A combination of routine laboratory findings and vital signs can predict survival of advanced cancer patients without physician evaluation: a fractional polynomial model

IntroductionThere have been no reports about predicting survival of patients with advanced cancer constructed entirely with objective variables. We aimed to develop a prognostic model based on laboratory findings and vital signs using a fractional polynomial (FP) model.MethodsA multicentre prospective cohort study was conducted at 58 specialist palliative care services in Japan from September 2012 to April 2014. Eligible patients were older than 20 years and had advanced cancer. We developed models for predicting 7-day, 14-day, 30-day, 56-day and 90-day survival by using the FP modelling method.ResultsData from 1039 patients were analysed to develop each prognostic model (Objective Prognostic Index for advanced cancer [OPI-AC]). All models included the heart rate, urea and albumin, while some models included the respiratory rate, creatinine, C-reactive protein, lymphocyte count, neutrophil count, total bilirubin, lactate dehydrogenase and platelet/lymphocyte ratio. The area under the curve was 0.77, 0.81, 0.90, 0.90 and 0.92 for the 7-day, 14-day, 30-day, 56-day and 90-day model, respectively. The accuracy of the OPI-AC predicting 30-day, 56-day and 90-day survival was significantly higher than that of the Palliative Prognostic Score or the Prognosis in Palliative Care Study model, which are based on a combination of symptoms and physician estimation.ConclusionWe developed highly accurate prognostic indexes for predicting the survival of patients with advanced cancer from objective variables alone, which may be useful for end-of-life management. The FP modelling method could be promising for developing other prognostic models in future research

Development and validation of a set of six adaptable prognosis prediction (SAP) models based on time-series real-world big data analysis for patients with cancer receiving chemotherapy: A multicenter case crossover study - Fig 1

<p>(A) Flow of the study. (B) Comparison of time-series real-world big data analysis with conventional methods. Upper: Time-series real-world big data analysis included every time point monitored within 1 year before the death event in the analysis as an explanatory variable. Each laboratory variable was used as time-inculsive data, classified event data and control data bounded by <i>n</i> months before the date death. Lower: The conventional method involved single time point (such as admission date or baseline assessment date) as an explanatory variable. AUC, area under the curve; ROC, receiver operating characteristic curve; Black arrow, explanatory variable.</p

Performance of albumin, lactate dehydrogenase, and neutrophil models for prediction of death events within 1–6 months.

<p>Performance of albumin, lactate dehydrogenase, and neutrophil models for prediction of death events within 1–6 months.</p

Regression equation corresponding to each prediction period.

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Left: Time-series heat map transition of albumin (Alb), neutrophil (Neu), and lactate dehydrogenase (LDH) levels.

<p>Right: Mean Alb, Neu, and LDH levels with 95% confidence intervals.</p

Area under the receiver operating characteristic curve values for the prediction of death events within 1–6 months among 10 different tumor types.

<p>Area under the receiver operating characteristic curve values for the prediction of death events within 1–6 months among 10 different tumor types.</p