European Geriatric Medicine 2 (2011) 327–331

Research paper

Prognostic factors of elderly patients admitted in a medical intermediate care unit

S. Duque a,*, P. Freitas a, J. Silvestre b, L. Fernandes a, M. Pinto a, A. Sousa a, V. Batalha a, L. Campos a

a Ocidental Lisbon Hospital Centre, Sao Francisco Xavier Hospital, Medicine 4 Department, Lisbon, Portugalb Ocidental Lisbon Hospital Centre, Sao Francisco Xavier Hospital, Polivalent Intensive Care Unit, Lisbon, Portugal

A R T I C L E I N F O

Article history:

Received 26 May 2011

Accepted 27 July 2011

Available online 9 September 2011

Keywords:

Elderly

Medical intermediate care unit

Mortality

Prognostic factors

A B S T R A C T

Introduction: Population ageing has determined increased prevalence of chronic illness and functional

impairment. Consequently, increased hospital admission of elderly patients is observed, not only in

general wards but also in high dependency units such as medical intermediate care units (IntCU), despite

uncertainty on their prognostic factors.

Objective: To identify prognostic factors of older patients admitted in an IntCU, namely acute illness

severity, comorbidity and functional status.

Design: Prospective observational study.

Setting: IntCU of a Central and University Hospital.

Subjects: One hundred and seventy-six elderly patients consecutively admitted during 32 months,

compared with 112 non-elderly patients.

Main outcome measure: IntCU mortality and In-hospital mortality.

Methods: At admission acute illness severity, comorbidity and previous functional status were measured.

Outcome measurements included IntCU mortality and in-hospital mortality.

Results: Comorbidity and functional impairment were higher in elderly. Higher IntCU mortality in elderly

patients (13.1%) was also observed as well as a higher in-hospital mortality. SAPS II was the best

predictor of both IntCU and in-hospital mortality in elderly group. Charlson index was not a good

predictor of IntCU and in-hospital mortality in elderly patients nor Barthel index a protective factor.

Conclusions: This study supports using SAPS II as the standard prognosis assessment tool of elderly

admitted in IntCU. Despite Charlson and Barthel indexes were not associated with higher mortality in

our study, a comprehensive assessment should be carried out when admitting elderly patients in IntCU,

not being age the best criteria. Risk stratification is mandatory to predict benefit of IntCU admission and

accurate tools need to be developed.

� 2011 Elsevier Masson SAS and European Union Geriatric Medicine Society. All rights reserved.

1. Introduction

Recent medical improvement has determined an increase inaverage life expectancy in developed countries.

Consequently, most patients admitted in medical wards areelderly, presenting high prevalence of chronic diseases andcomorbidity, resulting frequently in severe acute illness thatrequire high level of medical care not available in general wards.

However, many of these critically ill elderly patients do notrequire full intensive medical care, being Intermediate Care Units(IntCU) the most appropriate setting to treat this subset of patients.Ip et al. have proved that an exclusive geriatric high-dependencycare unit is worthwhile comparing to intensive care units (ICU), asresults achieved are similar to those in ICU and significantly cost-saving [1]. Ranhoff et al. have considered this type of unit an

* Corresponding author.

E-mail address: sofia.b.duque@gmail.com (S. Duque).

1878-7649/$ – see front matter � 2011 Elsevier Masson SAS and European Union Ger

doi:10.1016/j.eurger.2011.07.013

innovative method to treat frail elderly with more severeconditions [2]. However, this type of unit is very rare worldwide.

Therefore, medical IntCU admit significant proportion of elderlypatients despite scarcity on specific knowledge on geriatrics anduncertainty on prognostic factors.

Several studies have focused on prognostic factors of elderlypatients admitted in internal medicine or geriatrics wards orintensive care units. However, very few studies were performed inIntCU [1,3].

The first studies on older hospitalized patients outcome usedindices that only assessed acute and chronic illness severity,lacking inclusion of functional parameters, which has beenconsidered its major limitation [4].

Most recent studies on prognostic factors of elderly patientshave shown that short term outcome depends not only of acuteillness severity and comorbidity but also depend of functionalstatus [5–9], cognitive [5,7,8] and psychological status, nutritionalstatus [5,8] and polypharmacy [5,7].

