| Title: | The Acute COPD Exacerbation Prediction Tool (ACCEPT) |
| Version: | 1.0.2 |
| Description: | Allows clinicians to predict the rate and severity of future acute exacerbation in Chronic Obstructive Pulmonary Disease (COPD) patients, based on the clinical prediction models published in Adibi et al. (2020) <doi:10.1016/S2213-2600(19)30397-2> and Safari et al. (2022) <doi:10.1016/j.eclinm.2022.101574>. |
| Depends: | R (≥ 4.0.0) |
| License: | GPL-3 |
| Encoding: | UTF-8 |
| LazyData: | true |
| Imports: | stats, tidyselect(≥ 1.2.0), dplyr, reldist, splines, tibble, hardhat, vctrs, vetiver (≥ 0.2.1) |
| Suggests: | jsonlite, plotly, spelling, testthat (≥ 3.0.0) |
| RoxygenNote: | 7.3.2 |
| Language: | en-US |
| Config/testthat/edition: | 3 |
| NeedsCompilation: | no |
| Packaged: | 2025-06-05 20:10:08 UTC; amin |
| Author: | Amin Adibi [aut, cre], Mohsen Sadatsafavi [aut, cph], Abdollah Safari [aut], Ainsleigh Hill [aut] |
| Maintainer: | Amin Adibi <adibi@alumni.ubc.ca> |
| Repository: | CRAN |
| Date/Publication: | 2025-06-05 22:20:22 UTC |
A flexible version of ACCEPT 2.0 model, which imputes predictors using MICE approach.
Description
A flexible version of ACCEPT 2.0 model, which imputes predictors using MICE approach.
Usage
accept(
newdata,
format = "tibble",
version = "accept2",
prediction_interval = FALSE,
return_predictors = FALSE,
...
)
Arguments
newdata |
new patient data with missing values to be imputed before prediction with the same format as accept samplePatients. |
format |
default is "tibble". Can also be set to "json". |
version |
indicates which version of ACCEPT needs to be called. Options include "accept1", "accept2", and "flexccept" |
prediction_interval |
default is FALSE. If set to TRUE, returns prediction intervals of the predictions. |
return_predictors |
default is FALSE. IF set to TRUE, returns the predictors along with prediction results. |
... |
for other versions of accept. |
Value
patientData with prediction.
Examples
results <- accept(newdata = samplePatients)
Predicts COPD exacerbation rate by severity level based on Acute COPD Exacerbation Tool (ACCEPT)
Description
Predicts COPD exacerbation rate by severity level based on Acute COPD Exacerbation Tool (ACCEPT)
Usage
accept1(
patientData,
random_sampling_N = 100,
lastYrExacCol = "LastYrExacCount",
lastYrSevExacCol = "LastYrSevExacCount",
...
)
Arguments
patientData |
patient data matrix. Can have one or many patients in it |
random_sampling_N |
number of random sampling. Default is 100. |
lastYrExacCol |
the column specifying last year all exacerbation count |
lastYrSevExacCol |
the column specifying last year severe exacerbation count |
... |
for backward compatibility |
Value
patientData with prediction
Examples
results <- accept1(samplePatients)
Predicts COPD exacerbation rate by severity level based on the updated accept2 model, which improves accuracy in patients without an exacerbation history.
Description
Predicts COPD exacerbation rate by severity level based on the updated accept2 model, which improves accuracy in patients without an exacerbation history.
Usage
accept2(
patientData,
random_sampling_N = 100,
lastYrExacCol = "LastYrExacCount",
lastYrSevExacCol = "LastYrSevExacCount",
KeepSGRQ = TRUE,
KeepMeds = TRUE,
...
)
Arguments
patientData |
patient data matrix. Can have one or many patients in it |
random_sampling_N |
number of random sampling. Default is 100. |
lastYrExacCol |
the column specifying last year all exacerbation count |
lastYrSevExacCol |
the column specifying last year severe exacerbation count |
KeepSGRQ |
default is TRUE. If set to false, the reduced model without SGRQ will be used. |
KeepMeds |
default is TRUE. If set to false, the reduced model without medication predictors will be used. |
... |
for backward compatibility |
Value
patientData with prediction
Examples
results <- accept2(samplePatients)
Model handler functions for API endpoint
Description
Useful for supporting accept_model class in vetiver
Usage
## S3 method for class 'accept_model'
handler_predict(vetiver_model, ...)
Arguments
vetiver_model |
A deployable [vetiver_model()] object |
... |
any other inputs |
Value
A 'handler_startup' function should return invisibly, while a 'handler_predict' function should return a function with the signature 'function(req)'. The request body ('req$body') consists of the new data at prediction time; this function should return predictions either as a tibble or as a list coercable to a tibble via [tibble::as_tibble()].
