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1. Evaluation of the performance of Dutch Lipid Clinic Network score in an Italian FH population: The LIPIGEN study

Author

Casula, M. a., B, Be, Olmastroni, E. a., Pirillo, A. c., Catapano, D, A. L. b., D, Jemail, Author, Lipigen, Group, Averna, M. g., Bertolini, S. h., Calandra, S. i., Tarugi, P. i., Pellegatta, F. k., Angelico, F. f., Bartuli, A. l., Biasucci, G. m., Biolo, G. n., Bonanni, L. o., Bonomo, K. p., Borghi, C. q., Bossi, A. C. r., Branchi, A. s., Carubbi, F. t., Cipollone, F. u., Citroni, N. v., Federici, M. w., Ferri, C. x., Fiorenza, A. M. y., Giaccari, A. z., Giorgino, Aa, F., Guardamagna, Ab, O., Iannuzzi, Ac, A., Iughetti, Ad, L., Lupattelli, Ae, G., Lupi, Af, A., Mandraffino, Giuseppe, Ag, G., Marcucci, Ah, R., Maroni, Ai, L., Miccoli, Aj, R., Mombelli, Ak, G., Muntoni, Al, S., Pecchioli, Am, V., Pederiva, An, C., Pipolo, Ao, A., Pisciotta, Ap, L., Pujia, Aq, A., Purrello, Ar, F., Repetti, As, E., Rubba, At, P., Sabbà, Au, C., Sampietro, Av, T., Sarzani, Aw, R., Tagliabue, M. P., Ax, Trenti, Ay, C., Vigna, G. B., Az, Werba, J. P., Ba, Zambon, Bb, S., Zenti, M. G., Bc, Minicocci, I. f., Noto, D. g., Fortunato, Bd, G., Banderali, An, G., Benso, Ax, A., Bigolin, Bb, P., Bonora, Bc, E., Bruzzi, Ad, P., Bucci, M. u., Buonuomo, P. S. l., Capra, M. E. m., Cardolini, I. w., Cefalù, B. g., Cervelli, N. x., Chiariello, Ac, G., Cocci, Aw, G., Colombo, E. y., Cremonini, A. L., Ap, D'Addato, S. q., D'Erasmo, L. f., Dal, Pino, Av, B., Sanctis, De, Ab, L., Vita, De, Ao, E., Del, Ben, M. f., Costanzo, Di, A. f., Taranto, Di, M. D., Bd, Fasano, Ay, T., Gentile, As, L., At, M., Ghirardello, Az, O., Grigore, L. k., Lussu, Al, M., Meregalli, G. r., Moffa, S. z., Montalcini, Aq, T., Morgia, Nascimbeni, F. t., Pasta, A. h., Pavanello, Ak, C., Saitta, Antonino, Ag, A., Scicali, Ar, R., Siepi, Ae, D., Spagnolli, W. v., Spina, R. g., Sticchi, Ah, E., Suppressa, Au, P., Vigo, Ba, L., Vinci, P. n., Manzato, Bf, E., Tragni, Be, E., Zampoleri, Bg, V., Casula, Manuela, Olmastroni, Elena, Pirillo, Angela, Catapano, Alberico Luigi, Arca, Marcello, Averna, Maurizio, Bertolini, Stefano, Calandra, Sebastiano, Tarugi, Patrizia, Pellegatta, Fabio, Angelico, Francesco, Bartuli, Andrea, Biasucci, Giacomo, Biolo, Gianni, Bonanni, Luca, Bonomo, Katia, Borghi, Claudio, Bossi, Antonio Carlo, Branchi, Adriana, Carubbi, Francesca, Cipollone, Francesco, Citroni, Nadia, Federici, Massimo, Ferri, Claudio, Fiorenza, Anna Maria, Giaccari, Andrea, Giorgino, Francesco, Guardamagna, Ornella, Iannuzzi, Arcangelo, Iughetti, Lorenzo, Lupattelli, Graziana, Lupi, Alessandro, Mandraffino, Giuseppe, Marcucci, Rossella, Maroni, Lorenzo, Miccoli, Roberto, Mombelli, Giuliana, Muntoni, Sandro, Pecchioli, Valerio, Pederiva, Cristina, Pipolo, Antonio, Pisciotta, Livia, Pujia, Arturo, Purrello, Francesco, Repetti, Elena, Rubba, Paolo, Sabbà, Carlo, Sampietro, Tiziana, Sarzani, Riccardo, Tagliabue, Milena Paola, Trenti, Chiara, Vigna, Giovanni Battista, Werba, Josè Pablo, Zambon, Sabina, Zenti, Maria Grazia, Minicocci, Ilenia, Noto, Davide, Fortunato, Giuliana, Banderali, Giuseppe, Benso, Andrea, Bigolin, Paola, Bonora, Enzo, Bruzzi, Patrizia, Bucci, Marco, Buonuomo, Paola Sabrina, Capra, Maria Elena, Cardolini, Iri, Cefalù, Baldassarre, Cervelli, Nazzareno, Chiariello, Giuseppe, Cocci, Guido, Colombo, Emanuela, Cremonini, Anna Laura, D'Addato, Sergio, D'Erasmo, Laura, Dal Pino, Beatrice, De Sanctis, Luisa, De Vita, Emanuele, Del Ben, Maria, Di Costanzo, Alessia, Di Taranto, Maria Donata, Fasano, Tommaso, Gentile, Luigi, Gentile, Marco, Ghirardello, Omar, Grigore, Liliana, Lussu, Milena, Meregalli, Giancarla, Moffa, Simona, Montalcini, Tiziana, Morgia, Valeria, Nascimbeni, Fabio, Pasta, Andrea, Pavanello, Chiara, Saitta, Antonino, Scicali, Roberto, Siepi, Donatella, Spagnolli, Walter, Spina, Rossella, Sticchi, Elena, Suppressa, Patrizia, Vigo, Lorenzo, Vinci, Pierandrea, Manzato, Enzo, Tragni, Elena, Zampoleri, Veronica, D'addato, Sergio, D'erasmo, Laura, Casula, M, Olmastroni, E, Pirillo, A, Catapano, A, Averna, M, Noto, D, Cefalu, B, and Spina, R

