Study protocol for two stepped-wedge interventional trials evaluating the effects of holistic information technology-based patient-oriented management in older multimorbid patients with cancer: The GERONTE trials.

INTRODUCTION: Current hospital-based care pathways are generally single-disease centred. As a result, coexisting morbidities are often suboptimally evaluated and managed, a deficiency becoming increasingly apparent among older patients who exhibit heterogeneity in health status, functional abilities, frailty, and other geriatric impairments. To address this issue, our study aims to assess a newly developed patient-centred care pathway for older patients with multimorbidity and cancer. The new care pathway was based on currently available evidence and co-designed by end-users including health care professionals, patients, and informal caregivers. Within this care pathway, all healthcare professionals involved in the care of older patients with multimorbidity and cancer will form a Health Professional Consortium (HPC). The role of the HPC will be to centralise oncologic and non-oncologic treatment recommendations in accordance with the patient's priorities. Moreover, an Advanced Practice Nurse will act as case-manager by being the primary point of contact for the patient, thus improving coordination between specialists, and by organising and leading the consortium. Patient monitoring and the HPC collaboration will be facilitated by digital communication tools designed specifically for this purpose, with the added benefit of being customisable for each patient. MATERIALS AND METHODS: The GERONTE study is a prospective international, multicentric study consisting of two stepped-wedge trials performed at 16 clinical sites across three European countries. Each trial will include 720 patients aged 70 years and over with a new or progressive cancer (breast, lung, colorectal, prostate) and at least one moderate or severe multimorbidity. The patients in the intervention group will receive the new care pathway whereas patients in the control group will receive usual oncologic care. DISCUSSION: GERONTE will evaluate whether this kind of holistic, patient-oriented healthcare management can improve quality of life (primary outcome) and other valuable endpoints in older patients with multimorbidity and cancer. An ancillary study will assess in depth the socio-economic impact of the intervention and deliver concrete implementation guidelines for the GERONTE intervention care pathway. TRIAL REGISTRATION: FRONE: NCT05720910 TWOBE: NCT05423808.

Assessing the excess costs of the in-hospital adverse events covered by the AHRQ's Patient Safety Indicators in Switzerland.

There currently exists no comprehensive and up-to date overview on the financial impact of the different adverse events covered by the Patient Safety Indicators (PSIs) from the Agency for Healthcare Research and Quality. We conducted a retrospective case-control study using propensity score matching on a national administrative data set of 1 million inpatients in Switzerland to compare excess costs associated with 16 different adverse events both individually and on a nationally aggregated level. After matching 8,986 cases with adverse events across the investigated PSIs to 26,931 controls, we used regression analyses to determine the excess costs associated with the adverse events and to control for other cost-related influences. The average excess costs associated with the PSI-related adverse events ranged from CHF 1,211 (PSI 18, obstetric trauma with instrument) to CHF 137,967 (PSI 10, postoperative acute kidney injuries) with an average of CHF 27,409 across all PSIs. In addition, adverse events were associated with 7.8-day longer stays, 2.5 times more early readmissions (within 18 days), and 4.1 times higher mortality rates on average. At a national level, the PSIs were associated with CHF 347 million higher inpatient costs in 2019, which corresponds to about 2.2% of the annual inpatient costs in Switzerland. By comparing the excess costs of different PSIs on a nationally aggregated level, we offer a financial perspective on the implications of in-hospital adverse events and provide recommendations for policymakers regarding specific investments in patient safety to reduce costs and suffering.

Predicting low cognitive ability at age 5 years using perinatal data and machine learning.

BACKGROUND: There are no early, accurate, scalable methods for identifying infants at high risk of poor cognitive outcomes in childhood. We aim to develop an explainable predictive model, using machine learning and population-based cohort data, for this purpose. METHODS: Data were from 8858 participants in the Growing Up in Ireland cohort, a nationally representative study of infants and their primary caregivers (PCGs). Maternal, infant, and socioeconomic characteristics were collected at 9-months and cognitive ability measured at age 5 years. Data preprocessing, synthetic minority oversampling, and feature selection were performed prior to training a variety of machine learning models using ten-fold cross validated grid search to tune hyperparameters. Final models were tested on an unseen test set. RESULTS: A random forest (RF) model containing 15 participant-reported features in the first year of infant life, achieved an area under the receiver operating characteristic curve (AUROC) of 0.77 for predicting low cognitive ability at age 5. This model could detect 72% of infants with low cognitive ability, with a specificity of 66%. CONCLUSIONS: Model performance would need to be improved before consideration as a population-level screening tool. However, this is a first step towards early, individual, risk stratification to allow targeted childhood screening. IMPACT: This study is among the first to investigate whether machine learning methods can be used at a population-level to predict which infants are at high risk of low cognitive ability in childhood. A random forest model using 15 features which could be easily collected in the perinatal period achieved an AUROC of 0.77 for predicting low cognitive ability. Improved predictive performance would be required to implement this model at a population level but this may be a first step towards early, individual, risk stratification.

