Intensive Care Med (2017) 43:1403–1406 DOI 10.1007/s00134-017-4825-x WHAT ’S NE W IN INTENSIVE C ARE Intensive care medicine in 2050: statistical tools for development of prognostic models (why clinicians should not be ignored) 1* 2 2 Daniele Poole , Greta Carrara and Guido Bertolini © 2017 Springer-Verlag Berlin Heidelberg and ESICM Predictive ability assessment of prognostic models (Gruppo Italiano per la Valutazione degli interventi in When physicians admit a patient to the intensive care Terapia Intensiva) calibration belt (Fig. 1), which provides unit (ICU), they automatically grade the degree of sever- statistically rigorous information on deviation from the ity and formulate an initial prognosis. This is a com - ideal perfect matching between predicted and observed plex integration process of anamnestic information and death rates [3, 4]. physiological data with data from experience and culture. Traditionally, calibration has been assessed with the This approach, however, does not numerically quantify Hosmer–Lemeshow statistics , which, although detecting overall miscalibration, does not indicate the patient’s risk and may be heavily influenced by sub - whether the model predicts more or less deaths than jectivity. Prognostic models, such as the severity scores those observed. The combination with traditional cali of the APACHE and SAPS series, have been developed
Intensive Care Medicine – Springer Journals
Published: Jun 2, 2017
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