Variation and trends in reasons for knee replacement revision: a multi-registry study of revision burden

Background and purpose — Studies describing time-related change in reasons for knee replacement revision have been limited to single regions or institutions, commonly analyze only 1st revisions, and may not reflect true caseloads or findings from other areas. We used revision procedure data from 3 arthroplasty registries to determine trends and differences in knee replacement revision diagnoses.Patients and methods — We obtained aggregated data for 78,151 revision knee replacement procedures recorded by the Swedish Knee Arthroplasty Register (SKAR), the Australian Orthopaedic Association National Joint Replacement Registry (AOANJRR), and the Kaiser Permanente Joint Replacement Registry (KPJRR) for the period 2003–2017. Equivalent diagnosis groups were created. We calculated the annual proportions of the most common reasons for revision.Results — Infection, loosening, and instability were among the 5 most common reasons for revision but magnitude and ranking varied between registries. Over time there were increases in proportions of revisions for infection and decreases in revisions for wear. There were inconsistent proportions and trends for the other reasons for revision. The incidence of revision for infection showed a uniform increase.Interpretation — Despite some differences in terminology, comparison of registry-recorded revision diagnoses is possible, but defining a single reason for revision is not always clear-cut. There were common increases in revision for infection and decreases in revision for wear, but variable changes in other categories. This may reflect regional practice differences and therefore generalizability of studies regarding reasons for revision is unwise.

Although the survivorship of knee arthroplasty has improved over the last 15 years, the increased volume of primary knee replacement has led to growing numbers of revision procedures (Kumar et al. 2015, Patel et al. 2015). A prior study we undertook outlined changes in the volume and incidence of revision rates in Sweden, Australia, and the Kaiser Permanente registry from the USA (Lewis et al. 2020b).Factors influencing revision change with time. Patient factors may affect the rate of primary procedures, such as rising patient and surgeon acceptance of knee replacement (Hamilton et al. 2015), increasing rates of osteoarthritis (Hunter and Bierma-Zeinstra 2019), growing use in younger patients (Leyland et al. 2016, Karas et al. 2019), and also survivorship, such as longer life expectancy, increasing obesity, and higher physical activity of those receiving a replacement (Hamilton et al. 2015). In addition, prosthesis designs change to improve perceived shortcomings such as wear, instability, and patellofemoral pain and tracking (Lewis et al. 2020a). Methods to improve surgical precision, such as computer navigation (Jones and Jerabek 2018), image-derived instrumentation (Kizaki et al. 2019), and robotic assistance (Jacofsky et al. 2016) may decrease revision requirements (Price et al. 2018)These changing factors alter the reasons for revision. Previous studies observed a decrease in revisions for wear and loosening (Sharkey et al. 2014, Thiele et al. 2015), and related this to improved prosthesis design and materials. Other studies note infection is now the most common reason for revision (Koh et al. 2017, Postler et al. 2018). Studies of changing knee replacement failure modes are limited by being derived from single institutions or regions and may not accurately reflect what is occurring elsewhere (Sharkey et al. 2014, Thiele et al. 2015, Dyrhovden et al. 2017, Koh et al. 2017, Lum et al. 2018, Postler et al. 2018). Additionally, these studies do not show the true revision burden as they are restricted to 1st revision procedures, or only revisions of previous total knee replacements (TKR), and do not include revisions of partial knee replacement procedures.Combining registry data can be difficult due to inconsistency in the definition of revision (Liebs et al. 2015), and lack of consensus in defining modes of failure, with different terminologies used (Niinimaki 2015, Siqueira et al. 2015). Some have attempted to overcome this by defining equivalent diagnoses (Havelin et al. 2011, Paxton et al. 2011, Rasmussen et al. 2016).We determined variations and trends in reasons for knee replacement revision using data on all knee arthroplasty revision procedures from the national registries of Sweden and Australia and the institutional registry of Kaiser Permanente in the USA by using equivalent diagnosis groups (Table 1, see Supplementary data).     

Associations of Pre- and Postnatal Air Pollution Exposures with Child Blood Pressure and Modification by Maternal Nutrition: A Prospective Study in the CANDLE Cohort

