The Experts below are selected from a list of 5628 Experts worldwide ranked by ideXlab platform

Paul J Harrison - One of the best experts on this subject based on the ideXlab platform.

  • bidirectional associations between covid 19 and psychiatric disorder retrospective cohort studies of 62 354 covid 19 cases in the usa
    The Lancet Psychiatry, 2021
    Co-Authors: Maxime Taquet, Sierra Luciano, John R Geddes, Paul J Harrison
    Abstract:

    Summary Background Adverse mental health consequences of COVID-19, including anxiety and depression, have been widely predicted but not yet accurately measured. There are a range of physical health risk factors for COVID-19, but it is not known if there are also psychiatric risk factors. In this electronic health record Network cohort study using data from 69 million individuals, 62 354 of whom had a diagnosis of COVID-19, we assessed whether a diagnosis of COVID-19 (compared with other health events) was associated with increased rates of subsequent psychiatric diagnoses, and whether patients with a history of psychiatric illness are at a higher risk of being diagnosed with COVID-19. Methods We used the TriNetX Analytics Network, a global Federated Network that captures anonymised data from electronic health records in 54 health-care organisations in the USA, totalling 69·8 million patients. TriNetX included 62 354 patients diagnosed with COVID-19 between Jan 20, and Aug 1, 2020. We created cohorts of patients who had been diagnosed with COVID-19 or a range of other health events. We used propensity score matching to control for confounding by risk factors for COVID-19 and for severity of illness. We measured the incidence of and hazard ratios (HRs) for psychiatric disorders, dementia, and insomnia, during the first 14 to 90 days after a diagnosis of COVID-19. Findings In patients with no previous psychiatric history, a diagnosis of COVID-19 was associated with increased incidence of a first psychiatric diagnosis in the following 14 to 90 days compared with six other health events (HR 2·1, 95% CI 1·8–2·5 vs influenza; 1·7, 1·5–1·9 vs other respiratory tract infections; 1·6, 1·4–1·9 vs skin infection; 1·6, 1·3–1·9 vs cholelithiasis; 2·2, 1·9–2·6 vs urolithiasis, and 2·1, 1·9–2·5 vs fracture of a large bone; all p Interpretation Survivors of COVID-19 appear to be at increased risk of psychiatric sequelae, and a psychiatric diagnosis might be an independent risk factor for COVID-19. Although preliminary, our findings have implications for clinical services, and prospective cohort studies are warranted. Funding National Institute for Health Research.

Maxime Taquet - One of the best experts on this subject based on the ideXlab platform.

  • bidirectional associations between covid 19 and psychiatric disorder retrospective cohort studies of 62 354 covid 19 cases in the usa
    The Lancet Psychiatry, 2021
    Co-Authors: Maxime Taquet, Sierra Luciano, John R Geddes, Paul J Harrison
    Abstract:

    Summary Background Adverse mental health consequences of COVID-19, including anxiety and depression, have been widely predicted but not yet accurately measured. There are a range of physical health risk factors for COVID-19, but it is not known if there are also psychiatric risk factors. In this electronic health record Network cohort study using data from 69 million individuals, 62 354 of whom had a diagnosis of COVID-19, we assessed whether a diagnosis of COVID-19 (compared with other health events) was associated with increased rates of subsequent psychiatric diagnoses, and whether patients with a history of psychiatric illness are at a higher risk of being diagnosed with COVID-19. Methods We used the TriNetX Analytics Network, a global Federated Network that captures anonymised data from electronic health records in 54 health-care organisations in the USA, totalling 69·8 million patients. TriNetX included 62 354 patients diagnosed with COVID-19 between Jan 20, and Aug 1, 2020. We created cohorts of patients who had been diagnosed with COVID-19 or a range of other health events. We used propensity score matching to control for confounding by risk factors for COVID-19 and for severity of illness. We measured the incidence of and hazard ratios (HRs) for psychiatric disorders, dementia, and insomnia, during the first 14 to 90 days after a diagnosis of COVID-19. Findings In patients with no previous psychiatric history, a diagnosis of COVID-19 was associated with increased incidence of a first psychiatric diagnosis in the following 14 to 90 days compared with six other health events (HR 2·1, 95% CI 1·8–2·5 vs influenza; 1·7, 1·5–1·9 vs other respiratory tract infections; 1·6, 1·4–1·9 vs skin infection; 1·6, 1·3–1·9 vs cholelithiasis; 2·2, 1·9–2·6 vs urolithiasis, and 2·1, 1·9–2·5 vs fracture of a large bone; all p Interpretation Survivors of COVID-19 appear to be at increased risk of psychiatric sequelae, and a psychiatric diagnosis might be an independent risk factor for COVID-19. Although preliminary, our findings have implications for clinical services, and prospective cohort studies are warranted. Funding National Institute for Health Research.

Walter Van Dyck - One of the best experts on this subject based on the ideXlab platform.

