Steven Counsell, MD

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Impact of an inpatient geriatric consultative service on outcomes for cognitively impaired patients

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Malaz Boustani, MD, MPH

Under the Patient Protection and Affordable Care Act of 2010, commonly referred to as the Affordable Care Act, hospitals face up to a 3% penalty in Medicare reimbursements for patients readmitted within 30 days of initial discharge, and measures have been proposed for modifying payments to hospitals based on their performance on this metric.[1] Cognitive impairment (CI) is considered a major risk factor for poor postdischarge outcomes including mortality and hospital readmission.[2, 3] Hospitals are seeking strategies to reduce postdischarge mortality and rehospitalization among patients with and without CI.[4] Such strategies include use of transitional care coaches, patient and caregiver education, postdischarge follow‐up, and provision of geriatric consultative services (GCS) for the care of complex patients in the hospital setting.[5, 6, 7]

GCS utilize comprehensive geriatric assessments and multidisciplinary processes to recognize and modify risk factors that may lead to poor outcomes among hospitalized patients.[8, 9, 10, 11] Implementation of GCS models including Acute Care for Elders and, recently, the Mobile Acute Care of the Elderly services have shown many benefits among older patients including a reduction in the hospital length of stay and readmission rates.[12, 13] The benefits of such services among hospitalized elders suffering from CI, however, are not well established. The objective of this article was to evaluate the impact of GCS on the readmission and mortality rates of older adults with CI within 12 months of their hospitalization to an urban, public hospital. We hypothesized that GCS will reduce both 12‐month hospital readmissions and mortality rates among this vulnerable group of older adults.

METHODS

The study was approved by the Indiana University institutional review board, and informed consent for identifiable chart review was obtained from subjects or their legally authorized representatives.

Setting

The study was conducted at Eskenazi hospital, Indianapolis, Indiana, a 340‐bed, university‐affiliated, public hospital with over 2300 admissions of patients aged 65 years or older every year.

Population

Four hundred fifteen hospitalized patients aged 65 years or older suffering from CI were enrolled into an original, randomized, controlled trial that evaluated the effect of a computerized decision support system on their quality and outcome of care between July 1, 2006 and May 30, 2008.[14] The computerized decision support included reminders for physicians to reduce the prescription of 18 anticholinergics, minimize physical restraints and Foley catheterization, and increase referral to the local GCS.[15] That previous trial neither showed an impact on quality of care nor health utilization among older patients, including mortality and hospital readmission rates. The current study uses the data from the clinical trial cohort to evaluate the effect of GCS on the 12‐month mortality and hospital readmission rates for hospitalized elders with CI (Figure 1).

Flowchart depicting patients who received intervention.

Inclusion and Exclusion Criteria

Individuals were eligible for enrollment if they were aged 65 years or older, hospitalized on a medical ward, able to speak English, and had evidence of CI within 48 hours of hospital admission. Individuals were excluded if they were previously enrolled, were aphasic, or unresponsive. The presence of CI was based on the Short Portable Mental Status Questionnaire (SPMSQ),[16] a brief 10‐item screening test with a sensitivity of 86% and specificity of 99% for dementia using a score of 7 or less (maximum possible score of 10).[16] The SPMSQ scoring process adjusts for participant educational and racial status, which was a benefit to its use given the urban setting of our hospital serving a large proportion of minority and low‐education patients. A physician‐trained research assistant administered the SPMSQ within 48 hours of hospital admission.

Geriatric Consultative Services

GCS is an interdisciplinary team of a geriatrician, a geriatric pharmacist, a case manager nurse, a social worker, a medical assistant, physical therapists, and a representative of the local Area Agency on Aging. There may be a geriatric fellow and/or medicine resident available to the team based on their rotation structure. Team‐based bedside rounds are performed on new consults only, but all patients are seen individually by the team clinicians. The team emphasizes prevention of functional decline and polypharmacy, recognition and treatment of geriatric syndromes including dementia and delirium, and early discharge/transition planning. Consensus recommendations are prepared and documented in the consult notes section of the electronic medical records. Recommendations deemed critical are discussed directly with the primary teams, but no orders are placed by the GCS team. The GCS team is available on all weekdays but not on weekends or major holidays.

Study Outcomes

For this secondary analysis, we used the Regenstrief Medical Record System (RMRS) to measure 2 outcomes: hospital readmission and mortality rates up to 1 year from discharge following index hospitalization, defined as the first admission in the original clinical trial. The RMRS is the primary instrument for processing data and monitoring patient and physician activity for the hospital.[17, 18] The RMRS is linked with a state‐wide health information exchange to capture data on hospitalization outside the hospital. The RMRS also contains death certificate information for all registered patients who die in or outside the Eskenazi hospital.

Other Data Collections

Delirium was assessed at screening and then every weekday using the Confusion Assessment Method (CAM) by a trained research assistant.[19] CAM evaluates 10 symptoms of delirium specified in the Diagnostic and Statistical Manual of Mental Disorders‐III‐Revision: acute onset, fluctuating course, inattention, disorganized thinking, altered level of consciousness, disorientation, memory impairment, perceptual disturbances, psychomotor agitation or retardation, and sleep/wake disturbance. Participant demographic characteristics, including age, sex, ethnicity, and years of education, were collected from the RMRS and from interviews performed at the time of cognitive screening. Information on length of hospital stay and discharge destination (eg, home vs facility, including skilled nursing and acute rehabilitation facilities) was also obtained from the RMRS. Charlson Comorbidity Index score was calculated using International Classification of Diseases, Ninth Revision codes gathered from 1 year before admission until the time of each participant's discharge from the hospital.[20] The Acute Physiology Score (APS) from the Acute Physiology and Chronic Health Evaluation (APACHE) III was derived from data available in the RMRS to measure the severity of illness.[21] Although the APACHE III was developed in the intensive care unit using data from the first 24 hours after admission, for our study we used the worst laboratory test value during the entire hospital stay to calculate the APS.[22]

Statistical Analysis

Baseline variables are presented as means and standard deviations for continuous variables, and percentages for binary categorical variables. Comparisons between patients receiving GCS and those who did not were performed using ² tests for categorical variables and Kruskal‐Wallis test for continuous variables. Cox proportional hazard models were used to determine the association between receiving GCS and time to hospital readmission or mortality within 30 days or 1‐year postindex admission while adjusting for other covariates. For the models using time to readmission, patients without readmission were censored either at the endpoint (30 days or 1 year) or at time of death for those who died within the time frame in each model. Because GCS was not randomly assigned, we also conducted a propensity score analysis.[23] A logistic model for the probability of receiving GCS was conducted using patient demographic variables and information collected before and at the time of GCS. Stratified Cox proportional models using quintiles of predicted probability of receiving GCS were used in a propensity‐adjusted Cox model. All data analyses were performed using SAS version 9.3 (SAS Institute, Inc., Cary, NC).

RESULTS

Between July 1, 2006 and May 30, 2008, 415 CI patients were enrolled in the original trial, with 176 receiving the GCS. As shown in Table 1, the GCS and non‐GCS groups differed significantly. The GCS group was older (79.2 years old, 8.1 standard deviation [SD] vs 75.8 years old, 7.8 SD; P0.001), scored lower on the SPMSQ (4.7, 2.7 SD vs 5.5, 2.7 SD; P=0.002), had fewer chronic conditions with a lower mean Charlson Comorbidity Index Score (2.1, 1.86 SD vs 2.8, 2.6 SD; P=0.023), but a higher percentage of delirium (48.9% vs 29.3%), a lower percentage of being discharged home (37.5% vs 56.1%), and a higher mean length of stay (6.4 days, 6.4 SD vs 5.6 days, 5.9 SD; P=0.004). They also had a lower malignancy rate (6.2% vs 14.6%; P=0.007) and a lower number of hospitalizations in the previous year (0.5 admissions, 0.9 SD vs 0.7 admissions, 1.1 SD; P=0.035). No differences were observed in regard to gender, ethnicity, history of myocardial infarctions, chronic obstructive pulmonary disease, cerebrovascular disease, peripheral vascular disease, diabetes, and use of anticholinergic medicines.

Table 1.Baseline Characteristics and Summary Outcomes Within 1 Year of Hospital Discharge Based on Exposure to Geriatric Consult Services
	No GCS, n=239	GCS, n=176	P Value*
NOTE: Abbreviations: ACB, Anticholinergic Burden Scale; APS, Acute Physiology Score; CHF, congestive heart failure; COPD, chronic obstructive pulmonary disease; GCS, geriatric consult services; MI, myocardial infarction; PVD, peripheral vascular disease; SD, standard deviation; SPMSQ, Short Portable Mental Status Questionnaire. *P value was calculated using ² tests for categorical variables and Kruskal‐Wallis test for continuous variables.
Baseline characteristics
Mean age (SD)	75.8 (7.8)	79.2 (8.1)	<0.001
% Female	66.1 [n=158]	68.2 [n=120]	0.657
% African American	54.8 [n=131]	63.6 [n=112]	0.071
Mean SPMSQ score (SD)	5.5 (2.7)	4.7 (2.7)	0.002
Admission diagnoses
MI	15.5 [n=37]	13.6 [n=24]	0.675
CHF	38.1 [n=91]	34.7 [n=61]	0.475
PVD	7.1 [n=17]	9.7 [n=17]	0.370
Cerebrovascular	13.8 [n=33]	19.3 [n=34]	0.140
COPD	41.0 [n=98]	33.0 [n=58]	0.094
Diabetes	47.7 [n=114]	40.9 [n=72]	0.169
Malignancy	14.6 [n=35]	6.2 [n=11]	0.007
Metastatic cancer	8.8 [n=21]	1.7 [n=3]	0.002
Mean Charlson Comorbidity (SD)	2.8 (2.6)	2.1 (1.8)	0.023
Mean APS (SD)	24.5 (13.8)	25.9 (13.5)	0.231
Definite ACB Use	35.2 [n=84]	27.8 [n=49]	0.136
Length of stay	5.6 (5.9)	6.4 (6.4)	0.004
% Any delirium	29.3 [n=70]	48.9 [n=156]	<0.001
% Discharged home	56.1 [n=134]	37.5 [n=66]	<0.001
No. of inpatient stays prior year	0.7 (1.1)	0.5 (0.9)	0.035
Follow‐up outcomes
% Readmission within 30 days	15.1 [n=36]	22.7 [n=40]	0.054
% Readmission within 1 year	54.4 [n=130]	56.3 [n=99]	0.765
% Death within 30 days	4.2 [n=10]	1.7 [n=3]	0.253
% Death within 1 year	26.8 [n=64]	23.9 [n=42]	0.569
% Readmission or death within 30 days	18.0 [n=43]	24.4 [n=43]	0.113
% Readmission or death within 1 year	64.8 [n=155]	63.1 [n=111]	0.708

Table 2 describes the association of various factors with receiving GCS. Patients who were positive for delirium (odds ratio [OR]=1.65; 95% confidence interval=0.98‐2.77) and were older (OR=1.04; 95% confidence interval=1.01‐1.08) had a higher propensity to receive GCS, whereas, the presence of metastatic cancer resulted in a lower propensity (OR=0.15; 95% confidence interval=0.02‐1.16) of receiving GCS. The logistic model estimated area under the receiver operating characteristic curve was 0.707.

