Microbiome Profiling Demonstrates Concordance of Endotracheal Tube Aspirates With Direct Lower Airway Sampling in Intubated Patients.

BACKGROUND: Endotracheal aspirates (ETAs) are widely used for microbiologic studies of the respiratory tract in intubated patients. However, they involve sampling through an established endotracheal tube using suction catheters, both of which can acquire biofilms that may confound results. RESEARCH QUESTION: Does standard clinical ETA in intubated patients accurately reflect the authentic lower airway bacterial microbiome? STUDY DESIGN AND METHODS: Comprehensive quantitative bacterial profiling using 16S rRNA V1-V2 gene sequencing was applied to compare bacterial populations captured by standard clinical ETA vs contemporaneous gold standard samples acquired directly from the lower airways through a freshly placed sterile tracheostomy tube. The study included 13 patients undergoing percutaneous tracheostomy following prolonged (median, 15 days) intubation. Metrics of bacterial composition, diversity, and relative quantification were applied to samples. RESULTS: Pre-tracheostomy ETAs closely resembled the gold standard immediate post-tracheostomy airway microbiomes in bacterial composition and community features of diversity and quantification. Endotracheal tube and suction catheter biofilms also resembled cognate ETA and fresh tracheostomy communities. INTERPRETATION: Unbiased molecular profiling shows that standard clinical ETA sampling has good concordance with the authentic lower airway microbiome in intubated patients.

Well-Being Among Respiratory Therapists in an Academic Medical Center During the COVID-19 Pandemic.

BACKGROUND: Recent studies have revealed high rates of burnout among respiratory therapists (RTs), which has implications for patient care and outcomes as well as for the health care workforce. We sought to better understand RT well-being during the COVID-19 pandemic. The purpose of this study was to determine rates and identify determinants of well-being, including burnout and professional fulfillment, among RTs in ICUs. METHODS: We conducted a mixed-methods study comprised of a survey administered quarterly from July 2020-May 2021 to critical-care health care professionals and semi-structured interviews from April-May 2021 with 10 ICU RTs within a single health center. We performed multivariable analyses to compare RT well-being to other professional groups and to evaluate changes in well-being over time. We analyzed qualitative interview data using thematic analysis, followed by mapping themes to the Maslow needs hierarchy. RESULTS: One hundred eight RTs responded to at least one quarterly survey. Eighty-two (75%) experienced burnout; 39 (36%) experienced professional fulfillment, and 62 (58%) reported symptoms of depression. Compared to clinicians of other professions in multivariable analyses, RTs were significantly more likely to experience burnout (odds ratio 2.32 [95% CI 1.41-3.81]) and depression (odds ratio 2.73 [95% CI 1.65-4.51]) and less likely to experience fulfillment (odds ratio 0.51 [95% CI 0.31-0.85]). We found that staffing challenges, safety concerns, workplace conflict, and lack of work-life balance led to burnout. Patient care, use of specialized skills, appreciation and a sense of community at work, and purpose fostered professional fulfillment. Themes identified were mapped to Maslow's hierarchy of needs; met needs led to professional fulfillment, and unmet needs led to burnout. CONCLUSIONS: ICU RTs experienced burnout during the pandemic at rates higher than other professions. To address RT needs, institutions should design and implement strategies to reduce burnout across all levels.

Racial Disparities in Occult Hypoxemia and Clinically Based Mitigation Strategies to Apply in Advance of Technological Advancements.