  • real world evidence gathering in oncology the need for a biomedical big data insight providing Federated Network
    Frontiers in Medicine, 2019
    Co-Authors: Tine Geldof, Isabelle Huys, Walter Van Dyck
    Abstract:

    Moving towards new adaptive pathways for the development and access to innovative medicines implies that real-world data (RWD) collected throughout the medicinal product life cycle is becoming increasingly important. Big data analytics on RWD can obtain new and powerful insights into medicines’ effectiveness. However, the healthcare ecosystem still faces many sector-specific challenges that hamper the use of big data analytics delivering real world evidence (RWE). We distinguish between exploratory (ExTE) and hypotheses-evaluating (HETE) studies testing treatment effectiveness in the real world. From our experience and in the context of the four V’s of data management, we show that to get meaningful results data Variety and Veracity are needed regardless of the type of study conducted. More so, for ExTE studies high data Volume is needed while for HETE studies high Velocity becomes essential. Next, we highlight what are needed within the biomedical big data ecosystem, being: (a) international data reusability; (b) real-time RWD processing information systems; and (c) longitudinal RWD. Finally, in an effort to manage the four V’s whilst respecting patient privacy laws we argue for the development of an underlying Federated RWD infrastructure on a common data model, capable of bringing the centrally-conducted big data analysis to the de-centrally kept biomedical data.

John R Geddes - One of the best experts on this subject based on the ideXlab platform.

  • bidirectional associations between covid 19 and psychiatric disorder retrospective cohort studies of 62 354 covid 19 cases in the usa
    The Lancet Psychiatry, 2021
    Co-Authors: Maxime Taquet, Sierra Luciano, John R Geddes, Paul J Harrison
    Abstract:

    Summary Background Adverse mental health consequences of COVID-19, including anxiety and depression, have been widely predicted but not yet accurately measured. There are a range of physical health risk factors for COVID-19, but it is not known if there are also psychiatric risk factors. In this electronic health record Network cohort study using data from 69 million individuals, 62 354 of whom had a diagnosis of COVID-19, we assessed whether a diagnosis of COVID-19 (compared with other health events) was associated with increased rates of subsequent psychiatric diagnoses, and whether patients with a history of psychiatric illness are at a higher risk of being diagnosed with COVID-19. Methods We used the TriNetX Analytics Network, a global Federated Network that captures anonymised data from electronic health records in 54 health-care organisations in the USA, totalling 69·8 million patients. TriNetX included 62 354 patients diagnosed with COVID-19 between Jan 20, and Aug 1, 2020. We created cohorts of patients who had been diagnosed with COVID-19 or a range of other health events. We used propensity score matching to control for confounding by risk factors for COVID-19 and for severity of illness. We measured the incidence of and hazard ratios (HRs) for psychiatric disorders, dementia, and insomnia, during the first 14 to 90 days after a diagnosis of COVID-19. Findings In patients with no previous psychiatric history, a diagnosis of COVID-19 was associated with increased incidence of a first psychiatric diagnosis in the following 14 to 90 days compared with six other health events (HR 2·1, 95% CI 1·8–2·5 vs influenza; 1·7, 1·5–1·9 vs other respiratory tract infections; 1·6, 1·4–1·9 vs skin infection; 1·6, 1·3–1·9 vs cholelithiasis; 2·2, 1·9–2·6 vs urolithiasis, and 2·1, 1·9–2·5 vs fracture of a large bone; all p Interpretation Survivors of COVID-19 appear to be at increased risk of psychiatric sequelae, and a psychiatric diagnosis might be an independent risk factor for COVID-19. Although preliminary, our findings have implications for clinical services, and prospective cohort studies are warranted. Funding National Institute for Health Research.

Tine Geldof - One of the best experts on this subject based on the ideXlab platform.

  • real world evidence gathering in oncology the need for a biomedical big data insight providing Federated Network
    Frontiers in Medicine, 2019
    Co-Authors: Tine Geldof, Isabelle Huys, Walter Van Dyck
    Abstract:

    Moving towards new adaptive pathways for the development and access to innovative medicines implies that real-world data (RWD) collected throughout the medicinal product life cycle is becoming increasingly important. Big data analytics on RWD can obtain new and powerful insights into medicines’ effectiveness. However, the healthcare ecosystem still faces many sector-specific challenges that hamper the use of big data analytics delivering real world evidence (RWE). We distinguish between exploratory (ExTE) and hypotheses-evaluating (HETE) studies testing treatment effectiveness in the real world. From our experience and in the context of the four V’s of data management, we show that to get meaningful results data Variety and Veracity are needed regardless of the type of study conducted. More so, for ExTE studies high data Volume is needed while for HETE studies high Velocity becomes essential. Next, we highlight what are needed within the biomedical big data ecosystem, being: (a) international data reusability; (b) real-time RWD processing information systems; and (c) longitudinal RWD. Finally, in an effort to manage the four V’s whilst respecting patient privacy laws we argue for the development of an underlying Federated RWD infrastructure on a common data model, capable of bringing the centrally-conducted big data analysis to the de-centrally kept biomedical data.