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To evaluate the impact of nurse care changes in implementing a blood pressure management protocol on achieving rapid, intensive and sustained blood pressure reduction in acute intracerebral haemorrhage patients.
Retrospective cohort study of prospectively collected data over 6 years.
Intracerebral haemorrhage patients within 6 h and systolic blood pressure ≥ 150 mmHg followed a rapid (starting treatment at computed tomography suite with a target achievement goal of ≤60 min), intensive (target systolic blood pressure < 140 mmHg) and sustained (maintaining target stability for 24 h) blood pressure management plan. We differentiated six periods: P1, stroke nurse at computed tomography suite (baseline period); P2, antihypertensive titration by stroke nurse; P3, retraining by neurologists; P4, integration of a stroke advanced practice nurse; P5, after COVID-19 impact; and P6, retraining by stroke advanced practice nurse. Outcomes included first-hour target achievement (primary outcome), tomography-to-treatment and treatment-to-target times, first-hour maximum dose of antihypertensive treatment and 6-h and 24-h systolic blood pressure variability.
Compared to P1, antihypertensive titration by stroke nurses (P2) reduced treatment-to-target time and increased the rate of first-hour target achievement, retraining of stroke nurses by neurologists (P3) maintained a higher rate of first-hour target achievement and the integration of a stroke advanced practice nurse (P4) reduced both 6-h and 24-h systolic blood pressure variability. However, 6-h systolic blood pressure variability increased from P4 to P5 following the impact of the COVID-19 pandemic. Finally, compared to P1, retraining of stroke nurses by stroke advanced practice nurse (P6) reduced tomography-to-treatment time and increased the first-hour maximum dose of antihypertensive treatment.
Changes in nursing care and continuous education can significantly enhance the time metrics and blood pressure outcomes in acute intracerebral haemorrhage patients.
STROBE guidelines.
No Patient or Public Contribution.
by Anthony Muchai Manyara, Elizabeth Mwaniki, Jason M. R. Gill, Cindy M. Gray
BackgroundType 2 diabetes is increasing in Kenya, especially in urban settings, and prevention interventions based on local evidence and context are urgently needed. Therefore, this study aimed to explore diabetes risk and co-create a diabetes prevention theory of change in two socioeconomically distinct communities to inform future diabetes prevention interventions.
MethodsIn-depth interviews were conducted with middle-aged residents in two communities in Nairobi (one low-income (n = 15), one middle-income (n = 14)), and thematically analysed. The theory of change for diabetes prevention was informed by analysis of the in-depth interviews and the Behaviour Change Wheel framework, and reviewed by a sub-set (n = 13) of interviewees.
ResultsThe key factors that influenced diabetes preventive practices in both communities included knowledge and skills for diabetes prevention, understanding of the benefits/consequences of (un)healthy lifestyle, social influences (e.g., upbringing, societal perceptions), and environmental contexts (e.g., access to (un)healthy foods and physical activity facilities). The proposed strategies for diabetes prevention included: increasing knowledge and understanding about diabetes risk and preventive measures particularly in the low-income community; supporting lifestyle modification (e.g., upskilling, goal setting, action planning) in both communities; identifying people at high risk of diabetes through screening in both communities; and creating social and physical environments for lifestyle modification (e.g., positive social influences on healthy living, access to healthy foods and physical activity infrastructure) particularly in the low-income community. Residents from both communities agreed that the strategies were broadly feasible for diabetes prevention but proposed the addition of door-to-door campaigns and community theatre for health education. However, residents from the low-income community were concerned about the lack of government prioritisation for implementing population-level interventions, e.g., improving access to healthy foods and physical activity facilities/infrastructure.
ConclusionDiabetes prevention initiatives in Kenya should involve multicomponent interventions for lifestyle modification including increasing education and upskilling at individual level; promoting social and physical environments that support healthy living at population level; and are particularly needed in low-income communities.
Conventional prediction models fail to integrate the constantly evolving nature of critical illness. Alternative modelling approaches to study dynamic changes in critical illness progression are needed. We compare static risk prediction models to dynamic probabilistic models in early critical illness.
We developed models to simulate disease trajectories of critically ill COVID-19 patients across different disease states. Eighty per cent of cases were randomly assigned to a training and 20% of the cases were used as a validation cohort. Conventional risk prediction models were developed to analyse different disease states for critically ill patients for the first 7 days of intensive care unit (ICU) stay. Daily disease state transitions were modelled using a series of multivariable, multinomial logistic regression models. A probabilistic dynamic systems modelling approach was used to predict disease trajectory over the first 7 days of an ICU admission. Forecast accuracy was assessed and simulated patient clinical trajectories were developed through our algorithm.
We retrospectively studied patients admitted to a Cleveland Clinic Healthcare System in Ohio, for the treatment of COVID-19 from March 2020 to December 2022.
5241 patients were included in the analysis. For ICU days 2–7, the static (conventional) modelling approach, the accuracy of the models steadily decreased as a function of time, with area under the curve (AUC) for each health state below 0.8. But the dynamic forecasting approach improved its ability to predict as a function of time. AUC for the dynamic forecasting approach were all above 0.90 for ICU days 4–7 for all states.
We demonstrated that modelling critical care outcomes as a dynamic system improved the forecasting accuracy of the disease state. Our model accurately identified different disease conditions and trajectories, with a
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