Impact of adrenomedullin levels on clinical risk stratification and outcome in subarachnoid haemorrhage

AbstractLabeling clinical data from electronic health records (EHR) in health systems requires extensive knowledge of human expert, and painstaking review by clinicians. Furthermore, existing phenotyping algorithms are not uniformly applied across large datasets and can suffer from inconsistencies in case definitions across different algorithms. We describe here quantitative disease risk scores based on almost unsupervised methods that require minimal input from clinicians, can be applied to large datasets, and alleviate some of the main weaknesses of existing phenotyping algorithms. We show applications to phenotypic data on approximately 100,000 individuals in eMERGE, and focus on several complex diseases, including Chronic Kidney Disease, Coronary Artery Disease, Type 2 Diabetes, Heart Failure, and a few others. We demonstrate that relative to existing approaches, the proposed methods have higher prediction accuracy, can better identify phenotypic features relevant to the disease under consideration, can perform better at clinical risk stratification, and can identify undiagnosed cases based on phenotypic features available in the EHR. Using genetic data from the eMERGE-seq panel that includes sequencing data for 109 genes on 21,363 individuals from multiple ethnicities, we also show how the new quantitative disease risk scores help improve the power of genetic association studies relative to the standard use of disease phenotypes. The results demonstrate the effectiveness of quantitative disease risk scores derived from rich phenotypic EHR databases to provide a more meaningful characterization of clinical risk for diseases of interest beyond the prevalent binary (case-control) classification.

Download Full-text

Development and validation of a prognostic COVID-19 severity assessment (COSA) score and machine learning models for patient triage at a tertiary hospital

Journal of Translational Medicine ◽

10.1186/s12967-021-02720-w ◽

2021 ◽

Vol 19 (1) ◽

Cited By ~ 2

Author(s):

Verena Schöning ◽

Evangelia Liakoni ◽

Christine Baumgartner ◽

Aristomenis K. Exadaktylos ◽

Wolf E. Hautz ◽

...

Keyword(s):

Machine Learning ◽

Risk Stratification ◽

Clinical Outcomes ◽

Predictive Value ◽

Validation Cohort ◽

Tertiary Hospital ◽

Learning Models ◽

Clinical Risk ◽

Machine Learning Models ◽

Risk Stratification Score

Abstract Background Clinical risk scores and machine learning models based on routine laboratory values could assist in automated early identification of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) patients at risk for severe clinical outcomes. They can guide patient triage, inform allocation of health care resources, and contribute to the improvement of clinical outcomes. Methods In- and out-patients tested positive for SARS-CoV-2 at the Insel Hospital Group Bern, Switzerland, between February 1st and August 31st (‘first wave’, n = 198) and September 1st through November 16th 2020 (‘second wave’, n = 459) were used as training and prospective validation cohort, respectively. A clinical risk stratification score and machine learning (ML) models were developed using demographic data, medical history, and laboratory values taken up to 3 days before, or 1 day after, positive testing to predict severe outcomes of hospitalization (a composite endpoint of admission to intensive care, or death from any cause). Test accuracy was assessed using the area under the receiver operating characteristic curve (AUROC). Results Sex, C-reactive protein, sodium, hemoglobin, glomerular filtration rate, glucose, and leucocytes around the time of first positive testing (− 3 to + 1 days) were the most predictive parameters. AUROC of the risk stratification score on training data (AUROC = 0.94, positive predictive value (PPV) = 0.97, negative predictive value (NPV) = 0.80) were comparable to the prospective validation cohort (AUROC = 0.85, PPV = 0.91, NPV = 0.81). The most successful ML algorithm with respect to AUROC was support vector machines (median = 0.96, interquartile range = 0.85–0.99, PPV = 0.90, NPV = 0.58). Conclusion With a small set of easily obtainable parameters, both the clinical risk stratification score and the ML models were predictive for severe outcomes at our tertiary hospital center, and performed well in prospective validation.

Download Full-text

Clinical risk stratification in COVID-19: the need for a revised approach?

