Screening Tool to Identify Patients with Advanced Aortic Valve Stenosis
(1) Background: The clinical burden of aortic stenosis (AS) remains high in Western countries. Yet, there are no screening algorithms for this condition. We developed a risk prediction model to guide targeted screening for patients with AS. (2) Methods: We performed a cross-sectional analysis of all...
| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Article |
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MDPI AG
2022-07-01
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| Series: | Journal of Clinical Medicine |
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| Online Access: | https://www.mdpi.com/2077-0383/11/15/4386 |
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| author | Sameh Yousef Andrea Amabile Chirag Ram Huang Huang Varun Korutla Saket Singh Ritu Agarwal Roland Assi Rita K. Milewski Yawei Zhang Prakash A. Patel Markus Krane Arnar Geirsson Prashanth Vallabhajosyula |
| author_facet | Sameh Yousef Andrea Amabile Chirag Ram Huang Huang Varun Korutla Saket Singh Ritu Agarwal Roland Assi Rita K. Milewski Yawei Zhang Prakash A. Patel Markus Krane Arnar Geirsson Prashanth Vallabhajosyula |
| author_sort | Sameh Yousef |
| collection | DOAJ |
| description | (1) Background: The clinical burden of aortic stenosis (AS) remains high in Western countries. Yet, there are no screening algorithms for this condition. We developed a risk prediction model to guide targeted screening for patients with AS. (2) Methods: We performed a cross-sectional analysis of all echocardiographic studies performed between 2013 and 2018 at a tertiary academic care center. We included reports of unique patients aged from 40 to 95 years. A logistic regression model was fitted for the risk of moderate and severe AS, with readily available demographics and comorbidity variables. Model performance was assessed by the C-index, and its calibration was judged by a calibration plot. (3) Results: Among the 38,788 reports yielded by inclusion criteria, there were 4200 (10.8%) patients with ≥moderate AS. The multivariable model demonstrated multiple variables to be associated with AS, including age, male gender, Caucasian race, Body Mass Index ≥ 30, and cardiovascular comorbidities and medications. C-statistics of the model was 0.77 and was well calibrated according to the calibration plot. An integer point system was developed to calculate the predicted risk of ≥moderate AS, which ranged from 0.0002 to 0.7711. The lower 20% of risk was approximately 0.15 (corresponds to a score of 252), while the upper 20% of risk was about 0.60 (corresponds to a score of 332 points). (4) Conclusions: We developed a risk prediction model to predict patients’ risk of having ≥moderate AS based on demographic and clinical variables from a large population cohort. This tool may guide targeted screening for patients with advanced AS in the general population. |
| first_indexed | 2024-03-09T05:18:26Z |
| format | Article |
| id | doaj.art-b57dabdcaaad419caa571aca3ac32008 |
| institution | Directory Open Access Journal |
| issn | 2077-0383 |
| language | English |
| last_indexed | 2024-03-09T05:18:26Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Clinical Medicine |
| spelling | doaj.art-b57dabdcaaad419caa571aca3ac320082023-12-03T12:42:54ZengMDPI AGJournal of Clinical Medicine2077-03832022-07-011115438610.3390/jcm11154386Screening Tool to Identify Patients with Advanced Aortic Valve StenosisSameh Yousef0Andrea Amabile1Chirag Ram2Huang Huang3Varun Korutla4Saket Singh5Ritu Agarwal6Roland Assi7Rita K. Milewski8Yawei Zhang9Prakash A. Patel10Markus Krane11Arnar Geirsson12Prashanth Vallabhajosyula13Division of Cardiac Surgery, Yale School of Medicine, 330 Cedar Street BB204, New Haven, CT 06511, USADivision of Cardiac Surgery, Yale School of Medicine, 330 Cedar Street BB204, New Haven, CT 06511, USADivision of Cardiac Surgery, Yale School of Medicine, 330 