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Erschienen in: Intensive Care Medicine 11/2013

01.11.2013 | Original

Prediction of long-term mortality in ICU patients: model validation and assessing the effect of using in-hospital versus long-term mortality on benchmarking

verfasst von: Sylvia Brinkman, Ameen Abu-Hanna, Evert de Jonge, Nicolette F. de Keizer

Erschienen in: Intensive Care Medicine | Ausgabe 11/2013

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Abstract

Purpose

To analyze the influence of using mortality 1, 3, and 6 months after intensive care unit (ICU) admission instead of in-hospital mortality on the quality indicator standardized mortality ratio (SMR).

Methods

A cohort study of 77,616 patients admitted to 44 Dutch mixed ICUs between 1 January 2008 and 1 July 2011. Four Acute Physiology and Chronic Health Evaluation (APACHE) IV models were customized to predict in-hospital mortality and mortality 1, 3, and 6 months after ICU admission. Models’ performance, the SMR and associated SMR rank position of the ICUs were assessed by bootstrapping.

Results

The customized APACHE IV models can be used for prediction of in-hospital mortality as well as for mortality 1, 3, and 6 months after ICU admission. When SMR based on mortality 1, 3 or 6 months after ICU admission was used instead of in-hospital SMR, 23, 36, and 30 % of the ICUs, respectively, received a significantly different SMR. The percentages of patients discharged from ICU to another medical facility outside the hospital or to home had a significant influence on the difference in SMR rank position if mortality 1 month after ICU admission was used instead of in-hospital mortality.

