7 results on '"Carlotta Rogati"'
Search Results
2. Better prognosis in females with severe COVID-19 pneumonia: possible role of inflammation as potential mediator
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Cristina Mussini, Alessandro Cozzi-Lepri, Marianna Menozzi, Marianna Meschiari, Erica Franceschini, Carlotta Rogati, Gianluca Cuomo, Andrea Bedini, Vittorio Iadisernia, Sara Volpi, Jovana Milic, Roberto Tonelli, Lucio Brugioni, Antonello Pietrangelo, Massimo Girardis, Andrea Cossarizza, Enrico Clini, Giovanni Guaraldi, Erica Bacca, Vanni Borghi, Giulia Burastero, Federica Carli, Giacomo Ciusa, Luca Corradi, Margherita Di Gaetano, Matteo Faltoni, Giacomo Franceschi, Gabriella Orlando, Francesco Pellegrino, Cinzia Puzzolante, Alessandro Raimondi, Antonella Santoro, Marco Tutone, Dina Yaacoub, Alberto Andreotti, Emanuela Biagioni, Filippo Bondi, Stefano Busani, Giovanni Chierego, Marzia Scotti, Lucia Serio, Caterina Bellinazzi, Rebecca Borella, Sara De Biasi, Anna De Gaetano, Lucia Fidanza, Lara Gibellini, Anna Iannone, Domenico Lo Tartaro, Marco Mattioli, Annamaria Paolini, Rossella Fogliani, Grazia Righini, and Mario Lugli
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Male ,sex differences ,0301 basic medicine ,Microbiology (medical) ,Mediation (statistics) ,medicine.medical_specialty ,medicine.medical_treatment ,030106 microbiology ,Context (language use) ,Pathogenesis ,Cohort Studies ,03 medical and health sciences ,Sex Factors ,0302 clinical medicine ,Risk Factors ,Internal medicine ,Humans ,Medicine ,030212 general & internal medicine ,Respiratory system ,female sex ,Survival analysis ,Aged ,Retrospective Studies ,Inflammation ,Mechanical ventilation ,SARS-CoV-2 ,business.industry ,Absolute risk reduction ,COVID-19 ,General Medicine ,Middle Aged ,Prognosis ,medicine.disease ,Respiration, Artificial ,Hospitalization ,Pneumonia ,Infectious Diseases ,SARS-CoV-2, COVID-19, female sex, prognosis, inflammation ,inflammation ,Commentary ,Female ,prognosis ,CRP ,business ,Cohort study - Abstract
Objectives Sex differences in COVID-19 severity and mortality have been described. Key aims of this analysis were to compare the risk of invasive mechanical ventilation (IMV) and mortality by sex and to explore whether variation in specific biomarkers could mediate this difference. Methods This was a retrospective, observational cohort study among patients with severe COVID-19 pneumonia. A survival analysis was conducted to compare time to the composite endpoint of IMV or death by sex. Interaction was formally tested to compare the risk difference by sex in subsets. Mediation analysis with a binary endpoint IMV or death (yes/no) by end of follow-up for a number of inflammation/coagulation biomarkers in the context of counterfactual prediction was also conducted. Results Among 415 patients, 134 were females (32%) and 281 males (67%), median age 66 years (IQR 54-77). At admission, females showed a significantly less severe clinical and respiratory profiles with a higher PaO2/FiO2 (254 mmHg vs 191 mmHg; p=0.023). By 28 days from admission, 49.2% (95% CI: 39.6-58.9%) of males vs. 31.7% (17.9-45.4%) of females underwent IMV or death (log-rank p-value Conclusions Our analysis confirms a difference in the risk of COVID-19 clinical progression by sex and provides a hypothesis for potential mechanisms leading to this. CRP showed a predominant role to mediate the difference in risk by sex.
