Using Multi-Modal Electronic Health Record Data for the Development and Validation of Risk Prediction Models for Long COVID Using the Super Learner Algorithm
BACKGROUND: Post-Acute Sequelae of COVID-19 (PASC) have emerged as a global public health and healthcare challenge. This study aimed to uncover predictive factors for PASC from multi-modal data to develop a predictive model for PASC diagnoses.
METHODS: We analyzed electronic health records from 92,301 COVID-19 patients, covering medical phenotypes, medications, and lab results. We used a Super Learner-based prediction approach to identify predictive factors. We integrated the model outputs into individual and composite risk scores and evaluated their predictive performance.
RESULTS: Our analysis identified several factors predictive of diagnoses of PASC, including being overweight/obese and the use of HMG CoA reductase inhibitors prior to COVID-19 infection, and respiratory system symptoms during COVID-19 infection. We developed a composite risk score with a moderate discriminatory ability for PASC (covariate-adjusted AUC (95% confidence interval): 0.66 (0.63, 0.69)) by combining the risk scores based on phenotype and medication records. The combined risk score could identify 10% of individuals with a 2.2-fold increased risk for PASC.
CONCLUSIONS: We identified several factors predictive of diagnoses of PASC and integrated the information into a composite risk score for PASC prediction, which could contribute to the identification of individuals at higher risk for PASC and inform preventive efforts.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:12 |
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| Enthalten in: |
Journal of clinical medicine - 12(2023), 23 vom: 25. Nov. |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Jin, Weijia [VerfasserIn] |
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| Links: |
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| Themen: |
COVID-19 |
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| Anmerkungen: |
Date Revised 10.02.2024 published: Electronic Citation Status PubMed-not-MEDLINE |
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| doi: |
10.3390/jcm12237313 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM365591815 |
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| 500 | |a published: Electronic | ||
| 500 | |a Citation Status PubMed-not-MEDLINE | ||
| 520 | |a BACKGROUND: Post-Acute Sequelae of COVID-19 (PASC) have emerged as a global public health and healthcare challenge. This study aimed to uncover predictive factors for PASC from multi-modal data to develop a predictive model for PASC diagnoses | ||
| 520 | |a METHODS: We analyzed electronic health records from 92,301 COVID-19 patients, covering medical phenotypes, medications, and lab results. We used a Super Learner-based prediction approach to identify predictive factors. We integrated the model outputs into individual and composite risk scores and evaluated their predictive performance | ||
| 520 | |a RESULTS: Our analysis identified several factors predictive of diagnoses of PASC, including being overweight/obese and the use of HMG CoA reductase inhibitors prior to COVID-19 infection, and respiratory system symptoms during COVID-19 infection. We developed a composite risk score with a moderate discriminatory ability for PASC (covariate-adjusted AUC (95% confidence interval): 0.66 (0.63, 0.69)) by combining the risk scores based on phenotype and medication records. The combined risk score could identify 10% of individuals with a 2.2-fold increased risk for PASC | ||
| 520 | |a CONCLUSIONS: We identified several factors predictive of diagnoses of PASC and integrated the information into a composite risk score for PASC prediction, which could contribute to the identification of individuals at higher risk for PASC and inform preventive efforts | ||
| 650 | 4 | |a Journal Article | |
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| 650 | 4 | |a post-acute sequelae of SARS-CoV-2 infection | |
| 650 | 4 | |a predictive models | |
| 700 | 1 | |a Hao, Wei |e verfasserin |4 aut | |
| 700 | 1 | |a Shi, Xu |e verfasserin |4 aut | |
| 700 | 1 | |a Fritsche, Lars G |e verfasserin |4 aut | |
| 700 | 1 | |a Salvatore, Maxwell |e verfasserin |4 aut | |
| 700 | 1 | |a Admon, Andrew J |e verfasserin |4 aut | |
| 700 | 1 | |a Friese, Christopher R |e verfasserin |4 aut | |
| 700 | 1 | |a Mukherjee, Bhramar |e verfasserin |4 aut | |
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