Learning Clinical Concepts for Predicting Risk of Progression to Severe COVID-19 (preprint)

With COVID-19 now pervasive, identification of high-risk individuals is crucial. Using data from a major healthcare provider in Southwestern Pennsylvania, we develop survival models predicting severe COVID-19 progression. In this endeavor, we face a tradeoff between more accurate models relying on many features and less accurate models relying on a few features aligned with clinician intuition. Complicating matters, many EHR features tend to be under-coded, degrading the accuracy of smaller models. In this study, we develop two sets of high-performance risk scores: (i) an unconstrained model built from all available features; and (ii) a pipeline that learns a small set of clinical concepts before training a risk predictor. Learned concepts boost performance over the corresponding features (C-index 0.858 vs. 0.844) and demonstrate improvements over (i) when evaluated out-of-sample (subsequent time periods). Our models outperform previous works (C-index 0.844-0.872 vs. 0.598-0.810).

An Open Repository of Real-Time COVID-19 Indicators (preprint)

The COVID-19 pandemic presented enormous data challenges in the United States. Policy makers, epidemiological modelers, and health researchers all require up-to-date data on the pandemic and relevant public behavior, ideally at fine spatial and temporal resolution. The COVIDcast API is our attempt to fill this need: operational since April 2020, it provides open access to both traditional public health surveillance signals (cases, deaths, and hospitalizations) and many auxiliary indicators of COVID- 19 activity, such as signals extracted from de-identified medical claims data, massive online surveys, cell phone mobility data, and internet search trends. These are available at a fine geographic resolution (mostly at the county level) and are updated daily. The COVIDcast API also tracks all revisions to historical data, allowing modelers to account for the frequent revisions and backfill that are common for many public health data sources. All of the data is available in a common format through the API and accompanying R and Python software packages. This paper describes the data sources and signals, and provides examples demonstrating that the auxiliary signals in the COVIDcast API present information relevant to tracking COVID activity, augmenting traditional public health reporting and empowering research and decision-making.

Unpacking the Drop in COVID-19 Case Fatality Rates: A Study of National and Florida Line-Level Data (preprint)

Since the COVID-19 pandemic first reached the United States, the case fatality rate has fallen precipitously. Several possible explanations have been floated, including greater detection of mild cases due to expanded testing, shifts in age distribution among the infected, lags between confirmed cases and reported deaths, improvements in treatment, mutations in the virus, and decreased viral load as a result of mask-wearing. Using both Florida line-level data and recently released (but incomplete) national line level data from April 1, 2020 to November 1, 2020 on cases, hospitalizations, and deaths--each stratified by age--we unpack the drop in case fatality rate (CFR). Under the hypothesis that improvements in treatment efficacy should correspond to decreases in hospitalization fatality rate (HFR), we find that improvements in the national data do not always match the story told by Florida data. In the national data, treatment improvements between the first wave and the second wave appear substantial, but modest when compared to the drop in aggregate CFR. By contrast, possibly due to constrained resources in a much larger second peak, Florida data suggests comparatively little difference between the first and second wave, with HFR slightly increasing in every age group. However, by November 1st, both Florida and national data suggest significant decreases in age-stratified HFR since April 1st. By accounting for several confounding factors, our analysis shows how age-stratified HFR can provide a more realistic picture of treatment improvements than CFR. One key limitation of our analysis is that the national line-level data remains incomplete and plagued by artifacts. Our analysis highlights the crucial role that this data can play but also the pressing need for public, complete, and high-quality age-stratified line-level data for both cases, hospitalizations, and deaths for all states.

Predicting Mortality Risk in Viral and Unspecified Pneumonia to Assist Clinicians with COVID-19 ECMO Planning (preprint)

Respiratory complications due to coronavirus disease COVID-19 have claimed tens of thousands of lives in 2020. Many cases of COVID-19 escalate from Severe Acute Respiratory Syndrome (SARS-CoV-2) to viral pneumonia to acute respiratory distress syndrome (ARDS) to death. Extracorporeal membranous oxygenation (ECMO) is a life-sustaining oxygenation and ventilation therapy that may be used for patients with severe ARDS when mechanical ventilation is insufficient to sustain life. While early planning and surgical cannulation for ECMO can increase survival, clinicians report the lack of a risk score hinders these efforts. In this work, we leverage machine learning techniques to develop the PEER score, used to highlight critically ill patients with viral or unspecified pneumonia at high risk of mortality or decompensation in a subpopulation eligible for ECMO. The PEER score is validated on two large, publicly available critical care databases and predicts mortality at least as well as other existing risk scores. Stratifying our cohorts into low-risk and high-risk groups, we find that the high-risk group also has a higher proportion of decompensation indicators such as vasopressor and ventilator use. Finally, the PEER score is provided in the form of a nomogram for direct calculation of patient risk, and can be used to highlight at-risk patients among critical care patients eligible for ECMO.