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- 2021
3. The impact of tocilizumab on respiratory support states transition and clinical outcomes in COVID-19 patients. A Markov model multi-state study
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Giovanni Guaraldi, Gianluca Cuomo, Marianna Meschiari, Andrea Cossarizza, Massimo Girardis, Cristina Mussini, Stefano Busani, Luca Corradi, Alessandro Raimondi, Erica Bacca, Erica Franceschini, Giovanni Dolci, Margherita Digaetano, Antonella Santoro, Giacomo Ciusa, Dina Yaacoub, Marianna Menozzi, Sara Volpi, Marco Tutone, Cinzia Puzzolante, Gabriella Orlando, Giacomo Franceschi, Jovana Milic, Andrea Bedini, Licia Gozzi, Federico Banchelli, Vittorio Iadisernia, Giulia Burastero, Roberto D'Amico, Federica Carli, Matteo Faltoni, Rossella Miglio, Carlotta Rogati, Milic J., Banchelli F., Meschiari M., Franceschini E., Ciusa G., Gozzi L., Volpi S., Faltoni M., Franceschi G., Iadisernia V., Yaacoub D., Dolci G., Bacca E., Rogati C., Tutone M., Burastero G., Raimondi A., Menozzi M., Cuomo G., Corradi L., Orlando G., Santoro A., Digaetano M., Puzzolante C., Carli F., Bedini A., Busani S., Girardis M., Cossarizza A., Miglio R., Mussini C., Guaraldi G., and D'Amico R.
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Male ,medicine.medical_specialty ,Respiratory Therapy ,Time Factors ,Time Factor ,medicine.medical_treatment ,Science ,Antibodies, Monoclonal, Humanized ,law.invention ,chemistry.chemical_compound ,Tocilizumab ,Randomized controlled trial ,law ,Oxygen therapy ,Internal medicine ,medicine ,Humans ,Aged ,Mechanical ventilation ,Multidisciplinary ,Noninvasive Ventilation ,business.industry ,Mortality rate ,Oxygen Inhalation Therapy ,COVID-19 ,Markov Chain ,Middle Aged ,medicine.disease ,Respiration, Artificial ,Markov Chains ,COVID-19 Drug Treatment ,Pneumonia ,Treatment Outcome ,chemistry ,Breathing ,Medicine ,Observational study ,Female ,business ,Research Article ,Human - Abstract
Background The benefit of tocilizumab on mortality and time to recovery in people with severe COVID pneumonia may depend on appropriate timing. The objective was to estimate the impact of tocilizumab administration on switching respiratory support states, mortality and time to recovery. Methods In an observational study, a continuous-time Markov multi-state model was used to describe the sequence of respiratory support states including: no respiratory support (NRS), oxygen therapy (OT), non-invasive ventilation (NIV) or invasive mechanical ventilation (IMV), OT in recovery, NRS in recovery. Results Two hundred seventy-one consecutive adult patients were included in the analyses contributing to 695 transitions across states. The prevalence of patients in each respiratory support state was estimated with stack probability plots, comparing people treated with and without tocilizumab since the beginning of the OT state. A positive effect of tocilizumab on the probability of moving from the invasive and non-invasive mechanical NIV/IMV state to the OT in recovery state (HR = 2.6, 95% CI = 1.2–5.2) was observed. Furthermore, a reduced risk of death was observed in patients in NIV/IMV (HR = 0.3, 95% CI = 0.1–0.7) or in OT (HR = 0.1, 95% CI = 0.0–0.8) treated with tocilizumab. Conclusion To conclude, we were able to show the positive impact of tocilizumab used in different disease stages depicted by respiratory support states. The use of the multi-state Markov model allowed to harmonize the heterogeneous mortality and recovery endpoints and summarize results with stack probability plots. This approach could inform randomized clinical trials regarding tocilizumab, support disease management and hospital decision making.
