Calculating Measures of Comorbidity
Using Administrative Data
Vicki Stagg
Statistical Programmer
Department of Community Health Sciences
Dr. Robert Hilsden
Associate Professor
Departments of Medicine and Community Health Sciences
Dr. Hude Quan
Associate Professor
Centre for Health and Policy Studies (CHAPS)
Department of Community Health Sciences
University of Calgary
Calgary, Alberta, Canada
Background
• Medical Administrative Data
• Inpatient hospital visit information
• Comorbidity
• Pre-existing diagnosis / additional complication of admitted patient
• Comorbidity Index
• For measurement of burden of disease and case-mix adjustment
• Allows for stratification or adjustment by severity of illness
• Two common tools – Charlson and Elixhauser
• Clinical Conditions
• International Classification of Disease
– 9th Revision, Clinical Modification (ICD-9-CM codes)
– 10th Revision (ICD-10 codes)
(International statistical classification of disease and related health problems, 1992)
Algorithms included in ado programs
• Charlson (17 comorbidity definitions)
• Presence/absence, weighted sum (Charlson index)
• Charlson index developed to predict risk of one-year mortality from
comorbid illness
(J Chron Dis, 1987;40(5):373-383)
• Deyo modification for ICD-9-CM
(J Clin Epi, 1992;45(6):613-619)
• Quan’s Enhanced ICD-9-CM
• Quan’s ICD-10
(Medical Care, 2005;43(11):1130-1139)
• Elixhauser (30 comorbidity definitions)
• Presence/absence, sum
(Medical Care, 1998:36(1):8-27)
• Quan’s Enhanced ICD-9-CM
• Quan’s ICD-10
(Medical Care, 2005;43(11):1130-1139)
Algorithms –
development of ICD-10 & enhanced ICD-9-CM
• ICD-10 comorbidity coding algorithm
•
•
•
Based on Charlson index
Swiss, Australian, Canadian collaborative groups
ICD-10 Canadian version (ICD-10-CA)
• Enhanced ICD-9-CM coding algorithm
•
•
Back-translated from new ICD-10 coding algorithm
To improve original Deyo (Charlson) and Elixhauser
comorbidity classifications
Charlson Comorbidities with Corresponding ICD-9-CM and ICD-10 Codes
(Medical Care. 2005;43:1130-1139)
Example coding algorithm
Comorbidity
Deyo
Enhanced
ICD-10
Myocardial
infarction
410.x, 412.x
410.x, 412.x
I21.x, I22.x,
I25.2
Congestive
heart failure
428.x
398.91, 402.01,
402.11, 402.91,
404.01, 404.03,
404.11, 404.13,
404.91, 404.93,
425.4-425.9,
428.x
I09.9, I11.0,
I13.0, I13.2,
I12.5, I42.0,
I42.5-I42.9,
I43.x, I40.x,
P29.0
(Medical Care, 2005;43(11):1130-1139)
Charlson Comorbidities & Weights
CHARLSON COMORBIDITY
ASSIGNED WEIGHTS
1.
Myocardial infarction
1
2.
Congestive heart failure
1
3.
Peripheral vascular disease
1
4.
Cerebrovascular disease
1
5.
Dementia
1
6.
Chronic pulmonary disease
1
7.
Rheumatic disease
1
8.
Peptic ulcer disease
1
9.
Mild liver disease
1
10.
Diabetes without chronic complication
1
11.
Diabetes with end organ damage
2
12.
Hemiplegia / paraplegia
2
13.
Renal disease
2
14.
Any malignancy/lymphoma/leukemia
2
15.
Moderate or severe liver disease
3
16.
Metastatic solid tumor
6
17.
