The validity and reliability of the clinical assessment of increased work of breathing in acutely ill patients

Development of clinical tools to estimate the breathing effort during high-flow oxygen therapy: A multicenter cohort study

INTRODUCTION AND OBJECTIVES

Quantifying breathing effort in non-intubated patients is important but difficult. We aimed to develop two models to estimate it in patients treated with high-flow oxygen therapy.

PATIENTS AND METHODS

We analyzed the data of 260 patients from previous studies who received high-flow oxygen therapy. Their breathing effort was measured as the maximal deflection of esophageal pressure (ΔPes). We developed a multivariable linear regression model to estimate ΔPes (in cmH2O) and a multivariable logistic regression model to predict the risk of ΔPes being >10 cmH2O. Candidate predictors included age, sex, diagnosis of the coronavirus disease 2019 (COVID-19), respiratory rate, heart rate, mean arterial pressure, the results of arterial blood gas analysis, including base excess concentration (BEa) and the ratio of arterial tension to the inspiratory fraction of oxygen (PaO2:FiO2), and the product term between COVID-19 and PaO2:FiO2.

RESULTS

We found that ΔPes can be estimated from the presence or absence of COVID-19, BEa, respiratory rate, PaO2:FiO2, and the product term between COVID-19 and PaO2:FiO2. The adjusted R2 was 0.39. The risk of ΔPes being >10 cmH2O can be predicted from BEa, respiratory rate, and PaO2:FiO2. The area under the receiver operating characteristic curve was 0.79 (0.73-0.85). We called these two models BREF, where BREF stands for BReathing EFfort and the three common predictors: BEa (B), respiratory rate (RE), and PaO2:FiO2 (F).

CONCLUSIONS

We developed two models to estimate the breathing effort of patients on high-flow oxygen therapy. Our initial findings are promising and suggest that these models merit further evaluation.

Validity and reliability of outcome measures to assess dysfunctional breathing: a systematic review

OBJECTIVE

This study aimed to systematically review the psychometric properties of outcome measures that assess dysfunctional breathing (DB) in adults.

METHODS

Studies on developing and evaluating measurement properties to assess DB were included. The study investigated the empirical research published between 1990 and February 2022, with an updated search in May 2023 in the Cochrane Library database of systematic reviews and the Cochrane Central Register of Controlled Trials, the Ovid Medline (full), the Ovid Excerta Medica Database, the Ovid allied and complementary medicines database, the Ebscohost Cumulative Index to Nursing and Allied Health Literature and the Physiotherapy Evidence Database. The included studies' methodological quality was assessed using the COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN) risk of bias checklist. Data analysis and synthesis followed the COSMIN methodology for reviews of outcome measurement instruments.

RESULTS

Sixteen studies met the inclusion criteria, and 10 outcome measures were identified. The psychometric properties of these outcome measures were evaluated using COSMIN. The Nijmegen Questionnaire (NQ) is the only outcome measure with 'sufficient' ratings for content validity, internal consistency, reliability and construct validity. All other outcome measures did not report characteristics of content validity in the patients' group.

DISCUSSION

The NQ showed high-quality evidence for validity and reliability in assessing DB. Our review suggests that using NQ to evaluate DB in people with bronchial asthma and hyperventilation syndrome is helpful. Further evaluation of the psychometric properties is needed for the remaining outcome measures before considering them for clinical use.

PROSPERO REGISTRATION NUMBER

CRD42021274960.

RESPIRATORY MECHANICS AND NEURAL RESPIRATORY DRIVE OF UNTREATED GASPING DURING CARDIAC ARREST IN A PORCINE MODEL

ABSTRACT

Introduction: Although the effects on hemodynamics of gasping during cardiac arrest (CA) have received a lot of attention, less is known about the respiratory mechanics and physiology of respiration in gasping. This study aimed to investigate the respiratory mechanics and neural respiratory drive of gasping during CA in a porcine model. Method: Pigs weighing 34.9 ± 5.7 kg were anesthetized intravenously. Ventricular fibrillation (VF) was electrically induced and untreated for 10 min. Mechanical ventilation (MV) was ceased immediately after the onset of VF. Hemodynamic and respiratory parameters, pressure signals, diaphragmatic electromyogram data, and blood gas analysis data were recorded. Results: Gasping was observed in all the animals at a significantly lower rate (2-5 gaps/min), with higher tidal volume ( VT ; 0.62 ± 0.19 L, P < 0.01), and with lower expired minute volume (2.51 ± 1.49 L/min, P < 0.001) in comparison with the baseline. The total respiratory cycle time and the expiratory time tended to be lengthened. Statistically significant elevations in transdiaphragmatic pressure, the pressure-time product of diaphragmatic pressure, and the mean of root mean square diaphragmatic electromyogram values (RMSmean) were observed ( P < 0.05, P < 0.05, and P < 0.001, respectively); however, VT /RMSmean and transdiaphragmatic pressure/RMSmean were reduced at all time points. The partial pressure of oxygen showed a continuous decline after VF to reach statistical significance in the 10th minute (9.46 ± 0.96 kPa, P < 0.001), whereas the partial pressure of carbon dioxide tended to first rise and then fall. Conclusions: Gasping during CA was characterized by high VT , extremely low frequency, and prolonged expiratory time, which may improve hypercapnia. During gasping, increased work of breathing and insufficient neuromechanical efficacy of neural respiratory drive suggested the necessity of MV and appropriate management strategies for MV during resuscitation after CA.

