The number of comorbidities as an important cofactor to ASA class in predicting postoperative outcome: An international multicentre cohort study.

BACKGROUND: Multimorbidity is a growing burden in our ageing society and is associated with perioperative morbidity and mortality. Despite several modifications to the ASA physical status classification, multimorbidity as such is still not considered. Thus, the aim of this study was to quantify the burden of comorbidities in perioperative patients and to assess, independent of ASA class, its potential influence on perioperative outcome. METHODS: In a subpopulation of the prospective ClassIntra® validation study from eight international centres, type and severity of anaesthesia-relevant comorbidities were additionally extracted from electronic medical records for the current study. Patients from the validation study were of all ages, undergoing any type of in-hospital surgery and were followed up until 30 days postoperatively to assess perioperative outcomes. Primary endpoint was the number of comorbidities across ASA classes. The associated postoperative length of hospital stay (pLOS) and Comprehensive Complication Index (CCI®) were secondary endpoints. On a scale from 0 (no complication) to 100 (death) the CCI® measures the severity of postoperative morbidity as a weighted sum of all postoperative complications. RESULTS: Of 1421 enrolled patients, the mean number of comorbidities significantly increased from 1.5 in ASA I (95% CI, 1.1-1.9) to 10.5 in ASA IV (95% CI, 8.3-12.7) patients. Furthermore, independent of ASA class, postoperative complications measured by the CCI® increased per each comorbidity by 0.81 (95% CI, 0.40-1.23) and so did pLOS (geometric mean ratio, 1.03; 95% CI, 1.01-1.06). CONCLUSIONS: These data quantify the high prevalence of multimorbidity in the surgical population and show that the number of comorbidities is predictive of negative postoperative outcomes, independent of ASA class.

Breathlessness assessment, management and impact in the intensive care unit: a rapid review and narrative synthesis.

BACKGROUND: Adults in the intensive care unit (ICU) commonly experience distressing symptoms and other concerns such as pain, delirium, and breathlessness. Breathlessness management is not supported by any ICU guidelines, unlike other symptoms. AIM: To review the literature relating to (i) prevalence, intensity, assessment, and management of breathlessness in critically ill adults in the ICU receiving invasive and non-invasive mechanical ventilation (NIV) and high-flow oxygen therapy, (HFOT), (ii) the impact of breathlessness on ICU patients with regard to engagement with rehabilitation. METHODS: A rapid review and narrative synthesis using the Cochrane Methods Group Recommendations was conducted and reported in accordance with PRISMA. All study designs investigating breathlessness in adult ICU patients receiving either invasive mechanical ventilation (IMV), NIV or HFOT were eligible. PubMed, MEDLINE, The Cochrane Library and CINAHL databased were searched from June 2013 to June 2023. Studies were quality appraised. RESULTS: 19 studies representing 2822 ICU patients were included (participants mean age 48 years to 71 years; proportion of males 43-100%). The weighted mean prevalence of breathlessness in ICU patients receiving IMV was 49% (range 34-66%). The proportion of patients receiving NIV self-reporting moderate to severe dyspnoea was 55% prior to initiation. Breathlessness assessment tools included visual analogue scale, (VAS), numerical rating scale, (NRS) and modified BORG scale, (mBORG). In patients receiving NIV the highest reported median (interquartile range [IQR]) VAS, NRS and mBORG scores were 6.2cm (0-10 cm), 5 (2-7) and 6 (2.3-7) respectively (moderate to severe breathlessness). In patients receiving either NIV or HFOT the highest reported median (IQR) VAS, NRS and mBORG scores were 3 cm (0-6 cm), 8 (5-10) and 4 (3-5) respectively. CONCLUSION: Breathlessness in adults receiving IMV, NIV or HFOT in the ICU is prevalent and clinically important with median intensity ratings indicating the presence of moderate to severe symptoms.

The IRIS clinic: A Protocol for a mixed-methods study evaluating the management of Hyperemesis Gravidarum.