AIDS/HIV
6
Input data
• Patient demographic data
• ID variable (string) required if multiple visits
• Comorbidity diagnoses codes (strings)
• Charlson
– ICD-9-CM / ICD-10
• Elixhauser
– ICD-9-CM / ICD-10
• Additional medical information
• For subsequent modeling, if desired
Syntax
• Charlson
• charlson varlist [if exp] [in range], index(string)
[idvar(varname) diagprfx(string) assign0 wtchrl
cmorb noshow]
by may be used with charlson
• Elixhauser
• elixhauser varlist [if exp] [in range], index(string)
[idvar(varname) diagprfx(string) smelix cmorb
noshow]
by may be used with elixhauser
Input options
• index (string)
• ICD-9-CM (charlson)
• Enhanced ICD-9-CM (charlson/elixhauser)
• ICD-10 (charlson/elixhauser)
c
e
10
• idvar(varname)
• Required when multiple records per patient
• diagprfx(string)
• Gives common root of the comorbidity variables
• Necessary only when varlist not used
• assign0
• Only applicable to charlson
• Flag to apply hierarchical method
Output options
• wtchrl (charlson command)
• Presents summary of Charlson Index (frequencies of weighted
sums)
• wtelix (elixhauser command)
• Displays frequencies of sum of elixhauser comorbidities
• cmorb
• Displays frequencies of individual comorbidities
• noshow
• Controls display of chosen options
Sample program #1 – charlson
(Enhanced ICD-9-CM Algorithm)
• Input data (ICD-9-CM codes) Small Sample Data
patientid
diag1
diag2
id1
39891
id2
0930
id3
2500
2507
id4
342
2500
id5
3441
342
id6
2500
5722
id7
5722
196
id8
042
id9
V427
4561
176
197
id10
5834
diag3
342
3441
V427
Sample program #1 – charlson
• Command –
. charlson, index(e) diagprfx(diag) wtchrl
cmorb
Sample program #1 – charlson – Output (part 1)
• (Option noshow omitted)
(0 observations deleted)
COMORBIDITY INDEX MACRO
Providing COMORBIDITY INDEX Summary
OPTIONS SELECTED:
INPUT DATA:
Enhanced ICD-9
OBSERVATIONAL UNIT: Visits
ID VARIABLE NAME (Given only if Unit is Patients):
PREFIX of COMORBIDITY VARIABLES: diag
HIERARCHY METHOD APPLIED: NO
SUMMARIZE CHARLSON INDEX and WEIGHTS: YES
SUMMARIZE INDIVIDUAL COMORBIDITIES: YES
Please wait. Thank you!
Program takes a few minutes - there are up to 3 ICD codes per
subject.
Iteration 1 of 3 - Program is running - Please wait
Iteration 2 of 3 - Program is running - Please wait
Iteration 3 of 3 - Program is running - Please wait
Total Number of Observational Units (Visits OR Patients): 10
Sample program #1 – charlson – Output (part 2)
CHARLSON |
INDEX |
Freq.
Percent
Cum.
------------+----------------------------------1 |
1
10.00
10.00
2 |
1
10.00
20.00
3 |
2
20.00
40.00
4 |
2
20.00
60.00
5 |
1
10.00
70.00
6 |
1
10.00
80.00
9 |
2
20.00
100.00
------------+----------------------------------Total |
10
100.00
• (option wtchrl)
GROUPED |
CHARLSON |
INDEX |
Freq.
Percent
Cum.
------------+----------------------------------1 |
1
10.00
10.00
2 |
9
90.00
100.00
------------+----------------------------------Total |
10
100.00
Variable |
Obs
Mean
Std. Dev.
Min
Max
-------------+-------------------------------------------------------charlindex |
10
4.6
2.716207
1
9
Sample program #1 – charlson – Output (part 3)
• (option cmorb)
• selected comorbidities displayed
Diabetes |
Freq.
Percent
Cum.
------------+----------------------------------Absent |
7
70.00
70.00
Present |
3
30.00
100.00
------------+----------------------------------Total |
10
100.00
Diabetes + |
Complicatio |
ns |
Freq.
Percent
Cum.
