Is success written in the stars? The effect of zodiac sign on the outcome of total knee arthroplasty.

Abstract

There are 12 signs of the zodiac, each attributed with its own specific personality traits, desires and attitudes. The aim of the study was to evaluate the effect of zodiac sign on patient-reported outcome measures (PROMS) following primary total knee arthroplasty (TKA). Patients undergoing primary TKA during a 2-year period (January 2019 to December 2020) were identified retrospectively. Patient demographics, Oxford Knee Score (OKS), EuroQol 5-dimension (EQ-5D) (baseline, 1 and 2 years) and patient satisfaction scores (1 and 2 years) were collected. Each patient's zodiac sign was assigned from their date of birth. There were 509 patients (228 males [44.8%] and 281 females [55.2%]) with a mean age of 70.9 years and a mean BMI of 30.3. There were no significant differences in gender (p=0.712), age (p=0.088), BMI (p=0.660), or pre-operative OKS (p=0.539). Aries and Gemini (0.366) had the worst and Pisces the best (0.595) pre-operative EQ-5D scores (p=0.038). When adjusting for confounding, Aries (p=0.031) had a greater improvement in EQ-5D at 1 year, although this was not maintained at 2 years. When adjusting for confounding, Pisceans had significantly less of an improvement in OKS at both 1 (p=0.022) and 2 years (p=0.042) and also had a significantly lower risk of satisfaction at 2 years (odds ratio 0.41, p=0.043). Zodiac sign was associated with outcome following TKA. Pisceans had the best pre-operative EQ-5D scores, but the least improvement in the post-operative joint specific score (OKS) and were less likely to be satisfied, despite achieving an equal improvement in their health-related quality of life (EQ-5D). Aries started with the lowest pre-operative EQ-5D scores but achieved the best scores at 1 year. Our study shows that an individual's zodiac sign may serve as a useful predictive factor for functional outcomes and satisfaction following TKA. However, our findings are the result of multiple testing in a large dataset following a data trawl, and correlation does not necessarily equal causation even in a real-world registry.