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.

Web Applications for Teaching the Respiratory System: Content Validation

The subject of respiratory mechanics has complex characteristics, functions, and interactions that can be difficult to understand in training and medical education contexts. As such, education strategies based on computational simulations comprise useful tools, but their application in the medical area requires stricter validation processes. This paper shows a statistical and a Delphi validation for two modules of a web application used for respiratory system learning: (I) “Anatomy and Physiology” and (II) “Work of Breathing Indexes”. For statistical validation, population and individual analyses were made using a database of healthy men to compare experimental and model-predicted data. For both modules, the predicted values followed the trend marked by the experimental data in the population analysis, while in the individual analysis, the predicted errors were 9.54% and 25.38% for maximal tidal volume and airflow, respectively, and 6.55%, 9.33%, and 11.77% for rapid shallow breathing index, work of breathing, and maximal inspiratory pressure, respectively. For the Delphi validation, an average higher than 4 was obtained after health professionals evaluated both modules from 1 to 5. In conclusion, both modules are good tools for respiratory system learning processes. The studied parameters behaved consistently with the expressions that describe ventilatory dynamics and were correlated with experimental data; furthermore, they had great acceptance by specialists.

Analysis of respiratory kinematics: a method to characterize breaths from motion signals

Breathing motion (respiratory kinematics) can be characterized by the interval and depth of each breath, and by magnitude-synchrony relationships between locations. Such characteristics and their breath-by-breath variability might be useful indicators of respiratory health. To enable breath-by-breath characterization of respiratory kinematics, we developed a method to detect breaths using motion sensors. In 34 volunteers who underwent maximal exercise testing, we used 8 motion sensors to record upper rib, lower rib and abdominal kinematics at 3 exercise stages (rest, lactate threshold and exhaustion). We recorded volumetric air flow signals using clinical exercise laboratory equipment and synchronized them with kinematic signals. Using instantaneous phase landmarks from the analytic representation of kinematic and flow signals, we identified individual breaths and derived respiratory rate (RR) signals at 1Hz. To evaluate the fidelity of kinematics-derived RR, we calculated bias, limits of agreement, and cross-correlation coefficients (CCC) relative to flow-derived RR. To identify coupling between kinematics and flow, we calculated the Shannon entropy of the relative frequency with which flow landmarks were distributed over the phase of the kinematic cycle. We found good agreement in the kinematics-derived and flow-derived RR signals [bias (95% limit of agreement) = 0.1 (± 7) breaths/minute; CCC median (IQR) = 0.80 (0.48 - 0.91)]. In individual signals, kinematics and flow were well-coupled (entropy 0.9-1.4 across sensors), but the relationship varied within (by exercise stage) and between individuals. The final result was that the flow landmarks did not consistently localize to any particular phase of the kinematic signals (entropy 2.2-3.0 across sensors). The Analysis of Respiratory Kinematics method can yield highly resolved respiratory rate signals by separating individual breaths. This method will facilitate characterization of clinically significant breathing motion patterns on a breath-by-breath basis. The relationship between respiratory kinematics and flow is much more complex than expected, varying between and within individuals.

A Year of Critical Care: The Changing Face of the ICU During COVID-19

During the SARS-CoV-2 pandemic, admissions to hospital intensive care units (ICUs) surged, exerting unprecedented stress on ICU resources and operations. The novelty of the highly infectious coronavirus disease 2019 (COVID-19) required significant changes to the way critically ill patients were managed. Houston Methodist’s incident command center team navigated this health crisis by ramping up its bed capacity, streamlining treatment algorithms, and optimizing ICU staffing while ensuring adequate supplies of personal protective equipment (PPE), ventilators, and other ICU essentials. A tele–critical-care program and its infrastructure were deployed to meet the demands of the pandemic. Community hospitals played a vital role in creating a collaborative ecosystem for the treatment and referral of critically ill patients. Overall, the healthcare industry’s response to COVID-19 forced ICUs to become more efficient and dynamic, with improved patient safety and better resource utilization. This article provides an experiential account of Houston Methodist’s response to the pandemic and discusses the resulting impact on the function of ICUs.

