scholarly journals Machine learning risk prediction model for acute coronary syndrome and death from use of non-steroidal anti-inflammatory drugs in administrative data

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Juan Lu ◽  
Ling Wang ◽  
Mohammed Bennamoun ◽  
Isaac Ward ◽  
Senjian An ◽  
...  
Keyword(s):  
Machine Learning ◽  
Acute Coronary Syndrome ◽  
Risk Prediction ◽  
Administrative Data ◽  
Adverse Outcome ◽  
Gradient Boosting ◽  
Coronary Syndrome ◽  
Gradient Boosting Machine ◽  
Inflammatory Drugs ◽  
One Year

AbstractOur aim was to investigate the usefulness of machine learning approaches on linked administrative health data at the population level in predicting older patients’ one-year risk of acute coronary syndrome and death following the use of non-steroidal anti-inflammatory drugs (NSAIDs). Patients from a Western Australian cardiovascular population who were supplied with NSAIDs between 1 Jan 2003 and 31 Dec 2004 were identified from Pharmaceutical Benefits Scheme data. Comorbidities from linked hospital admissions data and medication history were inputs. Admissions for acute coronary syndrome or death within one year from the first supply date were outputs. Machine learning classification methods were used to build models to predict ACS and death. Model performance was measured by the area under the receiver operating characteristic curve (AUC-ROC), sensitivity and specificity. There were 68,889 patients in the NSAIDs cohort with mean age 76 years and 54% were female. 1882 patients were admitted for acute coronary syndrome and 5405 patients died within one year after their first supply of NSAIDs. The multi-layer neural network, gradient boosting machine and support vector machine were applied to build various classification models. The gradient boosting machine achieved the best performance with an average AUC-ROC of 0.72 predicting ACS and 0.84 predicting death. Machine learning models applied to linked administrative data can potentially improve adverse outcome risk prediction. Further investigation of additional data and approaches are required to improve the performance for adverse outcome risk prediction.

Machine Learning Based Prediction Model for Using Non-steroidal Anti-inflammatory Drugs on Risk of Adverse Events

2021 ◽  
Author(s):  
Juan Lu ◽  
Ling Wang ◽  
Mohammed Bennamoun ◽  
Isaac Ward ◽  
Senjian An ◽  
...  
Keyword(s):  
Machine Learning ◽  
Acute Coronary Syndrome ◽  
Adverse Event ◽  
Adverse Events ◽  
Risk Prediction ◽  
Coronary Syndrome ◽  
Event Risk ◽  
Inflammatory Drugs ◽  
Anti Inflammatory ◽  
One Year

Abstract To investigate the usefulness of machine learning approaches on linked administrative health data at the population level in predicting older patients’ one-year risk of adverse events after supplied with non-steroidal anti-inflammatory drugs (NSAIDs). Patients from a Western Australian cardiovascular population who were supplied with NSAIDs between 1 Jan 2003 and 31 Dec 2004 were identified from Pharmaceutical Benefits Scheme data. Comorbidities from linked hospital admissions data and medication history were inputs. Admissions for acute coronary syndrome or deaths within one year from the last supply date were outputs. Machine learning classification methods were used to build models to predict ACS and optimise their performance, measured by the area under the receiver operating characteristic curve, sensitivity and specificity. There were 69007 patients in the NSAIDs cohort with mean age 76 years and 54.3% were female. 1882 patients were admitted for acute coronary syndrome and 5405 patients dead within one year after their last supply of NSAIDs. The multi-layer neural network, gradient boosting machine and support vector machine were applied to build various classification models. The models achieved an average AUC-ROC score of 0.72 predicting ACS and 0.84 predicting death. Machine learning models applied to linked administrative data can potentially improve adverse event risk prediction. Further investigation of additional data and approaches are required further to improve the performance for adverse event risk prediction.

Validation of a machine learned model to predict the diagnosis of myocardial infarction

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
D Doudesis ◽  
J Yang ◽  
A Tsanas ◽  
C Stables ◽  
A Shah ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
Acute Coronary Syndrome ◽  
Predictive Value ◽  
High Probability ◽  
Cardiovascular Death ◽  
Gradient Boosting ◽  
Funding Source ◽  
Coronary Syndrome ◽  
Low Probability ◽  
One Year

