Big Data.2013

Published on March 2017 | Categories: Documents | Downloads: 34 | Comments: 0 | Views: 262
of 8
Download PDF   Embed   Report




Medications as a Predictor
of Medical Complexity
Roger Higdon,1–3 Elizabeth Stewart,1,3
Jared C. Roach,4 Caroline Dombrowski,5
Larissa Stanberry,1–3 Holly Clifton,6
Natali Kolker,2,3 Gerald van Belle,7
Mark A. Del Beccaro,8–10
and Eugene Kolker1–3,8,10
Children with special health-care needs (CSHCN) require health and related services that exceed those required by
most hospitalized children. A small but growing and important subset of the CSHCN group includes medically
complex children (MCCs). MCCs typically have comorbidities and disproportionately consume health-care resources. To enable strategic planning for the needs of MCCs, simple screens to identify potential MCCs rapidly in a
hospital setting are needed. We assessed whether the number of medications used and the class of those medications
correlated with MCC status. Retrospective analysis of medication data from the inpatients at Seattle Children’s
Hospital found that the numbers of inpatient and outpatient medications significantly correlated with MCC status.
Numerous variables based on counts of medications, use of individual medications, and use of combinations of
medications were considered, resulting in a simple model based on three different counts of medications: outpatient
and inpatient drug classes and individual inpatient drug names. The combined model was used to rank the patient
population for medical complexity. As a result, simple, objective admission screens for predicting the complexity of
patients based on the number and type of medications were implemented.
Health-care expenses are a large and growing burden
on the budgets of consumers, the country, and even the
global community as a whole. Children with special healthcare needs (CSHCN) are particularly challenging. It is estimated that 13–18% of children/youth fall into this category
and thus require health services of a type or amount beyond
that required by most children.1 Within this group is a
growing subset of children who, because of multiple chronic

conditions or exceptionally complex medical issues, are
termed medically complex children (MCCs).2 The consequence is a growing population of patients who require ongoing and often intensive care. The burden on the families is
substantial, with 54.1% reporting that a family member
stopped working because of a child’s health and 56.8% reporting financial difficulties.3
MCCs require a disproportionate amount of hospital resources. For example, one study of hospital discharge records


Bioinformatics and High-Throughput Data Analysis Laboratory, Seattle Children’s Research Institute, Seattle, Washington.
Predictive Analytics, Seattle Children’s Hospital, Seattle, Washington.
Data Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington.
Institute for Systems Biology, Seattle, Washington.
The Information School, University of Washington, Seattle, Washington.
Center for Children with Special Needs, Seattle Children’s Research Institute, Seattle, Washington.
Departments of Biostatistics and Environmental and Occupational Health Sciences, University of Washington, Seattle, Washington.
Department of Pediatrics, University of Washington, Seattle, Washington.
Medical Affairs, Seattle Children’s Hospital, Seattle, Washington.
Department of Biomedical Informatics & Medical Education, University of Washington, Seattle, Washington.

DOI: 10.1089/big.2013.0024  MARY ANN LIEBERT, INC.  VOL. 1 NO. 00  XXX 2013 BIG DATA


Higdon et al.

in Ontario, Canada, found that children with medical complexity had a median of 13 outpatient physicians and 6 distinct subspecialists.4 Another study found that pediatric
patients with medically extreme ‘‘catastrophic’’ disease (excluding malignancy) constitute 0.4% of the population enrolled in the Washington state health plan yet were
responsible for 24% of pediatric inpatient charges.5 MCCs are
also more likely to have an adverse event when hospitalized6
and more likely to have frequent readmissions.7 This is a
major concern to children’s hospitals because, although the
Centers for Medicare & Medicaid Services are currently only
penalizing adult hospitals for high readmission rates as part
of the U.S. Patient Protection and Affordable Care Act’s
Hospital Readmission Reduction Program, federal policies
for adult hospitals have been extended to children’s hospitals
in the past.8 This policy extension has the potential to disproportionately affect hospitals with larger populations of
MCCs. An examination of admittance rates found that between 2004 and 2009 the cumulative rate of hospitalization
for MCCs rose 32.5%.9
This growing demand for resources
shows a clear need to identify and
manage the care of MCCs. Coordinated managed care of MCCs
can improve outcomes and reduce
health-care costs.10,11 Furthermore,
forecasting the numbers of MCCs
and other patient groups can enable
predictions of hospital occupancy,
economic resource utilization, and
outcomes.12–14 Identifying MCCs at
admission can guide treatment decisions, psychosocial interventions, and
strategic planning at institutional and
governmental levels.15

