scholarly journals SYmptom-Based STratification of DiabEtes Mellitus by Renal Function Decline (SYSTEM): A Retrospective Cohort Study and Modeling Assessment

2021 ◽  
Vol 8 ◽  
Author(s):  
Kam Wa Chan ◽  
Tak Yee Chow ◽  
Kam Yan Yu ◽  
Yulong Xu ◽  
Nevin Lianwen Zhang ◽  
...  
Keyword(s):  
Statistical Models ◽  
Retrospective Cohort ◽  
Predictive Power ◽  
Information Criterion ◽  
Symptom Clusters ◽  
Diabetic Patients ◽  
Delphi Consensus ◽  
Epidemiological Factors ◽  
Gfr Decline ◽  
Independent Variable

Background: Previous UK Biobank studies showed that symptoms and physical measurements had excellent prediction on long-term clinical outcomes in general population. Symptoms and signs could intuitively and non-invasively predict and monitor disease progression, especially for telemedicine, but related research is limited in diabetes and renal medicine.Methods: This retrospective cohort study aimed to evaluate the predictive power of a symptom-based stratification framework and individual symptoms for diabetes. Three hundred two adult diabetic patients were consecutively sampled from outpatient clinics in Hong Kong for prospective symptom assessment. Demographics and longitudinal measures of biochemical parameters were retrospectively extracted from linked medical records. The association between estimated glomerular filtration rate (GFR) (independent variable) and biochemistry, epidemiological factors, and individual symptoms was assessed by mixed regression analyses. A symptom-based stratification framework of diabetes using symptom clusters was formulated by Delphi consensus method. Akaike information criterion (AIC) and Bayesian information criterion (BIC) were compared between statistical models with different combinations of biochemical, epidemiological, and symptom variables.Results: In the 4.2-year follow-up period, baseline presentation of edema (−1.8 ml/min/1.73m2, 95%CI: −2.5 to −1.2, p < 0.001), epigastric bloating (−0.8 ml/min/1.73m2, 95%CI: −1.4 to −0.2, p = 0.014) and alternating dry and loose stool (−1.1 ml/min/1.73m2, 95%CI: −1.9 to −0.4, p = 0.004) were independently associated with faster annual GFR decline. Eleven symptom clusters were identified from literature, stratifying diabetes predominantly by gastrointestinal phenotypes. Using symptom clusters synchronized by Delphi consensus as the independent variable in statistical models reduced complexity and improved explanatory power when compared to using individual symptoms. Symptom-biologic-epidemiologic combined model had the lowest AIC (4,478 vs. 5,824 vs. 4,966 vs. 7,926) and BIC (4,597 vs. 5,870 vs. 5,065 vs. 8,026) compared to the symptom, symptom-epidemiologic and biologic-epidemiologic models, respectively. Patients co-presenting with a constellation of fatigue, malaise, dry mouth, and dry throat were independently associated with faster annual GFR decline (−1.1 ml/min/1.73m2, 95%CI: −1.9 to −0.2, p = 0.011).Conclusions: Add-on symptom-based diagnosis improves the predictive power on renal function decline among diabetic patients based on key biochemical and epidemiological factors. Dynamic change of symptoms should be considered in clinical practice and research design.

scholarly journals When Uncontrolled Diabetes Mellitus and Severe COVID-19 Converge: The Perfect Storm for Mucormycosis

2021 ◽  
Vol 7 (4) ◽  
pp. 298
Author(s):  
Teny M. John ◽  
Ceena N. Jacob ◽  
Dimitrios P. Kontoyiannis
Keyword(s):  
Diabetes Mellitus ◽  
Risk Factors ◽  
Clinical Features ◽  
The Other ◽  
Diabetic Patients ◽  
Epidemiological Factors ◽  
Uncontrolled Diabetes ◽  
Diagnostic Certainty ◽  
Patients With Diabetes ◽  
Perfect Storm

