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Published in: BMC Medical Research Methodology 1/2014

Open Access 01-12-2014 | Research article

Identifying unusual performance in Australian and New Zealand intensive care units from 2000 to 2010

Authors: Patricia J Solomon, Jessica Kasza, John L Moran, the Australian and New Zealand Intensive Care Society (ANZICS) Centre for Outcome and Resource Evaluation (CORE)

Published in: BMC Medical Research Methodology | Issue 1/2014

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Abstract

Background

The Australian and New Zealand Intensive Care Society (ANZICS) Adult Patient Database (APD) collects voluntary data on patient admissions to Australian and New Zealand intensive care units (ICUs). This paper presents an in-depth statistical analysis of risk-adjusted mortality of ICU admissions from 2000 to 2010 for the purpose of identifying ICUs with unusual performance.

Methods

A cohort of 523,462 patients from 144 ICUs was analysed. For each ICU, the natural logarithm of the standardised mortality ratio (log-SMR) was estimated from a risk-adjusted, three-level hierarchical model. This is the first time a three-level model has been fitted to such a large ICU database anywhere. The analysis was conducted in three stages which included the estimation of a null distribution to describe usual ICU performance. Log-SMRs with appropriate estimates of standard errors are presented in a funnel plot using 5% false discovery rate thresholds. False coverage-statement rate confidence intervals are also presented. The observed numbers of deaths for ICUs identified as unusual are compared to the predicted true worst numbers of deaths under the model for usual ICU performance.

Results

Seven ICUs were identified as performing unusually over the period 2000 to 2010, in particular, demonstrating high risk-adjusted mortality compared to the majority of ICUs. Four of the seven were ICUs in private hospitals. Our three-stage approach to the analysis detected outlying ICUs which were not identified in a conventional (single) risk-adjusted model for mortality using SMRs to compare ICUs. We also observed a significant linear decline in mortality over the decade. Distinct yearly and weekly respiratory seasonal effects were observed across regions of Australia and New Zealand for the first time.