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- 2021
4. Darunavir/Cobicistat Is Associated with Negative Outcomes in HIV-Negative Patients with Severe COVID-19 Pneumonia
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Sara Volpi, Carlotta Rogati, Marco Tutone, Marianna Menozzi, Giulia Burastero, Andrea Cossarizza, Giacomo Franceschi, Marianna Meschiari, Giacomo Ciusa, Luca Pasina, Alessio Novella, Andrea Bedini, Giovanni Guaraldi, Jovana Milic, Dina Yaacoub, Cristina Mussini, Margherita Digaetano, Federica Carli, Erica Bacca, Giovanni Dolci, Matteo Faltoni, Vittorio Iadisernia, Erica Franceschini, Antonella Santoro, and Gianluca Cuomo
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Adult ,Male ,0301 basic medicine ,medicine.medical_specialty ,Anti-HIV Agents ,medicine.medical_treatment ,Immunology ,03 medical and health sciences ,0302 clinical medicine ,Virology ,Internal medicine ,medicine ,Humans ,030212 general & internal medicine ,Survival analysis ,Darunavir ,Retrospective Studies ,Mechanical ventilation ,business.industry ,SARS-CoV-2 ,Cobicistat ,COVID-19 ,darunavir/cobicistat ,negative outcomes ,Confounding ,Retrospective cohort study ,Middle Aged ,medicine.disease ,COVID-19 Drug Treatment ,Clinical trial ,Drug Combinations ,Pneumonia ,030104 developmental biology ,Infectious Diseases ,Female ,business ,medicine.drug - Abstract
The aim of this study was to evaluate both positive outcomes, including reduction of respiratory support aid and duration of hospital stay, and negative ones, including mortality and a composite of invasive mechanical ventilation or death, in patients with coronavirus disease 2019 (COVID-19) pneumonia treated with or without oral darunavir/cobicistat (DRV/c, 800/150 mg/day) used in different treatment durations. The secondary objective was to evaluate the percentage of patients treated with DRV/c who were exposed to potentially severe drug-drug interactions (DDIs) and died during hospitalization. This observational retrospective study was conducted in consecutive patients with COVID-19 pneumonia admitted to a tertiary care hospital in Modena, Italy. Kaplan-Meier survival curves and Cox proportional hazards regression were used to compare patients receiving standard of care with or without DRV/c. Adjustment for key confounders was applied. Two hundred seventy-three patients (115 on DRV/c) were included, 75.8% males, mean age was 64.6 (±13.2) years. Clinical improvement was similar between the groups, depicted by respiratory aid switch (p > .05). The same was observed for duration of hospital stay [13.2 (±8.9) for DRV/c vs. 13.4 (±7.2) days for no-DRV/c, p = .9]. Patients on DRV/c had higher rates of mortality (25.2% vs. 10.1%, p < .0001. The rate of composite outcome of mechanical ventilation and death was higher in the DRV/c group (37.4% vs. 25.3%, p = .03). Multiple serious DDI associated with DRV/c were observed in the 19 patients who died. DRV/c should not be recommended as a treatment option for COVID-19 pneumonia outside clinical trials.
