Machine Learning to Identify Predictors of Glycemic Control in Type 2 Diabetes: An Analysis of Target HbA1c Reduction Using Empagliflozin/Linagliptin Data
Introduction Outcomes in type 2 diabetes mellitus (T2DM) could be optimized by identifying which treatments are likely to produce the greatest improvements in glycemic control for each patient. Objectives We aimed to identify patient characteristics associated with achieving and maintaining a target...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Del Parigi, Angelo [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2019 |
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| Anmerkung: |
© The Author(s) 2019 |
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| Übergeordnetes Werk: |
Enthalten in: Pharmaceutical medicine - Auckland : Wolters Kluwer Health Adis, 2008, 33(2019), 3 vom: 20. Mai, Seite 209-217 |
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| Übergeordnetes Werk: |
volume:33 ; year:2019 ; number:3 ; day:20 ; month:05 ; pages:209-217 |
| Links: |
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| DOI / URN: |
10.1007/s40290-019-00281-4 |
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SPR035368586 |
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| 245 | 1 | 0 | |a Machine Learning to Identify Predictors of Glycemic Control in Type 2 Diabetes: An Analysis of Target HbA1c Reduction Using Empagliflozin/Linagliptin Data |
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| 520 | |a Introduction Outcomes in type 2 diabetes mellitus (T2DM) could be optimized by identifying which treatments are likely to produce the greatest improvements in glycemic control for each patient. Objectives We aimed to identify patient characteristics associated with achieving and maintaining a target glycated hemoglobin (HbA1c) of ≤ 7% using machine learning methodology to analyze clinical trial data on combination therapy for T2DM. By applying a new machine learning methodology to an existing clinical dataset, the practical application of this approach was evaluated and the potential utility of this new approach to clinical decision making was assessed. Methods Data were pooled from two phase III, randomized, double-blind, parallel-group studies of empagliflozin/linagliptin single-pill combination therapy versus each monotherapy in patients who were treatment-naïve or receiving background metformin. Descriptive analysis was used to assess univariate associations between HbA1c target categories and each baseline characteristic. After the descriptive analysis results, a machine learning analysis was performed (classification tree and random forest methods) to estimate and predict target categories based on patient characteristics at baseline, without a priori selection. Results In the descriptive analysis, lower mean baseline HbA1c and fasting plasma glucose (FPG) were both associated with achieving and maintaining the HbA1c target. The machine learning analysis also identified HbA1c and FPG as the strongest predictors of attaining glycemic control. In contrast, covariates including body weight, waist circumference, blood pressure, or other variables did not contribute to the outcome. Conclusions Using both traditional and novel data analysis methodologies, this study identified baseline glycemic status as the strongest predictor of target glycemic control attainment. Machine learning algorithms provide an hypothesis-free, unbiased methodology, which can greatly enhance the search for predictors of therapeutic success in T2DM. The approach used in the present analysis provides an example of how a machine learning algorithm can be applied to a clinical dataset and used to develop predictions that can facilitate clinical decision making. | ||
| 520 | |a Plain Language Summary What did this study look at?This study looked at whether a computer program could predict which people with type 2 diabetes would respond best to a particular treatment.The study treatment was a single-pill combination of two medicines, empagliflozin [em-PAH-gli-FLOW-zin] and linagliptin [LYNN-nah-GLIP-tin]. It is used to lower blood sugar (blood glucose) in people with type 2 diabetes.The researchers used machine learning to analyze data from people who received this treatment. Machine learning uses computer models to find patterns in information.The results helped to predict which people might respond best to the treatment. Who took part in this study?The researchers looked at results collected from two earlier studies of the treatment.1363 people took part.Approximately half of them were male.Their average age was 55 years.Approximately half of them had not received any previous diabetes treatment, and approximately half (50.3%) had received metformin treatment for diabetes. What did the study show?The researchers found that two blood tests commonly used in clinical practice helped them predict who would have the best response to treatment.These tests were HbA1c levels (a measure of long-term blood glucose control), and their fasting plasma glucose (blood glucose levels when they had not eaten for 10–16 h).This study suggests that machine learning could be a useful tool to help doctors decide which treatments will work best for individuals with type 2 diabetes. | ||
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| 700 | 1 | |a Liu, Dacheng |4 aut | |
| 700 | 1 | |a Lee, Christopher |4 aut | |
| 700 | 1 | |a Pratley, Richard |4 aut | |
