We’re excited to share that the American Journal of Obstetrics and Gynecology...
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We’re excited to share that the American Journal of Obstetrics and Gynecology has published a paper authored by Delfina’s accomplished Senior Data Scientist, Sara Sauer, PhD, and Chief Scientific Officer, Isabel Fulcher, PhD in collaboration with University of Alabama at Birmingham researchers, Ashley Battarbee, MD and Ayodeji Sanusi, MD. This paper, an important contribution to enhancing pregnancy outcomes in individuals with Type I and Type II diabetes, leverages continuous glucose monitor (CGM) data to provide valuable insights into the relationship between diabetes control during pregnancy and adverse pregnancy outcomes.
In traditional diabetes care, blood sugar readings are taken manually several times a day. However, with CGMs, glucose measurements are gathered every 5 minutes, allowing us to uncover and analyze glycemic trends among people with diabetes. When pregnant people with diabetes wear CGMs, we can get a clearer picture of how diabetes control during pregnancy could impact pregnancy outcomes.
Our study, “Discrete glucose profiles identified using continuous glucose monitoring data and their association with adverse pregnancy outcomes,” fills a crucial gap in the existing scholarship by identifying distinct glycemic patterns during pregnancy and relating these to adverse pregnancy outcomes.
First, our data scientists used machine learning techniques to identify and classify CGM data into four discrete glucose profiles: “well-controlled,” two different “suboptimally controlled” profiles, and one “poorly controlled” profile. Then, the researchers examined the association between these profiles and adverse pregnancy outcomes like preterm birth, c-section, preeclampsia, large-for-gestational-age babies, and NICU admission.
According to the study’s results, individuals with suboptimally or poorly controlled diabetes had a higher risk of adverse pregnancy outcomes. For example, the poorly controlled profile characterized by prolonged hyperglycemia overnight was associated with a higher risk of preeclampsia, large-for-gestational-age newborn, NICU admission, and having a baby with hypoglycemia.
“CGM provides a rich longitudinal data source that is often overlooked in clinical practice because the sheer volume of data is overwhelming and difficult for providers to interpret,” Dr. Sara Sauer, one of the paper’s authors, said. “Our analysis is a step towards making CGM data more interpretable and, most importantly, more actionable. ”
Delfina is dedicated to data-driven analysis and intervention, and this paper exemplifies that value. With more data, as CGMs offer, we can better understand how to improve pregnancy outcomes for individuals with diabetes.
Dr. Isabel Fulcher, Delfina’s CSO, believes in the potential of CGMs. “Our research team is excited about the future of CGM in pregnancy given its potential for delivering a more personalized patient care experience.”
Read the full paper here.
We’re excited to share that the American Journal of Obstetrics and Gynecology...
We’re excited to share that the American Journal of Obstetrics and Gynecology has published a paper authored by Delfina’s accomplished Senior Data Scientist, Sara Sauer, PhD, and Chief Scientific Officer, Isabel Fulcher, PhD in collaboration with University of Alabama at Birmingham researchers, Ashley Battarbee, MD and Ayodeji Sanusi, MD. This paper, an important contribution to enhancing pregnancy outcomes in individuals with Type I and Type II diabetes, leverages continuous glucose monitor (CGM) data to provide valuable insights into the relationship between diabetes control during pregnancy and adverse pregnancy outcomes.
In traditional diabetes care, blood sugar readings are taken manually several times a day. However, with CGMs, glucose measurements are gathered every 5 minutes, allowing us to uncover and analyze glycemic trends among people with diabetes. When pregnant people with diabetes wear CGMs, we can get a clearer picture of how diabetes control during pregnancy could impact pregnancy outcomes.
Our study, “Discrete glucose profiles identified using continuous glucose monitoring data and their association with adverse pregnancy outcomes,” fills a crucial gap in the existing scholarship by identifying distinct glycemic patterns during pregnancy and relating these to adverse pregnancy outcomes.
First, our data scientists used machine learning techniques to identify and classify CGM data into four discrete glucose profiles: “well-controlled,” two different “suboptimally controlled” profiles, and one “poorly controlled” profile. Then, the researchers examined the association between these profiles and adverse pregnancy outcomes like preterm birth, c-section, preeclampsia, large-for-gestational-age babies, and NICU admission.
According to the study’s results, individuals with suboptimally or poorly controlled diabetes had a higher risk of adverse pregnancy outcomes. For example, the poorly controlled profile characterized by prolonged hyperglycemia overnight was associated with a higher risk of preeclampsia, large-for-gestational-age newborn, NICU admission, and having a baby with hypoglycemia.