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- 2021
5. Prospective Study on Incidence, Risk Factors and Outcome of Recurrent
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Erica Franceschini, Malgorzata Mikulska, Andrea Lombardi, Gregorio Basile, Laura Isabella Lupo, Giambattista Lobreglio, Daniele Roberto Giacobbe, Giancarlo Ceccarelli, Maria Adriana Cataldo, Anna Maria De Luca, Mario Venditti, Stefano Di Bella, Emanuela Caraffa, Michele Bartoletti, Enrica Giacometti, Nicola Petrosillo, Matteo Bassetti, Alessandra Oliva, Guido Granata, Ivan Gentile, Pierluigi Viale, Cristina Mussini, Alessandro Pandolfo, Paolo Bonfanti, Raffaella Borromeo, Sara Fossati, Alessandra Mularoni, Lucia Adamoli, Fabrizio Ingrassia, Filippo Lagi, Alberto Enrico Maraolo, Carlotta Rogati, Filippo Trapani, Roberto Luzzati, Alessandro Bartoloni, Mario U. Mondelli, Granata, G, Petrosillo, N, Adamoli, L, Bartoletti, M, Bartoloni, A, Basile, G, Bassetti, M, Bonfanti, P, Borromeo, R, Ceccarelli, G, De Luca, A, Di Bella, S, Fossati, S, Franceschini, E, Gentile, I, Giacobbe, D, Giacometti, E, Ingrassia, F, Lagi, F, Lobreglio, G, Lombardi, A, Lupo, L, Luzzati, R, Maraolo, A, Mikulska, M, Mondelli, M, Mularoni, A, Mussini, C, Oliva, A, Pandolfo, A, Rogati, C, Trapani, F, Venditti, M, Viale, P, Caraffa, E, Cataldo, M, Granata, Guido, Petrosillo, Nicola, Adamoli, Lucia, Bartoletti, Michele, Bartoloni, Alessandro, Basile, Gregorio, Bassetti, Matteo, Bonfanti, Paolo, Borromeo, Raffaella, Ceccarelli, Giancarlo, De Luca, Anna Maria, Di Bella, Stefano, Fossati, Sara, Franceschini, Erica, Gentile, Ivan, Giacobbe, Daniele Roberto, Giacometti, Enrica, Ingrassia, Fabrizio, Lagi, Filippo, Lobreglio, Giambattista, Lombardi, Andrea, Lupo, Laura Isabella, Luzzati, Roberto, Maraolo, Alberto Enrico, Mikulska, Malgorzata, Mondelli, Mario Umberto, Mularoni, Alessandra, Mussini, Cristina, Oliva, Alessandra, Pandolfo, Alessandro, Rogati, Carlotta, Trapani, Filippo Fabio, Venditti, Mario, Viale, Pierluigi, Caraffa, Emanuela, Cataldo, Maria Adriana, Granata G., Petrosillo N., Adamoli L., Bartoletti M., Bartoloni A., Basile G., Bassetti M., Bonfanti P., Borromeo R., Ceccarelli G., De Luca A.M., Di Bella S., Fossati S., Franceschini E., Gentile I., Giacobbe D.R., Giacometti E., Ingrassia F., Lagi F., Lobreglio G., Lombardi A., Lupo L.I., Luzzati R., Maraolo A.E., Mikulska M., Mondelli M.U., Mularoni A., Mussini C., Oliva A., Pandolfo A., Rogati C., Trapani F.F., Venditti M., Viale P., Caraffa E., and Cataldo M.A.
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medicine.medical_specialty ,recurrence ,genetic structures ,lcsh:Medicine ,Logistic regression ,Clostridioides difficile ,Article ,03 medical and health sciences ,0302 clinical medicine ,Internal medicine ,Medicine ,risk factors ,030212 general & internal medicine ,Risk factor ,Prospective cohort study ,0303 health sciences ,Adult patients ,030306 microbiology ,business.industry ,Incidence (epidemiology) ,Mortality rate ,lcsh:R ,General Medicine ,risk factor ,outcome ,incidence ,business ,Clostridioides ,Cohort study - Abstract
Background: Limited and wide-ranging data are available on the recurrent Clostridioides difficile infection (rCDI) incidence rate. Methods: We performed a cohort study with the aim to assess the incidence of and risk factors for rCDI. Adult patients with a first CDI, hospitalized in 15 Italian hospitals, were prospectively included and followed-up for 30 d after the end of antimicrobial treatment for their first CDI. A case–control study was performed to identify risk factors associated with 30-day onset rCDI. Results: Three hundred nine patients with a first CDI were included in the study, 32% of the CDI episodes (99/309) were severe/complicated, complete follow-up was available for 288 patients (19 died during the first CDI episode, and 2 were lost during follow-up). At the end of the study, the crude all-cause mortality rate was 10.7% (33 deaths/309 patients). Two hundred seventy-one patients completed the follow-up, rCDI occurred in 21% of patients (56/271) with an incidence rate of 72/10,000 patient-days. Logistic regression analysis identified exposure to cephalosporin as an independent risk factor associated with rCDI (RR: 1.7, 95% CI: 1.1–2.7, p = 0.03). Conclusion: Our study confirms the relevance of rCDI in terms of morbidity and mortality and provides a reliable estimation of its incidence.
