Kamaleswaran, Rishikesan

OBJECTIVE: A definition of refractory septic shock is necessary to guide diagnosis, management, prognostication, research, and future guidelines for this most severe form of the disease. We sought to achieve consensus on clinical criteria that would be used to define refractory septic shock. DESIGN: Review of literature, expert panel position statements, and Delphi rounds with an international expert group. SETTING: Consensus was defined as having at least 75% of panellists in agreement or disagreement on the three highest or lowest levels of a 7-point Likert scale or based on responses to single- or multiple-choice questions, respectively. SUBJECTS: A panel of multinational, multiprofessional and multidisciplinary critical care experts assembled by the Society of Critical Care Medicine and the European Society of Intensive Care Medicine (57 invitations and 56 participants). MEASUREMENTS AND MAIN RESULTS: A five-round Delphi process was conducted for consensus and stability. The steering committee proposed 34 statements, and five of them were rejected by panel experts after round 2. Among 29 statements selected from eight domains, consensus was reached for 13. The panel agreed on the need for a comprehensive consensus set of clinical criteria for refractory septic shock. Markers of organ dysfunction (75%, 2 rounds), tissue perfusion (91.1%, 2 rounds) including lactate (94.6%, 2 rounds) and capillary refill time (76.8%, 2 rounds), assessment of fluid-responsiveness after initial resuscitation (92.9%, 5 rounds), and use of vasoactive drugs at norepinephrine equivalents greater than 0.5 µg/kg/min (75.0%, 3 rounds), were selected as clinical criteria of refractory septic shock. The use of critical care ultrasound (CCUS) (92.9%, 3 rounds) was the single diagnostic modality that reached a consensus-based agreement. CONCLUSIONS: A consensus for 13 criteria to frame the definition of refractory septic shock was reached. Refractory septic shock is characterised by persistently elevated lactate concentrations and or prolonged capillary refill time in patients with septic shock who are fluid unresponsive, require a norepinephrine base equivalent dose greater than 0.5 micrograms per kilogram per minute, and undergo CCUS assessment when mixed shock is suspected.

BACKGROUND: The implementation of data science concepts, skills, and tools in critical care research and practice faces multiple, complex barriers. METHODS: We developed an implementation science-based framework and method for measuring the adoption, implementation, and sustainment of data science concepts, skills, and tools in critical care—the Society of Critical Care Medicine (SCCM) Discovery Data Science Campaign (DSC) Implementation Research Logic Model (IRLM). Our IRLM specifies constructs for: 1) key determinants (i.e., barriers and facilitators) influencing the implementation of data science concepts, skills, and tools in critical care; 2) implementation strategies deployed by the SCCM Discovery DSC to address these determinants; 3) theorized mechanisms of action by which these strategies affect outcomes; and 4) upstream and downstream implementation outcomes influenced by implementation strategies. RESULTS AND CONCLUSIONS: We believe that our model can facilitate more rigorous measurement of theoretically grounded, empirically assessable factors driving implementation of data science concepts, skills, and tools in critical care.

OBJECTIVES: To develop the first core Critical Care Data Dictionary (C2D2) with common data elements (CDEs) to characterize critical illness and injuries. DESIGN: Group consensus process using modified Delphi approach. SETTING: Electronic surveys and in-person meetings. SUBJECTS: A multidisciplinary workgroup of clinicians and researchers with expertise in the care of the critically ill and injured. INTERVENTIONS: The Delphi process was divided into domain and CDE portions with each composed of two item generation rounds and one item reduction/refinement rounds. Two in-person meetings augmented this process to facilitate review and consideration of the domains and by panel members. The final set of domains and CDEs was then reviewed by the group to meet the competing criteria of utility and feasibility, resulting in the core dataset. MEASUREMENTS AND MAIN RESULTS: The 23-member Delphi panel was provided 1833 candidate variables for potential dataset inclusion. The final dataset includes 226 patient-level CDCs in nine domains, which include anthropometrics and demographics (8), chronic comorbid illnesses (18), advanced directives (1), ICU diagnoses (61), diagnostic tests (42), interventions (27), medications (38), objective assessments (26), and hospital course and outcomes (5). Upon final review, 91% of the panel endorsed the CDCs as meeting criteria for a minimum viable data dictionary. Data elements cross the lifespan of neonate through adult patients. CONCLUSIONS: The resulting C2D2 provides a foundation to facilitate rapid collection, analyses, and dissemination of information necessary for research, quality improvement, and clinical practice to optimize critical care outcomes. Further work is needed to validate the effectiveness of the dataset in a variety of critical care settings.

Abstract Background Easy identification of immunocompromised hosts (ICHs) would allow for stratification of culture results based on host type. Methods We utilized antimicrobial stewardship program (ASP) team notes written during handshake stewardship rounds in the pediatric intensive care unit (PICU) as the gold standard for host status; clinical notes from the primary team, medication orders during the encounter, problem list, and billing diagnoses documented prior to the ASP documentation were extracted to develop models that predict host status. We calculated performance for three models based on diagnoses/medications, with and without natural language processing from clinical notes. The susceptibility of pathogens causing bacteremia to commonly used empiric antibiotic regimens was then stratified by host status. Results We identified 844 antimicrobial episodes from 666 unique patients; 160 (18.9%) were identified as ICHs. We randomly selected 675 initiations (80%) for model training and 169 initiations (20%) for testing. A rule-based model using diagnoses and medications alone yielded a sensitivity of 0.87 (08.6–0.88), specificity of 0.93 (0.92–0.93), and positive predictive value (PPV) of 0.74 (0.73–0.75). Adding clinical notes into XGBoost model led to improved specificity of 0.98 (0.98–0.98) and PPV of 0.9 (0.88–0.91), but with decreased sensitivity 0.77 (0.76–0.79). There were 77 bacteremia episodes during the study period identified and a host-specific visualization was created. Conclusions An electronic health record–based phenotype based on notes, diagnoses, and medications identifies ICH in the PICU with high specificity.