Global Epidemiology, Pharmatelligence, Cardiff, United Kingdom.
Norgine Pharmaceuticals Limited, Harefield, Uxbridge, United Kingdom.
PLoS One. 2019 Oct 3;14(10):e0223253. doi: 10.1371/journal.pone.0223253. eCollection 2019.
The purpose of this study was to produce two statistical survival models in those with cirrhosis utilising only routine parameters, including non-liver-related clinical factors that influence survival. The first model identified and utilised factors impacting short-term survival to 90-days post incident diagnosis, and a further model characterised factors that impacted survival following this acute phase. Data were from the Clinical Practice Research Datalink linked with Hospital Episode Statistics. Incident cases in patients ≥18 years were identified between 1998 and 2014. Patients that had prior history of cancer or had received liver transplants prior were excluded. Model-1 used a logistic regression model to predict mortality. Model-2 used data from those patients who survived 90 days, and used an extension of the Cox regression model, adjusting for time-dependent covariables. At 90 days, 23% of patients had died. Overall median survival was 3.7 years. Model-1: numerous predictors, prior comorbidities and decompensating events were incorporated. All comorbidities contributed to increased odds of death, with renal disease having the largest adjusted odds ratio (OR = 3.35, 95%CI 2.97-3.77). Model-2: covariables included cumulative admissions for liver disease-related events and admissions for infections. Significant covariates were renal disease (adjusted hazard ratio (HR = 2.89, 2.47-3.38)), elevated bilirubin levels (aHR = 1.38, 1.26-1.51) and low sodium levels (aHR = 2.26, 1.84-2.78). An internal validation demonstrated reliability of both models. In conclusion: two survival models that included parameters commonly recorded in routine clinical practice were generated that reliably forecast the risk of death in patients with cirrhosis: in the acute, post diagnosis phase, and following this critical, 90 day phase. This has implications for practice and helps better forecast the risk of mortality from cirrhosis using routinely recorded parameters without inputs from specialists.
本研究的目的是利用仅包括影响生存的非肝脏相关临床因素的常规参数，为肝硬化患者生成两个统计生存模型。第一个模型确定并利用了影响发病后 90 天内短期生存的因素，进一步的模型描述了影响急性阶段后生存的因素。数据来自于临床实践研究数据库与医院入院统计数据的链接。1998 年至 2014 年期间，在≥18 岁的患者中确定了首发病例。排除了有癌症既往史或曾接受过肝移植的患者。模型 1 使用逻辑回归模型预测死亡率。模型 2 使用了在 90 天内存活的患者的数据，并使用 Cox 回归模型的扩展，调整了时间相关的协变量。在 90 天时，有 23%的患者死亡。总体中位生存时间为 3.7 年。模型 1：纳入了许多预测因素、既往合并症和失代偿事件。所有合并症都增加了死亡的可能性，其中肾脏疾病的调整后比值比（OR = 3.35，95%CI 2.97-3.77）最大。模型 2：协变量包括与肝脏疾病相关事件的累积入院和感染入院。显著的协变量是肾脏疾病（调整后的危险比（HR = 2.89，2.47-3.38））、胆红素水平升高（aHR = 1.38，1.26-1.51）和低钠水平（aHR = 2.26，1.84-2.78）。内部验证表明两个模型都具有可靠性。总之：生成了两个包含常规临床实践中常见记录参数的生存模型，可可靠预测肝硬化患者的死亡风险：在发病后的急性期和这一关键的 90 天阶段之后。这对实践具有影响，并有助于使用常规记录的参数而无需专家输入来更好地预测肝硬化的死亡率风险。
