mohcinemadkour / AKI-risk-Calculator-

Real time AKI risk Calculator using changes in serum creatinine

Real time AKI risk Calculator using changes in serum creatinine

This program analyses a series of creatinine laboratory values and calculates the number of AKI, and their dates. It also has the ability to plot the results.

How to use the program

In order to use the program, you have to include patients creatinine values in the input folder as Labsxx.csv files. If you also want the estimated glomerular filtration rate, then a Demographics.csv file is needed with patients identifiers, age, gender, and race

Input

Please see the current files in the input folder as an example of how the files are structured.

Demographics.csv
MRN     Age     Gender     Race
1       50      MALE       WHITE
2       55      FEMALE     BLACK
3       44      FEMALE     ASIAN
4       58      MALE       BLACK
5       61      FEMALE     WHITE

Labs01.csv
MRN     Creatinine1     TestDate1
1       1               2001-09-12
1       1.2             2001-09-13
1       1.1             2001-09-14
1       1.2             2001-09-15

Output

Upon running AKIPredictor.py, the information in the Input folder is proccessed, and the following files are written in the Output folder:

• aki.csv: This files contains a list of all patients, their estimated baseline creatinine, and baseline GFR, number of AKI episodes based on the AKIN criteria.
• Dates folder: This folder contains filed named by the patient's MRN, and lists the dates when AKI were detected for these patients listed.
• Graphs folder: This folder contains a list of .png names by patients MRNs, and illustrate the patients creatinine trend. The points identified as AKI episodes are highlighted in red.

Here is an example of the output:

MRN     baseCr     eGFR     numAKI     anyAKI     CKD
1       1.0        87.3     2          True       2.0
2       0.9        83.4     1          True       2.0
3       0.9        77.7     1          True       2.0
4       0.7        120.5    4          True          
5       0.8        79.5     0          False      2.0