Prediction of 2-Year Cognitive Outcomes in Very Preterm Infants Using Machine Learning Methods.

Importance: Early intervention can improve cognitive outcomes for very preterm infants but is resource intensive. Identifying those who need early intervention most is important. Objective: To evaluate a model for use in very preterm infants to predict cognitive delay at 2 years of age using routinely available clinical and sociodemographic data. Design, Setting, and Participants: This prognostic study was based on the Swedish Neonatal Quality Register. Nationwide coverage of neonatal data was reached in 2011, and registration of follow-up data opened on January 1, 2015, with inclusion ending on September 31, 2022. A variety of machine learning models were trained and tested to predict cognitive delay. Surviving infants from neonatal units in Sweden with a gestational age younger than 32 weeks and complete data for the Bayley Scales of Infant and Toddler Development, Third Edition cognitive index or cognitive scale scores at 2 years of corrected age were assessed. Infants with major congenital anomalies were excluded. Exposures: A total of 90 variables (containing sociodemographic and clinical information on conditions, investigations, and treatments initiated during pregnancy, delivery, and neonatal unit admission) were examined for predictability. Main Outcomes and Measures: The main outcome was cognitive function at 2 years, categorized as screening positive for cognitive delay (cognitive index score <90) or exhibiting typical cognitive development (score ≥90). Results: A total of 1062 children (median [IQR] birth weight, 880 [720-1100] g; 566 [53.3%] male) were included in the modeling process, of whom 231 (21.8%) had cognitive delay. A logistic regression model containing 26 predictive features achieved an area under the receiver operating curve of 0.77 (95% CI, 0.71-0.83). The 5 most important features for cognitive delay were non-Scandinavian family language, prolonged duration of hospitalization, low birth weight, discharge to other destination than home, and the infant not receiving breastmilk on discharge. At discharge from the neonatal unit, the full model could correctly identify 605 of 650 infants who would have cognitive delay at 24 months (sensitivity, 0.93) and 1081 of 2350 who would not (specificity, 0.46). Conclusions and Relevance: The findings of this study suggest that predictive modeling in neonatal care could enable early and targeted intervention for very preterm infants most at risk for developing cognitive impairment.

Lifetime occupational and recreational physical activity and risk of lymphoma subtypes. Results from the European Epilymph case-control study.

Physical activity is known to convey protection against several cancers. However, results on the risk of lymphoma overall and its subtypes have been inconsistent. The aim of this study was to investigate occupational and recreational physical activity in relation to risk of lymphoma subtypes adjusting for established occupational risk factors. We applied standardized tools to assess energy expenditure at work and in recreational physical activities to the questionnaire information on lifetime work and exercise history in 1117 lymphoma cases, including Hodgkin lymphoma, and B-cell (including chronic lymphocytic leukemia, and multiple myeloma) and T-cell non-Hodgkin's lymphoma (NHL) subtypes, and 1207 controls who took part in the multicentre European EpiLymph case-control study. We calculated the risk of lymphoma (all subtypes), B-cell NHL and its most represented subtypes, and Hodgkin's lymphoma (all subtypes) associated with weekly average Metabolic Equivalent of Task (MET-hours/week) and cumulative MET-hours of lifetime recreational, occupational, and total physical activity, with unconditional logistic regression and polytomous regression analysis adjusting by age, centre, sex, education, body mass index, history of farm work and solvent use. We observed an inverse association of occupational, and total physical activity with risk of lymphoma (all subtypes), and B-cell non-Hodgkin's lymphoma among women, and an upward trend in risk of Hodgkin's lymphoma with recreational and total physical activity among men, for which we cannot exclude chance or bias. Our results suggest no effect of overall physical activity on risk of lymphoma and its subtypes.

