Using Highly Detailed Administrative Data to Predict Pneumonia Mortality
et al. (2014) Using Highly Detailed Administrative Data to Predict Pneumonia Mortality. PLoS
ONE 9(1): e87382. doi:10.1371/journal.pone.0087382
Using Highly Detailed Administrative Data to Predict Pneumonia Mortality
Michael B. Rothberg 0
Penelope S. Pekow 0
Aruna Priya 0
Marya D. Zilberberg 0
Raquel Belforti 0
Daniel Skiest 0
Tara Lagu 0
Thomas L. Higgins 0
Peter K. Lindenauer 0
Measures: In hospital mortality. 0
Olivier Baud, H opital Robert Debre, France
0 1 Department of Medicine, Medicine Institute, Cleveland Clinic , Cleveland , Ohio, United States of America, 2 Division of General Medicine, Baystate Medical Center, Springfield, Massachusetts, United States of America, 3 Center for Quality of Care Research, Baystate Medical Center, Springfield, Massachusetts, United States of America, 4 Department of Medicine, Tufts University School of Medicine, Boston, Massachusetts, United States of America, 5 University of Massachusetts Amherst, Amherst, Massachusetts, United States of America, 6 EviMed Research Group, LLC, Goshen, Massachusetts, United States of America, 7 Division of Infectious Diseases, Baystate Medical Center, Springfield, Massachusetts, United States of America, 8 Division of Pulmonary and Critical Care, Baystate Medical Center , Springfield, Massachusetts , United States of America
Background: Mortality prediction models generally require clinical data or are derived from information coded at discharge, limiting adjustment for presenting severity of illness in observational studies using administrative data. Objectives: To develop and validate a mortality prediction model using administrative data available in the first 2 hospital days. Research Design: After dividing the dataset into derivation and validation sets, we created a hierarchical generalized linear mortality model that included patient demographics, comorbidities, medications, therapies, and diagnostic tests administered in the first 2 hospital days. We then applied the model to the validation set. Results: The derivation cohort included 200,870 patients and the validation cohort had 50,037. Mortality was 7.2%. In the multivariable model, 3 demographic factors, 25 comorbidities, 41 medications, 7 diagnostic tests, and 9 treatments were associated with mortality. Factors that were most strongly associated with mortality included receipt of vasopressors, noninvasive ventilation, and bicarbonate. The model had a c-statistic of 0.85 in both cohorts. In the validation cohort, deciles of predicted risk ranged from 0.3% to 34.3% with observed risk over the same deciles from 0.1% to 33.7%. Conclusions: A mortality model based on detailed administrative data available in the first 2 hospital days had good discrimination and calibration. The model compares favorably to clinically based prediction models and may be useful in observational studies when clinical data are not available.
Funding: The study was funded by the Agency for Healthcare Research and Quality (1 R01 HS018723-01A1). The funders had no role in study design, data
collection and analysis, decision to publish, or preparation of the manuscript.
Bacterial pneumonia is a leading cause of morbidity and
mortality in the United States. Every year, more than 8 million
patients are admitted to US hospitals with pneumonia; 8.8% of
them will die.  Despite the common nature of this condition,
there are large gaps in our knowledge regarding how best to care
for pneumonia patients. Most recommendations in national
treatment guidelines are not based on randomized trials, and
there is a paucity of comparative effectiveness research.
Administrative databases derived from billing records are
attractive candidates for health services research, as well as for
use in hospital profiling initiatives, because the number of patient
records is large and the acquisition cost is low. Observational
studies using administrative data can be used to assess comparative
effectiveness in real world settings, and findings from such studies
are sometimes confirmed in randomized trials. One concern,
however, is that such studies are often biased by confounding by
indication, in which the choice of treatment is influenced by a
patients severity of illness. This threat can be limited through the
use of validated risk prediction instruments that are capable of
adjusting for pre-treatment severity of illness, as well as
There exist a number of validated pneumonia mortality
prediction instruments for use in clinical care. [2,3] All of these
require clinical data, such as respiratory rate or blood urea
nitrogen, which are not generally available in administrative data
sets. Others have attempted to construct predictive mortality
models from administrative data. International Classification of
Diseases, Ninth Revision, Clinical Modification (ICD-9-CM)
codes assigned at discharge are highly predictive of mortality, in
great part because they include complications of hospitalization
which often precede death.  Such models are not useful for
severity adjustment because they incorporate the results of
treatment (e.g., complications) as predictors. Models restricted to
demographics and comorbidities at the time of admission have
much lower predictive accuracy .
