Initial Clinical Impressions of the Critical Care of COVID-19 Patients in Seattle, New York City, and Chicago.

Since the first recognition of a cluster of novel respiratory viral infections in China in late December 2019, intensivists in the United States have watched with growing concern as infections with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus-now named coronavirus disease of 2019 (COVID-19)-have spread to hospitals in the United States. Because COVID-19 is extremely transmissible and can progress to a severe form of respiratory failure, the potential to overwhelm available critical care resources is high and critical care management of COVID-19 patients has been thrust into the spotlight. COVID-19 arrived in the United States in January and, as anticipated, has dramatically increased the usage of critical care resources. Three of the hardest-hit cities have been Seattle, New York City, and Chicago with a combined total of over 14,000 cases as of March 23, 2020.In this special article, we describe initial clinical impressions of critical care of COVID-19 in these areas, with attention to clinical presentation, laboratory values, organ system effects, treatment strategies, and resource management. We highlight clinical observations that align with or differ from already published reports. These impressions represent only the early empiric experience of the authors and are not intended to serve as recommendations or guidelines for practice, but rather as a starting point for intensivists preparing to address COVID-19 when it arrives in their community.

Machine learning applied to multi-sensor information to reduce false alarm rate in the ICU.

Studies reveal that the false alarm rate (FAR) demonstrated by intensive care unit (ICU) vital signs monitors ranges from 0.72 to 0.99. We applied machine learning (ML) to ICU multi-sensor information to imitate a medical specialist in diagnosing patient condition. We hypothesized that applying this data-driven approach to medical monitors will help reduce the FAR even when data from sensors are missing. An expert-based rules algorithm identified and tagged in our dataset seven clinical alarm scenarios. We compared a random forest (RF) ML model trained using the tagged data, where parameters (e.g., heart rate or blood pressure) were (deliberately) removed, in detecting ICU signals with the full expert-based rules (FER), our ground truth, and partial expert-based rules (PER), missing these parameters. When all alarm scenarios were examined, RF and FER were almost identical. However, in the absence of one to three parameters, RF maintained its values of the Youden index (0.94-0.97) and positive predictive value (PPV) (0.98-0.99), whereas PER lost its value (0.54-0.8 and 0.76-0.88, respectively). While the FAR for PER with missing parameters was 0.17-0.39, it was only 0.01-0.02 for RF. When scenarios were examined separately, RF showed clear superiority in almost all combinations of scenarios and numbers of missing parameters. When sensor data are missing, specialist performance worsens with the number of missing parameters, whereas the RF model attains high accuracy and low FAR due to its ability to fuse information from available sensors, compensating for missing parameters.

Transport of Critically Ill Patients by the Anesthesia Versus the Intensive Care Unit Service: A Before-After Study of Operating Room Workflows.

BACKGROUND: We implemented a new policy at our institution where the responsibility for intensive care unit (ICU) patient transports to the operating room (OR) was changed from the anesthesia to the ICU service. We hypothesized that this approach would be associated with increased on-time starts and decreased turnover times. METHODS: In the historical model, intubated patients or those on mechanical circulatory assistance (MCA) were transported by the anesthesia service to the OR ("pre-ICU Pickup"). In our new model, these patients are transported by the ICU service to the preoperative holding area (Pre-op) where care is transferred to the anesthesia service ("post-ICU Transfer"). If judged necessary by the ICU or anesthesia attending, the patient was transported by the anesthesia service ("post-ICU Pickup"). We retrospectively reviewed case tracking data for patients undergoing surgery before (January 2014 to May 2015) and after implementation (July 2016 to June 2017) of the new policy. The primary outcome was the proportion of elective, weekday first-case, on-time starts. To adjust for confounders including comorbidities and time trends, we performed a segmented logistic regression analysis assessing the effect of our intervention on the primary outcome. Secondary outcomes were turnover times and compliance with preoperative checklist documentation. RESULTS: We identified 95 first-start and 86 turnover cases in the pre-ICU Pickup, 70 first-start and 88 turnover cases in the post-ICU Transfer, and 6 turnover cases in the post-ICU Pickup group. Ignoring time trends, the crude proportion of on-time starts increased from 32.6% in the pre-ICU Pickup to 77.1% in the post-ICU Transfer group. After segmented logistic regression adjusting for age, sex, American Society of Anesthesiologists (ASA) physical status, Sequential Organ Failure Assessment (SOFA) score, respiratory failure, endotracheal intubation, MCA, congestive heart failure (CHF), valvular heart disease, and cardiogenic and hemorrhagic shock, the post-ICU Transfer group was more likely to have an on-time start at the start of the intervention than the pre-ICU Pickup group at the end of the preintervention period (odds ratio, 11.1; 95% confidence interval [CI], 1.3-125.7; P = .043). After segmented linear regression adjusting for the above confounders, the estimated difference in mean turnover times between the post-ICU Pickup and pre-ICU Transfer group was not significant (-6.9 minutes; 95% CI, -17.09 to 3.27; P = .17). In post-ICU Transfer patients, consent, history and physical examination (H&P), and site marking were verified before leaving the ICU in 92.9%, 93.2%, and 89.2% of the cases, respectively. No adverse events were reported during the study period. CONCLUSIONS: A transition from the anesthesia to the ICU service for transporting ICU patients to the OR did not change turnover times but resulted in more on-time starts and high compliance with preoperative checklist documentation.

