Early Physiologic Numerical and Waveform Characteristics of Simulated Hemorrhagic Events With Healthy Volunteers Donating Blood.

OBJECTIVES: Early signs of bleeding are often masked by the physiologic compensatory responses delaying its identification. We sought to describe early physiologic signatures of bleeding during the blood donation process. SETTING: Waveform-level vital sign data including electrocardiography, photoplethysmography (PPG), continuous noninvasive arterial pressure, and respiratory waveforms were collected before, during, and after bleeding. SUBJECTS: Fifty-five healthy volunteers visited blood donation center to donate whole blood. INTERVENTION: After obtaining the informed consent, 3 minutes of resting time was given to each subject. Then 3 minutes of orthostasis was done, followed by another 3 minutes of resting before the blood donation. After the completion of donating blood, another 3 minutes of postbleeding resting time, followed by 3 minutes of orthostasis period again. MEASUREMENTS AND MAIN RESULTS: From 55 subjects, waveform signals as well as numerical vital signs (heart rate [HR], respiratory rate, blood pressure) and clinical characteristics were collected, and data from 51 subjects were analyzable. Any adverse events (AEs; dizziness, lightheadedness, nausea) were documented. Statistical and physiologic features including HR variability (HRV) metrics and other waveform morphologic parameters were modeled. Feature trends for all participants across the study protocol were analyzed. No significant changes in HR, blood pressure, or estimated cardiac output were seen during bleeding. Both orthostatic challenges and bleeding significantly decreased time domain and high-frequency domain HRV, and PPG amplitude, whereas increasing PPG amplitude variation. During bleeding, time-domain HRV feature trends were most sensitive to the first 100 mL of blood loss, and incremental changes of different HRV parameters (from 300 mL of blood loss), as well as a PPG morphologic feature (from 400 mL of blood loss), were shown with statistical significance. The AE group (n = 6) showed decreased sample entropy compared with the non-AE group during postbleed orthostatic challenge (p = 0.003). No significant other trend differences were observed during bleeding between AE and non-AE groups. CONCLUSIONS: Various HRV-related features were changed during rapid bleeding seen within the first minute. Subjects with AE during postbleeding orthostasis showed decreased sample entropy. These findings could be leveraged toward earlier identification of donors at risk for AE, and more broadly building a data-driven hemorrhage model for the early treatment of critical bleeding.

Engaging Multidisciplinary Clinical Users in the Design of an Artificial Intelligence-Powered Graphical User Interface for Intensive Care Unit Instability Decision Support.

BACKGROUND: Critical instability forecast and treatment can be optimized by artificial intelligence (AI)-enabled clinical decision support. It is important that the user-facing display of AI output facilitates clinical thinking and workflow for all disciplines involved in bedside care. OBJECTIVES: Our objective is to engage multidisciplinary users (physicians, nurse practitioners, physician assistants) in the development of a graphical user interface (GUI) to present an AI-derived risk score. METHODS: Intensive care unit (ICU) clinicians participated in focus groups seeking input on instability risk forecast presented in a prototype GUI. Two stratified rounds (three focus groups [only nurses, only providers, then combined]) were moderated by a focus group methodologist. After round 1, GUI design changes were made and presented in round 2. Focus groups were recorded, transcribed, and deidentified transcripts independently coded by three researchers. Codes were coalesced into emerging themes. RESULTS: Twenty-three ICU clinicians participated (11 nurses, 12 medical providers [3 mid-level and 9 physicians]). Six themes emerged: (1) analytics transparency, (2) graphical interpretability, (3) impact on practice, (4) value of trend synthesis of dynamic patient data, (5) decisional weight (weighing AI output during decision-making), and (6) display location (usability, concerns for patient/family GUI view). Nurses emphasized having GUI objective information to support communication and optimal GUI location. While providers emphasized need for recommendation interpretability and concern for impairing trainee critical thinking. All disciplines valued synthesized views of vital signs, interventions, and risk trends but were skeptical of placing decisional weight on AI output until proven trustworthy. CONCLUSION: Gaining input from all clinical users is important to consider when designing AI-derived GUIs. Results highlight that health care intelligent decisional support systems technologies need to be transparent on how they work, easy to read and interpret, cause little disruption to current workflow, as well as decisional support components need to be used as an adjunct to human decision-making.

Strategies for success in a nursing PhD program and beyond.