Conclusions

The SMR and SMR rank position of ICUs were significantly influenced by the chosen endpoint of follow-up. Case-mix-adjusted in-hospital mortality is still influenced by discharge policies, therefore SMR based on mortality at a fixed time point after ICU admission should preferably be used as a quality indicator for benchmarking purposes.
Literatur
1.
Zurück zum Zitat de Vos M, Graafmans W, Keesman E, Westert G et al (2007) Quality measurement at intensive care units: which indicators should we use? J Crit Care 22:267–274PubMedCrossRef de Vos M, Graafmans W, Keesman E, Westert G et al (2007) Quality measurement at intensive care units: which indicators should we use? J Crit Care 22:267–274PubMedCrossRef
2.
Zurück zum Zitat Sirio CA, Shepardson LB, Rotondi AJ, Cooper GS et al (1999) Community-wide assessment of intensive care outcomes using a physiologically based prognostic measure: implications for critical care delivery from Cleveland health quality choice. Chest 115:793–801PubMedCrossRef Sirio CA, Shepardson LB, Rotondi AJ, Cooper GS et al (1999) Community-wide assessment of intensive care outcomes using a physiologically based prognostic measure: implications for critical care delivery from Cleveland health quality choice. Chest 115:793–801PubMedCrossRef
3.
Zurück zum Zitat Zimmerman JE, Kramer AA, McNair DS, Malila FM (2006) Acute Physiology and Chronic Health Evaluation (APACHE) IV: hospital mortality assessment for today’s critically ill patients. Crit Care Med 34:1297–1310PubMedCrossRef Zimmerman JE, Kramer AA, McNair DS, Malila FM (2006) Acute Physiology and Chronic Health Evaluation (APACHE) IV: hospital mortality assessment for today’s critically ill patients. Crit Care Med 34:1297–1310PubMedCrossRef
4.
Zurück zum Zitat Brinkman S, de Jonge E, Abu-Hanna A, De Lange DW et al (2012) The use of linked registries to assess long-term mortality of ICU patients. Stud Health Technol Inform 180:230–234PubMed Brinkman S, de Jonge E, Abu-Hanna A, De Lange DW et al (2012) The use of linked registries to assess long-term mortality of ICU patients. Stud Health Technol Inform 180:230–234PubMed
7.
Zurück zum Zitat Brinkman S, Bakhshi-Raiez F, Abu-Hanna A, de Jonge E et al (2013) Determinants of mortality after hospital discharge in ICU patients: literature review and Dutch cohort study. Crit Care Med 41:1237–1251PubMedCrossRef Brinkman S, Bakhshi-Raiez F, Abu-Hanna A, de Jonge E et al (2013) Determinants of mortality after hospital discharge in ICU patients: literature review and Dutch cohort study. Crit Care Med 41:1237–1251PubMedCrossRef
8.
Zurück zum Zitat Harrell FE Jr (2001) Regression modeling strategies, with applications to linear models, logistic regression, and survival analysis. Springer, New York Harrell FE Jr (2001) Regression modeling strategies, with applications to linear models, logistic regression, and survival analysis. Springer, New York
9.
Zurück zum Zitat Hanley JA, McNeil BJ (1982) The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143:29–36PubMed Hanley JA, McNeil BJ (1982) The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143:29–36PubMed
10.
Zurück zum Zitat Hilden J, Habbema JD, Bjerregaard B (1978) The measurement of performance in probabilistic diagnosis. III. Methods based on continuous functions of the diagnostic probabilities. Methods Inf Med 17:238–246PubMed Hilden J, Habbema JD, Bjerregaard B (1978) The measurement of performance in probabilistic diagnosis. III. Methods based on continuous functions of the diagnostic probabilities. Methods Inf Med 17:238–246PubMed
11.
Zurück zum Zitat Salter K, Jutai JW, Teasell R, Foley NC et al (2005) Issues for selection of outcome measures in stroke rehabilitation: ICF participation. Disabil Rehabil 27:507–528PubMedCrossRef Salter K, Jutai JW, Teasell R, Foley NC et al (2005) Issues for selection of outcome measures in stroke rehabilitation: ICF participation. Disabil Rehabil 27:507–528PubMedCrossRef
12.
Zurück zum Zitat Biancari F, Vasques F, Mikkola R, Martin M et al (2012) Validation of EuroSCORE II in patients undergoing coronary artery bypass surgery. Ann Thorac Surg 93:1930–1935PubMedCrossRef Biancari F, Vasques F, Mikkola R, Martin M et al (2012) Validation of EuroSCORE II in patients undergoing coronary artery bypass surgery. Ann Thorac Surg 93:1930–1935PubMedCrossRef
13.
Zurück zum Zitat Efron B (1983) Estimating the error rate of a prediction rule: improvement on cross-validation. Am Stat Assoc 78:316–331CrossRef Efron B (1983) Estimating the error rate of a prediction rule: improvement on cross-validation. Am Stat Assoc 78:316–331CrossRef
14.
