'predictive framework' in healthcare technology

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Effectiveness of ML Approaches for Predicting Gastrointestinal Bleeds in Patients Receiving Antithrombotic Treatment | healthcare technology | Scoop.it

From pubmed.ncbi.nlm.nih.gov - May 27, 2021 4:56 AM

Anticipating the risk of gastrointestinal bleeding (GIB) when initiating antithrombotic treatment (oral antiplatelets or anticoagulants) is limited by existing risk prediction models. Machine learning algorithms may result in superior predictive models to aid in clinical decision-making.

Objective: To compare the performance of 3 machine learning approaches with the commonly used HAS-BLED (hypertension, abnormal kidney and liver function, stroke, bleeding, labile international normalized ratio, older age, and drug or alcohol use) risk score in predicting antithrombotic-related GIB.

Design, setting, and participants: This retrospective cross-sectional study used data from the OptumLabs Data Warehouse, which contains medical and pharmacy claims on privately insured patients and Medicare Advantage enrollees in the US. The study cohort included patients 18 years or older with a history of atrial fibrillation, ischemic heart disease, or venous thromboembolism who were prescribed oral anticoagulant and/or thienopyridine antiplatelet agents between January 1, 2016, and December 31, 2019.

In this cross-sectional study, the machine learning models examined showed similar performance in identifying patients at high risk for GIB after being prescribed antithrombotic agents. Two models (RegCox and XGBoost) performed modestly better than the HAS-BLED score. A prospective evaluation of the RegCox model compared with HAS-BLED may provide a better understanding of the clinical impact of improved performance.

link to the original investigation paper https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2780274

read the pubmed article at https://pubmed.ncbi.nlm.nih.gov/34019087/

From academic.oup.com - May 13, 2021 5:18 PM

We developed and validated an accurate in-hospital mortality prediction score in a live EHR for automatic and continuous calculation using a novel model that improved upon SOFA.

The COVID-19 pandemic created an emergent need for a novel, accurate, and location and context-sensitive EHR-computable tool to predict mortality in hospitalized patients with and without COVID-19. Because developing a new score can take years, a predictive model must rely on well-validated scores.

In contrast, COVID-19 is a novel disease for which existing scores may be of limited but unknown predictive value.

As such, a predictive framework relying on multiple previously validated scores that can incorporate new information but only keeps the new inputs that explicitly improve performance is required. Stacked generalization provides a solution. A stacked model is built upon one or more baseline model(s) (e.g. SOFA) and incorporates additional models only when they improve prediction.

Materials and Methods

We developed, verified, and deployed a stacked generalization model to predict mortality using data available in the EHR by combining five previously validated scores and additional novel variables reported to be associated with COVID-19-specific mortality.

We verified the model with prospectively collected data from 12 hospitals in Colorado between March 2020 and July 2020. We compared the area under the receiver operator curve (AUROC) for the new model to the SOFA score and the Charlson Comorbidity Index.

Results

The prospective cohort included 27,296 encounters, of which 1,358 (5.0%) were positive for SARS-CoV-2, 4,494 (16.5%) required intensive care unit care, 1,480 (5.4%) required mechanical ventilation, and 717 (2.6%) ended in death. The Charlson Comorbidity Index and SOFA scores predicted mortality with an AUROC of 0.72 and 0.90, respectively. Our novel score predicted mortality with AUROC 0.94. In the subset of patients with COVID-19, the stacked model predicted mortality with AUROC 0.90, whereas SOFA had AUROC of 0.85.

Discussion

Stacked regression allows a flexible, updatable, live-implementable, ethically defensible predictive analytics tool for decision support that begins with validated models and includes only novel information that improves prediction.

read the paper abstract at https://academic.oup.com/jamia/advance-article/doi/10.1093/jamia/ocab100/6273353

read the entire paper at https://academic.oup.com/jamia/advance-article-pdf/doi/10.1093/jamia/ocab100/37905236/ocab100.pdf

Effectiveness of ML Approaches for Predicting Gastrointestinal Bleeds in Patients Receiving Antithrombotic Treatment

Real-Time EHR Mortality Prediction During the COVID-19 Pandemic