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Bone & Joint Research
Vol. 13, Issue 4 | Pages 184 - 192
18 Apr 2024
Morita A Iida Y Inaba Y Tezuka T Kobayashi N Choe H Ike H Kawakami E

Aims

This study was designed to develop a model for predicting bone mineral density (BMD) loss of the femur after total hip arthroplasty (THA) using artificial intelligence (AI), and to identify factors that influence the prediction. Additionally, we virtually examined the efficacy of administration of bisphosphonate for cases with severe BMD loss based on the predictive model.

Methods

The study included 538 joints that underwent primary THA. The patients were divided into groups using unsupervised time series clustering for five-year BMD loss of Gruen zone 7 postoperatively, and a machine-learning model to predict the BMD loss was developed. Additionally, the predictor for BMD loss was extracted using SHapley Additive exPlanations (SHAP). The patient-specific efficacy of bisphosphonate, which is the most important categorical predictor for BMD loss, was examined by calculating the change in predictive probability when hypothetically switching between the inclusion and exclusion of bisphosphonate.


Bone & Joint Open
Vol. 4, Issue 3 | Pages 168 - 181
14 Mar 2023
Dijkstra H Oosterhoff JHF van de Kuit A IJpma FFA Schwab JH Poolman RW Sprague S Bzovsky S Bhandari M Swiontkowski M Schemitsch EH Doornberg JN Hendrickx LAM

Aims

To develop prediction models using machine-learning (ML) algorithms for 90-day and one-year mortality prediction in femoral neck fracture (FNF) patients aged 50 years or older based on the Hip fracture Evaluation with Alternatives of Total Hip arthroplasty versus Hemiarthroplasty (HEALTH) and Fixation using Alternative Implants for the Treatment of Hip fractures (FAITH) trials.

Methods

This study included 2,388 patients from the HEALTH and FAITH trials, with 90-day and one-year mortality proportions of 3.0% (71/2,388) and 6.4% (153/2,388), respectively. The mean age was 75.9 years (SD 10.8) and 65.9% of patients (1,574/2,388) were female. The algorithms included patient and injury characteristics. Six algorithms were developed, internally validated and evaluated across discrimination (c-statistic; discriminative ability between those with risk of mortality and those without), calibration (observed outcome compared to the predicted probability), and the Brier score (composite of discrimination and calibration).


The Bone & Joint Journal
Vol. 102-B, Issue 7 Supple B | Pages 99 - 104
1 Jul 2020
Shah RF Bini S Vail T

Aims

Natural Language Processing (NLP) offers an automated method to extract data from unstructured free text fields for arthroplasty registry participation. Our objective was to investigate how accurately NLP can be used to extract structured clinical data from unstructured clinical notes when compared with manual data extraction.

Methods

A group of 1,000 randomly selected clinical and hospital notes from eight different surgeons were collected for patients undergoing primary arthroplasty between 2012 and 2018. In all, 19 preoperative, 17 operative, and two postoperative variables of interest were manually extracted from these notes. A NLP algorithm was created to automatically extract these variables from a training sample of these notes, and the algorithm was tested on a random test sample of notes. Performance of the NLP algorithm was measured in Statistical Analysis System (SAS) by calculating the accuracy of the variables collected, the ability of the algorithm to collect the correct information when it was indeed in the note (sensitivity), and the ability of the algorithm to not collect a certain data element when it was not in the note (specificity).


The Bone & Joint Journal
Vol. 102-B, Issue 7 Supple B | Pages 11 - 19
1 Jul 2020
Shohat N Goswami K Tan TL Yayac M Soriano A Sousa R Wouthuyzen-Bakker M Parvizi J

