Forming a Partnership to Save Lives

What's in this Special Issue

Introduction: The process and outcomes of a novel Critical Care Outreach Team (CCOT) staffed with dedicated critical-care advanced practice providers (APPs) collaborating virtually with an intensivist responding to rapid response team (RRT) activations is described. Methods: Upon RRT activation, the CCOT APP responds to the bedside to assess the patient and acute clinical situation while the CCOT intensivist reviews the electronic medical record for situational awareness of the patient’s hospital course. The CCOT APP and Intensivist in conjunction with the primary team develop a clinical care plan to manage the acute event. The CCOT APP remains with the patient until stabilization or escalation to the intensive care unit (ICU). The CCOT intensivist helps facilitate communication with primary and ICU teams, ensuring timely ICU transfers. CCOT interventions include medications, diagnostics, facilitating consults, transport, escalation of care, transitioning to comfort care, timely follow-up, and multidisciplinary communication. Results: CCOT evaluated 1,862 patients from July 2021 to June 2022 in a large, academic adult hospital. The average time spent on activations was 52 minutes with an average of seven interventions/patient. The ICU transfer rate decreased (37.0% from 46.6%) with no change in mortality (9.0% to 9.8%). Stepdown transfer increased from 3.7% to 9.3%. RRT documentation increased to 100% from 74%. Conclusions: A CCOT with dedicated APPs collaborating with a virtual intensivist can stabilize acutely deteriorating patients by remaining at the bedside, providing advanced interventions, improving communication with bedside providers, and decreasing the need for ICU transfers without increasing mortality. Improved documentation provided improved communication and billing for the institution.

Evidence-Based Review and Discussion Points

Objective Little is understood about what contributes to perceived workload for those providing overnight coverage to hospitalized patients overnight, which limits the ability to modify these factors or to proactively identify appropriate staffing levels. The objective of this study is to understand the major contributors to perceived overnight cross-coverage workload. Methods Cross-covering advanced practice providers (APPs) in a large academic hospitalist group completed the National Aeronautics and Space Administration Task Load Index (NASA-TLX) at the end of each night shift. Other shift characteristics were collected, including patient load, assigned action items, watcher/unstable patients, newly admitted patients, number of units covered, total pages, peak pager density, rapid response team (RRT) activations, and intensive care unit (ICU) transfers. Results For 14 APP participants, who completed 271 post-shift surveys, the mean (SD) patient load was 49.9 (6.4) patients per night, and providers received a mean (SD) of 40.8 (13.7) total pages per shift. Mean (SD) NASA-TLX score was 35.1 (19.0). In multivariate modeling, total pages, action items, and any RRT or ICU transfer were associated with significant increases in the mean NASA-TLX score, with estimated effect sizes of 0.5, 0.8, and 14.3, respectively, per 1-unit increase in each shift characteristic. The greatest cumulative contributor to perceived workload was total number of pages, followed by the presence of any RRT activation/ICU transfer, with estimated effect sizes of 20.4 and 14.9, respectively. Conclusions Total number of pages was the greatest contributor to perceived workload. This study suggests that quality improvement initiatives designed to improve pager communication may considerably improve provider-perceived workload.

