In-hospital Complications and Mortality of Unilateral, Bilateral, and Revision TKA: Based on an estimate of 4,159,661 Discharges

Memtsoudis, Stavros G.; Valle, Alejandro González Della; Besculides, Melanie; Gaber, Licia; Sculco, Thomas P.

doi:10.1007/s11999-008-0402-5

Cited by 175 publications

(150 citation statements)

References 58 publications

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“…Analyses of risk-adjusted mortality rates should adjust mortality rates only for baseline comorbid diseases, not complications that arise from surgery [15]. The degree to which this issue influenced our results is unclear, although it has been reported that the majority of common diagnoses are comorbidities rather than adverse events [15,30,36]. Fourth, the NHDS enabled only ascertainment of inpatient outcomes, and thus postdischarge complications [24,40,58].…”

Section: Discussionmentioning

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

434

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: Discussionmentioning

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

434

292

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“…We used ICD-9 codes to select adult (18 years and older) patients who had a THA (procedure code 81.51) for hip osteoarthritis [3,15] (Table 2) and adverse events (Table 3), based on ICD-9 codes.…”

Section: Methodsmentioning

confidence: 99%

“…We entered variables with a minimum 2% prevalence (with the exception of clinically relevant adverse events [ Table 3] with variables with a 1% minimum prevalence) and p values of 0.001 or less [15] for statistical significance based on the large dataset and to correct for multiple comparisons [13] in stepwise backward logistic regressions for mortality and adverse events and a backward linear regression for days of care. We excluded discharge status in the model of mortality since discharge status contains death as a category.…”

Section: Methodsmentioning

confidence: 99%

The National Hospital Discharge Survey and Nationwide Inpatient Sample: The Databases Used Affect Results in THA Research

Bekkers

Bot

Makarawung

et al. 2014

Clinical Orthopaedics &Amp; Related Research

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Background The National Hospital Discharge Survey (NHDS) and the Nationwide Inpatient Sample (NIS) collect sample data and publish annual estimates of inpatient care in the United States, and both are commonly used in orthopaedic research. However, there are important differences between the databases, and because of these differences, asking these two databases the same question may result in different answers. The degree to which this is true for arthroplasty-related research has, to our knowledge, not been characterized. Question/purposes We tested the following null hypotheses: (1) there are no differences between the NHDS and NIS in patient characteristics, comorbidities, and adverse events in patients with hip osteoarthritis treated with THA, and (2) there are no differences between databases in factors associated with inpatient mortality, adverse events, and length of hospital stay after THA. Methods The NHDS and NIS databases use different methods of data collection and weighting to provide data representative of all nonfederal hospital discharges in the United States. In 2006 the NHDS database contained 203,149 patients with hip arthritis treated with hip arthroplasty, and the NIS database included 193,879 patients. Multivariable analyses for factors associated with inpatient mortality, adverse events, and days of care were constructed for each database. Results We found that 26 of 42 of the factors in demographics, comorbidities, and adverse events after THA in the NIS and NHDS databases differed more than 10%. Age and days of care were associated with inpatient mortality with the NHDS and the NIS although the effect rates differ more than 10%. The NIS identified seven other factors not identified by the NHDS: wound complications, congestive heart failure, new mental disorder, chronic pulmonary disease, dementia, geographic region Northeast, acute postoperative anemia, and sex, that were associated with inpatient mortality even after controlling for potentially confounding variables. For inpatient adverse events, atrial fibrillation, osteoporosis, and female sex were associated with the NHDS and the NIS although the effect rates differ One of the authors (SB) certifies that he or she has received, during the study period, funding from VU University Amsterdam, faculty fund. One of the authors (AGJB) certifies that he or she has received, during the study period, funding from an AnnaFonds Travel grant (Dutch Orthopaedic travel grant) (less than USD 10,000), VSB-fonds, a nonmedical study grant (USD 10,000 to USD 100,000) and Prins Bernhard Cultuurfonds, Banning-de Jong fonds, a nonmedical study grant for excellent Dutch students USD 10,000 to USD 100,000). One of the authors (DR) certifies that he or she, or a member of his or her immediate family, has received or may receive payments or benefits, during the study period, an amount of USD (less than USD 10,000), from Wright Medical Technologies, Inc (Arlington, TN, USA), Biomet, Inc (Warsaw, IN, USA), Skeletal Dynamics Inc (Miami, FL, USA), AO Nor...

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“…Advocates of simultaneous TKA have cited benefits of a single anesthetic, shorter cumulative hospital stays, patient convenience and satisfaction, and increased cost effectiveness for the healthcare system without increasing perioperative morbidity and mortality and without compromising the clinical outcomes of the operation [8, 9, 11, 12, 17, 18, 24, 27-30, 32, 35]. Opponents of the simultaneous approach cite increased perioperative complications, including pulmonary embolism, major cardiac events, ileus, higher transfusion rates, confusion, and death [1,4,12,15,21,23,27,33,35]. Concern also exists regarding the financial disincentive to the surgeon for performing bilateral simultaneous arthroplasty procedures [2,6,28].…”

Section: Introductionmentioning

confidence: 99%

Perioperative Complications of Simultaneous versus Staged Unicompartmental Knee Arthroplasty

Berend¹,

Morris²,

Skeels³

et al. 2011

Clinical Orthopaedics &Amp; Related Research

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In-hospital Complications and Mortality of Unilateral, Bilateral, and Revision TKA: Based on an estimate of 4,159,661 Discharges

Cited by 175 publications

References 58 publications

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

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

The National Hospital Discharge Survey and Nationwide Inpatient Sample: The Databases Used Affect Results in THA Research

Perioperative Complications of Simultaneous versus Staged Unicompartmental Knee Arthroplasty

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