Apply established comorbidity algorithms to ICD-coded data. Supported methods include several variants of the Charlson comorbidity system, Elixhauser, and the Pediatric Complex Chronic Conditions (PCCC).
Usage
comorbidities(
data,
icd.codes,
method,
id.vars = NULL,
icdv.var = NULL,
icdv = NULL,
dx.var = NULL,
dx = NULL,
poa.var = NULL,
poa = NULL,
age.var = NULL,
primarydx.var = NULL,
primarydx = NULL,
flag.method = c("current", "cumulative"),
full.codes = TRUE,
compact.codes = TRUE,
subconditions = FALSE
)Arguments
- data
A
data.framein a "long" format. The inputdata.frameis expected to have one column of ICD codes (one code per row) with additional (optional) columns for patient/encounter ids, ICD version, diagnostic/procedure status, present-on-admission flags, primary diagnostic flags, or age.- icd.codes
Character scalar naming the column in
datathat contains ICD codes (character strings). Codes may be provided in full form (with decimal points, e.g., C84.2), compact form (dots omitted, e.g., C842), or any mix of the two. Matching against lookup tables is governed byicdv.var/icdv,dx.var/dx, and thefull.codes/compact.codesflags.- method
Character string indicating the comorbidity algorithm to apply to
data.- id.vars
Optional character vector of column names. When missing, the entire input
datais treated as a single encounter from a single patient. If you want to setflag.method = "current"thenlength(id.vars) >= 2is expected. The last element should be the encounter order (must be sortable).- icdv.var
Character scalar naming the column in
datathat indicates the ICD version (9 or 10). If present it must be integer values9or10.icdv.vartakes precedence overicdvif both are provided.- icdv
An integer value of
9Lor10Lindicating that alldata[[icd.codes]]are ICD version 9 or 10, respectively. Ignored (with a warning) ificdv.varis provided.- dx.var
Character scalar naming the column in
datathat indicates diagnostic (1) vs procedural (0) codes. If present it must be integer values0or1.dx.vartakes precedence overdxif both are provided.- dx
An integer indicating that all
data[[icd.codes]]are diagnostic (1) or procedure (0) codes. Ignored (with a warning) ifdx.varis provided.- poa.var
Character scalar naming the column with present-on-admission flags: integer
1L(present),0L(not present), orNA. PCCC and Charlson will only flag conditions when the code is present-on-admission. Elixhauser has a mix of conditions; some require present-on-admission while others do not.poa.vartakes precedence overpoaif both are provided.- poa
Integer scalar
0or1. Use when allicd.codesshare the same present-on-admission status. Ignored with a warning ifpoaandpoa.varare both provided.- age.var
Character scalar naming the column in
datathat contains patient age in years. Only applicable to Charlson comorbidities.- primarydx.var
Character scalar naming the column in
datathat indicates whetherdata[[icd.codes]]are primary diagnostic codes (1L) or not (0L). Primary diagnosis is used only for Elixhauser and Charlson comorbidities and is ignored when the method is a PCCC variant.primarydx.vartakes precedence overprimarydxif both are provided.- primarydx
An integer value of
0or1. If0, treat all codes as non-primary diagnoses; if1, treat all codes as primary diagnoses. Ignored, with a warning, ifprimarydx.varis provided.- flag.method
When
flag.method = 'current'(default) only codes associated with the currentid.varsare considered when flagging comorbidities. Whenflag.method = 'cumulative'then all prior encounters are considered when flagging comorbidities. See Details.- full.codes, compact.codes
Logical; when
TRUEcomparedata[[icd.codes]]against full and/or compact ICD codes in the method’s lookup tables. Full ICD codes include a decimal point (when applicable) and compact codes omit the decimal point. For example:B95.0is the full ICD-10-CM diagnostic code for “Streptococcus, group A, as the cause of disease classified elsewhere,” whereasB950is the associated compact code.- subconditions
Logical scalar; when
TRUE, report both conditions and subconditions (PCCC only).
Value
The return object will be slightly different depending on the value of
method and subconditions.
When
subconditions = FALSE, amedicalcoder_comorbiditiesobject (adata.framewith attributes) is returned. Column(s) forid.vars, if defined in the function call. For all methods there will be the following columns:num_cmrba count of comorbidities/conditions flaggedcmrb_flaga 0/1 integer indicator for at least one comorbidity/condition.
