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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

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calculating measures of comorbidity using administrative data

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
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
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 EnhancedICD-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
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
Charlson Comorbidities with Corresponding ICD-9-CM and ICD-10 Codes(Medical Care. 2005;43:1130-1139)
example coding algorithm
Example coding algorithm

(Medical Care, 2005;43(11):1130-1139)

input data
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
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
Input options
  • index (string)
      • ICD-9-CM (charlson) c
      • Enhanced ICD-9-CM (charlson/elixhauser) e
      • ICD-10 (charlson/elixhauser) 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
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
Sample program #1 – charlson
  • Command –

. charlson, index(e) diagprfx(diag) wtchrl cmorb

sample program 1 charlson output part 1
Sample program #1 – charlson – Output (part 1)

(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

  • (Option noshow omitted)
sample program 1 charlson output part 2
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

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

  • (option wtchrl)
sample program 1 charlson output part 3
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
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
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
Output dataset – selected variables

. list id ynch10 ynch11 ynch15

+------------------------------------+

| id ynch10 ynch11 ynch15 |

|------------------------------------|

1. | id1 Absent Absent Absent |

2. | id2 Absent Absent Absent |

3. | id3 Present Present Absent |

4. | id4 Present Absent Absent |

5. | id5 Absent Absent Absent |

|------------------------------------|

6. | id6 Present Absent Present |

7. | id7 Absent Absent Present |

8. | id8 Absent Absent Absent |

9. | id9 Absent Absent Present |

10. | id10 Absent Absent Absent |

+------------------------------------+

. list id weightch10 weightch11 weightch15, c

+------------------------------+

| id we~10 we~11 we~15 |

|------------------------------|

1. | id1 0 0 0 |

2. | id2 0 0 0 |

3. | id3 1 2 0 |

4. | id4 1 0 0 |

5. | id5 0 0 0 |

|------------------------------|

6. | id6 1 0 3 |

7. | id7 0 0 3 |

8. | id8 0 0 0 |

9. | id9 0 0 3 |

10. | id10 0 0 0 |

+------------------------------+

slide20

Output dataset –Charlson index & grouped Charlson index

. list id charlindex grpci

+-------------------------+

| id charli~x grpci |

|-------------------------|

1. | id1 5 2 |

2. | id2 1 1 |

3. | id3 3 2 |

4. | id4 3 2 |

5. | id5 2 2 |

|-------------------------|

6. | id6 4 2 |

7. | id7 9 2 |

8. | id8 6 2 |

9. | id9 4 2 |

10. | id10 9 2 |

+-------------------------+

program rerun with assign0 option changes frequencies
Program rerun with assign0 option – (changes frequencies)

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

+-------------------------+

| id charli~x grpci |

|-------------------------|

1. | id1 5 2 |

2. | id2 1 1 |

3. | id3 2 2 |

4. | id4 3 2 |

5. | id5 2 2 |

|-------------------------|

6. | id6 4 2 |

7. | id7 9 2 |

8. | id8 6 2 |

9. | id9 3 2 |

10. | id10 7 2 |

+-------------------------+

. comorbid, index(e) diagprfx(diag) wtchrl cmorb assign0

selected comorbidities revisited
Selected comorbidities revisited-

Diabetes | 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
Sample program #2 – elixhauser(ICD-10 Algorithm)

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

-------------------------------------------------------------------------------

  • Input - real inpatient data -
sample program 2 elixhauser
Sample program #2 – elixhauser
  • Command –

. elixhauser dx1-dx25, index(10) smelix cmorb

sample program 2 elixhauser output
Sample program #2 – elixhauserOutput

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

acknowledgements
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

SUGGESTIONS / COMMENTSWELCOME

vlstagg@ucalgary.ca

Thank you!