Additional Features and Common Use Cases¶

For most use cases, you will just need to specify the AKI definition methodology (i.e. rolling minimum window, historical baseline trumping, or baseline creatinine imputation) and the AKI-column will be returned. There are a slew of other features, some of which are listed below. For a full listing of the features and appropriate use cases, see the Documentation at akiflagger.readthedocs.io.

→ Adding padding to the rolling window¶

It’s often the case that you want to add some padding to the window to account for variations occurring on the floor (52 hour & 172 hour windows instead, for example). If the amount of padding you would like to add is the same for both the smaller and larger window, simply pass padding='_hours' filling the blank with the number of hours to add to the windows. If the pad times are different between windows, the parameters pad1time and pad2time allow you to add just this padding to the initial windows of 48 and 172 hours. In fact, if you wanted a window of 36 hours, you could even set pad1time = ‘-12hours’; this is one way in which you could modify the rolling window.

Python¶

# Example 0: Adding 4-hour padding to windows

flagger = AKIFlagger(padding = '4hours')

example0 = flagger.returnAKIpatients(toy)

example0[example0.aki > 0].head(3)

patient_id	time	inpatient	creatinine	aki
12732	2020-02-24 23:42:42	False	1.61	1
19845	2020-05-12 18:02:54	True	0.76	2
19845	2020-05-14 18:02:54	True	0.89	2

R¶

# Example 0: Adding 4-hour padding to windows

example0 <- returnAKIpatients(toy, padding = as.difftime(4, units = 'hours'))

head(example0[example0$aki > 0])

	patient_id	inpatient	creatinine	time	aki
1	19008	False	2.06	2019-11-26 08:37:33	1
2	13264	False	0.75	2019-08-11 17:16:57	3
3	13264	False	0.5	2019-11-07 05:16:57	1
4	13264	False	0.63	2019-11-08 23:16:57	2
5	18752	False	1.18	2019-09-13 01:18:00	1
6	10537	False	1.34	2019-11-08 07:55:12	1

→ Working with different column names¶

Python¶

As an additional example, the patient identifier will often come in as ‘PAT_MRN_ID’ or ‘PAT_ENC_CSN_ID’ (or something of the sort) if it is coming from a typical clinical data warehouse/repository. Accordingly, these should be passed in as options to the flagger.

# Example 1: Working with different column names

dataframe = toy.rename(columns = {'patient_id': 'PAT_MRN_ID', 'creatinine':'CREATININE', 'inpatient': 'INPATIENT', 'time': 'TIME'
                                  'age': 'AGE', 'female': 'SEX'})

flagger = AKIFlagger(patient_id = 'PAT_MRN_ID', inpatient = 'INPATIENT', time = 'TIME', creatinine = 'CREATININE', age = 'AGE', sex = 'SEX')

example1 = flagger.returnAKIpatients(dataframe)

example1.head(3)

	PAT_MRN_ID	TIME	AGE	SEX	INPATIENT	CREATININE	aki
0	12732	2020-02-22 11:42:42	64.5	True	False	1.62	0
1	12732	2020-02-23 11:42:42	64.5	True	False	1.52	0
2	12732	2020-02-24 23:42:42	64.5	True	False	1.63	0

R¶

Say we had a dataframe which looked like this:

	PAT_MRN_ID	OUTPATIENT	TIME	CREATININE
1	12732	True	2019-11-16 05:42:42	1.05
2	12732	True	2019-11-20 05:42:42	1.61
3	12732	True	2020-01-15 05:42:42	1.42

In order to pass it to the flagger, we need to shape our data in a way that the flagger will understand. This means converting the outpatient columns to inpatient, and specifying the names of the columns as follows