Additional columns:
PCCC methods:
For
method = "pccc_v2.0"andmethod = "pccc_v2.1", there is one indicator column per condition.For
method = "pccc_v3.0"andmethod = "pccc_v3.1", there are four columns per condition:<condition>_dxpr_or_tech: the condition was flagged due to the presence of either a diagnostic or procedure code, or was flagged due to the presence of a technology dependence code along with at least one comorbidity being flagged by a diagnostic or procedure code.<condition>_dxpr_only: the condition was flagged due to the presence of a non-technology dependent diagnostic or procedure code only.<condition>_tech_only: the condition was flagged due to the presence of a technology dependent code only and at least one other comorbidity was flagged by a non-technology dependent code.<condition>_dxpr_and_tech: The patient had both diagnostic or procedure codes and a technology dependence code for the condition.
For Charlson variants, indicator columns are returned for the relevant conditions,
cci(Charlson Comorbidity Index), andage_score.For Elixhauser variants, indicator columns are returned for all relevant comorbidities, mortality, and readmission indices.
When
subconditions = TRUEand the method is a PCCC variant, a list of length two is returned: the first element contains condition indicators; the second element is a named list ofdata.frames with indicators for subconditions within each condition.
Details
When flag.method = "current", only codes from the index encounter
contribute to flags. When a longitudinal method is selected (e.g.,
"cumulative"), prior encounters for the same id.vars
combination may contribute to condition flags. For the cumulative method to
work, id.vars needs to be a character vector of length 2 or more. The last
element is treated as the encounter identifier and must be sortable. For
example, say you have data with a hospital, patient, and encounter id. The
id.vars could be one of two entries: c("hospital", "patient", "encounter")
or c("patient", "hospital", "encounter"). In both cases the return will be
the same because the encounter identifier is unchanged regardless of whether
hospital or patient is listed first.
It is critically important that the data[[tail(id.vars, 1)]] variable can
be sorted. Just because your data is sorted in temporal order does not mean
that the results will be correct if the tail(id.vars, 1) is not in the same
order as the data. For example, say you had the following:
| patid | enc_id | date |
| P1 | 10823090 | Aug 2023 |
| P1 | 10725138 | Jul 2025 |
id.vars = c("patid", "enc_id") will give the wrong result as enc_id
10725138 would be sorted to come before enc_id 10823090. id.vars = c("patid", "date") would be sufficient input, assuming that date has been
correctly stored. Adding a column enc_seq, e.g.,
| patid | enc_id | date | enc_seq |
| P1 | 10823090 | Aug 2023 | 1 |
| P1 | 10725138 | Jul 2025 | 2 |
and calling comorbidities() with id.vars = c("patid", "enc_seq") will
have better performance than using the date and will clear up any possible
issues with non-sequential encounter ids from the source data.
References
Pediatric Complex Chronic Conditions:
Feudtner, C., Feinstein, J.A., Zhong, W. et al. Pediatric complex chronic conditions classification system version 2: updated for ICD-10 and complex medical technology dependence and transplantation. BMC Pediatr 14, 199 (2014). https://doi.org/10.1186/1471-2431-14-199
Feinstein JA, Hall M, Davidson A, Feudtner C. Pediatric Complex Chronic Condition System Version 3. JAMA Netw Open. 2024;7(7):e2420579. https://doi.org/10.1001/jamanetworkopen.2024.20579
Charlson Comorbidities:
Mary E. Charlson, Peter Pompei, Kathy L. Ales, C.Ronald MacKenzie, A new method of classifying prognostic comorbidity in longitudinal studies: Development and validation, Journal of Chronic Diseases, Volume 40, Issue 5, 1987, Pages 373-383, ISSN 0021-9681, https://doi.org/10.1016/0021-9681(87)90171-8.
Deyo RA, Cherkin DC, Ciol MA. Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases. J Clin Epidemiol. 1992 Jun;45(6):613-9. https://doi.org/10.1016/0895-4356(92)90133-8. PMID: 1607900.
Quan H, Sundararajan V, Halfon P, Fong A, Burnand B, Luthi JC, Saunders LD, Beck CA, Feasby TE, Ghali WA. Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data. Med Care. 2005 Nov;43(11):1130-9. https://doi.org/10.1097/01.mlr.0000182534.19832.83. PMID: 16224307.