Subjects
Male, Settore MED/09 - Medicina Interna, Genetic testing, Predictive Value of Test, Familial hypercholesterolemia, 030204 cardiovascular system & hematology, Decision Support Technique, 0302 clinical medicine, Retrospective Studie, Risk Factors, Cardiovascular Disease, Genetic Marker, Prospective Studies, 030212 general & internal medicine, Age of Onset, Prospective cohort study, education.field_of_study, medicine.diagnostic_test, Middle Aged, Dutch Lipid Clinic Network score, Adult, Biomarkers, Cardiovascular Diseases, Cholesterol, LDL, Female, Genetic Markers, Genetic Predisposition to Disease, Genetic Testing, Humans, Hyperlipoproteinemia Type II, Italy, Phenotype, Predictive Value of Tests, Reproducibility of Results, Retrospective Studies, Risk Assessment, Decision Support Techniques, Mutation, 3. Good health, Cholesterol, Cardiology and Cardiovascular Medicine, Human, medicine.medical_specialty, Population, Reproducibility of Result, Physical examination, LDL, 03 medical and health sciences, Internal medicine, medicine, First-degree relatives, education, business.industry, Risk Factor, Settore MED/13 - ENDOCRINOLOGIA, Biomarker, medicine.disease, Missing data, Dutch Lipid Clinic Network score, Familial hypercholesterolemia, Genetic testing, Prospective Studie, Age of onset, business
Abstract

Background and aims Familial hypercholesterolemia (FH) is an inherited disorder characterized by high levels of blood cholesterol from birth and premature coronary heart disease. Thus, the identification of FH patients is crucial to prevent or delay the onset of cardiovascular events, and the availability of a tool helping with the diagnosis in the setting of general medicine is essential to improve FH patient identification. Methods This study evaluated the performance of the Dutch Lipid Clinic Network (DLCN) score in FH patients enrolled in the LIPIGEN study, an Italian integrated network aimed at improving the identification of patients with genetic dyslipidaemias, including FH. Results The DLCN score was applied on a sample of 1377 adults (mean age 42.9 ± 14.2 years) with genetic diagnosis of FH, resulting in 28.5% of the sample classified as probable FH and 37.9% as classified definite FH. Among these subjects, 43.4% had at least one missing data out of 8, and about 10.0% had 4 missing data or more. When analyzed based on the type of missing data, a higher percentage of subjects with at least 1 missing data in the clinical history or physical examination was classified as possible FH (DLCN score 3–5). We also found that using real or estimated pre-treatment LDL-C levels may significantly modify the DLCN score. Conclusions Although the DLCN score is a useful tool for physicians in the diagnosis of FH, it may be limited by the complexity to retrieve all the essential information, suggesting a crucial role of the clinical judgement in the identification of FH subjects.

