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Original Research: CRITICAL CARE |

Do Elderly Patients Fare Well in the ICU? FREE TO VIEW

Brian H. Nathanson, PhD; Thomas L. Higgins, MD, MBA; Maura J. Brennan, MD; Andrew A. Kramer, PhD; Maureen Stark, MS; Daniel Teres, MD
Author and Funding Information

From OptiStatim, LLC (Dr Nathanson), Longmeadow, MA; Baystate Medical Center (Drs Higgins and Brennan), Tufts University School of Medicine, Springfield, MA; Cerner Corporation (Dr Kramer and Ms Stark), Kansas City, MO; and Tufts University School of Medicine (Dr Teres), Springfield, MA.

Correspondence to: Brian H. Nathanson, PhD, OptiStatim, LLC, PO Box 60844, Longmeadow, MA 01116; e-mail: brian.h.nathanson@att.net


Funding/Support: This work was supported by the Cerner Corporation.

Reproduction of this article is prohibited without written permission from the American College of Chest Physicians (http://www.chestpubs.org/site/misc/reprints.xhtml).


© 2011 American College of Chest Physicians


Chest. 2011;139(4):825-831. doi:10.1378/chest.10-1233
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Published online

Background:  A recent update of the Mortality Probability Model (MPM)-III found 14% of intensive care patients had age as their only MPM risk factor for hospital mortality. This subgroup had a low mortality rate (2% vs 14% overall), and pronounced differences were noted among elderly patients. This article is an expanded analysis of age-related mortality rates in patients in the ICU.

Methods:  Project IMPACT data from 135 ICUs for 124,885 patients treated from 2001 to 2004 were analyzed. Patients were stratified as elective surgical, emergency/unscheduled surgical, and medical and then further stratified by age and whether additional MPM risk factors were present or absent.

Results:  Mortality rose with advancing age within all patient categories. Elective surgical patients without other risk factors were the least likely to die at all ages (0.4% for patients aged 18-29 years to 9.2% for patients aged ≥ 90 years), whereas medical patients with one or more additional risk factors had the highest mortality rate (12.1% for patients aged 18-29 years to 36.0% for patients aged ≥ 90 years). In these two subsets, mortality rates approximately doubled in the elective surgical group among patients aged in their 70s (2.4%), 80s (4.3%), and 90s (9.2%) but rose less dramatically in the medical group (27.0%, 30.7%, and 36.0%, respectively).

Conclusions:  Although mortality increased with age, the risk differed significantly by patient subset, even among elderly patients, which may reflect a selection bias. Advanced age alone does not preclude successful surgical and ICU interventions, although the presence of serious comorbidities decreases the likelihood of survival to discharge for all age groups.

Figures in this Article

People are living longer around the world. Ten percent of the world’s population is aged > 60 years, and this age group is expected to double in size by 2050.1 Hospitalized patients’ acuity in the United States also is rising.2 Between 1985 and 2000, the number of ICU beds in the United States grew to 26% (from 69,300-87,400 beds) despite a decrease in other inpatient beds during the same period.3

These two trends, the “graying” of the population and rising inpatient acuity, are profoundly affecting our health-care system. The mean age of intensive care patients is climbing,4 and studies have shown that 11% of Medicare patients spend > 1 week in the ICU in their last 6 months of life.5 In short, today’s ICUs are largely occupied by older and sicker patients.

It is unethical to deny access to potentially life-saving therapies on the basis of age alone. However, the suffering of critically ill geriatric patients is significant, and care that merely prolongs the dying process is neither sensible nor compassionate. In addition, ICUs account for nearly one-third of acute care expenditures6,7 and are routinely at full capacity, making resource allocation challenging.8 Finally, it is difficult to offer appropriate counsel if the expected outcomes for older patients are unclear. Physicians often make inaccurate predictions on the life expectancy of critically ill patients,9 which highlights the importance of analyzing severity-adjusted mortality in diverse populations.

