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1. Rationale and design of the CV-PREVITAL study: an Italian multiple cohort randomised controlled trial investigating innovative digital strategies in primary cardiovascular prevention

Author

Baldassarre, D, Iacoviello, L, Baetta, R, Roncaglioni, M, Condorelli, G, Remuzzi, G, Gensini, G, Frati, L, Ricciardi, W, Conaldi, P, Uccelli, A, Blandini, F, Bosari, S, Scambia, G, Fini, M, Di Malta, A, Amato, M, Veglia, F, Bonomi, A, Klersy, C, Colazzo, F, Pengo, M, Gorini, F, Auteri, L, Ferrante, G, Baviera, M, Ambrosio, G, Catapano, A, Gialluisi, A, Malavazos, A, Castelvecchio, S, Corsi-Romanelli, M, Cardani, R, La Rovere, M, Agnese, V, Pane, B, Prati, D, Spinardi, L, Liuzzo, G, Arbustini, E, Volterrani, M, Visconti, M, Werba, J, Genovese, S, Bilo, G, Invitti, C, Di Blasio, A, Lombardi, C, Faini, A, Rosa, D, Ojeda-Fernandez, L, Foresta, A, De Curtis, A, Di Castelnuovo, A, Scalvini, S, Pierobon, A, Gorini, A, Valenti, L, Luzi, L, Racca, A, Bandi, M, Tremoli, E, Menicanti, L, Parati, G, Pompilio, G, Colombo, G, Vavassori, C, Biondi, M, Frigerio, B, Ravani, A, Sansaro, D, Coggi, D, Romandini, A, Giroli, M, Giuliani, M, Bonmi, A, Rondinelli, M, Trudu, C, Cinieri, C, Monturano, M, Colazo, F, Inviti, C, Di Blasi, A, Torlasco, C, Gilardini, L, Soranna, D, Zambon, A, Perger, E, Zanotti, L, Badano, L, Cova, L, Gentilini, D, Grappiolo, L, Condoreli, G, Ferante, G, Papa, L, Savevski, V, Ieva, F, Romano, I, Remzzi, G, Ojeda, L, Clerici, F, Palumbo, A, Genini, G, Catpano, A, Mattioli, R, Longhi, E, Mantovani, L, Madotto, F, Bonaccio, M, Gianfagna, F, Ghulam, A, Magnacca, S, Noro, F, Costanzo, S, Esposito, S, Orlandi, S, Persichillo, M, Bracone, F, Panzera, T, Ruggiero, E, Parisi, R, Franciosa, S, Morelli, M, De Rita, F, Cerletti, C, de Gaetano, G, Donati, M, Mencanti, L, Romanelli, M, Cerri, A, Dubini, C, Trevisan, M, Renna, L, Milani, V, Boveri, S, Giubbilini, P, Ramputi, L, Baroni, I, De Angeli, G, Riciardi, W, Olmetti, F, Bussotti, M, Gaetano, C, Baiardi, P, Bachetti, T, Balbi, M, Comini, L, Lorenzoni, M, Olivares, A, Traversi, E, Garre, C, Sideri, R, Clemenza, F, Gentile, G, Caruana, G, Cuscino, N, Di Gesaro, G, Greco, A, Loddo, I, Tuzzolino, F, Ucelli, A, Palombo, D, Spinella, G, Mozzetta, G, Ameri, P, Zoppoli, G, Finotello, A, Porto, I, Pratesi, G, Bladini, F, Spnardi, L, Clerici, M, Pelusi, S, Bianco, C, Carpani, R, Periti, G, Margarita, S, Lanza, G, Severino, A, Pedicino, D, D'Amario, D, D'Aiello, A, Vinci, R, Bonanni, A, Brecciaroli, M, Filomia, S, Pastorino, R, Boccia, S, Urbani, A, Sanguinetti, M, Santoliquido, A, Proto, L, Tarquini, D, Grimaldi, M, Leonardi, S, Elia, A, Currao, A, Urtis, M, Di Toro, A, Giuliani, L, Caminiti, G, Marcolongo, F, Sposato, B, Guadagni, F, Morsella, V, Marziale, A, Protti, G, Baldassarre D., Iacoviello L., Baetta R., Roncaglioni M. C., Condorelli G., Remuzzi G., Gensini G., Frati L., Ricciardi W., Conaldi P. G., Uccelli A., Blandini F., Bosari S., Scambia G., Fini M., Di Malta A., Amato M., Veglia F., Bonomi A., Klersy C., Colazzo F., Pengo M., Gorini F., Auteri L., Ferrante G., Baviera M., Ambrosio G., Catapano A., Gialluisi A., Malavazos A. E., Castelvecchio S., Corsi-Romanelli M. M., Cardani R., La Rovere M. T., Agnese V., Pane B., Prati D., Spinardi L., Liuzzo G., Arbustini E., Volterrani M., Visconti M., Werba J. P., Genovese S., Bilo G., Invitti C., Di Blasio A., Lombardi C., Faini A., Rosa D., Ojeda-Fernandez L., Foresta A., De Curtis A., Di Castelnuovo A., Scalvini S., Pierobon A., Gorini A., Valenti L., Luzi L., Racca A., Bandi M., Tremoli E., Menicanti L., Parati G., Pompilio G., Colombo G., Vavassori C., Biondi M. L., Frigerio B., Ravani A., Sansaro D., Coggi D., Romandini A., Giroli M., Giuliani M., Bonmi A., Rondinelli M., Trudu C., Cinieri C., Monturano M., Colazo F., Inviti C., Di Blasi A., Torlasco C., Gilardini L., Soranna D., Zambon A., Perger E., Zanotti L., Badano L., Cova L., Gentilini D., Grappiolo L., Condoreli G., Ferante G., Papa L., Savevski V., Ieva F., Romano I., Remzzi G., Ojeda L., Clerici F., Palumbo A., Genini G. F., Catpano A., Mattioli R., Longhi E., Mantovani L. G., Madotto F., Bonaccio M., Gianfagna F., Ghulam A., Magnacca S., Noro F., Costanzo S., Esposito S., Orlandi S., Persichillo M., Bracone F., Panzera T., Ruggiero E., Parisi R., Franciosa S., Morelli M., De Rita F., Cerletti C., de Gaetano G., Donati M. B., Mencanti L., Romanelli M. M. C., Cerri A., Dubini C., Trevisan M. B., Renna L. V., Milani V., Boveri S., Giubbilini P., Ramputi L., Baroni I., De Angeli G., Riciardi W., Olmetti F., Bussotti M., Gaetano C., Baiardi P., Bachetti T., Balbi M., Comini L., Lorenzoni M., Olivares A., Traversi E., Garre C., Sideri R., Clemenza F., Gentile G., Caruana G., Cuscino N., Di Gesaro G., Greco A., Loddo I., Tuzzolino F., Ucelli A., Palombo D., Spinella G., Mozzetta G., Ameri P., Zoppoli G., Finotello A., Porto I., Pratesi G., Bladini F., Spnardi L., Clerici M., Pelusi S., Bianco C., Carpani R., Periti G., Margarita S., Lanza G. A., Severino A., Pedicino D., D'Amario D., D'Aiello A., Vinci R., Bonanni A., Brecciaroli M., Filomia S., Pastorino R., Boccia S., Urbani A., Sanguinetti M., Santoliquido A., Proto L., Tarquini D., Grimaldi M. C., Leonardi S., Elia A., Currao A., Urtis M., Di Toro A., Giuliani L., Caminiti G., Marcolongo F., Sposato B., Guadagni F., Morsella V., Marziale A., Protti G., Baldassarre, D, Iacoviello, L, Baetta, R, Roncaglioni, M, Condorelli, G, Remuzzi, G, Gensini, G, Frati, L, Ricciardi, W, Conaldi, P, Uccelli, A, Blandini, F, Bosari, S, Scambia, G, Fini, M, Di Malta, A, Amato, M, Veglia, F, Bonomi, A, Klersy, C, Colazzo, F, Pengo, M, Gorini, F, Auteri, L, Ferrante, G, Baviera, M, Ambrosio, G, Catapano, A, Gialluisi, A, Malavazos, A, Castelvecchio, S, Corsi-Romanelli, M, Cardani, R, La Rovere, M, Agnese, V, Pane, B, Prati, D, Spinardi, L, Liuzzo, G, Arbustini, E, Volterrani, M, Visconti, M, Werba, J, Genovese, S, Bilo, G, Invitti, C, Di Blasio, A, Lombardi, C, Faini, A, Rosa, D, Ojeda-Fernandez, L, Foresta, A, De Curtis, A, Di Castelnuovo, A, Scalvini, S, Pierobon, A, Gorini, A, Valenti, L, Luzi, L, Racca, A, Bandi, M, Tremoli, E, Menicanti, L, Parati, G, Pompilio, G, Colombo, G, Vavassori, C, Biondi, M, Frigerio, B, Ravani, A, Sansaro, D, Coggi, D, Romandini, A, Giroli, M, Giuliani, M, Bonmi, A, Rondinelli, M, Trudu, C, Cinieri, C, Monturano, M, Colazo, F, Inviti, C, Di Blasi, A, Torlasco, C, Gilardini, L, Soranna, D, Zambon, A, Perger, E, Zanotti, L, Badano, L, Cova, L, Gentilini, D, Grappiolo, L, Condoreli, G, Ferante, G, Papa, L, Savevski, V, Ieva, F, Romano, I, Remzzi, G, Ojeda, L, Clerici, F, Palumbo, A, Genini, G, Catpano, A, Mattioli, R, Longhi, E, Mantovani, L, Madotto, F, Bonaccio, M, Gianfagna, F, Ghulam, A, Magnacca, S, Noro, F, Costanzo, S, Esposito, S, Orlandi, S, Persichillo, M, Bracone, F, Panzera, T, Ruggiero, E, Parisi, R, Franciosa, S, Morelli, M, De Rita, F, Cerletti, C, de Gaetano, G, Donati, M, Mencanti, L, Romanelli, M, Cerri, A, Dubini, C, Trevisan, M, Renna, L, Milani, V, Boveri, S, Giubbilini, P, Ramputi, L, Baroni, I, De Angeli, G, Riciardi, W, Olmetti, F, Bussotti, M, Gaetano, C, Baiardi, P, Bachetti, T, Balbi, M, Comini, L, Lorenzoni, M, Olivares, A, Traversi, E, Garre, C, Sideri, R, Clemenza, F, Gentile, G, Caruana, G, Cuscino, N, Di Gesaro, G, Greco, A, Loddo, I, Tuzzolino, F, Ucelli, A, Palombo, D, Spinella, G, Mozzetta, G, Ameri, P, Zoppoli, G, Finotello, A, Porto, I, Pratesi, G, Bladini, F, Spnardi, L, Clerici, M, Pelusi, S, Bianco, C, Carpani, R, Periti, G, Margarita, S, Lanza, G, Severino, A, Pedicino, D, D'Amario, D, D'Aiello, A, Vinci, R, Bonanni, A, Brecciaroli, M, Filomia, S, Pastorino, R, Boccia, S, Urbani, A, Sanguinetti, M, Santoliquido, A, Proto, L, Tarquini, D, Grimaldi, M, Leonardi, S, Elia, A, Currao, A, Urtis, M, Di Toro, A, Giuliani, L, Caminiti, G, Marcolongo, F, Sposato, B, Guadagni, F, Morsella, V, Marziale, A, Protti, G, Baldassarre D., Iacoviello L., Baetta R., Roncaglioni M. C., Condorelli G., Remuzzi G., Gensini G., Frati L., Ricciardi W., Conaldi P. G., Uccelli A., Blandini F., Bosari S., Scambia G., Fini M., Di Malta A., Amato M., Veglia F., Bonomi A., Klersy C., Colazzo F., Pengo M., Gorini F., Auteri L., Ferrante G., Baviera M., Ambrosio G., Catapano A., Gialluisi A., Malavazos A. E., Castelvecchio S., Corsi-Romanelli M. M., Cardani R., La Rovere M. T., Agnese V., Pane B., Prati D., Spinardi L., Liuzzo G., Arbustini E., Volterrani M., Visconti M., Werba J. P., Genovese S., Bilo G., Invitti C., Di Blasio A., Lombardi C., Faini A., Rosa D., Ojeda-Fernandez L., Foresta A., De Curtis A., Di Castelnuovo A., Scalvini S., Pierobon A., Gorini A., Valenti L., Luzi L., Racca A., Bandi M., Tremoli E., Menicanti L., Parati G., Pompilio G., Colombo G., Vavassori C., Biondi M. L., Frigerio B., Ravani A., Sansaro D., Coggi D., Romandini A., Giroli M., Giuliani M., Bonmi A., Rondinelli M., Trudu C., Cinieri C., Monturano M., Colazo F., Inviti C., Di Blasi A., Torlasco C., Gilardini L., Soranna D., Zambon A., Perger E., Zanotti L., Badano L., Cova L., Gentilini D., Grappiolo L., Condoreli G., Ferante G., Papa L., Savevski V., Ieva F., Romano I., Remzzi G., Ojeda L., Clerici F., Palumbo A., Genini G. F., Catpano A., Mattioli R., Longhi E., Mantovani L. G., Madotto F., Bonaccio M., Gianfagna F., Ghulam A., Magnacca S., Noro F., Costanzo S., Esposito S., Orlandi S., Persichillo M., Bracone F., Panzera T., Ruggiero E., Parisi R., Franciosa S., Morelli M., De Rita F., Cerletti C., de Gaetano G., Donati M. B., Mencanti L., Romanelli M. M. C., Cerri A., Dubini C., Trevisan M. B., Renna L. V., Milani V., Boveri S., Giubbilini P., Ramputi L., Baroni I., De Angeli G., Riciardi W., Olmetti F., Bussotti M., Gaetano C., Baiardi P., Bachetti T., Balbi M., Comini L., Lorenzoni M., Olivares A., Traversi E., Garre C., Sideri R., Clemenza F., Gentile G., Caruana G., Cuscino N., Di Gesaro G., Greco A., Loddo I., Tuzzolino F., Ucelli A., Palombo D., Spinella G., Mozzetta G., Ameri P., Zoppoli G., Finotello A., Porto I., Pratesi G., Bladini F., Spnardi L., Clerici M., Pelusi S., Bianco C., Carpani R., Periti G., Margarita S., Lanza G. A., Severino A., Pedicino D., D'Amario D., D'Aiello A., Vinci R., Bonanni A., Brecciaroli M., Filomia S., Pastorino R., Boccia S., Urbani A., Sanguinetti M., Santoliquido A., Proto L., Tarquini D., Grimaldi M. C., Leonardi S., Elia A., Currao A., Urtis M., Di Toro A., Giuliani L., Caminiti G., Marcolongo F., Sposato B., Guadagni F., Morsella V., Marziale A., and Protti G.

