The utilization of critical care ultrasound to assess hemodynamics and lung pathology on ICU admission and the potential for predicting outcome

Triage of Critically Ill Patients: Characteristics and Outcomes of Patients Refused as Too Well for Intensive Care

BACKGROUND

The appropriate selection of patients for the intensive care unit (ICU) is a concern in acute care settings. However, the description of patients deemed too well for the ICU has been rarely reported.

METHODS

We conducted a single-centre retrospective observational study of all patients either deemed "too well" for or admitted to the ICU during one year. Refused patients were screened for unexpected events within 7 days, defined as either ICU admission without another indication, or death without treatment limitations. Patients' characteristics and organisational factors were analysed according to refusal status, outcome and delay in ICU admission.

RESULTS

Among 2219 enrolled patients, the refusal rate was 10.4%. Refusal was associated with diagnostic groups, treatment limitations, patients' location on a ward, night time and ICU occupancy. Unexpected events occurred in 16 (6.9%) refused patients. A worse outcome was associated with time spent in hospital before refusal, patients' location on a ward, SOFA score and physician's expertise. Delayed ICU admissions were associated with ICU and hospital length of stay.

CONCLUSIONS

ICU triage selected safely most patients who would have probably not benefited from the ICU. We identified individual and organisational factors associated with ICU refusal, subsequent ICU admission or death.

Lack of Association Between a Quantified Lung Ultrasound Score and Illness Severity in Pediatric Emergency Department Patients With Acute Lower Respiratory Infections

OBJECTIVES

Lung ultrasound (LUS) may help determine illness severity in children with acute lower respiratory tract infections (LRTI) but limited pediatric studies exist. Our objective was to determine the association between LUS findings and illness severity in children with LRTI.

METHODS

We conducted a prospective study of patients <20 years with LRTI. Trained investigators performed standardized LUS examinations of 12 regions. Blinded sonologists reviewed examinations for individual pathologic features and also calculated a Quantified Lung Ultrasound Score (QLUS). We defined focal severity as QLUS of ≥2 in ≥1 region, and diffuse severity as QLUS of ≥1 in ≥3 regions. The primary outcome was the Respiratory component of the Pediatric Early Warning Score (RPEWS), a 14-item scale measuring respiratory illness severity. Secondary outcomes included hospital admission, length of stay, supplemental oxygen, and antibiotic use.

RESULTS

We enrolled 85 patients with LRTIs, 46 (54%) whom were hospitalized (5.4% intensive care). Median RPEWS was 1 (interquartile range 2). Neither individual features on ultrasound nor total QLUS were associated with RPEWS, hospitalization, length of stay, or oxygen use. Mean RPEWS was similar for participants regardless of focal (1.46 versus 1.26, P = .57) or diffuse (1.47 versus 1.21, P = .47) severity findings, but those with focal or diffuse severity, or isolated consolidation, had greater antibiotic administration (P < .001).

CONCLUSIONS

In children with LRTI, neither individual features nor QLUS were associated with illness severity. Antibiotics were more likely in patients with either focal or diffuse severity or presence of consolidation on ultrasound.

Lung Ultrasound Patterns in Multisystem Inflammatory Syndrome in Children (MIS-C)-Characteristics and Prognostic Value

Objective and design: Following COVID-19 infection, children can develop an hyperinflammatory state termed Multisystem Inflammatory Syndrome in Children (MIS-C). Lung Ultrasound (LUS) features of COVID-19 in children have been described, but data describing the LUS findings of MIS-C are limited. The aim of this retrospective observational study conducted between 1 March and 31 December 2020, at a tertiary pediatric hospital in Milano, is to describe LUS patterns in patients with MIS-C and to verify correlation with illness severity. The secondary objective is to evaluate concordance of LUS with Chest X-ray (CXR). Methodology: Clinical and laboratory data were collected for all patients (age 0−18 years) admitted with MIS-C, as well as LUS and CXR patterns at admission. PICU admission, needed for respiratory support and inotrope administration, hospital, and PICU length of stay, were considered as outcomes and evaluated in the different LUS patterns. An agreement between LUS and CXR evaluation was assessed with Cohen’ k. Results: 24 children, who had a LUS examination upon admission, were enrolled. LUS pattern of subpleural consolidations < or > 1 cm with or without pleural effusion were associated with worse Left Ventricular Ejection Fraction at admission and need for inotropes. Subpleural consolidations < 1 cm were also associated with PICU length of stay. Agreement of CXR with LUS for consolidations and effusion was slight. Conclusion: LUS pattern of subpleural consolidations and consolidations with or without pleural effusion are predictors of disease severity; under this aspect, LUS can be used at admission to stratify risk of severe disease.

