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ORIGINAL RESEARCH

Do LUTS Predict Mortality? An Analysis Using Random Forest Algorithms

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Pages 237-245 | Received 25 Jul 2023, Accepted 17 Jan 2024, Published online: 12 Feb 2024

References

  • Irwin DE, Kopp ZS, Agatep B, Milsom I, Abrams P. Worldwide prevalence estimates of lower urinary tract symptoms, overactive bladder, urinary incontinence and bladder outlet obstruction. BJU Int. 2011;108(7):1132–1138. doi:10.1111/j.1464-410X.2010.09993.x
  • He Q, Wang Z, Liu G, Daneshgari F, MacLennan GT, Gupta S. Metabolic syndrome, inflammation and lower urinary tract symptoms: possible translational links. Prostate Cancer Prostatic Dis. 2016;19(1):7–13. doi:10.1038/pcan.2015.43
  • Gacci M, Vignozzi L, Sebastianelli A, et al. Metabolic syndrome and lower urinary tract symptoms: the role of inflammation. Prostate Cancer Prostatic Dis. 2013;16(1):100–105. doi:10.1038/pcan.2012.44
  • Pesonen JS, Cartwright R, Vernooij RWM, et al. The impact of nocturia on mortality: a systematic review and meta-analysis. J Urol. 2020;203(3):486–495. doi:10.1097/JU.0000000000000463
  • Åkerla J, Pesonen JS, Pöyhönen A, et al. Impact of lower urinary tract symptoms on mortality: a 21-year follow-up among middle-aged and elderly Finnish men. Prostate Cancer Prostatic Dis. 2019;22(2):317–323. doi:10.1038/s41391-018-0108-z
  • Gacci M, Corona G, Sebastianelli A, et al. Male lower urinary tract symptoms and cardiovascular events: a systematic review and meta-analysis. Eur Urol. 2016;70(5):788–796. doi:10.1016/j.eururo.2016.07.007
  • Nuotio M, Tammela TLJ, Luukkaala T, Jylha M. Urgency and urge incontinence in an older population: ten-year changes and their association with mortality. Aging Clin Exp Res. 2002;14(5):412–419. doi:10.1007/BF03324470
  • Matta R, Hird AE, Saskin R, et al. Is there an association between urinary incontinence and mortality? A retrospective cohort study. J Urol. 2020;203(3):591–597. doi:10.1097/JU.0000000000000574
  • Weng SF, Vaz L, Qureshi N, Kai J. Prediction of premature all-cause mortality: a prospective general population cohort study comparing machine-learning and standard epidemiological approaches. PLoS One. 2019;14(3):e0214365. doi:10.1371/journal.pone.0214365
  • Ajnakina O, Agbedjro D, Mccammon R, et al. Development and validation of prediction model to estimate 10-year risk of all-cause mortality using modern statistical learning methods: a large population-based cohort study and external validation. BMC Med. Res. Method. 2021;21:1. doi:10.1186/s12874-020-01204-7
  • Wilson PWF, D’Agostino RB, Levy D, Belanger AM, Silbershatz H, Kannel WB. Prediction of coronary heart disease using risk factor categories. Circulation. 1998;97(18):1837–1847. doi:10.1161/01.CIR.97.18.1837
  • Costantino JP, Gail MH, Pee D, et al. Validation studies for models projecting the risk of invasive and total breast cancer incidence. J Natl Cancer Inst. 1999;91(18):1541–1548. doi:10.1093/jnci/91.18.1541
  • Hippisley-Cox J, Coupland C, Robson J, Brindle P. Derivation, validation, and evaluation of a new QRISK model to estimate lifetime risk of cardiovascular disease: cohort study using QResearch database. BMJ. 2010;341(7788):93. doi:10.1136/BMJ.C6624
  • Koskimäki J, Hakama M, Huhtala H, Tammela T. Prevalence of lower urinary tract symptoms in Finnish men: a population-based study. Br J Urol. 1998;81(3):364–369. doi:10.1046/j.1464-410x.1998.00565.x
  • Häkkinen JT, Hakama M, Shiri R, Auvinen A, Tammela TLJ, Koskimäki J. Incidence of nocturia in 50 to 80-year-old Finnish men. J Urol. 2006;176:2541–2545. doi:10.1016/j.juro.2006.08.017
