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Andrea Spini,1 Luca L’Abbate,2 Ylenia Ingrasciotta,1 Giorgia Pellegrini,1 Massimo Carollo,1 Valentina Ientile,2 Olivia Leoni,3 Martina Zanforlini,4 Domenica Ancona,5 Paolo Stella,5 Anna Cavazzana,6 Angela Scapin,6 Sara Lopes,7 Valeria Belleudi,7 Gianluca Trifirò1 1Department of Diagnostics and Public Health, University of Verona, Verona, Italy; 2Department of Biomedical and Dental Sciences and Morphofunctional Imaging, University of Messina, Messina, Italy; 3Lombardy Regional Centre of Pharmacovigilance and Regional Epidemiologic Observatory, Milan, Italy; 4Azienda Regionale per l’Innovazione e gli Acquisti, S.p.A, Milan, Italy; 5Apulian Regional Health Department, Bari, Italy; 6Azienda Zero, Regione Veneto, Italy; 7Department of Epidemiology, Lazio Regional Health Service, Rome, ItalyCorrespondence: Gianluca Trifirò, Department of Diagnostics and Public Health, University of Verona, P. le L.A. Scuro 10, Verona, 37134, Italy, Tel +39 045 802 7679, Email gianluca.trifiro@univr.itPurpose: This research aimed to develop and validate a META-algorithm combining individual immune-mediated inflammatory disease (IMID)-specific algorithms to identify the exact IMID indications for incident biological drug users from claims data within the context of the Italian VALORE project.Methods and Patients: All subjects with at least one dispensing of TNF-alpha inhibitors, anti-interleukin agents, and selective immunosuppressants approved for IMIDs were identified from claims databases of Latium region in Italy (observation period: 2010– 2020). Validated coding algorithms for identifying individual IMIDs from claims databases were found from published literature and combined into a META-algorithm. Positive predictive value (PPV), sensitivity (Se), negative predictive value (NPV), specificity (Sp), and accuracy (Acc) were estimated for each indication against the electronic therapeutic plans (ETPs) of the Latium region as the reference standard. Lastly, the frequency of the indication of use across individual biologic drugs was compared with that reported in three other Italian regions (Lombardy, Apulia, and the Veneto region).Results: In total, 9755 incident biological drug users with a single IMID indication were identified. Using the newly developed META-algorithm, an indication of use was detected in 95% (n=9255) of the total cohort. The estimated Acc, Se, Sp, PPV, and NPV, against the reference standard were as follows: 0.96, 0.86, 0.97, 0.82, and 0.98 for Crohn’s disease, 0.96, 0.80, 0.98, 0.85, and 0.97 for ulcerative colitis, 0.93, 0.76, 0.99, 0.95, and 0.92 for rheumatoid arthritis, 0.97, 0.75, 0.99, 0.85, and 0.98 for spondylarthritis, and 0.91, 0.92, 0.91, 0.88, and 0.94 for psoriatic arthritis/psoriasis, respectively. Additionally, no substantial difference was observed in the frequency of indication of use by active ingredient among Latium and the other three Italian regions included in the study.Conclusion: The newly developed META-algorithm demonstrated high validity estimates in the Italian claims data and was capable of discriminating with good performance among the most frequent IMID indications.Plain Language Summary: In the claims database, the lack of information on the indication of use represents a well-known limitation for the conduct of observational studies. This study was conducted to develop and validate a META-algorithm that accurately identifies the exact indication for the use of biological drugs in treating various immune-mediated inflammatory diseases. Using claims databases from the Latium region, we developed and validated a META-algorithm. The META-algorithm combines disease-specific algorithms for different immune-mediated inflammatory diseases (ie, Crohn’s disease, ulcerative colitis, rheumatoid