“CGM provides a rich longitudinal data source that is often overlooked in clinical practice because the sheer volume of data is overwhelming and difficult for providers to interpret,” Dr. Sara Sauer, one of the paper’s authors, said. “Our analysis is a step towards making CGM data more interpretable and, most importantly, more actionable. ”
Delfina is dedicated to data-driven analysis and intervention, and this paper exemplifies that value. With more data, as CGMs offer, we can better understand how to improve pregnancy outcomes for individuals with diabetes.
Dr. Isabel Fulcher, Delfina’s CSO, believes in the potential of CGMs. “Our research team is excited about the future of CGM in pregnancy given its potential for delivering a more personalized patient care experience.”
Read the full paper here.
We’re excited to share that the American Journal of Obstetrics and Gynecology...
We’re excited to share that the American Journal of Obstetrics and Gynecology has published a paper authored by Delfina’s accomplished Senior Data Scientist, Sara Sauer, PhD, and Chief Scientific Officer, Isabel Fulcher, PhD in collaboration with University of Alabama at Birmingham researchers, Ashley Battarbee, MD and Ayodeji Sanusi, MD. This paper, an important contribution to enhancing pregnancy outcomes in individuals with Type I and Type II diabetes, leverages continuous glucose monitor (CGM) data to provide valuable insights into the relationship between diabetes control during pregnancy and adverse pregnancy outcomes.
In traditional diabetes care, blood sugar readings are taken manually several times a day. However, with CGMs, glucose measurements are gathered every 5 minutes, allowing us to uncover and analyze glycemic trends among people with diabetes. When pregnant people with diabetes wear CGMs, we can get a clearer picture of how diabetes control during pregnancy could impact pregnancy outcomes.
Our study, “Discrete glucose profiles identified using continuous glucose monitoring data and their association with adverse pregnancy outcomes,” fills a crucial gap in the existing scholarship by identifying distinct glycemic patterns during pregnancy and relating these to adverse pregnancy outcomes.
First, our data scientists used machine learning techniques to identify and classify CGM data into four discrete glucose profiles: “well-controlled,” two different “suboptimally controlled” profiles, and one “poorly controlled” profile. Then, the researchers examined the association between these profiles and adverse pregnancy outcomes like preterm birth, c-section, preeclampsia, large-for-gestational-age babies, and NICU admission.
According to the study’s results, individuals with suboptimally or poorly controlled diabetes had a higher risk of adverse pregnancy outcomes. For example, the poorly controlled profile characterized by prolonged hyperglycemia overnight was associated with a higher risk of preeclampsia, large-for-gestational-age newborn, NICU admission, and having a baby with hypoglycemia.
“CGM provides a rich longitudinal data source that is often overlooked in clinical practice because the sheer volume of data is overwhelming and difficult for providers to interpret,” Dr. Sara Sauer, one of the paper’s authors, said. “Our analysis is a step towards making CGM data more interpretable and, most importantly, more actionable. ”
Delfina is dedicated to data-driven analysis and intervention, and this paper exemplifies that value. With more data, as CGMs offer, we can better understand how to improve pregnancy outcomes for individuals with diabetes.
Dr. Isabel Fulcher, Delfina’s CSO, believes in the potential of CGMs. “Our research team is excited about the future of CGM in pregnancy given its potential for delivering a more personalized patient care experience.”
Read the full paper here.
We’re excited to share that the American Journal of Obstetrics and Gynecology...
We’re excited to share that the American Journal of Obstetrics and Gynecology has published a paper authored by Delfina’s accomplished Senior Data Scientist, Sara Sauer, PhD, and Chief Scientific Officer, Isabel Fulcher, PhD in collaboration with University of Alabama at Birmingham researchers, Ashley Battarbee, MD and Ayodeji Sanusi, MD. This paper, an important contribution to enhancing pregnancy outcomes in individuals with Type I and Type II diabetes, leverages continuous glucose monitor (CGM) data to provide valuable insights into the relationship between diabetes control during pregnancy and adverse pregnancy outcomes.
In traditional diabetes care, blood sugar readings are taken manually several times a day. However, with CGMs, glucose measurements are gathered every 5 minutes, allowing us to uncover and analyze glycemic trends among people with diabetes. When pregnant people with diabetes wear CGMs, we can get a clearer picture of how diabetes control during pregnancy could impact pregnancy outcomes.
