Swiss Registry Study Protocol Evaluates Machine Learning for Joint Replacement Outcomes

Researchers in Switzerland have outlined a comprehensive study protocol to compare traditional logistic regression against modern supervised machine learning algorithms for predicting clinical outcomes and early revision risks after total joint replacement surgery. Published in Diagnostic and Prognostic Research, the protocol details a planned historical cohort investigation designed to determine whether complex decision-tree algorithms deliver superior predictive accuracy over standard regression when forecasting recovery trajectories following total hip arthroplasty and total knee arthroplasty. By leveraging nationwide data recorded in the Swiss National Joint Registry, known as SIRIS, the investigative team intends to evaluate how well distinct computational models forecast both patient-reported pain and satisfaction at twelve months and the likelihood of undergoing revision surgery within five years. The authors observe that while supervised machine learning methods are increasingly proposed across orthopaedic surgery, empirical evidence verifying their practical superiority over established multivariable regression remains inconsistent, warranting a standardized comparative methodology adhering to the updated TRIPOD plus AI reporting guidelines.

Total hip arthroplasty and total knee arthroplasty represent two of the most frequently performed orthopaedic surgical procedures across Switzerland, routinely indicated when conservative, non-surgical management fails to alleviate severe symptoms of primary osteoarthritis. Although joint replacement prostheses demonstrate an average expected functional lifespan exceeding twenty-five years, a subset of recipients experiences unfavorable long-term outcomes or premature construct failure requiring surgical revision. Epidemiological observations cited in the protocol indicate that between seven and twenty-three percent of patients report unfavorable pain outcomes following total hip replacement, whereas ten to thirty-four percent experience unfavorable pain following total knee replacement. Premature revision operations generate substantial physical, psychological, and financial challenges for affected individuals, modern healthcare systems, and society at large. Implemented in 2012, the Swiss National Joint Registry captures approximately ninety-eight percent of all hip and knee arthroplasties conducted nationwide, registering an estimated annual volume of twenty-two thousand hip replacements and eighteen thousand knee replacements across participating Swiss healthcare institutions.

Facilitating shared decision-making prior to elective surgery requires accurate prognostic tools capable of estimating personalized postoperative trajectories based on individual prognostic factor profiles. Standardized patient-reported outcome measures, or PROMs, provide critical insights into recipient health status, enabling clinicians and patients to weigh surgical risks, calibrate expectations, and plan postoperative resource allocation. Despite surging interest in predictive artificial intelligence, existing orthopaedic prediction research exhibits critical methodological shortcomings. The authors highlight that only forty percent of machine learning models developed for hip or knee replacement outcomes have been directly benchmarked against traditional statistical prediction techniques such as multivariable logistic regression. Furthermore, a mere thirty percent of published algorithms have undergone rigorous external validation across independent patient cohorts. Compounding these deficits, concepts surrounding machine learning remain unfamiliar to many medical practitioners, fostering clinical skepticism and restricting the adoption of decision-support systems in everyday surgical management despite promising exploratory metrics.

To address these evidentiary gaps, the study protocol defines two specific investigative objectives aimed at comparing random forest and extreme gradient boosting against conventional logistic regression frameworks. The first objective focuses on predicting the individual risk of poor twelve-month postoperative patient-reported outcomes, specifically evaluating joint-specific pain intensity and overall surgical satisfaction following primary arthroplasty. The second objective evaluates each algorithm’s capability to predict the five-year cumulative risk of undergoing all-cause revision surgery following primary joint replacement. For the patient-reported outcome analyses, poor pain outcome is prespecified as achieving less than a fifty percent relative improvement from baseline or less than a three-point absolute reduction on an eleven-point numeric rating scale spanning from zero to ten. Similarly, poor satisfaction outcome is operationalized using an international Delphi consensus threshold encompassing patients who describe their postoperative status as very unsatisfied, unsatisfied, or neither satisfied nor unsatisfied on a standardized five-item survey scale.

The planned population-based historical cohort will evaluate surgical records registered in the Swiss National Joint Registry between 01 January 2015 and 31 December 2023. Eligible participants comprise adults aged forty years and older at the time of undergoing primary total hip or knee arthroplasty for primary osteoarthritis. Data integrity within the registry is maintained through systematic software-generated validation tests executed upon receipt, supplemented by quarterly automated script audits conducted by the registry statistician to flag potential errors for root cause investigation. Evaluation of twelve-month patient-reported outcomes will specifically utilize the Swiss orthopaedic minimal dataset, implemented in 2019 across all publicly funded hospitals performing elective arthroplasties in the Canton of Zurich. This minimal dataset captures pre- and postoperative health status via the five-dimension, five-level EuroQol questionnaire, visual analogue scales evaluating self-rated health from zero to one hundred, and joint-specific numeric rating scales assessing weekly pain averages.