Cedar Street BB204, New Haven, CT 06511, USASection of Surgical Outcomes and Epidemiology, Yale School of Public Health, New Haven, CT 06511, USADivision of Cardiac Surgery, Yale School of Medicine, 330 Cedar Street BB204, New Haven, CT 06511, USADivision of Cardiac Surgery, Yale School of Medicine, 330 Cedar Street BB204, New Haven, CT 06511, USAJoint Data Analytics Team, Information Technology Service, Yale University, New Haven, CT 06511, USADivision of Cardiac Surgery, Yale School of Medicine, 330 Cedar Street BB204, New Haven, CT 06511, USADivision of Cardiac Surgery, Yale School of Medicine, 330 Cedar Street BB204, New Haven, CT 06511, USASection of Surgical Outcomes and Epidemiology, Yale School of Public Health, New Haven, CT 06511, USADivision of Cardiac Anesthesiology, Yale School of Medicine, New Haven, CT 06511, USADivision of Cardiac Surgery, Yale School of Medicine, 330 Cedar Street BB204, New Haven, CT 06511, USADivision of Cardiac Surgery, Yale School of Medicine, 330 Cedar Street BB204, New Haven, CT 06511, USADivision of Cardiac Surgery, Yale School of Medicine, 330 Cedar Street BB204, New Haven, CT 06511, USA(1) Background: The clinical burden of aortic stenosis (AS) remains high in Western countries. Yet, there are no screening algorithms for this condition. We developed a risk prediction model to guide targeted screening for patients with AS. (2) Methods: We performed a cross-sectional analysis of all echocardiographic studies performed between 2013 and 2018 at a tertiary academic care center. We included reports of unique patients aged from 40 to 95 years. A logistic regression model was fitted for the risk of moderate and severe AS, with readily available demographics and comorbidity variables. Model performance was assessed by the C-index, and its calibration was judged by a calibration plot. (3) Results: Among the 38,788 reports yielded by inclusion criteria, there were 4200 (10.8%) patients with ≥moderate AS. The multivariable model demonstrated multiple variables to be associated with AS, including age, male gender, Caucasian race, Body Mass Index ≥ 30, and cardiovascular comorbidities and medications. C-statistics of the model was 0.77 and was well calibrated according to the calibration plot. An integer point system was developed to calculate the predicted risk of ≥moderate AS, which ranged from 0.0002 to 0.7711. The lower 20% of risk was approximately 0.15 (corresponds to a score of 252), while the upper 20% of risk was about 0.60 (corresponds to a score of 332 points). (4) Conclusions: We developed a risk prediction model to predict patients’ risk of having ≥moderate AS based on demographic and clinical variables from a large population cohort. This tool may guide targeted screening for patients with advanced AS in the general population.https://www.mdpi.com/2077-0383/11/15/4386aorticstenosismortalityriskprediction |
| spellingShingle | Sameh Yousef Andrea Amabile Chirag Ram Huang Huang Varun Korutla Saket Singh Ritu Agarwal Roland Assi Rita K. Milewski Yawei Zhang Prakash A. Patel Markus Krane Arnar Geirsson Prashanth Vallabhajosyula Screening Tool to Identify Patients with Advanced Aortic Valve Stenosis Journal of Clinical Medicine aortic stenosis mortality risk prediction |
| title | Screening Tool to Identify Patients with Advanced Aortic Valve Stenosis |
| title_full | Screening Tool to Identify Patients with Advanced Aortic Valve Stenosis |
| title_fullStr | Screening Tool to Identify Patients with Advanced Aortic Valve Stenosis |
| title_full_unstemmed | Screening Tool to Identify Patients with Advanced Aortic Valve Stenosis |
| title_short | Screening Tool to Identify Patients with Advanced Aortic Valve Stenosis |
| title_sort | screening tool to identify patients with advanced aortic valve stenosis |
| topic | aortic stenosis mortality risk prediction |
| url | https://www.mdpi.com/2077-0383/11/15/4386 |
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