Tracking of Systemic Lupus Erythematosus (SLE) Longitudinally Using Biosensor and Patient-Reported Data: A Report on the Fully Decentralized Mobile Study to Measure and Predict Lupus Disease Activity Using Digital Signals—The OASIS Study
(1) Objective: Systemic lupus erythematosus (SLE) is a complex disease involving immune dysregulation, episodic flares, and poor quality of life (QOL). For a decentralized digital study of SLE patients, machine learning was used to assess patient-reported outcomes (PROs), QOL, and biometric data for...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-11-01
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| Series: | BioTech |
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| Online Access: | https://www.mdpi.com/2673-6284/12/4/62 |
| _version_ | 1797381813064171520 |
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| author | Eldon R. Jupe Gerald H. Lushington Mohan Purushothaman Fabricio Pautasso Georg Armstrong Arif Sorathia Jessica Crawley Vijay R. Nadipelli Bernard Rubin Ryan Newhardt Melissa E. Munroe Brett Adelman |
| author_facet | Eldon R. Jupe Gerald H. Lushington Mohan Purushothaman Fabricio Pautasso Georg Armstrong Arif Sorathia Jessica Crawley Vijay R. Nadipelli Bernard Rubin Ryan Newhardt Melissa E. Munroe Brett Adelman |
| author_sort | Eldon R. Jupe |
| collection | DOAJ |
| description | (1) Objective: Systemic lupus erythematosus (SLE) is a complex disease involving immune dysregulation, episodic flares, and poor quality of life (QOL). For a decentralized digital study of SLE patients, machine learning was used to assess patient-reported outcomes (PROs), QOL, and biometric data for predicting possible disease flares. (2) Methods: Participants were recruited from the LupusCorner online community. Adults self-reporting an SLE diagnosis were consented and given a mobile application to record patient profile (PP), PRO, and QOL metrics, and enlisted participants received smartwatches for digital biometric monitoring. The resulting data were profiled using feature selection and classification algorithms. (3) Results: 550 participants completed digital surveys, 144 (26%) agreed to wear smartwatches, and medical records (MRs) were obtained for 68. Mining of PP, PRO, QOL, and biometric data yielded a 26-feature model for classifying participants according to MR-identified disease flare risk. ROC curves significantly distinguished true from false positives (ten-fold cross-validation: <i>p</i> < 0.00023; five-fold: <i>p</i> < 0.00022). A 25-feature Bayesian model enabled time-variant prediction of participant-reported possible flares (P(true) > 0.85, <i>p</i> < 0.001; P(nonflare) > 0.83, <i>p</i> < 0.0001). (4) Conclusions: Regular profiling of patient well-being and biometric activity may support proactive screening for circumstances warranting clinical assessment. |
| first_indexed | 2024-03-08T20:56:53Z |
| format | Article |
| id | doaj.art-8ba90c05cea54456b978d5cd7bb61a1a |
| institution | Directory Open Access Journal |
| issn | 2673-6284 |
| language | English |
| last_indexed | 2024-03-08T20:56:53Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | BioTech |
| spelling | doaj.art-8ba90c05cea54456b978d5cd7bb61a1a2023-12-22T13:56:28ZengMDPI AGBioTech2673-62842023-11-011246210.3390/biotech12040062Tracking of Systemic Lupus Erythematosus (SLE) Longitudinally Using Biosensor and Patient-Reported Data: A Report on the Fully Decentralized Mobile Study to Measure and Predict Lupus Disease Activity Using Digital Signals—The OASIS StudyEldon R. Jupe0Gerald H. Lushington1Mohan Purushothaman2Fabricio Pautasso3Georg Armstrong4Arif Sorathia5Jessica Crawley6Vijay R. Nadipelli7Bernard Rubin8Ryan Newhardt9Melissa E. Munroe10Brett Adelman11Progentec Diagnostics, Inc., Oklahoma City, OK 73104, USAProgentec Diagnostics, Inc., Oklahoma City, OK 73104, USAProgentec Diagnostics, Inc., Oklahoma City, OK 73104, USAProgentec Diagnostics, Inc., Oklahoma City, OK 73104, USAProgentec Diagnostics, Inc., Oklahoma City, OK 73104, USAProgentec Diagnostics, Inc., Oklahoma City, OK 73104, USAProgentec Diagnostics, Inc., Oklahoma City, OK 