Farmer, Joseph C.

Objectives: In the United States, training for physicians who manage critically ill adult patients (intensivists) evolved through parallel subspecialty critical care medicine (CCM) pathways with significant commonality. The Society of Critical Care Medicine Adult Critical Care Physician Core Knowledge and Skills Task Force aimed to delineate the common core knowledge and skills required of all intensivists. Design: A master list of content areas and procedural skills was compiled from all CCM subspecialty program requirements and blueprints of the certification examinations. Using a modified Delphi approach, participants were asked to categorize the knowledge items as “advanced knowledge is essential,” “general, but not advanced, knowledge is essential,” or “knowledge is not essential.” Procedures were categorized as “intensivist performs routinely,” “intensivist only performs in an emergency,” or “intensivist knows” about the procedure. Setting: Representatives from CCM stakeholder organizations, including accreditation and certification organizations, critical care societies, and program directors’ societies, were invited to participate. SUBJECTS: Members of the Adult Critical Care Physician Core Knowledge and Skills Task Force of the Society of Critical Care Medicine. Interventions: For the first two rounds of the modified Delphi process, Research Electronic Data Capture was used. For the third and fourth rounds, the process was completed through online meetings with Zoom (Zoom Video Corporations, San Jose, CA) utilizing Zoom’s polling feature. Measurements and Main Results: A total of 541 items were determined to be essential, with 145 requiring advanced knowledge and 323 requiring general knowledge. For 73 items, consensus regarding advanced vs. general could not be achieved, but they remained essential. Only eight items were felt to be nonessential. Of the 16 procedures, most were categorized as “intensivist performs.” Conclusions: The large number of items included in the list of essential knowledge and skills demonstrates the complexity of modern CCM. Utilization of a common framework across the subspecialties of CCM could lead to greater harmonization among the fellowship program requirements and certification examinations.

The COVID-19 pandemic caused tremendous disruption to the U.S. healthcare system and nearly crippled some hospitals during large patient surges. Limited ICU beds across the country further exacerbated these challenges. Telemedicine, specifically tele-critical care (TCC), can expand a hospital’s clinical capabilities through remote expertise and increase capacity by offloading some monitoring to remote teams. Unfortunately, the rapid deployment of telemedicine, especially TCC, is constrained by multiple barriers. In the summer of 2020, to support the National Emergency Tele-Critical Care Network (NETCCN) deployment, more than 50 national leaders in applying telemedicine technologies to critical care assembled to provide their opinions about barriers to NETCCN implementation and strategies to overcome them. Through consensus, these experts developed white papers that formed the basis of this article. Herein, the authors share their experience and propose multiple solutions to barriers presented by laws, local policies and cultures, and individual perspectives according to a minimum, better, best paradigm for TCC delivery in the setting of a national disaster. Cross-state licensure and local privileging of virtual experts were identified as the most significant barriers to rapid deployment of services, whereas refining the model of TCC to achieve the best outcomes and defining the best financial model is the most significant for long-term success. Ultimately, we conclude that a rapidly deployable national telemedicine response system is achievable.

Objectives: To deploy machine learning tools (random forests) to develop a model that reliably predicts hospital mortality in children with acute infections residing in low- and middle-income countries, using age and other variables collected at hospital admission. Design: Post hoc analysis of a single-center, prospective, before-and-after feasibility trial. Setting: Rural district hospital in Rwanda, a low-income country in Sub-Sahara Africa. Patients: Infants and children greater than 28 days and less than 18 years of life hospitalized because of an acute infection. Interventions: None. Measurements and Main Results: Age, vital signs (heart rate, respiratory rate, and temperature) capillary refill time, altered mental state collected at hospital admission, as well as survival status at hospital discharge were extracted from the trial database. This information was collected for 1,579 adult and pediatric patients admitted to a regional referral hospital with an acute infection in rural Rwanda. Nine-hundred forty-nine children were included in this analysis. We predicted survival in study subjects using random forests, a machine learning algorithm. Five prediction models, all including age plus two to five other variables, were tested. Three distinct optimization criteria of the algorithm were then compared. The in-hospital mortality was 1.5% (n = 14). All five models could predict in-hospital mortality with an area under the receiver operating characteristic curve ranging between 0.69 and 0.8. The model including age, respiratory rate, capillary refill time, altered mental state exhibited the highest predictive value area under the receiver operating characteristic curve 0.8 (95% CI, 0.78–0.8) with the lowest possible number of variables. Conclusions: A machine learning-based algorithm could reliably predict hospital mortality in a Sub-Sahara African population of 949 children with an acute infection using easily collected information at admission which includes age, respiratory rate, capillary refill time, and altered mental state. Future studies need to evaluate and strengthen this algorithm in larger pediatric populations, both in high- and low-/middle-income countries.

