate Cox proportional hazards model logistic regression model

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Regional-scale controls on the spatial activity of rockfalls (Turtmann Valley, Swiss Alps) - A multivariate modeling approach

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GEOMORPHOLOGY 2017年 287卷 29-45页

作者： Messenzehl, Karoline Meyer, Hanna Otto, Jan-Christoph Hoffmann, Thomas Dikau, Richard Univ Bonn Dept Geog Bonn Germany Philipps Univ Marburg Fac Geog Marburg Germany Univ Salzburg Dept Geog & Geol Salzburg Austria Fed Inst Hydrol BfG Koblenz Germany

In mountain geosystems, rockfalls are among the most effective sediment transfer processes, reflected in the regional-scale distribution of talus slopes. However, the understanding of the key controlling factors seems to decrease with increasing spatial scale, due to emergent and complex system behavior and not least to recent methodological shortcomings in rockfall modeling research. In this study, we aim (i) to develop a new approach to identify major regional-scale rockfall controls and (ii) to quantify the relative importance of these controls. Using a talus slope inventory in the Turtmann Valley (Swiss Alps), we applied for the first time the decision-tree based random forest algorithm (RF) in combination with a principal component logistic regression (PCLR) to evaluate the spatial distribution of rockfall activity. This study presents new insights into the discussion on whether periglacial rockfall events are controlled more by topo-climatic, cryospheric, paraglacial or/and rock mechanical properties. (i) Both models explain the spatial rockfall pattern very well, given the high areas under the Receiver Operating Characteristic (ROC) curves of >0.83. Highest accuracy was obtained by the RF, correctly predicting 88% of the rockfall source areas. The RF appears to have a great potential in geomorphic research involving multicollinear data. (ii) The regional permafrost distribution, coupled to the bedrock curvature and valley topography, was detected to be the primary rockfall control. Rockfall source areas cluster within a low-radiation elevation belt (2900-3300 m a.s.l,) consistent with a permafrost probability of >90%. The second most important factor is the time since deglaciation, reflected by the high abundance of rockfalls along recently deglaciated (<100 years), north-faring slopes. However, our findings also indicate a strong rock mechanical control on the paraglacial rockfall activity, declining either exponentially or linearly since deglaciation. The s

关键词： Rockfall logistic regression model Random Forests algorithm Rock mechanical properties Paraglacial Permafrost

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An epidemiologic study of antimicrobial resistance of Staphylococcus species isolated from equine samples submitted to a diagnostic laboratory

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BMC VETERINARY RESEARCH 2018年第20180227期14卷 42-42页

作者： Adams, Ronita Smith, Jackie Locke, Stephen Phillips, Erica Erol, Erdal Carter, Craig Odoi, Agricola Univ Tennessee Coll Vet Med Dept Biomed & Diagnost Sci 2407 River Dr Knoxville TN 37996 USA Univ Kentucky Vet Diagnost Lab 1490 Bull Lea Rd Lexington KY 40511 USA

Background: Antimicrobial resistance limits traditional treatment options and increases costs. It is therefore important to estimate the magnitude of the problem so as to provide empirical data to guide control efforts. The aim of this study was to investigate the burden and patterns of antimicrobial resistance (AMR) among equine Staphylococcus samples submitted to the University of Kentucky Veterinary Diagnostic Laboratory (UKVDL) from 1993 to 2009. Retrospective data of 1711 equine Staphylococcus samples submitted to the UKVDL during the time period 1993 to 2009 were included in the study. Antimicrobial susceptibility testing, that included 16 drugs, were performed using cultures followed by the Kirby-Bauer disk diffusion susceptibility test. The proportion of resistant isolates by animal breed, species of organism, sample source, and time period were computed. Chi-square and Cochran-Armitage trend tests were used to identify significant associations and temporal trends, respectively. logistic regression models were used to investigate predictors of AMR and multidrug resistance (MDR). Results: A total of 66.3% of the isolates were resistant to at least one antimicrobial, most of which were Staphylococcus aureus (77.1%), while 25.0% were MDR. The highest level of resistance was to penicillins (52.9%). Among drug classes, isolates had the highest rate of AMR to at least one type of ss-lactams (49.2%), followed by aminoglycosides (30.2%). Significant (p < 0.05) associations were observed between odds of AMR and horse breed, species of organism and year. Similarly, significant (p < 0.05) associations were identified between odds of MDR and breed and age. While some isolates had resistance to up to 12 antimicrobials, AMR profiles featuring single antimicrobials such as penicillin were more common than those with multiple antimicrobials. Conclusion: Demographic factors were significant predictors of AMR and MDR. The fact that some isolates had resistance to up to 12 of

