BREAST-Q Analysis of Reduction Mammaplasty: Do Postoperative Complications of Breast Reduction Surgery Negatively Affect Patient Satisfaction?

BACKGROUND: Reduction mammaplasty can provide symptomatic relief to patients suffering from macromastia, however complications such as dehiscence are common. It is unknown if the presence of complications may affect patient reported outcomes. OBJECTIVES: This study aims to (1) determine risk factors for development of complications, and (2) to examine the correlation between postoperative complications and patient reported outcomes in reduction mammaplasty. METHODS: A single-center retrospective chart review was performed on patients who received reduction mammaplasties (CPT19318) between 1/17-2/23 by thirteen surgeons. Breast cancer cases and oncoplastic reconstructions were excluded. Patients with >1 complication were grouped into the complications cohort. BREAST-Q-survey was used to assess satisfaction. RESULTS: A total of 661 patients were included for analysis, and 131 patients developed at least one complication. Patients in the complication group had significantly higher average ages and body mass indexes, and a higher likelihood of hypertension and diabetes (p<0.01). Among 180 BREAST-Q responders, 41 had at least one complication. There were no significant differences between the two groups across survey outcomes. Although obese patients were more likely to develop infection and require revisions (p<0.01), no significant differences in subgroup analysis of patient-reported outcomes focusing on obese patients were observed. CONCLUSIONS: Obesity, hypertension, and diabetes were associated with postoperative complications of reduction mammaplasty. Patients with complications had similar postoperative Breast-Q satisfaction to patients without complications. While risk optimization is critical, patients and surgeons should be reassured that satisfaction may be achieved even in the event of a complication.

AI-based differential diagnosis of dementia etiologies on multimodal data.

Differential diagnosis of dementia remains a challenge in neurology due to symptom overlap across etiologies, yet it is crucial for formulating early, personalized management strategies. Here, we present an artificial intelligence (AI) model that harnesses a broad array of data, including demographics, individual and family medical history, medication use, neuropsychological assessments, functional evaluations and multimodal neuroimaging, to identify the etiologies contributing to dementia in individuals. The study, drawing on 51,269 participants across 9 independent, geographically diverse datasets, facilitated the identification of 10 distinct dementia etiologies. It aligns diagnoses with similar management strategies, ensuring robust predictions even with incomplete data. Our model achieved a microaveraged area under the receiver operating characteristic curve (AUROC) of 0.94 in classifying individuals with normal cognition, mild cognitive impairment and dementia. Also, the microaveraged AUROC was 0.96 in differentiating the dementia etiologies. Our model demonstrated proficiency in addressing mixed dementia cases, with a mean AUROC of 0.78 for two co-occurring pathologies. In a randomly selected subset of 100 cases, the AUROC of neurologist assessments augmented by our AI model exceeded neurologist-only evaluations by 26.25%. Furthermore, our model predictions aligned with biomarker evidence and its associations with different proteinopathies were substantiated through postmortem findings. Our framework has the potential to be integrated as a screening tool for dementia in clinical settings and drug trials. Further prospective studies are needed to confirm its ability to improve patient care.

AI-based differential diagnosis of dementia etiologies on multimodal data.

Differential diagnosis of dementia remains a challenge in neurology due to symptom overlap across etiologies, yet it is crucial for formulating early, personalized management strategies. Here, we present an AI model that harnesses a broad array of data, including demographics, individual and family medical history, medication use, neuropsychological assessments, functional evaluations, and multimodal neuroimaging, to identify the etiologies contributing to dementia in individuals. The study, drawing on 51,269 participants across 9 independent, geographically diverse datasets, facilitated the identification of 10 distinct dementia etiologies. It aligns diagnoses with similar management strategies, ensuring robust predictions even with incomplete data. Our model achieved a micro-averaged area under the receiver operating characteristic curve (AUROC) of 0.94 in classifying individuals with normal cognition, mild cognitive impairment and dementia. Also, the micro-averaged AUROC was 0.96 in differentiating the dementia etiologies. Our model demonstrated proficiency in addressing mixed dementia cases, with a mean AUROC of 0.78 for two co-occurring pathologies. In a randomly selected subset of 100 cases, the AUROC of neurologist assessments augmented by our AI model exceeded neurologist-only evaluations by 26.25%. Furthermore, our model predictions aligned with biomarker evidence and its associations with different proteinopathies were substantiated through postmortem findings. Our framework has the potential to be integrated as a screening tool for dementia in various clinical settings and drug trials, with promising implications for person-level management.