Wong Ru Xin, Shirlynn Ho, Koh Yen Sin, Goh Seow Lin Stella, Quah Daniel, Zhuang Qingyuan
Division of Radiation Oncology, National Cancer Centre Singapore, Singapore, Singapore.
Division of Supportive and Palliative Care, National Cancer Centre Singapore, Singapore, Singapore.
Palliat Med Rep. 2021 Jan 6;2(1):9-14. doi: 10.1089/pmr.2020.0094. eCollection 2021.
End-of-life patients face difficulties in reporting respiratory distress. The Respiratory Distress Observation Scale (RDOS) is a well-known tool; however, field implementation has been challenging from ground feedback. We sought to develop a simpler scale. Patients referred for palliative consult in a tertiary hospital in Singapore were recruited. , we identified 18 dyspnea physical signs and documented their presence through bedside observation. Dyspnea severity was self-reported. The cohort was randomly split into training and test sets. Partial least square regression with leave-one-out cross-validation was used to develop a four-point model from the training set. Using the test set, data fit was compared using Akaike and Bayesian Information Criterion. Discrimination was assessed using receiver operating characteristics. Of 122 patients, mean age was 67.9 years (range 23-93, standard deviation 12.9), 71.3% had a primary cancer diagnosis, and 58.1% were chair/bedbound with a Palliative Performance Scale of ≤50. Median reported dyspnea scale was 5 (interquartile range 3-7). Our model (modRDOS-4) consisted of four predictors (grunting, respiratory rate, accessory muscle use, paradoxical breathing). A modRDOS-4 of ≥6 identified moderate-to-severe dyspnea with a sensitivity of 0.78 and specificity of 0.90. Using the test set, with the modRDOS-4, the Akaike Information Criterion (AIC) is 149.8, Bayesian Information Criteria (BIC) is 154.1, and the receiver operating characteristics (ROC) is 0.74. With the original RDOS, the AIC is 145.2, BIC is 149.5, and ROC is 0.76. For a quick assessment of dyspnea, we developed a four-item tool with a pilot web-based nomogram. External validation is needed.
临终患者在报告呼吸窘迫方面面临困难。呼吸窘迫观察量表(RDOS)是一种知名工具;然而,从实际反馈来看,在现场实施该量表具有挑战性。我们试图开发一种更简单的量表。招募了在新加坡一家三级医院接受姑息治疗咨询的患者。我们确定了18种呼吸困难的体征,并通过床边观察记录其存在情况。呼吸困难的严重程度由患者自行报告。该队列被随机分为训练集和测试集。使用留一法交叉验证的偏最小二乘回归从训练集中开发了一个四分模型。使用测试集,通过赤池信息准则和贝叶斯信息准则比较数据拟合情况。使用受试者工作特征曲线评估区分度。在122名患者中,平均年龄为67.9岁(范围23 - 93岁,标准差12.9),71.3%患有原发性癌症诊断,58.1%因姑息治疗表现量表≤50而需坐轮椅/卧床。报告的呼吸困难量表中位数为5(四分位间距3 - 7)。我们的模型(modRDOS - 4)由四个预测指标组成(呻吟声、呼吸频率、辅助肌使用、反常呼吸)。modRDOS - 4≥6可识别中度至重度呼吸困难,敏感性为0.78,特异性为0.90。使用测试集,对于modRDOS - 4,赤池信息准则(AIC)为149.8,贝叶斯信息准则(BIC)为154.1,受试者工作特征曲线(ROC)为0.74。对于原始的RDOS,AIC为145.2,BIC为149.5,ROC为0.76。为了快速评估呼吸困难,我们开发了一种包含四项的工具,并带有基于网络的初步列线图。尚需进行外部验证。