Tools for standardizing patient care can take many forms, including but not limited to, bundles, quality improvement and performance measures, guidelines, and protocols. Each is intended to improve compliance with interventions believed to be supported by the best available evidence, ensuring consistency of management across all patients with the ultimate goal of improving outcomes. However, in the ICU, patients typically present with complex acute illnesses and accompanying comorbidities, requiring careful tailoring of interventions and treatments for each individual patient. The rapidly changing nature of the underlying conditions also demands continuous reassessment and modification of each patient’s management on a frequent and sometimes moment-by-moment basis. Disrupting this individualized treatment approach by imposing prescriptive, overly restrictive, “one-size-fits-all” standardized treatments in the critical care setting may prevent the clinician from meeting individual patients’ needs and decrease care quality (1). This problem is compounded if the standardization tools adopted are not only inflexible but also have a poorly supported or entirely absent scientific basis. Importantly, identifiable patient subcategories often exist that fit poorly into the populations for which many interventions were developed and tested. Of equal concern, critical care trainees may become dependent on a standardized/cookbook approach to care and fail to recognize and learn how treatments must be tailored for the unique needs of each critically ill patient. Rather than rigidly standardizing critical care, approaches that recognize this complexity and are both scientifically sound and responsive to patient differences should be readily available to critical care clinicians without replacing sensible clinical judgment. Such strategies that acknowledge the limitations of available evidence hold more hope of improving, rather than inadvertently worsening, the outcome.

Background

Despite decades of research on predictors of extubation success, use of ventilatory support after extubation is common and 10-20% of patients require re-intubation. Proportional assist ventilation (PAV) mode automatically calculates estimated total work of breathing (total WOB). Here, we assessed the performance of total WOB to predict extubation failure in invasively ventilated subjects.

Methods

This prospective observational study was conducted in 6 adult ICUs at an academic medical center. We enrolled intubated subjects who successfully completed a spontaneous breathing trial, had a rapid shallow breathing index < 105 breaths/min/L, and were deemed ready for extubation by the primary team. Total WOB values were recorded at the end of a 30-min PAV trial. Extubation failure was defined as any respiratory support and/or re-intubation within 72 h of extubation. We compared total WOB scores between groups and performance of total WOB for predicting extubation failure with receiver operating characteristic curves.

Results

Of 61 subjects enrolled, 9.8% (n = 6) required re-intubation, and 50.8% (n = 31) required any respiratory support within 72 h of extubation. Median total WOB at 30 min on PAV was 0.9 J/L (interquartile range 0.7-1.3 J/L). Total WOB was significantly different between subjects who failed or were successfully extubated (median 1.1 J/L vs 0.7 J/L, P = .004). The area under the curve was 0.71 [95% CI 0.58-0.85] for predicting any requirement of respiratory support and 0.85 [95% CI 0.69-1.00] for predicting re-intubation alone within 72 h of extubation. Total WOB cutoff values maximizing sensitivity and specificity equally were 1.0 J/L for any respiratory support (positive predictive value [PPV] 70.0%, negative predictive value [NPV] 67.7%) and 1.3 J/L for re-intubation (PPV 26.3%, NPV 97.6%).

Conclusions

The discriminative performance of a PAV-derived total WOB value to predict extubation failure was good, indicating total WOB may represent an adjunctive tool for assessing extubation readiness. However, these results should be interpreted as preliminary, with specific thresholds of PAV-derived total WOB requiring further investigation in a large multi-center study.

Future healthcare systems will rely heavily on clinical decision support systems (CDSS) to improve the decision-making processes of clinicians. To explore the design of future CDSS, we developed a research-focused CDSS for the management of patients in the intensive care unit that leverages Internet of Things (IoT) devices capable of collecting streaming physiologic data from ventilators and other medical devices. We then created machine learning (ML) models that could analyze the collected physiologic data to determine if the ventilator was delivering potentially harmful therapy and if a deadly respiratory condition, acute respiratory distress syndrome (ARDS), was present. We also present work to aggregate these models into a mobile application that can provide responsive, real-time alerts of changes in ventilation to providers. As illustrated in the recent COVID-19 pandemic, being able to accurately predict ARDS in newly infected patients can assist in prioritizing care. We show that CDSS may be used to analyze physiologic data for clinical event recognition and automated diagnosis, and we also highlight future research avenues for hospital CDSS.

