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FINDRISC in Latin America: a systematic review of diagnosis and prognosis models

Rodrigo M Carrillo-Larco, Diego J Aparcana-Granda, Jhonatan R Mejia, Antonio Bernabé-Ortiz
2020 BMJ Open Diabetes Research & Care  
None of the studies conducted an independent external validation of the FINDRISC; conversely, they used the same (or very similar) predictors to fit a new model.  ...  There is a need for big data to develop—or improve—T2DM diagnostic and prognostic models in LAC. This could benefit T2DM screening and early diagnosis.  ...  Table 3 3 Performance metrics First author, and assessed model Discrimination (%) Classification measures Gomez-Arbelaez et al 25 74.77 (95% CI 57.22 to 92.32) (men) and 71.75 (95% CI 58.68 to 84.81  ... 
doi:10.1136/bmjdrc-2019-001169 pmid:32327446 pmcid:PMC7202717 fatcat:2ovyfjlhx5dbhazwg6ain4t7rm

Glucose Variability and Mortality in Patients Hospitalized With Acute Myocardial Infarction

K. J. Lipska, L. Venkitachalam, K. Gosch, B. Kovatchev, G. Van den Berghe, G. Meyfroidt, P. G. Jones, S. E. Inzucchi, J. A. Spertus, J. H. DeVries, M. Kosiborod
2012 Circulation. Cardiovascular Quality and Outcomes  
Background-Mean blood glucose (BG) during acute myocardial infarction (AMI) is an important predictor of inpatient mortality but does not capture glucose variability (GV), which has been shown to be independently  ...  Five different GV metrics were compared for their ability to predict in-hospital mortality (range, standard deviation, mean amplitude of glycemic excursions, mean absolute glucose change, and average daily  ...  Specifically, we sought to identify GV metrics that are most predictive of inhospital mortality and to examine whether GV provides additional prognostic information above and beyond mean BG. mortality.  ... 
doi:10.1161/circoutcomes.111.963298 pmid:22693348 fatcat:i5mlguva3rd6jkioygmeymh2iq

Dynamic Stress Factor (DySF): A Significant Predictor of Severe Hypoglycemic Events in Children with Type 1 Diabetes

Rawlings RA, Yuan L, Brehm W
2012 Journal of Diabetes & Metabolism  
We introduce and compare the Dynamic Stress Factor, DySF, a newly developed metric that quantifies glycemic volatility based on patient-specific glucose transition density profiles with HbA1c and with  ...  models.  ...  Acknowledgments We thank James Henderson for his helpful discussion, and editing.  ... 
doi:10.4172/2155-6156.1000177 pmid:24349871 pmcid:PMC3859451 fatcat:ednm4am3sbedxo44ben7juyt7i

Risk scores for Type 2 diabetes mellitus in Latin America: a systematic review of population‐based studies

R. M. Carrillo‐Larco, D. J. Aparcana‐Granda, J. R. Mejia, N. C. Barengo, A. Bernabe‐Ortiz
2019 Diabetic Medicine  
The most common predictors were age, waist circumference and family history of diabetes, and only one study used oral glucose tolerance test as the outcome.  ...  the development and/or validation of a multivariable regression model.  ...  A general recommendation could be to conduct a systematic review of available models in the field to identify the most common and relevant predictors; alternatively, expert knowledge should be included  ... 
doi:10.1111/dme.14114 pmid:31441090 pmcid:PMC6900051 fatcat:jom2zeagejdrvcpuivh5mcrdcq

Glucometrics in Patients Hospitalized With Acute Myocardial Infarction: Defining the Optimal Outcomes-Based Measure of Risk

M. Kosiborod, S. E. Inzucchi, H. M. Krumholz, L. Xiao, P. G. Jones, S. Fiske, F. A. Masoudi, S. P. Marso, J. A. Spertus
2008 Circulation  
Models were then used to evaluate the relationship between mean glucose and in-hospital mortality. All average glucose metrics performed better than admission glucose.  ...  Whether metrics that incorporate multiple glucose assessments during acute myocardial infarction hospitalization are better predictors of mortality than admission glucose alone is not well defined.  ...  We sought to identify the summary metric of persistently elevated glucose during hospitalization with the greatest association with inpatient mortality and to establish whether this metric is a superior  ... 
doi:10.1161/circulationaha.107.740498 pmid:18268145 fatcat:5c3d54ujnvdkflzxfmjthwjhhi

