MIDAS COVID-19 Special Seminar Series: Karandeep Singh, Validating a Widely Implemented Deterioration Index Model Among Hospitalized COVID-19 Patients

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Karandeep Singh, Assistant Professor, Learning Health Sciences and Medicine, University of Michigan

Validating a Widely Implemented Deterioration Index Model Among Hospitalized COVID-19 Patients

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The coronavirus disease 2019 (COVID-19) pandemic is straining the capacity of hospitals and healthcare systems across the United States. This strain has led to an expansion of both advanced ICU capabilities within hospitals as well as the opening of lower-acuity hospital beds (e.g., field hospitals) with limited resources for more stable patients. For example, Michigan has one of the largest number of COVID-19 caseloads in the country and recently approved over 4,000 new hospital beds to provide care to COVID-19 patients. Accurately identifying subgroups of COVID-19 patients at high- and low-risk for adverse outcomes using automated risk-score systems could help to alleviate this strain. Recent work, however, has demonstrated a paucity of validated high-quality models in COVID-19 patients.

The Epic Deterioration Index (EDI) is a proprietary early-warning prediction model that assesses the probability of hospitalized patients requiring a rapid response, resuscitation, intensive care unit (ICU)-level care, or dying. Although there is no published research on the validity of the EDI, ubiquitous use of the Epic EHR across the U.S. has led to growing anecdotal reports of its value in critically ill patients. Implemented in over 100 hospitals across the U.S., advocates have further promoted its use among COVID-19 patients despite the absence of published data on its validity in this population. In this talk, we will present preliminary results from our evaluation of the Epic Deterioration Index at Michigan Medicine. Our findings have potential implications for how this widespread tool may be validated and used by other healthcare systems during the COVID-19 pandemic.

MIDAS COVID-19 Special Seminar Series: Jon Zelner, “If the outbreak ended, does that mean the interventions worked?”

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Jon Zelner, Assistant Professor, Epidemiology, University of Michigan

If the outbreak ended, does that mean the interventions worked?

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In this talk Dr. Zelner will discuss some ongoing modeling work focused on understanding when we can and cannot infer that interventions meant to stop or slow infectious disease transmission have actually worked, and when observed outcomes cannot be distinguished from selection bias.

MIDAS Special Seminar: Qianying Lin – MIDAS Data Science Fellow

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Qianying Lin

Michigan Institute for Data Science – Data Science Fellow

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COVID-19 outbreak in Wuhan, China: in retrospect and in prospect

Since first confirmation in December 2019, the novel coronavirus diseases (COVID-19) infected more than 50,000 people and claimed over 2000 lives in Wuhan, China. It was transmitted across the whole country shortly, and now swept the world by causing more 20,000 infections in countries other than China. Using official reported cases and assuming changing reporting ratio, we investigated the early stage of the epidemic of COVID-19 in Wuhan and analysed its transmissibility. We then built up a conceptual model and incorporated the zoonotic introduction, emigration, individual reaction, and governmental action to simulate the trends of the outbreak in Wuhan and predicted the disease would be completely controlled by the end of April under current policies. These studies provide insights into not only the characteristics of COVID-19 itself, but the impact of governmental actions.

Read Global Reach’s article here. Read full transcript here.

For more information on MIDAS or the Seminar Series, please contact midas-contact@umich.edu.