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- 2020
6. Machine learning in predicting respiratory failure in patients with COVID-19 pneumonia—Challenges, strengths, and opportunities in a global health emergency
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Sara Volpi, Vittorio Iadisernia, Massimo Girardis, Federica Mandreoli, Jovana Milic, Marianna Meschiari, Roberto Tonelli, Cinzia Puzzolante, Enrico Clini, Francesco Ghinelli, Ivana Castaniere, Margherita Digaetano, Luca Corradi, Alessandro Raimondi, Luca Tabbì, Marianna Menozzi, Andrea Cossarizza, Erica Bacca, Gianluca Cuomo, Giacomo Ciusa, Stefano Busani, Giacomo Franceschi, Cristina Mussini, Federica Carli, Giulia Burastero, Mario Sarti, Davide Ferrari, Carlotta Rogati, Paolo Missier, Riccardo Fantini, Matteo Faltoni, Vanni Borghi, Gabriella Orlando, Andrea Bedini, Marco Tutone, Giovanni Guaraldi, Dina Yaacoub, Erica Franceschini, and Antonella Santoro
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Male ,Viral Diseases ,Critical Care and Emergency Medicine ,020205 medical informatics ,Pulmonology ,Epidemiology ,medicine.medical_treatment ,02 engineering and technology ,computer.software_genre ,Biochemistry ,Machine Learning ,0302 clinical medicine ,Medical Conditions ,Mathematical and Statistical Techniques ,0202 electrical engineering, electronic engineering, information engineering ,Medicine and Health Sciences ,030212 general & internal medicine ,Prospective Studies ,Prospective cohort study ,Multidisciplinary ,Statistics ,Middle Aged ,Chemistry ,Infectious Diseases ,Italy ,Physical Sciences ,Medicine ,Female ,Coronavirus Infections ,Respiratory Insufficiency ,Research Article ,Chemical Elements ,Computer and Information Sciences ,Science ,Pneumonia, Viral ,MEDLINE ,Machine learning ,Research and Analysis Methods ,03 medical and health sciences ,Respiratory Disorders ,Betacoronavirus ,Respiratory Failure ,Artificial Intelligence ,medicine ,Humans ,Medical history ,Computer Simulation ,Statistical Methods ,Pandemics ,Aged ,Mechanical ventilation ,Models, Statistical ,business.industry ,SARS-CoV-2 ,Biology and Life Sciences ,COVID-19 ,Covid 19 ,Pneumonia ,medicine.disease ,Respiration, Artificial ,Oxygen ,Respiratory failure ,Analytics ,respiratory failure ,prediction ,Medical Risk Factors ,Observational study ,Artificial intelligence ,Blood Gas Analysis ,business ,computer ,Mathematics ,Biomarkers ,Forecasting - Abstract
Aims The aim of this study was to estimate a 48 hour prediction of moderate to severe respiratory failure, requiring mechanical ventilation, in hospitalized patients with COVID-19 pneumonia. Methods This was an observational prospective study that comprised consecutive patients with COVID-19 pneumonia admitted to hospital from 21 February to 6 April 2020. The patients’ medical history, demographic, epidemiologic and clinical data were collected in an electronic patient chart. The dataset was used to train predictive models using an established machine learning framework leveraging a hybrid approach where clinical expertise is applied alongside a data-driven analysis. The study outcome was the onset of moderate to severe respiratory failure defined as PaO2/FiO2 ratio Results A total of 198 patients contributed to generate 1068 usable observations which allowed to build 3 predictive models based respectively on 31-variables signs and symptoms, 39-variables laboratory biomarkers and 91-variables as a composition of the two. A fourth “boosted mixed model” included 20 variables was selected from the model 3, achieved the best predictive performance (AUC = 0.84) without worsening the FN rate. Its clinical performance was applied in a narrative case report as an example. Conclusion This study developed a machine model with 84% prediction accuracy, which is able to assist clinicians in decision making process and contribute to develop new analytics to improve care at high technology readiness levels.
