Prediction of the effectiveness of the therapy of LUTS/BPH by Serenoa repens extracts
- Authors: Sivkov A.V1, Golovanov S.A1, Zhukova L.V2
-
Affiliations:
- N.A. Lopatkin Scientific Research Institute of Urology and Interventional Radiology - Branch of the National Medical Research Centre of Radiology of the Ministry of Health of Russian Federation
- National Research University Higher School of Economics
- Issue: No 3 (2019)
- Pages: 14-22
- Section: Articles
- URL: https://journals.eco-vector.com/1728-2985/article/view/303629
- DOI: https://doi.org/10.18565/urology.2019.3.14-22
- ID: 303629
Cite item
Abstract
Introduction. For the treatment of LUTS/BPH is used a wide range of drugs that patients have to take for a long time. Therefore, it is important to develop methods for predicting long-term results of therapy. The purpose of this work is to evaluate the possibility to predict long-term results of drug therapy of LUTS/BPH using mathematical modeling on the example of treatment with Serenoa repens extract (ESR - Permixon). Materials and methods. For prediction using the methods of predictive analytics of the therapeutic ESR effect in the long term, materials from the open study «Clinical and biological long-term tolerance of a lipidosterolic extract of Serenoa repens (Permixon) in patients with symptomatic benign prostatic hypertrophy» (No. P0048 95 GP 401) were used. The study took place in 1995-1999 in 3 Moscow medical centers: Research Institute of Urology of the Ministry of Health of the Russian Federation, Urological Clinic of the Moscow Medical Academy named after Sechenov and the urology department of Moscow Clinical Hospital No 60. The study included 155 patients aged 52 to 87 years (65.3) who received the drug in 320 mg capsules per day for two years. The target indicators of the prognosis identified key clinical parameters: a decrease IPSS of>25% or>3 points and an increase in Qmax>25% at 12 and 24 months of treatment. When evaluating the results, a binary approach was used: improvement achieved (1), not achieved (0). Results. Using the methods of predictive analytics, mathematical models were built to predict the long-term results of treatment according to the most significant 7 initial criterias (predictors): IPSS; Qmax; average urine flow rate; urination volume, urination time, residual urine volume, prostate volume. For each target field and time interval, mathematical models were built using ensembles from 7 selected machine learning algorithms with the best predictive qualities: BNet; C5.0; SVM; KNN; NNet; CHAID; C&RT. Verification of models on internal randomized samples showed their high prognostic properties: sensitivity 82.4-99.0; specificity 75.0-96.1; AUC 0,864-0,965. Conclusion. The potential for effective prediction by the methods of predictive analytics and data mining of the separated results of drug therapy of LUTS / BPH according to the main clinical criteria was demonstrated. It is necessary to continue training and testing the model with the inclusion of new clinical observations in the data set. This approach is applicable to the creation of similar models for predicting the effect of other drugs.
Full Text
![Restricted Access](https://journals.eco-vector.com/lib/pkp/templates/images/icons/text_lock.png)
About the authors
A. V Sivkov
N.A. Lopatkin Scientific Research Institute of Urology and Interventional Radiology - Branch of the National Medical Research Centre of Radiology of the Ministry of Health of Russian Federation
Email: uroinfo@yandex.ru
PhD, assistant director
S. A Golovanov
N.A. Lopatkin Scientific Research Institute of Urology and Interventional Radiology - Branch of the National Medical Research Centre of Radiology of the Ministry of Health of Russian Federation
Email: sergeygol124@mail.ru
Dr. Sc., head of scientific Laboratory Department
L. V Zhukova
National Research University Higher School of Economics
Email: lvzhukova@mail.ru
Senior Lecturer, Department of Applied Economics
References
- Management of Non-neurogenic Male LUTS. European Association of Urology Guidelines 2019. https://uroweb.org/guideline/treatment-of-non-neurogenic-male-luts/
- Xu J., Yang P., Xue S., Sharma B., Sanchez-Martin M., Wang F., Beaty K.A., Dehan E. Parikh B. Translating cancer genomics into precision medicine with artificial intelligence: applications, challenges and future perspectives. Hum Genet. 2019; 138(2): 109-124. doi: 10.1007/s00439-019-01970-5. Epub 2019 Jan 22.
- Dogan M.V., Beach S.R.H., Simons R.L., Lendasse A., Penaluna B., Philibert R.A. Blood-Based Biomarkers for Predicting the Risk for Five-Year Incident Coronary Heart Disease in the Framingham Heart Study via Machine Learning. Genes (Basel). 2018;9(12). pii: E641. Doi: 10.3390/ genes9120641.
- Bang S., Son S., Roh H., Lee J.3 Bae S., Lee K., Hong C., Shin H. Quad-phased data mining modeling for dementia diagnosis. BMC Med Inform Decis Mak. 2017;17(Suppl. 1):60. doi: 10.1186/s12911-017-0451-3.
- Naydenova E., Tsanas A., Howie S., Casals-Pascual C., De Vos M. The power of data mining in diagnosis of childhood pneumonia. J R Soc Interface. 2016;13(120). pii: 20160266. doi: 10.1098/rsif.2016.0266.
