Using the ROX Index to Predict Treatment Outcome for High-Flow Nasal Cannula and/or Noninvasive Ventilation in Patients With COPD Exacerbations.

BACKGROUND: The ratio of oxygen saturation index (ROX index; or SpO2 /FIO2 /breathing frequency) has been shown to predict risk of intubation after high-flow nasal cannula (HFNC) support among adults with acute hypoxemic respiratory failure primarily due to pneumonia. However, its predictive value for other subtypes of respiratory failure is unknown. This study investigated whether the ROX index predicts liberation from HFNC or noninvasive ventilation (NIV), intubation with mechanical ventilation, or death in adults admitted for respiratory failure due to an exacerbation of COPD. METHODS: We performed a retrospective study of 260 adults hospitalized with a COPD exacerbation and treated with HFNC and/or NIV (continuous or bi-level). ROX index scores were collected at treatment initiation and predefined time intervals throughout HFNC and/or NIV treatment or until the subject was intubated or died. A ROX index score of ≥ 4.88 was applied to the cohort to determine if the same score would perform similarly in this different cohort. Accuracy of the ROX index was determined by calculating the area under the receiver operator curve. RESULTS: A total of 47 subjects (18%) required invasive mechanical ventilation or died while on HFNC/NIV. The ROX index at treatment initiation, 1 h, and 6 h demonstrated the best prediction accuracy for avoidance of invasive mechanical ventilation or death (area under the receiver operator curve 0.73 [95% CI 0.66-0.80], 0.72 [95% CI 0.65-0.79], and 0.72 [95% CI 0.63-0.82], respectively). The optimal cutoff value for sensitivity (Sn) and specificity (Sp) was a ROX index score > 6.88 (sensitivity 62%, specificity 57%). CONCLUSIONS: The ROX index applied to adults with COPD exacerbations treated with HFNC and/or NIV required higher scores to achieve similar prediction of low risk of treatment failure when compared to subjects with hypoxemic respiratory failure/pneumonia. ROX scores < 4.88 did not accurately predict intubation or death.

Early Mobility Index and Patient Outcomes: A Retrospective Study in Multiple Intensive Care Units.

BACKGROUND: Early mobility interventions in intensive care units (ICUs) are safe and improve outcomes in subsets of critically ill adults. However, implementation varies, and the optimal mobility dose remains unclear. OBJECTIVE: To test for associations between daily dose of out-of-bed mobility and patient outcomes in different ICUs. METHODS: In this retrospective cohort study of electronic records from 7 adult ICUs in an academic quarternary hospital, multivariable linear regression was used to examine the effects of out-of-bed events per mobility-eligible day on mechanical ventilation duration and length of ICU and hospital stays. RESULTS: In total, 8609 adults hospitalized in ICUs from 2015 through 2018 were included. Patients were mobilized out of bed on 46.5% of ICU days and were eligible for mobility interventions on a median (IQR) of 2.0 (1-3) of 2.7 (2-9) ICU days. Median (IQR) out-of-bed events per mobility-eligible day were 0.5 (0-1.2) among all patients. For every unit increase in out-of-bed events per mobility-eligible day before extubation, mechanical ventilation duration decreased by 10% (adjusted coefficient [95% CI], -0.10 [-0.18 to -0.01]). Daily mobility increased ICU stays by 4% (adjusted coefficient [95% CI], 0.04 [0.03-0.06]) and decreased hospital stays by 5% (adjusted coefficient [95% CI], -0.05 [-0.07 to -0.03]). Effect sizes differed among ICUs. CONCLUSIONS: More daily out-of-bed mobility for ICU patients was associated with shorter mechanical ventilation duration and hospital stays, suggesting a dose-response relationship between daily mobility and patient outcomes. However, relationships differed across ICU subpopulations.

Work of Breathing During Proportional Assist Ventilation as a Predictor of Extubation Failure.

