JointCalc: A web-based personalised patient decision support tool for joint replacement

Expanding access to veterinary clinical decision support in resource-limited settings: a scoping review of clinical decision support tools in medicine and antimicrobial stewardship

Introduction

Digital clinical decision support (CDS) tools are of growing importance in supporting healthcare professionals in understanding complex clinical problems and arriving at decisions that improve patient outcomes. CDS tools are also increasingly used to improve antimicrobial stewardship (AMS) practices in healthcare settings. However, far fewer CDS tools are available in lowerand middle-income countries (LMICs) and in animal health settings, where their use in improving diagnostic and treatment decision-making is likely to have the greatest impact. The aim of this study was to evaluate digital CDS tools designed as a direct aid to support diagnosis and/or treatment decisionmaking, by reviewing their scope, functions, methodologies, and quality. Recommendations for the development of veterinary CDS tools in LMICs are then provided.

Methods

The review considered studies and reports published between January 2017 and October 2023 in the English language in peer-reviewed and gray literature.

Results

A total of 41 studies and reports detailing CDS tools were included in the final review, with 35 CDS tools designed for human healthcare settings and six tools for animal healthcare settings. Of the tools reviewed, the majority were deployed in high-income countries (80.5%). Support for AMS programs was a feature in 12 (29.3%) of the tools, with 10 tools in human healthcare settings. The capabilities of the CDS tools varied when reviewed against the GUIDES checklist.

Discussion

We recommend a methodological approach for the development of veterinary CDS tools in LMICs predicated on securing sufficient and sustainable funding. Employing a multidisciplinary development team is an important first step. Developing standalone CDS tools using Bayesian algorithms based on local expert knowledge will provide users with rapid and reliable access to quality guidance on diagnoses and treatments. Such tools are likely to contribute to improved disease management on farms and reduce inappropriate antimicrobial use, thus supporting AMS practices in areas of high need.

The effect of mobile application-based rehabilitation in patients with total knee arthroplasty: A systematic review and meta-analysis

OBJECTIVE

Mobile applications have been used frequently in post-operative orthopedic rehabilitation in recent years. However, no systematic review has emphasized the importance of mobile applications in the rehabilitation process after total knee arthroplasty (TKA). This systematic review and meta-analysis aimed to evaluate the effectiveness of mobile application-based rehabilitation practices in patients with TKA.

MATERIAL AND METHODS

PubMed, Web-of-Science, Scopus, ScienceDirect and Cochrane databases were searched. The Physiotherapy Evidence Database (PEDro) and the Revised Cochrane risk-of-bias tool randomized trials 2 (RoB2) tools were used to demonstrate the methodological quality and risk of bias.

RESULTS

A total of 584 articles were screened. Finally, six papers were included in the systematic review. PEDro scores ranged from 4 to 7 (median: 5.5), indicating fair to good methodological quality. All studies were classified as "some concerns" in RoB2. Mobile application-based rehabilitation demonstrated better scores on pain, range of motion (ROM), objective and subjective function, satisfaction and compliance in general. Meta-analysis proved that mobile application-based telerehabilitation demonstrated better results on subjective function (ES:0.57, 95% CI: 0.11-1.02).

CONCLUSION

Compared to conventional rehabilitation, application-based telerehabilitation provides more effective results in function, pain and ROM. Furthermore, mobile application-based rehabilitation should also be considered regarding patient satisfaction and compliance.

External Validation of Prediction Models for Surgical Complications in People Considering Total Hip or Knee Arthroplasty Was Successful for Delirium but Not for Surgical Site Infection, Postoperative Bleeding, and Nerve Damage: A Retrospective Cohort Study

Although several models for the prediction of surgical complications after primary total hip or total knee replacement (THA and TKA, respectively) are available, only a few models have been externally validated. The aim of this study was to externally validate four previously developed models for the prediction of surgical complications in people considering primary THA or TKA. We included 2614 patients who underwent primary THA or TKA in secondary care between 2017 and 2020. Individual predicted probabilities of the risk for surgical complication per outcome (i.e., surgical site infection, postoperative bleeding, delirium, and nerve damage) were calculated for each model. The discriminative performance of patients with and without the outcome was assessed with the area under the receiver operating characteristic curve (AUC), and predictive performance was assessed with calibration plots. The predicted risk for all models varied between <0.01 and 33.5%. Good discriminative performance was found for the model for delirium with an AUC of 84% (95% CI of 0.82-0.87). For all other outcomes, poor discriminative performance was found; 55% (95% CI of 0.52-0.58) for the model for surgical site infection, 61% (95% CI of 0.59-0.64) for the model for postoperative bleeding, and 57% (95% CI of 0.53-0.61) for the model for nerve damage. Calibration of the model for delirium was moderate, resulting in an underestimation of the actual probability between 2 and 6%, and exceeding 8%. Calibration of all other models was poor. Our external validation of four internally validated prediction models for surgical complications after THA and TKA demonstrated a lack of predictive accuracy when applied in another Dutch hospital population, with the exception of the model for delirium. This model included age, the presence of a heart disease, and the presence of a disease of the central nervous system as predictor variables. We recommend that clinicians use this simple and straightforward delirium model during preoperative counselling, shared decision-making, and early delirium precautionary interventions.

