Open Operative Readiness Standard

import pandas as pd # import seaborn as sns # import numpy as np from scipy.stats import pearsonr, spearmanr import plotly_express as px import plotly import plotly.graph_objects as go import re # import statsmodels # import math

df = pd.read_csv('rawdata.csv')

PGY_LEVEL = 'pgy_level' OPERATION = 'operation' SCORE = 'score' PGY_DICT = { 'PGY1' : 1, 'PGY2' : 2, 'PGY3' : 3, 'Research Year Resident' : 4, 'PGY4' : 5, 'PGY5' : 6 } PGY_REVERSE_DICT = { 1: 'PGY1', 2: 'PGY2', 3: 'PGY3', 4: 'Research', 5: 'PGY4', 6: 'PGY5' } PGY_DOUBLE_REVERESE_DICT = { 'PGY1' : 1, 'PGY2' : 2, 'PGY3' : 3, 'Research' : 4, 'PGY4' : 5, 'PGY5' : 6 } PORT_SCORE_DICT = { 'Observation Only' : 1, 'Direct Supervision' : 2, 'Indirect Supervision' : 3, 'Unsupervised Practice' : 4, 'Supervising Others' : 5 }

THRESHOLD = 4

df.replace({PGY_LEVEL: PGY_DICT}, inplace=True) proc_cols = list(new_cols.values())[2:] for col in proc_cols: df.replace({col : PORT_SCORE_DICT}, inplace=True) df

Timestampobject

6/14/2022 13:19:042.3%

6/14/2022 13:21:082.3%

41 others95.3%

pgy_levelint64

1 - 6

0

6/14/2022 13:19:04

4

1

6/14/2022 13:21:08

3

2

6/14/2022 13:51:22

3

6/14/2022 14:06:29

6

4

6/14/2022 14:36:15

1

5

6/14/2022 15:13:28

3

6

6/14/2022 15:26:49

1

7

6/14/2022 15:34:56

3

8

6/14/2022 15:55:01

6

9

6/14/2022 18:51:39

6

## Set a Color R1 = '69' B1 = '145' G1 = '226' ## Set a Color R2 = '15' B2 = '149' G2 = '215' ## Set a Color R3 = '174' B3 = '214' G3 = '234' ## Set a Color R4 = '243' B4 = '243' G4 = '243' ## Set a Color R5 = '190' B5 = '190' G5 = '190' darkblue = 'rgba(' + R2 + ', ' + B2 + ', ' + G2 + ', 1.0)' lightblue = 'rgba(' + R3 + ', ' + B3 + ', ' + G3 + ', 1.0)' lightgray = 'rgba(' + R4 + ', ' + B4 + ', ' + G4 + ', 1.0)' darkgray = 'rgba(' + R5 + ', ' + B5 + ', ' + G5 + ', 1.0)' # solid_color = 'rgba(' + R + ', ' + B + ', ' + G + ', 1.0)' # bg_color = 'rgba(' + R + ', ' + B + ', ' + G + ', 0.2)' no_color = 'rgba(' + R1 + ', ' + B1 + ', ' + G1 + ', 0.0)'

df_response = df.groupby(PGY_LEVEL).agg( responses = ('Timestamp', 'count') ) df_response.reset_index(inplace=True) df_response.replace({PGY_LEVEL: PGY_REVERSE_DICT}, inplace=True) df_response['total'] = [7, 7, 8, 11, 6, 7] df_response['percent'] = ((df_response['responses']/df_response['total']).round(2)*100).astype(int).astype(str) + '%' df_response

pgy_levelobject

responsesint64

0

PGY1

6

1

PGY2

7

2

PGY3

8

3

Research

9

4

PGY4

6

5

PGY5

7

df_melt = pd.melt(df, id_vars=PGY_LEVEL, value_vars=df.columns[2:], var_name='operation', value_name='score') df_melt.sort_values(by=[OPERATION, PGY_LEVEL], inplace=True) # print(df) df_melt = df_melt.dropna() df_melt = df_melt.reset_index(drop=True) df_melt # df.to_csv('rawscores.csv')

