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Start Over Author "Pulici, M" Topic intensive care units
2 results on '"Pulici, M"'

1. The influence of length of stay in the ICU on power of discrimination of a multipurpose severity score (SAPS). ARCHIDIA.

Author

Sicignano A, Carozzi C, Giudici D, Merli G, Arlati S, and Pulici M

Subjects
Adult, Aged, Cohort Studies, Diagnosis-Related Groups, Discriminant Analysis, Female, Health Care Surveys, Humans, Italy, Logistic Models, Male, Middle Aged, Reproducibility of Results, Sensitivity and Specificity, Intensive Care Units statistics & numerical data, Length of Stay, Severity of Illness Index
Abstract

Objective: To assess how the power of discrimination of a multipurpose severity score (Simplified Acute Physiology Score; SAPS) changes in relation to the length of stay (LOS) in the intensive care unit (ICU)., Design: In order to compute the SAPS probability, a model derived from logistic regression was developed in a cohort of 8059 patients. Measures of calibration (goodness-of-fit statistics) and discrimination [receiver operating characteristic (ROC) curve and relative area under the curve (AUC)] were adopted in a developmental set (5389 patients) and a validation set (2670 patients), both randomly selected. Once the logit was developed and the model validated, the whole database (8059 patients) was again assembled. To evaluate the accuracy of first-day SAPS probability over time, area under the ROC curve was computed for each of the initial 10 days of ICU care and for day 15., Setting: 24 Italian ICUs., Patients: A total of 8059 patients out of 10065 consecutive admissions over a period of 3 years (1990-1992) were included in this study. Patients whose SAPS was not correctly compiled (n = 687), patients younger than 18 years (n = 442), and patients whose LOS was less than 24 h (n = 877) were excluded from this analysis., Interventions: None., Measurements and Results: The logistic model gave good results in terms of calibration and discrimination, both in the developmental set (goodness-of-fit: chi 2 = 9.24, p = 0.32; AUC = 0.79 +/- 0.01) and in the validation set (goodness-of-fit: chi 2 = 8.95, p = 0.537; AUC = 0.78 +/- 0.01). The AUC for the whole database showed a loss in discrimination closely related to LOS: 0.79 +/- 0.01 at a day 1 and 0.59 +/- 0.02 at day 15., Conclusion: The logistic model that we developed meets high standards for discrimination and calibration. However, SAPS loses its discriminative power over time; accuracy of prediction is maintained at an acceptable level only in patients who stay in the ICU no longer than 5 days. The stay in the ICU represents a complex variable, which is not predictable, that influences the performance of SAPS on the first day.

Published
1996
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2. External validation of the Simplified Acute Physiology Score (SAPS) 3 in a cohort of 28,357 patients from 147 Italian intensive care units

