Li, Heyi

Abstract ObjectivePrevious studies suggested that hypernatremia or hyperosmolarity may have protective effects in lung injury. We hypothesized that hypernatremia and/or hyperosmolarity would prevent ARDS in those at risk of lung injury and associated with favorable outcomes in ARDS patients.SubjectsAll admissions at medical, surgical, and multidisciplinary intensive care units in Mayo Clinic, Rochester from the year of 2009 to 2019.DesignRetrospective cohort study. The occurrence of ARDS was identified using a validated computerized search strategy. The association between serum sodium/osmolarity and the occurrence of ARDS was analyzed using a multivariable logistic regression model. The relationship between serum sodium/osmolarity and outcomes of ARDS was analyzed using linear and logistic regression models.ResultsAmong 50,498 patients, the serum sodium level on admission did not have a significant association with the occurrence of ARDS, with an adjusted odds ratio of 0.95 [95% CI (0.86, 1.05)]. There was no significant association between calculated serum osmolarity and the occurrence of ARDS, with an adjusted odds ratio of 1.03 [95% CI (1.00, 1.07)]. 1560 patients developed ARDS during the ICU stay. Their serum sodium level did not have a significant association with their outcomes [adjusted estimated difference in 28-day ICU-free days, 0.08 days, 95% CI (-0.85, 1.02), adjusted estimated difference in 28-day ventilator-free days, -0.02, 95% CI (-0.62, 0.58), adjusted odds ratio of 28-day mortality 1.02, 95%CI (0.84, 1.24)]. There was no significant association between their calculated serum osmolarity and their outcomes [adjusted estimated difference in 28-day ICU-free days, -0.26, 95% CI, (-0.63, 0.12); adjusted estimated difference in 28-day ventilator-free days, -0.05, 95% CI (-0.28, 0.19); adjusted odds ratio of 28-day mortality, 1.07, 95% CI (0.99, 1.15)].ConclusionsAdmission serum sodium or serum osmolarity were not associated with the occurrence or outcomes of ARDS in ICU.

BACKGROUND: Several markers of oxygenation are used as prognostic markers in acute hypoxemic respiratory failure. Real-world use is limited by the need for invasive measurements and unreliable availability in the electronic health record. A pragmatic, reliable, and accurate marker of acute hypoxemic respiratory failure is needed to facilitate epidemiologic studies, clinical trials, and shared decision-making with patients. F IO 2 is easily obtained at the bedside and from the electronic health record. The F IO 2 trajectory may be a valuable marker of recovery in patients with acute hypoxemic respiratory failure. METHODS: This was a historical cohort study of adult subjects admitted to an ICU with acute hypoxemic respiratory failure secondary to community-acquired pneumonia and/or ARDS. RESULTS: Our study included 2,670 subjects. F IO 2 and S pO 2 were consistently more available than was P aO 2 in the electronic health record: (F IO 2 vs S pO 2 vs P aO 2 : 100 vs 100 vs 72.8% on day 1, and 100 vs 99 vs 21% on day 5). A worsening F IO 2 trajectory was associated with reduced ventilator-free days. From days 2 to 5, every increase in F IO 2 by 10% from the previous day was associated with fewer ventilator-free days (on day 2: adjusted mean –1.25 [95% CI –1.45 to –1.05] d, P < .001). The S pO 2 /F IO 2 trajectory also provided prognostic information. On days 3 – 5, an increase in S pO 2 /F IO 2 from the previous day was associated with increased ventilator-free days (on day 3: adjusted mean 2.09 (95% CI 1.44–2.74) d; P < .001). S pO 2 /F IO 2 models did not add predictive information compared with models with F IO 2 alone (on day 2: adjusted F IO 2 vs S pO 2 /F IO 2 R2 0.122 vs 0.119; and on day 3: 0.153 vs 0.163). CONCLUSIONS: F IO 2 and S pO 2 /F IO 2 are pragmatic and readily available intermediate prognostic markers in acute hypoxic respiratory failure. The F IO 2 trajectory in the first 5 d of ICU admission provided important prognostic information (ventilator-free days). Although the S pO 2 /F IO 2 trajectory was also associated with ventilator-free days, it did not provide more information than the F IO 2 trajectory alone.

Abstract Background CERTAIN (Checklist for Early Recognition and Treatment of Acute Illness and iNjury) has been shown to improve critical care process and patient outcomes in international ICUs with variable resources. Methods CERTAIN education program derived from this approach is designed, promoted, and implemented following the Logic model. Through the roadmap of the Logic model, we presented a dynamic, longitudinal implementation framework that had sufficient rigor yet offers flexibility to reach the need of the existing and emerging diversified medical education projects. Results Using the Logic model, the delivery of the CERTAIN education program is optimized to deliver relevant education content in various environments. During the COVID-19 outbreak, the implementational framework demonstrated that it could serve as an excellent template for effective response to global pandemics. Conclusions The Logical model is useful as a facilitation tool for planning and evaluating innovative education delivery and dissemination. The CERTAIN program provided an example for other continuous professional education projects.

