Machine Learning Tools for Acute Respiratory Distress Syndrome Detection and Prediction.

Machine learning (ML) tools for acute respiratory distress syndrome (ARDS) detection and prediction are increasingly used. Therefore, understanding risks and benefits of such algorithms is relevant at the bedside. ARDS is a complex and severe lung condition that can be challenging to define precisely due to its multifactorial nature. It often arises as a response to various underlying medical conditions, such as pneumonia, sepsis, or trauma, leading to widespread inflammation in the lungs. ML has shown promising potential in supporting the recognition of ARDS in ICU patients. By analyzing a variety of clinical data, including vital signs, laboratory results, and imaging findings, ML models can identify patterns and risk factors associated with the development of ARDS. This detection and prediction could be crucial for timely interventions, diagnosis and treatment. In summary, leveraging ML for the early prediction and detection of ARDS in ICU patients holds great potential to enhance patient care, improve outcomes, and contribute to the evolving landscape of precision medicine in critical care settings. This article is a concise definitive review on artificial intelligence and ML tools for the prediction and detection of ARDS in critically ill patients.

A scoping review of machine learning for sepsis prediction- feature engineering strategies and model performance: a step towards explainability.

BACKGROUND: Sepsis, an acute and potentially fatal systemic response to infection, significantly impacts global health by affecting millions annually. Prompt identification of sepsis is vital, as treatment delays lead to increased fatalities through progressive organ dysfunction. While recent studies have delved into leveraging Machine Learning (ML) for predicting sepsis, focusing on aspects such as prognosis, diagnosis, and clinical application, there remains a notable deficiency in the discourse regarding feature engineering. Specifically, the role of feature selection and extraction in enhancing model accuracy has been underexplored. OBJECTIVES: This scoping review aims to fulfill two primary objectives: To identify pivotal features for predicting sepsis across a variety of ML models, providing valuable insights for future model development, and To assess model efficacy through performance metrics including AUROC, sensitivity, and specificity. RESULTS: The analysis included 29 studies across diverse clinical settings such as Intensive Care Units (ICU), Emergency Departments, and others, encompassing 1,147,202 patients. The review highlighted the diversity in prediction strategies and timeframes. It was found that feature extraction techniques notably outperformed others in terms of sensitivity and AUROC values, thus indicating their critical role in improving sepsis prediction models. CONCLUSION: Key dynamic indicators, including vital signs and critical laboratory values, are instrumental in the early detection of sepsis. Applying feature selection methods significantly boosts model precision, with models like Random Forest and XG Boost showing promising results. Furthermore, Deep Learning models (DL) reveal unique insights, spotlighting the pivotal role of feature engineering in sepsis prediction, which could greatly benefit clinical practice.

Clinical practice, decision-making, and use of clinical decision support systems in invasive mechanical ventilation: a narrative review.

Invasive mechanical ventilation is a key supportive therapy for patients on intensive care. There is increasing emphasis on personalised ventilation strategies. Clinical decision support systems (CDSS) have been developed to support this. We conducted a narrative review to assess evidence that could inform device implementation. A search was conducted in MEDLINE (Ovid) and EMBASE. Twenty-nine studies met the inclusion criteria. Role allocation is well described, with interprofessional collaboration dependent on culture, nurse:patient ratio, the use of protocols, and perception of responsibility. There were no descriptions of process measures, quality metrics, or clinical workflow. Nurse-led weaning is well-described, with factors grouped by patient, nurse, and system. Physician-led weaning is heterogenous, guided by subjective and objective information, and 'gestalt'. No studies explored decision-making with CDSS. Several explored facilitators and barriers to implementation, grouped by clinician (facilitators: confidence using CDSS, retaining decision-making ownership; barriers: undermining clinician's role, ambiguity moving off protocol), intervention (facilitators: user-friendly interface, ease of workflow integration, minimal training requirement; barriers: increased documentation time), and organisation (facilitators: system-level mandate; barriers: poor communication, inconsistent training, lack of technical support). One study described factors that support CDSS implementation. There are gaps in our understanding of ventilation practice. A coordinated approach grounded in implementation science is required to support CDSS implementation. Future research should describe factors that guide clinical decision-making throughout mechanical ventilation, with and without CDSS, map clinical workflow, and devise implementation toolkits. Novel research design analogous to a learning organisation, that considers the commercial aspects of device design, is required.