Geriatric assessment for older people with cancer: policy recommendations.

Most cancers occur in older people and the burden in this age group is increasing. Over the past two decades the evidence on how best to treat this population has increased rapidly. However, implementation of new best practices has been slow and needs involvement of policymakers. This perspective paper explains why older people with cancer have different needs than the wider population. An overview is given of the recommended approach for older people with cancer and its benefits on clinical outcomes and cost-effectiveness. In older patients, the geriatric assessment (GA) is the gold standard to measure level of fitness and to determine treatment tolerability. The GA, with multiple domains of physical health, functional status, psychological health and socio-environmental factors, prevents initiation of inappropriate oncologic treatment and recommends geriatric interventions to optimize the patient's general health and thus resilience for receiving treatments. Multiple studies have proven its benefits such as reduced toxicity, better quality of life, better patient-centred communication and lower healthcare use. Although GA might require investment of time and resources, this is relatively small compared to the improved outcomes, possible cost-savings and compared to the large cost of oncologic treatments as a whole.

COVID-19 incidence and outcome by affluence/deprivation across three pandemic waves in Ireland: A retrospective cohort study using routinely collected data.

BACKGROUND: Since the pandemic onset, deprivation has been seen as a significant determinant of COVID-19 incidence and mortality. This study explores outcomes of COVID-19 in the context of material deprivation across three pandemic waves in Ireland. METHODS: Between 1st March 2020 and 13th May 2021, 252,637 PCR-confirmed COVID-19 cases were notified in Ireland. Cases were notified to the national Computerised Infectious Disease Reporting (CIDR) system. Each case was geo-referenced and assigned a deprivation category according to the Haase-Pratschke (HP) Deprivation Index. Regression modelling examined three outcomes: admission to hospital; admission to an intensive care unit (ICU) and death. RESULTS: Deprivation increased the likelihood of contracting COVID-19 in all age groups and across all pandemic waves, except for the 20-39 age group. Deprivation, age, comorbidity and male gender carried increased risk of hospital admission. Deprivation was not a factor in predicting ICU admission or death, and diagnosis in wave 2 was associated with the lowest risk of all three outcomes. CONCLUSIONS: Our study suggests that COVID-19 spreads easily through all strata of society and particularly in the more deprived population; however this was not a consistent finding. Ireland is ethnically more homogenous than other countries reporting a larger deprivation gradient, and in such societies, structural racial differences may contribute more to poor COVID outcomes than elements of deprivation.

Big data, machine learning, and population health: predicting cognitive outcomes in childhood.

The application of machine learning (ML) to address population health challenges has received much less attention than its application in the clinical setting. One such challenge is addressing disparities in early childhood cognitive development-a complex public health issue rooted in the social determinants of health, exacerbated by inequity, characterised by intergenerational transmission, and which will continue unabated without novel approaches to address it. Early life, the period of optimal neuroplasticity, presents a window of opportunity for early intervention to improve cognitive development. Unfortunately for many, this window will be missed, and intervention may never occur or occur only when overt signs of cognitive delay manifest. In this review, we explore the potential value of ML and big data analysis in the early identification of children at risk for poor cognitive outcome, an area where there is an apparent dearth of research. We compare and contrast traditional statistical methods with ML approaches, provide examples of how ML has been used to date in the field of neurodevelopmental disorders, and present a discussion of the opportunities and risks associated with its use at a population level. The review concludes by highlighting potential directions for future research in this area. IMPACT: To date, the application of machine learning to address population health challenges in paediatrics lags behind other clinical applications. This review provides an overview of the public health challenge we face in addressing disparities in childhood cognitive development and focuses on the cornerstone of early intervention. Recent advances in our ability to collect large volumes of data, and in analytic capabilities, provide a potential opportunity to improve current practices in this field. This review explores the potential role of machine learning and big data analysis in the early identification of children at risk for poor cognitive outcomes.

Predicting Low Cognitive Ability at Age 5-Feature Selection Using Machine Learning Methods and Birth Cohort Data.