Conclusions

The statistical approach proposed in this paper is intended to be used for the review of observed ICU and hospital mortality. Two important messages from our study are firstly, that comprehensive risk-adjustment is essential in modelling patient mortality for comparing performance, and secondly, that the appropriate statistical analysis is complicated.
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Literature
1.
go back to reference Schoenfeld D: Survival methods, including those using competing risk analysis, are not appropriate for intensive care unit outcome studies. Crit Care. 2006, 10: 103-CrossRefPubMed Schoenfeld D: Survival methods, including those using competing risk analysis, are not appropriate for intensive care unit outcome studies. Crit Care. 2006, 10: 103-CrossRefPubMed
2.
go back to reference Freemantle N, Richardson M, Wood J, Ray D, Khosla S, Shahian D, Roche W, Stephens I, Keogh B, Pagano D: Weekend hospitalization and additional risk of death: an analysis of inpatient data. J R Soc Med. 2012, 105: 74-84. 10.1258/jrsm.2012.120009.CrossRefPubMedPubMedCentral Freemantle N, Richardson M, Wood J, Ray D, Khosla S, Shahian D, Roche W, Stephens I, Keogh B, Pagano D: Weekend hospitalization and additional risk of death: an analysis of inpatient data. J R Soc Med. 2012, 105: 74-84. 10.1258/jrsm.2012.120009.CrossRefPubMedPubMedCentral
3.
go back to reference Stow PJ, Hart GK, Higlett T, George C, Herkes R, McWilliam D, Bellomo R: Development and implementation of a high-quality clinical database: the Australian and New Zealand Intensive Care Society Adult Patient Database. J Crit Care. 2006, 21: 133-41. 10.1016/j.jcrc.2005.11.010.CrossRefPubMed Stow PJ, Hart GK, Higlett T, George C, Herkes R, McWilliam D, Bellomo R: Development and implementation of a high-quality clinical database: the Australian and New Zealand Intensive Care Society Adult Patient Database. J Crit Care. 2006, 21: 133-41. 10.1016/j.jcrc.2005.11.010.CrossRefPubMed
4.
go back to reference Knaus WA, Draper EA, Wagner DP, Zimmerman JE: APACHE II: a severity of disease classification system. Crit Care Med. 1985, 13: 818-29. 10.1097/00003246-198510000-00009.CrossRefPubMed Knaus WA, Draper EA, Wagner DP, Zimmerman JE: APACHE II: a severity of disease classification system. Crit Care Med. 1985, 13: 818-29. 10.1097/00003246-198510000-00009.CrossRefPubMed
5.
go back to reference Knaus WA, Wagner DP, Draper EA, Zimmerman JE, Bergner M, Bastos PG, Sirio CA, Murphy DJ, Lotring T, Damiano A: The APACHE III prognostic system. Risk prediction of hospital mortality for critically ill hospitalized adults. Chest. 1991, 100: 1619-36. 10.1378/chest.100.6.1619.CrossRefPubMed Knaus WA, Wagner DP, Draper EA, Zimmerman JE, Bergner M, Bastos PG, Sirio CA, Murphy DJ, Lotring T, Damiano A: The APACHE III prognostic system. Risk prediction of hospital mortality for critically ill hospitalized adults. Chest. 1991, 100: 1619-36. 10.1378/chest.100.6.1619.CrossRefPubMed
6.
go back to reference Le Gall JR, Lemeshow S, Saulnier F: A new simplified acute physiology score (SAPS II) based on a European/North American multicenter study. JAMA. 1993, 270: 2957-63. 10.1001/jama.1993.03510240069035.CrossRefPubMed Le Gall JR, Lemeshow S, Saulnier F: A new simplified acute physiology score (SAPS II) based on a European/North American multicenter study. JAMA. 1993, 270: 2957-63. 10.1001/jama.1993.03510240069035.CrossRefPubMed
8.
go back to reference Gallagher MP, Krumholz HM: Public reporting of hospital outcomes: a challenging road ahead. MJA. 2011, 194: 658-60.PubMed Gallagher MP, Krumholz HM: Public reporting of hospital outcomes: a challenging road ahead. MJA. 2011, 194: 658-60.PubMed
9.
go back to reference Goldstein H, Spiegelhalter DJ: League tables and their limitations: statistical issues in comparisons of institutional performance. JRSS A. 1996, 159: 285-443. Goldstein H, Spiegelhalter DJ: League tables and their limitations: statistical issues in comparisons of institutional performance. JRSS A. 1996, 159: 285-443.
10.
go back to reference DeLong ER, Peterson ED, DeLong DM, Muhlbaier LH, Hackett S, Mark DB: Comparing risk-adjustment methods for provider profiling. Statist Med. 1997, 16: 2645-64.CrossRef DeLong ER, Peterson ED, DeLong DM, Muhlbaier LH, Hackett S, Mark DB: Comparing risk-adjustment methods for provider profiling. Statist Med. 1997, 16: 2645-64.CrossRef
11.
go back to reference Jones HE, Spiegelhalter DJ: The identification of ‘unusual’ health-care providers from a hierarchical model. Am Stat. 2011, 65: 154-63. 10.1198/tast.2011.10190.CrossRef Jones HE, Spiegelhalter DJ: The identification of ‘unusual’ health-care providers from a hierarchical model. Am Stat. 2011, 65: 154-63. 10.1198/tast.2011.10190.CrossRef