pgy_levelint64

1 - 6

operationobject

Arteriovenous Fistula Creation3.6%

Mastectomy3.6%

26 others92.9%

339

6

Exploratory Laparotomy for Acute Abdomen

171

6

Central Venous Catheter Placement

119

5

Bronchoscopy

118

5

Bronchoscopy

117

5

Bronchoscopy

116

5

Bronchoscopy

639

6

Laparoscopic Lysis of Adhesions

641

6

Laparoscopic Lysis of Adhesions

340

6

Exploratory Laparotomy for Acute Abdomen

642

6

Laparoscopic Lysis of Adhesions

dict = {OPERATION:[],'correlation':[],'pvalue':[]} for name, group in df_melt.groupby('operation'): corr, pvalue = spearmanr(group[PGY_LEVEL], group[SCORE]) dict['operation'].append(name) dict['correlation'].append(corr) dict['pvalue'].append(pvalue) corr_df = pd.DataFrame(dict) corr_df['correlation'] = corr_df['correlation'].round(2) corr_df['p-value'] = corr_df.apply(lambda x: '{:.2e}'.format(x['pvalue']), axis=1) # corr_df['p-value'] = corr_df.apply(lambda x: round(x['pvalue'], 3), axis=1) # corr_df.drop('pvalue', inplace=True, axis=1) corr_df

operationobject

Arteriovenous Fistula Creation3.6%

Breast Lumpectomy3.6%

26 others92.9%

correlationfloat64

0.28 - 0.79

0

Arteriovenous Fistula Creation

0.51

1

Breast Lumpectomy

0.71

2

Bronchoscopy

0.63

3

Central Venous Catheter Placement

0.61

4

Colonoscopy

0.61

5

Endoscopy

0.79

6

Evaluation of Trauma Patient

0.69

7

Exploratory Laparotomy for Acute Abdomen

0.69

8

Exploratory Laparotomy for Penetrating Trauma

0.77

9

Hemorrhoidectomy

0.28

10

Incision and Drainage of Abscess

0.61

11

Laparoscopic Appendectomy

0.72

12

Laparoscopic Cholecystectomy

0.7

13

Laparoscopic Inguinal Hernia Repair

0.62

14

Laparoscopic Lysis of Adhesions

0.77

15

Laparoscopic Right Colectomy

0.63

16

Laparoscopic Sigmoid Colectomy

0.64

17

Laparoscopic Ventral Hernia Repair

0.66

18

Management of Critically Ill Surgical Patient

0.57

19

Management of Medical Comorbidities for Surgical Patients

0.44

20

Mastectomy

0.59

21

Necrosectomy for Pancreatitis

0.46

22

Open Inguinal Hernia Repair

0.72

23

Open Lysis of Adhesions

0.74

24

Open Ventral Hernia Repair

0.59

25

Resection of Skin Melanoma

0.51

26

See a General Surgery consult

0.46

27

Thyroidectomy

0.72

df_count_per_score = df_melt.groupby([OPERATION, PGY_LEVEL, SCORE]).agg( count_per_score = ('score', 'count') ) df_count_per_score.reset_index(inplace=True) df_count_per_score df_scatter = pd.merge(df_melt, df_count_per_score, on=[OPERATION, PGY_LEVEL, SCORE]) df_scatter