Table 2.Results of Logistic Regression Model for Receiving a Geriatric Consult Service Consult Based on Patients' Demographic and Hospital Variables
	Adjusted OR (95% CI)	P Value
NOTE: Abbreviations: CI, confidence interval; OR, odds ratio. *Based on the Anticholinergic Burden Scale.
Age	1.04 (1.011.08)	0.006
Female	1.02 (0.641.63)	0.942
African American	1.11 (0.711.72)	0.657
Short Portable Mental Status Questionnaire score	1.00 (0.911.10)	0.990
Acute Physiology Score	1.00 (0.981.02)	0.769
Charlson Comorbidity Score	1.11 (0.841.46)	0.471
Length of hospital stay	1.02 (0.981.07)	0.299
Definite anticholinergic use*	0.74 (0.461.20)	0.219
Any delirium during hospital stay	1.65 (0.982.77)	0.061
Diabetes mellitus	0.72 (0.411.26)	0.253
Myocardial infarction	0.83 (0.411.66)	0.593
Congestive heart failure	0.83 (0.471.47)	0.524
Peripheral vascular disease	1.39 (0.613.18)	0.433
Cerebrovascular disease	1.30 (0.652.59)	0.464
Malignancy	0.45 (0.171.21)	0.113
Metastatic cancer	0.15 (0.021.16)	0.069
Chronic obstructive pulmonary disease	0.91 (0.531.55)	0.727

Table 3 provides results from the Cox models for receiving GCS on readmission and mortality outcomes adjusting for various sets of covariates and with the propensity score adjustment. Model 1 presents unadjusted hazard ratio (HR). Model 2 presents HRs adjusting for a common set of covariates that were significantly associated with at least 1 of the outcomes, whereas model 3 presents the results adjusting for all covariates. All 4 models yielded similar results. As evident from this table, propensity‐adjusted HR for 30‐day readmission was still significantly higher among patients receiving GCS (HR=1.75; 95% confidence interval=1.06‐2.88) but not at 1 year (HR=1.19; 95% confidence interval=0.89‐1.59). There was a trend for decreased mortality for the GCS group at 30 days (HR=0.35; 95% confidence interval=0.09‐1.35), but it disappeared at 1 year (HR=0.91; 95% confidence interval=0.59‐1.40). A composite outcome of readmissions and mortality did not show any difference between the GCS and no‐GCS groups.

Table 3.Adjusted Hazard Ratios for Geriatric Consult Service Consult Estimated Using Cox Proportional Hazard Models and Propensity‐Adjusted Models
Outcome Variables	Model 1		Model 2		Model 3		Propensity Adjusted
Outcome Variables	HR (95% CI)	P Value	HR (95% CI)	P Value	HR (95% CI)	P Value	HR (95% CI)	P Value
NOTE: Abbreviations: CI, confidence interval; HR, hazard ratio. Model 1: unadjusted; Model 2: adjusting for age, Charlson Comorbidity Score, Acute Physiology Scale, disposition status, congestive heart failure, and peripheral vascular disease; Model 3: adjusting for all demographic and hospital variables.
Readmission within 30 days	1.65 (1.05, 2.59)	0.030	1.73 (1.08, 2.78)	0.024	1.84 (1.133.00)	0.015	1.75 (1.062.88)	0.029
Readmission within 1 year	1.13 (0.87, 1.46)	0.373	1.24 (0.94, 1.63)	0.125	1.26 (0.941.68)	0.117	1.19 (0.891.59)	0.245
Death within 30 days	0.43 (0.12, 1.56)	0.199	0.34 (0.09, 1.28)	0.110	0.25 (0.061.02)	0.053	0.35 (0.091.35)	0.126
Death within 1 year	0.95 (0.65, 1.45)	0.806	0.87 (0.58 1.31)	0.506	0.93 (0.601.42)	0.724	0.91 (0.591.40)	0.669
Readmission or Death within 30 days	1.48 (0.97, 2.26)	0.070	1.49 (0.96, 2.33)	0.078	1.56 (0.982.47)	0.061	1.55 (0.972.48)	0.069
Readmission or death within 1 year	1.05 (0.82, 1.34)	0.699	1.11 (0.86, 1.43)	0.412	1.15 (0.881.50)	0.318	1.08 (0.831.42)	0.569

DISCUSSION

To our knowledge, this is the first study to analyze the impact of GCS on hospital readmission and mortality rates of CI patients. Our results did not show any short‐term or long‐term benefits of GCS for CI patients. Recent studies exploring cost benefits of the GCS have found trends toward lower readmission, but none focused on patients with CI.[6, 24, 25] It is important to note that our study did not use random allocation to assigning the patient into the GCS or control group, thus raising the possibility that patients who received GCS were sicker and were medically and socially more complex than those who did not receive the consult. Moreover, GCS consultation is preferentially sought for and completed for patients with CI and functional limitations, consistent with our finding that GCS patients more often have delirium and are less‐often discharged home.

The nature of the GCS team is another important consideration. Our GCS model did not include unit cohorting of patients, an important component of other proposed GCS models.[26] A recent meta‐analysis found that the GCS models without unit cohorting of patients did not have an impact on 1‐ or 12‐month readmission rates.[27] Low adherence to consultant recommendations (less than 33%) was thought to be a reason for such results. Importance of cohorting with regard to accomplishing recommendations by primary teams, importance of unit staff expertise in geriatric principles, and impact of a unit model on teamwork has also been highlighted by another review.[28] These findings lend to the hypothesis that unit cohorting and direct order placement by the GCS team may improve outcomes among CI patients, including a reduction in readmission rates.

Although readmissions rates were not statistically different between GCS and control groups at 1‐year postdischarge, 30‐day readmission rates were higher among the GCS group. Previous research among older heart failure patients found that a comprehensive transitional care intervention at the time of hospital discharge significantly shortened the time to readmission in the intervention group (P=0.026).[29] The factors identified by the study authors included enhanced supervision by the transitional healthcare teams along with improved awareness and education among treated patients that may have facilitated early recognition of clinical deterioration.[29] A recent study with intensive outpatient care that resulted in increased admissions among chronically ill adults provided a similar conclusion.[30]

GCS patients showed a trend toward decreased mortality as did patients enrolled in previous studies evaluating GCS models in the inpatient setting, as suggested by a recent review.[27] A caveat to note is that these trends favored ward‐styled GCS services as compared to our open GCS model,[27, 28] although the factors cited in these dedicated units affecting mortality included prompt attention to early rehabilitation, delirium management, and prevention of pressure ulcers and are also frequently implemented for patients in our GCS service model and therefore may have produced similar results.

Our neutral results in regard to the readmissions need to be interpreted with caution. First, this study was conducted in a hospital that supports expert geriatric and palliative care teams, both in the inpatient and the ambulatory settings, that provide consultative services and train medicine teams and hospital nursing staff. On the outpatient side, the presence of a robust geriatrics house‐calls program and the Geriatric Resources for Assessment and Care of Elders team results in above‐average care for the control group, and thus may also impact apparent outcomes.[31, 32] Second, 30‐day readmissions represent a complex outcome. Two recent reviews of hospital‐initiated interventions have shown that evidence regarding best strategies to decrease 30‐day readmissions is unclear.[33] Neither review included studies that targeted patients with CI only. The 2 programs that reduced 30‐day readmissions were multifaceted and included personnel who provide bridging between the hospital and the outpatient setting.[34] The GCS does include a focus on postdischarge resources, but does that on a case‐by‐case basis and no formal posthospital follow‐ups are provided. Moreover, the value of 30‐day readmission rates as a marker of quality, even though used by policymakers as an indicator of hospital quality, remains controversial.[35, 36] Broadening the outcomes of interest to include patient‐centered outcomes including satisfaction with care, that have shown to impact other health outcomes, may help improve understanding the benefits of GCS in hospitals.[37] Other comprehensive transitional care models that failed to show a benefit on 30‐day readmissions in older patients still resulted in higher satisfaction among patients.[38] Unfortunately, our evaluation did not include an assessment of patient satisfaction and quality of transitions.

Since the study period, GCS at our hospital now has incorporated a more robust focus on advance care planning (ACP) and execution of Physician Orders for Scope of Treatment that were legislated in the state in July 2013. The GCS team members are expert in carrying out complex ACP discussions and also partner with the inpatient palliative care team. It is quite possible that a study of more recent outcomes will yield more positive results for the selected outcomes. Thus, for future trials that aim to study the impact of GCS in the inpatient settings, it may be advisable to include important quality markers such as implementation of ACP and patient satisfaction along with the health utilization outcomes.

Limitations

As mentioned previously, it is possible that our risk adjustment was insufficient to account for all the medical and psychosocial differences among groups. For example, the overall anticholinergic impact of various medications such as antipsychotic medications and histamine‐2 blockers was assessed via the Anticholinergic Burden Scale on admission, but we did not have information on medication prescribing during the stay. We were further limited by lack of baseline functional status and socioeconomic details, both of which are related to 30‐day readmissions. For example, living alone, prior use of assist devices, and belonging to lower socioeconomic status are correlated with higher readmission rates.[39, 40] Patients with available social support may receive more intense supervision and may seek medical attention sooner. On the other hand, worsening health among CI patients without any approximate social support may be unnoticed for days. Absence of details of inpatient interventions may also have resulted in unmeasurable confounders that could have impacted our study outcomes. Finally, lack of information on the uptake of GCS recommendations by the primary teams is another limitation of this analysis. Future trials should include strategies to address these information gaps.