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10.1007/s40290-019-00281-4 doi (DE-627)SPR035368586 (SPR)s40290-019-00281-4-e DE-627 ger DE-627 rakwb eng Del Parigi, Angelo verfasserin aut Machine Learning to Identify Predictors of Glycemic Control in Type 2 Diabetes: An Analysis of Target HbA1c Reduction Using Empagliflozin/Linagliptin Data 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2019 Introduction Outcomes in type 2 diabetes mellitus (T2DM) could be optimized by identifying which treatments are likely to produce the greatest improvements in glycemic control for each patient. Objectives We aimed to identify patient characteristics associated with achieving and maintaining a target glycated hemoglobin (HbA1c) of ≤ 7% using machine learning methodology to analyze clinical trial data on combination therapy for T2DM. By applying a new machine learning methodology to an existing clinical dataset, the practical application of this approach was evaluated and the potential utility of this new approach to clinical decision making was assessed. Methods Data were pooled from two phase III, randomized, double-blind, parallel-group studies of empagliflozin/linagliptin single-pill combination therapy versus each monotherapy in patients who were treatment-naïve or receiving background metformin. Descriptive analysis was used to assess univariate associations between HbA1c target categories and each baseline characteristic. After the descriptive analysis results, a machine learning analysis was performed (classification tree and random forest methods) to estimate and predict target categories based on patient characteristics at baseline, without a priori selection. Results In the descriptive analysis, lower mean baseline HbA1c and fasting plasma glucose (FPG) were both associated with achieving and maintaining the HbA1c target. The machine learning analysis also identified HbA1c and FPG as the strongest predictors of attaining glycemic control. In contrast, covariates including body weight, waist circumference, blood pressure, or other variables did not contribute to the outcome. Conclusions Using both traditional and novel data analysis methodologies, this study identified baseline glycemic status as the strongest predictor of target glycemic control attainment. Machine learning algorithms provide an hypothesis-free, unbiased methodology, which can greatly enhance the search for predictors of therapeutic success in T2DM. The approach used in the present analysis provides an example of how a machine learning algorithm can be applied to a clinical dataset and used to develop predictions that can facilitate clinical decision making. Plain Language Summary What did this study look at?This study looked at whether a computer program could predict which people with type 2 diabetes would respond best to a particular treatment.The study treatment was a single-pill combination of two medicines, empagliflozin [em-PAH-gli-FLOW-zin] and linagliptin [LYNN-nah-GLIP-tin]. It is used to lower blood sugar (blood glucose) in people with type 2 diabetes.The researchers used machine learning to analyze data from people who received this treatment. Machine learning uses computer models to find patterns in information.The results helped to predict which people might respond best to the treatment. Who took part in this study?The researchers looked at results collected from two earlier studies of the treatment.1363 people took part.Approximately half of them were male.Their average age was 55 years.Approximately half of them had not received any previous diabetes treatment, and approximately half (50.3%) had received metformin treatment for diabetes. What did the study show?The researchers found that two blood tests commonly used in clinical practice helped them predict who would have the best response to treatment.These tests were HbA1c levels (a measure of long-term blood glucose control), and their fasting plasma glucose (blood glucose levels when they had not eaten for 10–16 h).This study suggests that machine learning could be a useful tool to help doctors decide which treatments will work best for individuals with type 2 diabetes. Tang, Wenbo aut Liu, Dacheng aut Lee, Christopher aut Pratley, Richard aut Enthalten in Pharmaceutical medicine Auckland : Wolters Kluwer Health Adis, 2008 33(2019), 3 vom: 20. Mai, Seite 209-217 (DE-627)56017375X (DE-600)2415180-4 1179-1993 nnns volume:33 year:2019 number:3 day:20 month:05 pages:209-217 https://dx.doi.org/10.1007/s40290-019-00281-4 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 33 2019 3 20 05 209-217 |
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10.1007/s40290-019-00281-4 doi (DE-627)SPR035368586 (SPR)s40290-019-00281-4-e DE-627 ger DE-627 rakwb eng Del Parigi, Angelo verfasserin aut Machine Learning to Identify Predictors of Glycemic Control in Type 2 Diabetes: An Analysis of Target HbA1c Reduction Using Empagliflozin/Linagliptin Data 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2019 Introduction Outcomes in type 2 diabetes mellitus (T2DM) could be optimized by identifying which treatments are likely to produce the greatest improvements in glycemic control for each patient. Objectives We aimed to identify patient characteristics associated with achieving and maintaining a target glycated hemoglobin (HbA1c) of ≤ 7% using machine learning methodology to analyze clinical trial data on combination therapy for T2DM. By applying a new machine learning methodology to an existing clinical dataset, the practical application of this approach was evaluated and the potential utility of this new approach to clinical decision making was assessed. Methods Data were pooled from two phase III, randomized, double-blind, parallel-group studies of empagliflozin/linagliptin single-pill combination therapy versus each monotherapy in patients who were treatment-naïve or receiving background metformin. Descriptive analysis was used to assess univariate associations between HbA1c target categories and each baseline characteristic. After the descriptive analysis results, a machine learning analysis was performed (classification tree and random forest methods) to estimate