Pathway patterns throughout the day in an example single stroke team

Aims

Show key pathway statistics broken down by time of day (3 hour epochs) in a single example team.

Import libraries and data

Data has been restricted to stroke teams with at least 300 admissions, with at least 10 patients receiving thrombolysis, over three years.

# import libraries
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd

# Display entire dataframes
pd.set_option("display.max_rows", 999, "display.max_columns", 150)

# import data
raw_data = pd.read_csv(
    './../data/2019-11-04-HQIP303-Exeter_MA.csv', low_memory=False)

headings = list(raw_data)
print (headings)

['StrokeTeam', 'PatientUID', 'Pathway', 'S1AgeOnArrival', 'MoreEqual80y', 'S1Gender', 'S1Ethnicity', 'S1OnsetInHospital', 'S1OnsetToArrival_min', 'S1OnsetDateType', 'S1OnsetTimeType', 'S1ArriveByAmbulance', 'S1AdmissionHour', 'S1AdmissionDay', 'S1AdmissionQuarter', 'S1AdmissionYear', 'CongestiveHeartFailure', 'Hypertension', 'AtrialFibrillation', 'Diabetes', 'StrokeTIA', 'AFAntiplatelet', 'AFAnticoagulent', 'AFAnticoagulentVitK', 'AFAnticoagulentDOAC', 'AFAnticoagulentHeparin', 'S2INR', 'S2INRHigh', 'S2INRNK', 'S2NewAFDiagnosis', 'S2RankinBeforeStroke', 'Loc', 'LocQuestions', 'LocCommands', 'BestGaze', 'Visual', 'FacialPalsy', 'MotorArmLeft', 'MotorArmRight', 'MotorLegLeft', 'MotorLegRight', 'LimbAtaxia', 'Sensory', 'BestLanguage', 'Dysarthria', 'ExtinctionInattention', 'S2NihssArrival', 'S2BrainImagingTime_min', 'S2StrokeType', 'S2Thrombolysis', 'Haemorrhagic', 'TimeWindow', 'Comorbidity', 'Medication', 'Refusal', 'Age', 'Improving', 'TooMildSevere', 'TimeUnknownWakeUp', 'OtherMedical', 'S2ThrombolysisTime_min', 'S2TIAInLastMonth']

Restrict original data to hospitals with at least 300 admissions + 10 thrombolysis patients

keep = []

groups = raw_data.groupby('StrokeTeam') # creates a new object of groups of data

for index, group_df in groups: # each group has an index and a dataframe of data
    
    # Skip if total admissions less than 300 or total thrombolysis < 10
    admissions = group_df.shape[0]
    thrombolysis_received = group_df['S2Thrombolysis'] == 'Yes' 
    if (admissions < 300) or (thrombolysis_received.sum() < 10):
        continue
    
    else: 
        keep.append(group_df)

# Concatenate output
data = pd.DataFrame()
data = pd.concat(keep)

Remove in-hospital onset

mask = data['S1OnsetInHospital'] == 'No'
data = data[mask]

Restrict to single stroke_team

team_pick = 0
teams = list(set(data['StrokeTeam']))
teams.sort()
team = teams[team_pick]
mask = data['StrokeTeam'] == team
selected_data = data[mask]
print (f'Arrivals {mask.sum()}')

Arrivals 2015

Group by time of day

# Work on copy of data
data_time = data.copy()

# Set up results DataFrame
time_summary = pd.DataFrame()

# Count arrivals
time_summary['Arrivals'] = \
    data_time.groupby('S1AdmissionHour').count()['StrokeTeam']

# Get thrombolysis rate
thrombolysed = data_time['S2Thrombolysis'] == 'Yes'
data_time['thrombolysed'] = thrombolysed
time_summary['thrombolyse_all'] = \
    data_time.groupby('S1AdmissionHour').mean()['thrombolysed']

# Get proportion of strokes with known onset
onset_known = (data_time['S1OnsetTimeType'] == 'Best estimate') | \
    (data_time['S1OnsetTimeType'] == 'Precise')
data_time['onset_known'] = onset_known
time_summary['onset_known'] = \
    data_time.groupby('S1AdmissionHour').mean()['onset_known']

