Nocturnal polyuria may decrease the treatment efficacy of lower urinary tract dysfunction; and adjuvant medication, such as desmopressin, may be needed for the treatment of nocturnal polyuria. The knowledge of prevalence and predictors of nocturnal polyuria might be important for the treatment of women with lower urinary tract symptoms (LUTS). Thus, our aim is to describe prevalence and predictors of nocturnal polyuria in women with LUTS.

Between September 2010 and January 2019, all women with LUTS visiting urogynecological department of a medical center for urodynamic evaluation were reviewed. Nocturnal polyuria was defined when the proportion of night-time voided volume over 24-hour voided volume was greater than 33% for ≥65-year-old women, and when the proportion of night-time voided volume over 24-hour voided volume was greater than 20% for <65-year-old women. Backward stepwise multivariable logistic regression analysis was performed using all statistical variables in the univariate logistic regression analysis. P < 0.05 was considered as statistically significant.

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).

Nocturnal polyuria was not uncommon in women with LUTS. It seems that the rate of nocturnal polyuria increases with aging. However, daytime frequency episodes is a poor predictor due to low area under the ROC curve. Nocturia episodes is a good predictor due to higher area under the ROC curve. Besides, we can use propensity score matching method  to derive logit(p), and logit(p) can be used to predict nocturnal polyuria.

Nocturnal polyuria is not uncommon in women with LUTS, especially in women with ≥ 5 nocturia episodes in the three-day bladder diary. Nocturia episodes and daytime frequency episodes are predictors of nocturnal polyuria. In addition, we can use logit(p), derived from nocturia episodes and daytime frequency, to predict nocturnal polyuria.