Hospital Nurses\u27 Moral Distress and Coping during COVID-19: A Pilot Study

The COVID-19 pandemic caused a wave of critically ill patients, overwhelming hospitals, and creating unprecedented conditions for hospital employees, particularly bedside nurses. Concerns about the emotional and mental well-being of nurses have already been raised prior to the pandemic and depression, anxiety, and PTSD symptoms among nurses during the pandemic have been observed. Given the increased infection and safety risks, staffing shortages, inadequate personal protective equipment and resources, and hospital restrictions causing many nurses to be patients’ only support, there is also a growing concern about how these institutional and personal restrictions to providing best care and practicing ethically have impacted nurses. Moral distress is the psychological disequilibrium that occurs when a professional knows what they need to do but is/feels unable to take that action due to perceived or actual internal and/or external constraints. Much has been researched about causes of moral distress in the last 20 years, but to date, there is limited evidence around the impact of organizational support, emotional intelligence (EI), and coping strategies. This pilot study aimed to describe and explore the relationships among EI, coping, organizational support, and moral distress, and to provide pilot data and methodological information to inform a national study. Participants were Kentucky registered nurses currently working or who have worked at an inpatient hospital facility within the last six months. Participants were recruited using non-randomized purposive and snowball sampling techniques to complete an anonymous online survey using Qualtrics. Using SPSS, descriptive statistics and regression analyses examined the relationships between the independent study variables, using the Wong and Law Emotional Intelligence Scale, COVID-19 Organizational Support, Survey of Perceived Organizational Support, and the Ways of Coping Checklist Revised, and the dependent variable, moral distress, as measured by the Measurement of Moral Distress for Healthcare Professionals. Regression analysis adjusted for hospital ethical climate, intensive care setting, and age. After data cleaning and screening, there were 544 nurse participants who completed most of the demographic questionnaire items with 271 participants completing all instruments included in the regression analysis. The sample included mostly white females with Bachelor of Science in nursing working with adult populations in non-ICU settings in central Kentucky. In support of predicted relationships, organizational support was associated with a reduction in moral distress and emotion-focused coping was positively related. However, in contrast of predicted relationships, problem-focused coping was also positively related to moral distress; and while EI had a negative relationship in correlational testing, EI resulted in a non-significant positive relationship in regression analysis. Additional regression analysis showed EI maintained a negative relationship only when entered with problem-focused coping adjusted for control variables. Future research should strive for larger, more diverse samples that would allow for mediation testing to further explore the relationship between EI, coping, and moral distress. Metadata from Qualtrics will be used to inform potential changes in the structure, length and presentation of the survey for a national study

Prediction of acute myeloid leukaemia risk in healthy individuals

The incidence of acute myeloid leukaemia (AML) increases with age and mortality exceeds 90% when diagnosed after age 65. Most cases arise without any detectable early symptoms and patients usually present with the acute complications of bone marrow failure(1). The onset of such de novo AML cases is typically preceded by the accumulation of somatic mutations in preleukaemic haematopoietic stem and progenitor cells (HSPCs) that undergo clonal expansion(2,3). However, recurrent AML mutations also accumulate in HSPCs during ageing of healthy individuals who do not develop AML, a phenomenon referred to as age-related clonal haematopoiesis (ARCH)(4-8). Here we use deep sequencing to analyse genes that are recurrently mutated in AML to distinguish between individuals who have a high risk of developing AML and those with benign ARCH. We analysed peripheral blood cells from 95 individuals that were obtained on average 6.3 years before AML diagnosis (pre-AML group), together with 414 unselected age- and gender-matched individuals (control group). Pre-AML cases were distinct from controls and had more mutations per sample, higher variant allele frequencies, indicating greater clonal expansion, and showed enrichment of mutations in specific genes. Genetic parameters were used to derive a model that accurately predicted AML-free survival; this model was validated in an independent cohort of 29 pre-AML cases and 262 controls. Because AML is rare, we also developed an AML predictive model using a large electronic health record database that identified individuals at greater risk. Collectively our findings provide proof-of-concept that it is possible to discriminate ARCH from pre-AML many years before malignant transformation. This could in future enable earlier detection and monitoring, and may help to inform intervention

An app with brief behavioural support to promote physical activity after a cancer diagnosis (APPROACH): study protocol for a pilot randomised controlled trial

Background- There are multiple health benefits from participating in physical activity after a cancer diagnosis, but many people living with and beyond cancer (LWBC) are not meeting physical activity guidelines. App-based interventions offer a promising platform for intervention delivery. This trial aims to pilot a theory-driven, app-based intervention that promotes brisk walking among people living with and beyond cancer. The primary aim is to investigate the feasibility and acceptability of study procedures before conducting a larger randomised controlled trial (RCT). Methods- This is an individually randomised, two-armed pilot RCT. Patients with localised or metastatic breast, prostate, or colorectal cancer, who are aged 16 years or over, will be recruited from a single hospital site in South Yorkshire in the UK. The intervention includes an app designed to encourage brisk walking (Active 10) supplemented with habit-based behavioural support in the form of two brief telephone/video calls, an information leaflet, and walking planners. The primary outcomes will be feasibility and acceptability of the study procedures. Demographic and medical characteristics will be collected at baseline, through self-report and hospital records. Secondary outcomes for the pilot (assessed at 0 and 3 months) will be accelerometer measured and self-reported physical activity, body mass index (BMI) and waist circumference, and patient-reported outcomes of quality of life, fatigue, sleep, anxiety, depression, self-efficacy, and habit strength for walking. Qualitative interviews will explore experiences of participating or reasons for declining to participate. Parameters for the intended primary outcome measure (accelerometer measured average daily minutes of brisk walking (≥ 100 steps/min)) will inform a sample size calculation for the future RCT and a preliminary economic evaluation will be conducted. Discussion- This pilot study will inform the design of a larger RCT to investigate the efficacy and cost-effectiveness of this intervention in people LWBC. Trial registration- ISRCTN registry, ISRCTN18063498. Registered 16 April 2021

