Gajic, Ognjen

OBJECTIVES: Accurate identification of acute respiratory distress syndrome is essential for understanding its epidemiology, patterns of care, and outcomes. We aimed to design a computable phenotyping strategy to detect acute respiratory distress syndrome in electronic health records of critically ill patients. DESIGN: This is a retrospective cohort study. Using a near real-time copy of the electronic health record, we developed a computable phenotyping strategy to detect acute respiratory distress syndrome based on the Berlin definition. SETTING: Twenty multidisciplinary ICUs in Mayo Clinic Health System. SUBJECTS: The phenotyping strategy was applied to 196,487 consecutive admissions from year 2009 to 2019. INTERVENTIONS: The acute respiratory distress syndrome cohort generated by this novel strategy was compared with the acute respiratory distress syndrome cohort documented by clinicians during the same period. The sensitivity and specificity of the phenotyping strategy were calculated in randomly selected patient cohort (50 patients) using the results from manual medical record review as gold standard. MEASUREMENTS AND MAIN RESULTS: Among the patients who did not have acute respiratory distress syndrome documented, the computable phenotyping strategy identified 3,169 adult patients who met the Berlin definition, 676 patients (21.3%) were classified to have severe acute respiratory distress syndrome (Pao 2/Fio 2 ratio ≤ 100), 1,535 patients (48.4%) had moderate acute respiratory distress syndrome (100 < Pao 2/Fio 2 ratio ≤ 200), and 958 patients (30.2%) had mild acute respiratory distress syndrome (200 < Pao 2/Fio 2 ratio ≤ 300). The phenotyping strategy achieved a sensitivity of 94.4%, specificity of 96.9%, positive predictive value of 94.4%, and negative predictive value of 96.9% in a randomly selected patient cohort. The clinicians documented acute respiratory distress syndrome in 1,257 adult patients during the study period. The clinician documentation rate of acute respiratory distress syndrome was 28.4%. Compared with the clinicians’ documentation, the phenotyping strategy identified a cohort that had higher acuity and complexity of illness suggested by higher Sequential Organ Failure Assessment score (9 vs 7; p < 0.0001), higher Acute Physiology and Chronic Health Evaluation score (76 vs 63; p < 0.0001), higher rate of requiring invasive mechanical ventilation (99.1% vs 71.8%; p < 0.0001), higher ICU mortality (20.6% vs 16.8%; p < 0.0001), and longer ICU length of stay (5.1 vs 4.2 d; p < 0.0001). CONCLUSIONS: Our rule-based computable phenotyping strategy can accurately detect acute respiratory distress syndrome in critically ill patients in the setting of high clinical complexity. This strategy can be applied to enhance early recognition of acute respiratory distress syndrome and to facilitate best-care delivery and clinical research in acute respiratory distress syndrome.

Objectives: Our preliminary data and observational studies suggested an increasing “off label” use of oral midodrine as a vasopressor sparing agent in various groups of critically ill patients, including those with sepsis. We designed this clinical trial to evaluate the feasibility of use of midodrine hydrochloride in early sepsis to reduce the duration for IV vasopressors and decrease ICU and hospital length of stay. Design: Pilot, two-center, placebo-controlled, double blinded randomized clinical trial. Setting: Medical ICUs at Mayo Clinic Rochester and Cleveland Clinic Abu Dhabi were the study sites. Patients and Methods: Adult patients (≥ 18 yr old) were included within 24 hours of meeting the Sepsis-3 definition if the mean arterial pressure remained less than 70 mm Hg despite receiving timely antibiotics and initial IV fluid bolus of 30 cc/kg. Intervention: Three doses of 10 mg midodrine versus placebo were administered. Measurements and Main Results: Total 32 patients were randomized into midodrine (n = 17) and placebo groups (n = 15). There were no major differences in baseline variables between the groups except for higher baseline creatinine in the midodrine group (2.0 ± 0.9 mg/dL) versus placebo group (1.4 ± 0.6 mg /dL), p = 0.03. The median duration of IV vasopressor requirement was 14.5 ± 8.1 hours in midodrine group versus 18.8 ± 7.1 hours in the placebo group, p value equals to 0.19. Patients in the midodrine group needed 729 ± 963 norepinephrine equivalent compared with 983 ± 1,569 norepinephrine equivalent in the placebo group, p value equals to 0.59. ICU length of stay was 2.29 days (interquartile range, 1.65–3.9 d) in the midodrine group, compared with 2.45 days (interquartile range, 1.6–3.2 d) in the placebo group, p value equals to 0.36. No serious adverse events were observed in either group. Conclusions: Phase II clinical trial powered for clinical outcomes (duration of vasopressor use, need for central venous catheter, and ICU and hospital length of stay) is justified.

Background: Surviving critical illness often creates a lasting psychological impact, including depression, anxiety, and post-traumatic stress. Memories of frightening and delusional experiences are the largest potentially modifiable risk factor, but currently, there is no proven intervention to improve these inciting factors. Psychological support based on positive suggestion is a psychotherapeutic approach that can be provided even to patients in altered cognitive states and is therefore a viable psychotherapy intervention throughout the ICU stay. Traditional ICU care team members have limited time and training to provide such psychological support to patients. Doulas are trained supportive companions who have been effectively used to provide patient advocacy and emotional support in other clinical settings and may address this need. Our aim was to train and implement a psychological support based on positive suggestion program for the critically ill using doulas, and measure acceptance of this intervention through stakeholder feedback. Methods: Doula training included three objectives: an introduction to ICU practice structure and policies; education about fundamental aspects of critical care conditions and procedures; and didactic and hands-on learning experiences in effective use of psychological support based on positive suggestion in the critically ill. Doulas were evaluated at the end of their training and during subsequent clinical activities using competency-based assessments as well as through survey-based questions and interviews with key stakeholders. Results: The ICU doulas performed psychological support based on positive suggestion on 43 critically ill patients in the ICU setting. Stakeholder feedback from nurses, patients, and patient families was positive. The majority (28/32) of surveyed bedside ICU nurses reported that the doulas’ involvement was helpful for both patients and nurses alike. All interviewed family members offered positive comments about the ICU doula presence and of the 40 patients who recalled the intervention 37 found it comforting. Conclusions: Our program successfully trained two doulas to work effectively in the ICU setting performing patient-centered psychological support based on positive suggestion interventions. Their training improved their skill sets and was reported as beneficial by patients, families, and critical care nursing. This training program offers a proof of concept that could be applied in other medical centers, bringing doulas more commonly into the ICU practice to help humanize the experience for patients, families, and medical teams.