and predict target categories based on patient characteristics at baseline, without a priori selection. Results In the descriptive analysis, lower mean baseline HbA1c and fasting plasma glucose (FPG) were both associated with achieving and maintaining the HbA1c target. The machine learning analysis also identified HbA1c and FPG as the strongest predictors of attaining glycemic control. In contrast, covariates including body weight, waist circumference, blood pressure, or other variables did not contribute to the outcome. Conclusions Using both traditional and novel data analysis methodologies, this study identified baseline glycemic status as the strongest predictor of target glycemic control attainment. Machine learning algorithms provide an hypothesis-free, unbiased methodology, which can greatly enhance the search for predictors of therapeutic success in T2DM. The approach used in the present analysis provides an example of how a machine learning algorithm can be applied to a clinical dataset and used to develop predictions that can facilitate clinical decision making. Plain Language Summary What did this study look at?This study looked at whether a computer program could predict which people with type 2 diabetes would respond best to a particular treatment.The study treatment was a single-pill combination of two medicines, empagliflozin [em-PAH-gli-FLOW-zin] and linagliptin [LYNN-nah-GLIP-tin]. It is used to lower blood sugar (blood glucose) in people with type 2 diabetes.The researchers used machine learning to analyze data from people who received this treatment. Machine learning uses computer models to find patterns in information.The results helped to predict which people might respond best to the treatment. Who took part in this study?The researchers looked at results collected from two earlier studies of the treatment.1363 people took part.Approximately half of them were male.Their average age was 55 years.Approximately half of them had not received any previous diabetes treatment, and approximately half (50.3%) had received metformin treatment for diabetes. What did the study show?The researchers found that two blood tests commonly used in clinical practice helped them predict who would have the best response to treatment.These tests were HbA1c levels (a measure of long-term blood glucose control), and their fasting plasma glucose (blood glucose levels when they had not eaten for 10–16 h).This study suggests that machine learning could be a useful tool to help doctors decide which treatments will work best for individuals with type 2 diabetes. Tang, Wenbo aut Liu, Dacheng aut Lee, Christopher aut Pratley, Richard aut Enthalten in Pharmaceutical medicine Auckland : Wolters Kluwer Health Adis, 2008 33(2019), 3 vom: 20. Mai, Seite 209-217 (DE-627)56017375X (DE-600)2415180-4 1179-1993 nnns volume:33 year:2019 number:3 day:20 month:05 pages:209-217 https://dx.doi.org/10.1007/s40290-019-00281-4 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 33 2019 3 20 05 209-217 |
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10.1007/s40290-019-00281-4 doi (DE-627)SPR035368586 (SPR)s40290-019-00281-4-e DE-627 ger DE-627 rakwb eng Del Parigi, Angelo verfasserin aut Machine Learning to Identify Predictors of Glycemic Control in Type 2 Diabetes: An Analysis of Target HbA1c Reduction Using Empagliflozin/Linagliptin Data 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2019 Introduction Outcomes in type 2 diabetes mellitus (T2DM) could be optimized by identifying which treatments are likely to produce the greatest improvements in glycemic control for each patient. Objectives We aimed to identify patient characteristics associated with achieving and maintaining a target glycated hemoglobin (HbA1c) of ≤ 7% using machine learning methodology to analyze clinical trial data on combination therapy for T2DM. By applying a new machine learning methodology to an existing clinical dataset, the practical application of this approach was evaluated and the potential utility of this new approach to clinical decision making was assessed. Methods Data were pooled from two phase III, randomized, double-blind, parallel-group studies of empagliflozin/linagliptin single-pill combination therapy versus each monotherapy in patients who were treatment-naïve or receiving background metformin. Descriptive analysis was used to assess univariate associations between HbA1c target categories and each baseline characteristic. After the descriptive analysis results, a machine learning analysis was performed (classification tree and random forest methods) to estimate and predict target categories based on patient characteristics at baseline, without a priori selection. Results In the descriptive analysis, lower mean baseline HbA1c and fasting plasma glucose (FPG) were both associated with achieving and maintaining the HbA1c target. The machine learning analysis also identified HbA1c and FPG as the strongest predictors of attaining glycemic control. In contrast, covariates including body weight, waist circumference, blood pressure, or other variables did not contribute to the outcome. Conclusions Using both traditional and novel data analysis methodologies, this study identified baseline glycemic status as the strongest predictor of target glycemic control attainment. Machine learning algorithms provide an hypothesis-free, unbiased methodology, which can greatly enhance the search for predictors of therapeutic success in T2DM. The approach used in the present analysis provides an example of how a machine learning algorithm can be applied to a clinical dataset and used to develop predictions that can facilitate clinical decision making. Plain Language Summary What did this study look at?This study looked at whether a computer program could predict which people with type 2 