Higher Charlson Comorbidity Index Scores are Associated With Readmission After Orthopaedic Surgery

Voskuijl, Timothy; Hageman, Michiel G.J.S.; Ring, David

doi:10.1007/s11999-013-3394-8

Cited by 178 publications

(102 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…This index provides a score ranging from 0 to 24 with a higher score representing more severe comorbidity status based on 12 weighted comorbidities (congestive heart failure, dementia, chronic pulmonary disease, rheumatologic disease, mild liver disease, diabetes with chronic complications, hemiplegia or paraplegia, renal disease, any malignancy, moderate or severe liver disease, metastatic solid tumor, and HIV/ AIDS). We determined the modified Charlson Comorbidity Index through a previously described algorithm based on ICD-9 codes given before the day of surgery [6,30,38] (Appendix 2. Supplemental material is available with the online version of CORR 1 .).…”

Section: Methodsmentioning

confidence: 99%

2015 Marshall Urist Young Investigator Award: Prognostication in Patients With Long Bone Metastases: Does a Boosting Algorithm Improve Survival Estimates?

Janssen

Heijden

Dijke

et al. 2015

Clin Orthop Relat Res

View full text Add to dashboard Cite

Background Survival estimation guides surgical decisionmaking in metastatic bone disease. Traditionally, classic scoring systems, such as the Bauer score, provide survival estimates based on a summary score of prognostic factors. Identification of new factors might improve the accuracy of these models. Additionally, the use of different algorithms-nomograms or boosting algorithms-could further improve accuracy of prognostication relative to classic scoring systems. A nomogram is an extension of a classic scoring system and generates a more-individualized survival probability based on a patient's set of characteristics using a figure. Boosting is a method that automatically trains to classify outcomes by applying classifiers (variables) in a sequential way and subsequently combines them. A boosting algorithm provides survival probabilities based on every possible combination of variables. Questions/purposes We wished to (1) assess factors independently associated with decreased survival in patients with metastatic long bone fractures and (2) compare the accuracy of a classic scoring system, nomogram, and boosting algorithms in predicting 30-, 90-, and 365-day survival. Methods We included all 927 patients in our retrospective study who underwent surgery for a metastatic long bone fracture at two institutions between January 1999 and December 2013. We included only the first procedure if patients underwent multiple surgical procedures or had more than one fracture. Median followup was 8 months (interquartile range, 3-25 months); 369 of 412 (90%) patients who where alive at 1 year were still in followup. Multivariable Cox regression analysis was used to identify One author (SJ) certifies that he has received, an amount less than USD 10,000, from the Anna Foundation (Oegstgeest, The Netherlands), an amount less than USD 10,000 from the De Drie Lichten Foundation (Hilversum, The Netherlands), an amount less than USD 10,000 from the KWF Kankerbestrijding (Amsterdam, The Netherlands), and an amount less than USD 10,000, from the Michael van Vloten Foundation (Rotterdam, The Netherlands). One author (JS) certifies that he or a member of his family has or may receive payments or benefits, an amount less than USD 10,000, from Stryker (Kalamazoo, MI, USA), and an amount less than USD 10,000, from Biom'up (Saint-Priest, Lyon, France). All ICMJE Conflict of Interest Forms for authors and Clinical Orthopaedics and Related Research 1 editors and board members are on file with the publication and can be viewed on request. Each author certifies that his or her institution approved the human protocol for this investigation, that all investigations were conducted in conformity with ethical principles of research. This work was performed at Massachusetts General Hospital, Boston, MA, USA. clinical and laboratory factors independently associated with decreased survival. We created a classic scoring system, nomogram, and boosting algorithms based on identified variables. Accuracy of the algorithms was assessed using area unde...

show abstract

Section: Methodsmentioning

confidence: 99%

2015 Marshall Urist Young Investigator Award: Prognostication in Patients With Long Bone Metastases: Does a Boosting Algorithm Improve Survival Estimates?