CONCLUSION

Our results comparing inpatient geriatrics consultative services with usual care in hospitalized elders having cognitive impairment failed to demonstrate an impact on readmissions and mortality. A clinical lesson learned, however, is that much work is still required to reduce readmission and mortality rates in this especially vulnerable patient population.

Disclosures

Disclosures: This work was supported by grants from a Geriatric Academic Career Award (K01HP20517) through Health Resources and Services Administration, R01AG034205 and K23‐AG043476 from the National Institute on Aging, and the John A. Hartford Foundation Center for Excellence in Geriatric Medicine. The sponsors had no role in the study design, evaluation, or manuscript development. The authors report no conflicts of interest.

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Under the Patient Protection and Affordable Care Act of 2010, commonly referred to as the Affordable Care Act, hospitals face up to a 3% penalty in Medicare reimbursements for patients readmitted within 30 days of initial discharge, and measures have been proposed for modifying payments to hospitals based on their performance on this metric.[1] Cognitive impairment (CI) is considered a major risk factor for poor postdischarge outcomes including mortality and hospital readmission.[2, 3] Hospitals are seeking strategies to reduce postdischarge mortality and rehospitalization among patients with and without CI.[4] Such strategies include use of transitional care coaches, patient and caregiver education, postdischarge follow‐up, and provision of geriatric consultative services (GCS) for the care of complex patients in the hospital setting.[5, 6, 7]

GCS utilize comprehensive geriatric assessments and multidisciplinary processes to recognize and modify risk factors that may lead to poor outcomes among hospitalized patients.[8, 9, 10, 11] Implementation of GCS models including Acute Care for Elders and, recently, the Mobile Acute Care of the Elderly services have shown many benefits among older patients including a reduction in the hospital length of stay and readmission rates.[12, 13] The benefits of such services among hospitalized elders suffering from CI, however, are not well established. The objective of this article was to evaluate the impact of GCS on the readmission and mortality rates of older adults with CI within 12 months of their hospitalization to an urban, public hospital. We hypothesized that GCS will reduce both 12‐month hospital readmissions and mortality rates among this vulnerable group of older adults.

METHODS

The study was approved by the Indiana University institutional review board, and informed consent for identifiable chart review was obtained from subjects or their legally authorized representatives.

Setting

The study was conducted at Eskenazi hospital, Indianapolis, Indiana, a 340‐bed, university‐affiliated, public hospital with over 2300 admissions of patients aged 65 years or older every year.

Population

Four hundred fifteen hospitalized patients aged 65 years or older suffering from CI were enrolled into an original, randomized, controlled trial that evaluated the effect of a computerized decision support system on their quality and outcome of care between July 1, 2006 and May 30, 2008.[14] The computerized decision support included reminders for physicians to reduce the prescription of 18 anticholinergics, minimize physical restraints and Foley catheterization, and increase referral to the local GCS.[15] That previous trial neither showed an impact on quality of care nor health utilization among older patients, including mortality and hospital readmission rates. The current study uses the data from the clinical trial cohort to evaluate the effect of GCS on the 12‐month mortality and hospital readmission rates for hospitalized elders with CI (Figure 1).

Inclusion and Exclusion Criteria

Individuals were eligible for enrollment if they were aged 65 years or older, hospitalized on a medical ward, able to speak English, and had evidence of CI within 48 hours of hospital admission. Individuals were excluded if they were previously enrolled, were aphasic, or unresponsive. The presence of CI was based on the Short Portable Mental Status Questionnaire (SPMSQ),[16] a brief 10‐item screening test with a sensitivity of 86% and specificity of 99% for dementia using a score of 7 or less (maximum possible score of 10).[16] The SPMSQ scoring process adjusts for participant educational and racial status, which was a benefit to its use given the urban setting of our hospital serving a large proportion of minority and low‐education patients. A physician‐trained research assistant administered the SPMSQ within 48 hours of hospital admission.

Geriatric Consultative Services

GCS is an interdisciplinary team of a geriatrician, a geriatric pharmacist, a case manager nurse, a social worker, a medical assistant, physical therapists, and a representative of the local Area Agency on Aging. There may be a geriatric fellow and/or medicine resident available to the team based on their rotation structure. Team‐based bedside rounds are performed on new consults only, but all patients are seen individually by the team clinicians. The team emphasizes prevention of functional decline and polypharmacy, recognition and treatment of geriatric syndromes including dementia and delirium, and early discharge/transition planning. Consensus recommendations are prepared and documented in the consult notes section of the electronic medical records. Recommendations deemed critical are discussed directly with the primary teams, but no orders are placed by the GCS team. The GCS team is available on all weekdays but not on weekends or major holidays.

Study Outcomes

For this secondary analysis, we used the Regenstrief Medical Record System (RMRS) to measure 2 outcomes: hospital readmission and mortality rates up to 1 year from discharge following index hospitalization, defined as the first admission in the original clinical trial. The RMRS is the primary instrument for processing data and monitoring patient and physician activity for the hospital.[17, 18] The RMRS is linked with a state‐wide health information exchange to capture data on hospitalization outside the hospital. The RMRS also contains death certificate information for all registered patients who die in or outside the Eskenazi hospital.

Other Data Collections

Delirium was assessed at screening and then every weekday using the Confusion Assessment Method (CAM) by a trained research assistant.[19] CAM evaluates 10 symptoms of delirium specified in the Diagnostic and Statistical Manual of Mental Disorders‐III‐Revision: acute onset, fluctuating course, inattention, disorganized thinking, altered level of consciousness, disorientation, memory impairment, perceptual disturbances, psychomotor agitation or retardation, and sleep/wake disturbance. Participant demographic characteristics, including age, sex, ethnicity, and years of education, were collected from the RMRS and from interviews performed at the time of cognitive screening. Information on length of hospital stay and discharge destination (eg, home vs facility, including skilled nursing and acute rehabilitation facilities) was also obtained from the RMRS. Charlson Comorbidity Index score was calculated using International Classification of Diseases, Ninth Revision codes gathered from 1 year before admission until the time of each participant's discharge from the hospital.[20] The Acute Physiology Score (APS) from the Acute Physiology and Chronic Health Evaluation (APACHE) III was derived from data available in the RMRS to measure the severity of illness.[21] Although the APACHE III was developed in the intensive care unit using data from the first 24 hours after admission, for our study we used the worst laboratory test value during the entire hospital stay to calculate the APS.[22]

Statistical Analysis

Baseline variables are presented as means and standard deviations for continuous variables, and percentages for binary categorical variables. Comparisons between patients receiving GCS and those who did not were performed using ² tests for categorical variables and Kruskal‐Wallis test for continuous variables. Cox proportional hazard models were used to determine the association between receiving GCS and time to hospital readmission or mortality within 30 days or 1‐year postindex admission while adjusting for other covariates. For the models using time to readmission, patients without readmission were censored either at the endpoint (30 days or 1 year) or at time of death for those who died within the time frame in each model. Because GCS was not randomly assigned, we also conducted a propensity score analysis.[23] A logistic model for the probability of receiving GCS was conducted using patient demographic variables and information collected before and at the time of GCS. Stratified Cox proportional models using quintiles of predicted probability of receiving GCS were used in a propensity‐adjusted Cox model. All data analyses were performed using SAS version 9.3 (SAS Institute, Inc., Cary, NC).

RESULTS

Between July 1, 2006 and May 30, 2008, 415 CI patients were enrolled in the original trial, with 176 receiving the GCS. As shown in Table 1, the GCS and non‐GCS groups differed significantly. The GCS group was older (79.2 years old, 8.1 standard deviation [SD] vs 75.8 years old, 7.8 SD; P0.001), scored lower on the SPMSQ (4.7, 2.7 SD vs 5.5, 2.7 SD; P=0.002), had fewer chronic conditions with a lower mean Charlson Comorbidity Index Score (2.1, 1.86 SD vs 2.8, 2.6 SD; P=0.023), but a higher percentage of delirium (48.9% vs 29.3%), a lower percentage of being discharged home (37.5% vs 56.1%), and a higher mean length of stay (6.4 days, 6.4 SD vs 5.6 days, 5.9 SD; P=0.004). They also had a lower malignancy rate (6.2% vs 14.6%; P=0.007) and a lower number of hospitalizations in the previous year (0.5 admissions, 0.9 SD vs 0.7 admissions, 1.1 SD; P=0.035). No differences were observed in regard to gender, ethnicity, history of myocardial infarctions, chronic obstructive pulmonary disease, cerebrovascular disease, peripheral vascular disease, diabetes, and use of anticholinergic medicines.