Further complicating the analysis of MCCs is the scale and
complexity of hospital databases. A medical record of any
single patient may have hundreds if not thousands of associated variables, and this complexity is multiplied many fold
for MCCs. In addition, the data are longitudinal, and measurements, procedures, medications, and medical tests may
not be applied or recorded consistently as the database
evolves. Extraction, standardization, and analysis of these
data require considerable resources and expertise.19

To manage the complexity of care and data involved with
MCCs, it is necessary to identify them early in the treatment
process through some type of screening. Previous efforts have
identified variables associated with medical complexity in
adult patients, including length of stay, number of days with
laboratory tests, number of days
with diagnostic procedures, number
of consultations by specialists, and
number of nonstandard nurse in‘‘IDENTIFYING MCCS AT
terventions.20 In addition, the
number of medications at admisTREATMENT DECISIONS,
sion, the number of medications
during hospital stay, and the number of medications at discharge were
found to be significantly correlated
with the risk for extended hospital
stay.21 A manual screen for comGOVERNMENTAL LEVELS.’’
plexity in adults based on these
predictors has been developed and
tested in Europe.22

One major problem inhibiting care management of MCCs is
the lack of a clear definition of MCCs. The definition and
even the label of medical complexity is challenging and
evolving with such variations as ‘‘children with’’ complex
medical needs, complex medical conditions, complex chronic
conditions, or complex health conditions.16 It is imperative
to establish such a standard as it would allow research studies
to be reproducible and comparable, and integrated patient
care to be targeted to those who need it.
Such a definition would also support efforts to determine a
simple, robust, and rapid way of identifying MCCs upon admission in order to provide the best possible care as soon as
they are admitted. Classifying patients by complexity has been
done from insurance claim information.17 At Seattle Children’s
Hospital (SCH), a medically complex child is defined as a
patient who has two or more chronic conditions, with some
exceptions for extremely complex single-condition patients.18
These definitions, however, require a thorough review of the


medical history and require data not available at admission. To
be useful and applicable in hospital settings, the admissions
stage screening should be simple and accurate and utilize patient information upon admission as well as previous records.

The need for a simple screen and the observation that medication usage is associated with MCC status led to assessing
whether the patient data gathered during the admission
process—in particular, the patient’s medication data—can
rapidly predict MCC status. The number and type of medications were found to be informative predictors of MCC status.
These findings were used by SCH to create a ranking system that
identified patients for MCC evaluation utilizing the number of
patient medications. The ranking system was incorporated into
standard operating procedures to enable improved care and
resource optimization. In what follows, we describe the methodology used in the analysis and compare competing models.

Patients and Methods
This project was primarily carried out by the Predictive
Analytics Group at SCH. SCH is the tertiary referral center
(inpatient and ambulatory clinics) for a four-state region
(Washington, Alaska, Montana, and Idaho) and the primary

Higdon et al.

Table 1. Patient Demographics

Mean age
Percent female
Percent Hispanic
Mean patient-days

complex patients


8.3 years

7.5 years

that lasted less than 24 hours, such as patients admitted for
observation or for day surgery. During our study, there were
7,916 individual children admitted to SCH. For our primary
analysis, MCCs totaled 263 and non-MCCs totaled 7,653.
Our analysis included patients later deceased (5 MCCs and 96
non-MCCs). The demographics of the patients are described
in Table 1. The Institutional Review Board at SCH approved
our protocol (no. 12509).