# Get proportion over 80
data_time['age_80_plus'] = data_time['MoreEqual80y'] == 'Yes'
time_summary['age_80_plus'] = \
    data_time.groupby('S1AdmissionHour').mean()['age_80_plus']

# Get Rankin score
time_summary['rankin_all'] = \
    data_time.groupby('S1AdmissionHour').mean()['S2RankinBeforeStroke']

# Get NIHSS
time_summary['nihss_all'] = \
    data_time.groupby('S1AdmissionHour').mean()['S2NihssArrival']

# Get onset to arrival <4hrs and then restrict data
data_time['4hr_arrival'] = data_time['S1OnsetToArrival_min'] <= 240
time_summary['4hr_arrival'] = \
    data_time.groupby('S1AdmissionHour').mean()['4hr_arrival']
mask = data_time['4hr_arrival']
data_time = data_time[mask]

# Get Rankin score of arrivals within 4hrs onset
time_summary['rankin_4hr'] = \
    data_time.groupby('S1AdmissionHour').mean()['S2RankinBeforeStroke']

# Get NIHSS of arrivals within 4hrs onset
time_summary['nihss_4hr'] = \
    data_time.groupby('S1AdmissionHour').mean()['S2NihssArrival']

# Get onset to arrival (of those arriving within 4 hours)
time_summary['onset_arrival'] = \
    data_time.groupby('S1AdmissionHour').mean()['S1OnsetToArrival_min']

# Get scan in four hours (and remove rest)
data_time['4hr_scan'] = data_time['S2BrainImagingTime_min'] <= 240
time_summary['scan_4hrs'] = \
    data_time.groupby('S1AdmissionHour').mean()['4hr_scan']
mask = data_time['4hr_scan']
data_time = data_time[mask]

# Get arrival to scan (of those arriving within 4 hours and scanned in 4hrs)
time_summary['arrival_scan'] = \
    data_time.groupby('S1AdmissionHour').mean()['S2BrainImagingTime_min']

# Filter down to acanned within 4 hrs onset
onset_to_scan = (
    data_time['S1OnsetToArrival_min'] + data_time['S2BrainImagingTime_min'])
data_time['onset_to_scan'] = onset_to_scan
mask = data_time['onset_to_scan'] <= 240
data_time = data_time[mask]

# Get thrombolysis given and remove rest
time_summary['thrombolyse_4hr'] = \
    data_time.groupby('S1AdmissionHour').mean()['thrombolysed']
mask = data_time['thrombolysed']

# Get scan to needle
scan_to_needle = (
    data_time['S2ThrombolysisTime_min'] - data_time['S2BrainImagingTime_min'])
data_time['scan_needle'] = scan_to_needle
time_summary['scan_to_needle'] = \
    data_time.groupby('S1AdmissionHour').mean()['scan_needle']

# Get arrival to needle
time_summary['arrival_to_needle'] = \
    data_time.groupby('S1AdmissionHour').mean()['S2ThrombolysisTime_min']

# Get onset to needle 
onset_to_needle = (
    data_time['S1OnsetToArrival_min'] + data_time['S2ThrombolysisTime_min'])
data_time['onset_to_needle'] = onset_to_needle
time_summary['onset_to_needle'] = \
    data_time.groupby('S1AdmissionHour').mean()['onset_to_needle']

# Add short time start (string) column for plots
hour = ['0', '3', '6', '9', '12', '15', '19', '21']
time_summary['hour'] = hour