pgy_levelint64

1 - 6

operationobject

Arteriovenous Fistula Creation3.6%

Breast Lumpectomy3.6%

26 others92.9%

0

1

Arteriovenous Fistula Creation

1

Arteriovenous Fistula Creation

2

1

Arteriovenous Fistula Creation

3

1

Arteriovenous Fistula Creation

4

1

Arteriovenous Fistula Creation

5

1

Arteriovenous Fistula Creation

6

2

Arteriovenous Fistula Creation

7

2

Arteriovenous Fistula Creation

8

2

Arteriovenous Fistula Creation

9

2

Arteriovenous Fistula Creation

fig = px.scatter(df_scatter, x=PGY_LEVEL, y=SCORE, trendline='ols', size='count_per_score', facet_col=OPERATION, facet_col_wrap=4, facet_row_spacing=0.04, facet_col_spacing=0.02, color_discrete_sequence=[darkblue], opacity=1 ) fig.update_layout(title = 'Correlation between PGY Level and Operative Comfort', xaxis_title = 'PGY Level', yaxis_title = 'score', width = 1200, height = 1600, font_family='Inter', plot_bgcolor=lightgray) fig.update_traces(marker_line_width=0) fig.for_each_annotation(lambda a: a.update(text=a.text.split("=")[1])) fig.update_xaxes(tickvals=[1,2,3,4,5,6], ticktext=['1', '2', '3', 'R', '4', '5']), fig.update_yaxes(tickvals=[1, 2, 3, 4, 5], ticktext=['OB', 'DS', 'IS', 'UP', 'SO']) fig.show() fig.write_image('fig_scatter.svg')

df_scatter

pgy_levelint64

1 - 6

operationobject

Arteriovenous Fistula Creation3.6%

Breast Lumpectomy3.6%

26 others92.9%

0

1

Arteriovenous Fistula Creation

1

Arteriovenous Fistula Creation

2

1

Arteriovenous Fistula Creation

3

1

Arteriovenous Fistula Creation

4

1

Arteriovenous Fistula Creation

5

1

Arteriovenous Fistula Creation

6

2

Arteriovenous Fistula Creation

7

2

Arteriovenous Fistula Creation

8

2

Arteriovenous Fistula Creation

9

2

Arteriovenous Fistula Creation

df_stats = df_melt.groupby([OPERATION, PGY_LEVEL]).agg( num_residents = ('score', 'count'), min = ('score', 'min'), mean = ('score', 'mean'), median = ('score', 'median'), max = ('score', 'max'), stdev = ('score', 'std') ) df_stats.reset_index(inplace=True) df_stats['mean'] = df_stats['mean'].round(2) df_stats['stdev'] = df_stats['stdev'].round(2) df_stats['min'] = df_stats['min'].astype(int) df_stats['max'] = df_stats['max'].astype(int) df_stats['range'] = df_stats['max']-df_stats['min'] df_stats.sort_values(by=['operation', 'pgy_level'], ascending=False, inplace=True) df_stats.replace({PGY_LEVEL: PGY_REVERSE_DICT}, inplace=True) df_stats

