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1.
PLoS One ; 18(12): e0295430, 2023.
Article in English | MEDLINE | ID: mdl-38060569

ABSTRACT

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.


Subject(s)
Exercise Test , Lung Neoplasms , Humans , Lung Neoplasms/surgery , Prospective Studies , Oxygen Consumption , Lung
2.
Article in English | MEDLINE | ID: mdl-34769590

ABSTRACT

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.


Subject(s)
Air Pollutants , Air Pollution , Asthma , Aged , Air Pollutants/analysis , Air Pollutants/toxicity , Air Pollution/analysis , Air Pollution/statistics & numerical data , Asthma/chemically induced , Asthma/epidemiology , Environmental Exposure/analysis , Environmental Exposure/statistics & numerical data , Humans , Nitrogen Dioxide/analysis , Particulate Matter/analysis , Particulate Matter/toxicity , Sulfur Dioxide/analysis , Sulfur Dioxide/toxicity
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