diabetes would respond best to a particular treatment.The study treatment was a single-pill combination of two medicines, empagliflozin [em-PAH-gli-FLOW-zin] and linagliptin [LYNN-nah-GLIP-tin]. It is used to lower blood sugar (blood glucose) in people with type 2 diabetes.The researchers used machine learning to analyze data from people who received this treatment. Machine learning uses computer models to find patterns in information.The results helped to predict which people might respond best to the treatment. Who took part in this study?The researchers looked at results collected from two earlier studies of the treatment.1363 people took part.Approximately half of them were male.Their average age was 55 years.Approximately half of them had not received any previous diabetes treatment, and approximately half (50.3%) had received metformin treatment for diabetes. What did the study show?The researchers found that two blood tests commonly used in clinical practice helped them predict who would have the best response to treatment.These tests were HbA1c levels (a measure of long-term blood glucose control), and their fasting plasma glucose (blood glucose levels when they had not eaten for 10–16 h).This study suggests that machine learning could be a useful tool to help doctors decide which treatments will work best for individuals with type 2 diabetes. Tang, Wenbo aut Liu, Dacheng aut Lee, Christopher aut Pratley, Richard aut Enthalten in Pharmaceutical medicine Auckland : Wolters Kluwer Health Adis, 2008 33(2019), 3 vom: 20. Mai, Seite 209-217 (DE-627)56017375X (DE-600)2415180-4 1179-1993 nnns volume:33 year:2019 number:3 day:20 month:05 pages:209-217 https://dx.doi.org/10.1007/s40290-019-00281-4 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 33 2019 3 20 05 209-217 |
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10.1007/s40290-019-00281-4 doi (DE-627)SPR035368586 (SPR)s40290-019-00281-4-e DE-627 ger DE-627 rakwb eng Del Parigi, Angelo verfasserin aut Machine Learning to Identify Predictors of Glycemic Control in Type 2 Diabetes: An Analysis of Target HbA1c Reduction Using Empagliflozin/Linagliptin Data 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2019 Introduction Outcomes in type 2 diabetes mellitus (T2DM) could be optimized by identifying which treatments are likely to produce the greatest improvements in glycemic control for each patient. Objectives We aimed to identify patient characteristics associated with achieving and maintaining a target glycated hemoglobin (HbA1c) of ≤ 7% using machine learning methodology to analyze clinical trial data on combination therapy for T2DM. By applying a new machine learning methodology to an existing clinical dataset, the practical application of this approach was evaluated and the potential utility of this new approach to clinical decision making was assessed. Methods Data were pooled from two phase III, randomized, double-blind, parallel-group studies of empagliflozin/linagliptin single-pill combination therapy versus each monotherapy in patients who were treatment-naïve or receiving background metformin. Descriptive analysis was used to assess univariate associations between HbA1c target categories and each baseline characteristic. After the descriptive analysis results, a machine learning analysis was performed (classification tree and random forest methods) to estimate and predict target categories based on patient characteristics at baseline, without a priori selection. Results In the descriptive analysis, lower mean baseline HbA1c and fasting plasma glucose (FPG) were both associated with achieving and maintaining the HbA1c target. The machine learning analysis also identified HbA1c and FPG as the strongest predictors of attaining glycemic control. In contrast, covariates including body weight, waist circumference, blood pressure, or other variables did not contribute to the outcome. Conclusions Using both traditional and novel data analysis methodologies, this study identified baseline glycemic status as the strongest predictor of target glycemic control attainment. Machine learning algorithms provide an hypothesis-free, unbiased methodology, which can greatly enhance the search for predictors of therapeutic success in T2DM. The approach used in the present analysis provides an example of how a machine learning algorithm can be applied to a clinical dataset and used to develop predictions that can facilitate clinical decision making. Plain Language Summary What did this study look at?This study looked at whether a computer program could predict which people with type 2 diabetes would respond best to a particular treatment.The study treatment was a single-pill combination of two medicines, empagliflozin [em-PAH-gli-FLOW-zin] and linagliptin [LYNN-nah-GLIP-tin]. It is used to lower blood sugar (blood glucose) in people with type 2 diabetes.The researchers used machine learning to analyze data from people who received this treatment. Machine learning uses computer models to find patterns in information.The results helped to predict which people might respond best to the treatment. Who took part in this study?The researchers looked at results collected from two earlier studies of the treatment.1363 people took part.Approximately half of them were male.Their average age was 55 years.Approximately half of them had not received any previous diabetes treatment, and approximately half (50.3%) had received metformin treatment for diabetes. What did the study show?The researchers found that two blood tests commonly used in clinical practice helped them predict who would have the best response to treatment.These tests were HbA1c levels (a measure of long-term blood glucose control), and their fasting plasma glucose (blood glucose levels when they had not eaten for 10–16 h).This study suggests that machine learning could be a useful tool to help doctors decide which treatments will work best for individuals with type 2 diabetes. Tang, Wenbo aut Liu, Dacheng aut Lee, Christopher aut Pratley, Richard aut Enthalten in Pharmaceutical medicine Auckland : Wolters Kluwer Health Adis, 2008 33(2019), 3 vom: 20. Mai, Seite 209-217 (DE-627)56017375X (DE-600)2415180-4 1179-1993 nnns volume:33 year:2019 number:3 day:20 month:05 pages:209-217 https://dx.doi.org/10.1007/s40290-019-00281-4 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 33 2019 3 20 05 209-217 |
