Outcome-specific Charlson Comorbidity Indices for Predicting Poor Inpatient Outcomes Following Noncardiac Surgery Using Hospital Administrative Data.
BACKGROUND
A need exists for adapting existing perioperative risk stratification methods such as the Charlson Comorbidity Index (CCI) for application with hospital administrative data in noncardiac surgery populations.


OBJECTIVE
Develop and validate outcome-specific CCIs for predicting inpatient mortality, and cardiac and renal morbidity in noncardiac surgery patients using hospital administrative data.


METHODS
We used hospital administrative data from the 2010 and 2011 California State Inpatient Database (SID) to develop (derivation cohort: 2010 SID, n=177,280) and validate (validation cohort: 2011 SID, n=179,145) 3 outcome-specific CCIs. Along with the 17 CCI comorbidities, the clinical importance and weighted point scores for age, male sex, race, emergent admission, and high-risk surgery were also determined from the coefficients of a logistic regression model. Cumulative outcome-specific CCI, CCI, and age-adjusted CCI (AACCI) scores were calculated for each patient. Receiver-operator characteristic curve analyses were used to determine the prognostic accuracy (area under the curve) of each outcome-specific CCIs, the CCI, and the AACCI. Risk was stratified according to cumulative point scores for each outcome-specific CCI, and posttest probabilities for each risk category were calculated.


RESULTS
All outcome-specific CCIs showed good performance as a prognostic tools (area under the curve>0.800 for all) and performed better than the CCI and AACCI. We attached clinical relevance to a given cumulative point score by determining posttest probabilities for each outcome-specific index.


CONCLUSIONS
We successfully adapted and validated 3 outcome-specific CCIs for use in noncardiac surgery patients based on ICD-9 and hospital admission data. Further validation of these outcome-specific CCIs is warranted.
Discipline of Anaesthesiology and Critical Care Medicine, Nelson R. Mandela School of Medicine, University of KwaZulu-Natal, Congella, South Africa.
Yoshan
Y
Moodley
D000368:Aged; D000369:Aged, 80 and over; D002140:California / Q000453:epidemiology; D015897:Comorbidity; D005260:Female; D006331:Heart Diseases / Q000453:epidemiology; D006761:Hospitals / Q000706:statistics & numerical data*; D006801:Humans; D007674:Kidney Diseases / Q000453:epidemiology; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D018570:Risk Assessment; D012307:Risk Factors; D012720:Severity of Illness Index; D013514:Surgical Procedures, Operative / Q000009:adverse effects* / Q000401:mortality