International urology and nephrology · 2015
Continence Index: a new screening questionnaire to predict the probability of future incontinence in older women in the community.
СтатьяПоддержка NIH: внешняя программаНазвание и аннотация: Перевод в очереди
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Diokno Ananias C, Ogunyemi Theophilus, Siadat Mohammad-Reza, Arslanturk Suzan, Killinger Kim A
Аннотация
PURPOSE: Urinary incontinence (UI) is a chronic, costly condition that impairs quality of life. To identify older women most at risk, the Medical Epidemiologic and Social Aspects of Aging (MESA) datasets were mined to create a set of questions that can reliably predict future UI.
METHODS: MESA data were collected during four household interviews at approximately 1 year intervals. Factors associated with becoming incontinent at the second interview (HH2) were identified using logistic regression (construction datasets). Based on p values and odds ratios, eight potential predictive factors with their 256 combinations and corresponding prediction probabilities formed the Continence Index. Its predictive and discriminatory capability was tested against the same cohort's outcome in the fourth survey (HH4 validation datasets). Sensitivity analysis, area under receiver operating characteristic (ROC) curve, predicted probabilities and confidence intervals were used to statistically validate the Continence Index.
RESULTS: Body mass index, sneezing, post-partum UI, urinary frequency, mild UI, belief of developing UI in the future, difficulty stopping urinary stream and remembering names emerged as the strongest predictors of UI. The confidence intervals for prediction probabilities strongly agreed between construction and validation datasets. Calculated sensitivity, specificity, false-positive and false-negative values revealed that the areas under the ROCs (0.802 and 0.799) for the construction and validation datasets, respectively, indicated good discriminatory capabilities of the index as a predictor.
CONCLUSION: The Continence Index will help identify older women most at risk of UI in order to apply targeted prevention strategies in women that are most likely to benefit.
METHODS: MESA data were collected during four household interviews at approximately 1 year intervals. Factors associated with becoming incontinent at the second interview (HH2) were identified using logistic regression (construction datasets). Based on p values and odds ratios, eight potential predictive factors with their 256 combinations and corresponding prediction probabilities formed the Continence Index. Its predictive and discriminatory capability was tested against the same cohort's outcome in the fourth survey (HH4 validation datasets). Sensitivity analysis, area under receiver operating characteristic (ROC) curve, predicted probabilities and confidence intervals were used to statistically validate the Continence Index.
RESULTS: Body mass index, sneezing, post-partum UI, urinary frequency, mild UI, belief of developing UI in the future, difficulty stopping urinary stream and remembering names emerged as the strongest predictors of UI. The confidence intervals for prediction probabilities strongly agreed between construction and validation datasets. Calculated sensitivity, specificity, false-positive and false-negative values revealed that the areas under the ROCs (0.802 and 0.799) for the construction and validation datasets, respectively, indicated good discriminatory capabilities of the index as a predictor.
CONCLUSION: The Continence Index will help identify older women most at risk of UI in order to apply targeted prevention strategies in women that are most likely to benefit.