Background: Hyperemesis Gravidarum (HG) is a severe form of nausea and vomiting in pregnancy that affects 0.3-3% of women and has profound nutritional, physical and psychological consequences. Research is lacking regarding the most effective management of the condition. In response to patient feedback, a multidisciplinary HG day-case service (IRIS Clinic) was launched in 2020 at The National Maternity Hospital, Ireland. The clinic provides routine, day-case care in a comfortable space with pre-booked appointments. The MDT involves midwives, dietitians, perinatal mental health, obstetrics and pharmacy, and the nature of the clinic enables peer-to-peer support. As this clinic is the first of its kind in Ireland, we aim to assess its effectiveness and feasibility, and suggest recommendations for improvement. Methods: This is a sequential, mixed-methods study that commenced in August 2021. The prospective arm of the study is ongoing and involves enrolling women (n = 50) who are attending the IRIS clinic. Data are collected on first admission (pre-intervention) and approximately 8 weeks' later (post-intervention) relating to symptoms of HG, well-being, food tolerances, quality of life and nutritional intake. Qualitative, semi-structured interviews will be conducted to evaluate women's experiences of attending the clinic. The retrospective arm of the study will be a chart review (n = 200) of women diagnosed with HG to describe assessments, treatments and pregnancy and birth outcomes. Conclusion: The IRIS clinic has the potential to improve pregnancy outcomes and nutritional status among women with HG. If found to be effective and feasible, the model for this clinic could be replicated elsewhere.

Time-Dependent Deep Learning Prediction of Multiple Sclerosis Disability.

The majority of deep learning models in medical image analysis concentrate on single snapshot timepoint circumstances, such as the identification of current pathology on a given image or volume. This is often in contrast to the diagnostic methodology in radiology where presumed pathologic findings are correlated to prior studies and subsequent changes over time. For multiple sclerosis (MS), the current body of literature describes various forms of lesion segmentation with few studies analyzing disability progression over time. For the purpose of longitudinal time-dependent analysis, we propose a combinatorial analysis of a video vision transformer (ViViT) benchmarked against traditional recurrent neural network of Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) architectures and a hybrid Vision Transformer-LSTM (ViT-LSTM) to predict long-term disability based upon the Extended Disability Severity Score (EDSS). The patient cohort was procured from a two-site institution with 703 patients' multisequence, contrast-enhanced MRIs of the cervical spine between the years 2002 and 2023. Following a competitive performance analysis, a VGG-16-based CNN-LSTM was compared to ViViT with an ablation analysis to determine time-dependency of the models. The VGG16-LSTM predicted trinary classification of EDSS score in 6 years with 0.74 AUC versus the ViViT with 0.84 AUC (p-value < 0.001 per 5 × 2 cross-validation F-test) on an 80:20 hold-out testing split. However, the VGG16-LSTM outperformed ViViT when patients with only 2 years of MRIs (n = 94) (0.75 AUC versus 0.72 AUC, respectively). Exact EDSS classification was investigated for both models using both classification and regression strategies but showed collectively worse performance. Our experimental results demonstrate the ability of time-dependent deep learning models to predict disability in MS using trinary stratification of disability, mimicking clinical practice. Further work includes external validation and subsequent observational clinical trials.

Anxiety and depression in patients with non-site-specific cancer symptoms: data from a rapid diagnostic clinic.

Background: Rapid diagnostic clinics (RDCs) provide a streamlined holistic pathway for patients presenting with non-site specific (NSS) symptoms concerning of malignancy. The current study aimed to: 1) assess the prevalence of anxiety and depression, and 2) identify a combination of patient characteristics and symptoms associated with severe anxiety and depression at Guy's and St Thomas' Foundation Trust (GSTT) RDC in Southeast London. Additionally, we compared standard statistical methods with machine learning algorithms for predicting severe anxiety and depression. Methods: Patients seen at GSTT RDC between June 2019 and January 2023 completed the General Anxiety Disorder Questionnaire (GAD-7) and Patient Health Questionnaire (PHQ-8) questionnaires, at baseline. We used logistic regression (LR) and 2 machine learning (ML) algorithms (random forest (RF), support vector machine (SVM)) to predict risk of severe anxiety and severe depression. The models were constructed using a set of sociodemographic and clinical variables. Results: A total of 1734 patients completed GAD-7 and PHQ-8 questionnaires. Of these, the mean age was 59 years (Standard Deviation: 15.5), and 61.5% (n:1067) were female. Prevalence of severe anxiety (GAD-7 score ≥15) was 13.8% and severe depression (PHQ-8 score≥20) was 9.3%. LR showed that a combination of previous mental health condition (PMH, Adjusted Odds Rario (AOR) 3.28; 95% confidence interval (CI) 2.36-4.56), symptom duration >6 months (AOR 2.20; 95%CI 1.28-3.77), weight loss (AOR 1.88; 95% CI 1.36-2.61), progressive pain (AOR 1.71; 95%CI 1.26-2.32), and fatigue (AOR 1.36; 95%CI 1.01-1.84), was positively associated with severe anxiety. Likewise, a combination PMH condition (AOR 3.95; 95%CI 2.17-5.75), fatigue (AOR 2.11; 95%CI 1.47-3.01), symptom duration >6 months (AOR 1.98; 95%CI 1.06-3.68), weight loss (AOR 1.66; 95%CI 1.13-2.44), and progressive pain (AOR 1.50; 95%CI 1.04-2.16), was positively associated with severe depression. LR and SVM had highest accuracy levels for severe anxiety (LR: 86%, SVM: 85%) and severe depression (SVM: 89%, LR: 86%). Conclusion: High prevalence of severe anxiety and severe depression was found. PMH, fatigue, weight loss, progressive pain, and symptoms >6 months emerged as combined risk factors for both these psychological comorbidities. RDCs offer an opportunity to alleviate distress in patients with concerning symptoms by expediting diagnostic evaluations.

