Development and Validation of Dynamic Multivariate Prediction Models of Sexual Function Recovery in Patients with Prostate Cancer Undergoing Radical Prostatectomy: Results from the MUSIC Statewide Collaborative
Background: Radical prostatectomy (RP) is the most common definitive treatment for men with intermediate-risk prostate cancer and is frequently complicated by erectile dysfunction. Objective: To develop and validate models to predict 12- and 24-month post-RP sexual function. Design, setting, and par...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-06-01
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| Series: | European Urology Open Science |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666168322000623 |
| _version_ | 1818240568555208704 |
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| author | Nnenaya Agochukwu-Mmonu Adharsh Murali Daniela Wittmann Brian Denton Rodney L. Dunn James Montie James Peabody David Miller Karandeep Singh |
| author_facet | Nnenaya Agochukwu-Mmonu Adharsh Murali Daniela Wittmann Brian Denton Rodney L. Dunn James Montie James Peabody David Miller Karandeep Singh |
| author_sort | Nnenaya Agochukwu-Mmonu |
| collection | DOAJ |
| description | Background: Radical prostatectomy (RP) is the most common definitive treatment for men with intermediate-risk prostate cancer and is frequently complicated by erectile dysfunction. Objective: To develop and validate models to predict 12- and 24-month post-RP sexual function. Design, setting, and participants: Using Michigan Urological Surgery Improvement Collaborative (MUSIC) registry data from 2016 to 2021, we developed dynamic, multivariate, random-forest models to predict sexual function recovery following RP. Model factors (established a priori) included baseline patient characteristics and repeated assessments of sexual satisfaction, and Expanded Prostate Cancer Index Composite 26 (EPIC-26) overall scores and sexual domain questions. Outcome measurements and statistical analysis: We evaluated three outcomes related to sexual function: (1) the EPIC-26 sexual domain score (range 0–100); (2) the EPIC-26 sexual domain score dichotomized at ≥73 for “good” function; and (3) a dichotomized variable for erection quality at 12 and 24 months after RP. A gradient-boosting decision tree was used for the prediction models, which combines many decision trees into a single model. We evaluated the performance of our model using the root mean squared error (RMSE) and mean absolute error (MAE) for the EPIC-26 score as a continuous variable, and the area under the receiver operating characteristic curve (AUC) for the dichotomized EPIC-26 sexual domain score (SDS) and erection quality. All analyses were conducted using R v3.6.3. Results and limitations: We identified 3983 patients at 12 months and 2494 patients at 24 months who were randomized to the derivation cohort at 12 and 24 months, respectively. Using baseline information only, our model predicted the 12-month EPIC-26 SDS with RMSE of 24 and MAE of 20. The AUC for predicting EPIC-26 SDS ≥73 (a previously published threshold) was 0.82. Our model predicted 24-month EPIC-26 SDS with RMSE of 26 and MAE of 21, and AUC for SDS ≥73 of 0.81. Inclusion of post-RP data improved the AUC to 0.91 and 0.94 at 12 and 24 months, respectively. A web tool has also been developed and is available at https://ml4lhs.shinyapps.io/askmusic_prostate_pro/. Conclusions: Our model provides a valid way to predict sexual function recovery at 12 and 24 months after RP. With this dynamic, multivariate (multiple outcomes) model, accurate predictions can be made for decision-making and during survivorship, which may reduce decision regret. Patient summary: Our prediction model allows patients considering prostate cancer surgery to understand their probability before and after surgery of recovering their erectile function and may reduce decision regret. |
| first_indexed | 2024-12-12T13:15:31Z |
| format | Article |
| id | doaj.art-46b81dbc4fb647b7bf5a0fb03a2b6d9e |
| institution | Directory Open Access Journal |
| issn | 2666-1683 |
| language | English |
| last_indexed | 2024-12-12T13:15:31Z |
| publishDate | 2022-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | European Urology Open Science |
| spelling | doaj.art-46b81dbc4fb647b7bf5a0fb03a2b6d9e2022-12-22T00:23:25ZengElsevierEuropean Urology Open Science2666-16832022-06-014018Development and Validation of Dynamic Multivariate Prediction Models of Sexual Function Recovery in Patients with Prostate Cancer Undergoing Radical Prostatectomy: Results from the MUSIC Statewide CollaborativeNnenaya Agochukwu-Mmonu0Adharsh Murali1Daniela Wittmann2Brian Denton3Rodney L. Dunn4James Montie5James Peabody6David Miller7Karandeep Singh8Department of Urology, New York University, New York, NY, USA; Department of Population Health, New York University, New York, NY, USA; Corresponding author. Department of Urology, New York University, 221 East 41st Street, New York, NY 10017, USA. Tel. +1 646 5010732; Fax: +1 646 7549551.Providence Health and Services, Rentan, WA, USADepartment of Urology, University of Michigan Medical School, Ann Arbor, MI, USADepartment of Urology, University of Michigan Medical School, Ann Arbor, MI, USA; Department of Industrial and Operations Engineering, University of Michigan College of Engineering, Ann Arbor, MI, USADepartment of