Patient "No-Show" Increases the Risk of 90-Day Complications Following Primary Total Knee Arthroplasty: A Retrospective Cohort Study of 6,776 Patients.

BACKGROUND: Patients who "no-show" (NS) clinical appointments are at a high risk of adverse health outcomes. The objective of this study was to evaluate and characterize the relationship between NS visits prior to primary total knee arthroplasty (TKA) and 90-day complications after TKA. METHODS: We retrospectively reviewed 6,776 consecutive patients undergoing primary TKA. Study groups were separated based on whether patients who NS versus always attended their appointment. A NS was defined as an intended appointment that was not canceled or rescheduled ≤2 hours before the appointment in which the patient did not show. Data collected included total number of follow-up appointments prior to surgery, patient demographics, comorbidities, and 90-day postoperative complications. RESULTS: Patients who have ≥3 NS appointments had 1.5 times increased odds of a surgical site infection (odds ratio (OR) 1.54, P = .002) compared to always attended patients. Patients who were ≤65 years old (OR: 1.41, P < .001), smokers (OR: 2.01, P < .001), and had a Charlson comorbidity index ≥3 (OR: 4.48, P < .001) were more likely to miss clinical appointments. CONCLUSION: Patients who have ≥3 NS appointments prior to TKA had an increased risk for surgical site infection. Sociodemographic factors were associated with higher odds of missing a scheduled clinical appointment. These data suggest that orthopaedic surgeons should consider NS data as an important clinical decision-making tool to assess risk for postoperative complications to minimize complications following TKA.

Patterns of Misdiagnosis and Discordance in Detecting Osteoporosis: A Comparison of Dual-energy X-ray Absorptiometry and Lumbar Computed Tomography Hounsfield Units.

STUDY DESIGN: This was a retrospective chart review. OBJECTIVE: This study aims to identify the prevalence of osteoporosis (OP) by lumbar computed tomography (CT) Hounsfield units (HUs) in patients who have normal or osteopenic bone determined by dual-energy x-ray absorptiometry (DEXA). SUMMARY OF BACKGROUND DATA: OP is a critical issue in the postmenopausal and aging population. Bone mineral density assessment by DEXA has been described as insensitive for diagnosing OP in the lumbar spine. Improving the detection of OP can bring more patients to treatment and reduce the risks associated with low bone mineral density. PATIENTS AND METHODS: We retrospectively reviewed all patients with DEXA scans and noncontrast CTs of the lumbar spine over a 15-year period. Patients were diagnosed as non-OP if they had a normal DEXA T -score (≥ -1) or osteopenic DEXA T -score (between -1.1 and -2.4). Patients in this cohort were considered osteoporotic by CT if L1-HU ≤110. Demographics and lumbar HUs were compared between these stratified groups. RESULTS: A total of 74 patients were included for analysis. All patients were demographically, similar, and the average patient age was 70 years. The prevalence of OP determined by CT L1-HU ≤110 was 46% (normal DEXA: 9%, osteopenic DEXA: 63%). A significant number of males in our study were considered osteoporotic by L1-HU ≤110 (74%, P = 0.03). All individual axial and sagittal lumbar HU measurements including L1-L5 average lumbar HUs were statistically significant among non-OP and OP groups except for the lower lumbar levels ( P > 0.05 for L4 axial HUs, and L4-L5 sagittal HUs). CONCLUSIONS: The prevalence of OP in patients with normal or osteopenic T -scores is high. Among those with osteopenia by DEXA, more than 50% may lack appropriate medical treatment. The DEXA scan may be particularly insensitive to male bone quality making the CT HU the diagnostic method of choice for detecting OP. LEVEL OF EVIDENCE: Level III.

Patient Perspectives on Artificial Intelligence in Healthcare Decision Making: A Multi-Center Comparative Study.

Objective: Investigate the patient opinion on the use of Artificial Intelligence (AI) in Orthopaedics. Methods: 397 orthopaedic patients from a large urban academic center and a rural health system completed a 37-component survey querying patient demographics and perspectives on clinical scenarios involving AI. An average comfort score was calculated from thirteen Likert-scale questions (1, not comfortable; 10, very comfortable). Secondary outcomes requested a binary opinion on whether it is acceptable for patient healthcare data to be used to create AI (yes/no) and the impact of AI on: orthopaedic care (positive/negative); healthcare cost (increase/decrease); and their decision to refuse healthcare if cost increased (yes/no). Bivariate and multivariable analyses were employed to identify characteristics that impacted patient perspectives. Results: The average comfort score across the population was 6.4, with significant bivariate differences between age (p = 0.0086), gender (p = 0.0001), education (p = 0.0029), experience with AI/ML (p < 0.0001), survey format (p < 0.0001), and four binary outcomes (p < 0.05). When controlling for age and education, multivariable regression identified significant relationships between comfort score and experience with AI/ML (p = 0.0018) and each of the four binary outcomes (p < 0.05). In the final multivariable model gender, survey format, perceived impact of AI on orthopaedic care, and the decision to refuse care if it were to increase cost remained significantly associated with the average AI comfort score (p < 0.05). Additionally, patients were not comfortable undergoing surgery entirely by a robot with distant physician supervision compared to close supervision. Conclusion: The orthopaedic patient appears comfortable with AI joining the care team.