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Abstract Background Specialist palliative care (SPC) includes care for incurably ill patients and their family caregivers at home or on a palliative care ward until the very end of life. However, in the last days of life, patients can rarely express their needs and little is known about SPC outcomes as reported by multiprofessional SPC teams and family caregivers. Methods Using the Palliative Care Outcome Scale (POS; Score 0–40), proxy assessments of SPC outcomes in the patient’s last 3 days of life were performed by SPC teams and primary family caregivers of three home care and three inpatient services. Additional questions were asked about problems solved ‘particularly well’ or ‘inadequately’ (last 7 days), which were content analyzed and quantified. Results Proxy assessments by SPC teams were available in 142 patients (of whom 51% had died at home). Family caregiver assessments exist for a subgroup of 60 of these patients. SPC teams (POS total score: mean 13.8, SD 6.3) reported SPC outcomes slightly better than family caregivers (mean 16.7, SD 6.8). The POS items consistently rated as least affected (= 0) by both, SPC teams and family caregivers, were ‘not wasted time’ (team 99%/family caregivers 87%), ‘information’ (84%/47%) and ‘support’ (53%/31%). Items rated as most affected (= 4) were ‘patient anxiety’ (31%/51%), ‘life not worthwhile’ (26%/35%) and ‘no self-worth’ (19%/30%). Both groups indicated more problems solved ‘particularly well’ than ‘inadequately’; the latter concerned mainly clinically well-known challenges during end-of-life care and family caregiver care. Conclusions This study shows the range and type of symptoms and other concerns reported in the patient’s last days. Starting points for further improvements in family caregiver care and psychosocial and spiritual issues were identified.

Abstract Background Personal last wishes of people facing a life-limiting illness may change closer to death and may vary across different forms of specialist palliative care (SPC). Aims To explore the presence and common themes of last wishes over time and according to the SPC settings (inpatient vs. home-based SPC), and to identify factors associated to having a last wish. Methods Patients enrolled in a longitudinal study completed questionnaires at the onset (baseline, t0) and within the first 6 weeks (follow-up, t1) of SPC including an open-ended question on their personal last wishes. Last wishes were content analyzed, and all wishes were coded for presence or absence of each of the identified themes. Changes of last wishes (t0-t1) were analyzed by a McNemar test. The chi-square-test was used to compare the two SPC settings. Predictors for the presence of a last wish were identified by logistic regression analysis. Results Three hundred sixty-one patients (mean age, 69.5 years; 49% female) answered at t0, and 130 at t1. In cross-sectional analyses, the presence of last wishes was higher at t0 (67%) than at t1 (59%). Comparisons revealed a higher presence of last wishes among inpatients than those in home-based SPC at t0 (78% vs. 62%; p = .002), but not at t1. Inpatient SPC (OR = 1.987, p = .011) and greater physical symptom burden over the past week (OR = 1.168, p

Abstract Background We investigated the need for additional professional support and associated factors in patients (pts) at initiation and in the course of in- and outpatient specialist palliative care (I-SPC/O-SPC). Methods Pts entering an urban SPC network consecutively completed questionnaires on psychosocial/spiritual problems and support needs within 72 h (T0) as well as within the first 6 weeks (T1) of SPC. Hierarchical linear regression analysis was used to investigate the impact of sociodemographic / disease-related variables, psychological / physical burden, social support, and SPC setting on the extent of support needs. Results Four hundred twenty-five pts (70 years, 48% female, 91% cancer, 67% O-SPC) answered at T0, and 167 at T1. At T0, main problems related to transportation, usual activities, and dependency (83–89%). At T1, most prevalent problems also related to transportation and usual activities and additionally to light housework (82–86%). At T0, support needs were highest for transportation, light housework, and usual activities (35–41%). Cross-sectional comparisons of SPC settings revealed higher problem scores in O-SPC compared to I-SPC at T0 (p = .039), but not at T1. Support need scores were higher in O-SPC at T0 (p

Abstract Background Spontaneous pneumomediastinum unrelated to mechanical ventilation is a newly described complication of COVID-19 pneumonia. The objective of this case presentation is to highlight an important complication and to explore potential predisposing risk factors and possible underlying pathophysiology of this phenomenon. Case presentation We present two patients with COVID-19 pneumonia complicated by spontaneous pneumomediastinum, pneumopericardium, pneumothorax and subcutaneous emphysema without positive pressure ventilation. Both patients had multiple comorbidities, received a combination of antibiotics, steroids and supportive oxygen therapy, and underwent routine laboratory workup. Both patients then developed spontaneous pneumomediastinum and ultimately required intubation and mechanical ventilation, which proved to be challenging to manage. Conclusions Spontaneous pneumomediastinum is a serious complication of COVID-19 pneumonia, of which clinicians should be aware. Further studies are needed to determine risk factors and laboratory data predictive of development of spontaneous pneumomediastinum in COVID-19 pneumonia.

