scholarly journals Estudo da Susceptibilidade à Erosão Laminar em Bacia Hidrográfica da Zona da Mata, Minas Gerais, Brasil

2021 ◽  
Vol 14 (3) ◽  
pp. 1707
Author(s):  
Heitor Carvalho Lacerda ◽  
André Luiz Lopes De Faria ◽  
Humberto Paiva Fonseca ◽  
Marco Antônio Saraiva Silva ◽  
Wesley Oliveira Soares ◽  
...  
Keyword(s):  
Statistical Difference ◽  
Agricultural Sector ◽  
Prediction Models ◽  
Multicriteria Analysis ◽  
Minas Gerais ◽  
Descriptive Statistics ◽  
Surface Erosion ◽  
Risk Models ◽  
Box Plot ◽  
Sheet Erosion

O estudo da susceptibilidade a erosão laminar é pertinente na mesorregião da Zona da Mata de Minas Gerais, visto a predominância da cobertura de pastagem e pela expressiva degradação do solo. Neste estudo, objetivou-se compreender quais variáveis geodinâmicas são importantes na predição dos processos erosivos laminares e o melhor modelo preditivo entre oito, através de comparações multicritérios, possibilitando entender o fenômeno em uma bacia hidrográfica da mesorregião. Assim, utilizou-se o método de atribuição de notas pela Literatura (L) e Realidade de campo (RC), cuja ponderação de parcela dos processos erosivos (60%) laminares mapeados ponderou a nota das classes das variáveis pela área das mesmas. A integração das variáveis foi por testes de ponderação e integração total e parcial. A avaliação dos modelos gerados foi por estatística descritiva (Box-Plot), diferentes métodos de categorização (Manual, Natural Breaks e Geometrical Interval) e curva ROC com cálculo de eficiência AUC (40% das erosões mapeadas). Os resultados apontaram que a falta umidade é um fator importante para a ocorrência dos processos erosivos laminares, por outro lado, as variáveis morfométricas não foram importantes para a predição. Modelos baseados na RC (72,41% AUC médio) obteve eficiência consideravelmente maior do que a L (65,41% AUC médio), já quando comparado a integração de todas as variáveis geodinâmicas e somente as mais importantes e quando integrado com ponderação e sem ponderação, não houve considerável diferença estatística. O modelo mais eficiente obteve 76,3% AUC, considerado boa e estava adequado a realidade da área estudada.   Study of Susceptibility to Sheet Erosion in a Watershed in Zona da Mata, Minas Gerais, BrazilABSTRACTThe study of susceptibility to surface erosion is relevant in the mesoregion of the Zona da Mata of Minas Gerais, given the predominance of pasture cover, the significant degradation of the soil and the stagnation of the agricultural sector. In this study, the objective was to understand which geodynamic variables are important in the prediction of surface erosive processes and the best predictive model among eight, through multicriteria comparisons, making it possible to understand the phenomenon in a watershed in the mesoregion. Thus, it was used the method of attributing grades by Literature (L) and Field Reality (RC), whose weighting of the mapped surface erosive (60%) processes weighted the grade of the variable classes by their area. The integration of the variables was through weighting tests and total and partial integration. The evaluation of the models generated was by descriptive statistics (Box-Plot), different methods of categorization (Manual, Natural Breaks and Geometrical Interval) and ROC curve with AUC efficiency calculation (40% of the mapped erosions). The results showed that the lack of moisture is an important factor for the occurrence of surface erosive processes, on the other hand, the morphometric variables were not important for the prediction. Models based on RC (72.41% average AUC) achieved considerably greater efficiency than L (65.41% average AUC), when compared to the integration of all geodynamic variables and only the most important ones and when integrated with weighting and without weighting, there was no considerable statistical difference. The most efficient model obtained 76.3% AUC, considered good and was adequate to the reality of the studied area.Key words: Geotechnologies; Comparison of Risk Models; Multicriteria Analysis

scholarly journals Risk Factors and Prediction Models for Venous Thromboembolism in Ambulatory Patients with Lung Cancer

Healthcare ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 778
Author(s):  
Ann-Rong Yan ◽  
Indira Samarawickrema ◽  
Mark Naunton ◽  
Gregory M. Peterson ◽  
Desmond Yip ◽  
...  
Keyword(s):  
Risk Factors ◽  
Lung Cancer ◽  
Venous Thromboembolism ◽  
High Risk ◽  
Prediction Models ◽  
Poor Performance ◽  
Risk Models ◽  
Related Risk ◽  
Wide Range ◽  
Ambulatory Patients