Is artificial intelligence ready to solve mechanical ventilation? Computer says blow.

Artificial intelligence (AI) has the potential to identify treatable phenotypes, optimise ventilation strategies, and provide clinical decision support for patients who require mechanical ventilation. Gallifant and colleagues performed a systematic review to identify studies using AI to solve a diverse range of clinical problems in the ventilated patient. They identify 95 studies, the majority of which were reported in the last 5 yr. Their findings indicate that the majority of studies have significant methodological bias and are a long way from deployment.

Temporal Trends in Critical Care Outcomes in U.S. Minority-Serving Hospitals.

Rationale: Whether critical care improvements over the last 10 years extend to all hospitals has not been described.Objectives: To examine the temporal trends of critical care outcomes in minority and non-minority-serving hospitals using an inception cohort of critically ill patients.Measurements and Main Results: Using the Philips Health Care electronic ICU Research Institute Database, we identified minority-serving hospitals as those with an African American or Hispanic ICU census more than twice its regional mean. We examined almost 1.1 million critical illness admissions among 208 ICUs from across the United States admitted between 2006 and 2016. Adjusted hospital mortality (primary) and length of hospitalization (secondary) were the main outcomes. Large pluralities of African American (25%, n = 27,242) and Hispanic individuals (48%, n = 26,743) were cared for in minority-serving hospitals, compared with only 5.2% (n = 42,941) of white individuals. Over the last 10 years, although the risk of critical illness mortality steadily decreased by 2% per year (95% confidence interval [CI], 0.97-0.98) in non-minority-serving hospitals, outcomes within minority-serving hospitals did not improve comparably. This disparity in temporal trends was particularly noticeable among African American individuals, where each additional calendar year was associated with a 3% (95% CI, 0.96-0.97) lower adjusted critical illness mortality within a non-minority-serving hospital, but no change within minority-serving hospitals (hazard ratio, 0.99; 95% CI, 0.97-1.01). Similarly, although ICU and hospital lengths of stay decreased by 0.08 (95% CI, -0.08 to -0.07) and 0.16 (95% CI, -0.16 to -0.15) days per additional calendar year, respectively, in non-minority-serving hospitals, there was little temporal change for African American individuals in minority-serving hospitals.Conclusions: Critically ill African American individuals are disproportionately cared for in minority-serving hospitals, which have shown significantly less improvement than non-minority-serving hospitals over the last 10 years.

The global mental health burden of COVID-19 on critical care staff.

BACKGROUND: Although the mental health burden in healthcare workers caused by COVID-19 has gained increasing attention both within the profession and through public opinion, there has been a lack of data describing their experience; specifically, the mental wellbeing of healthcare workers in the intensive care unit (ICU), including those redeployed. AIMS: The authors aimed to compare the mental health status of ICU healthcare workers (physicians, nurses and allied health professionals) affected by various factors during the COVID-19 pandemic; and highlight to policymakers areas of staff vulnerabilities in order to improve wellbeing strategies within healthcare systems. METHODS: An online survey using three validated scales was conducted in France, the UK, Italy, Mainland China, Taiwan, Egypt and Belgium. FINDINGS: The proportion of respondents who screened positive on the three scales across the countries was 16-49% for depression, 60-86% for insomnia and 17-35% for post-traumatic stress disorder. The authors also identified an increase in the scores with longer time spent in personal protective equipment, female gender, advancing age and redeployed status. CONCLUSION: The high prevalence of mental disorders among ICU staff during the COVID-19 crisis should inform local and national wellbeing policies.

Implications of Obesity for the Management of Severe Coronavirus Disease 2019 Pneumonia.