Show summary table by time of day

time_summary.T

S1AdmissionHour	00:00 to 02:59	03:00 to 05:59	06:00 to 08:59	09:00 to 11:59	12:00 to 14:59	15:00 to 17:59	18:00 to 20:59	21:00 to 23:59
Arrivals	9890	7367	15871	52049	48687	39838	32200	20918
thrombolyse_all	0.131446	0.0651554	0.109004	0.119464	0.118882	0.115945	0.119658	0.135338
onset_known	0.75996	0.679381	0.636633	0.63006	0.646004	0.658241	0.708571	0.75251
age_80_plus	0.378564	0.379938	0.387562	0.436589	0.423768	0.414027	0.411677	0.412037
rankin_all	1.02053	1.0566	1.05003	1.07894	1.03551	1.02995	1.03165	1.06062
nihss_all	7.94191	8.35454	8.80986	7.71318	7.0694	6.80621	7.38032	7.62235
4hr_arrival	0.464307	0.353197	0.388066	0.378778	0.36938	0.365304	0.423137	0.46362
rankin_4hr	0.93162	1.04343	1.01153	1.13178	1.06611	1.02185	1.07134	1.03722
nihss_4hr	9.47393	9.53222	10.0548	9.30759	8.91874	8.91479	9.32159	9.1475
onset_arrival	122.127	115.238	102.11	108.779	113.879	111.436	110.78	112.673
scan_4hrs	0.922474	0.895465	0.958597	0.95861	0.95863	0.954786	0.939523	0.926892
arrival_scan	40.8057	47.2502	44.7102	41.357	42.0989	40.9557	42.8849	41.8893
thrombolyse_4hr	0.334958	0.223671	0.310397	0.360533	0.373564	0.366626	0.333631	0.349285
scan_to_needle	44.0687	44.2937	39.3118	31.7291	32.1522	33.7953	40.7675	44.3519
arrival_to_needle	68.2312	68.0112	63.5706	53.7009	53.7034	55.1399	63.8725	69.2026
onset_to_needle	177.516	175.269	150.697	146.731	150.852	149.811	158.123	165.162
hour	0	3	6	9	12	15	19	21

Show summary charts of key metrics by time of day

# Set up figure
fig = plt.figure(figsize=(12,15))

# Subplot 1: Arrivals
ax1 = fig.add_subplot(4,4,1)
x = time_summary['hour']
y = time_summary['Arrivals'] / time_summary['Arrivals'].sum() * 8
ax1.plot(x,y)
# Add line at 1
y1 = np.repeat(1,8)
ax1.plot(x,y1, color='0.5', linestyle=':')
ax1.set_ylim(ymin=0) # must be after plot method
ax1.set_xlabel('Time of day')
ax1.set_ylabel('Normlalised arrivals')
ax1.set_title('Arrivals\n(normalised to average)')

# Subplot 2: Thrombolysis
ax2 = fig.add_subplot(4,4,2)
x = time_summary['hour']
y = time_summary['thrombolyse_all'] * 100
ax2.plot(x,y)
ax2.set_xlabel('Time of day')
ax2.set_ylabel('Thrombolysis (%)')
ax2.set_title('Thrombolysis use\n(all arrivals)')

# Subplot 3: Known onset
ax3 = fig.add_subplot(4,4,3)
x = time_summary['hour']
y = time_summary['onset_known'] * 100
ax3.plot(x,y)
ax3.set_xlabel('Time of day')
ax3.set_ylabel('Onset known (%)')
ax3.set_title('Proportion with known onset')

# Subplot 4: age_80_plus 
ax4 = fig.add_subplot(4,4,4)
x = time_summary['hour']
y = time_summary['age_80_plus'] * 100
ax4.plot(x,y)
ax4.set_xlabel('Time of day')
ax4.set_ylabel('Aged 80+')
ax4.set_title('Proportion aged 80+')

# Subplot 5: Rankin (all arrivals)
ax5 = fig.add_subplot(4,4,5)
x = time_summary['hour']
y = time_summary['rankin_all'] 
ax5.plot(x,y)
ax5.set_xlabel('Time of day')
ax5.set_ylabel('Rankin (mean)')
ax5.set_title('Mean pre-stroke\nmodified Rankin\n(all arrivals)')

# Subplot 6: NIHSS (all arrivals)
ax6 = fig.add_subplot(4,4,6)
x = time_summary['hour']
y = time_summary['nihss_all'] 
ax6.plot(x,y)
ax6.set_xlabel('Time of day')
ax6.set_ylabel('NIHSS (mean)')
ax6.set_title('Mean NIH Stroke Scale\n(all arrivals)')

