Augmented Curation of Clinical Notes from a Massive EHR System Reveals Symptoms of Impending COVID-19 Diagnosis

Abstract

Understanding the temporal dynamics of COVID-19 patient phenotypes is necessary to derive finegrained resolution of pathophysiology. Here we use state-of-the-art deep neural networks over an institution-wide machine intelligence platform for the augmented curation of 15.8 million clinical notes from 30,494 patients subjected to COVID-19 PCR diagnostic testing. By contrasting the Electronic Health Record (EHR)-derived clinical phenotypes of COVID-19-positive (COVIDpos, n=635) versus COVID-19-negative (COVIDneg, n=29,859) patients over each day of the week preceding the PCR testing date, we identify anosmia/dysgeusia (37.4-fold), myalgia/arthralgia (2.6-fold), diarrhea (2.2-fold), fever/chills (2.1-fold), respiratory difficulty (1.9-fold), and cough (1.8-fold) as significantly amplified in COVIDpos over COVIDneg patients. The specific combination of cough and diarrhea has a 3.2-fold amplification in COVIDpos patients during the week prior to PCR testing, and along with anosmia/dysgeusia, constitutes the earliest EHR-derived signature of COVID-19 (4-7 days prior to typical PCR testing date). This study introduces an Augmented Intelligence platform for the realtime synthesis of institutional knowledge captured in EHRs. The platform holds tremendous potential for scaling up curation throughput, with minimal need for retraining underlying neural networks, thus promising EHR-powered early diagnosis for a broad spectrum of diseases.

Inference from longitudinal laboratory tests characterizes temporal evolution of COVID-19-associated coagulopathy (CAC)

“…To identify laboratory test results which differ between COVID pos and COVID neg (matched) patients, we analyzed longitudinal trends for 194 laboratory test results in the days prior to and after the day of PCR testing (designated as day 0). As most patients did not undergo laboratory testing for each assay on a daily basis, we grouped the measurements into 9 time windows reflecting potential stages of infection as follows: pre-infection (days -30 to -11), pre-PCR (days -10 to -2), time of clinical presentation (days -1 to 0), and post-PCR phases 1 (days 1 to 3), 2 (days 4-6), 3 (days 7-9), 4 (days 10-12), 5 (days 13-15), and 6 (days [16][17][18][19][20][21][22][23][24][25][26][27][28][29][30]. We only considered test-time window pairs in which there were at least three patients contributing laboratory test results in both groups.…”

“…A state-of-the-art BERT-based neural network ( Devlin et al, 2018 ) was previously developed to classify sentiment regarding a diagnosis in the EHR ( Wagner et al, 2020 ). Sentences containing phenotypes were classified into the following categories: Yes (confirmed diagnosis), No (ruled out diagnosis), Maybe (possibility of disease), and Other (alternate context, e.g.…”

“…For the COVID pos cohort, we center the 2-month observation period around the date of the first positive PCR test for SARS-CoV-2, and for the COVID neg cohort, we center the 2-month observation period around the date of the first PCR test for SARS-CoV-2 (see Materials and methods). It is important to note that not all individuals infected by SARS-CoV-2 develop symptoms of COVID-19, but rather that a majority of patients are either asymptomatic or have mild-to-moderate symptoms not requiring hospitalization for COVID-19 ( Wagner et al, 2020 ). Furthermore, the guidelines followed for PCR-testing included a routine screening of individuals, patients displaying COVID-19 symptoms as per the Mayo Clinic ( Coronavirus disease, 2019 ) and CDC definitions ( Website, 2020 ), and possibly contact with infected persons or underlying predisposing conditions ( Wagner et al, 2020 ).…”

SARS-CoV-2 selectively mimics a cleavable peptide of human ENaC in a strategic hijack of host proteolytic machinery

“…3 Methods, Figure 1c) 11 . Interestingly, ENaC-ɑ is expressed in the nasal epithelial cells, type II alveolar cells of the lung, tongue keratinocytes, and colon enterocytes (Figure 1c and Figures S1-S6), which are all implicated in COVID-19 pathophysiology 11,12 . Further, ACE2 and ENaC-ɑ are known to be expressed generally in the apical membranes of polarized epithelial cells 13,14 .…”

Multi-pronged human protein mimicry by SARS-CoV-2 reveals bifurcating potential for MHC detection and immune evasion

“…Viral infection typically leads to T cell stimulation in the host, and autoimmune response associated with viral infection has been observed 1 . SARS-CoV-2, the causative agent of the ongoing COVID-19 pandemic, has complex manifestations ranging from mild symptoms like loss of sense of smell (anosmia) 2 to severe and critical illness 3,4 . While some molecular factors governing SARS-CoV-2 infection of lung tissues, such as the ACE2 receptor expressing cells have been characterized recently 5 , the mechanistic rationale underlying immune evasion and multi-system inflammation (Kawasaki-like disease) remains poorly understood 6,7 .…”