Bioimpedance spectroscopy for assessment of volume status in patients before and after general anaesthesia

BackgroundTechnically assisted assessment of volume status before surgery may be useful to direct intraoperative fluid administration. We therefore tested a recently developed whole-body bioimpedance spectroscopy device to determine pre- to postoperative fluid distribution.MethodsUsing a three-compartment physiologic tissue model, the body composition monitor (BCM, Fresenius Medical Care, Germany) measures total body fluid volume, extracellular volume, intracellular volume and fluid overload as surplus or deficit of 'normal' extracellular volume. BCM-measurements were performed before and after standardized general anaesthesia for gynaecological procedures (laparotomies, laparoscopies and vaginal surgeries). BCM results were blinded to the attending anaesthesiologist and data analysed using the 2-sided, paired Student's t-test and multiple linear regression.ResultsIn 71 females aged 45 ± 15 years with body weight 67 ± 13 kg and Duration of anesthesia 154 ± 69 minutes [corrected] duration of anaesthesia 154 ± 68 min, pre- to postoperative fluid overload increased from -0.7 ± 1.1 L to 0.1 ± 1.0 L, corresponding to -5.1 ± 7.5% and 0.8 ± 6.7% of normal extracellular volume, respectively (both pConclusionsRoutine intraoperative fluid administration results in a significant, and clinically meaningful increase in the extracellular compartment. BCM-measurements yielded plausible results and may become useful to guide intraoperative fluid therapy in future studies

Over-Detection of Melanoma-Suspect Lesions by a CE-Certified Smartphone App: Performance in Comparison to Dermatologists, 2D and 3D Convolutional Neural Networks in a Prospective Data Set of 1204 Pigmented Skin Lesions Involving Patients’ Perception

The exponential increase in algorithm-based mobile health (mHealth) applications (apps) for melanoma screening is a reaction to a growing market. However, the performance of available apps remains to be investigated. In this prospective study, we investigated the diagnostic accuracy of a class 1 CE-certified smartphone app in melanoma risk stratification and its patient and dermatologist satisfaction. Pigmented skin lesions ≥ 3 mm and any suspicious smaller lesions were assessed by the smartphone app SkinVision® (SkinVision® B.V., Amsterdam, the Netherlands, App-Version 6.8.1), 2D FotoFinder ATBM® master (FotoFinder ATBM® Systems GmbH, Bad Birnbach, Germany, Version 3.3.1.0), 3D Vectra® WB360 (Canfield Scientific, Parsippany, NJ, USA, Version 4.7.1) total body photography (TBP) devices, and dermatologists. The high-risk score of the smartphone app was compared with the two gold standards: histological diagnosis, or if not available, the combination of dermatologists’, 2D and 3D risk assessments. A total of 1204 lesions among 114 patients (mean age 59 years; 51% females (55 patients at high-risk for developing a melanoma, 59 melanoma patients)) were included. The smartphone app’s sensitivity, specificity, and area under the receiver operating characteristics (AUROC) varied between 41.3–83.3%, 60.0–82.9%, and 0.62–0.72% according to two study-defined reference standards. Additionally, all patients and dermatologists completed a newly created questionnaire for preference and trust of screening type. The smartphone app was rated as trustworthy by 36% (20/55) of patients at high-risk for melanoma, 49% (29/59) of melanoma patients, and 8.8% (10/114) of dermatologists. Most of the patients rated the 2D TBP imaging (93% (51/55) resp. 88% (52/59)) and the 3D TBP imaging (91% (50/55) resp. 90% (53/59)) as trustworthy. A skin cancer screening by combination of dermatologist and smartphone app was favored by only 1.8% (1/55) resp. 3.4% (2/59) of the patients; no patient preferred an assessment by a smartphone app alone. The diagnostic accuracy in clinical practice was not as reliable as previously advertised and the satisfaction with smartphone apps for melanoma risk stratification was scarce. MHealth apps might be a potential medium to increase awareness for melanoma screening in the lay population, but healthcare professionals and users should be alerted to the potential harm of over-detection and poor performance. In conclusion, we suggest further robust evidence-based evaluation before including market-approved apps in self-examination for public health benefits

Educational level-dependent melanoma awareness in a high-risk population in Switzerland

