Howell, Harrison J.

OBJECTIVE Coexisting medical conditions are increasingly prevalent in surgical populations. The impact of multiple comorbidities on patient-reported outcomes (PROs) and endotypes of frequently co-occurring conditions for cervical spondylotic myelopathy (CSM) remain unclear. This study explores whether CSM patients with multimorbidity have worse baseline and postoperative PROs and less functional improvement after surgery compared to those with few or no comorbidities. The authors also investigated whether distinct comorbidity endotypes exist among CSM surgical patients and whether they influence postoperative outcomes. METHODS The prospective Quality Outcomes Database (QOD) was used to assess patients undergoing surgery for CSM. Multimorbidity was defined as ≥ 2 chronic conditions, including diabetes, coronary artery disease, peripheral vascular disease, arthritis, chronic renal disease, chronic obstructive pulmonary disease, Parkinson’s disease, multiple sclerosis, depression, and anxiety. Baseline characteristics and 24-month PROs were assessed across multiple-comorbidity status, including modified Japanese Orthopaedic Association (mJOA), Neck Disability Index (NDI), visual analog scale for neck and arm pain, EQ-5D, and patient satisfaction scores. Clusters were identified from the full cohort using k-medoids, revealing subgroups with similar comorbidity endotypes. RESULTS The final cohort included 1141 CSM patients (83.1% reaching 24-month follow-up), with 761 (66.7%) having 0 or 1 comorbidity and 380 (33.3%) ≥ 2 comorbidities. The multimorbidity cohort was older (mean age 62.6 ± 11.2 vs 59.5 ± 12.0 years, p < 0.001), more likely to be female (52.9% vs 44.7%, p = 0.011), and had a higher BMI (mean 31.1 ± 6.7 vs 29.7 ± 6.2 kg/m 2 , p < 0.001). Multimorbidity patients exhibited worse mJOA, NDI, and EQ-5D scores at baseline and 24 months (p < 0.05). On multivariable analysis, the total number of comorbidities was not significantly associated with any PRO measures. Four comorbidity clusters were identified: low burden, arthritis, diabetes, and high burden. On one-way ANOVA, the baseline mJOA score was significantly different across clusters (p = 0.003). At 24 months, the mJOA score was significantly lower in the diabetes and high-burden endotypes. Twenty-four-month score change and minimal clinically important difference (MCID) achievement of all PROs remained similar across clusters (p > 0.05). CONCLUSIONS While patients with multimorbidity have worse baseline and postoperative PROs, they achieve similar functional and pain-related improvements following CSM surgery. Similarly, the comorbidity endotypes identified in this QOD cohort suggest that certain patterns of coexisting chronic conditions, such as overlapping diabetes and arthritis, are associated with different levels of disability but may not diminish the effectiveness of surgical intervention.

Background: Hip arthroscopy has seen a significant surge in utilization, but complications remain, and optimal functional outcomes are not guaranteed. Artificial intelligence (AI) has emerged as an effective supportive decision-making tool for surgeons. The purpose of this systematic review was to characterize the outcomes, performance, and validity (generalizability) of AI-based prediction models for hip arthroscopy in current literature. Methods: Two reviewers independently completed structured searches using PubMed/MEDLINE and Embase databases on August 10, 2022. The search query used the terms as follows: (artificial intelligence OR machine learning OR deep learning) AND (hip arthroscopy). Studies that investigated AI-based risk prediction models in hip arthroscopy were included. The primary outcomes of interest were the variable(s) predicted by the models, best model performance achieved (primarily based on area under the curve, but also accuracy, etc), and whether the model(s) had been externally validated (generalizable). Results: Seventy-seven studies were identified from the primary search. Thirteen studies were included in the final analysis. Six studies (n = 6,568) applied AI for predicting the achievement of minimal clinically important difference for various patient-reported outcome measures such as the visual analog scale and the International Hip Outcome Tool 12-Item Questionnaire, with area under a receiver-operating characteristic curve (AUC) values ranging from 0.572 to 0.94. Three studies used AI for predicting repeat hip surgery with AUC values between 0.67 and 0.848. Four studies focused on predicting other risks, such as prolonged postoperative opioid use, with AUC values ranging from 0.71 to 0.76. None of the 13 studies assessed the generalizability of their models through external validation. Conclusion: AI is being investigated for predicting clinical outcomes after hip arthroscopy. However, the performance of AI models varies widely, with AUC values ranging from 0.572 to 0.94. Critically, none of the models have undergone external validation, limiting their clinical applicability. Further research is needed to improve model performance and ensure generalizability before these tools can be reliably integrated into patient care. Level of Evidence: Level IV . See Instructions for Authors for a complete description of levels of evidence.