The role of computer-based clinical decision support systems to deliver protective mechanical ventilation

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.

Data Representation Structure to Support Clinical Decision-Making in the Pediatric Intensive Care Unit: Interview Study and Preliminary Decision Support Interface Design

BACKGROUND

Clinical decision-making is a complex cognitive process that relies on the interpretation of a large variety of data from different sources and involves the use of knowledge bases and scientific recommendations. The representation of clinical data plays a key role in the speed and efficiency of its interpretation. In addition, the increasing use of clinical decision support systems (CDSSs) provides assistance to clinicians in their practice, allowing them to improve patient outcomes. In the pediatric intensive care unit (PICU), clinicians must process high volumes of data and deal with ever-growing workloads. As they use multiple systems daily to assess patients' status and to adjust the health care plan, including electronic health records (EHR), clinical systems (eg, laboratory, imaging and pharmacy), and connected devices (eg, bedside monitors, mechanical ventilators, intravenous pumps, and syringes), clinicians rely mostly on their judgment and ability to trace relevant data for decision-making. In these circumstances, the lack of optimal data structure and adapted visual representation hinder clinician's cognitive processes and clinical decision-making skills.

OBJECTIVE

In this study, we designed a prototype to optimize the representation of clinical data collected from existing sources (eg, EHR, clinical systems, and devices) via a structure that supports the integration of a home-developed CDSS in the PICU. This study was based on analyzing end user needs and their clinical workflow.

METHODS

First, we observed clinical activities in a PICU to secure a better understanding of the workflow in terms of staff tasks and their use of EHR on a typical work shift. Second, we conducted interviews with 11 clinicians from different staff categories (eg, intensivists, fellows, nurses, and nurse practitioners) to compile their needs for decision support. Third, we structured the data to design a prototype that illustrates the proposed representation. We used a brain injury care scenario to validate the relevance of integrated data and the utility of main functionalities in a clinical context. Fourth, we held design meetings with 5 clinicians to present, revise, and adapt the prototype to meet their needs.

RESULTS

We created a structure with 3 levels of abstraction-unit level, patient level, and system level-to optimize clinical data representation and display for efficient patient assessment and to provide a flexible platform to host the internally developed CDSS. Subsequently, we designed a preliminary prototype based on this structure.

CONCLUSIONS

The data representation structure allows prioritizing patients via criticality indicators, assessing their conditions using a personalized dashboard, and monitoring their courses based on the evolution of clinical values. Further research is required to define and model the concepts of criticality, problem recognition, and evolution. Furthermore, feasibility tests will be conducted to ensure user satisfaction.

Understanding clinical and biological heterogeneity to advance precision medicine in paediatric acute respiratory distress syndrome

Paediatric acute respiratory distress syndrome (PARDS) is a heterogeneous clinical syndrome that is associated with high rates of mortality and long-term morbidity. Factors that distinguish PARDS from adult acute respiratory distress syndrome (ARDS) include changes in developmental stage and lung maturation with age, precipitating factors, and comorbidities. No specific treatment is available for PARDS and management is largely supportive, but methods to identify patients who would benefit from specific ventilation strategies or ancillary treatments, such as prone positioning, are needed. Understanding of the clinical and biological heterogeneity of PARDS, and of differences in clinical features and clinical course, pathobiology, response to treatment, and outcomes between PARDS and adult ARDS, will be key to the development of novel preventive and therapeutic strategies and a precision medicine approach to care. Studies in which clinical, biomarker, and transcriptomic data, as well as informatics, are used to unpack the biological and phenotypic heterogeneity of PARDS, and implementation of methods to better identify patients with PARDS, including methods to rapidly identify subphenotypes and endotypes at the point of care, will drive progress on the path to precision medicine.

Clinical Nurse Specialist Practice: Impact on Improving Sedation Practice in Critical Care

PURPOSE/OBJECTIVES

Prolonged mechanical ventilation results from deeper levels of sedation. This may lead to impaired respiratory muscle functioning that develops into pneumonia, increases antibiotic use, increases delirium risk, and increases length of hospitalization. A trauma and surgical intensive care unit interdisciplinary team conducted a quality improvement project to lighten sedation levels and shorten mechanical ventilation time.

DESCRIPTION OF THE PROJECT

The project included multimodal elements to improve sedation practice. Standardizing the spontaneous awakening trial algorithm, creation of electronic health record tools, integration of sedation practices into daily rounds, and focused education for nursing were implemented in April 2021 through October 2021.

OUTCOME

A reduction of median hours spent on mechanical ventilation was achieved. Mechanical ventilation hours decreased from 77 to 70. Richmond Agitation Sedation Scale levels improved from a median of -2 to -1, and daily spontaneous awakening trials increased from 10% to 27% completed.

CONCLUSION

The quality improvement project demonstrated that, with increased daily spontaneous awakening trials and lighter sedation levels, the time patients spent on mechanical ventilation was shortened. There was no increase to self-extubation with lighter sedations levels. Shorter time on mechanical ventilation can reduce patient harm risks.

