Kaufman, David A.

Background: Cardiovascular instability occurring during endotracheal intubation (ETI) in the critically ill is a commonly recognized phenomenon. However, this complication has not been evaluated in terms of the physiological cause (ie, decreased preload, contractility, or afterload) leading to the instability. Thus, the aim of the current investigation was to describe the hemodynamics occurring during ETI with noninvasive physiologic monitoring and to collect preliminary data on the hemodynamic effects of induction agents and positive pressure ventilation. Methods: A multicenter prospective study enrolling adult (≥18 years) critically ill patients undergoing ETI with noninvasive cardiac output monitoring in a medical/surgical intensive care unit from June 2018 to May 2019 was conducted. This study used the Cheetah Medical noninvasive cardiac output monitor to collect hemodynamic data during the peri-intubation period. Additional data collected included baseline characteristics such as illness severity, peri-intubation pharmacologic administration, and mechanical ventilation settings. Results: From the original 27 patients, only 19 (70%) patients had complete data and were included in the final analysis. Propofol was the most common sedative 8 (42%) followed by ketamine 6 (32%) and etomidate 5 (26%). Patients given propofol demonstrated a decrease in total peripheral resistance index (delta change [dynes × s/cm −5 /m 2 ]: −2.7 ± 778.2) but stabilization in cardiac index (delta change (L/min/m 2 ]: 0.1 ± 1.5) while etomidate and ketamine demonstrated increases in total peripheral resistance index (etomidate delta change [dynes × s/cm −5 /m 2 ]: 302.1 ± 414.3; ketamine delta change [dynes × s/cm −5 /m 2 ]: 278.7 ± 418.9) but only etomidate resulted in a decrease in cardiac index (delta change [L/min/m 2 ]: −0.3 ± 0.5). Positive pressure ventilation resulted in minimal changes to hemodynamics during ETI. Conclusions: The current study demonstrates that although propofol administration leads to a decrease in total peripheral resistance index, cardiac index is maintained while etomidate leads to a decrease in cardiac index with both etomidate and ketamine increasing total peripheral resistance index. These hemodynamic profiles are minimally affected by positive pressure ventilation. Study registration: ClinicalTrials.gov ID, NCT03525743.

Background: Little is known about hypoxemia surrounding endotracheal intubation in the critically ill. Thus, we sought to identify risk factors associated with peri-intubation hypoxemia and its effects’ on the critically ill. Methods: Data from a multicenter, prospective, cohort study enrolling 1,033 critically ill adults who underwent endotracheal intubation across 16 medical/surgical ICUs in the United States from July 2015-January 2017 were used to identify risk factors associated with peri-intubation hypoxemia and its effects on patient outcomes. We defined hypoxemia as any pulse oximetry ≤ 88% during and up to 30 minutes following endotracheal intubation. Results: In the full analysis (n = 1,033), 123 (11.9%) patients experienced the primary outcome. Five risk factors independently associated with our outcome were identified on multiple logistic regression: cardiac related reason for endotracheal intubation (OR 1.67, [95% CI 1.04, 2.69]); pre-intubation noninvasive ventilation (OR 1.66, [95% CI 1.09, 2.54]); emergency intubation (OR 1.65, [95% CI 1.06, 2.55]); moderate-severe difficult bag-mask ventilation (OR 2.68, [95% CI 1.72, 4.19]); and crystalloid administration within the preceding 24 hours (OR 1.24, [95% CI 1.07, 1.45]; per liter up to 4 liters). Higher baseline S p O 2 was found to be protective (OR 0.93, [95% CI 0.91, 0.96]; per percent up to 97%). Consistent results were seen in a separate analysis on only stable patients (n = 921, 93 [10.1%]) (those without baseline hypoxemia ≤ 88%). Peri-intubation hypoxemia was associated with in-hospital mortality (OR 2.40, [95% CI 1.33, 4.31]; stable patients: OR 2.67, [95% CI 1.38, 5.17]) but not ICU length of stay (point estimate 0.9 days, [95% CI −1.0, 2.8 days]; stable patients: point estimate 1.5 days, [95% CI −0.4, 3.4 days]) after adjusting for age, body mass index, illness severity, airway related reason for intubation (i.e., acute respiratory failure), and baseline S P O 2 . Conclusions: Patients with pre-existing noninvasive ventilation and volume loading who were intubated emergently in the setting of hemodynamic compromise with bag-mask ventilation described as moderate-severe were at increased risk for peri-intubation hypoxemia. Higher baseline oxygenation was found to be protective against peri-intubation hypoxemia. Peri-intubation hypoxemia was associated with in-hospital mortality but not ICU length of stay. Trial registration: Clinicaltrials.gov identifier: NCT02508948 and Registered Report Identifier: RR2-10.2196/11101.

