Hemoglobin A1c control is an independent predictor of circulating troponin concentrations using machine learning
Abstract Diabetes mellitus (DM) is associated with multiple comorbidities that may precipitate cardiac damage and elevated cardiac troponin (cTn) concentrations including renal failure, hyperlipidemia (HLD), hypertension (HTN), coronary artery disease (CAD), and congestive heart failure (CHF). Diabe...
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description | Abstract
Diabetes mellitus (DM) is associated with multiple comorbidities that may precipitate cardiac damage and elevated cardiac troponin (cTn) concentrations including renal failure, hyperlipidemia (HLD), hypertension (HTN), coronary artery disease (CAD), and congestive heart failure (CHF). Diabetics >70 years old have a higher likelihood of adverse outcomes including major cardiovascular events and cardiac mortality. However, the risk to younger diabetic patients and the precipitating factor(s) that facilitate cardiac injury in those with DM are unknown. The objective of this study was to investigate factors predictive of cTn concentrations in young, relatively healthy diabetic patients. We collected 1,533 remnant plasma samples from outpatients between 06/22-09/22 with physician ordered hemoglobin A1c testing. cTn was measured using the Abbott ARCHITECT High Sensitivity Troponin-I assay (limit of detection = 1.7 ng/L, imprecision = 4.76% at 50 ng/L). Demographic information (sex, race, BMI) and pertinent medical history (diabetes, HTN, HLD, CAD, CHF) were collected from the electronic medical record, along with estimated glomerular filtration rate (eGFR) and hemoglobin A1c. Exclusion criteria included: patients with missing laboratory data, those undergoing cancer treatment, or a history of myocardial infarction/cardiomyopathy/ cardiac surgery. Troponin results were classified as normal- or high-risk using a cut-off of 10 ng/L for females and 12 ng/L for males, thresholds previously shown to correlate with incidence of cardiovascular disease risk within 15 years. Univariate statistics were calculated using bootstrap resampling. An XGBoost model was trained to predict high-risk troponinemia, and summary statistics were calculated on a held-out test set. Of the 1,135 patients that met inclusion criteria, 621 (54.7%) were female. The median age was 60 years (IQR: 49-69 years) and the median A1c was 6.2% (IQR: 5.8-7.0%). A total of 746 patients (65.7%) had a prior diabetes diagnosis, with 156 patients (13.7%) having prediabetes, 42 patients (3.7%) having Type 1, and 548 patients (48.3%) having Type 2. Median troponin concentration for patients with an A1c |
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Diabetes mellitus (DM) is associated with multiple comorbidities that may precipitate cardiac damage and elevated cardiac troponin (cTn) concentrations including renal failure, hyperlipidemia (HLD), hypertension (HTN), coronary artery disease (CAD), and congestive heart failure (CHF). Diabetics >70 years old have a higher likelihood of adverse outcomes including major cardiovascular events and cardiac mortality. However, the risk to younger diabetic patients and the precipitating factor(s) that facilitate cardiac injury in those with DM are unknown. The objective of this study was to investigate factors predictive of cTn concentrations in young, relatively healthy diabetic patients. We collected 1,533 remnant plasma samples from outpatients between 06/22-09/22 with physician ordered hemoglobin A1c testing. cTn was measured using the Abbott ARCHITECT High Sensitivity Troponin-I assay (limit of detection = 1.7 ng/L, imprecision = 4.76% at 50 ng/L). Demographic information (sex, race, BMI) and pertinent medical history (diabetes, HTN, HLD, CAD, CHF) were collected from the electronic medical record, along with estimated glomerular filtration rate (eGFR) and hemoglobin A1c. Exclusion criteria included: patients with missing laboratory data, those undergoing cancer treatment, or a history of myocardial infarction/cardiomyopathy/ cardiac surgery. Troponin results were classified as normal- or high-risk using a cut-off of 10 ng/L for females and 12 ng/L for males, thresholds previously shown to correlate with incidence of cardiovascular disease risk within 15 years. Univariate statistics were calculated using bootstrap resampling. An XGBoost model was trained to predict high-risk troponinemia, and summary statistics were calculated on a held-out test set. Of the 1,135 patients that met inclusion criteria, 621 (54.7%) were female. The median age was 60 years (IQR: 49-69 years) and the median A1c was 6.2% (IQR: 5.8-7.0%). A total of 746 patients (65.7%) had a prior diabetes diagnosis, with 156 patients (13.7%) having