Quan H, Li B, Couris CM, Fushimi K, Graham P, Hider P, Januel JM, Sundararajan V. Updating and validating the Charlson comorbidity index and score for risk adjustment in hospital discharge abstracts using data from 6 countries. Am J Epidemiol. 2011 Mar 15;173(6):676-82. https://doi.org/10.1093/aje/kwq433. Epub 2011 Feb 17. PMID: 21330339.
Glasheen WP, Cordier T, Gumpina R, Haugh G, Davis J, Renda A. Charlson Comorbidity Index: ICD-9 Update and ICD-10 Translation. Am Health Drug Benefits. 2019 Jun-Jul;12(4):188-197. PMID: 31428236; PMCID: PMC6684052.
Elixhauser Comorbidities:
Agency for Healthcare Research and Quality (AHRQ). Elixhauser Comorbidity Software Refined for ICD-10-CM Diagnoses, v2025.1 [Internet]. 2025. Available from: https://www.hcup-us.ahrq.gov/toolssoftware/comorbidityicd10/comorbidity_icd10.jsp
See also
vignettes(topic = "comorbidities", package = "medicalcoder")vignettes(topic = "pccc", package = "medicalcoder")vignettes(topic = "charlson", package = "medicalcoder")vignettes(topic = "elixhauser", package = "medicalcoder")
Examples
pccc_v3.1_results <-
comorbidities(data = mdcr,
icd.codes = "code",
id.vars = "patid",
dx.var = "dx",
method = "pccc_v3.1",
flag.method = 'current',
poa = 1)
summary(pccc_v3.1_results)
#> condition label dxpr_or_tech_count
#> 1 congeni_genetic Other Congenital or Genetic Defect 3225
#> 2 cvd Cardiovascular 5147
#> 3 gi Gastrointestinal 5652
#> 4 hemato_immu Hematologic or Immunologic 2832
#> 5 malignancy Malignancy 3784
#> 6 metabolic Metabolic 3407
#> 7 misc Miscellaneous, Not Elsewhere Classified 764
#> 8 neonatal Premature & Neonatal 1516
#> 9 neuromusc Neurologic or Neuromuscular 5828
#> 10 renal Renal Urologic 2787
#> 11 respiratory Respiratory 3260
#> 12 any_tech_dep Any Technology Dependence 7012
#> 13 any_transplant Any Transplantation 1585
#> 14 cmrb_flag Any Condition 20871
#> 15 num_cmrb >= 2 conditions 10657
#> 16 num_cmrb >= 3 conditions 4582
#> 17 num_cmrb >= 4 conditions 1571
#> 18 num_cmrb >= 5 conditions 418
#> 19 num_cmrb >= 6 conditions 88
#> 20 num_cmrb >= 7 conditions 13
#> 21 num_cmrb >= 8 conditions 2
#> 22 num_cmrb >= 9 conditions 0
#> 23 num_cmrb >= 10 conditions 0
#> 24 num_cmrb >= 11 conditions 0
#> dxpr_or_tech_percent dxpr_only_count dxpr_only_percent tech_only_count
#> 1 8.428728242 3225 8.4287282 0
#> 2 13.451988919 4560 11.9178297 283
#> 3 14.771836287 1420 3.7112540 3855
#> 4 7.401599498 2832 7.4015995 0
#> 5 9.889707804 3784 9.8897078 0
#> 6 8.904396006 3356 8.7711045 40
#> 7 1.996759187 121 0.3162407 643
#> 8 3.962155664 1516 3.9621557 0
#> 9 15.231822696 4564 11.9282839 316
#> 10 7.283989337 1876 4.9030370 555
#> 11 8.520202812 1848 4.8298573 755
#> 12 18.326276724 NA NA NA
#> 13 4.142491245 NA NA NA
#> 14 54.547592912 NA NA NA
#> 15 27.852699807 NA NA NA
#> 16 11.975328002 NA NA NA
#> 17 4.105901417 NA NA NA
#> 18 1.092467723 NA NA NA
#> 19 0.229993205 NA NA NA
#> 20 0.033976269 NA NA NA
#> 21 0.005227118 NA NA NA
#> 22 0.000000000 NA NA NA
#> 23 0.000000000 NA NA NA
#> 24 0.000000000 NA NA NA
#> tech_only_percent dxpr_and_tech_count dxpr_and_tech_percent
#> 1 0.0000000 0 0.00000000
#> 2 0.7396372 304 0.79452198
#> 3 10.0752705 377 0.98531180
#> 4 0.0000000 0 0.00000000