BACKGROUND: In experimental canine septic shock, depressed circulating granulocyte counts were associated with a poor outcome and increasing counts with prophylactic granulocyte colony-stimulating factor (G-CSF) improved outcome. Therapeutic G-CSF, in contrast, did not improve circulating counts or outcome, and therefore investigation was undertaken to determine whether transfusing granulocytes therapeutically would improve outcome. STUDY DESIGN AND METHODS: Twenty-eight purpose-bred beagles underwent an intrabronchial Staphylococcus aureus challenge and 4 hours later were randomly assigned to granulocyte (40-100 × 109 cells) or plasma transfusion. RESULTS: Granulocyte transfusion significantly expanded the low circulating counts for hours compared to septic controls but was not associated with significant mortality benefit (1/14, 7% vs. 2/14, 14%, respectively; p = 0.29). Septic animals with higher granulocyte count at 4 hours (median [interquartile range] of 3.81 3.39-5.05] vs. 1.77 [1.25-2.50]) had significantly increased survival independent of whether they were transfused with granulocytes. In a subgroup analysis, animals with higher circulating granulocyte counts receiving donor granulocytes had worsened lung injury compared to septic controls. Conversely, donor granulocytes decreased lung injury in septic animals with lower counts. CONCLUSION: During bacterial pneumonia, circulating counts predict the outcome of transfusing granulocytes. With low but normal counts, transfusing granulocytes does not improve survival and injures the lung, whereas for animals with very low counts, but not absolute neutropenia, granulocyte transfusion improves lung function.

Dysfunction in the nitric oxide (NO) signaling pathway can lead to the development of pulmonary hypertension (PH) in mammals. Discovery of an alternative pathway to NO generation involving reduction from nitrate to nitrite and to NO has motivated the evaluation of nitrite as an alternative to inhaled NO for PH. In contrast, inhaled nitrate has not been evaluated to date, and potential benefits include a prolonged half-life and decreased risk of methemoglobinemia. In a canine model of acute hypoxia-induced PH we evaluated the effects of inhaled nitrate to reduce pulmonary arterial pressure (PAP). In a randomized controlled trial, inhaled nitrate was compared to inhaled nitrite and inhaled saline. Exhaled NO, PAP and systemic blood pressures were continuously monitored. Inhaled nitrite significantly decreased PAP and increased exhaled NO. In contrast, inhaled nitrate and inhaled saline did not decrease PAP or increase exhaled NO. Unexpectedly, we found that inhaled nitrite resulted in prolonged (>5 h) exhaled NO release, increase in nitrate venous/arterial levels and a late surge in venous nitrite levels. These findings do not support a therapeutic role for inhaled nitrate in PH but may have therapeutic implications for inhaled nitrite in various disease states.

To develop and characterize a machine learning algorithm to discriminate acute respiratory distress syndrome from other causes of respiratory failure using only ventilator waveform data.

Design

Retrospective, observational cohort study.

Setting

Academic medical center ICU.

Patients

Adults admitted to the ICU requiring invasive mechanical ventilation, including 50 patients with acute respiratory distress syndrome and 50 patients with primary indications for mechanical ventilation other than hypoxemic respiratory failure.

Interventions

None.

Measurements and main results

Pressure and flow time series data from mechanical ventilation during the first 24-hours after meeting acute respiratory distress syndrome criteria (or first 24-hr of mechanical ventilation for non-acute respiratory distress syndrome patients) were processed to extract nine physiologic features. A random forest machine learning algorithm was trained to discriminate between the patients with and without acute respiratory distress syndrome. Model performance was assessed using the area under the receiver operating characteristic curve, sensitivity, specificity, positive predictive value, and negative predictive value. Analyses examined performance when the model was trained using data from the first 24 hours and tested using withheld data from either the first 24 hours (24/24 model) or 6 hours (24/6 model). Area under the receiver operating characteristic curve, sensitivity, specificity, positive predictive value, and negative predictive value were 0.88, 0.90, 0.71, 0.77, and 0.90 (24/24); and 0.89, 0.90, 0.75, 0.83, and 0.83 (24/6).

Conclusions

Use of machine learning and physiologic information derived from raw ventilator waveform data may enable acute respiratory distress syndrome screening at early time points after intubation. This approach, combined with traditional diagnostic criteria, could improve timely acute respiratory distress syndrome recognition and enable automated clinical decision support, especially in settings with limited availability of conventional diagnostic tests and electronic health records.

Polyunsaturated fatty acids are metabolized into regulatory lipids important for initiating inflammatory responses in the event of disease or injury and for signaling the resolution of inflammation and return to homeostasis. The epoxides of linoleic acid (leukotoxins) regulate skin barrier function, perivascular and alveolar permeability and have been associated with poor outcomes in burn patients and in sepsis. It was later reported that blocking metabolism of leukotoxins into the vicinal diols ameliorated the deleterious effects of leukotoxins, suggesting that the leukotoxin diols are contributing to the toxicity. During quantitative profiling of fatty acid chemical mediators (eicosanoids) in COVID-19 patients, we found increases in the regioisomeric leukotoxin diols in plasma samples of hospitalized patients suffering from severe pulmonary involvement. In rodents these leukotoxin diols cause dramatic vascular permeability and are associated with acute adult respiratory like symptoms. Thus, pathways involved in the biosynthesis and degradation of these regulatory lipids should be investigated in larger biomarker studies to determine their significance in COVID-19 disease. In addition, incorporating diols in plasma multi-omics of patients could illuminate the COVID-19 pathological signature along with other lipid mediators and blood chemistry.