State of the Art of Machine Learning-Enabled Clinical Decision Support in Intensive Care Units: Literature Review

Background

Modern clinical care in intensive care units is full of rich data, and machine learning has great potential to support clinical decision-making. The development of intelligent machine learning–based clinical decision support systems is facing great opportunities and challenges. Clinical decision support systems may directly help clinicians accurately diagnose, predict outcomes, identify risk events, or decide treatments at the point of care.

Objective

We aimed to review the research and application of machine learning–enabled clinical decision support studies in intensive care units to help clinicians, researchers, developers, and policy makers better understand the advantages and limitations of machine learning–supported diagnosis, outcome prediction, risk event identification, and intensive care unit point-of-care recommendations.

Methods

We searched papers published in the PubMed database between January 1980 and October 2020. We defined selection criteria to identify papers that focused on machine learning–enabled clinical decision support studies in intensive care units and reviewed the following aspects: research topics, study cohorts, machine learning models, analysis variables, and evaluation metrics.

Results

A total of 643 papers were collected, and using our selection criteria, 97 studies were found. Studies were categorized into 4 topics—monitoring, detection, and diagnosis (13/97, 13.4%), early identification of clinical events (32/97, 33.0%), outcome prediction and prognosis assessment (46/97, 47.6%), and treatment decision (6/97, 6.2%). Of the 97 papers, 82 (84.5%) studies used data from adult patients, 9 (9.3%) studies used data from pediatric patients, and 6 (6.2%) studies used data from neonates. We found that 65 (67.0%) studies used data from a single center, and 32 (33.0%) studies used a multicenter data set; 88 (90.7%) studies used supervised learning, 3 (3.1%) studies used unsupervised learning, and 6 (6.2%) studies used reinforcement learning. Clinical variable categories, starting with the most frequently used, were demographic (n=74), laboratory values (n=59), vital signs (n=55), scores (n=48), ventilation parameters (n=43), comorbidities (n=27), medications (n=18), outcome (n=14), fluid balance (n=13), nonmedicine therapy (n=10), symptoms (n=7), and medical history (n=4). The most frequently adopted evaluation metrics for clinical data modeling studies included area under the receiver operating characteristic curve (n=61), sensitivity (n=51), specificity (n=41), accuracy (n=29), and positive predictive value (n=23).

Conclusions

Early identification of clinical and outcome prediction and prognosis assessment contributed to approximately 80% of studies included in this review. Using new algorithms to solve intensive care unit clinical problems by developing reinforcement learning, active learning, and time-series analysis methods for clinical decision support will be greater development prospects in the future.

Functional mitral regurgitation combined with increased early diastolic transmitral velocity to early mitral annulus diastolic velocity ratio is associated with a poor prognosis in patients with shock

BACKGROUND

Functional mitral regurgitation (FMR) is common in critically ill patients and may cause left atrial (LA) pressure elevation. This study aims to explore the prognostic impact of synergistic LA pressure elevation and FMR in patients with shock.

METHODS

We retrospectively screened 130 consecutive patients of 175 patients with shock from April 2016 to June 2017. The incidence and impact of FMR and early diastolic transmitral velocity to early mitral annulus diastolic velocity ratio (E/e') ≥ 4 within 6 h of shock on the prognosis of patients were evaluated. Finally, the synergistic effect of FMR and E/e' were assessed by combination, grouping, and trend analyses.

