Steps To Prevent Mortality in a Patient with Coinciding Severe Sepsis and Cardiogenic Shock Post-Non-ST-Elevation Myocardial Infarction (NSTEMI): A Case Report.

Severe sepsis is characterized by acute organ dysfunction secondary to an infective source, often requiring emergent medical intervention. The severity of sepsis is determined by a criterion that focuses on the presence of fever, tachycardia, tachypnea, leukocytosis, lactic acidosis, hypotension, evidence of organ failure, and the presence of an infective source. Management of sepsis in patients with a coinciding ischemic event such as a myocardial infarction (MI), is difficult, given the prognosis is poor and there is a high risk for mortality. This case report explores methodical medical measures taken to prevent mortality in an 81-year-old Hispanic male that developed severe sepsis in conjunction with a complicated presentation of a non-ST-elevation myocardial infarction (NSTEMI).

Cohort profile: St. Michael's Hospital Tuberculosis Database (SMH-TB), a retrospective cohort of electronic health record data and variables extracted using natural language processing.

BACKGROUND: Tuberculosis (TB) is a major cause of death worldwide. TB research draws heavily on clinical cohorts which can be generated using electronic health records (EHR), but granular information extracted from unstructured EHR data is limited. The St. Michael's Hospital TB database (SMH-TB) was established to address gaps in EHR-derived TB clinical cohorts and provide researchers and clinicians with detailed, granular data related to TB management and treatment. METHODS: We collected and validated multiple layers of EHR data from the TB outpatient clinic at St. Michael's Hospital, Toronto, Ontario, Canada to generate the SMH-TB database. SMH-TB contains structured data directly from the EHR, and variables generated using natural language processing (NLP) by extracting relevant information from free-text within clinic, radiology, and other notes. NLP performance was assessed using recall, precision and F1 score averaged across variable labels. We present characteristics of the cohort population using binomial proportions and 95% confidence intervals (CI), with and without adjusting for NLP misclassification errors. RESULTS: SMH-TB currently contains retrospective patient data spanning 2011 to 2018, for a total of 3298 patients (N = 3237 with at least 1 associated dictation). Performance of TB diagnosis and medication NLP rulesets surpasses 93% in recall, precision and F1 metrics, indicating good generalizability. We estimated 20% (95% CI: 18.4-21.2%) were diagnosed with active TB and 46% (95% CI: 43.8-47.2%) were diagnosed with latent TB. After adjusting for potential misclassification, the proportion of patients diagnosed with active and latent TB was 18% (95% CI: 16.8-19.7%) and 40% (95% CI: 37.8-41.6%) respectively. CONCLUSION: SMH-TB is a unique database that includes a breadth of structured data derived from structured and unstructured EHR data by using NLP rulesets. The data are available for a variety of research applications, such as clinical epidemiology, quality improvement and mathematical modeling studies.