A Regional Observational Study on COVID-19-Associated Pulmonary Aspergillosis (CAPA) within Intensive Care Unit: Trying to Break the Mold.

The reported incidence of COVID-19-associated pulmonary aspergillosis (CAPA) ranges between 2.4% and 35% in intensive care unit (ICU) patients, and awareness in the medical community is rising. We performed a regional retrospective observational study including patients diagnosed with CAPA defined according to the Modified AspICU Dutch/Belgian Mycosis Study Group and CAPA-EECMM, from five different ICUs, admitted between March, 2020 and September, 2021. Forty-five patients were included. The median age was 64 (IQR 60-72), mostly (73%) males. At ICU admission, the median Charlson comorbidity index was 3 (2-5), and the simplified acute physiology score (SAPS)-II score was 42 (31-56). The main underlying diseases were hypertension (46%), diabetes (36%) and pulmonary diseases (15%). CAPA was diagnosed within a median of 17 days (IQR 10-21.75) after symptoms onset and 9 days (IQR 3-11) after ICU admission. The overall 28-day mortality rate was 58%, and at univariate analysis, it was significantly associated with older age (p = 0.009) and SAPS-II score at admission (p = 0.032). The use of immunomodulatory agents, p = 0.061; broad-spectrum antibiotics, p = 0.091; positive culture for Aspergillus on BAL, p = 0.065; and hypertension, p = 0.083, were near reaching statistical significance. None of them were confirmed in multivariate analysis. In critically ill COVID-19 patients, CAPA acquired clinical relevance in terms of incidence and reported mortality. However, the risk between underdiagnosis-in the absence of specific invasive investigations, and with a consequent possible increase in mortality-and over-diagnosis (case identification with galactomannan on broncho-alveolar fluid alone) might be considered. Realistic incidence rates, based on local, real-life epidemiological data, might be helpful in guiding clinicians.

The COVID-19 Worsening Score (COWS)-a predictive bedside tool for critical illness.

OBJECTIVES: To evaluate the accuracy of a new COVID-19 prognostic score based on lung ultrasound (LUS) and previously validated variables in predicting critical illness. METHODS: We conducted a single-center retrospective cohort development and internal validation study of the COVID-19 Worsening Score (COWS), based on a combination of the previously validated COVID-GRAM score (GRAM) variables and LUS. Adult COVID-19 patients admitted to the emergency department (ED) were enrolled. Ten variables previously identified by GRAM, days from symptom onset, LUS findings, and peripheral oxygen saturation/fraction of inspired oxygen (P/F) ratio were analyzed. LUS score as a single predictor was assessed. We evaluated GRAM model's performance, the impact of adding LUS, and then developed a new model based on the most predictive variables. RESULTS: Among 274 COVID-19 patients enrolled, 174 developed critical illness. The GRAM score identified 51 patients at high risk of developing critical illness and 132 at low risk. LUS score over 15 (range 0 to 36) was associated with a higher risk ratio of critical illness (RR, 2.05; 95% confidence interval [CI], 1.52-2.77; area under the curve [AUC], 0.63; 95% CI 0.676-0.634). The newly developed COVID-19 Worsening Score relies on five variables to classify high- and low-risk patients with an overall accuracy of 80% and negative predictive value of 93% (95% CI, 87%-98%). Patients scoring more than 0.183 on COWS showed a RR of developing critical illness of 8.07 (95% CI, 4.97-11.1). CONCLUSIONS: COWS accurately identify patients who are unlikely to need intensive care unit (ICU) admission, preserving resources for the remaining high-risk patients.

Accuracy of cardiac magnetic resonance generated 3D models of the aortic annulus compared to cardiovascular computed tomography generated 3D models.

To evaluate the accuracy of 3D models of the aortic-root generated from non-contrast cardiac magnetic resonance (CMR). Data were retrospectively collected from 30 consecutive patients who underwent surgical aortic valve replacement and had available records of both intra-operative assessment and pre-surgery annulus assessment by cardiovascular computed tomography (CCT) and CMR. The 3D models were independently segmented, modelled and printed by two blinded "manufacturers". The measurements on the models were carried out by two cardiac surgeons with Hegar dilator. Data were analyzed with non-parametric tests. There was no significant intra- or inter-observer variability (p ≥ 0.13). The agreement between the diameter of the 3D model derived from CMR images and either the anatomical reference of the intraoperative measurement (p = 0.10, r = 0.97) or the radiological reference of the 3D model generated from CCT (p = 0.71, r = 0.92) was very good. The process of segmentation plus the post-processing was about 17 ± 2 min for a model created by CMR, significantly higher than a model created from CCT (7 ± 2 min; p < 0.001). The printing time for a single model did not differ between the two modalities (p = 0.61) and was less than 60 min. The cost for a single model was approximately 0.5 €. 3D models generated from non-contrast CMR performed well when compared to the anatomical reference standard and are comparable to the pair CCT derived models.