Your neighborhood matters: A machine-learning approach to the geospatial and social determinants of health in 9-1-1 activated chest pain.
Your neighborhood matters: A machine-learning approach to the geospatial and social determinants of health in 9-1-1 activated chest pain.
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DOI：
10.1002/nur.22199
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发表时间：
2022-04
影响因子：
2
通讯作者：
Al-Zaiti S
中科院分区：
文献类型：
--
作者：
Faramand Z;Alrawashdeh M;Helman S;Bouzid Z;Martin-Gill C;Callaway C;Al-Zaiti S
急性冠状动脉综合征（ACS）患者的初始管理存在医疗差异。然而，健康的人口、社会、经济和地理空间决定因素之间相互作用的复杂性阻碍了将此类预测因素纳入现有的风险分层模型中。我们试图探索一种基于机器学习的方法来研究健康的地理空间和社会决定因素之间的复杂相互作用，以解释城市社区ACS可能性的差异。本研究确定了匹兹堡EMS运送的胸痛或ACS等效症状的主诉的连续患者。我们从电子健康记录中提取了人口统计学、临床数据和位置坐标。收入中位数是根据美国人口普查数据按邮政编码。使用随机森林分类器和正则化逻辑回归模型来确定ACS可能性的最重要预测因子。我们的最终样本包括2，400例患者（年龄59±17岁，47%女性，41%黑人，15.8%判定为ACS）。在我们的随机森林模型（AUC为0.71±0.03）中，年龄、既往血运重建、收入、离医院的距离和居住区是ACS可能性的最重要预测因素。在正则化回归中（AIC = 1843，BIC = 1912，卡方= 193，df = 10，p < 0.001），居民区仍然是ACS可能性的重要和独立的预测因素。我们的研究结果表明，居民区构成了一个上游因素，可以解释观察到的ACS风险预测中的医疗保健差异，独立于已知的人口统计学，社会和经济健康决定因素，这可以为未来的ACS预防，住院护理和患者出院工作提供信息。
Healthcare disparities in the initial management of patients with acute coronary syndrome (ACS) exist. Yet, the complexity of interactions between demographic, social, economic, and geospatial determinants of health hinders incorporating such predictors in existing risk stratification models. We sought to explore a machine-learning-based approach to study the complex interactions between the geospatial and social determinants of health to explain disparities in ACS likelihood in an urban community. This study identified consecutive patients transported by Pittsburgh EMS for a chief complaint of chest pain or ACS-equivalent symptoms. We extracted demographics, clinical data, and location coordinates from electronic health records. Median income was based on US census data by zip code. A random forest classifier and a regularized logistic regression model were used to identify the most important predictors of ACS likelihood. Our final sample included 2,400 patients (age 59±17 years, 47% Females, 41% Blacks, 15.8% adjudicated ACS). In our random forest model (AUC of 0.71±0.03) age, prior revascularization, income, distance from hospital, and residential neighborhood were the most important predictors of ACS likelihood. In regularized regression (AIC = 1843, BIC = 1912, chi square = 193, df = 10, p < 0.001), residential neighborhood remained a significant and independent predictor of ACS likelihood. Findings from our study suggest that residential neighborhood constitutes an upstream factor to explain the observed healthcare disparity in ACS risk prediction, independent from known demographic, social, and economic determinants of health, which can inform future work on ACS prevention, in-hospital care, and patient discharge.
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影响因子：
2
作者：
Bereitschaft, Bradley
通讯作者：
Bereitschaft, Bradley
心血管疾病的环境决定因素。
影响因子：
20.1
作者：
Bhatnagar A
通讯作者：
Bhatnagar A
影响因子：
5.4
作者：
Mochari-Greenberger H;Xian Y;Hellkamp AS;Schulte PJ;Bhatt DL;Fonarow GC;Saver JL;Reeves MJ;Schwamm LH;Smith EE
通讯作者：
Smith EE
影响因子：
5.4
作者：
Agarwal, Shikhar;Garg, Aatish;Menon, Venu
通讯作者：
Menon, Venu
影响因子：
13.8
作者：
Hanchate, Amresh D.;Paasche-Orlow, Michael K.;Feldman, James
通讯作者：
Feldman, James