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Article ; Online: Deriving "definitive" results in observational COVID-19 research: A problematic endeavor.

Clift, Ash K

2020 Volume 93, Issue 2, Page(s) 681–682

MeSH term(s)	COVID-19/complications ; COVID-19/etiology ; COVID-19/physiopathology ; Humans ; Meta-Analysis as Topic ; Observational Studies as Topic/standards ; Research/standards ; Smoking
Keywords	covid19
Language	English
Publishing date	2020-09-29
Publishing country	United States
Document type	Letter
ZDB-ID	752392-0
ISSN	1096-9071 ; 0146-6615
ISSN (online)	1096-9071
ISSN	0146-6615
DOI	10.1002/jmv.26481
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Deriving "definitive" results in observational COVID-19 research: A problematic endeavor

Clift, Ash K

J. med. virol

Keywords	covid19
Publisher	WHO
Document type	Article
Note	WHO #Covidence: #812704
Database	COVID19

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Article ; Online: Deriving “definitive” results in observational COVID‐19 research

Clift, Ash K.

Journal of Medical Virology ; ISSN 0146-6615 1096-9071

A problematic endeavor

2020

Keywords	Virology ; Infectious Diseases ; covid19
Language	English
Publisher	Wiley
Publishing country	us
Document type	Article ; Online
DOI	10.1002/jmv.26481
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Living risk prediction algorithm (QCOVID) for risk of hospital admission and mortality from coronavirus 19 in adults: national derivation and validation cohort study.

BMJ (Clinical research ed.)

2020 Volume 371, Page(s) m3731

Abstract: Objective: To derive and validate a risk prediction algorithm to estimate hospital admission and mortality outcomes from coronavirus disease 2019 (covid-19) in adults.: Design: Population based cohort study.: Setting and participants: QResearch ... ...

Abstract	Objective: To derive and validate a risk prediction algorithm to estimate hospital admission and mortality outcomes from coronavirus disease 2019 (covid-19) in adults. Design: Population based cohort study. Setting and participants: QResearch database, comprising 1205 general practices in England with linkage to covid-19 test results, Hospital Episode Statistics, and death registry data. 6.08 million adults aged 19-100 years were included in the derivation dataset and 2.17 million in the validation dataset. The derivation and first validation cohort period was 24 January 2020 to 30 April 2020. The second temporal validation cohort covered the period 1 May 2020 to 30 June 2020. Main outcome measures: The primary outcome was time to death from covid-19, defined as death due to confirmed or suspected covid-19 as per the death certification or death occurring in a person with confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in the period 24 January to 30 April 2020. The secondary outcome was time to hospital admission with confirmed SARS-CoV-2 infection. Models were fitted in the derivation cohort to derive risk equations using a range of predictor variables. Performance, including measures of discrimination and calibration, was evaluated in each validation time period. Results: 4384 deaths from covid-19 occurred in the derivation cohort during follow-up and 1722 in the first validation cohort period and 621 in the second validation cohort period. The final risk algorithms included age, ethnicity, deprivation, body mass index, and a range of comorbidities. The algorithm had good calibration in the first validation cohort. For deaths from covid-19 in men, it explained 73.1% (95% confidence interval 71.9% to 74.3%) of the variation in time to death (R Conclusion: The QCOVID population based risk algorithm performed well, showing very high levels of discrimination for deaths and hospital admissions due to covid-19. The absolute risks presented, however, will change over time in line with the prevailing SARS-C0V-2 infection rate and the extent of social distancing measures in place, so they should be interpreted with caution. The model can be recalibrated for different time periods, however, and has the potential to be dynamically updated as the pandemic evolves.
MeSH term(s)	Adult ; Aged, 80 and over ; Algorithms ; Betacoronavirus/isolation & purification ; COVID-19 ; Clinical Decision Rules ; Cohort Studies ; Coronavirus Infections/mortality ; Coronavirus Infections/therapy ; Databases, Factual/statistics & numerical data ; England/epidemiology ; Female ; Hospitalization/statistics & numerical data ; Humans ; Male ; Mortality ; Pandemics ; Pneumonia, Viral/mortality ; Pneumonia, Viral/therapy ; Prognosis ; Reproducibility of Results ; Risk Assessment/methods ; Risk Assessment/standards ; SARS-CoV-2
Keywords	covid19
Language	English
Publishing date	2020-10-20
Publishing country	England
Document type	Journal Article
ZDB-ID	1362901-3
ISSN	1756-1833 ; 0959-8154 ; 0959-8146 ; 0959-8138 ; 0959-535X ; 1759-2151
ISSN (online)	1756-1833
ISSN	0959-8154 ; 0959-8146 ; 0959-8138 ; 0959-535X ; 1759-2151
DOI	10.1136/bmj.m3731
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Living risk prediction algorithm (QCOVID) for risk of hospital admission and mortality from coronavirus 19 in adults: national derivation and validation cohort study