OBJECTIVES: To investigate patients' characteristics, management, and outcomes in the critically ill population admitted to the ICU for severe acute respiratory syndrome coronavirus disease 2019 pneumonia causing an acute respiratory distress syndrome. DESIGN: Retrospective case-control study. SETTING: A 34-bed ICU of a tertiary hospital. PATIENTS: The first 44 coronavirus disease 2019 acute respiratory distress syndrome patients were compared with a historical control group of 39 consecutive acute respiratory distress syndrome patients admitted to the ICU just before the coronavirus disease 2019 crisis. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Obesity was the most frequent comorbidity exhibited by coronavirus disease 2019 patients (n = 32, 73% vs n = 11, 28% in controls; p < 0.001). Despite the same severity of illness and level of hypoxemia at admission, coronavirus disease 2019 patients failed more high flow oxygen via nasal cannula challenges (n = 16, 100% vs n = 5, 45% in controls; p = 0.002), were more often intubated (n = 44, 100% vs n = 22, 56% in controls; p < 0.001) and paralyzed (n = 34, 77% vs n = 3, 14% in controls; p < 0.001), required higher level of positive end-expiratory pressure (15 vs 8 cm H2O in controls; p < 0.001), more prone positioning (n = 33, 75% vs n = 6, 27% in controls; p < 0.001), more dialysis (n = 16, 36% vs n = 3, 8% in controls; p = 0.003), more hemodynamic support by vasopressors (n = 36, 82% vs n = 22, 56% in controls; p = 0.001), and had more often a prolonged weaning from mechanical ventilation (n = 28, 64% vs n = 10, 26% in controls; p < 0.01) resulting in a more frequent resort to tracheostomy (n = 18, 40.9% vs n = 2, 9% in controls; p = 0.01). However, an intensive management requiring more staff per patient for positioning coronavirus disease 2019 subjects (6 [5-7] vs 5 [4-5] in controls; p < 0.001) yielded the same ICU survival rate in the two groups (n = 34, 77% vs n = 29, 74% in controls; p = 0.23). CONCLUSIONS: In its most severe form, coronavirus disease 2019 pneumonia striked preferentially the vulnerable obese population, evolved toward a multiple organ failure, required prolonged mechanical ventilatory support, and resulted in a high workload for the caregivers.

Tracheal intubation in microgravity: a simulation study comparing direct laryngoscopy and videolaryngoscopy†.

BACKGROUND: The risk of severe medical and surgical events during long-duration spaceflight is significant. In space, many environmental and psychological factors may make tracheal intubation more difficult than on Earth. We hypothesised that, in microgravity, tracheal intubation may be facilitated by the use of a videolaryngoscope compared with direct laryngoscopy. METHODS: In a non-randomised, controlled, cross-over simulation study, we compared intubation performance of novice operators and experts, using either a direct laryngoscope or a videolaryngoscope, in weightlessness and in normogravity. The primary outcome was the success rate of tracheal intubation. Time to intubation and the confidence score into the success of tube placement were also recorded. RESULTS: When novices attempted to intubate the trachea in microgravity, the success rate of tracheal intubation using a videolaryngoscope was significantly higher (20/25 [80%]; 95% confidence interval [CI], 64.3-95.7 vs eight/20 [40%]; 95% CI, 18.5-61.5; P=0.006), and intubation time was shorter, compared with using a direct laryngoscope. In normogravity, the success rate of tracheal intubation by experts was significantly higher than that by novices (16/20 [80%]; 95% CI, 62.5-97.5 vs seven/25 [28%]; 95% CI, 10.4-45.6; P=0.001), but in microgravity, there was no significant difference between the experts and novices (19/20 [95%]; 95% CI, 85.4-100 vs 20/25 [80%]; 95% CI, 64.3-95.7; P=0.113). Higher confidence scores were achieved with videolaryngoscopy compared with direct laryngoscopy by both experts and novices in both microgravity and normogravity. CONCLUSIONS: Videolaryngoscopy was associated with higher intubation success rate and speed, and higher confidence for correct tube placement by novice operators in microgravity, and as such may represent the best technique for advanced airway management during long-duration spaceflight.

