Mhairi M Kerr1, Stephen E Graves1,2, Katherine M Duszynski1, Maria C Inacio3, Richard N de Steiger2,4, Ian A Harris2,5, Ilana N Ackerman6, Louisa R Jorm7, Michelle F Lorimer2,8, Aarti Gulyani1, Nicole L Pratt1. 1. Quality Use of Medicines and Pharmacy Research Centre, Clinical and Health Sciences, University of South Australia, Adelaide, Australia. 2. Australian Orthopaedic Association National Joint Replacement Registry, South Australian Health and Medical Research Institute, Adelaide, Australia. 3. Registry of Senior Australians, South Australian Health and Medical Research Institute, Adelaide, Australia. 4. Department of Surgery, Epworth HealthCare, University of Melbourne, Richmond, Australia. 5. South Western Sydney Clinical School, University of New South Wales, Liverpool Hospital, Liverpool, Australia. 6. School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia. 7. Centre for Big Data Research in Health, University of New South Wales, Sydney, Australia. 8. South Australian Health and Medical Research Institute, Adelaide, Australia.
Abstract
BACKGROUND: When analyzing the outcomes of joint arthroplasty, an important factor to consider is patient comorbidities. The presence of multiple comorbidities has been associated with longer hospital stays, more postoperative complications, and increased mortality. The American Society of Anesthesiologists (ASA) physical status classification system score is a measure of a patient's overall health and has been shown to be associated with complications and mortality after joint arthroplasty. The Rx-Risk score is another measure for determining the number of different health conditions for which an individual is treated, with a possible score ranging from 0 to 47. QUESTIONS/PURPOSES: For patients undergoing THA or TKA, we asked: (1) Which metric, the Rx-Risk score or the ASA score, correlates more closely with 30- and 90-day mortality after TKA or THA? (2) Is the Rx-Risk score correlated with the ASA score? METHODS: This was a retrospective analysis of the Australian Orthopaedic Association National Joint Replacement Registry (AOANJRR) database linked to two other national databases, the National Death Index (NDI) database and the Pharmaceutical Benefits Scheme (PBS), a dispensing database. Linkage to the NDI provided outcome information on patient death, including the fact of and date of death. Linkage to the PBS was performed to obtain records of all medicines dispensed to patients undergoing a joint replacement procedure. Patients were included if they had undergone either a THA (119,076 patients, 131,336 procedures) or TKA (182,445 patients, 215,712 procedures) with a primary diagnosis of osteoarthritis, performed between 2013 and 2017. We excluded patients with missing ASA information (THA: 3% [3055 of 119,076]; TKA: 2% [4095 of 182,445]). This left 127,761 primary THA procedures performed in 116,021 patients (53% [68,037 of 127,761] were women, mean age 68 ± 11 years) and 210,501 TKA procedures performed in 178,350 patients (56% [117,337 of 210,501] were women, mean age 68 ± 9 years) included in this study. Logistic regression models were used to determine the concordance of the ASA and Rx-Risk scores and 30-day and 90-day postoperative mortality. The Spearman correlation coefficient (r) was used to estimate the correlation between the ASA score and Rx-Risk score. All analyses were performed separately for THAs and TKAs. RESULTS: We found both the ASA and Rx-Risk scores had high concordance with 30-day mortality after THA (ASA: c-statistic 0.83 [95% CI 0.79 to 0.86]; Rx-Risk: c-statistic 0.82 [95% CI 0.79 to 0.86]) and TKA (ASA: c-statistic 0.73 [95% CI 0.69 to 0.78]; Rx-Risk: c-statistic 0.74 [95% CI 0.70 to 0.79]). Although both scores were strongly associated with death, their correlation was moderate for patients undergoing THA (r = 0.45) and weak for TKA (r = 0.38). However, the median Rx-Risk score did increase with increasing ASA score. For example, for THAs, the median Rx-Risk score was 1, 3, 5, and 7 for ASA scores 1, 2, 3, and 4, respectively. For TKAs, the median Rx-Risk score was 2, 4, 5, and 7 for ASA scores 1, 2, 3, and 4, respectively. CONCLUSION: The ASA physical status and RxRisk were associated with 30-day and 90-day mortality; however, the scores were only weakly to moderately correlated with each other. This suggests that although both scores capture a similar level of patient illness, each score may be capturing different aspects of health. The Rx-Risk may be used as a complementary measure to the ASA score. LEVEL OF EVIDENCE: Level III, therapeutic study.
