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Prediction of short-term atrial fibrillation risk using primary care electronic health records
Conclusions FIND-AF, a machine learning algorithm applicable at scale in routinely collected primary care data, identifies people at higher risk of short-term AF.
Source: Heart - June 26, 2023 Category: Cardiology Authors: Nadarajah, R., Wu, J., Hogg, D., Raveendra, K., Nakao, Y. M., Nakao, K., Arbel, R., Haim, M., Zahger, D., Parry, J., Bates, C., Cowan, C., Gale, C. P. Tags: Open access, Editor's choice Arrhythmias and sudden death Source Type: research

Perioperative adverse cardiac events and mortality after non-cardiac surgery: a multicenter study
CONCLUSIONS: A nationwide multicenter study showed that PACE was significantly associated with increased one-year mortality. This association was stronger in high-risk surgery, older, male, and chronic kidney disease subgroups. Further studies to improve mortality associated with PACE are needed.PMID:37169362 | DOI:10.4097/kja.23043
Source: Korean Journal of Anesthesiology - May 11, 2023 Category: Anesthesiology Authors: Byungjin Choi Ah Ran Oh Jungchan Park Jong-Hwan Lee Kwangmo Yang Dong Yun Lee Sang Youl Rhee Sang-Soo Kang Seung Do Lee Sun Hack Lee Chang Won Jeong Bumhee Park Soobeen Seol Rae Woong Park Seunghwa Lee Source Type: research

Prediction of incident atrial fibrillation in community-based electronic health records: a systematic review with meta-analysis
Conclusions Models externally validated for prediction of incident AF in community-based EHR demonstrate moderate predictive ability and high risk of bias. Novel methods may provide stronger discriminative performance. Systematic review registration PROSPERO CRD42021245093.
Source: Heart - June 10, 2022 Category: Cardiology Authors: Nadarajah, R., Alsaeed, E., Hurdus, B., Aktaa, S., Hogg, D., Bates, M. G. D., Cowan, C., Wu, J., Gale, C. P. Tags: Open access Arrhythmias and sudden death Source Type: research

Effects of ACE inhibitors and angiotensin receptor blockers: protocol for a UK cohort study using routinely collected electronic health records with validation against the ONTARGET trial
Introduction Cardiovascular disease is a leading cause of death globally, responsible for nearly 18 million deaths worldwide in 2017. Medications to reduce the risk of cardiovascular events are prescribed based on evidence from clinical trials which explore treatment effects in an indicated sample of the general population. However, these results may not be fully generalisable because of trial eligibility criteria that generally restrict to younger patients with fewer comorbidities. Therefore, evidence of effectiveness of medications for groups underrepresented in clinical trials such as those aged ≥75 years, from ethni...
Source: BMJ Open - March 8, 2022 Category: General Medicine Authors: Baptiste, P. J., Wong, A. Y. S., Schultze, A., Cunnington, M., Mann, J. F. E., Clase, C., Leyrat, C., Tomlinson, L. A., Wing, K. Tags: Open access, Epidemiology Source Type: research

P-043 Elevated D-dimer levels predicts mortality in COVID-19 with stroke: analysis of multi-center electronic health record data
ConclusionsPeak D-dimer levels above 5.15 µg/ml feu are associated with increased mortality in COVID-19 patients with AIS.Disclosures Y. Kim: None. S. Khose: None. R. Abdelkhaleq: None. S. Salazar-Marioni: None. S. Sheth: None.
Source: Journal of NeuroInterventional Surgery - July 26, 2021 Category: Neurosurgery Authors: Kim, Y., Khose, S., Abdelkhaleq, R., Salazar-Marioni, S., Sheth, S. Tags: Oral poster abstracts Source Type: research

Developing a New Score: How Machine Learning Improves Risk Prediction
Composite risk scores have been used for decades to identify disease risk and health status in the general population. However, current approaches often fail to identify people who would benefit from intervention or recommend unnecessary intervention. Machine learning promises to improve accuracy, ensuring targeted treatment for patients that need it and reducing unnecessary intervention. Framingham Risk Score, the gold standard for predicting the likelihood of heart disease, predicts hospitalizations with about 56% accuracy. It uses factors such as age, gender, smoking, cholesterol levels, and systolic blood pressure to...
Source: MDDI - November 17, 2017 Category: Medical Devices Authors: Heather R. Johnson Tags: R & D Source Type: news

Hypoglycaemia seriousness and weight gain as determinants of cardiovascular disease outcomes among sulfonylurea users
ConclusionsThis study provides evidence of increased CVD risk associated with hypoglycaemia, especially serious hypoglycaemia events. While associations were attenuated with non‐serious hypoglycaemia, the results were suggestive of a potential increased risk.
Source: Diabetes, Obesity and Metabolism - May 1, 2017 Category: Endocrinology Authors: Anthony P. Nunes, Kristy Iglay, Larry Radican, Samuel S. Engel, Jing Yang, Michael C. Doherty, David D. Dore Tags: ORIGINAL ARTICLE Source Type: research

Abstract 166: Developing the Veterans Affairs Cardiac Risk Score Session Title: Poster Session I
Conclusion: We demonstrated that an EHR in a specific population could risk-stratify patients as well those from as organized cohort studies and greatly improve calibration. Further, our finding that the ASCVD score greatly underpredicted in our population, while previous work have reported the ASCVD over-predictind in other cohorts, suggests that rather than arguing about which risk tool is best, our patients may be better served by us focusing on calibrating CV risk tools for our specific patient population using their EHR data.
Source: Circulation: Cardiovascular Quality and Outcomes - April 29, 2015 Category: Cardiology Authors: Sussman, J. B., Wiitala, W., Hofer, T., Zawitowski, M., Vijan, S., Hayward, R. Tags: Session Title: Poster Session I Source Type: research