Clinical data
pediatric inpatient hospital for the Puget Sound area of
Washington state. A basic unit of analysis is a patient, not a
visit; a patient with multiple admissions in a year is considered a single count. Individual patients were selected using
nonduplicative patient identifiers that were matched to
medical record numbers. We excluded data from admissions

The records were manually curated to determine MCC status
through use of diagnostic codes to identify patients with at
least two chronic conditions or extremely complex cases with a
single chronic condition.18 This approach was considered to be
the most likely to give an accurate accounting of status given
the complexity and variability of the data. Details about the
medication data and the flow of analysis are shown in Figure 1.

FIG. 1. Strategy for analysis of medication data and MCC status. AIC, Akaike information criterion; MCC, medically complex child; PPV,
positive predictive value; ROC, receiver operator characteristic.


Higdon et al.

Demographic and clinical data including the medication dataset for all outpatient and inpatient drug orders were recorded
in the clinical database (Cerner Millennium). The inpatient
record tends to be more complete, as all orders are entered
through computerized provider order entry and the integrated
pharmacy system (PharmNet, Cerner Millenium). Outpatient (ambulatory) orders include self-reported historical
medications obtained at admission, including prescription,
over-the-counter medications and supplements. However,
because the patients cared for at SCH are not a captured
population, the outpatient medication records do not contain
every medication the patients may have received from their
primary care providers. We considered outpatient and inpatient medication records separately. By dividing the medical
record into inpatient and outpatient categories, we buffer our
analysis from a loss of power that might result if one of the
categories produced particularly poor predictors.
PowerInsight (Business Objects, Enterprise XI, release 2)
was used to extract data from the clinical system. We tabulated unique drug classes as defined in the Cerner Millennium
system (e.g., proton pump inhibitor) and drug names (e.g.,
lansoprazole) for both inpatient and outpatient medications
for all children in the dataset. Multiple orders for the same
drugs or drug classes were counted only once for each patient.

Variable generation
Because complexity is often driven by comorbidity, we hypothesized that combinations of medications would be more
predictive than use of single particular medications. To gain
further insight into underlying causes of associations, we also
considered the presence or absence of particular drugs or
drug classes as predictors. Simple counts of medications were
also considered, as a potentially simple manual screen.
Using the data, we generated a number of different variables
as potential MCC predictors. These included counts of
unique medications by drug name or drug class separately for
inpatient and outpatient records. Boolean variables for individual and pairs of medications for each patient were also
created. For definitions and formulas, see Table 2.

Variable selection
A composite model of drug counts was fit using stepwise
logistic regression models.23 The count variables were transformed to a log scale after adding a pseudo count to remove
skewness and improve the model fit. Logistic regression
models fit the log odds of being medically complex as a
weighted average of individual predictors (Table 2). Logistic
regression models were chosen because of the ability to model
the anticipated monotone relationship between drug counts
and increasing medical complexity The Akaike information
criterion was used to select the best-fitting model.24 A 10-fold
cross-validation incorporating the stepwise selection procedure was applied to each model to estimate the prediction
accuracy.25 The final medication count model chosen by this


Table 2. Definitions and Formulas
Medically complex status





True positive
False negative

False positive
True negative


Sensitivity: Probability of a positive prediction for an individual who is
medically complex (TP/NMCC).
Specificity: Probability of a negative prediction for an individual who is not
medically complex (TN/Nnon-MCC).
Prevalence: Proportion of medically complex children (MCCs) in the
population (NMCC/N).
Positive predictive value (PPV): The probability of being medically complex
for an individual predicted to be medically complex (TP/NA).
Akaike information criterion (AIC): Criterion for estimating the predictive
ability of statistical models.
10-fold cross-validation: Estimating prediction accuracy by predicting each
10% partition of the data from the remaining 90%.
Receiver operator characteristic (ROC): An ROC curve summarizes the
trade-off between sensitivity and specificity over the range of possible

approach was compared with the predictions by individual
count variables, patient-days, and models based on either
inpatient or outpatient drug counts. The comparisons were
made using receiver operator characteristic (ROC) curves
built on the cross-validation data. ROC curves plot the
tradeoff between the percentage of correctly identified MCC
patients (sensitivity) and the percentage of non-MCC patients (1 – specificity) identified at all possible thresholds.26
The ROC curves are compared statistically using the area
under the curve (AUC), and test models based on individual
drugs and drug combinations were also fit.