PURPOSE: The purpose of this article is to inform newly enrolled PhD students of program expectations, strategies for success, and next steps in the career of a nurse scientist. METHODS: We used empirical evidence and insights from the authors to describe strategies for success during a nursing PhD program and continued career development following graduation. FINDINGS: Measures of success included maintaining health, focus, integrity, and a supportive network, identifying mentors, pursuing new knowledge and advancing research to transform health outcomes. CONCLUSION: Nursing PhD programs help to shape future researchers and leaders. Choosing to obtain a PhD in nursing is an investment in oneself, the discipline, and the science. CLINICAL RELEVANCE: Nursing PhD programs offer opportunities to advance science, impact healthcare and health outcomes, and prepare for a variety of career opportunities. Informing newly enrolled PhD students may better prepare them for what lies ahead and facilitate student retention.

Engaging clinicians early during the development of a graphical user display of an intelligent alerting system at the bedside.

BACKGROUND: Artificial Intelligence (AI) is increasingly used to support bedside clinical decisions, but information must be presented in usable ways within workflow. Graphical User Interfaces (GUI) are front-facing presentations for communicating AI outputs, but clinicians are not routinely invited to participate in their design, hindering AI solution potential. PURPOSE: To inform early user-engaged design of a GUI prototype aimed at predicting future Cardiorespiratory Insufficiency (CRI) by exploring clinician methods for identifying at-risk patients, previous experience with implementing new technologies into clinical workflow, and user perspectives on GUI screen changes. METHODS: We conducted a qualitative focus group study to elicit iterative design feedback from clinical end-users on an early GUI prototype display. Five online focus group sessions were held, each moderated by an expert focus group methodologist. Iterative design changes were made sequentially, and the updated GUI display was presented to the next group of participants. RESULTS: 23 clinicians were recruited (14 nurses, 4 nurse practitioners, 5 physicians; median participant age ∼35 years; 60% female; median clinical experience 8 years). Five themes emerged from thematic content analysis: trend evolution, context (risk evolution relative to vital signs and interventions), evaluation/interpretation/explanation (sub theme: continuity of evaluation), clinician intuition, and clinical operations. Based on these themes, GUI display changes were made. For example, color and scale adjustments, integration of clinical information, and threshold personalization. CONCLUSIONS: Early user-engaged design was useful in adjusting GUI presentation of AI output. Next steps involve clinical testing and further design modification of the AI output to optimally facilitate clinician surveillance and decisions. Clinicians should be involved early and often in clinical decision support design to optimize efficacy of AI tools.

Patterns of Adherence to Secondary Prevention Measures Among Chinese Patients With Coronary Artery Disease: A Longitudinal Study.

BACKGROUND: Adherence to secondary prevention measures among patients with coronary artery disease (CAD) affects patient prognosis, whereas patients' adherence behaviors change over time. OBJECTIVES: The aims of this study were to identify adherence trajectories to secondary prevention measures including medication-taking and a heart-healthy lifestyle and to estimate predictors of adherence trajectories among patients with CAD. METHODS: This longitudinal study enrolled 698 patients with CAD who received a percutaneous coronary intervention in China. Demographics, clinical characteristics, adherence to secondary prevention measures, and patient-related factors including disease knowledge, self-efficacy, and health literacy were measured during hospitalization. Adherence behaviors were followed at 1, 3, and 6 months, and 1 year after discharge. Group-based trajectory models estimated adherence trajectories, and multinomial logistic regression identified trajectory group predictors. RESULTS: Four trajectory groups were identified for medication-taking adherence: sustained adherence (39.9%), increasing and then decreasing adherence (23.1%), increasing adherence (23.4%), and nonadherence (13.6%). The 3 adherence trajectory groups for a heart-healthy lifestyle were sustained adherence (59.7%), increasing adherence (28.3%), and nonadherence (12.0%). Married patients were more likely (odds ratio [OR], 3.42; 95% confidence interval [CI], 1.56-7.52) to have sustained adherence to medication-taking. However, patients with higher disease knowledge were less likely (OR, 0.93; 95% CI, 0.87-0.99) to be adherent. Patients who were not working (OR, 2.25; 95% CI, 1.03-4.92) had higher self-efficacy (OR, 1.21; 95% CI, 1.08-1.37). Those with higher health literacy (OR, 1.18; 95% CI, 1.01-1.38) were more likely to have sustained adherence to a heart-healthy lifestyle. However, patients having no coronary stents (OR, 0.36; 95% CI, 0.19-0.70) were less likely to have done so. CONCLUSIONS: Trajectories of adherence to secondary prevention measures among mainland Chinese patients with CAD are multipatterned. Healthcare providers should formulate targeted adherence support, which considers the influence of disease knowledge, self-efficacy, and health literacy.

