Supplementary Material for: Building and prospectively evaluating a prediction model to forecast urgent dialysis needs across four tertiary hospitals

Background: Urgent dialysis is labour-intensive and expensive because it requires specialized nursing staff. Most hospitals schedule a fixed number of nurses daily for urgent dialysis needs, but daily dialysis demand fluctuates, leading to inefficiencies.

Methods: We developed statistical, machine learning, and deep learning models to predict the next seven days' dialysis needs. Our study included a retrospective (Apr 1, 2018 to Mar 31, 2023) and prospective component (Nov 1 to 30, 2023 and May 31 to June 27, 2024) across four hospitals (Hospital A for one hospital and Hospital B for three hospitals combined). To avoid model over-fitting, we divided our data into three sets: training, testing, and validation. The latter was performed prospectively during two silent deployment periods. The primary outcome measure was the mean absolute error (MAE).

Results: The mean daily dialysis volume in the retrospective data was 16.0 (standard deviation [SD], 5.7) for Hospital A and 4.5 (SD, 2.3) for Hospital B. The best performing models were autoregressive integrated moving average (ARIMA) and temporal convolutional network; both resulted in an MAE of 3.0 procedures for Hospital A and 1.5 procedures for Hospital B, compared to 4.4 and 1.9 respectively for the benchmark. During our two prospective evaluations, the mean daily dialysis volume was 16.8 (SD, 4.5) for Hospital A and 4.2 (SD, 2.5) for Hospital B. The ARIMA model resulted in the lowest MAE at 2.2 and 1.5 procedures, respectively.

Conclusions: Our multicentre, six-year study demonstrated that urgent in-hospital dialysis needs can be accurately forecasted.

背景：紧急透析工作强度大且成本高昂，因其需配备专业护理人员。多数医院会为急诊透析需求每日安排固定数量的护士，但每日透析需求存在波动，进而导致运营效率低下。
研究方法：本研究开发了统计模型、机器学习模型与深度学习模型，用于预测未来七日的透析需求。研究数据集涵盖回顾性（2018年4月1日至2023年3月31日）与前瞻性两部分（2023年11月1日至30日、2024年5月31日至6月27日），涉及四家医院：其中医院A对应单家医院，医院B对应三家医院的合并数据集。为避免模型过拟合，我们将数据集划分为训练集、测试集与验证集；验证集在两次静默部署阶段通过前瞻性方式采集。本研究的主要结局指标为平均绝对误差（mean absolute error, MAE）。
研究结果：回顾性数据中，医院A的日均透析量为16.0（标准差[SD]，5.7），医院B为4.5（SD，2.3）。表现最优的模型为自回归积分滑动平均模型（autoregressive integrated moving average, ARIMA）与时间卷积网络（temporal convolutional network）：二者针对医院A的平均绝对误差均为3.0例次透析，针对医院B均为1.5例次透析；而基准模型的对应误差分别为4.4与1.9。在两次前瞻性评估中，医院A的日均透析量为16.8（SD，4.5），医院B为4.2（SD，2.5）；其中ARIMA模型的平均绝对误差最低，分别为2.2与1.5例次透析。
研究结论：这项多中心、为期六年的研究证实，院内急诊透析需求可被精准预测。