| allfieldsSound |
10.1007/s40290-019-00281-4 doi (DE-627)SPR035368586 (SPR)s40290-019-00281-4-e DE-627 ger DE-627 rakwb eng Del Parigi, Angelo verfasserin aut Machine Learning to Identify Predictors of Glycemic Control in Type 2 Diabetes: An Analysis of Target HbA1c Reduction Using Empagliflozin/Linagliptin Data 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2019 Introduction Outcomes in type 2 diabetes mellitus (T2DM) could be optimized by identifying which treatments are likely to produce the greatest improvements in glycemic control for each patient. Objectives We aimed to identify patient characteristics associated with achieving and maintaining a target glycated hemoglobin (HbA1c) of ≤ 7% using machine learning methodology to analyze clinical trial data on combination therapy for T2DM. By applying a new machine learning methodology to an existing clinical dataset, the practical application of this approach was evaluated and the potential utility of this new approach to clinical decision making was assessed. Methods Data were pooled from two phase III, randomized, double-blind, parallel-group studies of empagliflozin/linagliptin single-pill combination therapy versus each monotherapy in patients who were treatment-naïve or receiving background metformin. Descriptive analysis was used to assess univariate associations between HbA1c target categories and each baseline characteristic. After the descriptive analysis results, a machine learning analysis was performed (classification tree and random forest methods) to estimate and predict target categories based on patient characteristics at baseline, without a priori selection. Results In the descriptive analysis, lower mean baseline HbA1c and fasting plasma glucose (FPG) were both associated with achieving and maintaining the HbA1c target. The machine learning analysis also identified HbA1c and FPG as the strongest predictors of attaining glycemic control. In contrast, covariates including body weight, waist circumference, blood pressure, or other variables did not contribute to the outcome. Conclusions Using both traditional and novel data analysis methodologies, this study identified baseline glycemic status as the strongest predictor of target glycemic control attainment. Machine learning algorithms provide an hypothesis-free, unbiased methodology, which can greatly enhance the search for predictors of therapeutic success in T2DM. The approach used in the present analysis provides an example of how a machine learning algorithm can be applied to a clinical dataset and used to develop predictions that can facilitate clinical decision making. Plain Language Summary What did this study look at?This study looked at whether a computer program could predict which people with type 2 diabetes would respond best to a particular treatment.The study treatment was a single-pill combination of two medicines, empagliflozin [em-PAH-gli-FLOW-zin] and linagliptin [LYNN-nah-GLIP-tin]. It is used to lower blood sugar (blood glucose) in people with type 2 diabetes.The researchers used machine learning to analyze data from people who received this treatment. Machine learning uses computer models to find patterns in information.The results helped to predict which people might respond best to the treatment. Who took part in this study?The researchers looked at results collected from two earlier studies of the treatment.1363 people took part.Approximately half of them were male.Their average age was 55 years.Approximately half of them had not received any previous diabetes treatment, and approximately half (50.3%) had received metformin treatment for diabetes. What did the study show?The researchers found that two blood tests commonly used in clinical practice helped them predict who would have the best response to treatment.These tests were HbA1c levels (a measure of long-term blood glucose control), and their fasting plasma glucose (blood glucose levels when they had not eaten for 10–16 h).This study suggests that machine learning could be a useful tool to help doctors decide which treatments will work best for individuals with type 2 diabetes. Tang, Wenbo aut Liu, Dacheng aut Lee, Christopher aut Pratley, Richard aut Enthalten in Pharmaceutical medicine Auckland : Wolters Kluwer Health Adis, 2008 33(2019), 3 vom: 20. Mai, Seite 209-217 (DE-627)56017375X (DE-600)2415180-4 1179-1993 nnns volume:33 year:2019 number:3 day:20 month:05 pages:209-217 https://dx.doi.org/10.1007/s40290-019-00281-4 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 33 2019 3 20 05 209-217 |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR035368586</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230519114438.