Estimating incidence rates of periprosthetic joint infection after hip and knee arthroplasty for osteoarthritis using linked registry and administrative health data.

AIMS: The aim of this study was to estimate the 90-day periprosthetic joint infection (PJI) rates following total knee arthroplasty (TKA) and total hip arthroplasty (THA) for osteoarthritis (OA). METHODS: This was a data linkage study using the New South Wales (NSW) Admitted Patient Data Collection (APDC) and the Australian Orthopaedic Association National Joint Replacement Registry (AOANJRR), which collect data from all public and private hospitals in NSW, Australia. Patients who underwent a TKA or THA for OA between 1 January 2002 and 31 December 2017 were included. The main outcome measures were 90-day incidence rates of hospital readmission for: revision arthroplasty for PJI as recorded in the AOANJRR; conservative definition of PJI, defined by T84.5, the PJI diagnosis code in the APDC; and extended definition of PJI, defined by the presence of either T84.5, or combinations of diagnosis and procedure code groups derived from recursive binary partitioning in the APDC. RESULTS: The mean 90-day revision rate for infection was 0.1% (0.1% to 0.2%) for TKA and 0.3% (0.1% to 0.5%) for THA. The mean 90-day PJI rates defined by T84.5 were 1.3% (1.1% to 1.7%) for TKA and 1.1% (0.8% to 1.3%) for THA. The mean 90-day PJI rates using the extended definition were 1.9% (1.5% to 2.2%) and 1.5% (1.3% to 1.7%) following TKA and THA, respectively. CONCLUSION: When reporting the revision arthroplasty for infection, the AOANJRR substantially underestimates the rate of PJI at 90 days. Using combinations of infection codes and PJI-related surgical procedure codes in linked hospital administrative databases could be an alternative way to monitor PJI rates.Cite this article: Bone Joint J 2022;104-B(9):1060-1066.

Predictive validity of the CriSTAL tool for short-term mortality in older people presenting at Emergency Departments: a prospective study.

ABSTRACT: To determine the validity of the Australian clinical prediction tool Criteria for Screening and Triaging to Appropriate aLternative care (CRISTAL) based on objective clinical criteria to accurately identify risk of death within 3 months of admission among older patients. METHODS: Prospective study of ≥ 65 year-olds presenting at emergency departments in five Australian (Aus) and four Danish (DK) hospitals. Logistic regression analysis was used to model factors for death prediction; Sensitivity, specificity, area under the ROC curve and calibration with bootstrapping techniques were used to describe predictive accuracy. RESULTS: 2493 patients, with median age 78-80 years (DK-Aus). The deceased had significantly higher mean CriSTAL with Australian mean of 8.1 (95% CI 7.7-8.6 vs. 5.8 95% CI 5.6-5.9) and Danish mean 7.1 (95% CI 6.6-7.5 vs. 5.5 95% CI 5.4-5.6). The model with Fried Frailty score was optimal for the Australian cohort but prediction with the Clinical Frailty Scale (CFS) was also good (AUROC 0.825 and 0.81, respectively). Values for the Danish cohort were AUROC 0.764 with Fried and 0.794 using CFS. The most significant independent predictors of short-term death in both cohorts were advanced malignancy, frailty, male gender and advanced age. CriSTAL's accuracy was only modest for in-hospital death prediction in either setting. CONCLUSIONS: The modified CriSTAL tool (with CFS instead of Fried's frailty instrument) has good discriminant power to improve prognostic certainty of short-term mortality for ED physicians in both health systems. This shows promise in enhancing clinician's confidence in initiating earlier end-of-life discussions.

Pre-existing risk factors for in-hospital death among older patients could be used to initiate end-of-life discussions rather than Rapid Response System calls: A case-control study.

AIM: To investigate associations between clinical parameters - beyond the evident physiological deterioration and limitations of medical treatment - with in-hospital death for patients receiving Rapid Response System (RRS) attendances. METHODS: Retrospective case-control analysis of clinical parameters for 328 patients aged 60 years and above at their last RRS call during admission to a single teaching hospital in the 2012-2013 calendar years. Generalised estimating equation modelling was used to compare the deceased with a randomly selected sample of those who had RRS calls and survived admission (controls), matched by age group, sex, and hospital ward. RESULTS: In addition to a pre-existing order for limitation of treatment or cardiac arrest (OR 6.92; 95%CI 4.61-10.27), nursing home residence, proteinuria, advanced malignancy, acute myocardial infarction, chronic kidney disease, cognitive impairment and frailty were associated with high risk of death. After adjusting for all the clinical indicators investigated, the strongest risk factors for in-hospital death for patients with a RRS call were advanced malignancy (OR 3.95; 95%CI 2.16-7.21) and new myocardial infarction (OR 2.79; 95%CI 1.86-4.20). Patients with cognitive impairment, frailty indicator or chronic kidney disease were twice as likely to die as patients without those risk factors. CONCLUSION: In a sample of older deteriorated patients requiring a RRS attendance, multiple indicators of chronic illness, cognitive impairment and frailty were significantly associated with high risk of death. These clinical features beyond the evident orders for limitation of medical treatment should signal the need for clinicians to initiate end-of-life discussions that may prevent futile interventions.