Highly detailed administrative datasets include a date-stamped
record for each item administered during a hospitalization; this
allows for differentiation between factors present at the time of
hospitalization and those arising during the stay. We used one such
dataset to create and validate a mortality risk prediction model
that included only tests and treatments administered in the first 2
hospital days along with patient demographics and comorbidities.
Setting and Patients
We identified patients discharged between July 1, 2007 and
June 30, 2010 from 347 US hospitals that participated in Premier,
Inc.s Perspective, a database developed for measuring quality and
healthcare utilization that has been described previously. 
Member hospitals represent all regions of the US, and are
generally reflective of US hospitals; although larger hospitals,
hospitals in the South and those in urban areas are over
represented. Perspective contains all data elements found in the
uniform billing 04 form, such as sociodemographic information,
ICD-9-CM diagnosis and procedure codes, as well as hospital and
physician information. It also includes a date-stamped log of all
billed items and services, including diagnostic tests, medications,
and other treatments. Because the data do not contain identifiable
information, the Institutional Review Board at Baystate Medical
Center determined that this study did not constitute human
We included all patients aged $18 years with a principal
diagnosis of pneumonia, or a secondary diagnosis of pneumonia
paired with a principal diagnosis of respiratory failure, ARDS,
respiratory arrest, sepsis or influenza (Figure S1). Diagnoses were
assessed using International Classification of Diseases, Ninth
Revision, Clinical Modification (ICD-9-CM) codes. We excluded
all patients transferred in from or out to other acute care facilities,
because we either could not assess initial severity or could not
assess outcomes; those with a length of stay of 1 day or less;
patients with cystic fibrosis; those whose attending physician of
record was in a specialty that would not be expected to treat
pneumonia (e.g., psychiatry); those with a diagnosis related
grouping (DRG) inconsistent with pneumonia (e.g., Prostate OR
procedure); those with a code indicating that the pneumonia was
not present on admission; and any patient who did not have a
chest radiograph and did not begin antibiotics on hospital day 1 or
2. For patients with multiple eligible admissions in the study
period, 1 admission was randomly selected for inclusion.
Markers of Comorbid Illness and Pneumonia Severity
For each patient, we extracted age, gender, race/ethnicity,
insurance status, principal diagnosis, comorbidities, and specialty
of the attending physician. Comorbidities were identified from
ICD-9-CM secondary diagnosis codes and DRGs using
Healthcare Cost and Utilization Project Comorbidity Software, version
3.1, based on the work of Elixhauser.  We identified a group of
medications, tests, and services that are typically associated with
chronic medical conditions (e.g., spironolactone, warfarin, need
for a special bed to reduce pressure ulcers), as well as acute
medications that may indicate severe illness (e.g., vasopressors,
intravenous steroids). We also identified early use of diagnostic
tests (e.g., arterial blood gas, serum lactate) and therapies (e.g.,
mechanical ventilation, blood transfusion, restraints) that are
associated with more severe presentations of pneumonia. The
complete list of medications, tests, and treatments appears in
Table S1. To avoid conflating initial severity with complications of
treatment, we limited our analysis to those markers received in the
first 2 hospital days. We used the first 2 days because hospital days
are demarcated at midnight and the first day often represents only
a few hours.
Individual predictors of mortality were assessed using
Chisquare tests using the full study cohort. Stratifying by hospital,
80% of the eligible admissions were randomly assigned to a
derivation and 20% to a validation cohort, and the two cohorts
were compared for differences in potential predictors. Using the
derivation cohort, we developed a series of multivariable logistic
regression models to predict in-hospital death. Hierarchical
generalized linear models (HGLM) with a logit link (SAS PROC
GLIMMIX) were used to account for the clustering of patients
within hospitals. We grouped predictors into the following
categories: demographics, comorbid conditions, and severity
markers. We developed separate mortality models for each of
these categories, including main effects and significant pairwise
interactions. Factors significant at p,0.05 were retained. For each
model we calculated the area under the receiver operating
characteristic (AUROC) curve, together with 95% confidence
intervals.  The final model was developed by sequentially
adding effects retained in individual category models and
evaluating pairwise interaction terms. Main effects that were
dropped at earlier stages were re-evaluated for inclusion in the
The purpose of the model was accurate prediction of mortality
and risk stratification. We did not attempt to determine which
individual factors were associated with mortality or to imply
causality. Therefore, we did not require a priori information about
the association of the various risk factors or interaction terms with
the outcome. Although such an approach may result in spurious
associations of individual risk factors, it need not necessarily
detract from the models accuracy of prediction, which was our
primary concern .