Technological Distractions (Part 2): A Summary of Approaches to Manage Clinical Alarms With Intent to Reduce Alarm Fatigue.

OBJECTIVE: Alarm fatigue is a widely recognized safety and quality problem where exposure to high rates of clinical alarms results in desensitization leading to dismissal of or slowed response to alarms. Nonactionable alarms are thought to be especially problematic. Despite these concerns, the number of clinical alarm signals has been increasing as an everincreasing number of medical technologies are added to the clinical care environment. DATA SOURCES: PubMed, SCOPUS, Embase, and CINAHL. STUDY SELECTION: We performed a systematic review of the literature focused on clinical alarms. We asked a primary key question; "what interventions have been attempted and resulted in the success of reducing alarm fatigue?" and 3-secondary key questions; "what are the negative effects on patients/families; what are the balancing outcomes (unintended consequences of interventions); and what human factor approaches apply to making an effective alarm?" DATA EXTRACTION: Articles relevant to the Key Questions were selected through an iterative review process and relevant data was extracted using a standardized tool. DATA SYNTHESIS: We found 62 articles that had relevant and usable data for at least one key question. We found that no study used/developed a clear definition of "alarm fatigue." For our primary key question 1, the relevant studies focused on three main areas: quality improvement/bundled activities; intervention comparisons; and analysis of algorithm-based false and total alarm suppression. All sought to reduce the number of total alarms and/or false alarms to improve the positive predictive value. Most studies were successful to varying degrees. None measured alarm fatigue directly. CONCLUSIONS: There is no agreed upon valid metric(s) for alarm fatigue, and the current methods are mostly indirect. Assuming that reducing the number of alarms and/or improving positive predictive value can reduce alarm fatigue, there are promising avenues to address patient safety and quality problem. Further investment is warranted not only in interventions that may reduce alarm fatigue but also in defining how to best measure it.

Cardiac Arrest in the Operating Room: Part 2-Special Situations in the Perioperative Period.

As noted in part 1 of this series, periprocedural cardiac arrest (PPCA) can differ greatly in etiology and treatment from what is described by the American Heart Association advanced cardiac life support algorithms, which were largely developed for use in out-of-hospital cardiac arrest and in-hospital cardiac arrest outside of the perioperative space. Specifically, there are several life-threatening causes of PPCA of which the management should be within the skill set of all anesthesiologists. However, previous research has demonstrated that continued review and training in the management of these scenarios is greatly needed and is also associated with improved delivery of care and outcomes during PPCA. There is a growing body of literature describing the incidence, causes, treatment, and outcomes of common causes of PPCA (eg, malignant hyperthermia, massive trauma, and local anesthetic systemic toxicity) and the need for a better awareness of these topics within the anesthesiology community at large. As noted in part 1 of this series, these events are always witnessed by a member of the perioperative team, frequently anticipated, and involve rescuer-providers with knowledge of the patient and the procedure they are undergoing or have had. Formulation of an appropriate differential diagnosis and rapid application of targeted interventions are critical for good patient outcome. Resuscitation algorithms that include the evaluation and management of common causes leading to cardiac in the perioperative setting are presented. Practicing anesthesiologists need a working knowledge of these algorithms to maximize good outcomes.

