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Purpose: A majority of clinical decisions use the electronic health record (EHR) and there is an unmet need to use its capability to help providers to make evidence-based decisions that improve care for heart failure patients. These electronic nudges are rooted in the human psychology of decision-making and often target specific cognitive biases. This review outlines the development of novel EHR nudges and specific lessons learned from each experience to inform the development of future interventions. Recent Findings: There have been several randomized clinical trials examining the impact of EHR alerts on quality of care for heart failure patients. These interventions have targeted both clinicians and patients. There are features of each trial that inform best practices and future directions for EHR nudges. Summary: Recent clinical trials have demonstrated that some EHR alerts can improve care for heart failure patients. These trials utilized default options, involved clinicians in the alert design process, provided actionable recommendations, and aimed to minimize disruptions to typical workflow. Alerts aimed at improving care should be examined in a randomized fashion in order to evaluate their impact on clinician satisfaction and patient care. [ABSTRACT FROM AUTHOR]

Background: Self‐care and patient engagement are important elements of heart failure (HF) care, endorsed in the guidelines. Digital health tools may improve quality of life (QOL) in HF patients by promoting care, knowledge, and engagement. This manuscript describes the rationale and challenges of the design and implementation of a pragmatic randomized controlled trial to evaluate the efficacy of three digital health technologies in improving QOL for patients with HF. Hypothesis: We hypothesize that digital health interventions will improve QOL of HF patients through the early detection of warning signs of disease exacerbation, the opportunity of self‐tracking symptoms, and the education provided, which enhances patient empowerment. Methods: Using a fully electronic enrollment and consent platform, the trial will randomize 200 patients across HF clinics in the Yale New Haven Health system to receive either usual care or one of three digital technologies designed to promote self‐management and provide critical data to clinicians. The primary outcome is the change in QOL as assessed by the Kansas City Cardiomyopathy Questionnaire at 3 months. Results: First enrollment occurred in September 2021. Recruitment was anticipated to last 6–8 months and participants were followed for 6 months after randomization. Our recruitment efforts have highlighted the large digital divide in our population of interest. Conclusion: Assessing clinical outcomes, patient usability, and ease of clinical integration of digital technologies will be beneficial in determining the feasibility of the integration of such technologies into the healthcare system. [ABSTRACT FROM AUTHOR]

Background There are limited and nonconcordant data on the rapidity and safety of blood pressure response to clonidine in the setting of asymptomatic severe hypertension. We evaluated the blood pressure response to clonidine in hospitalized patients with asymptomatic severe hypertension. Methods We performed a review of hospitalized, noncritically ill patients receiving clonidine within 6 hours of developing asymptomatic severe hypertension (systolic blood pressure [SBP] >180 or diastolic blood pressure [DBP] >110 mm Hg in the absence of acute hypertension-mediated target organ damage). The incidence of mean arterial pressure (MAP) reduction by ≥30% at 4 hours after clonidine was the primary endpoint. Results We identified 200 relevant patient encounters (median age 63 years, 48.5% women). Median time to clonidine following asymptomatic severe hypertension was 2.8 hours. A total of 20 (10%) patients had ≥30% MAP reduction within 4 hours after clonidine, and 32 (16%) patients had ≥30% reduction in either SBP, DBP, or MAP. Older age, female sex, and preexisting vascular disease were associated with ≥30% MAP reductions (P < 0.05). Only patient sex and clonidine dose of 0.3 mg were significant in multivariable models. There were 14 adverse events observed within 24 hours of administration of clonidine; most (9) were acute kidney injury. There were no ischemic (myocardial, cerebrovascular) events. Conclusions A substantial minority of hospitalized patients with asymptomatic severe hypertension experience precipitous blood pressure decline with clonidine, and though blood pressure declines more precipitously in women and those receiving higher doses (0.3 mg specifically), the response to clonidine is generally not predictable on clinical grounds. [ABSTRACT FROM AUTHOR]

Introduction Acute kidney injury (AKI) is common among hospitalised patients and under-recognised by providers and yet carries a significant risk of morbidity and mortality. Electronic alerts for AKI have become more common despite a lack of strong evidence of their benefits. We designed a multicentre, randomised, controlled trial to evaluate the effectiveness of AKI alerts. Our aim is to highlight several challenges faced in the design of this trial, which uses electronic screening, enrolment, randomisation, intervention and data collection. Methods and analysis The design and implementation of an electronic alert system for AKI was a reiterative process involving several challenges and limitations set by the confines of the electronic medical record system. The trial will electronically identify and randomise 6030 adults with AKI at six hospitals over a 1.5-2 year period to usual care versus an electronic alert containing an AKI-specific order set. Our primary outcome will be a composite of AKI progression, inpatient dialysis and inpatient death within 14 days of randomisation. During a 1-month pilot in the medical intensive care unit of Yale New Haven Hospital, we have demonstrated feasibility of automating enrolment and data collection. Feedback from providers exposed to the alerts was used to continually improve alert clarity, user friendliness and alert specificity through refined inclusion and exclusion criteria. Ethics and dissemination This study has been approved by the appropriate ethics committees for each of our study sites. Our study qualified for a waiver of informed consent as it presents no more than minimal risk and cannot be feasibly conducted in the absence of a waiver. We are committed to open dissemination of our data through clinicaltrials. gov and submission of results to the NIH data sharing repository. Results of our trial will be submitted for publication in a peer-reviewed journal. [ABSTRACT FROM AUTHOR]