arthritis, spondyloarthritis, psoriasis, and psoriatic arthritis) and was tested against a reference standard (electronic therapeutic plans of the Lazio region). The META-algorithm reported high validity estimates and was able to distinguish with a good performance among the most frequent IMIDs as indications for use. Applying this META-algorithm may facilitate post-marketing surveillance of biological drugs such as TNF-alpha inhibitors, anti-interleukin, and selective immunosuppressants in specific therapeutic areas in an Italian setting.Keywords: immune-mediated inflammatory diseases, biological drugs, validation, claims data, META-algorithm, indication for use

Andrea Spini,1 Luca L’Abbate,2 Ylenia Ingrasciotta,1 Giorgia Pellegrini,1 Massimo Carollo,1 Valentina Ientile,2 Olivia Leoni,3 Martina Zanforlini,4 Domenica Ancona,5 Paolo Stella,5 Anna Cavazzana,6 Angela Scapin,6 Sara Lopes,7 Valeria Belleudi,7 Gianluca Trifirò1 1Department of Diagnostics and Public Health, University of Verona, Verona, Italy; 2Department of Biomedical and Dental Sciences and Morphofunctional Imaging, University of Messina, Messina, Italy; 3Lombardy Regional Centre of Pharmacovigilance and Regional Epidemiologic Observatory, Milan, Italy; 4Azienda Regionale per l’Innovazione e gli Acquisti, S.p.A, Milan, Italy; 5Apulian Regional Health Department, Bari, Italy; 6Azienda Zero, Regione Veneto, Italy; 7Department of Epidemiology, Lazio Regional Health Service, Rome, ItalyCorrespondence: Gianluca Trifirò, Department of Diagnostics and Public Health, University of Verona, P. le L.A. Scuro 10, Verona, 37134, Italy, Tel +39 045 802 7679, Email gianluca.trifiro@univr.it

Ylenia Ingrasciotta,1,2 Valentina Isgrò,1 Saveria Serena Foti,2 Valentina Ientile,3 Andrea Fontana,4 Luca L’Abbate,3 Roberto Benoni,1 Elena Sofia Fiore,1 Michele Tari,5 Angela Alibrandi,6 Gianluca Trifirò1,2 1Department of Diagnostics and Public Health, University of Verona, Verona, Italy; 2Academic Spin-off “INSPIRE – Innovative Solutions for Medical Prediction and Big Data Integration in Real World Setting” – Azienda Ospedaliera Universitaria “G. Martino”, Messina, Italy; 3Department of Biomedical and Dental Sciences and Morphofunctional Imaging, University of Messina, Messina, Italy; 4Unit of Biostatistics, Fondazione IRCCS Casa Sollievo della Sofferenza, San Giovanni, Rotondo, Italy; 5Caserta-1 Local Health Service, Caserta, Italy; 6Department of Economics, University of Messina, Messina, ItalyCorrespondence: Ylenia Ingrasciotta, Department of Diagnostics and Public Health, University of Verona, P. le L.A. Scuro 10, Verona, 37134, Italy, Tel +39 045 802 7679, Email ylenia.ingrasciotta@univr.itBackground: Inflammatory bowel diseases (IBDs), Crohn’s disease (CD) and ulcerative colitis (UC), are chronic diseases that have been increasingly treated with biological drugs in recent years. Newly developed coding algorithms for IBD identification using claims databases are needed to improve post-marketing surveillance of biological drugs.Objective: To test algorithms to identify CD and UC, as indication for use of biological drugs approved for IBD treatment, using a claims database.Methods: Data were extracted from the Caserta Local Health Unit database between 2015 and 2018. CD/UC diagnoses reported by specialists in electronic therapeutic plans (ETPs) were considered as gold standard. Five algorithms were developed based on ICD-9-CM codes as primary cause of hospital admissions, exemption from healthcare service co-payment codes and drugs dispensing with only indication for CD/UC. The accuracy was assessed by sensitivity (Se), specificity (Sp), positive (PPV) and negative predicted values (NPV) along with computation of the Youden Index and F-score.Results: In the study period, 1205 subjects received at least one biological drug dispensing approved for IBD and 134 (11.1%) received ≥ 1 ETP with IBD as use indication. Patients with CD and CU were 83 (61.9%) and 51 (38.1%), respectively. Sensitivity of the different algorithms ranged from 71.1% (95% CI: 60.1– 80.5) to 98.8 (95% CI: 93.5– 100.0) for CD and from 64.7% (95% CI: 50.1– 77.6) to 94.1 (95% CI: 83.8– 98.8) for UC, while specificity was always higher than 91%. The best CD algorithm was “Algorithm 3”, based on hospital CD diagnosis code OR CD exemption code OR [IBD exemption code AND dispensing of non-biological drugs with only CD indication] (Se: 98.8%; Sp: 97.2%; PPV: 84.5%, NPV: 99.8%), achieving the highest diagnostic accuracy (Youden Index=0.960). The best UC algorithm was “Algorithm 3”, based on specific hospital UC diagnosis code OR UC exemption code OR [IBD exemption code AND golimumab dispensing] OR dispensing of non-biological drugs with only UC indication (Se: 94.1%; Sp: 91.6%; PPV: 50.0%; NPV: 99.4%), and achieving the highest diagnostic accuracy (Youden Index=0.857).Conclusion: In a population-based claims database, newly coding algorithms including diagnostic and exemption codes plus specific drug dispensing yielded highly accurate identification of CD and UC as distinct indication for biological drug use.Keywords: biological drugs, algorithm, healthcare database, ulcerative colitis, Crohn’s disease

Eliana Ferroni,1 Paolo Giorgi Rossi,2 Stefania Spila Alegiani,3 Gianluca Trifirò,4 Gisella Pitter,1 Olivia Leoni,5 Danilo Cereda,5 Massimiliano Marino,2 Michele Pellizzari,1 Massimo Fabiani,6 Flavia Riccardo,6 Janet Sultana,4 Marco Massari3 On behalf of The ITA-COVID Working Group1Azienda Zero of the Veneto Region, Padua, Italy; 2Azienda Unità Sanitaria Locale – IRCCS, Reggio Emilia, Italy; 3National Center for Drug Research and Evaluation, Istituto Superiore Di Sanità, Rome, Italy; 4Department of Biomedical and Dental Sciences and Morphofunctional Imaging – University of Messina, Messina, Italy; 5Department of Health of Lombardy Region, Milan, Italy; 6Department of Infectious Diseases, Istituto Superiore Di Sanità, Rome, ItalyCorrespondence: Stefania Spila AlegianiIstituto Superiore Di Sanità, Viale Regina Elena, 299, Rome 00161, ItalyTel +39 0649904249Fax +39 0649904248Email stefania.spila@iss.itPaolo Giorgi RossiAzienda Unità Sanitaria Locale – IRCCS, Reggio Emilia, via Amendola 2, Reggio Emilia 42122, ItalyTel +39 0522335490Email paolo.giorgirossi@ausl.re.itIntroduction: COVID-19 case fatality rate in hospitalized patients varies across countries and studies. Reliable estimates, specific for age, sex, and comorbidities, are needed to monitor the epidemic, to compare the outcome in different settings, and to correctly design trials for COVID-19 interventions. The aim of this study was to provide population-based survival curves of hospitalized COVID-19 patients.Materials and Methods: A cohort study was conducted in three areas of Northern Italy, heavily affected by SARS-CoV-2 infection (Lombardy and Veneto Regions, and Reggio Emilia province), using a loco-regional COVID-19 surveillance system, linked to hospital discharge databases. We included all patients testing positive for SARS-CoV-2 RNA by RT-PCR on nasopharyngeal/throat swab samples who were hospitalized from 21 February to 21 April 2020. Kaplan–Meier survival estimates were calculated at 14 and 30 days for death in any setting, stratifying by age, sex, and the Charlson Index.Results: Overall, 42,926 hospitalized COVID-19 patients were identified. Patients’ median age was 69 years (IQR: 57– 79), 62.6% were males, and 6.0% had a Charlson Index ≥ 3. Survival curves showed that 22.0% (95% CI 21.6– 22.4) of patients died within 14 days and 27.6% (95% CI 27.2– 28.1) within 30 days from hospitalization. Survival was higher in younger patients and in females. The negative impact of comorbidities on survival was more pronounced in younger age groups.Conclusion: The high fatality rate observed in the study (28% at 30 days) suggests that studies should focus on death as primary endpoint during a follow-up of at least one month.Keywords: COVID-19, survival, cohort study, hospitalized patients, Italy

