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Journal Abstract Search

94 related items for PubMed ID: 18787441

  • 21. Severity of illness and organ failure assessment in adult intensive care units.
    Afessa B, Gajic O, Keegan MT.
    Crit Care Clin; 2007 Jul; 23(3):639-58. PubMed ID: 17900487
    [Abstract] [Full Text] [Related]

  • 22. 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.
    Brinkman S, Abu-Hanna A, van der Veen A, de Jonge E, de Keizer NF.
    Crit Care Med; 2012 Feb; 40(2):373-8. PubMed ID: 21983367
    [Abstract] [Full Text] [Related]

  • 23. Admission patterns and outcomes of patients admitted to critical care in the UK with surgically treated facial infection: an analysis of the Intensive Care National Audit and Research Centre Case Mix Programme database.
    McGoldrick DM, Edwards J, Praveen P, Parmar S.
    Br J Oral Maxillofac Surg; 2022 Oct; 60(8):1074-1079. PubMed ID: 35760643
    [Abstract] [Full Text] [Related]

  • 24. Admission patterns and outcomes of postoperative oral cavity and oropharyngeal cancer patients admitted to critical care in the UK: an analysis of the Intensive Care National Audit and Research Centre Case Mix Programme database.
    McGoldrick DM, Edwards J, Abdelrahman A, Praveen P, Parmar S.
    Br J Oral Maxillofac Surg; 2022 Oct; 60(8):1108-1113. PubMed ID: 35680462
    [Abstract] [Full Text] [Related]

  • 25. Outcome prediction in critical care: the Mortality Probability Models.
    Higgins TL, Teres D, Nathanson B.
    Curr Opin Crit Care; 2008 Oct; 14(5):498-505. PubMed ID: 18787440
    [Abstract] [Full Text] [Related]

  • 26. Prognostic models based on administrative data alone inadequately predict the survival outcomes for critically ill patients at 180 days post-hospital discharge.
    Bohensky MA, Jolley D, Pilcher DV, Sundararajan V, Evans S, Brand CA.
    J Crit Care; 2012 Aug; 27(4):422.e11-21. PubMed ID: 22591572
    [Abstract] [Full Text] [Related]

  • 27. The suitability of the Pediatric Index of Mortality (PIM), PIM2, the Pediatric Risk of Mortality (PRISM), and PRISM III for monitoring the quality of pediatric intensive care in Australia and New Zealand.
    Slater A, Shann F, ANZICS Paediatric Study Group.
    Pediatr Crit Care Med; 2004 Sep; 5(5):447-54. PubMed ID: 15329160
    [Abstract] [Full Text] [Related]

  • 28. Outcome prediction in intensive care: why we need to reinvent the wheel.
    Moreno RP.
    Curr Opin Crit Care; 2008 Oct; 14(5):483-4. PubMed ID: 18787437
    [No Abstract] [Full Text] [Related]

  • 29. Comparison of clinical assessment with APACHE II for predicting mortality risk in patients admitted to a medical intensive care unit.
    Kruse JA, Thill-Baharozian MC, Carlson RW.
    JAMA; 2008 Oct; 260(12):1739-42. PubMed ID: 3137374
    [Abstract] [Full Text] [Related]

  • 30. Changes in health-related quality of life and factors predicting long-term outcomes in older adults admitted to intensive care units.
    Khouli H, Astua A, Dombrowski W, Ahmad F, Homel P, Shapiro J, Singh J, Nallamothu R, Mahbub H, Eden E, Delfiner J.
    Crit Care Med; 2011 Apr; 39(4):731-7. PubMed ID: 21263318
    [Abstract] [Full Text] [Related]

  • 31. Designing optimal mortality risk prediction scores that preserve clinical knowledge.
    Arzeno NM, Lawson KA, Duzinski SV, Vikalo H.
    J Biomed Inform; 2015 Aug; 56():145-56. PubMed ID: 26056073
    [Abstract] [Full Text] [Related]

  • 32. Comparing observed and predicted mortality among ICUs using different prognostic systems: why do performance assessments differ?
    Kramer AA, Higgins TL, Zimmerman JE.
    Crit Care Med; 2015 Feb; 43(2):261-9. PubMed ID: 25365725
    [Abstract] [Full Text] [Related]

  • 33. Outcome prediction in critical care: the Acute Physiology and Chronic Health Evaluation models.
    Zimmerman JE, Kramer AA.
    Curr Opin Crit Care; 2008 Oct; 14(5):491-7. PubMed ID: 18787439
    [Abstract] [Full Text] [Related]

  • 34. Association of automated data collection and data completeness with outcomes of intensive care. A new customised model for outcome prediction.
    Reinikainen M, Mussalo P, Hovilehto S, Uusaro A, Varpula T, Kari A, Pettilä V, Finnish Intensive Care Consortium.
    Acta Anaesthesiol Scand; 2012 Oct; 56(9):1114-22. PubMed ID: 22384799
    [Abstract] [Full Text] [Related]

  • 35. Modeling in-hospital patient survival during the first 28 days after intensive care unit admission: a prognostic model for clinical trials in general critically ill patients.
    Moreno RP, Metnitz PG, Metnitz B, Bauer P, Afonso de Carvalho S, Hoechtl A, SAPS 3 Investigators.
    J Crit Care; 2008 Sep; 23(3):339-48. PubMed ID: 18725038
    [Abstract] [Full Text] [Related]

  • 36. Severity-of-illness scoring systems and models: neurological and neurosurgical intensive care units.
    Murthy JM, Meena AK, Kumar SR.
    Neurol India; 2001 Jun; 49 Suppl 1():S91-4. PubMed ID: 11889480
    [Abstract] [Full Text] [Related]

  • 37. Why try to predict ICU outcomes?
    Power GS, Harrison DA.
    Curr Opin Crit Care; 2014 Oct; 20(5):544-9. PubMed ID: 25159474
    [Abstract] [Full Text] [Related]

  • 38. Development and testing of a hierarchical method to code the reason for admission to intensive care units: the ICNARC Coding Method. Intensive Care National Audit & Research Centre.
    Young JD, Goldfrad C, Rowan K.
    Br J Anaesth; 2001 Oct; 87(4):543-8. PubMed ID: 11878722
    [Abstract] [Full Text] [Related]

  • 39. [Analysis of outcome quality control in intensive care medicine using the Simplified Acute Physiology Score II].
    Schuster HP, Wilts S, Ritschel P.
    Med Klin (Munich); 1996 Jun 15; 91(6):343-8. PubMed ID: 8767306
    [Abstract] [Full Text] [Related]

  • 40. [Prognostic scores in intensive care].
    Unertl K, Kottler BM.
    Anaesthesist; 1997 Jun 15; 46(6):471-80. PubMed ID: 9297377
    [Abstract] [Full Text] [Related]

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