Nowadays, it is clear that elderly present a number of specificconditions, the geriatric syndromes, that usually are not assessed,

iatric Medicine Society. All rights reserved.

S. Duque et al. / European Geriatric Medicine 2 (2011) 327–331328

though its profound impact on health, implying a greatervulnerability to adverse events, such as the acute illness. Winogradet al. considered that geriatric syndromes are more predictive ofadverse outcomes than diagnosis per se [10]. Some authors havefound that functional status might be stronger predictor of outcomethan acute physiologic [11] and comorbidity measures [12].

Several studies have suggested that age alone does notrepresent a strong predictor for mortality [9,13,14], existing morereliable predictive parameters such as the severity of acute illness[9,13–15], comorbidity, functional [9] and cognitive status. Indeed,Dunlop et al. demonstrated that in a surgical geriatric populationthe severity of illness is a much better predictor of outcome thanage, recommending not to use age in surgical decisions in theelderly [16]. Some authors have reported no significant differencesbetween elderly and non-elderly mortality in ICU setting [3,9]while others found that in-hospital mortality differences betweenyoung and elderly patients disappeared when controlling forseverity of illness and prior health [17].

Identification of prognostic factors in elderly patients admittedin IntCU is of utmost importance in order to avoid decisionsexclusively based on age. Not only age determines outcome butalso previous functional and cognitive status, comorbidity andseverity of acute illness. Elderly patients of the same chronologicalage present distinct comorbidity, functional and cognitive status,factors that should be taken in account as important determinantsof biological age and predictors of recovery of acute illness.Therefore, identification of prognostic factors is essential forreliable risk stratification of older patients [8] and taking correctdiagnostic and therapeutic decisions [18].

The primary aim of this study was evaluation of mortality andanalysis of acute illness severity, comorbidity and previousfunctional status as prognostic factors of elderly patients admittedto a IntCU, comparing with non-elderly patients.

2. Patients and methods

This study was a prospective, single center, observational studyconducted during a 32 months period in the IntCU of Sao FranciscoXavier Hospital. Sao Francisco Xavier Hospital is a central anduniversity hospital of Lisbon, that belongs to an Hospital Centre of900 beds, serving a population of about 935,000 people as a tertiaryreferral center.

The IntCU is a four-bed unit that admits patients from theemergency department, from the internal medicine ward (as astep-up unit) and from the intensive care unit (as a step-downunit).

During 32 months (April 2008–December 2010) 377 patientswere admitted in the IntCU (mean age 67.1 � 19.3 years, 247patients aged 65 or over (66% elderly), mortality 10.6%).

A total of 288 patients were enrolled to this study: 176 elderlypatients and 112 non-elderly. These groups were establishedaccording chronological age, considering ‘‘elderly’’ if chronologicalage was 65 or more years old, and ‘‘non-elderly’’ those who wereyounger, according to World Health Organization. Patients wereexcluded if complete data were not available.

Patients data were recorded in a database including age, gender,previous functional status, previous comorbidity, length of stay,primary diagnosis on admission, severity of acute illness andoutcome. All data were obtained using standardized instruments.

2.1. Previous functional status

The previous functional status was determined applying theBarthel index of basic activities of daily living (BADL) [19],classifying patients as independent, slightly dependent, moder-

ately dependent, highly or totally dependent in BADL, according tothe number of basic care skills in which patients require physicalassistance. The BADL considered in Barthel index are feeding,bathing, grooming, dressing, toileting, transferring and walking.The Barthel index ranges from 0 to 100 points and five categoriesare established: less than 20 points: total dependency in BADL; 20–35 points: severe dependency in BADL; 40–55 points: moderatedependency in BADL; 60–95 points: slight dependency in BADLand 100 points: independency in BADL. Information was obtaineddirectly through interview to patient and/or caregiver and clinicalrecords of the origin departments.