Objectives Diagnostic error and delay is a prevalent and impactful problem. This study was part of a mixed-methods approach to understand the organizational, clinician, and patient factors contributing to diagnostic error and delay among acutely ill patients within a health system, as well as recommendations for the development of tailored, targeted, feasible, and effective interventions. Methods We did a multisite qualitative study using focus group methodology to explore the perspectives of key clinician stakeholders. We used a conceptual framework that characterized diagnostic error and delay as occurring within 1 of 3 stages of the patient’s diagnostic journey—critical information gathering, synthesis of key information, and decision making and communication. We developed our moderator guide based on the sociotechnical frameworks previously described by Holden and Singh for understanding noncognitive factors that lead to diagnostic error and delay. Deidentified focus group transcripts were coded in triplicate and to consensus over a series of meetings. A final coded data set was then uploaded into NVivo software. The data were then analyzed to generate overarching themes and categories. Results We recruited a total of 64 participants across 4 sites from emergency departments, hospital floor, and intensive care unit settings into 11 focus groups. Clinicians perceive that diverse organizational, communication and coordination, individual clinician, and patient factors interact to impede the process of making timely and accurate diagnoses. Conclusions This study highlights the complex sociotechnical system within which individual clinicians operate and the contributions of systems, processes, and institutional factors to diagnostic error and delay.

Purpose: To describe the epidemiology, outcomes, and temporal trends of respiratory failure in the cardiac intensive care unit (CICU). Materials and Methods: Retrospective cohort analysis of 2,986 unique Mayo Clinic CICU patients from 2007 to 2018 with respiratory failure. Temporal trends were analyzed, along with hospital and 1-year mortality. Multivariable logistic regression was used to determine adjusted hospital mortality trends. Results: The prevalence of respiratory failure in the CICU increased from 15% to 38% during the study period ( P < 0.001 for trend). Among patients with respiratory failure, the utilization of invasive ventilation decreased and noninvasive ventilation modalities increased over time. Hospital mortality and 1-year mortality were 24% and 54%, respectively, with variation according to the type of respiratory support (highest among patients receiving invasive ventilation alone: 35% and 46%, respectively). Hospital mortality was highest among patients with concomitant cardiac arrest and/or shock (52% for patients with both). Hospital mortality decreased in the overall population from 35% to 25% ( P < 0.001 for trend), but was unchanged among patients receiving positive-pressure ventilation. Conclusions: The prevalence of respiratory failure in CICU more than doubled during the last decade. The use of noninvasive respiratory support increased, while overall mortality declined over time. Cardiac arrest and shock accounted for the majority of deaths. Further research is needed to optimize the outcomes of high-risk CICU patients with respiratory failure.

OBJECTIVES: To determine whether the “Checklist for Early Recognition and Treatment of Acute Illness and Injury” decision support tool during ICU admission and rounding is associated with improvements in nonadherence to evidence-based daily care processes and outcomes in variably resourced ICUs. DESIGN, SETTINGS, PATIENTS: This before-after study was performed in 34 ICUs (15 countries) from 2013 to 2017. Data were collected for 3 months before and 6 months after Checklist for Early Recognition and Treatment of Acute Illness and Injury implementation. INTERVENTIONS: Checklist for Early Recognition and Treatment of Acute Illness and Injury implementation using remote simulation training. MEASUREMENTS AND MAIN RESULTS: The coprimary outcomes, modified from the original protocol before data analysis, were nonadherence to 10 basic care processes and ICU and hospital length of stay. There were 1,447 patients in the preimplementation phase and 2,809 patients in the postimplementation phase. After adjusting for center effect, Checklist for Early Recognition and Treatment of Acute Illness and Injury implementation was associated with reduced nonadherence to care processes (adjusted incidence rate ratio [95% CI]): deep vein thrombosis prophylaxis (0.74 [0.68–0.81), peptic ulcer prophylaxis (0.46 [0.38–0.57]), spontaneous breathing trial (0.81 [0.76–0.86]), family conferences (0.86 [0.81–0.92]), and daily assessment for the need of central venous catheters (0.85 [0.81–0.90]), urinary catheters (0.84 [0.80–0.88]), antimicrobials (0.66 [0.62–0.71]), and sedation (0.62 [0.57–0.67]). Analyses adjusted for baseline characteristics showed associations of Checklist for Early Recognition and Treatment of Acute Illness and Injury implementation with decreased ICU length of stay (adjusted ratio of geometric means [95% CI]) 0.86 [0.80–0.92]), hospital length of stay (0.92 [0.85–0.97]), and hospital mortality (adjusted odds ratio [95% CI], 0.81 (0.69–0.95). CONCLUSIONS: A quality-improvement intervention with remote simulation training to implement a decision support tool was associated with decreased nonadherence to daily care processes, shorter length of stay, and decreased mortality.