BACKGROUND: Pulse oximetry is the mainstay of patient oxygen monitoring. Measurement error from pulse oximetry is more common for those with darker skin pigmentation, yet this topic remains understudied, and evidence-based clinical mitigation strategies do not currently exist. Our objectives were to measure the rate of occult hypoxemia, defined as arterial oxygen saturation (SaO2 ) < 88% when pulse oximeter oxygen saturation was between 92-96%, in a racially diverse critically ill population; to analyze degree, direction, and consistency of measurement error; and to develop a mitigation strategy that minimizes occult hypoxemia in advance of technological advancements. METHODS: We performed a multi-center retrospective cohort study of critically ill subjects. RESULTS: Among 105,467 paired observations from 7,693 subjects, we found occult hypoxemia was more common among minority subjects. The frequency of occult hypoxemia was 7.9% versus 2.9% between Black and white subjects, respectively, (P < .001). Pulse oximeter measurement errors were inconsistent throughout a patient encounter, with 67% of encounters having a range of intra-subject measurement errors > 4 percentage points. In 75% of encounters, the intra-subject errors were bidirectional. SaO2 < 88% was less common at higher pulse oximeter oxygenation ranges (4.1% and 1.8% of observations among Black and white subjects at a pulse oximeter threshold of 94-98%). Although occult hypoxemia was further reduced at oxygenation saturation range 95-100%, the frequency of hyperoxemia (partial pressure of arterial oxygen > 110 mm Hg) became more common, occurring in 42.3% of Black and 46.0% of white observations. CONCLUSIONS: Measurement error in pulse oximetry is common for all racial groups, but occult hypoxemia occurred most commonly in Black subjects. The highly variable magnitude and direction of measurement error preclude an individualized mitigation approach. In advance of technological advancements, we recommend targeting a pulse oximetry saturation goal of 94-98% for all patients.

Adherence to Lung Protective Ventilation in Patients With Coronavirus Disease 2019.

Prior studies have demonstrated suboptimal adherence to lung protective ventilation among patients with acute respiratory distress syndrome. A common barrier to providing this evidence-based practice is diagnostic uncertainty. We sought to test the hypothesis that patients with acute respiratory distress syndrome due to coronavirus disease 2019, in whom acute respiratory distress syndrome is easily recognized, would be more likely to receive low tidal volume ventilation than concurrently admitted acute respiratory distress syndrome patients without coronavirus disease 2019. DESIGN: Retrospective cohort study. SETTING: Five hospitals of a single health system. PATIENTS: Mechanically ventilated patients with coronavirus disease 2019 or noncoronavirus disease 2019 acute respiratory distress syndrome as identified by an automated, electronic acute respiratory distress syndrome finder in clinical use at study hospitals. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Among 333 coronavirus disease 2019 patients and 234 noncoronavirus disease 2019 acute respiratory distress syndrome patients, the average initial tidal volume was 6.4 cc/kg predicted body weight and 6.8 cc/kg predicted body weight, respectively. Patients had tidal volumes less than or equal to 6.5 cc/kg predicted body weight for a mean of 70% of the first 72 hours of mechanical ventilation in the coronavirus disease 2019 cohort, compared with 52% in the noncoronavirus disease 2019 cohort (unadjusted p < 0.001). After adjusting for height, gender, admitting hospital, and whether or not the patient was admitted to a medical specialty ICU, coronavirus disease 2019 diagnosis was associated with a 21% higher percentage of time receiving tidal volumes less than or equal to 6.5 cc/kg predicted body weight within the first 72 hours of mechanical ventilation (95% CI, 14-28%; p < 0.001). CONCLUSIONS: Adherence to low tidal volume ventilation during the first 72 hours of mechanical ventilation is higher in patients with coronavirus disease 2019 than with acute respiratory distress syndrome without coronavirus disease 2019. This population may present an opportunity to understand facilitators of implementation of this life-saving evidence-based practice.

Implementing nudges to promote utilization of low tidal volume ventilation (INPUT): a stepped-wedge, hybrid type III trial of strategies to improve evidence-based mechanical ventilation management.