RESULTS

Forty-four patients (33.8%) had FMR, and 15 patients (11.5%) had E/e' elevation. A multivariate analysis revealed FMR and E/e' as independent correlated factors for 28-day mortality (P = 0.043 and 0.028, respectively). The Kaplan-Meier survival analysis revealed a significant difference in survival between patients with and without FMR (χ2 = 7.672, P = 0.006) and between the E/e' ≥ 14 and E/e' < 14 groups (χ2 = 19.351, P < 0.010). Twenty-eight-day mortality was significantly different among the four groups (χ2 = 30.141, P < 0.010). The risk of 28-day mortality was significantly higher in group 4 (E/e' ≥ 14 with FMR) compared with groups 1 (E/e' < 14 without FMR) and 2 (E/e' < 14 with FMR) (P = 0.001 and 0.046, respectively).

CONCLUSIONS

Patients with shock can be identified by the presence of FMR. FMR and E/e' are independent risk factors for a poor prognosis in these patients, and prognosis is worst when FMR and E/e' ≥ 14 are present. It may be possible to improve prognosis by reducing LA pressure and E/e'.

TRIAL REGISTRATION

ClinicalTrials.gov, NCT03082326.

A scoping review of registry captured indicators for evaluating quality of critical care in ICU

BACKGROUND

Excess morbidity and mortality following critical illness is increasingly attributed to potentially avoidable complications occurring as a result of complex ICU management (Berenholtz et al., J Crit Care 17:1-2, 2002; De Vos et al., J Crit Care 22:267-74, 2007; Zimmerman J Crit Care 1:12-5, 2002). Routine measurement of quality indicators (QIs) through an Electronic Health Record (EHR) or registries are increasingly used to benchmark care and evaluate improvement interventions. However, existing indicators of quality for intensive care are derived almost exclusively from relatively narrow subsets of ICU patients from high-income healthcare systems. The aim of this scoping review is to systematically review the literature on QIs for evaluating critical care, identify QIs, map their definitions, evidence base, and describe the variances in measurement, and both the reported advantages and challenges of implementation.

METHOD

We searched MEDLINE, EMBASE, CINAHL, and the Cochrane libraries from the earliest available date through to January 2019. To increase the sensitivity of the search, grey literature and reference lists were reviewed. Minimum inclusion criteria were a description of one or more QIs designed to evaluate care for patients in ICU captured through a registry platform or EHR adapted for quality of care surveillance.

RESULTS

The search identified 4780 citations. Review of abstracts led to retrieval of 276 full-text articles, of which 123 articles were accepted. Fifty-one unique QIs in ICU were classified using the three components of health care quality proposed by the High Quality Health Systems (HQSS) framework. Adverse events including hospital acquired infections (13.7%), hospital processes (54.9%), and outcomes (31.4%) were the most common QIs identified. Patient reported outcome QIs accounted for less than 6%. Barriers to the implementation of QIs were described in 35.7% of articles and divided into operational barriers (51%) and acceptability barriers (49%).

CONCLUSIONS

Despite the complexity and risk associated with ICU care, there are only a small number of operational indicators used. Future selection of QIs would benefit from a stakeholder-driven approach, whereby the values of patients and communities and the priorities for actionable improvement as perceived by healthcare providers are prioritized and include greater focus on measuring discriminable processes of care.

Findings and Prognostic Value of Lung Ultrasonography in Coronal Virus Disease 2019 (COVID-19) Pneumonia

PURPOSE

We used lung ultrasonography to identify features of COVID-19 pneumonia and to evaluate the prognostic value.

PATIENTS AND METHODS

We performed lung ultrasonography on 48 COVID-19 patients in an intensive care unit (ICU) (Wuhan, China) using a 12-zone method. The associations between lung ultrasonography score, PaO2/FiO2, APACHE II, SOFA, and PaCO2 with 28-day mortality were analyzed and the receiver operator characteristic curve was plotted.

RESULTS

25.9% areas in all scanning zones presented with B7 lines and 23.5% with B3 lines (B-pattern) on lung ultrasonography; 13% areas with confluent B lines (B-pattern), 24.9% in areas with consolidations, and 9.9% in areas with A lines. Pleural effusion was observed in 2.8% of areas. Lung ultrasonography score was negatively correlated with PaO2/FiO2 (n = 48, r = -0.498, P < 0.05) and positively correlated with APACHE II (n = 48, r = 0.435, P < 0.05). Lung ultrasonography score was independently associated with 28-day mortality. The areas under receiver operator characteristic curves of lung ultrasonography score were 0.735 (95% CI: 0.586-0.844). The sensitivity, specificity, and cutoff values were 0.833, 0.722, and 22.5, respectively.