Weakly Supervised Classification of Vital Sign Alerts as Real or Artifact.

A significant proportion of clinical physiologic monitoring alarms are false. This often leads to alarm fatigue in clinical personnel, inevitably compromising patient safety. To combat this issue, researchers have attempted to build Machine Learning (ML) models capable of accurately adjudicating Vital Sign (VS) alerts raised at the bedside of hemodynamically monitored patients as real or artifact. Previous studies have utilized supervised ML techniques that require substantial amounts of hand-labeled data. However, manually harvesting such data can be costly, time-consuming, and mundane, and is a key factor limiting the widespread adoption of ML in healthcare (HC). Instead, we explore the use of multiple, individually imperfect heuristics to automatically assign probabilistic labels to unlabeled training data using weak supervision. Our weakly supervised models perform competitively with traditional supervised techniques and require less involvement from domain experts, demonstrating their use as efficient and practical alternatives to supervised learning in HC applications of ML.

Accuracy of identifying hospital acquired venous thromboembolism by administrative coding: implications for big data and machine learning research.

Big data analytics research using heterogeneous electronic health record (EHR) data requires accurate identification of disease phenotype cases and controls. Overreliance on ground truth determination based on administrative data can lead to biased and inaccurate findings. Hospital-acquired venous thromboembolism (HA-VTE) is challenging to identify due to its temporal evolution and variable EHR documentation. To establish ground truth for machine learning modeling, we compared accuracy of HA-VTE diagnoses made by administrative coding to manual review of gold standard diagnostic test results. We performed retrospective analysis of EHR data on 3680 adult stepdown unit patients identifying HA-VTE. International Classification of Diseases, Ninth Revision (ICD-9-CM) codes for VTE were identified. 4544 radiology reports associated with VTE diagnostic tests were screened using terminology extraction and then manually reviewed by a clinical expert to confirm diagnosis. Of 415 cases with ICD-9-CM codes for VTE, 219 were identified with acute onset type codes. Test report review identified 158 new-onset HA-VTE cases. Only 40% of ICD-9-CM coded cases (n = 87) were confirmed by a positive diagnostic test report, leaving the majority of administratively coded cases unsubstantiated by confirmatory diagnostic test. Additionally, 45% of diagnostic test confirmed HA-VTE cases lacked corresponding ICD codes. ICD-9-CM coding missed diagnostic test-confirmed HA-VTE cases and inaccurately assigned cases without confirmed VTE, suggesting dependence on administrative coding leads to inaccurate HA-VTE phenotyping. Alternative methods to develop more sensitive and specific VTE phenotype solutions portable across EHR vendor data are needed to support case-finding in big-data analytics.

Intensive Care Unit Scoring Systems.

BACKGROUND: Illness severity scoring systems are commonly used in critical care. When applied to the populations for whom they were developed and validated, these tools can facilitate mortality prediction and risk stratification, optimize resource use, and improve patient outcomes. OBJECTIVE: To describe the characteristics and applications of the scoring systems most frequently applied to critically ill patients. METHODS: A literature search was performed using MEDLINE to identify original articles on intensive care unit scoring systems published in the English language from 1980 to 2020. Search terms associated with critical care scoring systems were used alone or in combination to find relevant publications. RESULTS: Two types of scoring systems are most frequently applied to critically ill patients: those that predict risk of in-hospital mortality at the time of intensive care unit admission (Acute Physiology and Chronic Health Evaluation, Simplified Acute Physiology Score, and Mortality Probability Models) and those that assess and characterize current degree of organ dysfunction (Multiple Organ Dysfunction Score, Sequential Organ Failure Assessment, and Logistic Organ Dysfunction System). This article details these systems' differing features and timing of use, score calculation, patient populations, and comparative performance data. CONCLUSION: Critical care nurses must be aware of the strengths, limitations, and specific characteristics of severity scoring systems commonly used in intensive care unit patients to effectively employ these tools in clinical practice and critically appraise research findings based on their use.