BACKGROUND: Behavioral economic insights have yielded strategies to overcome implementation barriers. For example, default strategies and accountable justification strategies have improved adherence to best practices in clinical settings. Embedding such strategies in the electronic health record (EHR) holds promise for simple and scalable approaches to facilitating implementation. A proven-effective but under-utilized treatment for patients who undergo mechanical ventilation involves prescribing low tidal volumes, which protects the lungs from injury. We will evaluate EHR-based implementation strategies grounded in behavioral economic theory to improve evidence-based management of mechanical ventilation. METHODS: The Implementing Nudges to Promote Utilization of low Tidal volume ventilation (INPUT) study is a pragmatic, stepped-wedge, hybrid type III effectiveness implementation trial of three strategies to improve adherence to low tidal volume ventilation. The strategies target clinicians who enter electronic orders and respiratory therapists who manage the mechanical ventilator, two key stakeholder groups. INPUT has five study arms: usual care, a default strategy within the mechanical ventilation order, an accountable justification strategy within the mechanical ventilation order, and each of the order strategies combined with an accountable justification strategy within flowsheet documentation. We will create six matched pairs of twelve intensive care units (ICUs) in five hospitals in one large health system to balance patient volume and baseline adherence to low tidal volume ventilation. We will randomly assign ICUs within each matched pair to one of the order panels, and each pair to one of six wedges, which will determine date of adoption of the order panel strategy. All ICUs will adopt the flowsheet documentation strategy 6 months afterwards. The primary outcome will be fidelity to low tidal volume ventilation. The secondary effectiveness outcomes will include in-hospital mortality, duration of mechanical ventilation, ICU and hospital length of stay, and occurrence of potential adverse events. DISCUSSION: This stepped-wedge, hybrid type III trial will provide evidence regarding the role of EHR-based behavioral economic strategies to improve adherence to evidence-based practices among patients who undergo mechanical ventilation in ICUs, thereby advancing the field of implementation science, as well as testing the effectiveness of low tidal volume ventilation among broad patient populations. TRIAL REGISTRATION: ClinicalTrials.gov , NCT04663802 . Registered 11 December 2020.

Improving Prone Positioning for Severe Acute Respiratory Distress Syndrome during the COVID-19 Pandemic. An Implementation-Mapping Approach.

Rationale: Prone positioning reduces mortality in patients with severe acute respiratory distress syndrome (ARDS), a feature of severe coronavirus disease 2019 (COVID-19). Despite this, most patients with ARDS do not receive this lifesaving therapy.Objectives: To identify determinants of prone-positioning use, to develop specific implementation strategies, and to incorporate strategies into an overarching response to the COVID-19 crisis.Methods: We used an implementation-mapping approach guided by implementation-science frameworks. We conducted semistructured interviews with 30 intensive care unit (ICU) clinicians who staffed 12 ICUs within the Penn Medicine Health System and the University of Michigan Medical Center. We performed thematic analysis using the Consolidated Framework for Implementation Research. We then conducted three focus groups with a task force of ICU leaders to develop an implementation menu, using the Expert Recommendations for Implementing Change framework. The implementation strategies were adapted as part of the Penn Medicine COVID-19 pandemic response.Results: We identified five broad themes of determinants of prone positioning, including knowledge, resources, alternative therapies, team culture, and patient factors, which collectively spanned all five Consolidated Framework for Implementation Research domains. The task force developed five specific implementation strategies, including educational outreach, learning collaborative, clinical protocol, prone-positioning team, and automated alerting, elements of which were rapidly implemented at Penn Medicine.Conclusions: We identified five broad themes of determinants of evidence-based use of prone positioning for severe ARDS and several specific strategies to address these themes. These strategies may be feasible for rapid implementation to increase use of prone positioning for severe ARDS with COVID-19.

Characteristics, Outcomes, and Trends of Patients With COVID-19-Related Critical Illness at a Learning Health System in the United States.

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic continues to surge in the United States and globally. OBJECTIVE: To describe the epidemiology of COVID-19-related critical illness, including trends in outcomes and care delivery. DESIGN: Single-health system, multihospital retrospective cohort study. SETTING: 5 hospitals within the University of Pennsylvania Health System. PATIENTS: Adults with COVID-19-related critical illness who were admitted to an intensive care unit (ICU) with acute respiratory failure or shock during the initial surge of the pandemic. MEASUREMENTS: The primary exposure for outcomes and care delivery trend analyses was longitudinal time during the pandemic. The primary outcome was all-cause 28-day in-hospital mortality. Secondary outcomes were all-cause death at any time, receipt of mechanical ventilation (MV), and readmissions. RESULTS: Among 468 patients with COVID-19-related critical illness, 319 (68.2%) were treated with MV and 121 (25.9%) with vasopressors. Outcomes were notable for an all-cause 28-day in-hospital mortality rate of 29.9%, a median ICU stay of 8 days (interquartile range [IQR], 3 to 17 days), a median hospital stay of 13 days (IQR, 7 to 25 days), and an all-cause 30-day readmission rate (among nonhospice survivors) of 10.8%. Mortality decreased over time, from 43.5% (95% CI, 31.3% to 53.8%) to 19.2% (CI, 11.6% to 26.7%) between the first and last 15-day periods in the core adjusted model, whereas patient acuity and other factors did not change. LIMITATIONS: Single-health system study; use of, or highly dynamic trends in, other clinical interventions were not evaluated, nor were complications. CONCLUSION: Among patients with COVID-19-related critical illness admitted to ICUs of a learning health system in the United States, mortality seemed to decrease over time despite stable patient characteristics. Further studies are necessary to confirm this result and to investigate causal mechanisms. PRIMARY FUNDING SOURCE: Agency for Healthcare Research and Quality.