Abstract

Introduction Prevention of cardiovascular disease (CVD) is of key importance in reducing morbidity, disability and mortality worldwide. Observational studies suggest that digital health interventions can be an effective strategy to reduce cardiovascular (CV) risk. However, evidence from large randomised clinical trials is lacking. Methods and analysis The CV-PREVITAL study is a multicentre, prospective, randomised, controlled, open-label interventional trial designed to compare the effectiveness of an educational and motivational mobile health (mHealth) intervention versus usual care in reducing CV risk. The intervention aims at improving diet, physical activity, sleep quality, psycho-behavioural aspects, as well as promoting smoking cessation and adherence to pharmacological treatment for CV risk factors. The trial aims to enrol approximately 80 000 subjects without overt CVDs referring to general practitioners' offices, community pharmacies or clinics of Scientific Institute for Research, Hospitalization and Health Care (Italian acronym IRCCS) affiliated with the Italian Cardiology Network. All participants are evaluated at baseline and after 12 months to assess the effectiveness of the intervention on short-term endpoints, namely improvement in CV risk score and reduction of major CV risk factors. Beyond the funded life of the study, a long-term (7 years) follow-up is also planned to assess the effectiveness of the intervention on the incidence of major adverse CV events. A series of ancillary studies designed to evaluate the effect of the mHealth intervention on additional risk biomarkers are also performed. Ethics and dissemination This study received ethics approval from the ethics committee of the coordinating centre (Monzino Cardiology Center; R1256/20-CCM 1319) and from all other relevant IRBs and ethics committees. Findings are disseminated through scientific meetings and peer-reviewed journals and via social media. Partners are informed about the study's

Published
2023

2. Synthetic Data Generation by Artificial Intelligence to Accelerate Research and Precision Medicine in Hematology

Author

D'Amico, S, Dall'Olio, D, Sala, C, Dall'Olio, L, Sauta, E, Zampini, M, Asti, G, Lanino, L, Maggioni, G, Campagna, A, Ubezio, M, Russo, A, Bicchieri, M, Riva, E, Tentori, C, Travaglino, E, Morandini, P, Savevski, V, Santoro, A, Prada-Luengo, I, Krogh, A, Santini, V, Kordasti, S, Platzbecker, U, Diez-Campelo, M, Fenaux, P, Haferlach, T, Castellani, G, Della Porta, M, D'Amico S., Dall'Olio D., Sala C., Dall'Olio L., Sauta E., Zampini M., Asti G., Lanino L., Maggioni G., Campagna A., Ubezio M., Russo A., Bicchieri M. E., Riva E., Tentori C. A., Travaglino E., Morandini P., Savevski V., Santoro A., Prada-Luengo I., Krogh A., Santini V., Kordasti S., Platzbecker U., Diez-Campelo M., Fenaux P., Haferlach T., Castellani G., Della Porta M. G., D'Amico, S, Dall'Olio, D, Sala, C, Dall'Olio, L, Sauta, E, Zampini, M, Asti, G, Lanino, L, Maggioni, G, Campagna, A, Ubezio, M, Russo, A, Bicchieri, M, Riva, E, Tentori, C, Travaglino, E, Morandini, P, Savevski, V, Santoro, A, Prada-Luengo, I, Krogh, A, Santini, V, Kordasti, S, Platzbecker, U, Diez-Campelo, M, Fenaux, P, Haferlach, T, Castellani, G, Della Porta, M, D'Amico S., Dall'Olio D., Sala C., Dall'Olio L., Sauta E., Zampini M., Asti G., Lanino L., Maggioni G., Campagna A., Ubezio M., Russo A., Bicchieri M. E., Riva E., Tentori C. A., Travaglino E., Morandini P., Savevski V., Santoro A., Prada-Luengo I., Krogh A., Santini V., Kordasti S., Platzbecker U., Diez-Campelo M., Fenaux P., Haferlach T., Castellani G., and Della Porta M. G.

Abstract

PURPOSE: Synthetic data are artificial data generated without including any real patient information by an algorithm trained to learn the characteristics of a real source data set and became widely used to accelerate research in life sciences. We aimed to (1) apply generative artificial intelligence to build synthetic data in different hematologic neoplasms; (2) develop a synthetic validation framework to assess data fidelity and privacy preservability; and (3) test the capability of synthetic data to accelerate clinical/translational research in hematology. METHODS: A conditional generative adversarial network architecture was implemented to generate synthetic data. Use cases were myelodysplastic syndromes (MDS) and AML: 7,133 patients were included. A fully explainable validation framework was created to assess fidelity and privacy preservability of synthetic data. RESULTS: We generated MDS/AML synthetic cohorts (including information on clinical features, genomics, treatment, and outcomes) with high fidelity and privacy performances. This technology allowed resolution of lack/incomplete information and data augmentation. We then assessed the potential value of synthetic data on accelerating research in hematology. Starting from 944 patients with MDS available since 2014, we generated a 300% augmented synthetic cohort and anticipated the development of molecular classification and molecular scoring system obtained many years later from 2,043 to 2,957 real patients, respectively. Moreover, starting from 187 MDS treated with luspatercept into a clinical trial, we generated a synthetic cohort that recapitulated all the clinical end points of the study. Finally, we developed a website to enable clinicians generating high-quality synthetic data from an existing biobank of real patients. CONCLUSION: Synthetic data mimic real clinical-genomic features and outcomes, and anonymize patient information. The implementation of this technology allows to increase the scientific

