accept

R package for the ACute COPD Exacerbation Prediction Tool (ACCEPT)

ACCEPT is a prediction model for predicting probability, rate, and severity of exacerbations (also known as lung attacks) in patients with Chronic Obstructive Pulmonary Disease.

ACCEPT has been developed by researchers at the University of British Columbia. Please refer to the published papers for more information:

Adibi A, Sin DD, Safari A, Jonhson KM, Aaron SD, FitzGerald JM, Sadatsafavi M. The Acute COPD Exacerbation Prediction Tool (ACCEPT): a modelling study. The Lancet Respiratory Medicine, Volume 8, Issue 10, 1013 - 1021; doi:10.1016/S2213-2600(19)30397-2

Safari, A., Adibi, A., Sin, D.D., Lee, T.Y., Ho, J.K., Sadatsafavi, M. and IMPACT study team, 2022. ACCEPT 2· 0: Recalibrating and externally validating the Acute COPD exacerbation prediction tool (ACCEPT). EClinicalMedicine, 51, p.101574. doi:10.1016/j.eclinm.2022.101574

The following animation explains the accept model in 90 seconds:

Installation

The latest stable version can be downloaded from CRAN:

install.packages('accept')

You can install the development version of accept from GitHub with:

# install.packages("pak")
pak::pak("resplab/accept")

Usage

The function accept() provides predictions from the latest version of the accept prediction model. accept1() provides predictions of exacerbations for COPD patients per original published manuscript. accept2() is an updated version of ACCEPT that is fine tuned for improved predictions in patients who do not have a prior history of exacerbations.

Example

Exacerbation Prediction

To get a prediction for exacerbation rate, you will need to pass in a patient vector. The accept package comes with a sample patient data tibble called samplePatients:

library(accept)
accept(samplePatients) #accept uses the latest updated prediction model
#>      ID  male age smoker oxygen statin LAMA LABA   ICS FEV1 BMI SGRQ
#> 1 10001  TRUE  70   TRUE   TRUE   TRUE TRUE TRUE  TRUE   33  25   50
#> 2 10002 FALSE  42  FALSE   TRUE  FALSE TRUE TRUE FALSE   40  35   40
#>   LastYrExacCount LastYrSevExacCount predicted_exac_probability
#> 1               2                  1                  0.8327888
#> 2               0                  0                  0.4366622
#>   predicted_exac_probability_lower_PI predicted_exac_probability_upper_PI
#> 1                           0.1929329                           0.9924159
#> 2                           0.0000000                           0.8998712
#>   predicted_exac_rate predicted_exac_rate_lower_PI predicted_exac_rate_upper_PI
#> 1           1.7884977                    0.2143485                     4.881703
#> 2           0.5738758                    0.0000000                     2.301298
#>   predicted_severe_exac_probability predicted_severe_exac_probability_lower_PI
#> 1                         0.6026383                                 0.09371195
#> 2                         0.1085515                                 0.02547523
#>   predicted_severe_exac_probability_upper_PI predicted_severe_exac_rate
#> 1                                  0.9575906                  0.9229084
#> 2                                  0.4784134                  0.1149076
#>   predicted_severe_exac_rate_lower_PI predicted_severe_exac_rate_upper_PI
#> 1                          0.09839809                            3.160385
#> 2                          0.02580535                            0.650880
#>   azithromycin_predicted_exac_probability
#> 1                               0.7633086
#> 2                               0.3291975
#>   azithromycin_predicted_exac_probability_lower_PI
#> 1                                        0.1286904
#> 2                                        0.0000000
#>   azithromycin_predicted_exac_probability_upper_PI
#> 1                                        0.9793698
#> 2                                        0.8524301
#>   azithromycin_predicted_exac_rate azithromycin_predicted_exac_rate_lower_PI
#> 1                        1.4409981                                 0.1377579
#> 2                        0.3992806                                 0.0000000
#>   azithromycin_predicted_exac_rate_upper_PI
#> 1                                  3.880998
#> 2                                  1.913453
#>   azithromycin_predicted_severe_exac_probability
#> 1                                     0.51103045
#> 2                                     0.08544925
#>   azithromycin_predicted_severe_exac_probability_lower_PI
#> 1                                              0.07570494
#> 2                                              0.02469734
#>   azithromycin_predicted_severe_exac_probability_upper_PI
#> 1                                               0.9221162
#> 2                                               0.3567065
#>   azithromycin_predicted_severe_exac_rate
#> 1                              0.71545506
#> 2                              0.08932232
#>   azithromycin_predicted_severe_exac_rate_lower_PI
#> 1                                       0.07872393
#> 2                                       0.02500744
#>   azithromycin_predicted_severe_exac_rate_upper_PI
#> 1                                        2.5525368
#> 2                                        0.4411541