#> 5 0.0000000 0 0.00000000
#> 6 0.1045424 11 0.02874915
#> 7 1.6805185 0 0.00000000
#> 8 0.0000000 0 0.00000000
#> 9 0.8258847 948 2.47765407
#> 10 1.4505253 356 0.93042706
#> 11 1.9732372 657 1.71710836
#> 12 NA NA NA
#> 13 NA NA NA
#> 14 NA NA NA
#> 15 NA NA NA
#> 16 NA NA NA
#> 17 NA NA NA
#> 18 NA NA NA
#> 19 NA NA NA
#> 20 NA NA NA
#> 21 NA NA NA
#> 22 NA NA NA
#> 23 NA NA NA
#> 24 NA NA NA
pccc_v3.1_subcondition_results <-
comorbidities(data = mdcr,
icd.codes = "code",
id.vars = "patid",
dx.var = "dx",
method = "pccc_v3.1",
flag.method = 'current',
poa = 1,
subconditions = TRUE)
summary(pccc_v3.1_subcondition_results)
#> condition subcondition count
#> 1 congeni_genetic <NA> 3225
#> 2 congeni_genetic bone_and_joint_anomalies 825
#> 3 congeni_genetic chromosomal_anomalies 1544
#> 4 congeni_genetic diaphragm_and_abdominal_wall_anomalies 300
#> 5 congeni_genetic other_congenital_anomalies 754
#> 6 cvd <NA> 5147
#> 7 cvd cardiomyopathies 240
#> 8 cvd conduction_disorder 653
#> 9 cvd device_and_technology_use 587
#> 10 cvd dysrhythmias 1130
#> 11 cvd endocardium_diseases 278
#> 12 cvd heart_and_great_vessel_malformations 2320
#> 13 cvd other 1247
#> 14 cvd transplantation 271
#> 15 gi <NA> 5652
#> 16 gi chronic_liver_disease_and_cirrhosis 290
#> 17 gi congenital_anomalies 709
#> 18 gi device_and_technology_use 4232
#> 19 gi inflammatory_bowel_disease 264
#> 20 gi other 295
#> 21 gi transplantation 343
#> 22 hemato_immu <NA> 2832
#> 23 hemato_immu acquired_immunodeficiency 11
#> 24 hemato_immu aplastic_anemias 823
#> 25 hemato_immu coagulation_hemorrhagic 100
#> 26 hemato_immu diffuse_diseases_of_connective_tissue 125
#> 27 hemato_immu hemophagocytic_syndromes 59
#> 28 hemato_immu hereditary_anemias 771
#> 29 hemato_immu hereditary_immunodeficiency 909
#> 30 hemato_immu leukopenia 28
#> 31 hemato_immu other 12
#> 32 hemato_immu polyarteritis_nodosa_and_related_conditions 46
#> 33 hemato_immu sarcoidosis 3
#> 34 hemato_immu transplantation 181
#> 35 malignancy <NA> 3784
#> 36 malignancy neoplasms 3525
#> 37 malignancy transplantation 452
#> 38 metabolic <NA> 3407
#> 39 metabolic amino_acid_metabolism 194
#> 40 metabolic carbohydrate_metabolism 130
#> 41 metabolic device_and_technology_use 51
#> 42 metabolic endocrine_disorders 884
#> 43 metabolic lipid_metabolism 354
#> 44 metabolic other_metabolic_disorders 1870
#> 45 metabolic storage_disorders 82
#> 46 misc <NA> 764
#> 47 misc device_and_technology_use 643
#> 48 misc transplantation 121
#> 49 neonatal <NA> 1516
#> 50 neonatal birth_asphyxia 11
#> 51 neonatal cerebral_hemorrhage_at_birth 84
#> 52 neonatal extreme_immaturity 349
#> 53 neonatal fetal_malnutrition 45
#> 54 neonatal hypoxic_ischemic_encephalopathy 130
#> 55 neonatal other 247
#> 56 neonatal respiratory_diseases 950
#> 57 neonatal spinal_cord_injury_at_birth 0
#> 58 neuromusc <NA> 5828
#> 59 neuromusc brain_and_spinal_cord_malformations 1767
#> 60 neuromusc cns_degeneration_and_diseases 1474
#> 61 neuromusc device_and_technology_use 1264
#> 62 neuromusc epilepsy 833
#> 63 neuromusc infantile_cerebral_palsy 1322
#> 64 neuromusc intellectual_disabilities 161
#> 65 neuromusc movement_diseases 146
#> 66 neuromusc muscular_dystrophies_and_myopathies 147