In order to guard against the possibility of overfitting our model,
parameter estimates derived from the model were used to compute
individual mortality risk in the remaining 20% of the admissions
(the validation cohort). Discrimination of the final model in the
validation set was assessed by the c-statistic as well as the
expected/observed ratio. Both cohorts were categorized by decile
of risk based on the probability distribution in the derivation
cohort, and observed mortality was compared to that predicted by
the model. We also used the integrated discrimination
improvement (IDI) index  to measure the improvement of the final
model over a basic model including only demographics and
We next evaluated model performance in subpopulations of the
entire cohort based on hospital and patient characteristics.
Specifically, we assessed model performance in strata defined by
hospital size, teaching status, patient age, ICU and non-ICU
admissions, and pneumonia type [healthcare-associated (HCAP)
vs. community-acquired (CAP)]. All analyses were performed
using the Statistical Analysis System (version 9.2, SAS Institute,
Inc., Cary, NC) and STATA (StataCorp. 2007. Stata Statistical
Software: Release 10. College Station, TX: StataCorp LP).
The dataset included 200,870 patients in the derivation cohort
and 50,037 patients in the validation cohort. Patient characteristics
of the full study cohort appear in Table 1. Most patients were over
age 65, 53.3% were female and 68.0% were white. The most
common comorbidities were hypertension (46.5%), diabetes
(23.8%), chronic pulmonary disease (48.6%), and anemia
(22.2%). Patients in the validation cohort were similar (Table S2).
Overall in-hospital mortality in the derivation cohort was 7.2%.
A large number of patient and hospital factors were associated
with mortality (Table 1). Due to the large sample size, even weak
associations appear highly statistically significant. Figure 1 shows
the model discrimination, as measured by the area under the
ROC curve, when subgroups of factors were used to model
mortality. Including only patient demographics produced a model
with poor discrimination (AUROC = 0.66). Using traditional
ICD-9-CM based measures of comorbidity showed greater
discrimination (AUROC = 0.71), as did a model that used
admission to the ICU in day 1 or 2 as the only predictor
(AUROC = 0.73). As an alternative measure of comorbidity,
chronic medications were superior to ICD-9-CM codes in
predicting mortality (AUROC 0.74 vs. 0.71, p,.001). Combining
demographics, comorbidities, and markers of severity of illness on
presentation (other infections present-on-admission, admission to
ICU, the ability to take oral medications, and acute medications,
tests and therapies used in first 2 days) offered excellent
discrimination in the derivation cohort (AUROC = 0.85). We also
assessed model discrimination using the IDI. Compared to the
model including only demographics and ICD-9-CM
comorbidities, the full model had an IDI which was 12 percentage points
higher (16.6% vs. 4.6%, p,.001).
The final multivariable model included 3 demographic factors,
25 comorbidities, 41 medications, 7 diagnostic tests, and 9
treatments, as well as a large number of interaction terms (Table
S3). The strongest predictors were early vasopressors (OR 1.71,
95% CI 1.621.81), early non-invasive ventilation (OR 1.55, 95%
CI 1.471.64), and early bicarbonate treatment (OR 1.70, 95% CI
1.591.82). The final model produced deciles of mean predicted
risk from 0.3% to 34.5%, while mean observed risk over the same
deciles ranged from 0.1% to 34.1% (Figure 2).
Model discrimination measured by the c-statistic in the
validation set was 0.85 (95%CI: 0.8440.856). Deciles of predicted
risk ranged from 0.3% to 34.3% with observed risk over the same
deciles from 0.1% to 33.7% (Figure 2). The expected mortality
rate according to the model was 7.1% (expected/observed ratio:
1.00 [95% CI 0.971.03]).