2.2. Previous comorbidity

Comorbidity was determined applying the Charlson comorbidityindex [20], which is an instrument performed to predict 10-yearmortality in longitudinal studies, according to comorbid diseasesthat patient presents. Each of 18 conditions is given a score from 1 to6 points, according its illness burden. The final score corresponds to apredicted 10-year mortality. Existence of comorbid conditions wasassessed through research of previous hospital archives and/orinterview to patient and/or caregiver.

2.3. Primary diagnosis

Fifteen categories of primary diagnosis were established: heartfailure, arrhythmias, myocardial infarction or ischemia, pulmonarythromboembolism, pulmonary disease, sepsis, neurological dis-ease, hepatic disease, metabolic or endocrinologic disease, diges-tive bleeding, other gastroenterologic disease, intoxication,pancreatitis, kidney diseases and others.

2.4. Severity of acute illness

Severity of acute illness was determined applying intensive carescores: Acute Physiology and Chronic Health Evaluation II(APACHE II) [21], the Simplified Acute Physiology Score II (SAPSII) [22] and the Sequential Organ Failure Assessment (SOFA) [23],which were calculated in the first 24 hours of admission.

2.5. Outcome

The main outcome was mortality in the IntCU and thesecondary one was in-hospital mortality. In-hospital mortalitywas obtained reviewing hospital electronic records.

2.6. Statistical analysis

Continuous variables were expressed as mean � standarddeviation, median and 95% confidence interval (IC) for the median.

Elderly and non-elderly groups were compared using theKruskal-Wallis test. Categorical variables were compared betweenelderly and non-elderly using the Fisher’s exact test.

Bivariate and multiple logistic regression with forwardsstepwise selection were used to identify prognostic factors ofIntCU and in-hospital mortality. The entry criterion for themultivariate model was p � 0.05. The receiver operating char-acteristics (ROC) area under the curve (AUC) was used to assessmodels discrimination. Tests were two-tailed and reportedstatistically significant at p < 0.05.

SPSS version 18 was used to statistical analysis.

3. Results

Main baseline characteristics of patients enrolled in this studyare resumed in Table 1.

Table 1Main baseline characteristics of patients.

Elderly (n = 176) Non-elderly (n = 112) p

Age (years)

Mean � SD 79.4 � 6.9 43.6 � 14.4

95% CI 78.4–80.5 40.8–46.3

Median 79.0 48.0

Comorbidity–Charlson index < 0.001

Mean � SD 7.4 � 2.7 2.4 � 2.5

95% CI 6.9–7.8 1.9–2.8

Median 7.0 2.0

Pre-admission basic activities of daily living–Barthel index < 0.001

Mean � SD 76.5 � 31.4 91.7 � 23.3

95% CI 71.7–81.3 87.3–96.1

Median 95.0 100

Acute illness severity

APACHE II < 0.001

Mean � SD 17.8 � 5.9 9.6 � 4.8

95% CI 16.9–18.7 8.7–10.5

Median 17.0 9.0

SAPS II < 0.001

Mean � SD 38.2 � 8.0 21.3 � 8.5

95% CI 36.9–39.4 19.7–22.9

Median 37.0 21.0

SOFA < 0.001

Mean � SD 5.5 � 2.3 4.5 � 2.2

95% CI 5.1–5.9 4.1–4.9

Median 5.0 4.0

Primary Diagnosis (%)

Heart Failure 28.4 8.0

Arrhythmias 2.3 0.9

Myocardial infarction or ischemia 5.7 0.9

Pulmonary thromboembolism 6.8 6.3

Pulmonary disease 26.1 31.3

Sepsis 10.8 11.6

Neurological disease 2.8 2.7

Hepatic disease 0.6 0.9

Metabolic or endocrinologic disease 1.7 10.7

Digestive bleeding 2.3 1.8

Other gastroenterologic disease 1.1 0.9

Intoxication 2.3 4.5

Pancreatitis 1.7 7.1

Kidney diseases 2.8 3.6

Others 4.5 8.9

S. Duque et al. / European Geriatric Medicine 2 (2011) 327–331 329

Comorbidity and acute illness severity were significantly higherin elderly patients. A difference of five points was observedbetween median score of Charlson index of both groups. Themedian scores of APACHE II, SAPS II and SOFA were higher inelderly, translating more severe acute conditions with highermortality risk.

