Mesoscale spatiotemporal predictive models of daily human- and lightning-caused wildland fire occurrence in British Columbia

We developed three models of daily human- and lightning-caused fire occurrence to support fire management preparedness and detection planning in the province of British Columbia, Canada, using a lasso-logistic framework. Novel aspects of our work involve (1) using an ensemble of models that were created using 500 datasets balanced (through response-selective sampling) to have equal numbers of fire and non-fire observations; (2) the use of a new ranking algorithm to address the difficulty in interpreting variable importance in models with a large number of covariates. We also introduce the use of cause-specific average spatial daily fire occurrence, termed baseline risk, as a covariate for missing or poorly estimated factors that influence human and lightning fire occurrence. All three models have strong predictive ability, with areas under the Receiver Operator Characteristic curve exceeding 0.9.

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Vaginal examination vs. cervical length – which is superior in predicting preterm birth?

Journal of Perinatal Medicine ◽

10.1515/jpm-2016-0273 ◽

2017 ◽

Vol 45 (8) ◽

Cited By ~ 4

Author(s):

Merav Sharvit ◽

Reut Weiss ◽

Yael Ganor Paz ◽

Keren Tzadikevitch Geffen ◽

Netanella Danielli Miller ◽

...

Keyword(s):

Preterm Birth ◽

Medical Center ◽

Characteristic Curve ◽

Multiple Pregnancy ◽

Predictive Ability ◽

Cervical Length ◽

Receiver Operator Characteristic Curve ◽

Retrospective Case ◽

Nulliparous Women ◽

Significant Difference

AbstractObjective:To compare the predictive value of preterm birth (PTB) by transvaginal sonographic cervical length (CL) measurement to digital examination of the cervix (Bishop score – BS), in patients with premature contractions (PC) and intact membranes.Design:A retrospective case-control study.Setting:Meir Medical Center, Kfar Saba, Israel.Population:Women at 24–34 weeks of gestation who were hospitalized with PC and intact membranes.Methods:All patients underwent CL and BS measurements upon admission. Power analysis revealed that 375 patients were needed to show a significant difference between the two methods for predicting PTB. Each one served as her own control.Main outcome measures:PTB<37 and<34 weeks.Results:Receiver-operator characteristic curve (ROC) and logistic regression analyses indicated a correlation between both shortened CL and increased BS to PTB (P<0.001). Neither test offered an advantage in predicting PTB. Areas under the curve for BS and CL ROC were similar for PTB before 37 weeks gestation (0.611 vs. 0.640, P=0.28). For nulliparous women, CL predicted PTB better that BS (0.642 vs. 0.724, P=0.03). For singleton and multiple pregnancy pregnancies, BS and CL did not differ significantly in predicting PTB (P=0.9, P=0.2, respectively). For nulliparous with multiple pregnancy, the BS and CL ROC curves differ nearly significantly (0.554 vs. 0.709, P=0.07), with better predictive ability for CL.Conclusions:CL and BS have similar value in predicting PTB in patients with PC. For nulliparous women, CL is superior over the BS.

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External Validation and Comparison of Clostridioides difficile Severity Scoring Systems

Clinical Infectious Diseases ◽

10.1093/cid/ciab737 ◽

2021 ◽

Author(s):

D Alexander Perry ◽

Daniel Shirley ◽

Dejan Micic ◽

C Pratish Patel ◽

Rosemary Putler ◽

...

Keyword(s):

