About 3 million surgical pigmented skin lesion biopsies are performed each year in the USA alone to diagnose fewer than 200 000 new cases of invasive melanoma and melanoma in situ using the current standard of care that includes visual assessment and histopathology. A recently described noninvasive adhesive patch-based gene expression rule-out test [pigmented lesion assay (PLA)] may be helpful in identifying high-risk pigmented skin lesions to aid with surgical biopsy decisions. The main objective of this utility study was to determine the real-world clinical performance of PLA use and assess how the PLA changes physician behavior in an observational cohort analysis of 381 patients assessed with the PLA. All (100%) of 51 PLA(+) test results were clinically managed with surgical biopsy. Of these, 19 (37%) were melanomas, corresponding to a number needed to biopsy of 2.7 and a biopsy ratio of 1.7. All melanomas were histopathologically classified as melanoma in situ or stage 1. Nearly all (99%) of 330 PLA(-) test results were clinically managed with surveillance. None of the three follow-up biopsies performed in the following 3-6 months, were diagnosed as melanoma histopathologically. The estimated sensitivity and specificity of the PLA from these data sets are 95 and 91%, respectively. Overall, 93% of PLA results positive for both LINC00518 and PRAME were diagnosed histopathologically as melanoma. PRAME-only and LINC00518-only lesions were melanomas histopathologically in 50 and 7%, respectively. The PLA alters clinical management of pigmented lesions and shows high clinical performance. The likelihood of positive histopathologic diagnosis of melanoma is higher in PLA results that are positive for both LINC00518 and PRAME.

Impact on clinical practice of a non-invasive gene expression melanoma rule-out test: 12month follow-up of negative test results and utility data from a large US registry study Permalink

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Impact on clinical practice of a non-invasive gene expression melanoma ruleout test: 12-month followup of negative test results and utility data from a large US registry study Permalink

Introduction In case of COVID-19 related scarcity of critical care resources, an early French triage algorithm categorized critically ill patients by probability of survival based on medical history and severity, with four priority levels for initiation or continuation of critical care: P1 –high priority, P2 –intermediate priority, P3 –not needed, P4 –not appropriate. This retrospective multi-center study aimed to assess its classification performance and its ability to help saving lives under capacity saturation. Methods ICU patients admitted for severe COVID-19 without triage in spring 2020 were retrospectively included from three hospitals. Demographic data, medical history and severity items were collected. Priority levels were retrospectively allocated at ICU admission and on ICU day 7–10. Mortality rate, cumulative incidence of death and of alive ICU discharge, length of ICU stay and of mechanical ventilation were compared between priority levels. Calculated mortality and survival were compared between full simulated triage and no triage. Results 225 patients were included, aged 63.1±11.9 years. Median SAPS2 was 40 (IQR 29–49). At the end of follow-up, 61 (27%) had died, 26 were still in ICU, and 138 had been discharged. Following retrospective initial priority allocation, mortality rate was 53% among P4 patients (95CI 34–72%) versus 23% among all P1 to P3 patients (95CI 17–30%, chi-squared p = 5.2e-4). The cumulative incidence of death consistently increased in the order P3, P1, P2 and P4 both at admission (Gray’s test p = 3.1e-5) and at reassessment (p = 8e-5), and conversely for that of alive ICU discharge. Reassessment strengthened consistency. Simulation under saturation showed that this two-step triage protocol could have saved 28 to 40 more lives than no triage. Conclusion Although it cannot eliminate potentially avoidable deaths, this triage protocol proved able to adequately prioritize critical care for patients with highest probability of survival, hence to save more lives if applied.