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      Identification of high-risk subgroups in very elderly intensive care unit patients

      research-article
      Author(s): 1 , , 2 , 3 , 4
      Publication date (Electronic):
      Journal: Critical Care
      Publisher: BioMed Central

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          Abstract

          Introduction

          Current prognostic models for intensive care unit (ICU) patients have not been specifically developed or validated in the very elderly. The aim of this study was to develop a prognostic model for ICU patients 80 years old or older to predict in-hospital mortality by means of data obtained within 24 hours after ICU admission. Aside from having good overall performance, the model was designed to reliably and specifically identify subgroups at very high risk of dying.

          Methods

          A total of 6,867 consecutive patients 80 years old or older from 21 Dutch ICUs were studied. Data necessary to calculate the Glasgow Coma Scale, Acute Physiology and Chronic Health Evaluation II, Simplified Acute Physiology Score II (SAPS II), Mortality Probability Models II scores, and ICU and hospital survival were recorded. Data were randomly divided into a developmental ( n = 4,587) and a validation ( n = 2,289) set. By means of recursive partitioning analysis, a classification tree predicting in-hospital mortality was developed. This model was compared with the original SAPS II model and with the SAPS II model after recalibration for very elderly ICU patients in the Netherlands.

          Results

          Overall performance measured by the area under the receiver operating characteristic curve and by the Brier score was similar for the classification tree, the original SAPS II model, and the recalibrated SAPS II model. The tree identified most patients with very high risk of mortality (9.2% of patients versus 8.9% for the original SAPS II and 5.9% for the recalibrated SAPS II had a risk of more than 80%). With a cut-point at a risk of 80%, the positive predictive values were 0.88 for the tree, 0.83 for the original SAPS II, and 0.87 for the recalibrated SAPS II.

          Conclusion

          Prognostic models with good overall performance may also reliably identify subgroups of very elderly ICU patients who have a very high risk of dying before hospital discharge. The classification tree has the advantage of identifying the separate factors contributing to bad outcome and of using few variables. Up to 9.5% of patients were found to have a risk to die of more than 85%.

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          Most cited references23

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          The APACHE III prognostic system. Risk prediction of hospital mortality for critically ill hospitalized adults.

          W A Knaus, D P Wagner, E A Draper … (1991)
          The objective of this study was to refine the APACHE (Acute Physiology, Age, Chronic Health Evaluation) methodology in order to more accurately predict hospital mortality risk for critically ill hospitalized adults. We prospectively collected data on 17,440 unselected adult medical/surgical intensive care unit (ICU) admissions at 40 US hospitals (14 volunteer tertiary-care institutions and 26 hospitals randomly chosen to represent intensive care services nationwide). We analyzed the relationship between the patient's likelihood of surviving to hospital discharge and the following predictive variables: major medical and surgical disease categories, acute physiologic abnormalities, age, preexisting functional limitations, major comorbidities, and treatment location immediately prior to ICU admission. The APACHE III prognostic system consists of two options: (1) an APACHE III score, which can provide initial risk stratification for severely ill hospitalized patients within independently defined patient groups; and (2) an APACHE III predictive equation, which uses APACHE III score and reference data on major disease categories and treatment location immediately prior to ICU admission to provide risk estimates for hospital mortality for individual ICU patients. A five-point increase in APACHE III score (range, 0 to 299) is independently associated with a statistically significant increase in the relative risk of hospital death (odds ratio, 1.10 to 1.78) within each of 78 major medical and surgical disease categories. The overall predictive accuracy of the first-day APACHE III equation was such that, within 24 h of ICU admission, 95 percent of ICU admissions could be given a risk estimate for hospital death that was within 3 percent of that actually observed (r2 = 0.41; receiver operating characteristic = 0.90). Recording changes in the APACHE III score on each subsequent day of ICU therapy provided daily updates in these risk estimates. When applied across the individual ICUs, the first-day APACHE III equation accounted for the majority of variation in observed death rates (r2 = 0.90, p less than 0.0001).
          Article 0 comments Cited 455 times – based on 0 reviews     Review now
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            Use of intensive care at the end of life in the United States: an epidemiologic study.