Objectives: To assess—by literature review and expert consensus—workforce, workload, and burnout considerations among intensivists and advanced practice providers. Design: Data were synthesized from monthly expert consensus and literature review. Setting: Workforce and Workload section workgroup of the Academic Leaders in Critical Care Medicine Task Force. Measurements and Main Results: Multidisciplinary care teams led by intensivists are an essential component of critical care delivery. Advanced practice providers (nurse practitioners and physician assistants) are progressively being integrated into ICU practice models. The ever-increasing number of patients with complex, life-threatening diseases, concentration of ICU beds in few centralized hospitals, expansion of specialty ICU services, and desire for 24/7 availability have contributed to growing intensivist staffing concerns. Such staffing challenges may negatively impact practitioner wellness, team perception of care quality, time available for teaching, and length of stay when the patient to intensivist ratio is greater than or equal to 15. Enhanced team communication and reduction of practice variation are important factors for improved patient outcomes. A diverse workforce adds value and enrichment to the overall work environment. Formal succession planning for ICU leaders is crucial to the success of critical care organizations. Implementation of a continuous 24/7 ICU coverage care model in high-acuity, high-volume centers should be based on patient-centered outcomes. High levels of burnout syndrome are common among intensivists. Prospective analyses of interventions to decrease burnout within the ICU setting are limited. However, organizational interventions are felt to be more effective than those directed at individuals. Conclusions: Critical care workforce and staffing models are myriad and based on several factors including local culture and resources, ICU organization, and strategies to reduce burden on the ICU provider workforce. Prospective studies to assess and avoid the burnout syndrome among intensivists and advanced practice providers are needed.

Objective: To evaluate whether a focused education program and implementation of a treatment bundle increases the rate of early evidence-based interventions in patients with acute infections. Design: Single-center, prospective, before-and-after feasibility trial. Setting: Emergency department of a sub-Saharan African district hospital. Patients: Patients > 28 days of life admitted to the study hospital for an acute infection. Interventions: The trial had three phases (each of four months). Interventions took place during the second (educational program followed by implementation of the treatment bundle) and third (provision of resources to implement treatment bundle) phases. Measurements and Main Results: Demographic, clinical, and laboratory data were collected at study enrollment; 24, 48, and 72 hours after hospital admission; and at discharge. A total of 1,594 patients were enrolled (pre-intervention, n = 661; intervention I, n = 531; intervention II, n = 402). The rate of early evidence-based interventions per patient during Intervention Phase I was greater than during the pre-intervention phase (74 ± 17 vs. 79 ± 15%, p < 0.001). No difference was detected when data were compared between Intervention Phases I and II (79 ± 15 vs. 80 ± 15%, p = 0.58). No differences in the incidence of blood transfusion (pre-intervention, 6%; intervention I, 7%; intervention II, 7%) or severe adverse events in the first 24 hours (allergic reactions: pre-intervention, 0.2%; intervention I, 0%; intervention II, 0%; respiratory failure: pre-intervention, 2%; intervention I, 2%; intervention II, 2%; acute renal failure: pre-intervention, 2%; intervention I, 2%; intervention II, 1%) were observed. Conclusions: Our results indicate that a focused education program and implementation of an infection treatment bundle in clinical practice increased the rate of early evidence-based interventions in patients with acute infections (mostly malaria) admitted to a sub-Saharan African district hospital. Provision of material resources did not further increase this rate. While no safety issues were detected, this could be related to the very low disease severity of the enrolled patient population (www.clinicaltrials.gov: NCT02697513).