Published
2023

3. Real-World Validation of Molecular International Prognostic Scoring System for Myelodysplastic Syndromes

Author

Sauta, E, Robin, M, Bersanelli, M, Travaglino, E, Meggendorfer, M, Zhao, L, Caballero Berrocal, J, Sala, C, Maggioni, G, Bernardi, M, Di Grazia, C, Vago, L, Rivoli, G, Borin, L, D'Amico, S, Tentori, C, Ubezio, M, Campagna, A, Russo, A, Mannina, D, Lanino, L, Chiusolo, P, Giaccone, L, Voso, M, Riva, M, Oliva, E, Zampini, M, Riva, E, Nibourel, O, Bicchieri, M, Bolli, N, Rambaldi, A, Passamonti, F, Savevski, V, Santoro, A, Germing, U, Kordasti, S, Santini, V, Diez-Campelo, M, Sanz, G, Sole, F, Kern, W, Platzbecker, U, Ades, L, Fenaux, P, Haferlach, T, Castellani, G, Della Porta, M, Sauta, Elisabetta, Robin, Marie, Bersanelli, Matteo, Travaglino, Erica, Meggendorfer, Manja, Zhao, Lin-Pierre, Caballero Berrocal, Juan Carlos, Sala, Claudia, Maggioni, Giulia, Bernardi, Massimo, Di Grazia, Carmen, Vago, Luca, Rivoli, Giulia, Borin, Lorenza, D'Amico, Saverio, Tentori, Cristina Astrid, Ubezio, Marta, Campagna, Alessia, Russo, Antonio, Mannina, Daniele, Lanino, Luca, Chiusolo, Patrizia, Giaccone, Luisa, Voso, Maria Teresa, Riva, Marta, Oliva, Esther Natalie, Zampini, Matteo, Riva, Elena, Nibourel, Olivier, Bicchieri, Marilena, Bolli, Niccolo', Rambaldi, Alessandro, Passamonti, Francesco, Savevski, Victor, Santoro, Armando, Germing, Ulrich, Kordasti, Shahram, Santini, Valeria, Diez-Campelo, Maria, Sanz, Guillermo, Sole, Francesc, Kern, Wolfgang, Platzbecker, Uwe, Ades, Lionel, Fenaux, Pierre, Haferlach, Torsten, Castellani, Gastone, Della Porta, Matteo Giovanni, Sauta, E, Robin, M, Bersanelli, M, Travaglino, E, Meggendorfer, M, Zhao, L, Caballero Berrocal, J, Sala, C, Maggioni, G, Bernardi, M, Di Grazia, C, Vago, L, Rivoli, G, Borin, L, D'Amico, S, Tentori, C, Ubezio, M, Campagna, A, Russo, A, Mannina, D, Lanino, L, Chiusolo, P, Giaccone, L, Voso, M, Riva, M, Oliva, E, Zampini, M, Riva, E, Nibourel, O, Bicchieri, M, Bolli, N, Rambaldi, A, Passamonti, F, Savevski, V, Santoro, A, Germing, U, Kordasti, S, Santini, V, Diez-Campelo, M, Sanz, G, Sole, F, Kern, W, Platzbecker, U, Ades, L, Fenaux, P, Haferlach, T, Castellani, G, Della Porta, M, Sauta, Elisabetta, Robin, Marie, Bersanelli, Matteo, Travaglino, Erica, Meggendorfer, Manja, Zhao, Lin-Pierre, Caballero Berrocal, Juan Carlos, Sala, Claudia, Maggioni, Giulia, Bernardi, Massimo, Di Grazia, Carmen, Vago, Luca, Rivoli, Giulia, Borin, Lorenza, D'Amico, Saverio, Tentori, Cristina Astrid, Ubezio, Marta, Campagna, Alessia, Russo, Antonio, Mannina, Daniele, Lanino, Luca, Chiusolo, Patrizia, Giaccone, Luisa, Voso, Maria Teresa, Riva, Marta, Oliva, Esther Natalie, Zampini, Matteo, Riva, Elena, Nibourel, Olivier, Bicchieri, Marilena, Bolli, Niccolo', Rambaldi, Alessandro, Passamonti, Francesco, Savevski, Victor, Santoro, Armando, Germing, Ulrich, Kordasti, Shahram, Santini, Valeria, Diez-Campelo, Maria, Sanz, Guillermo, Sole, Francesc, Kern, Wolfgang, Platzbecker, Uwe, Ades, Lionel, Fenaux, Pierre, Haferlach, Torsten, Castellani, Gastone, and Della Porta, Matteo Giovanni

Abstract

Purpose: Myelodysplastic syndromes (MDS) are heterogeneous myeloid neoplasms in which a risk-adapted treatment strategy is needed. Recently, a new clinical-molecular prognostic model, the Molecular International Prognostic Scoring System (IPSS-M) was proposed to improve the prediction of clinical outcome of the currently available tool (Revised International Prognostic Scoring System [IPSS-R]). We aimed to provide an extensive validation of IPSS-M. Methods: A total of 2,876 patients with primary MDS from the GenoMed4All consortium were retrospectively analyzed. Results: IPSS-M improved prognostic discrimination across all clinical end points with respect to IPSS-R (concordance was 0.81 v 0.74 for overall survival and 0.89 v 0.76 for leukemia-free survival, respectively). This was true even in those patients without detectable gene mutations. Compared with the IPSS-R based stratification, the IPSS-M risk group changed in 46% of patients (23.6% and 22.4% of subjects were upstaged and downstaged, respectively).In patients treated with hematopoietic stem cell transplantation (HSCT), IPSS-M significantly improved the prediction of the risk of disease relapse and the probability of post-transplantation survival versus IPSS-R (concordance was 0.76 v 0.60 for overall survival and 0.89 v 0.70 for probability of relapse, respectively). In high-risk patients treated with hypomethylating agents (HMA), IPSS-M failed to stratify individual probability of response; response duration and probability of survival were inversely related to IPSS-M risk.Finally, we tested the accuracy in predicting IPSS-M when molecular information was missed and we defined a minimum set of 15 relevant genes associated with high performance of the score. Conclusion: IPSS-M improves MDS prognostication and might result in a more effective selection of candidates to HSCT. Additional factors other than gene mutations can be involved in determining HMA sensitivity. The definition of a minimum set of relevan

Published
2023

4. A sex-informed approach to improve the personalised decision making process in myelodysplastic syndromes: a multicentre, observational cohort study

Author

Maggioni, G, Bersanelli, M, Travaglino, E, Alfonso Piérola, A, Kasprzak, A, Sangerman Montserrat, A, Sauta, E, Sala, C, Matteuzzi, T, Meggendorfer, M, Gnocchi, M, Zhao, L, Astrid Tentori, C, Nachtkamp, K, Dall'Olio, D, Mosca, E, Ubezio, M, Campagna, A, Russo, A, Rivoli, G, Bernardi, M, Borin, L, Teresa Voso, M, Riva, M, Oliva, E, Zampini, M, Riva, E, Saba, E, D'Amico, S, Lanino, L, Tinterri, B, Re, F, Bicchieri, M, Giordano, L, Angelotti, G, Morandini, P, Sophie Kubasch, A, Passamonti, F, Rambaldi, A, Savevski, V, Santoro, A, A van de Loosdrecht, A, Brogi, A, Santini, V, Kordasti, S, Sanz, G, Sole, F, Gattermann, N, Kern, W, Platzbecker, U, Ades, L, Fenaux, P, Haferlach, T, Castellani, G, Germing, U, Diez-Campelo, M, G Della Porta, M, Giulia Maggioni, Matteo Bersanelli, Erica Travaglino, Ana Alfonso Piérola, Annika Kasprzak, Arnan Sangerman Montserrat, Elisabetta Sauta, Claudia Sala, Tommaso Matteuzzi, Manja Meggendorfer, Matteo Gnocchi, Lin-Pierre Zhao, Cristina Astrid Tentori, Kathrin Nachtkamp, Daniele Dall'Olio, Ettore Mosca, Marta Ubezio, Alessia Campagna, Antonio Russo, Giulia Rivoli, Massimo Bernardi, Lorenza Borin, Maria Teresa Voso, Marta Riva, Esther Oliva, Matteo Zampini, Elena Riva, Elena Saba, Saverio D'Amico, Luca Lanino, Benedetta Tinterri, Francesca Re, Marilena Bicchieri, Laura Giordano, Giovanni Angelotti, Pierandrea Morandini, Anne Sophie Kubasch, Francesco Passamonti, Alessandro Rambaldi, Victor Savevski, Armando Santoro, Arjan A van de Loosdrecht, Alice Brogi, Valeria Santini, Shahram Kordasti, Guillermo Sanz, Francesc Sole, Norbert Gattermann, Wolfgang Kern, Uwe Platzbecker, Lionel Ades, Pierre Fenaux, Torsten Haferlach, Gastone Castellani, Ulrich Germing, Maria Diez-Campelo, Matteo G Della Porta, Maggioni, G, Bersanelli, M, Travaglino, E, Alfonso Piérola, A, Kasprzak, A, Sangerman Montserrat, A, Sauta, E, Sala, C, Matteuzzi, T, Meggendorfer, M, Gnocchi, M, Zhao, L, Astrid Tentori, C, Nachtkamp, K, Dall'Olio, D, Mosca, E, Ubezio, M, Campagna, A, Russo, A, Rivoli, G, Bernardi, M, Borin, L, Teresa Voso, M, Riva, M, Oliva, E, Zampini, M, Riva, E, Saba, E, D'Amico, S, Lanino, L, Tinterri, B, Re, F, Bicchieri, M, Giordano, L, Angelotti, G, Morandini, P, Sophie Kubasch, A, Passamonti, F, Rambaldi, A, Savevski, V, Santoro, A, A van de Loosdrecht, A, Brogi, A, Santini, V, Kordasti, S, Sanz, G, Sole, F, Gattermann, N, Kern, W, Platzbecker, U, Ades, L, Fenaux, P, Haferlach, T, Castellani, G, Germing, U, Diez-Campelo, M, G Della Porta, M, Giulia Maggioni, Matteo Bersanelli, Erica Travaglino, Ana Alfonso Piérola, Annika Kasprzak, Arnan Sangerman Montserrat, Elisabetta Sauta, Claudia Sala, Tommaso Matteuzzi, Manja Meggendorfer, Matteo Gnocchi, Lin-Pierre Zhao, Cristina Astrid Tentori, Kathrin Nachtkamp, Daniele Dall'Olio, Ettore Mosca, Marta Ubezio, Alessia Campagna, Antonio Russo, Giulia Rivoli, Massimo Bernardi, Lorenza Borin, Maria Teresa Voso, Marta Riva, Esther Oliva, Matteo Zampini, Elena Riva, Elena Saba, Saverio D'Amico, Luca Lanino, Benedetta Tinterri, Francesca Re, Marilena Bicchieri, Laura Giordano, Giovanni Angelotti, Pierandrea Morandini, Anne Sophie Kubasch, Francesco Passamonti, Alessandro Rambaldi, Victor Savevski, Armando Santoro, Arjan A van de Loosdrecht, Alice Brogi, Valeria Santini, Shahram Kordasti, Guillermo Sanz, Francesc Sole, Norbert Gattermann, Wolfgang Kern, Uwe Platzbecker, Lionel Ades, Pierre Fenaux, Torsten Haferlach, Gastone Castellani, Ulrich Germing, Maria Diez-Campelo, and Matteo G Della Porta