------------+----------------------------------Absent |
9
90.00
90.00
Present |
1
10.00
100.00
------------+----------------------------------Total |
10
100.00
Output dataset – describe
obs:
10
vars:
41
17 Oct 2007 10:08
size:
1,880 (99.9% of memory free)
------------------------------------------------------------------------------storage display
value
variable name
type
format
label
variable label
------------------------------------------------------------------------------id
str23 %23s
diag1
str5
%9s
diag2
str4
%9s
diag3
str4
%9s
ynch1
float %9.0g
ynlab
AMI (Acute Myocardial)
ynch2
float %9.0g
ynlab
CHF (Congestive Heart)
ynch3
float %9.0g
ynlab
PVD (Peripheral Vascular)
ynch4
float %9.0g
ynlab
CEVD (Cerebrovascular
ynch5
float %9.0g
ynlab
Dementia
ynch6
float %9.0g
ynlab
COPD (Chronic Obstructive
Pulmonary)
ynch7
float %9.0g
ynlab
Rheumatoid Disease
ynch8
float %9.0g
ynlab
PUD (Peptic Ulcer)
ynch9
float %9.0g
ynlab
Mild LD (Liver)
ynch10
float %9.0g
ynlab
Diabetes
ynch11
float %9.0g
ynlab
Diabetes + Complications
ynch12
float %9.0g
ynlab
HP/PAPL (Hemiplegia or
Paraplegia)
Output dataset – describe continued
ynch13
float %9.0g
ynlab
RD (Renal)
ynch14
float %9.0g
ynlab
Cancer
ynch15
float %9.0g
ynlab
Moderate/Severe LD (Liver)
ynch16
float %9.0g
ynlab
Metastic Cancer
ynch17
float %9.0g
ynlab
AIDS
weightch1
float %9.0g
weightch2
float %9.0g
weightch3
float %9.0g
weightch4
float %9.0g
weightch5
float %9.0g
weightch6
float %9.0g
weightch7
float %9.0g
weightch8
float %9.0g
weightch9
float %9.0g
weightch10
float %9.0g
weightch11
float %9.0g
weightch12
float %9.0g
weightch13
float %9.0g
weightch14
float %9.0g
weightch15
float %9.0g
weightch16
float %9.0g
weightch17
float %9.0g
charlindex
float %9.0g
CHARLSON INDEX
grpci
float %9.0g
GROUPED CHARLSON INDEX
------------------------------------------------------------------------------Sorted by:
Note: dataset has changed since last saved
Output dataset – selected variables
. list id ynch10 ynch11 ynch15
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
+------------------------------------+
|
id
ynch10
ynch11
ynch15 |
|------------------------------------|
| id1
Absent
Absent
Absent |
| id2
Absent
Absent
Absent |
| id3
Present
Present
Absent |
| id4
Present
Absent
Absent |
| id5
Absent
Absent
Absent |
|------------------------------------|
| id6
Present
Absent
Present |
| id7
Absent
Absent
Present |
| id8
Absent
Absent
Absent |
| id9
Absent
Absent
Present |
| id10
Absent
Absent
Absent |
+------------------------------------+
. list id weightch10 weightch11
weightch15, c
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
+------------------------------+
|
id
we~10
we~11
we~15 |
|------------------------------|
| id1
0
0
0 |
| id2
0
0
0 |
| id3
1
2
0 |
| id4
1
0
0 |
| id5
0
0
0 |
|------------------------------|
| id6
1
0
3 |
| id7
0
0
3 |
| id8
0
0
0 |
| id9
0
0
3 |
| id10
0
0
0 |
+------------------------------+
Output dataset –
Charlson index & grouped Charlson index
. list id
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
charlindex grpci
+-------------------------+
|
id
charli~x
grpci |
|-------------------------|
| id1
5
2 |
| id2
1
1 |
| id3
3
2 |
| id4
3
2 |
| id5
2
2 |
|-------------------------|
| id6
4
2 |
| id7
9
2 |
| id8
6
2 |
| id9
4
2 |
| id10
9
2 |
+-------------------------+
Program rerun with assign0 option –
(changes frequencies)
. comorbid, index(e) diagprfx(diag) wtchrl cmorb assign0
CHARLSON |
INDEX |
Freq.
Percent
Cum.
------------+----------------------------------1 |
1
10.00
10.00
2 |
2
20.00
30.00
3 |
2
20.00
50.00
4 |
1
10.00
60.00
5 |
1
10.00
70.00
6 |
1
10.00
80.00
7 |
1
10.00
90.00
9 |
1
10.00
100.00
------------+----------------------------------Total |
10
100.00
GROUPED |
CHARLSON |
INDEX |
Freq.
Percent
Cum.
------------+----------------------------------1 |
1
10.00
10.00
2 |
9
90.00
100.00
------------+----------------------------------Total |
10
100.00
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
+-------------------------+
|
id
charli~x
grpci |
|-------------------------|
| id1
5
2 |
| id2
1
1 |
| id3
2
2 |
| id4
3
2 |
| id5
2
2 |
|-------------------------|
| id6
4
2 |
| id7
9
2 |
| id8
6
2 |
| id9
3
2 |
| id10
7
2 |
+-------------------------+
Selected comorbidities revisitedDiabetes |
Freq.
Percent
Cum.
------------+----------------------------------Absent |
8
80.00
80.00
Present |
2
20.00
100.00
------------+----------------------------------Total |
10
100.00
Diabetes + |
Complicatio |
ns |
Freq.
Percent
Cum.
