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Evaluating outcome from intensive care: A preliminary multihospital comparison
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The APACHE III prognostic system. Risk prediction of hospital mortality for critically ill hospitalized adults
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The use of APACHE III to evaluate ICU length of stay, resource use, and mortality after coronary artery bypass surgery
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Becker RB, Zimmerman JE, Knaus WA, et al. The use of APACHE III to evaluate ICU length of stay, resource use, and mortality after coronary artery bypass surgery. J Cardiovasc Surg 1995; 36:1-11.
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J Cardiovasc Surg
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0031880379
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Evaluation of acute physiology and chronic health evaluation III predictions of hospital mortality in an independent database
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Zimmerman JE, Wagner DP, Draper EA, et al. Evaluation of acute physiology and chronic health evaluation III predictions of hospital mortality in an independent database. Crit Care Med 1998; 26:1317-1326.
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Zimmerman, J.E.1
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6
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0033791746
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Intensive care unit length of stay: Recent changes and future challenges
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Rosenberg AL, Zimmerman JE, Alzola C, et al. Intensive care unit length of stay: recent changes and future challenges. Crit Care Med 2000; 28:3465-3473.
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Crit Care Med
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Rosenberg, A.L.1
Zimmerman, J.E.2
Alzola, C.3
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7
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0036743113
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Rating the quality of intensive care units: Is it a function of the intensive care unit scoring system?
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Glance LG, Osler TM, Dick A. Rating the quality of intensive care units: is it a function of the intensive care unit scoring system? Crit Care Med 2002; 30:1976-1982.
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Glance, L.G.1
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33646585120
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Harrison DA, Brady AR, Parry GJ, et al. Recalibration of risk prediction models In a large multicenter cohort of admissions to adult, general critical care units in the United Kingdom. Crit Care Med 2006; 34:1378-1388. This study describes a head-to head comparison of the accuracy of APACHE II, APACHE III, SAPS II, and MPM II for predicting mortality among 231 930 ICU admissions in the UK. The results demonstrate the limitations of recalibration, the need to use quantitative measures to assess calibration, and the hazards of using risk-adjustment models internationally.
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Harrison DA, Brady AR, Parry GJ, et al. Recalibration of risk prediction models In a large multicenter cohort of admissions to adult, general critical care units in the United Kingdom. Crit Care Med 2006; 34:1378-1388. This study describes a head-to head comparison of the accuracy of APACHE II, APACHE III, SAPS II, and MPM II for predicting mortality among 231 930 ICU admissions in the UK. The results demonstrate the limitations of recalibration, the need to use quantitative measures to assess calibration, and the hazards of using risk-adjustment models internationally.
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9
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31744439227
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Predictive mortality models are not like fine wine
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Kramer A. Predictive mortality models are not like fine wine. Crit Care 2005; 9:636-637.
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Crit Care
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Kramer, A.1
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10
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33646592518
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Zimmerman JE, Kramer AA, McNair DS, Malila FM. Acute Physiology and Chronic Health Evaluation (APACHE) IV: hospital mortality assessment for today's critically ill patients. Crit Care Med 2006; 34:1297-1310. This study describes the APACHE IV model for predicting group hospital mortality. The obsolescence of APACHE II and III for mortality prediction, the importance of ICU admission diagnosis as an outcome predictor, and the value of including new variables in enhancing prognostic accuracy are documented.
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Zimmerman JE, Kramer AA, McNair DS, Malila FM. Acute Physiology and Chronic Health Evaluation (APACHE) IV: hospital mortality assessment for today's critically ill patients. Crit Care Med 2006; 34:1297-1310. This study describes the APACHE IV model for predicting group hospital mortality. The obsolescence of APACHE II and III for mortality prediction, the importance of ICU admission diagnosis as an outcome predictor, and the value of including new variables in enhancing prognostic accuracy are documented.
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11
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55349091298
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Kramer AA, Zimmerman JE. Predicting outcomes for cardiac surgery patients after intensive care admission. J Cardiothorac Vasc Anesth (in press). A comprehensive review of the models used to predict hospital mortality and resource use based on data obtained after CABG surgery and cardiac valve surgery. This study describes the evolution, rationale, and accuracy of APACHE models used for cardiac surgery patients as well as patients who have had vascular and thoracic procedures.
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Kramer AA, Zimmerman JE. Predicting outcomes for cardiac surgery patients after intensive care admission. J Cardiothorac Vasc Anesth (in press). A comprehensive review of the models used to predict hospital mortality and resource use based on data obtained after CABG surgery and cardiac valve surgery. This study describes the evolution, rationale, and accuracy of APACHE models used for cardiac surgery patients as well as patients who have had vascular and thoracic procedures.
