Multidimensional Geriatric Prognostic Index, Based on a Geriatric Assessment, for Long-Term Survival in Older Adults in Korea
Multidimensional Geriatric Prognostic Index, Based on a Geriatric Assessment, for Long- Term Survival in Older Adults in Korea
Hee-Won Jung 0 1
Jin Won Kim 0 1
Ji Won Han 0 1
Kayoung Kim 0 1
Jee Hyun Kim 0 1
Kwang- Il Kim 0 1
Cheol-Ho Kim 0 1
Ki Woong Kim 0 1
0 1 Division of Geriatrics, Department of Internal Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine , Seongnam , Korea , 2 Division of Hematology and Medical Oncology, Department of Internal Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine , Seongnam , Korea , 3 Department of Neuropsychiatry, Seoul National University Bundang Hospital , Seongnam , Korea , 4 Department of Psychiatry, Seoul National University College of Medicine , Seoul , Korea , 5 Department of Brain and Cognitive Science, Seoul National University College of Natural Sciences , Seoul , Korea
1 Editor: Valquiria Bueno, UNIFESP Federal University of São Paulo , BRAZIL
The patient´s survival estimate is important for clinical decision-making, especially in frail patients with multimorbidities. We aimed to develop a multidimensional geriatric prognosis index (GPI) for 3- and 5-year mortality in community-dwelling elderly and to validate the GPI in a separate hospital-based population. The GPI was constructed using data for 988 participants in the Korean Longitudinal Study on Health and Aging (KLoSHA) and cross-validated with 1109 patients who underwent a geriatric assessment at the Seoul National University Bundang Hospital (SNUBH). The GPI, with a total possible score of 8, included age, gender, activities of daily living, instrumental activities of daily living, comorbidities, mood, cognitive function, and nutritional status. During the 5-year observation period, 179 KLoSHA participants (18.1%) and 340 SNUBH patients (30.7%) died. The c-indices for 3- and 5-year mortality were 0.78 and 0.80, respectively, in the KLoSHA group and 0.73 and 0.80, respectively, in the SNUBH group. Positive linear trends were observed for GPI scores and both 3- and 5year mortality in both groups. In conclusions, using common components of a geriatric assessment, the GPI can stratify the risk of 3- and 5-year mortality in Korean elderly people both in the community and hospital.
Data Availability Statement: All relevant data are
within the paper and its Supporting Information files.
Funding: This study was supported by grants from
the National R&D Program for Cancer Control,
Ministry for Health and Welfare, Republic of Korea
(study no: 1320370) and from the Korean Health
Technology R&D Project, Ministry for Health, Welfare
and Family Affairs, Republic of Korea (grant No.
A092077). The funding organizations had no role in
the design and conduct of the study; in the collection,
analysis, and interpretation of the data; or in the
preparation, review, or approval of the manuscript.
The global population is aging and the proportion of older adults is increasing in South Korea
]. The patient’s survival estimate is important for individualized decision-making, especially
in frail patients with multimorbidities [
Competing Interests: The authors have declared
that no competing interests exist.
Frailty is associated with increased mortality [
] and vulnerability to surgery and
chemotherapy for cancer [
]. Furthermore, frailty is closely associated with multimorbidities and
functional impairment [
]. Although frailty is correlated with chronological age, there are
inter-individual differences that must be considered [
]. Geriatric assessment (GA) has the
ability to assess the level of frailty in individual patients [
]. Therefore, when estimating the
survival of older adults, a GA, which includes the characteristics of frailty, can be beneficial for
personalized decisions regarding treatments that might result in complications.
A number of short-term [
] and long-term  prediction models for mortality have
been developed in various settings in different countries [
]. However, there are some quality
barriers including potential for bias, compatibility in another population, and accuracy to use
routinely in general practice [
]. Because specific data acquisition is difficult, practical issues
remain for routinely adaptation.