Table 1.Baseline Characteristics and Summary Outcomes Within 1 Year of Hospital Discharge Based on Exposure to Geriatric Consult Services
	No GCS, n=239	GCS, n=176	P Value*
NOTE: Abbreviations: ACB, Anticholinergic Burden Scale; APS, Acute Physiology Score; CHF, congestive heart failure; COPD, chronic obstructive pulmonary disease; GCS, geriatric consult services; MI, myocardial infarction; PVD, peripheral vascular disease; SD, standard deviation; SPMSQ, Short Portable Mental Status Questionnaire. *P value was calculated using ² tests for categorical variables and Kruskal‐Wallis test for continuous variables.
Baseline characteristics
Mean age (SD)	75.8 (7.8)	79.2 (8.1)	<0.001
% Female	66.1 [n=158]	68.2 [n=120]	0.657
% African American	54.8 [n=131]	63.6 [n=112]	0.071
Mean SPMSQ score (SD)	5.5 (2.7)	4.7 (2.7)	0.002
Admission diagnoses
MI	15.5 [n=37]	13.6 [n=24]	0.675
CHF	38.1 [n=91]	34.7 [n=61]	0.475
PVD	7.1 [n=17]	9.7 [n=17]	0.370
Cerebrovascular	13.8 [n=33]	19.3 [n=34]	0.140
COPD	41.0 [n=98]	33.0 [n=58]	0.094
Diabetes	47.7 [n=114]	40.9 [n=72]	0.169
Malignancy	14.6 [n=35]	6.2 [n=11]	0.007
Metastatic cancer	8.8 [n=21]	1.7 [n=3]	0.002
Mean Charlson Comorbidity (SD)	2.8 (2.6)	2.1 (1.8)	0.023
Mean APS (SD)	24.5 (13.8)	25.9 (13.5)	0.231
Definite ACB Use	35.2 [n=84]	27.8 [n=49]	0.136
Length of stay	5.6 (5.9)	6.4 (6.4)	0.004
% Any delirium	29.3 [n=70]	48.9 [n=156]	<0.001
% Discharged home	56.1 [n=134]	37.5 [n=66]	<0.001
No. of inpatient stays prior year	0.7 (1.1)	0.5 (0.9)	0.035
Follow‐up outcomes
% Readmission within 30 days	15.1 [n=36]	22.7 [n=40]	0.054
% Readmission within 1 year	54.4 [n=130]	56.3 [n=99]	0.765
% Death within 30 days	4.2 [n=10]	1.7 [n=3]	0.253
% Death within 1 year	26.8 [n=64]	23.9 [n=42]	0.569
% Readmission or death within 30 days	18.0 [n=43]	24.4 [n=43]	0.113
% Readmission or death within 1 year	64.8 [n=155]	63.1 [n=111]	0.708

Table 2 describes the association of various factors with receiving GCS. Patients who were positive for delirium (odds ratio [OR]=1.65; 95% confidence interval=0.98‐2.77) and were older (OR=1.04; 95% confidence interval=1.01‐1.08) had a higher propensity to receive GCS, whereas, the presence of metastatic cancer resulted in a lower propensity (OR=0.15; 95% confidence interval=0.02‐1.16) of receiving GCS. The logistic model estimated area under the receiver operating characteristic curve was 0.707.

Table 2.Results of Logistic Regression Model for Receiving a Geriatric Consult Service Consult Based on Patients' Demographic and Hospital Variables
	Adjusted OR (95% CI)	P Value
NOTE: Abbreviations: CI, confidence interval; OR, odds ratio. *Based on the Anticholinergic Burden Scale.
Age	1.04 (1.011.08)	0.006
Female	1.02 (0.641.63)	0.942
African American	1.11 (0.711.72)	0.657
Short Portable Mental Status Questionnaire score	1.00 (0.911.10)	0.990
Acute Physiology Score	1.00 (0.981.02)	0.769
Charlson Comorbidity Score	1.11 (0.841.46)	0.471
Length of hospital stay	1.02 (0.981.07)	0.299
Definite anticholinergic use*	0.74 (0.461.20)	0.219
Any delirium during hospital stay	1.65 (0.982.77)	0.061
Diabetes mellitus	0.72 (0.411.26)	0.253
Myocardial infarction	0.83 (0.411.66)	0.593
Congestive heart failure	0.83 (0.471.47)	0.524
Peripheral vascular disease	1.39 (0.613.18)	0.433
Cerebrovascular disease	1.30 (0.652.59)	0.464
Malignancy	0.45 (0.171.21)	0.113
Metastatic cancer	0.15 (0.021.16)	0.069
Chronic obstructive pulmonary disease	0.91 (0.531.55)	0.727

Table 3 provides results from the Cox models for receiving GCS on readmission and mortality outcomes adjusting for various sets of covariates and with the propensity score adjustment. Model 1 presents unadjusted hazard ratio (HR). Model 2 presents HRs adjusting for a common set of covariates that were significantly associated with at least 1 of the outcomes, whereas model 3 presents the results adjusting for all covariates. All 4 models yielded similar results. As evident from this table, propensity‐adjusted HR for 30‐day readmission was still significantly higher among patients receiving GCS (HR=1.75; 95% confidence interval=1.06‐2.88) but not at 1 year (HR=1.19; 95% confidence interval=0.89‐1.59). There was a trend for decreased mortality for the GCS group at 30 days (HR=0.35; 95% confidence interval=0.09‐1.35), but it disappeared at 1 year (HR=0.91; 95% confidence interval=0.59‐1.40). A composite outcome of readmissions and mortality did not show any difference between the GCS and no‐GCS groups.

Table 3.Adjusted Hazard Ratios for Geriatric Consult Service Consult Estimated Using Cox Proportional Hazard Models and Propensity‐Adjusted Models
Outcome Variables	Model 1		Model 2		Model 3		Propensity Adjusted
Outcome Variables	HR (95% CI)	P Value	HR (95% CI)	P Value	HR (95% CI)	P Value	HR (95% CI)	P Value
NOTE: Abbreviations: CI, confidence interval; HR, hazard ratio. Model 1: unadjusted; Model 2: adjusting for age, Charlson Comorbidity Score, Acute Physiology Scale, disposition status, congestive heart failure, and peripheral vascular disease; Model 3: adjusting for all demographic and hospital variables.
Readmission within 30 days	1.65 (1.05, 2.59)	0.030	1.73 (1.08, 2.78)	0.024	1.84 (1.133.00)	0.015	1.75 (1.062.88)	0.029
Readmission within 1 year	1.13 (0.87, 1.46)	0.373	1.24 (0.94, 1.63)	0.125	1.26 (0.941.68)	0.117	1.19 (0.891.59)	0.245
Death within 30 days	0.43 (0.12, 1.56)	0.199	0.34 (0.09, 1.28)	0.110	0.25 (0.061.02)	0.053	0.35 (0.091.35)	0.126
Death within 1 year	0.95 (0.65, 1.45)	0.806	0.87 (0.58 1.31)	0.506	0.93 (0.601.42)	0.724	0.91 (0.591.40)	0.669
Readmission or Death within 30 days	1.48 (0.97, 2.26)	0.070	1.49 (0.96, 2.33)	0.078	1.56 (0.982.47)	0.061	1.55 (0.972.48)	0.069
Readmission or death within 1 year	1.05 (0.82, 1.34)	0.699	1.11 (0.86, 1.43)	0.412	1.15 (0.881.50)	0.318	1.08 (0.831.42)	0.569

DISCUSSION

To our knowledge, this is the first study to analyze the impact of GCS on hospital readmission and mortality rates of CI patients. Our results did not show any short‐term or long‐term benefits of GCS for CI patients. Recent studies exploring cost benefits of the GCS have found trends toward lower readmission, but none focused on patients with CI.[6, 24, 25] It is important to note that our study did not use random allocation to assigning the patient into the GCS or control group, thus raising the possibility that patients who received GCS were sicker and were medically and socially more complex than those who did not receive the consult. Moreover, GCS consultation is preferentially sought for and completed for patients with CI and functional limitations, consistent with our finding that GCS patients more often have delirium and are less‐often discharged home.

The nature of the GCS team is another important consideration. Our GCS model did not include unit cohorting of patients, an important component of other proposed GCS models.[26] A recent meta‐analysis found that the GCS models without unit cohorting of patients did not have an impact on 1‐ or 12‐month readmission rates.[27] Low adherence to consultant recommendations (less than 33%) was thought to be a reason for such results. Importance of cohorting with regard to accomplishing recommendations by primary teams, importance of unit staff expertise in geriatric principles, and impact of a unit model on teamwork has also been highlighted by another review.[28] These findings lend to the hypothesis that unit cohorting and direct order placement by the GCS team may improve outcomes among CI patients, including a reduction in readmission rates.

Although readmissions rates were not statistically different between GCS and control groups at 1‐year postdischarge, 30‐day readmission rates were higher among the GCS group. Previous research among older heart failure patients found that a comprehensive transitional care intervention at the time of hospital discharge significantly shortened the time to readmission in the intervention group (P=0.026).[29] The factors identified by the study authors included enhanced supervision by the transitional healthcare teams along with improved awareness and education among treated patients that may have facilitated early recognition of clinical deterioration.[29] A recent study with intensive outpatient care that resulted in increased admissions among chronically ill adults provided a similar conclusion.[30]

GCS patients showed a trend toward decreased mortality as did patients enrolled in previous studies evaluating GCS models in the inpatient setting, as suggested by a recent review.[27] A caveat to note is that these trends favored ward‐styled GCS services as compared to our open GCS model,[27, 28] although the factors cited in these dedicated units affecting mortality included prompt attention to early rehabilitation, delirium management, and prevention of pressure ulcers and are also frequently implemented for patients in our GCS service model and therefore may have produced similar results.

Our neutral results in regard to the readmissions need to be interpreted with caution. First, this study was conducted in a hospital that supports expert geriatric and palliative care teams, both in the inpatient and the ambulatory settings, that provide consultative services and train medicine teams and hospital nursing staff. On the outpatient side, the presence of a robust geriatrics house‐calls program and the Geriatric Resources for Assessment and Care of Elders team results in above‐average care for the control group, and thus may also impact apparent outcomes.[31, 32] Second, 30‐day readmissions represent a complex outcome. Two recent reviews of hospital‐initiated interventions have shown that evidence regarding best strategies to decrease 30‐day readmissions is unclear.[33] Neither review included studies that targeted patients with CI only. The 2 programs that reduced 30‐day readmissions were multifaceted and included personnel who provide bridging between the hospital and the outpatient setting.[34] The GCS does include a focus on postdischarge resources, but does that on a case‐by‐case basis and no formal posthospital follow‐ups are provided. Moreover, the value of 30‐day readmission rates as a marker of quality, even though used by policymakers as an indicator of hospital quality, remains controversial.[35, 36] Broadening the outcomes of interest to include patient‐centered outcomes including satisfaction with care, that have shown to impact other health outcomes, may help improve understanding the benefits of GCS in hospitals.[37] Other comprehensive transitional care models that failed to show a benefit on 30‐day readmissions in older patients still resulted in higher satisfaction among patients.[38] Unfortunately, our evaluation did not include an assessment of patient satisfaction and quality of transitions.

Since the study period, GCS at our hospital now has incorporated a more robust focus on advance care planning (ACP) and execution of Physician Orders for Scope of Treatment that were legislated in the state in July 2013. The GCS team members are expert in carrying out complex ACP discussions and also partner with the inpatient palliative care team. It is quite possible that a study of more recent outcomes will yield more positive results for the selected outcomes. Thus, for future trials that aim to study the impact of GCS in the inpatient settings, it may be advisable to include important quality markers such as implementation of ACP and patient satisfaction along with the health utilization outcomes.