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12
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0036209168
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A multicenter description of intermediate care patients: Comparison with ICU low risk monitor patients
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Junker C, Zimmerman JE, Alzola C, et al. A multicenter description of intermediate care patients: comparison with ICU low risk monitor patients. Chest 2002; 121:1253-1261.
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Chest
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Junker, C.1
Zimmerman, J.E.2
Alzola, C.3
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13
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33745698681
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Hospital volume and the outcomes of mechanical ventilation
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Kahn JM, Goss CH, Heagerty PJ, et al. Hospital volume and the outcomes of mechanical ventilation. N Engl J Med 2006; 355:41-50.
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N Engl J Med
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Kahn, J.M.1
Goss, C.H.2
Heagerty, P.J.3
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14
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33748771981
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Intensive care unit length of stay: Benchmarking based on Acute Physiology and Chronic Health Evaluation (APACHE) IV
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Zimmerman JE, Kramer AA, McNair DS, et al. Intensive care unit length of stay: benchmarking based on Acute Physiology and Chronic Health Evaluation (APACHE) IV. Crit Care Med 2006; 34:2517-2529.
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Crit Care Med
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Zimmerman, J.E.1
Kramer, A.A.2
McNair, D.S.3
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What's in a day? Determining intensive care unit length of stay
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Marik PE, Hedman L. What's in a day? Determining intensive care unit length of stay. Crit Care Med 2000; 28:2090-2093.
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Crit Care Med
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Marik, P.E.1
Hedman, L.2
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Variation in outcomes in Veterans Affairs intensive care units with a computerized severity measure
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Render ML, Kim HM, Deddens J, et al. Variation in outcomes in Veterans Affairs intensive care units with a computerized severity measure. Crit Care Med 2005; 33:930-939.
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Render, M.L.1
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17
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0037635418
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The use of benchmarking to identify top performing critical care units: A preliminary assessment of their policies and practices
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Zimmerman JE, Alzola C, Von Rueden KT. The use of benchmarking to identify top performing critical care units: a preliminary assessment of their policies and practices. J Crit Care 2003; 18:76-86.
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Zimmerman, J.E.1
Alzola, C.2
Von Rueden, K.T.3
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18
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0028111550
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Daily prognostic estimates for critically ill adults in intensive care units: Results from a prospective multicenter, inception cohort analysis
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Wagner, D.P.1
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Harrell, F.E.3
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19
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0029905274
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Intensive care unit admissions with cirrhosis: Risk stratifying patient groups and predicting individual survival
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Zimmerman JE, Wagner DP, Seneff MG, et al. Intensive care unit admissions with cirrhosis: risk stratifying patient groups and predicting individual survival. Hepatology 1996; 23:1393-1401.
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Hepatology
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Seneff, M.G.3
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Potentially ineffective care: A new outcome to assess the limits of critical care
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Esserman L, Beklora J, Lenart L. Potentially ineffective care: a new outcome to assess the limits of critical care. JAMA 1995; 274:1544-1551.
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Identifying potentially ineffective care in a community hospital
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Fleegler BM, Jackson DK, Turnbull J, et al. Identifying potentially ineffective care in a community hospital. Crit Care Med 2002; 30:1803-1807.
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22
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26444506063
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SAPS 3: From evaluation of the patient to evaluation of the intensive care unit. Part 2: Development of a prognostic model for hospital mortality at ICU admission
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Moreno RP, Metnitz PGH, Almeida E, et al. SAPS 3: from evaluation of the patient to evaluation of the intensive care unit. Part 2: Development of a prognostic model for hospital mortality at ICU admission. Intensive Care Med 2005; 31:1345-1355.
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Almeida, E.3
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33947137881
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o-III). Crit Care Med 2007; 35:827-835. This manuscript describes the extent of over prediction by the 1993 version of the Mortality Probability Admission Model in contemporary practice and the development and validation of an updated and revised version.
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o-III). Crit Care Med 2007; 35:827-835. This manuscript describes the extent of over prediction by the 1993 version of the Mortality Probability Admission Model in contemporary practice and the development and validation of an updated and revised version.
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24
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34247145553
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A new risk prediction model for critical care: The Intensive Care National Audit & Research Centre (ICNARC) model
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Using a database comprised of data used for APACHE II, APACHE III, SAPS II, and MPM II mortality predictions, this model was developed to provide risk adjustment among UK ICUs
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Harrison DA, Parry GJ, Carpenter JR, et al. A new risk prediction model for critical care: the Intensive Care National Audit & Research Centre (ICNARC) model. Crit Care Med 2007; 35:1091-1098. Using a database comprised of data used for APACHE II, APACHE III, SAPS II, and MPM II mortality predictions, this model was developed to provide risk adjustment among UK ICUs.
