Melatonin Does Not Improve Sleep Quality in a Randomized Placebo-controlled Trial After Primary Total Joint Arthroplasty.

INTRODUCTION: Sleep disturbance is a common concern among patients who have undergone total joint arthroplasty (TJA). Poor sleep during the postoperative period affect quality of life directly and may influence pain recovery after TJA. The purpose of this prospective study was to investigate whether the daily use of exogenous melatonin for 6 weeks after TJA can mitigate the effects of TJA on sleep. METHODS: A cohort of 118 patients undergoing primary total hip arthroplasty or total knee arthroplasty from 2018 to 2020 were randomized to melatonin (6 mg) or placebo for 42 days after surgery. Inclusion criterion was patients undergoing unilateral primary TJA. Patients who underwent bilateral TJA and revision TJA, with a history of sleep disturbance, and on opioid medication or sleep aids preoperatively were excluded. Sleep quality was assessed at baseline and at 2 and 6 weeks postoperatively using the validated self-administered questionnaire, Pittsburgh Sleep Quality Index (PSQI). Continuous and categorical variables were analyzed using Student t-test and chi-square analysis, respectively. Multivariate linear regression analysis was also conducted. RESULTS: Patients in both groups exhibited higher PSQI scores, representing lower sleep quality, at both 2 and 6 weeks postoperatively compared with that at baseline. Overall, global PSQI scores were 6.8, 9.8, and 8.8 at baseline, week 2, and week 6, respectively. No significant differences were noted between melatonin and placebo groups at baseline (6.8 versus 6.8, P = 0.988), week 2 (10.2 versus 9.3, P = 0.309), or week 6 (8.8 versus 8.7, P = 0.928). In multivariable regression, the only significant predictors of increased PSQI scores were an elevated baseline PSQI score (at both time points), a decreased length of stay (at week 2 only), and patients undergoing total hip arthroplasty versus total knee arthroplasty (at week 6 only). CONCLUSION: Patients undergoing TJA had poor sleep quality both preoperatively and postoperatively. The use of exogenous melatonin did not demonstrate any notable effect on sleep quality

2020 Frank Stinchfield Award: Identifying who will fail following irrigation and debridement for prosthetic joint infection A MACHINE LEARNING-BASED VALIDATED TOOL

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

Improved Patient Outcomes by Normalizing Sympathovagal Balance: Differentiating Syncope—Precise Subtype Differentiation Leads to Improved Outcomes

Syncope is difficult to definitively diagnose, even with tilt-table testing and beat-to-beat blood pressure measurements, the gold-standard. Both are qualitative, subjective assessments. There are subtypes of syncope associated with autonomic conditions for which tilt-table testing is not useful. Heart rate variability analyses also include too much ambiguity. Three subtypes of syncope are differentiated: vasovagal syncope (VVS) due to parasympathetic excess (VVS-PE), VVS with abnormal heart rate response (VVS-HR), and VVS without PE (VVS-PN). P&S monitoring (ANSAR, Inc., Philadelphia, PA) differentiates subtypes in 2727 cardiology patients (50.5% female; average age: 57 years; age range: 12–100 years), serially tested over four years (3.3 tests per patient, average). P&S monitoring noninvasively, independently, and simultaneously measures parasympathetic and sympathetic (P&S) activity, including the normal P-decrease followed by an S-increase with head-up postural change (standing). Syncope, as an S-excess (SE) with stand, is differentiated from orthostatic dysfunction (e.g., POTS) as S-withdrawal with stand. Upon standing, VVS-PE is further differentiated as SE with PE, VVS-HR as SE with abnormal HR, and VVS-PN as SE with normal P- and HR-responses. Improved understanding of the underlying pathophysiology by more accurate subtyping leads to more precise therapy and improved outcomes