An Evaluation of the Influence of Body Mass Index on Severity Scoring.

OBJECTIVES: Although one third or more of critically ill patients in the United States are obese, obesity is not incorporated as a contributing factor in any of the commonly used severity of illness scores. We hypothesize that selected severity of illness scores would perform differently if body mass index categorization was incorporated and that the performance of these score models would improve after consideration of body mass index as an additional model feature. DESIGN: Retrospective cohort analysis from a multicenter ICU database which contains deidentified data for more than 200,000 ICU admissions from 208 distinct ICUs across the United States between 2014 and 2015. SETTING: First ICU admission of patients with documented height and weight. PATIENTS: One-hundred eight-thousand four-hundred two patients from 189 different ICUs across United States were included in the analyses, of whom 4,661 (4%) were classified as underweight, 32,134 (30%) as normal weight, 32,278 (30%) as overweight, 30,259 (28%) as obese, and 9,070 (8%) as morbidly obese. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: To assess the effect of adding body mass index as a risk adjustment element to the Acute Physiology and Chronic Health Evaluation IV and Oxford Acute Severity of Illness scoring systems, we examined the impact of this addition on both discrimination and calibration. We performed three assessments based upon 1) the original scoring systems, 2) a recalibrated version of the systems, and 3) a recalibrated version incorporating body mass index as a covariate. We also performed a subgroup analysis in groups defined using World Health Organization guidelines for obesity. Incorporating body mass index into the models provided a minor improvement in both discrimination and calibration. In a subgroup analysis, model discrimination was higher in groups with higher body mass index, but calibration worsened. CONCLUSIONS: The performance of ICU prognostic models utilizing body mass index category as a scoring element was inconsistent across body mass index categories. Overall, adding body mass index as a risk adjustment variable led only to a minor improvement in scoring system performance.

Airway management in microgravity: A systematic review.

INTRODUCTION: In the near future, space programs will shift their focus toward long-duration interplanetary missions, in particular to the Moon and Mars. These exploration missions will be associated with an increased risk of acute medical problems, which will need to be handled by an autonomous crew operating in extreme isolation. An important skill in emergencies is represented by airway management. Many airway devices are available and it is unclear which one would be the most suitable in the context of a space mission. The aim of this systematic review was to analyze the existing literature on airway management in the special situation of weightlessness during space missions. MATERIAL AND METHODS: We performed a standardized review of published literature on airway management in spaceflight and analogue environments using the database PubMed. RESULTS: We identified a total of 3111 publications of which 3039 were initially excluded after evaluation. The screening identified three randomized comparative manikin studies, two of them in parabolic flights, one in a submerged setup. Under free-floating conditions, the insertion success rate of supraglottic airway devices (SGA) was excellent (91%-97%). The administration of artificial ventilation could be successfully achieved in weightlessness with SGA. The success rate of conventional laryngoscopy under free-floating conditions fluctuated between 15% and 86%. CONCLUSION: It appears possible to safely manage the airway in weightlessness, provided that certain conditions are ensured, such as restraining the patient and operator for conventional orotracheal intubation. If airway protection is required with endotracheal intubation, both the operator and the patient should be restrained.

Severity of Illness Scores May Misclassify Critically Ill Obese Patients.