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Crit Care Med
, vol.35
, pp. 1091-1098
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Harrison, D.A.1
Parry, G.J.2
Carpenter, J.R.3
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25
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34547662014
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A revised method to assess intensive care unit clinical performance and resource utilization
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Nathanson BH, Higgins TL, Teres D, et al. A revised method to assess intensive care unit clinical performance and resource utilization. Crit Care Med 2007; 35:1853-1862.
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Nathanson, B.H.1
Higgins, T.L.2
Teres, D.3
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26
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34547115213
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Variability in outcome and resource use in intensive care units
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This article describes differences in standardized mortality ratios and resource use based on ICU LOS among ICUs that were part of the SAPS 3 database
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Rothen HU, Stricker K, Einfalt J, et al. Variability in outcome and resource use in intensive care units. Intensive Care Med 2007; 33:1329-1336. This article describes differences in standardized mortality ratios and resource use based on ICU LOS among ICUs that were part of the SAPS 3 database.
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Intensive Care Med
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Rothen, H.U.1
Stricker, K.2
Einfalt, J.3
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Bakhshi-Raiez F, Peek N, Bosman RJ, et al. The impact of different prognostic models and their customization on institutional comparison of intensive care units. Crit Care Med 2007; 35:2553-2560. The results from this study show that ICU performance rankings based on case-mix adjusted league tables are sensitive to the prognostic model used, but not to customization. The study also documents the importance of displaying the uncertainty associated with performance ranking based on league tables.
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Bakhshi-Raiez F, Peek N, Bosman RJ, et al. The impact of different prognostic models and their customization on institutional comparison of intensive care units. Crit Care Med 2007; 35:2553-2560. The results from this study show that ICU performance rankings based on case-mix adjusted league tables are sensitive to the prognostic model used, but not to customization. The study also documents the importance of displaying the uncertainty associated with performance ranking based on league tables.
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29
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34548258778
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Assessing the calibration of mortality benchmarks in critical care: The Hosmer-Lemeshow test revisited
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This study describes information that must be considered when evaluating model calibration using the Hosmer-Lemeshow test. It highlights the importance of the number of patients, observed and predicted probabilities by decile, and quantitative measures of calibration
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Kramer AA, Zimmerman JE. Assessing the calibration of mortality benchmarks in critical care: the Hosmer-Lemeshow test revisited. Crit Care Med 2007; 35:2052-2056. This study describes information that must be considered when evaluating model calibration using the Hosmer-Lemeshow test. It highlights the importance of the number of patients, observed and predicted probabilities by decile, and quantitative measures of calibration.
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Crit Care Med
, vol.35
, pp. 2052-2056
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Kramer, A.A.1
Zimmerman, J.E.2
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30
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0000306511
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Comparing ICU populations: Background and current methods
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Sibbald WJ, Bion JF, editors, Berlin: Springer Verlag;
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Zimmerman JE, Draper EA, Wagner DP. Comparing ICU populations: background and current methods. In: Sibbald WJ, Bion JF, editors. Evaluating critical care. Berlin: Springer Verlag; 2001. pp. 121-139.
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Evaluating critical care
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Zimmerman, J.E.1
Draper, E.A.2
Wagner, D.P.3
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31
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External validation of the SAPS II, APACHE II, and APACHE III prognostic models in South England: A multicentre study
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Beck DH, Smith GB, Pappachan JV, et al. External validation of the SAPS II, APACHE II, and APACHE III prognostic models in South England: a multicentre study. Intensive Care Med 2003; 29:249-256.
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Beck, D.H.1
Smith, G.B.2
Pappachan, J.V.3
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32
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41649120320
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The VA ICU risk adjustment model: Validation, updating, recalibration
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This article describes and updates the fully automated Veterans Administration ICU risk adjustment model. This model demonstrates the power of computerized measurement and its utility for widespread evaluation of variability in ICU outcomes
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Render ML, Deddens J, Freyberg R, et al. The VA ICU risk adjustment model: validation, updating, recalibration. Crit Care Med 2008; 36:1031-1042. This article describes and updates the fully automated Veterans Administration ICU risk adjustment model. This model demonstrates the power of computerized measurement and its utility for widespread evaluation of variability in ICU outcomes.
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Crit Care Med
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, pp. 1031-1042
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Render, M.L.1
Deddens, J.2
Freyberg, R.3
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33
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34249297414
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Effect of interhospital transfer on resource utilization and outcomes at a tertiary care referral center
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This analysis confirms the accuracy of the adjustments used by APACHE III for predicting mortality and length of stay among patients transferred to a large tertiary care center. It includes an excellent discussion of why these results differ from those of prior studies
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Golestanian E, Scruggs JE, Gagnon RE, et al. Effect of interhospital transfer on resource utilization and outcomes at a tertiary care referral center. Crit Care Med 2007; 35:1470-1476. This analysis confirms the accuracy of the adjustments used by APACHE III for predicting mortality and length of stay among patients transferred to a large tertiary care center. It includes an excellent discussion of why these results differ from those of prior studies.
