Acute cystitis is one of most common infections among women;. Diagnosis of AC may be made with a high probability based on a focused history of urinary ...
Online Urinary Symptoms and Quality of Life Assessment Tool (e-USQOLAT)
Jakhongir F. Alidjanov 1 JSC “Republican Specialized Center of Urology. Tashkent, Uzbekistan 2 Klinik Und Poliklinik für Urologie, Kinderurologie und Andrologie, Universitätsklinikum Gießen und Marburg, Giessen, Germany
Background Acute cystitis is one of most common infections
among women; Diagnosis of AC may be made with a high probability based
on a focused history of urinary symptoms and the absence of vaginal discharge or irritation; Empiric therapy of suspected UTIs without indication of additional tests may be most cost-effective;
Background Acute cystitis symptom score (ACSS)
ALIDJANOV ET AL 2014 UROL INT
Background Acute cystitis symptom score (ACSS)
ALIDJANOV ET AL 2014 UROL INT
Background ACSS is available in following languages: Uzbek (Cyrillic and Latin); Russian; German; Hungarian; UK English; Ukrainian; Polish; Romanian; Tajik (under validation); US English (under validation)
Hypothesis/Aim/Objective Process of “Diagnosis based on symptoms” follows
certain algorithms, thus It is possible to find standard algorithms for diagnosis of acute cystitis with high levels of probability, based only on symptoms, and to develop the software (AI) able to establish the diagnosis of acute cystitis.
Methods Study design:
depends (let’s leave it for the discussion); Recruitment: female respondents, visiting doctor’s office for any reason; Investigations: ACSS, lab tests (urinalyses, urine culture, US); Analysis of probability.
Results Study population – 819 cases; After exclusion of cases with any missing value –
579, aged (Mean±SD) 33.2±13.4; of them – 329 (56.8%) Controls (32.6±12.3 y.o.) vs 250 (43.2%) Patients (34.0±14.7 y.o.). “Cut-off” value between Patients and Controls – summary score ≥6.
Results Total "Typical" cutoff ≥6 Sensitivity Specificity Likelihood Ratio + Likelihood Ratio False positive rate False negative rate Prob of disease Pos. predictive value Neg. predictive value Overall accuracy** Pre-test probability of positive result Posttest probability of positive result Posttest probability of negative result
Value
CI 95% Lower Upper 0,92 0,88 0,95 0,91 0,88 0,94 10,39 7,33 14,74 0,09 0,06 0,14 0,09 0,06 0,12 0,08 0,05 0,11 0,43 0,39 0,47 0,89 0,85 0,93 0,93 0,91 0,96 0,91 0,89 0,94
43,2%
39,1%
47,2%
88,8% 84,9%
92,8%
6,5%
3,8%
9,2%
Results
Results
Results
Results
Results
Results Patient-Reported Outcome (PRO) Differentiation between Success and Non-success in 48 female patients treated for acute uncomplicated cystitis (AUC) using part B of the ACSS QoL = Quality of Life; N – number
Dynamics 1 = I feel much better (Majority of symptoms has gone away) Poster, 14th UAA Congress, Singapore, 20 - 24 July 2016
Discussion/ Conclusions It is possible to “educate” AI to
make correct diagnosis, and assess the efficacy of the treatment, based on developed algorithms
Discussion/ Conclusions
Acknowledgements The ACSS team Prof. Kurt G. Naber (DE) Prof. Florian M. Wagenlehner (DE) Dr. Ulugbek A. Abdufattaev (UZ) Dr. Adrian Pilatz (DE) Mrs. Ozoda T. Alidjanova Special thanks to Prof. Tomas Hooton (US) Prof. Robert Pickard (GB) Dr. Magyar Andras (HU) Dr. Béla Köves (HU) Ms. Angela Terberg (NE) Prof. Oleg I. Apolikhin (RU) Abdukhamid Radjabov (TJ) Dr. Igor Shaderkin (RU)
Special thanks to Dr. Veronika Piskovatska (UA) Dr. Valentina Sklyarova (UA) Mr. Boris Yugay (PL) Mrs. Evgeniya Yugay (PL) Dr. Konstantin Kross (DE)
Thank you for your attention
Let’s discuss?
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