BACKGROUND: When analyzing the outcomes of joint arthroplasty, an important factor to consider is patient comorbidities. The presence of multiple comorbidities has been associated with longer hospital stays, more postoperative complications, and increased mortality. The American Society of Anesthesiologists (ASA) physical status classification system score is a measure of a patient's overall health and has been shown to be associated with complications and mortality after joint arthroplasty. The Rx-Risk score is another measure for determining the number of different health conditions for which an individual is treated, with a possible score ranging from 0 to 47. QUESTIONS/PURPOSES: For patients undergoing THA or TKA, we asked: (1) Which metric, the Rx-Risk score or the ASA score, correlates more closely with 30- and 90-day mortality after TKA or THA? (2) Is the Rx-Risk score correlated with the ASA score? METHODS: This was a retrospective analysis of the Australian Orthopaedic Association National Joint Replacement Registry (AOANJRR) database linked to two other national databases, the National Death Index (NDI) database and the Pharmaceutical Benefits Scheme (PBS), a dispensing database. Linkage to the NDI provided outcome information on patient death, including the fact of and date of death. Linkage to the PBS was performed to obtain records of all medicines dispensed to patients undergoing a joint replacement procedure. Patients were included if they had undergone either a THA (119,076 patients, 131,336 procedures) or TKA (182,445 patients, 215,712 procedures) with a primary diagnosis of osteoarthritis, performed between 2013 and 2017. We excluded patients with missing ASA information (THA: 3% [3055 of 119,076]; TKA: 2% [4095 of 182,445]). This left 127,761 primary THA procedures performed in 116,021 patients (53% [68,037 of 127,761] were women, mean age 68 ± 11 years) and 210,501 TKA procedures performed in 178,350 patients (56% [117,337 of 210,501] were women, mean age 68 ± 9 years) included in this study. Logistic regression models were used to determine the concordance of the ASA and Rx-Risk scores and 30-day and 90-day postoperative mortality. The Spearman correlation coefficient (r) was used to estimate the correlation between the ASA score and Rx-Risk score. All analyses were performed separately for THAs and TKAs. RESULTS: We found both the ASA and Rx-Risk scores had high concordance with 30-day mortality after THA (ASA: c-statistic 0.83 [95% CI 0.79 to 0.86]; Rx-Risk: c-statistic 0.82 [95% CI 0.79 to 0.86]) and TKA (ASA: c-statistic 0.73 [95% CI 0.69 to 0.78]; Rx-Risk: c-statistic 0.74 [95% CI 0.70 to 0.79]). Although both scores were strongly associated with death, their correlation was moderate for patients undergoing THA (r = 0.45) and weak for TKA (r = 0.38). However, the median Rx-Risk score did increase with increasing ASA score. For example, for THAs, the median Rx-Risk score was 1, 3, 5, and 7 for ASA scores 1, 2, 3, and 4, respectively. For TKAs, the median Rx-Risk score was 2, 4, 5, and 7 for ASA scores 1, 2, 3, and 4, respectively. CONCLUSION: The ASA physical status and RxRisk were associated with 30-day and 90-day mortality; however, the scores were only weakly to moderately correlated with each other. This suggests that although both scores capture a similar level of patient illness, each score may be capturing different aspects of health. The Rx-Risk may be used as a complementary measure to the ASA score. LEVEL OF EVIDENCE: Level III, therapeutic study.
Authors: Jordan F Schaeffer; Daniel J Scott; Jonathan A Godin; David E Attarian; Samuel S Wellman; Richard C Mather Journal: J Arthroplasty Date: 2014-12-16 Impact factor: 4.757
Authors: Nicolas R Thompson; Youran Fan; Jarrod E Dalton; Lara Jehi; Benjamin P Rosenbaum; Sumeet Vadera; Sandra D Griffith Journal: Med Care Date: 2015-04 Impact factor: 2.983
Authors: Robert G Whitmore; James H Stephen; Coleen Vernick; Peter G Campbell; Sanjay Yadla; George M Ghobrial; Mitchell G Maltenfort; John K Ratliff Journal: Spine J Date: 2013-04-17 Impact factor: 4.166
Authors: Alan J Silman; Christophe Combescure; Rory J Ferguson; Stephen E Graves; Elizabeth W Paxton; Chris Frampton; Ove Furnes; Anne Marie Fenstad; Gary Hooper; Anne Garland; Anneke Spekenbrink-Spooren; J Mark Wilkinson; Keijo Mäkelä; Anne Lübbeke; Ola Rolfson Journal: Acta Orthop Date: 2021-03-01 Impact factor: 3.717
Authors: Nicole L Pratt; Mhairi Kerr; John D Barratt; Anna Kemp-Casey; Lisa M Kalisch Ellett; Emmae Ramsay; Elizabeth Ellen Roughead Journal: BMJ Open Date: 2018-04-13 Impact factor: 2.692