Abstract

BACKGROUND: Sex is a major source of diversity among patients and a sex-informed approach is becoming a new paradigm in precision medicine. We aimed to describe sex diversity in myelodysplastic syndromes in terms of disease genotype, phenotype, and clinical outcome. Moreover, we sought to incorporate sex information into the clinical decision-making process as a fundamental component of patient individuality. METHODS: In this multicentre, observational cohort study, we retrospectively analysed 13 284 patients aged 18 years or older with a diagnosis of myelodysplastic syndrome according to 2016 WHO criteria included in the EuroMDS network (n=2025), International Working Group for Prognosis in MDS (IWG-PM; n=2387), the Spanish Group of Myelodysplastic Syndromes registry (GESMD; n=7687), or the Düsseldorf MDS registry (n=1185). Recruitment periods for these cohorts were between 1990 and 2016. The correlation between sex and genomic features was analysed in the EuroMDS cohort and validated in the IWG-PM cohort. The effect of sex on clinical outcome, with overall survival as the main endpoint, was analysed in the EuroMDS population and validated in the other three cohorts. Finally, novel prognostic models incorporating sex and genomic information were built and validated, and compared to the widely used revised International Prognostic Scoring System (IPSS-R). This study is registered with ClinicalTrials.gov, NCT04889729. FINDINGS: The study included 7792 (58·7%) men and 5492 (41·3%) women. 10 906 (82·1%) patients were White, and race was not reported for 2378 (17·9%) patients. Sex biases were observed at the single-gene level with mutations in seven genes enriched in men (ASXL1, SRSF2, and ZRSR2 p<0·0001 in both cohorts; DDX41 not available in the EuroMDS cohort vs p=0·0062 in the IWG-PM cohort; IDH2 p<0·0001 in EuroMDS vs p=0·042 in IWG-PM; TET2 p=0·031 vs p=0·035; U2AF1 p=0·033 vs p<0·0001) and mutations in two genes were enriched in women (DNMT3A p<0·000

Published
2023

5. Real-World Validation of Molecular International Prognostic Scoring System for Myelodysplastic Syndromes

Author

Sauta, E., Robin, M., Bersanelli, M., Travaglino, E., Meggendorfer, M., Zhao, L. -P., Caballero Berrocal, J. C., Sala, C., Maggioni, G., Bernardi, M., Di Grazia, C., Vago, L., Rivoli, G., Borin, L., D'Amico, S., Tentori, C. A., Ubezio, M., Campagna, A., Russo, A., Mannina, D., Lanino, L., Chiusolo, Patrizia, Giaccone, L., Voso, Maria Teresa, Riva, M., Oliva, E. N., Zampini, M., Riva, E., Nibourel, O., Bicchieri, M., Bolli, N., Rambaldi, A., Passamonti, F., Savevski, V., Santoro, A., Germing, U., Kordasti, S., Santini, V., Diez-Campelo, M., Sanz, G., Sole, F., Kern, W., Platzbecker, U., Ades, L., Fenaux, P., Haferlach, T., Castellani, G., Della Porta, M. G., Chiusolo P. (ORCID:0000-0002-1355-1587), Voso M. T., Sauta, E., Robin, M., Bersanelli, M., Travaglino, E., Meggendorfer, M., Zhao, L. -P., Caballero Berrocal, J. C., Sala, C., Maggioni, G., Bernardi, M., Di Grazia, C., Vago, L., Rivoli, G., Borin, L., D'Amico, S., Tentori, C. A., Ubezio, M., Campagna, A., Russo, A., Mannina, D., Lanino, L., Chiusolo, Patrizia, Giaccone, L., Voso, Maria Teresa, Riva, M., Oliva, E. N., Zampini, M., Riva, E., Nibourel, O., Bicchieri, M., Bolli, N., Rambaldi, A., Passamonti, F., Savevski, V., Santoro, A., Germing, U., Kordasti, S., Santini, V., Diez-Campelo, M., Sanz, G., Sole, F., Kern, W., Platzbecker, U., Ades, L., Fenaux, P., Haferlach, T., Castellani, G., Della Porta, M. G., Chiusolo P. (ORCID:0000-0002-1355-1587), and Voso M. T.

Abstract

PURPOSEMyelodysplastic syndromes (MDS) are heterogeneous myeloid neoplasms in which a risk-adapted treatment strategy is needed. Recently, a new clinical-molecular prognostic model, the Molecular International Prognostic Scoring System (IPSS-M) was proposed to improve the prediction of clinical outcome of the currently available tool (Revised International Prognostic Scoring System [IPSS-R]). We aimed to provide an extensive validation of IPSS-M.METHODSA total of 2,876 patients with primary MDS from the GenoMed4All consortium were retrospectively analyzed.RESULTSIPSS-M improved prognostic discrimination across all clinical end points with respect to IPSS-R (concordance was 0.81 v 0.74 for overall survival and 0.89 v 0.76 for leukemia-free survival, respectively). This was true even in those patients without detectable gene mutations. Compared with the IPSS-R based stratification, the IPSS-M risk group changed in 46% of patients (23.6% and 22.4% of subjects were upstaged and downstaged, respectively).In patients treated with hematopoietic stem cell transplantation (HSCT), IPSS-M significantly improved the prediction of the risk of disease relapse and the probability of post-transplantation survival versus IPSS-R (concordance was 0.76 v 0.60 for overall survival and 0.89 v 0.70 for probability of relapse, respectively). In high-risk patients treated with hypomethylating agents (HMA), IPSS-M failed to stratify individual probability of response; response duration and probability of survival were inversely related to IPSS-M risk.Finally, we tested the accuracy in predicting IPSS-M when molecular information was missed and we defined a minimum set of 15 relevant genes associated with high performance of the score.CONCLUSIONIPSS-M improves MDS prognostication and might result in a more effective selection of candidates to HSCT. Additional factors other than gene mutations can be involved in determining HMA sensitivity. The definition of a minimum set of relevant genes may

Published
2023

6. Artificial intelligence and colonoscopy experience: Lessons from two randomised trials

Author

Repici, A., Spadaccini, M., Antonelli, G., Correale, L., Maselli, R., Galtieri, P. A., Pellegatta, G., Capogreco, A., Milluzzo, S. M., Lollo, G., Di Paolo, D., Badalamenti, M., Ferrara, E., Fugazza, A., Carrara, S., Anderloni, A., Rondonotti, E., Amato, A., De Gottardi, A., Spada, Cristiano, Radaelli, F., Savevski, V., Wallace, M. B., Sharma, P., Rosch, T., Hassan, Cesare, Spada C. (ORCID:0000-0002-5692-0960), Hassan C., Repici, A., Spadaccini, M., Antonelli, G., Correale, L., Maselli, R., Galtieri, P. A., Pellegatta, G., Capogreco, A., Milluzzo, S. M., Lollo, G., Di Paolo, D., Badalamenti, M., Ferrara, E., Fugazza, A., Carrara, S., Anderloni, A., Rondonotti, E., Amato, A., De Gottardi, A., Spada, Cristiano, Radaelli, F., Savevski, V., Wallace, M. B., Sharma, P., Rosch, T., Hassan, Cesare, Spada C. (ORCID:0000-0002-5692-0960), and Hassan C.

Abstract

Background and aims Artificial intelligence has been shown to increase adenoma detection rate (ADR) as the main surrogate outcome parameter of colonoscopy quality. To which extent this effect may be related to physician experience is not known. We performed a randomised trial with colonoscopists in their qualification period (AID-2) and compared these data with a previously published randomised trial in expert endoscopists (AID-1). Methods In this prospective, randomised controlled non-inferiority trial (AID-2), 10 non-expert endoscopists (<2000 colonoscopies) performed screening/surveillance/diagnostic colonoscopies in consecutive 40-80 year-old subjects using high-definition colonoscopy with or without a real-time deep-learning computer-aided detection (CADe) (GI Genius, Medtronic). The primary outcome was ADR in both groups with histology of resected lesions as reference. In a post-hoc analysis, data from this randomised controlled trial (RCT) were compared with data from the previous AID-1 RCT involving six experienced endoscopists in an otherwise similar setting. Results In 660 patients (62.3±10 years; men/women: 330/330) with equal distribution of study parameters, overall ADR was higher in the CADe than in the control group (53.3% vs 44.5%; relative risk (RR): 1.22; 95% CI: 1.04 to 1.40; p<0.01 for non-inferiority and p=0.02 for superiority). Similar increases were seen in adenoma numbers per colonoscopy and in small and distal lesions. No differences were observed with regards to detection of non-neoplastic lesions. When pooling these data with those from the AID-1 study, use of CADe (RR 1.29; 95% CI: 1.16 to 1.42) and colonoscopy indication, but not the level of examiner experience (RR 1.02; 95% CI: 0.89 to 1.16) were associated with ADR differences in a multivariate analysis. Conclusions In less experienced examiners, CADe assistance during colonoscopy increased ADR and a number of related polyp parameters as compared with the control group. Experien