accept2() and accept1() functions return a more detailed dataframe with the predictions for different treatment options with measures of uncertainty.

To visualize the data, there is a graphing function called plotExacerbations(), which creates a Plotly bar graph. You have the option of selecting probability or rate for which prediction you want to see, and either CI or PI to select the confidence interval or prediction interval respectively.

results <- accept2(samplePatients[1,])

plotExacerbations(results, type="probability")

plotExacerbations(results, type="rate")

Probability of N Exacerbations (Poisson)

You can also calculate the predicted number of exacerbations in a year:

results <- accept2(samplePatients[1,]) 
exacerbationsMatrix <- predictCountProb(results, n = 10, shortened = TRUE)
print(exacerbationsMatrix)
#>                         none severe   1 severe   2 severe 3 or more severe
#> no exacerbations         0.16721119 0.00000000 0.00000000       0.00000000
#> 1 exacerbation           0.11883372 0.18022310 0.00000000       0.00000000
#> 2 exacerbations          0.04222640 0.12808103 0.09712378       0.00000000
#> 3 or more exacerbations  0.01206628 0.05851757 0.10055944       0.07102149

The shortened parameter groups the probabilities from 3-10 exacerbations into one category, “3 or more exacerbations.” To see all n exacerbation probabilities:

exacerbationsMatrix <- predictCountProb(results, n = 10, shortened = FALSE)
print(exacerbationsMatrix)
#>                       0 severe     1 severe     2 severe     3 severe
#> 0 exacerbation(s) 1.672112e-01 0.000000e+00 0.000000e+00 0.000000e+00
#> 1 exacerbation(s) 1.188337e-01 1.802231e-01 0.000000e+00 0.000000e+00
#> 2 exacerbation(s) 4.222640e-02 1.280810e-01 9.712378e-02 0.000000e+00
#> 3 exacerbation(s) 1.000316e-02 4.551234e-02 6.902397e-02 3.489389e-02
#> 4 exacerbation(s) 1.777262e-03 1.078158e-02 2.452699e-02 2.479841e-02
#> 5 exacerbation(s) 2.526131e-04 1.915564e-03 5.810285e-03 8.811872e-03
#> 6 exacerbation(s) 2.992120e-05 2.722708e-04 1.032314e-03 2.087475e-03
#> 7 exacerbation(s) 3.037773e-06 3.224959e-05 1.467291e-04 3.708819e-04
#> 8 exacerbation(s) 2.698608e-07 3.274164e-06 1.737958e-05 5.271571e-05
#> 9 exacerbation(s) 2.130942e-08 2.908606e-07 1.764475e-06 6.244004e-06
#>                       4 severe     5 severe     6 severe     7 severe
#> 0 exacerbation(s) 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
#> 1 exacerbation(s) 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
#> 2 exacerbation(s) 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
#> 3 exacerbation(s) 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
#> 4 exacerbation(s) 9.402310e-03 0.000000e+00 0.000000e+00 0.000000e+00
#> 5 exacerbation(s) 6.682038e-03 2.026794e-03 0.000000e+00 0.000000e+00
#> 6 exacerbation(s) 2.374397e-03 1.440403e-03 3.640856e-04 0.000000e+00
#> 7 exacerbation(s) 5.624791e-04 5.118332e-04 2.587485e-04 5.605968e-05
#> 8 exacerbation(s) 9.993573e-05 1.212499e-04 9.194377e-05 3.984052e-05
#> 9 exacerbation(s) 1.420447e-05 2.154249e-05 2.178088e-05 1.415694e-05
#>                       8 severe     9 severe
#> 0 exacerbation(s) 0.000000e+00 0.000000e+00
#> 1 exacerbation(s) 0.000000e+00 0.000000e+00
#> 2 exacerbation(s) 0.000000e+00 0.000000e+00
#> 3 exacerbation(s) 0.000000e+00 0.000000e+00
#> 4 exacerbation(s) 0.000000e+00 0.000000e+00
#> 5 exacerbation(s) 0.000000e+00 0.000000e+00
#> 6 exacerbation(s) 0.000000e+00 0.000000e+00
#> 7 exacerbation(s) 0.000000e+00 0.000000e+00
#> 8 exacerbation(s) 7.552761e-06 0.000000e+00
#> 9 exacerbation(s) 5.367600e-06 9.044996e-07