Glycemic trend prediction using empirical model identification

Marzia Cescon, Rolf Johansson
2009 Proceedings of the 48h IEEE Conference on Decision and Control (CDC) held jointly with 2009 28th Chinese Control Conference  
we will remove access to the work immediately and investigate your claim.  ...  authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. • Users may download and print  ...  Prediction evaluation The quality of the predictors developed was assessed by statistical model validation and mathematical metrics in order to quantify the error between the predicted profile vs. the  ... 
doi:10.1109/cdc.2009.5400219 dblp:conf/cdc/CesconJ09 fatcat:47oi75gycrdx5kgf7tnloupkia

Linear Modeling and Prediction in Diabetes Physiology [chapter]

Marzia Cescon, Rolf Johansson
2014 Data-driven Modeling for Diabetes  
In order to address model-based control for blood glucose regulation, low-order, individualized, data-driven, stable, physiological relevant models were identified from a population of 9 T1DM patients  ...  This thesis presents work on data-driven glucose metabolism modeling and short-term, that is, up to 120 minutes, blood-glucose prediction in Type 1 Diabetes Mellitus (T1DM) subjects.  ...  The quality of the predictors developed was assessed by mathematical metrics in order to quantify the error between the predicted blood glucose profile vs. the actual ones.  ... 
doi:10.1007/978-3-642-54464-4_9 fatcat:g367i7kn3bgbtiffsxkpu56bnq

Use of reclassification for assessment of improved prediction: an empirical evaluation

Ioanna Tzoulaki, George Liberopoulos, John P A Ioannidis
2011 International Journal of Epidemiology  
We focused on articles that included any analyses comparing the performance of a baseline predictive model vs the baseline model plus some additional predictor for a prospectively assessed outcome.  ...  Methods Two independent investigators searched PubMed and citations to the article that introduced the currently most popular reclassification metric (net reclassification index, NRI) to identify studies  ...  outcomes, predictors, and study-specific biases.  ... 
doi:10.1093/ije/dyr013 pmid:21325392 fatcat:r2spxvxmwvfuzgzfnarl2sxn2a

Impact of statistical models on the prediction of type 2 diabetes using non-targeted metabolomics profiling

Loic Yengo, Abdelilah Arredouani, Michel Marre, Ronan Roussel, Martine Vaxillaire, Mario Falchi, Abdelali Haoudi, Jean Tichet, Beverley Balkau, Amélie Bonnefond, Philippe Froguel
2016 Molecular Metabolism  
ABSTRACT Objective: Characterizing specific metabolites in sub-clinical phases preceding the onset of type 2 diabetes to enable efficient preventive and personalized interventions.  ...  Research design and methods: We developed predictive models of type 2 diabetes using two strategies.  ...  ACKNOWLEDGMENTS We are grateful to all participants of this study.  ... 
doi:10.1016/j.molmet.2016.08.011 pmid:27689004 pmcid:PMC5034686 fatcat:lgarutlcjvat5ns4qrh6iz6j6u

Machine Learning Models for Nocturnal Hypoglycemia Prediction in Hospitalized Patients with Type 1 Diabetes

Vladimir B. Berikov, Olga A. Kutnenko, Julia F. Semenova, Vadim V. Klimontov
2022 Journal of Personalized Medicine  
Eight CGM-derived metrics of glycemic control and glucose variability were included in the models. Combinations of CGM and clinical data (23 parameters) were also assessed.  ...  The models were trained on continuous glucose monitoring (CGM) data obtained from 406 adult patients admitted to a tertiary referral hospital.  ...  Assessment of NH Predictors We used RF as a standard tool for estimating the value of predictors in a model [26] .  ... 
doi:10.3390/jpm12081262 pmid:36013211 pmcid:PMC9409948 fatcat:px6olaqqdra2bpdans3pymkouy