Educating PhD students in research-intensive nursing doctorate programs regarding teaching competencies.

In October 2019 an invitational summit was held addressing nursing PhD program competencies within research-intensive universities. One topic of discussion was related to whether or not teaching competencies should be included in the PhD program curricula of research-intensive universities, and where competencies should be learned. The discussion indicated a lack of uniform consensus. Rather, schools should be clear about their goals-to focus solely on developing nurse scientists, or a broader mission of preparing graduates to embrace the full scope of academic work inclusive of discovery, teaching, application and integration, or even possibly roles outside of academia. The discussion group coalesced around the notion that preparation in teaching be dependent upon mission clarification. Schools could then decide whether to incorporate teaching competencies or whether the best way to achieve their mission was to limit the acquisition of teaching competencies to elective experiential learning alone, or a combination of didactics, supervised practica and experiential learning. Based upon the summit conversation, this is something that each PhD program will have to decide based upon its own purpose and the environment it is preparing graduates to occupy. Nevertheless, preparing for a too narrow and specific career trajectory may not accommodate flexibility in the marketplace.

Choosing Wisely For Critical Care: The Next Five.

OBJECTIVES: To formulate new "Choosing Wisely" for Critical Care recommendations that identify best practices to avoid waste and promote value while providing critical care. DATA SOURCES: Semistructured narrative literature review and quantitative survey assessments. STUDY SELECTION: English language publications that examined critical care practices in relation to reducing cost or waste. DATA EXTRACTION: Practices assessed to add no value to critical care were grouped by category. Taskforce assessment, modified Delphi consensus building, and quantitative survey analysis identified eight novel recommendations to avoid wasteful critical care practices. These were submitted to the Society of Critical Care Medicine membership for evaluation and ranking. DATA SYNTHESIS: Results from the quantitative Society of Critical Care Medicine membership survey identified the top scoring five of eight recommendations. These five highest ranked recommendations established Society of Critical Care Medicine's Next Five "Choosing" Wisely for Critical Care practices. CONCLUSIONS: Five new recommendations to reduce waste and enhance value in the practice of critical care address invasive devices, proactive liberation from mechanical ventilation, antibiotic stewardship, early mobilization, and providing goal-concordant care. These recommendations supplement the initial critical care recommendations from the "Choosing Wisely" campaign.

Prediction of hypotension events with physiologic vital sign signatures in the intensive care unit.

BACKGROUND: Even brief hypotension is associated with increased morbidity and mortality. We developed a machine learning model to predict the initial hypotension event among intensive care unit (ICU) patients and designed an alert system for bedside implementation. MATERIALS AND METHODS: From the Medical Information Mart for Intensive Care III (MIMIC-3) dataset, minute-by-minute vital signs were extracted. A hypotension event was defined as at least five measurements within a 10-min period of systolic blood pressure ≤ 90 mmHg and mean arterial pressure ≤ 60 mmHg. Using time series data from 30-min overlapping time windows, a random forest (RF) classifier was used to predict risk of hypotension every minute. Chronologically, the first half of extracted data was used to train the model, and the second half was used to validate the trained model. The model's performance was measured with area under the receiver operating characteristic curve (AUROC) and area under the precision recall curve (AUPRC). Hypotension alerts were generated using risk score time series, a stacked RF model. A lockout time were applied for real-life implementation. RESULTS: We identified 1307 subjects (1580 ICU stays) as the hypotension group and 1619 subjects (2279 ICU stays) as the non-hypotension group. The RF model showed AUROC of 0.93 and 0.88 at 15 and 60 min, respectively, before hypotension, and AUPRC of 0.77 at 60 min before. Risk score trajectories revealed 80% and > 60% of hypotension predicted at 15 and 60 min before the hypotension, respectively. The stacked model with 15-min lockout produced on average 0.79 alerts/subject/hour (sensitivity 92.4%). CONCLUSION: Clinically significant hypotension events in the ICU can be predicted at least 1 h before the initial hypotension episode. With a highly sensitive and reliable practical alert system, a vast majority of future hypotension could be captured, suggesting potential real-life utility.

Clinical Distancing and Mitigation of Coronavirus Disease 2019.