IntroductionThe worldwide incidence of melanoma has been increasing rapidly in recent decades with Switzerland having one of the highest rates in Europe. Ultraviolet (UV) radiation is one of the main risk factors for skin cancer. Our objective was to investigate UV protective behavior and melanoma awareness in a high-risk cohort for melanoma.MethodsIn this prospective monocentric study, we assessed general melanoma awareness and UV protection habits in at-risk patients (≥100 nevi, ≥5 dysplastic nevi, known CDKN2A mutation, and/or positive family history) and melanoma patients using questionnaires. ResultsBetween 01/2021 and 03/ 2022, a total of 269 patients (53.5% at-risk patients, 46.5% melanoma patients) were included. We observed a significant trend toward using a higher sun protection factor (SPF) in melanoma patients compared with at-risk patients (SPF 50+: 48% [n=60] vs. 26% [n=37]; p=0.0016). Those with a college or university degree used a high SPF significantly more often than patients with lower education levels (p=0.0007). However, higher educational levels correlated with increased annual sun exposure (p=0.041). Neither a positive family history for melanoma, nor gender or Fitzpatrick skin type influenced sun protection behavior. An age of ≥ 50 years presented as a significant risk factor for melanoma development with an odd’s ratio of 2.32. Study participation resulted in improved sun protection behavior with 51% reporting more frequent sunscreen use after study inclusion. DiscussionUV protection remains a critical factor in melanoma prevention. We suggest that melanoma awareness should continue to be raised through public skin cancer prevention campaigns with a particular focus on individuals with low levels of education

Improved diagnosis by automated macro‐ and micro‐anatomical region mapping of skin photographs

Background: The exact location of skin lesions is key in clinical dermatology. On one hand, it supports differential diagnosis (DD) since most skin conditions have specific predilection sites. On the other hand, location matters for dermatosurgical interventions. In practice, lesion evaluation is not well standardized and anatomical descriptions vary or lack altogether. Automated determination of anatomical location could benefit both situations. Objective: Establish an automated method to determine anatomical regions in clinical patient pictures and evaluate the gain in DD performance of a deep learning model (DLM) when trained with lesion locations and images. Methods: Retrospective study based on three datasets: macro-anatomy for the main body regions with 6000 patient pictures partially labelled by a student, micro-anatomy for the ear region with 182 pictures labelled by a student and DD with 3347 pictures of 16 diseases determined by dermatologists in clinical settings. For each dataset, a DLM was trained and evaluated on an independent test set. The primary outcome measures were the precision and sensitivity with 95% CI. For DD, we compared the performance of a DLM trained with lesion pictures only with a DLM trained with both pictures and locations. Results: The average precision and sensitivity were 85% (CI 84-86), 84% (CI 83-85) for macro-anatomy, 81% (CI 80-83), 80% (CI 77-83) for micro-anatomy and 82% (CI 78-85), 81% (CI 77-84) for DD. We observed an improvement in DD performance of 6% (McNemar test P-value 0.0009) for both average precision and sensitivity when training with both lesion pictures and locations. Conclusion: Including location can be beneficial for DD DLM performance. The proposed method can generate body region maps from patient pictures and even reach surgery relevant anatomical precision, e.g. the ear region. Our method enables automated search of large clinical databases and make targeted anatomical image retrieval possible

Allergic contact dermatitis

Allergic contact dermatitis (ACD) is a common skin disease caused by a T cell-mediated immune reaction to usually innocuous allergens. ACD can have grave medical and socioeconomic consequences. ACD and irritant contact dermatitis often occur together. A detailed history and clinical examination are crucial and guide patch testing, which is the gold standard to diagnose ACD. T-cell clones persisting in the skin may explain the tendency of ACD to relapse even after years of allergen avoidance. Traditional treatments for ACD are topical steroids, calcineurin inhibitors, phototherapy, retinoids (including the recent alitretinoin), and immunosuppressants. Targeted therapies are lacking

Vital signs, volume status and body composition before and after anaesthesia.

<p>Delta was calculated by subtracting any patient’s preoperative value from the postoperative value. Significance was determined by Student’s t-test (p<0.05). Italics: Data were incomplete for body temperature (number of missing values: N = 7 [pre], N = 20 [post], N = 20 [delta]), serum protein (number of missing values: N = 15 [pre], N = 39 [post], N = 46 [delta]), serum albumin (number of missing values: N = 42 [pre], N = 40 [post], N = 54 [delta]), C-reactive protein (number of missing values: N = 11 [pre], N = 39 [post], N = 42 [delta]) and Capillary Leak Index (number of missing values: N = 43 [pre], N = 43 [post], N = 55 [delta]), despite significant findings. Note that postoperative laboratory measurements were usually performed on the day after surgery (not simultaneous to the postoperative BCM-measurement). Bold: findings with p<0.05. Abbreviations: ECV = Extracellular Volume, ICV = Intracellular Volume, TBV = Total Body Fluid Volume.</p><p>Vital signs, volume status and body composition before and after anaesthesia.</p

Association between net perioperative fluid balance and changes in pre- to postoperative extracellular volume, with multiple levels of adjustments.