12.
go back to reference Normand S-LT, Shahian DM: Statistical and clinical aspects of hospital outcomes profiling. Stat Sci. 2007, 22: 206-26. 10.1214/088342307000000096.CrossRef Normand S-LT, Shahian DM: Statistical and clinical aspects of hospital outcomes profiling. Stat Sci. 2007, 22: 206-26. 10.1214/088342307000000096.CrossRef
13.
go back to reference Ohlssen DI, Sharples L, Spiegelhalter DJ: A hierarchical modelling framework for identifying unusual performance in health care providers. JRSS A. 2007, 170: 265-90. Ohlssen DI, Sharples L, Spiegelhalter DJ: A hierarchical modelling framework for identifying unusual performance in health care providers. JRSS A. 2007, 170: 265-90.
14.
go back to reference Kasza J, Moran JL, Solomon PJ: Evaluating the performance of Australian and New Zealand intensive care units in 2009 and 2010. Statist Med. 2013, 13: 3720-36.CrossRef Kasza J, Moran JL, Solomon PJ: Evaluating the performance of Australian and New Zealand intensive care units in 2009 and 2010. Statist Med. 2013, 13: 3720-36.CrossRef
15.
go back to reference Kalbfleisch JD, Wolfe R: On monitoring outcomes of medical providers. Stat Bio. 2013, 5: 286-302. 10.1007/s12561-013-9093-x.CrossRef Kalbfleisch JD, Wolfe R: On monitoring outcomes of medical providers. Stat Bio. 2013, 5: 286-302. 10.1007/s12561-013-9093-x.CrossRef
16.
go back to reference Pouw ME, Peelen LM, Lingsma HF, Pieter D, Steyerberg E, Kalkman CJ, Moons KGM: Hospital standardized mortality ratio: consequences of adjusting hospital mortality with indirect standardization. PLOS One. 2013, 8 (4): e59160-10.1371/journal.pone.0059160. doi:10.1371/journal.pone.0059160CrossRefPubMedPubMedCentral Pouw ME, Peelen LM, Lingsma HF, Pieter D, Steyerberg E, Kalkman CJ, Moons KGM: Hospital standardized mortality ratio: consequences of adjusting hospital mortality with indirect standardization. PLOS One. 2013, 8 (4): e59160-10.1371/journal.pone.0059160. doi:10.1371/journal.pone.0059160CrossRefPubMedPubMedCentral
17.
go back to reference Seaton SE, Barker L, Lingsm HF, Steyerberg EW, Manktelow BN: What is the probability of detecting poorly performing hospitals using funnel plots?. BMJ Qual Saf. 2013, doi:10.1136/bmjqs-2012-001689 Seaton SE, Barker L, Lingsm HF, Steyerberg EW, Manktelow BN: What is the probability of detecting poorly performing hospitals using funnel plots?. BMJ Qual Saf. 2013, doi:10.1136/bmjqs-2012-001689
19.
go back to reference Moran JL, Bristow P, Solomon PJ, George C, Hart G: Mortality and length-of-stay outcomes, 1993-2003 in the binational Australian and New Zealand intensive care Adult Patient Database. Crit Care Med. 2008, 36: 46-61. 10.1097/01.CCM.0000295313.08084.58.CrossRefPubMed Moran JL, Bristow P, Solomon PJ, George C, Hart G: Mortality and length-of-stay outcomes, 1993-2003 in the binational Australian and New Zealand intensive care Adult Patient Database. Crit Care Med. 2008, 36: 46-61. 10.1097/01.CCM.0000295313.08084.58.CrossRefPubMed
20.
go back to reference Hosmer DW, Lemeshow S: Confidence interval estimates of an index of quality performance based on logistic regression models. Statist Med. 1995, 14: 2161-72. 10.1002/sim.4780141909.CrossRef Hosmer DW, Lemeshow S: Confidence interval estimates of an index of quality performance based on logistic regression models. Statist Med. 1995, 14: 2161-72. 10.1002/sim.4780141909.CrossRef
21.
go back to reference StataCorp: Stata™: Release 12. 2011, College Station, TX, USA: StataCorp LP, Statistical Software StataCorp: Stata™: Release 12. 2011, College Station, TX, USA: StataCorp LP, Statistical Software
22.
go back to reference Iezzoni L: The risks of risk-adjustment. JAMA. 1997, 278: 1600-7. 10.1001/jama.1997.03550190064046.CrossRefPubMed Iezzoni L: The risks of risk-adjustment. JAMA. 1997, 278: 1600-7. 10.1001/jama.1997.03550190064046.CrossRefPubMed
23.
go back to reference Sauerbrei W, Royston P, Binder H: Selection of important variables and determination of functional form for continuous predictors in multivariable model building. Stat Med. 2007, 26: 5512-8. 10.1002/sim.3148.CrossRefPubMed Sauerbrei W, Royston P, Binder H: Selection of important variables and determination of functional form for continuous predictors in multivariable model building. Stat Med. 2007, 26: 5512-8. 10.1002/sim.3148.CrossRefPubMed
24.