Published
2022

7. A sex-informed approach to improve the personalised decision making process in myelodysplastic syndromes

Author

Giulia Maggioni, Matteo Bersanelli, Erica Travaglino, Ana Alfonso Piérola, Annika Kasprzak, Arnan Sangerman Montserrat, Elisabetta Sauta, Claudia Sala, Tommaso Matteuzzi, Manja Meggendorfer, Matteo Gnocchi, Lin-Pierre Zhao, Cristina Astrid Tentori, Kathrin Nachtkamp, Daniele Dall'Olio, Ettore Mosca, Marta Ubezio, Alessia Campagna, Antonio Russo, Giulia Rivoli, Massimo Bernardi, Lorenza Borin, Maria Teresa Voso, Marta Riva, Esther Oliva, Matteo Zampini, Elena Riva, Elena Saba, Saverio D'Amico, Luca Lanino, Benedetta Tinterri, Francesca Re, Marilena Bicchieri, Laura Giordano, Giovanni Angelotti, Pierandrea Morandini, Anne Sophie Kubasch, Francesco Passamonti, Alessandro Rambaldi, Victor Savevski, Armando Santoro, Arjan A. van de Loosdrecht, Alice Brogi, Valeria Santini, Shahram Kordasti, Guillermo Sanz, Francesc Sole, Norbert Gattermann, Wolfgang Kern, Uwe Platzbecker, Lionel Ades, Pierre Fenaux, Torsten Haferlach, Gastone Castellani, Ulrich Germing, Maria Diez-Campelo, Matteo G. Della Porta, Hematology, AII - Cancer immunology, AII - Inflammatory diseases, CCA - Cancer biology and immunology, Maggioni, G, Bersanelli, M, Travaglino, E, Alfonso Piérola, A, Kasprzak, A, Sangerman Montserrat, A, Sauta, E, Sala, C, Matteuzzi, T, Meggendorfer, M, Gnocchi, M, Zhao, L, Astrid Tentori, C, Nachtkamp, K, Dall'Olio, D, Mosca, E, Ubezio, M, Campagna, A, Russo, A, Rivoli, G, Bernardi, M, Borin, L, Teresa Voso, M, Riva, M, Oliva, E, Zampini, M, Riva, E, Saba, E, D'Amico, S, Lanino, L, Tinterri, B, Re, F, Bicchieri, M, Giordano, L, Angelotti, G, Morandini, P, Sophie Kubasch, A, Passamonti, F, Rambaldi, A, Savevski, V, Santoro, A, A van de Loosdrecht, A, Brogi, A, Santini, V, Kordasti, S, Sanz, G, Sole, F, Gattermann, N, Kern, W, Platzbecker, U, Ades, L, Fenaux, P, Haferlach, T, Castellani, G, Germing, U, Diez-Campelo, M, and G Della Porta, M

Subjects
Malalties hematològiques, Hematologic diseases, Myelodysplastic Syndrome, Factors sexuals en les malalties, Sex factors in disease, sex, Hematology, personalized medicine, Settore MED/15
Abstract

Background Sex is a major source of diversity among patients and a sex-informed approach is becoming a new paradigm in precision medicine. We aimed to describe sex diversity in myelodysplastic syndromes in terms of disease genotype, phenotype, and clinical outcome. Moreover, we sought to incorporate sex information into the clinical decision-making process as a fundamental component of patient individuality. Methods In this multicentre, observational cohort study, we retrospectively analysed 13 284 patients aged 18 years or older with a diagnosis of myelodysplastic syndrome according to 2016 WHO criteria included in the EuroMDS network (n=2025), International Working Group for Prognosis in MDS (IWG-PM; n=2387), the Spanish Group of Myelodysplastic Syndromes registry (GESMD; n=7687), or the Dusseldorf MDS registry (n=1185). Recruitment periods for these cohorts were between 1990 and 2016. The correlation between sex and genomic features was analysed in the EuroMDS cohort and validated in the IWG-PM cohort. The effect of sex on clinical outcome, with overall survival as the main endpoint, was analysed in the EuroMDS population and validated in the other three cohorts. Finally, novel prognostic models incorporating sex and genomic information were built and validated, and compared to the widely used revised International Prognostic Scoring System (IPSS-R). This study is registered with ClinicalTrials.gov, NCT04889729. Findings The study included 7792 (58middot7%) men and 5492 (41middot3%) women. 10 906 (82middot1%) patients were White, and race was not reported for 2378 (17middot9%) patients. Sex biases were observed at the single-gene level with mutations in seven genes enriched in men (ASXL1, SRSF2, and ZRSR2 p

Published
2023
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8. Real-World Validation of Molecular International Prognostic Scoring System for Myelodysplastic Syndromes

Author

Elisabetta Sauta, Marie Robin, Matteo Bersanelli, Erica Travaglino, Manja Meggendorfer, Lin-Pierre Zhao, Juan Carlos Caballero Berrocal, Claudia Sala, Giulia Maggioni, Massimo Bernardi, Carmen Di Grazia, Luca Vago, Giulia Rivoli, Lorenza Borin, Saverio D'Amico, Cristina Astrid Tentori, Marta Ubezio, Alessia Campagna, Antonio Russo, Daniele Mannina, Luca Lanino, Patrizia Chiusolo, Luisa Giaccone, Maria Teresa Voso, Marta Riva, Esther Natalie Oliva, Matteo Zampini, Elena Riva, Olivier Nibourel, Marilena Bicchieri, Niccolo’ Bolli, Alessandro Rambaldi, Francesco Passamonti, Victor Savevski, Armando Santoro, Ulrich Germing, Shahram Kordasti, Valeria Santini, Maria Diez-Campelo, Guillermo Sanz, Francesc Sole, Wolfgang Kern, Uwe Platzbecker, Lionel Ades, Pierre Fenaux, Torsten Haferlach, Gastone Castellani, Matteo Giovanni Della Porta, Sauta, E, Robin, M, Bersanelli, M, Travaglino, E, Meggendorfer, M, Zhao, L, Caballero Berrocal, J, Sala, C, Maggioni, G, Bernardi, M, Di Grazia, C, Vago, L, Rivoli, G, Borin, L, D'Amico, S, Tentori, C, Ubezio, M, Campagna, A, Russo, A, Mannina, D, Lanino, L, Chiusolo, P, Giaccone, L, Voso, M, Riva, M, Oliva, E, Zampini, M, Riva, E, Nibourel, O, Bicchieri, M, Bolli, N, Rambaldi, A, Passamonti, F, Savevski, V, Santoro, A, Germing, U, Kordasti, S, Santini, V, Diez-Campelo, M, Sanz, G, Sole, F, Kern, W, Platzbecker, U, Ades, L, Fenaux, P, Haferlach, T, Castellani, G, and Della Porta, M

Subjects
Cancer Research, Oncology, Myelodysplastic Syndrome, MDS, IPSS-M, Hematology, Settore MED/15
Abstract

PURPOSE Myelodysplastic syndromes (MDS) are heterogeneous myeloid neoplasms in which a risk-adapted treatment strategy is needed. Recently, a new clinical-molecular prognostic model, the Molecular International Prognostic Scoring System (IPSS-M) was proposed to improve the prediction of clinical outcome of the currently available tool (Revised International Prognostic Scoring System [IPSS-R]). We aimed to provide an extensive validation of IPSS-M. METHODS A total of 2,876 patients with primary MDS from the GenoMed4All consortium were retrospectively analyzed. RESULTS IPSS-M improved prognostic discrimination across all clinical end points with respect to IPSS-R (concordance was 0.81 v 0.74 for overall survival and 0.89 v 0.76 for leukemia-free survival, respectively). This was true even in those patients without detectable gene mutations. Compared with the IPSS-R based stratification, the IPSS-M risk group changed in 46% of patients (23.6% and 22.4% of subjects were upstaged and downstaged, respectively). In patients treated with hematopoietic stem cell transplantation (HSCT), IPSS-M significantly improved the prediction of the risk of disease relapse and the probability of post-transplantation survival versus IPSS-R (concordance was 0.76 v 0.60 for overall survival and 0.89 v 0.70 for probability of relapse, respectively). In high-risk patients treated with hypomethylating agents (HMA), IPSS-M failed to stratify individual probability of response; response duration and probability of survival were inversely related to IPSS-M risk. Finally, we tested the accuracy in predicting IPSS-M when molecular information was missed and we defined a minimum set of 15 relevant genes associated with high performance of the score. CONCLUSION IPSS-M improves MDS prognostication and might result in a more effective selection of candidates to HSCT. Additional factors other than gene mutations can be involved in determining HMA sensitivity. The definition of a minimum set of relevant genes may facilitate the clinical implementation of the score.

Published
2023

9. Clinical Outcomes in the Second versus First Pandemic Wave in Italy: Impact of Hospital Changes and Reorganization

Author

Daniele Piovani, Antonio Voza, Stefanos Bonovas, Mauro Giordano, Victor Savevski, Maria Kogan, Alessio Aghemo, Antonio Desai, Giovanni Angelotti, Alice Giotta Lucifero, Claudio Angelini, Giulia Goretti, Elena Costantini, Ana Lleo, Massimiliano Greco, Elena Azzolini, Maurizio Cecconi, Sabino Luzzi, Salvatore Badalamenti, Voza, A., Desai, A., Luzzi, S., Lucifero, A. G., Azzolini, E., Kogan, M., Goretti, G., Piovani, D., Bonovas, S., Angelotti, G., Savevski, V., Aghemo, A., Greco, M., Costantini, E., Lleo, A., Angelini, C., Giordano, M., Badalamenti, S., and Cecconi, M.