Predictor variable selection for the predictive models was conducted according to established multivariable prognostic guidelines, beginning with an exhaustive synthesis of published orthopaedic literature followed by interdisciplinary panel consensus. Candidate predictor domains encompass demographic variables, baseline comorbidity scores, surgical parameters, and baseline patient-reported health measurements. To preserve maximum prognostic information, continuous predictors will remain continuous rather than being converted into arbitrary categorical tiers, while nominal categorical variables will undergo one-hot dummy encoding and ordinal metrics will be transformed into numerical scales. Because patient-reported outcome instruments were systematically integrated into registry workflows only in 2019, patient-reported metrics will be excluded from the five-year revision prediction models to prevent a substantial reduction in eligible study sample size across earlier registry years. The multivariable models will follow a full-model approach, incorporating all clinically prespecified variables simultaneously without implementing automated backward elimination.

Sample size estimations were calculated using specialized statistical software packages based on established prognostic methodology, setting Nagelkerke’s R-squared value at 0.15, Cox-Snell’s adjusted R-squared at 0.09, and a shrinkage factor of 0.9. Based on observed registry prevalence rates of approximately 9.8 percent for poor pain and 7.2 percent for poor satisfaction after hip arthroplasty, minimum development sample sizes with twenty predictor parameters were established at 2,434 and 2,850 participants, respectively. For knee arthroplasty, where observed prevalence reaches approximately 16.3 percent for poor pain and 15.1 percent for poor satisfaction, required sample sizes were determined to be 1,944 and 1,992 participants. Predicting five-year revision surgery requires 6,158 patients across twenty-three predictor parameters for hip replacements, with an observed event rate of 2.7 percent, and 5,232 patients across twenty-five parameters for knee replacements, reflecting an observed 3.9 percent event rate. Fixed registry sample cohorts exceed all calculated development and external validation thresholds.

Addressing incomplete observational records forms a critical component of the statistical analysis plan, as missing outcome records will lead to patient exclusion from specific outcome analyses. Approximately sixty percent of eligible registry participants lacked twelve-month postoperative pain or satisfaction questionnaires, though comparative sensitivity analyses indicated comparable demographic profiles between responders and non-responders. Individual candidate predictors exhibiting missingness greater than thirty percent will be excluded from modelling, while remaining missing predictor values will be handled under a missing-at-random assumption using multivariate imputation by chained equations across five imputed datasets. To properly address right-censored survival observations among individuals who exited observation before reaching the five-year postoperative horizon without experiencing revision surgery, the protocol incorporates Kaplan-Meier conditional survival imputation using specialized R software packages. This conditional survival approach imputes time-to-event outcomes based on observed survival distributions derived from uncensored registry cohorts.

The computational architecture encompasses both classical statistical models and ensemble machine learning algorithms executed within the R programming environment. Multivariable logistic regression will establish baseline prognostic associations, supplemented by penalized Elastic Net regression combining Ridge and least absolute shrinkage and selection operator penalties to mitigate potential model overfitting. In parallel, random forest models will construct extensive ensembles of independent decision trees trained on random data subsets to aggregate decentralized predictions. The extreme gradient boosting algorithm, or XGBoost, will build decision trees sequentially, utilizing gradient-based optimization to correct residual errors generated by preceding iterations, with hyperparameter tuning directed via systematic grid search. Model overfitting within the development cohorts will be evaluated through repeated five-fold cross-validation, iteratively partitioning development data into four training folds and one reserved testing fold. Baseline regression models will report explicit intercept values, regression coefficients, and multivariable odds ratios accompanied by ninety-five percent confidence intervals.

Comparative model evaluation will measure overall predictive accuracy, calibration plots with loess smoothing, and discriminative capacity quantified via the concordance statistic and receiver operating characteristic curves. Relative predictor contributions across all architectures will be established using variable importance metrics. External generalizability will be tested through geographic validation, dividing Canton Zurich districts for patient-reported outcomes and Swiss territorial statistical regions for revision endpoints. To determine whether clinical deployment offers tangible net benefit over default strategies of treating all or treating none, the authors will perform decision curve analysis across two concrete clinical scenarios: assessing whether primary arthroplasty should be recommended for borderline surgical candidates, and evaluating whether identified high-risk patients should receive intensified pre- and postoperative rehabilitation. The authors note that while retrospective registry data collection restricts available clinical variables and limits patient-reported metrics to Canton Zurich cohorts, nationwide registration ensures substantial real-world statistical power.

Subject of Research: Comparative protocol assessing machine learning and logistic regression for joint arthroplasty outcomes

Article Title: Predicting patient outcomes and risk for revision surgery after hip and knee replacement surgery: study protocol for a comparison of modelling approaches using the Swiss National Joint Registry (SIRIS)

Article References: Hofstetter, L., Schweyckart, N., Seiler, C., Brand, C., Rosella, L. C., Farshad, M., Puhan, M. A., & Hincapié, C. A. (2025). Predicting patient outcomes and risk for revision surgery after hip and knee replacement surgery: study protocol for a comparison of modelling approaches using the Swiss National Joint Registry (SIRIS). Diagnostic and Prognostic Research, 9(1), Article 16. https://doi.org/10.1186/s41512-025-00200-z

Image Credits: AI Generated

DOI: 10.1186/s41512-025-00200-z

Keywords: total hip arthroplasty, total knee arthroplasty, Swiss National Joint Registry, SIRIS, machine learning, logistic regression, random forest, extreme gradient boosting, revision surgery, patient-reported outcome measures, EuroQol EQ-5D-5L, decision curve analysis, TRIPOD AI, geographic validation, elastic net, primary osteoarthritis