A number of researchers have examined outcomes for older ICU patients.1,10-13 Most of these studies were small (sample sizes < 1,000 patients), but the findings suggest that advanced age alone should not preclude ICU admission. The presence of other comorbidities appeared to have a greater impact on in-hospital mortality. Nevertheless, there is still some reluctance to admit very elderly patients to ICUs, even when critical care is otherwise appropriate.14

Project IMPACT (PI)14 is a large, modern, multicenter database of intensive care patients. We recently used PI data from 135 ICUs for 124,885 patients treated in 2001 to 2004 to update the Mortality Probability Model (MPM)-III4 in which we observed that 14% of patients had no MPM risk factor other than age. This “zero factor” subgroup of elective surgical patients had a notably low mortality risk (2% vs 14% overall). We also saw that age modified the effect of seven risk factors on mortality.4 For reference, we provide the model and its variable definitions in e-Appendixes 1 and 2, respectively. Here, we further analyze PI data to determine more definitively how age is associated with mortality among critically ill patients.

Database

Data were provided by PI, and additional information on PI is available online.15 Participating ICUs in PI submitted data at least quarterly to a central repository. User software performed extensive checks for data accuracy and completeness. The central site performs supplementary data analyses and queries participants when questionable data are identified.16,17 Previous studies using this database have been published,4,18,19 and good agreement between the PI central database and reabstracted patient charts exists.16 PI data were stripped of identifiers in accordance with Health Insurance Portability and Accountability Act regulations, and the data set provided was limited to variables needed for this project. We analyzed data for patients treated in 156 PI ICUs in 116 primarily North American hospitals between March 2001 and June 2004. To eliminate potential bias from very-low-volume ICUs and new participants, we excluded data from ICUs with < 100 patient records in the study database, and records for patients not meeting MPM-II applicability criteria (ie, cardiac surgery, acute myocardial infarction, burns, or aged < 18 years) were further excluded, leaving 124,885 patients from 135 ICUs at 98 hospitals. The MPM-III project was reviewed by the Institutional Review Board at Baystate Medical Center (Springfield, Massachusetts) and was determined to be exempt from the need for approval (IRB-6-170) because it met all requirements for use of anonymized data.

Statistical Analysis

We stratified the data set by age (18-29, 30-39, 40-49, 50-59, 60-69, 70-79, 80-89, 90+ y) and by patient type (medical, unscheduled/emergency surgical, and elective/scheduled surgical). We also identified patients with at least one additional MPM risk factor other than age, patient type, zero factor, or full-code status. Counts and in-hospital mortality rates among groups were compared using the χ2 test. All statistical analyses were done using Stata/SE version 10.1 (StataCorp; College Station, Texas).

Analysis of MPM-III

The seven interaction terms with age in the MPM-III model (e-Appendix 1) all had negative coefficients, meaning that these risk factors interacted with age in a nonadditive way (ie, mortality probability for each of these risk factors depended on the patient’s age). In other words, the presence of these risk factors increased the risk of dying, but this increased risk was not constant and instead lessened as patients aged. Figure 1 depicts this increase for three commonly encountered risk factors: hypotension, cirrhosis, and metastatic cancer. Had age not modified the effect of these risk factors, the lines representing the mortality rates of those patients with each factor would have been parallel to the line plotted by age alone (denoted as the solid line).

Figure Jump LinkFigure 1. Graphic display of the effect of selected MPM-III risk factors and age. MPM = Mortality Probability Model; SBP = systolic BP.Grahic Jump Location

Table 1 provides information on patient characteristics. A histogram of the patients’ ages is shown in Figure 2. The median age was 63 years (interquartile range, 48-75 years). The histogram shows that age was not normally distributed, but instead, a disproportionate number of patients were aged > 65 years, which confirms known demographic trends.4

Table Graphic Jump Location
Table 1 —Characteristics of the Study Population

Data are presented as No. (%) or mean ± SD. CMO = comfort measures only; DNR = do not resuscitate; LTV = long-term ventilation facility; PACU = postanesthesia recovery unit; SNF = skilled nursing facility.

Figure Jump LinkFigure 2. Histogram of age from 124,885 MPM-III-eligible patients in Project IMPACT. The histogram shows that patient age in Project IMPACT does not follow a normal distribution, as shown as a bell-shaped curve, but instead has a disproportionate number of elderly patients. See Figure 1 legend for expansion of abbreviation.Grahic Jump Location

Table 2 shows the counts and prevalences by age group and patient type. For example, 17,315 patients (13.9% of the data set) were aged 80 to 89 years, and 12.7% of this group were elective surgical patients with no risk factors (ie, the “zero factor” patients in MPM-III), whereas 45.9% were medical patients with at least one MPM risk factor other than age. Table 2 also reveals that older medical patients admitted to the ICU were more likely to have additional MPM risk factors, whereas this trend was not seen among the surgical patients.