Objectives: In this study, we applied the random forest (RF) algorithm to birth-cohort data to train a model to predict low cognitive ability at 5 years of age and to identify the important predictive features. Methods: Data was from 1,070 participants in the Irish population-based BASELINE cohort. A RF model was trained to predict an intelligence quotient (IQ) score ≤90 at age 5 years using maternal, infant, and sociodemographic features. Feature importance was examined and internal validation performed using 10-fold cross validation repeated 5 times. Results The five most important predictive features were the total years of maternal schooling, infant Apgar score at 1 min, socioeconomic index, maternal BMI, and alcohol consumption in the first trimester. On internal validation a parsimonious RF model based on 11 features showed excellent predictive ability, correctly classifying 95% of participants. This provides a foundation suitable for external validation in an unseen cohort. Conclusion: Machine learning approaches to large existing datasets can provide accurate feature selection to improve risk prediction. Further validation of this model is required in cohorts representative of the general population.

Feasibility of a peer-led, after-school physical activity intervention for disadvantaged adolescent females during the COVID-19 pandemic: results from the Girls Active Project (GAP).

INTRODUCTION: There is a critical need for interventions that can be feasibly implemented and are effective in successfully engaging adolescent females in physical activity (PA). A theory-based, peer-led, after-school PA intervention, the Girls Active Project (GAP), was codesigned with adolescent females. This study aimed to assess the feasibility of implementing and evaluating the GAP programme. SETTING: One single-sex, female-only, designated disadvantaged postprimary school (students aged 12-18) in Dublin, Ireland. METHODS: Mixed methods were applied with multiple stakeholders over a 12-week trial (March to May 2021). A single-arm study design was used to examine intervention: reach, dose, fidelity, acceptability, compatibility and context. Feasibility of using proposed self-reported outcome measures (moderate-to-vigorous PA levels, self-rated health, life satisfaction, PA self-efficacy and PA enjoyment) was also explored. Due to school closure resulting from the COVID-19 pandemic, the intervention was delivered both online and in person in the school setting. RESULTS: Eight exercise classes were peer delivered by project leaders (n = 6, students aged 15-17) to intervention recipients (students aged 13-14). Recruitment was low (n = 8, 10% of eligible students, mean age: 13.3 SD: 0.46), yet retention was high (n = 7/8, 88%). Attendance rates were satisfactory (68%), and the intervention was implemented with high fidelity (87%). Data completion rates suggested proposed self-reported outcome measures were deemed appropriate (≥ 95%), except for weight (50%) and height data (80%). Despite COVID-19 hindering intervention implementation, both quantitative and qualitative data suggested that stakeholders were satisfied and perceived the in-person delivered intervention to be compatible with the school setting. Recommended refinements included extending class duration, introducing different rewards, and boosting programme awareness. CONCLUSIONS: Further thought must be given on how to increase recruitment. Overall, the in-person delivered after-school PA programme was well-received by stakeholders and shows promise as an intervention that can be feasibly implemented and evaluated. Suggested improvements to the GAP intervention programme are recommended, before continuing to a more robust evaluation. TRIAL REGISTRATION: 10.17605/OSF.IO/75HWJ (prospectively registered, date of registration: 9th December 2020).

Autistic Adult Services Availability, Preferences, and User Experiences: Results From the Autism Spectrum Disorder in the European Union Survey.

There is very little knowledge regarding autistic adult services, practices, and delivery. The study objective was to improve understanding of current services and practices for autistic adults and opportunities for improvement as part of the Autism Spectrum Disorder in the European Union (ASDEU) project. Separate survey versions were created for autistic adults, carers of autistic adults, and professionals in adult services. 2,009 persons responded to the survey and 1,085 (54%) of them completed at least one of the services sections: 469 autistic adults (65% female; 55% <35 years old), 441 carers of autistic adults (27% female; 6% <35 years old), 175 professionals in adult services (76% female; 67% in non-medical services). Top choices by autistic adults, carers or professionals for services best suiting their current needs were: residential services: "help in own home" (adults, carers of high independent adults, professionals), "fulltime residential facility" (carers of low independent adults); employment services: "job mentors" (adults, carers of high independent adults, professionals), "Sheltered employment" (carers of low independent adults); education services: "support in regular education setting" (all groups); financial services: financial support in lieu of employment ("Supplementary income for persons unable to have full employment" for adults, "full pension" for carers of low independent adults) or to supplement employment earnings for carers of high independent adults and professionals; social services: "behavior training" (adults) and "life skills training" (carers and professionals). Waiting times for specific services were generally < 1 month or 1-3 months, except for residential services which could be up to 6 months; most professionals were uninformed of waiting times (>50% responded "don't know"). Five of seven residential services features recommended for autistic adults were experienced by <50% of adults. The knowledge of good local services models that work well for autistic adults was generally low across all services areas. The variation in services experiences and perceptions reported by autistic adults, carers, or professionals underscore the need to query all groups for a complete picture of community services availability and needs. The results showed areas for potential improvement in autistic adult services delivery in the EU to achieve recommended standards.