Janet Sultana,1,2 Andrea Fontana,3 Francesco Giorgianni,1 Giorgio Basile,1 Elisabetta Patorno,4 Alberto Pilotto,5 Mariam Molokhia,6 Robert Stewart,7 Miriam Sturkenboom,2 Gianluca Trifirò1,2 1Department of Biomedical and Dental Sciences and Morphofunctional Imaging, University of Messina, Messina, Italy; 2Department of Medical Informatics, Erasmus University Medical Centre, Rotterdam, the Netherlands; 3Unit of Biostatistics, IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy; 4Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, USA; 5Geriatrics Unit, Department of Geriatric Care, Ortho Geriatrics and Rehabilitation, Frailty Area, E.O. Galliera Hospital, Genova, Italy; 6Department of Primary Care and Public Health Sciences, King’s College, London, UK; 7Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience King’s College London, UK Background: Functional and cognitive domains have rarely been evaluated for their prognostic value in general practice databases. The aim of this study was to identify functional and cognitive domains in The Health Improvement Network (THIN) and to evaluate their additional value for the prediction of 1-month and 1-year mortality in elderly people.Materials and methods: A cohort study was conducted using a UK nationwide general practitioner database. A total of 1,193,268 patients aged 65 years or older, of whom 15,300 had dementia, were identified from 2000 to 2012. Information on mobility, dressing and accommodation was recorded frequently enough to be analyzed further in THIN. Cognition data could not be used due to very poor recording of data in THIN. One-year and 1-month mortality was predicted using logistic models containing variables such as age, sex, disease score and functionality status.Results: A significant but moderate improvement in 1-year and 1-month mortality prediction in elderly people was observed by adding accommodation to the variables age, sex and disease score, as the c-statistic (95% confidence interval [CI]) increased from 0.71 (0.70–0.72) to 0.76 (0.75–0.77) and 0.73 (0.71–0.75) to 0.79 (0.77–0.80), respectively. A less notable improvement in the prediction of 1-year and 1-month mortality was observed in people with dementia.Conclusion: Functional domains moderately improved the accuracy of a model including age, sex and comorbidities in predicting 1-year and 1-month mortality risk among community-dwelling older people, but they were much less able to predict mortality in people with dementia. Cognition could not be explored as a predictor of mortality due to insufficient data being recorded. Keywords: elderly, frailty, database, mortality

Raffaella Palumbo,1 Federico Sottotetti,1 Giuseppe Trifirò,2 Elena Piazza,3 Antonella Ferzi,4 Anna Gambaro,3 Elena Giulia Spinapolice,2 Emma Pozzi,1 Barbara Tagliaferri,1 Cristina Teragni,1 Antonio Bernardo1 1Departmental Unit of Oncology, IRCCS Fondazione Salvatore Maugeri, Pavia, Italy; 2Unit of Nuclear Medicine, IRCCS Fondazione Salvatore Maugeri, Pavia, Italy; 3Medical Oncology Luigi Sacco Hospital, Milano, Italy; 4Medical Oncology, Legnano Hospital, Legnano, Italy Background: A prospective, multicenter trial was undertaken to assess the activity, safety, and quality of life of nanoparticle albumin-bound paclitaxel (nab-paclitaxel) as second-line chemotherapy in HER2-negative, taxane-pretreated metastatic breast cancer (MBC).Patients and methods: Fifty-two women with HER2-negative MBC who were candidates for second-line chemotherapy for the metastatic disease were enrolled and treated at three centers in Northern Italy. All