Coronavirus disease 2019 (COVID-19) is characterized by a clinical spectrum of diseases ranging from asymptomatic or mild cases to severe pneumonia with acute respiratory distress syndrome (ARDS) requiring mechanical ventilation. Extracorporeal membrane oxygenation (ECMO) has been used as rescue therapy in appropriate patients with COVID-19 complicated by ARDS refractory to mechanical ventilation. In this study, we review the indications, challenges, complications, and clinical outcomes of ECMO utilization in critically ill patients with COVID-19-related ARDS. Most of these patients required venovenous ECMO. Although the risk of mortality and complications is very high among patients with COVID-19 requiring ECMO, it is similar to that of non-COVID-19 patients with ARDS requiring ECMO. ECMO is a resource-intensive therapy, with an inherent risk of complications, which makes its availability limited and its use challenging in the midst of a pandemic. Well-maintained data registries, with timely reporting of outcomes and evidence-based clinical guidelines, are necessary for the careful allocation of resources and for the development of standardized utilization protocols.

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.

ABSTRACT Introduction: The impact of albumin resuscitation on sepsis outcomes is debated, particularly in the initial phase of resuscitation. We aimed to investigate the association between albumin use in the initial 6 h of resuscitation and subsequent outcomes in adult septic patients. Methods: This single-center, retrospective, propensity score-matched cohort study included adult patients admitted to intensive care units (ICUs) with sepsis or septic shock from January 1, 2006, to May 4, 2018, at a tertiary referral hospital. We compared two groups based on albumin receipt within the initial six resuscitation hours (albumin group vs. non-albumin group). We performed a 1:2 propensity score matching to assess shock-free time in ICU as the primary outcome. Results: Of 2,732 patients with medical sepsis, 286 cases in the albumin group were matched with 549 individuals in the non-albumin group. Compared to the non-albumin group, the albumin group required more intravenous fluids and had higher net fluid balance, lower mean arterial pressure, and lower serum base excess level in the initial 6 and 24 h of resuscitation. Shock-free time, ICU and hospital length of stay, and 28-day mortality were not different between albumin and non-albumin groups (56 vs. 66 h, P = 0.18; 3.5 days vs. 3.7 days, P = 0.61; 9.1 days vs. 9.5 days, P = 0.27; 36% vs. 32%, P = 0.25, respectively). Conclusions: Using albumin during the initial 6 h of resuscitation was not associated with benefits in clinical outcomes of patients with medical sepsis.

Objectives: To develop and verify a digital twin model of critically ill patient using the causal artificial intelligence approach to predict the response to specific treatment during the first 24 hours of sepsis. Design: Directed acyclic graphs were used to define explicitly the causal relationship among organ systems and specific treatments used. A hybrid approach of agent-based modeling, discrete-event simulation, and Bayesian network was used to simulate treatment effect across multiple stages and interactions of major organ systems (cardiovascular, neurologic, renal, respiratory, gastrointestinal, inflammatory, and hematology). Organ systems were visualized using relevant clinical markers. The application was iteratively revised and debugged by clinical experts and engineers. Agreement statistics was used to test the performance of the model by comparing the observed patient response versus the expected response (primary and secondary) predicted by digital twin. Setting: Medical ICU of a large quaternary- care academic medical center in the United States. Patients or Subjects: Adult (> 18 year yr old), medical ICU patients were included in the study. Interventions: No additional interventions were made beyond the standard of care for this study. Measurements and Main Results: During the verification phase, model performance was prospectively tested on 145 observations in a convenience sample of 29 patients. Median age was 60 years (54–66 d) with a median Sequential Organ Failure Assessment score of 9.5 (interquartile range, 5.0–14.0). The most common source of sepsis was pneumonia, followed by hepatobiliary. The observations were made during the first 24 hours of the ICU admission with one-step interventions, comparing the output in the digital twin with the real patient response. The agreement between the observed versus and the expected response ranged from fair (kappa coefficient of 0.41) for primary response to good (kappa coefficient of 0.65) for secondary response to the intervention. The most common error detected was coding error in 50 observations (35%), followed by expert rule error in 29 observations (20%) and timing error in seven observations (5%). Conclusions: We confirmed the feasibility of development and prospective testing of causal artificial intelligence model to predict the response to treatment in early stages of critical illness. The availability of qualitative and quantitative data and a relatively short turnaround time makes the ICU an ideal environment for development and testing of digital twin patient models. An accurate digital twin model will allow the effect of an intervention to be tested in a virtual environment prior to use on real patients.