Creates bar graph comparing no treatment with azithromycin treatment
Description
Creates bar graph comparing no treatment with azithromycin treatment
Usage
plotExacerbations(
patientResults,
type = "rate",
interval = "PI",
colors = c("#007bff", "rgb(204,204,204)")
)
Arguments
patientResults |
patient results vector, produced by accept. |
type |
string: either "probability" or "rate" |
interval |
string: either "CI" or "PI" PI = Predicted Interval CI = Confidence Interval |
colors |
vector: a vector of colors to be used in the graph must be length 2 can use hexadecimal, rgb, or R color codes |
Value
a bar graph
Examples
## Not run:
results <- accept1(samplePatients[1,])
plotExacerbations(results)
## End(Not run)
Creates heatmap of number of exacerbations
Description
Creates heatmap of number of exacerbations
Usage
plotHeatMap(patientResults, n = 10, shortened = TRUE)
Arguments
patientResults |
patient results vector, produced by accept. |
n |
how many exacerbations to consider |
shortened |
boolean |
Value
a heatmap
Examples
## Not run:
results <- accept1(samplePatients[1,])
plotHeatMap(results)
## End(Not run)
Model predictions
Description
Model predictions
Usage
## S3 method for class 'accept_model'
predict(object, ...)
Arguments
object |
a model object |
... |
new data for which the prediction is needed |
Value
prediction results from ACCEPT model
Predicts probability of observing n exacerbations in the next year
Description
Predicts probability of observing n exacerbations in the next year
Usage
predictCountProb(patientResults, n = 10, shortened = TRUE)
Arguments
patientResults |
patient results vector, produced by accept. |
n |
how many exacerbations |
shortened |
boolean: Shortened results groups into 0, 1, 2, and 3 or more exacerbations |
Value
a matrix of probabilities with the number of exacerbations as rows and number of severe exacerbations as columns
Examples
results <- accept2(samplePatients[1,])
predictCountProb (results)
Sample Patient Characteristics Inputs
Description
A dataset containing sample patient characteristics to run the prediction model variables are as follows:
Format
A data frame with 2 rows and 19 variables
Details
ID. A unique character string identifying a patients
male. whether the patient is male
age. the age of the patient (40–90)
smoker. whether the patient is currently a smoker
oxygen. whether the patient has had supplemental oxygen therapy within the past year (0,1)
FEV1. forced expiratory volume in 1 second in percent predicted
BMI. body mass index (10–60)
SGRQ. St. George’s Respiratory Questionnaire score (0–100)
statin. whether the patient is taking statins due to cardiovascular conditions
LAMA. whether the patient is on long acting muscarinic antagonist
LABA. whether the patient is on long acting beta agonist
ICS. whether the patient is on inhaled corticosteroids
LastYrExacCount. total number of exacerbations in the previous year
LastYrSevExacCount. number of severe exacerbations in the previous year
Set OpenAI API key
Description
NLP functionalities require a paid OpenAI API key to communicate with Open AI models.
Usage
set_openai_api_key(key, overwrite = FALSE, temporary = FALSE)
Arguments
key |
an OpenAI API key. For more information on keys see the OpenAI API blog |
overwrite |
Option to overwrite any existing OpenAI keys already stored locally. |
temporary |
Option to not store thew API key for use across sessions. |
Examples
## Not run:
set_openai_api_key("YOUR_OPENAI_API_KEY")
# This will set the key permanently until overwritten again
set_openai_api_key("YOUR_OPENAI_API_KEY")
## End(Not run)
View saved openAI API key
Description
View saved API key'
Usage
show_openai_api_key()
Examples
show_openai_api_key()
Create a vetiver model object description
Description
Create a vetiver model object description
Usage
## S3 method for class 'accept_model'
vetiver_create_description(model)
Arguments
model |
model name |
Value
the description of the model
Metadata constructors for 'vetiver_model()' object
Description
Useful for supporting accept_model class in vetiver
Usage
## S3 method for class 'accept_model'
vetiver_create_meta(model, metadata)
Arguments
model |
a trained model object |
metadata |
list containing additional metadata to store with the pin |
Value
The vetiver_create_meta function returns a 'vetiver_meta()' list.
Create a vetiver input data prototype
Description
Create a vetiver input data prototype
Usage
## S3 method for class 'accept_model'
vetiver_ptype(model, ...)
Arguments
model |
a vetiver model object |
... |
any other inputs |
Value
A 'vetiver_ptype' method returns a zero-row dataframe, and 'vetiver_create_ptype()' returns either such a zero-row dataframe, 'NULL', or the dataframe passed to 'save_prototype'.