Mortality in Elderly Patients After Cervical Spine Fractures

Harris, Mitchel B.; Reichmann, William M.; Bono, Christopher M.; Bouchard, Kim; Corbett, Kelly; Warholic, Natalie M.; Simon, J; Schoenfeld, Andrew J.; Maciolek, Lawrence; Corsello, Paul; Losina, Elena; Katz, Jeffrey N.

doi:10.2106/jbjs.i.00003

Cited by 105 publications

(67 citation statements)

References 33 publications

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“…Patients undergoing orthopaedic surgery often have multiple coexisting medical conditions (comorbidities) [13,21,24,28,39,45,58]. Preoperative risk assessment can help with decision-making and management strategies [11,37,50].…”

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

442

292

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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

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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

442

292

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“…Trauma injuries resulting in head injury also raise the risk for cervical spine (C-spine) injury in the elderly (14,15). The incidence of C-spine injuries in patients > 65 years of age is twice that of younger patients, with a disproportionately high rate of odontoid fractures (14).…”

Section: Introductionmentioning

confidence: 99%

Falls and Implementation of NEXUS in the Elderly (The FINE Study)

Denver

Shetty

Unwin

2015

The Journal of Emergency Medicine

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“…By excluding these patients in the current study, the performance of the GTOS in predicting the probability of in-hospital mortality was improved. (17). Finally, patient disposition following discharge has significant impact on the patient's 'true' functional outcome and prognosis since discharge to a hospice reflects expected death whereas discharge to the home signifies the chance of good or full recovery.…”

Section: Discussionmentioning

confidence: 99%

“…Due to the previously observed increased risk of mortality in the elderly beyond the index admission (7,17) the authors wished to test the goodness of fit of the GTOS predictor model by analyzing the death rate at one year following hospital discharge. With a misclassification rate of 17.6% and Brier score of 0.13 at 1-year post-discharge, the results of the current study imply that the GTOS is not appropriate for predicting mortality rates beyond the index admission.…”

Section: A C C E P T E D Accepted Manuscriptmentioning

confidence: 99%