Abstract Tuberculosis (TB) is a life‐threatening infectious disease. The standard treatment is up to 90% effective; however, it requires the administration of four antibiotics (isoniazid, rifampicin, pyrazinamide, and ethambutol [HRZE]) over long time periods. This harsh treatment process causes adherence issues for patients because of the long treatment times and a myriad of adverse effects. Therefore, the World Health Organization has focused goals of shortening standard treatment regimens for TB in their End TB Strategy efforts, which aim to reduce TB‐related deaths by 95% by 2035. For this purpose, many novel and promising combination antibiotics are being explored that have recently been discovered, such as the bedaquiline, pretomanid, and linezolid (BPaL) regimen. As a result, testing the number of possible combinations with all possible novel regimens is beyond the limit of experimental resources. In this study, we present a unique framework that uses a primate granuloma modeling approach to screen many combination regimens that are currently under clinical and experimental exploration and assesses their efficacies to inform future studies. We tested well‐studied regimens such as HRZE and BPaL to evaluate the validity and accuracy of our framework. We also simulated additional promising combination regimens that have not been sufficiently studied clinically or experimentally, and we provide a pipeline for regimen ranking based on their efficacies in granulomas. Furthermore, we showed a correlation between simulation rankings and new marmoset data rankings, providing evidence for the credibility of our framework. This framework can be adapted to any TB regimen and can rank any number of single or combination regimens.

Abstract Medical digital twins are computational models of human biology relevant to a given medical condition, which are tailored to an individual patient, thereby predicting the course of disease and individualized treatments, an important goal of personalized medicine. The immune system, which has a central role in many diseases, is highly heterogeneous between individuals, and thus poses a major challenge for this technology. In February 2023, an international group of experts convened for two days to discuss these challenges related to immune digital twins. The group consisted of clinicians, immunologists, biologists, and mathematical modelers, representative of the interdisciplinary nature of medical digital twin development. A video recording of the entire event is available. This paper presents a synopsis of the discussions, brief descriptions of ongoing digital twin projects at different stages of progress. It also proposes a 5-year action plan for further developing this technology. The main recommendations are to identify and pursue a small number of promising use cases, to develop stimulation-specific assays of immune function in a clinical setting, and to develop a database of existing computational immune models, as well as advanced modeling technology and infrastructure.

A fundamental challenge for personalized medicine is to capture enough of the complexity of an individual patient to determine an optimal way to keep them healthy or restore their health. This will require personalized computational models of sufficient resolution and with enough mechanistic information to provide actionable information to the clinician. Such personalized models are increasingly referred to as medical digital twins. Digital twin technology for health applications is still in its infancy, and extensive research and development is required. This article focuses on several projects in different stages of development that can lead to specific—and practical–medical digital twins or digital twin modeling platforms. It emerged from a two-day forum on problems related to medical digital twins, particularly those involving an immune system component. Open access video recordings of the forum discussions are available.

Abstract Tuberculosis (TB) is the deadliest infectious disease worldwide. The design of new treatments for TB is hindered by the large number of candidate drugs, drug combinations, dosing choices, and complex pharmaco-kinetics/dynamics (PK/PD). Here we study the interplay of these factors in designing combination therapies by linking a machine-learning model, INDIGO-MTB, which predicts in vitro drug interactions using drug transcriptomics, with a multi-scale model of drug PK/PD and pathogen-immune interactions called GranSim. We calculate an in vivo drug interaction score (iDIS) from dynamics of drug diffusion, spatial distribution, and activity within lesions against various pathogen sub-populations. The iDIS of drug regimens evaluated against non-replicating bacteria significantly correlates with efficacy metrics from clinical trials. Our approach identifies mechanisms that can amplify synergistic or mitigate antagonistic drug interactions in vivo by modulating the relative distribution of drugs. Our mechanistic framework enables efficient evaluation of in vivo drug interactions and optimization of combination therapies.

Finding more effective vaccines against tuberculosis (TB) and improved preventive treatments against endogenous reactivation of latent TB is strategic to block transmission and reach the WHO goal of eliminating TB by 2050. Key related open questions in TB research include: i) what are the determinants of a strong memory response upon primary infection? ii) what is the role of cytokines towards protective memory response against a secondary infection? iii) what are the mechanisms responsible for the increased risk of reactivation in elderly individuals? To address these questions, we explored a computational model of the immune response to Mycobacterium tuberculosis including a mathematical description of immunosenescence and the generation and maintenance of immune memory. Sensitivity analysis techniques, together with extensive model characterization and in silico experiments, were applied to identify key mechanisms controlling TB reactivation and immunological memory. Key findings of this study are summarized by the following model predictions: i) increased strength and duration of memory protection is associated with higher levels of Tumor Necrosis Factor-[Formula: see text] (TNF) during primary infection; ii) production of TNF, but not of interferon-[Formula: see text], by memory T cells during secondary infection is a major determinant of effective protection; iii) impaired recruitment of CD4+ T cells may promote reactivation of latent TB infections in aging hosts. This study is a first attempt to consider the immune dynamics of a persistent infection throughout the lifetime of the host, taking into account immunosenescence and memory. While the model is TB specific, the results are applicable to other persistent bacterial infections and can aid in the development, evaluation and refinement of TB treatment and/or vaccine protocols.