Subjects
Technology, medicine.medical_specialty, Coronavirus disease 2019 (COVID-19), QH301-705.5, QC1-999, medicine.medical_treatment, SARS‐CoV‐2, law.invention, COVID‐19, law, Oxygen therapy, Pandemic, medicine, General Materials Science, Biology (General), QD1-999, Instrumentation, Management algorithm, Fluid Flow and Transfer Processes, SARS-CoV-2, Emergency department, business.industry, Physics, Process Chemistry and Technology, Mortality rate, General Engineering, COVID-19, Admission rate, Engineering (General). Civil engineering (General), Intensive care unit, Computer Science Applications, Chemistry, Propensity score weighting, Emergency medicine, Lean, TA1-2040, business
Abstract

The region of Lombardy was the epicenter of the COVID-19 outbreak in Italy. Emergency Hospital 19 (EH19) was built in the Milan metropolitan area during the pandemic’s second wave as a facility of Humanitas Clinical and Research Center (HCRC). The present study aimed to assess whether the implementation of EH19 was effective in improving the quality of care of COVID-19 patients during the second wave compared with the first one. The demographics, mortality rate, and in-hospital length of stay (LOS) of two groups of patients were compared: the study group involved patients admitted at HCRC and managed in EH19 during the second pandemic wave, while the control group included patients managed exclusively at HCRC throughout the first wave. The study and control group included 903 (56.7%) and 690 (43.3%) patients, respectively. The study group was six years older on average and had more pre-existing comorbidities. EH19 was associated with a decrease in the intensive care unit admission rate (16.9% vs. 8.75%, p &lt, 0.001), and an equal decrease in invasive oxygen therapy (3.8% vs. 0.23%, p &lt, 0.001). Crude mortality was similar but overlap propensity score weighting revealed a trend toward a potential small decrease. The adjusted difference in LOS was not significant. The implementation of an additional COVID-19 hospital facility was effective in improving the overall quality of care of COVID-19 patients during the first wave of the pandemic when compared with the second. Further studies are necessary to validate the suggested approach.

Published
2021
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11. Machine Learning Prediction Model to Predict Length of Stay of Patients Undergoing Hip or Knee Arthroplasties: Results from a High-Volume Single-Center Multivariate Analysis.

Author

Di Matteo V, Tommasini T, Morandini P, Savevski V, Grappiolo G, and Loppini M

Abstract

Background: The growth of arthroplasty procedures requires innovative strategies to reduce inpatients' hospital length of stay (LOS). This study aims to develop a machine learning prediction model that may aid in predicting LOS after hip or knee arthroplasties. Methods: A collection of all the clinical notes of patients who underwent elective primary or revision arthroplasty from 1 January 2019 to 31 December 2019 was performed. The hospitalization was classified as "short LOS" if it was less than or equal to 6 days and "long LOS" if it was greater than 7 days. Clinical data from pre-operative laboratory analysis, vital parameters, and demographic characteristics of patients were screened. Final data were used to train a logistic regression model with the aim of predicting short or long LOS. Results: The final dataset was composed of 1517 patients (795 "long LOS", 722 "short LOS", p = 0.3196) with a total of 1541 hospital admissions (729 "long LOS", 812 "short LOS", p < 0.001). The complete model had a prediction efficacy of 78.99% (AUC 0.7899). Conclusions: Machine learning may facilitate day-by-day clinical practice determination of which patients are suitable for a shorter LOS and which for a longer LOS, in which a cautious approach could be recommended.

Published
2024
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12. Multimodal deep learning for COVID-19 prognosis prediction in the emergency department: a bi-centric study.

Author

Dipaola F, Gatti M, Giaj Levra A, Menè R, Shiffer D, Faccincani R, Raouf Z, Secchi A, Rovere Querini P, Voza A, Badalamenti S, Solbiati M, Costantino G, Savevski V, and Furlan R

Subjects
Humans, Hospitalization, Prognosis, Emergency Service, Hospital, Retrospective Studies, COVID-19 diagnosis, Deep Learning
Abstract

Predicting clinical deterioration in COVID-19 patients remains a challenging task in the Emergency Department (ED). To address this aim, we developed an artificial neural network using textual (e.g. patient history) and tabular (e.g. laboratory values) data from ED electronic medical reports. The predicted outcomes were 30-day mortality and ICU admission. We included consecutive patients from Humanitas Research Hospital and San Raffaele Hospital in the Milan area between February 20 and May 5, 2020. We included 1296 COVID-19 patients. Textual predictors consisted of patient history, physical exam, and radiological reports. Tabular predictors included age, creatinine, C-reactive protein, hemoglobin, and platelet count. TensorFlow tabular-textual model performance indices were compared to those of models implementing only tabular data. For 30-day mortality, the combined model yielded slightly better performances than the tabular fastai and XGBoost models, with AUC 0.87 ± 0.02, F1 score 0.62 ± 0.10 and an MCC 0.52 ± 0.04 (p < 0.32). As for ICU admission, the combined model MCC was superior (p < 0.024) to the tabular models. Our results suggest that a combined textual and tabular model can effectively predict COVID-19 prognosis which may assist ED physicians in their decision-making process., (© 2023. The Author(s).)

Published
2023
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13. Connecting the use of innovative treatments and glucocorticoids with the multidisciplinary evaluation through rule-based natural-language processing: a real-world study on patients with rheumatoid arthritis, psoriatic arthritis, and psoriasis.

Author

Motta F, Morandini P, Maffia F, Vecellio M, Tonutti A, De Santis M, Costanzo A, Puggioni F, Savevski V, and Selmi C

Abstract

Background: The impact of a multidisciplinary management of rheumatoid arthritis (RA), psoriatic arthritis (PsA), and psoriasis on systemic glucocorticoids or innovative treatments remains unknown. Rule-based natural language processing and text extraction help to manage large datasets of unstructured information and provide insights into the profile of treatment choices., Methods: We obtained structured information from text data of outpatient visits between 2017 and 2022 using regular expressions (RegEx) to define elastic search patterns and to consider only affirmative citation of diseases or prescribed therapy by detecting negations. Care processes were described by binary flags which express the presence of RA, PsA and psoriasis and the prescription of glucocorticoids and biologics or small molecules in each cases. Logistic regression analyses were used to train the classifier to predict outcomes using the number of visits and the other specialist visits as the main variables., Results: We identified 1743 patients with RA, 1359 with PsA and 2,287 with psoriasis, accounting for 5,677, 4,468 and 7,770 outpatient visits, respectively. Among these, 25% of RA, 32% of PsA and 25% of psoriasis cases received biologics or small molecules, while 49% of RA, 28% of PsA, and 40% of psoriasis cases received glucocorticoids. Patients evaluated also by other specialists were treated more frequently with glucocorticoids (70% vs. 49% for RA, 60% vs. 28% for PsA, 51% vs. 40% for psoriasis; p < 0.001) as well as with biologics/small molecules (49% vs. 25% for RA, 64% vs. 32% in PsA; 51% vs. 25% for psoriasis; p < 0.001) compared to cases seen only by the main specialist., Conclusion: Patients with RA, PsA, or psoriasis undergoing multiple evaluations are more likely to receive innovative treatments or glucocorticoids, possibly reflecting more complex cases., Competing Interests: FP received reimbursements for lectures, presentations, speakers bureaus, and manuscript writing or educational events from AstraZeneca, Mundipharma, Menarini, Almirall, Chiesi, Valeas, Malesci Guidotti, Boehringer Ingelheim, Sanofi, GSK, Novartis, Stallergenes-Greer. CS received fees for consulting/speakers (AbbVie, Amgen, Alfa-Sigma, Biogen, Eli-Lilly, Galapagos, Janssen, Novartis, Pfizer, SOBI) and Research support (AbbVie, Amgen, Pfizer). The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Motta, Morandini, Maffia, Vecellio, Tonutti, De Santis, Costanzo, Puggioni, Savevski and Selmi.)

Published
2023
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14. Artificial intelligence in gastroenterology: Where are we heading?

Author

Koleth G, Emmanue J, Spadaccini M, Mascagni P, Khalaf K, Mori Y, Antonelli G, Maselli R, Carrara S, Galtieri PA, Pellegatta G, Fugazza A, Anderloni A, Selvaggio C, Bretthauer M, Aghemo A, Spinelli A, Savevski V, Sharma P, Hassan C, and Repici A

Abstract

Background and study aims  Artificial intelligence (AI) is set to impact several fields within gastroenterology. In gastrointestinal endoscopy, AI-based tools have translated into clinical practice faster than expected. We aimed to evaluate the status of research for AI in gastroenterology while predicting its future applications. Methods  All studies registered on Clinicaltrials.gov up to November 2021 were analyzed. The studies included used AI in gastrointestinal endoscopy, inflammatory bowel disease (IBD), hepatology, and pancreatobiliary diseases. Data regarding the study field, methodology, endpoints, and publication status were retrieved, pooled, and analyzed to observe underlying temporal and geographical trends. Results  Of the 103 study entries retrieved according to our inclusion/exclusion criteria, 76 (74 %) were based on AI application to gastrointestinal endoscopy, mainly for detection and characterization of colorectal neoplasia (52/103, 50 %). Image analysis was also more frequently reported than data analysis for pancreaticobiliary (six of 10 [60 %]), liver diseases (eight of nine [89 %]), and IBD (six of eight [75 %]). Overall, 48 of 103 study entries (47 %) were interventional and 55 (53 %) observational. In 2018, one of eight studies (12.5 %) were interventional, while in 2021, 21 of 34 (61.8 %) were interventional, with an inverse ratio between observational and interventional studies during the study period. The majority of the studies were planned as single-center (74 of 103 [72 %]) and more were in Asia (45 of 103 [44 %]) and Europe (44 of 103 [43 %]). Conclusions  AI implementation in gastroenterology is dominated by computer-aided detection and characterization of colorectal neoplasia. The timeframe for translational research is characterized by a swift conversion of observational into interventional studies., Competing Interests: Competing interets The authors declare that they have no conflict of interest., (The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/).)

Published
2022
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15. Artificial Intelligence Applied to Pancreatic Imaging: A Narrative Review.

Author

Laino ME, Ammirabile A, Lofino L, Mannelli L, Fiz F, Francone M, Chiti A, Saba L, Orlandi MA, and Savevski V

Abstract

The diagnosis, evaluation, and treatment planning of pancreatic pathologies usually require the combined use of different imaging modalities, mainly, computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET). Artificial intelligence (AI) has the potential to transform the clinical practice of medical imaging and has been applied to various radiological techniques for different purposes, such as segmentation, lesion detection, characterization, risk stratification, or prediction of response to treatments. The aim of the present narrative review is to assess the available literature on the role of AI applied to pancreatic imaging. Up to now, the use of computer-aided diagnosis (CAD) and radiomics in pancreatic imaging has proven to be useful for both non-oncological and oncological purposes and represents a promising tool for personalized approaches to patients. Although great developments have occurred in recent years, it is important to address the obstacles that still need to be overcome before these technologies can be implemented into our clinical routine, mainly considering the heterogeneity among studies.