Background: The current acute kidney injury (AKI) risk prediction model for patients undergoing percutaneous coronary intervention (PCI) from the American College of Cardiology (ACC) National Cardiovascular Data Registry (NCDR) employed regression techniques. This study aimed to evaluate whether models using machine learning techniques could significantly improve AKI risk prediction after PCI.Methods and Findings: We used the same cohort and candidate variables used to develop the current NCDR CathPCI Registry AKI model, including 947,091 patients who underwent PCI procedures between June 1, 2009, and June 30, 2011. The mean age of these patients was 64.8 years, and 32.8% were women, with a total of 69,826 (7.4%) AKI events. We replicated the current AKI model as the baseline model and compared it with a series of new models. Temporal validation was performed using data from 970,869 patients undergoing PCIs between July 1, 2016, and March 31, 2017, with a mean age of 65.7 years; 31.9% were women, and 72,954 (7.5%) had AKI events. Each model was derived by implementing one of two strategies for preprocessing candidate variables (preselecting and transforming candidate variables or using all candidate variables in their original forms), one of three variable-selection methods (stepwise backward selection, lasso regularization, or permutation-based selection), and one of two methods to model the relationship between variables and outcome (logistic regression or gradient descent boosting). The cohort was divided into different training (70%) and test (30%) sets using 100 different random splits, and the performance of the models was evaluated internally in the test sets. The best model, according to the internal evaluation, was derived by using all available candidate variables in their original form, permutation-based variable selection, and gradient descent boosting. Compared with the baseline model that uses 11 variables, the best model used 13 variables and achieved a significantly better area under the receiver operating characteristic curve (AUC) of 0.752 (95% confidence interval [CI] 0.749-0.754) versus 0.711 (95% CI 0.708-0.714), a significantly better Brier score of 0.0617 (95% CI 0.0615-0.0618) versus 0.0636 (95% CI 0.0634-0.0638), and a better calibration slope of observed versus predicted rate of 1.008 (95% CI 0.988-1.028) versus 1.036 (95% CI 1.015-1.056). The best model also had a significantly wider predictive range (25.3% versus 21.6%, p < 0.001) and was more accurate in stratifying AKI risk for patients. Evaluated on a more contemporary CathPCI cohort (July 1, 2015-March 31, 2017), the best model consistently achieved significantly better performance than the baseline model in AUC (0.785 versus 0.753), Brier score (0.0610 versus 0.0627), calibration slope (1.003 versus 1.062), and predictive range (29.4% versus 26.2%). The current study does not address implementation for risk calculation at the point of care, and potential challenges include the availability and accessibility of the predictors.Conclusions: Machine learning techniques and data-driven approaches resulted in improved prediction of AKI risk after PCI. The results support the potential of these techniques for improving risk prediction models and identification of patients who may benefit from risk-mitigation strategies. [ABSTRACT FROM AUTHOR]

Background. Electronic alerts (e-alerts) for acute kidney injury (AKI) in hospitalized patients are increasingly being implemented; however, their impact on outcomes remains uncertain. Methods. We performed a systematic review. Electronic databases and grey literature were searched for original studies published between 1990 and 2016. Randomized, quasi-randomized, observational and before-and-after studies that included hospitalized patients, implemented e-alerts for AKI and described their impact on one of care processes, patient-centred outcomes or resource utilizationmeasures were included. Results. Our search yielded six studies (n=10 165 patients). E-alerts were generally automated,triggered through electronic health records and not linked to clinical decision support. In pooled analysis, e-alerts did not improve mortality [odds ratio (OR) 1.05; 95% confidence intervals (CI), 0.84-1.31; n=3 studies; n=3425 patients; I2=0%] or reduce renal replacement therapy (RRT) use (OR 1.20; 95% CI, 0.91-1.57; n=2 studies; n=3236 patients; I2=0%). Isolated studies reported improvements in selected care processes. Pooled analysis found no significant differences in prescribed fluid therapy. Conclusions. In the available studies, e-alerts for AKI do not improve survival or reduce RRT utilization. The impact of e-alerts on processes of care was variable. Additional research is needed to understand those aspects of e-alerts [ABSTRACT FROM AUTHOR]

