Management of COVID-19 ICU-survivors in primary care: - a narrative review.

Many survivors of critical illness suffer from long-lasting physical, cognitive, and mental health sequelae. The number of affected patients is expected to markedly increase due to the COVID-19 pandemic. Many ICU survivors receive long-term care from a primary care physician. Hence, awareness and appropriate management of these sequelae is crucial. An interdisciplinary authorship team participated in a narrative literature review to identify key issues in managing COVID-19 ICU-survivors in primary care. The aim of this perspective paper is to synthesize important literature to understand and manage sequelae of critical illness due to COVID-19 in the primary care setting.

Frequency and risk factors of post-intensive care syndrome components in a multicenter randomized controlled trial of German sepsis survivors.

BACKGROUND: Post-intensive care syndrome (PICS) is a combination of cognitive, psychiatric and physical impairments in survivors of critical illness and intensive care. There is little data on long-term co-occurrence of associated impairments. METHODS: Analysis of data from 289 sepsis survivors from a German multicenter RCT. Impairments associated with PICS (depression, PTSD, cognitive impairment, chronic pain, neuropathic symptoms, dysphagia) during 24 months follow-up are used to explore the frequency and risk factors of PICS components in three classification models. RESULTS: The majority of participants showed impairments in 2-3 of 6 domains during follow-up. The overall frequency of PICS according to the classification models ranged from 32.9% to 98.6%. In regression analyses, there were no significant effects in selected ICU-related exposures or covariates for PICS classification models. Regarding individual components, only higher age and longer duration of ICU treatment and mechanical ventilation showed significant positive associations with the occurrence of cognitive impairment during follow-up, as did male gender and higher age for dysphagia. CONCLUSIONS: Almost all study participants showed impairments associated with PICS in at least one domain. The proposed classification models for PICS appear to be too broad to identify specific risk factors beyond its individual components.

Diagnostic use of facial image analysis software in endocrine and genetic disorders: review, current results and future perspectives.

Cushing's syndrome (CS) and acromegaly are endocrine diseases that are currently diagnosed with a delay of several years from disease onset. Novel diagnostic approaches and increased awareness among physicians are needed. Face classification technology has recently been introduced as a promising diagnostic tool for CS and acromegaly in pilot studies. It has also been used to classify various genetic syndromes using regular facial photographs. The authors provide a basic explanation of the technology, review available literature regarding its use in a medical setting, and discuss possible future developments. The method the authors have employed in previous studies uses standardized frontal and profile facial photographs for classification. Image analysis is based on applying mathematical functions evaluating geometry and image texture to a grid of nodes semi-automatically placed on relevant facial structures, yielding a binary classification result. Ongoing research focuses on improving diagnostic algorithms of this method and bringing it closer to clinical use. Regarding future perspectives, the authors propose an online interface that facilitates submission of patient data for analysis and retrieval of results as a possible model for clinical application.

Automatic face classification of Cushing's syndrome in women - a novel screening approach.

OBJECTIVE: Cushing's syndrome causes considerable harm to the body if left untreated, yet often remains undiagnosed for prolonged periods of time. In this study we aimed to test whether face classification software might help in discriminating patients with Cushing's syndrome from healthy controls. DESIGN: Diagnostic study. PATIENTS: Using a regular digital camera, we took frontal and profile pictures of 20 female patients with Cushing's syndrome and 40 sex- and age-matched controls. MEASUREMENTS: Semi-automatic analysis of the pictures was performed by comparing texture and geometry within a grid of nodes placed on the pictures. The leave-one-out cross-validation method was employed to classify subjects by the software. RESULTS: The software correctly classified 85.0% of patients and 95.0% of controls, resulting in a total classification accuracy of 91.7%. CONCLUSIONS: In this preliminary analysis we found a good classification accuracy of Cushing's syndrome by face classification software. Testing accuracy is comparable to that of currently employed screening tests.