关键词： Staphylococcus Antimicrobial resistance Multidrug resistance Horses Equine Kentucky Burden Predictors logistic regression model

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A prediction score for significant coronary artery disease in Chinese patients ≥50 years old referred for rheumatic valvular heart disease surgery

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INTERACTIVE CARDIOVASCULAR AND THORACIC SURGERY 2018年第4期26卷 623-630页

作者： Xu, Zhenjun Pan, Jun Chen, Tao Zhou, Qing Wang, Qiang Cao, Hailong Fan, Fudong Luo, Xuan Ge, Min Wang, Dongjin Nanjing Univ Med Sch Affiliated Nanjing Drum Tower Hosp Dept Thorac & Cardiovasc Surg 321 Zhongshan Rd Nanjing 210008 Jiangsu Peoples R China

OBJECTIVES: Our goal was to establish a prediction score and protocol for the preoperative prediction of significant coronary artery disease (CAD) in patients with rheumatic valvular heart disease. METHODS: Using multivariate logistic regression analysis, we validated the model based on 490 patients without a history of myocardial infarction and who underwent preoperative screening coronary angiography. Significant CAD was defined as >= 50% narrowing of the diameter of the lumen of the left main coronary artery or >= 70% narrowing of the diameter of the lumen of the left anterior descending coronary artery, left circumflex artery or right coronary artery. RESULTS: Significant CAD was present in 9.8% of patients. Age, smoking, diabetes mellitus, diastolic blood pressure, low-density lipoprotein cholesterol and ischaemia evident on an electrocardiogram were independently associated with significant CAD and were entered into the multivariate model. According to the logistic regression predictive risk score, preoperative coronary angiography is recommended in (i) postmenopausal women between 50 and 59 years of age with >= 9.1% logistic regression predictive risk score;(ii) postmenopausal women who are >= 60 years old with a logistic regression predictive risk score >= 6.6% and (iii) men >= 50 years old whose logistic regression predictive risk score was >= 2.8%. Based on this predictive model, 246 (50.2%) preoperative coronary angiograms could be safely avoided. The negative predictive value of the model was 98.8% (246 of 249). CONCLUSIONS: This model was accurate for the preoperative prediction of significant CAD in patients with rheumatic valvular heart disease. This model must be validated in larger cohorts and various populations.

关键词： Rheumatic valvular heart disease Coronary artery disease Coronary angiography logistic regression model

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Diagnosis of Compressed Nerve Root in Lumbar Disc Herniation Patients by Surface Electromyography

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ORTHOPAEDIC SURGERY 2018年第1期10卷 47-55页

作者： Li, Wei Liu, Yan-cheng Zheng, Chen-fan Miao, Jun Chen, Hui Quan, Hai-ying Yan, Song-hua Zhang, Kuan Capital Med Univ Sch Biomed Engn 10 Xitoutiao Beijing 100069 Peoples R China Capital Med Univ Beijing Key Lab Fundamental Res Biomech Clin Appl Beijing Peoples R China Tianjin Hosp Dept Spinal Surg Tianjin Peoples R China Chinese Acad Sci Suzhou Inst Biomed Engn & Technol Suzhou Peoples R China

