Predicting adverse events, length of stay, and discharge disposition following shoulder arthroplasty: a comparison of the Elixhauser Comorbidity Measure and Charlson Comorbidity Index

2018 ◽  
Vol 27 (10) ◽  
pp. 1748-1755 ◽  
Author(s):  
Chang-Yeon Kim ◽  
Lakshmanan Sivasundaram ◽  
Mark W. LaBelle ◽  
Nikunj N. Trivedi ◽  
Raymond W. Liu ◽  
...  
Author(s):  
Richard Ofori-Asenso ◽  
Ella Zomer ◽  
Ken Chin ◽  
Si Si ◽  
Peter Markey ◽  
...  

The burden of comorbidity among stroke patients is high. The aim of this study was to examine the effect of comorbidity on the length of stay (LOS), costs, and mortality among older adults hospitalised for acute stroke. Among 776 older adults (mean age 80.1 ± 8.3 years; 46.7% female) hospitalised for acute stroke during July 2013 to December 2015 at a tertiary hospital in Melbourne, Australia, we collected data on LOS, costs, and discharge outcomes. Comorbidity was assessed via the Charlson Comorbidity Index (CCI), where a CCI score of 0–1 was considered low and a CCI ≥ 2 was high. Negative binomial regression and quantile regression were applied to examine the association between CCI and LOS and cost, respectively. Survival was evaluated with the Kaplan–Meier and Cox regression analyses. The median LOS was 1.1 days longer for patients with high CCI than for those with low CCI. In-hospital mortality rate was 18.2% (22.1% for high CCI versus 11.8% for low CCI, p < 0.0001). After controlling for confounders, high CCI was associated with longer LOS (incidence rate ratio [IRR]; 1.35, p < 0.0001) and increased likelihood of in-hospital death (hazard ratio [HR]; 1.91, p = 0.003). The adjusted median, 25th, and 75th percentile costs were AUD$2483 (26.1%), AUD$1446 (28.1%), and AUD$3140 (27.9%) higher for patients with high CCI than for those with low CCI. Among older adults hospitalised for acute stroke, higher global comorbidity (CCI ≥ 2) was associated adverse clinical outcomes. Measures to better manage comorbidities should be considered as part of wider strategies towards mitigating the social and economic impacts of stroke.


1997 ◽  
Vol 86 (1) ◽  
pp. 92-100 ◽  
Author(s):  
Alex Macario ◽  
Terry S. Vitez ◽  
Brian Dunn ◽  
Tom McDonald ◽  
Byron Brown

Background If patients who are more severely ill have greater hospital costs for surgery, then health-care reimbursements need to be adjusted appropriately so that providers caring for more seriously ill patients are not penalized for incurring higher costs. The authors' goal for this study was to determine if severity of illness, as measured by either the American Society of Anesthesiologists Physical Status (ASA PS) or the comorbidity index developed by Charlson, can predict anesthesia costs, operating room costs, total hospital costs, or length of stay for elective surgery. Methods The authors randomly selected 224 inpatients (60% sampling fraction) having either colectomy (n = 30), total knee replacement (n = 100), or laparoscopic cholecystectomy (n = 94) from September 1993 to September 1994. For each surgical procedure, backward-elimination multiple regression was used to build models to predict (1) total hospital costs, (2) operating room costs, (3) anesthesia costs, and (4) length of stay. Explanatory candidate variables included patient age (years), sex, ASA PS, Charlson comorbidity index (which weighs the number and seriousness of coexisting diseases), and type of insurance (Medicare/Medicaid, managed care, or indemnity). These analyses were repeated for the pooled data of all 224 patients. Costs (not patient charges) were obtained from the hospital cost accounting software. Results Mean total hospital costs were $3,778 (95% confidence interval +/- 299) for laparoscopic cholecystectomy, $13,614 (95% CI +/- 3,019) for colectomy, and $18,788 (95% CI +/- 573) for knee replacement. The correlation (r) between ASA PS and Charlson comorbidity scores equaled 0.34 (P &lt; .001). No consistent relation was found between hospital costs and either of the two severity-of-illness indices. The Charlson comorbidity index (but not the ASA PS) predicted hospital costs only for knee replacement (P = .003). The ASA PS, but not the Charlson index, predicted operating room and anesthesia costs only for colectomy (P &lt; .03). Conclusions Severity of illness, as categorized by ASA PS categories 1-3 or by the Charlson comorbidity index, was not a consistent predictor of hospital costs and lengths of stay for three types of elective surgery. Hospital resources for these lower-risk elective procedures may be expended primarily to manage the consequences of the surgical disease, rather than to manage the patient's coexisting diseases.


2019 ◽  
Vol 27 (15) ◽  
pp. e696-e701 ◽  
Author(s):  
Jacob E. Berman ◽  
Ana Mata-Fink ◽  
Hafiz F. Kassam ◽  
Theodore A. Blaine ◽  
David Kovacevic

2018 ◽  
Vol 47 (6) ◽  
Author(s):  
Timothy S. Leroux ◽  
Bryce A. Basques ◽  
Bryan M. Saltzman ◽  
Gregory P. Nicholson ◽  
Anthony A. Romeo ◽  
...  

2010 ◽  
Vol 113 (5) ◽  
pp. 1026-1037 ◽  
Author(s):  
Daniel I. Sessler ◽  
Jeffrey C. Sigl ◽  
Paul J. Manberg ◽  
Scott D. Kelley ◽  
Armin Schubert ◽  
...  

Background Hospitals are increasingly required to publicly report outcomes, yet performance is best interpreted in the context of population and procedural risk. We sought to develop a risk-adjustment method using administrative claims data to assess both national-level and hospital-specific performance. Methods A total of 35,179,507 patient stay records from 2001-2006 Medicare Provider Analysis and Review (MEDPAR) files were randomly divided into development and validation sets. Risk stratification indices (RSIs) for length of stay and mortality endpoints were derived from aggregate risk associated with individual diagnostic and procedure codes. Performance of RSIs were tested prospectively on the validation database, as well as a single institution registry of 103,324 adult surgical patients, and compared with the Charlson comorbidity index, which was designed to predict 1-yr mortality. The primary outcome was the C statistic indicating the discriminatory power of alternative risk-adjustment methods for prediction of outcome measures. Results A single risk-stratification model predicted 30-day and 1-yr postdischarge mortality; separate risk-stratification models predicted length of stay and in-hospital mortality. The RSIs performed well on the national dataset (C statistics for median length of stay and 30-day mortality were 0.86 and 0.84). They performed significantly better than the Charlson comorbidity index on the Cleveland Clinic registry for all outcomes. The C statistics for the RSIs and Charlson comorbidity index were 0.89 versus 0.60 for median length of stay, 0.98 versus 0.65 for in-hospital mortality, 0.85 versus 0.76 for 30-day mortality, and 0.83 versus 0.77 for 1-yr mortality. Addition of demographic information only slightly improved performance of the RSI. Conclusion RSI is a broadly applicable and robust system for assessing hospital length of stay and mortality for groups of surgical patients based solely on administrative data.


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