Published
2018

2. Continuous quality improvement in intensive care medicine. The GiViTI Margherita project - Report 2005

Author

Ffelli, S. a, Rossi, C. a, Anghileri, A. a, Giardino, M. a, Carnevale, L. b, Messina, M. c, Neri, M. d, Langer, M. e, Bertolini, G. a., Abastanotti, M. f, Alberti, A. g, Alborghetti, A. h, Angiolini, P. i, Anticoli, B. S. j, Archi, D. k, Arditi, E. l, Badii, F. m, Balata, A. n, Barattini, M. i, Barbagli, R. i, Bartoccini, A. o, Bartoli, T. p, Bassi, F. q, Beck, E. r, Belloni, U. G. s, Berardino, M. t, Bernasconi, M. O. u, Bianchin, A. v, Blasetti, A. w, Boccalatte, Rosa, D. x, Bonaccorso, G. y, Bonfà, A. z, Bonfiglio, Aa, M., Bonizzoli, M. i, Bottari, Ab, V., Breschi, Ac, C., Bressan, S. t, Brunori, Ad, E., Burgio, Ae, G., Buzzetti, Af, V., Cabano, G. V., Ag, Calicchio, Ah, G., Caracciolo, Ai, M., Casagli, Aj, S., Casagrande, Ak, L., Casciani, M. j, Castiglione, Al, G., Cavallo, R. t, Chiarello, Am, M., Chieregato, An, A., Chinelli, Ao, E., Chini, Ap, G., Clemente, Aq, S., Coaloa, Ar, M., Colombo, As, R., Colonna, S. S., At, Cominotti, Au, S., Corsini, Av, W., Constanzo, Aw, E., Crema, Ax, L., Crestan, Ay, E., Dal, Cero, Az, P., Dal, Ferro, Ba, M., Dal, Poggetto, Bb, L., Dal, Pos, Bc, L., David, Bd, A., Blasi, De, R. A. j., Luca, De, A. i., Negri, De, Be, P., Dei, Poli, Bf, M., Del, Sarto, Bg, P., Masi, Di, Bh, P., Pasquale, Di, Bi, D., Elvio, D. B., Bj, Fabbri, Bk, E., Fabbri, L. i, P. G., Ae, Fabi, M. C., Bl, Faccio, L. t, Falconio, Bm, A., Fasiolo, Bn, S., Febbrari, Bo, P., Ferrante, Bo, M., Ferraro, Bp, F., Fiore, Bq, G., Fontaneto, Br, C., Gabini, Bs, R., Galeotti, Bt, E., Gamberini, An, E., Garelli, Bu, A., Garofalo, Giacopuzzi, Bv, L., Gianni, Bw, M., Giannoni, Bx, S., Girardis, Massimo, Giudici, As, D., Giugiaro, P. M., Bz, Gorietti, Ca, A., Grassi, Cb, P., Boffelli, S, Rossi, C, Anghileri, A, Giardino, M, Carnevale, L, Messina, M, Neri, N, Langer, M, Bertolini G., for the GiViTI group, gruppo italiano per la valutazione degli interventi in terapia, Intensiva, and Ferraro, Fausto

Subjects
Adult, Aged, 80 and over, Male, Intensive Care Units, Quality, Gruppo Italiano per la Valutazione degli Interventi in Terapia Intensiva, Adolescent, Critical Care, adolescent, adult, aged, calibration, clinical assessment, clinical medicine, computer program, controlled study, data analysis, female, health care quality, health care system, hospital admission, human, information processing, intensive care unit, Italy, major clinical study, male, medical record, mortality, multivariate logistic regression analysis, parity, predictive validity, quantitative analysis, review, statistical significance, Middle Aged, Humans, Intensive care unit, Female, Child, Aged, Total Quality Management
Abstract

AIM: The assessment of the quality of intensive care medicine is mandatory in the modern healthcare system. In Italy, the GiViTI (Gruppo Italiano per la Valutazione degli Interventi in Terapia Intensiva) network is working in this field since 1991 and it now involves 295 out of the about 450 Italian intensive care units (ICU). In 2002 GiViTI launched a project for the continuous quality assessment and improvement that is now joined by 180 ICUs. Data collected in 2005 are analyzed and presented. METHODS: All admitted patients were entered in a validated software, which performs a multitude of validity checks during the data entry. Data were further reviewed by the co-ordinating center; patients admitted in months with more than 10% of incomplete or inconsistent records in each ICU were excluded from the analysis. Each year, a multivariate logistic regression model is fitted to identify predictors of hospital mortality. Starting from the SAPS 2 and the 2004 GiViTI model predictions of hospital mortality, two calibration tables and curves are presented. RESULTS: In 2005, 180 Italian ICUs collected data on 55 246 patients. After excluding those admitted in months with an unjustified lower recruitment rate or with less than 90% of complete and consistent data, we had 52 816 (95.6%) valid cases. Although the rough hospital mortality in 2005 was 1% higher than in 2004 (22.6% vs 21.5%), the adjusted mortality shows a statistically significant 4% reduction (obser-ved-to-expected ratio: 0.96; 95% CI: 0.94-0.97). CONCLUSIONS: Italian ICUs in 2005 performed better than in 2004, at a parity of patient severity.

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