Characteristic Curve ◽

External Validation ◽

Predictive Ability ◽

Scoring Systems ◽

Receiver Operator Characteristic Curve ◽

Adverse Outcomes ◽

Risk Scores ◽

Clostridioides Difficile ◽

Multicenter Cohort ◽

Severity Scores

Abstract Background Many models have been developed to predict severe outcomes from Clostridioides difficile infection. These models are usually developed at a single institution and largely are not externally validated. This aim of this study was to validate previously published risk scores in a multicenter cohort of patients with CDI. Methods Retrospective study on four separate inpatient cohorts with CDI from three distinct sites: The Universities of Michigan (2010-2012 and 2016), Chicago (2012), and Wisconsin (2012). The primary composite outcome was admission to an intensive care unit, colectomy, and/or death attributed to CDI within 30 days of positive testing. Both within each cohort and combined across all cohorts, published CDI severity scores were assessed and compared to each other and the IDSA guideline definitions of severe and fulminant CDI. Results A total of 3,646 patients were included for analysis. Including the two IDSA guideline definitions, fourteen scores were assessed. Performance of scores varied within each cohort and in the combined set (mean area under the receiver operator characteristic curve(AUC 0.61, range 0.53-0.66). Only half of the scores had performance at or better than IDSA severe and fulminant definitions (AUCs 0.64 and 0.63, respectively). Most of the scoring systems had more false than true positives in the combined set (mean: 81.5%, range:0-91.5%). Conclusions No published CDI severity score showed stable, good predictive ability for adverse outcomes across multiple cohorts/institutions or in a combined multicenter cohort.

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Lightning and lightning fire, central cordillera, Canada

International Journal of Wildland Fire ◽

10.1071/wf01048 ◽

2002 ◽

Vol 11 (1) ◽

pp. 41 ◽

Cited By ~ 59

Author(s):

Jack Wierzchowski ◽

Mark Heathcott ◽

Michael D. Flannigan

Keyword(s):

British Columbia ◽

Forest Fires ◽

Fire Occurrence ◽

Fire Weather Index ◽

Lightning Strikes ◽

Severity Rating ◽

Continental Divide ◽

Significant Difference ◽

Lightning Discharges ◽

Lightning Fire

This study examines the influences of fuel, weather and topography on lightning-caused forest fires in portions of southern British Columbia and Alberta, Canada. The results show a significant difference in lightning and lightning-caused fires east and west of the Continental Divide. In British Columbia, on average there was one fire for every 50 lightning discharges whereas in Alberta there was one fire for every 1400 lightning discharges. Elevation, the distribution of lightning strikes, the Daily Severity Rating (a component of the Canadian Forest Fire Weather Index System) and vegetation composition were identified as primary agents controlling lightning fire occurrence. However, the multivariate analysis does suggest that there are other factors influencing fire occurrence other than the biophysical factors we tested. The implications of the lightning and lightning-ignited fires for land managers are discussed.

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AVALIAÇÃO DE UM INSTRUMENTO DE MENSURAÇÃO DE MORBIDADE PSÍQUICA: ESTUDO DE VALIDAÇÃO DO SELF-REPORTING QUESTIONNAIRE (SRQ-20)

Revista Baiana Saúde Pública ◽

10.22278/2318-2660.2010.v34.n3.a54 ◽

1970 ◽

Vol 34 (3) ◽

pp. 544 ◽

Cited By ~ 25

Author(s):

Kionna Oliveira Bernardes Santos ◽

Tânia Maria de Araújo ◽

Paloma de Sousa Pinho ◽

Ana Cláudia Conceição Silva

Keyword(s):

Characteristic Curve ◽

Receiver Operator Characteristic Curve ◽

Clinical Interview ◽

Receiver Operator Characteristic ◽

Interview Schedule ◽

Para Determinar ◽

Self Reporting Questionnaire

O Self-Reporting Questionnaire (SRQ-20), desenvolvido pela Organização Mundial de Saúde, tem sido utilizado para mensuração de nível de suspeição de transtornos mentais em estudos brasileiros, especialmente em grupos de trabalhadores. O objetivo deste estudo foi avaliar o desempenho do SRQ-20, com base em indicadores de validade (sensibilidade, especificidade, taxa de classificação incorreta e valores preditivos), e determinar o melhor ponto de corte para classificação dos transtornos mentais comuns na população estudada. O estudo incluiu 91 indivíduos selecionados aleatoriamente de um estudo de corte transversal realizado com população residente em áreas urbanas de Feira de Santana (BA). Entrevistas clínicas, realizadas por psicólogas, utilizando o Revised Clinical Interview Schedule (CIS-R), foi adotada como padrão-ouro. Na avaliação do desempenho do SRQ-20 foram estimados indicadores de validade (sensibilidade e especificidade). A curva Receiver Operator Characteristic Curve (ROC) foi utilizada para determinar o melhor ponto de corte para classificação de suspeitos/não suspeitos. O ponto de corte de melhor desempenho foi de 6/7 para a população investigada, revelando desempenho razoável com área sob a curva de 0,789. Os resultados indicam que o SRQ-20 possui característica discriminante regular.