Abstract Introduction The myocardial-ischemic-injury-index (MI3) is a promising machine learned algorithm that predicts the likelihood of myocardial infarction in patients with suspected acute coronary syndrome. Whether this algorithm performs well in unselected patients or predicts recurrent events is unknown. Methods In an observational analysis from a multi-centre randomised trial, we included all patients with suspected acute coronary syndrome and serial high-sensitivity cardiac troponin I measurements without ST-segment elevation myocardial infarction. Using gradient boosting, MI3 incorporates age, sex, and two troponin measurements to compute a value (0–100) reflecting an individual's likelihood of myocardial infarction, and estimates the negative predictive value (NPV) and positive predictive value (PPV). Model performance for an index diagnosis of myocardial infarction, and for subsequent myocardial infarction or cardiovascular death at one year was determined using previously defined low- and high-probability thresholds (1.6 and 49.7, respectively). Results In total 20,761 of 48,282 (43%) patients (64±16 years, 46% women) were eligible of whom 3,278 (15.8%) had myocardial infarction. MI3 was well discriminated with an area under the receiver-operating-characteristic curve of 0.949 (95% confidence interval 0.946–0.952) identifying 12,983 (62.5%) patients as low-probability (sensitivity 99.3% [99.0–99.6%], NPV 99.8% [99.8–99.9%]), and 2,961 (14.3%) as high-probability (specificity 95.0% [94.7–95.3%], PPV 70.4% [69–71.9%]). At one year, subsequent myocardial infarction or cardiovascular death occurred more often in high-probability compared to low-probability patients (17.6% [520/2,961] versus 1.5% [197/12,983], P<0.001). Conclusions In unselected consecutive patients with suspected acute coronary syndrome, the MI3 algorithm accurately estimates the likelihood of myocardial infarction and predicts probability of subsequent adverse cardiovascular events. Performance of MI3 at example thresholds Funding Acknowledgement Type of funding source: Foundation. Main funding source(s): Medical Research Council

scholarly journals RISK PREDICTION OF SUFFERING AN ACUTE CORONARY SYNDROME THROUGH THE USE OF MACHINE LEARNING CLASSIFIERS

2020 ◽  
Vol 75 (11) ◽  
pp. 247
Author(s):  
Juan Pablo Costabel ◽  
Luis Poler ◽  
Cristian Garmendia ◽  
Florencia Lambardi ◽  
Paula Ariznavarreta ◽  
...  
Keyword(s):  
Machine Learning ◽  
Acute Coronary Syndrome ◽  
Risk Prediction ◽  
Machine Learning Classifiers ◽  
Coronary Syndrome ◽  
Learning Classifiers

Abstract 13455: Predicting Emergency Department Disposition at Triage for Suspected Patients With Acute Coronary Syndrome Using Machine Learning Algorithms

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
Stephanie O Frisch ◽  
Zeineb Bouzid ◽  
Jessica Zègre-Hemsey ◽  
Clifton W CALLAWAY ◽  
Holli A Devon ◽  
...  
Keyword(s):  
Machine Learning ◽  
Acute Coronary Syndrome ◽  
Critical Care ◽  
Random Forest ◽  
Characteristic Curve ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
Coronary Syndrome ◽  
Critical Care Admission

Introduction: Overcrowded emergency departments (ED) and undifferentiated patients make the provision of care and resources challenging. We examined whether machine learning algorithms could identify ED patients’ disposition (hospitalization and critical care admission) using readily available objective triage data among patients with symptoms suggestive of acute coronary syndrome (ACS). Methods: This was a retrospective observational cohort study of adult patients who were triaged at the ED for a suspected coronary event. A total of 162 input variables (k) were extracted from the electronic health record: demographics (k=3), mode of transportation (k=1), past medical/surgical history (k=57), first ED vital signs (k=7), home medications (k=31), symptomology (k=40), and the computer generated automatic interpretation of 12-lead electrocardiogram (k=23). The primary outcomes were hospitalization and critical care admission (i.e., admission to intensive or step-down care unit). We used 10-fold stratified cross validation to evaluate the performance of five machine learning algorithms to predict the study outcomes: logistic regression, naïve Bayes, random forest, gradient boosting and artificial neural network classifiers. We determined the best model by comparing the area under the receiver operating characteristic curve (AUC) of all models. Results: Included were 1201 patients (age 64±14, 39% female; 10% Black) with a total of 956 hospitalizations, and 169 critical care admissions. The best performing machine learning classifier for the outcome of hospitalization was gradient boosting machine with an AUC of 0.85 (95% CI, 0.82–0.89), 89% sensitivity, and F-score of 0.83; random forest classifier performed the best for the outcome of critical care admission with an AUC of 0.73 (95% CI, 0.70–0.77), 76% sensitivity, and F-score of 0.56. Conclusion: Predictive machine learning algorithms demonstrate excellent to good discriminative power to predict hospitalization and critical care admission, respectively. Administrators and clinicians could benefit from machine learning approaches to predict hospitalization and critical care admission, to optimize and allocate scarce ED and hospital resources and provide optimal care.

