scholarly journals Development and Validation of A Simple Clinical Risk Prediction Model for New-Onset Postoperative Atrial Fibrillation After Cardiac Surgery: Nopaf Score

2020 ◽  
Vol 13 (2) ◽  
Author(s):  
Dhanunjaya Lakkireddy
Keyword(s):  
Atrial Fibrillation ◽  
Cardiac Surgery ◽  
Prediction Model ◽  
Risk Prediction ◽  
Risk Prediction Model ◽  
Postoperative Atrial Fibrillation ◽  
Clinical Risk ◽  
Development And Validation ◽  
New Onset

scholarly journals Genetic and Clinical Risk Prediction Model for Postoperative Atrial Fibrillation

2015 ◽  
Vol 8 (1) ◽  
pp. 25-31 ◽  
Author(s):  
Matthew J. Kolek ◽  
J. Daniel Muehlschlegel ◽  
William S. Bush ◽  
Babar Parvez ◽  
Katherine T. Murray ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Prediction Model ◽  
Risk Prediction ◽  
Risk Prediction Model ◽  
Postoperative Atrial Fibrillation ◽  
Clinical Risk

scholarly journals Development and validation of a 2-year new-onset stroke risk prediction model for people over age 45 in China

Medicine ◽  
2020 ◽  
Vol 99 (41) ◽  
pp. e22680
Author(s):  
Qiang Yao ◽  
Jing Zhang ◽  
Ke Yan ◽  
Qianwen Zheng ◽  
Yawen Li ◽  
...  
Keyword(s):  
Prediction Model ◽  
Risk Prediction ◽  
Stroke Risk ◽  
Risk Prediction Model ◽  
Development And Validation ◽  
New Onset

Development and Validation of a New Risk Prediction Model for New-Onset Diabetes: Multicohort Study

2020 ◽  
Author(s):  
Shinje Moon ◽  
Ji-Yong Jang ◽  
Yumin Kim ◽  
Wankyo Chung ◽  
Chang-Myung Oh
Keyword(s):  
Prediction Model ◽  
Risk Prediction ◽  
Risk Prediction Model ◽  
Onset Diabetes ◽  
Development And Validation ◽  
New Onset

Development and Validation of a Risk Prediction Model for Patients with COVID-19 Using Linked Population Health Data (Preprint)

2021 ◽  
Author(s):  
Nikolaos Mastellos ◽  
Richard Betteridge ◽  
Prasanth Peddaayyavarla ◽  
Andrew Moran ◽  
Jurgita Kaubryte ◽  
...  
Keyword(s):  
Health Care ◽  
Prediction Model ◽  
Population Health ◽  
Risk Prediction ◽  
Validation Cohort ◽  
Risk Prediction Model ◽  
Adult Patients ◽  
Internal Validation ◽  
Temporal Validation ◽  
Development And Validation

BACKGROUND The impact of the COVID-19 pandemic on health care utilisation and associated costs has been significant, with one in ten patients becoming severely ill and being admitted to hospital with serious complications during the first wave of the pandemic. Risk prediction models can help health care providers identify high-risk patients in their populations and intervene to improve health outcomes and reduce associated costs. OBJECTIVE To develop and validate a hospitalisation risk prediction model for adult patients with laboratory confirmed Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). METHODS The model was developed using pre-linked and standardised data of adult patients with laboratory confirmed SARS-CoV-2 from Cerner’s population health management platform (HealtheIntent®) in the London Borough of Lewisham. A total of 14,203 patients who tested positive for SARS-CoV-2 between 1st March 2020 and 28th February 2021 were included in the development and internal validation cohorts. A second temporal validation cohort covered the period between 1st March 2021 to 30th April 2021. The outcome variable was hospital admission in adult patients with laboratory confirmed SARS-CoV-2. A generalised linear model was used to train the model. The predictive performance of the model was assessed using the area under the receiver operator characteristic curve (ROC-AUC). RESULTS Overall, 14,203 patients were included. Of those, 9,755 (68.7%) were assigned to the development cohort, 2,438 (17.2%) to the internal validation cohort, and 2,010 (14.1%) to the temporal validation cohort. A total of 917 (9.4%) patients were admitted to hospital in the development cohort, 210 (8.6%) in the internal validation cohort, and a further 204 (10.1%) in the temporal validation cohort. The model had a ROC-AUC of 0.85 in both the development and validation cohorts. The most predictive factors were older age, male sex, Asian or Other ethnic minority background, obesity, chronic kidney disease, hypertension and diabetes. CONCLUSIONS The COVID-19 hospitalisation risk prediction model demonstrated very good performance and can be used to stratify risk in the Lewisham population to help providers reduce unnecessary hospital admissions and associated costs, improve patient outcomes, and target those at greatest risk to ensure full vaccination against SARS-CoV-2. Further research may examine the external validity of the model in other populations.

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