Published
2022
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16. Artificial intelligence processing electronic health records to identify commonalities and comorbidities cluster at Immuno Center Humanitas.

Author

Morandini P, Laino ME, Paoletti G, Carlucci A, Tommasini T, Angelotti G, Pepys J, Canonica GW, Heffler E, Savevski V, and Puggioni F

Abstract

Background: Comorbidities are common in chronic inflammatory conditions, requiring multidisciplinary treatment approach. Understanding the link between a single disease and its comorbidities is important for appropriate treatment and management. We evaluate the ability of an NLP-based process for knowledge discovery to detect information about pathologies, patients' phenotype, doctors' prescriptions and commonalities in electronic medical records, by extracting information from free narrative text written by clinicians during medical visits, resulting in the extraction of valuable information and enriching real world evidence data from a multidisciplinary setting., Methods: We collected clinical notes from the Allergy Department of Humanitas Research Hospital written in the last 3 years and used it to look for diseases that cluster together as comorbidities associated to the main pathology of our patients, and for the extent of prescription of systemic corticosteroids, thus evaluating the ability of NLP-based tools for knowledge discovery to extract structured information from free text., Results: We found that the 3 most frequent comorbidities to appear in our clusters were asthma, rhinitis, and urticaria, and that 991 (of 2057) patients suffered from at least one of these comorbidities. The clusters which co-occur particularly often are oral allergy syndrome and urticaria (131 patients), angioedema and urticaria (105 patients), rhinitis and asthma (227 patients). With regards to systemic corticosteroid prescription volume by our clinicians, we found it was lower when compared to the therapy the patients followed before coming to our attention, with the exception of two diseases: Chronic obstructive pulmonary disease and Angioedema., Conclusions: This analysis seems to be valid and is confirmed by the data from the literature. This means that NLP tools could have significant role in many other research fields of medicine, as it may help identify other important, and possibly previously neglected clusters of patients with comorbidities and commonalities. Another potential benefit of this approach lies in its potential ability to foster a multidisciplinary approach, using the same drugs to treat pathologies normally treated by physicians in different branches of medicine, thus saving resources and improving the pharmacological management of patients., Competing Interests: Francesca Puggioni received reimbursements for lectures, presentations, speakers bureaus, manuscript writing or educational events from AstraZeneca, Mundipharma, Menarini, Almirall, Chiesi, Valeas, Malesci Guidotti, Boehringer Ingelheim, Sanofi, GSK, Novartis, Stallergenes‐Greer; for Consulting fees from Sanofi, Novartis, Stallergenes‐Greer. Giovanni Paoletti received reimbursements for lectures, presentations, speakers bureaus, manuscript writing or educational events from Lusopharma and Novartis. Enrico Heffler received reimbursements for lectures, presentations, speakers bureaus, manuscript writing or educational events from AstraZeneca, Sanofi, GSK, Novartis, Circassia, Nestlè Purina, Stallergenes‐Greer; for Consulting fees from AstraZeneca, Sanofi, GSK, Novartis, Circassia, Nestlè Purina, Stallergenes‐Greer. Giorgio Walter Canonica received reimbursements for lectures, presentations, speakers bureaus, manuscript writing or educational events from AstraZeneca, Sanofi, GSK, Novartis, Chiesi Farmaceutici, Hal Allergy, Menarini, Stallergenes‐Greer; for Consulting fees from AstraZeneca, Sanofi, GSK, Novartis, Chiesi Farmaceutici, Hal Allergy, Menarini, Stallergenes‐Greer; for Participation on a Data Safety Monitoring Board or Advisory Board from AstraZeneca, Sanofi, GSK, Novartis, Chiesi Farmaceutici, Hal Allergy, Menarini, Stallergenes‐Greer. The other authors declare that they have no conflict of interest to disclose regarding the publication of this manuscript., (© 2022 The Authors. Clinical and Translational Allergy published by John Wiley and Sons Ltd on behalf of European Academy of Allergy and Clinical Immunology.)

Published
2022
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17. Generative Adversarial Networks in Brain Imaging: A Narrative Review.

Author

Laino ME, Cancian P, Politi LS, Della Porta MG, Saba L, and Savevski V

Abstract

Artificial intelligence (AI) is expected to have a major effect on radiology as it demonstrated remarkable progress in many clinical tasks, mostly regarding the detection, segmentation, classification, monitoring, and prediction of diseases. Generative Adversarial Networks have been proposed as one of the most exciting applications of deep learning in radiology. GANs are a new approach to deep learning that leverages adversarial learning to tackle a wide array of computer vision challenges. Brain radiology was one of the first fields where GANs found their application. In neuroradiology, indeed, GANs open unexplored scenarios, allowing new processes such as image-to-image and cross-modality synthesis, image reconstruction, image segmentation, image synthesis, data augmentation, disease progression models, and brain decoding. In this narrative review, we will provide an introduction to GANs in brain imaging, discussing the clinical potential of GANs, future clinical applications, as well as pitfalls that radiologists should be aware of.

Published
2022
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18. An individualized algorithm to predict mortality in COVID-19 pneumonia: a machine learning based study.

Author

Laino ME, Generali E, Tommasini T, Angelotti G, Aghemo A, Desai A, Morandini P, Stefanini GG, Lleo A, Voza A, and Savevski V

Abstract

Introduction: Identifying SARS-CoV-2 patients at higher risk of mortality is crucial in the management of a pandemic. Artificial intelligence techniques allow one to analyze large amounts of data to find hidden patterns. We aimed to develop and validate a mortality score at admission for COVID-19 based on high-level machine learning., Material and Methods: We conducted a retrospective cohort study on hospitalized adult COVID-19 patients between March and December 2020. The primary outcome was in-hospital mortality. A machine learning approach based on vital parameters, laboratory values and demographic features was applied to develop different models. Then, a feature importance analysis was performed to reduce the number of variables included in the model, to develop a risk score with good overall performance, that was finally evaluated in terms of discrimination and calibration capabilities. All results underwent cross-validation., Results: 1,135 consecutive patients (median age 70 years, 64% male) were enrolled, 48 patients were excluded, and the cohort was randomly divided into training (760) and test (327) groups. During hospitalization, 251 (22%) patients died. After feature selection, the best performing classifier was random forest (AUC 0.88 ±0.03). Based on the relative importance of each variable, a pragmatic score was developed, showing good performances (AUC 0.85 ±0.025), and three levels were defined that correlated well with in-hospital mortality., Conclusions: Machine learning techniques were applied in order to develop an accurate in-hospital mortality risk score for COVID-19 based on ten variables. The application of the proposed score has utility in clinical settings to guide the management and prognostication of COVID-19 patients., Competing Interests: The authors declare no conflict of interest., (Copyright: © 2022 Termedia & Banach.)

Published
2022
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19. The Applications of Artificial Intelligence in Chest Imaging of COVID-19 Patients: A Literature Review.

Author

Laino ME, Ammirabile A, Posa A, Cancian P, Shalaby S, Savevski V, and Neri E

Abstract

Diagnostic imaging is regarded as fundamental in the clinical work-up of patients with a suspected or confirmed COVID-19 infection. Recent progress has been made in diagnostic imaging with the integration of artificial intelligence (AI) and machine learning (ML) algorisms leading to an increase in the accuracy of exam interpretation and to the extraction of prognostic information useful in the decision-making process. Considering the ever expanding imaging data generated amid this pandemic, COVID-19 has catalyzed the rapid expansion in the application of AI to combat disease. In this context, many recent studies have explored the role of AI in each of the presumed applications for COVID-19 infection chest imaging, suggesting that implementing AI applications for chest imaging can be a great asset for fast and precise disease screening, identification and characterization. However, various biases should be overcome in the development of further ML-based algorithms to give them sufficient robustness and reproducibility for their integration into clinical practice. As a result, in this literature review, we will focus on the application of AI in chest imaging, in particular, deep learning, radiomics and advanced imaging as quantitative CT.

Published
2021
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20. Development of a Deep-Learning Pipeline to Recognize and Characterize Macrophages in Colo-Rectal Liver Metastasis.

Author

Cancian P, Cortese N, Donadon M, Di Maio M, Soldani C, Marchesi F, Savevski V, Santambrogio MD, Cerina L, Laino ME, Torzilli G, Mantovani A, Terracciano L, Roncalli M, and Di Tommaso L

Abstract

Quantitative analysis of Tumor Microenvironment (TME) provides prognostic and predictive information in several human cancers but, with few exceptions, it is not performed in daily clinical practice since it is extremely time-consuming. We recently showed that the morphology of Tumor Associated Macrophages (TAMs) correlates with outcome in patients with Colo-Rectal Liver Metastases (CLM). However, as for other TME components, recognizing and characterizing hundreds of TAMs in a single histopathological slide is unfeasible. To fasten this process, we explored a deep-learning based solution. We tested three Convolutional Neural Networks (CNNs), namely UNet, SegNet and DeepLab-v3, with three different segmentation strategies, semantic segmentation, pixel penalties and instance segmentation. The different experiments are compared according to the Intersection over Union (IoU), a metric describing the similarity between what CNN predicts as TAM and the ground truth, and the Symmetric Best Dice (SBD), which indicates the ability of CNN to separate different TAMs. UNet and SegNet showed intrinsic limitations in discriminating single TAMs (highest SBD 61.34±2.21), whereas DeepLab-v3 accurately recognized TAMs from the background (IoU 89.13±3.85) and separated different TAMs (SBD 79.00±3.72). This deep-learning pipeline to recognize TAMs in digital slides will allow the characterization of TAM-related metrics in the daily clinical practice, allowing the implementation of prognostic tools.

Published
2021
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21. Artificial Intelligence & Tissue Biomarkers: Advantages, Risks and Perspectives for Pathology.