Limitations

As mentioned previously, it is possible that our risk adjustment was insufficient to account for all the medical and psychosocial differences among groups. For example, the overall anticholinergic impact of various medications such as antipsychotic medications and histamine‐2 blockers was assessed via the Anticholinergic Burden Scale on admission, but we did not have information on medication prescribing during the stay. We were further limited by lack of baseline functional status and socioeconomic details, both of which are related to 30‐day readmissions. For example, living alone, prior use of assist devices, and belonging to lower socioeconomic status are correlated with higher readmission rates.[39, 40] Patients with available social support may receive more intense supervision and may seek medical attention sooner. On the other hand, worsening health among CI patients without any approximate social support may be unnoticed for days. Absence of details of inpatient interventions may also have resulted in unmeasurable confounders that could have impacted our study outcomes. Finally, lack of information on the uptake of GCS recommendations by the primary teams is another limitation of this analysis. Future trials should include strategies to address these information gaps.

CONCLUSION

Our results comparing inpatient geriatrics consultative services with usual care in hospitalized elders having cognitive impairment failed to demonstrate an impact on readmissions and mortality. A clinical lesson learned, however, is that much work is still required to reduce readmission and mortality rates in this especially vulnerable patient population.

Disclosures

Disclosures: This work was supported by grants from a Geriatric Academic Career Award (K01HP20517) through Health Resources and Services Administration, R01AG034205 and K23‐AG043476 from the National Institute on Aging, and the John A. Hartford Foundation Center for Excellence in Geriatric Medicine. The sponsors had no role in the study design, evaluation, or manuscript development. The authors report no conflicts of interest.

Under the Patient Protection and Affordable Care Act of 2010, commonly referred to as the Affordable Care Act, hospitals face up to a 3% penalty in Medicare reimbursements for patients readmitted within 30 days of initial discharge, and measures have been proposed for modifying payments to hospitals based on their performance on this metric.[1] Cognitive impairment (CI) is considered a major risk factor for poor postdischarge outcomes including mortality and hospital readmission.[2, 3] Hospitals are seeking strategies to reduce postdischarge mortality and rehospitalization among patients with and without CI.[4] Such strategies include use of transitional care coaches, patient and caregiver education, postdischarge follow‐up, and provision of geriatric consultative services (GCS) for the care of complex patients in the hospital setting.[5, 6, 7]

GCS utilize comprehensive geriatric assessments and multidisciplinary processes to recognize and modify risk factors that may lead to poor outcomes among hospitalized patients.[8, 9, 10, 11] Implementation of GCS models including Acute Care for Elders and, recently, the Mobile Acute Care of the Elderly services have shown many benefits among older patients including a reduction in the hospital length of stay and readmission rates.[12, 13] The benefits of such services among hospitalized elders suffering from CI, however, are not well established. The objective of this article was to evaluate the impact of GCS on the readmission and mortality rates of older adults with CI within 12 months of their hospitalization to an urban, public hospital. We hypothesized that GCS will reduce both 12‐month hospital readmissions and mortality rates among this vulnerable group of older adults.

METHODS

The study was approved by the Indiana University institutional review board, and informed consent for identifiable chart review was obtained from subjects or their legally authorized representatives.

Setting

The study was conducted at Eskenazi hospital, Indianapolis, Indiana, a 340‐bed, university‐affiliated, public hospital with over 2300 admissions of patients aged 65 years or older every year.

Population

Four hundred fifteen hospitalized patients aged 65 years or older suffering from CI were enrolled into an original, randomized, controlled trial that evaluated the effect of a computerized decision support system on their quality and outcome of care between July 1, 2006 and May 30, 2008.[14] The computerized decision support included reminders for physicians to reduce the prescription of 18 anticholinergics, minimize physical restraints and Foley catheterization, and increase referral to the local GCS.[15] That previous trial neither showed an impact on quality of care nor health utilization among older patients, including mortality and hospital readmission rates. The current study uses the data from the clinical trial cohort to evaluate the effect of GCS on the 12‐month mortality and hospital readmission rates for hospitalized elders with CI (Figure 1).

Inclusion and Exclusion Criteria

Individuals were eligible for enrollment if they were aged 65 years or older, hospitalized on a medical ward, able to speak English, and had evidence of CI within 48 hours of hospital admission. Individuals were excluded if they were previously enrolled, were aphasic, or unresponsive. The presence of CI was based on the Short Portable Mental Status Questionnaire (SPMSQ),[16] a brief 10‐item screening test with a sensitivity of 86% and specificity of 99% for dementia using a score of 7 or less (maximum possible score of 10).[16] The SPMSQ scoring process adjusts for participant educational and racial status, which was a benefit to its use given the urban setting of our hospital serving a large proportion of minority and low‐education patients. A physician‐trained research assistant administered the SPMSQ within 48 hours of hospital admission.

Geriatric Consultative Services

GCS is an interdisciplinary team of a geriatrician, a geriatric pharmacist, a case manager nurse, a social worker, a medical assistant, physical therapists, and a representative of the local Area Agency on Aging. There may be a geriatric fellow and/or medicine resident available to the team based on their rotation structure. Team‐based bedside rounds are performed on new consults only, but all patients are seen individually by the team clinicians. The team emphasizes prevention of functional decline and polypharmacy, recognition and treatment of geriatric syndromes including dementia and delirium, and early discharge/transition planning. Consensus recommendations are prepared and documented in the consult notes section of the electronic medical records. Recommendations deemed critical are discussed directly with the primary teams, but no orders are placed by the GCS team. The GCS team is available on all weekdays but not on weekends or major holidays.

Study Outcomes

For this secondary analysis, we used the Regenstrief Medical Record System (RMRS) to measure 2 outcomes: hospital readmission and mortality rates up to 1 year from discharge following index hospitalization, defined as the first admission in the original clinical trial. The RMRS is the primary instrument for processing data and monitoring patient and physician activity for the hospital.[17, 18] The RMRS is linked with a state‐wide health information exchange to capture data on hospitalization outside the hospital. The RMRS also contains death certificate information for all registered patients who die in or outside the Eskenazi hospital.

Other Data Collections

Delirium was assessed at screening and then every weekday using the Confusion Assessment Method (CAM) by a trained research assistant.[19] CAM evaluates 10 symptoms of delirium specified in the Diagnostic and Statistical Manual of Mental Disorders‐III‐Revision: acute onset, fluctuating course, inattention, disorganized thinking, altered level of consciousness, disorientation, memory impairment, perceptual disturbances, psychomotor agitation or retardation, and sleep/wake disturbance. Participant demographic characteristics, including age, sex, ethnicity, and years of education, were collected from the RMRS and from interviews performed at the time of cognitive screening. Information on length of hospital stay and discharge destination (eg, home vs facility, including skilled nursing and acute rehabilitation facilities) was also obtained from the RMRS. Charlson Comorbidity Index score was calculated using International Classification of Diseases, Ninth Revision codes gathered from 1 year before admission until the time of each participant's discharge from the hospital.[20] The Acute Physiology Score (APS) from the Acute Physiology and Chronic Health Evaluation (APACHE) III was derived from data available in the RMRS to measure the severity of illness.[21] Although the APACHE III was developed in the intensive care unit using data from the first 24 hours after admission, for our study we used the worst laboratory test value during the entire hospital stay to calculate the APS.[22]

Statistical Analysis

Baseline variables are presented as means and standard deviations for continuous variables, and percentages for binary categorical variables. Comparisons between patients receiving GCS and those who did not were performed using ² tests for categorical variables and Kruskal‐Wallis test for continuous variables. Cox proportional hazard models were used to determine the association between receiving GCS and time to hospital readmission or mortality within 30 days or 1‐year postindex admission while adjusting for other covariates. For the models using time to readmission, patients without readmission were censored either at the endpoint (30 days or 1 year) or at time of death for those who died within the time frame in each model. Because GCS was not randomly assigned, we also conducted a propensity score analysis.[23] A logistic model for the probability of receiving GCS was conducted using patient demographic variables and information collected before and at the time of GCS. Stratified Cox proportional models using quintiles of predicted probability of receiving GCS were used in a propensity‐adjusted Cox model. All data analyses were performed using SAS version 9.3 (SAS Institute, Inc., Cary, NC).

RESULTS

Between July 1, 2006 and May 30, 2008, 415 CI patients were enrolled in the original trial, with 176 receiving the GCS. As shown in Table 1, the GCS and non‐GCS groups differed significantly. The GCS group was older (79.2 years old, 8.1 standard deviation [SD] vs 75.8 years old, 7.8 SD; P0.001), scored lower on the SPMSQ (4.7, 2.7 SD vs 5.5, 2.7 SD; P=0.002), had fewer chronic conditions with a lower mean Charlson Comorbidity Index Score (2.1, 1.86 SD vs 2.8, 2.6 SD; P=0.023), but a higher percentage of delirium (48.9% vs 29.3%), a lower percentage of being discharged home (37.5% vs 56.1%), and a higher mean length of stay (6.4 days, 6.4 SD vs 5.6 days, 5.9 SD; P=0.004). They also had a lower malignancy rate (6.2% vs 14.6%; P=0.007) and a lower number of hospitalizations in the previous year (0.5 admissions, 0.9 SD vs 0.7 admissions, 1.1 SD; P=0.035). No differences were observed in regard to gender, ethnicity, history of myocardial infarctions, chronic obstructive pulmonary disease, cerebrovascular disease, peripheral vascular disease, diabetes, and use of anticholinergic medicines.