OBJECTIVE: Severity of illness scores rest on the assumption that patients have normal physiologic values at baseline and that patients with similar severity of illness scores have the same degree of deviation from their usual state. Prior studies have reported differences in baseline physiology, including laboratory markers, between obese and normal weight individuals, but these differences have not been analyzed in the ICU. We compared deviation from baseline of pertinent ICU laboratory test results between obese and normal weight patients, adjusted for the severity of illness. DESIGN: Retrospective cohort study in a large ICU database. SETTING: Tertiary teaching hospital. PATIENTS: Obese and normal weight patients who had laboratory results documented between 3 days and 1 year prior to hospital admission. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Seven hundred sixty-nine normal weight patients were compared with 1,258 obese patients. After adjusting for the severity of illness score, age, comorbidity index, baseline laboratory result, and ICU type, the following deviations were found to be statistically significant: WBC 0.80 (95% CI, 0.27-1.33) × 10/L; p = 0.003; log (blood urea nitrogen) 0.01 (95% CI, 0.00-0.02); p = 0.014; log (creatinine) 0.03 (95% CI, 0.02-0.05), p < 0.001; with all deviations higher in obese patients. A logistic regression analysis suggested that after adjusting for age and severity of illness at least one of these deviations had a statistically significant effect on hospital mortality (p = 0.009). CONCLUSIONS: Among patients with the same severity of illness score, we detected clinically small but significant deviations in WBC, creatinine, and blood urea nitrogen from baseline in obese compared with normal weight patients. These small deviations are likely to be increasingly important as bigger data are analyzed in increasingly precise ways. Recognition of the extent to which all critically ill patients may deviate from their own baseline may improve the objectivity, precision, and generalizability of ICU mortality prediction and severity adjustment models.

Trends in mortality from pneumonia in the Europe union: a temporal analysis of the European detailed mortality database between 2001 and 2014.

BACKGROUND: Pneumonia is responsible for approximately 230,000 deaths in Europe, annually. Comprehensive and comparable reports on pneumonia mortality trends across the European Union (EU) are lacking. METHODS: A temporal analysis of national mortality statistics to compare trends in pneumonia age-standardised death rates (ASDR) of EU countries between 2001 and 2014 was performed. International Classification of Diseases version 10 (ICD-10) codes were used to extract data from the World Health Organisation European Detailed Mortality Database and trends were analysed using Joinpoint regression. RESULTS: Median pneumonia mortality across the EU for the last recorded observation was 19.8 / 100,000 and 6.9 / 100,000 for males and females, respectively. Mortality was higher in males across all EU countries, most notably in Estonia and Lithuania where the ratio of male to female ASDR was 4.0 and 3.7, respectively. Gender mortality differences were lowest in the UK and Demark with ASDR ratios of 1.1 and 1.5, respectively. Pneumonia mortality across all countries decreased by a median of 31.0% over the observation period. Countries that demonstrated an increase in pneumonia mortality were Poland (males + 33.1%, females + 10.2%), and Lithuania (males + 6.0%). CONCLUSIONS: Mortality from pneumonia is improving in most EU countries, however substantial variation in trends remains between countries and between genders.

Bridging the Health Data Divide.

Fundamental quality, safety, and cost problems have not been resolved by the increasing digitization of health care. This digitization has progressed alongside the presence of a persistent divide between clinicians, the domain experts, and the technical experts, such as data scientists. The disconnect between clinicians and data scientists translates into a waste of research and health care resources, slow uptake of innovations, and poorer outcomes than are desirable and achievable. The divide can be narrowed by creating a culture of collaboration between these two disciplines, exemplified by events such as datathons. However, in order to more fully and meaningfully bridge the divide, the infrastructure of medical education, publication, and funding processes must evolve to support and enhance a learning health care system.

Anaesthesia in outer space: the ultimate ambulatory setting?

PURPOSE OF REVIEW: Missions to the Moon or more distant planets are planned in the next future, and will push back the limits of our experience in providing medical support in remote environments. Medical preparedness is ongoing, and involves planning for emergency surgical interventions and anaesthetic procedures. This review will summarize what principles of ambulatory anaesthesia on Earth could benefit the environment of a space mission with its unique constraints. RECENT FINDINGS: Ambulatory anaesthesia relies on several principles such as improved patient pathway, correct patient selection, optimized procedural strategies to hasten recovery and active prevention of postoperative complications. Severe limitations in the equipment available and the skills of the crew members represent the key factors to be taken into account when designing the on-board medical system for future interplanetary space missions. SUMMARY: The application of some of the key principles of ambulatory anaesthesia, as well as recent advances in anaesthetic techniques and better understanding of human adaptation to the space environment might allow nonanaesthesiologist physicians to perform common anaesthetic procedures, whilst maximizing crew safety and minimizing the impact of medical events on the mission.