PMID- 38507059
OWN - NLM
STAT- MEDLINE
DCOM- 20240322
LR  - 20240322
IS  - 1433-8726 (Electronic)
IS  - 0724-4983 (Linking)
VI  - 42
IP  - 1
DP  - 2024 Mar 20
TI  - Combination of urinary biomarkers and machine-learning models provided a higher 
      predictive accuracy to predict long-term treatment outcomes of patients with 
      interstitial cystitis/bladder pain syndrome.
PG  - 173
LID - 10.1007/s00345-024-04843-3 [doi]
AB  - PURPOSE: To identify predictive factors for satisfactory treatment outcome of the 
      patients with IC/BPS using urine biomarkers and machine-learning models. METHODS: 
      The IC/BPS patients were prospectively enrolled and provide urine samples. The 
      targeted analytes included inflammatory cytokines, neurotrophins, and oxidative 
      stress biomarkers. The patients with overall subjective symptom improvement 
      of >/= 50% were considered to have satisfactory results. Binary logistic 
      regression, receiver-operating characteristic (ROC) curve, machine-learning 
      decision tree, and random forest models were used to analyze urinary biomarkers 
      to predict satisfactory results. RESULTS: Altogether, 57.4% of the 291 IC/BPS 
      patients obtained satisfactory results. The patients with satisfactory results 
      had lower levels of baseline urinary inflammatory cytokines and oxidative 
      biomarkers than patients without satisfying results, including interleukin-6, 
      monocyte chemoattractant protein-1 (MCP-1), C-X-C motif chemokine 10 (CXCL10), 
      oxidative stress biomarkers 8-hydroxy-2'-deoxyguanosine (8-OHDG), 8-isoprostane, 
      and total antioxidant capacity (TAC). Logistic regression and multivariable 
      analysis revealed that lower levels of urinary CXCL10, MCP-1, 8-OHDG, and 
      8-isoprostane were independent factors. The ROC curve revealed that MCP-1 level 
      had best area under curve (AUC: 0.797). In machine-learning decision tree model, 
      combination of urinary C-C motif chemokine 5, 8-isoprostane, TAC, MCP-1, and 
      8-OHDG could predict satisfactory results (accuracy: 0.81). The random forest 
      model revealed that urinary 8-isoprostance, MCP-1, and 8-OHDG levels had the most 
      important influence on accuracy. CONCLUSION: Machine learning decision tree model 
      provided a higher accuracy for predicting treatment outcome of patients with 
      IC/BPS than logistic regression, and levels of 8-isoprostance, MCP-1, and 8-OHDG 
      had the most important influence on accuracy.
CI  - (c) 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, 
      part of Springer Nature.
FAU - Jhang, Jia-Fong
AU  - Jhang JF
AD  - Department of Urology, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical 
      Foundation, Hualien, Taiwan.
AD  - Department of Urology, School of Medicine, Tzu Chi University, Hualien, 970, 
      Taiwan.
FAU - Yu, Wan-Ru
AU  - Yu WR
AD  - Department of Nursing, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical 
      Foundation, Hualien, Taiwan.
AD  - Institute of Medical Sciences, Tzu Chi University, Hualien, Taiwan.
FAU - Huang, Wan-Ting
AU  - Huang WT
AD  - Epidemiology and Biostatistics Center, Hualien Tzu Chi Hospital, Buddhist Tzu Chi 
      Medical Foundation, Haulien, Taiwan.
FAU - Kuo, Hann-Chorng
AU  - Kuo HC
AUID- ORCID: 0000-0001-7165-4771
AD  - Department of Urology, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical 
      Foundation, Hualien, Taiwan. hck@tzuchi.com.tw.
AD  - Department of Urology, Buddhist Tzu Chi General Hospital, 707 Chung-Yang Road, 
      Section 3, Hualien, 970, Taiwan. hck@tzuchi.com.tw.
LA  - eng
GR  - TCMMP 109‑02‑03/Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation/
PT  - Journal Article
DEP - 20240320
PL  - Germany
TA  - World J Urol
JT  - World journal of urology
JID - 8307716
RN  - 0 (Biomarkers)
RN  - 0 (Chemokines)
RN  - 0 (Cytokines)
RN  - 0 (Antioxidants)
SB  - IM
MH  - Humans
MH  - *Cystitis, Interstitial/diagnosis
MH  - Biomarkers/urine
MH  - Chemokines
MH  - Cytokines
MH  - Treatment Outcome
MH  - Antioxidants
OTO - NOTNLM
OT  - Biomarker
OT  - Prediction
OT  - Prognosis
OT  - Protein
OT  - Satisfaction
EDAT- 2024/03/20 18:45
MHDA- 2024/03/22 06:44
CRDT- 2024/03/20 12:13
PHST- 2023/12/12 00:00 [received]
PHST- 2024/01/24 00:00 [accepted]
PHST- 2024/03/22 06:44 [medline]
PHST- 2024/03/20 18:45 [pubmed]
PHST- 2024/03/20 12:13 [entrez]
AID - 10.1007/s00345-024-04843-3 [pii]
AID - 10.1007/s00345-024-04843-3 [doi]
PST - epublish
SO  - World J Urol. 2024 Mar 20;42(1):173. doi: 10.1007/s00345-024-04843-3.