Background

During sepsis, higher plasma cell-free hemoglobin (CFH) levels portend worse outcomes. In sepsis models, plasma proteins that bind CFH improve survival. In our canine antibiotic-treated Staphylococcus aureus pneumonia model, with and without red blood cell (RBC) exchange transfusion, commercial human haptoglobin (Hp) concentrates bound and compartmentalized CFH intravascularly, increased CFH clearance, and lowered iron levels, improving shock, lung injury, and survival. We now investigate in our model how very high CFH levels and treatment time affect Hp's beneficial effects.

Materials and methods

Two separate canine pneumonia sepsis Hp studies were undertaken: one with exchange transfusion of RBCs after prolonged storage to raise CFH to very high levels and another with rapidly lethal sepsis alone to shorten time to treat. All animals received continuous standard intensive care unit supportive care for 96 hours.

Results

Older RBCs markedly elevated plasma CFH levels and, when combined with Hp therapy, created supraphysiologic CFH-Hp complexes that did not increase CFH or iron clearance or improve lung injury and survival. In a rapidly lethal bacterial challenge model without RBC transfusion, Hp binding did not increase clearance of complexes or iron or show benefits seen previously in the less lethal model.

Discussion

High-level CFH-Hp complexes may impair clearance mechanisms and eliminate Hp's beneficial effect during sepsis. Rapidly lethal sepsis narrows the therapeutic window for CFH and iron clearance, also decreasing Hp's beneficial effects. In designing clinical trials, dosing and kinetics may be critical factors if Hp infusion is used to treat sepsis.

AIM: To translate and culturally adapt the FRAIL scale into Spanish and perform a preliminary test of diagnostic accuracy in patients admitted to intensive care units. DESIGN: Cross-sectional diagnostic study. METHODS: Five intensive care units (ICU) in Spain were participated. Stage 1: Three native Spanish-speaking bilingual translators familiar with the field of critical care translated the scale from English into Spanish. Stage 2: Three native English-speaking bilingual translators familiar with critical care medicine. Stage 3: Authors of the original scale compared the English original and back-translated versions of the scale. Stage 4: Five nurses with more than 5 years of ICU experience and five critical care physicians assessed the comprehension and relevance of each of the items of the Spanish version in 30 patients of 3 different age ranges (<50, 50-65 and >65 years). RESULTS: The FRAIL scale was translated and adapted cross-culturally for patients admitted to intensive care units in Spain. The process consisted of four stages: translation, back translation, comparison and pilot test. There was good correspondence between the original scale and the Spanish version in 100% of the items. The participating patients assessed the relevance (content validity) and comprehensibility (face validity) of each of the items of the first Spanish version. The relevance of some of the items scored low when the scale was used in patients younger than 65 years. CONCLUSIONS: We have cross-culturally adapted the FRAIL scale, originally in English, to Spanish for its use in the critical care medical setting in Spanish-speaking countries. IMPLICATIONS FOR PROFESSIONALS: Physicians and nurses can apply the new scale to all patients admitted to the intensive care units. Nursing care can be adapted according to frailty, trying to reduce the side effects of admission to these units for the most fragile patients. REPORTING METHOD: The manuscripts authors have adhered to the EQUATOR guidelines, using the COSMIN reporting guideline for studies on the measurement properties of patient-reported outcome measures. PATIENT OR PUBLIC CONTRIBUTION: In a pilot clinical study, we applied the first version of the FRAIL-Spain scale to intensive care unit (ICU) patients. Five nurses with more than 5 years of ICU experience and five critical care physicians assessed the relevance (content validity) and comprehensibility (face validity) of the five items of the first Spanish version. Relevance was assessed using a 4-point Likert scale ranging from 1 (no relevance) to 4 (high relevance), and comprehensibility was assessed as poor, acceptable or good. Each health professional applied the scale to three patients (total number of patients = 30) of three different age ranges (<50, 50-65 and >65 years) and recorded the time of application of the scale to each patient. Although the frailty scales were initially created by geriatricians to be applied to the elders, there is little experience with their application in critically ill patients of any age. Therefore, more information is needed to determine the relevance of using this scale in critical care patients. In this pilot study, we considered that nurses and critical care physicians should evaluate frailty using this adapted scale in adult patients admitted to the Intensive Care Units.