Design, Development and Implementation of iALERTS (Informatics Analytics for Long-term Evaluation and Repercussions Tracking of SARS-CoV-2 Infection): A Research Protocol
Introduction: The global pandemic caused by SARS-CoV-2 has led to significant morbidity and mortality, with many survivors experiencing long-term sequelae known as Post Acute Sequelae of Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) infection (PASC) or “long Coronavirus Disease (COVID...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
JCDR Research and Publications Private Limited
2025-02-01
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| Series: | Journal of Clinical and Diagnostic Research |
| Subjects: | |
| Online Access: | https://jcdr.net/articles/PDF/20601/72559_CE[Ra1]__F(SHU)_QC(PS_OM)_PF1(VD_SHU)_redo_PFA(IS)_PB(VD_IS)_PN(IS).pdf |
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| Summary: | Introduction: The global pandemic caused by SARS-CoV-2 has led to significant morbidity and mortality, with many survivors experiencing long-term sequelae known as Post Acute Sequelae of Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) infection (PASC) or “long Coronavirus Disease (COVID).” Addressing the healthcare challenges posed by PASC requires an innovative approach to monitoring and management.
Need of the study: Traditional systems are often under-equipped to monitor and manage these prolonged effects comprehensively. An alert system is crucial for filling this gap, offering a data-driven approach to track, analyse, and manage the long-term repercussions of SARS-CoV-2 infection.
Aim: To design, develop, and implement an informatics decision support tool that leverages data analytics to track and analyse the long-term effects of COVID-19, facilitating early identification and management of PASC.
Materials and Methods: A quasi-experimental study will be conducted at Panimalar Medical College Hospital and Research Institute (PMCHRI), Chennai, Tamil Nadu, India, from December 2023 to December 2024. Primary data will be collected through a predictive survey tool from patients who have recovered from COVID-19, in addition to secondary data from hospital records, to develop a clinical decision support system. This platform employs machine learning algorithms to predict the likelihood of PASC development among Coronavirus Disease-19 (COVID-19) survivors. The project involves three phases: the creation of the predictive survey tool, the development of the iALERTS platform (Informatics Analytics for Long-term Evaluation and Repercussions Tracking of SARS-CoV-2 infection), and the implementation and evaluation of the system in clinical settings. Evaluation metrics will include user satisfaction, predictive accuracy, and the impact on clinical decision-making and patient outcomes. Statistical analysis will be conducted using multivariable regression models to identify predictors of PASC and to evaluate the association between SARS-CoV-2 infection characteristics and long-term outcomes. A p-value of <0.05 will be considered statistically significant for all analyses. |
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| ISSN: | 2249-782X 0973-709X |