Mucormycosis (MCR) has been increasingly described in patients with coronavirus disease 2019 (COVID-19) but the epidemiological factors, presentation, diagnostic certainty, and outcome of such patients are not well described. We review the published COVID-19-associated mucormycosis (CAMCR) cases (total 41) to identify risk factors, clinical features, and outcomes. CAMCR was typically seen in patients with diabetes mellitus (DM) (94%) especially the ones with poorly controlled DM (67%) and severe or critical COVID-19 (95%). Its presentation was typical of MCR seen in diabetic patients (mostly rhino-orbital and rhino-orbital-cerebral presentation). In sharp contrast to reported COVID-associated aspergillosis (CAPA) cases, nearly all CAMCR infections were proven (93%). Treating physicians should have a high suspicion for CAMCR in patients with uncontrolled diabetes mellitus and severe COVID-19 presenting with rhino-orbital or rhino-cerebral syndromes. CAMR is the convergence of two storms, one of DM and the other of COVID-19.

Explanation Plus Prediction—The Logical Focus of Project Management Research

2021 ◽  
pp. 875697282199994
Author(s):  
Joseph F. Hair ◽  
Marko Sarstedt
Keyword(s):  
Project Management ◽  
Statistical Models ◽  
Predictive Power ◽  
Mean Absolute Error ◽  
Explanatory Power ◽  
Absolute Error ◽  
Model Parameters ◽  
Mean Square ◽  
Management Research ◽  
The Mean

Most project management research focuses almost exclusively on explanatory analyses. Evaluation of the explanatory power of statistical models is generally based on F-type statistics and the R 2 metric, followed by an assessment of the model parameters (e.g., beta coefficients) in terms of their significance, size, and direction. However, these measures are not indicative of a model’s predictive power, which is central for deriving managerial recommendations. We recommend that project management researchers routinely use additional metrics, such as the mean absolute error or the root mean square error, to accurately quantify their statistical models’ predictive power.

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.

Predicting matches in international football tournaments with random forests

2018 ◽  
Vol 18 (5-6) ◽  
pp. 460-482 ◽  
Author(s):  
Gunther Schauberger ◽  
Andreas Groll
Keyword(s):  
Random Forests ◽  
Statistical Models ◽  
Regression Models ◽  
Predictive Power ◽  
Statistical Modelling ◽  
World Cup ◽  
Fifa World Cup ◽  
Specific Data ◽  
Covariate Information ◽  
Different Types

Many approaches that analyse and predict results of international matches in football are based on statistical models incorporating several potentially influential covariates with respect to a national team's success, such as the bookmakers’ ratings or the FIFA ranking. Based on all matches from the four previous FIFA World Cups 2002–2014, we compare the most common regression models that are based on the teams’ covariate information with regard to their predictive performances with an alternative modelling class, the so-called random forests. Random forests can be seen as a mixture between machine learning and statistical modelling and are known for their high predictive power. Here, we consider two different types of random forests depending on the choice of response. One type of random forests predicts the precise numbers of goals, while the other type considers the three match outcomes—win, draw and loss—using special algorithms for ordinal responses. To account for the specific data structure of football matches, in particular at FIFA World Cups, the random forest methods are slightly altered compared to their standard versions and adapted to the specific needs of the application to FIFA World Cup data.

scholarly journals Relative importance analysis with multivariate models: Shifting the focus from independent variables to parameter estimates

2020 ◽  
Vol 4 (2) ◽  
pp. 1-20
Author(s):  
Joseph N. Luchman ◽  
Xue Lei ◽  
Seth Kaplan
Keyword(s):  
Statistical Models ◽  
Likelihood Estimation ◽  
Parameter Estimate ◽  
Theory And Practice ◽  
Parameter Estimates ◽  
Relative Importance ◽  
Multivariate Models ◽  
Independent Variables ◽  
Dependent Variables ◽  
Independent Variable

