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Abstract Background Several scores aimed at predicting COVID-19 progression have been proposed. As the variables vaccination and early SARS-CoV-2 treatment were systematically excluded from the prognostic scores, the present study's objective was to develop a new model adapted to the current epidemiological scenario. Methods We included all patients evaluated by the Infectious Disease Unit in Sassari, with SARS-CoV-2 infection and without signs of respiratory failure at the first evaluation (P/F > 300). Disease progression was defined by the prescription of supplemental oxygen. In addition, variables related to demographics, vaccines, comorbidities, symptoms, CT scans, blood tests, and therapies were collected. Multivariate logistic regression modelling was performed to determine factors associated with progression; any variable with significant univariate test or clinical relevance was selected as a candidate for multivariate analysis. Hosmer–Lemeshow (HL) goodness of fit statistic was calculated. Odds ratio values were used to derive an integer score for developing an easy-to-use progression risk score. The discrimination performance of the risk index was determined using the AUC, and the best cut-off point, according to the Youden index, sensitivity, specificity, predictive value, and likelihood ratio, was chosen. Results 1145 patients [median (IQR) age 74 (62–83) years; 53.5% males] were enrolled; 336 (29.3%) had disease progression. Patients with a clinical progression were older and showed more comorbidities; furthermore, they were less vaccinated and exposed to preventive therapy. In the multivariate logistic regression analysis, age ≥ 60 years, COPD, dementia, haematological tumours, heart failure, exposure to no or one vaccine dose, fever, dyspnoea, GGO, consolidation, ferritin, De Ritis ≥ 1.2, LDH, and no exposure to early anti-SARS-CoV-2 treatment were associated with disease progression. The final risk score ranged from 0 to 45. The ROC curve analysis showed an AUC of 0.92 (95% CI 0.90–0.93) with a 93.7% specificity and 72.9% sensitivity. Low risk was defined when the cut-off value was less than 23. Three risk levels were identified: low (0–23 points), medium (24–35), and high (≥ 36). Conclusions The proportion of patients with progression increases with high scores: the assessment of the risk could be helpful for clinicians to plan appropriate therapeutic strategies.

Background: Several scores aimed at predicting COVID-19 progression have been proposed. As the variables vaccination and early SARS-CoV-2 treatment were systematically excluded from the prognostic scores, the present study's objective was to develop a new model adapted to the current epidemiological scenario. Methods: We included all patients evaluated by the Infectious Disease Unit in Sassari, with SARS-CoV-2 infection and without signs of respiratory failure at the first evaluation (P/F > 300). Disease progression was defined by the prescription of supplemental oxygen. In addition, variables related to demographics, vaccines, comorbidities, symptoms, CT scans, blood tests, and therapies were collected. Multivariate logistic regression modelling was performed to determine factors associated with progression; any variable with significant univariate test or clinical relevance was selected as a candidate for multivariate analysis. Hosmer–Lemeshow (HL) goodness of fit statistic was calculated. Odds ratio values were used to derive an integer score for developing an easy-to-use progression risk score. The discrimination performance of the risk index was determined using the AUC, and the best cut-off point, according to the Youden index, sensitivity, specificity, predictive value, and likelihood ratio, was chosen. Results: 1145 patients [median (IQR) age 74 (62–83) years; 53.5% males] were enrolled; 336 (29.3%) had disease progression. Patients with a clinical progression were older and showed more comorbidities; furthermore, they were less vaccinated and exposed to preventive therapy. In the multivariate logistic regression analysis, age ≥ 60 years, COPD, dementia, haematological tumours, heart failure, exposure to no or one vaccine dose, fever, dyspnoea, GGO, consolidation, ferritin, De Ritis ≥ 1.2, LDH, and no exposure to early anti-SARS-CoV-2 treatment were associated with disease progression. The final risk score ranged from 0 to 45. The ROC curve analysis showed an AUC of 0.92 (95% CI 0.90–0.93) with a 93.7% specificity and 72.9% sensitivity. Low risk was defined when the cut-off value was less than 23. Three risk levels were identified: low (0–23 points), medium (24–35), and high (≥ 36). Conclusions: The proportion of patients with progression increases with high scores: the assessment of the risk could be helpful for clinicians to plan appropriate therapeutic strategies. [ABSTRACT FROM AUTHOR]

Introduction/Objective. Prognostication of chronic lymphocytic leukemia (CLL) has been substantially improved in recent times. Among several prognostic models (PMs) focused on the prediction of time to first treatment (TTFT), progression-risk score (PRS), and MD Anderson Cancer Center score 2011 (MDACC 2011) are the most relevant, while CLL-International Prognostic Index (CLL-IPI), although originally developed to predict overall survival (OS), is also being used to estimate TTFT. The aim of this study was to investigate CLL-IPI, PRS, and MDACC 2011 prognostic values regarding TTFT and OS. Methods. The analyzed cohort included 57 unselected Serbian CLL patients from a single institution, with the basic characteristics reflecting more aggressive disease than in the general de novo CLL population. The eligible patients were assigned investigated PMs, and TTFT and OS analyses were performed. Results. Patients with higher risk scores according to CLL-IPI, PRS, and MDACC 2011 underwent treatment significantly earlier than patients with lower risk scores (p = 0.002, p = 0.019, and p < 0.001, respectively). In multivariate analysis, MDACC 2011 and CLL-IPI retained their significance regarding TTFT (p = 0.001 and p = 0.018, respectively), while PRS did not. CLL-IPI was the only significant predictor of OS both at the univariate (p = 0.005) and multivariate (p = 0.013) levels. Conclusion. CLL-IPI, PRS, and particularly MDACC 2011 are able to predict TTFT even in cohorts with more advanced-disease patients, while for prediction of OS, CLL-IPI is the only applicable among the three PMs. These results imply that PMs should be investigated in more diverse CLL populations, as it is in real-life setting.