scholarly journals LO17: A comparative evaluation of ED crowding metrics and associations with patient mortality

CJEM ◽  
2017 ◽  
Vol 19 (S1) ◽  
pp. S33
Author(s):  
A. McRae ◽  
I. Usman ◽  
D. Wang ◽  
G. Innes ◽  
E. Lang ◽  
...  
Keyword(s):  
Comparative Evaluation ◽  
Regression Models ◽  
Wait Time ◽  
Information Criterion ◽  
Data Partition ◽  
Data Partitions ◽  
Ed Visits ◽  
Ed Crowding ◽  
Strength Of Association ◽  
Better Than

Introduction: Over 700 different input, throughput and output metrics have been used to quantify ED crowding. Of these, only ED length-of-stay (ED LOS) has been shown to be associated with mortality. No comparative evaluation of ED crowding metrics has been performed to determine which ones have the strongest association with patient mortality. The objective of this study was to compare the strength of association of common ED input, throughput and output metrics to patient mortality. Methods: Administrative data from five years of ED visits (2011-2014) at three urban EDs were linked to develop a database of over 900,000 ED visits with patient demographics, electronic time stamps for care processes, dispositions and outcomes. The data were randomly divided into three partitions of equal size. Here we report the findings from one partition of 253,938 ED visits. The remaining two data partitions will be used to validate these findings. Commonly-used crowding metrics were quantified and aggregated by day or by shift (0800-1600, 1600-2400, 2400-0800), and the shift-specific metrics assigned to each patient. The primary outcome was 7-day all-cause mortality. Multilevel logistic regression models were developed for 7-day mortality, with selected ED crowding metrics and a common set of confounders as predictors. The strength of association between the crowding metrics and mortality was compared using Akaike’s Information Criterion (AIC) and the Bayesian Information Criterion (BIC): ED crowding metrics with lower AIC and BIC have stronger associations with 7-day mortality. Results: Of 909,000 ED encounters, 124,679 (16.5%) arrived by EMS, 149,233(19.7%) were admitted, and 3,808 patients (0.5%) died within 7 days of ED arrival. Of input metrics, the model with ED wait-time was better (i.e. had a smaller AIC and BIC) than models for daily census, ED occupancy or LWBS proportion for predicting 7-day mortality. Of throughput metrics, the model with mean ED LOS was better than the model for mean MD care time. Of output metrics, the model with daily inpatient hospital occupancy was better than the model with mean boarding time. Conclusion: Based on one data partition, regression models based on the average wait-time, ED LOS and inpatient occupancy best predicted 7-day mortality. These results will be validated in the two other data partitions to confirm the best-performing ED input, throughput and output metrics.

Optimizing age at first use of semen for higher fertilityin Sahiwal breeding bulls

2016 ◽  
Author(s):  
B. C. Naha ◽  
A. K. Chakravarty ◽  
M. A. Mir ◽  
M. Bhakat
Keyword(s):  
Comparative Evaluation ◽  
Regression Models ◽  
Information Criterion ◽  
Conception Rate ◽  
Coefficient Of Determination ◽  
Progeny Testing ◽  
Testing Programme ◽  
Multiple Regression Models ◽  
Improvement Programme ◽  
Selection Of

The objective of the study was to optimise the age at first use (AAFU) of semen in Sahiwal breeding bulls which will help in early selection of bulls under progeny testing programme. The data on AAFU, conception rate based on first A.I. (CRFAI), overall conception rate (OCR) and birth weight (B.WT) of 43 Sahiwal bulls during 1987 to 2013 at NDRI centre pertaining to 8 sets of Sahiwal improvement programme at ICAR-NDRI, Karnal, India were adjusted for significant environmental influences and subsequently analyzed. Simple and multiple regression models were used for prediction of CRFAI and OCR of Sahiwal bulls. Comparative evaluation of three developed models (I to III) have showed that Model III, having AAFU and B.WT which fulfill the accuracy of model as revealed by high coefficient of determination, low mean sum of square to due error, low conceptual predictive value and low Bayesian information criterion . The results showed that average predicted CRFAI was highest (49.34%) at less than 5 years and lowest (44.79%) at > 6 years of age at first A.I. /use. Similarly average predicted OCR was highest (48.50%) at less than 5 years and lowest (44.56%) at >6 years of age at first A.I. / use of Sahiwal bulls. In organized herd under progeny testing programme, Sahiwal bulls should be used prior to 5 years which is expected to result in 4.45% better CRFAI and 3.94% better OCR in comparison to Sahiwal bulls used after 6 years of age.

scholarly journals LO10: Associations between ED crowding metrics and 72h-hour ED re-visits: Which crowding metrics are most highly associated with patient-oriented adverse outcomes?