How long do revised and multiply revised hip replacements last? A retrospective observational study of the National Joint Registry

Background

Hip replacements are common and effective operations but patients that undergo this intervention are at risk of the replacements failing, requiring costly and often complex revision surgery with poorer outcomes than primary surgery. There is paucity of reliable data examining the treatment pathway for hip replacements over the life of the patient in terms of risk of revision and re-revisions. We aim to provide detailed information on the longevity of hip revision surgery.

Methods

We did a retrospective observational registry-based study of the National Joint Registry, using data on hip replacements from all participating hospitals in England and Wales, UK. We included data on all first revisions, with an identifiable primary procedure, with osteoarthritis as the sole indication for the original primary procedure. Kaplan-Meier estimates were used to determine the cumulative probability of revision and subsequent re-revision after primary hip replacement. Analyses were stratified by age and gender, and the influence of time from first to second revision on the risk of further revision was explored.

Findings

Between April 1, 2003, and Dec 31, 2019, there were 29 010 revision hip replacements with a linked primary episode. Revision rates of revision hip replacements were higher in patients younger than 55 years than in older age groups. After revision of primary total hip replacement, 21·3% (95% CI 18·6-24·4) of first revisions were revised again within 15 years, 22·3% (20·3-24·4) of second revisions were revised again within 7 years, and 22·3% (18·3-27·0) of third revisions were revised again within 3 years. After revision of hip resurfacing, 23·7% (95% CI 19·6-28·5) of these revisions were revised again within 15 years, 21·0% (17·0-25·8) of second revisions were revised again within 7 years, and 19·3% (11·9-30·4) of third revisions were revised again within 3 years. A shorter time between revision episodes was associated with earlier subsequent revision.

Interpretation

Younger patients are at an increased risk of multiple revisions. Patients who undergo a revision have a steadily increasing risk of further revision the more procedures they undergo, and each subsequent revision lasts for approximately half the time of the previous one. Although hip replacements are effective for improving pain and function and usually last a remarkably long time, if they are revised, successive revisions are progressively and markedly less successful.

Funding

NIHR Biomedical Research Centre at the University Hospitals Bristol and Weston NHS Foundation Trust and the University of Bristol, Healthcare Quality Improvement Partnership; and the National Joint Registry.

A comparison of survival models for prediction of eight-year revision risk following total knee and hip arthroplasty

Background

There is increasing interest in the development and use of clinical prediction models, but a lack of evidence-supported guidance on the merits of different modelling approaches. This is especially true for time-to-event outcomes, where limited studies have compared the vast number of modelling approaches available. This study compares prediction accuracy and variable importance measures for four modelling approaches in prediction of time-to-revision surgery following total knee arthroplasty (TKA) and total hip arthroplasty (THA).

Methods

The study included 321,945 TKA and 151,113 THA procedures performed between 1 January 2003 and 31 December 2017. Accuracy of the Cox model, Weibull parametric model, flexible parametric model, and random survival forest were compared, with patient age, sex, comorbidities, and prosthesis characteristics considered as predictors. Prediction accuracy was assessed using the Index of Prediction Accuracy (IPA), c-index, and smoothed calibration curves. Variable importance rankings from the Cox model and random survival forest were also compared.

Results

Overall, the Cox and flexible parametric survival models performed best for prediction of both TKA (integrated IPA 0.056 (95% CI [0.054, 0.057]) compared to 0.054 (95% CI [0.053, 0.056]) for the Weibull parametric model), and THA revision. (0.029 95% CI [0.027, 0.030] compared to 0.027 (95% CI [0.025, 0.028]) for the random survival forest). The c-index showed broadly similar discrimination between all modelling approaches. Models were generally well calibrated, but random survival forest underfitted the predicted risk of TKA revision compared to regression approaches. The most important predictors of revision were similar in the Cox model and random survival forest for TKA (age, opioid use, and patella resurfacing) and THA (femoral cement, depression, and opioid use).

Conclusion

The Cox and flexible parametric models had superior overall performance, although all approaches performed similarly. Notably, this study showed no benefit of a tuned random survival forest over regression models in this setting.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12874-022-01644-3.

