A Nomogram to Predict Patients with Obstructive Coronary Artery Disease: Development and Validation
<p class="first" id="d17686013e152"> <b>Objective:</b> To develop and validate clinical prediction models for the development of a nomogram to estimate the probability of patients having coronary artery disease (CAD)....
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Compuscript Ltd
2021-04-01
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| Series: | Cardiovascular Innovations and Applications |
| Online Access: | https://www.scienceopen.com/hosted-document?doi=10.15212/CVIA.2021.0001 |
| _version_ | 1827914722829139968 |
|---|---|
| author | Zesen Han Lihong Lai Zhaokun Pu Lan Yang |
| author_facet | Zesen Han Lihong Lai Zhaokun Pu Lan Yang |
| author_sort | Zesen Han |
| collection | DOAJ |
| description | <p class="first" id="d17686013e152">
<b>Objective:</b> To develop and validate clinical prediction models for the development of a nomogram
to estimate the probability of patients having coronary artery disease (CAD).
</p><p id="d17686013e157">
<b>Methods and Results:</b> A total of 1,025 patients referred for coronary angiography were included in a retrospective,
single-center study. Randomly, 720 patients (70%) were selected as the development
group and the other patients were selected as the validation group. Multivariate logistic
regression analysis showed that the seven risk factors age, sex, systolic blood pressure,
lipoprotein-associated phospholipase A
<sub>2</sub>, type of angina, hypertension, and diabetes were significant for diagnosis of CAD,
from which we established model A. We established model B with the risk factors age,
sex, height, systolic blood pressure, low-density lipoprotein cholesterol, lipoprotein-associated
phospholipase A
<sub>2</sub>, type of angina, hypertension, and diabetes via the Akaike information criterion.
The risk factors from the original Framingham Risk Score were used for model C. From
comparison of the areas under the receiver operating characteristic curve, net reclassification
improvement, and integrated discrimination improvement of models A, B, and C, we chose
model B to develop the nomogram because of its fitness in discrimination, calibration,
and clinical efficiency. The nomogram for diagnosis of CAD could be used easily and
conveniently.
</p><p id="d17686013e168">
<b>Conclusion:</b> An individualized clinical prediction model for patients with CAD allowed an accurate
estimation in Chinese populations. The Akaike information criterion is a better method
in screening risk factors. The net reclassification improvement and integrated discrimination
improvement are better than the area under the receiver operating characteristic curve
in discrimination. Decision curve analysis can be used to evaluate the efficiency
of clinical prediction models.
</p> |
| first_indexed | 2024-03-13T02:48:16Z |
| format | Article |
| id | doaj.art-39a79c8650184fa68272f6a7dc1bfa66 |
| institution | Directory Open Access Journal |
| issn | 2009-8618 2009-8782 |
| language | English |
| last_indexed | 2024-03-13T02:48:16Z |
| publishDate | 2021-04-01 |
| publisher | Compuscript Ltd |
| record_format | Article |
| series | Cardiovascular Innovations and Applications |
| spelling | doaj.art-39a79c8650184fa68272f6a7dc1bfa662023-06-28T14:12:44ZengCompuscript LtdCardiovascular Innovations and Applications2009-86182009-87822021-04-015424510.15212/CVIA.2021.0001A Nomogram to Predict Patients with Obstructive Coronary Artery Disease: Development and ValidationZesen HanLihong LaiZhaokun PuLan Yang<p class="first" id="d17686013e152"> <b>Objective:</b> To develop and validate clinical prediction models for the development of a nomogram to estimate the probability of patients having coronary artery disease (CAD). </p><p id="d17686013e157"> <b>Methods and Results:</b> A total of 1,025 patients referred for coronary angiography were included in a retrospective, single-center study. Randomly, 720 patients (70%) were selected as the development group and the other patients were selected as the validation group. Multivariate logistic regression analysis showed that the seven risk factors age, sex, systolic blood pressure, lipoprotein-associated phospholipase A <sub>2</sub>, type of angina, hypertension, and diabetes were significant for diagnosis of CAD, from which we established model A. We established model B with the risk factors age, sex, height, systolic blood pressure, low-density lipoprotein cholesterol, lipoprotein-associated phospholipase A <sub>2</sub>, type of angina, hypertension, and diabetes via the Akaike information criterion. The risk factors from the original Framingham Risk Score were used for model C. From comparison of the areas under the receiver operating characteristic curve, net reclassification improvement, and integrated discrimination improvement of models A, B, and C, we chose model B to develop the nomogram because of its fitness in discrimination, calibration, and clinical efficiency. The nomogram for diagnosis of CAD could be used easily and conveniently. </p><p id="d17686013e168"> <b>Conclusion:</b> An individualized clinical prediction model for patients with CAD allowed an accurate estimation in Chinese populations. The Akaike information criterion is a better method in screening risk factors. The net reclassification improvement and integrated discrimination improvement are better than the area under the receiver operating characteristic curve in discrimination. Decision curve analysis can be used to evaluate the efficiency of clinical prediction models. </p>https://www.scienceopen.com/hosted-document?doi=10.15212/CVIA.2021.0001 |
| spellingShingle | Zesen Han Lihong Lai Zhaokun Pu Lan Yang A Nomogram to Predict Patients with Obstructive Coronary Artery Disease: Development and Validation Cardiovascular Innovations and Applications |
| title | A Nomogram to Predict Patients with Obstructive Coronary Artery Disease: Development and Validation |
| title_full | A Nomogram to Predict Patients with Obstructive Coronary Artery Disease: Development and Validation |
| title_fullStr | A Nomogram to Predict Patients with Obstructive Coronary Artery Disease: Development and Validation |
| title_full_unstemmed | A Nomogram to Predict Patients with Obstructive Coronary Artery Disease: Development and Validation |
| title_short | A Nomogram to Predict Patients with Obstructive Coronary Artery Disease: Development and Validation |
| title_sort | nomogram to predict patients with obstructive coronary artery disease development and validation |
| url | https://www.scienceopen.com/hosted-document?doi=10.15212/CVIA.2021.0001 |
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