BACKGROUND: Despite decades of research on predictors of extubation success, use of ventilatory support after extubation is common and 10-20% of patients require re-intubation. Proportional assist ventilation (PAV) mode automatically calculates estimated total work of breathing (total WOB). Here, we assessed the performance of total WOB to predict extubation failure in invasively ventilated subjects. METHODS: This prospective observational study was conducted in 6 adult ICUs at an academic medical center. We enrolled intubated subjects who successfully completed a spontaneous breathing trial, had a rapid shallow breathing index < 105 breaths/min/L, and were deemed ready for extubation by the primary team. Total WOB values were recorded at the end of a 30-min PAV trial. Extubation failure was defined as any respiratory support and/or re-intubation within 72 h of extubation. We compared total WOB scores between groups and performance of total WOB for predicting extubation failure with receiver operating characteristic curves. RESULTS: Of 61 subjects enrolled, 9.8% (n = 6) required re-intubation, and 50.8% (n = 31) required any respiratory support within 72 h of extubation. Median total WOB at 30 min on PAV was 0.9 J/L (interquartile range 0.7-1.3 J/L). Total WOB was significantly different between subjects who failed or were successfully extubated (median 1.1 J/L vs 0.7 J/L, P = .004). The area under the curve was 0.71 [95% CI 0.58-0.85] for predicting any requirement of respiratory support and 0.85 [95% CI 0.69-1.00] for predicting re-intubation alone within 72 h of extubation. Total WOB cutoff values maximizing sensitivity and specificity equally were 1.0 J/L for any respiratory support (positive predictive value [PPV] 70.0%, negative predictive value [NPV] 67.7%) and 1.3 J/L for re-intubation (PPV 26.3%, NPV 97.6%). CONCLUSIONS: The discriminative performance of a PAV-derived total WOB value to predict extubation failure was good, indicating total WOB may represent an adjunctive tool for assessing extubation readiness. However, these results should be interpreted as preliminary, with specific thresholds of PAV-derived total WOB requiring further investigation in a large multi-center study.

Use of Machine Learning to Screen for Acute Respiratory Distress Syndrome Using Raw Ventilator Waveform Data.

To develop and characterize a machine learning algorithm to discriminate acute respiratory distress syndrome from other causes of respiratory failure using only ventilator waveform data. DESIGN: Retrospective, observational cohort study. SETTING: Academic medical center ICU. PATIENTS: Adults admitted to the ICU requiring invasive mechanical ventilation, including 50 patients with acute respiratory distress syndrome and 50 patients with primary indications for mechanical ventilation other than hypoxemic respiratory failure. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Pressure and flow time series data from mechanical ventilation during the first 24-hours after meeting acute respiratory distress syndrome criteria (or first 24-hr of mechanical ventilation for non-acute respiratory distress syndrome patients) were processed to extract nine physiologic features. A random forest machine learning algorithm was trained to discriminate between the patients with and without acute respiratory distress syndrome. Model performance was assessed using the area under the receiver operating characteristic curve, sensitivity, specificity, positive predictive value, and negative predictive value. Analyses examined performance when the model was trained using data from the first 24 hours and tested using withheld data from either the first 24 hours (24/24 model) or 6 hours (24/6 model). Area under the receiver operating characteristic curve, sensitivity, specificity, positive predictive value, and negative predictive value were 0.88, 0.90, 0.71, 0.77, and 0.90 (24/24); and 0.89, 0.90, 0.75, 0.83, and 0.83 (24/6). CONCLUSIONS: Use of machine learning and physiologic information derived from raw ventilator waveform data may enable acute respiratory distress syndrome screening at early time points after intubation. This approach, combined with traditional diagnostic criteria, could improve timely acute respiratory distress syndrome recognition and enable automated clinical decision support, especially in settings with limited availability of conventional diagnostic tests and electronic health records.

Meaningful Engagement of Patient Advisors in Research: Towards Mutually Beneficial Relationships.

Patient engagement in research improves trustworthiness of the research findings, increases relevance, and ensures designs include the most meaningful outcomes for patients living with targeted health conditions. The Patient Centered Outcomes Research Institute (PCORI) requires engagement of patient stakeholders. There is limited description of both the context and the processes used to engage patients effectively. This paper discusses engagement activities, roles and responsibilities, value of a Patient Advisory Board (PAB), and lessons learned. Data include program notes, research team reflections, PCORI reporting, and an advisor survey.Facilitators of meaningful engagement included creating a learning community, co-defining clear roles, reimbursing advisors, establishing clear avenues for communication, and welcoming unique contributions. Lessons learned were the value of time, the importance of building trust, and the benefits of diverse perspectives. The approach to meaningful engagement of patient advisors in research has the potential to enhance the relevance and usefulness of research for improving lives.

Standardisation, multi-measure, data quality and trending: A qualitative study on multidisciplinary perspectives to improve intensive care early mobility monitoring.

OBJECTIVE: To explore multi-clinician perspectives on intensive care early mobility, monitoring and to assess the perceived value of technology-generated mobility metrics to provide user feedback to inform research, practice improvement, and technology development. METHODS: We performed a qualitative descriptive study. Three focus groups were conducted with critical care clinicians, including nurses (n = 10), physical therapists (n = 8) and physicians (n = 8) at an academic medical centre that implemented an intensive care early mobility programme in 2012. Qualitative thematic analysis was used to code transcripts and identify overarching themes. FINDINGS: Along with reaffirming the value of performing early mobility interventions, four themes for improving mobility monitoring emerged, including the need for: 1) standardised indicators for documenting mobility; 2) inclusion of both quantitative and qualitative metrics to measure mobility 3) a balance between quantity and quality of data; and 4) trending mobility metrics over time. CONCLUSION: Intensive care mobility monitoring should be standardised and data generated should be high quality, capable of supporting trend analysis, and meaningful. By improving measurement and monitoring of mobility, future researchers can examine the arc of activity that patients in the intensive care unit undergo and develop models to understand factors that influence successful implementation.