Evaluation of Respiratory Therapist Extender Comfort With Mechanical Ventilation During COVID-19 Pandemic.

BACKGROUND: Staffing strategies used to meet the needs of respiratory care departments during the COVID-19 pandemic included the deployment of respiratory therapist extenders. The purpose of this study was to evaluate respiratory therapist extenders' comfort level with critical care ventilators while caring for patients with COVID-19. To our knowledge, this is the first study to evaluate the deployment of certified registered nurse anesthetists (CRNAs) in a critical care setting. METHODS: A qualitative survey method was used to assess CRNA experience with critical care ventilators. Prior to deployment in the ICU, CRNAs were trained by clinical lead respiratory therapists. Education included respiratory clinical practices and ventilator management. Sixty-minute sessions were held with demonstration stations set up in ICUs for hands-on experience. RESULTS: Fifty-six CRNAs responded to our survey (63%). A mean ± SD of 9.48 ± 12.27 h was spent training prior to deployment in the ICU. CRNAs were at the bedside a mean ± SD of 73.0 ± 40.6 h during the pandemic. While CRNA comfort level with critical care ventilators increased significantly (P < .001) from the beginning to the end of their work experience, no statistically significant differences were found between CRNA comfort based on years of experience. Differences in comfort level were not found after training (chi-squared test 23.82, P = .09) or after ICU experience was completed (chi-squared test = 15.99, P = .45). Similarly, mean comfort level did not increase based on the number of hours spent working in the ICU (chi-squared test = 13.67, P = .55). CONCLUSIONS: Comfort level with mechanical ventilation increased for CRNAs working alongside respiratory therapists during the COVID-19 pandemic.

A Machine Learning Algorithm to Predict Severe Sepsis and Septic Shock: Development, Implementation, and Impact on Clinical Practice.

OBJECTIVES: Develop and implement a machine learning algorithm to predict severe sepsis and septic shock and evaluate the impact on clinical practice and patient outcomes. DESIGN: Retrospective cohort for algorithm derivation and validation, pre-post impact evaluation. SETTING: Tertiary teaching hospital system in Philadelphia, PA. PATIENTS: All non-ICU admissions; algorithm derivation July 2011 to June 2014 (n = 162,212); algorithm validation October to December 2015 (n = 10,448); silent versus alert comparison January 2016 to February 2017 (silent n = 22,280; alert n = 32,184). INTERVENTIONS: A random-forest classifier, derived and validated using electronic health record data, was deployed both silently and later with an alert to notify clinical teams of sepsis prediction. MEASUREMENT AND MAIN RESULT: Patients identified for training the algorithm were required to have International Classification of Diseases, 9th Edition codes for severe sepsis or septic shock and a positive blood culture during their hospital encounter with either a lactate greater than 2.2 mmol/L or a systolic blood pressure less than 90 mm Hg. The algorithm demonstrated a sensitivity of 26% and specificity of 98%, with a positive predictive value of 29% and positive likelihood ratio of 13. The alert resulted in a small statistically significant increase in lactate testing and IV fluid administration. There was no significant difference in mortality, discharge disposition, or transfer to ICU, although there was a reduction in time-to-ICU transfer. CONCLUSIONS: Our machine learning algorithm can predict, with low sensitivity but high specificity, the impending occurrence of severe sepsis and septic shock. Algorithm-generated predictive alerts modestly impacted clinical measures. Next steps include describing clinical perception of this tool and optimizing algorithm design and delivery.

Clinician Perception of a Machine Learning-Based Early Warning System Designed to Predict Severe Sepsis and Septic Shock.