Cardiac Arrest in the Operating Room: Resuscitation and Management for the Anesthesiologist: Part 1.

Cardiac arrest in the operating room and procedural areas has a different spectrum of causes (ie, hypovolemia, gas embolism, and hyperkalemia), and rapid and appropriate evaluation and management of these causes require modification of traditional cardiac arrest algorithms. There is a small but growing body of literature describing the incidence, causes, treatments, and outcomes of circulatory crisis and perioperative cardiac arrest. These events are almost always witnessed, frequently known, and involve rescuer providers with knowledge of the patient and their procedure. In this setting, there can be formulation of a differential diagnosis and a directed intervention that treats the likely underlying cause(s) of the crisis while concurrently managing the crisis itself. Management of cardiac arrest of the perioperative patient is predicated on expert opinion, physiologic rationale, and an understanding of the context in which these events occur. Resuscitation algorithms should consider the evaluation and management of these causes of crisis in the perioperative setting.

Technologic Distractions (Part 1): Summary of Approaches to Manage Alert Quantity With Intent to Reduce Alert Fatigue and Suggestions for Alert Fatigue Metrics.

OBJECTIVE: To provide ICU clinicians with evidence-based guidance on tested interventions that reduce or prevent alert fatigue within clinical decision support systems. DESIGN: Systematic review of PubMed, Embase, SCOPUS, and CINAHL for relevant literature from 1966 to February 2017. PATIENTS: Focus on critically ill patients and included evaluations in other patient care settings, as well. INTERVENTIONS: Identified interventions designed to reduce or prevent alert fatigue within clinical decision support systems. MEASUREMENTS AND MAIN RESULTS: Study selection was based on one primary key question to identify effective interventions that attempted to reduce alert fatigue and three secondary key questions that covered the negative effects of alert fatigue, potential unintended consequences of efforts to reduce alert fatigue, and ideal alert quantity. Data were abstracted by two reviewers independently using a standardized abstraction tool. Surveys, meeting abstracts, "gray" literature, studies not available in English, and studies with non-original data were excluded. For the primary key question, articles were excluded if they did not provide a comparator as key question 1 was designed as a problem, intervention, comparison, and outcome question. We anticipated that reduction in alert fatigue, including the concept of desensitization may not be directly measured and thus considered interventions that reduced alert quantity as a surrogate marker for alert fatigue. Twenty-six articles met the inclusion criteria. CONCLUSION: Approaches for managing alert fatigue in the ICU are provided as a result of reviewing tested interventions that reduced alert quantity with the anticipated effect of reducing fatigue. Suggested alert management strategies include prioritizing alerts, developing sophisticated alerts, customizing commercially available alerts, and including end user opinion in alert selection. Alert fatigue itself is studied less frequently, as an outcome, and there is a need for more precise evaluation. Standardized metrics for alert fatigue is needed to advance the field. Suggestions for standardized metrics are provided in this document.

Anesthesia for Patients with Concomitant Hepatic and Pulmonary Dysfunction.

Hepatic function and pulmonary function are interrelated with failure of one organ system affecting the other. With improved therapies, patients with concomitant hepatic and pulmonary failure increasingly enjoy a good quality of life and life expectancy. Therefore, the prevalence of such patients is increasing with more presenting for both emergent and elective surgical procedures. Hypoxemia requires a thorough evaluation in patients with end-stage liver disease. The most common etiologies respond to appropriate therapy. Portopulmonary hypertension and hepatopulmonary syndrome are associated with increased perioperative morbidity and mortality. It is incumbent on the anesthesiologist to understand the physiology of liver failure and its early effect on pulmonary function to ensure a successful outcome.

Concurrence of Intraoperative Hypotension, Low Minimum Alveolar Concentration, and Low Bispectral Index Is Associated with Postoperative Death.

