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Beta-lactam antibiotics are widely used in the intensive care unit due to their favorable effectiveness and safety profiles. Beta-lactams given to patients with sepsis must be delivered as soon as possible after infection recognition (early), treat the suspected organism (appropriate), and be administered at a dose that eradicates the infection (adequate). Early and appropriate antibiotic delivery occurs in >90% of patients, but less than half of patients with sepsis achieve adequate antibiotic exposure. This project aimed to address this quality gap and improve beta-lactam adequacy using the Define, Measure, Analyze, Improve, and Control Lean Six Sigma quality improvement framework. A multidisciplinary steering committee was formed, which completed a stakeholder analysis to define the gap in practice. An Ishikawa cause and effect (Fishbone) diagram was used to identify the root causes and an impact/effort grid facilitated prioritization of interventions. An intervention that included bundled education with the use of therapeutic drug monitoring (TDM; i.e. drug-level testing) was projected to have the highest impact relative to the amount of effort and selected to address beta-lactam inadequacy in the critically ill. The education and TDM intervention were deployed through a Plan, Do, Study, Act cycle. In the 3 months after "go-live," 54 episodes of beta-lactam TDM occurred in 41 unique intensive care unit patients. The primary quality metric of beta-lactam adequacy was achieved in 94% of individuals after the intervention. Ninety-four percent of clinicians gauged the education provided as sufficient. The primary counterbalance of antimicrobial days of therapy, a core antimicrobial stewardship metric, was unchanged over time (favorable result; P  = .73). Application of the Define, Measure, Analyze, Improve, and Control Lean Six Sigma quality improvement framework effectively improved beta-lactam adequacy in critically ill patients. The approach taken in this quality improvement project is widely generalizable to other drugs, drug classes, or settings to increase the adequacy of drug exposure. [ABSTRACT FROM AUTHOR]

BackgroundTransfusion-related acute lung injury (TRALI) and transfusion-associated circulatory overload (TACO) are leading causes of transfusion-related mortality. Notably, poor syndrome recognition and underreporting likely result in an underestimate of their true attributable burden. We aimed to develop accurate electronic health record-based screening algorithms for improved detection of TRALI/transfused acute lung injury (ALI) and TACO.Study design and methodsThis was a retrospective observational study. The study cohort, identified from a previous National Institutes of Health-sponsored prospective investigation, included 223 transfused patients with TRALI, transfused ALI, TACO, or complication-free controls. Optimal case detection algorithms were identified using classification and regression tree (CART) analyses. Algorithm performance was evaluated with sensitivities, specificities, likelihood ratios, and overall misclassification rates.ResultsFor TRALI/transfused ALI detection, CART analysis achieved a sensitivity and specificity of 83.9% (95% confidence interval [CI], 74.4%-90.4%) and 89.7% (95% CI, 80.3%-95.2%), respectively. For TACO, the sensitivity and specificity were 86.5% (95% CI, 73.6%-94.0%) and 92.3% (95% CI, 83.4%-96.8%), respectively. Reduced PaO2 /FiO2 ratios and the acquisition of posttransfusion chest radiographs were the primary determinants of case versus control status for both syndromes. Of true-positive cases identified using the screening algorithms (TRALI/transfused ALI, n = 78; TACO, n = 45), only 11 (14.1%) and five (11.1%) were reported to the blood bank by physicians, respectively.ConclusionsElectronic screening algorithms have shown good sensitivity and specificity for identifying patients with TRALI/transfused ALI and TACO at our institution. This supports the notion that active electronic surveillance may improve case identification, thereby providing a more accurate understanding of TRALI/transfused ALI and TACO epidemiology.

