Prediction of acute kidney injury from a post-surgical metabolic blood panel

Abstract

Systems and methods are provided for predicting the likelihood of acute kidney injury. An input interface is configured to receive a plurality of features derived from the results of a post-surgical metabolic blood panel and either a pre-surgical metabolic blood panel or a perisurgical metabolic blood panel. A predictive model is configured to calculate a parameter representing a likelihood of acute kidney injury from the plurality of features. A user interface is configured to provide the calculated parameter to a user in a human comprehensible form.

Having described the invention, the following is claimed: 
     
         1 . A non-transitory computer readable medium storing machine executable instructions for predicting the likelihood of acute kidney injury comprising:
 an input interface configured to receive a plurality of features derived from the results of a post-surgical metabolic blood panel and one of a pre-surgical metabolic blood panel and a perisurgical metabolic blood panel;   a predictive model configured to calculate a parameter representing a likelihood of acute kidney injury from the plurality of features; and   a user interface configured to provide the calculated parameter to a user in a human comprehensible form.   
     
     
         2 . The non-transitory computer readable medium of  claim 1 , wherein the plurality of features includes a change in a creatinine level between the one of the pre-surgical metabolic blood panel and the perisurgical metabolic blood panel and the post-surgical blood panel. 
     
     
         3 . The non-transitory computer readable medium of  claim 2 , wherein the change in the creatinine level is normalized by a time elapsed between a surgery and the post-surgical metabolic blood panel. 
     
     
         4 . The non-transitory computer readable medium of  claim 1 , wherein the plurality of features includes a sodium level from the post-surgical metabolic blood panel. 
     
     
         5 . The non-transitory computer readable medium of  claim 1 , wherein the plurality of features includes a potassium level from the post-surgical metabolic blood panel. 
     
     
         6 . The non-transitory computer readable medium of  claim 1 , wherein the plurality of features includes a bicarbonate level from the post-surgical metabolic blood panel. 
     
     
         7 . The non-transitory computer readable medium of  claim 1 , wherein the plurality of features includes a creatinine level from the post-surgical metabolic blood panel. 
     
     
         8 . The non-transitory computer readable medium of  claim 1 , wherein the plurality of features includes an albumin level from the post-surgical metabolic blood panel. 
     
     
         9 . The non-transitory computer readable medium of  claim 1 , wherein the plurality of features includes a change in a blood urea nitrogen level between the one of the pre-surgical metabolic blood panel and the perisurgical metabolic blood panel and the post-surgical blood panel. 
     
     
         10 . The non-transitory computer readable medium of  claim 9 , wherein the change in the blood urea nitrogen level is normalized by a time elapsed between a surgery and the post-surgical metabolic blood panel. 
     
     
         11 . The non-transitory computer readable medium of  claim 1 , wherein the predictive model is configured to calculate the parameter representing a likelihood of acute kidney injury as a weighted combination of the plurality of features. 
     
     
         12 . The non-transitory computer readable medium of  claim 11 , wherein a first feature of the plurality of features is a non-linear function of a second feature of the plurality of features. 
     
     
         13 . A method for predicting acute kidney injury associated with a surgical procedure comprising:
 isolating a first blood serum sample from a blood sample drawn from a patient before an end of the surgical procedure;   determining at least a first creatinine level from the first blood serum sample;   isolating a second blood serum sample from a blood sample drawn from the patient after the end of the surgical procedure;   determining at least a second creatinine level from the second blood serum sample;   calculating, from a predictive model, a parameter representing a likelihood of acute kidney injury to the patient from at least a difference between the first creatinine level and the second creatinine level; and   displaying the calculated parameter to a user.   
     
     
         14 . The method of  claim 13 , wherein the difference between the first creatinine level is normalized according to a length of time between the end of the surgical procedure and drawing of the blood sample from the patient after the end of the surgical procedure. 
     
     
         15 . The method of  claim 14 , wherein calculating the parameter representing the likelihood of acute kidney injury to the patient comprises calculating the parameter from the normalized difference between the first creatinine level and the second creatinine level and the first creatinine level. 
     
     
         16 . The method of  claim 15 , further comprising:
 determining a first blood urea nitrogen level from the first blood serum sample; and   determining a second blood urea nitrogen level from the second blood serum sample;   wherein calculating the parameter representing the likelihood of acute kidney injury to the patient comprises calculating the parameter from the normalized difference between the first creatinine level and the second creatinine level and a difference between the first second blood urea level and the second second blood urea level, normalized by the length of time.   
     
     
         17 . The method of  claim 16 , further comprising determining each of a sodium level, a potassium level, a bicarbonate level, and an albumin level from the second blood serum sample, and calculating the parameter representing the likelihood of acute kidney injury to the patient comprises calculating the parameter from the normalized difference between the first creatinine level and the second creatinine level, the normalized difference between the first blood urea nitrogen level and the second blood urea nitrogen level, the first creatinine level, the sodium level, the potassium level, the bicarbonate level, and the albumin level. 
     
     
         18 . A diagnostic system for predicting acute kidney injury associated with a surgical procedure comprising:
 a processor; and   a non-transitory computer readable medium storing machine executable instructions executable by the processor to predict a likelihood of acute kidney injury, the instructions comprising:
 an input interface configured to receive a plurality of features derived from the results of a post-surgical metabolic blood panel and one of a pre-surgical metabolic blood panel and a perisurgical metabolic blood panel, the plurality of features including at least a difference in a serum creatinine level between the one of the pre-surgical metabolic blood panel and the perisurgical metabolic blood panel and the post-surgical metabolic blood panel; 
 a predictive model configured to calculate a parameter representing a likelihood of acute kidney injury from the plurality of features; and 
 a user interface configured to provide the calculated parameter to a user in a human comprehensible form. 
   
     
     
         19 . The diagnostic system of  claim 18 , wherein the input interface is configured to receive an input from a remote user via an Internet connection, and the user interface is configured to provide the calculated parameter to a display associated with the remote user via the Internet connection. 
     
     
         20 . The diagnostic system of  claim 18 , wherein the predictive model is configured to calculate the parameter representing a likelihood of acute kidney injury as a weighted non-linear combination of a ratio of the difference in a serum creatinine level and a period of time between an end of the surgical procedure and a drawing of the post-surgical metabolic blood panel, a ratio of a difference between a presurgical blood urea nitrogen level and a post-surgical blood urea nitrogen level to the period of time, the presurgical creatinine level, a post-surgical sodium level, a post-surgical potassium level, a post-surgical bicarbonate level, and a post-surgical albumin level.