Literature DB >> 18787438

Outcome prediction in critical care: the Simplified Acute Physiology Score models.

Maurizia Capuzzo1, Rui P Moreno, Jean-Roger Le Gall.   

Abstract

PURPOSE OF REVIEW: Outcome prediction models measuring severity of illness of patients admitted to the intensive care unit should predict hospital mortality. This review describes the state-of-the-art of Simplified Acute Physiology Score models from the clinical and managerial perspectives. Methodological issues concerning the effects of differences between new samples and original databases in which the models were developed are considered. RECENT
FINDINGS: The progressive lack of fit of the Simplified Acute Physiology Score II in independent intensive care unit populations induced investigators to propose customizations and expansions as potential evolutions for Simplified Acute Physiology Score II. We do not know whether those solutions did solve the issue because there are no demonstrations of consistent good fit in new databases. The recently developed Simplified Acute Physiology Score 3 Admission Score with customization for geographical areas is discussed. The points shared by the Simplified Acute Physiology Score models and the pros and cons for each of them are introduced.
SUMMARY: Comparisons of intensive care unit performance should take into account not only the patient severity of illness, but also the effect of the 'intensive care unit variable', that is, differences in human resources, structure, equipment, management and organization of the intensive care unit. In the future, moving from patient and geographical area adjustment to resource use could allow the user to adjust for differences in healthcare provision.

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Mesh:

Year:  2008        PMID: 18787438     DOI: 10.1097/MCC.0b013e32830864d7

Source DB:  PubMed          Journal:  Curr Opin Crit Care        ISSN: 1070-5295            Impact factor:   3.687


  12 in total

1.  Comparison between SAPS II and SAPS 3 in predicting hospital mortality in a cohort of 103 Italian ICUs. Is new always better?

Authors:  Daniele Poole; Carlotta Rossi; Nicola Latronico; Giancarlo Rossi; Stefano Finazzi; Guido Bertolini
Journal:  Intensive Care Med       Date:  2012-05-15       Impact factor: 17.440

2.  Intensive care medicine in 2050: statistical tools for development of prognostic models (why clinicians should not be ignored).

Authors:  Daniele Poole; Greta Carrara; Guido Bertolini
Journal:  Intensive Care Med       Date:  2017-06-02       Impact factor: 17.440

3.  The Association Between Serum Anion Gap and All-Cause Mortality in Disseminated Intravascular Coagulation Patients: A Retrospective Analysis.

Authors:  Bin Hu; Jinxia Cao; Yangyang Hu; Zuoan Qin; Jun Wang
Journal:  Int J Gen Med       Date:  2021-08-16

Review 4.  [Intensive care medicine in old age : The individual status is the determining factor].

Authors:  A Valentin
Journal:  Med Klin Intensivmed Notfmed       Date:  2017-04-24       Impact factor: 0.840

5.  An in-hospital mortality equation for mechanically ventilated patients in intensive care units.

Authors:  Takeshi Umegaki; Masaji Nishimura; Kimitaka Tajimi; Kiyohide Fushimi; Hiroshi Ikai; Yuichi Imanaka
Journal:  J Anesth       Date:  2013-03-09       Impact factor: 2.078

6.  [End-of life decisions in intensive care units. Influence of patient wishes on therapeutic decision making].

Authors:  R Riessen; C Bantlin; U Wiesing; M Haap
Journal:  Med Klin Intensivmed Notfmed       Date:  2013-03-17       Impact factor: 0.840

7.  Hypoxia and Outcome Prediction in Early-Stage Coma (Project HOPE): an observational prospective cohort study.

Authors:  Alex Lopez-Rolon; Andreas Bender
Journal:  BMC Neurol       Date:  2015-05-15       Impact factor: 2.474

8.  Determinants of the calibration of SAPS II and SAPS 3 mortality scores in intensive care: a European multicenter study.

Authors:  Antoine Poncet; Thomas V Perneger; Paolo Merlani; Maurizia Capuzzo; Christophe Combescure
Journal:  Crit Care       Date:  2017-04-04       Impact factor: 9.097

Review 9.  The Role of Oliguria and the Absence of Fluid Administration and Balance Information in Illness Severity Scores.

Authors:  Neil J Glassford; Rinaldo Bellomo
Journal:  Korean J Crit Care Med       Date:  2017-05-31

10.  Is SAPS 3 better than APACHE II at predicting mortality in critically ill transplant patients?

Authors:  Vanessa M de Oliveira; Janete S Brauner; Edison Rodrigues Filho; Ruth G A Susin; Viviane Draghetti; Simone T Bolzan; Silvia R R Vieira
Journal:  Clinics (Sao Paulo)       Date:  2013       Impact factor: 2.365

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