#> 67 neuromusc occlusion_of_cerebral_arteries 92
#> 68 neuromusc other_neurologic_disorders 848
#> 69 renal <NA> 2787
#> 70 renal chronic_bladder_diseases 519
#> 71 renal chronic_renal_failure 627
#> 72 renal congenital_anomalies 915
#> 73 renal device_and_technology_use 911
#> 74 renal other 217
#> 75 renal transplantation 322
#> 76 respiratory <NA> 3260
#> 77 respiratory chronic_respiratory_diseases 1092
#> 78 respiratory cystic_fibrosis 400
#> 79 respiratory device_and_technology_use 1412
#> 80 respiratory other 22
#> 81 respiratory respiratory_malformations 1091
#> 82 respiratory transplantation 39
#> percent_of_cohort percent_of_those_with_condition
#> 1 8.428728242 NA
#> 2 2.156186294 25.5813953
#> 3 4.035335320 47.8759690
#> 4 0.784067743 9.3023256
#> 5 1.970623595 23.3798450
#> 6 13.451988919 NA
#> 7 0.627254195 4.6629104
#> 8 1.706654122 12.6870021
#> 9 1.534159218 11.4047018
#> 10 2.953321834 21.9545366
#> 11 0.726569442 5.4012046
#> 12 6.063457216 45.0748009
#> 13 3.259108254 24.2277055
#> 14 0.708274528 5.2652030
#> 15 14.771836287 NA
#> 16 0.757932152 5.1309271
#> 17 1.853013434 12.5442321
#> 18 11.060582301 74.8761500
#> 19 0.689979614 4.6709130
#> 20 0.770999948 5.2193914
#> 21 0.896450787 6.0686483
#> 22 7.401599498 NA
#> 23 0.028749151 0.3884181
#> 24 2.150959176 29.0607345
#> 25 0.261355914 3.5310734
#> 26 0.326694893 4.4138418
#> 27 0.154199990 2.0833333
#> 28 2.015054101 27.2245763
#> 29 2.375725263 32.0974576
#> 30 0.073179656 0.9887006
#> 31 0.031362710 0.4237288
#> 32 0.120223721 1.6242938
#> 33 0.007840677 0.1059322
#> 34 0.473054205 6.3912429
#> 35 9.889707804 NA
#> 36 9.212795986 93.1553911
#> 37 1.181328733 11.9450317
#> 38 8.904396006 NA
#> 39 0.507030474 5.6941591
#> 40 0.339762689 3.8156736
#> 41 0.133291516 1.4969181
#> 42 2.310386284 25.9465806
#> 43 0.925199937 10.3903728
#> 44 4.887355601 54.8869974
#> 45 0.214311850 2.4068095
#> 46 1.996759187 NA
#> 47 1.680518530 84.1623037
#> 48 0.316240657 15.8376963
#> 49 3.962155664 NA
#> 50 0.028749151 0.7255937
#> 51 0.219538968 5.5408971
#> 52 0.912132142 23.0211082
#> 53 0.117610162 2.9683377
#> 54 0.339762689 8.5751979
#> 55 0.645549109 16.2928760
#> 56 2.482881188 62.6649077
#> 57 0.000000000 0.0000000
#> 58 15.231822696 NA
#> 59 4.618159009 30.3191489
#> 60 3.852386179 25.2916953
#> 61 3.303538759 21.6884008
#> 62 2.177094768 14.2930679
#> 63 3.455125189 22.6835964
#> 64 0.420783022 2.7625257
#> 65 0.381579635 2.5051476
#> 66 0.384193194 2.5223061
#> 67 0.240447441 1.5785861
#> 68 2.216298155 14.5504461
#> 69 7.283989337 NA
#> 70 1.356437196 18.6221744
#> 71 1.638701584 22.4973089
#> 72 2.391406618 32.8310011
#> 73 2.380952381 32.6874776
#> 74 0.567142334 7.7861500
#> 75 0.841566045 11.5536419
#> 76 8.520202812 NA
#> 77 2.854006586 33.4969325
#> 78 1.045423658 12.2699387
#> 79 3.690345513 43.3128834
#> 80 0.057498301 0.6748466
#> 81 2.851393027 33.4662577
#> 82 0.101928807 1.1963190
charlson_results <-
comorbidities(data = mdcr,
icd.codes = "code",
id.vars = "patid",
dx.var = "dx",
method = "charlson_quan2011",
flag.method = 'current',
poa = 1)
#> Warning: Assuming all codes provided are secondary diagnostic codes. Define `primarydx.var` or `primarydx` if this assumption is incorrect.