Table 1.Baseline Characteristics and Summary Outcomes Within 1 Year of Hospital Discharge Based on Exposure to Geriatric Consult Services
	No GCS, n=239	GCS, n=176	P Value*
NOTE: Abbreviations: ACB, Anticholinergic Burden Scale; APS, Acute Physiology Score; CHF, congestive heart failure; COPD, chronic obstructive pulmonary disease; GCS, geriatric consult services; MI, myocardial infarction; PVD, peripheral vascular disease; SD, standard deviation; SPMSQ, Short Portable Mental Status Questionnaire. *P value was calculated using ² tests for categorical variables and Kruskal‐Wallis test for continuous variables.
Baseline characteristics
Mean age (SD)	75.8 (7.8)	79.2 (8.1)	<0.001
% Female	66.1 [n=158]	68.2 [n=120]	0.657
% African American	54.8 [n=131]	63.6 [n=112]	0.071
Mean SPMSQ score (SD)	5.5 (2.7)	4.7 (2.7)	0.002
Admission diagnoses
MI	15.5 [n=37]	13.6 [n=24]	0.675
CHF	38.1 [n=91]	34.7 [n=61]	0.475
PVD	7.1 [n=17]	9.7 [n=17]	0.370
Cerebrovascular	13.8 [n=33]	19.3 [n=34]	0.140
COPD	41.0 [n=98]	33.0 [n=58]	0.094
Diabetes	47.7 [n=114]	40.9 [n=72]	0.169
Malignancy	14.6 [n=35]	6.2 [n=11]	0.007
Metastatic cancer	8.8 [n=21]	1.7 [n=3]	0.002
Mean Charlson Comorbidity (SD)	2.8 (2.6)	2.1 (1.8)	0.023
Mean APS (SD)	24.5 (13.8)	25.9 (13.5)	0.231
Definite ACB Use	35.2 [n=84]	27.8 [n=49]	0.136
Length of stay	5.6 (5.9)	6.4 (6.4)	0.004
% Any delirium	29.3 [n=70]	48.9 [n=156]	<0.001
% Discharged home	56.1 [n=134]	37.5 [n=66]	<0.001
No. of inpatient stays prior year	0.7 (1.1)	0.5 (0.9)	0.035
Follow‐up outcomes
% Readmission within 30 days	15.1 [n=36]	22.7 [n=40]	0.054
% Readmission within 1 year	54.4 [n=130]	56.3 [n=99]	0.765
% Death within 30 days	4.2 [n=10]	1.7 [n=3]	0.253
% Death within 1 year	26.8 [n=64]	23.9 [n=42]	0.569
% Readmission or death within 30 days	18.0 [n=43]	24.4 [n=43]	0.113
% Readmission or death within 1 year	64.8 [n=155]	63.1 [n=111]	0.708

Table 2 describes the association of various factors with receiving GCS. Patients who were positive for delirium (odds ratio [OR]=1.65; 95% confidence interval=0.98‐2.77) and were older (OR=1.04; 95% confidence interval=1.01‐1.08) had a higher propensity to receive GCS, whereas, the presence of metastatic cancer resulted in a lower propensity (OR=0.15; 95% confidence interval=0.02‐1.16) of receiving GCS. The logistic model estimated area under the receiver operating characteristic curve was 0.707.

Table 2.Results of Logistic Regression Model for Receiving a Geriatric Consult Service Consult Based on Patients' Demographic and Hospital Variables
	Adjusted OR (95% CI)	P Value
NOTE: Abbreviations: CI, confidence interval; OR, odds ratio. *Based on the Anticholinergic Burden Scale.
Age	1.04 (1.011.08)	0.006
Female	1.02 (0.641.63)	0.942
African American	1.11 (0.711.72)	0.657
Short Portable Mental Status Questionnaire score	1.00 (0.911.10)	0.990
Acute Physiology Score	1.00 (0.981.02)	0.769
Charlson Comorbidity Score	1.11 (0.841.46)	0.471
Length of hospital stay	1.02 (0.981.07)	0.299
Definite anticholinergic use*	0.74 (0.461.20)	0.219
Any delirium during hospital stay	1.65 (0.982.77)	0.061
Diabetes mellitus	0.72 (0.411.26)	0.253
Myocardial infarction	0.83 (0.411.66)	0.593
Congestive heart failure	0.83 (0.471.47)	0.524
Peripheral vascular disease	1.39 (0.613.18)	0.433
Cerebrovascular disease	1.30 (0.652.59)	0.464
Malignancy	0.45 (0.171.21)	0.113
Metastatic cancer	0.15 (0.021.16)	0.069
Chronic obstructive pulmonary disease	0.91 (0.531.55)	0.727

Table 3 provides results from the Cox models for receiving GCS on readmission and mortality outcomes adjusting for various sets of covariates and with the propensity score adjustment. Model 1 presents unadjusted hazard ratio (HR). Model 2 presents HRs adjusting for a common set of covariates that were significantly associated with at least 1 of the outcomes, whereas model 3 presents the results adjusting for all covariates. All 4 models yielded similar results. As evident from this table, propensity‐adjusted HR for 30‐day readmission was still significantly higher among patients receiving GCS (HR=1.75; 95% confidence interval=1.06‐2.88) but not at 1 year (HR=1.19; 95% confidence interval=0.89‐1.59). There was a trend for decreased mortality for the GCS group at 30 days (HR=0.35; 95% confidence interval=0.09‐1.35), but it disappeared at 1 year (HR=0.91; 95% confidence interval=0.59‐1.40). A composite outcome of readmissions and mortality did not show any difference between the GCS and no‐GCS groups.

Table 3.Adjusted Hazard Ratios for Geriatric Consult Service Consult Estimated Using Cox Proportional Hazard Models and Propensity‐Adjusted Models
Outcome Variables	Model 1		Model 2		Model 3		Propensity Adjusted
Outcome Variables	HR (95% CI)	P Value	HR (95% CI)	P Value	HR (95% CI)	P Value	HR (95% CI)	P Value
NOTE: Abbreviations: CI, confidence interval; HR, hazard ratio. Model 1: unadjusted; Model 2: adjusting for age, Charlson Comorbidity Score, Acute Physiology Scale, disposition status, congestive heart failure, and peripheral vascular disease; Model 3: adjusting for all demographic and hospital variables.
Readmission within 30 days	1.65 (1.05, 2.59)	0.030	1.73 (1.08, 2.78)	0.024	1.84 (1.133.00)	0.015	1.75 (1.062.88)	0.029
Readmission within 1 year	1.13 (0.87, 1.46)	0.373	1.24 (0.94, 1.63)	0.125	1.26 (0.941.68)	0.117	1.19 (0.891.59)	0.245
Death within 30 days	0.43 (0.12, 1.56)	0.199	0.34 (0.09, 1.28)	0.110	0.25 (0.061.02)	0.053	0.35 (0.091.35)	0.126
Death within 1 year	0.95 (0.65, 1.45)	0.806	0.87 (0.58 1.31)	0.506	0.93 (0.601.42)	0.724	0.91 (0.591.40)	0.669
Readmission or Death within 30 days	1.48 (0.97, 2.26)	0.070	1.49 (0.96, 2.33)	0.078	1.56 (0.982.47)	0.061	1.55 (0.972.48)	0.069
Readmission or death within 1 year	1.05 (0.82, 1.34)	0.699	1.11 (0.86, 1.43)	0.412	1.15 (0.881.50)	0.318	1.08 (0.831.42)	0.569

DISCUSSION

To our knowledge, this is the first study to analyze the impact of GCS on hospital readmission and mortality rates of CI patients. Our results did not show any short‐term or long‐term benefits of GCS for CI patients. Recent studies exploring cost benefits of the GCS have found trends toward lower readmission, but none focused on patients with CI.[6, 24, 25] It is important to note that our study did not use random allocation to assigning the patient into the GCS or control group, thus raising the possibility that patients who received GCS were sicker and were medically and socially more complex than those who did not receive the consult. Moreover, GCS consultation is preferentially sought for and completed for patients with CI and functional limitations, consistent with our finding that GCS patients more often have delirium and are less‐often discharged home.

The nature of the GCS team is another important consideration. Our GCS model did not include unit cohorting of patients, an important component of other proposed GCS models.[26] A recent meta‐analysis found that the GCS models without unit cohorting of patients did not have an impact on 1‐ or 12‐month readmission rates.[27] Low adherence to consultant recommendations (less than 33%) was thought to be a reason for such results. Importance of cohorting with regard to accomplishing recommendations by primary teams, importance of unit staff expertise in geriatric principles, and impact of a unit model on teamwork has also been highlighted by another review.[28] These findings lend to the hypothesis that unit cohorting and direct order placement by the GCS team may improve outcomes among CI patients, including a reduction in readmission rates.

Although readmissions rates were not statistically different between GCS and control groups at 1‐year postdischarge, 30‐day readmission rates were higher among the GCS group. Previous research among older heart failure patients found that a comprehensive transitional care intervention at the time of hospital discharge significantly shortened the time to readmission in the intervention group (P=0.026).[29] The factors identified by the study authors included enhanced supervision by the transitional healthcare teams along with improved awareness and education among treated patients that may have facilitated early recognition of clinical deterioration.[29] A recent study with intensive outpatient care that resulted in increased admissions among chronically ill adults provided a similar conclusion.[30]

GCS patients showed a trend toward decreased mortality as did patients enrolled in previous studies evaluating GCS models in the inpatient setting, as suggested by a recent review.[27] A caveat to note is that these trends favored ward‐styled GCS services as compared to our open GCS model,[27, 28] although the factors cited in these dedicated units affecting mortality included prompt attention to early rehabilitation, delirium management, and prevention of pressure ulcers and are also frequently implemented for patients in our GCS service model and therefore may have produced similar results.

Our neutral results in regard to the readmissions need to be interpreted with caution. First, this study was conducted in a hospital that supports expert geriatric and palliative care teams, both in the inpatient and the ambulatory settings, that provide consultative services and train medicine teams and hospital nursing staff. On the outpatient side, the presence of a robust geriatrics house‐calls program and the Geriatric Resources for Assessment and Care of Elders team results in above‐average care for the control group, and thus may also impact apparent outcomes.[31, 32] Second, 30‐day readmissions represent a complex outcome. Two recent reviews of hospital‐initiated interventions have shown that evidence regarding best strategies to decrease 30‐day readmissions is unclear.[33] Neither review included studies that targeted patients with CI only. The 2 programs that reduced 30‐day readmissions were multifaceted and included personnel who provide bridging between the hospital and the outpatient setting.[34] The GCS does include a focus on postdischarge resources, but does that on a case‐by‐case basis and no formal posthospital follow‐ups are provided. Moreover, the value of 30‐day readmission rates as a marker of quality, even though used by policymakers as an indicator of hospital quality, remains controversial.[35, 36] Broadening the outcomes of interest to include patient‐centered outcomes including satisfaction with care, that have shown to impact other health outcomes, may help improve understanding the benefits of GCS in hospitals.[37] Other comprehensive transitional care models that failed to show a benefit on 30‐day readmissions in older patients still resulted in higher satisfaction among patients.[38] Unfortunately, our evaluation did not include an assessment of patient satisfaction and quality of transitions.