# Example 1: Working with different column names

library(dplyr) # rename function from dplyr library

dataframe$OUTPATIENT <- !dataframe$OUTPATIENT # turn the dataframe into inpatient instead of outpatient by logically inverting it

dataframe <- dataframe %>% rename('patient_id' = 'PAT_MRN_ID', 'inpatient' = 'OUTPATIENT', 'time' = 'TIME', 'creatinine' = 'CREATININE')

head(returnAKIpatients(dataframe), n = 3L)

	patient_id	inpatient	creatinine	time	aki
1	19008	False	2.05	2019-07-08 14:37:33	0
2	19008	False	1.65	2019-07-09 08:37:33	0
3	19008	False	1.58	2019-07-29 08:37:33	0

→ Adding in rolling window minimum creatinines¶

To add in the baseline creatinine, simply pass the flag add_min_creat = True to the flagger. This will add in two columns which contain the minimum values in the rolling window, which is an intermediate column generated to calculate AKI; the flag adds in the column which the current creatinine is checked against.

Python¶

# Example 2: Adding in rolling-window minima

flagger = AKIFlagger(add_min_creat = True)

example2 = flagger.returnAKIpatients(toy)

example2.head(3)

patient_id	time	inpatient	creatinine	min_creat48	min_creat168	aki
12732	2020-02-22 17:42:42	False	1.05	1.05	1.05	0
12732	2020-02-26 05:42:42	False	1.26	1.26	1.05	0
12732	2020-02-29 05:42:42	True	1.06	1.06	1.05	0

R¶

# Example 2: Adding in rolling window minima

example2 <- returnAKIpatients(toy, add_min_creat = T)

head(example2)

	patient_id	inpatient	creatinine	time	min_creat48	min_creat7d	aki
1	19008	False	2.05	2019-07-08 14:37:33	2.05	2.05	0
2	19008	False	1.65	2019-07-09 08:37:33	1.65	1.65	0
3	19008	False	1.58	2019-07-29 08:37:33	1.58	1.58	0
4	19008	False	1.77	2019-08-10 08:37:33	1.77	1.77	0
5	19008	False	1.47	2019-09-25 02:37:33	1.47	1.47	0
6	19008	False	1.64	2019-11-25 14:37:33	1.64	1.64	0

→ Adding in baseline creatinine¶

To add in the baseline creatinine, simply pass the flag add_baseline_creat = True to the flagger. Note that the baseline creatinine is not defined for outpatient measurements. Baseline creatinine can be thought of as the “resting” creatinine before coming into the hospital, so it doesn’t make much sense to define the baseline creatinine outside of a hospital visit.

Python¶

# Example 3: Adding in baseline creatinine

toy = generate_toy_data(include_demographic_info = True)

flagger = AKIFlagger(HB_trumping = True, eGFR_impute = True, #Specifying both calculation methods
                     add_baseline_creat = True, # Additional parameter to add in baseline creatinine values
                     age = 'age', sex = 'female')

example3 = flagger.returnAKIpatients(toy)

example3[~example3.baseline_creat.isnull()].head(3)

patient_id	time	age	female	inpatient	creatinine	baseline_creat	aki
12732	2020-02-22 11:42:42	64.5	True	False	1.62	0.9300765849293292	0
12732	2020-02-25 11:42:42	64.5	True	False	1.63	0.9300765849293292	0
12732	2020-02-25 17:42:42	64.5	True	False	1.52	0.9300765849293292	0

R¶

# Example 3: Adding in baseline creatinine

example3 <- returnAKIpatients(toy, add_baseline_creat = T)

head(example3)

	patient_id	inpatient	creatinine	time	age	sex	baseline_creat	aki
1	19008	False	1.94	2019-12-30 02:37:33	52.9	True	0.8806117024042	0
2	19008	True	1.41	2020-01-02 02:37:33	52.9	True	0.8806117024042	1
3	19008	True	1.2	2020-01-02 14:37:33	52.9	True	0.8806117024042	1
4	19008	True	1.4	2020-01-03 02:37:33	52.9	True	0.8806117024042	1
5	19008	True	1.49	2020-01-03 14:37:33	52.9	True	0.8806117024042	1
6	19008	True	1.71	2020-01-03 20:37:33	52.9	True	0.8806117024042	1