Venous thromboembolism (VTE) is a significant cause of mortality in patients with lung cancer. Despite the availability of a wide range of anticoagulants to help prevent thrombosis, thromboprophylaxis in ambulatory patients is a challenge due to its associated risk of haemorrhage. As a result, anticoagulation is only recommended in patients with a relatively high risk of VTE. Efforts have been made to develop predictive models for VTE risk assessment in cancer patients, but the availability of a reliable predictive model for ambulate patients with lung cancer is unclear. We have analysed the latest information on this topic, with a focus on the lung cancer-related risk factors for VTE, and risk prediction models developed and validated in this group of patients. The existing risk models, such as the Khorana score, the PROTECHT score and the CONKO score, have shown poor performance in external validations, failing to identify many high-risk individuals. Some of the newly developed and updated models may be promising, but their further validation is needed.

scholarly journals Family Businesses in Global Economy and Evaluating their Financial Health Using Bankruptcy Models

2021 ◽  
Vol 129 ◽  
pp. 03031
Author(s):  
Maria Truchlikova
Keyword(s):  
Financial Distress ◽  
Global Economy ◽  
Agricultural Sector ◽  
Prediction Models ◽  
Predictive Accuracy ◽  
Value Added ◽  
Business Environment ◽  
Bankruptcy Prediction ◽  
Family Businesses ◽  
Financial Health

Research background: Predicting and assessing financial health should be one of the most important activities for each business especially in context of turbulent business environment and global economy. The financial sustainability of family businesses has a direct and significant influence on the development and growth of the economy because they still represent the backbone of the economy and play an important role in national economies worldwide accounting. Purpose of the article: We used in this article the financial distress and bankruptcy prediction models for assessing financial status of family businesses in agricultural sector. The aim of the paper is to compare models developed by using three different methods to identify a model with the highest predictive accuracy of financial distress and assess financial health. Methods: The data was obtained from Finstat database. For assessing the financial health of selected family businesses bankruptcy models were used: Chrastinova’s CH-Index, Gurcik’s G-Index (defined for Slovak agricultural enterprises) and Altman Z-score. Findings & Value added: This article summarizes existing models and compares results of assessing financial health of family businesses using three different models.

Abstract 196: Development and Validation of Machine Learning-based Race-specific Models to Predict 10-year Risk of Heart Failure: A Multi-cohort Analysis

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
Matthew W Segar ◽  
Byron Jaeger ◽  
Kershaw V Patel ◽  
Vijay Nambi ◽  
Chiadi E Ndumele ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Heart Failure ◽  
Risk Prediction ◽  
Prediction Models ◽  
External Validation ◽  
Superior Performance ◽  
Risk Models ◽  
Black And White ◽  
White Adults

Introduction: Heart failure (HF) risk and the underlying biological risk factors vary by race. Machine learning (ML) may improve race-specific HF risk prediction but this has not been fully evaluated. Methods: The study included participants from 4 cohorts (ARIC, DHS, JHS, and MESA) aged > 40 years, free of baseline HF, and with adjudicated HF event follow-up. Black adults from JHS and white adults from ARIC were used to derive race-specific ML models to predict 10-year HF risk. The ML models were externally validated in subgroups of black and white adults from ARIC (excluding JHS participants) and pooled MESA/DHS cohorts and compared to prior established HF risk scores developed in ARIC and MESA. Harrell’s C-index and Greenwood-Nam-D’Agostino chi-square were used to assess discrimination and calibration, respectively. Results: In the derivation cohorts, 288 of 4141 (7.0%) black and 391 of 8242 (4.7%) white adults developed HF over 10 years. The ML models had excellent discrimination in both black and white participants (C-indices = 0.88 and 0.89). In the external validation cohorts for black participants from ARIC (excluding JHS, N = 1072) and MESA/DHS pooled cohorts (N = 2821), 131 (12.2%) and 115 (4.1%) developed HF. The ML model had adequate calibration and demonstrated superior discrimination compared to established HF risk models (Fig A). A consistent pattern was also observed in the external validation cohorts of white participants from the MESA/DHS pooled cohorts (N=3236; 100 [3.1%] HF events) (Fig A). The most important predictors of HF in both races were NP levels. Cardiac biomarkers and glycemic parameters were most important among blacks while LV hypertrophy and prevalent CVD and traditional CV risk factors were the strongest predictors among whites (Fig B). Conclusions: Race-specific and ML-based HF risk models that integrate clinical, laboratory, and biomarker data demonstrated superior performance when compared to traditional risk prediction models.