Single-variable predictions
As expected, the number of medications for MCCs was higher
than that for non-MCCs. The number of medications tabulated by numbers of unique drug classes or unique individual
medications for both outpatient and inpatient was significantly
associated with MCC status (all p-values < 0.0001). The
number of patient-days was also strongly associated with MCC
status ( p < 0.0001). Each of the four medication count variables was highly correlated with the others. Correlation coefficients ranged from 0.50 to 0.98 for the two inpatient counts,
indicating that they contain redundant information. Medication counts were also strongly correlated with the number of
bed-days (correlation coefficients ranging from 0.3 to 0.6).
The presence of particular medications (and medication classes) was associated with MCC status; for example, outpatient
use of baclofen was the most predictive single medication (34/
7,653 or 4.4% of non-MCC patients, and 20/263 or 7.6% of
MCC patients). We also observed that pairs of drugs were

Higdon et al.

FIG. 2. ROC curves comparing the predictive abilities: trade-off
between identifying MCCs and excluding non-MCCs of composite
models of medication counts, individual medication counts, or
patient-days. The drug count model is simple and performs nearly
as well as or better than all other models. AUC, area under the

often better predictors than single drugs (e.g., tacrolimus and
acyclovir). Combinations of drug classes did not appear to add
much value because of taxonomic redundancy. The most
predictive drug class is proton pump inhibitors.

Predictive models

FIG. 3. Comparison of the distribution of scores (log-odds of
prediction probabilities) between medically complex and other
patients. The plot shows the scores of medically complex patients
shifted to the right but still overlapping other patients.

equal to 0.80 and 0.75, respectively). Inpatient drug counts
were better than outpatient drug counts at predicting MCC
( p < 0.0001) (Fig. 3). All of the drug counts were more predictive than patient bed-days as a sole predictor ( p < 0.0001).
Predictive models based on individual and combinations of
medications were also evaluated. The cross-validation of
these models showed no improvement over the prediction
made by the corresponding simple count of medications.
This result is possibly because of the large number of possible
medications and low amount of overlapping usage of any
particular combination.

A composite medication count model was selected by a
stepwise approach. The best model includes counts of outpatient and inpatient drug classes and the count of individual
Model to maximize positive predictive value
inpatient drug names. All of these counts were statistically
Positive predictive value (PPV) is an important measure of
significant (p < 0.0001) after adjusting for other variables in
the clinical utility of a screening test. PPV measures the
spite of the high degree of correlation between counts. The
probability of a patient actually being MCC when passing the
distribution of these scores from the medication count model
screen.27 Although there is a significant correlation to each
between MCCs and non-MCCs shows a large separation in
predictor with MCC status, the PPV
the number of medications beof individual counts is not of high
tween MCCs and non-MCCs, but
operational value. As can be seen in
with considerable overlap in the
Figure 4 (3% prevalence), a threshold
tails of the distributions (Fig. 2).
The composite model improved PAIRS OF DRUGS WERE OFTEN identifying 60% of MCCs results in a
relatively low PPV of 10%. However, it
upon the individual medication
is probable that many patients in the
count models in terms of AUC
hospital population are medically
(Fig. 3). The p-values respectively
complex but have yet to be classified as
for comparing AUC for combined
such. To account for this, we performed a sensitivity analysis. In
model versus counts inpatient drug classes, inpatient drug
Figure 4, PPV is compared with sensitivity for the medication
names, outpatient drug classes, and outpatient drug names
model, assuming different prevalences of MCCs in the patient
were respectively. Models based on inpatient drug counts
population. Prevalence could range from 3% (assumes that
only or outpatient drug counts only were no better than innone of the non-MCCs are medically complex) to 20%. When
dividual inpatient or outpatient drug count variables (AUCs


