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Risk assessment models for venous thromboembolism in hospitalised adult patients: a systematic review

INTRODUCTION: Hospital-acquired thrombosis accounts for a large proportion of all venous thromboembolism (VTE), with significant morbidity and mortality. This subset of VTE can be reduced through accurate risk assessment and tailored pharmacological thromboprophylaxis. This systematic review aimed t...

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Autores principales: Pandor, Abdullah, Tonkins, Michael, Goodacre, Steve, Sworn, Katie, Clowes, Mark, Griffin, Xavier L, Holland, Mark, Hunt, Beverley J, de Wit, Kerstin, Horner, Daniel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8323381/
https://www.ncbi.nlm.nih.gov/pubmed/34326045
http://dx.doi.org/10.1136/bmjopen-2020-045672
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author Pandor, Abdullah
Tonkins, Michael
Goodacre, Steve
Sworn, Katie
Clowes, Mark
Griffin, Xavier L
Holland, Mark
Hunt, Beverley J
de Wit, Kerstin
Horner, Daniel
author_facet Pandor, Abdullah
Tonkins, Michael
Goodacre, Steve
Sworn, Katie
Clowes, Mark
Griffin, Xavier L
Holland, Mark
Hunt, Beverley J
de Wit, Kerstin
Horner, Daniel
author_sort Pandor, Abdullah
collection PubMed
description INTRODUCTION: Hospital-acquired thrombosis accounts for a large proportion of all venous thromboembolism (VTE), with significant morbidity and mortality. This subset of VTE can be reduced through accurate risk assessment and tailored pharmacological thromboprophylaxis. This systematic review aimed to determine the comparative accuracy of risk assessment models (RAMs) for predicting VTE in patients admitted to hospital. METHODS: A systematic search was performed across five electronic databases (including MEDLINE, EMBASE and the Cochrane Library) from inception to February 2021. All primary validation studies were eligible if they examined the accuracy of a multivariable RAM (or scoring system) for predicting the risk of developing VTE in hospitalised inpatients. Two or more reviewers independently undertook study selection, data extraction and risk of bias assessments using the PROBAST (Prediction model Risk Of Bias ASsessment Tool) tool. We used narrative synthesis to summarise the findings. RESULTS: Among 6355 records, we included 51 studies, comprising 24 unique validated RAMs. The majority of studies included hospital inpatients who required medical care (21 studies), were undergoing surgery (15 studies) or receiving care for trauma (4 studies). The most widely evaluated RAMs were the Caprini RAM (22 studies), Padua prediction score (16 studies), IMPROVE models (8 studies), the Geneva risk score (4 studies) and the Kucher score (4 studies). C-statistics varied markedly between studies and between models, with no one RAM performing obviously better than other models. Across all models, C-statistics were often weak (<0.7), sometimes good (0.7–0.8) and a few were excellent (>0.8). Similarly, estimates for sensitivity and specificity were highly variable. Sensitivity estimates ranged from 12.0% to 100% and specificity estimates ranged from 7.2% to 100%. CONCLUSION: Available data suggest that RAMs have generally weak predictive accuracy for VTE. There is insufficient evidence and too much heterogeneity to recommend the use of any particular RAM. PROSPERO REGISTRATION NUMBER: Steve Goodacre, Abdullah Pandor, Katie Sworn, Daniel Horner, Mark Clowes. A systematic review of venous thromboembolism RAMs for hospital inpatients. PROSPERO 2020 CRD42020165778. Available from https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=165778https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=165778
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spelling pubmed-83233812021-08-19 Risk assessment models for venous thromboembolism in hospitalised adult patients: a systematic review Pandor, Abdullah Tonkins, Michael Goodacre, Steve Sworn, Katie Clowes, Mark Griffin, Xavier L Holland, Mark Hunt, Beverley J de Wit, Kerstin Horner, Daniel BMJ Open Haematology (Incl Blood Transfusion) INTRODUCTION: Hospital-acquired thrombosis accounts for a large proportion of all venous thromboembolism (VTE), with significant morbidity and mortality. This subset of VTE can be reduced through accurate risk assessment and tailored pharmacological thromboprophylaxis. This systematic review aimed to determine the comparative accuracy of risk assessment models (RAMs) for predicting VTE in patients admitted to hospital. METHODS: A systematic search was performed across five electronic databases (including MEDLINE, EMBASE and the Cochrane Library) from inception to February 2021. All primary validation studies were eligible if they examined the accuracy of a multivariable RAM (or scoring system) for predicting the risk of developing VTE in hospitalised inpatients. Two or more reviewers independently undertook study selection, data extraction and risk of bias assessments using the PROBAST (Prediction model Risk Of Bias ASsessment Tool) tool. We used narrative synthesis to summarise the findings. RESULTS: Among 6355 records, we included 51 studies, comprising 24 unique validated RAMs. The majority of studies included hospital inpatients who required medical care (21 studies), were undergoing surgery (15 studies) or receiving care for trauma (4 studies). The most widely evaluated RAMs were the Caprini RAM (22 studies), Padua prediction score (16 studies), IMPROVE models (8 studies), the Geneva risk score (4 studies) and the Kucher score (4 studies). C-statistics varied markedly between studies and between models, with no one RAM performing obviously better than other models. Across all models, C-statistics were often weak (<0.7), sometimes good (0.7–0.8) and a few were excellent (>0.8). Similarly, estimates for sensitivity and specificity were highly variable. Sensitivity estimates ranged from 12.0% to 100% and specificity estimates ranged from 7.2% to 100%. CONCLUSION: Available data suggest that RAMs have generally weak predictive accuracy for VTE. There is insufficient evidence and too much heterogeneity to recommend the use of any particular RAM. PROSPERO REGISTRATION NUMBER: Steve Goodacre, Abdullah Pandor, Katie Sworn, Daniel Horner, Mark Clowes. A systematic review of venous thromboembolism RAMs for hospital inpatients. PROSPERO 2020 CRD42020165778. Available from https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=165778https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=165778 BMJ Publishing Group 2021-07-29 /pmc/articles/PMC8323381/ /pubmed/34326045 http://dx.doi.org/10.1136/bmjopen-2020-045672 Text en © Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY. Published by BMJ. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See: https://creativecommons.org/licenses/by/4.0/.
spellingShingle Haematology (Incl Blood Transfusion)
Pandor, Abdullah
Tonkins, Michael
Goodacre, Steve
Sworn, Katie
Clowes, Mark
Griffin, Xavier L
Holland, Mark
Hunt, Beverley J
de Wit, Kerstin
Horner, Daniel
Risk assessment models for venous thromboembolism in hospitalised adult patients: a systematic review
title Risk assessment models for venous thromboembolism in hospitalised adult patients: a systematic review
title_full Risk assessment models for venous thromboembolism in hospitalised adult patients: a systematic review
title_fullStr Risk assessment models for venous thromboembolism in hospitalised adult patients: a systematic review
title_full_unstemmed Risk assessment models for venous thromboembolism in hospitalised adult patients: a systematic review
title_short Risk assessment models for venous thromboembolism in hospitalised adult patients: a systematic review
title_sort risk assessment models for venous thromboembolism in hospitalised adult patients: a systematic review
topic Haematology (Incl Blood Transfusion)
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8323381/
https://www.ncbi.nlm.nih.gov/pubmed/34326045
http://dx.doi.org/10.1136/bmjopen-2020-045672
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