------------+----------------------------------Absent |
9
90.00
90.00
Present |
1
10.00
100.00
------------+----------------------------------Total |
10
100.00
Sample program #2 – elixhauser
(ICD-10 Algorithm)
• Input - real inpatient data obs:
2,987
vars:
43
18 Oct 2007 10:18
size:
1,000,645 (90.5% of memory free)
------------------------------------------------------------------------------storage display
value
variable name
type
format
label
variable label
------------------------------------------------------------------------------dx1
str6
%9s
DIAG1
dx2
str6
%9s
DIAG2
. . .
dx24
str6
%9s
DIAG24
dx25
str6
%9s
DIAG25
cdr_keyforqsh~e long
%12.0g
CDR_KEY (for QSHI use)
admitdate
str20 %20s
Admit Date
dischargedate
str20 %20s
Discharge Date
acutelosdays
int
%8.0g
ACUTE LOS (days)
birthdate
str11 %11s
Birth Date
age
int
%8.0g
AGE
pc
str6
%9s
PC
residence
str7
%9s
RESIDENCE
entrycodetoho~l str61 %61s
ENTRY CODE to hospital
strokediagtyp~a str25 %25s
Stroke Diag Type when Stroke
not the Main Diag
gender
long
%8.0g
gender
gender
site
long
%8.0g
site
site
stroketype
long
%13.0g
stroke
Stroke type
disposition
long
%60.0g
disp
discharge disposition
cohort
float %9.0g
cohort
cohort
-------------------------------------------------------------------------------
Sample program #2 – elixhauser
• Command –
. elixhauser dx1-dx25, index(10) smelix
cmorb
Sample program #2 – elixhauser
Output
ELIX |
COMORBIDITY |
SUM |
Freq.
Percent
Cum.
------------+----------------------------------0 |
402
13.46
13.46
1 |
715
23.94
37.40
2 |
751
25.14
62.54
3 |
529
17.71
80.25
4 |
303
10.14
90.39
5 |
174
5.83
96.22
6 |
71
2.38
98.59
7 |
30
1.00
99.60
8 |
10
0.33
99.93
9 |
1
0.03
99.97
10 |
1
0.03
100.00
------------+----------------------------------Total |
2,987
100.00
Variable |
Obs
Mean
Std. Dev.
Min
Max
-------------+-------------------------------------------------------elixsum |
2987
2.216605
1.621281
0
10
ELIXHAUSER COMORBIDITY
Congestive Heart Failure
Cardiac Arrhythmias
PERCENT
8.20
22.23
Valvular Disease
4.49
Pulmonary Circulation Disorders
1.77
Peripheral Vascular Disorders
5.49
Hypertension, Uncomplicated
58.49
Paralysis
25.68
Other Neurological Disorders
23.84
Chronic Pulmonary Disease
Diabetes, Uncomplicated
7.20
15.80
Diabetes, Complicated
4.08
Hypothyroidism
3.85
Renal Failure
4.69
Liver Disease
0.77
Peptic Ulcer Disease Excluding Bleeding
0.40
AIDS/HIV
0.13
…continued
ELIXHAUSER COMORBIDITY
PERCENT
Lymphoma
0.57
Metastatic Cancer
1.61
Solid Tumor Without Metastasis
2.98
Rheumatoid Arthritis/Collagen Vascular
1.47
Coagulopathy
2.68
Obesity
3.31
Weight Loss
0.67
Fluid and Electrolyte Disorders
6.46
Blood Loss Anemia
0.50
Deficiency Anemia
1.31
Alcohol Abuse
3.35
Drug Abuse
0.74
Psychoses
0.50
Depression
4.59
Hypertension, Complicated
3.82
Acknowledgements
I would like to express sincere gratitude to:
• Dr. Robert Hilsden
Depts. of Medicine/ Community Health Sciences, U of Calgary
For supervising this work and for all his advice and support.
• Dr. Hude Quan
Centre for Health and Policy Studies
Dept. of Community Health Sciences, U of Calgary
For providing the SAS code and databases and for his support.
• Haifeng Zhu
MSc Graduate Student
Dept. of Community Health Sciences
For her assistance with converting the Elixhauser algorithms to Stata.
• Malcolm Stagg
Student, Vista Virtual School, Calgary AB
My son, for his help with preparing this PowerPoint presentation and all his encouragement.
• Andrew Stagg
Intern, Google Inc., Mountain View CA
My son, for his encouragement.
SUGGESTIONS / COMMENTS
WELCOME
vlstagg@ucalgary.ca
Thank you!