scholarly journals Assessing the Clinical Impact of Risk Prediction Models With Decision Curves: Guidance for Correct Interpretation and Appropriate Use

2016 ◽  
Vol 34 (21) ◽  
pp. 2534-2540 ◽  
Author(s):  
Kathleen F. Kerr ◽  
Marshall D. Brown ◽  
Kehao Zhu ◽  
Holly Janes
Keyword(s):  
Prostate Cancer ◽  
Risk Prediction ◽  
Prediction Models ◽  
Risk Model ◽  
Characteristic Curve ◽  
Clinical Impact ◽  
Adjuvant Radiation ◽  
Risk Models ◽  
Personal Benefit ◽  
The Impact

The decision curve is a graphical summary recently proposed for assessing the potential clinical impact of risk prediction biomarkers or risk models for recommending treatment or intervention. It was applied recently in an article in Journal of Clinical Oncology to measure the impact of using a genomic risk model for deciding on adjuvant radiation therapy for prostate cancer treated with radical prostatectomy. We illustrate the use of decision curves for evaluating clinical- and biomarker-based models for predicting a man’s risk of prostate cancer, which could be used to guide the decision to biopsy. Decision curves are grounded in a decision-theoretical framework that accounts for both the benefits of intervention and the costs of intervention to a patient who cannot benefit. Decision curves are thus an improvement over purely mathematical measures of performance such as the area under the receiver operating characteristic curve. However, there are challenges in using and interpreting decision curves appropriately. We caution that decision curves cannot be used to identify the optimal risk threshold for recommending intervention. We discuss the use of decision curves for miscalibrated risk models. Finally, we emphasize that a decision curve shows the performance of a risk model in a population in which every patient has the same expected benefit and cost of intervention. If every patient has a personal benefit and cost, then the curves are not useful. If subpopulations have different benefits and costs, subpopulation-specific decision curves should be used. As a companion to this article, we released an R software package called DecisionCurve for making decision curves and related graphics.

Prevalence of pressure ulcers in patients hospitalized in a surgical unit of a university hospital

2011 ◽  
Vol 5 (7) ◽  
pp. 1684
Author(s):  
Miguir Terezinha Vieccelli Donoso ◽  
Eline Lima Borges ◽  
Camila Patrícia Rennó Carazzato
Keyword(s):  
Nursing Care ◽  
Pressure Ulcers ◽  
Ethics Committee ◽  
Minas Gerais ◽  
Descriptive Statistics ◽  
University Hospital ◽  
Surgical Patients ◽  
Not At Risk ◽  
Transversal Study ◽  
Surgical Unit

