MyMedR

Displaying all 4 publications

Abstract:

Sort:

Evaluation of model-based methods in estimating respiratory mechanics in the presence of variable patient effort

Redmond DP, Chiew YS, Major V, Chase JG

Comput Methods Programs Biomed, 2019 Apr;171:67-79.
PMID: 27697371 DOI: 10.1016/j.cmpb.2016.09.011

Monitoring of respiratory mechanics is required for guiding patient-specific mechanical ventilation settings in critical care. Many models of respiratory mechanics perform poorly in the presence of variable patient effort. Typical modelling approaches either attempt to mitigate the effect of the patient effort on the airway pressure waveforms, or attempt to capture the size and shape of the patient effort. This work analyses a range of methods to identify respiratory mechanics in volume controlled ventilation modes when there is patient effort. The models are compared using 4 Datasets, each with a sample of 30 breaths before, and 2-3 minutes after sedation has been administered. The sedation will reduce patient efforts, but the underlying pulmonary mechanical properties are unlikely to change during this short time. Model identified parameters from breathing cycles with patient effort are compared to breathing cycles that do not have patient effort. All models have advantages and disadvantages, so model selection may be specific to the respiratory mechanics application. However, in general, the combined method of iterative interpolative pressure reconstruction, and stacking multiple consecutive breaths together has the best performance over the Dataset. The variability of identified elastance when there is patient effort is the lowest with this method, and there is little systematic offset in identified mechanics when sedation is administered.

Matched MeSH terms: Respiratory Insufficiency/physiopathology
Fulltext Stochastic Modelling of Respiratory System Elastance for Mechanically Ventilated Respiratory Failure Patients

Lee JWW, Chiew YS, Wang X, Tan CP, Mat Nor MB, Damanhuri NS, et al.

Ann Biomed Eng, 2021 Dec;49(12):3280-3295.
PMID: 34435276 DOI: 10.1007/s10439-021-02854-4

While lung protective mechanical ventilation (MV) guidelines have been developed to avoid ventilator-induced lung injury (VILI), a one-size-fits-all approach cannot benefit every individual patient. Hence, there is significant need for the ability to provide patient-specific MV settings to ensure safety, and optimise patient care. Model-based approaches enable patient-specific care by identifying time-varying patient-specific parameters, such as respiratory elastance, Ers, to capture inter- and intra-patient variability. However, patient-specific parameters evolve with time, as a function of disease progression and patient condition, making predicting their future values crucial for recommending patient-specific MV settings. This study employs stochastic modelling to predict future Ers values using retrospective patient data to develop and validate a model indicating future intra-patient variability of Ers. Cross validation results show stochastic modelling can predict future elastance ranges with 92.59 and 68.56% of predicted values within the 5-95% and the 25-75% range, respectively. This range can be used to ensure patients receive adequate minute ventilation should elastance rise and minimise the risk of VILI should elastance fall. The results show the potential for model-based protocols using stochastic model prediction of future Ers values to provide safe and patient-specific MV. These results warrant further investigation to validate its clinical utility.

Matched MeSH terms: Respiratory Insufficiency/physiopathology*
Fulltext Prevalence, clinical predictors and diagnosis of gastro-oesophageal reflux in children with persistent respiratory symptoms