Author

Poole, D, Rossi, C, Anghileri, A, Giardino, M, Latronico, N, Radrizzani, D, Langer, M, Bertolini, G, Acciarri, C, Acquarolo, A, Adorni, A, Alberti, A, Alborghetti, A, Antonini, B, Archi, D, Arditi, E, Badii, F, Balata, A, Barattini, M, Barbagli, R, Barbieri, P, Baroncia, M, Bartoccini, A, Bartoli, T, Bassi, F, Beck, E, Bellato, V, Bellorini, M, Belluomo Anello, C, Benanti, C, Berardino, M, Bernasconi, Mo, Besozzi, A, Bianchin, A, Bindi, Ml, Biolino, P, Blasetti, A, Boccalatte, D, Bonaccorso, G, Bonfà, A, Bonfiglio, M, Boniotti, C, Bonizzoli, M, Bottari, V, Breschi, C, Brunori, E, Buonanno, R, Buzzetti, V, Calicchio, G, Calva, S, Capannolo, B, Caracciolo, A, Carnevale, L, Carsana, C, Casadei, E, Casagli, S, Casagrande, L, Casalini, P, Castiglione, G, Cavallo, R, Chiarello, M, Chieregato, A, Chinelli, E, Cigada, M, Coaloa, M, Colombo, R, Colombo, S, Corsini, W, Corvi, D, Cosentino, E, Costa, R, Costanzo, E, Cottignoli, T, Crema, L, Crestan, E, Da Re, D, Dal Cero, P, Dal Ferro, M, David, Antonio, De Blasi RA, De Blasio, E, De Luca, A, De Negri, P, Dei Poli, M, Del Sarto, P, Di Lorenzo, G, Di Masi, P, Di Pasquale, D, Di Serafino, G, Donato, I, Fabbri, L, Fabbri, E, Fabbri, Pg, Fabi, Mc, Faccio, L, Ferla, F, Ferri, F, Fiore, G, Fognani, G, Galeotti, E, Gamberini, E, Garelli, A, Garofalo, G, Garzilli, T, Giacopuzzi, L, Gianni, M, Giannoni, S, Giugiaro, Pm, Gorietti, A, Grassi, P, Greco, M, Guadagnucci, A, Guagliardi, C, Isetta, M, Lapolla, A, Lefons, U, Livigni, S, Madonna, R, Maestrone, C, Magatti, Mf, Maggiolo, C, Malacarne, P, Mancosu, S, Mangani, Va, Mantovani, G, Marafon, S, Martinez Escobar, R, Mastroianni, A, Mastropierro, R, Meloni, A, Messina, M, Miglioranzi, R, Molino, Fm, Morigi, A, Mosca, C, Murri, V, Nardi, G, Nascimben, E, Natalini, G, Negri, G, Negro, G, Neri, M, Nicolini, A, Nuovo, D, Odetto, L, Olivieri, C, Ortoleva, A, Pacini, Dm, Paganini, G, Paganoni, G, Palmer, M, Papiri, S, Parma, A, Parrini, V, Pastorelli, M, Pastorini, S, Pegoraro, M, Pezzi, A, Pinciroli, D, Piredda, G, Piva, S, Pizzaballa, M, Possamai, C, Postiglione, M, Potalivo, A, Pulici, M, Quattrocchi, P, Raffaeli, M, Ranzini, L, Righini, E, Rona, R, Rossi, G, Rossi, S, Rossi, M, Rottoli, F, Salcuni, R, Salvi, G, Savioli, M, Scarrone, S, Schellino, Mm, Segala, V, Segalini, Pa, Sera, G, Sforza, D, Sicignano, A, Sorbara, C, Spadini, E, Staccioli, P, Stefani, M, Sucre, Mj, Tartari, S, Tavola, M, Tetamo, R, Tibaldi, G, Tinacci, S, Todesco, L, Torta, M, Tosi, L, Tripepi, C, Ughi, L, Vaj, M, Vecchiarelli, P, Vespignani, Mg, Visconti, Mg, Vulcano, Ga, Zappa, S, Zocaro, R, and Zuccaro, F.

Subjects
Male, medicine.medical_specialty, Time Factors, genetic structures, Critical Care, Critical Illness, Critical Care and Intensive Care Medicine, law.invention, Bias, law, Predictive Value of Tests, Risk Factors, Intensive care, Anesthesiology, Outcome Assessment, Health Care, medicine, Humans, Hospital Mortality, Prospective Studies, Simplified Acute Physiology Score, Intensive care medicine, Prospective cohort study, Diagnosis-Related Groups, APACHE, Aged, Chi-Square Distribution, business.industry, Data Collection, Benchmarking, Critical care, Hospital mortality, Outcome assessment, Validation study, Discriminant Analysis, Middle Aged, Prognosis, Intensive care unit, Intensive Care Units, Logistic Models, Italy, ROC Curve, Predictive value of tests, Cohort, Calibration, Female, business, Chi-squared distribution
Abstract

To evaluate the SAPS 3 score predictive ability of hospital mortality in a large external validation cohort.Prospective observational study.A total of 28,357 patients from 147 Italian ICUs joining the Project Margherita national database of the Gruppo italiano per la Valutazione degli interventi in Terapia Intensiva (GiViTI).None.Evaluation of discrimination through ROC analysis and of overall goodness-of-fit through the Cox calibration test.Although discrimination was good, calibration turned out to be poor. The general and the South-Europe Mediterranean countries equations overestimated hospital mortality overall (SMR values 0.73 with 95% CI 0.72-0.75 for both equations) and homogeneously across risk classes. Overprediction was confirmed among important subgroups, with SMR values ranging between 0.47 and 0.82.The result strictly supported by our data is that the SAPS 3 score calibrates inadequately in a large sample of Italian ICU patients and thus should not be used for benchmarking, at least in Italian settings.

Published
2009

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