Enhancing Diagnosis Through Technology: Decision Support, Artificial Intelligence, and Beyond

Patient care in intensive care environments is complex, time-sensitive, and data-rich, factors that make these settings particularly well-suited to clinical decision support (CDS). A wide range of CDS interventions have been used in intensive care unit environments. The field needs well-designed studies to identify the most effective CDS approaches. Evolving artificial intelligence and machine learning models may reduce information-overload and enable teams to take better advantage of the large volume of patient data available to them. It is vital to effectively integrate new CDS into clinical workflows and to align closely with the cognitive processes of frontline clinicians.

Adherence to Lung-Protective Ventilation Principles in Pediatric Acute Respiratory Distress Syndrome: A Pediatric Acute Respiratory Distress Syndrome Incidence and Epidemiology Study

OBJECTIVES

To describe mechanical ventilation management and factors associated with nonadherence to lung-protective ventilation principles in pediatric acute respiratory distress syndrome.

DESIGN

A planned ancillary study to a prospective international observational study. Mechanical ventilation management (every 6 hr measurements) during pediatric acute respiratory distress syndrome days 0-3 was described and compared with Pediatric Acute Lung Injury Consensus Conference tidal volume recommendations (< 7 mL/kg in children with impaired respiratory system compliance, < 9 mL/kg in all other children) and the Acute Respiratory Distress Syndrome Network lower positive end-expiratory pressure/higher Fio2 grid recommendations.

SETTING

Seventy-one international PICUs.

PATIENTS

Children with pediatric acute respiratory distress syndrome.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Analyses included 422 children. On pediatric acute respiratory distress syndrome day 0, median tidal volume was 7.6 mL/kg (interquartile range, 6.3-8.9 mL/kg) and did not differ by pediatric acute respiratory distress syndrome severity. Plateau pressure was not recorded in 97% of measurements. Using delta pressure (peak inspiratory pressure - positive end-expiratory pressure), median tidal volume increased over quartiles of median delta pressure (p = 0.007). Median delta pressure was greater than or equal to 18 cm H2O for all pediatric acute respiratory distress syndrome severity levels. In severe pediatric acute respiratory distress syndrome, tidal volume was greater than or equal to 7 mL/kg 62% of the time, and positive end-expiratory pressure was lower than recommended by the positive end-expiratory pressure/Fio2 grid 70% of the time. In multivariable analysis, tidal volume nonadherence was more common with severe pediatric acute respiratory distress syndrome, fewer PICU admissions/yr, non-European PICUs, higher delta pressure, corticosteroid use, and pressure control mode. Adherence was associated with underweight stature and cuffed endotracheal tubes. In multivariable analysis, positive end-expiratory pressure/Fio2 grid nonadherence was more common with higher pediatric acute respiratory distress syndrome severity, ventilator decisions made primarily by the attending physician, pre-ICU cardiopulmonary resuscitation, underweight stature, and age less than 2 years. Adherence was associated with respiratory therapist involvement in ventilator management and longer time from pediatric acute respiratory distress syndrome diagnosis. Higher nonadherence to tidal volume and positive end-expiratory pressure recommendations were independently associated with higher mortality and longer duration of ventilation after adjustment for confounding variables. In stratified analyses, these associations were primarily influenced by children with severe pediatric acute respiratory distress syndrome.

CONCLUSIONS

Nonadherence to lung-protective ventilation principles is common in pediatric acute respiratory distress syndrome and may impact outcome. Modifiable factors exist that may improve adherence.

Real-Time Effort Driven Ventilator Management: A Pilot Study

OBJECTIVES

Mechanical ventilation of patients with acute respiratory distress syndrome should balance lung and diaphragm protective principles, which may be difficult to achieve in routine clinical practice. Through a Phase I clinical trial, we sought to determine whether a computerized decision support-based protocol (real-time effort-driven ventilator management) is feasible to implement, results in improved acceptance for lung and diaphragm protective ventilation, and improves clinical outcomes over historical controls.

DESIGN

Interventional nonblinded pilot study.

SETTING

PICU.

PATIENTS

Mechanically ventilated children with acute respiratory distress syndrome.

INTERVENTIONS

A computerized decision support tool was tested which prioritized lung-protective management of peak inspiratory pressure-positive end-expiratory pressure, positive end-expiratory pressure/FIO2, and ventilatory rate. Esophageal manometry was used to maintain patient effort in a physiologic range. Protocol acceptance was reported, and enrolled patients were matched 4:1 with respect to age, initial oxygenation index, and percentage of immune compromise to historical control patients for outcome analysis.

MEASUREMENTS AND MAIN RESULTS

Thirty-two patients were included. Acceptance of protocol recommendations was over 75%. One-hundred twenty-eight matched historical controls were used for analysis. Compared with historical controls, patients treated with real-time effort-driven ventilator management received lower peak inspiratory pressure-positive end-expiratory pressure and tidal volume, and higher positive end-expiratory pressure when FIO2 was greater than 0.60. Real-time effort-driven ventilator management was associated with 6 more ventilator-free days, shorter duration until the first spontaneous breathing trial and 3 fewer days on mechanical ventilation among survivors (all p ≤ 0.05) in comparison with historical controls, while maintaining no difference in the rate of reintubation.

CONCLUSIONS

A computerized decision support-based protocol prioritizing lung-protective ventilation balanced with reduction of controlled ventilation to maintain physiologic levels of patient effort can be implemented and may be associated with shorter duration of ventilation.