Higdon et al.

these datasets is high but incomplete, and separate consideration can yield more information than a combined predictor. In
addition, both inpatient and outpatient medication variables
have drawbacks relative to one another; inclusion of both can
make up for these deficiencies. In the present dataset, many
patients have no outpatient drug orders (11.8% of MCCs and
51% of non-MCCs) because of not capturing prescriptions
filled outside SCH. Most patients have recorded inpatient drug
orders (99.3% of MCCs and 98.7% of non-MCCs); however,
inpatient records often have many medications related to acute
episodes or standard order sets that are less correlated with
MCC status than outpatient medication lists.

FIG. 4. PPV versus sensitivity at different prevalences for the
medication count model. The true prevalence of MCCs in the
Seattle Children’s Hospital population is unknown, but is unlikely
to be more than 20. A 3% prevalence would exist if none of the
non-MCCs are medically complex. PPV increases with prevalence
and decreasing sensitivity.

prevalence is low, it requires high sensitivity to detect MCCs,
resulting in a relatively low PPV (Fig. 4).

Identifying MCCs at admission is critical to improving care,
optimizing resource usage, and reducing costs. Developing a
simple, accurate screening method is complicated by inherent
variability in MCC cases and large data volumes accumulated
by the hospitals. Health-care data can also be heavily influenced
by the human factor; for example, doctor-to-doctor variations
in treatment can cause data variations that must be considered.
In addition, data that are generated outside the hospital (outpatient drug counts) are not always
reliably captured and therefore can
lead to incorrect evaluations.

MCC status is largely determined by complex illness and comorbidities. Patients with more complex illnesses tend to have
more medications, and additional medications may be prescribed for each comorbidity. Our non-MCC group likely
contains MCCs who were not identified even by the manual
curation process before this study. This effect, as seen in Figure
4, likely artificially lowered the PPV of the medication screen
applied to our data. Even if the test’s ability to find MCCs is
high, the PPV may be low because of the low prevalence of
identified MCCs. Figure 3 indicates that unless prevalence is
high, the medication model by itself is insufficient to completely identify MCCs. However, the model provides a useful
component to rank patients for additional screening.
Applying big data analysis models to a classification process
must be done carefully in circumstances such as this, in which
the determined classifier might in the future be used to define the
category, thereby corrupting any further analysis. Identification
of predictor variables could lead to a circular process in which
future classification of MCCs overly relies on these variables. If,
in turn, this newly identified set of patients is used to revise
successive predictive models, the association of these variables
with medical complexity will be artificially inflated. Our predictive medication count model can aid in classifying patients as
medically complex. However, it should not be used as an exclusive aid. Training sets for future predictive models should
not be chosen entirely on the basis of
our prior predictive models or should
at least account for prior models in
training to avoid bias.


At SCH, we have demonstrated that
information in the medication record is highly associated with medical complexity and can aid in
classifying pediatric MCCs. The best
predictive model was based on both
outpatient and inpatient medication
counts. For most classification purposes, we concluded that
medication predictors should be used in combination with
other predictors.

Our results demonstrate value in considering the inpatient and
outpatient medical records separately. Correlation between


Our primary goal is to efficiently
screen for CSHCN and MCCs. Once
these patients are identified, the
most appropriate specialized resources can be assigned to the patient. Our analysis of admissions
data has led to predictive models that open the door for
creation of one or more standardized ‘‘complexity scores.’’
These scores need to rely on a simple and robust model that
can be reliably applied with accuracy in diverse hospital settings. The models should also reflect family fragility and social support since these factors greatly influence the care of

Higdon et al.