CONCLUSIONS

Lung ultrasonography could be used to assess the severity of COVID-19 pneumonia, and it could also reveal the pathological signs of the disease. The lung ultrasonography score on ICU admission was independently related to the ICU 28-day mortality.

Lung ultrasound score based on the BLUE-plus protocol is associated with the outcomes and oxygenation indices of intensive care unit patients

PURPOSE

The primary objective was to demonstrate the relationship between lung ultrasound (LUS) manifestations and the outcomes of intensive care unit (ICU) patients. The secondary objective was to determine the characteristics of LUS manifestations in different subgroups of ICU patients.

METHODS

This prospective multi-center cohort study was conducted in 17 ICUs. A total of 1702 patients admitted between August 31, 2017 and February 16, 2019 were included. LUS was performed according to the bedside lung ultrasound in emergency (BLUE)-plus protocol, and LUS scores were calculated. Data on the outcomes and oxygenation indices were analyzed and compared between different primary indication groups.

RESULTS

The LUS scores were significantly higher for non-survivors than for survivors and were significantly different between the oxygenation index groups, with higher scores in the lower oxygenation index groups. The LUS score was an independent risk factor for the 28-day mortality. The area under the receiver operating characteristic curve was 0.663 for prediction of the 28-day mortality and 0.748 for prediction of an oxygenation index ≤100.

CONCLUSIONS

The LUS score based on the BLUE-plus protocol was an independent risk factor for the 28-day mortality and was important for the prediction of an oxygenation index ≤100. An early LUS score within 24 hours of ICU admission helps predicting the outcome of ICU patients.

Hemodynamics in Shock Patients Assessed by Critical Care Ultrasound and Its Relationship to Outcome: A Prospective Study

BACKGROUND

Shock is one of the causes of mortality in the intensive care unit (ICU). Traditionally, hemodynamics related to shock have been monitored by broad-spectrum devices with treatment guided by many inaccurate variables to describe the pathophysiological changes. Critical care ultrasound (CCUS) has been widely advocated as a preferred tool to monitor shock patients. The purpose of this study was to analyze and broaden current knowledge of the characteristics of ultrasonic hemodynamic pattern and investigate their relationship to outcome.

METHODS

This prospective study of shock patients in CCUS was conducted in 181 adult patients between April 2016 and June 2017 in the Department of Intensive Care Unit of West China Hospital. CCUS was performed within the initial 6 hours after shock patients were enrolled. The demographic and clinical characteristics, ultrasonic pattern of hemodynamics, and outcome were recorded. A stepwise bivariate logistic regression model was established to identify the correlation between ultrasonic variables and the 28-day mortality.

RESULTS

A total of 181 patients with shock were included in our study (male/female: 113/68). The mean age was 58.2 ± 18.0 years; the mean Acute Physiology and Chronic Health Evaluation II (APACHE II score) was 23.7 ± 8.7, and the 28-day mortality was 44.8% (81/181). The details of ultrasonic pattern were well represented, and the multivariate analysis revealed that mitral annular plane systolic excursion (MAPSE), mitral annular peak systolic velocity (S'-MV), tricuspid annular plane systolic excursion (TAPSE), and lung ultrasound score (LUSS) were the independent risk factors for 28-day mortality in our study, as well as APACHE II score, PaO2/FiO2, and lactate (p = 0.047, 0.041, 0.022, 0.002, 0.027, 0.028, and 0.01, respectively).

CONCLUSIONS

CCUS exam on admission provided valuable information to describe the pathophysiological changes of shock patients and the mechanism of shock. Several critical variables obtained by CCUS were related to outcome, hence deserving more attention in clinical decision-making. Trial Registration. The study was approved by the Ethics Committee of West China Hospital Review Board for human research with the following reference number 201736 and was registered on ClinicalTrials. This trial is registered with NCT03082326 on 3 March 2017 (retrospectively registered).