CJEM ◽  
2019 ◽  
Vol 21 (S1) ◽  
pp. S10
Author(s):  
A. McRae ◽  
G. Innes ◽  
M. Schull ◽  
E. Lang ◽  
E. Grafstein ◽  
...  
Keyword(s):  
Patient Outcomes ◽  
Regression Models ◽  
Information Criterion ◽  
Patient Characteristics ◽  
Adverse Outcomes ◽  
Logistic Regression Models ◽  
Return Visits ◽  
Offload Delay ◽  
Ed Crowding ◽  
Meaningful Measure

Introduction: Emergency Department (ED) crowding is a pervasive problem and is associated with adverse patient outcomes. Yet, there are no widely accepted, universal ED crowding metrics. The objective of this study is to identify ED crowding metrics with the strongest association to the risk of ED revisits within 72 hours, which is a patient-oriented adverse outcome. Methods: Crowding metrics, patient characteristics and outcomes were obtained from administrative data for all ED encounters from 2011-2014 for three adult EDs in Calgary, AB. The data were randomly divided into three partitions for cross-validation, and further divided by CTAS category 1, 2/3 and 4/5. Twenty unique ED crowding metrics were calculated and assigned to each patient seen on each calendar day or shift, to standardize the exposure. Logistic regression models were fitted with 72h ED revisit as the dependent variable, and an individual crowding metric along with a common list of confounders as independent variables. Adjusted odds ratios (OR) for the 72h return visits were obtained for each crowding metric. The strength of associations between 72h revisits and crowding metrics were compared using Akaike's Information Criterion and Akaike weights. Results: This analysis is based on 1,149,939 ED encounters. Across all CTAS groups, INPUT metrics (ED census, ED occupancy, waiting time, EMS offload delay, LWBS%) were only weakly associated with the risk of 72h re-visit. Among THROUGHPUT metrics, ED Length of Stay and MD Care Time had similar adjusted ORs for 72h ED re-visit (range 0.99-1.15). Akaike weights ranging from 0.3/1.00 to 0.4/1.00 indicate that both THROUGHPUT metrics are reasonable predictors of 72h ED re-visits. All OUTPUT metrics (boarding time, # of boarded patients, % of beds occupied by boarded patients, hospital occupancy) had statistically significant ORs for 72h ED re-visits. The median boarding time had the highest adjusted OR for 72h ED re-visit (adjusted OR 1.40, 95% CI 1.33-1.47) and highest Akaike weight (0.97/1.00) compared to all other OUTPUT metrics, indicating that median boarding time had the strongest association with 72h re-visits. Conclusion: ED THROUGHPUT and OUTPUT metrics had consistent associations with 72h ED re-visits, while INPUT metrics had little to no association with 72h re-visits. Median boarding time is the strongest predictor of 72h re-visits, indicating that this may be the most meaningful measure of ED crowding.

STUDY ON THRIVE SCORE IN THE PROGNOSIS OF ISCHEMIC STROKE

2017 ◽  
pp. 22-24
Author(s):  
Thi Thao Nhi Tran ◽  
Dinh Toan Nguyen
Keyword(s):  
Ischemic Stroke ◽  
Clinical Outcome ◽  
Regression Models ◽  
Cross Sectional Study ◽  
High Rate ◽  
Vascular Events ◽  
Cross Sectional ◽  
Key Words Ischemic Stroke ◽  
Outcome Mortality ◽  
Better Than