Objectives: The objectives of this study were to: 1) determine the association between vasopressor dosing intensity during the first 6 hours and first 24 hours after the onset of septic shock and 30-day in-hospital mortality; 2) determine whether the effect of vasopressor dosing intensity varies by fluid resuscitation volume; and 3) determine whether the effect of vasopressor dosing intensity varies by dosing titration pattern. Design: Multicenter prospective cohort study between September 2017 and February 2018. Vasopressor dosing intensity was defined as the total vasopressor dose infused across all vasopressors in norepinephrine equivalents. Setting: Thirty-three hospital sites in the United States ( n = 32) and Jordan ( n = 1). Patients: Consecutive adults requiring admission to the ICU with septic shock treated with greater than or equal to 1 vasopressor within 24 hours of shock onset. Interventions: None. Measurements and Main Results: Out of 1,639 patients screened, 616 were included. Norepinephrine (93%) was the most common vasopressor. Patients received a median of 3,400 mL (interquartile range, 1,851–5,338 mL) during the 24 hours after shock diagnosis. The median vasopressor dosing intensity during the first 24 hours of shock onset was 8.5 μg/min norepinephrine equivalents (3.4–18.1 μg/min norepinephrine equivalents). In the first 6 hours, increasing vasopressor dosing intensity was associated with increased odds ratio of 30-day in-hospital mortality, with the strength of association dependent on concomitant fluid administration. Over the entire 24 hour period, every 10 μg/min increase in vasopressor dosing intensity was associated with an increased risk of 30-day mortality (adjusted odds ratio, 1.33; 95% CI, 1.16–1.53), and this association did not vary with the amount of fluid administration. Compared to an early high/late low vasopressor dosing strategy, an early low/late high or sustained high vasopressor dosing strategy was associated with higher mortality. Conclusions: Increasing vasopressor dosing intensity during the first 24 hours after septic shock was associated with increased mortality. This association varied with the amount of early fluid administration and the timing of vasopressor titration.

Objectives: To characterize the association between the use of physiologic assessment (central venous pressure, pulmonary artery occlusion pressure, stroke volume variation, pulse pressure variation, passive leg raise test, and critical care ultrasound) with fluid and vasopressor administration 24 hours after shock onset and with in-hospital mortality. Design: Multicenter prospective cohort study between September 2017 and February 2018. Settings: Thirty-four hospitals in the United States and Jordan. Patients: Consecutive adult patients requiring admission to the ICU with systolic blood pressure less than or equal to 90 mm Hg, mean arterial blood pressure less than or equal to 65 mm Hg, or need for vasopressor. Interventions: None. Measurement and Main Results: Of 1,639 patients enrolled, 39% had physiologic assessments. Use of physiologic assessment was not associated with cumulative fluid administered within 24 hours of shock onset, after accounting for baseline characteristics, etiology and location of shock, ICU types, Acute Physiology and Chronic Health Evaluation III, and hospital (beta coefficient, 0.04; 95% CI, −0.07 to 0.15). In multivariate analysis, the use of physiologic assessment was associated with a higher likelihood of vasopressor use (adjusted odds ratio, 1.98; 95% CI, 1.45−2.71) and higher 24-hour cumulative vasopressor dosing as norepinephrine equivalent (beta coefficient, 0.37; 95% CI, 0.19−0.55). The use of vasopressor was associated with increased odds of in-hospital mortality (adjusted odds ratio, 1.88; 95% CI, 1.27−2.78). In-hospital mortality was not associated with the use of physiologic assessment (adjusted odds ratio, 0.86; 95% CI, 0.63−1.18). Conclusions: The use of physiologic assessment in the 24 hours after shock onset is associated with increased use of vasopressor but not with fluid administration.

Abstract Rationale No previous studies have examined the role of prehospital vulnerability in acute respiratory distress syndrome (ARDS) development and mortality in an acutely ill adult population. Objectives To describe the association between prehospital vulnerability and 1) the development of ARDS, 2) 28-day mortality, and 3) 1-year mortality. Methods This was a longitudinal prospective cohort study nested within the multicenter LIPS-A (Lung Injury Prevention Study-Aspirin) trial. We analyzed 301 participants who completed Vulnerable Elders Survey (VES) at baseline. Multivariable logistic regression and Cox regression analyses were used to describe the association between vulnerability and short-term outcomes (ARDS and 28-day mortality) and long-term outcomes (1-year mortality), respectively. Results The VES score ranged from 0 to 10 (median [interquartile range], 2.0 [0–6]); 143 (47.5%) fit criteria for prehospital vulnerability (VES ≥ 3). Vulnerability was not significantly associated with ARDS development (10 [7.0%] vulnerable patients developed ARDS as per LIPS-A study criteria vs. 20 [12.7%] without vulnerability; P = 0.10; adjusted odds ratio [95% confidence interval (CI)], 0.54 [0.24–1.24]; P = 0.15). Nor was vulnerability associated with 28-day mortality (15 [10.5%] vulnerable patients were dead by Day 28 vs. 11 [7.0%] nonvulnerable patients; P = 0.28; adjusted odds ratio [95% CI], 0.95 [0.39–2.26]; P = 0.90). Vulnerability was significantly associated with 1-year mortality in hospital survivors (35 [26.9%] vs. 13 [9.3%]; adjusted hazard ratio [95% CI], 2.20 [1.10–4.37]; P = 0.02). Conclusions In a population of adults recruited for their high risk of ARDS, prehospital vulnerability, measured by VES, was highly prevalent and strongly associated with 1-year mortality.