Prediction of acute myeloid leukaemia risk in healthy individuals

The incidence of acute myeloid leukaemia (AML) increases with age and mortality exceeds 90% when diagnosed after age 65. Most cases arise without any detectable early symptoms and patients usually present with the acute complications of bone marrow failure1. The onset of such de novo AML cases is typically preceded by the accumulation of somatic mutations in preleukaemic haematopoietic stem and progenitor cells (HSPCs) that undergo clonal expansion2,3. However, recurrent AML mutations also accumulate in HSPCs during ageing of healthy individuals who do not develop AML, a phenomenon referred to as age-related clonal haematopoiesis (ARCH)4-8. Here we use deep sequencing to analyse genes that are recurrently mutated in AML to distinguish between individuals who have a high risk of developing AML and those with benign ARCH. We analysed peripheral blood cells from 95 individuals that were obtained on average 6.3 years before AML diagnosis (pre-AML group), together with 414 unselected age- and gender-matched individuals (control group). Pre-AML cases were distinct from controls and had more mutations per sample, higher variant allele frequencies, indicating greater clonal expansion, and showed enrichment of mutations in specific genes. Genetic parameters were used to derive a model that accurately predicted AML-free survival; this model was validated in an independent cohort of 29 pre-AML cases and 262 controls. Because AML is rare, we also developed an AML predictive model using a large electronic health record database that identified individuals at greater risk. Collectively our findings provide proof-of-concept that it is possible to discriminate ARCH from pre-AML many years before malignant transformation. This could in future enable earlier detection and monitoring, and may help to inform intervention

Prediction of acute myeloid leukaemia risk in healthy individuals

The incidence of acute myeloid leukaemia (AML) increases with age and mortality exceeds 90% when diagnosed after age 65. Most cases arise without any detectable early symptoms and patients usually present with the acute complications of bone marrow failure1. The onset of such de novo AML cases is typically preceded by the accumulation of somatic mutations in preleukaemic haematopoietic stem and progenitor cells (HSPCs) that undergo clonal expansion2,3. However, recurrent AML mutations also accumulate in HSPCs during ageing of healthy individuals who do not develop AML, a phenomenon referred to as age-related clonal haematopoiesis (ARCH)4-8. Here we use deep sequencing to analyse genes that are recurrently mutated in AML to distinguish between individuals who have a high risk of developing AML and those with benign ARCH. We analysed peripheral blood cells from 95 individuals that were obtained on average 6.3 years before AML diagnosis (pre-AML group), together with 414 unselected age- and gender-matched individuals (control group). Pre-AML cases were distinct from controls and had more mutations per sample, higher variant allele frequencies, indicating greater clonal expansion, and showed enrichment of mutations in specific genes. Genetic parameters were used to derive a model that accurately predicted AML-free survival; this model was validated in an independent cohort of 29 pre-AML cases and 262 controls. Because AML is rare, we also developed an AML predictive model using a large electronic health record database that identified individuals at greater risk. Collectively our findings provide proof-of-concept that it is possible to discriminate ARCH from pre-AML many years before malignant transformation. This could in future enable earlier detection and monitoring, and may help to inform intervention

Prediction of acute myeloid leukaemia risk in healthy individuals

The incidence of acute myeloid leukaemia (AML) increases with age and mortality exceeds 90% when diagnosed after age 65. Most cases arise without any detectable early symptoms and patients usually present with the acute complications of bone marrow failure1. The onset of such de novo AML cases is typically preceded by the accumulation of somatic mutations in preleukaemic haematopoietic stem and progenitor cells (HSPCs) that undergo clonal expansion2,3. However, recurrent AML mutations also accumulate in HSPCs during ageing of healthy individuals who do not develop AML, a phenomenon referred to as age-related clonal haematopoiesis (ARCH)4-8. Here we use deep sequencing to analyse genes that are recurrently mutated in AML to distinguish between individuals who have a high risk of developing AML and those with benign ARCH. We analysed peripheral blood cells from 95 individuals that were obtained on average 6.3 years before AML diagnosis (pre-AML group), together with 414 unselected age- and gender-matched individuals (control group). Pre-AML cases were distinct from controls and had more mutations per sample, higher variant allele frequencies, indicating greater clonal expansion, and showed enrichment of mutations in specific genes. Genetic parameters were used to derive a model that accurately predicted AML-free survival; this model was validated in an independent cohort of 29 pre-AML cases and 262 controls. Because AML is rare, we also developed an AML predictive model using a large electronic health record database that identified individuals at greater risk. Collectively our findings provide proof-of-concept that it is possible to discriminate ARCH from pre-AML many years before malignant transformation. This could in future enable earlier detection and monitoring, and may help to inform intervention