Fig 1.
(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 n 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.
Table 1.
Patient characteristics.
Fig 2.
Left: Time-series heat map transition of albumin (Alb), neutrophil (Neu), and lactate dehydrogenase (LDH) levels.
Right: Mean Alb, Neu, and LDH levels with 95% confidence intervals.
Table 2.
Performance of albumin, lactate dehydrogenase, and neutrophil models for prediction of death events within 1–6 months.
Table 3.
Regression equation corresponding to each prediction period.
Fig 3.
Area under the receiver operating characteristic curve values for the prediction of death events within 1–6 months among 10 different tumor types.
Fig 4.
Comparison of area under the receiver operating characteristic curve (AUC) values for the prediction of death events among the (A) test cohort at the Kyoto University Hospital and (B) validation cohort (blue: Kyoto Mitsubishi Hospital, green: Kyoto Min-iren Chuo Hospital, red: J-ProVal study).