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Purpose: To analyze the effects of 1 or more patient-reported allergies on clinical outcomes, in particular graft failure rate, and patient-reported outcomes (PROs) following osteochondral allograft transplantation (OCA) of the knee. Methods: Retrospective review of patients who underwent knee OCA from August 2010 to May 2021 with a minimum of 2-year follow-up. Patients were initially divided into 2 cohorts: those with at least 1 allergy and those without any allergies. Clinical outcomes assessed included graft failure, reoperation rates, deep vein thrombosis/pulmonary embolism, and manipulation under anesthesia/lysis of adhesions (MUA/LOA). PROs assessed, including the visual analog scale (VAS) for pain and satisfaction, the Knee injury and Osteoarthritis Outcome Score (KOOS), and return to sport rates, were compared. Results: In total, 285 patients were included with a mean clinical follow-up of 4.8 ± 2.0 years. The allergy cohort had a significantly higher rate of graft failure (P = .008). In a regression analysis controlling for confounding variables, graft failure remained significantly associated with the presence of medication allergies (odds ratio [OR], 3.631; 95% CI, 1.139-11.577; P = .029). Furthermore, an increasing number of allergies were associated with an increased rate of graft failure (OR, 1.644; 95% CI, 1.074-2.515; P = .022). There was no difference in rate of reoperation, complications, infection, and MUA/LOA. Of the 100 patients who completed PROs, there was no difference in VAS satisfaction, pain, and any of the KOOS outcome scores or return to sport. Conclusions: The presence of 1 or more patient-reported allergies was shown to be significantly associated with OCA graft failure. Furthermore, an increasing number of patient-reported allergies were associated with a higher rate of graft failure. However, there were no significant differences in VAS satisfaction or pain, KOOS symptom, quality of life, pain, or return to sport in patients with at least 1 patient-reported allergy and those without allergies. Level of Evidence: Level III, retrospective cohort study.
RESUMO
Objectives: As computational methods for detecting symptoms can help us better attend to patient suffering, the objectives of this study were to develop and evaluate the performance of a natural language processing keyword library for detecting symptom talk, and to describe symptom communication within our dataset to generate insights for future model building. Materials and Methods: This was a secondary analysis of 121 transcribed outpatient oncology conversations from the Communication in Oncologist-Patient Encounters trial. Through an iterative process of identifying symptom expressions via inductive and deductive techniques, we generated a library of keywords relevant to the Patient-Reported Outcome version of the Common Terminology Criteria for Adverse Events (PRO-CTCAE) framework from 90 conversations, and tested the library on 31 additional transcripts. To contextualize symptom expressions and the nature of misclassifications, we qualitatively analyzed 450 mislabeled and properly labeled symptom-positive turns. Results: The final library, comprising 1320 terms, identified symptom talk among conversation turns with an F1 of 0.82 against a PRO-CTCAE-focused gold standard, and an F1 of 0.61 against a broad gold standard. Qualitative observations suggest that physical symptoms are more easily detected than psychological symptoms (eg, anxiety), and ambiguity persists throughout symptom communication. Discussion: This rudimentary keyword library captures most PRO-CTCAE-focused symptom talk, but the ambiguity of symptom speech limits the utility of rule-based methods alone, and limits to generalizability must be considered. Conclusion: Our findings highlight opportunities for more advanced computational models to detect symptom expressions from transcribed clinical conversations. Future improvements in speech-to-text could enable real-time detection at scale.
