Short-Form Charlson Comorbidity Index for Assessment of Perioperative Mortality After Radical Cystectomy.

Background: The Deyo adaptation of the Charlson comorbidity index (DaCCI), which relies on 17 comorbid condition groupings, represents one of the most frequently used baseline comorbidity assessment tools in retrospective database studies. However, this index is not specific for patients with bladder cancer (BCa) treated with radical cystectomy (RC). The goal of this study was to develop a short-form of the original DaCCI (DaCCI-SF) that may specifically predict 90-day mortality after RC, with equal or better accuracy. Patients and Methods: Between 2000 and 2009, we identified 7,076 patients in the SEER-Medicare database with stage T1 through T4 nonmetastatic BCa treated with RC. We randomly divided the population into development (n=6,076) and validation (n=1,000) cohorts. Within the development cohort, logistic regression models tested the ability to predict 90-day mortality with various iterations of the DaCCI-SF, wherein <17 original comorbid condition groupings were included after adjusting for age, sex, race, T stage, and N stage. We relied on the Akaike information criterion to identify the most parsimonious and informative set of comorbid condition groupings. Accuracy of the DaCCI and the DaCCI-SF was tested in the external validation cohort. Results: Within the development cohort, the most parsimonious and informative model resulted in the inclusion of 3 of the 17 (17.6%) original comorbid condition groupings: congestive heart failure, cerebrovascular disease, and chronic pulmonary disease. Within the validation cohort, the accuracy was 68.4% for the DaCCI versus 69.7% for the DaCCI-SF. Higher accuracy of the DaCCI-SF was confirmed in subgroup analyses performed according to age (≤75 vs >75 years), stage (organ-confined vs non-organ-confined), type of diversion (ileal-conduit vs non-ileal-conduit), and treatment period. Conclusions: DaCCI-SF relies on 17.6% of the original comorbid condition groupings and provides higher accuracy for predicting 90-day mortality after RC compared with the original DaCCI, especially in most contemporary patients.

[1]  E. Compérat,et al.  Updated 2016 EAU Guidelines on Muscle-invasive and Metastatic Bladder Cancer. , 2017, European urology.

[2]  J. McKiernan,et al.  Frailty as a marker of adverse outcomes in patients with bladder cancer undergoing radical cystectomy. , 2016, Urologic oncology.

[3]  J. Oh,et al.  Association between diabetes mellitus and oncological outcomes in bladder cancer patients undergoing radical cystectomy , 2015, International journal of urology : official journal of the Japanese Urological Association.

[4]  F. Saad,et al.  Contemporary 90-day mortality rates after radical cystectomy in the elderly. , 2014, European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology.

[5]  Mark W. Ball,et al.  Comorbidities and causes of death in the management of localized T1a kidney cancer , 2014, International journal of urology : official journal of the Japanese Urological Association.

[6]  J. Cheville,et al.  Sarcopenia in patients with bladder cancer undergoing radical cystectomy: Impact on cancer‐specific and all‐cause mortality , 2014, Cancer.

[7]  C. Torp-Pedersen,et al.  Time elapsed after ischemic stroke and risk of adverse cardiovascular events and mortality following elective noncardiac surgery. , 2014, JAMA.

[8]  W. Otto,et al.  Predictive capacity of four comorbidity indices estimating perioperative mortality after radical cystectomy for urothelial carcinoma of the bladder , 2012, BJU international.

[9]  Zhi Huang,et al.  An improved comorbidity index for outcome analyses among dialysis patients. , 2010, Kidney international.

[10]  M. Hynynen,et al.  Mortality in diabetic patients undergoing non‐cardiac surgery: a 7‐year follow‐up study , 2009, Acta anaesthesiologica Scandinavica.

[11]  C. O'connor,et al.  Impact of Heart Failure on Patients Undergoing Major Noncardiac Surgery , 2008, Anesthesiology.

[12]  C. Klabunde,et al.  A refined comorbidity measurement algorithm for claims-based studies of breast, prostate, colorectal, and lung cancer patients. , 2007, Annals of epidemiology.

[13]  W. Henderson,et al.  Morbidity and mortality after liver resection: results of the patient safety in surgery study. , 2007, Journal of the American College of Surgeons.

[14]  J. Tschopp,et al.  Operative mortality and respiratory complications after lung resection for cancer: impact of chronic obstructive pulmonary disease and time trends. , 2006, The Annals of thoracic surgery.

[15]  L. Goldman,et al.  Assessing and Reducing the Cardiac Risk of Noncardiac Surgery , 2006, Circulation.

[16]  J. Daurès,et al.  A new simplified comorbidity score as a prognostic factor in non-small-cell lung cancer patients: description and comparison with the Charlson's index , 2005, British Journal of Cancer.

[17]  M. Sorror,et al.  Hematopoietic cell transplantation (HCT)-specific comorbidity index: a new tool for risk assessment before allogeneic HCT. , 2005, Blood.

[18]  David R. Anderson,et al.  Multimodel Inference , 2004 .

[19]  Edward L Spitznagel,et al.  Prognostic importance of comorbidity in a hospital-based cancer registry. , 2004, JAMA.

[20]  A. Tsiatis,et al.  The prognostic importance of comorbidity for mortality in patients with stable coronary artery disease. , 2004, Journal of the American College of Cardiology.

[21]  K. Pearce,et al.  The development and validation of a comorbidity index for prostate cancer among Black men. , 2003, Journal of clinical epidemiology.

[22]  David R. Anderson,et al.  Model selection and multimodel inference : a practical information-theoretic approach , 2003 .

[23]  Hude Quan,et al.  Adapting the Charlson Comorbidity Index for use in patients with ESRD. , 2003, American journal of kidney diseases : the official journal of the National Kidney Foundation.

[24]  Deborah Schrag,et al.  Overview of the SEER-Medicare Data: Content, Research Applications, and Generalizability to the United States Elderly Population , 2002, Medical care.

[25]  A. Dmitrienko,et al.  A comprehensive prognostic index to predict survival based on multiple comorbidities: a focus on breast cancer. , 1999, Medical care.

[26]  E. Jones,et al.  Influence of diabetes mellitus on early and late outcome after coronary artery bypass grafting. , 1999, The Annals of thoracic surgery.

[27]  R. Deyo,et al.  Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases. , 1992, Journal of clinical epidemiology.

[28]  G. Beck,et al.  Stratification of morbidity and mortality outcome by preoperative risk factors in coronary artery bypass patients. A clinical severity score. , 1992, JAMA.

[29]  E. McFadden,et al.  Toxicity and response criteria of the Eastern Cooperative Oncology Group , 1982, American journal of clinical oncology.

[30]  Meyer Saklad,et al.  GRADING OF PATIENTS FOR SURGICAL PROCEDURES , 1941 .

[31]  G. Moneta.  Risk of Surgery Following Recent Myocardial Infarction , 2011 .

[32]  V. Gudnason,et al.  Diabetes Mellitus, Fasting Glucose, and Risk of Cause-Specific Death , 2011 .

[33]  J. Stockman.  Fasting Serum Glucose Level and Cancer Risk in Korean Men and Women , 2006 .

[34]  John T. Wei,et al.  The impact of co-morbid disease on cancer control and survival following radical cystectomy. , 2003, The Journal of urology.

[35]  C. Mackenzie,et al.  A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. , 1987, Journal of chronic diseases.