A research group at the University of Tsukuba has developed a method for estimating the prognosis of patients with advanced cancer with high accuracy using only blood test data, heart rate, and respiratory rate.

 So far, the prognosis of patients with advanced cancer has been estimated mainly based on the experience of doctors.However, inferences based on the experience of physicians tended to estimate life prognosis longer than they actually were, and were inaccurate.Therefore, several guessing methods including objective information have been developed, but all of them have a drawback that they cannot be guessed without seeing a doctor.

 With the method developed this time, the survival probability after 7 days, 14 days, 30 days, 56 days, and 90 days can be calculated for each patient at once by simply inputting the patient's blood test data, heart rate, and respiratory rate.Moreover, it is said to be more accurate than the conventional guessing method used worldwide.In addition, since it is possible to make repeated guesses at each blood test, it is possible to obtain results that sharply reflect changes in therapeutic effects and physical condition.

 The results of this research can be used not only to properly judge the prognosis of a patient and to consider the best treatment, but also to help the patient understand his / her own situation and think about future medical care / care. It can also be used in telemedicine.

 However, since this study analyzes only advanced cancer patients who have received palliative care, it is yet to be verified whether it can be used for all advanced cancer patients who do not receive palliative care.Also, if this method is used, the prognosis of life can be estimated when the patient or family wants to know it, but is that good for the patient or family?It is said that this point needs to be discussed in the future.

Paper information:[European Journal of Cancer] A combination of routine laboratory findings and vital signs can predict survival of advanced cancer patients without physician evaluation: a fractional polynomial model

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