Treatment of end-stage hip osteoarthritis was revolutionized in the 1960s with the newly invented low-friction total hip arthroplasty (THA). Since then, an increasing number of both primary and revision THAs have been performed annually, especially over the past two decades. To achieve better outcomes, orthopedic surgeons should carefully select optimal patients and appropriate methods and devices. Risk prediction models have been developed to inform the surgeon and patient more precisely about the expected outcomes of the surgery. The use of such a tool could engage patients more closely in the decision-making process and guide surgeons in avoiding unnecessary risk.

The aims of this doctoral thesis were: 1) to determine the risk factors for revision due to dislocation after primary THA; 2) to determine the risk factors for revision due to periprosthetic joint infection (PJI) after primary THA; 3) to develop risk prediction models for assessing the risk of the most common adverse outcomes after primary THA, based on versatile registry data from Finland; and 4) to develop risk prediction models for early revisions and death, and to evaluate the predictive potential of various machine learning algorithms for complications following primary THA, based on the Nordic Arthroplasty Register Association (NARA) dataset.
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We found that posterior approach, fracture diagnosis, and American Society of Anesthesiologists class III–IV were associated with an increased risk of revision for dislocation after primary THA. The use of a 36 mm femoral head size decreased the risk of revision for dislocation. For PJI, we identified several modifiable variables increasing and decreasing the risk of revision. Especially patients with a high body mass index may be at even higher risk of developing infection than previously reported. We also successfully developed preoperative risk prediction models for PJI, dislocation, periprosthetic fracture, and death after primary THA. Based on the NARA dataset, we were able to demonstrate that complex risk prediction methods are not required to achieve maximum predictive potential. Hence, simpler models can improve usability. All the developed models can easily be used in clinical practice to serve individual risk estimations for adverse outcomes.