Can sitting and lying blood pressure measurements be considered interchangeable in older frail adults?

BACKGROUND: To compare blood pressure (BP) values in the lying and sitting positions, and the effect of orthostatism when moving from each of these positions to the upright position in a geriatric population with various frailty levels. METHODS: In two sub-studies, we included a total of 157 consecutive patients, aged 75+ admitted to the Geriatric Department of Nancy University Hospital. BP and heart rate were sequentially measured three times in 1-min intervals each in lying, sitting and upright positions (Protocol#1, n = 107) or lying and upright positions (Protocol#2, n = 50) with an automatic validated Blood Pressure device. Patients were classified into two increasing frailty status (FS) categories: Low/Moderate (L/M-FS, n = 98) and High (H-FS, n = 59). RESULTS: BP levels were similar in the lying and sitting positions (Protocol#1, SBP 141 ± 22 mmHg vs. 142 ± 21 mmHg, respectively, and DBP 72 ± 12 mmHg vs. 72 ± 12 mmHg, respectively) in both frailty groups. In the H-FS, orthostatic drop of SBP was more pronounced from the lying (22.1 ± 5.8 mmHg, Protocol#2) as compared to the sitting to upright position (9.4 ± 1.9 mmHg, Protocol#1) (p < 0.008), and the same trend was observed for DBP. No such differences were observed in the L-M/FS frailty individuals. CONCLUSIONS: Orthostatic BP changes are more pronounced in the frailest patients when going from lying to the upright position than from the sitting to the upright position. Consequently, in these individuals, lying and sitting BP measurements cannot be interchangeable baseline positions to investigate orthostatic BP effects, and therefore, precise patient positioning should be specified when referring to "baseline BP measurements".

A deep learning system for differential diagnosis of skin diseases.

Skin conditions affect 1.9 billion people. Because of a shortage of dermatologists, most cases are seen instead by general practitioners with lower diagnostic accuracy. We present a deep learning system (DLS) to provide a differential diagnosis of skin conditions using 16,114 de-identified cases (photographs and clinical data) from a teledermatology practice serving 17 sites. The DLS distinguishes between 26 common skin conditions, representing 80% of cases seen in primary care, while also providing a secondary prediction covering 419 skin conditions. On 963 validation cases, where a rotating panel of three board-certified dermatologists defined the reference standard, the DLS was non-inferior to six other dermatologists and superior to six primary care physicians (PCPs) and six nurse practitioners (NPs) (top-1 accuracy: 0.66 DLS, 0.63 dermatologists, 0.44 PCPs and 0.40 NPs). These results highlight the potential of the DLS to assist general practitioners in diagnosing skin conditions.