Zurück zum Zitat Hosmer DW, Hosmer T, Le Cessie S, Lemeshow S (1997) A comparison of goodness-of-fit tests for the logistic regression model. Stat Med 16:965–980PubMedCrossRef Hosmer DW, Hosmer T, Le Cessie S, Lemeshow S (1997) A comparison of goodness-of-fit tests for the logistic regression model. Stat Med 16:965–980PubMedCrossRef
15.
Zurück zum Zitat Zhu BP, Lemeshow S, Hosmer DW, Klar J et al (1996) Factors affecting the performance of the models in the mortality probability model II system and strategies of customization: a simulation study. Crit Care Med 24:57–63PubMedCrossRef Zhu BP, Lemeshow S, Hosmer DW, Klar J et al (1996) Factors affecting the performance of the models in the mortality probability model II system and strategies of customization: a simulation study. Crit Care Med 24:57–63PubMedCrossRef
16.
Zurück zum Zitat Knaus WA, Harrell FE Jr, Lynn J, Goldman L et al (1995) The SUPPORT prognostic model. Objective estimates of survival for seriously ill hospitalized adults. Study to understand prognoses and preferences for outcomes and risks of treatments. Ann Intern Med 122:191–203PubMedCrossRef Knaus WA, Harrell FE Jr, Lynn J, Goldman L et al (1995) The SUPPORT prognostic model. Objective estimates of survival for seriously ill hospitalized adults. Study to understand prognoses and preferences for outcomes and risks of treatments. Ann Intern Med 122:191–203PubMedCrossRef
17.
Zurück zum Zitat Ho KM, Knuiman M, Finn J, Webb SA (2008) Estimating long-term survival of critically ill patients: the PREDICT model. PLoS ONE 3:e3226PubMedCrossRef Ho KM, Knuiman M, Finn J, Webb SA (2008) Estimating long-term survival of critically ill patients: the PREDICT model. PLoS ONE 3:e3226PubMedCrossRef
18.
Zurück zum Zitat Brinkman S, Abu-Hanna A, van der Veen A, de Jonge E et al (2012) A comparison of the performance of a model based on administrative data and a model based on clinical data: effect of severity of illness on standardized mortality ratios of intensive care units. Crit Care Med 40:373–378PubMedCrossRef Brinkman S, Abu-Hanna A, van der Veen A, de Jonge E et al (2012) A comparison of the performance of a model based on administrative data and a model based on clinical data: effect of severity of illness on standardized mortality ratios of intensive care units. Crit Care Med 40:373–378PubMedCrossRef
19.
Zurück zum Zitat Bakhshi-Raiez F, Peek N, Bosman RJ, de Jonge E et al (2007) The impact of different prognostic models and their customization on institutional comparison of intensive care units. Crit Care Med 35:2553–2560PubMedCrossRef Bakhshi-Raiez F, Peek N, Bosman RJ, de Jonge E et al (2007) The impact of different prognostic models and their customization on institutional comparison of intensive care units. Crit Care Med 35:2553–2560PubMedCrossRef
20.
Zurück zum Zitat Lilford R, Pronovost P (2010) Using hospital mortality rates to judge hospital performance: a bad idea that just won’t go away. BMJ 340:c2016PubMedCrossRef Lilford R, Pronovost P (2010) Using hospital mortality rates to judge hospital performance: a bad idea that just won’t go away. BMJ 340:c2016PubMedCrossRef
21.
Zurück zum Zitat Brinkman S, Bakhshi-Raiez F, Abu-Hanna A, de Jonge E et al (2011) External validation of acute physiology and chronic health evaluation IV in Dutch intensive care units and comparison with acute physiology and chronic health evaluation II and simplified acute physiology score II. J Crit Care 26:105–108PubMedCrossRef Brinkman S, Bakhshi-Raiez F, Abu-Hanna A, de Jonge E et al (2011) External validation of acute physiology and chronic health evaluation IV in Dutch intensive care units and comparison with acute physiology and chronic health evaluation II and simplified acute physiology score II. J Crit Care 26:105–108PubMedCrossRef
22.
Zurück zum Zitat Tromp M, Ravelli AC, Bonsel GJ, Hasman A et al (2011) Results from simulated data sets: probabilistic record linkage outperforms deterministic record linkage. J Clin Epidemiol 64:565–572PubMedCrossRef Tromp M, Ravelli AC, Bonsel GJ, Hasman A et al (2011) Results from simulated data sets: probabilistic record linkage outperforms deterministic record linkage. J Clin Epidemiol 64:565–572PubMedCrossRef
Metadaten
Titel
Prediction of long-term mortality in ICU patients: model validation and assessing the effect of using in-hospital versus long-term mortality on benchmarking
verfasst von
Sylvia Brinkman
Ameen Abu-Hanna
Evert de Jonge
Nicolette F. de Keizer
Publikationsdatum
01.11.2013
Verlag
Springer Berlin Heidelberg
Erschienen in
Intensive Care Medicine / Ausgabe 11/2013
Print ISSN: 0342-4642
Elektronische ISSN: 1432-1238
DOI
https://doi.org/10.1007/s00134-013-3042-5

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