Ahuva Weiss-Meilik

Background: liver test abnormalities have been described in patients with Coronavirus-2019 (COVID-19), and hepatic involvement may correlate with disease severity. With the relaxing of COVID-19 restrictions, seasonal respiratory viruses now circulate alongside SARS-CoV-2. Aims: we aimed to compare patterns of abnormal liver function tests in patients suffering from COVID-19 infection and seasonal respiratory viruses: respiratory syncytial virus (RSV) and influenza (A and B). Methods: a retrospective cohort study was performed including 4140 patients admitted to a tertiary medical center between 2010–2020. Liver test abnormalities were classified as hepatocellular, cholestatic or mixed type. Clinical outcomes were defined as 30-day mortality and mechanical ventilation. Results: liver function abnormalities were mild to moderate in most patients, and mainly cholestatic. Hepatocellular injury was far less frequent but had a strong association with adverse clinical outcome in RSV, COVID-19 and influenza (odds ratio 5.29 (CI 1.2–22), 3.45 (CI 1.7–7), 3.1 (CI 1.7–6), respectively) COVID-19 and influenza patients whose liver functions did not improve or alternatively worsened after 48 h had a significantly higher risk of death or ventilation. Conclusion: liver function test abnormalities are frequent among patients with COVID-19 and seasonal respiratory viruses, and are associated with poor clinical outcome. The late liver tests’ peak had a twofold risk for adverse outcome. Though cholestatic injury was more common, hepatocellular injury had the greatest prognostic significance 48 h after admission. Our study may provide a viral specific auxiliary prognostic tool for clinicians facing patients with a respiratory virus.

Antimicrobial resistance (AMR) has consistently been linked to antibiotic use. However, the roles of commonly prescribed non-antimicrobial drugs as drivers of AMR may be under-appreciated. Here, we studied a cohort of patients with community-acquired pyelonephritis and assessed the association of exposure to non-antimicrobial drugs at the time of hospital admission with infection with drug-resistant organisms (DRO). Associations identified on bivariate analyses were tested using a treatment effects estimator that models both outcome and treatment probability. Exposure to proton-pump inhibitors, beta-blockers, and antimetabolites was significantly associated with multiple resistance phenotypes. Clopidogrel, selective serotonin reuptake inhibitors, and anti-Xa agents were associated with single-drug resistance phenotypes. Antibiotic exposure and indwelling urinary catheters were covariates associated with AMR. Exposure to non-antimicrobial drugs significantly increased the probability of AMR in patients with no other risk factors for resistance. Non-antimicrobial drugs may affect the risk of infection with DRO through multiple mechanisms. If corroborated using additional datasets, these findings offer novel directions for predicting and mitigating AMR.

Background: Patients who are admitted to the Department of Internal Medicine with apparently normal C-reactive protein (CRP) concentration impose a special challenge due the assumption that they might not harbor a severe and potentially lethal medical condition. Methods: A retrospective cohort of all patients who were admitted to the Department of Internal Medicine with a CRP concentration of ≤31.9 mg/L and had a second CRP test obtained within the next 24 h. Seven day mortality data were analyzed. Results: Overall, 3504 patients were analyzed with a mean first and second CRP of 8.8 (8.5) and 14.6 (21.6) mg/L, respectively. The seven day mortality increased from 1.8% in the first quartile of the first CRP to 7.5% in the fourth quartile of the first CRP (p < 0.0001) and from 0.6% in the first quartile of the second CRP to 9.5% in the fourth quartile of the second CRP test (p < 0.0001), suggesting a clear relation between the admission CRP and in hospital seven day mortality. Conclusions: An association exists between the quartiles of CRP and 7-day mortality as well as sepsis related cause of death. Furthermore, the CRP values 24 h after hospital admission improved the discrimination.

Hypoalbuminemia is common in hypoalbuminemia-associated disorders (HAD), e.g., liver and kidney disease. We hypothesize that hospitalized patients with hypoalbuminemia have poor prognosis irrespective of their underlying disease. Records of patients admitted to Medicine (2010–2018), with and without HAD were analyzed, comparing low (n = 1542) vs. normal albumin (n = 6216) were older, had a higher Charlson Comorbidity Index (CCI, 5 vs. 4), longer median hospital stay (5 vs. 4), higher one year re-admission rate (49.9% vs. 39.8%), and one year mortality (48.9% vs. 15.3%, p < 0.001 for all). LR model predicting 3 month, 1 year and 5 year mortality confirmed the predictive power of albumin (1 year: OR = 4.49 for hypoalbuminema, p < 0.01). Hypoalbuminemia portends poor long-term prognosis in hospitalized patients regardless of the underlying disease and could be added to prognostic predictive models.

Bloodstream infections (BSI) are a main cause of infectious disease morbidity and mortality worldwide. Early prediction of BSI patients at high risk of poor outcomes is important for earlier decision making and effective patient stratification. We developed electronic medical record-based machine learning models that predict patient outcomes of BSI. The area under the receiver-operating characteristics curve was 0.82 for a full featured inclusive model, and 0.81 for a compact model using only 25 features. Our models were trained using electronic medical records that include demographics, blood tests, and the medical and diagnosis history of 7889 hospitalized patients diagnosed with BSI. Among the implications of this work is implementation of the models as a basis for selective rapid microbiological identification, toward earlier administration of appropriate antibiotic therapy. Additionally, our models may help reduce the development of BSI and its associated adverse health outcomes and complications.

During the recent pandemic, the fact that the clinical manifestation of COVID-19 may be indistinguishable from bacterial infection, as well as concerns of bacterial co-infection, have been associated with an increased use of antibiotics. The objective of this study was to assess the effect of targeted antibiotic stewardship programs (ASP) on the use of antibiotics in designated COVID-19 departments and to compare it to the antibiotic use in the equivalent departments in the same periods of 2018 and 2019. Antibiotic consumption was assessed as days of treatment (DOT) per 1000 patient days (PDs). The COVID-19 pandemic was divided into three periods (waves) according to the pandemic dynamics. The proportion of patients who received at least one antibiotic was significantly lower in COVID-19 departments compared to equivalent departments in 2018 and 2019 (Wave 2: 30.2% vs. 45.6% and 44.9%, respectively; Wave 3: 30.5% vs. 47.8% and 50.1%, respectively, p < 0.001). The DOT/1000PDs in every COVID-19 wave was lower than during similar periods in 2018 and 2019 (179-282 DOT/1000PDs vs. 452-470 DOT/1000PDs vs. 426-479 DOT/1000PDs, respectively). Moreover, antibiotic consumption decreased over time during the pandemic. In conclusion, a strong ASP is effective in restricting antibiotic consumption, particularly for COVID-19 which is a viral disease that may mimic bacterial sepsis but has a low rate of concurrent bacterial infection.

Bloodstream infections (BSI) are a main cause of infectious disease morbidity and mortality world-wide. Early prediction of patients at high risk of poor outcomes of BSI is important for earlier decision making and effective patient stratification. We developed electronic medical record-based machine learning models that predict patient outcomes of BSI. The area under the receiver-operating characteristics curve was 0.82 for a full featured inclusive model, and 0.81 for a compact model using only 25 features. Our models were trained, using electronic medical records that include demographics, blood tests, and the medical and diagnosis history of 7,889 hospitalized patients diagnosed with BSI. Among the implications of this work is implementation of the models as a basis for selective rapid microbiological identification, toward earlier administration of appropriate antibiotic therapy. Additionally, our models may help reduce the development of BSI and its associated adverse health outcomes and complications.