To visualize the matrix as a heatmap, we can use the function plotHeatMap:

plotHeatMap(results, shortened = FALSE)

Web App for ACCEPT

ACCEPT is also available as web app, accessible at http://resp.core.ubc.ca/ipress/accept

API using vetiver and plumber

You can use vetiver and plumber packages to create, deploy, and monitor an API for ACCEPT:

library(vetiver)
v_accept <- vetiver_model(accept, 
                   "accept-model")

To test to API locally, you can use

library(plumber)
pr() |> 
    vetiver_api(v_accept) |>
  pr_run()

Cloud-based API Access through Peer Models Network

The Peer Models Network allows users to access ACCEPT through the cloud. A MACRO-enabled Excel-file can be used to interact with the model and see the results. To download the PRISM Excel template file for ACCEPT, please refer to the Peer Models Network model repository.

Python

import json
import requests
url = 'https://prism.peermodelsnetwork.com/route/accept/run'
headers = {'x-prism-auth-user': YOUR_API_KEY}
model_run = requests.post(url, headers=headers,
json = {"func":["prism_model_run"],"model_input":[{"ID": "10001","male": 1,"age": 57,"smoker": 0,"oxygen": 0,"statin": 0,"LAMA": 1,"LABA": 1,"ICS": 1,"FEV1": 51,"BMI": 18,"SGRQ": 63,"LastYrExacCount": 2,"LastYrSevExacCount": 1,"randomized_azithromycin": 0,"randomized_statin": 0,"randomized_LAMA": 0,"randomized_LABA": 0,"randomized_ICS": 0, "random_sampling_N" : 100,  "calculate_CIs" : "TRUE"}]})
print(model_run)
results = json.loads(model_run.text)
print(results)

Linux Bash

In Ubuntu, you can call the API with curl:

curl \
-X POST \
-H "x-prism-auth-user: REPLACE_WITH_API_KEY" \
-H "Content-Type: application/json" \
-d '{"func":["prism_model_run"],"model_input":[{"ID": "10001","male": 1,"age": 57,"smoker": 0,"oxygen": 0,"statin": 0,"LAMA": 1,"LABA": 1,"ICS": 1,"FEV1": 51,"BMI": 18,"SGRQ": 63,"LastYrExacCount": 2,"LastYrSevExacCount": 1,"randomized_azithromycin": 0,"randomized_statin": 0,"randomized_LAMA": 0,"randomized_LABA": 0,"randomized_ICS": 0, "random_sampling_N" : 100, 
"calculate_CIs" : "TRUE"}]}' \
https://prism.peermodelsnetwork.com/route/accept/run

Citation

Please cite:

Adibi A, Sin DD, Safari A, Jonhson KM, Aaron SD, FitzGerald JM, Sadatsafavi M. The Acute COPD Exacerbation Prediction Tool (ACCEPT): a modelling study. The Lancet Respiratory Medicine. Volume 8, Issue 10, 1013 - 1021 doi:10.1016/S2213-2600(19)30397-2

accept

Installation

Usage

Example

Exacerbation Prediction

Probability of N Exacerbations (Poisson)

Web App for ACCEPT

API using vetiver and plumber

Cloud-based API Access through Peer Models Network

Python

Linux Bash

Citation

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Install

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Functions in accept (1.0.2)