Model-Based Insulin Sensitivity as a Sepsis Diagnostic in Critical Care

Amy Blakemore, Sheng-Hui Wang, Aaron Le Compte, Geoffrey M. Shaw, Xing-Wei Wong, Jessica Lin, Thomas Lotz, Christopher E. Hann, J. Geoffrey Chase
2008 Journal of Diabetes Science and Technology  
METHODS ROC curves and cutoff insulin sensitivity values for diagnosing sepsis were calculated for model based insulin sensitivity (S I ) and a simpler metric (SS I ) that was estimated from the glycaemic  ...  Model-based S I fell below this value in 15% of all patient hours. The S I test has a negative predictive value of 99.8% The test sensitivity is 78% and specificity is 82%.  ...  of sepsis (ss ≥ 3) ROC of Insulin Sensitivity (SS I ) metric evaluated in real time as a predictor of sepsis Figure 4 . 4 Correlation between SS I and S I the simple and model based metrics for insulin  ... 
doi:10.1177/193229680800200317 pmid:19885212 pmcid:PMC2769723 fatcat:xecc4holy5conavgnngija5qn4

Novel Approach to Inpatient Glucometric Monitoring and Variability in a Community Hospital Setting

James Koziol, Keith Johnson, Kathy Brenner, Addie Fortmann, Robin Morrisey, Athena Philis-Tsimikas
2014 Journal of Diabetes Science and Technology  
Standardized metrics are needed to assess the efficacy and safety of glucose management interventions.  ...  Moreover, a standardized metric is needed to assess and 564992D STXXX10.1177/1932296814564992Journal of Diabetes Science and TechnologyKoziol et al research-article2014 Abstract Background: Hyperglycemia  ...  grant from Sanofi, and a research grant from Novo Nordisk.  ... 
doi:10.1177/1932296814564992 pmid:25539653 pmcid:PMC4604585 fatcat:fgsi5q3on5ckjmpmsd4msygcmm

Characterising frailty, metrics of continuous glucose monitoring, and mortality hazards in older adults with type 2 diabetes on insulin therapy (HARE): a prospective, observational cohort study

Erik Fung, Leong-Ting Lui, Lei Huang, King Fai Cheng, Gloria H W Lau, Yi Ting Chung, Behzad Nasiri Ahmadabadi, Suyi Xie, Jenny S W Lee, Elsie Hui, Wing Yee So, Joseph J Y Sung (+7 others)
2021 The Lancet Healthy Longevity  
We used continuous glucose monitors (CGMs) to profile this patient population and determine the prognostic value of CGM metrics.  ...  assessment and CGM recording.  ...  Acknowledgments EF is recipient of an investigator-initiated grant from the Health and Medical Research Fund (HMRF number 15162161) of the Food and Health Bureau, Hong Kong Special Administrative Region  ... 
doi:10.1016/s2666-7568(21)00251-8 pmid:36098029 fatcat:nso3go6irjgbfgwmww2qhd7qyy

Clinical Application of Time in Range and Other Metrics

Grazia Aleppo
2021 Diabetes Spectrum  
Application of TIR to clinical practice can be easily done with a stepped approach to the analysis and interpretation of CGM-derived metrics and the ambulatory glucose profile report.  ...  Time in range (TIR) and other continuous glucose monitoring (CGM)-derived metrics have been standardized in international consensus conferences.  ...  evidence building around TIR as a predictor of long-term diabetes complications, and offers a practical approach to applying the 10 core CGM metrics and ambulatory glucose profile (AGP) in clinical practice  ... 
doi:10.2337/ds20-0093 pmid:34149251 pmcid:PMC8178724 fatcat:j5gwm6grunajfpyaypkffgrc7m

Model-fusion-based online glucose concentration predictions in people with type 1 diabetes

Xia Yu, Kamuran Turksoy, Mudassir Rashid, Jianyuan Feng, Nicole Hobbs, Iman Hajizadeh, Sediqeh Samadi, Mert Sevil, Caterina Lazaro, Zacharie Maloney, Elizabeth Littlejohn, Laurie Quinn (+1 others)
2018 Control Engineering Practice  
In this work, a novel glucose forecasting paradigm based on a model fusion strategy is developed to accurately characterize the variability and transient dynamics of glycemic measurements.  ...  To this end, four different adaptive filters and a fusion mechanism are proposed for use in the online prediction of future glucose trajectories.  ...  Acknowledgments This work is supported by the National Institutes of Health (NIH) under grants 1DP3DK101075-01 and 1DP3DK101077-01. Dr.  ... 
doi:10.1016/j.conengprac.2017.10.013 pmid:29276347 pmcid:PMC5736323 fatcat:nu6wv5u5qrgthadjm55zcrvkja
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