BACKGROUND: An intraoperative concurrence of mean arterial pressure less than 75 mmHg, minimum alveolar concentration less than 0.8, and bispectral index less than 45 has been termed a "triple low" state. An association between triple low and postoperative mortality has been reported but was not replicated in a subsequent study. The authors pooled existing data from clinical trials to further evaluate the purported association in an observational study. METHODS: This retrospective observational study included 13,198 patients from three clinical trials: B-Unaware, BAG-RECALL, and Michigan Awareness Control Study. Patients with greater than 15 not necessarily consecutive minutes of triple low were propensity matched to controls with similar characteristics and comorbidities. A multivariable Cox proportional hazards model was used to evaluate the association between triple low duration and postoperative mortality. RESULTS: Thirty-day mortality was 0.8% overall, 1.9% in the triple low cohort, and 0.4% in the nontriple low cohort (odds ratio, 5.16; 95% CI, 4.21 to 6.34). After matching and adjusting for comorbidities, cumulative duration of triple low was significantly associated with an increased risk of mortality at 30 days (hazard ratio, 1.09; 95% CI, 1.07 to 1.11, per 15 min) and 90 days (hazard ratio, 1.09; 95% CI, 1.08 to 1.11, per 15 min). CONCLUSION: There is a weak independent association between the triple low state and postoperative mortality, and the propensity-matched analysis does not suggest that this is an epiphenomenon.

The incidence and risk factors for perioperative cardiac arrest observed in the national anesthesia clinical outcomes registry.

BACKGROUND: Cardiac arrest is a rare but important event in the operating room and postanesthesia care unit, when surgical patients are most intensively monitored. Several recent publications have reported the rate of cardiac arrest in surgical patients during the subsequent hospital stay but have not uniquely identified the immediate perioperative period. We hypothesized that cardiac arrest during this time (intraprocedure and postanesthesia care) would occur at a lower frequency than that described for inpatient hospital care in the available literature. METHODS: We extracted data from all cardiac arrests and immediate perioperative deaths reported to the National Anesthesia Clinical Outcomes Registry for the period from 2010 to 2013 and analyzed for anesthesia-related risk factors. We compared these data to published rates of in-hospital cardiac arrest after surgery. RESULTS: Overall, the risk of cardiac arrest was 5.6 per 10,000 cases, which is less than in previous reports of in-hospital arrests in surgical patients overall, with an associated mortality from the arrest of 58.4%. The rate of cardiac arrest increased with age and ASA physical status. The rate of cardiac arrest was significantly higher for males, as was the mortality. CONCLUSIONS: The National Anesthesia Clinical Outcomes Registry is an emerging resource for examination of perioperative and anesthesia-related outcomes. Cardiac arrest is less frequent in the periprocedural setting than later in the hospital course, with most arrests predictably occurring in patients with ASA physical status III-V. The finding of increased risk of mortality in male patients cannot be readily explained and should prompt future research attention.

Validity and reliability assessment of detailed scoring checklists for use during perioperative emergency simulation training.

INTRODUCTION: Few valid and reliable grading checklists have been published for the evaluation of performance during simulated high-stakes perioperative event management. As such, the purposes of this study were to construct valid scoring checklists for a variety of perioperative emergencies and to determine the reliability of scores produced by these checklists during continuous video review. METHODS: A group of anesthesiologists, intensivists, and educators created a set of simulation grading checklists for the assessment of the following scenarios: severe anaphylaxis, cerebrovascular accident, hyperkalemic arrest, malignant hyperthermia, and acute coronary syndrome. Checklist items were coded as critical or noncritical. Nonexpert raters evaluated 10 simulation videos in a random order, with each video being graded 4 times. A group of faculty experts also graded the videos to create a reference standard to which nonexpert ratings were compared. P < 0.05 was considered significant. RESULTS: Team leaders in the simulation videos were scored by the expert panel as having performed 56.5% of all items on the checklist (range, 43.8%-84.0%), and 67.2% of the critical items (range, 30.0%-100%). Nonexpert raters agreed with the expert assessment 89.6% of the time (95% confidence interval, 87.2%-91.6%). No learning curve development was found with repetitive video assessment or checklist use. The κ values comparing nonexpert rater assessments to the reference standard averaged 0.76 (95% confidence interval, 0.71-0.81). CONCLUSIONS: The findings indicate that the grading checklists described are valid, are reliable, and could be used in perioperative crisis management assessment.

Expect the unexpected: clinical trials are key to understanding post-intensive care syndrome.

Long-term follow-up of randomized prospective trials of treatments in the intensive care unit may allow us to attain some understanding of the causes of post-intensive care syndrome. This in turn may allow us to produce better long-term outcomes among survivors of critical illness.