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217 lines
9.5 KiB
217 lines
9.5 KiB
# -*- coding: utf-8 -*-
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"""
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Created on Thu Feb 25 11:13:35 2021
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@author: Dijkhofmf
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"""
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# Import stuff
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import os
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import pandas as pd
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import numpy as np
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#import seaborn as sns
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#import matplotlib.pyplot as plt
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pd.options.mode.chained_assignment = None # default='warn'
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#%% Define filenames and path
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Filename_T0 = 'BaselineT0.csv'
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Filename_T1 = 'DischargeT1.csv'
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#Filename_T2 = 'FollowUpT2Data.csv'
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FilenameOutc = 'SurgAdmComp.csv'
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FilenameComplete = 'Complete.csv'
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Path = 'I:\Mike Dijkhof\Connecare MGP\Data\FinalFiles'
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# Set path
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os.chdir(Path)
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DFT0 = pd.DataFrame(pd.read_csv(Filename_T0))
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DFT1 = pd.DataFrame(pd.read_csv(Filename_T1))
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#DFT2 = pd.DataFrame(pd.read_csv(Filename_T2))
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DFComplete = pd.DataFrame(pd.read_csv(FilenameComplete))
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DFCompl = pd.DataFrame(pd.read_csv(FilenameOutc))
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#%%
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DFT0['Complete'] = DFComplete['Has patient completed study?']
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DFT0 = DFT0.drop(DFT0[DFT0['Complete'] !='Yes'].index)
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DFT0 = DFT0.astype('str')
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DFT0 = DFT0.set_index(['Study ID'])
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DFT1['Complete'] = DFComplete['Has patient completed study?']
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DFT1 = DFT1.drop(DFT1[DFT1['Complete'] !='Yes'].index)
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DFT1 = DFT1.astype('str')
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DFT1 = DFT1.set_index(['Study ID'])
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# DFT2['Complete data'] = DFComplete['Has patient completed study?']
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# DFT2 = DFT2.drop(DFT2[DFT2['Complete data'] !='Yes'].index)
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# DFT2 = DFT2.astype('str')
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# DFT2 = DFT2.set_index(['Study ID'])
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DFCompl['Complete'] = DFComplete['Has patient completed study?']
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DFCompl = DFCompl.drop(DFCompl[DFCompl['Complete'] !='Yes'].index)
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DFCompl = DFCompl.set_index(['Study ID'])
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#%%
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DFT0 = DFT0.apply(lambda x: x.str.replace(',','.'), axis=1)
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DFT1 = DFT1.apply(lambda x: x.str.replace(',','.'), axis=1)
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#DFT2 = DFT2.apply(lambda x: x.str.replace(',','.'), axis=1)
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#%%
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FinalDF_T0 = pd.DataFrame()
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FinalDF_T0[['BMI','GFI', 'HADS_A', 'HADS_D', 'ADL', 'iADL']] = DFT0[['BMI', 'Groningen Frailty Index', 'Anxiety - Hospital Anxiety Depression Scale', 'Depression - Hospital Anxiety Depression Scale', 'ADL', 'iADL']].astype('float64')
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FinalDF_T1 = pd.DataFrame()
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FinalDF_T1[['GFI', 'HADS_A', 'HADS_D', 'ADL', 'iADL']] = DFT1[['Groningen Frailty Index', 'Anxiety - Hospital Anxiety Depression Scale', 'Depression - Hospital Anxiety Depression Scale', 'ADL', 'iADL']].astype('float64')
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FinalDF_T2 = pd.DataFrame()
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#FinalDF_T2[['GFI', 'HADS_A', 'HADS_D', 'ADL', 'iADL']] = DFT2[['Groningen Frailty Index', 'Anxiety - Hospital Anxiety Depression Scale', 'Depression - Hospital Anxiety Depression Scale', 'ADL', 'iADL']].astype('float64')
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#%% TUG_T0
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FinalDF_T0['TUG1'] = DFT0['Timed to Up&Go - attempt 1 (sec)'].astype('float64').fillna(0)
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FinalDF_T0['TUG2'] = DFT0['Timed to Up&Go - attempt 2 (sec)'].astype('float64').fillna(0)
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for i, r in FinalDF_T0.iterrows():
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if FinalDF_T0.loc[i,'TUG1'] != 0 and FinalDF_T0.loc[i,'TUG2'] != 0:
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FinalDF_T0.loc[i,'TUGTot'] = (FinalDF_T0.loc[i,'TUG1']+FinalDF_T0.loc[i,'TUG2'])/2
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else:
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FinalDF_T0.loc[i,'TUGTot'] = (FinalDF_T0.loc[i,'TUG1']+FinalDF_T0.loc[i,'TUG2'])/1
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FinalDF_T0['TUG1'] = FinalDF_T0['TUG1'].replace(0, np.nan)
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FinalDF_T0['TUG2'] = FinalDF_T0['TUG2'].replace(0, np.nan)
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FinalDF_T0['TUGTot'] = FinalDF_T0['TUGTot'].replace(0, np.nan)
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FinalDF_T0 = FinalDF_T0.drop(['TUG1', 'TUG2'], axis=1)
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# TUG_T1 Asuming that all missing data were due to physical disabilties --> NaNs to 30 seconds
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FinalDF_T1['TUG1'] = DFT1['Timed to Up&Go - attempt 1 (sec)'].astype('float64').fillna(30)
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FinalDF_T1['TUG2'] = DFT1['Timed to Up&Go - attempt 2 (sec)'].astype('float64').fillna(30)
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FinalDF_T1['TUGTot'] = (FinalDF_T1['TUG1']+FinalDF_T1['TUG2'])/2
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FinalDF_T1 = FinalDF_T1.drop(['TUG1', 'TUG2'], axis=1)
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# TUG_T2
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#FinalDF_T2['TUG1'] = DFT2['Timed to Up&Go - attempt 1 (sec)'].astype('float64').fillna(0)
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#FinalDF_T2['TUG2'] = DFT2['Timed to Up&Go - attempt 2 (sec)'].astype('float64').fillna(0)
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# for i, r in FinalDF_T2.iterrows():
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# if FinalDF_T2.loc[i,'TUG1'] != 0 and FinalDF_T2.loc[i,'TUG2'] != 0:
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# FinalDF_T2.loc[i,'TUGTot'] = (FinalDF_T2.loc[i,'TUG1']+FinalDF_T2.loc[i,'TUG2'])/2
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# else:
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# FinalDF_T2.loc[i,'TUGTot'] = (FinalDF_T2.loc[i,'TUG1']+FinalDF_T2.loc[i,'TUG2'])/1
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# FinalDF_T2['TUG1'] = FinalDF_T2['TUG1'].replace(0, np.nan)
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# FinalDF_T2['TUG2'] = FinalDF_T2['TUG2'].replace(0, np.nan)
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# FinalDF_T2['TUGTot'] = FinalDF_T2['TUGTot'].replace(0, np.nan)
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#%%
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FinalDF_T0[['HGSR1','HGSR2','HGSR3']] = DFT0[['Handgrip Strength test Attempt 1 rigth','Handgrip Strength test Attempt 2 rigth','Handgrip Strength test Attempt 3 right']].astype('float64')
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FinalDF_T0['HGSRAvg'] = (FinalDF_T0['HGSR1'] + FinalDF_T0['HGSR2'] + FinalDF_T0['HGSR3'])/3
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FinalDF_T0[['HGSL1','HGSL2','HGSL3']] = DFT0[['Handgrip Strength test Attempt 1 left', 'Handgrip Strength test Attempt 2 left', 'Handgrip Strength test Attempt 3 left']].astype('float64')
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FinalDF_T0['HGSLAvg'] = (FinalDF_T0['HGSL1'] + FinalDF_T0['HGSL2'] + FinalDF_T0['HGSL3'])/3
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FinalDF_T0['Dominance'] = DFT0['Hand dominance']
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FinalDF_T1[['HGSR1','HGSR2','HGSR3']] = DFT1[['Handgrip Strength test Attempt 1 rigth','Handgrip Strength test Attempt 2 rigth','Handgrip Strength test Attempt 3 right']].astype('float64')
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FinalDF_T1['HGSRAvg'] = (FinalDF_T1['HGSR1'] + FinalDF_T1['HGSR2'] + FinalDF_T1['HGSR3'])/3
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FinalDF_T1[['HGSL1','HGSL2','HGSL3']] = DFT1[['Handgrip Strength test Attempt 1 left', 'Handgrip Strength test Attempt 2 left', 'Handgrip Strength test Attempt 3 left']].astype('float64')
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FinalDF_T1['HGSLAvg'] = (FinalDF_T1['HGSL1'] + FinalDF_T1['HGSL2'] + FinalDF_T1['HGSL3'])/3
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for i, r in DFT1.iterrows():
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if DFT1.loc[i,'Handgrip Strength Test'] == 'No':
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FinalDF_T1.loc[i,['HGSR1','HGSR2','HGSR3','HGSRAvg','HGSL1','HGSL2','HGSL3','HGSLAvg']] = 0
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for index, rows in FinalDF_T1.iterrows():
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if FinalDF_T0.loc[index, 'Dominance'] == 'Rigth':
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FinalDF_T0.loc[index, 'HGSDom'] = FinalDF_T0.loc[index,'HGSRAvg']
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FinalDF_T1.loc[index, 'HGSDom'] = FinalDF_T1.loc[index,'HGSRAvg']
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elif FinalDF_T0.loc[index, 'Dominance'] == 'Left':
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FinalDF_T0.loc[index, 'HGSDom'] = FinalDF_T0.loc[index,'HGSLAvg']
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FinalDF_T1.loc[index, 'HGSDom'] = FinalDF_T1.loc[index,'HGSLAvg']
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else:
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FinalDF_T0.loc[index, 'HGSDom'] = (FinalDF_T0.loc[index,'HGSRAvg']+FinalDF_T0.loc[index,'HGSLAvg'])/2
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FinalDF_T1.loc[index, 'HGSDom'] = (FinalDF_T1.loc[index,'HGSRAvg']+FinalDF_T1.loc[index,'HGSLAvg'])/2