Elderly patients were more dependent in basic activities of dailyliving than non-elderly, but only a 5-point difference was observedbetween median scores of Barthel index (95 vs 100 points).

In this study the IntCU mortality was 9.3% (n = 27), being higherin elderly patients (13.1% vs 3.6%, p = 0.007). We also observed a

Table 2Intermediate care unit and hospital outcomes.

Elderly

(n = 176)

Non-elderly

(n = 112)

p

Length of stay (days) 0.004

Mean � SD 11.3 � 10.0 8.4 � 7.0

95% CI 9.8–12.8 7.1–9.7

Median 8.0 7.0

IntCU mortality (%; n = 27/288) 13.1% 3.6% 0.007

In-hospital mortality (%; n = 51/288) 25 6.3 < 0.001

higher length of stay and a higher in-hospital mortality in theelderly group. These data are presented in Table 2.

Bivariate logistic regression studies by age group wereundertaken (Table 3).

Our data demonstrated that SAPS II was the best predictor ofIntCU mortality (AUC 0.717 [0.608–0.825]) in elderly patients.However, concerning in-hospital mortality none of the acuteillness severity scores demonstrated to be a good marker ofprognosis. In the non-elderly patients group both IntCU and in-hospital mortality were best predicted by these severity scores.

Concerning previous comorbidity, in elderly patients Charlsonindex was not a significant predictor of mortality. However, in non-elderly patients a higher Charlson score was associated with highermortality.

Regarding previous functional status, Barthel index showed notto be a protective factor of mortality in both groups.

These data were corroborated by multiple logistic regression(Table 3). We also observed in this model that APACHE II was thestrongest predictor of IntCU mortality in non-elderly patients.

4. Discussion

Our results demonstrate the usefulness and reliability of acuteillness severity scores in predicting mortality of patients admitted

Table 3Results of the bivariate and multiple logistic regression analyses by age (OR odds ratio).

Predictors OR (95% CI) p AUC (95% CI)

Bivariated logistic regression

Elderly

IntCU mortality

Charlson index 1.156 (0.993–1.346) 0.062 0.576 (0.435–0.716)

Barthel index 0.988 (0.975–1.000) 0.058 0.630 (0.502–0.758)

APACHE II 1.097 (1.020–1.180) 0.013 0.646 (0.529–0.763)

SAPS II 1.087 (1.030–1.148) 0.003 0.717 (0.608–0.825)

SOFA 1.256 (1.053–1.498) 0.011 0.671 (0.562–0.780)

In-hospital mortality

Charlson index 1.156 (1.018–1.313) 0.025 0.590 (0.489–0.691)

Barthel index 0.990 (0.980–1.001) 0.064 0.615 (0.517–0.713)

APACHE II 1.095 (1.031–1.161) 0.003 0.653 (0.562–0.743)

SAPS II 1.081 (1.034–1.131) 0.001 0.671 (0.572–0.770)

SOFA 1.277 (1.098–1.485) 0.001 0.675 (0.585–0.765)

Non-elderly

IntCU mortality

Charlson index 1.456 (1.078–1.966) 0.014 0.822 (0.0–1.0)

Barthel index 0.967 (0.944–0.992) 0.009 0.706 (0.338–1.0)

APACHE II 1.404 (1.117–1.764) 0.004 0.950 (0.0–1.0)

SAPS II 1.273 (1.080–1.501) 0.004 0.898 (0.0–1.0)

SOFA 1.407 (1.014–1.952) 0.041 0.852 (0.718–0.985)

In-hospital mortality

Charlson index 1.582 (1.179–2.123) 0.002 0.852 (0.660–1.0)

Barthel index 0.974 (0.954–0.995) 0.014 0.661 (0.416–0.905)

APACHE II 1.324 (1.121–1.564) 0.001 0.911 (0.846–0.976)

SAPS II 1.245 (1.100–1.410) 0.001 0.917 (0.0–1.0)

SOFA 1.566 (1.128–2.175) 0.007 0.841 (0.699–0.984)

Multiple logistic regression

Elderly

IntCU mortality 0.717 (0.608–0.825)

