Baseline Histological Findings Do Not Predict the Risk of Subsequent Extension in Patients with Limited Ulcerative Colitis.

BACKGROUND: Among patients with limited ulcerative colitis (UC), 30% ultimately extend to pancolitis and are at increased risk of adverse clinical outcomes. Risk of endoscopic extension has been found to correlate with clinical features such as early age of onset. AIMS: We sought to determine whether histologic features correlate with disease extension. METHODS: The study population consisted of 40 patients with UC from two large academic centers diagnosed between 2006 and 2017. Eligible cases had a diagnosis of endoscopically limited UC (Montreal E1 or E2) at baseline and ≥ 2 subsequent endoscopic examinations with biopsies. Severity of inflammation was scored using both the Mount Sinai Activity Index and Nancy Histological Index. RESULTS: Patients were divided into two cohorts: those who progressed to pancolitis (Montreal E3) were defined as "Extenders" (n = 21), whereas "Non-extenders" (n = 19) were cases without progression in the follow-up period. The median follow-up time was 58.4 months. The histologic scores in the endoscopically involved mucosa of the index biopsies were not associated with subsequent extension of disease, overall. However, among extender cohort, the index histology scores correlated with biopsy scores at extension (r = 0.455, P = 0.044) and index severity was associated with a shorter time to extension (r = - 0.611, P = 0.003). Furthermore, female patients had a shorter time to extension (P = 0.013). CONCLUSIONS: Histological severity of limited UC is not an independent predictor of extension in UC. However, among patients who subsequently extend, severe inflammation at baseline correlates with shorter progression time and severe inflammation when extension occurs. Patients with limited UC but severe histologic inflammation may warrant more frequent endoscopic surveillance.

An accessible, efficient, and accurate natural language processing method for extracting diagnostic data from pathology reports.

Context: Analysis of diagnostic information in pathology reports for the purposes of clinical or translational research and quality assessment/control often requires manual data extraction, which can be laborious, time-consuming, and subject to mistakes. Objective: We sought to develop, employ, and evaluate a simple, dictionary- and rule-based natural language processing (NLP) algorithm for generating searchable information on various types of parameters from diverse surgical pathology reports. Design: Data were exported from the pathology laboratory information system (LIS) into extensible markup language (XML) documents, which were parsed by NLP-based Python code into desired data points and delivered to Excel spreadsheets. Accuracy and efficiency were compared to a manual data extraction method with concordance measured by Cohen's κ coefficient and corresponding P values. Results: The automated method was highly concordant (90%-100%, P<.001) with excellent inter-observer reliability (Cohen's κ: 0.86-1.0) compared to the manual method in 3 clinicopathological research scenarios, including squamous dysplasia presence and grade in anal biopsies, epithelial dysplasia grade and location in colonoscopic surveillance biopsies, and adenocarcinoma grade and amount in prostate core biopsies. Significantly, the automated method was 24-39 times faster and inherently contained links for each diagnosis to additional variables such as patient age, location, etc., which would require additional manual processing time. Conclusions: A simple, flexible, and scaleable NLP-based platform can be used to correctly, safely, and quickly extract and deliver linked data from pathology reports into searchable spreadsheets for clinical and research purposes.

An inflammatory cytokine signature predicts COVID-19 severity and survival.

Several studies have revealed that the hyper-inflammatory response induced by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a major cause of disease severity and death. However, predictive biomarkers of pathogenic inflammation to help guide targetable immune pathways are critically lacking. We implemented a rapid multiplex cytokine assay to measure serum interleukin (IL)-6, IL-8, tumor necrosis factor (TNF)-α and IL-1ß in hospitalized patients with coronavirus disease 2019 (COVID-19) upon admission to the Mount Sinai Health System in New York. Patients (n = 1,484) were followed up to 41 d after admission (median, 8 d), and clinical information, laboratory test results and patient outcomes were collected. We found that high serum IL-6, IL-8 and TNF-α levels at the time of hospitalization were strong and independent predictors of patient survival (P < 0.0001, P = 0.0205 and P = 0.0140, respectively). Notably, when adjusting for disease severity, common laboratory inflammation markers, hypoxia and other vitals, demographics, and a range of comorbidities, IL-6 and TNF-α serum levels remained independent and significant predictors of disease severity and death. These findings were validated in a second cohort of patients (n = 231). We propose that serum IL-6 and TNF-α levels should be considered in the management and treatment of patients with COVID-19 to stratify prospective clinical trials, guide resource allocation and inform therapeutic options.