The association between cardiopulmonary exercise testing and postoperative outcomes in patients with lung cancer undergoing lung resection surgery: A systematic review and meta-analysis.

BACKGROUND: Exercise capacity should be determined in all patients undergoing lung resection for lung cancer surgery and cardiopulmonary exercise testing (CPET) remains the gold standard. The purpose of this study was to investigate associations between preoperative CPET and postoperative outcomes in patients undergoing lung resection surgery for lung cancer through a review of the existing literature. METHODS: A search was conducted on PubMed, Scopus, Cochrane Library and CINAHL from inception until December 2022. Studies investigating associations between preoperative CPET and postoperative outcomes were included. Risk of bias was assessed using the QUIPS tool. A random effect model meta-analysis was performed. I2 > 40% indicated a high level of heterogeneity. RESULTS: Thirty-seven studies were included with 6450 patients. Twenty-eight studies had low risk of bias. [Formula: see text] peak is the oxygen consumption at peak exercise and serves as a marker of cardiopulmonary fitness. Higher estimates of [Formula: see text] peak, measured and as a percentagege of predicted, showed significant associations with a lower risk of mortality [MD: 3.66, 95% CI: 0.88; 6.43 and MD: 16.49, 95% CI: 6.92; 26.07] and fewer complications [MD: 2.06, 95% CI: 1.12; 3.00 and MD: 9.82, 95% CI: 5.88; 13.76]. Using a previously defined cutoff value of > 15mL/kg/min for [Formula: see text] peak, showed evidence of decreased odds of mortality [OR: 0.55, 95% CI: 0.28-0.81] and but not decreased odds of postoperative morbidity [OR: 0.82, 95% CI: 0.64-1.00]. There was no relationship between [Formula: see text] slope, which depicts ventilatory efficiency, with mortality [MD: -9.60, 95% CI: -27.74; 8.54] however, patients without postoperative complications had a lower preoperative [Formula: see text] [MD: -2.36, 95% CI: -3.01; -1.71]. Exercise load and anaerobic threshold did not correlate with morbidity or mortality. There was significant heterogeneity between studies. CONCLUSIONS: Estimates of cardiopulmonary fitness as evidenced by higher [Formula: see text] peak, measured and as a percentage of predicted, were associated with decreased morbidity and mortality. A cutoff value of [Formula: see text] peak > 15mL/kg/min was consistent with improved survival but not with fewer complications. Ventilatory efficiency was associated with decreased postoperative morbidity but not with improved survival. The heterogeneity in literature could be remedied with large scale, prospective, blinded, standardised research to improve preoperative risk stratification in patients with lung cancer scheduled for lung resection surgery.

Ensemble averaging using remote sensing data to model spatiotemporal PM10 concentrations in sparsely monitored South Africa.

There is a paucity of air quality data in sub-Saharan African countries to inform science driven air quality management and epidemiological studies. We investigated the use of available remote-sensing aerosol optical depth (AOD) data to develop spatially and temporally resolved models to predict daily particulate matter (PM10) concentrations across four provinces of South Africa (Gauteng, Mpumalanga, KwaZulu-Natal and Western Cape) for the year 2016 in a two-staged approach. In stage 1, a Random Forest (RF) model was used to impute Multiangle Implementation of Atmospheric Correction AOD data for days where it was missing. In stage 2, the machine learner algorithms RF, Gradient Boosting and Support Vector Regression were used to model the relationship between ground-monitored PM10 data, AOD and other spatial and temporal predictors. These were subsequently combined in an ensemble model to predict daily PM10 concentrations at 1 km × 1 km spatial resolution across the four provinces. An out-of-bag R2 of 0.96 was achieved for the first stage model. The stage 2 cross-validated (CV) ensemble model captured 0.84 variability in ground-monitored PM10 with a spatial CV R2 of 0.48 and temporal CV R2 of 0.80. The stage 2 model indicated an optimal performance of the daily predictions when aggregated to monthly and annual means. Our results suggest that a combination of remote sensing data, chemical transport model estimates and other spatiotemporal predictors has the potential to improve air quality exposure data in South Africa's major industrial provinces. In particular, the use of a combined ensemble approach was found to be useful for this area with limited availability of air pollution ground monitoring data.

Supervised Kohonen Self-Organizing Maps of Acute Asthma from Air Pollution Exposure.

There are unanswered questions with regards to acute respiratory outcomes, particularly asthma, due to environmental exposures. In contribution to asthma research, the current study explored a computational intelligence paradigm of artificial neural networks (ANNs) called self-organizing maps (SOM). To train the SOM, air quality data (nitrogen dioxide, sulphur dioxide and particulate matter), interpolated to geocoded addresses of asthmatics, were used with clinical data to classify asthma outcomes. Socio-demographic data such as age, gender and race were also used to perform the classification by the SOM. All pollutants and demographic traits appeared to be important for the correct classification of asthma outcomes. Age was more important: older patients were more likely to have asthma. The resultant SOM model had low quantization error. The study concluded that Kohonen self-organizing maps provide effective classification models to study asthma outcomes, particularly when using multidimensional data. SO2 was concluded to be an important pollutant that requires strict regulation, particularly where frail subpopulations such as the elderly may be at risk.