SAPS II 1.085 (1.025–1.148) 0.005

In-hospital mortality 0.671 (0.572–0.770)

SAPS II 1.076 (1.027–1.127) 0.002

Non-elderly

IntCU mortality 0.95 (0.0–1.0)

APACHE II 1.402 (1.117–1.759) 0.004

In-hospital mortality 0.917 (0.0–1.0)

SAPS II 1.269 (1.106–1.455) 0.001

S. Duque et al. / European Geriatric Medicine 2 (2011) 327–331330

to IntCU, both in elderly and non-elderly patients. Acute illnessseverity should be considered when admitting patients in IntCU,not being age the exclusive criteria of admission. SAPS II seems tobe the strongest predictor of IntCU mortality in elderly with a gooddiscriminative performance. Auriant et al. has already suggestedthat SAPS II was reliable to assess severity of illness of patientsadmitted to an IntCU, though considering that results should beconfirmed, using different samples from other IntCU [23]. Ip et al.also found that SAPS II was stronger than APACHE II predictingoutcome of elderly patients admitted to a geriatric high-dependency unit [1].

Our study showed a higher mortality in older patients, though itis not possible to assume their age to be the determinant ofmortality as comorbidity was significantly higher in elderlycomparing to younger patients. However, Charlson index wasnot a statistically significant predictor of IntCU mortality in elderlypatients, possibly because of methodological constraints, namelythe relatively small number of patients. Indeed, Charlson index hasrevealed to be a statistically significant predictor of IntCU and in-hospital mortality of non-elderly patients. We believe that bettercomorbidity indexes for elderly might exist [24] as Charlson indexoverestimates conditions that are not usual in elderly andunderestimates typical conditions of elderly. Zekry et al. consid-ered the Geriatrics Index of Comorbidity (GIC) the most accurate

predictor of in-hospital mortality [25]. According Salvi et al.Cumulative Illness Rating Scale for Geriatrics (CIRS-G) might bebetter as it grades 14 domains, according the grade of insufficiencyor pathology present, obtaining a more accurate and completemeasure of comorbidity [26]. Nagaratnam et al. considered CIRS-Ga potentially useful tool in predicting outcome of hospitalizednonagenarians patients [27]. Importance of comorbidity in elderlyoutcome has been highlighted by several authors. Incalzy et al.concluded that age has prognostic implication by modulating theeffect of comorbidity [7].

Our study is not the first one failing in proving comorbidity asshort term prognostic factor [13,15]. Mahul et al. have studied agroup of elderly in ICU setting and also have identified SAPS II theonly good predictor of short term mortality, being comorbidity notrelated to mortality [15].

Concerning functional status previous to admission, Barthelindex was not statistically significant as a protective factor inelderly, probably because of small sample size. Other studies havefailed to show correlation between previous functional status andmortality [28]. However, most studies, both in medicine orgeriatric wards and ICU, revealed that functional impairment isa predictive factor of mortality. Bo et al. found that onlydependence in BADL (and not in instrumental ADL) is predictiveof in-hospital mortality [8].

S. Duque et al. / European Geriatric Medicine 2 (2011) 327–331 331

Limitations of our study are mainly related to small sample sizeand being a single-centre study. Another potential limitationmight be the difficulty in obtaining reliable information onfunctional status [29] and the potential bias introduced by theinvestigators when inquiring about dependence instead difficulty[30].

5. Conclusion

Despite our results we consider that admission in IntCU shouldnot be age-based, being chronological age an insufficient criteria.Instead, a comprehensive assessment including not only age butalso chronic diseases, previous autonomy and acute illness severitymust be done, looking for more accuracy in prognosis estimative.

Our results support the choice of SAPS II as the standardprognosis assessment tool of elderly admitted in IntCU to evaluatemultiorganic failure and acute illness severity.

A more accurate risk stratification could help and clarifydecisions such as admission in high-dependency units andwithdrawal of life support treatment. Further investigation isneeded to identify the most accurate systems to evaluatecomorbidity, functional status and other domains usually affectedin elderly, in order to accurately predict in-hospital mortality andcompare outcomes.

Disclosure of interest

The authors declare that they have no conflicts of interestconcerning this article.

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