OBJECTIVE: To assess clinician perceptions of a machine learning-based early warning system to predict severe sepsis and septic shock (Early Warning System 2.0). DESIGN: Prospective observational study. SETTING: Tertiary teaching hospital in Philadelphia, PA. PATIENTS: Non-ICU admissions November-December 2016. INTERVENTIONS: During a 6-week study period conducted 5 months after Early Warning System 2.0 alert implementation, nurses and providers were surveyed twice about their perceptions of the alert's helpfulness and impact on care, first within 6 hours of the alert, and again 48 hours after the alert. MEASUREMENTS AND MAIN RESULTS: For the 362 alerts triggered, 180 nurses (50% response rate) and 107 providers (30% response rate) completed the first survey. Of these, 43 nurses (24% response rate) and 44 providers (41% response rate) completed the second survey. Few (24% nurses, 13% providers) identified new clinical findings after responding to the alert. Perceptions of the presence of sepsis at the time of alert were discrepant between nurses (13%) and providers (40%). The majority of clinicians reported no change in perception of the patient's risk for sepsis (55% nurses, 62% providers). A third of nurses (30%) but few providers (9%) reported the alert changed management. Almost half of nurses (42%) but less than a fifth of providers (16%) found the alert helpful at 6 hours. CONCLUSIONS: In general, clinical perceptions of Early Warning System 2.0 were poor. Nurses and providers differed in their perceptions of sepsis and alert benefits. These findings highlight the challenges of achieving acceptance of predictive and machine learning-based sepsis alerts.

Clinical Impact of an Electronic Dashboard and Alert System for Sedation Minimization and Ventilator Liberation: A Before-After Study.

Sedation minimization and ventilator liberation protocols improve outcomes but are challenging to implement. We sought to demonstrate proof-of-concept and impact of an electronic application promoting sedation minimization and ventilator liberation. DESIGN: Multi-ICU proof-of-concept study and a single ICU before-after study. SETTING: University hospital ICUs. PATIENTS: Adult patients receiving mechanical ventilation. INTERVENTIONS: An automated application consisting of 1) a web-based dashboard with real-time data on spontaneous breathing trial readiness, sedation depth, sedative infusions, and nudges to wean sedation and ventilatory support and 2) text-message alerts once patients met criteria for a spontaneous breathing trial and spontaneous awakening trial. Pre-intervention, sedation minimization, and ventilator liberation were reviewed daily during a multidisciplinary huddle. Post-intervention, the dashboard was used during the multidisciplinary huddle, throughout the day by respiratory therapists, and text alerts were sent to bedside providers. MEASUREMENTS AND MAIN RESULTS: We enrolled 115 subjects in the proof-of-concept study. Spontaneous breathing trial alerts were accurate (98.3%), usually sent while patients were receiving mandatory ventilation (88.5%), and 61.9% of patients received concurrent spontaneous awakening trial alerts. We enrolled 457 subjects in the before-after study, 221 pre-intervention and 236 post-intervention. After implementation, patients were 28% more likely to be extubated (hazard ratio, 1.28; 95% CI, 1.01-1.63; p = 0.042) and 31% more likely to be discharged from the ICU (hazard ratio, 1.31; 95% CI, 1.03-1.67; p = 0.027) at any time point. After implementation, the median duration of mechanical ventilation was 2.20 days (95% CI, 0.09-4.31 d; p = 0.042) shorter and the median ICU length of stay was 2.65 days (95% CI, 0.13-5.16 d; p = 0.040) shorter, compared with the expected durations without the application. CONCLUSIONS: Implementation of an electronic dashboard and alert system promoting sedation minimization and ventilator liberation was associated with reductions in the duration of mechanical ventilation and ICU length of stay.

Combined Biomarkers Predict Acute Mortality Among Critically Ill Patients With Suspected Sepsis.