Background: Limited data suggest air pollution exposures may contribute to pediatric high blood pressure (HBP), a known predictor of adult cardiovascular diseases.Methods: We investigated this association in the Conditions Affecting Neurocognitive Development and Learning in Early Childhood (CANDLE) study, a sociodemographically diverse pregnancy cohort in the southern United States with participants enrolled from 2006 to 2011. We included 822 mother–child dyads with available address histories and a valid child blood pressure measurement at 4–6 y. Systolic (SBP) and diastolic blood pressures (DBP) were converted to age-, sex-, and height-specific percentiles for normal-weight U.S. children. HBP was classified based on SBP or DBP ≥90th percentile. Nitrogen dioxide (NO2) and particulate matter ≤2.5μm in aerodynamic diameter (PM2.5) estimates in both pre- and postnatal windows were obtained from annual national models and spatiotemporal models, respectively. We fit multivariate Linear and Poisson regressions and explored multiplicative joint effects with maternal nutrition, child sex, and maternal race using interaction terms.Results: Mean PM2.5 and NO2 in the prenatal period were 10.8 [standard deviation (SD): 0.9] μg/m3 and 10.0 (SD: 2.4) ppb, respectively, and 9.9 (SD: 0.6) μg/m3 and 8.8 (SD: 1.9) ppb from birth to the 4-y-old birthday. On average, SBP percentile increased by 14.6 (95% CI: 4.6, 24.6), and DBP percentile increased by 8.7 (95% CI: 1.4, 15.9) with each 2-μg/m3 increase in second-trimester PM2.5. PM2.5 averaged over the prenatal period was only significantly associated with higher DBP percentiles [β= 11.6 (95% CI: 2.9, 20.2)]. Positive associations of second-trimester PM2.5 with SBP and DBP percentiles were stronger in children with maternal folate concentrations in the lowest quartile (pinteraction= 0.05 and 0.07, respectively) and associations with DBP percentiles were stronger in female children (pinteraction= 0.05). We did not detect significant association of NO2, road proximity, and postnatal PM2.5 with any outcomes.Conclusions: The findings suggest that higher prenatal PM2.5 exposure, particularly in the second trimester, is associated with elevated early childhood blood pressure. This adverse association could be modified by pregnancy folate concentrations. https://doi.org/10.1289/EHP7486     

Erratum to “Applicability of in vitro tests for skin irritation and corrosion to regulatory classification schemes: Substantiating test strategies with data from routine studies” [Regul. Toxicol. Pharmacol. (2012) 402–414]

Skin corrosion or irritation refers to the production of irreversible or reversible damage to the skin following the application of a test substance, respectively. Traditionally, hazard assessments are conducted using the in vivo Draize skin test, but recently in vitro tests using reconstructed human epidermis (RhE) models have gained regulatory acceptance. In this study, skin corrosion (SCT) and irritation tests (SIT) using a RhE model were implemented to reduce the number of in vivo tests required by regulatory bodies. One hundred and thirty-four materials were tested from a wide range of substance classes included 46 agrochemical formulations. Results were assessed according to UN GHS, EU-CLP, ANVISA and US EPA classification schemes. There was high correlation between the two in vitro tests. Assessment of the SCT sensitivity was not possible due to the limited number of corrosives in the data set; SCT specificity and accuracy were 89% for all classification systems. Accuracy (63–76%) and sensitivity (53–67%) were low in the SIT. Specificity and concordance for agrochemical formulations alone in both the SCT and SIT were comparable to the values for the complete data set (SCT: 91% vs. 89% specificity, 91% vs. 89% accuracy and SIT: 64–88% vs. 70–85% specificity, 56–75% vs. 63–76% accuracy).     

Predicting prostate tumour location from multiparametric MRI using Gaussian kernel support vector machines: a preliminary study

The performance of a support vector machine (SVM) algorithm was investigated to predict prostate tumour location using multi-parametric MRI (mpMRI) data. The purpose was to obtain information of prostate tumour location for the implementation of bio-focused radiotherapy. In vivo mpMRI data were collected from 16 patients prior to radical prostatectomy. Sequences included T2-weighted imaging, diffusion-weighted imaging and dynamic contrast enhanced imaging. In vivo mpMRI was registered with ‘ground truth’ histology, using ex vivo MRI as an intermediate registration step to improve accuracy. Prostate contours were delineated by a radiation oncologist and tumours were annotated on histology by a pathologist. Five patients with minimal imaging artefacts were selected for this study. A Gaussian kernel SVM was trained and tested on different patient data subsets. Parameters were optimised using leave-oneout cross validation. Signal intensities of mpMRI were used as features and histology annotations as true labels. Prediction accuracy, as well as area under the curve (AUC) of the receiver operating characteristics (ROC) curve, were used to assess performance. Results demonstrated the prediction accuracy ranged from 70.4 to 87.1% and AUC of ROC ranged from 0.81 to 0.94. Additional investigations showed the apparent diffusion coefficient map from diffusion weighted imaging was the most important imaging modality for predicting tumour location. Future work will incorporate additional patient data into the framework to increase the sensitivity and specificity of the model, and will be extended to incorporate predictions of biological characteristics of the tumour which will be used in bio-focused radiotherapy optimisation.