Veno-venous extracorporeal membrane oxygenation in managing acute respiratory distress syndrome associated with hemolytic uremic syndrome and septic shock: a case report.

Veno-venous extracorporeal membrane oxygenation (VV-ECMO) is a rescue therapy for severe respiratory failure in which conventional mechanical ventilation therapy is unsuccessful. Hemolysis during VV-ECMO support arises from multiple factors associated with organ damage and poor outcomes. Therefore, close and prompt monitoring is needed. Hemolytic uremic syndrome (HUS) is characterized by hemolysis, acute renal failure, and thrombocytopenia. Hemolytic features of the disease may complicate VV-ECMO management. A 26-year-old man with a history of cerebral palsy underwent VV-ECMO for acute respiratory distress syndrome (ARDS) due to septic shock caused by bacterial translocation during treatment for HUS. He showed features of hemolysis, with elevated lactate dehydrogenase (LDH), fragmented red blood cells, and low haptoglobin levels. Plasma free hemoglobin was measured daily throughout the whole course of ECMO with levels higher than 10 mg/dL but not exceeding 50 mg/dL. The extracorporeal membrane oxygenation (ECMO) circuit pressures were carefully monitored to ensure the pump generated no excessive negative pressure. The patient was weaned off ECMO on the eleventh day. There have been several cases of VA-ECMO in patients with HUS; however, there is limited literature on VV-ECMO. As the days on VV-ECMO tend to be longer than those on VA-ECMO, features of hemolysis may complicate management. Although HUS did not directly influence the clinical course in the present case, features of hemolysis were continuously observed. This case highlighted the importance of standard ECMO monitoring, especially daily measurement of plasma free hemoglobin.

Correlation Between the Social Network Structure and Well-Being of Health Care Workers in Intensive Care Units: Prospective Observational Study.

BACKGROUND: Effective communication strategies are becoming increasingly important in intensive care units (ICUs) where patients at high risk are treated. Distributed leadership promotes effective communication among health care professionals (HCPs). Moreover, beyond facilitating patient care, it may improve well-being among HCPs by fostering teamwork. However, the impact of distributed leadership on the communication structure and well-being of HCPs remains unclear. OBJECTIVE: We performed a social network analysis (SNA) to assess the characteristics of each HCP in the network, identify the number of HCP connections, analyze 4 centralities that can measure an HCP's importance, and evaluate the impact of distributed leadership structure on the well-being and communication structure of the medical staff. METHODS: Wearable sensors were used to obtain face-to-face interaction data from the ICU medical staff at Mie University Hospital, Japan. Participants wore a badge on the front of their clothing during working hours to measure the total frequency of face-to-face interactions. We collected data about the well-being of medical staff using the Center for Epidemiological Studies-Depression (CES-D) questionnaire and measured 4 centralities using SNA analysis. A CES-D questionnaire was administered during the study to measure the well-being of the HCPs. RESULTS: Overall, 247 ICU workers participated in this clinical study for 4 weeks yearly in February 2016, 2017, and 2018. The distributed leadership structure was established within the ICU in 2017 and 2018. We compared these results with those of the traditional leadership structure used in 2016. Most face-to-face interactions in the ICU were among nurses or between nurses and other professionals. In 2016, overall, 10 nurses could perform leadership tasks, which significantly increased to 24 in 2017 (P=.046) and 20 in 2018 (P=.046). Considering the increased number of nurses who could perform leadership duties and the collaboration created within the organization, SNA in 2018 showed that the betweenness (P=.001), degree (P<.001), and closeness (P<.001) centralities significantly increased compared with those in 2016. However, the eigenvector centrality significantly decreased in 2018 compared with that in 2016 (P=.01). The CES-D scores in 2018 also significantly decreased compared with those in 2016 (P=.01). The betweenness (r=0.269; P=.02), degree (r=0.262; P=.03), and eigenvector (r=0.261; P=.03) centralities and CES-D scores were positively correlated in 2016, whereas the closeness centrality and CES-D scores were negatively correlated (r=-0.318; P=.01). In 2018, the degree (r=-0.280; P=.01) and eigenvector (r=-0.284; P=.01) centralities were negatively correlated with CES-D scores. CONCLUSIONS: Face-to-face interactions of HCPs in the ICU were measured using wearable sensors, and nurses were found to be centrally located. However, the introduction of distributed leadership created collaboration and informal leadership in the organization, altering the social network structure of HCPs and increasing organizational well-being. TRIAL REGISTRATION: University Hospital Medical Information Network (UMIN) UMIN000037046; https://center6.umin.ac.jp/cgi-open-bin/icdr_e/ctr_view.cgi?recptno=R000042211.