BACKGROUND: Transfusion-associated circulatory overload is characterized by new respiratory distress and hydrostatic pulmonary edema within 6 hours after blood transfusion, but its risk factors and outcomes are poorly characterized. METHODS: Using a case control design, we enrolled 83 patients with severe transfusion-associated circulatory overload identified by active surveillance for hypoxemia and 163 transfused controls at the University of California, San Francisco (UCSF) and Mayo Clinic (Rochester, Minn) hospitals. Odds ratios (OR) and 95% confidence intervals (CI) were calculated using multivariable logistic regression, and survival and length of stay were analyzed using proportional hazard models. RESULTS: Transfusion-associated circulatory overload was associated with chronic renal failure (OR 27.0; 95% CI, 5.2-143), a past history of heart failure (OR 6.6; 95% CI, 2.1-21), hemorrhagic shock (OR 113; 95% CI, 14.1-903), number of blood products transfused (OR 1.11 per unit; 95% CI, 1.01-1.22), and fluid balance per hour (OR 9.4 per liter; 95% CI, 3.1-28). Patients with transfusion-associated circulatory overload had significantly increased in-hospital mortality (hazard ratio 3.20; 95% CI, 1.23-8.10) after controlling for Acute Physiology and Chronic Health Evaluation-II (APACHE-II) score, and longer hospital and intensive care unit lengths of stay. CONCLUSIONS: The risk of transfusion-associated circulatory overload increases with the number of blood products administered and a positive fluid balance, and in patients with pre-existing heart failure and chronic renal failure. These data, if replicated, could be used to construct predictive algorithms for transfusion-associated circulatory overload, and subsequent modifications of transfusion practice might prevent morbidity and mortality associated with this complication.

BackgroundTransfusion-associated circulatory overload is characterized by new respiratory distress and hydrostatic pulmonary edema within 6 hours after blood transfusion, but its risk factors and outcomes are poorly characterized.MethodsUsing a case control design, we enrolled 83 patients with severe transfusion-associated circulatory overload identified by active surveillance for hypoxemia and 163 transfused controls at the University of California, San Francisco (UCSF) and Mayo Clinic (Rochester, Minn) hospitals. Odds ratios (OR) and 95% confidence intervals (CI) were calculated using multivariable logistic regression, and survival and length of stay were analyzed using proportional hazard models.ResultsTransfusion-associated circulatory overload was associated with chronic renal failure (OR 27.0; 95% CI, 5.2-143), a past history of heart failure (OR 6.6; 95% CI, 2.1-21), hemorrhagic shock (OR 113; 95% CI, 14.1-903), number of blood products transfused (OR 1.11 per unit; 95% CI, 1.01-1.22), and fluid balance per hour (OR 9.4 per liter; 95% CI, 3.1-28). Patients with transfusion-associated circulatory overload had significantly increased in-hospital mortality (hazard ratio 3.20; 95% CI, 1.23-8.10) after controlling for Acute Physiology and Chronic Health Evaluation-II (APACHE-II) score, and longer hospital and intensive care unit lengths of stay.ConclusionsThe risk of transfusion-associated circulatory overload increases with the number of blood products administered and a positive fluid balance, and in patients with pre-existing heart failure and chronic renal failure. These data, if replicated, could be used to construct predictive algorithms for transfusion-associated circulatory overload, and subsequent modifications of transfusion practice might prevent morbidity and mortality associated with this complication.

Transfusion-related acute lung injury (TRALI) is the leading cause of transfusion-related mortality. To determine TRALI incidence by prospective, active surveillance and to identify risk factors by a case-control study, 2 academic medical centers enrolled 89 cases and 164 transfused controls. Recipient risk factors identified by multivariate analysis were higher IL-8 levels, liver surgery, chronic alcohol abuse, shock, higher peak airway pressure while being mechanically ventilated, current smoking, and positive fluid balance. Transfusion risk factors were receipt of plasma or whole blood from female donors (odds ratio = 4.5, 95% confidence interval [CI], 1.85-11.2, P = .001), volume of HLA class II antibody with normalized background ratio more than 27.5 (OR = 1.92/100 mL, 95% CI, 1.08-3.4, P = .03), and volume of anti-human neutrophil antigen positive by granulocyte immunofluoresence test (OR = 1.71/100 mL, 95% CI, 1.18-2.5, P = .004). Little or no risk was associated with older red blood cell units, noncognate or weak cognate class II antibody, or class I antibody. Reduced transfusion of plasma from female donors was concurrent with reduced TRALI incidence: 2.57 (95% CI, 1.72-3.86) in 2006 versus 0.81 (95% CI, 0.44-1.49) in 2009 per 10 000 transfused units (P = .002). The identified risk factors provide potential targets for reducing residual TRALI.