Pulmonary Circulation ◽

10.1177/2045894020988630 ◽

2021 ◽

Vol 11 (1) ◽

pp. 204589402098863

Author(s):

Robin Cherian

Keyword(s):

Risk Stratification ◽

Clinical Risk

Download Full-text

Clinical Risk Stratification Optimizes Value of Biomarkers to Predict New-Onset Heart Failure in a Community-Based Cohort

Circulation Heart Failure ◽

10.1161/circheartfailure.114.001185 ◽

2014 ◽

Vol 7 (5) ◽

pp. 723-731 ◽

Cited By ~ 40

Author(s):

Frank P. Brouwers ◽

Wiek H. van Gilst ◽

Kevin Damman ◽

Maarten P. van den Berg ◽

Ron T. Gansevoort ◽

...

Keyword(s):

Heart Failure ◽

Risk Stratification ◽

Community Based ◽

Clinical Risk ◽

New Onset

Download Full-text

062 Clinical risk stratification for hydroxychloroquine prescribing in a large urban district general hospital

Rheumatology ◽

10.1093/rheumatology/key075.286 ◽

2018 ◽

Vol 57 (suppl_3) ◽

Author(s):

Amrita Saravanan ◽

Tazeen J Ahmed

Keyword(s):

Risk Stratification ◽

General Hospital ◽

District General Hospital ◽

Urban District ◽

Clinical Risk

Download Full-text

Simple electrocardiographic measures improve sudden arrhythmic death prediction in coronary disease

European Heart Journal ◽

10.1093/eurheartj/ehaa177 ◽

2020 ◽

Vol 41 (21) ◽

pp. 1988-1999 ◽

Cited By ~ 8

Author(s):

Neal A Chatterjee ◽

Jani T Tikkanen ◽

Gopi K Panicker ◽

Dhiraj Narula ◽

Daniel C Lee ◽

...

Keyword(s):

Risk Factors ◽

High Risk ◽

Risk Stratification ◽

Validation Cohort ◽

Clinical Risk Factors ◽

Left Ventricular Ejection ◽

Derivation Cohort ◽

Arrhythmic Death ◽

Clinical Risk ◽

Ecg Score

Abstract Aims To determine whether the combination of standard electrocardiographic (ECG) markers reflecting domains of arrhythmic risk improves sudden and/or arrhythmic death (SAD) risk stratification in patients with coronary heart disease (CHD). Methods and results The association between ECG markers and SAD was examined in a derivation cohort (PREDETERMINE; N = 5462) with adjustment for clinical risk factors, left ventricular ejection fraction (LVEF), and competing risk. Competing outcome models assessed the differential association of ECG markers with SAD and competing mortality. The predictive value of a derived ECG score was then validated (ARTEMIS; N = 1900). In the derivation cohort, the 5-year cumulative incidence of SAD was 1.5% [95% confidence interval (CI) 1.1–1.9] and 6.2% (95% CI 4.5–8.3) in those with a low- and high-risk ECG score, respectively (P for Δ < 0.001). A high-risk ECG score was more strongly associated with SAD than non-SAD mortality (adjusted hazard ratios = 2.87 vs. 1.38 respectively; P for Δ = 0.003) and the proportion of deaths due to SAD was greater in the high vs. low risk groups (24.9% vs. 16.5%, P for Δ = 0.03). Similar findings were observed in the validation cohort. The addition of ECG markers to a clinical risk factor model inclusive of LVEF improved indices of discrimination and reclassification in both derivation and validation cohorts, including correct reclassification of 28% of patients in the validation cohort [net reclassification improvement 28 (7–49%), P = 0.009]. Conclusion For patients with CHD, an externally validated ECG score enriched for both absolute and proportional SAD risk and significantly improved risk stratification compared to standard clinical risk factors including LVEF. Clinical Trial Registration https://clinicaltrials.gov/ct2/show/NCT01114269. ClinicalTrials.gov ID NCT01114269.

Download Full-text