Severe hypocalcemia after denosumab treatment leading to refractory ventricular tachycardia and veno-arterial extracorporeal membrane oxygenation support: a case report.

BACKGROUND: Severe hypocalcemia may lead to life-threatening arrhythmias. Denosumab is an effective treatment for osteoporosis that allows long intervals between doses. However, there is a risk of hypocalcemia in some patients. Due to the long half-life of denosumab, emergency physicians caring for patients presenting with symptoms of hypocalcemia may not be aware of the medication, and adverse effects may last longer. CASE PRESENTATION: A 55-year-old woman with a history of systemic lupus erythematosus (SLE) and anxiety disorder called for an ambulance for symptoms of hyperventilation and muscle cramps. After evaluation at the local hospital, she developed pulseless ventricular tachycardia and was resuscitated by defibrillation by the hospital staff. After conversion to sinus rhythm, she was transported to a tertiary center. Upon arrival, pulseless ventricular tachycardia occurred again, and veno-arterial extracorporeal membrane oxygenation (ECMO) and intra-aortic balloon pumping (IABP) were implemented. Laboratory results showed severe hypocalcemia (corrected calcium level of 5.3 mg/dL) whereupon intravenous calcium supplementation was started. She had received the first dose of denosumab (60 mg) by subcutaneous injection 24 days prior to hospitalization. She was eventually weaned from ECMO and IABP support. CONCLUSION: Cardiac arrest due to hypocalcemia is relatively rare but can be fatal. In the present case, hyperventilation may have acutely exacerbated pre-existing hypocalcemia, leading to ventricular tachycardia. The patient had a slightly decreased serum calcium level prior to denosumab. Close monitoring may be preferable after the primary dose of denosumab in selected patients. Emergency physicians caring for patients who may be suffering from symptoms/signs of hypocalcemia must be mindful of medications that have long half-lives and affect electrolyte balance when treating fatal arrhythmia due to hypocalcemia.

Effect of an Internet-Delivered Cognitive Behavioral Therapy-Based Sleep Improvement App for Shift Workers at High Risk of Sleep Disorder: Single-Arm, Nonrandomized Trial.

BACKGROUND: Shift workers are at high risk of developing sleep disorders such as shift worker sleep disorder or chronic insomnia. Cognitive behavioral therapy (CBT) is the first-line treatment for insomnia, and emerging evidence shows that internet-based CBT is highly effective with additional features such as continuous tracking and personalization. However, there are limited studies on internet-based CBT for shift workers with sleep disorders. OBJECTIVE: This study aimed to evaluate the impact of a 4-week, physician-assisted, internet-delivered CBT program incorporating machine learning-based well-being prediction on the sleep duration of shift workers at high risk of sleep disorders. We evaluated these outcomes using an internet-delivered CBT app and fitness trackers in the intensive care unit. METHODS: A convenience sample of 61 shift workers (mean age 32.9, SD 8.3 years) from the intensive care unit or emergency department participated in the study. Eligible participants were on a 3-shift schedule and had a Pittsburgh Sleep Quality Index score ≥5. The study comprised a 1-week baseline period, followed by a 4-week intervention period. Before the study, the participants completed questionnaires regarding the subjective evaluation of sleep, burnout syndrome, and mental health. Participants were asked to wear a commercial fitness tracker to track their daily activities, heart rate, and sleep for 5 weeks. The internet-delivered CBT program included well-being prediction, activity and sleep chart, and sleep advice. A job-based multitask and multilabel convolutional neural network-based model was used for well-being prediction. Participant-specific sleep advice was provided by sleep physicians based on daily surveys and fitness tracker data. The primary end point of this study was sleep duration. For continuous measurements (sleep duration, steps, etc), the mean baseline and week-4 intervention data were compared. The 2-tailed paired t test or Wilcoxon signed rank test was performed depending on the distribution of the data. RESULTS: In the fourth week of intervention, the mean daily sleep duration for 7 days (6.06, SD 1.30 hours) showed a statistically significant increase compared with the baseline (5.54, SD 1.36 hours; P=.02). Subjective sleep quality, as measured by the Pittsburgh Sleep Quality Index, also showed statistically significant improvement from baseline (9.10) to after the intervention (7.84; P=.001). However, no significant improvement was found in the subjective well-being scores (all P>.05). Feature importance analysis for all 45 variables in the prediction model showed that sleep duration had the highest importance. CONCLUSIONS: The physician-assisted internet-delivered CBT program targeting shift workers with a high risk of sleep disorders showed a statistically significant increase in sleep duration as measured by wearable sensors along with subjective sleep quality. This study shows that sleep improvement programs using an app and wearable sensors are feasible and may play an important role in preventing shift work-related sleep disorders. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/24799.