prediabetes, 42 patients (3.7%) having Type 1, and 548 patients (48.3%) having Type 2. Median troponin concentration for patients with an A1c <5.7% was 1.6 ng/L (IQR: 0.8-3.4), 5.7-6.4% was 2.2 ng/L (IQR:1.3-4.8), and ≥6.5% was 2.9 ng/L (IQR: 1.6-6.7). Univariate analysis demonstrated significant differences in troponinemia by age (CI of difference: 4.4-8.9 years), A1c (0.2-0.7%), and eGFR (28-38mL/min/1.73m2). The machine learning model demonstrated strong predictive capacity (sensitivity: 0.74, specificity: 0.86, PPV: 0.5, NPV: 0.92, area under ROC curve: 0.91). The features with the greatest impact on area under the ROC curve when removed were eGFR, Age, A1c, and CHF, suggesting a combination of clinical and laboratory variables can be used to predict circulating troponin concentrations in outpatients. Results of both univariate and multivariate analyses suggest that A1c control is an independent contributor to cTn concentrations. Ultimately, this study provides evidence that glucose control may be associated with cardiac damage and future cardiovascular events, warranting longitudinal outcome studies.</description><identifier>ISSN: 0002-9173</identifier><identifier>EISSN: 1943-7722</identifier><identifier>DOI: 10.1093/ajcp/aqad150.305</identifier><language>eng</language><publisher>US: Oxford University Press</publisher><subject>Age ; Cancer therapies ; Cardiomyopathy ; Cardiovascular disease ; Cardiovascular diseases ; Comorbidity ; Congestive heart failure ; Coronary artery disease ; Diabetes ; Diabetes mellitus ; Electronic medical records ; Epidermal growth factor receptors ; Glomerular filtration rate ; Hemoglobin ; Hyperlipidemia ; Learning algorithms ; Machine learning ; Myocardial infarction ; Patients ; Renal failure ; Statistical analysis ; Troponin</subject><ispartof>American journal of clinical pathology, 2023-11, Vol.160 (Supplement_1), p.S141-S142</ispartof><rights>The Author(s) 2023. Published by Oxford University Press on behalf of American Society for Clinical Pathology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com 2023</rights><rights>The Author(s) 2023. Published by Oxford University Press on behalf of American Society for Clinical Pathology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,1578,27901,27902</link.rule.ids></links><search><creatorcontrib>Brown, Hannah</creatorcontrib><creatorcontrib>Spies, Nicholas</creatorcontrib><creatorcontrib>Zaydman, Mark</creatorcontrib><creatorcontrib>Farnsworth, Christopher</creatorcontrib><title>Hemoglobin A1c control is an independent predictor of circulating troponin concentrations using machine learning</title><title>American journal of clinical pathology</title><description>Abstract
Diabetes mellitus (DM) is associated with multiple comorbidities that may precipitate cardiac damage and elevated cardiac troponin (cTn) concentrations including renal failure, hyperlipidemia (HLD), hypertension (HTN), coronary artery disease (CAD), and congestive heart failure (CHF). Diabetics >70 years old have a higher likelihood of adverse outcomes including major cardiovascular events and cardiac mortality. However, the risk to younger diabetic patients and the precipitating factor(s) that facilitate cardiac injury in those with DM are unknown. The objective of this study was to investigate factors predictive of cTn concentrations in young, relatively healthy diabetic patients. We collected 1,533 remnant plasma samples from outpatients between 06/22-09/22 with physician ordered hemoglobin A1c testing. cTn was measured using the Abbott ARCHITECT High Sensitivity Troponin-I assay (limit of detection = 1.7 ng/L, imprecision = 4.76% at 50 ng/L). Demographic information (sex, race, BMI) and pertinent medical history (diabetes, HTN, HLD, CAD, CHF) were collected from the electronic medical record, along with estimated glomerular filtration rate (eGFR) and hemoglobin A1c. Exclusion criteria included: patients with missing laboratory data, those undergoing cancer treatment, or a history of myocardial infarction/cardiomyopathy/ cardiac surgery. Troponin results were classified as normal- or high-risk using a cut-off of 10 ng/L for females and 12 ng/L for males, thresholds previously shown to correlate with incidence of cardiovascular disease risk within 15 years. Univariate statistics were calculated using bootstrap resampling. An XGBoost model was trained to predict high-risk troponinemia, and summary statistics were calculated on a held-out test set. Of the 1,135 patients that met inclusion criteria, 621 (54.7%) were female. The median age was 60 years (IQR: 49-69 years) and the median A1c was 6.2% (IQR: 5.8-7.0%). A total of 746 patients (65.7%) had a prior diabetes diagnosis, with 156 patients (13.7%) having prediabetes, 42 