BMJ

Abstract: OBJECTIVE: To derive and validate a risk prediction algorithm to estimate hospital admission and mortality outcomes from coronavirus disease 2019 (covid-19) in adults. DESIGN: Population based cohort study. SETTING AND PARTICIPANTS: QResearch database, ... ...

Abstract	OBJECTIVE: To derive and validate a risk prediction algorithm to estimate hospital admission and mortality outcomes from coronavirus disease 2019 (covid-19) in adults. DESIGN: Population based cohort study. SETTING AND PARTICIPANTS: QResearch database, comprising 1205 general practices in England with linkage to covid-19 test results, Hospital Episode Statistics, and death registry data. 6.08 million adults aged 19-100 years were included in the derivation dataset and 2.17 million in the validation dataset. The derivation and first validation cohort period was 24 January 2020 to 30 April 2020. The second temporal validation cohort covered the period 1 May 2020 to 30 June 2020. MAIN OUTCOME MEASURES: The primary outcome was time to death from covid-19, defined as death due to confirmed or suspected covid-19 as per the death certification or death occurring in a person with confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in the period 24 January to 30 April 2020. The secondary outcome was time to hospital admission with confirmed SARS-CoV-2 infection. Models were fitted in the derivation cohort to derive risk equations using a range of predictor variables. Performance, including measures of discrimination and calibration, was evaluated in each validation time period. RESULTS: 4384 deaths from covid-19 occurred in the derivation cohort during follow-up and 1722 in the first validation cohort period and 621 in the second validation cohort period. The final risk algorithms included age, ethnicity, deprivation, body mass index, and a range of comorbidities. The algorithm had good calibration in the first validation cohort. For deaths from covid-19 in men, it explained 73.1% (95% confidence interval 71.9% to 74.3%) of the variation in time to death (R2); the D statistic was 3.37 (95% confidence interval 3.27 to 3.47), and Harrell's C was 0.928 (0.919 to 0.938). Similar results were obtained for women, for both outcomes, and in both time periods. In the top 5% of patients with the highest predicted risks of death, the sensitivity for identifying deaths within 97 days was 75.7%. People in the top 20% of predicted risk of death accounted for 94% of all deaths from covid-19. CONCLUSION: The QCOVID population based risk algorithm performed well, showing very high levels of discrimination for deaths and hospital admissions due to covid-19. The absolute risks presented, however, will change over time in line with the prevailing SARS-C0V-2 infection rate and the extent of social distancing measures in place, so they should be interpreted with caution. The model can be recalibrated for different time periods, however, and has the potential to be dynamically updated as the pandemic evolves.
Keywords	covid19
Publisher	WHO
Document type	Article
Note	WHO #Covidence: #883340
Database	COVID19

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Book ; Online: Living risk prediction algorithm (QCOVID) for risk of hospital admission and mortality from coronavirus 19 in adults

national derivation and validation cohort study

2020

Abstract: ... Objective ... To derive and validate a risk prediction algorithm to estimate hospital admission and mortality outcomes from coronavirus disease 2019 (covid-19) in adults. ... ... Design ... Population based cohort study. ... ... < ... ...