Aims. Failure of irrigation and debridement (I&D) for prosthetic joint infection (PJI) is influenced by numerous host, surgical, and pathogen-related factors. We aimed to develop and validate a practical, easy-to-use tool based on machine learning that may accurately predict outcome following I&D surgery taking into account the influence of numerous factors. Methods. This was an international, multicentre retrospective study of 1,174 revision total hip (THA) and knee arthroplasties (TKA) undergoing I&D for PJI between January 2005 and December 2017. PJI was defined using the Musculoskeletal Infection Society (MSIS) criteria. A total of 52 variables including demographics, comorbidities, and clinical and laboratory findings were evaluated using random forest machine learning analysis. The algorithm was then verified through cross-validation. Results. Of the 1,174 patients that were included in the study, 405 patients (34.5%) failed treatment. Using random forest analysis, an algorithm that provides the probability for failure for each specific patient was created. By order of importance, the ten most important variables associated with failure of I&D were serum CRP levels, positive blood cultures, indication for index arthroplasty other than osteoarthritis, not exchanging the modular components, use of immunosuppressive medication, late acute (haematogenous) infections, methicillin-resistant Staphylococcus aureus infection, overlying skin infection, polymicrobial infection, and older age. The algorithm had good discriminatory capability (area under the curve = 0.74). Cross-validation showed similar probabilities comparing predicted and observed failures indicating high accuracy of the model. Conclusion. This is the first study in the orthopaedic literature to use machine learning as a tool for predicting outcomes following I&D surgery. The developed algorithm provides the medical profession with a tool that can be employed in clinical decision-making and improve patient care. Future studies should aid in further validating this tool on additional cohorts. Cite this article: Bone Joint J 2020;102-B(7 Supple B):11–19


The Bone & Joint Journal
Vol. 106-B, Issue 7 | Pages 656 - 661
1 Jul 2024
Bolbocean C Hattab Z O'Neill S Costa ML

Aims

Cemented hemiarthroplasty is an effective form of treatment for most patients with an intracapsular fracture of the hip. However, it remains unclear whether there are subgroups of patients who may benefit from the alternative operation of a modern uncemented hemiarthroplasty – the aim of this study was to investigate this issue. Knowledge about the heterogeneity of treatment effects is important for surgeons in order to target operations towards specific subgroups who would benefit the most.

Methods

We used causal forest analysis to compare subgroup- and individual-level treatment effects between cemented and modern uncemented hemiarthroplasty in patients aged > 60 years with an intracapsular fracture of the hip, using data from the World Hip Trauma Evaluation 5 (WHiTE 5) multicentre randomized clinical trial. EuroQol five-dimension index scores were used to measure health-related quality of life at one, four, and 12 months postoperatively.


The Bone & Joint Journal
Vol. 102-B, Issue 7 Supple B | Pages 90 - 98
1 Jul 2020
Florissi I Galea VP Sauder N Colon Iban Y Heng M Ahmed FK Malchau H Bragdon CR

Aims

The primary aim of this paper was to outline the processes involved in building the Partners Arthroplasty Registry (PAR), established in April 2016 to capture baseline and outcome data for patients undergoing arthroplasty in a regional healthcare system. A secondary aim was to determine the quality of PAR’s data. A tertiary aim was to report preliminary findings from the registry and contributions to quality improvement initiatives and research up to March 2019.

Methods

Structured Query Language was used to obtain data relating to patients who underwent total hip or knee arthroplasty (THA and TKA) from the hospital network’s electronic medical record (EMR) system to be included in the PAR. Data were stored in a secure database and visualized in dashboards. Quality assurance of PAR data was performed by review of the medical records. Capture rate was determined by comparing two months of PAR data with operating room schedules. Linear and binary logistic regression models were constructed to determine if length of stay (LOS), discharge to a care home, and readmission rates improved between 2016 and 2019.


The Bone & Joint Journal
Vol. 101-B, Issue 6_Supple_B | Pages 68 - 76
1 Jun 2019
Jones CW Choi DS Sun P Chiu Y Lipman JD Lyman S Bostrom MPG Sculco PK

Aims

Custom flange acetabular components (CFACs) are a patient-specific option for addressing large acetabular defects at revision total hip arthroplasty (THA), but patient and implant characteristics that affect survivorship remain unknown. This study aimed to identify patient and design factors related to survivorship.

Patients and Methods

A retrospective review of 91 patients who underwent revision THA using 96 CFACs was undertaken, comparing features between radiologically failed and successful cases. Patient characteristics (demographic, clinical, and radiological) and implant features (design characteristics and intraoperative features) were collected. There were 74 women and 22 men; their mean age was 62 years (31 to 85). The mean follow-up was 24.9 months (sd 27.6; 0 to 116). Two sets of statistical analyses were performed: 1) univariate analyses (Pearson’s chi-squared and independent-samples Student’s t-tests) for each feature; and 2) bivariable logistic regressions using features identified from a random forest analysis.