[…] commonly used equations for eGFR should not be applied in AKI patients, neither at the debut of AKI nor during its recovery. The common equations for eGFR will markedly overestimate GFR and may potentially result in overdosing of drugs with renal excretion and risk of side effects. […]Background Existing systems for grading severity of acute kidney injury (AKI) rely on a change of serum creatinine concentration over a defined time interval. The rate of change in serum creatinine increases by degree of reduction in glomerular filtration rate, but is mitigated by low creatinine generation rate (CGR). Failure to appreciate variation in CGR may lead to erroneous conclusions regarding severity of AKI and distorted predictions regarding patient outcomes based on AKI severity. Methods Cohort study of 103 patients who received continuous venovenous hemodialysis (CVVHD) over a 2-year period in a tertiary care hospital setting. Study participants entered the cohort when they were anuric, receiving a stable and uninterrupted dose of CVVHD with serum creatinine in steady state. They were followed until hospital discharge. CGR was measured based on dialyzate effluent volume and effluent creatinine concentration (prospective cohort) and via effluent volume and serum creatinine concentration (retrospective cohort). Results CGR (mean 10.5, range 1.7–22.4 mg/kg/day) was substantially lower in this patient population than what would be predicted from existing equations. Correlates of CGR in multivariable analysis included the length of hospitalization prior to measurement and presence of an oncologic diagnosis. Lower CGR was independently associated with in-hospital mortality in unadjusted analysis and after multivariable adjustment for measures of severity of illness. Conclusions Grading systems for severity of AKI fail to account for variation in CGR, limiting their ability to predict relevant outcomes. Calculation of CGR is superior to other risk metrics in predicting hospital mortality in this population. [ABSTRACT FROM AUTHOR]

Francis Bacon's utopian fragment New Atlantis was originally prized by its 17th-century readers for its connection with the 'Great Instauration' (the author's plan for reforming the sciences), and for its close link with Sylva Sylvarum, the natural history with which it was first published. Modern scholarship has ignored the importance of Sylva to New Atlantis, which was intended to demonstrate how advances gained through the Instauration might actually be implemented for the greater benefit of humankind. I hope to show that, although Bacon's more theoretical philosophical treatises argue for a scientific method purged of fanciful language and back-door theology, the 'simpler', more referential language used in Sylva Sylvarum and New Atlantis nevertheless demonstrates the author's clear recognition that the success of the new science depended to some extent upon the strength of its figurative language, and that very often it was through the 'poetry' of metaphor, analogy and symbol that religion was re-inserted back into Bacon's natural philosophy. [ABSTRACT FROM AUTHOR]

We sought to determine the associations between hemoglobin A1c (A1c) and admission glucose with in-hospital mortality among patients with diabetes mellitus (DM) hospitalized with COVID-19. Adults hospitalized between 3/5/20 and 12/1/20 in a Connecticut health care system were included if they had prior DM diagnosis, an in-hospital A1c, and a positive RT-PCR nasopharyngeal swab for SARS-CoV-2. A1c was stratified into <7%, 7-<9%, and ≥9%. Both bivariate and multi-variable adjusted logistic regression analyses were performed to determine the association of A1c categories and admission glucose >200 mg/dL with mortality (in-hospital death or transition to hospice) and with intensive care unit (ICU) use. Models were adjusted for demographics and 8 relevant comorbidities. Among 733 patients (median age 67 years [interquartile range, 56-77], 48.3% female, 43.11% White, 35.47% Black, 24.97% Hispanic, 1.64% Asian), 31.7% had A1c <7%, 40.5% 7-<9%, 27.8% ≥9%, and 38.1% admission glucose >200 mg/dL. During hospitalization, 111 (15.1%) patients died or transitioned to hospice and 230 (31.4%) required ICU care. In 2 multi-variable adjusted analyses, neither A1c category nor high admission glucose were significantly associated with mortality (A1c 7-<9%: OR 0.89, 95% CI 0.53-1.49; A1c >9% OR 1.3, CI 0.72-2.35 compared with A1c <7%; glucose >200 OR 1.34, CI 0.72-2.35) or ICU care (A1c 7-<9% OR 1.30, 95% CI 0.88-1.93; A1c ≥9% OR 1.35, CI 0.86-2.1 compared with A1c <7%; glucose >200 OR 1.26, CI 0.9-1.78). Age (per year OR 1.06, CI 1.04-1.08), male sex (OR 1.78, CI 1.14-2.81), obesity (OR 1.85, CI 1.16-2.96) and CKD (OR 1.90, CI 1.19-3.03) were significantly associated with mortality. Only female sex (OR 0.67, CI 0.48-0.93) was significantly associated with ICU care. In our retrospective study of hospitalized patients with DM, neither A1c nor admission glucose were prognostic of COVID-19 mortality or ICU care. In those with DM, male sex, obesity and CKD predicted worse outcomes. Disclosure: J. Brancale: None. A. M. Jastreboff: Consultant; Self; Boehringer Ingelheim Pharmaceuticals, Inc., Eli Lilly and Company, Novo Nordisk, Other Relationship; Spouse/Partner; Elucidata. K. J. Lipska: None. D. Athonvarangkul: None. M. Simonov: None. J. Alausa: None. L. K. Subair: None. A. M. Hajduk: None. Y. Kang: None. T. Arora: None. F. P. Wilson: Other Relationship; Self; Efference LLC, Medscape. Funding: National Institutes of Health (1T32GM136651-01) [ABSTRACT FROM AUTHOR]