Social distancing as a technique to limit transmission of infectious disease has come into common parlance following the arrival and rapid spread of a novel coronavirus disease around the world in 2019 and 2020. But in the face of an emerging pandemic threat, it is crucial that we start to apply these principles to the clinic, the emergency department, and the hospital ward. We propose that this dynamic situation calls for a parallel "Clinical Distancing" in which we as a medical culture go against many of our fundamental instincts and, at least in the short term, begin to reduce unnecessary patient-care contacts for the benefit of our patients and our ability to continue to provide care to those who need it most. In this commentary, we provide specific recommendations for the rapid implementation of clinical distancing techniques.

Prediction of Changes in Adherence to Secondary Prevention Among Patients With Coronary Artery Disease.

BACKGROUND: Healthcare providers are concerned about adherence to provider recommendations in coronary artery disease management. Seeking patient-related factors influencing changes in adherence over time is necessary for formulating suitable intervention measures-especially among diverse populations. OBJECTIVE: To explore whether health literacy, self-efficacy, and disease knowledge predict changes in adherence over time (between baseline and 3 months) to secondary prevention recommendations for Chinese coronary artery disease patients. METHODS: A longitudinal study was performed for 662 patients following percutaneous coronary intervention in China. Self-reported data were collected at baseline during hospitalization and at a 3-month telephone follow-up. Variables included demographics, health literacy, self-efficacy, disease knowledge, and adherence to secondary prevention recommendations for medication taking and a heart-healthy lifestyle. Multinomial logistic regression identified predictors of adherence changes over time. RESULTS: Patients were categorized into three groups: sustained/declined to nonadherence between baseline and 3 months, improved to adherence, and sustained adherence. The number of patients in sustained/declined to nonadherence group was small. Absence of stents predicted sustained/declined to nonadherence to medication and lifestyle over time. Health literacy was not associated with adherence changes over time. Higher self-efficacy scores were associated with lower likelihood of sustained/declined to nonadherence to a healthy lifestyle over time, whereas higher disease knowledge scores were associated with higher sustained/declined to nonadherence to medication. CONCLUSIONS: Adherence to secondary prevention 3 months after discharge was relatively good in Chinese patients with coronary artery disease who received percutaneous coronary intervention. Absence of stents and lower self-efficacy can predict the poor adherence changes, which should be considered in formulating follow-up care.

Tele-Critical Care: An Update From the Society of Critical Care Medicine Tele-ICU Committee.

OBJECTIVES: In 2014, the Tele-ICU Committee of the Society of Critical Care Medicine published an article regarding the state of ICU telemedicine, one better defined today as tele-critical care. Given the rapid evolution in the field, the authors now provide an updated review. DATA SOURCES AND STUDY SELECTION: We searched PubMed and OVID for peer-reviewed literature published between 2010 and 2018 related to significant developments in tele-critical care, including its prevalence, function, activity, and technologies. Search terms included electronic ICU, tele-ICU, critical care telemedicine, and ICU telemedicine with appropriate descriptors relevant to each sub-section. Additionally, information from surveys done by the Society of Critical Care Medicine was included given the relevance to the discussion and was referenced accordingly. DATA EXTRACTION AND DATA SYNTHESIS: Tele-critical care continues to evolve in multiple domains, including organizational structure, technologies, expanded-use case scenarios, and novel applications. Insights have been gained in economic impact and human and organizational factors affecting tele-critical care delivery. Legislation and credentialing continue to significantly influence the pace of tele-critical care growth and adoption. CONCLUSIONS: Tele-critical care is an established mechanism to leverage critical care expertise to ICUs and beyond, but systematic research comparing different models, approaches, and technologies is still needed.

The Association Between Patient Outcomes and the Initial Emergency Severity Index Triage Score in Patients With Suspected Acute Coronary Syndrome.

BACKGROUND: The Emergency Severity Index (ESI) is a widely used tool to triage patients in emergency departments. The ESI tool is used to assess all complaints and has significant limitation for accurately triaging patients with suspected acute coronary syndrome (ACS). OBJECTIVE: We evaluated the accuracy of ESI in predicting serious outcomes in suspected ACS and aimed to assess the incremental reclassification performance if ESI is supplemented with a clinically validated tool used to risk-stratify suspected ACS. METHODS: We used existing data from an observational cohort study of patients with chest pain. We extracted ESI scores documented by triage nurses during routine medical care. Two independent reviewers adjudicated the primary outcome, incidence of 30-day major adverse cardiac events. We compared ESI with the well-established modified HEAR/T (patient History, Electrocardiogram, Age, Risk factors, but without Troponin) score. RESULTS: Our sample included 750 patients (age, 59 ± 17 years; 43% female; 40% black). A total of 145 patients (19%) experienced major adverse cardiac event. The area under the receiver operating characteristic curve for ESI score for predicting major adverse cardiac event was 0.656, compared with 0.796 for the modified HEAR/T score. Using the modified HEAR/T score, 181 of the 391 false positives (46%) and 16 of the 19 false negatives (84%) assigned by ESI could be reclassified correctly. CONCLUSION: The ESI score is poorly associated with serious outcomes in patients with suspected ACS. Supplementing the ESI tool with input from other validated clinical tools can greatly improve the accuracy of triage in patients with suspected ACS.

