Neurourology and urodynamics · 2008
Modeling the clinical assessment of men with suspected obstructed voiding using Bayes' Theorem.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Caffarel Jennifer, Griffiths Clive, Pickard Robert, Robson Wendy, Drinnan Michael
Аннотация
AIMS: Pressure-flow studies (PFS) are the only reliable way to diagnose bladder outlet obstruction (BOO) in men with lower urinary tract symptoms (LUTS). However, in routine clinical practice, BOO is usually inferred by any of a number of tests (symptoms, flow rate, prostate size...). Bayes' Theorem provides a mathematical method, which may be similar to the process used by clinicians, for combining the results of multiple tests to reach a diagnosis. We have applied Bayes' Theorem to the results of several tests known weakly to predict BOO in men with LUTS to assess if they improve the diagnostic accuracy of a flow rate test which alone is known to predict obstruction moderately well.
METHODS: We applied Bayes' Theorem to data from 50 patients using Q(max) alone and with the inclusion of additional variables (IPSS, PSA, and residual urine), to establish individual probabilities of BOO. The chi-squared statistic (with trend) was used to compare the relative diagnostic values, against the BOO index calculated from the results of subsequent PFS.
RESULTS: The diagnostic value of Q(max) alone (chi-squared = 9.2, P = 0.002), was superior than that for the Bayesian model using the combination of tests available (chi-squared = 4.9, P = 0.026).
CONCLUSIONS: Although in our sample relevant additional tests do not improve the diagnostic power of Q(max) as a predictor of BOO, we believe the Bayesian approach is conceptually suited to modeling clinical decision making but may be better tested for a more clinically relevant outcome such as treatment response.
METHODS: We applied Bayes' Theorem to data from 50 patients using Q(max) alone and with the inclusion of additional variables (IPSS, PSA, and residual urine), to establish individual probabilities of BOO. The chi-squared statistic (with trend) was used to compare the relative diagnostic values, against the BOO index calculated from the results of subsequent PFS.
RESULTS: The diagnostic value of Q(max) alone (chi-squared = 9.2, P = 0.002), was superior than that for the Bayesian model using the combination of tests available (chi-squared = 4.9, P = 0.026).
CONCLUSIONS: Although in our sample relevant additional tests do not improve the diagnostic power of Q(max) as a predictor of BOO, we believe the Bayesian approach is conceptually suited to modeling clinical decision making but may be better tested for a more clinically relevant outcome such as treatment response.