Table Graphic Jump Location
Table 2 —Counts of Patients by Age Group

Data are presented as No. (%). The percentage in each cell shows the proportion of the respective age group (column) (eg 5.7% of patients aged 18-29 years had elective surgery with no other MPM risk factors). The percentages in the last row and column represent the fraction of the total sample size of 124,885 patients. MPM = Mortality Probability Model.

Table 3 presents the in-hospital mortality rate for each cell. For example, the mortality rate for patients aged 80 to 89 years with elective surgery and no other MPM risk factors was 4.3%, whereas for elective surgical patients with at least one other MPM risk factor, the rate was 17.7% (P < .001). As expected, mortality rates increased with age for all patient subtypes as shown in Figure 3. The elective surgical group with no other MPM risk factors had the lowest mortality rates (0.4%-9.2%), whereas medical patients with at least one other MPM risk factor had the highest mortality rates across all age groups (12.1%-36.0%). In these two subsets, mortality rates approximately doubled in the elective surgical group among patients in their 70s (2.4%), 80s (4.3%), and 90s (9.2%) but rose less dramatically in the medical patients (27.0%, 30.7%, and 36.0%, respectively).

Table Graphic Jump Location
Table 3 —Mortality Rate of Each Patient Type by Age Group

Data are presented as percentages. See Table 2 legend for expansion of abbreviation.

Figure Jump LinkFigure 3. Mortality rate by age, stratified by patient type.Grahic Jump Location

Mortality rose considerably for all patient types when MPM risk factors were present. The smallest relative increase in deaths occurred in medical patients aged > 90 years. However, even for these nonagenarians, the mortality rate was 19.3% for those with no other MPM risk factors but almost doubled to 36.0% (relative risk ratio, 1.87; P < .001) for those with at least one additional risk factor.

Table 3 and Figure 3 also highlight low mortality rates for certain patient subsets. Among elective surgical patients aged < 40 years with no MPM risk factors, the mortality rate was just 0.4%, although these same patients in their 80s and 90s had mortality rates of 4.3% and 9.2%, respectively, which may be considered quite high. However, “acceptable” mortality rates are context driven because the youngest (aged 18-29 years) patients with at least one MPM risk factor had similar mortality rates as the elderly patients (elective surgical, 4.7%; emergency surgical, 10.8%; medical, 12.1%).

The primary reason for ICU admission varied by patient type (Fig 4). Postoperative observation was more common in elective than unscheduled surgical patients (21.1% vs 15.1%; P < .001). However, the proportion of postoperative observation surgical patients aged ≥ 70 years (38.2%) was similar to surgical patients overall (40.0%).

Figure Jump LinkFigure 4. Primary reason for ICU admission by patient type; 0.3% of medical patients were admitted for postoperative observation and are not shown in the figure.Grahic Jump Location

This study demonstrates that select older patients admitted to ICUs have low in-hospital mortality rates, particularly when MPM risk factors are absent and surgery is scheduled. Because MPM risk factors are quite serious conditions known at ICU admission (eg, coma/deep stupor, metastatic neoplasm, CPR within 24 h), it is both clinically intuitive and empirically supported that their presence increases the chance of dying.4 We note, though, that mortality rates of nonagenarians undergoing elective surgery were somewhat high (9.2%-23.2%), and this finding must be considered carefully when deciding treatment options.

Caution also must be taken when generalizing these findings to all elderly critically ill patients due to the selection bias present in these results. Patients aged in their 80s or 90s who are terminally ill or in poor overall health are more likely to choose palliative care or less aggressive therapies that do not require ICU monitoring. We also observed that older medical patients were more likely to have additional MPM risk factors, but this trend was not seen in the surgical patients. So in some fundamental ways, the study’s population of elderly ICU surgical patients may be healthier than other hospitalized geriatric patients outside the ICU with the same MPM risk factors. This selection bias, however, reflects the real-world decision-making of clinicians, patients, and families as well as suggests that ICU case selection is reasonably judicious, at least among PI hospitals and for the outcome of in-hospital mortality. Further research is needed to examine the association between age and resource use in the ICU to further ensure that intensive care is given to the most suitable populations.