ABSTRACTObjective: to identify the prevalence, staging, and risk for developing pressure ulcers (PU) of patients hospitalized in a surgical unit. Method: this is a transversal study, carried out with 20 surgical patients hospitalized in a university hospital in Minas Gerais, from both sexes, and older than 18 years. For the analysis the descriptive statistics – with distribution of frequency, minimum and maximum values, mean, standard deviation, and prevalence of PU – was used. The project was approved by the Universidade Federal de Minas Gerais Research Ethics Committee (process ETIC 150/05), Results: the prevalence of PU was 10%, 90% are not at risk for developing PU, two patients with PU presented 2 and 3 ulcers, respectively, classified as belonging to the stages I and II. Conclusion: considering the prevalence of PU, the need of an appropriate and individualized nursing care planning emerges, having as a reference each patient’s risk for developing this kind of ulcer. The need of adopting appropriate nursing practices has been realized, according to each patient’s risk score for developing PU. Descriptors: pressure ulcer; nursing; prevalence. RESUMOObjetivo: identificar a prevalência, o estadiamento e o risco de desenvolvimento de úlceras por pressão (UP) em pacientes internados em uma unidade cirúrgica, Método: estudo transversal, realizado com 20 pacientes cirúrgicos, internados em um hospital universitário de Minas Gerais, de ambos os sexos e com idade superior a 18 anos. Para análise utilizou-se estatística descritiva com a distribuição de freqüência, valores mínimos e máximos, mediana, desvio-padrão e prevalência de UP. O projeto foi aprovado pelo Comitê de Ética em Pesquisa da Universidade Federal de Minas Gerais com parecer ETIC 150/05, Resultados: a prevalência de UP foi de 10%, 90% eram sem risco para formação de UP, dois pacientes com UP apresentaram duas e três úlceras, respectivamente, classificadas em estágio I e II, Conclusão: diante da prevalência de UP, surge a necessidade de uma planificação de cuidados adequados e individualizada, tendo como referência o risco que cada paciente apresenta para o desenvolvimento dessa úlcera. Percebeu-se a necessidade de implementação de cuidados adequados, de acordo com o escore que cada paciente apresente para o desenvolvimento da UP. Descritores: úlcera por pressão; enfermagem; prevalência.RESUMENObjetivo: identificar la prevalencia, estadiamiento y el riesgo de desarrollo de úlceras por presión (UP) en pacientes internados en una unidad quirúrgica. Método: estudio transversal, realizado con 20 pacientes quirúrgicos, internados en un hospital universitario de Minas Gerais, de ambos sexos y con edad superior a 18 años. Para el análisis se utilizó la estadística descriptiva mediante la distribución de frecuencia, valores mínimos y máximos, mediana, desvío-estándar y prevalencia de UP. El proyecto se aprobó por el Comité de Ética en Pesquisa de la Universidad Federal de Minas Gerais con parecer/laudo ETIC 150/05. Resultados: la prevalencia de UP fue de 10%, 90% lo eran sin riesgo para formación de UP, dos pacientes con UP presentaron dos o tres úlceras, respectivamente, clasificadas en estadio I y II. Conclusión: cara a la prevalencia de UP, surge la necesidad de una planificación de cuidados y de forma individualizada, teniendo como referencia el riesgo que cada paciente presenta al desarrollo de esta úlcera. Se detectó la necesidad de implementación de cuidados adecuados, según el marcador que cada paciente presente al desarrollo de la UP. Descriptores: úlcera por presión; enfermería; prevalencia.

scholarly journals External validation of risk prediction models for incident colorectal cancer using UK Biobank

2018 ◽  
Vol 118 (5) ◽  
pp. 750-759 ◽  
Author(s):  
J A Usher-Smith ◽  
A Harshfield ◽  
C L Saunders ◽  
S J Sharp ◽  
J Emery ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Prediction Models ◽  
Characteristic Curve ◽  
External Validation ◽  
Risk Scores ◽  
Prior History ◽  
Uk Biobank ◽  
Risk Models ◽  
Potential Health ◽  
Men And Women

Abstract Background: This study aimed to compare and externally validate risk scores developed to predict incident colorectal cancer (CRC) that include variables routinely available or easily obtainable via self-completed questionnaire. Methods: External validation of fourteen risk models from a previous systematic review in 373 112 men and women within the UK Biobank cohort with 5-year follow-up, no prior history of CRC and data for incidence of CRC through linkage to national cancer registries. Results: There were 1719 (0.46%) cases of incident CRC. The performance of the risk models varied substantially. In men, the QCancer10 model and models by Tao, Driver and Ma all had an area under the receiver operating characteristic curve (AUC) between 0.67 and 0.70. Discrimination was lower in women: the QCancer10, Wells, Tao, Guesmi and Ma models were the best performing with AUCs between 0.63 and 0.66. Assessment of calibration was possible for six models in men and women. All would require country-specific recalibration if estimates of absolute risks were to be given to individuals. Conclusions: Several risk models based on easily obtainable data have relatively good discrimination in a UK population. Modelling studies are now required to estimate the potential health benefits and cost-effectiveness of implementing stratified risk-based CRC screening.

scholarly journals Analysis of determinants of maize price variations in Nigeria (1978 - 2014)

2019 ◽  
Vol 11 (2) ◽  
pp. 237-234
Author(s):  
Opeyemi Eyitayo Ayinade ◽  
Ifedotun Victor Aina ◽  
Kayode Ayinade
Keyword(s):  
Food Security ◽  
Agricultural Production ◽  
Agricultural Sector ◽  
Price Change ◽  
Secondary Data ◽  
Descriptive Statistics ◽  
Price Variation ◽  
Factors Affecting ◽  
The Government ◽  
Food Staples