Points to consider when initiating clinical investigations in autistic paediatric populations-A White Paper.

Many individuals with autism spectrum disorder (ASD) experience various degrees of impairment in social interaction and communication, restricted, repetitive behaviours, interests/activities. These impairments make a significant contribution to poorer everyday adaptive functioning. Yet, there are no pharmacological therapies to effectively treat the core symptoms of ASD. Since symptoms of ASD likely emerge from a complex interplay of vulnerabilities, environmental factors and compensatory mechanisms during the early developmental period, pharmacological interventions arguably would have the greatest impact to improve long-term outcomes when implemented at a young age. It is essential therefore, that clinical development programmes of investigational drugs in ASD include the paediatric population early on in clinical trials. Such trials need to offer the prospect of direct benefit (PDB) for participants. In most cases in drug development this prospect is supported by evidence of efficacy in adults. However, the effectiveness of treatment approaches may be age-dependent, so that clinical trials in adults may not provide sufficient evidence for a PDB in children. In this white paper, we consolidate recommendations from regulatory guidelines, as well as advice from the Food and Drug Administration, USA (FDA) and the Committee for Human Medicinal Products (CHMP) consultations on various development programmes on: 1) elements to support a PDB to participants in early paediatric clinical trials in ASD, including single-gene neurodevelopment disorders, 2) aspects of study design to allow for a PDB. This white paper is intended to be complementary to existing regulatory guidelines in guiding industry and academic sponsors in their conduct of early paediatric clinical trials in ASD.

'There's never really been an opportunity': perspectives of those with long-term conditions on advance care planning, a qualitative interview study in primary care.

BACKGROUND: Advance care planning (ACP), the process of discussing and documenting people's wishes for future care, is increasingly researched within health and social care. However, it is sometimes assumed that ACP is only for those experiencing advanced cancer or those reaching the end of life. Little is known about how those living with other major conditions perceive ACP. AIM: To explore attitudes towards ACP in this population. METHOD: In-depth interview study in general practice. INCLUSION CRITERIA: adults living with major conditions other than cancer (diabetes, heart disease, chronic respiratory disease, and kidney disease). Interviews were conducted face-to-face, audio-recorded, and transcribed verbatim. Thematic analysis utilised a critical realist approach, underpinned by the COM-B Behaviour Change Model. NHS ethics approval (reference: 23/PR/0078). RESULTS: Eleven participants have been recruited (recruitment ongoing): aged 61-88 years, five males, and Rockwood Frailty Scores 1-5. Emergent COM-B themes relate to capability (wellbeing, organisational skills, and understanding); opportunity (setting, time, trust, involving others, and the 'right' moment); and motivation (motivating factors: helping loved ones, helping professionals, fearing undesirable outcomes, desiring a peaceful death, being organised, and valuing autonomy; and demotivating factors: trusting others with decisions, uncertainty regarding the future, distrusting health care, focusing on the present, and values-based objections). ACP is understood in three ways: as proactive planning, as anticipatory planning, and as acknowledgment of ill-health and mortality. Context influences understanding and understanding influences motivation. There is openness to discussing ACP as part of proactive care. CONCLUSION: Normalising ACP before ill-health may reduce distress. Public health campaigns and preventative healthcare consultations present opportunities for ACP signposting.

Understanding and addressing symptoms for those with kidney failure managed conservatively, without dialysis: considerations and models of care.

For those who have kidney failure and are managed conservatively without dialysis, symptoms are often prevalent, multiple, and troublesome. They interfere with quality of life, reduce wellbeing, and can affect family carers too. Symptoms can sometimes be difficult to manage, and-for professionals-they are often hard to assess and not always amenable to management with medications appropriate for use in kidney failure. Fatigue is one of the most common symptoms; alongside a general overview of symptoms in this population, we include a more detailed discussion of this often-neglected symptom. The solutions to the main symptoms experienced by those with kidney failure managed conservatively without dialysis lie in detailed assessment and monitoring of symptoms, working as a multi-disciplinary team to the maximum to draw on the full range of skills and expertise, and use of non-pharmacological, as well as pharmacological, approaches. Both nephrology and palliative care skills and expertise are important to optimise the recognition, assessment, and management of symptoms. There are few published descriptions of models of conservative kidney management (CKM) or supportive kidney care and there is a lack of evidence to suggest which model is most effective. We therefore consider the evidence on optimal models of CKM and make suggestions for best practice.