In addition to the risk of mortality, physicians may be reluctant to recommend surgery in geriatric patients for fear of postoperative delirium or permanent postoperative cognitive dysfunction.20,21 No large prospective study has examined short- and long-term cognitive outcomes among elderly patients requiring ICU admission after major surgery,21 and existing research has produced conflicting results. However, Avidan et al20 showed retrospectively that operating on older adults was not linked to cognitive decline, whereas Ouimet et al22 found that age was not a risk factor for ICU delirium.

The MPM-III was validated on external data19 and across a wide range of patient subsets.23 However, MPM-III and other models specifically designed for critically ill geriatric patients24 are not applicable to individuals but only to patient groups and as such, may help to identify elderly subgroups at high risk of dying after ICU admission.

Notably, the PI age-stratified risk-adjusted mortality rates dovetail with prior research. de Rooij et al11 reviewed 15 studies of geriatric ICU patients from 1966 to 2005. Although most studies were small (10 studies had < 1,000 patients) and from single institutions, the overall conclusion was that “ICU mortality was higher in elderly patients” but that “high age alone was not responsible for poorer outcomes, but premorbid functional status and severity of illness also contributed.” Marik25 also conducted a literature review and agreed, noting that “age alone should not be used to triage ICU patients.” He recommended that decisions to admit elderly patients rely on an assessment of their acuity, prehospital functional status, and personal treatment wishes. European studies also have found that advanced age alone does not preclude good outcomes for patients admitted to ICUs.26-28

Limitations

The MPM-III relies only on the most common and severe critical care risk factors by design.4 Consequently, some variables that influence ICU admissions and outcomes that are prevalent among the elderly, such as malnutrition and low BMI,29 are not included, and their effects cannot be assessed. Other absent factors such as dementia, depression, and prior nursing home residence are associated with poor outcomes for elderly patients requiring hospitalization and have not been comprehensively analyzed by intensivists.12,30,31 Geriatric patients with these risk factors likely will die at higher rates than predicted by the MPM-III, even in the absence of other MPM-III risk factors, and may be poor candidates for ICU admission.4

We stratified the elective surgical, emergency surgical, and medical patients into two groups: those with no additional MPM risk factors and those with at least one additional risk factor (excluding age, full-code status, and medical or emergency surgical status) and did not distinguish between acute and chronic factors. This decision was reasonable because the median number of additional risk factors after these exclusions was 1 (interquartile range, 0-1). However, an analysis of specific MPM-III variables or variable combinations may have provided more nuanced results though likely similar overall conclusions. We also did not have data on the type of surgery performed within the surgical groups, and so the association between surgical complexity and mortality is unknown.

Finally, we could only assess in-hospital mortality among patients admitted to the ICU. The association of age on long-term survival for these patients is unknown. Also unknown, although clearly of interest, would be how patients fared who were not admitted to the ICU because of their age, acuity, prognosis, or wishes. We also lacked data on the quality of life after discharge in this cohort. Recent studies showed that prolonged ICU stays, regardless of age, led to ICU-acquired paresis.32 Nevertheless, other studies indicated that elderly patients who survived had an acceptable quality of life thereafter.10,33,34 A more-robust analysis of outcomes for critically ill older patients would include data on patient quality of life, functional status outcomes, readmission rates, and ICU admission status.

Because of its size and recent data, this study definitively confirms that even the oldest ICU patients may fare well if other risk factors are absent. Our main finding is that advanced age alone does not preclude successful intensive care, especially in the cohort of elderly elective surgical patients admitted to ICUs. These findings may reflect the preferences of both clinicians and patients to avoid surgery or intensive care for those who are frail and nearing the end of their lives, but in a real-world population, the relatively low hospital mortality rate even in patients in their 80s is encouraging. This study also highlights that chronologic age alone offers much less prognostic information than age in the context of known MPM risk factors. Therefore, a holistic assessment of the critically ill patient, regardless of age, is needed to determine appropriate treatment.

Author contributions:Dr Nathanson: contributed to the study concept, analysis and interpretation of the data, statistical analysis, drafting of the manuscript, and review of the manuscript for important intellectual content.

Dr Higgins: contributed to the study concept, analysis and interpretation of the data, and review of the manuscript for important intellectual content.

Dr Brennan: contributed to the analysis and interpretation of the data and review of the manuscript for important intellectual content.