Therefore, we aimed to develop a practical and generally accepted multidimensional
geriatric prognosis index (GPI) based on GA to predict long-term mortality in Korean
communitydwelling elderly and validate the GPI in a separate hospital-based population.
Materials and Methods
Study design and population
This study included two populations: community-based prospective cohort and hospital-based
The community-based population was based on the dataset from the Korean Longitudinal
Study on Health and Aging (KLoSHA), which included people aged 65 years old in the city
of Seongnam city [
]. For the derivation of the GPI, we used the final dataset of baseline data
from KLoSHA, which included 721 randomly sampled people 65 years old and 278
people 85 years old who voluntarily participated. The baseline evaluation was performed at
the Seoul National University Bundang Hospital (SNUBH) from 2005 to 2006.
For the hospital-based cohort, we reviewed the medical records of 1282 patients 60 years
old who underwent a GA in the outpatient geriatric clinic or inpatient wards of the SNUBH
between 2004 and 2007.
Measurements and definitions
Variables for the GPI were selected in an a priori fashion to facilitate adaptation for a
widespread GA with generally accepted domains and to prevent over-fitting in the study dataset;
these variables included age, gender, activities of daily living, instrumental activities of daily
living, comorbidities, mood, cognitive function, and nutritional status, which are associated with
For these variables, the KLoSHA used the Korean Activities of Daily Living (K-ADL) [
Korean Instrumental Activities of Daily Living (K-IADL) [
], Cumulative Illness Rating Scale
for Geriatrics (CIRS-G) [
], Korean version of the Geriatric Depression Scale (GDS) [
Nutrition Screening Initiative (NSI) [
]. At the SNUBH, the modified Barthel index [
Lawton and Brody Index [
], Charlson’s comorbidity index (CCI) [
], Korean version of
the Geriatric Depression Scale Short Form (GDS-SF) , and Mini Nutritional Assessment
] were used. The Korean Mini-Mental State Examination (MMSE) [
] was used
both in the KLoSHA and at SNUBH.
Because the GAs in the KLoSHA and at SNUBH used different measures for each domain,
we created a method to categorically interpret each domain (Table 1). The coding and cutoff
values were determined based on the effects of the GA domains on mortality that were
observed in a previous study [
]. We tried to simplify the coded scores from the many tools
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ADL, activities of daily living; IADL, instrumental activities of daily living; CCI, Charlson’s comorbidity index;
CIRS-G, Cumulative Illness Rating Scale for Geriatrics; GDS, Korean version of the Geriatric Depression
Scale; GDS-SF, Korean version of the Geriatric Depression Scale Short Form; MMSE, Korean Mini-Mental
State Examination; MNA, Mini Nutritional Assessment; NSI, Nutrition Screening Initiative
(i.e., 0, 0.5, or 1) to facilitate the comparability between the GA domains from the KLoSHA
and SNUBH datasets. The final score of the GPI was calculated by summing the scores from
age, gender, and the 6 GA domains, resulting in a possible score ranging from 0 to 8.
The survival status, including cause and date of death, was acquired from the National
Statistical Office of Korea for all of the participants in the development (KLoSHA) and validation
(SNUBH) cohorts as of December 2011 (medial follow-up, 68.8 months) and December 2013
(median follow-up, 71.0 months), respectively. To analyze the 3- and 5-year mortality rates,
participants who were alive at 3 and 5 years from the baseline GA were censored. Also, to
remove the impact of acute illness on long-term mortality, we excluded participants who died
within 6 months from the baseline GA (n = 11, development cohort; n = 173, validation
cohort) from the final analysis.
Because 196 of the 988 participants in the development cohort had at least 1 missing GA
domain, we used a multiple imputation procedure with age, gender, K-ADL, K-IADL, CIRS-G,
GDS, MMSE, and NSI. This procedure provided complete imputation for the 196 participants,
resulting in a dataset of 988 participants. In the same manner, the data for 179 patients with at
least 1 missing GA domain in the validation cohort underwent a multiple imputation
procedure with age, gender, modified Barthel index, Lawton and Brody Index, CCI, GDS-SF,
MMSE, and MNA.