Background and Purpose: Stroke is the second cause of mortality and the leading cause of disability. Using the clinical scale to predict the outcome of the patient play an important role in clinical practice. The Totaled Health Risks in Vascular Events (THRIVE) score has shown broad utility, allowing prediction of clinical outcome and death. Methods: A cross-sectional study conducting on 102 patients with acute ischemic stroke using THRIVE score. The outcome of patient was assessed by mRankin in the day of 30 after stroke. Statistic analysis using SPSS 15.0. Results: There was 60.4% patient in the group with THRIVE score 0 – 2 points having a good outcome (mRS 0 - 2), patient group with THRIVE score 6 - 9 having a high rate of bad outcome and mortality. Having a positive correlation between THRIVE score on admission and mRankin score at the day 30 after stroke with r = 0.712. THRIVE score strongly predicts clinical outcome with ROC-AUC was 0.814 (95% CI 0.735 - 0.893, p<0.001), Se 69%, Sp 84% and the cut-off was 2. THRIVE score strongly predicts mortality with ROC-AUC was 0.856 (95% CI 0.756 - 0.956, p<0.01), Se 86%, Sp 77% and the cut-off was 3. Analysis of prognostic factors by multivariate regression models showed that THRIVE score was only independent prognostic factor for the outcome of post stroke patients. Conclusions: The THRIVE score is a simple-to-use tool to predict clinical outcome, mortality in patients with ischemic stroke. Despite its simplicity, the THRIVE score performs better than several other outcome prediction tools. Key words: Ischemic stroke, THRIVE, prognosis, outcome, mortality

scholarly journals An analysis of Italian university students’ performance through segmented regression models: gender differences in STEM courses

Genus ◽  
2021 ◽  
Vol 77 (1) ◽  
Author(s):  
Andrea Priulla ◽  
Nicoletta D’Angelo ◽  
Massimo Attanasio
Keyword(s):  
Gender Differences ◽  
Regression Models ◽  
First Year ◽  
Strongly Correlated ◽  
Segmented Regression ◽  
Freshmen Students ◽  
Female Performance ◽  
And Mathematics ◽  
The Relationship ◽  
Better Than

AbstractThis paper investigates gender differences in university performances in Science, Technology, Engineering and Mathematics (STEM) courses in Italy, proposing a novel application through the segmented regression models. The analysis concerns freshmen students enrolled at a 3-year STEM degree in Italian universities in the last decade, with a focus on the relationship between the number of university credits earned during the first year (a good predictor of the regularity of the career) and the probability of getting the bachelor degree within 4 years. Data is provided by the Italian Ministry of University and Research (MIUR). Our analysis confirms that first-year performance is strongly correlated to obtaining a degree within 4 years. Furthermore, our findings show that gender differences vary among STEM courses, in accordance with the care-oriented and technical-oriented dichotomy. Males outperform females in mathematics, physics, chemistry and computer science, while females are slightly better than males in biology. In engineering, female performance seems to follow the male stream. Finally, accounting for other important covariates regarding students, we point out the importance of high school background and students’ demographic characteristics.

Abstract 3775: Average Blood Glucose and Mortality in Patients Hospitalized with Acute Myocardial Infarction

Circulation ◽  
2007 ◽  
Vol 116 (suppl_16) ◽  
Author(s):  
Mikhail Kosiborod ◽  
Silvio Inzucchi ◽  
Harlan M Krumholz ◽  
Lan Xiao ◽  
Phillip G Jones ◽  
...  
Keyword(s):  
Blood Glucose ◽  
Hospital Mortality ◽  
Regression Models ◽  
Independent Predictor ◽  
Laboratory Data ◽  
Logistic Regression Models ◽  
Average Blood Glucose ◽  
The Relationship ◽  
Multivariable Adjustment ◽  
Better Than

Background: Elevated blood glucose (BG) on admission is associated with higher mortality risk in patients (pts) hospitalized with AMI. However, the prognostic value of average BG, which reflects overall glycemic exposure much better than admission BG, is unknown. Furthermore, the nature of the relationship between average BG and mortality has not been determined. Methods: We evaluated a cohort of 16,871 AMI pts hospitalized from January 2000-December 2005, using Cerner Corporation’s Health Facts® database from 40 hospitals, which contains demographics, clinical and comprehensive laboratory data. Logistic regression models evaluated the nature of the relationship between mean BG during the entire AMI hospitalization and in-hospital mortality, after adjusting for multiple patient factors and confounders. Similar analyses were performed in subgroups of pts with and without diabetes (DM). Results: A J-shaped relationship was observed between mean BG and in-hospital mortality, which persisted after multivariable adjustment (Figure ). Mortality increased with each 10 mg/dL incremental rise in mean BG over >120 mg/dL, and with incremental decline in mean BG <80 mg/dL. The slope of these relationships was much steeper in pts without DM. Conclusions: Average BG during the entire AMI hospitalization is a powerful independent predictor of in-hospital mortality. Both persistent hyper- and hypoglycemia are associated with adverse prognosis. Whether strategies directed at optimizing BG control will improve survival remains to be established. Association Between Mean BG and In-Hospital Mortality After Multivariable Adjustment (Reference: Mean BG 100 to <110)