Our study, “Discrete glucose profiles identified using continuous glucose monitoring data and their association with adverse pregnancy outcomes,” fills a crucial gap in the existing scholarship by identifying distinct glycemic patterns during pregnancy and relating these to adverse pregnancy outcomes.
First, our data scientists used machine learning techniques to identify and classify CGM data into four discrete glucose profiles: “well-controlled,” two different “suboptimally controlled” profiles, and one “poorly controlled” profile. Then, the researchers examined the association between these profiles and adverse pregnancy outcomes like preterm birth, c-section, preeclampsia, large-for-gestational-age babies, and NICU admission.
According to the study’s results, individuals with suboptimally or poorly controlled diabetes had a higher risk of adverse pregnancy outcomes. For example, the poorly controlled profile characterized by prolonged hyperglycemia overnight was associated with a higher risk of preeclampsia, large-for-gestational-age newborn, NICU admission, and having a baby with hypoglycemia.
“CGM provides a rich longitudinal data source that is often overlooked in clinical practice because the sheer volume of data is overwhelming and difficult for providers to interpret,” Dr. Sara Sauer, one of the paper’s authors, said. “Our analysis is a step towards making CGM data more interpretable and, most importantly, more actionable. ”
Delfina is dedicated to data-driven analysis and intervention, and this paper exemplifies that value. With more data, as CGMs offer, we can better understand how to improve pregnancy outcomes for individuals with diabetes.
Dr. Isabel Fulcher, Delfina’s CSO, believes in the potential of CGMs. “Our research team is excited about the future of CGM in pregnancy given its potential for delivering a more personalized patient care experience.”
Read the full paper here.
We’re excited to share that the American Journal of Obstetrics and Gynecology...
We’re excited to share that the American Journal of Obstetrics and Gynecology has published a paper authored by Delfina’s accomplished Senior Data Scientist, Sara Sauer, PhD, and Chief Scientific Officer, Isabel Fulcher, PhD in collaboration with University of Alabama at Birmingham researchers, Ashley Battarbee, MD and Ayodeji Sanusi, MD. This paper, an important contribution to enhancing pregnancy outcomes in individuals with Type I and Type II diabetes, leverages continuous glucose monitor (CGM) data to provide valuable insights into the relationship between diabetes control during pregnancy and adverse pregnancy outcomes.
In traditional diabetes care, blood sugar readings are taken manually several times a day. However, with CGMs, glucose measurements are gathered every 5 minutes, allowing us to uncover and analyze glycemic trends among people with diabetes. When pregnant people with diabetes wear CGMs, we can get a clearer picture of how diabetes control during pregnancy could impact pregnancy outcomes.
Our study, “Discrete glucose profiles identified using continuous glucose monitoring data and their association with adverse pregnancy outcomes,” fills a crucial gap in the existing scholarship by identifying distinct glycemic patterns during pregnancy and relating these to adverse pregnancy outcomes.
First, our data scientists used machine learning techniques to identify and classify CGM data into four discrete glucose profiles: “well-controlled,” two different “suboptimally controlled” profiles, and one “poorly controlled” profile. Then, the researchers examined the association between these profiles and adverse pregnancy outcomes like preterm birth, c-section, preeclampsia, large-for-gestational-age babies, and NICU admission.
According to the study’s results, individuals with suboptimally or poorly controlled diabetes had a higher risk of adverse pregnancy outcomes. For example, the poorly controlled profile characterized by prolonged hyperglycemia overnight was associated with a higher risk of preeclampsia, large-for-gestational-age newborn, NICU admission, and having a baby with hypoglycemia.
“CGM provides a rich longitudinal data source that is often overlooked in clinical practice because the sheer volume of data is overwhelming and difficult for providers to interpret,” Dr. Sara Sauer, one of the paper’s authors, said. “Our analysis is a step towards making CGM data more interpretable and, most importantly, more actionable. ”
Delfina is dedicated to data-driven analysis and intervention, and this paper exemplifies that value. With more data, as CGMs offer, we can better understand how to improve pregnancy outcomes for individuals with diabetes.
Dr. Isabel Fulcher, Delfina’s CSO, believes in the potential of CGMs. “Our research team is excited about the future of CGM in pregnancy given its potential for delivering a more personalized patient care experience.”
Read the full paper here.
We’re excited to share that the American Journal of Obstetrics and Gynecology...
