Wlodarczyk Aleksandra, Molek Patrycja, Bochenek Bogdan, Wypych Agnieszka, Nessler Jadwiga, Zalewski Jaroslaw
Department of Coronary Artery Disease and Heart Failure, Jagiellonian University Medical College, John Paul II Hospital, Kraków, Poland.
Institute of Meteorology and Water Management, National Research Institute, Warsaw, Poland.
Front Cardiovasc Med. 2022 Apr 8;9:830823. doi: 10.3389/fcvm.2022.830823. eCollection 2022.
The prediction of the number of acute coronary syndromes (ACSs) based on the weather conditions in the individual climate zones is not effective. We sought to investigate whether an artificial intelligence system might be useful in this prediction.
Between 2008 and 2018, a total of 105,934 patients with ACS were hospitalized in Lesser Poland Province, one covered by two meteorological stations. The predicted daily number of ACS has been estimated with the Random Forest machine learning system based on air temperature (°C), air pressure (hPa), dew point temperature (Td) (°C), relative humidity (RH) (%), wind speed (m/s), and precipitation (mm) and their daily extremes and ranges derived from the day of ACS and from 6 days before ACS.
Of 840 pairwise comparisons between individual weather parameters and the number of ACS, 128 (15.2%) were significant but weak with the correlation coefficients ranged from -0.16 to 0.16. None of weather parameters correlated with the number of ACS in all the seasons and stations. The number of ACS was higher in warm front days vs. days without any front [40 (29-50) vs. 38 (27-48), respectively, < 0.05]. The correlation between the predicted and observed daily number of ACS derived from machine learning was 0.82 with 95% CI of 0.80-0.84 ( < 0.001). The greatest importance for machine learning (range 0-1.0) among the parameters reached Td daily range with 1.00, pressure daily range with 0.875, pressure maximum daily range with 0.864, and RH maximum daily range with 0.853, whereas among the clinical parameters reached hypertension daily range with 1.00 and diabetes mellitus daily range with 0.28. For individual seasons and meteorological stations, the correlations between the predicted and observed number of ACS have ranged for spring from 0.73 to 0.77 (95% CI 0.68-0.82), for summer from 0.72 to 0.76 (95% CI 0.66-0.81), for autumn from 0.72 to 0.83 (95% CI 0.67-0.87), and for winter from 0.76 to 0.79 (95% CI 0.71-0.83) ( < 0.001 for each).
The weather parameters have proven useful in predicting the prevalence of ACS in a temperate climate zone for all the seasons, if analyzed with an artificial intelligence system. Simultaneously, the analysis of individual weather parameters or frontal scenarios has provided only weak univariate relationships. These findings will require validation in other climatic zones.
基于各个气候区的天气状况来预测急性冠脉综合征（ACS）的发病数量并不有效。我们试图研究人工智能系统在这一预测中是否有用。
2008年至2018年期间，小波兰省共有105934例ACS患者住院，该地区有两个气象站覆盖。基于气温（℃）、气压（hPa）、露点温度（Td）（℃）、相对湿度（RH）（%）、风速（m/s）和降水量（mm）及其每日极值和范围（源自ACS发生当天及ACS发生前6天），使用随机森林机器学习系统估计每日ACS发病数量。
在个体天气参数与ACS发病数量的840对比较中，128对（15.2%）具有显著相关性，但较弱，相关系数范围为-0.16至0.16。在所有季节和气象站中，没有一个天气参数与ACS发病数量相关。暖锋天气下的ACS发病数量高于无锋面天气[分别为40（29 - 50）例对38（27 - 48）例，P < 0.05]。机器学习得出的预测每日ACS发病数量与观察到的发病数量之间的相关性为0.82，95%置信区间为0.80 - 0.84（P < 0.001）。在这些参数中，对机器学习重要性最高（范围0 - 1.0）的是Td每日范围，为1.00；气压每日范围为0.875；气压每日最大值范围为0.864；RH每日最大值范围为0.853。而在临床参数中，高血压每日范围为1.00，糖尿病每日范围为0.28。对于各个季节和气象站，预测的和观察到的ACS发病数量之间的相关性在春季为0.73至0.77（95%置信区间0.68 - 0.82），夏季为0.72至0.76（95%置信区间0.66 - 0.81），秋季为0.72至0.83（95%置信区间0.67 - 0.87），冬季为0.76至0.79（95%置信区间0.71 - 0.83）（各季节P < 0.001）。
如果使用人工智能系统进行分析，天气参数已被证明有助于预测温带气候区所有季节的ACS患病率。同时，对个体天气参数或锋面情况的分析仅提供了较弱的单变量关系。这些发现需要在其他气候区进行验证。
