Online Urinary Symptoms and Quality of Life

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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)

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