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201007s2019 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s40290-019-00281-4</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR035368586</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s40290-019-00281-4-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Del Parigi, Angelo</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Machine Learning to Identify Predictors of Glycemic Control in Type 2 Diabetes: An Analysis of Target HbA1c Reduction Using Empagliflozin/Linagliptin Data</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2019</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Author(s) 2019</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Introduction Outcomes in type 2 diabetes mellitus (T2DM) could be optimized by identifying which treatments are likely to produce the greatest improvements in glycemic control for each patient. Objectives We aimed to identify patient characteristics associated with achieving and maintaining a target glycated hemoglobin (HbA1c) of ≤ 7% using machine learning methodology to analyze clinical trial data on combination therapy for T2DM. By applying a new machine learning methodology to an existing clinical dataset, the practical application of this approach was evaluated and the potential utility of this new approach to clinical decision making was assessed. Methods Data were pooled from two phase III, randomized, double-blind, parallel-group studies of empagliflozin/linagliptin single-pill combination therapy versus each monotherapy in patients who were treatment-naïve or receiving background metformin. Descriptive analysis was used to assess univariate associations between HbA1c target categories and each baseline characteristic. After the descriptive analysis results, a machine learning analysis was performed (classification tree and random forest methods) to estimate and predict target categories based on patient characteristics at baseline, without a priori selection. Results In the descriptive analysis, lower mean baseline HbA1c and fasting plasma glucose (FPG) were both associated with achieving and maintaining the HbA1c target. The machine learning analysis also identified HbA1c and FPG as the strongest predictors of attaining glycemic control. In contrast, covariates including body weight, waist circumference, blood pressure, or other variables did not contribute to the outcome. Conclusions Using both traditional and novel data analysis methodologies, this study identified baseline glycemic status as the strongest predictor of target glycemic control attainment. Machine learning algorithms provide an hypothesis-free, unbiased methodology, which can greatly enhance the search for predictors of therapeutic success in T2DM. The approach used in the present analysis provides an example of how a machine learning algorithm can be applied to a clinical dataset and used to develop predictions that can facilitate clinical decision making.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Plain Language Summary What did this study look at?This study looked at whether a computer program could predict which people with type 2 diabetes would respond best to a particular treatment.The study treatment was a single-pill combination of two medicines, empagliflozin [em-PAH-gli-FLOW-zin] and linagliptin [LYNN-nah-GLIP-tin]. It is used to lower blood sugar (blood glucose) in people with type 2 diabetes.The researchers used machine learning to analyze data from people who received this treatment. Machine learning uses computer models to find patterns in information.The results helped to predict which people might respond best to the treatment. Who took part in this study?The researchers looked at results collected from two earlier studies of the treatment.1363 people took part.Approximately half of them were male.Their average age was 55 years.Approximately half of them had not received any previous diabetes treatment, and approximately half (50.3%) had received metformin treatment for diabetes. What did the study show?The researchers found that two blood tests commonly used in clinical practice helped them predict who would have the best response to treatment.These tests were HbA1c levels (a measure of long-term blood glucose control), and their fasting plasma glucose (blood glucose levels when they had not eaten for 10–16 h).This study suggests that machine learning could be a useful tool to help doctors decide which treatments will work best for individuals with type 2 diabetes.</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Tang, Wenbo</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Liu, Dacheng</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Lee, Christopher</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Pratley, Richard</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Pharmaceutical medicine</subfield><subfield code="d">Auckland : Wolters Kluwer Health Adis, 2008</subfield><subfield code="g">33(2019), 3 vom: 20. 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Machine Learning to Identify Predictors of Glycemic Control in Type 2 Diabetes: An Analysis of Target HbA1c Reduction Using Empagliflozin/Linagliptin Data |
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Machine Learning to Identify Predictors of Glycemic Control in Type 2 Diabetes: An Analysis of Target HbA1c Reduction Using Empagliflozin/Linagliptin Data |
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Machine Learning to Identify Predictors of Glycemic Control in Type 2 Diabetes: An Analysis of Target HbA1c Reduction Using Empagliflozin/Linagliptin Data |
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Del Parigi, Angelo Tang, Wenbo Liu, Dacheng Lee, Christopher Pratley, Richard |
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machine learning to identify predictors of glycemic control in type 2 diabetes: an analysis of target hba1c reduction using empagliflozin/linagliptin data |
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Machine Learning to Identify Predictors of Glycemic Control in Type 2 Diabetes: An Analysis of Target HbA1c Reduction Using Empagliflozin/Linagliptin Data |
| abstract |
