The problem of recurrent, unplanned readmissions, where some patients return shortly after discharge from the hospital and are readmitted for the same or a related condition, has become a challenge worldwide due to care quality, health outcomes, and financial concerns. Predicting frequent, preventable readmissions and understanding the contributing factors is a critical problem that is being widely studied. However, few studies have examined longitudinal risk stratification, profiling, and prediction of multi-morbid, heterogeneous patient populations. We examine how readmission risk may progress over multiple emergency department visits of chronic disease patients, their early stratification into distinct trajectories with related frequencies, and the relationship of these trajectories to patient characteristics. We further extend this analysis to investigate the impact of time-stable and time-varying covariates in predicting future readmission conditional on latent class membership. Results indicate that longitudinal risk stratification can enable early identification of specific patient groups following distinct trajectories based on their presentation for emergency care. Prediction models that incorporate latent classes perform well and demonstrate the promise of trajectory modeling methods combined with advanced prediction models for longitudinal risk assessment in addressing readmission challenges. The methodology and insights from this study are generalizable to other important Information Systems problems.

中文翻译：

---

慢性病患者重复入院的轨迹：风险分层，概要分析和预测。

由于护理质量，健康结果和财务问题，经常性，计划外的再次入院问题已成为全球范围内的一个挑战，有些患者出院后不久便返回，并因相同或相关状况而再次入院。预测频繁的，可预防的再入院并了解影响因素是一个正在广泛研究的关键问题。但是，很少有研究检查纵向风险分层，分布图以及对多病态，异类患者群体的预测。我们研究了慢性病患者多次急诊就诊，将其早期分层成具有相关频率的不同轨迹以及这些轨迹与患者特征之间的关系时，再入院风险可能如何发展。我们进一步扩展了此分析，以研究时间稳定和时变协变量在预测潜在班级成员资格的未来再入学方面的影响。结果表明，纵向风险分层可以根据患者的急诊表现，按照不同的轨迹早期识别特定的患者组。结合潜在类别的预测模型表现良好，并证明了轨迹建模方法与先进的预测模型相结合的前景，可应对纵向风险评估，以应对再入院挑战。这项研究的方法论和见解可推广到其他重要的信息系统问题。结果表明，纵向风险分层可以根据患者的急诊表现，按照不同的轨迹早期识别特定的患者组。结合潜在类别的预测模型表现良好，并证明了轨迹建模方法与先进的预测模型相结合的前景，可应对纵向风险评估，以应对再入院挑战。这项研究的方法论和见解可推广到其他重要的信息系统问题。结果表明，纵向风险分层可以根据患者的急诊表现，按照不同的轨迹早期识别特定的患者组。结合潜在类别的预测模型表现良好，并证明了轨迹建模方法与先进的预测模型相结合的前景，可应对纵向风险评估，以应对再入院挑战。这项研究的方法论和见解可推广到其他重要的信息系统问题。结合潜在类别的预测模型表现良好，并证明了轨迹建模方法与先进的预测模型相结合的前景，可应对纵向风险评估，以应对再入院挑战。这项研究的方法论和见解可推广到其他重要的信息系统问题。结合潜在类别的预测模型表现良好，并证明了轨迹建模方法与先进的预测模型相结合的前景，可应对纵向风险评估，以应对再入院挑战。这项研究的方法论和见解可推广到其他重要的信息系统问题。