Elevated Plasma Soluble PD-L1 Levels in Out-of-Hospital Cardiac Arrest Patients.

Background: A deregulated immune system has been implicated in the pathogenesis of post-cardiac arrest syndrome (PCAS). A soluble form of programmed cell death-1 (PD-1) ligand (sPD-L1) has been found at increased levels in cancer and sustained inflammation, thereby deregulating immune functions. Here, we aim to study the possible involvement of sPD-L1 in PCAS. Methods: Thirty out-of-hospital cardiac arrest (OHCA) patients consecutively admitted to the ER of Mie University Hospital were prospectively enrolled. Plasma concentrations of sPD-L1 were measured by an enzyme-linked immunosorbent assay in blood samples of all 30 OHCA patients obtained during cardiopulmonary resuscitation (CPR). In 13 patients who achieved return-of-spontaneous-circulation (ROSC), sPD-L1 levels were also measured daily in the ICU. Results: The plasma concentrations of sPD-L1 in OHCA were significantly increased; in fact, to levels as high as those observed in sepsis. sPD-L1 levels during CPR correlated with reduced peripheral lymphocyte counts and increased C-reactive protein levels. Of 13 ROSC patients, 7 cases survived in the ICU for more than 4 days. A longitudinal analysis of sPD-L1 levels in the 7 ROSC cases revealed that sPD-L1 levels occurred in parallel with organ failure. Conclusions: This study suggests that ischemia- reperfusion during CPR may aberrantly activate immune and endothelial cells to release sPD-L1 into circulation, which may play a role in the pathogenesis of immune exhaustion and organ failures associated with PCAS.

Sociometric wearable devices for studying human behavior in corporate and healthcare workplaces.

Wearable sensor technology enables objective data collection of direct human interactions. The authors review sociometric wearable devices (SWD) and their application in healthcare. Human interactions captured by wearable sensors have been shown to correlate with social constructs such as teamwork and productivity in the office. Application of SWD in the field of healthcare requires special considerations: validation studies have shown technological disadvantages in acute medical settings. Application of SWD in healthcare should be considered based on the strengths and weaknesses of the methodology. SWD can also play an important role in investigation of human interaction and epidemic spread. When study designs and methodologies are carefully considered, incorporation of SWD in healthcare research has promising potential for new insights.

Internet-Based Individualized Cognitive Behavioral Therapy for Shift Work Sleep Disorder Empowered by Well-Being Prediction: Protocol for a Pilot Study.