OBJECTIVES: Sepsis is associated with high early and total in-hospital mortality. Despite recent revisions in the diagnostic criteria for sepsis that sought to improve predictive validity for mortality, it remains difficult to identify patients at greatest risk of death. We compared the utility of nine biomarkers to predict mortality in subjects with clinically suspected bacterial sepsis. DESIGN: Cohort study. SETTING: The medical and surgical ICUs at an academic medical center. SUBJECTS: We enrolled 139 subjects who met two or more systemic inflammatory response syndrome (systemic inflammatory response syndrome) criteria and received new broad-spectrum antibacterial therapy. INTERVENTIONS: We assayed nine biomarkers (α-2 macroglobulin, C-reactive protein, ferritin, fibrinogen, haptoglobin, procalcitonin, serum amyloid A, serum amyloid P, and tissue plasminogen activator) at onset of suspected sepsis and 24, 48, and 72 hours thereafter. We compared biomarkers between groups based on both 14-day and total in-hospital mortality and evaluated the predictive validity of single and paired biomarkers via area under the receiver operating characteristic curve. MEASUREMENTS AND MAIN RESULTS: Fourteen-day mortality was 12.9%, and total in-hospital mortality was 29.5%. Serum amyloid P was significantly lower (4/4 timepoints) and tissue plasminogen activator significantly higher (3/4 timepoints) in the 14-day mortality group, and the same pattern held for total in-hospital mortality (Wilcoxon p ≤ 0.046 for all timepoints). Serum amyloid P and tissue plasminogen activator demonstrated the best individual predictive performance for mortality, and combinations of biomarkers including serum amyloid P and tissue plasminogen activator achieved greater predictive performance (area under the receiver operating characteristic curve > 0.76 for 14-d and 0.74 for total mortality). CONCLUSIONS: Combined biomarkers predict risk for 14-day and total mortality among subjects with suspected sepsis. Serum amyloid P and tissue plasminogen activator demonstrated the best discriminatory ability in this cohort.

Accuracy of Acid-Base Diagnoses Using the Central Venous Blood Gas in the Medical Intensive Care Unit.

BACKGROUND: Acid-base disturbances are frequent in critically ill patients. Arterial blood gas (ABG) is the gold standard in the diagnosis of these disturbances, but it is invasive with potential hazards. For patients with a central venous catheter, venous blood gas (VBG) sampling may be an alternative, less-invasive diagnostic tool. However, the accuracy of a central VBG-based acid-base disorder diagnosis compared to an ABG is unknown. The primary objective of this study was to assess the accuracy of a central VBG-based acid-base disorder diagnosis compared to the "gold standard" ABG in critically ill patients. METHODS: This was a study of adult patients in a medical intensive care unit that had simultaneously drawn ABG and central VBG samples. Expert acid-base diagnosticians, all nephrologists, diagnosed the acid-base disorder(s) in each blood gas sample. The central VBG diagnostic accuracy was assessed with percent agreement, sensitivity, and specificity compared to the ABG-based diagnosis. RESULTS: The study involved 23 participants. Overall, the central VBG had 100% sensitivity for metabolic acidosis, metabolic alkalosis, and respiratory acidosis, and lower sensitivity (71%) for respiratory alkalosis, and high percent agreement, ranging from 75 to 94%. VBG-based diagnoses in vasopressor-dependent patients (n = 13, 56.5%) performed similarly to the entire sample. CONCLUSIONS: In critically ill adult patients, central VBG may be used to detect and diagnose acid-base disturbances with reasonable diagnostic accuracy, even in shock states, compared to the ABG. This study supports the use of central VBG for diagnosis of acid-base disturbances in critically ill patients.

Natural Language Processing to Assess Documentation of Features of Critical Illness in Discharge Documents of Acute Respiratory Distress Syndrome Survivors.