Abstract P36: Lesional Perfusion and Permeability Are Diagnostic and Prognostic Biomarkers of Cavernous Angioma With Symptomatic Hemorrhage

Stroke ◽  
2021 ◽  
Vol 52 (Suppl_1) ◽  
Author(s):  
Je Yeong Sone ◽  
Nicholas Hobson ◽  
Sharbel Romanos ◽  
Abhinav Srinath ◽  
Abdallah Shkoukani ◽  
...  
Keyword(s):  
Regression Models ◽  
Prognostic Biomarker ◽  
Information Criterion ◽  
Cavernous Angioma ◽  
Prognostic Biomarkers ◽  
Diagnostic Efficacy ◽  
Logistic Regression Models ◽  
Conventional Mri ◽  
Contrast Enhanced ◽  
Univariate Analyses

Introduction: Diagnosis of cavernous angioma with symptomatic hemorrhage (CASH) requires MRI evidence of lesional bleeding associated directly with attributable symptoms. However, hemorrhagic signs of CASH may become clinically silent on conventional MRI after 3 months. As CASH is likely to rebleed for several years, accurate diagnosis of CASH that bled more than 3 months prior is needed. Hypothesis: Perfusion and permeability derivations of dynamic contrast-enhanced quantitative perfusion (DCEQP) MRI can diagnose CASH and predict bleeding/growth in CAs. Methods: CAs of 205 consecutively enrolled patients scanned with DCEQP during clinical visits were classified as CASH that bled 3 - 12 months prior (N = 55) versus non-CASH (N = 658) or CA with (N = 23) versus without (N = 721) bleeding/growth within a year after MRI. Demographics and 13 perfusion and 13 permeability derivations of DCEQP were assessed via machine learning and univariate analyses. Logistic regression models ln ( P / 1 - P ) = Σ (β i x i ) + β 0 were selected as the best diagnostic and prognostic biomarkers by minimizing the Bayesian information criterion (BIC). Results: The best diagnostic biomarker of CASH that bled 3 - 12 months prior (BIC = 321.6, Figure A) showed 80% sensitivity and 82% specificity. Permeability derivations did not add diagnostic efficacy when combined with perfusion. The best prognostic biomarker of bleeding/growth (BIC = 201.5, Figure B) showed 77% sensitivity and 72% specificity. Conclusion: Perfusion imaging may diagnose CASH even after hemorrhagic signs disappear on conventional MRI. A combination of permeability and perfusion derivations may help predict bleeding/growth in CAs.

scholarly journals A NON-DESTRUCTIVE METHOD FOR ESTIMATING LEAF AREA OF Ceiba glaziovii (Kuntze) K. Schum.

FLORESTA ◽  
2019 ◽  
Vol 50 (1) ◽  
pp. 1063
Author(s):  
João Everthon da Silva Ribeiro ◽  
Francisco Romário Andrade Figueiredo ◽  
Ester Dos Santos Coêlho ◽  
Walter Esfrain Pereira ◽  
Manoel Bandeira de Albuquerque
Keyword(s):  
Leaf Area ◽  
Regression Models ◽  
Information Criterion ◽  
Coefficient Of Determination ◽  
Linear Measurements ◽  
Maximum Width ◽  
Bias Index ◽  
Different Shapes ◽  
Non Destructive

The determination of leaf area is of fundamental importance in studies involving ecological and ecophysiological aspects of forest species. The objective of this research was to adjust an equation to determine the leaf area of Ceiba glaziovii as a function of linear measurements of leaves. Six hundred healthy leaf limbs were collected in different matrices, with different shapes and sizes, in the Mata do Pau-Ferro State Park, Areia, Paraíba state, Northeast Brazil. The maximum length (L), maximum width (W), product between length and width (L.W), and leaf area of the leaf limbs were calculated. The regression models used to construct equations were: linear, linear without intercept, quadratic, cubic, power and exponential. The criteria for choosing the best equation were based on the coefficient of determination (R²), Akaike information criterion (AIC), root mean square error (RMSE), Willmott concordance index (d) and BIAS index. All the proposed equations satisfactorily estimate the leaf area of C. glaziovii, due to their high determination coefficients (R² ≥ 0.851). The linear model without intercept, using the product between length and width (L.W), presented the best criteria to estimate the leaf area of the species, using the equation 0.4549*LW.