Co-design of a school-based physical activity intervention for adolescent females in a disadvantaged community: insights from the Girls Active Project (GAP).

BACKGROUND: Globally, adolescents' physical activity (PA) participation rates are low, particularly among lower socioeconomic groups, with females consistently the least active. The aim of this study was to co-design, with adolescent females, a school-based PA intervention in a single-sex, females-only designated disadvantaged post-primary school in Ireland. This involved using the Behaviour Change Wheel (BCW) and Public and Patient Involvement (PPI). This paper outlines the novel methodological approach taken. METHODS: The three stages 1) understand the behaviour, 2) identify intervention options, and 3) identify content and implementation options of the BCW guide is described. A student PPI Youth Advisory Group (YAG) (n = 8, aged 15-17) was established. Mixed-methods were used with students (n = 287, aged 12-18) and teachers (n = 7) to capture current self-reported PA levels and to identify factors influencing adolescent females' PA behaviour in their school setting. The intervention options, content and implementation options were identified through discussion groups with the YAG. The Template for Intervention Description and Replication (TIDieR) checklist was used to specify details of the intervention. RESULTS: Just 1.4% of the students in this sample reported meeting the recommended PA guidelines. Students identified having more 'time' as the strongest predictor to becoming more active in school (Mean = 4.01, 95% CI 3.91 to 4.12). Social influences, environmental context and resources, behavioural regulation, beliefs about capabilities, goals, and reinforcement emerged from the qualitative data as factors influencing PA behaviour at school. The BCW co-design process resulted in the identification of seven intervention functions, four policy categories and 21 Behaviour Change Techniques. The Girls Active Project (GAP) intervention, a peer-led, after-school PA programme was proposed. CONCLUSIONS: This paper describes how the BCW, a comprehensive, evidence-based, theory-driven framework was used in combination with PPI to co-design a school-based intervention aimed to increase adolescent females' PA levels. This approach could be replicated in other settings to develop targeted behavioural interventions in populations with specific demographic characteristics.

Embracing multiple aims in healthcare improvement and innovation.

BACKGROUND: Healthcare and other industries have to manage and coordinate a number of different aims and longer-term ambitions. They must maintain quality, manage costs, support and retain the workforce, manage regulatory demands and consider wider societal objectives. These aims are all legitimate, but they are not necessarily aligned, neither in their time frame nor in their nature. Conflicts between aims have a profound influence on the implementation of safety and quality improvement and wider innovation system change. Healthcare leaders understand that these aims may conflict, but the extent and nature of such conflicts have been underestimated. OBJECTIVE: This paper aims to support medical and nursing leadership and executives in the complex task of managing multiple aims in relation to improvement and innovation. METHODS: We drew on our experience and the wider industrial and healthcare literature to find examples of studies and improvement projects with multiple aims and examples of innovation and change in which conflicting aims were apparent. We sought to identify principles that would enable the management of parallel aims and practical strategies that might facilitate implementation. RESULTS: We argue that almost all improvement and innovation in healthcare should address parallel aims, actively seek to articulate these aims and manage potential conflicts between them. We propose four underlying principles to support a more productive approach to the identification and management of parallel aims: embrace multiple aims, consider both short- and long-term aims and ambitions, consider the wider societal context and appreciate that all changes take place within an evolving, dynamic context. In terms of practical actions, we identified five key strategies: (i) identify and monitor the parallel aims and accept that some will conflict; (ii) slow down to accommodate the natural flexibility of the system; (iii) think both the short term and the long term; (iv) expect and endeavour to anticipate some unintended consequences and (v) resist downgrading the project to partial implementation. CONCLUSIONS: We have argued that most improvement and innovation, unlike controlled trials, should consider multiple aims. We set out some broad principles and practices to reduce conflict and suggest avenues to manage conflicts and support positive synergies. We suggest that if this is not done, conflicts are much more likely to arise, which will be detrimental to the change process. Articulating the multiple aims and actively seeking to manage them in parallel will promote a more flexible and productive approach to innovation and change.