Sepsis is a leading cause of death and is the most expensive condition to treat in U.S. hospitals. Despite targeted efforts to automate earlier detection of sepsis, current techniques rely exclusively on using either standard clinical data or novel biomarker measurements. In this study, we apply machine learning techniques to assess the predictive power of combining multiple biomarker measurements from a single blood sample with electronic medical record data (EMR) for the identification of patients in the early to peak phase of sepsis in a large community hospital setting. Combining biomarkers and EMR data achieved an area under the receiver operating characteristic (ROC) curve (AUC) of 0.81, while EMR data alone achieved an AUC of 0.75. Furthermore, a single measurement of six biomarkers (IL-6, nCD64, IL-1ra, PCT, MCP1, and G-CSF) yielded the same predictive power as collecting an additional 16 hours of EMR data(AUC of 0.80), suggesting that the biomarkers may be useful for identifying these patients earlier. Ultimately, supervised learning using a subset of biomarker and EMR data as features may be capable of identifying patients in the early to peak phase of sepsis in a diverse population and may provide a tool for more timely identification and intervention.

SARS-CoV2 is a novel coronavirus responsible for causing COVID-19, first identified in the city of Wuhan, China and officially declared a pandemic by the World Health Organization. SARS-CoV2 expresses high affinity to human ACE2 receptors, including within the gastrointestinal tract. Patients with COVID-19 exhibit a wide spectrum of GI symptoms including anorexia, nausea, vomiting, abdominal pain, and abnormal liver function tests. Pathogenesis behind gastrointestinal symptoms caused by SARS-CoV2 has been postulated to be multifactorial including disruption of the intestinal mechanical barrier integrity, alteration of the gut microbiome and systemic inflammatory response to the virus. SARS-CoV-2 RNA has also been found in stool samples of infected patients for a significantly longer period than in nasopharyngeal samples, though the implication of this finding is unclear at this time. Liver injury in patients with COVID-19 is usually mild, stemming from immune-mediated damage, drug induced hepatotoxicity, or ischemia from sepsis. Patients with pre-existing liver disease may be at a higher risk for hospitalization and mortality. Given the high degree of infectivity of this disease, healthcare providers will need to remain watchful for resurgence of this virus. Strict protocols should be implemented regarding hand hygiene, isolation, personal protective equipment, and appropriate disposal of waste. It is also imperative to identify patients with gastrointestinal symptoms at an early stage as these patients may have a prolonged course between symptom onset and viral clearance.

Sepsis is a major cause of mortality among hospitalized patients worldwide. Shorter time to administration of broad-spectrum antibiotics is associated with improved outcomes, but early recognition of sepsis remains a major challenge. In a two-center cohort study with prospective sample collection from 1400 adult patients in emergency departments suspected of sepsis, we sought to determine the diagnostic and prognostic capabilities of a machine-learning algorithm based on clinical data and a set of uncommonly measured biomarkers. Specifically, we demonstrate that a machine-learning model developed using this dataset outputs a score with not only diagnostic capability but also prognostic power with respect to hospital length of stay (LOS), thirty-day mortality, and thirty-day inpatient readmission both in our entire testing cohort and various subpopulations. The area under the Receiver Operating Curve (AUROC) for diagnosis of sepsis was 0.83. Predicted risk scores for patients with septic shock were higher compared to patients with sepsis but without shock (p < 0.0001). Scores for patients with infection and organ dysfunction were higher compared to those without either condition (p < 0.0001). Stratification based on predicted scores of the patients into low, medium and high-risk groups showed significant differences in length of stay (p < 0.0001), thirty-day mortality (p < 0.0001), and thirty-day inpatient readmission (p < 0.0001). In conclusion, a machine-learning algorithm based on EMR data and three non-routinely measured biomarkers demonstrated good diagnostic and prognostic capability at the time of initial blood culture.

OBJECTIVES: To identify and validate novel COVID-19 subphenotypes with potential heterogenous treatment effects (HTEs) using electronic health record (EHR) data and 33 unique biomarkers. DESIGN: Retrospective cohort study of adults presenting for acute care, with analysis of biomarkers from residual blood collected during routine clinical care. Latent profile analysis (LPA) of biomarker and EHR data identified subphenotypes of COVID-19 inpatients, which were validated using a separate cohort of patients. HTE for glucocorticoid use among subphenotypes was evaluated using both an adjusted logistic regression model and propensity matching analysis for in-hospital mortality. SETTING: Emergency departments from four medical centers. PATIENTS: Patients diagnosed with COVID-19 based on International Classification of Diseases, 10th Revision codes and laboratory test results. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Biomarker levels generally paralleled illness severity, with higher levels among more severely ill patients. LPA of 522 COVID-19 inpatients from three sites identified two profiles: profile 1 (n = 332), with higher levels of albumin and bicarbonate, and profile 2 (n = 190), with higher inflammatory markers. Profile 2 patients had higher median length of stay (7.4 vs 4.1 d; p < 0.001) and in-hospital mortality compared with profile 1 patients (25.8% vs 4.8%; p < 0.001). These were validated in a separate, single-site cohort (n = 192), which demonstrated similar outcome differences. HTE was observed (p = 0.03), with glucocorticoid treatment associated with increased mortality for profile 1 patients (odds ratio = 4.54). CONCLUSIONS: In this multicenter study combining EHR data with research biomarker analysis of patients with COVID-19, we identified novel profiles with divergent clinical outcomes and differential treatment responses.