MCCs. As further refinements in classification and analysis
Hendricks, Skip Smith, Drex DeFord, Nate Anderson,
occur, the model will continue to be improved. Rather than
Courtney MacNealy-Koch, Greg Yandl, Maggie Lackey, and
simply classifying a patient as an
John Neff for their critical reading
MCC or not, our models can proand editing of the article; and Tom
duce a quantitative score reflecting
Hansen, Jim Hendricks, David Fisher,
the likelihood that a patient is
Lisa Brandenburg, Bruder Stapleton,
medically complex as well as the
Kelly Wallace, Sandy Meltzer, and Wes
overall degree of complexity,
Wright for their encouragement and
COULD LEAD TO A CIRCULAR help in this project.
therefore providing an indication
of the quantity of care needed.
Such complexity scores could refine
Author Disclosure
hospital planning, resource allocaOVERLY
tion, and assignment of inpatients
to the MCC Service. Our current
No conflicting financial interests
analysis led to the amendment of
standard operating procedures in
admissions at SCH for improved patient care and better
management of resources.


A future goal will be to develop an automated screen to rank
all patients during admission. If this analysis can be done in
near real time, necessary services can be assigned quickly.
Predictive classification of new admits will have to be done
without any prior data from admissions. Therefore, additional predictive variables from intake records should be
identified to realize the full potential of automated real-time
screening for MCC or CSHCN status. As the pediatric healthcare field changes, more MCC care may be taken on by
community-based resources, and it may be increasingly
critical that data on these patients are adequately gathered
and shared for comprehensive care plans.28–30

Health-care data can be used to improve patient care and
hospital efficiency. There are challenges to overcome, such as
the high degree of variability and the degree of difficulty in
complete capture and access; however, even with these challenges, the potential for improving patient care and hospital
efficiencies is immense. The number of medications a patient
takes is predictive of medical complexity with simple models
based on counts of the number of medications as effective as
more complex models. A composite predictor based on both
outpatient and inpatient medication counts yielded the best
results. Numerous variables were considered, and the best
predictor of MCC status was a simple model based on three
medication variables. That model can be used to help rank
the patient population for medical complexity and thus improve patient care and resource allocation.

We would like to thank John Neff for the introduction to the
issue of medical complexity and Ron Dick for providing
details of the MCC Service; Ted Corbett, Ann Samuelson, and
Deborah Curley for their assistance with PowerInsight; Jim

1. Berry J, Hall D, Kuo D, et al. Hospital utilization and
characteristics of patients experiencing recurrent readmissions within children’s hospitals. JAMA 2011;
2. Burns K, Casey P, Lyle R, et al. Increasing prevalence of
medically complex children in US hospitals. Pediatrics
2010; 126:638–646.
3. Kuo D, Cohen E, Agrawai R, et al. A national profile of
caregiver challenges among more medically complex
children with special health care needs. Arch Pediatr
Adolesc Med 2011; 165:1020–1026.
4. Cohen E, Berry J, Camacho X, et al. Patterns and costs of
health care use of children with medical complexity.
Pediatrics 2012; 130:e1463–e1470.
5. Neff J, Sharp V, Muldoon J, et al. Profile of medical
charges for children by health status group and severity
level in a Washington State Health Plan. Health Serv Res
2004; 39:73–89.
6. Matlow A, Baker G, Flintoft V, et al. Adverse events
among children in Canadian hospitals: the Canadian
Paediatric Adverse Events Study. CMAJ 2012; 184:E709–
7. Berry J, Agrawal R, Kuo D, et al. Characteristics of hospitalizations for patients who use a structured clinical
care program for children with medical complexity. J
Pediatr 2011; 159:284–290.
8. Srivastava R, Keren R. Pediatric readmissions as a hospital quality measure. JAMA 2013; 309:396–398.
9. Berry J, Hall M, Hall D, et al. Inpatient growth and resource use in 28 children’s hospitals: a longitudinal, multiinstitutional study. JAMA Pediatr 2013; 167:170–177.
10. Srivastava R, Stone B, Murphy N. Hospitalist care of the
medically complex child. Pediatr Clin North Am 2005;
11. Cohen E, Friedman J, Nicholas D, et al. A home for
medically complex children: the role of hospital programs. J Healthc Qual 2008; 30:7–15.