We thank Overdyk and Broens1 for their educational comments on our systematic review and meta-analysis. The prospective randomized controlled trial by Ochroch et al2 found that continuous pulse oximetry (CPOX) did not reduce intensive care unit (ICU) transfers on a postcardiothoracic surgery floor. However, in a subgroup analysis, they found that CPOX significantly decreased ICU transfers due to pulmonary complications. It is important to note that the study by Ochroch et al2 was underpowered to detect real subgroup effects, thereby increasing the risk of false-positive results. The other prospective controlled study by Taenzer et al3 included the cohort of elderly patients undergoing joint replacement surgeries in a patient surveillance system unit. The comparison unit included the cohort of patients from urology, gynecology, vascular surgery, and general surgery. They found that CPOX monitoring reduced the need for ICU transfers; however, no subgroup analysis was performed to detect whether ICU transfers were due to respiratory complications.3 Overdyk and Broens1 suggested that ICU transfers in the study by Taenzer et al3 are less likely to be skewed by hemodynamic causes. This hypothesis is not supported in the growing literature which shows that there is an increased incidence of major complications like pulmonary embolism, sepsis, nonischemic cardiac complications, and pneumonia after joint replacement surgeries.4 Thus, ICU transfers were not exclusively due to pulmonary causes. Having extracted data from the prospective controlled studies,2,3,5,6 we decided that it was not appropriate to pool results from trials comparing outcomes that are dissimilar. In other words, it would be inappropriate to pool the data on pulmonary causes of ICU transfer from Ochroch et al2 and data on all-cause ICU transfer from Taenzer et al.3 Overdyk and Broens1 suggested combining the 2 data sets from Taenzer et al3 and Ochroch et al.2 This may increase the risk of bias, raising questions on the validity of the results. To avoid bias in our meta-analysis, we pooled the data on all-cause ICU transfer from both studies.2,3 Based on these findings, we concluded that all-cause ICU transfer had a risk reduction of 34% with CPOX when compared to standard monitoring. Due to lack of data in the literature, our meta-analysis failed to differentiate ICU transfers with respect to pulmonary versus other causes. We agree with Overdyk and Broens1 that pulse oximetry is not a measure of circulatory insufficiency. Pulse oximetry device is designed to measure specific physiologic outcome (oxygen saturation), and as such, it measures respiratory adverse events better than cardiac adverse events.7 Our systematic review makes it clear that there are not enough outcome studies using oxygen saturation to evaluate its utility for improving patient outcomes with respect to pulmonary causes of adverse events. Mahesh Nagappa, MDDepartment of Anesthesia and Perioperative MedicineLondon Health Sciences Centre and St. Joseph Health CareWestern UniversityLondon, Ontario, Canada Thach Lam, MDFrances Chung, MBBSDepartment of Anesthesiology and Pain MedicineToronto Western HospitalUniversity Health NetworkUniversity of TorontoToronto, Ontario, Canada[email protected]

The impact of a liaison nurse on ICU nurses' perceptions of discharge planning

Exploring Entrustment: Housestaff Autonomy and Patient Readmission

The 100,000 Lives Campaign: A Scientific and Policy Review

The predictive properties of four definitions of a frailty risk score (FRS) constructed using combinations of nursing flowsheet data, laboratory tests, and ICD-10 codes were examined for time to first intensive care unit (ICU) transfer in medical-surgical inpatients ≥50 years of age. Cox regression modeled time to first ICU transfer and Schemper-Henderson explained variance summarized predictive accuracy of FRS combinations. Modeling by age group and controlling for sex, all FRS measures significantly predicted time to first ICU transfer. Further multivariable modeling controlling for clinical characteristics substantially improved predictive accuracy. The effect of frailty on time to first ICU transfer depended on age, with highest risk in 50 to <60 years and ≥80 years age groups. Frailty prevalence ranged from 25.1% to 56.4%. Findings indicate that FRS-based frailty is a risk factor for time to first ICU transfer and should be considered in assessment and care-planning to address frailty in high-risk patients.Frailty prevalence was highest med-surg pts 60 to <70 years (56%); highest risk for time to first ICU transfer was in younger (50 to <60 years) and older (≥80 years) groups.

A subset of critically ill patients have end-of-life (EOL) goals that are unclear. Rapid response teams (RRTs) may aid in the identification of these patients and the delivery of their EOL care. To characterize the impact of RRT discussion on EOL care, and to examine how a preprinted order (PPO) set for EOL care influenced EOL discussions and outcomes. A single-centre retrospective chart review of all RRT calls (January 2009 to December 2010) was performed. The effect of RRT EOL discussions and the effect of a hospital-wide PPO set on EOL care was examined. Charts were from the Ontario Ministry of Health and Long-Term Care Critical Care Information Systemic database, and were interrogated by two reviewers. In patients whose EOL status changed following RRT EOL discussion, there were fewer intensive care unit (ICU) transfers (8.4% versus 17%; P<0.001), decreased ICU length of stay (5.8 days versus 20 days; P=0.08), increased palliative care consultations (34% versus 5.3%; P<0.001) and an increased proportion who died within 24 h of consultation (25% versus 8.3%; P<0.001). More patients experienced a change in EOL status following the introduction of an EOL PPO, from 20% (before) to 31% (after) (P<0.05). A change in EOL status following RRT-led EOL discussion was associated with reduced ICU transfers and enhanced access to palliative care services. Further study is required to identify and deconstruct barriers impairing timely and appropriate EOL discussions.

