EXPERIMENTAL DESIGN: Tumor tissue EGFRm status was determined at screening using the central cobas tissue test or a local tissue test. Baseline circulating tumor (ct)DNA EGFRm status was retrospectively determined with the central cobas plasma test.
RESULTS: Of 994 patients screened, 556 were randomized (289 and 267 with central and local EGFR test results, respectively) and 438 failed screening. Of those randomized from local EGFR test results, 217 patients had available central test results; 211/217 (97%) were retrospectively confirmed EGFRm positive by central cobas tissue test. Using reference central cobas tissue test results, positive percent agreements with cobas plasma test results for Ex19del and L858R detection were 79% [95% confidence interval (CI), 74-84] and 68% (95% CI, 61-75), respectively. Progression-free survival (PFS) superiority with osimertinib over comparator EGFR-TKI remained consistent irrespective of randomization route (central/local EGFRm-positive tissue test). In both treatment arms, PFS was prolonged in plasma ctDNA EGFRm-negative (23.5 and 15.0 months) versus -positive patients (15.2 and 9.7 months).
CONCLUSIONS: Our results support utility of cobas tissue and plasma testing to aid selection of patients with EGFRm advanced NSCLC for first-line osimertinib treatment. Lack of EGFRm detection in plasma was associated with prolonged PFS versus patients plasma EGFRm positive, potentially due to patients having lower tumor burden.
METHODS: This study shows the design and development of the "VENT" protocol, which integrates the single compartment linear lung model with clinical recommendations from landmark studies, to aid clinical decision-making in selecting mechanical ventilation settings. Using retrospective breath data from a cohort of 24 patients, 3,566 and 2,447 clinically implemented VC and PC settings were extracted respectively. Using this data, a VENT protocol application case study and clinical comparison is performed, and the prediction accuracy of the VENT protocol is validated against actual measured outcomes of pressure and volume.
RESULTS: The study shows the VENT protocols' potential use in narrowing an overwhelming number of possible mechanical ventilation setting combinations by up to 99.9%. The comparison with retrospective clinical data showed that only 33% and 45% of clinician settings were approved by the VENT protocol. The unapproved settings were mainly due to exceeding clinical recommended settings. When utilising the single compartment model in the VENT protocol for forecasting peak pressures and tidal volumes, median [IQR] prediction error values of 0.75 [0.31 - 1.83] cmH2O and 0.55 [0.19 - 1.20] mL/kg were obtained.
CONCLUSIONS: Comparing the proposed protocol with retrospective clinically implemented settings shows the protocol can prevent harmful mechanical ventilation setting combinations for which clinicians would be otherwise unaware. The VENT protocol warrants a more detailed clinical study to validate its potential usefulness in a clinical setting.
METHODS: Non-linear autoregressive (NARX) model is used to reconstruct missing airway pressure due to the presence of spontaneous breathing effort in mv patients. Then, the incidence of SB patients is estimated. The study uses a total of 10,000 breathing cycles collected from 10 ARDS patients from IIUM Hospital in Kuantan, Malaysia. In this study, there are 2 different ratios of training and validating methods. Firstly, the initial ratio used is 60:40 which indicates 600 breath cycles for training and remaining 400 breath cycles used for testing. Then, the ratio is varied using 70:30 ratio for training and testing data.
RESULTS AND DISCUSSION: The mean residual error between original airway pressure and reconstructed airway pressure is denoted as the magnitude of effort. The median and interquartile range of mean residual error for both ratio are 0.0557 [0.0230 - 0.0874] and 0.0534 [0.0219 - 0.0870] respectively for all patients. The results also show that Patient 2 has the highest percentage of SB incidence and Patient 10 with the lowest percentage of SB incidence which proved that NARX model is able to perform for both higher incidence of SB effort or when there is a lack of SB effort.
CONCLUSION: This model is able to produce the SB incidence rate based on 10% threshold. Hence, the proposed NARX model is potentially useful to estimate and identify patient-specific SB effort, which has the potential to further assist clinical decisions and optimize MV settings.
DESIGN: A multicenter, retrospective, descriptive cohort study.
SETTING: Ten multidisciplinary PICUs in Asia.
PATIENTS: All mechanically ventilated children meeting the Pediatric Acute Lung Injury Consensus Conference criteria for PARDS between 2009 and 2015.
INTERVENTIONS: None.
MEASUREMENTS AND MAIN RESULTS: Data on epidemiology, ventilation, adjunct therapies, and clinical outcomes were collected. Patients were followed for 100 days post diagnosis of PARDS. A total of 373 patients were included. There were 89 (23.9%), 149 (39.9%), and 135 (36.2%) patients with mild, moderate, and severe PARDS, respectively. The most common risk factor for PARDS was pneumonia/lower respiratory tract infection (309 [82.8%]). Higher category of severity of PARDS was associated with lower ventilator-free days (22 [17-25], 16 [0-23], 6 [0-19]; p < 0.001 for mild, moderate, and severe, respectively) and PICU free days (19 [11-24], 15 [0-22], 5 [0-20]; p < 0.001 for mild, moderate, and severe, respectively). Overall PICU mortality for PARDS was 113 of 373 (30.3%), and 100-day mortality was 126 of 317 (39.7%). After adjusting for site, presence of comorbidities and severity of illness in the multivariate Cox proportional hazard regression model, patients with moderate (hazard ratio, 1.88 [95% CI, 1.03-3.45]; p = 0.039) and severe PARDS (hazard ratio, 3.18 [95% CI, 1.68, 6.02]; p < 0.001) had higher risk of mortality compared with those with mild PARDS.
CONCLUSIONS: Mortality from PARDS is high in Asia. The Pediatric Acute Lung Injury Consensus Conference definition of PARDS is a useful tool for risk stratification.