- Get your PCI data from Richard Jung (
rjung@ottawaheart.ca
) as an EXCEL SPREADSHEET *.XLSX file. Make sure you save the file without a password so that the Terminal can open it (Open it in Excel -> Go to File - > Passwords and type the current password, then under 'New Password' leave the field empty)
-
Install Github https://gist.github.com/derhuerst/1b15ff4652a867391f03
-
On MacOS : Open Terminal by doing CMD + SPACE and searching for 'Terminal'
-
In the new window clone this GitHub reposity by typing 'git clone https://github.com/robertavram-md/UOHI_PCI_Log.git'
-
You will have a new folder where Terminal or command Prompt was opened called
UOHI_PCI_Log
-
Change directory to
UOHI_PCI_Log
by typing the followingcd UOHI_PCI_Log
-
Install Python https://www.python.org/downloads/
-
Put your PCI data .xlsx file into the folder called
UOHI_PCI_Log
-
Run in terminal
pip install pandas
-
Run this command in terminal
python ComputeMetrics.py '**filename**'
For example if your PCI Log book is named avram_18_11_2020.xlsx
then you should run python ComputeMetrics.py 'avram_18_11_2020.xlsx'
-
Install Github https://gist.github.com/derhuerst/1b15ff4652a867391f03
-
On Windows : Use Windows+R on your keyboard to open “Run” box. Type “cmd” and then click “OK” to open a regular Command Prompt.
-
In the new window clone this GitHub reposity by typing 'git clone https://github.com/robertavram-md/UOHI_PCI_Log.git'
-
You will have a new folder where Terminal or command Prompt was opened called
UOHI_PCI_Log
-
Install Python https://www.python.org/downloads/
-
Put your PCI data .xlsx file into the folder called
UOHI_PCI_Log
-
Run in Command Prompt
pip install pandas
-
Run in Command Prompt
python ComputeMetrics.py '**filename**'
For example if your PCI Log book is named avram_18_11_2020.xlsx
then you should run python ComputeMetrics.py 'avram_18_11_2020.xlsx'
You will get three files
- condensed_summary_.csv Contains the summary of number of cases breakdown as required by the royal college
- overall_.csv Contains all your cases, including diagnostics. The last columns in the file can be used to 'filter' the required data
- interventions_.csv Contains all the intervention data. The last columns in the file can be used to 'filter' the required data (i.e. select all the Bifurcation cases
- Calcified cases are defined by any case where you required a 'rotablator', 'guideliner' or 'cutting balloon' np.where(((df_intervention['guideliner_used']=='Yes') | (df_intervention['thrombectomy_used']=='Yes') | (df_intervention['distal_protection_used']=='Yes') | (df_intervention['rotablator_used']=='Yes') | (df_intervention['cutting_balloon_used']=='Yes')| (df_intervention['lm_interv']==True) | (df_intervention['bifurcation_interv']==True)
- Complicated cases are defined by any LEFT MAIN PCI, BIFURCATION, CALCIFIED LESION, USE OF ROTABLATOR, THROMBECTOMY CATHETER USED, DISTAL PROTECTION DEVICE USED or GUIDELINER
- It is currently impossible to know how many 'bypass' grafts or 'CTOs' you have done unless you specified them as 'free text' in the last box in the CAPITAL_PCI registry entry.
- PCI cases include any balloon angioplasty or stent deployment; it does not include simple diagnostic FFR, IFR, IVUS or OCT.
- Hemodynamic instability is defined by the presence of either of the following 'ROSC', 'Cardiac arrest complication', 'Arythmia complicated' or 'intubated patient'