Performance of the model in subpopulations of the entire cohort
is shown in Table 2. The model performed well in all
subpopulations tested, but discrimination was poorest among
patients in intensive care (c-statistic 0.78) and best among patients
aged 18 to 64 years (c-statistic 0.89). In all subgroups the range of
predicted mortality extended from #0.3% to .90%. Model
calibration was also good in all subgroups. The model tended to
underestimate the risk of mortality among patients with
healthcare-associated pneumonia, and to a lesser extent among patients
in teaching hospitals and those outside of the ICU. At the same
time, it overestimated the risk of mortality among patients with
In this retrospective cohort study, we used highly detailed
administrative data to derive and validate a pneumonia mortality
prediction model for use in observational studies. The model had
discriminatory ability comparable to those derived from clinical
data, but unlike most other administrative models, it included
information on illness severity that would be available in the first 2
hospital days. The model also had excellent calibration and
successfully divided patients into mortality deciles ranging from
,0.5% to .33%. Interestingly, the 30% of patients with the
lowest predicted mortality had an observed mortality of ,1%.
At least two clinical prediction tools have been developed for the
purposes of risk stratifying patients with community acquired
pneumoniathe CURB-65,  modified from earlier work by the
British Thoracic Society, and the Pneumonia Severity Index (PSI).
 The CURB-65 consists entirely of exam findings and
laboratory values, while the PSI incorporates some historical
information as well. At least 3 studies have prospectively compared
the predictive abilities of these two measures.  Perhaps due
to differences in study population, c-statistics for predicting 30-day
mortality ranged from 0.73 to 0.89 across studies; however, within
any given study, there were no statistically significant differences
between the two scales.
Because the clinical information required for these tools is not
available in administrative databases, others have attempted to
create models based solely on administrative claims. In general,
such models have modest discriminatory ability, unless they are
combined with laboratory data. For example, one administrative
claims model developed for profiling hospitals pneumonia
mortality rates, and containing age, sex, and 29 comorbidities
(based on ICD-9-CM codes from the index hospitalization and the
prior years outpatient visits) had a c-statistic of 0.72.  Addition
of laboratory values to administrative data can substantially
enhance discrimination. Tabak et al. demonstrated that
laboratory values alone contributed 3.6 times as much explanatory power
as ICD-9-CM codes and 2.5 times as much as vital signs to
mortality prediction.  For example, the c-statistic for a model
that only includes laboratory values and age was 0.80. Adding
ICD-9-CM codes and vital signs increased the c-statistic to 0.82.
 Pine et al. also found that ICD-9-CM codes alone produced a
c-statistic of 0.78, whereas addition of laboratory values increased
the c-statistic to 0.87.  Addition of chart-based data (e.g., vital
signs) had a small marginal effect on the models predictive ability
Our study takes a different approach to overcoming the
limitations of administrative data. In brief, our results suggest that
it is possible to tell a lot about patients by the tests, medications
and treatments they are prescribed. Although others have utilized
Markers of Initial Severitya
Pleural fluid analysis
Brain natriuretic peptide
Cerebrospinal fluid analysis
awithin first 2 hospital days.
ambulatory medications to predict outpatient costs and mortality,
these generally do not perform better than comorbidity models
based on ICD-9-CM codes. [19,20] In contrast, by assessing
medications, tests and treatments administered in the first 2
hospital days, we were able to identify chronic comorbid
conditions, as well as factors indicative of the severity of illness
on presentation. Indeed, use of chronic medications alone
predicted mortality better than ICD-9-CM codes. This could be
because billing codes are more sensitive than ICD-9-CM codes,
but also because medication use can identify not just the presence
of disease, but also provide information about disease severity. For
example, among patients with heart failure, spironolactone often
signifies severe systolic dysfunction, and nadolol in the presence of
liver disease likely indicates portal hypertension. Medications,
however, did not capture all the information present in
ICD-9CM codes, and the combination of the two was a more powerful
predictor than either one alone. This is likely because some
chronic conditions, such as metastatic cancer, may not be
associated with any routine medications, but are nonetheless
potent predictors of mortality.
The inclusion of certain initial tests and therapies also allowed
us to estimate the severity of illness at the time of admission in the
absence of laboratory or clinical data. Although it would be helpful
to know the results of a blood gas, the simple presence of that test
is indirect evidence that the treating physicians were concerned
about a patients respiratory condition. Similarly, a patient
receiving vasopressors is almost certainly hypotensive. More
importantly, our models predictive ability was comparable to
that seen with other administrative models that include laboratory
data, as well as those that are based on physiological information
obtained from review of medical records. An analogous model,
designed for use in sepsis patients, demonstrated that highly
detailed administrative data can achieve discrimination and
calibration similar to clinical mortality prediction models, 
with the majority of the additional explanatory power of the model
arising from the inclusion of initial treatments .