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Haemoconcentration as a predictor of severity in acute pancreatitis

Journal of Kathmandu Medical College ◽

10.3126/jkmc.v4i1.15020 ◽

2016 ◽

Vol 4 (1) ◽

pp. 3-7

Author(s):

Tanka Prasad Bohara ◽

Dimindra Karki ◽

Anuj Parajuli ◽

Shail Rupakheti ◽

Mukund Raj Joshi

Keyword(s):

Acute Pancreatitis ◽

Pearson Correlation ◽

Characteristic Curve ◽

Scoring Systems ◽

Cost Effective ◽

Receiver Operator Characteristic Curve ◽

First Episode ◽

P Value ◽

Receiver Operator Characteristic ◽

Chi Square

Background: Acute pancreatitis is usually a mild and self-limiting disease. About 25 % of patients have severe episode with mortality up to 30%. Early identification of these patients has potential advantages of aggressive treatment at intensive care unit or transfer to higher centre. Several scoring systems are available to predict severity of acute pancreatitis but are cumbersome, take 24 to 48 hours and are dependent on tests that are not universally available. Haematocrit has been used as a predictor of severity of acute pancreatitis but some have doubted its role.Objectives: To study the significance of haematocrit in prediction of severity of acute pancreatitis.Methods: Patients admitted with first episode of acute pancreatitis from February 2014 to July 2014 were included. Haematocrit at admission and 24 hours of admission were compared with severity of acute pancreatitis. Mean, analysis of variance, chi square, pearson correlation and receiver operator characteristic curve were used for statistical analysis.Results: Thirty one patients were included in the study with 16 (51.61%) male and 15 (48.4%) female. Haematocrit at 24 hours of admission was higher in severe acute pancreatitis (P value 0.003). Both haematocrit at admission and at 24 hours had positive correlation with severity of acute pancreatitis (r: 0.387; P value 0.031 and r: 0.584; P value 0.001) respectively.Area under receiver operator characteristic curve for haematocrit at admission and 24 hours were 0.713 (P value 0.175, 95% CI 0.536 - 0.889) and 0.917 (P value 0.008, 95% CI 0.813 – 1.00) respectively.Conclusion: Haematocrit is a simple, cost effective and widely available test and can predict severity of acute pancreatitis.Journal of Kathmandu Medical College, Vol. 4(1) 2015, 3-7

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MicroRNAs-1299, -126-3p and -30e-3p as Potential Diagnostic Biomarkers for Prediabetes

Diagnostics ◽

10.3390/diagnostics11060949 ◽

2021 ◽

Vol 11 (6) ◽

pp. 949

Author(s):

Cecil J. Weale ◽

Don M. Matshazi ◽

Saarah F. G. Davids ◽

Shanel Raghubeer ◽

Rajiv T. Erasmus ◽

...

Keyword(s):

Receiver Operating Characteristic ◽

Operating Characteristic ◽

Characteristic Curve ◽

Predictive Ability ◽

Roc Curves ◽

Tolerance Test ◽

Cross Sectional Study ◽

Oral Glucose ◽

Cross Sectional ◽

Receiver Operating

This cross-sectional study investigated the association of miR-1299, -126-3p and -30e-3p with and their diagnostic capability for dysglycaemia in 1273 (men, n = 345) South Africans, aged >20 years. Glycaemic status was assessed by oral glucose tolerance test (OGTT). Whole blood microRNA (miRNA) expressions were assessed using TaqMan-based reverse transcription quantitative-PCR (RT-qPCR). Receiver operating characteristic (ROC) curves assessed the ability of each miRNA to discriminate dysglycaemia, while multivariable logistic regression analyses linked expression with dysglycaemia. In all, 207 (16.2%) and 94 (7.4%) participants had prediabetes and type 2 diabetes mellitus (T2DM), respectively. All three miRNAs were significantly highly expressed in individuals with prediabetes compared to normotolerant patients, p < 0.001. miR-30e-3p and miR-126-3p were also significantly more expressed in T2DM versus normotolerant patients, p < 0.001. In multivariable logistic regressions, the three miRNAs were consistently and continuously associated with prediabetes, while only miR-126-3p was associated with T2DM. The ROC analysis indicated all three miRNAs had a significant overall predictive ability to diagnose prediabetes, diabetes and the combination of both (dysglycaemia), with the area under the receiver operating characteristic curve (AUC) being significantly higher for miR-126-3p in prediabetes. For prediabetes diagnosis, miR-126-3p (AUC = 0.760) outperformed HbA1c (AUC = 0.695), p = 0.042. These results suggest that miR-1299, -126-3p and -30e-3p are associated with prediabetes, and measuring miR-126-3p could potentially contribute to diabetes risk screening strategies.