Conclusions regarding the relative importance of different independent variables in a statistical model have meaningful implications for theory and practice. However, methods for determining relative importance have yet to extend beyond statistical models with a single dependent variable and a limited set of multivariate models. To accommodate multivariate models, the current work proposes shifting away from the concept of independent variable relative importance toward that of parameter estimate relative importance (PERI). This paper illustrates the PERI approach by comparing it to the evaluation of regression slopes and independent variable relative importance (IVRI) statistics to show the interpretive and methodological advantages of the new concept and associated methods. PERI’s advantages above standardized slopes stem from the same fit metric that is used to compute PERI statistics; this makes them more comparable to one another than standardized slopes. PERI’s advantages over IVRI stem from situations where independent variables do not predict all dependent variables; hence, PERI permits importance determination in situations where independent variables are nested in dependent variables they predict. We also provide recommendations for implementing PERI using dominance analysis with statistical models that can be estimated with maximum likelihood estimation combined with a series of model constraints using two examples.

scholarly journals Factor Associated with Abnormal Ankle-Brachial Index Among Diabetic Patients in Songklanagarind Hospital: A Retrospective Cohort Study

2018 ◽  
Vol 36 (4) ◽  
pp. 299 ◽  
Author(s):  
Supakorn Sripaew ◽  
Thanittha Sirirak
Keyword(s):  
Cohort Study ◽  
Retrospective Cohort Study ◽  
Retrospective Cohort ◽  
Coronary Revascularization ◽  
Ankle Brachial Index ◽  
Smoking History ◽  
Diabetic Patients ◽  
Coronary Syndrome ◽  
Previous Stroke ◽  
History Of

Objective: To find the correlation between type 2 diabetic patients who had abnormal ankle-brachial index (ABI) among factors affected diabetes and cardiovascular outcomes including acute coronary syndrome (ACS), myocardial infarction (MI), coronary revascularization stroke, renal replacement therapy, leg revascularization and limb amputation Material and Methods: Retrospective cohort study collecting the data of 548 diabetic patients examined ABI at Outpatient Departments from 1st January 2009 to 31st December 2015. Results: From 548 medical records including only normal-ABI group and low-ABI group, we found that hypertension, chronic kidney disease (CKD), smoking, history of previous MI, history of previous stroke and age were the significant associated factor of low-ABI. The survival analyses revealed the significantly higher rate of ACS, MI, and coronary revascularization in low-ABI group (p-value=0.04, <0.01, <0.01 respectively) after exposed to low-ABI around 4 years. However, the study found no significant difference of other outcomes between the 2 groups. Conclusion: Songklanagarind’s diabetic patients with low-ABI were associated with the significantly higher rate of multiple cardiovascular risk factors including  hypertension, CKD, smoking, history of previous MI, history of previous stroke and age and they tend to significantly experience more ACS, MI and coronary revascularization after 4 years exposed to low-ABI.

scholarly journals Survival Analysis on Time-To-Recovery of Diabetic Patients at Minlik Referral Hospital, Ethiopia: Retrospective Cohort Study

2021 ◽  
Author(s):  
Assaye Belay ◽  
Bizuwork Derebew ◽  
Solomon Abebaw
Keyword(s):  
Cohort Study ◽  
Retrospective Cohort Study ◽  
Retrospective Cohort ◽  
Intensive Therapy ◽  
Rank Test ◽  
Diabetic Patients ◽  
Fast Blood Glucose Level ◽  
Global Test ◽  
Oral Agents

Abstract AimThe study aimed to determine the time to recovery of diabetic patients who have been treated in the hospital under follow-up. Subject and MethodsA retrospective cohort study design was carried out. The fast blood glucose level of diabetic patients who are under follow-up in the hospital was measured from 2016 to 2020. One thousand seven hundred diabetic patients were included in the study. Kaplan-Meier, Log-rank test, global test, Schoenfeld residuals, and Cox-PH model were used for statistical analysis.ResultsOut of the total of 1278 patients, 27.4% were censored (withdrawal from follow-up) and 72.6% recovered from the diabetic disease. For sex, the expected hazard is 1.322 times higher in males than female diabetic patients or there is a 32.2% increase in the expected hazard in males relative to female diabetic patients. For Spdrt, The expected hazard is 1.164 times higher in the patients who had taken leute than diabetic patients who took doanied. For regimen, the expected hazard is 1.495 times higher in the patients who had been treated by insulin agent only than diabetic patients who were treated by oral agents only ConclusionThe intensive-therapy regimen, Spdrt, and gender differences were statistically significant and critically contribute to the survival time to recovery of diabetic patients.

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