Our study has several limitations. First, our main outcome was
in-hospital mortality. Others have modeled 30-day mortality and
the factors that are predictive of in-hospital mortality may be
different than those which predict 30-day mortality.  Second,
our study was conducted retrospectively and the model, therefore,
may perform differently in a prospective cohort. It would certainly
be premature to base treatment decisions on our model, but that is
not its intended purpose. Third, our definition of pneumonia was
based on diagnosis and charge codes. Some patients may not have
had pneumonia and some cases of pneumonia may have been
missed. These numbers are likely to be small, as the positive
predictive value of an ICD-9 diagnosis paired with an antibiotic
description is .95%.  Fourth, we excluded patients with
pneumonia not present on admission, as well as transfer patients,
so our model is not applicable to these groups. Finally, our model
derives much of its power from physician assessments of patients
disease, as represented by physician ordering. To the extent that
prescribing thresholds vary by institution, the model may be more
or less accurate in certain hospitals, and therefore could not be
used for benchmarking purposes. The fact that model
discrimination was good across various subgroups of hospitals is reassuring
in this regard.
This model could be used in various ways. It could be used for
adjustment in observational trials, including comparative
effectiveness or epidemiologic studies. Although such studies might also
be performed using clinical data, many institutions do not
Large Hospitals (.400 beds)
Medium (201400 beds)
Small hospitals (#200 beds)
Hospital teaching status
Patients aged 85+ years
Patients aged 7584 years
Patients aged 6574 years
Patients aged 1864 years
Admitted to ICU
Admitted to non-ICU care
Community acquired pneumonia
Healthcare associated pneumonia
AUROC (95% CI)
a95% CI: (Expected/Observed)*exp(2/+1.96*1/(!(# of deaths))).
currently have the ability to automatically extract clinical data
from electronic medical records and many administrative datasets
do not yet contain laboratory data. Our model represents a
lowcost yet accurate alternative. In addition, unlike existing clinical
models, our model was validated in several different
subpopulations, with excellent performance in small and large
hospitals, and in teaching and non-teaching institutions.
The model could, for example, be used for severity-adjustment
in a study to compare effectiveness of guideline recommended
therapies to alternative treatment options in community acquired
pneumonia. It could also be used to study the severity of an illness
such as healthcare associated pneumonia, in which multiple
comorbid illnesses might contribute to poor outcomes. It could
have application for studying the methods of hospital profiling for
public reporting (e.g., testing alternative definitions of diagnosis),
but may not be useful for profiling hospitals per se, because
thresholds for treatment might vary across hospitals making
patients appear more or less sick. Finally, some aspects of the
modelspecifically the chronic medicationscould be incorporated
into clinical prediction rules such as the PSI, in order to improve
their accuracy. To avoid showering clinicians with unnecessary
complexity, these could be embedded in clinical information
systems to provide prognostic information at the point of care.
However, prospective validation of such a hybrid model is
required before it can be applied in clinical care.
In conclusion, we have created a mortality prediction model
based on highly detailed administrative data available in the first 2
days of hospitalization. The performance of the model was
comparable to that of models based on clinical data, and the
performance was consistent across different patient
subpopulations. The model should be useful for comparative effectiveness
research using large, administrative databases.
Range of predicted
Expected vs. Observed (95% CI)a
Figure S1 Flow Diagram of Patient Selection. PN
Pneumonia; ARDS Acute Respiratory Distress Syndrome; CXR
Chest X-Ray; CH CT Chest CT; ABX Antibiotic; LOS
Length of Stay; MS DRG Medicare Diagnosis Related Group;
POA Present on Admission.
Table S1 Complete List of Medications, Tests, and
Table S2 Patient Characteristics in the Derivation and
HGLM Estimates from Multivariable
Society for Medical Decision Making Annual Meeting, Phoenix, AZ,
Conceived and designed the experiments: MBR PSP AP MZ RB DS TL
TH PKL. Performed the experiments: MBR PSP AP. Analyzed the data:
MBR PSP AP MZ RB DS TL TH PKL. Wrote the paper: MBR AP PSP.
Critical revision of the manuscript for important intellectual content: PSP
AP MZ RB DS TL TH PKL.
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