To investigate critical care delivery in intensive care units (ICUs), we propose a qualitative and quantitative coupling approach to developing an ICU digital twin model. The Systems Engineering Initiative for Patient Safety (SEIPS) 2.0 model was adapted to conceptualise the current ICU system. A hybrid simulation model was developed to characterise major care delivery processes as discrete-time events, feature patients, clinicians, and other artifacts as autonomous agents, and integrate them in the same simulation environment to capture their interactions under a variety of ICU production conditions. Electronic health record (EHR) data from a medical ICU of Mayo Clinic Rochester, Minnesota, were used to calibrate model parameters. Upon iterative refinement and validation, the model has the potential to be integrated with the hospital information system to simulate real-life events as a full-fledged digital twin of the system. It can be used as an in-silico testbed to investigate the real-time allocation of ICU resources such as medical equipment, flexible staffing, workflow change, and support decisions of patient admission, discharge, and transfer, for healthcare delivery innovation. The interdisciplinary nature of this framework demonstrates and promotes the partnership between healthcare and engineering communities to building a better delivery system. [ABSTRACT FROM AUTHOR]

BackgroundWe used a multicenter retrospective cohort study design to evaluate whether human leukocyte antigen (HLA) antibody donor screening would reduce the risk of transfusion-related acute lung injury (TRALI) or possible TRALI.Study design and methodsIn the Leukocyte Antibody Prevalence Study-II (LAPS-II), we evaluated pulmonary outcomes in recipients of 2596 plasma-rich blood components (transfusable plasma and plateletpheresis) sent to participating hospitals; half of the components were collected from anti-HLA-positive donors (study arm) and half from anti-HLA-negative donors (control arm) matched by sex, parity, and blood center. A staged medical record review process was used. Final recipient diagnosis was based on case review by a blinded expert panel of pulmonary or critical care physicians.ResultsTRALI incidence was 0.59% (seven cases) in study arm recipients versus 0.16% (two cases) in control arm recipients for an odds ratio (OR) of 3.6 (95% confidence interval [CI], 0.7-17.4; p = 0.10). For possible TRALI cases (nine study arm, eight control arm), the OR was 1.2 (95% CI, 0.4-3.0; p = 0.81), and for TRALI and possible TRALI aggregated together, it was 1.7 (95% CI, 0.7-3.7; p = 0.24). Transfusion-associated circulatory overload incidence was identical in the two arms (1.17 and 1.22%, respectively; OR, 1.0; p = 1.0).ConclusionsTRALI incidence in recipients of anti-HLA-positive components was relatively low for a lookback study (1 in 170) and was higher than in the control arm, but did not reach significance. Based on this trend, the data are consistent with the likelihood that TRALI risk is decreased by selecting high-volume plasma components for transfusion from donors at low risk of having HLA antibodies.

Objective: Given limited critical care resources and an aging population, early interventions to prevent critical illness are vital. In this work, we measured post-implementation outcomes after introducing a novel electronic scoring system (Elders Risk Assessment—ERA) and a risk-factor checklist, Checklist for Early Recognition and Treatment of Acute Illness (CERTAIN), to detect older patients at high risk of critical illness in a primary care setting. Methods: The study was conducted at a family medicine clinic in Kasson, MN. The ADAPT-ITT framework was used to modify the CERTAIN checklist for primary care during 2 co-design workshops involving interdisciplinary clinicians, held in April 2023. The ERA score and modified CERTAIN checklist were implemented between May and July 2023 and identify and assess all patients age ≥60 years at risk of critical illness during their primary care visits. Implementation outcomes were evaluated at the end of the study via an anonymous survey and EHR data extraction. Results: Fourteen clinicians participated in 2 co-design workshops. A total of 19 clinicians participated in a post-pilot survey. All survey items were rated on a 5-point Likert type scale. Mean acceptability of the ERA score and checklist was rated 3.35 (SD = 0.75) and 3.09 (SD = 0.64), respectively. Appropriateness had a mean rating of 3.38 (SD = 0.82) for the ERA score and 3.19 (SD = 0.59) for the checklist. Mean feasibility was rated 3.38(SD = 0.85) and 2.92 (SD = 0.76) for the ERA score and checklist, respectively. The adoption rate was 50% (19/38) among clinicians, but the reach was low at 17% (49/289) of eligible patients. Conclusions: This pilot study evaluated the implementation of an intervention that introduced the ERA score and CERTAIN checklist into a primary care practice. Results indicate moderate acceptability, appropriateness, and feasibility of the ERA score, and similar ratings for the checklist, with slightly lower feasibility. While checklist adoption was moderate, reach was limited, indicating inconsistent use. Recommendations: We plan to use the open-ended resurvey responses to further modify the CERTAIN-FM checklist and implementation process. The ADAPT-ITT framework is a useful model for adapting the checklist to meet the primary care clinician needs. [ABSTRACT FROM AUTHOR]