BACKGROUND: Shift work sleep disorders (SWSDs) are associated with the high turnover rates of nurses, and are considered a major medical safety issue. However, initial management can be hampered by insufficient awareness. In recent years, it has become possible to visualize, collect, and analyze the work-life balance of health care workers with irregular sleeping and working habits using wearable sensors that can continuously monitor biometric data under real-life settings. In addition, internet-based cognitive behavioral therapy for psychiatric disorders has been shown to be effective. Application of wearable sensors and machine learning may potentially enhance the beneficial effects of internet-based cognitive behavioral therapy. OBJECTIVE: In this study, we aim to develop and evaluate the effect of a new internet-based cognitive behavioral therapy for SWSD (iCBTS). This system includes current methods such as medical sleep advice, as well as machine learning well-being prediction to improve the sleep durations of shift workers and prevent declines in their well-being. METHODS: This study consists of two phases: (1) preliminary data collection and machine learning for well-being prediction; (2) intervention and evaluation of iCBTS for SWSD. Shift workers in the intensive care unit at Mie University Hospital will wear a wearable sensor that collects biometric data and answer daily questionnaires regarding their well-being. They will subsequently be provided with an iCBTS app for 4 weeks. Sleep and well-being measurements between baseline and the intervention period will be compared. RESULTS: Recruitment for phase 1 ended in October 2019. Recruitment for phase 2 has started in October 2020. Preliminary results are expected to be available by summer 2021. CONCLUSIONS: iCBTS empowered with well-being prediction is expected to improve the sleep durations of shift workers, thereby enhancing their overall well-being. Findings of this study will reveal the potential of this system for improving sleep disorders among shift workers. TRIAL REGISTRATION: UMIN Clinical Trials Registry UMIN000036122 (phase 1), UMIN000040547 (phase 2); https://tinyurl.com/dkfmmmje, https://upload.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000046284. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/24799.

How ICU Patient Severity Affects Communicative Interactions Between Healthcare Professionals: A Study Utilizing Wearable Sociometric Badges.

Numerous factors affecting the interactions between healthcare professionals in the workplace demand a comprehensive understanding if the quality of patient healthcare is to be improved. Our previous cross-sectional analysis showed that patient severity scores [i.e., Acute Physiology and Chronic Health Evaluation (APACHE) II] in the 24 h following admission positively correlated with the length of the face-to-face interactions among ICU healthcare professionals. The present study aims to address how the relationships between patient severity and interaction lengths can change over a period of time during both admission and treatment in the ICU. We retrospectively analyzed data prospectively collected between 19 February to 17 March 2016 from an open ICU in a University Hospital in Japan. We used wearable sensors to collect a spatiotemporal distribution dataset documenting the face-to-face interactions between ICU healthcare professionals, which involved 76 ICU staff members, each of whom worked for 160 h, on average, during the 4-week period of data collection. We studied the longitudinal relationships among these interactions, which occurred at the patient bedside, vis-à-vis the severity of the patient's condition [i.e., the Sequential Organ Failure Assessment (SOFA) score] assessed every 24 h. On Day 1, during which a total of 117 patients stayed in the ICU, we found statistically significant positive associations between the interaction lengths and their SOFA scores, as shown by the Spearman's correlation coefficient value (R) of 0.447 (p < 0.01). During the course of our observation from Day 1 to Day 10, the number of patients (N) who stayed in the ICU gradually decreased (N = 117, Day1; N = 10, Day 10), as they either were discharged or died. The statistically significant positive associations of the interaction lengths with the SOFA scores disappeared from Days 2 to 6, but re-emerged on Day 7 (R = 0.620, p < 0.05) and Day 8 (R = 0.625, p < 0.05), then disappearing again on Days 9 and 10. Whereas all 6 SOFA sub-scores correlated well with the interaction lengths on Day 1, only a few of the sub-scores (coagulation, cardiovascular, and central nervous system scores) did so; specifically, those on Days 7 and 8. The results suggest that patient severity may play an important role in affecting the interactions between ICU healthcare professionals in a time-related manner on ICU Day 1 and on Days 7/8.

Social Network Analysis of Intensive Care Unit Health Care Professionals Measured by Wearable Sociometric Badges: Longitudinal Observational Study.

BACKGROUND: Use of wearable sensor technology for studying human teamwork behavior is expected to generate a better understanding of the interprofessional interactions between health care professionals. OBJECTIVE: We used wearable sociometric sensor badges to study how intensive care unit (ICU) health care professionals interact and are socially connected. METHODS: We studied the face-to-face interaction data of 76 healthcare professionals in the ICU at Mie University Hospital collected over 4 weeks via wearable sensors. RESULTS: We detail the spatiotemporal distributions of staff members' inter- and intraprofessional active face-to-face interactions, thereby generating a comprehensive visualization of who met whom, when, where, and for how long in the ICU. Social network analysis of these active interactions, concomitant with centrality measurements, revealed that nurses constitute the core members of the network, while doctors remain in the periphery. CONCLUSIONS: Our social network analysis using the comprehensive ICU interaction data obtained by wearable sensors has revealed the leading roles played by nurses within the professional communication network.