METHODS: A stochastic model was developed using respiratory elastance (Ers) data from two clinical cohorts and averaged over 30-minute time intervals. The stochastic model was used to generate future Ers data based on current Ers values with added normally distributed random noise. Self-validation of the VPs was performed via Monte Carlo simulation and retrospective Ers profile fitting. A stochastic VP cohort of temporal Ers evolution was synthesised and then compared to an independent retrospective patient cohort data in a virtual trial across several measured patient responses, where similarity of profiles validates the realism of stochastic model generated VP profiles.
RESULTS: A total of 120,000 3-hour VPs for pressure control (PC) and volume control (VC) ventilation modes are generated using stochastic simulation. Optimisation of the stochastic simulation process yields an ideal noise percentage of 5-10% and simulation iteration of 200,000 iterations, allowing the simulation of a realistic and diverse set of Ers profiles. Results of self-validation show the retrospective Ers profiles were able to be recreated accurately with a mean squared error of only 0.099 [0.009-0.790]% for the PC cohort and 0.051 [0.030-0.126]% for the VC cohort. A virtual trial demonstrates the ability of the stochastic VP cohort to capture Ers trends within and beyond the retrospective patient cohort providing cohort-level validation.
CONCLUSION: VPs capable of temporal evolution demonstrate feasibility for use in designing, developing, and optimising bedside MV guidance protocols through in-silico simulation and validation. Overall, the temporal VPs developed using stochastic simulation alleviate the need for lengthy, resource intensive, high cost clinical trials, while facilitating statistically robust virtual trials, ultimately leading to improved patient care and outcomes in mechanical ventilation.
OBJECTIVE: The objective of this study is to collate the prognosis, symptomatology and clinical findings of COVID-19 adverse events causing STEMI.
METHODS: Databases were queried with various keyword combinations to find applicable articles. Cardiovascular risk factors, symptomatology, mortality and rates of PCI were analyzed using random-effect model.
RESULTS: 15 studies with a total of 379 patients were included in the final analysis. Mean age of patients was 62.82 ± 36.01, with a male predominance (72%, n = 274). Hypertension, dyslipidemia and diabetes mellitus were the most common cardiovascular risk factors among these patients, with a pooled proportion of 72%, 59% and 40% respectively. Dyspnea (61%, n = 131) was the most frequent presenting symptom, followed by chest pain (60%, n = 101) and fever (56%, n = 104). 62% of the patients had obstructive CAD during coronary angiography. The primary reperfusion method used in the majority of cases was percutaneous coronary intervention (64%, n = 124). Mortality, which is the primary outcome in our study, was relatively high, with a rate of 34% across studies.
CONCLUSION: Our findings show that most cases have been found in males, while the most common risk factors were Hypertension and Diabetes Mellitus. In most COVID-19 cases with ST-segment myocardial infarction, most hospitalized patients underwent primary percutaneous coronary intervention instead of fibrinolysis. The in-hospital mortality was significantly higher, making this report significant. As the sample size and reported study are considerably less, it warrants a further large-scale investigation to generalize it.
METHODS: Data was collected from the web-based MOH CSR. All consecutive cataract surgery patients from 1st June 2008 to 31st December 2014 were identified. Exclusion criteria were traumatic cataract or previous ocular surgery. Demographic data, ocular co-morbidities, intraoperative details and postoperative visual acuity (VA) at final ophthalmological follow-up were noted. All eyes were taken for analysis. Subjects with POE were compared against subjects with no POE for risk factor assessment using multiple logistic regressions.
RESULTS: A total of 163 503 subjects were screened. The incidence of POE was 0.08% (131/163 503). Demographic POE risk factors included male gender (OR: 2.121, 95%CI: 1.464-3.015) and renal disease (OR: 2.867, 95%CI: 1.503-5.467). POE risk increased with secondary causes of cataract (OR: 3.562, 95%CI: 1.740-7.288), uveitis (OR: 11.663, 95%CI: 4.292-31.693) and diabetic retinopathy (OR: 1.720, 95%CI: 1.078-2.744). Intraoperative factors reducing POE were shorter surgical time (OR: 2.114, 95%CI: 1.473-3.032), topical or intracameral anaesthesia (OR: 1.823, 95%CI: 1.278-2.602), posterior chamber intraocular lens (PCIOL; OR: 4.992, 95%CI: 2.689-9.266) and foldable IOL (OR: 2.276, 95%CI: 1.498-3.457). POE risk increased with posterior capsule rupture (OR: 3.773, 95%CI: 1.915-7.432) and vitreous loss (OR: 3.907, 95%CI: 1.720-8.873). Postoperative VA of 6/12 or better was achieved in 15.27% (20/131) subjects with POE.
CONCLUSION: This study concurs with other studies regarding POE risk factors. Further strengthening of MOH CSR data collection process will enable deeper analysis and optimization of POE treatment.
METHODS: Hysteresis loop analysis (HLA) which is a rule-based method (RBM) and a tri-input convolutional neural network (TCNN) machine learning model are used to classify 7 different types of PVA, including: 1) flow asynchrony; 2) reverse triggering; 3) premature cycling; 4) double triggering; 5) delayed cycling; 6) ineffective efforts; and 7) auto triggering. Class activation mapping (CAM) heatmaps visualise sections of respiratory waveforms the TCNN model uses for decision making, improving result interpretability. Both PVA classification methods were used to classify incidence in an independent retrospective clinical cohort of 11 mechanically ventilated patients for validation and performance comparison.
RESULTS: Self-validation with the training dataset shows overall better HLA performance (accuracy, sensitivity, specificity: 97.5 %, 96.6 %, 98.1 %) compared to the TCNN model (accuracy, sensitivity, specificity: 89.5 %, 98.3 %, 83.9 %). In this study, the TCNN model demonstrates higher sensitivity in detecting PVA, but HLA was better at identifying non-PVA breathing cycles due to its rule-based nature. While the overall AI identified by both classification methods are very similar, the intra-patient distribution of each PVA type varies between HLA and TCNN.
CONCLUSION: The collective findings underscore the efficacy of both HLA and TCNN in PVA detection, indicating the potential for real-time continuous monitoring of PVA. While ML methods such as TCNN demonstrate good PVA identification performance, it is essential to ensure optimal model architecture and diversity in training data before widespread uptake as standard care. Moving forward, further validation and adoption of RBM methods, such as HLA, offers an effective approach to PVA detection while providing clear distinction into the underlying patterns of PVA, better aligning with clinical needs for transparency, explicability, adaptability and reliability of these emerging tools for clinical care.
OBJECTIVE: This study aimed to assess early mortality and analyze the risk factors of early mortality among patients who underwent TEER.
METHODS: Using the all-payer, nationally representative Nationwide Readmissions Database, our study included patients aged 18 years or older who had TEER between January 2017 and November 2020. We categorized the cohort into two groups depending on the occurrence of early mortality (death within 30 days after the procedure). Based on the ICD-10, we identified the trend of early mortality after TEER and further analyzed the risk factors associated with early mortality.
RESULTS: A total of 15,931 patients who had TEER were included; 292 (1.8 %) with early mortality and 15,639 (98.2 %) without. There was a decreasing trend in early mortality from 2.8 % in the first quarter of 2017 to 1.2 % in the fourth quarter of 2020, but it was not statistically significant (p = 0.18). In multivariable analysis, the independent risk factors for early mortality were chronic kidney disease not requiring dialysis (adjusted odds ratio [aOR]: 1.57; 95 % confidence interval [CI]: 1.11-2.22, p = 0.01), end-stage renal disease (aOR: 2.34; CI: 1.44-3.79, p