patients (3.7%) having Type 1, and 548 patients (48.3%) having Type 2. Median troponin concentration for patients with an A1c <5.7% was 1.6 ng/L (IQR: 0.8-3.4), 5.7-6.4% was 2.2 ng/L (IQR:1.3-4.8), and ≥6.5% was 2.9 ng/L (IQR: 1.6-6.7). Univariate analysis demonstrated significant differences in troponinemia by age (CI of difference: 4.4-8.9 years), A1c (0.2-0.7%), and eGFR (28-38mL/min/1.73m2). The machine learning model demonstrated strong predictive capacity (sensitivity: 0.74, specificity: 0.86, PPV: 0.5, NPV: 0.92, area under ROC curve: 0.91). The features with the greatest impact on area under the ROC curve when removed were eGFR, Age, A1c, and CHF, suggesting a combination of clinical and laboratory variables can be used to predict circulating troponin concentrations in outpatients. Results of both univariate and multivariate analyses suggest that A1c control is an independent contributor to cTn concentrations. Ultimately, this study provides evidence that glucose control may be associated with cardiac damage and future cardiovascular events, warranting longitudinal outcome studies.</description><subject>Age</subject><subject>Cancer therapies</subject><subject>Cardiomyopathy</subject><subject>Cardiovascular disease</subject><subject>Cardiovascular diseases</subject><subject>Comorbidity</subject><subject>Congestive heart failure</subject><subject>Coronary artery disease</subject><subject>Diabetes</subject><subject>Diabetes mellitus</subject><subject>Electronic medical records</subject><subject>Epidermal growth factor receptors</subject><subject>Glomerular filtration rate</subject><subject>Hemoglobin</subject><subject>Hyperlipidemia</subject><subject>Learning algorithms</subject><subject>Machine learning</subject><subject>Myocardial infarction</subject><subject>Patients</subject><subject>Renal failure</subject><subject>Statistical analysis</subject><subject>Troponin</subject><issn>0002-9173</issn><issn>1943-7722</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>BENPR</sourceid><recordid>eNqFkD1vwyAQhlHVSk3T7h2ROlZOOLBjM0ZR21SK1CU7woBTIgcI2EP_fbGSPQOHdPe89_Ei9ApkAYSzpTyqsJRnqaEiC0aqOzQDXrKirim9RzNCCC041OwRPaV0JARoQ8oZCltz8ofet9bhNSisvBui77FNWDpsnTbB5OAGHKLRVg0-Yt9hZaMaezlYd8CZD95lfdaqTMac9i7hMU3Vk1S_1hncGxkzdHhGD53sk3m5_nO0__zYb7bF7ufre7PeFQrKuipM10LDpi1hRXWz0rxt85NMrxiUeX1oG8nyCVQaUmra1Q00wJSBlteMsjl6u7QN0Z9HkwZx9GN0eaKgvKoorWrOM0UulIo-pWg6EaI9yfgngIjJVjHZKq62imxrlrxfJH4Mt-l_RZ18XA</recordid><startdate>20231129</startdate><enddate>20231129</enddate><creator>Brown, Hannah</creator><creator>Spies, Nicholas</creator><creator>Zaydman, Mark</creator><creator>Farnsworth, Christopher</creator><general>Oxford University Press</general><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7RV</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8FE</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>KB0</scope><scope>LK8</scope><scope>M0S</scope><scope>M1P</scope><scope>M7P</scope><scope>NAPCQ</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope></search><sort><creationdate>20231129</creationdate><title>Hemoglobin A1c control is an independent predictor of circulating troponin concentrations using machine learning</title><author>Brown, Hannah ; Spies, Nicholas ; Zaydman, Mark ; Farnsworth, Christopher</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c1475-efb1830128162d86d9bbd9ba3d63140011b8a38042ae04d2f781813ce1b97323</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Age</topic><topic>Cancer therapies</topic><topic>Cardiomyopathy</topic><topic>Cardiovascular disease</topic><topic>Cardiovascular diseases</topic><topic>Comorbidity</topic><topic>Congestive heart failure</topic><topic>Coronary artery disease</topic><topic>Diabetes</topic><topic>Diabetes mellitus</topic><topic>Electronic medical records</topic><topic>Epidermal growth factor receptors</topic><topic>Glomerular filtration rate</topic><topic>Hemoglobin</topic><topic>Hyperlipidemia</topic><topic>Learning algorithms</topic><topic>Machine learning</topic><topic>Myocardial infarction</topic><topic>Patients</topic><topic>Renal failure</topic><topic>Statistical analysis</topic><topic>Troponin</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Brown, Hannah</creatorcontrib><creatorcontrib>Spies, Nicholas</creatorcontrib><creatorcontrib>Zaydman, Mark</creatorcontrib><creatorcontrib>Farnsworth, Christopher</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>ProQuest Nursing & Allied Health Database</collection><collection>Health Medical collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>ProQuest Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection (Proquest) (PQ_SDU_P3)</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>Biological Sciences</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>PML(ProQuest