Gonnella, JS

Grilli, R

Herrera, K

Louis, D Z

Maio, V

Richardson, D

Robeson, M

BMC Health Services Research

English

PubMed

D Z Louis

M Robeson

K Herrera

V Maio

JS Gonnella

D Richardson

R Grilli

Crossref

Who is most likely to require a higher level of care: predicting risk of hospitalization

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MEETING ABSTRACT Open Access
Who is most likely to require a higher level of
care: predicting risk of hospitalization
D Z Louis1*, M Robeson1, K Herrera1, V Maio1, JS Gonnella1, D Richardson2, R Grilli3
From 26th Patient Classification Systems International (PCSI) Working Conference
Munich, Germany. 15-18 September 2010
Introduction
Health-care services are increasingly focused on care of
individuals with chronic illness. Predicting those who
are at greatest risk is critical in the design of case or dis-
ease management programs. The goal of this project is
to develop models that can be used to identify people
who are at high risk for hospitalization, and who would
potentially benefit from participating in chronic disease
management programs.
Methods
We developed a predictive model using the 2002-2007
Regione Emilia-Romagna longitudinal health-care
administrative database, which includes linked demo-
graphic, hospital, outpatient pharmacy, and referral data
for laboratory, x-ray and specialist physician visits for
3.3 million adult residents of the region. We defined the
dependent variable as a “hospitalization (or death) for
problems that are potentially avoidable in patients who
have diseases or problems amenable to disease manage-
ment.” The independent variables included demographic
variables, morbidity indicators, and quality-of-care
indicators.
A boosted regression tree predictive model was devel-
oped using an enriched case-control training sample of
50,000 adults. Predictive performance and calibration of
the model was assessed on an independent validation
sample. Model discrimination was assessed using the
C-statistic, the area under the Receiver-Operator Char-
acteristics curve. Calibration was assessed using a
Hosmer-Lemeshow table of predicted versus observed
proportions hospitalized, stratified by risk category.
Results
Model discrimination was good (C-statistic = 0.82). The
overall rate of hospitalization in Emilia-Romagna for the
selected conditions was 6.0%. We tabulated the actual
(observed) and the predicted hospitalization rate within
each category. Model calibration was excellent. For
example, in the highest risk decile, 26% of the patients
were hospitalized. The model predicted a hospitalization
rate of 27% for this group.
We then applied the model to the entire adult popula-
tion of the Emilia-Romagna region (n = 3.3 million) to
identify those residents with the highest predicted risk
of hospitalization.
The population was divided into 4 groups based on
predicted risk of hospitalization:
Very high risk: predicted risk of hospitalization is
≥ 50%
High risk: predicted risk of hospitalization is 25-49%
Moderate risk: predicted risk of hospitalization is
10-24%
Low risk: predicted risk of hospitalization is < 10%
162,140 residents of the region, or 4.9% of the popula-
tion, were identified as high risk. Of these people, 34%
were hospitalized in the following year for one of the
selected conditions and 12.8% died. Despite being less
than 5% of the population, the high-risk group
accounted for more than 30% of hospital expenditures
for selected problems: €440.1 million.
In addition to patient age, the variables with the great-
est relative influence on predicted risk of hospitalization
included the number of chronic diseases, polypharmacy,
and the presence of potentially inappropriate prescrip-
tion drugs for elderly patients. Chronic diseases affecting
the cardiovascular, gastrointestinal, musculoskeletal,
respiratory and neurological systems were prevalent in
high-risk patients, as were mental health problems. For
those patients hospitalized in previous years, specific1Thomas Jefferson University, Philadelphia, PA, USA
Full list of author information is available at the end of the article
Louis et al. BMC Health Services Research 2010, 10(Suppl 2):A2
http://www.biomedcentral.com/1472-6963/10/S2/A2
© 2010 Louis et al; licensee BioMed Central Ltd.
severity stages of certain chronic diseases (e.g., coronary
artery disease, diabetes mellitus) were predictive of
future risk. Table 1.
Conclusions
The longitudinal database available in the Emilia-
Romagna region made it feasible to develop models that
could identify individuals at high risk of future hospitali-
zation. Of course, identifying those at risk is only one
step in helping patients modify those risks. The Emilia-
Romagna region includes 11 Local Health Authorities
(LHA); each LHA is further organized into up to 6
health districts. Model results were prepared for each
LHA and health district in order to assist the directors
general and medical directors to identify residents at
high risk. In addition, to assist them in their efforts to
improve chronic care management, a review of the
international literature was performed to identify
chronic disease management programs that have shown
promise.
Author details
1Thomas Jefferson University, Philadelphia, PA, USA. 2Philadelphia VA Medical
Center, Philadelphia, PA, USA. 3Regione Emilia-Romagna, Bologna, Italy.
Published: 6 October 2010
doi:10.1186/1472-6963-10-S2-A2
Cite this article as: Louis et al.: Who is most likely to require a higher
level of care: predicting risk of hospitalization. BMC Health Services
Research 2010 10(Suppl 2):A2.
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Table 1 Number of patients by predicted risk group, hospitalization, and cost
Risk of Hospitalization High Risk 25-49% Very High Risk 50-100% Total
No. % No. % No. %
Total population 162,140 4.9% 1,385 0.0% 3,333,943 100%
Hospitalized for selected conditions* 55,833 34.4% 815 58.8% 201,590 6.0%
Died from selected conditions 20,678 12.8% 453 32.7% 42,306 1.3%
Hospital costs for selected conditions * €440,109,568 €6,713,382 €1,446,983,397
% of hospital costs for selected conditions (row%) 30.4% 0.5%
* Patients who were hospitalized for >2 days for selected conditions.
Louis et al. BMC Health Services Research 2010, 10(Suppl 2):A2
http://www.biomedcentral.com/1472-6963/10/S2/A2
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