Statistical analysis plan: Development and validation of prediction models for new-onset atrial fibrillation in patients admitted to an intensive care unit
<p><strong>Introduction: </strong>New-onset atrial fibrillation (NOAF) is a common arrhythmia in patients on an intensive care unit (ICU). NOAF in this setting is associated with adverse short and long-term outcomes. Little is known about modifiable risk factors for NOAF....
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Report |
| Language: | English |
| Published: |
University of Oxford
2021
|
| _version_ | 1826310390707912704 |
|---|---|
| author | Bedford, JPN Hatch, R Redfern, O Gerry, S Mertes, G Clifton, D Watkinson, P Collins, G |
| author_facet | Bedford, JPN Hatch, R Redfern, O Gerry, S Mertes, G Clifton, D Watkinson, P Collins, G |
| author_sort | Bedford, JPN |
| collection | OXFORD |
| description | <p><strong>Introduction: </strong>New-onset atrial fibrillation (NOAF) is a common arrhythmia in patients on an intensive care unit (ICU). NOAF in this setting is associated with adverse short and long-term outcomes. Little is known about modifiable risk factors for NOAF. Developing a model to identify patients at high risk of developing NOAF is vital for future studies investigating prevention strategies to allow stratification, sample enrichment and efficient study design.</p>
<p><strong>Methods: </strong>We will use data from 7 general ICUs in the UK and USA to develop a predictive model for NOAF. We will assess whether including longitudinal, dynamic predictors, along with the use of machine learning approaches improves model predictive ability.</p>
<p><strong>Generalisability and Implications: </strong>This work will be the largest assessment of predictors of NOAF in patients treated on a general ICU. It will be the first to analyse international data to ensure worldwide applicability. A detailed understanding of modifiable risk factors underpins future work to prevent NOAF in these patients.</p> |
| first_indexed | 2024-03-07T07:51:01Z |
| format | Report |
| id | oxford-uuid:5546f6f3-e19c-4793-9843-7f036a5940a2 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T07:51:01Z |
| publishDate | 2021 |
| publisher | University of Oxford |
| record_format | dspace |
| spelling | oxford-uuid:5546f6f3-e19c-4793-9843-7f036a5940a22023-07-13T11:08:55ZStatistical analysis plan: Development and validation of prediction models for new-onset atrial fibrillation in patients admitted to an intensive care unitReporthttp://purl.org/coar/resource_type/c_93fcuuid:5546f6f3-e19c-4793-9843-7f036a5940a2EnglishSymplectic ElementsUniversity of Oxford2021Bedford, JPNHatch, RRedfern, OGerry, SMertes, GClifton, DWatkinson, PCollins, G<p><strong>Introduction: </strong>New-onset atrial fibrillation (NOAF) is a common arrhythmia in patients on an intensive care unit (ICU). NOAF in this setting is associated with adverse short and long-term outcomes. Little is known about modifiable risk factors for NOAF. Developing a model to identify patients at high risk of developing NOAF is vital for future studies investigating prevention strategies to allow stratification, sample enrichment and efficient study design.</p> <p><strong>Methods: </strong>We will use data from 7 general ICUs in the UK and USA to develop a predictive model for NOAF. We will assess whether including longitudinal, dynamic predictors, along with the use of machine learning approaches improves model predictive ability.</p> <p><strong>Generalisability and Implications: </strong>This work will be the largest assessment of predictors of NOAF in patients treated on a general ICU. It will be the first to analyse international data to ensure worldwide applicability. A detailed understanding of modifiable risk factors underpins future work to prevent NOAF in these patients.</p> |
| spellingShingle | Bedford, JPN Hatch, R Redfern, O Gerry, S Mertes, G Clifton, D Watkinson, P Collins, G Statistical analysis plan: Development and validation of prediction models for new-onset atrial fibrillation in patients admitted to an intensive care unit |
| title | Statistical analysis plan: Development and validation of prediction models for new-onset atrial fibrillation in patients admitted to an intensive care unit |
| title_full | Statistical analysis plan: Development and validation of prediction models for new-onset atrial fibrillation in patients admitted to an intensive care unit |
| title_fullStr | Statistical analysis plan: Development and validation of prediction models for new-onset atrial fibrillation in patients admitted to an intensive care unit |
| title_full_unstemmed | Statistical analysis plan: Development and validation of prediction models for new-onset atrial fibrillation in patients admitted to an intensive care unit |
| title_short | Statistical analysis plan: Development and validation of prediction models for new-onset atrial fibrillation in patients admitted to an intensive care unit |
| title_sort | statistical analysis plan development and validation of prediction models for new onset atrial fibrillation in patients admitted to an intensive care unit |
| work_keys_str_mv | AT bedfordjpn statisticalanalysisplandevelopmentandvalidationofpredictionmodelsfornewonsetatrialfibrillationinpatientsadmittedtoanintensivecareunit AT hatchr statisticalanalysisplandevelopmentandvalidationofpredictionmodelsfornewonsetatrialfibrillationinpatientsadmittedtoanintensivecareunit AT redferno statisticalanalysisplandevelopmentandvalidationofpredictionmodelsfornewonsetatrialfibrillationinpatientsadmittedtoanintensivecareunit AT gerrys statisticalanalysisplandevelopmentandvalidationofpredictionmodelsfornewonsetatrialfibrillationinpatientsadmittedtoanintensivecareunit AT mertesg statisticalanalysisplandevelopmentandvalidationofpredictionmodelsfornewonsetatrialfibrillationinpatientsadmittedtoanintensivecareunit AT cliftond statisticalanalysisplandevelopmentandvalidationofpredictionmodelsfornewonsetatrialfibrillationinpatientsadmittedtoanintensivecareunit AT watkinsonp statisticalanalysisplandevelopmentandvalidationofpredictionmodelsfornewonsetatrialfibrillationinpatientsadmittedtoanintensivecareunit AT collinsg statisticalanalysisplandevelopmentandvalidationofpredictionmodelsfornewonsetatrialfibrillationinpatientsadmittedtoanintensivecareunit |