Janssen

Heijden

Dijke

et al. 2015

Clin Orthop Relat Res

View full text Add to dashboard Cite

show abstract

“…Preoperative risk assessment can help with decision-making and management strategies [11,37,50]. Several models for estimating risk based on coded comorbidities are currently in use for orthopaedic patients, but there is no consensus regarding the optimal approach [22,37,49,51,56,57]. Selecting appropriate risk adjustment models can help hospitals contain costs while ensuring high levels of quality.…”

Section: Introductionmentioning

confidence: 99%

The Elixhauser Comorbidity Method Outperforms the Charlson Index in Predicting Inpatient Death After Orthopaedic Surgery

Menendez

Neuhaus

Dijk

et al. 2014

Clinical Orthopaedics &Amp; Related Research

Self Cite

442

292

View full text Add to dashboard Cite

Background Scores derived from comorbidities can help with risk adjustment of quality and safety data. The Charlson and Elixhauser comorbidity measures are wellknown risk adjustment models, yet the optimal score for orthopaedic patients remains unclear. Questions/purposes We determined whether there was a difference in the accuracy of the Charlson and Elixhauser comorbidity-based measures in predicting (1) in-hospital mortality after major orthopaedic surgery, (2) in-hospital adverse events, and (3) nonroutine discharge. Methods Among an estimated 14,007,813 patients undergoing orthopaedic surgery identified in the National Hospital Discharge Survey (1990Survey ( -2007, 0.80% died in the hospital. The association of each Charlson comorbidity measure and Elixhauser comorbidity measure with mortality was assessed in bivariate analysis. Two main multivariable logistic regression models were constructed, with in-hospital mortality as the dependent variable and one of the two comorbidity-based measures (and age, sex, and year of surgery) as independent variables. A base model that included only age, sex, and year of surgery also was evaluated. The discriminative ability of the models was quantified using the area under the receiver operating characteristic curve (AUC). The AUC quantifies the ability of our models to assign a high probability of mortality to patients who die. Values range from 0.50 to 1.0, with 0.50 indicating no ability to discriminate and 1.0 indicating perfect discrimination. Results Elixhauser comorbidity adjustment provided a better prediction of in-hospital case mortality (AUC, 0.86; 95% CI, 0.86-0.86) compared with the Charlson model (AUC, 0.83; 95% CI, and to the base model with no comorbidities (AUC, 0.81; 95% CI, 0.81-0.81). In terms of relative improvement in predictive performance, the Elixhauser measure performed 60% better than the Charlson score in predicting mortality. The Elixhauser model discriminated inpatient morbidity better than the Charlson measure, but the discriminative ability of the model was poor and the difference in the absolute improvement in predictive power between the two models (AUC, 0.01) is of dubious clinical importance. Both comorbidity models exhibited the same degree of discrimination for estimating nonroutine discharge (AUC, 0.81; 95% CI, 0.81-0.82 for both models). Conclusions Provider-specific outcomes, particularly inpatient mortality, may be evaluated differently depending on the comorbidity risk adjustment model selected. Future research assessing and comparing the performance of the

show abstract

“…Although many scoring systems have been desig- ned for hip fractures, CCI seems to be the frequently used in the prediction of mortality following fracture surgery 7,9,20,21 . In a study by Kirkland et al, 22 on hip fractures in elderly patients, a strong relationship was found between a CCI score of >6 and 30-day mortality rates but no comment was made about sensitivity or specificity of CCI scoring system.…”

Section: Discussionmentioning

confidence: 99%

“…Therefore, several scoring methods have been created taking comorbid factors into consideration, which would be of benefit in the prediction of mortality. The Charlson Comorbidity Index (CCI) and the American Society of Anaesthesiologists score (ASA) have been widely used in the prediction of mortality 1,7,[9][10][11] . Although many scoring systems have been described in the literature, the perfect risk model for predicting mortality following hip fracture surgery does not exist, and research on the subject is ongoing 12 .…”

Section: öZmentioning

confidence: 99%

Is ACE-27 a reliable method for predicting mortality of hip fractures treated with hemiarthroplasty in the elderly?

Tahta¹,

Bulut²,

Öztürk³

et al. 2016

MMJ

View full text Add to dashboard Cite

Higher Charlson Comorbidity Index Scores are Associated With Readmission After Orthopaedic Surgery

Cited by 178 publications

References 39 publications

2015 Marshall Urist Young Investigator Award: Prognostication in Patients With Long Bone Metastases: Does a Boosting Algorithm Improve Survival Estimates?

2015 Marshall Urist Young Investigator Award: Prognostication in Patients With Long Bone Metastases: Does a Boosting Algorithm Improve Survival Estimates?

The Elixhauser Comorbidity Method Outperforms the Charlson Index in Predicting Inpatient Death After Orthopaedic Surgery

Is ACE-27 a reliable method for predicting mortality of hip fractures treated with hemiarthroplasty in the elderly?

Contact Info

Product

Resources

About