Dr Kramer: contributed to the study concept, analysis and interpretation of the data, and review of the manuscript for important intellectual content.

Ms Stark: contributed to data acquisition and review of the manuscript for important intellectual content.

Dr Teres: contributed to the study concept, analysis and interpretation of the data, and review of the manuscript for important intellectual content.

Financial/nonfinancial disclosures: The authors have reported to CHEST the following conflicts of interest: Dr Nathanson’s company, OptiStatim, LLC, has an annual consulting agreement with Cerner for > $10,000; Dr Higgins has consulted for Cerner and holds stock in Cerner; Dr Kramer has been employed with Cerner and holds stock ownership and options with Cerner; and Ms Stark has been employed with Cerner. Drs Brennan and Teres have reported to CHEST that no potential conflicts of interest exist with any companies/organizations whose products or services may be discussed in this article.

Role of sponsors: Cerner provided the data for this study. This study was an additional analysis of a project funded by Cerner.

Additional information: The e-Appendices can be found in the Online Supplement at http://chestjournal.chestpubs.org/content/139/4/825/suppl/DC1.

MPM

Mortality Probability Model

PI

Project IMPACT

Boumendil A, Somme D, Garrouste-Orgeas M, Guidet B. Should elderly patients be admitted to the intensive care unit? Intensive Care Med. 2007;337:1252-1262. [CrossRef] [PubMed]
 
Vahey DC, Aiken LH, Sloane DM, Clarke SP, Vargas D. Nurse burnout and patient satisfaction. Med Care. 2004;422 suppl:II57-II66. [CrossRef] [PubMed]
 
Halpern NA, Pastores SM, Thaler HT, Greenstein RJ. Changes in critical care beds and occupancy in the United States 1985-2000: differences attributable to hospital size. Crit Care Med. 2006;348:2105-2112. [CrossRef] [PubMed]
 
Higgins TL, Teres D, Copes WS, Nathanson BH, Stark M, Kramer AA. Assessing contemporary intensive care unit outcome: an updated Mortality Probability Admission Model (MPM0-III). Crit Care Med. 2007;353:827-835. [CrossRef] [PubMed]
 
Mularski RA, Osborne ML. End-of-life care in the critically ill geriatric population. Crit Care Clin. 2003;194:789-810 viii.. [CrossRef] [PubMed]
 
Shorr AF. An update on cost-effectiveness analysis in critical care. Curr Opin Crit Care. 2002;84:337-343. [CrossRef] [PubMed]
 
Dasta JF, McLaughlin TP, Mody SH, Piech CT. Daily cost of an intensive care unit day: the contribution of mechanical ventilation. Crit Care Med. 2005;336:1266-1271. [CrossRef] [PubMed]
 
Higgins TL. Placing bets with a full house. Crit Care Med. 2008;363:1008-1009. [CrossRef] [PubMed]
 
Christakis NA, Lamont EB. Extent and determinants of error in doctors’ prognoses in terminally ill patients: prospective cohort study. BMJ. 2000;3207233:469-472. [CrossRef] [PubMed]
 
Hennessy D, Juzwishin K, Yergens D, Noseworthy T, Doig C. Outcomes of elderly survivors of intensive care: a review of the literature. Chest. 2005;1275:1764-1774. [CrossRef] [PubMed]
 
de Rooij SE, Abu-Hanna A, Levi M, de Jonge E. Factors that predict outcome of intensive care treatment in very elderly patients: a review. Crit Care. 2005;94:R307-R314. [CrossRef] [PubMed]
 
Sacanella E, Pérez-Castejón JM, Nicolás JM, et al. Mortality in healthy elderly patients after ICU admission. Intensive Care Med. 2009;353:550-555. [CrossRef] [PubMed]
 
Marik PE. Should age limit admission to the intensive care unit? Am J Hosp Palliat Care. 2007;241:63-66. [CrossRef] [PubMed]
 
Garrouste-Orgeas M, Boumendil A, Pateron D, et al; ICE-CUB Group ICE-CUB Group Selection of intensive care unit admission criteria for patients aged 80 years and over and compliance of emergency and intensive care unit physicians with the selected criteria: an observational, multicenter, prospective study. Crit Care Med. 2009;3711:2919-2928. [CrossRef] [PubMed]
 
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Cerner Web site. http://www.cerner.com/piccm. Accessed November 30, 2005.
 
