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      A calibration study of SAPS II with Norwegian intensive care registry data

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          Abstract

          Background

          Mortality prediction is important in intensive care. The Simplified Acute Physiology Score (SAPS) II is a tool for predicting such mortality. However, the original SAPS II is poorly calibrated to current intensive care unit (ICU) populations because it draws on data, which is more than 20 years old. We aimed to improve the calibration of SAPS II using data from the Norwegian Intensive Care Registry (NIR). This is the first recalibration of SAPS II for Nordic data.

          Methods

          A first-level customization was applied to improve calibration of the original SAPS II model (Model A). NIR data used covered more than 90% of adult patients admitted to ICUs in Norway from 2008 to 2010 ( n = 30712).

          Results

          The modified SAPS II, Model B, outperformed the original Model A with respect to calibration. Model B gave more accurate predictions of mortality than Model A (Hosmer–Lemeshow's C: 22.01 vs. 689.07; Brier score: 0.120 vs. 0.131; Cox's calibration regression: α = −0.093 vs. −0.747, β = 0.921 vs. 0.735, (α|β = 1) = −0.009 vs. −0.630). The standardized mortality ratio was 0.73 [95% confidence interval (CI) of 0.70–0.76] for Model A and 0.99 (95% CI of 0.95–1.04) for Model B. Discrimination was good for both models (area under receiver operating characteristic curve = 0.83 for both models).

          Conclusions

          As expected, Model B is better calibrated than Model A, and both models have similar uniformity of fit and equal discrimination. Introducing Model B into Norwegian ICUs may improve precision in decision-making. Units will have a more realistic benchmark for the assessment of ICU performance. Mortality risk estimates from Model B are better than previous SAPS II estimates have been.

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          Most cited references24

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          The meaning and use of the area under a receiver operating characteristic (ROC) curve.

          A representation and interpretation of the area under a receiver operating characteristic (ROC) curve obtained by the "rating" method, or by mathematical predictions based on patient characteristics, is presented. It is shown that in such a setting the area represents the probability that a randomly chosen diseased subject is (correctly) rated or ranked with greater suspicion than a randomly chosen non-diseased subject. Moreover, this probability of a correct ranking is the same quantity that is estimated by the already well-studied nonparametric Wilcoxon statistic. These two relationships are exploited to (a) provide rapid closed-form expressions for the approximate magnitude of the sampling variability, i.e., standard error that one uses to accompany the area under a smoothed ROC curve, (b) guide in determining the size of the sample required to provide a sufficiently reliable estimate of this area, and (c) determine how large sample sizes should be to ensure that one can statistically detect differences in the accuracy of diagnostic techniques.
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            A new Simplified Acute Physiology Score (SAPS II) based on a European/North American multicenter study.

            To develop and validate a new Simplified Acute Physiology Score, the SAPS II, from a large sample of surgical and medical patients, and to provide a method to convert the score to a probability of hospital mortality. The SAPS II and the probability of hospital mortality were developed and validated using data from consecutive admissions to 137 adult medical and/or surgical intensive care units in 12 countries. The 13,152 patients were randomly divided into developmental (65%) and validation (35%) samples. Patients younger than 18 years, burn patients, coronary care patients, and cardiac surgery patients were excluded. Vital status at hospital discharge. The SAPS II includes only 17 variables: 12 physiology variables, age, type of admission (scheduled surgical, unscheduled surgical, or medical), and three underlying disease variables (acquired immunodeficiency syndrome, metastatic cancer, and hematologic malignancy). Goodness-of-fit tests indicated that the model performed well in the developmental sample and validated well in an independent sample of patients (P = .883 and P = .104 in the developmental and validation samples, respectively). The area under the receiver operating characteristic curve was 0.88 in the developmental sample and 0.86 in the validation sample. The SAPS II, based on a large international sample of patients, provides an estimate of the risk of death without having to specify a primary diagnosis. This is a starting point for future evaluation of the efficiency of intensive care units.
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              Standardizing the power of the Hosmer-Lemeshow goodness of fit test in large data sets.

              The Hosmer-Lemeshow test is a commonly used procedure for assessing goodness of fit in logistic regression. It has, for example, been widely used for evaluation of risk-scoring models. As with any statistical test, the power increases with sample size; this can be undesirable for goodness of fit tests because in very large data sets, small departures from the proposed model will be considered significant. By considering the dependence of power on the number of groups used in the Hosmer-Lemeshow test, we show how the power may be standardized across different sample sizes in a wide range of models. We provide and confirm mathematical derivations through simulation and analysis of data on 31,713 children from the Collaborative Perinatal Project. We make recommendations on how to choose the number of groups in the Hosmer-Lemeshow test based on sample size and provide example applications of the recommendations. Copyright © 2012 John Wiley & Sons, Ltd.
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                Author and article information

                Journal
                Acta Anaesthesiol Scand
                Acta Anaesthesiol Scand
                aas
                Acta Anaesthesiologica Scandinavica
                Blackwell Publishing Ltd (Oxford, UK )
                0001-5172
                1399-6576
                July 2014
                12 May 2014
                : 58
                : 6
                : 701-708
                Affiliations
                [1 ]Department of Global Public Health and Primary Care, University of Bergen Bergen, Norway
                [2 ]Department of Research and Development, Haukeland University Hospital Bergen, Norway
                [3 ]Norwegian Intensive Care Registry, Helse Bergen HF Bergen, Norway
                [4 ]Department of Anesthesia and Intensive Care, Haukeland University Hospital Bergen, Norway
                Author notes
                Address: Øystein Ariansen Haaland, Department of Global Public Health and Primary Care, University of Bergen, IGS PO Box 7804, NO-5020 Bergen, Norway, e-mail: oystein.haaland@ 123456igs.uib.no
                Article
                10.1111/aas.12327
                4223997
                24819749
                7bb0be1e-1869-4af1-b682-c4f4cee75609
                © 2014 The Authors. The Acta Anaesthesiologica Scandinavica Foundation. Published by John Wiley & Sons Ltd.

                This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

                History
                : 24 March 2014
                Categories
                Intensive Care and Physiology

                Anesthesiology & Pain management
                Anesthesiology & Pain management

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