Quantifying Mobility in the ICU: Comparison of Electronic Health Record Documentation and Accelerometer-Based Sensors to Clinician-Annotated Video.

To compare the accuracy of electronic health record clinician documentation and accelerometer-based sensors with a gold standard dataset derived from clinician-annotated video to quantify early mobility activities in adult ICU patients. DESIGN: Prospective, observational study. SETTING: Medical ICU at an academic hospital. PATIENTS: Adult ICU patients (n = 30) were each continuously monitored over a median of 24.4 hours, yielding 711.5 hours of video, electronic health record, and sensor data. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Electronic health record documentation estimated ambulation (intraclass correlation coefficient, 0.89; 95% CI, 0.78-0.95), sitting out-of-bed (intraclass correlation coefficient, 0.85; 95% CI, 0.72-0.93), and turning events (intraclass correlation coefficient, 0.87; 95% CI, 0.75-0.94) with excellent agreement but underestimated the number of standing, transferring, and pregait activities performed per patient. The accelerometer-based sensor had excellent agreement with video annotation for estimating duration of time spent supine (intraclass correlation coefficient, 0.99; CI, 0.97-0.99) and sitting/standing upright (intraclass correlation coefficient, 0.92; CI, 0.82-0.96) but overestimated ambulation time. CONCLUSIONS: Our results show that electronic health record documentation and sensor-based technologies accurately capture distinct but complimentary metrics for ICU mobility measurement. Innovations in artifact detection, standardization of clinically relevant mobility definitions, and electronic health record documentation enhancements may enable further use of these technologies to drive critical care research and technology leveraged data-driven ICU models of care.

How much do hospitalized adults move? A systematic review and meta-analysis.

AIM: To quantify the type and duration of physical activity performed by hospitalized adults. BACKGROUND: Inactivity is pervasive among hospitalized patients and is associated with increased mortality, functional decline, and cognitive impairment. Objective measurement of activity is necessary to examine associations with clinical outcomes and quantify optimal inpatient mobility interventions. METHODS: We used PRISMA guidelines to search three databases in December 2017 to retrieve original research evaluating activity type and duration among adult acute-care inpatients. We abstracted data on inpatient population, measurement method, monitoring time, activity duration, and study quality. RESULTS: Thirty-eight articles were included in the review and 7 articles were included in the meta-analysis. Study populations included geriatric (n = 5), surgical (n = 5), medical (n = 12), post-stroke (n = 10), psychiatric (n = 2), and critical care inpatients (n = 4). To measure activity, 29% of studies used human observation and 71% used activity monitors. Among inpatient populations, 87-100% of time was spent sitting or lying in-bed. Among medical inpatients monitored over a continuous 24-hour period (n = 7), 70 min per day was spent standing/walking (95% CI 57-83 min). CONCLUSIONS: This review provides a baseline assessment and benchmark of inpatient activity, which can be used to compare inpatient mobility practices. While there is substantial heterogeneity in how researchers measure and define how much inpatients move, there is consistent evidence that patients are mostly inactive and in-bed during hospitalization. Future research is needed to establish standardized methods to accurately and consistently measure inpatient mobility over time.

Empowering Diabetes Self-Management Through Technology and Nurse Health Coaching.

PURPOSE: This research explores the impact of mobile health (mHealth) technology and nurse health coaching on views of diabetes self-management (DSM) for persons living with diabetes. METHODS: Three focus groups (N = 24) were conducted with individuals living with type 2 diabetes who participated in a nurse health coaching and mHealth technology intervention study. Qualitative thematic analysis was used to identify overarching themes in each group. RESULTS: Major themes identified following intervention participation included enhanced perspectives about living with diabetes, increased awareness of how health behaviors influence DSM, improved support, and increased ownership of DSM. CONCLUSIONS: The themes identified suggest that the mHealth technology and health coaching intervention together may have had an empowering effect on participants' DSM. These results suggest that providing nurse health coaching with mHealth technology may help individuals ameliorate some of the challenges of living with and managing diabetes.

More than A1C: Types of success among adults with type-2 diabetes participating in a technology-enabled nurse coaching intervention.

