Cluster analysis of weather and pollution features and its role in predicting acute cardiac or cerebrovascular events.

BACKGROUND: Despite mounting evidence, the impact of the interplay between weather and pollution features on the risk of acute cardiac and cerebrovascular events has not been entirely appraised. The aim of this study was to perform a comprehensive cluster analysis of weather and pollution features in a large metropolitan area, and their association with acute cardiac and cerebrovascular events. METHODS: Anonymized data on acute myocardial infarction (AMI) and acute cerebrovascular events were obtained from 3 tertiary care centers from a single large metropolitan area. Weather and pollution data were obtained averaging measurements from several city measurement stations managed by the competent regional agency for enviromental protection, and from the Metereological Center of Italian Military Aviation. Unsupervised machine learning was performed with hierarchical clustering to identify specific days with distinct weather and pollution features. Clusters were then compared for rates of acute cardiac and cerebrovascular events with Poisson models. RESULTS: As expected, significant pairwise correlations were found between weather and pollution features. Building upon these correlations, hierarchical clustering, from a total of 1169 days, generated 4 separate clusters: mostly winter days with low temperatures and high ozone concentrations (cluster 1, N.=60, 5.1%), days with moderately high temperatures and low pollutants concentrations (cluster 2, N.=419, 35.8%), mostly summer and spring days with high temperatures and high ozone concentrations (cluster 3, N.=673, 57.6%), and mostly winter days with low temperatures and low ozone concentrations (cluster 4, N.=17, 1.5%). Overall cluster-wise comparisons showed significant differences in adverse cardiac and cerebrovascular events (P<0.001), as well as in cerebrovascular events (P<0.001) and strokes (P=0.001). Between-cluster comparisons showed that cluster 1 was associated with an increased risk of any event, cerebrovascular events, and strokes in comparison to cluster 2, cluster 3 and cluster 4 (all P<0.05), as well as AMI in comparison to cluster 3 (P=0.047). In addition, cluster 2 was associated with a higher risk of strokes in comparison to cluster 4 (P=0.030). Analysis adjusting for season confirmed the increased risk of any event, cerebrovascular events and strokes for cluster 1 and cluster 2. CONCLUSIONS: Unsupervised machine learning can be leveraged to identify specific days with a unique clustering of adverse weather and pollution features which are associated with an increased risk of acute cardiovascular events, especially cerebrovascular events. These findings may improve collective and individual risk prediction and prevention.

Impact of environmental pollution and weather changes on the incidence of ST-elevation myocardial infarction.

BACKGROUND: Environmental pollution and weather changes unfavorably impact on cardiovascular disease. However, limited research has focused on ST-elevation myocardial infarction (STEMI), the most severe yet distinctive form of acute coronary syndrome. METHODS AND RESULTS: We appraised the impact of environmental and weather changes on the incidence of STEMI, analysing the bivariate and multivariable association between several environmental and atmospheric parameters and the daily incidence of STEMI in two large Italian urban areas. Specifically, we appraised: carbon monoxide (CO), nitrogen dioxide (NO2), nitric oxide (NOX), ozone, particulate matter smaller than 10 µm (PM10) and than 2.5 µm (PM2.5), temperature, atmospheric pressure, humidity and rainfall. A total of 4285 days at risk were appraised, with 3473 cases of STEMI. Specifically, no STEMI occurred in 1920 (44.8%) days, whereas one or more occurred in the remaining 2365 (55.2%) days. Multilevel modelling identified several pollution and weather predictors of STEMI. In particular, concentrations of CO (p = 0.024), NOX (p = 0.039), ozone (p = 0.003), PM10 (p = 0.033) and PM2.5 (p = 0.042) predicted STEMI as early as three days before the event, as well as subsequently, and NO predicted STEMI one day before (p = 0.010), as well as on the same day. A similar predictive role was evident for temperature and atmospheric pressure (all p < 0.05). CONCLUSIONS: The risk of STEMI is strongly associated with pollution and weather features. While causation cannot yet be proven, environmental and weather changes could be exploited to predict STEMI risk in the following days.

Renal arteries denervation with second generation systems: a remedy for resistant hypertension?

Initial studies on renal denervation (RDN) for the treatment of non-controlled arterial hypertension (HTN) through radiofrequency ablation of renal arteries demonstrated that RDN is an effective therapeutic strategy to reduce arterial blood pressure (BP). Nonetheless, the first randomized study, SYMPLICITY-HTN-3, failed to demonstrate a clear benefit for RND over the control group. Technologic evolution, with the introduction of new second generation multi-electrode devices, allowed deep energy delivery along the full circumference of the vessel. Two recent randomized studies involving patients assuming (SPYRAL HTN-ON MED) or not (SPYRAL HTN-OFF MED) antihypertensive pharmacologic treatment, demonstrated the efficacy and safety of RDN using second generation systems for radiofrequency ablation. Another recent randomized study demonstrated that RDN with ultrasounds (RADIANCE-HTN SOLO) of the main renal arteries led to a significant BP reduction compared to the control group. These studies have once again raised the interest of the scientific community towards attempting to define the appropriate role of RDN in the treatment of hypertension. Nonetheless, larger and longer clinical trials will be necessary to draw further conclusions.