Neurocardiac Injury Assessed by Strain Imaging Is Associated With In-Hospital Mortality in Patients With Subarachnoid Hemorrhage.

OBJECTIVES: This study sought to test the hypothesis that speckle tracking strain echocardiography can quantify neurocardiac injuries in patients with aneurysmal subarachnoid hemorrhage (SAH), which is associated with worse clinical outcome. BACKGROUND: SAH may be a life-threatening disease associated with variable degrees of neurocardiac injury. Strain imaging has the potential to detect subtle myocardial dysfunction which is additive to conventional measurements. METHODS: A total of 255 consecutive patients were prospectively enrolled with acute SAH, who were admitted to the intensive care unit with echocardiography studies within 72 h. Left ventricular (LV) and right ventricular (RV) strains were acquired from standard apical views. Abnormal LV global longitudinal strain (GLS) and RV free-wall strain were pre-defined as <17% and <23% (absolute values), respectively. RESULTS: Performing LV GLS was feasible in 221 patients (89%) 53 ± 10 years of age, 71% female, after excluding those with previous cardiac disease. Abnormal LV GLS findings were observed in 53 patients (24%) and were associated with worse clinical severity, including a Hunt-Hess grade >3 (34% vs. 15%; p = 0.005) and biomarker evidence of neurocardiac injury and higher troponin values (1.50 [interquartile range (IQR): 0.01 to 3.87] vs. 0.01 [IQR: 0.01 to 0.22] ng/ml; p < 0.001). A reverse Takotsubo pattern of segmental strain was observed in 49% of patients (apical sparing and reduced basal strain). Importantly, LV GLS was more strongly associated with in-hospital mortality than left ventricular ejection fraction (LVEF), even after adjusting for clinical severity (odds ratio [OR]: 3.11; 95% confidence interval [CI]: 1.12 to 8.63; p = 0.029). RV strain was measured in 159 subjects (72%); abnormal RV strain was added to LV GLS for predicting in-hospital mortality (p = 0.007). CONCLUSIONS: Neurocardiac injury can be detected by LV GLS and RV strain in patients with acute SAH. LV GLS was significantly associated with in-hospital mortality. RV strain, when available, added prognostic value to LV GLS. Abnormal myocardial strain is a marker for increased risk of in-hospital mortality in SAH and has clinical prognostic utility.

Relationship between adherence to secondary prevention and health literacy, self-efficacy and disease knowledge among patients with coronary artery disease in China.

BACKGROUND: Adherence to secondary prevention is an indispensable part of the management of patients with coronary artery disease. Finding patient factors affecting their adherence behaviours is important for improving the treatment effect and limiting further disease progression. AIMS: To examine the association between health literacy, self-efficacy, disease knowledge and adherence to secondary coronary artery disease prevention in patients in China. METHODS: In this cross-sectional study, 598 patients with coronary artery disease were enrolled in two tertiary hospitals in China during a hospitalisation for receiving percutaneous coronary intervention. Patient-reported data were collected on demographics, health literacy, self-efficacy, disease knowledge and adherence to secondary coronary artery disease prevention (medication-taking and heart-healthy lifestyle (exercise, reducing/eliminating alcohol intake and smoking, low salt and fat diet, stress reduction)). Chi-squared tests and regression analyses were performed. RESULTS: The proportions of recalled self-report of adherence to medication-taking and a heart-healthy lifestyle immediately prior to the coronary artery disease hospitalisation were 84.7% and 53.2%, respectively. In logistic regression, health literacy, self-efficacy and disease knowledge was significantly associated with non-adherence to secondary coronary artery disease prevention. Limited health literacy demonstrated a 1.61-fold odds for non-adherence to a heart-healthy lifestyle. Each score increase of self-efficacy and disease knowledge had 0.98-fold odds and 1.05-fold odds of non-adherence to a heart-healthy lifestyle. CONCLUSIONS: Adherence to medication-taking was relatively good in Chinese patients prior to coronary artery disease hospitalisation, but adherence to heart-healthy lifestyle behaviours should be improved. Health literacy, self-efficacy and disease knowledge should be taken into account when intervening to improve secondary coronary artery disease prevention.