Since the study period, GCS at our hospital now has incorporated a more robust focus on advance care planning (ACP) and execution of Physician Orders for Scope of Treatment that were legislated in the state in July 2013. The GCS team members are expert in carrying out complex ACP discussions and also partner with the inpatient palliative care team. It is quite possible that a study of more recent outcomes will yield more positive results for the selected outcomes. Thus, for future trials that aim to study the impact of GCS in the inpatient settings, it may be advisable to include important quality markers such as implementation of ACP and patient satisfaction along with the health utilization outcomes.

Limitations

As mentioned previously, it is possible that our risk adjustment was insufficient to account for all the medical and psychosocial differences among groups. For example, the overall anticholinergic impact of various medications such as antipsychotic medications and histamine‐2 blockers was assessed via the Anticholinergic Burden Scale on admission, but we did not have information on medication prescribing during the stay. We were further limited by lack of baseline functional status and socioeconomic details, both of which are related to 30‐day readmissions. For example, living alone, prior use of assist devices, and belonging to lower socioeconomic status are correlated with higher readmission rates.[39, 40] Patients with available social support may receive more intense supervision and may seek medical attention sooner. On the other hand, worsening health among CI patients without any approximate social support may be unnoticed for days. Absence of details of inpatient interventions may also have resulted in unmeasurable confounders that could have impacted our study outcomes. Finally, lack of information on the uptake of GCS recommendations by the primary teams is another limitation of this analysis. Future trials should include strategies to address these information gaps.

CONCLUSION

Our results comparing inpatient geriatrics consultative services with usual care in hospitalized elders having cognitive impairment failed to demonstrate an impact on readmissions and mortality. A clinical lesson learned, however, is that much work is still required to reduce readmission and mortality rates in this especially vulnerable patient population.

Disclosures

Disclosures: This work was supported by grants from a Geriatric Academic Career Award (K01HP20517) through Health Resources and Services Administration, R01AG034205 and K23‐AG043476 from the National Institute on Aging, and the John A. Hartford Foundation Center for Excellence in Geriatric Medicine. The sponsors had no role in the study design, evaluation, or manuscript development. The authors report no conflicts of interest.

References

Krumholz HM, Lin Z, Keenan PS, et al. Relationship between hospital readmission and mortality rates for patients hospitalized with acute myocardial infarction, heart failure, or pneumonia. JAMA. 2013;309(6):587–593.
Fields SD, MacKenzie CR, Charlson ME, Sax FL. Cognitive impairment. Can it predict the course of hospitalized patients? J Am Geriatr Soc. 1986;34(8):579–585.
Inouye SK, Peduzzi PN, Robison JT, Hughes JS, Horwitz RI, Concato J. Importance of functional measures in predicting mortality among older hospitalized patients. JAMA. 1998;279(15):1187–1193.
Lindquist LA, Baker DW. Understanding preventable hospital readmissions: masqueraders, markers, and true causal factors. J Hosp Med. 2011;6(2):51–53.
Inouye SK, Bogardus ST, Charpentier PA, et al. A multicomponent intervention to prevent delirium in hospitalized older patients. N Engl J Med. 1999;340(9):669–676.
Counsell SR, Holder CM, Liebenauer LL, et al. Effects of a multicomponent intervention on functional outcomes and process of care in hospitalized older patients: a randomized controlled trial of Acute Care for Elders (ACE) in a community hospital. J Am Geriatr Soc. 2000;48(12):1572–1581.
Coleman EA, Boult C; American Geriatrics Society Health Care Systems Committee. Improving the quality of transitional care for persons with complex care needs. J Am Geriatr Soc. 2003;51(4):556–557.
Sennour Y, Counsell SR, Jones J, Weiner M. Development and implementation of a proactive geriatrics consultation model in collaboration with hospitalists. J Am Geriatr Soc. 2009;57(11):2139–2145.
Benoit F, Bertiaux M, Schouterden R, et al. Screening of the risk of functional decline performed by an inpatient geriatric consultation team in a general hospital [in French]. Revue medicale de Bruxelles. 2013;34(6):462–468.
Cole MG, McCusker J, Bellavance F, et al. Systematic detection and multidisciplinary care of delirium in older medical inpatients: a randomized trial. CMAJ. 2002;167(7):753–759.
Edwards RF, Harrison TM, Davis SM. Potentially inappropriate prescribing for geriatric inpatients: an acute care of the elderly unit compared to a general medicine service. Consult Pharm. 2003;18(1):37–42, 47–39.
Hung WW, Ross JS, Farber J, Siu AL. Evaluation of the Mobile Acute Care of the Elderly (MACE) service. JAMA Intern Med. 2013;173(11):990–996.
Flood KL, Maclennan PA, McGrew D, Green D, Dodd C, Brown CJ. Effects of an acute care for elders unit on costs and 30‐day readmissions. JAMA Intern Med. 2013;173(11):981–987.
Boustani M, Baker MS, Campbell N, et al. Impact and recognition of cognitive impairment among hospitalized elders. J Hosp Med. 2010;5(2):69–75.
Boustani MA, Campbell NL, Khan BA, et al. Enhancing care for hospitalized older adults with cognitive impairment: a randomized controlled trial. J Gen Intern Med. 2012;27(5):561–567.
Erkinjuntti T, Sulkava R, Wikstrom J, Autio L. Short Portable Mental Status Questionnaire as a screening test for dementia and delirium among the elderly. J Am Geriatr Soc. 1987;35(5):412–416.
McDonald CJ, Overhage JM, Tierney WM, et al. The Regenstrief Medical Record System: a quarter century experience. Int J Med Inform. 1999;54(3):225–253.
Cohen CA, Gold DP, Shulman KI, Wortley JT, McDonald G, Wargon M. Factors determining the decision to institutionalize dementing individuals: a prospective study. Gerontologist. 1993;33(6):714–720.
Inouye SK, Dyck CH, Alessi CA, Balkin S, Siegal AP, Horwitz RI. Clarifying confusion: the confusion assessment method. A new method for detection of delirium. Ann Intern Med. 1990;113(12):941–948.
Charlson ME, Sax FL, MacKenzie CR, Fields SD, Braham RL, Douglas RG Resuscitation: how do we decide? A prospective study of physicians' preferences and the clinical course of hospitalized patients. JAMA. 1986;255(10):1316–1322.
Knaus WA, Wagner DP, Draper EA, et al. The APACHE III prognostic system. Risk prediction of hospital mortality for critically ill hospitalized adults. Chest. 1991;100(6):1619–1636.
Nazir A, LaMantia M, Chodosh J, et al. Interaction between cognitive impairment and discharge destination and its effect on rehospitalization. J Am Geriatr Soc. 2013;61(11):1958–1963.
Gayat E, Pirracchio R, Resche‐Rigon M, Mebazaa A, Mary JY, Porcher R. Propensity scores in intensive care and anaesthesiology literature: a systematic review. Intensive Care Med. 2010;36(12):1993–2003.
Covinsky KE, Palmer RM, Kresevic DM, et al. Improving functional outcomes in older patients: lessons from an acute care for elders unit. Jt Comm J Qual Improv. 1998;24(2):63–76.
Allen KR, Hazelett SE, Palmer RR, et al. Developing a stroke unit using the acute care for elders intervention and model of care. J Am Geriatr Soc. 2003;51(11):1660–1667.
Palmer RM, Landefeld CS, Kresevic D, Kowal J. A medical unit for the acute care of the elderly. J Am Geriatr Soc. 1994;42(5):545–552.
Deschodt M, Flamaing J, Haentjens P, Boonen S, Milisen K. Impact of geriatric consultation teams on clinical outcome in acute hospitals: a systematic review and meta‐analysis. BMC Med. 2013;11:48.
Ellis G, Whitehead MA, Robinson D, O'Neill D, Langhorne P. Comprehensive geriatric assessment for older adults admitted to hospital: meta‐analysis of randomised controlled trials. BMJ. 2011;343:d6553.
Rich MW, Vinson JM, Sperry JC, et al. Prevention of readmission in elderly patients with congestive heart failure: results of a prospective, randomized pilot study. J Gen Intern Med. 1993;8(11):585–590.
Kripalani S, Roumie CL, Dalal AK, et al. Effect of a pharmacist intervention on clinically important medication errors after hospital discharge: a randomized trial. Ann Intern Med. 2012;157(1):1–10.
Beck RA, Arizmendi A, Purnell C, Fultz BA, Callahan CM. House calls for seniors: building and sustaining a model of care for homebound seniors. J Am Geriatr Soc. 2009;57(6):1103–1109.
Counsell SR, Callahan CM, Clark DO, et al. Geriatric care management for low‐income seniors: a randomized controlled trial. JAMA. 2007;298(22):2623–2633.
Hansen LO, Young RS, Hinami K, Leung A, Williams MV. Interventions to reduce 30‐day rehospitalization: a systematic review. Ann Intern Med. 2011;155(8):520–528.
Naylor MD, McCauley KM. The effects of a discharge planning and home follow‐up intervention on elders hospitalized with common medical and surgical cardiac conditions. J Cardiovasc Nurs. 1999;14(1):44–54.
Clarke A. Readmission to hospital: a measure of quality or outcome? Qual Saf Health Care. 2004;13(1):10–11.
Naylor MD, Kurtzman ET, Grabowski DC, Harrington C, McClellan M, Reinhard SC. Unintended consequences of steps to cut readmissions and reform payment may threaten care of vulnerable older adults. Health Aff (Millwood). 2012;31(7):1623–1632.
Quaschning K, Korner M, Wirtz M. Analyzing the effects of shared decision‐making, empathy and team interaction on patient satisfaction and treatment acceptance in medical rehabilitation using a structural equation modeling approach. Patient Educ Couns. 2013;91(2):167–175.
Englander H, Michaels L, Chan B, Kansagara D. The Care Transitions Innovation (C‐TraIn) for Socioeconomically Disadvantaged Adults: results of a cluster randomized controlled trial. J Gen Intern Med. 2014;29(11):1460–1467.
Iloabuchi TC, Mi D, Tu W, Counsell SR. Risk factors for early hospital readmission in low‐income elderly adults. J Am Geriatr Soc. 2014;62(3):489–494.
Kangovi S, Barg FK, Carter T, Long JA, Shannon R, Grande D. Understanding why patients of low socioeconomic status prefer hospitals over ambulatory care. Health Aff (Millwood). 2013;32(7):1196–1203.