- Tseng C.J., Lu C.J., Chang C.C., Chen G.D., Cheewakriangkrai C. Integration of data mining classification techniques and ensemble learning to identify risk factors and diagnose ovarian cancer recurrence. Artif Intell Med. 2017;78:47-54. doi: 10.1016/j.artmed.2017.06.003. Epub 2017 Jun 10;
- Shahmoradi L., Langarizadeh M., Pourmand G., Fard Z.A., Borhani A. Comparing Three Data Mining Methods to Predict Kidney Transplant Survival. Acta Inform Med. 2016;24(5):322-327. Doi: 10.5455/ aim.2016.24.322-327.
- Tavares M., Paredes S., Rocha T., Carvalho P., Ramos J., Mendes D., Henriques J., Morais J. Expert knowledge integration in the data mining process with application to cardiovascular risk assessment. Conf Proc IEEE Eng Med Biol Soc. 2015;2015:2538-2542. Doi: 10.1109/ EMBC.2015.7318909
- Fusco F., D’Anzeo G., Henneges C., Rossi A., Buttner H., Nickel C. Predictors of Individual Response to Placebo or Tadalafil 5mg among Men with Lower Urinary Tract Symptoms Secondary to Benign Prostatic Hyperplasia: An Integrated Clinical Data Mining Analysis. PLOS ONE | Doi:10.1371/ journal.pone.0135484, 2015.
- Choo M.S., Yoo C., Cho S.Y., Jeong S.J., Jeong C.W., Ku J.H., Oh S.J. Development of Decision Support Formulas for the Prediction of Bladder Outlet Obstruction and Prostatic Surgery in Patients With Lower Urinary Tract Symptom/Benign Prostatic Hyperplasia: Part I, Development of the Formula and its Internal Validation. Int Neurourol J. 2017;21(Suppl. 1):S55-65. doi: 10.5213/inj.1734852.426.
- Пытель Ю.А., Лопаткин Н.А., Гориловский Л.М., Винаров А.З., Сивков А.В, Медведев А.А. Результаты долгосрочного применения Пермиксона у больных с симптомами нарушения функции нижних мочевых путей, обусловленными доброкачественной гиперплазией предстательной железы. Урология. 2004;2:3-7.
- McCormick K. IBM SPSS Modeler Cookbook. Packt Publishing. 2013, 382 p.
- Jensen K. Use balancing to produce more relevant models and data results. Learn how and why to use SPSS Modeler in data mining to balance data. Published September 19, 2016, URL: https://developer.ibm.com/articles/ ba-1608balancing-spss-modeler-trs/
- Chi Shu. Recipes for Predictive Modeling: Highly Imbalanced Data // January 11, 2016. URL: https://www.ironsidegroup.com/2016/01/11/ recipes-predictive-modeling-highly-imbalanced-data/
- Hosmer D.W., Lemeshow S. Assessing the fit of the model. In: Hosmer DW, Lemeshow S. Applied logistic regression. 2nd ed. New York: John Wiley & Sons; 2005. P. 143-202.
- DeLong E.R., DeLong D.M., Clarke-Pearson D.L. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 1988;44:837-845.
- Aso Y., Boccon-Gibod L., Calais Da Silva F. et al. Subjective response, objective response, impact on quality of life. The International Consultation on Benign Prostatic Hyperplasia (BPH), Proceedings, 1991. P. 87-90.
- Barry M.J., Williford W.O., Chang Y. et al. Benign prostatic hyperplasia specific health status measures in clinical research: how much change in the American Urological Association symptom index and the benign prostatic hyperplasia impact index is perceptible to patients? J Urol. 1995;154:1770-1774.
- American Urological Association (AUA) Benign Prostatic Hyperplasia (BPH) Guideline Update Panel. American Urological Association Guideline: management of Benign Prostatic Hyperplasia (BPH). Revised March 2010.
- Lepor H. Phase III multicenter placebo-controlled study of tamsulosin in benign prostatic hyperplasia. Tamsulosin Investigator Group. Urology 1998;51:892-900.
- Narayan P., Tewari A. Members of the United States 93-01 Study Group. A second phase III multicenter placebo controlled study of 2 dosages of modified release tamsulosin in patients with symptoms of benign prostatic hyperplasia. J Urol. 1998;160:1701-1706.
- Chapple C.R., Montorsi F., Tammela T.L. et al. Silodosin therapy for lower urinary tract symptoms in men with suspected benign prostatic hyperplasia: results of an international, randomized, double-blind, placebo- and active-controlled clinical trial performed in Europe. Eur Urol. 2011;59:342-352.
- Nickel J.C., Brock G.B., Herschorn S., Dickson R., Henneges C., Viktrup L. Proportion of tadalafil-treated patients with clinically meaningful improvement in lower urinary tract symptoms associated with benign prostatic hyperplasia - integrated data from 1 499 study participants. BJU Int. 2015;115:815-821.
- Denis L., Griffiths S., Khaury S. et al. In: Proceedings of the 4th International consultation on benign prostatic hyperplasia (BPH). Paris; 1997;439-491.
- Marberger M.J. Long-term effects of finasteride in patients with benign prostatic hyperplasia: a double-blind, placebo-controlled, multicenter study. PROWESS Study Group. Urology. 1998;51(5):677-686.
- Boyne C.W., Donnelly F., Ross M., Habib FK. Serenoa repens (Pcrmixon): a 5 alpha-reductase types I and II inhibitor - new evidence in a coculture model of BPH. Prostate 1999;40(4):232-241.
Supplementary files
![](/img/style/loading.gif)