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FinalDF_T0 = FinalDF_T0.drop(['HGSR1', 'HGSR2', 'HGSR3', 'HGSRAvg','HGSL1', 'HGSL2', 'HGSL3', 'HGSLAvg', 'Dominance'], axis=1)
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FinalDF_T1 = FinalDF_T1.drop(['HGSR1', 'HGSR2', 'HGSR3', 'HGSRAvg','HGSL1', 'HGSL2', 'HGSL3', 'HGSLAvg'], axis=1)
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# FinalDF_T2[['HGSR1','HGSR2','HGSR3']] = DFT2[['Handgrip Strength test Attempt 1 right','Handgrip Strength test Attempt 2 right','Handgrip Strength test Attempt 3 right']].astype('float64')
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# FinalDF_T2['HGSRAvg'] = (FinalDF_T2['HGSR1'] + FinalDF_T2['HGSR2'] + FinalDF_T2['HGSR3'])/3
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# FinalDF_T2[['HGSL1','HGSL2','HGSL3']] = DFT2[['Handgrip Strength test Attempt 1 left', 'Handgrip Strength test Attempt 2 left', 'Handgrip Strength test Attempt 3 left']].astype('float64')
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# FinalDF_T2['HGSLAvg'] = (FinalDF_T2['HGSL1'] + FinalDF_T2['HGSL2'] + FinalDF_T2['HGSL3'])/3
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# if FinalDF_T0['Dominance'] == 'Right':
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# FinalDF_T2['HSGDom'] = FinalDF_T0['HGSRAvg']
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# elif FinalDF_T0['Dominance'] == 'Left':
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# FinalDF_T2['HSGDom'] = FinalDF_T0['HGSLAvg']
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# else:
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# FinalDF_T2['HGSDom'] = (FinalDF_T0['HGSRAvg']+FinalDF_T0['HGSLAvg'])/2
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#%%
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EORTCCols = DFT0.columns[15:59].tolist()
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EORTCScoresT0 = DFT0[EORTCCols]
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#EORTCScoresT2 = DFT2[EORTCCols]
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#%%
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os.chdir('I:\Mike Dijkhof\Python')
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import EORTC as eor
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import SQUASH as sq
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NewEORTCScoresT0 = eor.EORTCCalculator(EORTCScoresT0, EORTCCols)
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#NewEORTCScoresT2 = eor.EORTCCalculator(EORTCScoresT2, EORTCCols)
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EORTCT0 = eor.EORTCScore(EORTCScoresT0)
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#EORTCT2 = eor.EORTCScore(EORTCScoresT2)
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os.chdir(Path)
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#%% plaatjes
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# for index, row in EORTC.iterrows():
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# plt.figure(figsize=(20,8))
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# plt.title('EORTC preoperative outcomes pt ' + str(index))
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# sns.barplot(x=EORTC.columns, y=EORTC.loc[index,:])
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#%%
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SQUASHScoresT0 = sq.SQUASHParse(DFT0)
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#SQUASHScoresT2 = sq.SQUASHParse(DFT2)
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ColsToDrop = ['SQUASH baseline afgenomen?', 'Woon werkverkeer?', 'Werk?', 'Huishoudelijk werk?']
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SQUASHScoresT0 = SQUASHScoresT0.drop(ColsToDrop, axis=1)
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SQUASHScoresT0 = SQUASHScoresT0.astype('float64')
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#SQUASHScoresT2 = SQUASHScoresT2.drop(ColsToDrop, axis=1)
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#SQUASHScoresT2 = SQUASHScoresT2.astype('float64')
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SQUASHT0 = sq.SQUASHScore(SQUASHScoresT0)
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#SQUASHT2 = sq.SQUASHScore(SQUASHScoresT2)
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#%%
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FinalDF_T0['Pt Type'] = DFCompl.loc[:,'Complications at home during monitoring ? '].values
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FinalDF_T1['Pt Type'] = DFCompl.loc[:,'Complications at home during monitoring ? '].values
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#FinalDF_T2['Pt Type'] = DFCompl.loc[:,'Complications at home during monitoring ? '].values
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FinalDF_T0['Pt Type'] = FinalDF_T0['Pt Type'].str.replace('Yes', 'Complication')
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FinalDF_T0['Pt Type'] = FinalDF_T0['Pt Type'].str.replace('No', 'Healthy')
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FinalDF_T1['Pt Type'] = FinalDF_T1['Pt Type'].str.replace('Yes', 'Complication')
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FinalDF_T1['Pt Type'] = FinalDF_T1['Pt Type'].str.replace('No', 'Healthy')
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#FinalDF_T2['Pt Type'] = FinalDF_T2['Pt Type'].str.replace('Yes', 'Complication')
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#FinalDF_T2['Pt Type'] = FinalDF_T2['Pt Type'].str.replace('No', 'Healthy')
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#%% Save FinalDF to .csv file
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FinalDF_T0.to_csv('FinalDF_T0.csv')
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FinalDF_T1.to_csv('FinalDF_T1.csv')
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#FinalDF_T2.to_csv('FinalDF_T2.csv')
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