Neurourological assessment in people with multiple sclerosis (MS): a new evaluated algorithm.

Assessment Lower urinary tract dysfunction Multicenter study Multiple Sclerosis Neurogenic bladder

Journal

Multiple sclerosis and related disorders
ISSN: 2211-0356
Titre abrégé: Mult Scler Relat Disord
Pays: Netherlands
ID NLM: 101580247

Informations de publication

Date de publication:
Sep 2020
Historique:
received: 03 04 2020
revised: 17 05 2020
accepted: 27 05 2020
pubmed: 21 6 2020
medline: 15 5 2021
entrez: 21 6 2020
Statut: ppublish

Résumé

Neurogenic lower urinary tract dysfunction (NULTD) is common in patients with multiple sclerosis (MS); nevertheless, it is often underestimated, underdiagnosed, and undertreated due to patients' sense of shame, variability of symptoms, as well as lack of communication between neurologists and urologists, despite the availability of several guidelines based on scientific evidence and expert opinion. This study was conducted to develop an easy-to-perform algorithm for diagnosing neurogenic lower urinary tract disease in patients with MS for daily neurological and urological routine, including the identification of red flags. In consensus group meetings, interprofessional experts (neurologists, urologists, neurourologists, nurses, nurse practitioners, occupational therapists, physical therapists as well as representatives of national MS centers, self-care groups, social care, residential care, and health-aid-providers) developed a diagnostic algorithm to detect NULTD in patients with MS. Subsequently, the group evaluated the algorithm in 121 patients with MS using micturition diary, post-void residual volume, uroflowmetry, and urodynamic studies. Statistical analysis was conducted on the basis of logistic regression models to compare patients with normal and abnormal urodynamic examinations. Differentiation was performed using selected diagnostic parameters as well as standard performance measures for binary classifiers to assess prognostic quality. The following four parameters allowed to diagnose NLUTD in patients with MS: post-void residual urine volume, rate of urinary tract infections during the past 6 months, micturition frequency, and incontinence. According to statistical analysis, the following thresholds could be defined: post-void residual volume (PVR) ≥70 mL (Odds Ratio (OR) = 1.24; 95% CI:[1.07,1.62]), urinary tract infection (UTI) rate - none in 6 months (OR = 2.03; 95% CI:[1.04,6.68]), and micturition frequency >13/day, standardized on 2000 mL urine excretion (OR = 1.24; 95% CI:[1.07,1.49]). Uroflowmetry served as a further predictor of urodynamically measurable urinary bladder dysfunction (OR = 4.80; 95% CI: [1.41, 19.21]). Interestingly, patients without any complaints of NLUTD had an abnormal urodynamic examination in >50% of the cases. The entire algorithm has a sensitivity of 95%. All patients with MS should undergo a basic examination to detect NLUTDs. Within the algorithm developed in this study, four easy-to-collect parameters may reveal NLUTD in patients with MS.

Sections du résumé

BACKGROUND BACKGROUND
Neurogenic lower urinary tract dysfunction (NULTD) is common in patients with multiple sclerosis (MS); nevertheless, it is often underestimated, underdiagnosed, and undertreated due to patients' sense of shame, variability of symptoms, as well as lack of communication between neurologists and urologists, despite the availability of several guidelines based on scientific evidence and expert opinion.
OBJECTIVE OBJECTIVE
This study was conducted to develop an easy-to-perform algorithm for diagnosing neurogenic lower urinary tract disease in patients with MS for daily neurological and urological routine, including the identification of red flags.
METHODS METHODS
In consensus group meetings, interprofessional experts (neurologists, urologists, neurourologists, nurses, nurse practitioners, occupational therapists, physical therapists as well as representatives of national MS centers, self-care groups, social care, residential care, and health-aid-providers) developed a diagnostic algorithm to detect NULTD in patients with MS. Subsequently, the group evaluated the algorithm in 121 patients with MS using micturition diary, post-void residual volume, uroflowmetry, and urodynamic studies. Statistical analysis was conducted on the basis of logistic regression models to compare patients with normal and abnormal urodynamic examinations. Differentiation was performed using selected diagnostic parameters as well as standard performance measures for binary classifiers to assess prognostic quality.
RESULTS RESULTS
The following four parameters allowed to diagnose NLUTD in patients with MS: post-void residual urine volume, rate of urinary tract infections during the past 6 months, micturition frequency, and incontinence. According to statistical analysis, the following thresholds could be defined: post-void residual volume (PVR) ≥70 mL (Odds Ratio (OR) = 1.24; 95% CI:[1.07,1.62]), urinary tract infection (UTI) rate - none in 6 months (OR = 2.03; 95% CI:[1.04,6.68]), and micturition frequency >13/day, standardized on 2000 mL urine excretion (OR = 1.24; 95% CI:[1.07,1.49]). Uroflowmetry served as a further predictor of urodynamically measurable urinary bladder dysfunction (OR = 4.80; 95% CI: [1.41, 19.21]). Interestingly, patients without any complaints of NLUTD had an abnormal urodynamic examination in >50% of the cases. The entire algorithm has a sensitivity of 95%.
CONCLUSIONS CONCLUSIONS
All patients with MS should undergo a basic examination to detect NLUTDs. Within the algorithm developed in this study, four easy-to-collect parameters may reveal NLUTD in patients with MS.

