Predictive model for bacterial co-infection in patients hospitalized for COVID-19: a multicenter observational cohort study

Objective: The aim of our study was to build a predictive model able to stratify the risk of bacterial co-infection at hospitalization in patients with COVID-19. Methods: Multicenter observational study of adult patients hospitalized from February to December 2020 with confirmed COVID-19 diagnosis. Endpoint was microbiologically documented bacterial co-infection diagnosed within 72 h from hospitalization. The cohort was randomly split into derivation and validation cohort. To investigate risk factors for co-infection univariable and multivariable logistic regression analyses were performed. Predictive risk score was obtained assigning a point value corresponding to β-coefficients to the variables in the multivariable model. ROC analysis in the validation cohort was used to estimate prediction accuracy. Results: Overall, 1733 patients were analyzed: 61.4% males, median age 69 years (IQR 57-80), median Charlson 3 (IQR 2-6). Co-infection was diagnosed in 110 (6.3%) patients. Empirical antibiotics were started in 64.2 and 59.5% of patients with and without co-infection (p = 0.35). At multivariable analysis in the derivation cohort: WBC ≥ 7.7/mm3, PCT ≥ 0.2 ng/mL, and Charlson index ≥ 5 were risk factors for bacterial co-infection. A point was assigned to each variable obtaining a predictive score ranging from 0 to 5. In the validation cohort, ROC analysis showed AUC of 0.83 (95%CI 0.75-0.90). The optimal cut-point was ≥2 with sensitivity 70.0%, specificity 75.9%, positive predictive value 16.0% and negative predictive value 97.5%. According to individual risk score, patients were classified at low (point 0), intermediate (point 1), and high risk (point ≥ 2). CURB-65 ≥ 2 was further proposed to identify patients at intermediate risk who would benefit from early antibiotic coverage. Conclusions: Our score may be useful in stratifying bacterial co-infection risk in COVID-19 hospitalized patients, optimizing diagnostic testing and antibiotic use

Whole brain radiotherapy with adjuvant or concomitant boost in brain metastasis: dosimetric comparison between helical and volumetric IMRT technique

To compare and evaluate the possible advantages related to the use of VMAT and helical IMRT and two different modalities of boost delivering, adjuvant stereotactic boost (SRS) or simultaneous integrated boost (SIB), in the treatment of brain metastasis (BM) in RPA classes I-II patients

Pattern of relapse of glioblastoma multiforme treated with radical radio-chemotherapy: Could a margin reduction be proposed?

To analyse the pattern of recurrence of patients treated with Stupp protocol in relation to technique, to compare in silico plans with reduced margin (1 cm) with the original ones and to analyse toxicity. 105 patients were treated: 85 had local recurrence and 68 of them were analysed. Recurrence was considered in field, marginal and distant if >80 %, 20-80 % or <20 % of the relapse volume was included in the 95 %-isodose. In silico plans were retrospectively recalculated using the same technique, fields angles and treatment planning system of the original ones. The pattern of recurrence was in field, marginal and distant in 88, 10 and 2 % respectively and was similar in in silico plans. The margin reduction appears to spare 100 cc of healthy brain by 57 Gy-volume (p = 0.02). The target coverage was worse in standard plans (pt student < 0.001), especially if the tumour was near to organs at risk (pχ2 < 0.001). PTV coverage was better with IMRT and helical-IMRT, than conformal-3D (pAnova test = 0.038). This difference was no more significant with in silico planning. A higher incidence of asthenia and leuko-encephalopathy was observed in patients with greater percentage of healthy brain included in 57 Gy-volume. No differences in the pattern of recurrence according to margins were found. The margin reduction determines sparing of healthy brain and could possibly reduce the incidence of late toxicity. Margin reduction could allow to use less sophisticated techniques, ensuring appropriate target coverage, and the choice of more costly techniques could be reserved to selected cases

Development and validation of a prediction model for severe respiratory failure in hospitalized patients with SARS-CoV-2 infection: a multicentre cohort study (PREDI-CO study)

Objectives: We aimed to develop and validate a risk score to predict severe respiratory failure (SRF) among patients hospitalized with coronavirus disease-2019 (COVID-19).Methods: We performed a multicentre cohort study among hospitalized (&gt;24 hours) patients diagnosed with COVID-19 from 22 February to 3 April 2020, at 11 Italian hospitals. Patients were divided into derivation and validation cohorts according to random sorting of hospitals. SRF was assessed from admission to hospital discharge and was defined as: SpO(2) &lt;93% with 100% FiO(2), respiratory rate &gt;30 breaths/min or respiratory distress. Multivariable logistic regression models were built to identify predictors of SRF, beta-coefficients were used to develop a risk score. Trial Registration NCT04316949.Results: We analysed 1113 patients (644 derivation, 469 validation cohort). Mean (+/- SD) age was 65.7 (+/- 15) years, 704 (63.3%) were male. SRF occurred in 189/644 (29%) and 187/469 (40%) patients in the derivation and validation cohorts, respectively. At multivariate analysis, risk factors for SRF in the derivation cohort assessed at hospitalization were age &gt;= 70 years (OR 2.74; 95% CI 1.66-4.50), obesity (OR 4.62; 95% CI 2.78-7.70), body temperature &gt;= 38 degrees C (OR 1.73; 95% CI 1.30-2.29), respiratory rate &gt;= 22 breaths/min (OR 3.75; 95% CI 2.01-7.01), lymphocytes &lt;= 900 cells/mm(3) (OR 2.69; 95% CI 1.60-4.51), creatinine &gt;= 1 mg/dL (OR 2.38; 95% CI 1.59-3.56), C-reactive protein &gt;= 10 mg/dL (OR 5.91; 95% CI 4.88 -7.17) and lactate dehydrogenase &gt;= 350 IU/L (OR 2.39; 95% CI 1.11-5.11). Assigning points to each variable, an individual risk score (PREDI-CO score) was obtained. Area under the receiver-operator curve was 0.89 (0.86-0.92). At a score of &gt;3, sensitivity, specificity, and positive and negative predictive values were 71.6% (65%-79%), 89.1% (86%-92%), 74% (67%-80%) and 89% (85%-91%), respectively. PREDI-CO score showed similar prognostic ability in the validation cohort: area under the receiver-operator curve 0.85 (0.81e0.88). At a score of &gt;3, sensitivity, specificity, and positive and negative predictive values were 80% (73%-85%), 76% (70%-81%), 69% (60%-74%) and 85% (80%-89%), respectively.Conclusion: PREDI-CO score can be useful to allocate resources and prioritize treatments during the COVID-19 pandemic. (c) 2020 European Society of Clinical Microbiology and Infectious Diseases. Published by Elsevier Ltd. All rights reserved