Author

Lancellotti C, Cancian P, Savevski V, Kotha SRR, Fraggetta F, Graziano P, and Di Tommaso L

Subjects
Animals, Humans, Organ Specificity, Risk, Software, Artificial Intelligence, Biomarkers metabolism, Pathology
Abstract

Tissue Biomarkers are information written in the tissue and used in Pathology to recognize specific subsets of patients with diagnostic, prognostic or predictive purposes, thus representing the key elements of Personalized Medicine. The advent of Artificial Intelligence (AI) promises to further reinforce the role of Pathology in the scenario of Personalized Medicine: AI-based devices are expected to standardize the evaluation of tissue biomarkers and also to discover novel information, which would otherwise be ignored by human review, and use them to make specific predictions. In this review we will present how AI has been used to support Tissue Biomarkers evaluation in the specific field of Pathology, give an insight to the intriguing field of AI-based biomarkers and discuss possible advantages, risk and perspectives for Pathology.

Published
2021
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22. Volume-of-Interest Aware Deep Neural Networks for Rapid Chest CT-Based COVID-19 Patient Risk Assessment.

Author

Chatzitofis A, Cancian P, Gkitsas V, Carlucci A, Stalidis P, Albanis G, Karakottas A, Semertzidis T, Daras P, Giannitto C, Casiraghi E, Sposta FM, Vatteroni G, Ammirabile A, Lofino L, Ragucci P, Laino ME, Voza A, Desai A, Cecconi M, Balzarini L, Chiti A, Zarpalas D, and Savevski V

Subjects
Humans, Neural Networks, Computer, Risk Assessment, SARS-CoV-2, Tomography, X-Ray Computed, COVID-19
Abstract

Since December 2019, the world has been devastated by the Coronavirus Disease 2019 (COVID-19) pandemic. Emergency Departments have been experiencing situations of urgency where clinical experts, without long experience and mature means in the fight against COVID-19, have to rapidly decide the most proper patient treatment. In this context, we introduce an artificially intelligent tool for effective and efficient Computed Tomography (CT)-based risk assessment to improve treatment and patient care. In this paper, we introduce a data-driven approach built on top of volume-of-interest aware deep neural networks for automatic COVID-19 patient risk assessment (discharged, hospitalized, intensive care unit) based on lung infection quantization through segmentation and, subsequently, CT classification. We tackle the high and varying dimensionality of the CT input by detecting and analyzing only a sub-volume of the CT, the Volume-of-Interest (VoI). Differently from recent strategies that consider infected CT slices without requiring any spatial coherency between them, or use the whole lung volume by applying abrupt and lossy volume down-sampling, we assess only the "most infected volume" composed of slices at its original spatial resolution. To achieve the above, we create, present and publish a new labeled and annotated CT dataset with 626 CT samples from COVID-19 patients. The comparison against such strategies proves the effectiveness of our VoI-based approach. We achieve remarkable performance on patient risk assessment evaluated on balanced data by reaching 88.88%, 89.77%, 94.73% and 88.88% accuracy, sensitivity, specificity and F1-score, respectively.

Published
2021
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23. The Use of Antiviral Agents against SARS-CoV-2: Ineffective or Time and Age Dependent Result? A Retrospective, Observational Study among COVID-19 Older Adults.

Author

Desai A, Caltagirone G, Sari S, Pocaterra D, Kogan M, Azzolini E, Savevski V, Martinelli-Boneschi F, Voza A, and On Behalf Of The Humanitas Covid-Task Force

Abstract

Background: Our aim was to investigate the impact of therapeutics with antiviral activity against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) on mortality of older adults affected by coronavirus disease 2019 (COVID-19), taking into consideration the time interval from symptoms onset to drugs administration., Methods: Data from 143 COVID-19 patients over 65 years of age admitted to the Humanitas Clinical and Research Center Emergency Department (Milan, Italy) and treated with Lopinavir/ritonavir (LPV/r) or Darunavir/cobicistat (DVR/c) associated to Hydroxychloroquine (HCQ) were retrospectively analyzed. Statistical analysis was performed by using a logistic regression model and survival analysis to assess the role of different predictors of in-hospital mortality, including an early (<6 days from symptoms onset) vs. late treatment onset, signs and symptoms at COVID-19 presentation, type of antiviral treatment (LPV/r or DVR/c) and patients' age (65-80 vs. >80 years old)., Results: Multivariate analysis showed that an older age (OR: 2.54) and dyspnea as presenting symptom (OR: 2.01) were associated with higher mortality rate, whereas cough as presenting symptom (OR: 0.53) and a timely drug administration (OR: 0.44) were associated with lower mortality. Survival analysis demonstrated that the timing of drug administration had an impact on mortality in 65-80 years-old patients ( p = 0.02), whereas no difference was seen in those >80 years-old. This impact was more evident in patients with dyspnea as primary symptom of COVID-19, in whom mortality decreased from 57.1% to 38.3% due to timely drug administration (OR: 0.5; p = 0.04)., Conclusions: There was a significant association between the use of a combined antiviral regimen and HCQ and lower mortality, when timely-administered, in COVID-19 patients aged 65-80 years. Our findings support timely treatment onset as a key component in the treatment of COVID-19.

Published
2021
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24. The antibody response to SARS-CoV-2 infection persists over at least 8 months in symptomatic patients.

Author

Levi R, Ubaldi L, Pozzi C, Angelotti G, Sandri MT, Azzolini E, Salvatici M, Savevski V, Mantovani A, and Rescigno M

Abstract

Background: Persistence of antibodies to SARS-CoV-2 viral infection may depend on several factors and may be related to the severity of disease or to the different symptoms., Methods: We evaluated the antibody response to SARS-CoV-2 in personnel from 9 healthcare facilities and an international medical school and its association with individuals' characteristics and COVID-19 symptoms in an observational cohort study. We enrolled 4735 subjects (corresponding to 80% of all personnel) for three time points over a period of 8-10 months. For each participant, we determined the rate of antibody increase or decrease over time in relation to 93 features analyzed in univariate and multivariate analyses through a machine learning approach., Results: Here we show in individuals positive for IgG (≥12 AU/mL) at the beginning of the study an increase [ p   =   0 . 0002 ] in antibody response in paucisymptomatic or symptomatic subjects, particularly with loss of taste or smell (anosmia/dysgeusia: OR 2.75, 95% CI 1.753 - 4.301), in a multivariate logistic regression analysis in the first three months. The antibody response persists for at least 8-10 months., Conclusions: SARS-CoV-2 infection induces a long lasting antibody response that increases in the first months, particularly in individuals with anosmia/dysgeusia. This may be linked to the lingering of SARS-CoV-2 in the olfactory bulb., Competing Interests: Competing interestsThe authors declare no competing interests., (© The Author(s) 2021.)

Published
2021
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25. Effectiveness of Streptococcus Pneumoniae Urinary Antigen Testing in Decreasing Mortality of COVID-19 Co-Infected Patients: A Clinical Investigation.

Author

Desai A, Santonocito OG, Caltagirone G, Kogan M, Ghetti F, Donadoni I, Porro F, Savevski V, Poretti D, Ciccarelli M, Martinelli Boneschi F, and Voza A

Subjects
Adult, Aged, Aged, 80 and over, Anticoagulants therapeutic use, Antigens, Bacterial urine, Azithromycin therapeutic use, Betacoronavirus, COVID-19, Ceftriaxone therapeutic use, Cobicistat therapeutic use, Coinfection urine, Coronavirus Infections complications, Cross-Sectional Studies, Darunavir therapeutic use, Drug Combinations, Female, Heparin, Low-Molecular-Weight therapeutic use, Humans, Hydroxychloroquine therapeutic use, Length of Stay statistics & numerical data, Levofloxacin therapeutic use, Lopinavir therapeutic use, Male, Mass Screening, Middle Aged, Pandemics, Pneumonia, Pneumococcal complications, Pneumonia, Pneumococcal diagnosis, Pneumonia, Pneumococcal urine, Pneumonia, Viral complications, Retrospective Studies, Ritonavir therapeutic use, SARS-CoV-2, Streptococcus pneumoniae immunology, COVID-19 Drug Treatment, Anti-Bacterial Agents therapeutic use, Antiviral Agents therapeutic use, Coinfection diagnosis, Coronavirus Infections drug therapy, Hospital Mortality, Pneumonia, Pneumococcal drug therapy, Pneumonia, Viral drug therapy
Abstract

Background and Objectives: Streptococcus pneumoniae urinary antigen (u-Ag) testing has recently gained attention in the early diagnosis of severe and critical acute respiratory syndrome coronavirus-2/pneumococcal co-infection. The aim of this study is to assess the effectiveness of Streptococcus pneumoniae u-Ag testing in coronavirus disease 2019 (COVID-19) patients, in order to assess whether pneumococcal co-infection is associated with different mortality rate and hospital stay in these patients., Materials and Methods: Charts, protocols, mortality, and hospitalization data of a consecutive series of COVID-19 patients admitted to a tertiary hospital in northern Italy during COVID-19 outbreak were retrospectively reviewed. All patients underwent Streptococcus pneumoniae u-Ag testing to detect an underlying pneumococcal co-infection. Covid19+/u-Ag+ and Covid19+/u-Ag- patients were compared in terms of overall survival and length of hospital stay using chi-square test and survival analysis., Results: Out of 575 patients with documented pneumonia, 13% screened positive for the u-Ag test. All u-Ag+ patients underwent treatment with Ceftriaxone and Azithromycin or Levofloxacin. Lopinavir/Ritonavir or Darunavir/Cobicistat were added in 44 patients, and hydroxychloroquine and low-molecular-weight heparin (LMWH) in 47 and 33 patients, respectively. All u-Ag+ patients were hospitalized. Mortality was 15.4% and 25.9% in u-Ag+ and u-Ag- patients, respectively ( p = 0.09). Survival analysis showed a better prognosis, albeit not significant, in u-Ag+ patients. Median hospital stay did not differ among groups (10 vs. 9 days, p = 0.71)., Conclusions: The routine use of Streptococcus pneumoniae u-Ag testing helped to better target antibiotic therapy with a final trend of reduction in mortality of u-Ag+ COVID-19 patients having a concomitant pneumococcal infection. Randomized trials on larger cohorts are necessary in order to draw definitive conclusion.

Published
2020
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