System and method for predicting risk of acute renal failure following non-cardiac surgery

A method for predicting and reducing a risk of acute kidney injury (AKI) after non-cardiac surgery, the method implemented by a computing apparatus including one or more processors and a memory storing one or more programs executed by the one or more processors, the method comprising:

• selecting factors associated with an occurrence of acute kidney injury after non-cardiac surgery, which includes clinical data before non-cardiac surgery of patients subjected to non-cardiac surgery as variables, and then, presetting an index set for each variable;
• calculating sensitivity and specificity to a sum of combinations of indexes for each index set, and setting cutoff values for classification according to the sensitivity and specificity;
• and
• calculating a sum of indexes determined according to the index set preset for each variable;
• and
• classifying risk of acute kidney injury after non-cardiac surgery of the non-cardiac surgery patient on the basis of the cutoff values into four grades;
• and
• administering, based on the classified grade, an AKI preventive agent to the patient,
• wherein the factors associated with the occurrence of acute kidney injury after non-cardiac surgery include at least one selected from the group consisting of age, estimated glomerular filtration rate (eGFR), dipstick albuminuria, sex, expected surgical duration, emergency operation, diabetes mellitus, use of renin-aldosterone-angiotensin-system blocker (use of RAAS blocker), hypoalbuminemia, anemia and hyponatremia,
• wherein the prediction of the risk of acute kidney injury after non-cardiac surgery is classified into total four (4) grades including A, B, C and D, based on a sum of indexes determined according to the index set preset for each variable in regard to the non-cardiac surgery patient in need of the prediction,
• wherein the grade A is classified when the sum of the indexes is less than 20, the grade B is classified when the sum of the indexes is 20 or more and less than 40, the grade C is classified when the sum of the indexes is 40 or more and less than 60, and the grade D is classified when the sum of the indexes is 60 or more,
• wherein the grade A involves less than 2% probability of both acute kidney injury and severe acute kidney injury after non-cardiac surgery of patients subjected to non-cardiac surgery (“non-cardiac surgery patients”) in need of the prediction;
• the grade B involves 2% or more probability of acute kidney injury and less than 2% probability of serious acute kidney injury after non-cardiac surgery of the non-cardiac surgery patients in need of the prediction;
• the grade C involves 10% or more probability of acute kidney injury and 2% or more probability of serious acute kidney injury after non-cardiac surgery of the non-cardiac surgery patients in need of the prediction;
• and
• the grade D involves 20% or more probability of acute kidney injury and 10% or more probability of serious acute kidney injury after non-cardiac surgery of the non-cardiac surgery patients in need of the prediction.

A method of reducing a risk of acute kidney injury after non-cardiac surgery, the method comprising:

• before the non-cardiac surgery, determining a risk of acute kidney injury of a patient after the non-cardiac surgery;
• and
• treating the patent with an acute kidney injury (AKI) preventive agent, wherein a dose of the AKI preventive agent is determined on the basis of the graded risk,
• wherein the determining is implemented by a computing apparatus including one or more processors and a memory storing one or more programs executed by the one or more processors, the determining comprising:
• selecting factors associated with an occurrence of acute kidney injury after non-cardiac surgery as variables;
• presetting an index set for each variable;
• calculating sensitivity and specificity to a sum of combinations of indexes for each index set;
• setting cutoff values for classification according to the sensitivity and specificity;
• calculating a sum of indexes determined according to the index set preset for each variable;
• determining, based on the calculated sum of indexes, a risk grade of a patient.