New York Institute of Technology

Abstract Purpose This study sought to examine the role of frailty in predicting inpatient mortality following early surgical fixation of intertrochanteric (IT) fractures. Methods The ACS-TQIP (2017–21) database was queried for patients with intertrochanteric fractures undergoing surgical fixation within 48 h. Frailty status was determined using the 5-item modified frailty index (mFI-5) (0 = nonfrail, 1 = prefrail, and, 2 = frail, and > 2 = severely frail). The primary end point was inpatient mortality. Secondary end points were unplanned ICU admission and thromboembolic events (DVT or PE). Significance was considered a p -value < 0.05. Results Frailty, as determined by the mFI-5, was a significant predictor of inpatient mortality and unplanned ICU admissions. Frail and severely frail patients had higher odds of mortality (OR: 2.00 and 3.58, respectively) and ICU admission (OR: 1.66, 3.09, 3.83, respectively). When compared with nonoperative management in frail (OR: 0.561) and severely frail (OR: 0.665) patients, surgical intervention within 48 h significantly reduced the risk of mortality. Conclusion Increasing frailty status, as measured by the mFI-5, has increased odds for unplanned ICU admission and inpatient mortality following surgery within 48 h for IT fractures. Although operative management is required in these patients, these results suggest frailty may be used to preoperatively identify patients at high risk for adverse events. Future studies may seek to identify modifiable factors in the preoperative period to improve outcomes in frail patients.

Dowling-Degos disease (DDD) is a rare genodermatosis characterized by reticulated hyperpigmented macules and papules, yet its surface architecture has not been quantitatively described. In this case, we utilized 3D surface metrology to objectively characterize its surface texture. A shave biopsy from the inner thigh of a 54-year-old woman with clinically and histopathologically confirmed DDD was scanned using the S Neox optical profiler (Sensofar, Barcelona, Spain) at 20x and 50x magnification. Roughness parameters, including mean roughness (Sa), maximum surface height (Sz), maximum valley depth (Sv), maximum peak height (Sp), root mean square roughness (Sq), skewness (Ssk), and sharpness (Sku), were extracted and compared with previously published values for both unaffected and psoriatic skin. When compared to unaffected skin, DDD showed markedly increased Sa, Sz, and Sv, indicating a more irregular and deeply sculpted skin surface. In contrast to psoriatic lesions, DDD demonstrated lower Sa. Between magnifications, Sp was significantly greater in the 20x scan. These findings indicate that DDD has a distinct topographic profile that could support noninvasive diagnosis and monitoring. Surface metrology may complement clinical, dermoscopic, and histopathologic evaluation by providing a quantitative description of disease-specific skin texture.

We present the case of a 26-year-old female patient with a history of asthma who initially presented with symptoms suggestive of a common infection. Her constellation of facial swelling, productive cough, and fatigue, coupled with imaging findings, first led to a diagnosis of right upper lobe pneumonia and suspected cellulitis. Despite appropriate antibiotic therapy, her symptoms persisted, and her clinical picture became more complex with the identification ofActinomycesin her sputum, shifting the differential diagnosis toward a more indolent cervicofacial infection. However, a progressively worsening and profound leukocytosis, along with a lack of clinical improvement, prompted further investigation. This ultimately revealed a large mediastinal mass, and subsequent tissue analysis confirmed a diagnosis of primary mediastinal large B-cell lymphoma. This case highlights a critical diagnostic challenge, illustrating how a rare and aggressive malignancy can masquerade as a common infectious process in a young, otherwise healthy individual, leading to a significant delay in diagnosis and treatment.

Intracoronary (IC) imaging-guided percutaneous coronary intervention (PCI) improves clinical outcomes in patients with high clinical and anatomical risk when compared to interventions guided by angiography alone. Recent Class I recommendations for the use of IC imaging guidance when performing PCI in left main stem or complex lesions may result in a significant uptake as the technology is embraced as standard of care. Routine application of IC imaging will provide interventional cardiologists with a wealth of high-fidelity intracoronary data on plaque composition and distribution. When paired with emerging data regarding the importance of plaque anatomical characteristics, developments in artificial intelligence and computational fluid dynamics, lesion stratification with IC imaging may herald the next paradigm shift in this field. In this review, we will explore this important emerging application of IC imaging to inform morphology-guided PCI, identify high-risk lesions for targeted therapies, and consider the prospects of harnessing automated image interpretation with artificial intelligence technologies to achieve an integrated physiological and morphological assessment. Lesion stratification with IC imaging has the potential to shape the future of interventional cardiology practice to guide therapies within and beyond the confines of the cardiac catheterisation laboratory.

In recent years, understanding the emotional dynamics of patient-clinician interactions has emerged as a critical topic in healthcare research. Speech Emotion Recognition (SER) provides critical insights to enhance patient care, diagnostic precision, and therapeutic effectiveness. In this paper, we present a text-based framework for Speech Emotion Recognition specifically designed for healthcare scenarios, integrating advanced transformer-based models including T5, BERT, and XLNet. Our proposed framework analyzes transcribed textual data, enabling the identification of potential emotions expressed by patients and healthcare providers. Audio recordings from interactions between patients and clinicians-including doctors and psychiatrists-are transcribed using the Whisper model, ensuring high transcription quality. We evaluated the framework’s performance on a dataset comprising clinical conversations capturing a variety of emotional expressions relevant to healthcare contexts. Our experimental results demonstrate that our framework predicts six primary emotional states, including Happiness, Anger, Fear, Sadness, and Surprise, as well as distinguishing between positive and negative sentiments. Among the evaluated models, T5 exhibited the highest mean confidence score at $89.12 \%$, significantly outperforming RoBERTa ($78.44 \%$) and XLNet ($36.02 \%$) in capturing emotional content from clinical dialogues. These findings highlight the potential of SER to aid healthcare professionals by providing deeper insights into patients’ emotional states, supporting communication, and improving understanding of patients’ sentiment.