OBJECTIVE: Success in diabetes research and self-management is often defined as a significant decrease in glycated hemoglobin (A1C). The aim of this article is to explore different types of successes experienced by adults with type-2 diabetes participating in a health technology and nurse coaching clinical trial. METHODS: A qualitative analysis was conducted using surveys and documentation from motivational interview-based coaching sessions between study nurses and intervention participants. RESULTS: Of the 132 cases reviewed, types of success predominantly fell into five categories: 1) change in health behaviors; 2) change in mindset or awareness; 3) change in engagement with healthcare resources; 4) change in physical or emotional health; and 5) change in health indicators. CONCLUSION: Experiences of success in diabetes are more varied than traditional A1C-based outcome models. Our findings suggest coaching and technology can assist patients to achieve a range of successes in diabetes management through goal setting, health tracking, resolving barriers, and aligning goals with factors that impact change. PRACTICE IMPLICATIONS: While A1C reduction is a critical factor in decreasing risk of diabetes-related complications, when healthcare professionals focus on A1C as the main indicator of diabetes management success, important changes in individuals' health and well-being may be overlooked or undervalued.

mHealth Technology and Nurse Health Coaching to Improve Health in Diabetes: Protocol for a Randomized Controlled Trial.

BACKGROUND: Chronic diseases, including diabetes mellitus, are the leading cause of mortality and disability in the United States. Current solutions focus primarily on diagnosis and pharmacological treatment, yet there is increasing evidence that patient-centered models of care are more successful in improving and addressing chronic disease outcomes. OBJECTIVE: The objective of this clinical trial is to evaluate the impact of a mobile health (mHealth) enabled nurse health coaching intervention on self-efficacy among adults with type-2 diabetes mellitus. METHODS: A randomized controlled trial was conducted at an academic health system in Northern California. A total of 300 participants with type-2 diabetes were scheduled to be enrolled through three primary care clinics. Participants were randomized to either usual care or intervention. All participants received training on use of the health system patient portal. Participants in the intervention arm received six scheduled health-coaching telephone calls with a registered nurse and were provided with an activity tracker and mobile application that integrated data into the electronic health record (EHR) to track their daily activity and health behavior decisions. All participants completed a baseline survey and follow-up surveys at 3 and 9 months. Primary and secondary outcomes include diabetes self-efficacy, hemoglobin A1c (HbA1c), and quality of life measures. RESULTS: Data collection for this trial, funded by the Patient-Centered Outcomes Research Institute, will be completed by December 2017. Results from the trial will be available mid-2018. CONCLUSIONS: This protocol details a patient-centered intervention using nurse health coaching, mHealth technologies, and integration of patient-generated data into the EHR. The aim of the intervention is to enhance self-efficacy and health outcomes by providing participants with a mechanism to track daily activity by offering coaching support to set reasonable and attainable health goals, and by creating a complete feedback loop by bringing patient-generated data into the EHR. TRIAL REGISTRATION: ClinicalTrials.gov NCT02672176; https://clinicaltrials.gov/ct2/show/NCT02672176 (Archived by WebCite at http://www.webcitation.org/6xEQXe1M5).

Managing Psychiatrist-Patient Relationships in the Digital Age: a Summary Review of the Impact of Technology-enabled Care on Clinical Processes and Rapport.

PURPOSE OF REVIEW: Participatory medicine and the availability of commercial technologies have given patients more options to view and track their health information and to communicate with their providers. This shift in the clinical process may be of particular importance in mental healthcare where rapport plays a significant role in the therapeutic process. RECENT FINDINGS: In this review, we examined literature related to the impact of technology on the clinical workflow and patient-provider rapport in the mental health field between January 2014 and June 2017. Thirty three relevant articles, of 226 identified articles, were summarized. The use of technology clinically has evolved from making care more accessible and efficient to leveraging technology to improve care, communication, and patient-provider rapport. Evidence exists demonstrating that information and communication technologies may improve care by better connecting patients and providers and by improving patient-provider rapport, although further research is needed.

Full disclosure of adverse events to patients and families in the ICU: wouldn't you want to know?

In the past several years, there has been an increasing focus in our Canadian health care system related to patient safety. The Canadian Disclosure Guidelines, which were released in May 2008, discuss various patient safety initiatives underway across Canada. They emphasize the importance of a clear and consistent approach to disclosure, regardless of the variability in the definitions and interpretations across health care institutions. In addition, they highlight that all patients have the right to be informed about all aspects of their care, and all harm must be communicated to patients regardless of the reason (Disclosure Working Group, 2008). In this article, the authors describe and share our learning experiences, as nurses and students, while working in critical care settings when these guidelines were needed to communicate a harmful incident. Often, health care practitioners only become aware of specific guidelines regarding the disclosure of an adverse event once the incident has occurred. A case study will be discussed to illustrate the benefits of having a policy and a systematic framework in place to support a critical care environment in disclosing errors and adverse events to affected patients and their families.