Target flow deviations on the cardiopulmonary bypass cause postoperative delirium in cardiothoracic surgery-a retrospective study evaluating temporal fluctuations of perfusion data.

OBJECTIVES: Postoperative delirium (POD) is common, costly and associated with long-term morbidity and increased mortality. We conducted a cohort study to assess the contribution of cardiopulmonary bypass (CPB) to the development of POD by means of algorithm-based data processing. METHODS: A database was compiled from 3 datasets of patients who underwent cardiac surgery between 2014 and 2019: intensive care unit discharge files, CPB protocols and medical quality management records. Following data extraction and structuring using novel algorithms, missing data were imputed. Ten independent imputations were analysed by multiple logistic regression with stepwise deletion of factors to arrive at a minimal adequate model. RESULTS: POD was diagnosed in 456/3163 patients (14.4%). In addition to known demographic risk factors and comorbidities like male sex, age, carotid disease, acute kidney failure and diabetes mellitus, cardiopulmonary parameters like total blood volume at the CPB [adjusted odds ratio (AOR) 1.001; confidence interval (CI) 1.1001-1.002] were independent predictors of POD. Higher values of the minimal blood flow were associated with a lower risk of POD (AOR 0.993; CI 0.988-0.997). Flow rates at least 30% above target did emerge in the minimal adequate model as a potential risk factor, but the confidence interval suggested a lack of statistical significance (AOR 1.819; 95% CI: 0.955-3.463). CONCLUSIONS: CPB data processing proved to be a useful tool for obtaining compact information to better identify the roles of individual operational states. Strict adherence to perfusion limits along with tighter control of blood flow and acid-base balance during CPB may help to further decrease the risk of POD.

Guideline-Based Cardiovascular Risk Assessment Delivered by an mHealth App: Development Study.

BACKGROUND: Identifying high-risk individuals is crucial for preventing cardiovascular diseases (CVDs). Currently, risk assessment is mostly performed by physicians. Mobile health apps could help decouple the determination of risk from medical resources by allowing unrestricted self-assessment. The respective test results need to be interpretable for laypersons. OBJECTIVE: Together with a patient organization, we aimed to design a digital risk calculator that allows people to individually assess and optimize their CVD risk. The risk calculator was integrated into the mobile health app HerzFit, which provides the respective background information. METHODS: To cover a broad spectrum of individuals for both primary and secondary prevention, we integrated the respective scores (Framingham 10-year CVD, Systematic Coronary Risk Evaluation 2, Systematic Coronary Risk Evaluation 2 in Older Persons, and Secondary Manifestations Of Arterial Disease) into a single risk calculator that was recalibrated for the German population. In primary prevention, an individual's heart age is estimated, which gives the user an easy-to-understand metric for assessing cardiac health. For secondary prevention, the risk of recurrence was assessed. In addition, a comparison of expected to mean and optimal risk levels was determined. The risk calculator is available free of charge. Data safety is ensured by processing the data locally on the users' smartphones. RESULTS: Offering a risk calculator to the general population requires the use of multiple instruments, as each provides only a limited spectrum in terms of age and risk distribution. The integration of 4 internationally recommended scores allows risk calculation in individuals aged 30 to 90 years with and without CVD. Such integration requires recalibration and harmonization to provide consistent and plausible estimates. In the first 14 months after the launch, the HerzFit calculator was downloaded more than 96,000 times, indicating great demand. Public information campaigns proved effective in publicizing the risk calculator and contributed significantly to download numbers. CONCLUSIONS: The HerzFit calculator provides CVD risk assessment for the general population. The public demonstrated great demand for such a risk calculator as it was downloaded up to 10,000 times per month, depending on campaigns creating awareness for the instrument.

Use of big data from health insurance for assessment of cardiovascular outcomes.

Outcome research that supports guideline recommendations for primary and secondary preventions largely depends on the data obtained from clinical trials or selected hospital populations. The exponentially growing amount of real-world medical data could enable fundamental improvements in cardiovascular disease (CVD) prediction, prevention, and care. In this review we summarize how data from health insurance claims (HIC) may improve our understanding of current health provision and identify challenges of patient care by implementing the perspective of patients (providing data and contributing to society), physicians (identifying at-risk patients, optimizing diagnosis and therapy), health insurers (preventive education and economic aspects), and policy makers (data-driven legislation). HIC data has the potential to inform relevant aspects of the healthcare systems. Although HIC data inherit limitations, large sample sizes and long-term follow-up provides enormous predictive power. Herein, we highlight the benefits and limitations of HIC data and provide examples from the cardiovascular field, i.e. how HIC data is supporting healthcare, focusing on the demographical and epidemiological differences, pharmacotherapy, healthcare utilization, cost-effectiveness and outcomes of different treatments. As an outlook we discuss the potential of using HIC-based big data and modern artificial intelligence (AI) algorithms to guide patient education and care, which could lead to the development of a learning healthcare system and support a medically relevant legislation in the future.