Medical Library)</collection><collection>Biological Science Database</collection><collection>Nursing & Allied Health Premium</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><jtitle>American journal of clinical pathology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Brown, Hannah</au><au>Spies, Nicholas</au><au>Zaydman, Mark</au><au>Farnsworth, Christopher</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Hemoglobin A1c control is an independent predictor of circulating troponin concentrations using machine learning</atitle><jtitle>American journal of clinical pathology</jtitle><date>2023-11-29</date><risdate>2023</risdate><volume>160</volume><issue>Supplement_1</issue><spage>S141</spage><epage>S142</epage><pages>S141-S142</pages><issn>0002-9173</issn><eissn>1943-7722</eissn><abstract>Abstract
Diabetes mellitus (DM) is associated with multiple comorbidities that may precipitate cardiac damage and elevated cardiac troponin (cTn) concentrations including renal failure, hyperlipidemia (HLD), hypertension (HTN), coronary artery disease (CAD), and congestive heart failure (CHF). Diabetics >70 years old have a higher likelihood of adverse outcomes including major cardiovascular events and cardiac mortality. However, the risk to younger diabetic patients and the precipitating factor(s) that facilitate cardiac injury in those with DM are unknown. The objective of this study was to investigate factors predictive of cTn concentrations in young, relatively healthy diabetic patients. We collected 1,533 remnant plasma samples from outpatients between 06/22-09/22 with physician ordered hemoglobin A1c testing. cTn was measured using the Abbott ARCHITECT High Sensitivity Troponin-I assay (limit of detection = 1.7 ng/L, imprecision = 4.76% at 50 ng/L). Demographic information (sex, race, BMI) and pertinent medical history (diabetes, HTN, HLD, CAD, CHF) were collected from the electronic medical record, along with estimated glomerular filtration rate (eGFR) and hemoglobin A1c. Exclusion criteria included: patients with missing laboratory data, those undergoing cancer treatment, or a history of myocardial infarction/cardiomyopathy/ cardiac surgery. Troponin results were classified as normal- or high-risk using a cut-off of 10 ng/L for females and 12 ng/L for males, thresholds previously shown to correlate with incidence of cardiovascular disease risk within 15 years. Univariate statistics were calculated using bootstrap resampling. An XGBoost model was trained to predict high-risk troponinemia, and summary statistics were calculated on a held-out test set. Of the 1,135 patients that met inclusion criteria, 621 (54.7%) were female. The median age was 60 years (IQR: 49-69 years) and the median A1c was 6.2% (IQR: 5.8-7.0%). A total of 746 patients (65.7%) had a prior diabetes diagnosis, with 156 patients (13.7%) having prediabetes, 42 patients (3.7%) having Type 1, and 548 patients (48.3%) having Type 2. Median troponin concentration for patients with an A1c <5.7% was 1.6 ng/L (IQR: 0.8-3.4), 5.7-6.4% was 2.2 ng/L (IQR:1.3-4.8), and ≥6.5% was 2.9 ng/L (IQR: 1.6-6.7). Univariate analysis demonstrated significant differences in troponinemia by age (CI of difference: 4.4-8.9 years), A1c (0.2-0.7%), and eGFR (28-38mL/min/1.73m2). The machine learning model demonstrated strong predictive capacity (sensitivity: 0.74, specificity: 0.86, PPV: 0.5, NPV: 0.92, area under ROC curve: 0.91). The features with the greatest impact on area under the ROC curve when removed were eGFR, Age, A1c, and CHF, suggesting a combination of clinical and laboratory variables can be used to predict circulating troponin concentrations in outpatients. Results of both univariate and multivariate analyses suggest that A1c control is an independent contributor to cTn concentrations. Ultimately, this study provides evidence that glucose control may be associated with cardiac damage and future cardiovascular events, warranting longitudinal outcome studies.</abstract><cop>US</cop><pub>Oxford University Press</pub><doi>10.1093/ajcp/aqad150.305</doi><oa>free_for_read</oa></addata></record> |
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subjects | Age Cancer therapies Cardiomyopathy Cardiovascular disease Cardiovascular diseases Comorbidity Congestive heart failure Coronary artery disease Diabetes Diabetes mellitus Electronic medical records Epidermal growth factor receptors Glomerular filtration rate Hemoglobin Hyperlipidemia Learning algorithms Machine learning Myocardial infarction Patients Renal failure Statistical analysis Troponin |
title | Hemoglobin A1c control is an independent predictor of circulating troponin concentrations using machine learning |
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