Taipei City Hospital, RenAi Branch Nursing Supervisor, Taipei 106, Taiwan.
Graduate Institute of Business Administration, Fu Jen Catholic University, New Taipei City 242, Taiwan.
Int J Environ Res Public Health. 2022 Apr 2;19(7):4263. doi: 10.3390/ijerph19074263.
Previous research mostly analyzed the utilization of palliative care for patients with cancer, and data regarding non-cancer inpatients are limited.
This research aimed to investigate the current situation regarding palliative care and the important factors that influence its utilization by inpatients (including inpatients with and without cancer) at the end of their lives. We also explored the feasibility of establishing a prediction model of palliative care utilization for inpatients at the end of their lives. These findings will allow medical staff to monitor and focus on those who may require palliative care, resulting in more end-of-life patients receiving palliative care and thereby reducing medical expense and improving their quality of life.
This was a retrospective study based on real-world health information system (HIS) data from 5 different branches of Taipei City Hospital between 1 January 2018 and 31 December 2018 that enrolled a total of 1668 deceased inpatients. To explore palliative care utilization at the end of life, we used 5-fold cross-validation in four different statistical models to obtain the performance of predictive accuracy: logistic regression (LGR), classification and regression tree (CART), multivariate adaptive regression spline (MARS), and gradient boosting (GB). The important variables that may affect palliative care utilization by inpatients were also identified.
The results were as follows: (1) 497 (29.8%) inpatients received palliative care; (2) the average daily hospitalization cost of patients with cancer who received palliative care (NTD 5789 vs. NTD 12,115; ≤ 0.001) and all patients who received palliative care (NTD 91,527 vs. NTD 186,981; = 0.0037) were statistically significantly lower than patients who did not receive palliative care; (3) diagnosis, hospital, and length of stay (LOS) may affect palliative care utilization of inpatient; diagnosis, hospitalization unit, and length of hospitalization were statistically significant by LGR; (4) 51.5% of patients utilized palliative consultation services, and 48.5% utilized palliative care units; and (5) MARS had the most consistent results; its accuracy was 0.751, and the main predictors of palliative care utilization are hospital, medical expense, LOS, diagnosis, and Palliative Care Screening Tool-Taiwan version (TW-PCST) scores.
The results reveal that palliative care utilization by inpatients remains low, and it is necessary to educate patients without cancer of the benefits and advantages of palliative care. Although data were limited, the predictability of the MARS model was 0.751; a better prediction model with more data is necessary for further research. Precisely predicting the need for palliative care may encourage patients and their family members to consider palliative care, which may balance both physical and mental care. Therefore, unnecessary medical care can be avoided and limited medical resources can be allocated to more patients in need.
先前的研究主要分析了癌症患者对姑息治疗的利用情况，而关于非癌症住院患者的数据有限。
本研究旨在调查姑息治疗的现状以及影响临终患者（包括癌症和非癌症患者）利用姑息治疗的重要因素。我们还探讨了建立临终患者姑息治疗利用预测模型的可行性。这些发现将使医务人员能够监测和关注那些可能需要姑息治疗的患者，从而使更多的终末期患者接受姑息治疗，从而降低医疗费用并提高他们的生活质量。
这是一项基于台北市立医院 5 个不同分支机构的真实世界健康信息系统（HIS）数据的回顾性研究，研究对象为 2018 年 1 月 1 日至 2018 年 12 月 31 日期间去世的 1668 名住院患者。为了探讨临终姑息治疗的利用情况，我们使用 5 折交叉验证在四个不同的统计模型中获得预测准确性的表现：逻辑回归（LGR）、分类和回归树（CART）、多元自适应回归样条（MARS）和梯度提升（GB）。还确定了可能影响住院患者姑息治疗利用的重要变量。
结果如下：（1）497 名（29.8%）住院患者接受了姑息治疗；（2）接受姑息治疗的癌症患者（新台币 5789 元与新台币 12115 元； ≤ 0.001）和所有接受姑息治疗的患者（新台币 91527 元与新台币 186981 元； = 0.0037）的平均每日住院费用明显低于未接受姑息治疗的患者；（3）诊断、医院和住院时间（LOS）可能影响住院患者姑息治疗的利用；诊断、住院科室和住院时间在 LGR 中有统计学意义；（4）51.5%的患者使用了姑息治疗咨询服务，48.5%的患者使用了姑息治疗病房；（5）MARS 的结果最为一致；其准确率为 0.751，姑息治疗利用的主要预测因素是医院、医疗费用、LOS、诊断和台湾版姑息治疗筛查工具（TW-PCST）评分。
研究结果表明，住院患者姑息治疗的利用率仍然较低，有必要向非癌症患者宣传姑息治疗的益处和优势。尽管数据有限，但 MARS 模型的预测率为 0.751；进一步研究需要一个更好的、有更多数据的预测模型。准确预测姑息治疗的需求可能会鼓励患者及其家属考虑姑息治疗，这可能会平衡身心护理。因此，可以避免不必要的医疗护理，并将有限的医疗资源分配给更多有需要的患者。