Siti Mazliah K, Norzila MZ, Deng CT, Zulfiqar A, Azizi BHO

Med J Malaysia, 2000 Jun;55(2):180-7.
PMID: 19839146

Objectives: This was a cross sectional study conducted in the Paediatric Institute among infants and children with chronic respiratory symptoms with the following objectives: i) to determine the prevalence of gastro-oesophageal reflux in children with persistent respiratory symptoms, ii) to identify the clinical predictors of GOR (Gastro-oesophageal reflux) in children with persistent respiratory symptoms and iii) assess the validity of abdominal ultrasound, barium oesophagogram and chest radiograph in diagnosing GOR in these patients.
Materials and Methods: Forty-four patients were recruited over a period of six months. All the presenting symptoms were identified. The patients were subjected to chest radiograph, abdominal ultrasound, barium oesophagogram and 24-hour pH oesophageal monitoring.
The predictive validity of clinical symptoms, chest radiograph, abdominal ultrasound and barium oesophagogram were assessed. Twenty-four hours oesophageal pH was the gold standard to diagnose GOR.
Results: The mean age of patients was 9.1 months (1-58 months). Thirty-one patients (70.5%) were confirmed to have GOR by pH study. Respiratory symptoms alone were not useful to predict GOR. Cough had the highest sensitivity of 51.6%. stridor, wheeze and choking each had a specificity of 76%. Wheeze, vomiting, choking and stridor were identified to have high specificity (90-100%) in diagnosing GOR when any two symptoms were taken in combination.
Collapse/consolidation was the commonest radiological abnormality but had low sensitivity (35.5%) and specificity (53.8%). However hyperinflation on chest radiograph had a specificity of 92.3% with positive predictive value at 80% in diagnosing GOR. Barium oesophagogram has low sensitivity (37.9%) and moderate specificity (75%) in diagnosing GOR in children with respiratory symptoms.
Abdominal ultrasound was a valid mode of diagnosing GOR when there were three or more reflux episodes demonstrated during the screening period with a specificity of 90.9%. However the sensitivity was low ie 20-25%. The specificity increased to 90-100% when two positive tests were taken in combination (abdominal ultrasound and barium oesophagogram). However the sensitivity remained low (10-20%). Chest radiograph did not improve the predictive value when considered with the above tests. Combination of clinical symptoms were useful as clinical predictors of GOR. In the absence of a pH oesophageal monitoring, a combination of barium oesophagogram and ultrasound may be helpful in diagnosing GOR.

Matched MeSH terms: Respiratory Insufficiency/physiopathology*
Non-steady State Modeling of the Ventilatory Depressant Effect of Remifentanil in Awake Patients Experiencing Moderate-to-severe Obstructive Sleep Apnea

Doufas AG, Shafer SL, Rashid NHA, Kushida CA, Capasso R

Anesthesiology, 2019 02;130(2):213-226.
PMID: 30247202 DOI: 10.1097/ALN.0000000000002430

BACKGROUND: Evidence suggests that obstructive sleep apnea promotes postoperative pulmonary complications by enhancing vulnerability to opioid-induced ventilatory depression. We hypothesized that patients with moderate-to-severe obstructive sleep apnea are more sensitive to remifentanil-induced ventilatory depression than controls.
METHODS: After institutional approval and written informed consent, patients received a brief remifentanil infusion during continuous monitoring of ventilation. We compared minute ventilation in 30 patients with moderate-to-severe obstructive sleep apnea diagnosed by polysomnography and 20 controls with no to mild obstructive sleep apnea per polysomnography. Effect site concentrations were estimated by a published pharmacologic model. We modeled minute ventilation as a function of effect site concentration and the estimated carbon dioxide. Obstructive sleep apnea status, body mass index, sex, age, use of continuous positive airway pressure, apnea/hypopnea events per hour of sleep, and minimum nocturnal oxygen saturation measured by pulse oximetry in polysomnography were tested as covariates for remifentanil effect site concentration at half-maximal depression of minute ventilation (Ce50) and included in the model if a threshold of 6.63 (P < 0.01) in the reduction of objective function was reached and improved model fit.
RESULTS: Our model described the observed minute ventilation with reasonable accuracy (22% median absolute error). We estimated a remifentanil Ce50 of 2.20 ng · ml (95% CI, 2.09 to 2.33). The estimated value for Ce50 was 2.1 ng · ml (95% CI, 1.9 to 2.3) in patients without obstructive sleep apnea and 2.3 ng · ml (95% CI, 2.2 to 2.5) in patients with obstructive sleep apnea, a statistically nonsignificant difference (P = 0.081). None of the tested covariates demonstrated a significant effect on Ce50. Likelihood profiling with the model including obstructive sleep apnea suggested that the effect of obstructive sleep apnea on remifentanil Ce50 was less than 5%.
CONCLUSIONS: Obstructive sleep apnea status, apnea/hypopnea events per hour of sleep, or minimum nocturnal oxygen saturation measured by pulse oximetry did not influence the sensitivity to remifentanil-induced ventilatory depression in awake patients receiving a remifentanil infusion of 0.2 μg · kg of ideal body weight per minute.

Matched MeSH terms: Respiratory Insufficiency/physiopathology*