A total of 3,000 women with LUTS were included in this study. The rate of nocturnal polyuria was 26.5% (795/3,000) with 17.6% (213/1,212) and 32.6% (582/1,788) in ≥65-year-old and <65-year-old women, respectively. In women under the age of 65, the highest rate (33%, 272/815) of nocturnal polyuria is between 51 and 60 year-old. In women more than the age of 65, the highest rate (27%, 32/120) is more than 81 year-old.
In univariate logistic regression analysis, age (odds ratio = 1.01, 95% confidence interval [CI] = 1.00 to 1.02, p = 0.008), daytime frequency episodes (odds ratio = 0.99, 95% CI = 0.98 to 1.00, p = 0.005), nocturia episodes (odds ratio = 1.41, 95% CI = 1.36 to 1.45, p < 0.001), urgency episodes (odds ratio = 1.01, 95% CI = 1.00 to 1.02, p = 0.02) and total fluid intake (odds ratio = 1.00, 95% CI = 1.00 to 1.00, p = 0.008) were statistically significant.
In addition, many symptoms (i.e., frequency, nocturia, urgency, nocturia enuresis, waterworks infections, bladder pain and voiding difficulty) and all domains (i.e., general health perception, incontinence impact, role limitations, physical limitations, social limitations, personal relationships, emotions, sleep/energy and severity measures) of King’s Health Questionnaire (all odds ratio = 1.00 to 1.01, all p < 0.01) were statistically significant.
However, in multivariable logistic regression analysis, daytime frequency episodes (odds ratio = 0.92, 95% CI = 0.90 to 0.93, p < 0.001) and nocturia episodes (odds ratio = 1.59, 95% CI = 1.53 to 1.65, p < 0.001) were independent predictors for nocturnal polyuria.
Receiver operating characteristic curve (ROC) analysis revealed daytime frequency episodes ≤ 24 in the three-day bladder diary was an optimal cut-off value to predict nocturnal polyuria (sensitivity = 56.2%, specificity = 51.3%; area = 0.54, 95% CI = 0.52 to 0.57, Fig 1). Nocturia episodes ≥ 5 in the three-day bladder diary was an optimal cut-off value to predict nocturnal polyuria (sensitivity = 82.3%, specificity = 67.7%; area = 0.82, 95% CI = 0.81 to 0.84). Thus, the predicted logit(p) for a given daytime frequency episodes (a) and nocturia episodes (b) can be denoted by logit(p) = -1.27 - 0.09 x a + 0.46 x b. The optimum cut-off values of logit(p) ≥ -1.49 to predict nocturnal polyuria were determined using ROC analysis (sensitivity = 88.2%, specificity = 69.0%; area = 0.85, 95% CI = 0.84 to 0.87, Fig 2).