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- 2020
7. Machine learning in predicting respiratory failure in patients with COVID-19 pneumonia - challenges, strengths, and opportunities in a global health emergency
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Giovanni Guaraldi, Erica Bacca, Luca Corradi, Andrea Cossarizza, Massimo Girardis, Stefano Busani, Alessandro Raimondi, Marianna Meschiari, Vittorio Iadisernia, Riccardo Fantini, Sara Volpi, G. Orlando, Luca Tabbì, Marco Tutone, Roberto Tonelli, Carlotta Rogati, Jovana Milic, Marianna Menozzi, Davide Ferrari, Paolo Missier, Ivana Castaniere, M. Di Gaetano, Cristina Mussini, Giacomo Franceschi, F. Ghinelli, Andrea Bedini, Federica Mandreoli, Giulia Burastero, Dina Yaacoub, Giacomo Ciusa, Federica Carli, Matteo Faltoni, Cinzia Puzzolante, Enrico Clini, Mario Sarti, G. Cuomo, Erica Franceschini, and Antonella Santoro
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Mechanical ventilation ,medicine.medical_specialty ,medicine.diagnostic_test ,business.industry ,medicine.medical_treatment ,Complete blood count ,Machine learning ,computer.software_genre ,medicine.disease ,Pneumonia ,Respiratory failure ,Epidemiology ,medicine ,Observational study ,Medical history ,Artificial intelligence ,business ,computer ,Predictive modelling - Abstract
Background Machine learning can assist clinicians in forecasting patients with COVID-19 who develop respiratory failure requiring mechanical ventilation. This analysis aimed to determine a 48 hours prediction of moderate to severe respiratory failure, as assessed with PaO2/FiO2 < 150 mmHg, in hospitalized patients with COVID-19 pneumonia. Methods This was an observational study that comprised all consecutive adult patients with COVID-19 pneumonia admitted to the Infectious Diseases Clinic of the University Hospital of Modena, Italy from 21 February to 6 April 2020. COVID-19 was confirmed with PCR positive nasopharyngeal swabs while the presence of pneumonia was radiologically confirmed. Patients received standard of care according to national guidelines for clinical management of SARS-CoV-2 infection. The patients' full medical history, demographic and epidemiological features, clinical data, complete blood count, coagulation, inflammatory and biochemical markers were routinely collected and aggregated in a clinically-oriented logical framework in order to build different datasets. The dataset was used to train a learning framework relying on Microsoft LightGBM and leveraging a hybrid approach, where clinical expertise is applied alongside a data-driven analysis. Shapley Additive exPlanations (SHAP) values were used to quantify the positive or negative impact of each variable included in the model on the predicted outcome. The study outcome was the onset of moderate to severe respiratory failure defined as PaO2/FiO2 ratio < 150 mmHg ([≥] 13.3 kPa) in at least one of two consecutive arterial blood gas analyses in the following 48 hours. Results A total of 198 patients contributed to generate 1068 valuable observations which allowed to build 3 prediction models based respectively on 31-variables signs and symptoms, 39-variables laboratory biomarkers and 91-variables as a composition of the two. A fourth boosted mixed model which included 20 variables was selected from the model 3, achieved the best predictive performance (AUC=0.84). Its clinical performance was applied in a narrative case report as an example. Conclusion This study developed a machine learning algorithm, with a 84% prediction accuracy, which is potentially able to assist clinicians in decision making process with therapeutic implications.
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- 2020
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