The baseline characteristics were compared using independent t-tests for continuous variables
and Chi-square tests for dichotomized variables. Linear regression analysis was used to evaluate
associations between age and GPI. To analyze mortality, the predicted 3- and 5-year probability
of mortality based on the GPI was calculated using a logistic regression model with GPI as the
linear term in the development cohort, and the observed mortality in the validation cohort was
compared with this prediction. The 95% confidence intervals (CIs) for the expected and observed
mortality were calculated using a binomial distribution. To validate the ability of the GPI to
predict 3- and 5-year survival, we used receiver operating characteristic analysis and calculated the
c-index. The data for the development and validation cohorts were pooled for sensitivity analysis.
Statistical analyses were conducted using STATA 12.0 (StataCorp, College Station, TX, USA)
The study adhered to the guidelines of the Declaration of Helsinki, and the Institutional Review
Board of the Seoul National University Bundang Hospital approved the study protocol (B-1211/
178-112). Written informed consent was acquired from all of the KLoSHA participants, and the
need for informed consent was waived by the Institutional Review Board for the retrospective
cohort from the Seoul National University Bundang Hospital (B-1211/178-112). Patient records/
information was anonymized and de-identified prior to analysis for the retrospective cohort.
The development cohort included 988 participants, and the validation cohort included 1109
participants (Table 2). The validation cohort was older and had worse functional status,
comorbidity scores, mood, cognition, and nutritional status than the community-based
development cohort. The mean GPI score was also significantly higher (3.6) in the validation cohort
than the development cohort (3.0).
The GPI score was positively associated with age in both the development (B = 0.14, 95% CI
0.12–0.16, R2 = 0.43) and validation (B = 0.07, 95% CI 0.07–0.08, R2 = 0.28) cohorts in the
linear regression analysis. Also, the GPI score was significantly higher in male than female in both
the development (3.3 ± 1.3 vs. 2.7 ± 1.6, P < 0.001) and validation (4.0 ± 1.9 vs. 3.4 ± 1.8, P <
Differences in 3- and 5-year mortality, estimated using the geriatric prognosis index
In the follow up, the mean period of observation was 63.4 +/- 15.4 months in the development
cohort. Two hundred and ten (21.3%) participants died. Also, 92 (9.3%) participants died
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Data are presented as mean ± SD or n (%).
ADL, activities of daily living; IADL, instrumental activities of daily living; CCI, Charlson’s comorbidity index; CIRS-G, Cumulative Illness Rating Scale for
Geriatrics; GDS, Korean version of the Geriatric Depression Scale; GDS-SF, Korean version of the Geriatric Depression Scale Short Form; MMSE,
Korean Mini-Mental State Examination; MNA, Mini Nutritional Assessment; NSI, Nutrition Screening Initiative; GPI, geriatric prognostic index.
within 3 years, and 179 (18.1%) participants died within 5 years from the baseline evaluation.
During the entire observation period of 65.6 ± 31.3 months in the validation cohort, 488
(44.0%) participants died. In addition, 225 (20.3%) participants died within 3 years, and 340
(30.7%) participants died within 5 years from the baseline evaluation.
In the logistic regression analysis with GPI as the linear term, a linear trend was observed in
the 3-year mortality in both the development (odds ratio [OR] 2.0, 95% CI 1.7–2.4, P < 0.001)
and validation (OR 1.6, 95% CI 1.4–1.7, P < 0.001) cohorts. The same trend was observed for
5-year mortality in both the development (OR 2.2, 95% CI 1.9–2.5, P < 0.001) and validation
(OR 1.7, 95% CI 1.5–1.9, P < 0.001) cohorts.