Patterns of acute hospital and specialist palliative care use among people with non-curative upper gastrointestinal cancer.

PURPOSE: Upper gastrointestinal (GI) cancers contribute to 16.7% of UK cancer deaths. These patients make high use of acute hospital services, but detail about palliative care use is lacking. We aimed to determine the patterns of use of acute hospital and hospital specialist palliative care services in patients with advanced non-curative upper GI cancer. METHODS: We conducted a service evaluation of hospital use and palliative care for all patients with non-curative upper GI cancer seen in one large hospital, using routinely collected data (2019-2022). We report and characterise hospital admissions and palliative care within the study time period, using descriptive statistics, and multivariable Poisson regression to estimate the unadjusted and adjusted incidence rate ratio of hospital admissions. RESULTS: The total with non-curative upper GI cancer was 960. 86.7% had at least one hospital admission, with 1239 admissions in total. Patients had a higher risk of admission to hospital if: aged ≤ 65 (IRR for 66-75 years 0.71, IRR 76-85 years 0.68; IRR > 85 years 0.53; p < 0.05), or lived in an area of lower socioeconomic status (IMD Deciles 1-5) (IRR 0.90; p < 0.05). Over the 4-year period, the rate of re-admission was higher in patients not referred to palliative care (rate 0.52 readmissions/patient versus rate 1.47 readmissions/patient). CONCLUSION: People with advanced non-curative gastrointestinal cancer have frequent hospital admissions, especially if younger or from areas of lower socioeconomic status. There is clear association between specialist palliative care referral and reduced risk of hospitalisation. This evidence supports referral to specialist palliative care.

Promoting planting in front gardens: a systematic approach to intervention development.

Planting in front gardens is associated with a range of human and environmental health benefits. Effective interventions aimed at cultivating this practice are, however, hampered by the paucity of theory- and evidence-based behavioural research in this context. This study aims to systematically determine a set of behaviour change interventions likely to be effective at promoting planting in front gardens amongst UK householders. The Behaviour Change Wheel framework was applied. Behavioural systems mapping was used to identify community actors relevant to front gardening. Potential behavioural influences on householders' front gardening were identified using the Capability, Opportunity, Motivation, Behaviour model. Using peer-reviewed scientific findings as evidence, behavioural influences were systematically linked to potential intervention strategies, behaviour change techniques and real-world implementation options. Finally, intervention recommendations were refined through expert evaluations and local councillor and public stakeholder feedback, evaluating them against the Acceptability, Practicability, Effectiveness, Affordability, Side effects and Equity criteria in a UK implementation context. This study formulated 12 intervention recommendations, implementable at a community level, to promote front gardening. Stakeholder feedback revealed a preference for educational and supportive (social and practical) strategies (e.g., community gardening workshops, front gardening 'starter kits') over persuasive and motivational approaches (e.g., social marketing, motivational letters from the council to householders). Householders' front gardening behaviour is complex and influenced by the behaviour of many other community actors. It also needs to be understood as a step in a continuum of other behaviours (e.g., clearing land, gardening, waste disposal). This study demonstrates the application of behavioural science to an understudied implementation context, that is, front gardening promotion, drawing on a rigorous development process promoting a transparent approach to intervention design. Stakeholder consultation allowed relevance, feasibility and practical issues to be considered. These improve the likely effectiveness of interventions in practice. The next steps include evaluating the proposed interventions in practice.

Reduced congestion and improved response to a fluid/sodium challenge in chronic heart failure patients after initiation of sacubitril/valsartan: The NATRIUM-HF study.