# Subplot 7: 4hr_arrival
ax7 = fig.add_subplot(4,4,7)
x = time_summary['hour']
y = time_summary['4hr_arrival'] * 100
ax7.plot(x,y)
ax7.set_xlabel('Time of day')
ax7.set_ylabel('Arrive within 4hrs of onset (%)')
ax7.set_title('Proportion arriving within\n4hrs of known onset')

# Subplot 8: Rankin (4hr arrivals)
ax8 = fig.add_subplot(4,4,8)
x = time_summary['hour']
y = time_summary['rankin_4hr'] 
ax8.plot(x,y)
ax8.set_xlabel('Time of day')
ax8.set_ylabel('Rankin (mean)')
ax8.set_title('Mean pre-stroke\nmodified Rankin\n(arrivals 4hrs from onset)')

# Subplot 9: NIHSS (4hr arrivals)
ax9 = fig.add_subplot(4,4,9)
x = time_summary['hour']
y = time_summary['nihss_4hr'] 
ax9.plot(x,y)
ax9.set_xlabel('Time of day')
ax9.set_ylabel('NIHSS (mean)')
ax9.set_title('Mean NIH Stroke Scale\n(arrivals 4hrs from onset)')

# Subplot 10: onset_arrival (4hr arrivals)
ax10 = fig.add_subplot(4,4,10)
x = time_summary['hour']
y = time_summary['onset_arrival'] 
ax10.plot(x,y)
ax10.set_xlabel('Time of day')
ax10.set_ylabel('Onset to arrival (minutes, mean)')
ax10.set_title('Mean onset to arrival\n(arrivals 4hrs from onset)')

# Subplot 11: scan_4hrs (4hr arrivals)
ax11 = fig.add_subplot(4,4,11)
x = time_summary['hour']
y = time_summary['scan_4hrs'] * 100
ax11.plot(x,y)
ax11.set_xlabel('Time of day')
ax11.set_ylabel('Proportion scanned within 4hrs (%)')
ax11.set_title('Proportion scanned within\n4hrs of arrival\n(arrivals 4hrs from onset)')

# Subplot 12: arrival_scan (4hr scan)
ax12 = fig.add_subplot(4,4,12)
x = time_summary['hour']
y = time_summary['arrival_scan'] 
ax12.plot(x,y)
ax12.set_xlabel('Time of day')
ax12.set_ylabel('Arrival to scan (minutes, mean)')
ax12.set_title('Mean arrival to scan\n(scanned 4hrs from onset)')

# Subplot 13: thrombolysis (4hr scan)
ax13 = fig.add_subplot(4,4,13)
x = time_summary['hour']
y = time_summary['thrombolyse_4hr']  * 100
ax13.plot(x,y)
ax13.set_xlabel('Time of day')
ax13.set_ylabel('Thrombolsyis(%)')
ax13.set_title('Thrombolysis use\n(scanned 4hrs from onset)')

# Subplot 14: scan_to_needle
ax14 = fig.add_subplot(4,4,14)
x = time_summary['hour']
y = time_summary['scan_to_needle'] 
ax14.plot(x,y)
ax14.set_xlabel('Time of day')
ax14.set_ylabel('Scan to needle (minutes, mean)')
ax14.set_title('Mean scan to needle')

# Subplot 15: arrival_to_needle
ax15 = fig.add_subplot(4,4,15)
x = time_summary['hour']
y = time_summary['arrival_to_needle'] 
ax15.plot(x,y)
ax15.set_xlabel('Time of day')
ax15.set_ylabel('Arrival to needle (minutes, mean)')
ax15.set_title('Mean arrival to needle')

# Subplot 16: onset_to_needle
ax16 = fig.add_subplot(4,4,16)
x = time_summary['hour']
y = time_summary['onset_to_needle'] 
ax16.plot(x,y)
ax16.set_xlabel('Time of day')
ax16.set_ylabel('Onset to needle (minutes, mean)')
ax16.set_title('Mean onset to needle')

# Save and show
plt.tight_layout(pad=2)
plt.savefig('output/stats_by_time_of_day_single_team.jpg', dpi=300)
plt.show();

Observations

• Individual hospitals may show similarities and differences in diurnal patterns to national average.

• This hospital shows a more marked reduction in night-time (midnight to 3am) use of thrombolysis than the national average.