Clinical prediction model for bacterial co-infection in hospitalized COVID-19 patients during four waves of the pandemic.

The reported estimates of bacterial co-infection in COVID-19 patients are highly variable. We aimed to determine the rates and risk factors of bacterial co-infection and develop a clinical prediction model to support early decision-making on antibiotic use. This is a retrospective cohort study conducted in a tertiary-level academic hospital in Israel between March 2020 and May 2022. All adult patients with severe COVID-19 who had a blood or lower respiratory specimen sent for microbiological analyses within 48 h of admission were included. The primary study endpoint was the prevalence of bacterial co-infection at the time of hospital admission. We created a prediction model using the R XGBoost package. The study cohort included 1,050 patients admitted with severe or critical COVID-19. Sixty-two patients (5.9%) had a microbiologically proven bacterial infection on admission. The variables with the greatest impact on the prediction model were age, comorbidities, functional capacity, and laboratory parameters. The model achieved perfect prediction on the training set (area under the curve = 1.0). When applied to the test dataset, the model achieved 56% and 78% specificity with the area under the receiver operating curve of 0.784. The negative and positive predictive values were 0.975 and 0.105, respectively. Applying the prediction model would have resulted in a 2.5-fold increase in appropriate antibiotic use and an 18% reduction in inappropriate use in patients with severe and critical COVID-19. The use of a clinical prediction model can support decisions to withhold empiric antimicrobial treatment at the time of hospital admission without adversely affecting patient outcomes. IMPORTANCE: Estimates of bacterial coinfection in COVID-19 patients are highly variable and depend on many factors. Patients with severe or critical COVID-19 requiring intensive care unit admission have the highest risk of infection-related complications and death. Thus, the study of the incidence and risk factors for bacterial coinfection in this population is of special interest and may help guide empiric antibiotic therapy and avoid unnecessary antimicrobial treatment. The prediction model based on clinical criteria and simple laboratory tests may be a useful tool to predict bacterial co-infection in patients hospitalized with severe COVID-19.

Poor Compliance to Clinical Guidelines in the Diagnosis of Acute Appendicitis: Insights from a National Survey.

Background: Many scoring systems, algorithms, and guidelines have been developed to aid in the evaluation and diagnosis of acute appendicitis (AA). Many of these algorithms advocate against the routine use of radiological investigations when there is a high clinical suspicion of AA. However, there has been a significant rise in the use of imaging techniques for diagnosing AA in the past two decades. This is a national study aimed at assessing the adherence of residents assigned to the emergency department to the clinical guidelines for diagnosing AA. Methods: We introduced a case study of a male patient with highly suspicious clinical findings of AA to all surgical and emergency medicine residents assigned to the emergency department with the autonomy to make critical decisions to determine the preferred way of diagnosing AA. Results: A total of 62.4% of all relevant residents participated in this survey; 69.6% reported that the Alvarado score was eight or higher, and 82.1% estimated that the next step recommended by most clinical guidelines was appendectomy without further abdominal imaging tests. However, 83.4% chose to perform an imaging test to establish the diagnosis of AA. Conclusions: Our study revealed a notable non-adherence to clinical guidelines in diagnosing AA. Given the significance of these guidelines, we assert that adopting medical recommendations should not solely depend on individual education but should also be incorporated as a departmental policy.

The predictive value of high sensitivity troponin measurements in patients treated with immune checkpoint inhibitors.

BACKGROUND: Immune checkpoint inhibitors (ICIs) have revolutionized the treatment of cancer; however, at the potential cost of serious adverse events including cardiac injury. OBJECTIVE: To assess the baseline and longitudinal changes in high sensitivity-Troponin (hs-Tn) in patients treated with pembrolizumab as a potential predictor for the development of major adverse cardiac events (MACE) and survival. METHODS: We performed a retrospective analysis of cancer patients treated with pembrolizumab at our center. All participants had baseline measurements of hs-TnI prior to initiation of pembrolizumab (T1), with half of the patients performing follow-up measurements at their second encounter for therapy introduction (T2). We first evaluated the prevalence of abnormally elevated serum hs-TnI (> 50 nanogram per liter) at T1 and T2. We then evaluated the predictive value of abnormal levels at T1 or T2 in relation to the development of MACE (composite outcomes of myocarditis, acute coronary syndrome, heart failure, venous thromboembolism, cardiovascular hospitalization and cardiovascular mortality) and all-cause mortality. RESULTS: Among 135 patients, the mean age was 72 years, predominantly male (61%). Abnormally elevated hs-TnI at T1 was observed in 7 (5%) patients and emerged as a significant independent predictor for MACE (HR 8.1, 95% CI 1.67-37.4, p = 0.009) and all-cause mortality (HR 5.37, 95% CI 2.1-13.57, p < 0.001). Abnormally elevated hs-TnI at T2 was observed in 8 (11%) patients and emerged as a significant independent predictor for MACE (HR 10.49, 95% CI 1.68-65.5, p = 0.009), but not for mortality (p = 0.200). CONCLUSIONS: Abnormally elevated baseline and follow-up hs-TnI served as significant independent predictors for MACE, with an increased risk of development being 8-tenfold. Furthermore, elevated baseline hs-TnI showed a predictive value for all-cause mortality. Central illustration: Novel immune checkpoint inhibitor (ICIs) therapy has been found to revolutionize cancer therapy through increased activation of host immune systems to target and reduce tumor burden, but may come at the cost of serious adverse cardiac events. Identification of early biomarkers for the prediction and detection of these events is necessary.

Valvular Heart Disease following Anthracycline Therapy-Is It Time to Look beyond Ejection Fraction?

The association between anthracycline (ANT) and left ventricle (LV) dysfunction is well known; however, data regarding its direct effect on cardiac valve function is limited. We aimed to evaluate how ANT therapy affected valvular function in patients diagnosed with breast cancer. Data were prospectively collected as part of the Israel Cardio-Oncology Registry (ICOR). Patients underwent echocardiography exams at baseline (T1), during ANT therapy (T2), and after completion within 3 months (T3) and 6 months (T4). A total of 141 female patients were included, with a mean age of 51 ± 12 years. From T1 to T4, we observed a significant deterioration in LV ejection fraction (60.2 ± 1.5 to 59.2 ± 2.7%, p = 0.0004) and LV global longitudinal strain (−21.6 (−20.0−−23.0) to −20.0 (−19.1−−21.1)%, p < 0.0001)), and an increase in LV end-systolic diameter (25 (22−27) to 27 (24−30) mm, p < 0.0001). We observed a significant increase in the incidence of new mitral regurgitation (MR) development (4 to 19%, p < 0.0001), worsening with concomitant trastuzumab therapy (6% to 31%, p = 0.003), and a trend for tricuspid regurgitation development (4% to 8%, p = 0.19). ANT therapy is associated with the development of a new valvular disease, mainly MR, which may imply the need for a valvular focus in the monitoring of cancer patients.