Minimal important changes and differences were estimated for Oxford hip and knee scores following primary and revision arthroplasty

OBJECTIVES

To create estimates for clinically meaningful changes and differences in pain and joint function for the Oxford Hip and Knee Scores (OHS/OKS) in primary and revision joint replacement.

STUDY DESIGN AND SETTING

694,487 primary and revision joint replacement procedures were analysed from the NHS PROMs dataset between 2012-2020. Minimal important changes (MIC) and differences (MID) were calculated using distribution and anchor-based methods (including receiver-operating characteristic (ROC) curve and predictive-modelling techniques).

RESULTS

For comparison of two or more groups (such as in a clinic trial), MID estimates were ∼5 points. For cohort studies investigating changes over time in a single group of patients, MIC_group estimates were 12.4 points (primary hip replacement), 8.6 points (revision hip replacement), 10.5 points (primary knee replacement) and 9.4 points (revision knee replacement). For studies investigating changes over time at the individual patient level, MIC_adjusted estimates were ∼8 points, ∼6 points, ∼7 points and ∼6 points respectively.

CONCLUSION

This study has calculated contemporary estimates of clinically important changes and differences for the OHS/OKS for primary and revision hip and knee replacement. These estimates can be used to inform sample size calculations and to interpret changes in joint function over time and differences between groups.

How long do revised and multiply revised knee replacements last? An analysis of the National Joint Registry

BACKGROUND

Knee replacements are common and effective operations but patients that undergo this intervention are at risk of needing subsequent costly and often complex revision surgery with poorer outcomes than primary surgery. The treatment pathway over the life of the patient in terms of risk of revision and re-revision(s) is poorly described. We aim to provide detailed information on the longevity of revision surgery.

METHODS

This was a retrospective observational registry-based study of the National Joint Registry. Kaplan-Meier estimates were used to determine the cumulative probability of revision and subsequent re-revision(s) following primary knee replacement. Analyses were stratified by age and gender and the influence of time from first to second revision on the risk of further revision was explored.

FINDINGS

There were 33,292 revision knee replacements with a linked primary episode. Revision rates of revision knee replacements were higher in males and younger patients. 19·9% of revisions were revised again within 13 years, 20·7% of second revisions were revised again within 5 years and 20·7% of third revisions were revised again within 3 years. A shorter time between revision episodes was associated with earlier subsequent revision.

INTERPRETATION

Males and younger patients are at higher risk of multiple revisions. Patients who undergo a revision have steadily increasing risk of further revision the more procedures they undergo, and each subsequent revision lasts for approximately half the time of the previous one.

FUNDING

This study was supported by the NIHR Biomedical Research Centre at University Hospitals Bristol and Weston National Health Service Foundation Trust and the University of Bristol. The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care. This study was also supported by funding from Healthcare Quality Improvement Partnership and the National Joint Registry. Posts of authors of this work are part funded by a grant from the National Joint Registry to conduct statistical analysis for the National Joint Registry. AS was funded by a Medical Research Council Strategic Skills Fellowship MR/L01226X/1.

Personalized estimation of one-year mortality risk after elective hip or knee arthroplasty for osteoarthritis

AIMS

To develop and validate patient-centred algorithms that estimate individual risk of death over the first year after elective joint arthroplasty surgery for osteoarthritis.

METHODS

A total of 763,213 hip and knee joint arthroplasty episodes recorded in the National Joint Registry for England and Wales (NJR) and 105,407 episodes from the Norwegian Arthroplasty Register were used to model individual mortality risk over the first year after surgery using flexible parametric survival regression.

RESULTS

The one-year mortality rates in the NJR were 10.8 and 8.9 per 1,000 patient-years after hip and knee arthroplasty, respectively. The Norwegian mortality rates were 9.1 and 6.0 per 1,000 patient-years, respectively. The strongest predictors of death in the final models were age, sex, body mass index, and American Society of Anesthesiologists grade. Exposure variables related to the intervention, with the exception of knee arthroplasty type, did not add discrimination over patient factors alone. Discrimination was good in both cohorts, with c-indices above 0.76 for the hip and above 0.70 for the knee. Time-dependent Brier scores indicated appropriate estimation of the mortality rate (≤ 0.01, all models).

CONCLUSION

Simple demographic and clinical information may be used to calculate an individualized estimation for one-year mortality risk after hip or knee arthroplasty (https://jointcalc.shef.ac.uk). These models may be used to provide patients with an estimate of the risk of mortality after joint arthroplasty. Cite this article: Bone Joint Res 2020;9(11):808-820.