73104, USAGSK, Philadelphia, PA 19104, USAGSK, Raleigh, NC 27709, USAProgentec Diagnostics, Inc., Oklahoma City, OK 73104, USAProgentec Diagnostics, Inc., Oklahoma City, OK 73104, USAProgentec Diagnostics, Inc., Oklahoma City, OK 73104, USA(1) Objective: Systemic lupus erythematosus (SLE) is a complex disease involving immune dysregulation, episodic flares, and poor quality of life (QOL). For a decentralized digital study of SLE patients, machine learning was used to assess patient-reported outcomes (PROs), QOL, and biometric data for predicting possible disease flares. (2) Methods: Participants were recruited from the LupusCorner online community. Adults self-reporting an SLE diagnosis were consented and given a mobile application to record patient profile (PP), PRO, and QOL metrics, and enlisted participants received smartwatches for digital biometric monitoring. The resulting data were profiled using feature selection and classification algorithms. (3) Results: 550 participants completed digital surveys, 144 (26%) agreed to wear smartwatches, and medical records (MRs) were obtained for 68. Mining of PP, PRO, QOL, and biometric data yielded a 26-feature model for classifying participants according to MR-identified disease flare risk. ROC curves significantly distinguished true from false positives (ten-fold cross-validation: <i>p</i> < 0.00023; five-fold: <i>p</i> < 0.00022). A 25-feature Bayesian model enabled time-variant prediction of participant-reported possible flares (P(true) > 0.85, <i>p</i> < 0.001; P(nonflare) > 0.83, <i>p</i> < 0.0001). (4) Conclusions: Regular profiling of patient well-being and biometric activity may support proactive screening for circumstances warranting clinical assessment.https://www.mdpi.com/2673-6284/12/4/62SLEdigitalbiosensorpatient-reported outcomessigns and symptoms of flarereal-world evidence |
| spellingShingle | Eldon R. Jupe Gerald H. Lushington Mohan Purushothaman Fabricio Pautasso Georg Armstrong Arif Sorathia Jessica Crawley Vijay R. Nadipelli Bernard Rubin Ryan Newhardt Melissa E. Munroe Brett Adelman Tracking of Systemic Lupus Erythematosus (SLE) Longitudinally Using Biosensor and Patient-Reported Data: A Report on the Fully Decentralized Mobile Study to Measure and Predict Lupus Disease Activity Using Digital Signals—The OASIS Study BioTech SLE digital biosensor patient-reported outcomes signs and symptoms of flare real-world evidence |
| title | Tracking of Systemic Lupus Erythematosus (SLE) Longitudinally Using Biosensor and Patient-Reported Data: A Report on the Fully Decentralized Mobile Study to Measure and Predict Lupus Disease Activity Using Digital Signals—The OASIS Study |
| title_full | Tracking of Systemic Lupus Erythematosus (SLE) Longitudinally Using Biosensor and Patient-Reported Data: A Report on the Fully Decentralized Mobile Study to Measure and Predict Lupus Disease Activity Using Digital Signals—The OASIS Study |
| title_fullStr | Tracking of Systemic Lupus Erythematosus (SLE) Longitudinally Using Biosensor and Patient-Reported Data: A Report on the Fully Decentralized Mobile Study to Measure and Predict Lupus Disease Activity Using Digital Signals—The OASIS Study |
| title_full_unstemmed | Tracking of Systemic Lupus Erythematosus (SLE) Longitudinally Using Biosensor and Patient-Reported Data: A Report on the Fully Decentralized Mobile Study to Measure and Predict Lupus Disease Activity Using Digital Signals—The OASIS Study |
| title_short | Tracking of Systemic Lupus Erythematosus (SLE) Longitudinally Using Biosensor and Patient-Reported Data: A Report on the Fully Decentralized Mobile Study to Measure and Predict Lupus Disease Activity Using Digital Signals—The OASIS Study |
| title_sort | tracking of systemic lupus erythematosus sle longitudinally using biosensor and patient reported data a report on the fully decentralized mobile study to measure and predict lupus disease activity using digital signals the oasis study |
| topic | SLE digital biosensor patient-reported outcomes signs and symptoms of flare real-world evidence |
| url | https://www.mdpi.com/2673-6284/12/4/62 |
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