About: Performing risk stratification for COVID-19 when individual level data is not available, the experience of a large healthcare organization

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About: Performing risk stratification for COVID-19 when individual level data is not available, the experience of a large healthcare organization Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Performing risk stratification for COVID-19 when individual level data is not available, the experience of a large healthcare organization
Creator	Akriv, Amichay Bachmat, Eitan Barda, Noam Dagan, Noa Finkel, Uriah Greenfeld, Daniel Levi, Joseph Netzer, Doron Riesel, Dan Rothblum, Guy Shalit, Uri Sheiba, Shimon Somer, Jonathan Yona, Gal
Source	MedRxiv
abstract	With the global coronavirus disease 2019 (COVID-19) pandemic, there is an urgent need for risk stratification tools to support prevention and treatment decisions. The Centers for Disease Control and Prevention (CDC) listed several criteria that define high-risk individuals, but multivariable prediction models may allow for a more accurate and granular risk evaluation. In the early days of the pandemic, when individual level data required for training prediction models was not available, a large healthcare organization developed a prediction model for supporting its COVID-19 policy using a hybrid strategy. The model was constructed on a baseline predictor to rank patients according to their risk for severe respiratory infection or sepsis (trained using over one-million patient records) and was then post-processed to calibrate the predictions to reported COVID-19 case fatality rates. Since its deployment in mid-March, this predictor was integrated into many decision-processes in the organization that involved allocating limited resources. With the accumulation of enough COVID-19 patients, the predictor was validated for its accuracy in predicting COVID-19 mortality among all COVID-19 cases in the organization (3,176, 3.1% death rate). The predictor was found to have good discrimination, with an area under the receiver-operating characteristics curve of 0.942. Calibration was also good, with a marked improvement compared to the calibration of the baseline model when evaluated for the COVID-19 mortality outcome. While the CDC criteria identify 41% of the population as high-risk with a resulting sensitivity of 97%, a 5% absolute risk cutoff by the model tags only 14% to be at high-risk while still achieving a sensitivity of 90%. To summarize, we found that even in the midst of a pandemic, shrouded in epidemiologic %22fog of war%22 and with no individual level data, it was possible to provide a useful predictor with good discrimination and calibration.
has issue date	2020-04-28(xsd:dateTime)
bibo:doi	10.1101/2020.04.23.20076976
has license	medrxiv
sha1sum (hex)	bba9d0841f99fabe4e5ab84376cc45ba843067b5
schema:url	https://doi.org/10.1101/2020.04.23.20076976
resource representing a document's title	Performing risk stratification for COVID-19 when individual level data is not available, the experience of a large healthcare organization
resource representing a document's body	covid:bba9d0841f99fabe4e5ab84376cc45ba843067b5#body_text
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