Urology, University of Michigan Medical School, Ann Arbor, MI, USADepartment of Urology, University of Michigan Medical School, Ann Arbor, MI, USADepartment of Urology, Henry Ford Health System, Detroit, MI, USADepartment of Urology, University of Michigan Medical School, Ann Arbor, MI, USADepartment of Urology, University of Michigan Medical School, Ann Arbor, MI, USA; Department of Learning Health Sciences, University of Michigan Medical School, Ann Arbor, MI, USABackground: Radical prostatectomy (RP) is the most common definitive treatment for men with intermediate-risk prostate cancer and is frequently complicated by erectile dysfunction. Objective: To develop and validate models to predict 12- and 24-month post-RP sexual function. Design, setting, and participants: Using Michigan Urological Surgery Improvement Collaborative (MUSIC) registry data from 2016 to 2021, we developed dynamic, multivariate, random-forest models to predict sexual function recovery following RP. Model factors (established a priori) included baseline patient characteristics and repeated assessments of sexual satisfaction, and Expanded Prostate Cancer Index Composite 26 (EPIC-26) overall scores and sexual domain questions. Outcome measurements and statistical analysis: We evaluated three outcomes related to sexual function: (1) the EPIC-26 sexual domain score (range 0–100); (2) the EPIC-26 sexual domain score dichotomized at ≥73 for “good” function; and (3) a dichotomized variable for erection quality at 12 and 24 months after RP. A gradient-boosting decision tree was used for the prediction models, which combines many decision trees into a single model. We evaluated the performance of our model using the root mean squared error (RMSE) and mean absolute error (MAE) for the EPIC-26 score as a continuous variable, and the area under the receiver operating characteristic curve (AUC) for the dichotomized EPIC-26 sexual domain score (SDS) and erection quality. All analyses were conducted using R v3.6.3. Results and limitations: We identified 3983 patients at 12 months and 2494 patients at 24 months who were randomized to the derivation cohort at 12 and 24 months, respectively. Using baseline information only, our model predicted the 12-month EPIC-26 SDS with RMSE of 24 and MAE of 20. The AUC for predicting EPIC-26 SDS ≥73 (a previously published threshold) was 0.82. Our model predicted 24-month EPIC-26 SDS with RMSE of 26 and MAE of 21, and AUC for SDS ≥73 of 0.81. Inclusion of post-RP data improved the AUC to 0.91 and 0.94 at 12 and 24 months, respectively. A web tool has also been developed and is available at https://ml4lhs.shinyapps.io/askmusic_prostate_pro/. Conclusions: Our model provides a valid way to predict sexual function recovery at 12 and 24 months after RP. With this dynamic, multivariate (multiple outcomes) model, accurate predictions can be made for decision-making and during survivorship, which may reduce decision regret. Patient summary: Our prediction model allows patients considering prostate cancer surgery to understand their probability before and after surgery of recovering their erectile function and may reduce decision regret.http://www.sciencedirect.com/science/article/pii/S2666168322000623Machine learningPatient educationProstate cancerPrediction modelSexual function |
| spellingShingle | Nnenaya Agochukwu-Mmonu Adharsh Murali Daniela Wittmann Brian Denton Rodney L. Dunn James Montie James Peabody David Miller Karandeep Singh Development and Validation of Dynamic Multivariate Prediction Models of Sexual Function Recovery in Patients with Prostate Cancer Undergoing Radical Prostatectomy: Results from the MUSIC Statewide Collaborative European Urology Open Science Machine learning Patient education Prostate cancer Prediction model Sexual function |
| title | Development and Validation of Dynamic Multivariate Prediction Models of Sexual Function Recovery in Patients with Prostate Cancer Undergoing Radical Prostatectomy: Results from the MUSIC Statewide Collaborative |
| title_full | Development and Validation of Dynamic Multivariate Prediction Models of Sexual Function Recovery in Patients with Prostate Cancer Undergoing Radical Prostatectomy: Results from the MUSIC Statewide Collaborative |
| title_fullStr | Development and Validation of Dynamic Multivariate Prediction Models of Sexual Function Recovery in Patients with Prostate Cancer Undergoing Radical Prostatectomy: Results from the MUSIC Statewide Collaborative |
| title_full_unstemmed | Development and Validation of Dynamic Multivariate Prediction Models of Sexual Function Recovery in Patients with Prostate Cancer Undergoing Radical Prostatectomy: Results from the MUSIC Statewide Collaborative |
| title_short | Development and Validation of Dynamic Multivariate Prediction Models of Sexual Function Recovery in Patients with Prostate Cancer Undergoing Radical Prostatectomy: Results from the MUSIC Statewide Collaborative |
| title_sort | development and validation of dynamic multivariate prediction models of sexual function recovery in patients with prostate cancer undergoing radical prostatectomy results from the music statewide collaborative |
| topic | Machine learning Patient education Prostate cancer Prediction model Sexual function |
| url | http://www.sciencedirect.com/science/article/pii/S2666168322000623 |
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