            Derek C Angus, Amber E. Barnato, Walter Linde-Zwirble … (2004)
            Despite concern over the appropriateness and quality of care provided in an intensive care unit (ICU) at the end of life, the number of Americans who receive ICU care at the end of life is unknown. We sought to describe the use of ICU care at the end of life in the United States using hospital discharge data from 1999 for six states and the National Death Index. Retrospective analysis of administrative data to calculate age-specific rates of hospitalization with and without ICU use at the end of life, to generate national estimates of end-of-life hospital and ICU use, and to characterize age-specific case mix of ICU decedents. All nonfederal hospitals in the states of Florida, Massachusetts, New Jersey, New York, Virginia, and Washington. All inpatients in nonfederal hospitals in the six states in 1999. None. We found that there were 552,157 deaths in the six states in 1999, of which 38.3% occurred in hospital and 22.4% occurred after ICU admission. Using these data to project nationwide estimates, 540,000 people die after ICU admission each year. The age-specific rate of ICU use at the end of life was highest for infants (43%), ranged from 18% to 26% among older children and adults, and fell to 14% for those >85 yrs. Average length of stay and costs were 12.9 days and $24,541 for terminal ICU hospitalizations and 8.9 days and $8,548 for non-ICU terminal hospitalizations. One in five Americans die using ICU services. The doubling of persons over the age of 65 yrs by 2030 will require a system-wide expansion in ICU care for dying patients unless the healthcare system pursues rationing, more effective advanced care planning, and augmented capacity to care for dying patients in other settings.
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              A simplified acute physiology score for ICU patients.

              P. Le Mercier, Stefan Glaser, D Mathieu … (1984)
              We used 14 easily measured biologic and clinical variables to develop a simple scoring system reflecting the risk of death in ICU patients. The simplified acute physiology score (SAPS) was evaluated in 679 consecutive patients admitted to eight multidisciplinary referral ICUs in France. Surgery accounted for 40% of admissions. Data were collected during the first 24 h after ICU admission. SAPS correctly classified patients in groups of increasing probability of death, irrespective of diagnosis, and compared favorably with the acute physiology score (APS), a more complex scoring system which has also been applied to ICU patients. SAPS was a simpler and less time-consuming method for comparative studies and management evaluation between different ICUs.
              Article 0 comments Cited 157 times – based on 0 reviews     Review now
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                Author and article information

                Journal
                Journal ID (nlm-ta): Crit Care
                Title: Critical Care
                Publisher: BioMed Central
                ISSN (Print): 1364-8535
                ISSN (Electronic): 1466-609X
                Publication date (Print): 2007
                Publication date (Electronic): 8 March 2007
                Volume: 11
                Issue: 2
                Page: R33
                Affiliations
                [1 ]Department of Geriatrics, Academic Medical Center, University of Amsterdam, Meibergdreef 9 1105 AZ, Amsterdam, The Netherlands
                [2 ]Department of Medical Informatics, Academic Medical Center, University of Amsterdam, Meibergdreef 9 1105 AZ, Amsterdam, The Netherlands
                [3 ]Department of Internal Medicine, Cardiology and Pulmonary Disease, Academic Medical Center, University of Amsterdam, Meibergdreef 9 1105 AZ, Amsterdam, The Netherlands
                [4 ]Department of Intensive Care, Academic Medical Center, University of Amsterdam, Meibergdreef 9 1105 AZ, Amsterdam, The Netherlands
                Article
                Publisher ID: cc5716
                DOI: 10.1186/cc5716
                PMC ID: 2206449
                PubMed ID: 17346348
                SO-VID: 76785c39-ad6f-4755-9526-235adaec59e3
                Copyright © Copyright © 2007 de Rooij et al.; licensee BioMed Central Ltd.
                License:

                This is an open access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                Date received : 16 November 2006
                Date revision requested : 14 December 2006
                Date revision received : 18 January 2007
                Date accepted : 8 March 2007
                Categories
                Subject: Research

                ScienceOpen disciplines: Emergency medicine & Trauma
                Data availability:
                ScienceOpen disciplines: Emergency medicine & Trauma

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