The life expectancy of patients treated with maintenance hemodialysis (MHD) is heterogeneous. Knowledge of life-expectancy may focus care decisions on near-term versus long-term goals. The current tools are limited and focus on near-term mortality. Here, we develop and assess potential utility for predicting near-term mortality and long-term survival on MHD. Predictive modeling study. 42,351 patients contributing 997,381 patient months over 11 years, abstracted from the electronic health record (EHR) system of midsize, nonprofit dialysis providers. Demographics, laboratory results, vital signs, and service utilization data available within dialysis EHR. For each patient month, we ascertained death within the next 6 months (ie, near-term mortality) and survival over more than 5 years during receipt of MHD or after kidney transplantation (ie, long-term survival). We used least absolute shrinkage and selection operator logistic regression and gradient-boosting machines to predict each outcome. We compared these to time-to-event models spanning both time horizons. We explored the performance of decision rules at different cut points. All models achieved an area under the receiver operator characteristic curve of≥0.80 and optimal calibration metrics in the test set. The long-term survival models had significantly better performance than the near-term mortality models. The time-to-event models performed similarly to binary models. Applying different cut points spanning from the 1st to 90th percentile of the predictions, a positive predictive value (PPV) of 54% could be achieved for near-term mortality, but with poor sensitivity of 6%. A PPV of 71% could be achieved for long-term survival with a sensitivity of 67%. The retrospective models would need to be prospectively validated before they could be appropriately used as clinical decision aids. A model built with readily available clinical variables to support easy implementation can predict clinically important life expectancy thresholds and shows promise as a clinical decision support tool for patients on MHD. Predicting long-term survival has better decision rule performance than predicting near-term mortality. Clinical prediction models (CPMs) are not widely used for patients undergoing maintenance hemodialysis (MHD). Although a variety of CPMs have been reported in the literature, many of these were not well-designed to be easily implementable. We consider the performance of an implementable CPM for both near-term mortality and long-term survival for patients undergoing MHD. Both near-term and long-term models have similar predictive performance, but the long-term models have greater clinical utility. We further consider how the differential performance of predicting over different time horizons may be used to impact clinical decision making. Although predictive modeling is not regularly used for MHD patients, such tools may help promote individualized care planning and foster shared decision making.

中文翻译：

---

设计一个可实施的临床预测模型，预测维持性血液透析患者的近期死亡率和长期生存率

接受维持性血液透析（MHD）治疗的患者的预期寿命存在差异。了解预期寿命可能会将护理决策的重点放在近期目标和长期目标上。当前的工具有限，并且侧重于近期死亡率。在这里，我们开发并评估了 MHD 预测近期死亡率和长期生存的潜在效用。预测模型研究。 42,351 名患者在 11 年间贡献了 997,381 个患者月，从中型非营利性透析提供商的电子健康记录 (EHR) 系统中提取。透析 EHR 中提供人口统计数据、实验室结果、生命体征和服务利用数据。对于每个患者月份，我们确定了接下来 6 个月内的死亡（即近期死亡率）以及接受 MHD 期间或肾移植后超过 5 年的生存（即长期生存）。我们使用最小绝对收缩和选择算子逻辑回归和梯度提升机来预测每个结果。我们将这些与跨越两个时间范围的事件时间模型进行了比较。我们探讨了决策规则在不同切点的性能。所有模型均实现了接收者算子特征曲线下面积≥0.80 和测试集中的最佳校准指标。长期生存模型的性能明显优于近期死亡率模型。事件时间模型的表现与二元模型类似。应用从第 1 个百分位数到第 90 个百分位数的不同切点，短期死亡率的阳性预测值 (PPV) 可以达到 54%，但敏感性较差，仅为 6%。长期生存的 PPV 可以达到 71%，敏感性为 67%。回顾性模型需要进行前瞻性验证，然后才能适当地用作临床决策辅助。使用现成的临床变量构建的模型可以轻松实现，可以预测临床上重要的预期寿命阈值，并有望成为 MHD 患者的临床决策支持工具。预测长期生存比预测近期死亡率具有更好的决策规则性能。临床预测模型 (CPM) 并未广泛用于接受维持性血液透析 (MHD) 的患者。尽管文献中报道了多种 CPM，但其中许多设计并不易于实施。我们考虑可实施的 CPM 对接受 MHD 的患者的近期死亡率和长期生存率的影响。近期和长期模型都具有相似的预测性能，但长期模型具有更大的临床实用性。我们进一步考虑如何利用不同时间范围内预测的差异性能来影响临床决策。尽管预测模型并不经常用于 MHD 患者，此类工具可能有助于促进个性化护理规划并促进共同决策。