Nathanson BH, Higgins TL, Teres D, Copes WS, Kramer A, Stark M. A revised method to assess intensive care unit clinical performance and resource utilization. Crit Care Med. 2007;358:1853-1862. [CrossRef] [PubMed]
 
Higgins TL, Kramer AA, Nathanson BH, et al. Prospective validation of the intensive care unit admission Mortality Probability Model Version 3 (MPM0-III). Crit Care Med. 2009;375:1619-1623. [CrossRef] [PubMed]
 
Avidan MS, Searleman AC, Storandt M, et al. Long-term cognitive decline in older subjects was not attributable to noncardiac surgery or major illness. Anesthesiology. 2009;1115:964-970. [CrossRef] [PubMed]
 
Leung JM, Sands LP. Long-term cognitive decline: is there a link to surgery and anesthesia? Anesthesiology. 2009;1115:931-932. [CrossRef] [PubMed]
 
Ouimet S, Kavanagh BP, Gottfried SB, Skrobik Y. Incidence, risk factors and consequences of ICU delirium. Intensive Care Med. 2007;331:66-73. [CrossRef] [PubMed]
 
Nathanson BH, Higgins TL, Kramer AA, Copes WS, Stark M, Teres D. Subgroup mortality probability models: are they necessary for specialized intensive care units? Crit Care Med. 2009;378:2375-2386. [CrossRef] [PubMed]
 
de Rooij SE, Abu-Hanna A, Levi M, de Jonge E. Identification of high-risk subgroups in very elderly intensive care unit patients. Crit Care. 2007;112:R33. [CrossRef] [PubMed]
 
Marik PE. Management of the critically ill geriatric patient. Crit Care Med. 2006;349 suppl:S176-S182. [CrossRef] [PubMed]
 
Rellos K, Falagas ME, Vardakas KZ, Sermaides G, Michalopoulos A. Outcome of critically ill oldest-old patients (aged 90 and older) admitted to the intensive care unit. J Am Geriatr Soc. 2006;541:110-114. [CrossRef] [PubMed]
 
Somme D, Maillet JM, Gisselbrecht M, Novara A, Ract C, Fagon JY. Critically ill old and the oldest-old patients in intensive care: short- and long-term outcomes. Intensive Care Med. 2003;2912:2137-2143. [CrossRef] [PubMed]
 
Udelnow A, Leinung S, Schreiter D, Schönfelder M, Würl P. Impact of age on in-hospital mortality of surgical patients in a German university hospital. Arch Gerontol Geriatr. 2005;413:281-288. [CrossRef] [PubMed]
 
Landi F, Onder G, Gambassi G, Pedone C, Carbonin P, Bernabei R. Body mass index and mortality among hospitalized patients. Arch Intern Med. 2000;16017:2641-2644. [CrossRef] [PubMed]
 
Covinsky KE, Justice AC, Rosenthal GE, Palmer RM, Landefeld CS. Measuring prognosis and case mix in hospitalized elders. The importance of functional status. J Gen Intern Med. 1997;124:203-208. [PubMed]
 
Pisani MA, Redlich C, McNicoll L, Ely EW, Inouye SK. Underrecognition of preexisting cognitive impairment by physicians in older ICU patients. Chest. 2003;1246:2267-2274. [CrossRef] [PubMed]
 
Ali NA, O’Brien JM Jr, Hoffmann SP, et al; Midwest Critical Care Consortium Midwest Critical Care Consortium Acquired weakness, handgrip strength, and mortality in critically ill patients. Am J Respir Crit Care Med. 2008;1783:261-268. [CrossRef] [PubMed]
 
Kleinpell RM, Ferrans CE. Quality of life of elderly patients after treatment in the ICU. Res Nurs Health. 2002;253:212-221. [CrossRef] [PubMed]
 
de Rooij SE, Govers AC, Korevaar JC, Giesbers AW, Levi M, de Jonge E. Cognitive, functional, and quality-of-life outcomes of patients aged 80 and older who survived at least 1 year after planned or unplanned surgery or medical intensive care treatment. J Am Geriatr Soc. 2008;565:816-822. [CrossRef] [PubMed]
 