patients had previously received taxane-based chemotherapy in the adjuvant or first-line metastatic setting. Single-agent nab-paclitaxel was given at the dose of 260 mg/m2 as a 30-minute intravenous infusion on day 1 each treatment cycle, which lasted 3 weeks, in the outpatient setting. No steroid or antihistamine premedication was provided. Treatment was stopped for documented disease progression, unacceptable toxicity, or patient refusal.Results: All of the enrolled patients were evaluable for the study endpoints. The objective response rate was 48% (95% CI, 31.5%–61.3%) and included complete responses from 13.5%. Disease stabilization was obtained in 19 patients and lasted >6 months in 15 of them; the overall clinical benefit rate was 77%. The median time to response was 70 days (range 52–86 days). The median progression-free survival time was 8.9 months (95% CI, 8.0–11.6 months, range 5–21+ months). The median overall survival point has not yet been reached. Toxicities were expected and manageable with good patient compliance and preserved quality of life in patients given long-term treatment.Conclusion: Our results showed that single-agent nab-paclitaxel 260 mg/m2 every 3 weeks is an effective and well tolerated regimen as second-line chemotherapy in HER2-negative, taxane-pretreated MBC patients, and that it produced interesting values of objective response rate and progression-free survival without the concern of significant toxicity. Specifically, the present study shows that such a regimen is a valid therapeutic option for that ‘difficult to treat’ patient population represented by women who at the time of disease relapse have already received the most active agents in the adjuvant and/or metastatic setting (ie, conventional taxanes). Keywords: nab-paclitaxel, metastatic breast cancer, taxanes, quality of life

Background: Evidence on neonatal withdrawal syndrome following antidepressant intrauterine exposure is limited, particularly for antidepressants other than selective serotonin reuptake inhibitor (SSRIs). Methods: In our case/non-case pharmacovigilance study, based on VigiBase®, the WHO database of suspected adverse drug reactions, we estimated reporting odds ratio (ROR) and the Bayesian information component (IC) with 95% confidence/credibility intervals (CI) as measures of disproportionate reporting of antidepressant-related neonatal withdrawal syndrome. Antidepressants were first compared to all other medications, then to methadone, and finally within each class of antidepressants: SSRIs, tricyclics (TCA) and other antidepressants. Antidepressants were ranked in terms of clinical priority, based on semiquantitative score ratings. Serious v. non-serious reports were compared. Results: A total of 406 reports of neonatal withdrawal syndrome in 379 neonates related to 15 antidepressants were included. Disproportionate reporting was detected for antidepressants as a group as compared to all other drugs (ROR: 6.18, 95% CI 5.45–7.01, IC: 2.07, 95% CI 1.92–2.21). Signals were found for TCAs (10.55, 95% CI 8.02–13.88), followed by other antidepressants (ROR: 5.90, 95% CI 4.74–7.36) and SSRIs (ROR: 4.68, 95% CI 4.04–5.42). Significant disproportionality emerged for all individual antidepressants except for bupropion, whereas no disproportionality for any antidepressant was detected v. methadone. Eleven antidepressants had a moderate clinical priority score and four had a weak one. Most frequent symptoms included respiratory symptoms (n = 106), irritability/agitation (n = 75), tremor (n = 52) and feeding problems (n = 40). Conclusions: Most antidepressants are associated with moderate signals of disproportionate reporting for neonatal withdrawal syndrome, which should be considered when prescribing an antidepressant during pregnancy, irrespective of class. [ABSTRACT FROM AUTHOR]