Purpose of review In this review, we will discuss efforts and challenges in understanding and developing meaningful outcomes of critical care research, quality improvement and policy, which are patient-centered and goal concordant, rather than mortality alone. We shall discuss different aspects of what could constitute outcomes of critical illness as meaningful to the patients and other stakeholders, including families and providers. Recent findings Different outcome pathways after critical illness impact the patients, families and providers in multiple ways. For patients who die, it is important to consider the experience of dying. For the increasing number of survivors of critical illness, challenges of survival have surfaced. The physical, mental and social debility that survivors experience has evolved into the entity called post-ICU syndrome. The importance of prehospital health state trajectory and the need for the outcome of critical care to be aligned with the patients’ goals and preferences have been increasingly recognized. Summary A theoretical framework is outlined to help understand the impact of critical care interventions on outcomes that are meaningful to patients, families and healthcare providers.

Objective: The Lung Injury Prediction Score identifies patients at risk for acute respiratory distress syndrome in the emergency department, but it has not been validated in non-emergency department hospitalized patients. We aimed to evaluate whether Lung Injury Prediction Score identifies non-emergency department hospitalized patients at risk of developing acute respiratory distress syndrome at the time of critical care contact. Design: Retrospective study. Setting: Five academic medical centers. Patients: Nine hundred consecutive patients (≥ 18 yr old) with at least one acute respiratory distress syndrome risk factor at the time of critical care contact. Interventions: None. Measurements and Main Results: Lung Injury Prediction Score was calculated using the worst values within the 12 hours before initial critical care contact. Patients with acute respiratory distress syndrome at the time of initial contact were excluded. Acute respiratory distress syndrome developed in 124 patients (13.7%) a median of 2 days (interquartile range, 2–3) after critical care contact. Hospital mortality was 22% and was significantly higher in acute respiratory distress syndrome than non-acute respiratory distress syndrome patients (48% vs 18%; p < 0.001). Increasing Lung Injury Prediction Score was significantly associated with development of acute respiratory distress syndrome (odds ratio, 1.31; 95% CI, 1.21–1.42) and the composite outcome of acute respiratory distress syndrome or death (odds ratio, 1.26; 95% CI, 1.18–1.34). A Lung Injury Prediction Score greater than or equal to 4 was associated with the development of acute respiratory distress syndrome (odds ratio, 4.17; 95% CI, 2.26–7.72), composite outcome of acute respiratory distress syndrome or death (odds ratio, 2.43; 95% CI, 1.68–3.49), and acute respiratory distress syndrome after accounting for the competing risk of death (hazard ratio, 3.71; 95% CI, 2.05–6.72). For acute respiratory distress syndrome development, the Lung Injury Prediction Score has an area under the receiver operating characteristic curve of 0.70 and a Lung Injury Prediction Score greater than or equal to 4 has 90% sensitivity (misses only 10% of acute respiratory distress syndrome cases), 31% specificity, 17% positive predictive value, and 95% negative predictive value. Conclusions: In a cohort of non-emergency department hospitalized patients, the Lung Injury Prediction Score and Lung Injury Prediction Score greater than or equal to 4 can identify patients at increased risk of acute respiratory distress syndrome and/or death at the time of critical care contact but it does not perform as well as in the original emergency department cohort.

Abstract Rationale Accurate, early identification of patients at risk for developing acute lung injury (ALI) provides the opportunity to test and implement secondary prevention strategies. Objectives To determine the frequency and outcome of ALI development in patients at risk and validate a lung injury prediction score (LIPS). Methods In this prospective multicenter observational cohort study, predisposing conditions and risk modifiers predictive of ALI development were identified from routine clinical data available during initial evaluation. The discrimination of the model was assessed with area under receiver operating curve (AUC). The risk of death from ALI was determined after adjustment for severity of illness and predisposing conditions. Measurements and Main Results Twenty-two hospitals enrolled 5,584 patients at risk. ALI developed a median of 2 (interquartile range 1–4) days after initial evaluation in 377 (6.8%; 148 ALI-only, 229 adult respiratory distress syndrome) patients. The frequency of ALI varied according to predisposing conditions (from 3% in pancreatitis to 26% after smoke inhalation). LIPS discriminated patients who developed ALI from those who did not with an AUC of 0.80 (95% confidence interval, 0.78–0.82). When adjusted for severity of illness and predisposing conditions, development of ALI increased the risk of in-hospital death (odds ratio, 4.1; 95% confidence interval, 2.9–5.7). Conclusions ALI occurrence varies according to predisposing conditions and carries an independently poor prognosis. Using routinely available clinical data, LIPS identifies patients at high risk for ALI early in the course of their illness. This model will alert clinicians about the risk of ALI and facilitate testing and implementation of ALI prevention strategies. Clinical trial registered with www.clinicaltrials.gov (NCT00889772).