  • Hansen BJ, Flyger H, Brasso K, et al. Validation of the self-administered Danish prostatic symptom score (DAN-PSS-1) system for use in benign prostatic hyperplasia. Br J Urol. 1995;76(4):451–458. doi:10.1111/j.1464-410X.1995.tb07744.x
  • Esteva A, Kuprel B, Novoa RA, et al. Dermatologist–level classification of skin cancer with deep neural networks. Nature. 2017;542(7639):115. doi:10.1038/NATURE21056
  • Gulshan V, Peng L, Coram M, et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA. 2016;316(22):2402–2410. doi:10.1001/jama.2016.17216
  • Weng SF, Reps J, Kai J, Garibaldi JM, Qureshi N. Can machine-learning improve cardiovascular risk prediction using routine clinical data? PLoS One. 2017;12(4):e0174944. doi:10.1371/JOURNAL.PONE.0174944
  • Young J, Kempton MJ, McGuire P. Using machine learning to predict outcomes in psychosis. Lancet Psychiatry. 2016;3(10):908–909. doi:10.1016/S2215-0366(16)30218-8
  • Yu KH, Zhang C, Berry GJ, et al. Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features. Nat Commun. 2016;7. doi:10.1038/ncomms12474
  • Yun K, Oh J, Hong TH, Kim EY. Prediction of mortality in surgical intensive care unit patients using machine learning algorithms. Front Med. 2021;8:406. doi:10.3389/FMED.2021.621861/BIBTEX
  • Spooner A, Chen E, Sowmya A, et al. A comparison of machine learning methods for survival analysis of high-dimensional clinical data for dementia prediction. Sci Rep. 2020;10(1):1–10. doi:10.1038/s41598-020-77220-w
  • Breiman L. Random Forests. Machine Learning. 2001;45(1):5–32. doi:10.1023/A:1010933404324
  • Speiser JL, Wolf BJ, Chung D, Karvellas CJ, Koch DG, Durkalski VL. BiMM forest: a random forest method for modeling clustered and longitudinal binary outcomes. Chemom Intell Lab Syst an Int J Spons by Chemom Soc. 2019;185:122. doi:10.1016/J.CHEMOLAB.2019.01.002
  • Speiser JL, Wolf BJ, Chung D, Karvellas CJ, Koch DG, Durkalski VL. BiMM tree: a decision tree method for modeling clustered and longitudinal binary outcomes. Commun Stat Simul Comput. 2020;49(4):1004–1023. doi:10.1080/03610918.2018.1490429
  • Hajjem A, Bellavance F, Larocque D. Mixed-effects random forest for clustered data. J Stat Comput Simul. 2014;84(6):1313–1328. doi:10.1080/00949655.2012.741599
  • Breiman L Manual for setting up, using, and understanding random forest V4.0; 2003. Available from: https://www.stat.berkeley.edu/~breiman/Using_random_forests_v4.0.pdf. Accessed February 07, 2024.
  • Hosmer DW, Lemeshow S, Sturdivant RX. Applied Logistic Regression. Hoboken, N.J.: Wiley; 2013:177.
  • Reps JM, Rijnbeek PR, Ryan PB. Identifying the DEAD: development and validation of a patient-level model to predict death status in population-level claims data. Drug Saf. 2019;42(11):1377–1386. doi:10.1007/s40264-019-00827-0
  • Funada S, Tabara Y, Setoh K, et al. Impact of nocturia on mortality: the nagahama study. J Urol. 2020;204(5):996–1002. doi:10.1097/JU.0000000000001138
  • Pashootan P, Ploussard G, Cocaul A, De Gouvello A, Desgrandchamps F. Association between metabolic syndrome and severity of lower urinary tract symptoms (LUTS): an observational study in a 4666 European men cohort. BJU Int. 2015;116(1):124–130. doi:10.1111/bju.12931
  • Russo GI, Castelli T, Privitera S, et al. Increase of Framingham cardiovascular disease risk score is associated with severity of lower urinary tract symptoms. BJU Int. 2015;116(5):791–796. doi:10.1111/bju.13053
  • Coyne KS, Kaplan SA, Chapple CR, et al. Risk factors and comorbid conditions associated with lower urinary tract symptoms: epiLUTS. BJU Int. 2009;103:24–32. doi:10.1111/j.1464-410X.2009.08438.x