Objective: To establish a logistic regression model using surface electromyography (SEMG) parameters for diagnosing the compressed nerve root at L5 or S1 level in patients with lumbar disc herniation (LDH). Methods: This study recruited 24 patients with L5 nerve root compression and 23 patients with S1 nerve root compression caused by LDH from May 2014 to May 2016. SEMG signals from the bilateral tibialis anterior and lateral gastrocnemius were measured. The root mean square (RMS), the RMS peak time, the mean power frequency (MPF), and the median frequency (MF) were analyzed. The accuracy, sensitivity, and specificity values were calculated separately. The areas under the curve (AUC) of the receiver-operating characteristic (ROC) curve and the kappa value were used to evaluate the accuracy of the SEMG diagnostic model. Results: The accuracy of the SEMG model ranged from 85.71% to 100%, with an average of 93.57%. The sensitivity, specificity, AUC, and kappa value of the logistic regression model were 0.98 +/- 0.05, 0.92 +/- 0.09, 0.95 +/- 0.04 (P = 0.006), and 0.87 +/- 0.11, respectively (P = 0.001). The final diagnostic model was: P = 1-1 1+ ey;y = 10.76 (5.95 x TA_RMS Ratio) -(0.38 x TA_RMS Peak Time Ratio) -(5.44 x 44 x LG_RMS Peak Time Ratio). L5 nerve root compression is diagnosed when P < 0.5 and S-1 nerve root compression when P = 0.5. Conclusions: The logistic regression model developed in this study showed high diagnostic accuracy in detecting the compressed nerve root (L5 and S1) in these patients with LDH.

关键词： Compressed nerve root Diagnosis logistic regression model Lumbar disc herniation Surface electromyography

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The creation and evaluation of a model predicting the probability of conception in seasonal-calving, pasture-based dairy cows

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JOURNAL OF DAIRY SCIENCE 2017年第7期100卷 5550-5563页

作者： Fenlon, Caroline O'Grady, Luke Doherty, Michael L. Dunnion, John Shalloo, Laurence Butler, Stephen T. Univ Coll Dublin Sch Comp Sci Dublin 4 Ireland Univ Coll Dublin Sch Vet Med Dublin 4 Ireland TEAGASC Anim & Grassland Res & Innovat Ctr Fermoy Cork Ireland

Reproductive performance in pasture-based production systems has a fundamentally important effect on economic efficiency. The individual factors affecting the probability of submission and conception are multifaceted and have been extensively researched. The present study analyzed some of these factors in relation to service-level probability of conception in seasonal-calving pasture-based dairy cows to develop a predictive model of conception. Data relating to 2,966 services from 737 cows on 2 research farms were used for model development and data from 9 commercial dairy farms were used for model testing, comprising 4,212 services from 1,471 cows. The data spanned a 15-yr period and originated from seasonal-calving pasture based dairy herds in Ireland. The calving season for the study herds extended from January to June, with peak calving in February and March. A base mixed-effects logistic regression model was created using a step-wise model-building strategy and incorporated parity, days in milk, interservice interval, calving difficulty, and predicted transmitting abilities for calving interval and milk production traits. To attempt to further improve the predictive capability of the model, the addition of effects that were not statistically significant was considered, resulting in a final model composed of the base model with the inclusion of BCS at service. The models' predictions were evaluated using discrimination to measure their ability to correctly classify positive and negative cases. Precision, recall, F-score, and area under the receiver operating characteristic curve (AUC) were calculated. Calibration tests measured the accuracy of the predicted probabilities. These included tests of overall goodness-of-fit, bias, and calibration error. Both models performed better than using the population average probability of conception. Neither of the models showed high levels of discrimination (base model AUC 0.61, final model AUC 0.62), possibly because of the

关键词： dairy cow fertility milk production logistic regression model external evaluation

