Minimizing Bias in Alopecia Diagnosis in Skin of Color Patients.

Alopecia is one of the most common dermatologic conditions affecting black patients, with a significantly negative impact on quality of life.1,2 Timely and accurate diagnosis is therefore critical in order to reverse or halt progression of disease.3 Unfortunately, lack of representation of skin of color (SOC) patients in the current literature may contribute to misdiagnosis as providers may be unfamiliar with the clinical spectrum of alopecia presenting in darker scalps.4 Some scarring alopecia subtypes such as Central Centrifugal Cicatricial Alopecia (CCCA) are more prevalent in certain racial groups. However, focusing solely on patient demographics and gross clinical findings may obscure accurate diagnoses. To distinguish alopecia findings in Black patients, a dedicated approach using a combination of clinical exam findings and patient history, along with trichoscopy and biopsy, is essential to prevent misdiagnosis and improve clinical and diagnostic outcomes. We present three cases of alopecia in patients of color which the initial suspected clinical diagnosis did not correspond with trichoscopic and biopsy results. We challenge clinicians to reexamine their biases and fully evaluate patients of color with alopecia. An examination should include a thorough history, clinical examination, trichoscopy, and potentially a biopsy, particularly when findings do not correlate. Our cases highlight the challenges and disparities that exist in diagnosis of alopecia in Black patients. We emphasize the need for continued research regarding alopecia in skin of color and the importance of a complete workup for alopecia to improve diagnostic outcomes.Balazic E, Axler E, Nwankwo C, et al. Minimizing bias in alopecia diagnosis in skin of color patients. J Drugs Dermatol. 2023;22(7):703-705. doi:10.36849/JDD.7117.  .

Risk assessment for indeterminate pulmonary nodules using a novel, plasma-protein based biomarker assay.

BACKGROUND: The increase in lung cancer screening is intensifying the need for a noninvasive test to characterize the many indeterminate pulmonary nodules (IPN) discovered. Correctly identifying non-cancerous nodules is needed to reduce overdiagnosis and overtreatment. Alternatively, early identification of malignant nodules may represent a potentially curable form of lung cancer. OBJECTIVE: To develop and validate a plasma-based multiplexed protein assay for classifying IPN by discriminating between those with a lung cancer diagnosis established pathologically and those found to be clinically and radiographically stable for at least one year. METHODS: Using a novel technology, we developed assays for plasma proteins associated with lung cancer into a panel for characterizing the risk that an IPN found on chest imaging is malignant. The assay panel was evaluated with a cohort of 277 samples, all from current smokers with an IPN 4-30 mm. Subjects were divided into training and test sets to identify a Support Vector Machine (SVM) model for risk classification containing those proteins and clinical factors that added discriminatory information to the Veteran's Affairs (VA) Clinical Factors Model. The algorithm was then evaluated in an independent validation cohort. RESULTS: Among the 97 validation study subjects, 68 were grouped as having intermediate risk by the VA model of which the SVM model correctly identified 44 (65%) of these intermediate-risk samples as low (n=16) or high risk (n=28). The SVM model negative predictive value (NPV) was 94% and its sensitivity was 94%. CONCLUSION: The performance of the novel plasma protein biomarker assay supports its use as a noninvasive risk assessment aid for characterizing IPN. The high NPV of the SVM model suggests its application as a rule-out test to increase the confidence of providers to avoid aggressive interventions for their patients for whom the VA model result is an inconclusive, intermediate risk.