Introduction Outcomes in type 2 diabetes mellitus (T2DM) could be optimized by identifying which treatments are likely to produce the greatest improvements in glycemic control for each patient. Objectives We aimed to identify patient characteristics associated with achieving and maintaining a target glycated hemoglobin (HbA1c) of ≤ 7% using machine learning methodology to analyze clinical trial data on combination therapy for T2DM. By applying a new machine learning methodology to an existing clinical dataset, the practical application of this approach was evaluated and the potential utility of this new approach to clinical decision making was assessed. Methods Data were pooled from two phase III, randomized, double-blind, parallel-group studies of empagliflozin/linagliptin single-pill combination therapy versus each monotherapy in patients who were treatment-naïve or receiving background metformin. Descriptive analysis was used to assess univariate associations between HbA1c target categories and each baseline characteristic. After the descriptive analysis results, a machine learning analysis was performed (classification tree and random forest methods) to estimate and predict target categories based on patient characteristics at baseline, without a priori selection. Results In the descriptive analysis, lower mean baseline HbA1c and fasting plasma glucose (FPG) were both associated with achieving and maintaining the HbA1c target. The machine learning analysis also identified HbA1c and FPG as the strongest predictors of attaining glycemic control. In contrast, covariates including body weight, waist circumference, blood pressure, or other variables did not contribute to the outcome. Conclusions Using both traditional and novel data analysis methodologies, this study identified baseline glycemic status as the strongest predictor of target glycemic control attainment. Machine learning algorithms provide an hypothesis-free, unbiased methodology, which can greatly enhance the search for predictors of therapeutic success in T2DM. The approach used in the present analysis provides an example of how a machine learning algorithm can be applied to a clinical dataset and used to develop predictions that can facilitate clinical decision making. Plain Language Summary What did this study look at?This study looked at whether a computer program could predict which people with type 2 diabetes would respond best to a particular treatment.The study treatment was a single-pill combination of two medicines, empagliflozin [em-PAH-gli-FLOW-zin] and linagliptin [LYNN-nah-GLIP-tin]. It is used to lower blood sugar (blood glucose) in people with type 2 diabetes.The researchers used machine learning to analyze data from people who received this treatment. Machine learning uses computer models to find patterns in information.The results helped to predict which people might respond best to the treatment. Who took part in this study?The researchers looked at results collected from two earlier studies of the treatment.1363 people took part.Approximately half of them were male.Their average age was 55 years.Approximately half of them had not received any previous diabetes treatment, and approximately half (50.3%) had received metformin treatment for diabetes. What did the study show?The researchers found that two blood tests commonly used in clinical practice helped them predict who would have the best response to treatment.These tests were HbA1c levels (a measure of long-term blood glucose control), and their fasting plasma glucose (blood glucose levels when they had not eaten for 10–16 h).This study suggests that machine learning could be a useful tool to help doctors decide which treatments will work best for individuals with type 2 diabetes. © The Author(s) 2019 |
| abstractGer |
Introduction Outcomes in type 2 diabetes mellitus (T2DM) could be optimized by identifying which treatments are likely to produce the greatest improvements in glycemic control for each patient. Objectives We aimed to identify patient characteristics associated with achieving and maintaining a target glycated hemoglobin (HbA1c) of ≤ 7% using machine learning methodology to analyze clinical trial data on combination therapy for T2DM. By applying a new machine learning methodology to an existing clinical dataset, the practical application of this approach was evaluated and the potential utility of this new approach to clinical decision making was assessed. Methods Data were pooled from two phase III, randomized, double-blind, parallel-group studies of empagliflozin/linagliptin single-pill combination therapy versus each monotherapy in patients who were treatment-naïve or receiving background metformin. Descriptive analysis was used to assess univariate associations between HbA1c target categories and each baseline characteristic. After the descriptive analysis results, a machine learning analysis was performed (classification tree and random forest methods) to estimate and predict target categories based on patient characteristics at baseline, without a priori selection. Results In the descriptive analysis, lower mean baseline HbA1c and fasting plasma glucose (FPG) were both associated with achieving and maintaining the HbA1c target. The machine learning analysis also identified HbA1c and FPG as the strongest predictors of attaining glycemic control. In contrast, covariates including body weight, waist circumference, blood pressure, or other variables did not contribute to the outcome. Conclusions Using both traditional and novel data analysis methodologies, this study identified baseline glycemic status as the strongest predictor of target glycemic control attainment. Machine learning algorithms provide an hypothesis-free, unbiased methodology, which can greatly enhance the search for predictors of therapeutic success in T2DM. The approach used in the present analysis provides an example of how a machine learning algorithm can be applied to a clinical