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Prediction of thrombophilia in patients with unexplained recurrent pregnancy loss using a statistical model

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INTERNATIONAL JOURNAL OF GYNECOLOGY & OBSTETRICS 2017年第3期138卷 283-287页

作者： Wang, Tongfei Kang, Xiaomin He, Liying Liu, Zhilan Xu, Haijing Zhao, Aimin Shanghai Jiao Tong Univ Ren Ji Hosp Sch Med Dept Obstet & Gynecol Shanghai Peoples R China

Objective: To establish a statistical model to predict thrombophilia in patients with unexplained recurrent pregnancy loss (URPL). Methods: A retrospective case-control study was conducted at Ren Ji Hospital, Shanghai, China, from March 2014 to October 2016. The levels of D-dimer (DD), fibrinogen degradation products (FDP), activated partial thromboplastin time (APTT), prothrombin time (PT), thrombin time (TT), fibrinogen (Fg), and platelet aggregation in response to arachidonic acid (AA) and adenosine diphosphate (ADP) were collected. Receiver operating characteristic curve analysis was used to analyze data from 158 UPRL patients (>= 3 previous first trimester pregnancy losses with unexplained etiology) and 131 non-RPL patients (no history of recurrent pregnancy loss). A logistic regression model (LRM) was built and the model was externally validated in another group of patients. Results: The LRM included AA, DD, FDP, TT, APTT, and PT. The overall accuracy of the LRM was 80.9%, with sensitivity and specificity of 78.5% and 78.3%, respectively. The diagnostic threshold of the possibility of the LRM was 0.6492, with a sensitivity of 78.5% and a specificity of 78.3%. Subsequently, the LRM was validated with an overall accuracy of 83.6%. Conclusion: The LRM is a valuable model for prediction of thrombophilia in URPL patients. Thrombophilia was associated with unexplained recurrent pregnancy loss (URPL). We built an effective statistical model for the prediction of thrombophilia in patients with URPL.

关键词： logistic regression model ROC curve Thrombophilia Unexplained recurrent pregnancy loss

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Rumen fluke (Calicophoron daubneyi) on Welsh farms: prevalence, risk factors and observations on co- infection with Fasciola hepatica

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PARASITOLOGY 2017年第2期144卷 237-247页

作者： Jones, Rhys Aled Brophy, Peter M. Mitchell, E. Sian Williams, Hefin Wyn Aberystwyth Univ Inst Biol Environm & Rural Sci Abersystwyth Ceredigion Wales Anim & Plant Hlth Agcy Carmarthen Vet Investigat Ctr Jobs Well Rd Carmarthen SA31 3EZ Dyfed Wales

Reports of Calicophoron daubneyi infecting livestock in Europe have increased substantially over the past decade;however, there has not been an estimate of its farm level prevalence and associated risk factors in the UK. Here, the prevalence of C. daubneyi across 100 participating Welsh farms was recorded, with climate, environmental and management factors attained for each farm and used to create logistic regression models explaining its prevalence. Sixty-one per cent of farms studied were positive for C. daubneyi, with herd-level prevalence for cattle (59%) significantly higher compared with flock-level prevalence for sheep (42%, P = 0.029). Co-infection between C. daubneyi and Fasciola hepatica was observed on 46% of farms;however, a significant negative correlation was recorded in the intensity of infection between each parasite within cattle herds (rho = -0.358, P = 0.007). Final models showed sunshine hours, herd size, treatment regularity against F. hepatica, the presence of streams and bog habitats, and Ollerenshaw index values as significant positive predictors for C. daubneyi (P < 0.05). The results raise intriguing questions regarding C. daubneyi epidemiology, potential competition with F. hepatica and the role of climate change in C. daubneyi establishment and its future within the UK.

关键词： Calicophoron daubneyi Fasciola hepatica co-infection cattle sheep logistic regression model null modelling UK

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