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R-CMD-check CRAN_Status_Badge metacran downloads Project Status: Active – The project has reached a stable, usable state and is being actively developed.

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:

IMAGE ALT TEXT HERE

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.

Country-Specific Calibration in ACCEPT v3 (accept3)

ACCEPT v3 (exposed via accept(..., version = "accept3") or the default accept() when a country is specified) provides country-specific recalibrated predictions based on the NOVELTY study. Important notes:

  • The country parameter is required for accept3 and must be a three-letter ISO country code (for example, "CAN", "USA", "GBR"). For countries not in the supported list you should provide an observed moderate-to-severe risk via the obs_modsev_risk column in your input data.
  • When accept3 is called a message is printed to inform the user of the underlying method: ACCEPT v3 is recalibrated using a Cox model.
  • accept3 returns the same core prediction columns as accept2: predicted_exac_probability, predicted_exac_rate, predicted_severe_exac_probability, and predicted_severe_exac_rate.
  • Rates are derived from the returned probabilities using the transformation rate = -log(1 - p).

Example:

# explicit country is required for accept3
library(accept)
accept(samplePatients, country = "CAN")
#> ACCEPT v3 is recalibrated using a Cox model
#> # A tibble: 2 × 5
#>   ID    predicted_exac_probability predicted_exac_rate predicted_severe_exac_p…¹
#>   <chr>                      <dbl>               <dbl>                     <dbl>
#> 1 10001                      0.245               0.281                    0.0631
#> 2 10002                      0.253               0.292                    0.0647
#> # ℹ abbreviated name: ¹​predicted_severe_exac_probability
#> # ℹ 1 more variable: predicted_severe_exac_rate <dbl>

ACCEPT v3 manuscript is currently under review.

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, country="CAN") #accept uses the latest updated prediction model
#> ACCEPT v3 is recalibrated using a Cox model
#> # A tibble: 2 × 5
#>   ID    predicted_exac_probability predicted_exac_rate predicted_severe_exac_p…¹
#>   <chr>                      <dbl>               <dbl>                     <dbl>
#> 1 10001                      0.245               0.281                    0.0631
#> 2 10002                      0.253               0.292                    0.0647
#> # ℹ abbreviated name: ¹​predicted_severe_exac_probability
#> # ℹ 1 more variable: predicted_severe_exac_rate <dbl>

Here’s another example with a specific patient profile:

# Create a patient tibble with specific properties
patient <- tibble::tibble(
  ID = "10003",
  age = 65,
  male = 1,
  BMI = 25.5,
  smoker = 1,  # 0=never, 1=former, 2=current
  FEV1 = 45.0,  # % predicted
  SGRQ = 45.0,
  oxygen = 0,
  LastYrExacCount = 2,
  LastYrSevExacCount = 1,
  statin = 1,
  LAMA = 1,
  LABA = 1,
  ICS = 1
)

# Get predictions for this patient
accept(patient, country="CAN")
#> ACCEPT v3 is recalibrated using a Cox model
#> # A tibble: 1 × 5
#>   ID    predicted_exac_probability predicted_exac_rate predicted_severe_exac_p…¹
#>   <chr>                      <dbl>               <dbl>                     <dbl>
#> 1 10003                      0.247               0.284                    0.0637
#> # ℹ abbreviated name: ¹​predicted_severe_exac_probability
#> # ℹ 1 more variable: predicted_severe_exac_rate <dbl>

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

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

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Jun 6, 2025
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