Continuous and wireless vital sign (VS) monitoring on hospital wards is superior to intermittent VS monitoring at detecting VS abnormalities; however, the impact on clinical outcomes remains to be confirmed. A recent propensity-matched study of primary surgical patients found decreased odds of intensive care unit (ICU) admission and mortality in patients receiving continuous monitoring. Primary surgical patients are inherently different from their medical counterparts who typically have high morbidity, including frailty. Continuous monitoring research has been limited in primary medical patients. This study aims to evaluate the clinical outcomes of primary medical patients who received either continuous or, as a contemporaneous control, intermittent vital monitoring as the standard of care using propensity matching. Propensity-matched analysis of a population-based sample of 7971 patients admitted to the medical wards between January 2018 and December 2019 at a single, tertiary United States medical center. The continuous monitoring device measures oxygen saturation, heart rate, respiratory rate, continuous noninvasive blood pressure, and either 3-lead or 5-lead electrocardiogram. Patients received either 12 hours or more of continuous and wireless VS monitoring (n=1450) or intermittent VS monitoring (n=6521). The primary outcome was the odds of a composite of in-hospital mortality or ICU transfer during hospitalization. Secondary outcomes were the odds of individual components of the primary outcome, as well as heart failure (HF), myocardial infarction (MI), acute kidney injury (AKI), and rapid response team (RRT) activations. Those who received intermittent VS monitoring had greater odds of a composite of in-hospital mortality or ICU admission (odds ratio [OR] 2.79, 95% CI 1.89-4.25; P<.001) compared with those who had continuous and wireless VS monitoring. The odds of HF (OR 1.03, 95% CI 0.83-1.28; P=.77), MI (OR 1.58, 95% CI 0.77-3.47; P=.23), AKI (OR 0.74, 95% CI 0.62-1.02; P=.06), and RRT activation (OR 0.94, 95% CI 0.75-1.19; P=.62) were similar in both groups. In this propensity-matched study, medical ward patients who received standard of care intermittent VS monitoring were at nearly 3 times greater odds of transfer to the ICU or death compared with those who received continuous VS monitoring.Our study was primarily limited by the inability to match patients on admission diagnosis due to limitations in electronic health record data. Other limitations included the number of and reasons for false alarms, which can be challenging with continuous monitoring strategies. Given the limitations of this work, these observations need to be confirmed with prospective interventional trials.

Objective To explore the status quo of pain assessment by the nurses in intensive care units (ICU) of Class Ⅲ hospitals, to analyze the factors influencing the implementation of pain assessment by ICU nurses, and to provide evidence for improving the practice of pain assessment by ICU nurses. Methods Totally 410 ICU nurses from 11 ClassⅢ hospitals in Hu'nan, Guangdong, Beijing and Hebei were selected by convenient sampling and investigated with the self-designed pain assessment questionnaire between April and July 2017. SPSS 21.0 was used to conduct the statistical analysis of the data. Results The implementation rate of pain assessment by ICU nurses was 77.6%. The application of subjective pain assessment tools was consistent mainly with the Numeric Rating Scale (NRS) (74.2%) , while objective assessment tools were less used, including the Critical Care Pain Observation Tool (CPOT) (40.6%) and Behavioral Pain Scale (BPS) (29.9%) . The standard rate of the use of pain assessment tools in ICU was 81.8%. The type of ICU and the frequency of continuing education in pain among ICU nurses were the factors that affected pain assessment by ICU nurses. Conclusions ICU nurses have not standardized their pain assessment, applied less objective pain assessment tools, and lacked norms for pain assessment in ICU. It is suggested that hospitals and departments should formulate relevant pain management rules and regulations, strengthen the continuous professional education for ICU nurses, and standardize and promote the clinical pain assessment of ICU nurses so as to improve the quality of pain care in ICU. Key words: Nurses; Intensive care unit; Pain; Assessment; Status quo; Influencing factors

One of the most important conclusions of a research study conducted by the Human Sciences Research Council in the late 1980s was that junior intensive care unit (ICU) nurses bear a heavy burden as a result of staff shortages in the ICU and do not receive sufficient support from nursing service managers. ICU nurses regularly present with work-related problems such as lack of motivation and frustration due to inadequate equipment, and staff turnover is high. Are ICU nurses unhappy because they do not receive enough support? The question arises, 'How do nurses experience the support they receive in the ICU?' The purpose of this study was to describe and explore the support received by ICU nurses in private hospitals in Gauteng. To answer the research question, a qualitative, descriptive, explorative and contextual research design was followed. The target population was qualified ICU nurses working in private hospitals in Gauteng. A purposeful convenience sampling was done. The sample consisted of 6 nurses working in five different ICUs in different private hospitals in the Gauteng area. The research question put forward to the group was 'Tell me about support in the ICU'. Trustworthiness was ensured and the data obtained from the interview were divided into four categories, namely stress and conflict, debriefing, interpersonal skills and communication, and demotivation. From these categories, guidelines were developed to help support ICU nurses better.

High-Intensity Care for Nursing Home Residents with Severe Dementia Hospitalized at the End of Life: A Mixed Methods Study

Predicting clinical deterioration in the hospital: The impact of outcome selection