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Using Artificial Intelligence to Revolutionise the Patient Care Pathway in Hip and Knee Arthroplasty (ARCHERY):
Luke Farrow1,2, George Patrick Ashcroft1,2, Mingjun Zhong1
1Institute of Applied Health Sciences, University of Aberdeen, Aberdeen, United Kingdom.
Insights
Artificial intelligence (AI) can improve hip and knee replacement surgery selection. Machine learning models predict patient suitability, aiming to reduce waiting times for arthroplasty procedures.
Area of Science:
- Orthopedic surgery
- Artificial intelligence in healthcare
- Machine learning applications
Background:
- Osteoarthritis affects many older adults globally, necessitating joint replacement surgery.
- Backlogs in elective surgeries and an aging population strain arthroplasty services.
- Improving primary care referral selection can enhance arthroplasty service efficiency.
Purpose of the Study:
- Develop a patient cohort for hip/knee replacement suitability assessment using NHS Grampian data.
- Identify factors influencing arthroplasty selection and create a predictive model.
- Guide arthroplasty referral pathways with a validated patient-specific predictive tool.
Main Methods:
- The ARCHERY project utilizes the Grampian Data Safe Haven and AI Platform.
- Machine learning models will be developed using linked patient data (demographics, clinical, imaging) from 2015-2022.
- Pattern classification and probabilistic prediction models will be built and validated using cross-validation and standard metrics.
Main Results:
- Study funded by Chief Scientist Office Scotland; data collection began May 2022.
- Results are anticipated for publication in Q1 2024.
- ISRCTN registration is complete, with privacy advisory committee approval obtained.
Conclusions:
- This project pioneers an automated solution for arthroplasty selection using routine healthcare data.
- External validation and clinical testing are necessary next steps.
- The developed model has the potential to increase surgery selection rates and decrease waiting times.
Background:
Hip and knee osteoarthritis is substantially prevalent worldwide, with large numbers of older adults undergoing joint replacement (arthroplasty) every year. A backlog of elective surgery due to the COVID-19 pandemic, and an aging population, has led to substantial issues with access to timely arthroplasty surgery. A potential method to improve the efficiency of arthroplasty services is by increasing the percentage of patients who are listed for surgery from primary care referrals. The use of artificial intelligence (AI) techniques, specifically machine learning, provides a potential unexplored solution to correctly and rapidly select suitable patients for arthroplasty surgery.
Objective:
This study has 2 objectives: (1) develop a cohort of patients with referrals by general practitioners regarding assessment of suitability for hip or knee replacement from National Health Service (NHS) Grampian data via the Grampian Data Safe Haven and (2) determine the demographic, clinical, and imaging characteristics that influence the selection of patients to undergo hip or knee arthroplasty, and develop a tested and validated patient-specific predictive model to guide arthroplasty referral pathways.
Methods:
The AI to Revolutionise the Patient Care Pathway in Hip and Knee Arthroplasty (ARCHERY) project will be delivered through 2 linked work packages conducted within the Grampian Data Safe Haven and Safe Haven Artificial Intelligence Platform. The data set will include a cohort of individuals aged ≥16 years with referrals for the consideration of elective primary hip or knee replacement from January 2015 to January 2022. Linked pseudo-anonymized NHS Grampian health care data will be acquired including patient demographics, medication records, laboratory data, theatre records, text from clinical letters, and radiological images and reports. Following the creation of the data set, machine learning techniques will be used to develop pattern classification and probabilistic prediction models based on radiological images. Supplemental demographic and clinical data will be used to improve the predictive capabilities of the models. The sample size is predicted to be approximately 2000 patients-a sufficient size for satisfactory assessment of the primary outcome. Cross-validation will be used for development, testing, and internal validation. Evaluation will be performed through standard techniques, such as the C statistic (area under curve) metric, calibration characteristics (Brier score), and a confusion matrix.
Results:
The study was funded by the Chief Scientist Office Scotland as part of a Clinical Research Fellowship that runs from August 2021 to August 2024. Approval from the North Node Privacy Advisory Committee was confirmed on October 13, 2021. Data collection started in May 2022, with the results expected to be published in the first quarter of 2024. ISRCTN registration has been completed.
Conclusions:
This project provides a first step toward delivering an automated solution for arthroplasty selection using routinely collected health care data. Following appropriate external validation and clinical testing, this project could substantially improve the proportion of referred patients that are selected to undergo surgery, with a subsequent reduction in waiting time for arthroplasty appointments.
Trial Registration:
ISRCTN Registry ISRCTN18398037; https://www.isrctn.com/ISRCTN18398037.
International Registered Report Identifier (Irrid):
PRR1-10.2196/37092.
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