RATIONALE: Transitions to outpatient care are crucial after critical illness, but the documentation practices in discharge documents after critical illness are unknown. OBJECTIVES: To characterize the rates of documentation of various features of critical illness in discharge documents of patients diagnosed with acute respiratory distress syndrome (ARDS) during their hospital stay. METHODS: We used natural language processing tools to build a keyword-based classifier that categorizes discharge documents by presence of terms from four groups of keywords related to critical illness. We used a multivariable modified Poisson regression model to infer patient- and hospital-level characteristics associated with documentation of relevant keywords. A manual chart review was used to validate the accuracy of the keyword-based classifier, and to assess for ARDS documentation during the hospital stay. MEASUREMENTS AND MAIN RESULTS: Of 815 discharge documents, ARDS was identified in only 111 (13%). Mechanical ventilation was identified in 770 (92%) and intensive care unit (ICU) admission in 693 (83%) of discharge documents. Symptoms or recommendations related to post-intensive care syndrome were included in 306 (38%) of discharge documents. Patient age (older; relative risk [RR] = 0.97/yr, 95% confidence interval [CI] = 0.96-0.98) and higher PaO2:FiO2 (decreasing illness severity; RR = 0.96/10-unit increment, 95% CI = 0.93-0.98) were associated with decreased documentation of ARDS. Being discharged from a surgical (RR = 0.33, 95% CI = 0.22-0.50) compared with a medicine service was also associated with decreased rates of ARDS documentation. The manual chart review revealed 98% concordance between ARDS documentation in the discharge summary and during the hospital stay. Accuracy of the document classifier was 100% for ARDS and mechanical ventilation, 98% for ICU admission, and 95% for symptoms of post-intensive care syndrome. CONCLUSIONS: In the discharge documents of survivors of ARDS, ARDS itself is rarely mentioned, but mechanical ventilation and ICU stay frequently are. The low rates of documentation of ARDS appear to be concordant with low rates of documentation during the hospital stay, consistent with known underrecognition in the ICU. Natural language processing tools can be used to effectively analyze large numbers of discharge documents of patients with critical illness.

Using a Post-Intubation Checklist and Time Out to Expedite Mechanical Ventilation Monitoring: Observational Study of a Quality Improvement Intervention.

BACKGROUND: Delayed mechanical ventilation monitoring may impede recognition of life-threatening acidemia. Coordination of multidisciplinary processes can be improved by using a checklist and time-out procedure. The study objective was to evaluate process-related outcomes after implementation of a post-intubation checklist and time out. METHODS: An observational study of a 24-bed medical ICU in Philadelphia, Pennsylvania, was conducted from January to December 2011. A random sample of mechanically ventilated adults was selected from the pre-intervention (n = 80) and post-intervention (n = 144) periods. The primary outcome was the proportion of subjects with an arterial blood gas (ABG) result within 60 min of mechanical ventilation initiation. Secondary outcomes included rates of respiratory acidosis, moderate-severe acidemia (pH <7.25), checklist initiation, and project sustainability. Chi-square analysis was used to evaluate differences in outcomes between time periods. RESULTS: After the intervention, the proportion of subjects with an ABG result within 60 min increased (56% vs 37%, P = .01), and time to ABG result improved (58 min vs 79 min, P = .004). Adjusting for illness severity, the proportion with an ABG result within 60 min remained significantly higher in the post-intervention period (odds ratio 2.42, 95% CI 1.25-4.68, P = .009). Checklist adherence was higher with ICU intubations than for intubations performed outside the ICU (71% vs 27% checklist initiation rate, P < .001). Transfer from referring institutions (23% checklist initiation rate, P = .006) negatively impacted checklist use. Implementation challenges included frequent stakeholder turnover, undefined process ownership, and lack of real-time performance feedback. CONCLUSIONS: A post-intubation checklist and time out improved the timeliness of mechanical ventilation monitoring through more rapid assessment of arterial blood gases. Implementing this peri-intubation procedure may reduce the risks associated with transitioning to full mechanical ventilatory support. Optimal implementation necessitates strategies to surmount organizational and behavioral barriers to change.

Combined biomarkers discriminate a low likelihood of bacterial infection among surgical intensive care unit patients with suspected sepsis.

Among surgical intensive care unit (SICU) patients, it is difficult to distinguish bacterial sepsis from other causes of systemic inflammatory response syndrome (SIRS). Biomarkers have proven useful to identify the presence of bacterial infection. We enrolled a prospective cohort of 69 SICU patients with suspected sepsis and assayed the concentrations of 9 biomarkers (α-2 macroglobulin [A2M], C-reactive protein, ferritin, fibrinogen, haptoglobin, procalcitonin [PCT], serum amyloid A, serum amyloid P, and tissue plasminogen activator) at baseline, 24, 48, and 72hours. Forty-two patients (61%) had bacterial sepsis by chart review. A2M concentrations were significantly lower, and PCT concentrations were significantly higher in subjects with bacterial sepsis at 3 of 4 time points. Using optimal cutoff values, the combination of baseline A2M and 72-hour PCT achieved a negative predictive value of 75% (95% confidence interval, 54-96%). The combination of A2M and PCT discriminated bacterial sepsis from other SIRS among SICU patients with suspected sepsis.