scholarly journals Description of blackberry fruit growth by nonlinear regression models

2020 ◽  
Vol 42 (2) ◽  
Author(s):  
Édipo Menezes da Silva ◽  
Maraísa Hellen Tadeu ◽  
Victor Ferreira da Silva ◽  
Rafael Pio ◽  
Tales Jesus Fernandes ◽  
...  
Keyword(s):  
Statistical Models ◽  
Regression Models ◽  
Least Squares Method ◽  
Growth Curves ◽  
Gompertz Model ◽  
Information Criterion ◽  
Financial Return ◽  
Nonlinear Regression Models ◽  
Small Fruit ◽  
Small Producers

Abstract Blackberry is a small fruit with several properties beneficial to human health and its cultivation is an alternative for small producers due to its fast and high financial return. Studying the growth of fruits over time is extremely important to understand their development, helping in the most appropriate crop management, avoiding post-harvest losses, which is one of the aggravating factors of blackberry cultivation, being a short shelf life fruit. Thus, growth curves are highlighted in this type of study and modeling through statistical models helps understanding how such growth occurs. Data from this study were obtained from an experiment conducted at the Federal University of Lavras in 2015. The aim of this study was to adjust nonlinear, double Logistic and double Gompertz models to describe the diameter growth of four blackberry cultivars (‘Brazos’, ‘Choctaw’, ‘Guarani’ and ‘Tupy’). Estimations of parameters were obtained using the least squares method and the Gauss-Newton algorithm, with the “nls” and “glns” functions of the R statistical software. The comparison of adjustments was made by the Akaike information criterion (AICc), residual standard deviation (RSD) and adjusted determination coefficient (R2 aj). The models satisfactorily described data, choosing the Logistic double model for ‘Brazos’ and ‘Guarani’ cultivars and the double Gompertz model for ‘Tupy’ and ‘Choctaw’ cultivars.

scholarly journals The updated Charlson comorbidity index is a useful predictor of mortality in patients with Staphylococcus aureus bacteraemia

2018 ◽  
Vol 146 (16) ◽  
pp. 2122-2130 ◽  
Author(s):  
H. G. Ternavasio-de la Vega ◽  
F. Castaño-Romero ◽  
S. Ragozzino ◽  
R. Sánchez González ◽  
M. P. Vaquero-Herrero ◽  
...  
Keyword(s):  
Staphylococcus Aureus ◽  
Charlson Comorbidity Index ◽  
Regression Models ◽  
Model Fit ◽  
Logistic Regression Models ◽  
Relevant Variables ◽  
Comorbidity Index ◽  
The Mean ◽  
Related Mortality ◽  
Better Than

AbstractThe objective was to compare the performance of the updated Charlson comorbidity index (uCCI) and classical CCI (cCCI) in predicting 30-day mortality in patients with Staphylococcus aureus bacteraemia (SAB). All cases of SAB in patients aged ⩾14 years identified at the Microbiology Unit were included prospectively and followed. Comorbidity was evaluated using the cCCI and uCCI. Relevant variables associated with SAB-related mortality, along with cCCI or uCCI scores, were entered into multivariate logistic regression models. Global model fit, model calibration and predictive validity of each model were evaluated and compared. In total, 257 episodes of SAB in 239 patients were included (mean age 74 years; 65% were male). The mean cCCI and uCCI scores were 3.6 (standard deviation, 2.4) and 2.9 (2.3), respectively; 161 (63%) cases had cCCI score ⩾3 and 89 (35%) cases had uCCI score ⩾4. Sixty-five (25%) patients died within 30 days. The cCCI score was not related to mortality in any model, but uCCI score ⩾4 was an independent factor of 30-day mortality (odds ratio, 1.98; 95% confidence interval, 1.05–3.74). The uCCI is a more up-to-date, refined and parsimonious prognostic mortality score than the cCCI; it may thus serve better than the latter in the identification of patients with SAB with worse prognoses.

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