Skyrocketing prices of food staples such as maize can lead to inefficient agricultural production and definitely have detrimental effects on the economic, social, and political growth of any country. Most studies on maize in Nigeria are focused on the increasing consumption or competitiveness, very few address the determinants of maize price change as a panacea for the increase of productivity. Filling this gap requires a study on the various factors that contribute to the variations in the price of maize. In this study, secondary data were used. The study used descriptive statistics tools to analyze the pattern of price variations and changes in the production of maize over a period of 36 years in Nigeria. Also, various factors affecting price variation of maize were examined. It was recommended that the positive and significant impact of country’s population to maize price change should serve as an impulse to encourage investment in agricultural sector of Nigeria in order to ensure food security in the country. Also, the government should use the inflation measures to regulate prices of maize in the country

scholarly journals External validation of six clinical models for prediction of unknown chronic kidney disease in a German population

2021 ◽  
Author(s):  
Susanne Stolpe ◽  
Bernd Kowall ◽  
Denise Zwanziger ◽  
Mirjam Frank ◽  
Karl-Heinz Joeckel ◽  
...  
Keyword(s):  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
General Population ◽  
Prediction Models ◽  
Screening Methods ◽  
German Population ◽  
Risk Models ◽  
Validation Data ◽  
Predictive Values ◽  
Diagnostic Properties

Background Chronic kidney disease (CKD) is responsible for large personal health and societal burdens. Screening populations at higher risk for CKD is effective to initiate earlier treatment and decelerate disease progress. We externally validated clinical prediction models for unknown CKD that might be used in population screening. Methods We validated six risk models for prediction of unknown CKD using only non-invasive parameters. Validation data came from 4,185 participants of the German Heinz-Nixdorf-Recall study (HNR), drawn in 2000 from a general population aged 45-75 years. We estimated discrimination and calibration using the full model information, and calculated the diagnostic properties applying the published scoring algorithms of the models using various thresholds for the sum of scores. Results The risk models used four to nine parameters. Age and hypertension were included in all models. Five out of six c-values ranged from 0.71 to 0.73, indicating fair discrimination. Positive predictive values ranged from 15% to 19%, negative predictive values were >93% using score thresholds that resulted in values for sensitivity and specificity above 60%. Conclusions Most of the selected CKD prediction models show fair discrimination in a German general population. The estimated diagnostic properties indicate that the models are suitable for identifying persons at higher risk for unknown CKD without invasive procedures.

scholarly journals Epidemic Models for Personalised COVID-19 Isolation and Exit Policies Using Clinical Risk Predictions

2020 ◽  
Author(s):  
Theodoros Evgeniou ◽  
Mathilde Fekom ◽  
Anton Ovchinnikov ◽  
Raphael Porcher ◽  
Camille Pouchol ◽  
...  
Keyword(s):  
Risk Prediction ◽  
Prediction Models ◽  
Epidemic Models ◽  
Sensitivity Analyses ◽  
Model Discrimination ◽  
Risk Models ◽  
Risk Prediction Models ◽  
Seir Model ◽  
Clinical Risk ◽  
Time Sensitivity

Background: In early May 2020, following social distancing measures due to COVID-19, governments consider relaxing lock-down. We combined individual clinical risk predictions with epidemic modelling to examine simulations of risk based differential isolation and exit policies. Methods: We extended a standard susceptible-exposed-infected-removed (SEIR) model to account for personalised predictions of severity, defined by the risk of an individual needing intensive care if infected, and simulated differential isolation policies using COVID-19 data and estimates in France as of early May 2020. We also performed sensitivity analyses. The framework may be used with other epidemic models, with other risk predictions, and for other epidemic outbreaks. Findings: Simulations indicated that, assuming everything else the same, an exit policy considering clinical risk predictions starting on May 11, as planned by the French government, could enable to immediately relax restrictions for an extra 10% (6 700 000 people) or more of the lowest-risk population, and consequently relax the restrictions on the remaining population significantly faster -- while abiding to the current ICU capacity. Similar exit policies without risk predictions would exceed the ICU capacity by a multiple. Sensitivity analyses showed that when the assumed percentage of severe patients among the population decreased, or the prediction model discrimination improved, or the ICU capacity increased, policies based on risk models had a greater impact on the results of epidemic simulations. At the same time, sensitivity analyses also showed that differential isolation policies require the higher risk individuals to comply with recommended restrictions. In general, our simulations demonstrated that risk prediction models could improve policy effectiveness, keeping everything else constant. Interpretation: Clinical risk prediction models can inform new personalised isolation and exit policies, which may lead to both safer and faster outcomes than what can be achieved without such prediction models.

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