Composition and dynamics of the respiratory tract microbiome in intubated patients.

BACKGROUND: Lower respiratory tract infection (LRTI) is a major contributor to respiratory failure requiring intubation and mechanical ventilation. LRTI also occurs during mechanical ventilation, increasing the morbidity and mortality of intubated patients. We sought to understand the dynamics of respiratory tract microbiota following intubation and the relationship between microbial community structure and infection. RESULTS: We enrolled a cohort of 15 subjects with respiratory failure requiring intubation and mechanical ventilation from the medical intensive care unit at an academic medical center. Oropharyngeal (OP) and deep endotracheal (ET) secretions were sampled within 24 h of intubation and every 48-72 h thereafter. Bacterial community profiling was carried out by purifying DNA, PCR amplification of 16S ribosomal RNA (rRNA) gene sequences, deep sequencing, and bioinformatic community analysis. We compared enrolled subjects to a cohort of healthy subjects who had lower respiratory tract sampling by bronchoscopy. In contrast to the diverse upper respiratory tract and lower respiratory tract microbiota found in healthy controls, critically ill subjects had lower initial diversity at both sites. Diversity further diminished over time on the ventilator. In several subjects, the bacterial community was dominated by a single taxon over multiple time points. The clinical diagnosis of LRTI ascertained by chart review correlated with low community diversity and dominance of a single taxon. Dominant taxa matched clinical bacterial cultures where cultures were obtained and positive. In several cases, dominant taxa included bacteria not detected by culture, including Ureaplasma parvum and Enterococcus faecalis. CONCLUSIONS: Longitudinal analysis of respiratory tract microbiota in critically ill patients provides insight into the pathogenesis and diagnosis of LRTI. 16S rRNA gene sequencing of endotracheal aspirate samples holds promise for expanded pathogen identification.

Association Between Index Hospitalization and Hospital Readmission in Sepsis Survivors.

OBJECTIVES: Hospital readmission is common after sepsis, yet the relationship between the index admission and readmission remains poorly understood. We sought to examine the relationship between infection during the index acute care hospitalization and readmission and to identify potentially modifiable factors during the index sepsis hospitalization associated with readmission. DESIGN: In a retrospective cohort study, we evaluated 444 sepsis survivors at risk of an unplanned hospital readmission in 2012. The primary outcome was 30-day unplanned hospital readmission. SETTING: Three hospitals within an academic healthcare system. SUBJECTS: Four hundred forty-four sepsis survivors. MEASUREMENTS AND MAIN RESULTS: Of 444 sepsis survivors, 23.4% (95% CI, 19.6-27.6%) experienced an unplanned 30-day readmission compared with 10.1% (95% CI, 9.6-10.7%) among 11,364 nonsepsis survivors over the same time period. The most common cause for readmission after sepsis was infection (69.2%, 72 of 104). Among infection-related readmissions, 51.4% were categorized as recurrent/unresolved. Patients with sepsis present on their index admission who also developed a hospital-acquired infection ("second hit") were nearly twice as likely to have an unplanned 30-day readmission compared with those who presented with sepsis at admission and did not develop a hospital-acquired infection or those who presented without infection and then developed hospital-acquired sepsis (38.6% vs 22.2% vs 20.0%, p = 0.04). Infection-related hospital readmissions, specifically, were more likely in patients with a "second hit" and patients receiving a longer duration of antibiotics. The use of total parenteral nutrition (p = 0.03), longer duration of antibiotics (p = 0.047), prior hospitalizations, and lower discharge hemoglobin (p = 0.04) were independently associated with hospital readmission. CONCLUSIONS: We confirmed that the majority of unplanned hospital readmissions after sepsis are due to an infection. We found that patients with sepsis at admission who developed a hospital-acquired infection, and those who received a longer duration of antibiotics, appear to be high-risk groups for unplanned, all-cause 30-day readmissions and infection-related 30-day readmissions.