The predicted 3- and 5-year mortality rates in the development cohort based on the logistic
models in addition to the observed 3- and 5-year mortality rates in the validation cohort are
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shown in the S1 and S2 Tables. In Fig 1, the 3- and 5-year predicted mortality rates by GPI
score in the development cohort are compared with the 3- and 5-year observed mortality rates
in the validation cohort; in addition, the fitted equations for the 3- and 5-year mortality rates
(%) are shown.
Internal and external validation of the geriatric prognosis index
In the development cohort, the c-index of the GPI to predict 3-year mortality was 0.78 (95% CI
0.74–0.82), which was not significantly different (P = 0.757) from the c-index of the model
including only age and gender (0.77, 95% CI 0.73–0.72). The c-index for predicting 5-year
mortality with the GPI was 0.80 (95% CI 0.76–0.83), which was not significantly different
(P = 0.639) from the c-index of the model using only age with gender (0.79, 95% CI 0.76–0.82).
In the validation cohort, the c-index of the GPI to predict 3-year mortality was 0.73 (95% CI
0.69–0.72), which was significantly higher (P = 0.028) than the c-index of the model including
Fig 1. Comparisons of predicted 3- (A) and 5-year (B) mortality by geriatric prognostic index (GPI)
score. Dotted lines show the 95% confidence interval. Predicted 3-year mortality (%) = 100 * exp(-4.903
+ 0.726 * [GPI]) / (1 + exp(-4.903 + 0.726 * [GPI])); Predicted 5-year mortality (%) = 100 * exp(-4.247 + 0.784
* [GPI]) / (1 + exp(-4.247 + 0.784 * [GPI])) KLoSHA, Korean Longitudinal Study on Health and Aging;
SNUBH, Seoul National University Bundang Hospital
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only age and gender (0.66, 95% CI 0.68–0.76). The c-index for predicting 5-year mortality with
the GPI was 0.80 (95% CI 0.77–0.82), which was significantly higher (P < 0.001) than the
cindex of the model using only age with gender (0.70, 95% CI 0.67–0.73).
In the sensitivity analysis, the same linear trend in mortality based on the GPI was observed
(3-year mortality, OR 1.6, 95% CI 1.5–1.7; 5-year mortality, OR 1.8, 95% CI 1.7–2.0). The
cindex for the prediction of 3-year mortality was significantly higher (P = 0.001) for GPI (0.75,
95% CI 0.72–0.78) than for only age with gender (0.70, 95% CI 0.67–0.73). The c-index for the
prediction of 5-year mortality was also significantly higher (P < 0.001) for GPI (0.77, 95% CI
0.75–0.79) than for only age with gender (0.72, 95% CI 0.70–0.75).
In the present study, multidimensional GPI was devised and validated, including mood,
cognitive function, and nutritional status as well as function of daily activities, which was based on
common GA domains, to predict long-term mortality in both community and hospital
settings. Also, GPI could predict 3- and 5-year mortality with a significantly higher c-index than
age combined with gender in a hospital-based population.
In previous studies, a number of prognostic indices were reported in various diseases and
certain setting such as community-dwelling older adults, nursing home residents, and
hospitalized older adults [
]. Pilotto index was developed to predict one-year mortality in
hospitalized older patients . Frailty index could also predict postoperative mortality [
However, because there are some quality barriers including potential for bias, compatibility in
another population, and accuracy, these tools are not used routinely in general practice across
patient groups that differ according to severity of diseases, data collection, location, and time.
Furthermore, previous indices were developed from administrative data sets or the information
that may not be routinely assessed in older patients [
]. Even if quality barriers are overcome,
practical issues remain for routinely adaptation. In our study, the GPI was based on GA which
could be tested routinely in practice for older patients. Variables for the GPI were selected in
an a priori fashion to facilitate adaptation for a widespread GA with generally accepted
domains and to prevent over-fitting in the study dataset. Therefore, our GPI could be more
practical and fruitful than other tools.
