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Systolic blood pressure, chronic obstructive pulmonary disease and cardiovascular risk
Conclusion Using deep learning for modelling EHR, we identified a monotonic association between SBP and risk of cardiovascular events in patients with COPD.
Source: Heart - July 27, 2023 Category: Cardiology Authors: Rao, S., Nazarzadeh, M., Li, Y., Canoy, D., Mamouei, M., Salimi-Khorshidi, G., Rahimi, K. Tags: Open access Cardiac risk factors and prevention Source Type: research

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

Common clinical blood and urine biomarkers for ischemic stroke: an Estonian Electronic Health Records database study
ConclusionsWe conclude that the EHR database and the risk factors uncovered are valuable resources in screening the population for risk of IS as well as constructing disease risk scores and refining prediction models for IS by ML.
Source: European Journal of Medical Research - March 25, 2023 Category: Research Source Type: research

A Real-World Exploration into Clinical Outcomes of Direct Oral Anticoagulant Dosing Regimens in Morbidly Obese Patients Using Data-Driven Approaches
ConclusionData-driven approaches can identify key factors associated with clinical outcomes following the dosing of DOACs in morbidly obese patients. This will help design further studies to explore well tolerated and effective DOAC doses for morbidly obese patients.
Source: American Journal of Cardiovascular Drugs - March 6, 2023 Category: Cardiology Source Type: research

A Learning Health System Infrastructure for Precision Rehabilitation After Stroke
We describe the creation of a Precision Rehabilitation Data Repository that facilitates access to systematically collected data from the electronic health record as part of a learning health system to drive precision rehabilitation. Specifically, we describe the process of (1) standardizing the documentation of functional assessments, (2) obtaining regulatory approval, (3) defining the patient cohort, and (4) extracting data for the Precision Rehabilitation Data Repository. The development of similar infrastructures at other institutions can help generate large, heterogeneous data sets to drive poststroke care toward preci...
Source: Health Physics - January 12, 2023 Category: Physics Authors: Margaret A French Kelly Daley Annette Lavezza Ryan T Roemmich Stephen T Wegener Preeti Raghavan Pablo Celnik Source Type: research

Risk of cardiovascular events in patients having had acute calcium pyrophosphate crystal arthritis
Conclusions Acute CPP crystal arthritis was significantly associated with elevated short and long-term risk for non-fatal CV event.
Source: Annals of the Rheumatic Diseases - August 11, 2022 Category: Rheumatology Authors: Tedeschi, S. K., Huang, W., Yoshida, K., Solomon, D. H. Tags: ARD, Crystal arthropathies 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

Predicting patient-level new-onset atrial fibrillation from population-based nationwide electronic health records: protocol of FIND-AF for developing a precision medicine prediction model using artificial intelligence
Introduction Atrial fibrillation (AF) is a major cardiovascular health problem: it is common, chronic and incurs substantial healthcare expenditure because of stroke. Oral anticoagulation reduces the risk of thromboembolic stroke in those at higher risk; but for a number of patients, stroke is the first manifestation of undetected AF. There is a rationale for the early diagnosis of AF, before the first complication occurs, but population-based screening is not recommended. Previous prediction models have been limited by their data sources and methodologies. An accurate model that uses existing routinely collected data is n...
Source: BMJ Open - November 2, 2021 Category: General Medicine Authors: Nadarajah, R., Wu, J., Frangi, A. F., Hogg, D., Cowan, C., Gale, C. Tags: Open access, Cardiovascular medicine Source Type: research

Application of Machine Learning Techniques to Identify Data Reliability and Factors Affecting Outcome After Stroke Using Electronic Administrative Records
Conclusion: Electronic administrative records from this cohort produced reliable outcome prediction and identified clinically appropriate factors negatively impacting most outcome variables following hospital admission with stroke. This presents a means of future identification of modifiable factors associated with patient discharge destination. This may potentially aid in patient selection for certain interventions and aid in better patient and clinician education regarding expected discharge outcomes.
Source: Frontiers in Neurology - September 27, 2021 Category: Neurology Source Type: research

Using electronic health records to develop and validate a machine-learning tool to predict type 2 diabetes outcomes: a study protocol
Introduction Type 2 diabetes mellitus (T2DM) is a major cause of blindness, kidney failure, myocardial infarction, stroke and lower limb amputation. We are still unable, however, to accurately predict or identify which patients are at a higher risk of deterioration. Most risk stratification tools do not account for novel factors such as sociodemographic determinants, self-management ability or access to healthcare. Additionally, most tools are based in clinical trials, with limited external generalisability. Objective The aim of this work is to design and validate a machine learning-based tool to identify patients with T2...
Source: BMJ Open - July 30, 2021 Category: General Medicine Authors: Neves, A. L., Pereira Rodrigues, P., Mulla, A., Glampson, B., Willis, T., Darzi, A., Mayer, E. Tags: Open access, Health informatics Source Type: research

Children with perinatal stroke are at increased risk for autism spectrum disorder: Prevalence and co-occurring conditions within a clinically followed sample
DISCUSSION: Children with perinatal stroke have an increased prevalence of ASD (11.4%) than in the general population. ASD concerns arise at a similar age as the general population, yet ASD is diagnosed almost two years later than the general population and 3.60 years after first concerns present. Co-occurring neurological conditions are common. Clinicians must be aware of increased prevalence and implement screening as part of routine care for all pediatric patients with perinatal stroke.PMID:34308766 | DOI:10.1080/13854046.2021.1955150
Source: The Clinical Neuropsychologist - July 26, 2021 Category: Psychiatry & Psychology Authors: Taralee Hamner Evelyn Shih Rebecca Ichord Lauren Krivitzky Source Type: research

Prediction of 30-Day Readmission After Stroke Using Machine Learning and Natural Language Processing
Conclusion: NLP-enhanced machine learning models potentially advance our ability to predict readmission after stroke. However, further improvement is necessary before being implemented in clinical practice given the weak discrimination.
Source: Frontiers in Neurology - July 13, 2021 Category: Neurology Source Type: research

Electronic Medical Record Risk Modeling of Cardiovascular Outcomes Among Patients with Type 2 Diabetes
ConclusionsThe Ochsner model overestimated 5-year CHD risk, but had relatively higher calibration than the other models in CHD. Risk equations fitted for local populations improved cardiovascular risk stratification for patients with T2DM. Application of machine learning simplified the models compared to “generalized” risk equations.
Source: Diabetes Therapy - June 18, 2021 Category: Endocrinology Source Type: research

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