go back to reference Moran JL, Solomon PJ: Conventional and advanced time series estimation: application to the Australian and New Zealand Intensive Care Society Adult Patient Database 1993-2006. JECP. 2011, 17: 45-60. Moran JL, Solomon PJ: Conventional and advanced time series estimation: application to the Australian and New Zealand Intensive Care Society Adult Patient Database 1993-2006. JECP. 2011, 17: 45-60.
25.
go back to reference Bhonagiri D, Pilcher DV, Bailey MJ: Increased mortality associated with after-hours and weekend admission to the intensive care unit: a retrospective analysis. MJA. 2011, 194: 287-92.PubMed Bhonagiri D, Pilcher DV, Bailey MJ: Increased mortality associated with after-hours and weekend admission to the intensive care unit: a retrospective analysis. MJA. 2011, 194: 287-92.PubMed
26.
go back to reference Diggle PJ: Time Series: a Biostatistical Introduction. 1990, Oxford: Oxford University Press Diggle PJ: Time Series: a Biostatistical Introduction. 1990, Oxford: Oxford University Press
27.
go back to reference Stolwijk AM, Straatman H, Zielhuis GA: Studying seasonality by using sine and cosine functions in regression analysis. J Epid Comm H. 1999, 53: 235-38. 10.1136/jech.53.4.235.CrossRef Stolwijk AM, Straatman H, Zielhuis GA: Studying seasonality by using sine and cosine functions in regression analysis. J Epid Comm H. 1999, 53: 235-38. 10.1136/jech.53.4.235.CrossRef
28.
go back to reference Paul P, Pennell ML, Lemeshow S: Standardizing the power of the Hosmer-Lemeshow goodness of fit test in large data sets. Stat Med. 2013, 32: 67-80. 10.1002/sim.5525.CrossRefPubMed Paul P, Pennell ML, Lemeshow S: Standardizing the power of the Hosmer-Lemeshow goodness of fit test in large data sets. Stat Med. 2013, 32: 67-80. 10.1002/sim.5525.CrossRefPubMed
29.
go back to reference Gelman A, Hill J: Data Analysis Using Regression and Multilevel/hierarchical Models. 2007, Cambridge: Cambridge University Press Gelman A, Hill J: Data Analysis Using Regression and Multilevel/hierarchical Models. 2007, Cambridge: Cambridge University Press
30.
go back to reference Verbeke G, Molenberghs G: The gradient function as an exploratory goodness-of-fit assessment of the random-effects distribution in mixed models. Biostat. 2013, 14: 477-90. 10.1093/biostatistics/kxs059.CrossRef Verbeke G, Molenberghs G: The gradient function as an exploratory goodness-of-fit assessment of the random-effects distribution in mixed models. Biostat. 2013, 14: 477-90. 10.1093/biostatistics/kxs059.CrossRef
31.
go back to reference Benjamini Y, Yekutieli D: False discovery rate-adjusted multiple confidence intervals for selected parameters. JASA. 2005, 100: 71-93. 10.1198/016214504000001907.CrossRef Benjamini Y, Yekutieli D: False discovery rate-adjusted multiple confidence intervals for selected parameters. JASA. 2005, 100: 71-93. 10.1198/016214504000001907.CrossRef
32.
go back to reference Benjamini Y, Hochberg Y: Controlling the false discovery rate: a practical and powerful approach to multiple testing. JRSS B. 1995, 57: 289-300. Benjamini Y, Hochberg Y: Controlling the false discovery rate: a practical and powerful approach to multiple testing. JRSS B. 1995, 57: 289-300.
33.
go back to reference Harrison DA, Lertsithichai P, Brady AR, Carpenter JR, Rowan K: Winter excess mortality in intensive care in the UK: an analysis of outcome adjusted for patient casemix and unit workload. Int Care Med. 2004, 30: 1900-7. 10.1007/s00134-004-2390-6.CrossRef Harrison DA, Lertsithichai P, Brady AR, Carpenter JR, Rowan K: Winter excess mortality in intensive care in the UK: an analysis of outcome adjusted for patient casemix and unit workload. Int Care Med. 2004, 30: 1900-7. 10.1007/s00134-004-2390-6.CrossRef
34.
go back to reference Twisk J, de Boer M, de Vente W, Heymans M: Multiple imputation of missing values was not necessary before performing a longitudinal mixed-model analysis. J Clin Epi. 2013, 66: 1022-28. 10.1016/j.jclinepi.2013.03.017.CrossRef Twisk J, de Boer M, de Vente W, Heymans M: Multiple imputation of missing values was not necessary before performing a longitudinal mixed-model analysis. J Clin Epi. 2013, 66: 1022-28. 10.1016/j.jclinepi.2013.03.017.CrossRef
Metadata
Title
Identifying unusual performance in Australian and New Zealand intensive care units from 2000 to 2010
Authors
Patricia J Solomon
Jessica Kasza
John L Moran
the Australian and New Zealand Intensive Care Society (ANZICS) Centre for Outcome and Resource Evaluation (CORE)
Publication date
01-12-2014
Publisher
BioMed Central
Published in
BMC Medical Research Methodology / Issue 1/2014
Electronic ISSN: 1471-2288
DOI
https://doi.org/10.1186/1471-2288-14-53

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