Abstract	<sec><st>Objective</st> To derive and validate a risk prediction algorithm to estimate hospital admission and mortality outcomes from coronavirus disease 2019 (covid-19) in adults. </sec> <sec><st>Design</st> Population based cohort study. </sec> <sec><st>Setting and participants</st> QResearch database, comprising 1205 general practices in England with linkage to covid-19 test results, Hospital Episode Statistics, and death registry data. 6.08 million adults aged 19-100 years were included in the derivation dataset and 2.17 million in the validation dataset. The derivation and first validation cohort period was 24 January 2020 to 30 April 2020. The second temporal validation cohort covered the period 1 May 2020 to 30 June 2020. </sec> <sec><st>Main outcome measures</st> The primary outcome was time to death from covid-19, defined as death due to confirmed or suspected covid-19 as per the death certification or death occurring in a person with confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in the period 24 January to 30 April 2020. The secondary outcome was time to hospital admission with confirmed SARS-CoV-2 infection. Models were fitted in the derivation cohort to derive risk equations using a range of predictor variables. Performance, including measures of discrimination and calibration, was evaluated in each validation time period. </sec> <sec><st>Results</st> 4384 deaths from covid-19 occurred in the derivation cohort during follow-up and 1722 in the first validation cohort period and 621 in the second validation cohort period. The final risk algorithms included age, ethnicity, deprivation, body mass index, and a range of comorbidities. The algorithm had good calibration in the first validation cohort. For deaths from covid-19 in men, it explained 73.1% (95% confidence interval 71.9% to 74.3%) of the variation in time to death (R2); the D statistic was 3.37 (95% confidence interval 3.27 to 3.47), and Harrell’s C was 0.928 (0.919 to 0.938). Similar results were obtained for women, for both outcomes, and in both time periods. In the top 5% of patients with the highest predicted risks of death, the sensitivity for identifying deaths within 97 days was 75.7%. People in the top 20% of predicted risk of death accounted for 94% of all deaths from covid-19. </sec> <sec><st>Conclusion</st> The QCOVID population based risk algorithm performed well, showing very high levels of discrimination for deaths and hospital admissions due to covid-19. The absolute risks presented, however, will change over time in line with the prevailing SARS-C0V-2 infection rate and the extent of social distancing measures in place, so they should be interpreted with caution. The model can be recalibrated for different time periods, however, and has the potential to be dynamically updated as the pandemic evolves. </sec>
Keywords	RESEARCH ; covid19
Language	English
Publishing date	2020-10-20 16:06:04.0
Publisher	BMJ Publishing Group Ltd
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Living risk prediction algorithm (QCOVID) for risk of hospital admission and mortality from coronavirus 19 in adults

national derivation and validation cohort study

2020

Abstract: Abstract: Objective: To derive and validate a risk prediction algorithm to estimate hospital admission and mortality outcomes from coronavirus disease 2019 (covid-19) in adults. Design: Population based cohort study. Setting and participants: QResearch ... ...

Abstract	Abstract: Objective: To derive and validate a risk prediction algorithm to estimate hospital admission and mortality outcomes from coronavirus disease 2019 (covid-19) in adults. Design: Population based cohort study. Setting and participants: QResearch database, comprising 1205 general practices in England with linkage to covid-19 test results, Hospital Episode Statistics, and death registry data. 6.08 million adults aged 19-100 years were included in the derivation dataset and 2.17 million in the validation dataset. The derivation and first validation cohort period was 24 January 2020 to 30 April 2020. The second temporal validation cohort covered the period 1 May 2020 to 30 June 2020. Main outcome measures: The primary outcome was time to death from covid-19, defined as death due to confirmed or suspected covid-19 as per the death certification or death occurring in a person with confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in the period 24 January to 30 April 2020. The secondary outcome was time to hospital admission with confirmed SARS-CoV-2 infection. Models were fitted in the derivation cohort to derive risk equations using a range of predictor variables. Performance, including measures of discrimination and calibration, was evaluated in each validation time period. Results: 4384 deaths from covid-19 occurred in the derivation cohort during follow-up and 1722 in the first validation cohort period and 621 in the second validation cohort period. The final risk algorithms included age, ethnicity, deprivation, body mass index, and a range of comorbidities. The algorithm had good calibration in the first validation cohort. For deaths from covid-19 in men, it explained 73.1% (95% confidence interval 71.9% to 74.3%) of the variation in time to death (R2); the D statistic was 3.37 (95% confidence interval 3.27 to 3.47), and Harrell’s C was 0.928 (0.919 to 0.938). Similar results were obtained for women, for both outcomes, and in both time periods. In the top 5% of patients with the highest predicted risks of death, the sensitivity for identifying deaths within 97 days was 75.7%. People in the top 20% of predicted risk of death accounted for 94% of all deaths from covid-19. Conclusion: The QCOVID population based risk algorithm performed well, showing very high levels of discrimination for deaths and hospital admissions due to covid-19. The absolute risks presented, however, will change over time in line with the prevailing SARS-C0V-2 infection rate and the extent of social distancing measures in place, so they should be interpreted with caution. The model can be recalibrated for different time periods, however, and has the potential to be dynamically updated as the pandemic evolves.
Keywords	Research ; covid19
Language	English
Publishing date	2020-10-21T15:12:16Z
Publisher	British Medical Journal Publishing Group
Publishing country	uk
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Living risk prediction algorithm (QCOVID) for risk of hospital admission and mortality from coronavirus 19 in adults