AIMS: The effects of initiating sacubitril/valsartan in patients with stable heart failure with reduced ejection fraction (HFrEF) on response to fluid and sodium expansion are unknown. METHODS AND RESULTS: We have explored changes in natriuresis, diuresis, and congestion in response to the administration of intravenous fluid/sodium load in patients with HFrEF before as compared to after the initiation of sacubitril/valsartan. At baseline (before sacubitril/valsartan initiation) and 2 and 3 months after the initiation, patients underwent an evaluation that consisted of three phases of 3 h: the rest phase (0-3 h), the load phase (3-6 h) in which 1 L of intravenous Ringer solution was administered, and the diuretic phase (6-9 h) at the beginning of which furosemide was administered. Overall, 216 patients completed the study. In comparison to baseline values, at 2 and 3 months after sacubitril/valsartan initiation, patients' diuresis and natriuresis in response to Ringer administration significantly increased (mean difference: 38.8 [17.38] ml, p = 0.0040, and 9.6 [2.02] mmol, p < 0.0001, respectively). Symptoms and signs of congestion after the fluid/sodium challenge were significantly decreased at months 2 and 3 compared to baseline. Compared to baseline, there was also an increment of natriuresis after furosemide administration on sacubitril/valsartan (9.8 [5.13] mmol, p = 0.0167). There was a significant decrease in body weight in subsequent visits when compared to baseline values (-0.50 [-12.7, 7.4] kg at 2 months, and -0.75 [-15.9, 7.5] kg at 3 months; both p < 0.0001). CONCLUSIONS: The initiation of sacubitril/valsartan in HFrEF patients was associated with improvements in natriuresis, diuresis, and weight loss and better clinical adaptation to potentially decongestive stressors.

Cognitive testing of the Children's Palliative Outcome Scale (C-POS) with children, young people and their parents/carers.

BACKGROUND: The Children's Palliative Outcome Scale (C-POS) is being developed using best methodological guidance on outcome measure development, This recommends cognitive testing, an established method of item improvement, prior to psychometric testing. AIM: To cognitively test C-POS within the target population to establish comprehensibility, comprehensiveness, relevance and acceptability. DESIGN: Cross-sectional cognitive interview study following COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN) methodology and Rothrock guidance on outcome measure development. Cognitive interviews were conducted using 'think aloud' and verbal probing techniques. SETTING/PARTICIPANTS: Children 5-⩽17 years old with life-limiting conditions and parents/carers of children with life-limiting conditions were recruited from 14 UK sites. RESULTS: Forty-eight individuals participated (36 parents; 12 children) in cognitively testing the five versions of C-POS over two to seven rounds. Content and length were acceptable, and all questions were considered important. Refinements were made to parent/carer versions to be inclusive of non-verbal children such as changing 'share' to 'express' feelings; and 'being able to ask questions' to 'having the appropriate information'. Changes to improve comprehensibility of items such as 'living life to the fullest' were also made. Parents reported that completing an outcome measure can be distressing but this is anticipated and that being asked is important. CONCLUSION: Cognitive interviewing has facilitated refinement of the C-POS, especially for non-verbal children who represent a large proportion of those with a life-limiting condition. This study has enhanced the face and content validity of the measure and provided preliminary evidence for acceptability for use in routine practice.

A roadmap for model-based bioprocess development.

The bioprocessing industry is undergoing a significant transformation in its approach to quality assurance, shifting from the traditional Quality by Testing (QbT) to Quality by Design (QbD). QbD, a systematic approach to quality in process development, integrates quality into process design and control, guided by regulatory frameworks. This paradigm shift enables increased operational efficiencies, reduced market time, and ensures product consistency. The implementation of QbD is framed around key elements such as defining the Quality Target Product Profile (QTPPs), identifying Critical Quality Attributes (CQAs), developing Design Spaces (DS), establishing Control Strategies (CS), and maintaining continual improvement. The present critical analysis delves into the intricacies of each element, emphasizing their role in ensuring consistent product quality and regulatory compliance. The integration of Industry 4.0 and 5.0 technologies, including Artificial Intelligence (AI), Machine Learning (ML), Internet of Things (IoT), and Digital Twins (DTs), is significantly transforming the bioprocessing industry. These innovations enable real-time data analysis, predictive modelling, and process optimization, which are crucial elements in QbD implementation. Among these, the concept of DTs is notable for its ability to facilitate bi-directional data communication and enable real-time adjustments and therefore optimize processes. DTs, however, face implementation challenges such as system integration, data security, and hardware-software compatibility. These challenges are being addressed through advancements in AI, Virtual Reality/ Augmented Reality (VR/AR), and improved communication technologies. Central to the functioning of DTs is the development and application of various models of differing types - mechanistic, empirical, and hybrid. These models serve as the intellectual backbone of DTs, providing a framework for interpreting and predicting the behaviour of their physical counterparts. The choice and development of these models are vital for the accuracy and efficacy of DTs, enabling them to mirror and predict the real-time dynamics of bioprocessing systems. Complementing these models, advancements in data collection technologies, such as free-floating wireless sensors and spectroscopic sensors, enhance the monitoring and control capabilities of DTs, providing a more comprehensive and nuanced understanding of the bioprocessing environment. This review offers a critical analysis of the prevailing trends in model-based bioprocessing development within the sector.