The 5-year mortality rate of the community-dwelling elderly in the present study was
similar to the 4-year and 5-year mortality in previous reports [
], as was the predictability
(cindex) of our model. In the hospital-based group, the mortality rate and GPI score were higher
than in the community-based group, as expected. The c-index of the GPI to predict 3-year
mortality and 5-year mortality was significantly higher in hospital-based population with more
comorbidity and function decline than in community-dwelling elderly.
Because of the growing multimorbid and frail population, there are a higher number of
elderly patients with cancer or cardiovascular disease requiring medical therapy or invasive
procedures that could cause severe complications. In addition, the cost-effectiveness and
futility of cancer screening or treatment strategies as well as therapeutic goal setting for
chronic diseases are important issues in frail, multimorbid people [
]. In a study that
included Medicare beneficiaries , comorbidity status was a significant factor for life
expectancy during cancer screening in elderly people, indicating that more than age should
be considered for mortality. The inclusion of life expectancy might also be helpful to select
patients who will benefit most from adjuvant or palliative chemotherapy for the treatment
of colon, lung, and breast cancers, which are increasingly detected in older patients [
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the present study, a positive linear trend was observed between GPI and 3- and 5-year
mortality in both the community and hospital-based populations. Therefore, clinicians could
choose not to screen for cancer or use a more conservative regimen for adjuvant or palliative
cancer treatment in this group. Also, health professionals involved in the multidisciplinary
care of older people should be advised to prevent and manage deterioration in functional
There are several strengths of the present study. First, our GPI encompassed most geriatric
factors including mood, cognitive function, and nutritional status as well as function of daily
activities. We selected components that are usually found in a standardized GA. To facilitate
the compatibility with other tools, we assigned a score for each domain rather than using the
score directly from the tool. Second, although hazard ratios for mortality could be used to
weight the GA domains in the development cohort, we did not use a weighting strategy to
prevent over-fitting. Using this scoring method, the GPI was able to predict 3- and 5-year
mortality in both community- and hospital-based populations, ensuring transportability of this
index, which is required for the compatibility of a prognostic index in another population
]. Third, we had access to survival data from the government for a relatively long period of
There are also certain limitations to the study. First, in the community-based population,
the GPI performed similarly to the model with age and gender for predicting mortality. This
finding could be explained by fewer comorbidities and better functional status in the
community-based population. In a hospital-based population with worse functional status and more
comorbidities, the GPI showed better performance and might be more appropriate. Second, we
had to interpret and compare scores from the different tools that were used for each GA
domain in each setting. However, the trends for mortality were similar in both settings. Third,
the number of patients with similar GPI score might relatively too small for GPI to be
generalized before more research would be done in the other dataset. Finally, the performance of this
novel GPI was not directly compared with previous prognostic tools. The direct comparison
was impossible in retrospective analysis because specific data were needed for each tool.
However, this novel GPI is valuable, which showed high c-index to predict long-term mortality in
both community and hospital settings.
In conclusion, a practical and generally accepted multidimensional GPI, which was
developed in this study based on common GA components, stratified mortality in Korean elderly
people. This index can provide information for clinicians regarding life expectancy and assist
in individualized decision making for the treatment of an aging population.
S1 Table. Observed and predicted 3-year mortality rate in the development cohort (Korean
Longitudinal Study on Health and Aging [KLoSHA]) and observed 3-year mortality rate in
the validation cohort (Seoul National University Bundang Hospital [SNUBH]), by geriatric
prognostic index (GPI) score.
S2 Table. Observed and predicted 5-year mortality rate in the development cohort (Korean
Longitudinal Study on Health and Aging [KLoSHA)] and observed 5-year mortality rate in
the validation cohort (Seoul National University Bundang Hospital [SNUBH]) group, by
geriatric prognostic index (GPI) score.
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Conceived and designed the experiments: JWK HWJ. Performed the experiments: JWK HWJ.
Analyzed the data: JWK HWJ KIK KWK. Contributed reagents/materials/analysis tools: KIK
KWK JWH KYK JHK CHK. Wrote the paper: JWK HWJ KIK KWK JHK.
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