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Crit Care Med
, vol.35
, pp. 1470-1476
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Golestanian, E.1
Scruggs, J.E.2
Gagnon, R.E.3
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Decisions to forgo life-sustaining therapy in ICU patients independently predict hospital death
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Azoulay E, Pochard F, Garrouste-Orgeas M, et al. Decisions to forgo life-sustaining therapy in ICU patients independently predict hospital death. Intensive Care Med 2003; 29:1895-1901.
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Intensive Care Med
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Azoulay, E.1
Pochard, F.2
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35
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New disseminated intravascular coagulation score: A useful tool to predict mortality in comparison with Acute Physiology and Chronic Health Evaluation II and Logistic Organ Dysfunction scores
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Angstwurm MWA, Dempfle CE, Spannagl M. New disseminated intravascular coagulation score: a useful tool to predict mortality in comparison with Acute Physiology and Chronic Health Evaluation II and Logistic Organ Dysfunction scores. Crit Care Med 2006; 34:314-320.
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Angstwurm, M.W.A.1
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Thrombocytomenia in patients in the medical intensive care unit: Bleeding prevalence, transfusion requirements, and outcome
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Strauss R, Wehler M, Mehler K, et al. Thrombocytomenia in patients in the medical intensive care unit: bleeding prevalence, transfusion requirements, and outcome. Crit Care Med 2002; 30:1765-1771.
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Strauss, R.1
Wehler, M.2
Mehler, K.3
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37
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33645837049
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Morbid obesity is an independent determinant of death among surgical critically ill patients
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Nasraway SA, Albert M, Donnelly AM, et al. Morbid obesity is an independent determinant of death among surgical critically ill patients. Crit Care Med 2006; 34:964-970.
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Nasraway, S.A.1
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33644589344
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Body mass index is independently associated with hospital mortality in mechanically ventilated adults with acute lung injury
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O'Brien JM, Phillips GS, Ali N, et al. Body mass index is independently associated with hospital mortality in mechanically ventilated adults with acute lung injury. Crit Care Med 2006; 34:738-744.
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O'Brien, J.M.1
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Low baseline serum creatinine concentration predicts mortality in critically ill patients independent of body mass index
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Cartin-Ceba R, Afessa B, Gajic O. Low baseline serum creatinine concentration predicts mortality in critically ill patients independent of body mass index. Crit Care Med 2007; 34:2420-2423.
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Chalfin DB, Trzeciak S, Likourezos A, et al. Impact of delayed transfer of critically ill patients from the emergency department to the intensive care unit. Crit Care Med 2007; 35:1477-1483. This analysis suggests that for critically ill emergency room patients, a delay in ICU admission hour of 6 h or greater is associated with increased hospital mortality and length of stay. This article highlights the need to consider admission delays as a cause of excess mortality and ICU stay compared to expected benchmarks.
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Chalfin DB, Trzeciak S, Likourezos A, et al. Impact of delayed transfer of critically ill patients from the emergency department to the intensive care unit. Crit Care Med 2007; 35:1477-1483. This analysis suggests that for critically ill emergency room patients, a delay in ICU admission hour of 6 h or greater is associated with increased hospital mortality and length of stay. This article highlights the need to consider admission delays as a cause of excess mortality and ICU stay compared to expected benchmarks.
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41
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0037791848
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Automated intensive care unit risk adjustment: Results from a National Veterans Affairs study
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Render ML, Kim HM, Welsh OE, et al. Automated intensive care unit risk adjustment: results from a National Veterans Affairs study. Crit Care Med 2003; 31:1638-1646.
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Render, M.L.1
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42
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Kahn JM, Kramer AA, Rubenfeld GD. Transferring critically ill patients out of the hospital improves the standardized mortality ratio. Chest 2007; 131:68-75. A simulation study that shows how a small increase in out of hospital transfers can bias an ICU's standardized mortality ratio. A 2 or 6% increase in transfers can make a low performing ICU appear to be high performing.
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Kahn JM, Kramer AA, Rubenfeld GD. Transferring critically ill patients out of the hospital improves the standardized mortality ratio. Chest 2007; 131:68-75. A simulation study that shows how a small increase in out of hospital transfers can bias an ICU's standardized mortality ratio. A 2 or 6% increase in transfers can make a low performing ICU appear to be high performing.
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43
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33646553862
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Critical care performance measurement: The time has come for all [editorial]
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Sirio CA. Critical care performance measurement: the time has come for all [editorial]. Crit Care Med 2006; 34:1538-1539.
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Lilford R, Mohammed MA, Spiegelhalter D, et al. Use and misuse of process and outcome data in managing performance of acute medical care: avoiding institutional stigma. Lancet 2004; 363:1147-1154.
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Lilford, R.1
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