Higdon et al.

12. McMillan J, Tristram D, Weiner L, et al. Prediction of the
duration of hospitalization in patients with respiratory
syncytial virus infection: use of clinical parameters. Pediatrics 1988; 81:22–26.
13. Brady A, Harrison D, Black S, et al. Assessment and
optimization of mortality prediction tools for admissions
to pediatric intensive care in the United Kingdom. Pediatrics 2006; 117:e733–e742.
14. Noe¨l PH, Parchman ML, Williams J, et al. The challenges
of multimorbidity from the patient perspective. J Gen
Intern Med 2007; 22 Suppl 3:419–424.
15. Wang K, Barnard A. Technology-dependent children and
their families: a review. J Adv Nurs 2004; 45:36–46.
16. Cohen E, Kuo D, Agrawal R, et al. Children with medical
complexity: an emerging population for clinical and research initiatives. Pediatrics 2011; 127:529–538.
17. Neff J, Sharp V, Muldoon J, et al. Identifying and classifying children with chronic conditions using administrative data with the clinical risk group classification
system. Ambul Pediatr 2002; 2:71–79.
18. Neff JM, Clifton H, Park KJ, et al. Identifying children
with lifelong chronic conditions for care coordination by
using hospital discharge data. Acad Pediatr 2010; 10:417–
19. Higdon R, Haynes W, Stanberry L, et al. Unraveling the
complexities of life sciences data. Big Data 2013; 1:42–50.
20. de Jonge P, Huyse F, Herzog T, Lobo A, et al. Risk factors
for complex care needs in general medical inpatients: results
from a European study. Psychosomatics 2001; 42:213–221.
21. de Jonge P, Bauer I, Huyse F, et al. Medical inpatients at
risk of extended hospital stay and poor discharge health
status: detection with COMPRI and INTERMED. Psychosom Med 2003; 65:534–541.
22. Huyse F, de Jonge P, Slaets J, et al. COMPRI—an instrument to detect patients with complex care needs:







results from a European study. Psychosomatics 2001;
McCullagh P, Nelder J. Generalized Linear Models.
London: Chapman Hall, 1999.
Akaike H. Information theory and the extension of the
maximum likelihood principle. In: Perov BN, Caski F
(eds.). Second International Symposium and Information Theory. Budapest: Akademiei Kiado, 1973, pp 267–
Hastie T, Tibshirani R, Friedman J. The Elements of
Statistical Learning, Data Mining, Inference and Prediction. New York: Springer, 2001.
Pepe M. The Statistical Evaluation of Medical Tests for
Classification and Prediction. Oxford: Oxford University
Press, 2003.
Zweig M, Campbell G. Receiver-operating characteristic
(ROC) plots: a fundamental evaluation tool in clinical
medicine. Clin Chem 1993; 39:561–577.
Simon T, Mahant S, Cohen E. Pediatric hospital medicine and children with medical complexity: past, present,
and future. Curr Probl Pediatr Adolesc Health Care 2012;
Wise P. The future pediatrician: the challenge of chronic
illness. J Pediatr 2007; 151:S6–S10.
Cohen E, Friedman J, Mahant S, et al. The impact of a
complex care clinic in a children’s hospital. Child Care
Health Dev 2010; 36:574–582.

Address correspondence to:
Eugene Kolker
Seattle Children’s Research Institute
Box C9S-10, 1900 Ninth Avenue
Seattle, WA 98101-1309
E-mail: [email protected]

This work is licensed under a Creative Commons Attribution 3.0 United States License. You are free to copy, distribute,
transmit and adapt this work, but you must attribute this work as ‘‘Big Data. Copyright 2013 Mary Ann Liebert, Inc., used under a Creative Commons Attribution License:



Sponsor Documents

Or use your account on


Forgot your password?

Or register your new account on


Lost your password? Please enter your email address. You will receive a link to create a new password.

Back to log-in