Identifiants

pubmed: 32562907
pii: S2211-0348(20)30324-2
doi: 10.1016/j.msard.2020.102248
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

102248

Informations de copyright

Copyright © 2020 The Authors. Published by Elsevier B.V. All rights reserved.

Auteurs

Burkhard Domurath (B)

Neuro-Urological Center, Clinic Beelitz GmbH, Neurological Rehabilitation Clinic, Paracelsusring 6 a, 14547 Beelitz Heilstätten, Germany. Electronic address: bdomurath@yahoo.de.

Ines Kurze (I)

Spinal Cord Injury Center, Clinic for Paraplegiologia and Neuro-Urology Robert-Koch-Allee 9, 99438 Bad Berka, Germany. Electronic address: ines.kurze@zentralklinik.de.

Ruth Kirschner-Hermanns (R)

Department Neurourology, University Clinic Bonn, Siegmund-Freud-Str.25, 53127 Bonn. Electronic address: ruth.kirschner-hermanns@ukbonn.de.

Albert Kaufmann (A)

Center for Continence & Neuro-Urology, Hospital St Franziskus, Viersener Straße 450, 41063 Mönchengladbach, Germany. Electronic address: albert.kaufmann@mariahilf.de.

Wolfgang Feneberg (W)

Marianne Strauss Clinic Berg, Therapeutic Center for Patients with Multiple Sclerosis Kempfenhausen GmbH, Milchberg 21, 82335 Berg, Germany. Electronic address: wolfgang.feneberg@ms-klinik.de.

Paul Schmidt (P)

Statistik. Große Seestraße 8, 13086 Berlin, Germany. Electronic address: paul.schmidt.mail@gmail.com.

Thomas Henze (T)

Praxisgemeinschaft for Neurology, Psychiatry, Psychotherapy, Günzstraße 1, 93059 Regensburg, Germany. Electronic address: thomas.henze@outlook.com.

Peter Flachenecker (P)

Neurological Rehabilitation Center, Kuranlagenallee 2, 75323 Bad Wildbad, Germany. Electronic address: peter.flachenecker@quellenhof.de.

Anna Brandt (A)

Clinic Segeberg, Neurological Center, Hammdorfer Weg 3, 23795 Bad Segeberg, Germany. Electronic address: anna.brandt@segebergerkliniken.de.

Will Nelson Vance (WN)

Neuro-Urological Center, Clinic Beelitz GmbH, Neurological Rehabilitation Clinic, Paracelsusring 6 a, 14547 Beelitz Heilstätten, Germany. Electronic address: vance@kliniken-beelitz.de.

Janina Beck (J)

Neurological Rehabilitation Center Godeshöhe, Waldstraße 2, 53177 Bonn - Bad Godeshöhe, Germany. Electronic address: janina.beck@gmx.de.

Manuela Vonthien (M)

Coloplast GmbH, Kuehnstraße 75, 22045 Hamburg, Germany. Electronic address: demvon@coloplast.com.

Kerstin Ratering (K)

Coloplast GmbH, Kuehnstraße 75, 22045 Hamburg, Germany. Electronic address: derat@coloplast.com.

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