Clinical presentation and antimicrobial resistance of invasive Escherichia coli disease in hospitalized older adults: a prospective multinational observational study

Abstract

Background Clinical data characterizing invasive Escherichia coli disease (IED) are limited. We assessed the clinical presentation of IED and antimicrobial resistance (AMR) patterns of causative E. coli isolates in older adults. Methods EXPECT-2 (NCT04117113) was a prospective, observational, multinational, hospital-based study conducted in patients with IED aged ≥ 60 years. IED was determined by the microbiological confirmation of E. coli from blood; or by the microbiological confirmation of E. coli from urine or an otherwise sterile body site in the presence of requisite criteria of systemic inflammatory response syndrome (SIRS), Sequential Organ Failure Assessment (SOFA), or quick SOFA (qSOFA). The primary outcomes were the clinical presentation of IED and AMR rates of E. coli isolates to clinically relevant antibiotics. Complications and in-hospital mortality were assessed through 28 days following IED diagnosis. Results Of 240 enrolled patients, 80.4% had bacteremic and 19.6% had non-bacteremic IED. One-half of infections (50.4%) were community-acquired. The most common source of infection was the urinary tract (62.9%). Of 240 patients, 65.8% fulfilled ≥ 2 SIRS criteria, and 60.4% had a total SOFA score of ≥ 2. Investigator-diagnosed sepsis and septic shock were reported in 72.1% and 10.0% of patients, respectively. The most common complication was kidney dysfunction (12.9%). The overall in-hospital mortality was 4.6%. Of 299 E. coli isolates tested, the resistance rates were: 30.4% for trimethoprim-sulfamethoxazole, 24.1% for ciprofloxacin, 22.1% for levofloxacin, 16.4% for ceftriaxone, 5.7% for cefepime, and 4.3% for ceftazidime. Conclusions The clinical profile of identified IED cases was characterized by high rates of sepsis. IED was associated with high rates of AMR to clinically relevant antibiotics. The identification of IED can be optimized by using a combination of clinical criteria (SIRS, SOFA, or qSOFA) and culture results.

Machine Learning Analysis of Biomarkers and Infectious Sites in Elderly Sepsis: Distinguishing Escherichia coli from Non-Escherichia coli Infections with a Random Forest Model

“…Our study revealed that urinary tract infections were the most common infectious site among elderly sepsis patients (38%), followed by pulmonary (30%), abdominal (20%), and other sites (18%). This nding aligns with prior research conducted by J. Doua et al 21 , which also identi ed the urinary tract as the primary source of infection (62.9%), followed by intraabdominal infection (20.4%), other (14.2%), and respiratory tract (2.5%).…”