scholarly journals Machine learning approaches as an alternative to traditional statistical methods in cardiovascular risk prediction

2021 ◽  
Vol 4 (s1) ◽  
Author(s):  
Michela Sperti ◽  
Fabrizio D’Ascenzo ◽  
Luca Navarini ◽  
Giacomo Di Benedetto ◽  
Antonella Afeltra ◽  
...  
Keyword(s):  
Machine Learning ◽  
Acute Coronary Syndrome ◽  
Cardiovascular Risk ◽  
Psoriatic Arthritis ◽  
Risk Prediction ◽  
Inflammatory Arthritis ◽  
Learning Approaches ◽  
Coronary Syndrome ◽  
Cardiovascular Risk Prediction ◽  
Risk Predictors

Machine Learning (ML) algorithms have proven promising methodologies in improving Cardiovascular (CV) risk predictors based on traditional statistics. In the present work, two case studies are reported: CV risk prediction in patients affected by Inflammatory Arthritis (IA), with attention to Psoriatic Arthritis (PsA), and patients who experienced Acute Coronary Syndrome (ACS).

scholarly journals Acute coronary syndrome – Still a valid contraindication to perform rotational atherectomy? Early and one-year outcomes

2018 ◽  
Vol 71 (4) ◽  
pp. 382-388 ◽  
Author(s):  
Piotr Kübler ◽  
Wojciech Zimoch ◽  
Michał Kosowski ◽  
Brunon Tomasiewicz ◽  
Artur Telichowski ◽  
...  
Keyword(s):  
Acute Coronary Syndrome ◽  
Rotational Atherectomy ◽  
Coronary Syndrome ◽  
One Year

Prognostic usefulness of an age-adapted equation for renal function assessment in older patients with acute coronary syndrome

2017 ◽  
Vol 7 (8) ◽  
pp. 703-709 ◽  
Author(s):  
Lucía Rioboo Lestón ◽  
Emad Abu-Assi ◽  
Sergio Raposeiras-Roubin ◽  
Rafael Cobas-Paz ◽  
Berenice Caneiro-Queija ◽  
...  
Keyword(s):  
Acute Coronary Syndrome ◽  
Glomerular Filtration Rate ◽  
Glomerular Filtration ◽  
Risk Prediction ◽  
Older Patients ◽  
Filtration Rate ◽  
Estimated Glomerular Filtration Rate ◽  
Coronary Syndrome ◽  
Function Assessment

Background: Renal dysfunction negatively impacts survival in acute coronary syndrome patients. The Berlin Initiative Study creatinine-based (BIScrea) equation has recently been proposed for renal function assessment in older persons. However, up to now it is unknown if the superiority of the new BIScrea equation, with respect to the most recommended chronic kidney disease epidemiology collaboration creatinine-based (CKD-EPIcrea) formula, would translate into better risk prediction of adverse events in older patients with acute coronary syndrome. Objectives: To study the impact of using estimated glomerular filtration rate calculated according to the BIScrea and CKD-EPIcrea equations on mortality in acute coronary syndrome patients aged 70 years and over. Methods: Retrospectively, between 2011 and 2016, a total of 2008 patients with acute coronary syndrome (64% men; age 79±7 years) were studied. Follow-up was 18±10 months. Measures of performance were evaluated using continuous data and stratifying patients into three estimated glomerular filtration rate subgroups: ≥60, 59.9–30 and <30 mL/min/1.73 m2. Results: The two formulas afforded independent prognostic information over follow-up. However, risk prediction was most accurate using the BIScrea formula as evaluated by Cox proportional hazards models (hazard ratio (for each 10 mL/min/1.73 m2 decrease) 1.47 vs. 1.27 with the CKD-EPI equation; P<0.001 for comparison), c-statistic values (0.69 vs. 0.65, respectively; P=0.04 for comparison) and Bayesian information criterion. Net reclassification improvement based on the estimated glomerular filtration rate categories significantly favoured BIScrea +9 (95% confidence interval 2–16%; P=0.02). Conclusions: Our findings suggest that the BIScrea formula may improve death risk prediction more than the CKD-EPIcrea formula in older patients with acute coronary syndrome.

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