operationobject

Bronchoscopy3.6%

Colonoscopy3.6%

26 others92.9%

pgy_levelobject

PGY316.7%

PGY516.7%

4 others66.7%

14

Bronchoscopy

PGY3

29

Colonoscopy

PGY5

153

Resection of Skin Melanoma

Research

69

Laparoscopic Appendectomy

Research

18

Central Venous Catheter Placement

PGY1

82

Laparoscopic Inguinal Hernia Repair

PGY4

53

Exploratory Laparotomy for Penetrating Trauma

PGY5

95

Laparoscopic Right Colectomy

PGY5

106

Laparoscopic Ventral Hernia Repair

PGY4

83

Laparoscopic Inguinal Hernia Repair

PGY5

5

Arteriovenous Fistula Creation

PGY5

56

Hemorrhoidectomy

PGY3

74

Laparoscopic Cholecystectomy

PGY3

60

Incision and Drainage of Abscess

PGY1

28

Colonoscopy

PGY4

159

See a General Surgery consult

Research

152

Resection of Skin Melanoma

PGY3

47

Exploratory Laparotomy for Acute Abdomen

PGY5

107

Laparoscopic Ventral Hernia Repair

PGY5

147

Open Ventral Hernia Repair

Research

75

Laparoscopic Cholecystectomy

Research

124

Mastectomy

PGY4

110

Management of Critically Ill Surgical Patient

PGY3

149

Open Ventral Hernia Repair

PGY5

17

Bronchoscopy

PGY5

143

Open Lysis of Adhesions

PGY5

140

Open Lysis of Adhesions

PGY3

117

Management of Medical Comorbidities for Surgical Patients

Research

8

Breast Lumpectomy

PGY3

36

Evaluation of Trauma Patient

PGY1

76

Laparoscopic Cholecystectomy

PGY4

148

Open Ventral Hernia Repair

PGY4

108

Management of Critically Ill Surgical Patient

PGY1

70

Laparoscopic Appendectomy

PGY4

112

Management of Critically Ill Surgical Patient

PGY4

87

Laparoscopic Lysis of Adhesions

Research

89

Laparoscopic Lysis of Adhesions

PGY5

151

Resection of Skin Melanoma

PGY2

38

Evaluation of Trauma Patient

PGY3

116

Management of Medical Comorbidities for Surgical Patients

PGY3

10

Breast Lumpectomy

PGY4

136

Open Inguinal Hernia Repair

PGY4

13

Bronchoscopy

PGY2

59

Hemorrhoidectomy

PGY5

141

Open Lysis of Adhesions

Research

9

Breast Lumpectomy

Research

122

Mastectomy

PGY3

45

Exploratory Laparotomy for Acute Abdomen

Research

16

Bronchoscopy

PGY4

52

Exploratory Laparotomy for Penetrating Trauma

PGY4

df_stats = df_melt.groupby([OPERATION, PGY_LEVEL]).agg( num_residents = ('score', 'count'), min = ('score', 'min'), # mean = ('score', 'mean'), median = ('score', 'median'), max = ('score', 'max'), # stdev = ('score', 'std') ) df_stats.reset_index(inplace=True) # df_stats['mean'] = df_stats['mean'].round(2) # df_stats['stdev'] = df_stats['stdev'].round(2) # df_stats['min'] = df_stats['min'].astype(int) # df_stats['max'] = df_stats['max'].astype(int) df_stats['range'] = df_stats['max']-df_stats['min'] df_stats.sort_values(by=['operation', 'pgy_level'], ascending=True, inplace=True) df_stats.replace({PGY_LEVEL: PGY_REVERSE_DICT}, inplace=True) df_stats

operationobject

Arteriovenous Fistula Creation3.6%

Breast Lumpectomy3.6%

26 others92.9%

pgy_levelobject

PGY116.7%

PGY216.7%

4 others66.7%

0

Arteriovenous Fistula Creation

PGY1

1

Arteriovenous Fistula Creation

PGY2

2

Arteriovenous Fistula Creation

PGY3

3

Arteriovenous Fistula Creation

Research

4

Arteriovenous Fistula Creation

PGY4

5

Arteriovenous Fistula Creation

PGY5

6

Breast Lumpectomy

PGY1

7

Breast Lumpectomy

PGY2

8

Breast Lumpectomy

PGY3

9

Breast Lumpectomy

Research

# fig = px.line(df_stats, x=PGY_LEVEL, y='min', facet_col=OPERATION, facet_col_wrap=4, # facet_row_spacing=0.04, # default is 0.07 when facet_col_wrap is used # facet_col_spacing=0.02, # default is 0.03` # ) # fig.update_traces(line_color=no_color) # for t in fig.data: # row = t['xaxis'] # col = t['yaxis'] # proc = t['hovertemplate'].strip('operation=') # proc = re.sub('(<br>.+)', '', proc) # dff = df_stats.loc[df_stats['operation'] == proc] # fig.add_trace(go.Scatter(x=dff[PGY_LEVEL], # y=dff['max'], # xaxis=row, # yaxis=col, # fill='tonexty', # fillcolor=lightblue, # line_color=no_color)) # fig.add_trace(go.Scatter(x=dff[PGY_LEVEL], # y=dff['min'], # xaxis=row, # yaxis=col, # fill='tonexty', # fillcolor=lightblue, # line_color=no_color)) # fig.add_trace(go.Scatter(x=dff[PGY_LEVEL], # y=dff['mean'], # xaxis=row, # yaxis=col, # line_color=darkblue, # line_width=4)) # fig.for_each_annotation(lambda a: a.update(text=a.text.split("=")[1])) # fig.update_xaxes(tickvals=[1,2,3,4,5,6], ticktext=['1', '2', '3', 'R', '4', '5']), # fig.update_yaxes(tickvals=[1, 2, 3, 4, 5], ticktext=['OB', 'DS', 'IS', 'UP', 'SO']) # fig.add_hline(y=4, line_dash="dot") # fig.update_layout(title = # 'Operative Comfort By PGY Level', # xaxis_title = 'PGY Level', yaxis_title = 'score', # width = 1200, height = 1600, # font_family='Inter', # showlegend=False, # plot_bgcolor=lightgray) # fig.write_image("op_readiness_curves.svg") # fig.show()