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Evaluation of factors that predict the success rate of trial of labor after the cesarean section

BMC Pregnancy and Childbirth ◽

10.1186/s12884-021-04004-z ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Yang Mi ◽

Pengfei Qu ◽

Na Guo ◽

Ruimiao Bai ◽

Jiayi Gao ◽

...

Keyword(s):

Logistic Regression ◽

Cesarean Section ◽

Receiver Operating Characteristic Curve ◽

Success Rate ◽

Operating Characteristic ◽

Characteristic Curve ◽

Predictive Ability ◽

Training Set ◽

History Of ◽

Operating Characteristic Curve

Abstract Background For most women who have had a previous cesarean section, vaginal birth after cesarean section (VBAC) is a reasonable and safe choice, but which will increase the risk of adverse outcomes such as uterine rupture. In order to reduce the risk, we evaluated the factors that may affect VBAC and and established a model for predicting the success rate of trial of the labor after cesarean section (TOLAC). Methods All patients who gave birth at Northwest Women’s and Children’s Hospital from January 2016 to December 2018, had a history of cesarean section and voluntarily chose the TOLAC were recruited. Among them, 80% of the population was randomly assigned to the training set, while the remaining 20% were assigned to the external validation set. In the training set, univariate and multivariate logistic regression models were used to identify indicators related to successful TOLAC. A nomogram was constructed based on the results of multiple logistic regression analysis, and the selected variables included in the nomogram were used to predict the probability of successfully obtaining TOLAC. The area under the receiver operating characteristic curve was used to judge the predictive ability of the model. Results A total of 778 pregnant women were included in this study. Among them, 595 (76.48%) successfully underwent TOLAC, whereas 183 (23.52%) failed and switched to cesarean section. In multi-factor logistic regression, parity = 1, pre-pregnancy BMI < 24 kg/m2, cervical score ≥ 5, a history of previous vaginal delivery and neonatal birthweight < 3300 g were associated with the success of TOLAC. The area under the receiver operating characteristic curve in the prediction and validation models was 0.815 (95% CI: 0.762–0.854) and 0.730 (95% CI: 0.652–0.808), respectively, indicating that the nomogram prediction model had medium discriminative power. Conclusion The TOLAC was useful to reducing the cesarean section rate. Being primiparous, not overweight or obese, having a cervical score ≥ 5, a history of previous vaginal delivery or neonatal birthweight < 3300 g were protective indicators. In this study, the validated model had an approving predictive ability.

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A microRNA panel compared to environmental and polygenic scores for colorectal cancer risk prediction

Nature Communications ◽

10.1038/s41467-021-25067-8 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Janhavi R. Raut ◽

Ben Schöttker ◽

Bernd Holleczek ◽

Feng Guo ◽

Megha Bhardwaj ◽

...

Keyword(s):

Colorectal Cancer ◽

Risk Prediction ◽

Risk Score ◽

Characteristic Curve ◽

Predictive Ability ◽

Population Based ◽

Polygenic Risk Score ◽

Nucleotide Polymorphisms ◽

Polygenic Scores

AbstractCirculating microRNAs (miRNAs) could improve colorectal cancer (CRC) risk prediction. Here, we derive a blood-based miRNA panel and evaluate its ability to predict CRC occurrence in a population-based cohort of adults aged 50–75 years. Forty-one miRNAs are preselected from independent studies and measured by quantitative-real-time-polymerase-chain-reaction in serum collected at baseline of 198 participants who develop CRC during 14 years of follow-up and 178 randomly selected controls. A 7-miRNA score is derived by logistic regression. Its predictive ability, quantified by the optimism-corrected area-under-the-receiver-operating-characteristic-curve (AUC) using .632+ bootstrap is 0.794. Predictive ability is compared to that of an environmental risk score (ERS) based on known risk factors and a polygenic risk score (PRS) based on 140 previously identified single-nucleotide-polymorphisms. In participants with all scores available, optimism-corrected-AUC is 0.802 for the 7-miRNA score, while AUC (95% CI) is 0.557 (0.498–0.616) for the ERS and 0.622 (0.564–0.681) for the PRS.