Predicting tachycardia as a surrogate for instability in the intensive care unit.

Tachycardia is a strong though non-specific marker of cardiovascular stress that proceeds hemodynamic instability. We designed a predictive model of tachycardia using multi-granular intensive care unit (ICU) data by creating a risk score and dynamic trajectory. A subset of clinical and numerical signals were extracted from the Multiparameter Intelligent Monitoring in Intensive Care II database. A tachycardia episode was defined as heart rate ≥ 130/min lasting for ≥ 5 min, with ≥ 10% density. Regularized logistic regression (LR) and random forest (RF) classifiers were trained to create a risk score for upcoming tachycardia. Three different risk score models were compared for tachycardia and control (non-tachycardia) groups. Risk trajectory was generated from time windows moving away at 1 min increments from the tachycardia episode. Trajectories were computed over 3 hours leading up to the episode for three different models. From 2809 subjects, 787 tachycardia episodes and 707 control periods were identified. Patients with tachycardia had increased vasopressor support, longer ICU stay, and increased ICU mortality than controls. In model evaluation, RF was slightly superior to LR, which accuracy ranged from 0.847 to 0.782, with area under the curve from 0.921 to 0.842. Risk trajectory analysis showed average risks for tachycardia group evolved to 0.78 prior to the tachycardia episodes, while control group risks remained < 0.3. Among the three models, the internal control model demonstrated evolving trajectory approximately 75 min before tachycardia episode. Clinically relevant tachycardia episodes can be predicted from vital sign time series using machine learning algorithms.

Determinants of Intensive Care Unit Telemedicine Effectiveness. An Ethnographic Study.

RATIONALE: Telemedicine is an increasingly common care delivery strategy in the ICU. However, ICU telemedicine programs vary widely in their clinical effectiveness, with some studies showing a large mortality benefit and others showing no benefit or even harm. OBJECTIVES: To identify the organizational factors associated with ICU telemedicine effectiveness. METHODS: We performed a focused ethnographic evaluation of 10 ICU telemedicine programs using site visits, interviews, and focus groups in both facilities providing remote care and the target ICUs. Programs were selected based on their change in risk-adjusted mortality after adoption (decreased mortality, no change in mortality, and increased mortality). We used a constant comparative approach to guide data collection and analysis. MEASUREMENTS AND MAIN RESULTS: We conducted 460 hours of direct observation, 222 interviews, and 18 focus groups across six telemedicine facilities and 10 target ICUs. Data analysis revealed three domains that influence ICU telemedicine effectiveness: 1) leadership (i.e., the decisions related to the role of the telemedicine, conflict resolution, and relationship building), 2) perceived value (i.e., expectations of availability and impact, staff satisfaction, and understanding of operations), and 3) organizational characteristics (i.e., staffing models, allowed involvement of the telemedicine unit, and new hire orientation). In the most effective telemedicine programs these factors led to services that are viewed as appropriate, integrated, responsive, and consistent. CONCLUSIONS: The effectiveness of ICU telemedicine programs may be influenced by several potentially modifiable factors within the domains of leadership, perceived value, and organizational structure.

A call to alarms: Current state and future directions in the battle against alarm fatigue.

Research demonstrates that the majority of alarms derived from continuous bedside monitoring devices are non-actionable. This avalanche of unreliable alerts causes clinicians to experience sensory overload when attempting to sort real from false alarms, causing desensitization and alarm fatigue, which in turn leads to adverse events when true instability is neither recognized nor attended to despite the alarm. The scope of the problem of alarm fatigue is broad, and its contributing mechanisms are numerous. Current and future approaches to defining and reacting to actionable and non-actionable alarms are being developed and investigated, but challenges in impacting alarm modalities, sensitivity and specificity, and clinical activity in order to reduce alarm fatigue and adverse events remain. A multi-faceted approach involving clinicians, computer scientists, industry, and regulatory agencies is needed to battle alarm fatigue.