tetano
Editor, Senior Moderator
Acad Emerg Med
. 2022 Jan 22.
doi: 10.1111/acem.14447. Online ahead of print.
Prediction Models for Severe Manifestations and Mortality due to COVID-19: A Systematic Review
Jamie L Miller[SUP] 1 [/SUP], Masafumi Tada[SUP] 2 [/SUP], Michihiko Goto[SUP] 3 [/SUP], Hao Chen[SUP] 4 [/SUP], Elizabeth Dang[SUP] 4 [/SUP], Nicholas Mohr[SUP] 5 [/SUP], Sangil Lee[SUP] 6 [/SUP]
Affiliations
Abstract
Background: Throughout 2020, the coronavirus disease 2019 (COVID-19) has become a threat to public health on national and global level. There has been an immediate need for research to understand the clinical signs and symptoms of COVID-19 that can help predict deterioration including mechanical ventilation, organ support, and death. Studies thus far have addressed the epidemiology of the disease, common presentations, and susceptibility to acquisition and transmission of the virus; however, an accurate prognostic model for severe manifestations of COVID-19 is still needed because of the limited healthcare resources available.
Objective: This systematic review aims to evaluate published reports of prediction models for severe illnesses caused COVID-19.
Methods: Searches were developed by the primary author and a medical librarian using an iterative process of gathering and evaluating terms. Comprehensive strategies, including both index and keyword methods, were devised for PubMed and EMBASE. The data of confirmed COVID-19 patients from randomized control studies, cohort studies, and case-control studies published between January 2020 and May 2021were retrieved. Studies were independently assessed for risk of bias and applicability using the Prediction Model Risk Of Bias Assessment Tool (PROBAST). We collected study type, setting, sample size, type of validation, and outcome including intubation, ventilation, any other type of organ support, or death. The combination of the prediction model, scoring system, performance of predictive models, and geographic locations were summarized.
Results: A primary review found 445 articles relevant based on title and abstract. After further review, 366 were excluded based on the defined inclusion and exclusion criteria. Seventy-nine articles were included in the qualitative analysis. Inter observer agreement on inclusion 0.84 (95%CI 0.78 - 0.89).When the PROBAST tool was applied, 70 of the 79 articles were identified to have high or unclear risk of bias, or high or unclear concern for applicability. Nine studies reported prediction models that were rated as low risk of bias and low concerns for applicability.
Conclusion: Several prognostic models for COVID-19 were identified, with varying clinical score performance. Nine studies that had a low risk of bias and low concern for applicability, one from a general public population and hospital setting. The most promising and well-validated scores include Clift et al.[SUP]15[/SUP] , and Knight et al.[SUP]18[/SUP] , which seem to have accurate prediction models that clinicians can use in the public health and emergency department setting.
. 2022 Jan 22.
doi: 10.1111/acem.14447. Online ahead of print.
Prediction Models for Severe Manifestations and Mortality due to COVID-19: A Systematic Review
Jamie L Miller[SUP] 1 [/SUP], Masafumi Tada[SUP] 2 [/SUP], Michihiko Goto[SUP] 3 [/SUP], Hao Chen[SUP] 4 [/SUP], Elizabeth Dang[SUP] 4 [/SUP], Nicholas Mohr[SUP] 5 [/SUP], Sangil Lee[SUP] 6 [/SUP]
Affiliations
- PMID: 35064988
- DOI: 10.1111/acem.14447
Abstract
Background: Throughout 2020, the coronavirus disease 2019 (COVID-19) has become a threat to public health on national and global level. There has been an immediate need for research to understand the clinical signs and symptoms of COVID-19 that can help predict deterioration including mechanical ventilation, organ support, and death. Studies thus far have addressed the epidemiology of the disease, common presentations, and susceptibility to acquisition and transmission of the virus; however, an accurate prognostic model for severe manifestations of COVID-19 is still needed because of the limited healthcare resources available.
Objective: This systematic review aims to evaluate published reports of prediction models for severe illnesses caused COVID-19.
Methods: Searches were developed by the primary author and a medical librarian using an iterative process of gathering and evaluating terms. Comprehensive strategies, including both index and keyword methods, were devised for PubMed and EMBASE. The data of confirmed COVID-19 patients from randomized control studies, cohort studies, and case-control studies published between January 2020 and May 2021were retrieved. Studies were independently assessed for risk of bias and applicability using the Prediction Model Risk Of Bias Assessment Tool (PROBAST). We collected study type, setting, sample size, type of validation, and outcome including intubation, ventilation, any other type of organ support, or death. The combination of the prediction model, scoring system, performance of predictive models, and geographic locations were summarized.
Results: A primary review found 445 articles relevant based on title and abstract. After further review, 366 were excluded based on the defined inclusion and exclusion criteria. Seventy-nine articles were included in the qualitative analysis. Inter observer agreement on inclusion 0.84 (95%CI 0.78 - 0.89).When the PROBAST tool was applied, 70 of the 79 articles were identified to have high or unclear risk of bias, or high or unclear concern for applicability. Nine studies reported prediction models that were rated as low risk of bias and low concerns for applicability.
Conclusion: Several prognostic models for COVID-19 were identified, with varying clinical score performance. Nine studies that had a low risk of bias and low concern for applicability, one from a general public population and hospital setting. The most promising and well-validated scores include Clift et al.[SUP]15[/SUP] , and Knight et al.[SUP]18[/SUP] , which seem to have accurate prediction models that clinicians can use in the public health and emergency department setting.