Figures

Figure Jump LinkFigure 1. Graphic display of the effect of selected MPM-III risk factors and age. MPM = Mortality Probability Model; SBP = systolic BP.Grahic Jump Location
Figure Jump LinkFigure 2. Histogram of age from 124,885 MPM-III-eligible patients in Project IMPACT. The histogram shows that patient age in Project IMPACT does not follow a normal distribution, as shown as a bell-shaped curve, but instead has a disproportionate number of elderly patients. See Figure 1 legend for expansion of abbreviation.Grahic Jump Location
Figure Jump LinkFigure 3. Mortality rate by age, stratified by patient type.Grahic Jump Location
Figure Jump LinkFigure 4. Primary reason for ICU admission by patient type; 0.3% of medical patients were admitted for postoperative observation and are not shown in the figure.Grahic Jump Location

Tables

Table Graphic Jump Location
Table 1 —Characteristics of the Study Population

Data are presented as No. (%) or mean ± SD. CMO = comfort measures only; DNR = do not resuscitate; LTV = long-term ventilation facility; PACU = postanesthesia recovery unit; SNF = skilled nursing facility.

Table Graphic Jump Location
Table 2 —Counts of Patients by Age Group

Data are presented as No. (%). The percentage in each cell shows the proportion of the respective age group (column) (eg 5.7% of patients aged 18-29 years had elective surgery with no other MPM risk factors). The percentages in the last row and column represent the fraction of the total sample size of 124,885 patients. MPM = Mortality Probability Model.

Table Graphic Jump Location
Table 3 —Mortality Rate of Each Patient Type by Age Group

Data are presented as percentages. See Table 2 legend for expansion of abbreviation.

References

Boumendil A, Somme D, Garrouste-Orgeas M, Guidet B. Should elderly patients be admitted to the intensive care unit? Intensive Care Med. 2007;337:1252-1262. [CrossRef] [PubMed]
 
Vahey DC, Aiken LH, Sloane DM, Clarke SP, Vargas D. Nurse burnout and patient satisfaction. Med Care. 2004;422 suppl:II57-II66. [CrossRef] [PubMed]
 
Halpern NA, Pastores SM, Thaler HT, Greenstein RJ. Changes in critical care beds and occupancy in the United States 1985-2000: differences attributable to hospital size. Crit Care Med. 2006;348:2105-2112. [CrossRef] [PubMed]
 
Higgins TL, Teres D, Copes WS, Nathanson BH, Stark M, Kramer AA. Assessing contemporary intensive care unit outcome: an updated Mortality Probability Admission Model (MPM0-III). Crit Care Med. 2007;353:827-835. [CrossRef] [PubMed]
 
Mularski RA, Osborne ML. End-of-life care in the critically ill geriatric population. Crit Care Clin. 2003;194:789-810 viii.. [CrossRef] [PubMed]
 
Shorr AF. An update on cost-effectiveness analysis in critical care. Curr Opin Crit Care. 2002;84:337-343. [CrossRef] [PubMed]
 
Dasta JF, McLaughlin TP, Mody SH, Piech CT. Daily cost of an intensive care unit day: the contribution of mechanical ventilation. Crit Care Med. 2005;336:1266-1271. [CrossRef] [PubMed]
 
Higgins TL. Placing bets with a full house. Crit Care Med. 2008;363:1008-1009. [CrossRef] [PubMed]
 
Christakis NA, Lamont EB. Extent and determinants of error in doctors’ prognoses in terminally ill patients: prospective cohort study. BMJ. 2000;3207233:469-472. [CrossRef] [PubMed]
 
Hennessy D, Juzwishin K, Yergens D, Noseworthy T, Doig C. Outcomes of elderly survivors of intensive care: a review of the literature. Chest. 2005;1275:1764-1774. [CrossRef] [PubMed]
 
de Rooij SE, Abu-Hanna A, Levi M, de Jonge E. Factors that predict outcome of intensive care treatment in very elderly patients: a review. Crit Care. 2005;94:R307-R314. [CrossRef] [PubMed]
 
Sacanella E, Pérez-Castejón JM, Nicolás JM, et al. Mortality in healthy elderly patients after ICU admission. Intensive Care Med. 2009;353:550-555. [CrossRef] [PubMed]
 
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Nathanson BH, Higgins TL, Kramer AA, Copes WS, Stark M, Teres D. Subgroup mortality probability models: are they necessary for specialized intensive care units? Crit Care Med. 2009;378:2375-2386. [CrossRef] [PubMed]
 
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