summary(charlson_results)
#> $conditions
#> condition_description condition count percent
#> 1 AIDS/HIV aidshiv 7 0.018294914
#> 2 Any malignancy mal 2301 6.013799592
#> 3 Cerebrovascular disease cebvd 411 1.074172809
#> 4 Chronic pulmonary disease copd 3415 8.925304480
#> 5 Congestive heart failure chf 684 1.787674455
#> 6 Dementia dem 13 0.033976269
#> 7 Diabetes with chronic complications dmc 13 0.033976269
#> 8 Diabetes without chronic complications dm 441 1.152579583
#> 9 Hemiplegia or paraplegia hp 1177 3.076159113
#> 10 Liver disease, mild mld 593 1.549840573
#> 11 Liver disease, moderate to severe msld 206 0.538393184
#> 12 Metastatic solid tumor mst 453 1.183942293
#> 13 Myocardial infarction mi 10 0.026135591
#> 14 Peptic ulcer disease pud 45 0.117610162
#> 15 Peripheral vascular disease pvd 217 0.567142334
#> 16 Renal disease rnd 898 2.346976112
#> 17 Rheumatic disease rhd 136 0.355444044
#> 18 >= 1 <NA> 9841 25.720035544
#> 19 >= 2 <NA> 1075 2.809576081
#> 20 >= 3 <NA> 94 0.245674560
#> 21 >= 4 <NA> 9 0.023522032
#> 22 >= 5 <NA> 1 0.002613559
#>
#> $age_summary
#> age_score count percent
#> 1 <NA> 38262 100
#>
#> $index_summary
#> min q1 median q3 max
#> 1 0 0 0 0 10
#>
elixhauser_results <-
comorbidities(data = mdcr,
icd.codes = "code",
id.vars = "patid",
dx.var = "dx",
method = "elixhauser_ahrq2025",
primarydx = 1,
flag.method = 'current',
poa = 1)
summary(elixhauser_results)
#> $conditions
#> condition count percent
#> 1 AIDS 0 0
#> 2 ALCOHOL 0 0
#> 3 ANEMDEF 0 0
#> 4 AUTOIMMUNE 0 0
#> 5 BLDLOSS 0 0
#> 6 CANCER_LEUK 0 0
#> 7 CANCER_LYMPH 0 0
#> 8 CANCER_METS 0 0
#> 9 CANCER_NSITU 0 0
#> 10 CANCER_SOLID 0 0
#> 11 CBVD 0 0
#> 12 COAG 0 0
#> 13 DEMENTIA 0 0
#> 14 DEPRESS 0 0
#> 15 DIAB_CX 0 0
#> 16 DIAB_UNCX 0 0
#> 17 DRUG_ABUSE 0 0
#> 18 HF 0 0
#> 19 HTN_CX 0 0
#> 20 HTN_UNCX 0 0
#> 21 LIVER_MLD 0 0
#> 22 LIVER_SEV 0 0
#> 23 LUNG_CHRONIC 0 0
#> 24 NEURO_MOVT 0 0
#> 25 NEURO_OTH 0 0
#> 26 NEURO_SEIZ 0 0
#> 27 OBESE 0 0
#> 28 PARALYSIS 0 0
#> 29 PERIVASC 0 0
#> 30 PSYCHOSES 0 0
#> 31 PULMCIRC 0 0
#> 32 RENLFL_MOD 0 0
#> 33 RENLFL_SEV 0 0
#> 34 THYROID_HYPO 0 0
#> 35 THYROID_OTH 0 0
#> 36 ULCER_PEPTIC 0 0
#> 37 VALVE 0 0
#> 38 WGHTLOSS 0 0
#>
#> $index_summary
#> index min q1 median q3 max
#> 1 readmission 0 0 0 0 0
#> 2 mortality 0 0 0 0 0
#>