Synthetic data in cancer and cerebrovascular disease research: A novel approach to big data.
<h4>Objectives</h4>Synthetic datasets are artificially manufactured based on real health systems data but do not contain real patient information. We sought to validate the use of synthetic data in stroke and cancer research by conducting a comparison study of cancer patients with ischem...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2024-01-01
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| Series: | PLoS ONE |
| Online Access: | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0295921&type=printable |
| _version_ | 1797304781977419776 |
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| author | Ronda Lun Deborah Siegal Tim Ramsay Grant Stotts Dar Dowlatshahi |
| author_facet | Ronda Lun Deborah Siegal Tim Ramsay Grant Stotts Dar Dowlatshahi |
| author_sort | Ronda Lun |
| collection | DOAJ |
| description | <h4>Objectives</h4>Synthetic datasets are artificially manufactured based on real health systems data but do not contain real patient information. We sought to validate the use of synthetic data in stroke and cancer research by conducting a comparison study of cancer patients with ischemic stroke to non-cancer patients with ischemic stroke.<h4>Design</h4>retrospective cohort study.<h4>Setting</h4>We used synthetic data generated by MDClone and compared it to its original source data (i.e. real patient data from the Ottawa Hospital Data Warehouse).<h4>Outcome measures</h4>We compared key differences in demographics, treatment characteristics, length of stay, and costs between cancer patients with ischemic stroke and non-cancer patients with ischemic stroke. We used a binary, multivariable logistic regression model to identify risk factors for recurrent stroke in the cancer population.<h4>Results</h4>Using synthetic data, we found cancer patients with ischemic stroke had a lower prevalence of hypertension (52.0% in the cancer cohort vs 57.7% in the non-cancer cohort, p<0.0001), and a higher prevalence of chronic obstructive pulmonary disease (COPD: 8.5% vs 4.7%, p<0.0001), prior ischemic stroke (1.7% vs 0.1%, p<0.001), and prior venous thromboembolism (VTE: 8.2% vs 1.5%, p<0.0001). They also had a longer length of stay (8 days [IQR 3-16] vs 6 days [IQR 3-13], p = 0.011), and higher costs associated with their stroke encounters: $11,498 (IQR $4,440 -$20,668) in the cancer cohort vs $8,084 (IQR $3,947 -$16,706) in the non-cancer cohort (p = 0.0061). A multivariable logistic regression model identified 5 predictors for recurrent ischemic stroke in the cancer cohort using synthetic data; 3 of the same predictors identified using real patient data with similar effect measures. Summary statistics between synthetic and original datasets did not significantly differ, other than slight differences in the distributions of frequencies for numeric data.<h4>Conclusion</h4>We demonstrated the utility of synthetic data in stroke and cancer research and provided key differences between cancer and non-cancer patients with ischemic stroke. Synthetic data is a powerful tool that can allow researchers to easily explore hypothesis generation, enable data sharing without privacy breaches, and ensure broad access to big data in a rapid, safe, and reliable fashion. |
| first_indexed | 2024-03-08T00:15:27Z |
| format | Article |
| id | doaj.art-bd8768067e704f4eacc47bbf97b92e54 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-03-08T00:15:27Z |
| publishDate | 2024-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-bd8768067e704f4eacc47bbf97b92e542024-02-17T05:33:08ZengPublic Library of Science (PLoS)PLoS ONE1932-62032024-01-01192e029592110.1371/journal.pone.0295921Synthetic data in cancer and cerebrovascular disease research: A novel approach to big data.Ronda LunDeborah SiegalTim RamsayGrant StottsDar Dowlatshahi<h4>Objectives</h4>Synthetic datasets are artificially manufactured based on real health systems data but do not contain real patient information. We sought to validate the use of synthetic data in stroke and cancer research by conducting a comparison study of cancer patients with ischemic stroke to non-cancer patients with ischemic stroke.<h4>Design</h4>retrospective cohort study.<h4>Setting</h4>We used synthetic data generated by MDClone and compared it to its original source data (i.e. real patient data from the Ottawa Hospital Data Warehouse).<h4>Outcome measures</h4>We compared key differences in demographics, treatment characteristics, length of stay, and costs between cancer patients with ischemic stroke and non-cancer patients with ischemic stroke. We used a binary, multivariable logistic regression model to identify risk factors for recurrent stroke in the cancer population.<h4>Results</h4>Using synthetic data, we found cancer patients with ischemic stroke had a lower prevalence of hypertension (52.0% in the cancer cohort vs 57.7% in the non-cancer cohort, p<0.0001), and a higher prevalence of chronic obstructive pulmonary disease (COPD: 8.5% vs 4.7%, p<0.0001), prior ischemic stroke (1.7% vs 0.1%, p<0.001), and prior venous thromboembolism (VTE: 8.2% vs 1.5%, p<0.0001). They also had a longer length of stay (8 days [IQR 3-16] vs 6 days [IQR 3-13], p = 0.011), and higher costs associated with their stroke encounters: $11,498 (IQR $4,440 -$20,668) in the cancer cohort vs $8,084 (IQR $3,947 -$16,706) in the non-cancer cohort (p = 0.0061). A multivariable logistic regression model identified 5 predictors for recurrent ischemic stroke in the cancer cohort using synthetic data; 3 of the same predictors identified using real patient data with similar effect measures. Summary statistics between synthetic and original datasets did not significantly differ, other than slight differences in the distributions of frequencies for numeric data.<h4>Conclusion</h4>We demonstrated the utility of synthetic data in stroke and cancer research and provided key differences between cancer and non-cancer patients with ischemic stroke. Synthetic data is a powerful tool that can allow researchers to easily explore hypothesis generation, enable data sharing without privacy breaches, and ensure broad access to big data in a rapid, safe, and reliable fashion.https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0295921&type=printable |
| spellingShingle | Ronda Lun Deborah Siegal Tim Ramsay Grant Stotts Dar Dowlatshahi Synthetic data in cancer and cerebrovascular disease research: A novel approach to big data. PLoS ONE |
| title | Synthetic data in cancer and cerebrovascular disease research: A novel approach to big data. |
| title_full | Synthetic data in cancer and cerebrovascular disease research: A novel approach to big data. |
| title_fullStr | Synthetic data in cancer and cerebrovascular disease research: A novel approach to big data. |
| title_full_unstemmed | Synthetic data in cancer and cerebrovascular disease research: A novel approach to big data. |
| title_short | Synthetic data in cancer and cerebrovascular disease research: A novel approach to big data. |
| title_sort | synthetic data in cancer and cerebrovascular disease research a novel approach to big data |
| url | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0295921&type=printable |
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