BMJ

national derivation and validation cohort study

2020 , Page(s) m3731

Abstract: Abstract Objective To derive and validate a risk prediction algorithm to estimate hospital admission and mortality outcomes from coronavirus disease 2019 (covid-19) in adults. Design Population based cohort study. Setting and participants QResearch ... ...

Abstract	Abstract Objective To derive and validate a risk prediction algorithm to estimate hospital admission and mortality outcomes from coronavirus disease 2019 (covid-19) in adults. Design Population based cohort study. Setting and participants QResearch database, comprising 1205 general practices in England with linkage to covid-19 test results, Hospital Episode Statistics, and death registry data. 6.08 million adults aged 19-100 years were included in the derivation dataset and 2.17 million in the validation dataset. The derivation and first validation cohort period was 24 January 2020 to 30 April 2020. The second temporal validation cohort covered the period 1 May 2020 to 30 June 2020. Main outcome measures The primary outcome was time to death from covid-19, defined as death due to confirmed or suspected covid-19 as per the death certification or death occurring in a person with confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in the period 24 January to 30 April 2020. The secondary outcome was time to hospital admission with confirmed SARS-CoV-2 infection. Models were fitted in the derivation cohort to derive risk equations using a range of predictor variables. Performance, including measures of discrimination and calibration, was evaluated in each validation time period. Results 4384 deaths from covid-19 occurred in the derivation cohort during follow-up and 1722 in the first validation cohort period and 621 in the second validation cohort period. The final risk algorithms included age, ethnicity, deprivation, body mass index, and a range of comorbidities. The algorithm had good calibration in the first validation cohort. For deaths from covid-19 in men, it explained 73.1% (95% confidence interval 71.9% to 74.3%) of the variation in time to death (R 2 ); the D statistic was 3.37 (95% confidence interval 3.27 to 3.47), and Harrell’s C was 0.928 (0.919 to 0.938). Similar results were obtained for women, for both outcomes, and in both time periods. In the top 5% of patients with the highest predicted risks of death, the sensitivity for identifying deaths within 97 days was 75.7%. People in the top 20% of predicted risk of death accounted for 94% of all deaths from covid-19. Conclusion The QCOVID population based risk algorithm performed well, showing very high levels of discrimination for deaths and hospital admissions due to covid-19. The absolute risks presented, however, will change over time in line with the prevailing SARS-C0V-2 infection rate and the extent of social distancing measures in place, so they should be interpreted with caution. The model can be recalibrated for different time periods, however, and has the potential to be dynamically updated as the pandemic evolves.
Keywords	covid19
Language	English
Publisher	BMJ
Publishing country	uk
Document type	Article ; Online
ZDB-ID	1362901-3
ISSN	1756-1833 ; 0959-8154 ; 0959-8146 ; 0959-8138 ; 0959-535X ; 1759-2151
ISSN (online)	1756-1833
ISSN	0959-8154 ; 0959-8146 ; 0959-8138 ; 0959-535X ; 1759-2151
DOI	10.1136/bmj.m3731
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Deriving "definitive" results in observational COVID-19 research: A problematic endeavor.

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Article ; Online: Deriving “definitive” results in observational COVID‐19 research

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Article ; Online: Living risk prediction algorithm (QCOVID) for risk of hospital admission and mortality from coronavirus 19 in adults: national derivation and validation cohort study.

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Article: Living risk prediction algorithm (QCOVID) for risk of hospital admission and mortality from coronavirus 19 in adults: national derivation and validation cohort study

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Book ; Online: Living risk prediction algorithm (QCOVID) for risk of hospital admission and mortality from coronavirus 19 in adults

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Article ; Online: Living risk prediction algorithm (QCOVID) for risk of hospital admission and mortality from coronavirus 19 in adults

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Article ; Online: Living risk prediction algorithm (QCOVID) for risk of hospital admission and mortality from coronavirus 19 in adults

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