Department of Clinical Neurosciences, Cumming School of Medicine, University of Calgary, 3330 Hospital Drive NW, Calgary, AB, T2N4N1, Canada.
ICES, Toronto, ON, Canada.
BMC Med Res Methodol. 2021 May 10;21(1):102. doi: 10.1186/s12874-021-01289-8.
Ninety-day hometime, the number of days a patient is living in the community in the first 90 after stroke, exhibits a non-normal bucket-shaped distribution, with lower and upper constraints making its analysis difficult. In this proof-of-concept study we evaluated the performance of random forests regression in the analysis of hometime.
Using administrative data we identified stroke hospitalizations between 2010 and 2017 in Ontario, Canada. We used random forests regression to predict 90-day hometime using 15 covariates. Model accuracy was determined using the r-squared statistic. Variable importance in prediction and the marginal effects of each covariate were explored.
We identified 75,745 eligible patients. Median 90-day hometime was 59 days (Q1: 2, Q3: 83). Random forests predicted hometime with reasonable accuracy (adjusted r-squared 0.3462); no implausible values were predicted but extreme values were predicted with low accuracy. Frailty, stroke severity, and age exhibited inverse non-linear relationships with hometime and patients arriving by ambulance had less hometime than those who did not.
Random forests may be a useful method for analyzing 90-day hometime and capturing the complex non-linear relationships which exist between predictors and hometime. Future work should compare random forests to other models and focus on improving the accuracy of predictions of extreme values of hometime.
90 天居家时间，即患者在中风后 90 天内居住在社区中的天数，呈非正态桶形分布，下限和上限的存在使其分析变得困难。在这项概念验证研究中，我们评估了随机森林回归在居家时间分析中的表现。
我们利用行政数据，在加拿大安大略省识别了 2010 年至 2017 年的中风住院患者。我们使用随机森林回归，使用 15 个协变量来预测 90 天居家时间。使用 r 平方统计量来确定模型的准确性。探索了预测中的变量重要性和每个协变量的边际效应。
我们确定了 75745 名符合条件的患者。中位数 90 天居家时间为 59 天（Q1：2，Q3：83）。随机森林对居家时间的预测具有合理的准确性（调整 r 平方 0.3462）；没有预测到不合理的值，但对极端值的预测准确性较低。脆弱性、中风严重程度和年龄与居家时间呈负相关的非线性关系，乘坐救护车到达的患者比未乘坐救护车的患者居家时间短。
随机森林可能是分析 90 天居家时间和捕捉预测因子与居家时间之间存在的复杂非线性关系的有用方法。未来的工作应该将随机森林与其他模型进行比较，并专注于提高居家时间极端值预测的准确性。
