Escalante C P, Martin C G, Elting L S, Price K J, Manzullo E F, Weiser M A, Harle T S, Cantor S B, Rubenstein E B
Department of Internal Medicine Specialties, Section of General Internal Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX 77030-4095, USA.
J Pain Symptom Manage. 2000 Nov;20(5):318-25. doi: 10.1016/s0885-3924(00)00193-7.
A substantial proportion of cancer patients presenting to an emergency center (EC) or clinic with acute dyspnea survives fewer than 2 weeks. If these patients could be identified at the time of admission, physicians and patients would have additional information on which to base decisions to continue therapy to extend life or to refocus treatment efforts on palliation and/or hospice care alone. The purpose of this study was to identify risk factors for imminent death (survival </= 2 weeks) and short-term survival (1, 3, or 6 months) in cancer patients presenting to an EC with acute dyspnea and to combine these factors into a model to help clinicians identify patients with short life expectancies. A random sample of 122 patients presenting to an EC with acute dyspnea was selected for a retrospective analysis. Data that were available to physicians during the initial EC visit included patient histories, triage and discharge vital signs, chest radiographs, and laboratory results. These variables were used in univariate and logistic regression models to develop predictive models for imminent death and short-term survival. Variables and interactions meeting a univariate criterion of P < 0.10 were included in stepwise regression by using forward and backward stepping. Models were compared with the use of Hosmer-Lemeshow statistics and receiver operating characteristics curves. Underlying cancers were 30% breast, 37% lung, and 34% other cancers. Triage respiration greater than 28/min., triage pulse greater than or equal to 110 bpm, uncontrolled progressive disease, and history of metastasis were found to be statistically significant predictors (alpha </= 0.05) of imminent death. Patients with uncontrolled progressive disease had a relative risk of imminent death of 21.93. Relative risks for triage respiration, pulse, and metastases were 12.72, 4.92, and 3.85, respectively. Cancer diagnosis was not predictive of imminent death but was predictive when longer time periods were modeled. It may be possible to identify patients whose death is imminent from a group of cancer patients with acute dyspnea. Some factors that predict imminent death (triage pulse and respiration) differ from those (cancer diagnosis) that predict short-term survival. Extent of disease/response to treatment is common to all models. These factors need further examination and validation. If these findings are confirmed, this quantified information can help physicians in making difficult end-of-life decisions.
大量因急性呼吸困难前往急诊中心(EC)或诊所就诊的癌症患者生存期不足2周。如果这些患者在入院时就能被识别出来,医生和患者就能获得更多信息,以便据此决定是继续治疗以延长生命,还是仅将治疗重点重新放在缓解症状和/或临终关怀上。本研究的目的是确定因急性呼吸困难前往急诊中心就诊的癌症患者即将死亡(生存期≤2周)和短期生存(1、3或6个月)的风险因素,并将这些因素整合到一个模型中,以帮助临床医生识别预期寿命较短的患者。选取了122例因急性呼吸困难前往急诊中心就诊的患者作为随机样本进行回顾性分析。医生在急诊中心初次就诊时可获得的数据包括患者病史、分诊和出院时的生命体征、胸部X光片以及实验室检查结果。这些变量被用于单变量和逻辑回归模型,以建立预测即将死亡和短期生存的模型。满足单变量P<0.10标准的变量和交互作用通过向前和向后逐步回归纳入逐步回归分析。通过使用Hosmer-Lemeshow统计量和受试者工作特征曲线对模型进行比较。潜在癌症中乳腺癌占30%,肺癌占37%,其他癌症占34%。分诊时呼吸频率大于28次/分钟、分诊时脉搏大于或等于110次/分钟、疾病进展无法控制以及有转移史被发现是即将死亡的统计学显著预测因素(α≤0.05)。疾病进展无法控制的患者即将死亡的相对风险为21.93。分诊时呼吸频率、脉搏和转移的相对风险分别为12.72、4.92和3.85。癌症诊断并非即将死亡的预测因素,但在对较长时间段进行建模时具有预测性。从一组因急性呼吸困难就诊的癌症患者中识别出即将死亡的患者是有可能的。一些预测即将死亡的因素(分诊时的脉搏和呼吸频率)与预测短期生存的因素(癌症诊断)不同。疾病程度/对治疗的反应在所有模型中都很常见。这些因素需要进一步研究和验证。如果这些发现得到证实,这些量化信息可以帮助医生做出艰难的临终决策。