References

Krumholz HM, Lin Z, Keenan PS, et al. Relationship between hospital readmission and mortality rates for patients hospitalized with acute myocardial infarction, heart failure, or pneumonia. JAMA. 2013;309(6):587–593.
Fields SD, MacKenzie CR, Charlson ME, Sax FL. Cognitive impairment. Can it predict the course of hospitalized patients? J Am Geriatr Soc. 1986;34(8):579–585.
Inouye SK, Peduzzi PN, Robison JT, Hughes JS, Horwitz RI, Concato J. Importance of functional measures in predicting mortality among older hospitalized patients. JAMA. 1998;279(15):1187–1193.
Lindquist LA, Baker DW. Understanding preventable hospital readmissions: masqueraders, markers, and true causal factors. J Hosp Med. 2011;6(2):51–53.
Inouye SK, Bogardus ST, Charpentier PA, et al. A multicomponent intervention to prevent delirium in hospitalized older patients. N Engl J Med. 1999;340(9):669–676.
Counsell SR, Holder CM, Liebenauer LL, et al. Effects of a multicomponent intervention on functional outcomes and process of care in hospitalized older patients: a randomized controlled trial of Acute Care for Elders (ACE) in a community hospital. J Am Geriatr Soc. 2000;48(12):1572–1581.
Coleman EA, Boult C; American Geriatrics Society Health Care Systems Committee. Improving the quality of transitional care for persons with complex care needs. J Am Geriatr Soc. 2003;51(4):556–557.
Sennour Y, Counsell SR, Jones J, Weiner M. Development and implementation of a proactive geriatrics consultation model in collaboration with hospitalists. J Am Geriatr Soc. 2009;57(11):2139–2145.
Benoit F, Bertiaux M, Schouterden R, et al. Screening of the risk of functional decline performed by an inpatient geriatric consultation team in a general hospital [in French]. Revue medicale de Bruxelles. 2013;34(6):462–468.
Cole MG, McCusker J, Bellavance F, et al. Systematic detection and multidisciplinary care of delirium in older medical inpatients: a randomized trial. CMAJ. 2002;167(7):753–759.
Edwards RF, Harrison TM, Davis SM. Potentially inappropriate prescribing for geriatric inpatients: an acute care of the elderly unit compared to a general medicine service. Consult Pharm. 2003;18(1):37–42, 47–39.
Hung WW, Ross JS, Farber J, Siu AL. Evaluation of the Mobile Acute Care of the Elderly (MACE) service. JAMA Intern Med. 2013;173(11):990–996.
Flood KL, Maclennan PA, McGrew D, Green D, Dodd C, Brown CJ. Effects of an acute care for elders unit on costs and 30‐day readmissions. JAMA Intern Med. 2013;173(11):981–987.
Boustani M, Baker MS, Campbell N, et al. Impact and recognition of cognitive impairment among hospitalized elders. J Hosp Med. 2010;5(2):69–75.
Boustani MA, Campbell NL, Khan BA, et al. Enhancing care for hospitalized older adults with cognitive impairment: a randomized controlled trial. J Gen Intern Med. 2012;27(5):561–567.
Erkinjuntti T, Sulkava R, Wikstrom J, Autio L. Short Portable Mental Status Questionnaire as a screening test for dementia and delirium among the elderly. J Am Geriatr Soc. 1987;35(5):412–416.
McDonald CJ, Overhage JM, Tierney WM, et al. The Regenstrief Medical Record System: a quarter century experience. Int J Med Inform. 1999;54(3):225–253.
Cohen CA, Gold DP, Shulman KI, Wortley JT, McDonald G, Wargon M. Factors determining the decision to institutionalize dementing individuals: a prospective study. Gerontologist. 1993;33(6):714–720.
Inouye SK, Dyck CH, Alessi CA, Balkin S, Siegal AP, Horwitz RI. Clarifying confusion: the confusion assessment method. A new method for detection of delirium. Ann Intern Med. 1990;113(12):941–948.
Charlson ME, Sax FL, MacKenzie CR, Fields SD, Braham RL, Douglas RG Resuscitation: how do we decide? A prospective study of physicians' preferences and the clinical course of hospitalized patients. JAMA. 1986;255(10):1316–1322.
Knaus WA, Wagner DP, Draper EA, et al. The APACHE III prognostic system. Risk prediction of hospital mortality for critically ill hospitalized adults. Chest. 1991;100(6):1619–1636.
Nazir A, LaMantia M, Chodosh J, et al. Interaction between cognitive impairment and discharge destination and its effect on rehospitalization. J Am Geriatr Soc. 2013;61(11):1958–1963.
Gayat E, Pirracchio R, Resche‐Rigon M, Mebazaa A, Mary JY, Porcher R. Propensity scores in intensive care and anaesthesiology literature: a systematic review. Intensive Care Med. 2010;36(12):1993–2003.
Covinsky KE, Palmer RM, Kresevic DM, et al. Improving functional outcomes in older patients: lessons from an acute care for elders unit. Jt Comm J Qual Improv. 1998;24(2):63–76.
Allen KR, Hazelett SE, Palmer RR, et al. Developing a stroke unit using the acute care for elders intervention and model of care. J Am Geriatr Soc. 2003;51(11):1660–1667.
Palmer RM, Landefeld CS, Kresevic D, Kowal J. A medical unit for the acute care of the elderly. J Am Geriatr Soc. 1994;42(5):545–552.
Deschodt M, Flamaing J, Haentjens P, Boonen S, Milisen K. Impact of geriatric consultation teams on clinical outcome in acute hospitals: a systematic review and meta‐analysis. BMC Med. 2013;11:48.
Ellis G, Whitehead MA, Robinson D, O'Neill D, Langhorne P. Comprehensive geriatric assessment for older adults admitted to hospital: meta‐analysis of randomised controlled trials. BMJ. 2011;343:d6553.
Rich MW, Vinson JM, Sperry JC, et al. Prevention of readmission in elderly patients with congestive heart failure: results of a prospective, randomized pilot study. J Gen Intern Med. 1993;8(11):585–590.
Kripalani S, Roumie CL, Dalal AK, et al. Effect of a pharmacist intervention on clinically important medication errors after hospital discharge: a randomized trial. Ann Intern Med. 2012;157(1):1–10.
Beck RA, Arizmendi A, Purnell C, Fultz BA, Callahan CM. House calls for seniors: building and sustaining a model of care for homebound seniors. J Am Geriatr Soc. 2009;57(6):1103–1109.
Counsell SR, Callahan CM, Clark DO, et al. Geriatric care management for low‐income seniors: a randomized controlled trial. JAMA. 2007;298(22):2623–2633.
Hansen LO, Young RS, Hinami K, Leung A, Williams MV. Interventions to reduce 30‐day rehospitalization: a systematic review. Ann Intern Med. 2011;155(8):520–528.
Naylor MD, McCauley KM. The effects of a discharge planning and home follow‐up intervention on elders hospitalized with common medical and surgical cardiac conditions. J Cardiovasc Nurs. 1999;14(1):44–54.
Clarke A. Readmission to hospital: a measure of quality or outcome? Qual Saf Health Care. 2004;13(1):10–11.
Naylor MD, Kurtzman ET, Grabowski DC, Harrington C, McClellan M, Reinhard SC. Unintended consequences of steps to cut readmissions and reform payment may threaten care of vulnerable older adults. Health Aff (Millwood). 2012;31(7):1623–1632.
Quaschning K, Korner M, Wirtz M. Analyzing the effects of shared decision‐making, empathy and team interaction on patient satisfaction and treatment acceptance in medical rehabilitation using a structural equation modeling approach. Patient Educ Couns. 2013;91(2):167–175.
Englander H, Michaels L, Chan B, Kansagara D. The Care Transitions Innovation (C‐TraIn) for Socioeconomically Disadvantaged Adults: results of a cluster randomized controlled trial. J Gen Intern Med. 2014;29(11):1460–1467.
Iloabuchi TC, Mi D, Tu W, Counsell SR. Risk factors for early hospital readmission in low‐income elderly adults. J Am Geriatr Soc. 2014;62(3):489–494.
Kangovi S, Barg FK, Carter T, Long JA, Shannon R, Grande D. Understanding why patients of low socioeconomic status prefer hospitals over ambulatory care. Health Aff (Millwood). 2013;32(7):1196–1203.

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Journal of Hospital Medicine - 10(5)

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Journal of Hospital Medicine - 10(5)

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Impact of an inpatient geriatric consultative service on outcomes for cognitively impaired patients

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Impact of an inpatient geriatric consultative service on outcomes for cognitively impaired patients

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Arif Nazir, MD

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Address for correspondence and reprint requests: Arif Nazir, MD, Associate Professor of Clinical Medicine, Indiana University School of Medicine, 720 Eskenazi Avenue, 5/3 Faculty Bldg, Ste E2121, Indianapolis, IN 46202; Telephone: 317‐880‐6585; Fax: 317‐630‐2667; E‐mail: [email protected]

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Impact of Inpatient GCS on CI Patients

METHODS

Setting

Population

Inclusion and Exclusion Criteria

Geriatric Consultative Services

Study Outcomes

Other Data Collections

Statistical Analysis

RESULTS

DISCUSSION

Limitations

CONCLUSION

Disclosures

METHODS

Setting

Population

Inclusion and Exclusion Criteria

Geriatric Consultative Services

Study Outcomes

Other Data Collections

Statistical Analysis

RESULTS

DISCUSSION

Limitations

CONCLUSION

Disclosures

METHODS

Setting

Population

Inclusion and Exclusion Criteria

Geriatric Consultative Services

Study Outcomes

Other Data Collections

Statistical Analysis

RESULTS

DISCUSSION

Limitations

CONCLUSION

Disclosures