Development and Initial Validation of a Frontline Health Worker mHealth Assessment Platform (MEDSINC®) for Children 2-60 Months of Age

Approximately 3 million children younger than 5 years living in low- and middle-income countries (LMICs) die each year from treatable clinical conditions such as pneumonia, dehydration secondary to diarrhea, and malaria. A majority of these deaths could be prevented with early clinical assessments and appropriate therapeutic intervention. In this study, we describe the development and initial validation testing of a mobile health (mHealth) platform, MEDSINC^®, designed for frontline health workers (FLWs) to perform clinical risk assessments of children aged 2–60 months. MEDSINC is a web browser–based clinical severity assessment, triage, treatment, and follow-up recommendation platform developed with physician-based Bayesian pattern recognition logic. Initial validation, usability, and acceptability testing were performed on 861 children aged between 2 and 60 months by 49 FLWs in Burkina Faso, Ecuador, and Bangladesh. MEDSINC-based clinical assessments by FLWs were independently and blindly correlated with clinical assessments by 22 local health-care professionals (LHPs). Results demonstrate that clinical assessments by FLWs using MEDSINC had a specificity correlation between 84% and 99% to LHPs, except for two outlier assessments (63% and 75%) at one study site, in which local survey prevalence data indicated that MEDSINC outperformed LHPs. In addition, MEDSINC triage recommendation distributions were highly correlated with those of LHPs, whereas usability and feasibility responses from LHP/FLW were collectively positive for ease of use, learning, and job performance. These results indicate that the MEDSINC platform could significantly increase pediatric health-care capacity in LMICs by improving FLWs’ ability to accurately assess health status and triage of children, facilitating early life-saving therapeutic interventions.

Derivation and Validation of an Objective Effort of Breathing Score in Critically Ill Children

OBJECTIVES

To derive and validate a score that correlates with an objective measurement of a child's effort of breathing.

DESIGN

Secondary analysis of a previously conducted observational study.

SETTING

The pediatric and cardiothoracic ICUs of a quaternary-care children's hospital.

PATIENTS

Patients more than 37 weeks gestational age to age 18 years who were undergoing extubation.

INTERVENTIONS

Effort of breathing was measured in patients following extubation using esophageal manometry to calculate pressure rate product. Simultaneously, members of a multidisciplinary team (nurse, physician, and respiratory therapist) assessed respiratory function using a previously validated tool. Elements of the tool that were significantly associated with pressure rate product in univariate analysis were identified and included in a multivariate model. An Effort of Breathing score was derived from the results of the model using data from half of the subjects (derivation cohort) and then validated using data from the remaining subjects (validation cohort) by calculating the area under the receiver operator characteristic curve for pressure rate product greater than 90th percentile and for the need for reintubation.

MEASUREMENTS AND MAIN RESULTS

Among 409 subjects, the median age was 5 months, and nearly half were cardiac surgery patients (49.1%). Retractions, stridor, and pulsus paradoxus were included in the Simple Score. Area under the receiver operator characteristic curve for pressure rate product greater than 90th percentile was 0.8359 (95% CI, 0.7996-0.8722) in the derivation cohort and 0.7930 (0.7524-0.8337) in the validation cohort. Area under the receiver operator characteristic curve for reintubation was 0.7280 (0.6807-0.7752) when all scores were analyzed individually and was 0.7548 (0.6644-0.8452) if scores from three clinicians from different disciplines were summated. Results were similar regardless of provider discipline or training.

CONCLUSIONS

A scoring system was derived and validated, performed acceptably to predict increased effort of breathing or need for advanced respiratory support and may function best when used by a team.

DiapHRaGM: A mnemonic to describe the work of breathing in patients with respiratory failure

Background

The assessment of the work of breathing in the definitions of respiratory failure is vague and variable.

Objective

Identify a parsimonious set of signs to describe the work of breathing in hypoxemic, acutely ill patients.

Methods

We examined consecutive medical ICU patients receiving oxygen with a mask, non-invasive ventilation, or T-piece. A physician inspected each patient for 10 seconds, rated the level of respiratory distress, and then examined the patient for vital signs and 17 other physical signs. We used the rating of distress as a surrogate for measuring the work of breathing, constructed three multivariate models to identify the one with the smallest number of signs and largest explained variance, and validated it with bootstrap analysis.

Results

We performed 402 observations on 240 patients. Respiratory distress was absent in 78, mild in 157, moderate in 107, and severe in 60. Respiratory rate, hypoxia, heart rate, and frequency of most signs increased as distress increased. Respiratory rate and hypoxia explained 43% of the variance in respiratory distress. Diaphoresis, gasping, and contraction of the sternomastoid explained an additional 28%. Heart rate, blood pressure, alertness, agitation, body posture, nasal flaring, audible breathing, cyanosis, tracheal tug, retractions, paradox, scalene or abdominal muscles contraction did not increase the explained variance in respiratory distress.

Conclusion

Most of the variance is respiratory distress can be explained by five signs summarized by the mnemonic DiapHRaGM (diaphoresis, hypoxia, respiratory rate, gasping, accessory muscle). This set of signs may allow for efficient, standardized assessments of the work of breathing of hypoxic patients.