dataset and used to develop predictions that can facilitate clinical decision making. Plain Language Summary What did this study look at?This study looked at whether a computer program could predict which people with type 2 diabetes would respond best to a particular treatment.The study treatment was a single-pill combination of two medicines, empagliflozin [em-PAH-gli-FLOW-zin] and linagliptin [LYNN-nah-GLIP-tin]. It is used to lower blood sugar (blood glucose) in people with type 2 diabetes.The researchers used machine learning to analyze data from people who received this treatment. Machine learning uses computer models to find patterns in information.The results helped to predict which people might respond best to the treatment. Who took part in this study?The researchers looked at results collected from two earlier studies of the treatment.1363 people took part.Approximately half of them were male.Their average age was 55 years.Approximately half of them had not received any previous diabetes treatment, and approximately half (50.3%) had received metformin treatment for diabetes. What did the study show?The researchers found that two blood tests commonly used in clinical practice helped them predict who would have the best response to treatment.These tests were HbA1c levels (a measure of long-term blood glucose control), and their fasting plasma glucose (blood glucose levels when they had not eaten for 10–16 h).This study suggests that machine learning could be a useful tool to help doctors decide which treatments will work best for individuals with type 2 diabetes. © The Author(s) 2019 |
| abstract_unstemmed |
Introduction Outcomes in type 2 diabetes mellitus (T2DM) could be optimized by identifying which treatments are likely to produce the greatest improvements in glycemic control for each patient. Objectives We aimed to identify patient characteristics associated with achieving and maintaining a target glycated hemoglobin (HbA1c) of ≤ 7% using machine learning methodology to analyze clinical trial data on combination therapy for T2DM. By applying a new machine learning methodology to an existing clinical dataset, the practical application of this approach was evaluated and the potential utility of this new approach to clinical decision making was assessed. Methods Data were pooled from two phase III, randomized, double-blind, parallel-group studies of empagliflozin/linagliptin single-pill combination therapy versus each monotherapy in patients who were treatment-naïve or receiving background metformin. Descriptive analysis was used to assess univariate associations between HbA1c target categories and each baseline characteristic. After the descriptive analysis results, a machine learning analysis was performed (classification tree and random forest methods) to estimate and predict target categories based on patient characteristics at baseline, without a priori selection. Results In the descriptive analysis, lower mean baseline HbA1c and fasting plasma glucose (FPG) were both associated with achieving and maintaining the HbA1c target. The machine learning analysis also identified HbA1c and FPG as the strongest predictors of attaining glycemic control. In contrast, covariates including body weight, waist circumference, blood pressure, or other variables did not contribute to the outcome. Conclusions Using both traditional and novel data analysis methodologies, this study identified baseline glycemic status as the strongest predictor of target glycemic control attainment. Machine learning algorithms provide an hypothesis-free, unbiased methodology, which can greatly enhance the search for predictors of therapeutic success in T2DM. The approach used in the present analysis provides an example of how a machine learning algorithm can be applied to a clinical dataset and used to develop predictions that can facilitate clinical decision making. Plain Language Summary What did this study look at?This study looked at whether a computer program could predict which people with type 2 diabetes would respond best to a particular treatment.The study treatment was a single-pill combination of two medicines, empagliflozin [em-PAH-gli-FLOW-zin] and linagliptin [LYNN-nah-GLIP-tin]. It is used to lower blood sugar (blood glucose) in people with type 2 diabetes.The researchers used machine learning to analyze data from people who received this treatment. Machine learning uses computer models to find patterns in information.The results helped to predict which people might respond best to the treatment. Who took part in this study?The researchers looked at results collected from two earlier studies of the treatment.1363 people took part.Approximately half of them were male.Their average age was 55 years.Approximately half of them had not received any previous diabetes treatment, and approximately half (50.3%) had received metformin treatment for diabetes. What did the study show?The researchers found that two blood tests commonly used in clinical practice helped them predict who would have the best response to treatment.These tests were HbA1c levels (a measure of long-term blood glucose control), and their fasting plasma glucose (blood glucose levels when they had not eaten for 10–16 h).This study suggests that machine learning could be a useful tool to help doctors decide which treatments will work best for individuals with type 2 diabetes. © The Author(s) 2019 |
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Machine Learning to Identify Predictors of Glycemic Control in Type 2 Diabetes: An Analysis of Target HbA1c Reduction Using Empagliflozin/Linagliptin Data |
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Tang, Wenbo Liu, Dacheng Lee, Christopher Pratley, Richard |
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| score |
7.401269 |