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fig = px.line(df_stats, x=PGY_LEVEL, y='min', facet_col=OPERATION, facet_col_wrap=4, facet_row_spacing=0.04, # default is 0.07 when facet_col_wrap is used facet_col_spacing=0.02, # default is 0.03` ) fig.update_traces(line_color=no_color) for t in fig.data: row = t['xaxis'] col = t['yaxis'] proc = t['hovertemplate'].strip('operation=') proc = re.sub('(<br>.+)', '', proc) dff = df_stats.loc[df_stats['operation'] == proc] fig.add_trace(go.Scatter(x=dff[PGY_LEVEL], y=dff['max'], xaxis=row, yaxis=col, fill='tonexty', fillcolor=lightblue, line_color=no_color)) fig.add_trace(go.Scatter(x=dff[PGY_LEVEL], y=dff['min'], xaxis=row, yaxis=col, fill='tonexty', fillcolor=lightblue, line_color=no_color)) fig.add_trace(go.Scatter(x=dff[PGY_LEVEL], y=dff['median'], xaxis=row, yaxis=col, line_color=darkblue, line_width=4)) fig.for_each_annotation(lambda a: a.update(text=a.text.split("=")[1])) fig.update_xaxes(tickvals=['PGY1', 'PGY2', 'PGY3', 'Research', 'PGY4','PGY5'], ticktext=['1', '2', '3', 'R', '4', '5']), fig.update_yaxes(tickvals=[1, 2, 3, 4, 5], ticktext=['OB', 'DS', 'IS', 'UP', 'SO']) fig.add_hline(y=4, line_dash="dot") fig.update_layout(title = 'Operative Comfort By PGY Level', xaxis_title = 'PGY Level', yaxis_title = 'score', width = 1200, height = 1600, font_family='Inter', showlegend=False, plot_bgcolor=lightgray) fig.write_image("op_readiness_curves.svg") fig.show()

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# THRESHOLD = 4 df_stats_reverse = df_stats df_stats_reverse df_stats_reverse.replace({PGY_LEVEL: PGY_DOUBLE_REVERESE_DICT}, inplace=True) df_stats_reverse df_threshold = {OPERATION : [], 'threshold_by': []} for name, group in df_stats_reverse.groupby(OPERATION): for index, row in group.iterrows(): if row['median'] >= THRESHOLD: if row[PGY_LEVEL] == 4: level = row[PGY_LEVEL] + 1 else: level = row[PGY_LEVEL] break else: level = 7 df_threshold[OPERATION].append(name) df_threshold['threshold_by'].append(level) df_threshold = pd.DataFrame(df_threshold) df_threshold.sort_values(by=['threshold_by', 'operation'], ascending=False, inplace=True) df_threshold

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fig = px.bar(df_threshold, x='threshold_by', y=OPERATION, orientation='h', text_auto=True) fig.update_layout(title = 'Achievement of Unsupervised Practice by Operation', xaxis_title = PGY_LEVEL, yaxis_title = SCORE, width = 1200, height = 800, font_family='Inter', showlegend=False) fig.update_xaxes(tickvals=[1,2,3,4,5,6,7], ticktext=['PGY1', 'PGY2', 'PGY3', 'Research', 'PGY4', 'PGY5', 'Not Reached']) fig.write_image('threshold.svg') fig.update_layout(title = 'PGY Level at Which Unsupervised Practice Threshold is Reached', xaxis_title = 'PGY Level', yaxis_title = 'Activity', width = 1200, height = 1600, font_family='Inter', showlegend=False) fig.show()

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SELECT * FROM 'rawdata.csv'

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