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Identification of candidate biomarkers of liver hydatid disease via microarray profiling, bioinformatics analysis, and machine learning

Journal of International Medical Research ◽

10.1177/0300060521993980 ◽

2021 ◽

Vol 49 (3) ◽

pp. 030006052199398

Author(s):

Jinwu Peng ◽

Zhili Duan ◽

Yamin Guo ◽

Xiaona Li ◽

Xiaoqin Luo ◽

...

Keyword(s):

Random Forest ◽

Hydatid Disease ◽

Characteristic Curve ◽

Receiver Operator Characteristic Curve ◽

Random Forest Model ◽

Hepatic Hydatid Disease ◽

Forest Model ◽

Protein Protein Interaction ◽

Ppi Networks ◽

Microarray Profiling

Objectives Liver echinococcosis is a severe zoonotic disease caused by Echinococcus (tapeworm) infection, which is epidemic in the Qinghai region of China. Here, we aimed to explore biomarkers and establish a predictive model for the diagnosis of liver echinococcosis. Methods Microarray profiling followed by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis was performed in liver tissue from patients with liver hydatid disease and from healthy controls from the Qinghai region of China. A protein–protein interaction (PPI) network and random forest model were established to identify potential biomarkers and predict the occurrence of liver echinococcosis, respectively. Results Microarray profiling identified 1152 differentially expressed genes (DEGs), including 936 upregulated genes and 216 downregulated genes. Several previously unreported biological processes and signaling pathways were identified. The FCGR2B and CTLA4 proteins were identified by the PPI networks and random forest model. The random forest model based on FCGR2B and CTLA4 reliably predicted the occurrence of liver hydatid disease, with an area under the receiver operator characteristic curve of 0.921. Conclusion Our findings give new insight into gene expression in patients with liver echinococcosis from the Qinghai region of China, improving our understanding of hepatic hydatid disease.

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Development and External Validation of Deep-Learning-Based Tumor Grading Models in Soft-Tissue Sarcoma Patients Using MR Imaging

Cancers ◽

10.3390/cancers13122866 ◽

2021 ◽

Vol 13 (12) ◽

pp. 2866

Author(s):

Fernando Navarro ◽

Hendrik Dapper ◽

Rebecca Asadpour ◽

Carolin Knebel ◽

Matthew B. Spraker ◽

...

Keyword(s):

Deep Learning ◽

Soft Tissue ◽

Mr Imaging ◽

Characteristic Curve ◽

External Validation ◽

Soft Tissue Sarcomas ◽

Treatment Decision ◽

Receiver Operator Characteristic Curve ◽

Low Grade ◽

Tumor Grading

Background: In patients with soft-tissue sarcomas, tumor grading constitutes a decisive factor to determine the best treatment decision. Tumor grading is obtained by pathological work-up after focal biopsies. Deep learning (DL)-based imaging analysis may pose an alternative way to characterize STS tissue. In this work, we sought to non-invasively differentiate tumor grading into low-grade (G1) and high-grade (G2/G3) STS using DL techniques based on MR-imaging. Methods: Contrast-enhanced T1-weighted fat-saturated (T1FSGd) MRI sequences and fat-saturated T2-weighted (T2FS) sequences were collected from two independent retrospective cohorts (training: 148 patients, testing: 158 patients). Tumor grading was determined following the French Federation of Cancer Centers Sarcoma Group in pre-therapeutic biopsies. DL models were developed using transfer learning based on the DenseNet 161 architecture. Results: The T1FSGd and T2FS-based DL models achieved area under the receiver operator characteristic curve (AUC) values of 0.75 and 0.76 on the test cohort, respectively. T1FSGd achieved the best F1-score of all models (0.90). The T2FS-based DL model was able to significantly risk-stratify for overall survival. Attention maps revealed relevant features within the tumor volume and in border regions. Conclusions: MRI-based DL models are capable of predicting tumor grading with good reproducibility in external validation.

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