<p>Multiple regression analysis: After stepwise adjustment the coefficients of determination (r²), the p-value¹ for the combined effect of all predictors for each model, the p-value² for the individual effect of a predictor and the correlation coefficient (Beta) represent the association between the outcome and the predictor variables. Boldface indicating statistical significance (p<0.05).</p><p>Association between net perioperative fluid balance and changes in pre- to postoperative extracellular volume, with multiple levels of adjustments.</p

Demographic characteristics of the study population.

<p>Continuous variables are reported as mean ± standard deviation, (5^th and 95^th percentile); categorical variables are presented as counts and frequencies. Abbreviations: ASA = American Society of Anaesthesiologists’ physical status classification system, NYHA = New York Heart Association functional classification, PONV = postoperative nausea and vomiting, IV = intravenous. Net perioperative fluid balance = total perioperative intravenous fluid volume, corrected for urinary excretion and blood loss. *In 5 cases, the interval between the BCM-measurements was >60 minutes longer than the duration of anaesthesia, due to logistical reasons.</p><p>Demographic characteristics of the study population.</p

Associations between pre- to postoperative changes in volume status and net perioperative fluid balance.

<p>Scatter plots. Regression equations are as follows: <b>A</b> Change in Extracellular Volume = 0.73×Net Perioperative Fluid Balance –0.37. <b>B</b> Change in Total Body volume = 0.91×Net Perioperative Fluid Balance –0.43. <b>C</b> No linear correlation between Change in Intracellular Volume and Net Perioperative Fluid Balance. <b>D</b> No linear correlation between Change in Intracellular Volume and Change in Extracellular Volume. Pearson correlation test. R² = Coefficient of determination.</p

Over-Detection of Melanoma-Suspect Lesions by a CE-Certified Smartphone App: Performance in Comparison to Dermatologists, 2D and 3D Convolutional Neural Networks in a Prospective Data Set of 1204 Pigmented Skin Lesions Involving Patients&rsquo; Perception

The exponential increase in algorithm-based mobile health (mHealth) applications (apps) for melanoma screening is a reaction to a growing market. However, the performance of available apps remains to be investigated. In this prospective study, we investigated the diagnostic accuracy of a class 1 CE-certified smartphone app in melanoma risk stratification and its patient and dermatologist satisfaction. Pigmented skin lesions &ge; 3 mm and any suspicious smaller lesions were assessed by the smartphone app SkinVision&reg; (SkinVision&reg; B.V., Amsterdam, the Netherlands, App-Version 6.8.1), 2D FotoFinder ATBM&reg; master (FotoFinder ATBM&reg; Systems GmbH, Bad Birnbach, Germany, Version 3.3.1.0), 3D Vectra&reg; WB360 (Canfield Scientific, Parsippany, NJ, USA, Version 4.7.1) total body photography (TBP) devices, and dermatologists. The high-risk score of the smartphone app was compared with the two gold standards: histological diagnosis, or if not available, the combination of dermatologists&rsquo;, 2D and 3D risk assessments. A total of 1204 lesions among 114 patients (mean age 59 years; 51% females (55 patients at high-risk for developing a melanoma, 59 melanoma patients)) were included. The smartphone app&rsquo;s sensitivity, specificity, and area under the receiver operating characteristics (AUROC) varied between 41.3&ndash;83.3%, 60.0&ndash;82.9%, and 0.62&ndash;0.72% according to two study-defined reference standards. Additionally, all patients and dermatologists completed a newly created questionnaire for preference and trust of screening type. The smartphone app was rated as trustworthy by 36% (20/55) of patients at high-risk for melanoma, 49% (29/59) of melanoma patients, and 8.8% (10/114) of dermatologists. Most of the patients rated the 2D TBP imaging (93% (51/55) resp. 88% (52/59)) and the 3D TBP imaging (91% (50/55) resp. 90% (53/59)) as trustworthy. A skin cancer screening by combination of dermatologist and smartphone app was favored by only 1.8% (1/55) resp. 3.4% (2/59) of the patients; no patient preferred an assessment by a smartphone app alone. The diagnostic accuracy in clinical practice was not as reliable as previously advertised and the satisfaction with smartphone apps for melanoma risk stratification was scarce. MHealth apps might be a potential medium to increase awareness for melanoma screening in the lay population, but healthcare professionals and users should be alerted to the potential harm of over-detection and poor performance. In conclusion, we suggest further robust evidence-based evaluation before including market-approved apps in self-examination for public health benefits