A new tool for screening severe sleep apnea syndrome

A tool for severe obstructive sleep apnea syndrome screening in patients with unexplained nocturnal polyuria

Status

Active, not recruiting

Phases

Unknown

Study type

Observational

Source

ISRCTN

Registry ID

ISRCTN13617639

Enrollment

200

Registered

2019-03-22

Start date

2016-01-21

Completion date

Unknown

Last updated

2019-04-08

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Nocturia Signs and Symptoms

Interventions

Medical records of patients diagnosed with nocturia due to nocturnal polyuria and screened for obstructive sleep apnea syndrome (OSAS) with a sleep study (overnight polygraphy) Patient

Eligibility

Sex/Gender

All

Inclusion criteria

Inclusion criteria: 1. Diagnosed with a nocturia due to nocturnal polyuria 2. Screened for sleep apnea syndrome with overnight polygraphy

Exclusion criteria

Exclusion criteria: 1. Patients presenting with the following potential causes of nocturnal polyuria: 1.1 Diabetes insipidus 1.2 Uncontrolled diabetes mellitus (defined by a serum glucose level > 200 mg/dL) 1.3 Severe renal impairment (defined by a glomerular filtration rate of <30 ml/min) 1.4 Hart failure 1.5 Oedematous state

Design outcomes

Primary

Measure	Time frame
To design and validate a new “score” (the Clinique Pasteur score) to detect severe OSAS in patients with unexplained nocturnal polyuria.	—

Secondary

Measure	Time frame
To validate this “score” (internal validation) Development of the score Step 1: logistic regression model The first step in constructing the score was to perform a multivariate logistic regression analysis. All variables associated with severe OSAS according to a p value <0.2 in the logistic regression were selected. For continuous variables, the log-linearity assumption had to be fulfilled to ensure the validity of the model. If this assumption was not fulfilled, the continuous variable was categorized. The continuous variables included in the final model were age and BMI, and the assumption of log-linearity was not fulfilled. The thresholds chosen for the categorization of BMI were 25 kg/m2 and 30 kg/m2 according to anthropometric definition (normal/overweight/obese). The threshold for age (70 years) was much more arbitrary and was choose for sample size and powerful of the prediction reasons. The results of the model are expressed by means of odd-ratio together with their 95% confidence intervals computed by Wald’s methods. The performance was assessed by the rate of prediction error, the receiver operating characteristic (ROC) curve and a graphical illustration of the specificity/sensitivity of the model. The area under this curve (AUC) and its 95% confidence interval computed by bootstrap procedure (2000 replicates) indicated the predictive performances of the model. The internal validity of the model was investigated by splitting the database into two cohorts: a learning cohort (65% of the sample size) to create the model and a validation cohort (35% of the sample size) to assess. Individuals were randomly assigned to one of the cohorts. The predictive performance was assessed by the rate of prediction error and ROC curves. Step 2: construction of the score As the variables involved in the model were discrete, it was possible to construct a simplifi	—

Measure

Time frame

To validate this “score” (internal validation) Development of the score Step 1: logistic regression model The first step in constructing the score was to perform a multivariate logistic regression analysis. All variables associated with severe OSAS according to a p value <0.2 in the logistic regression were selected. For continuous variables, the log-linearity assumption had to be fulfilled to ensure the validity of the model. If this assumption was not fulfilled, the continuous variable was categorized. The continuous variables included in the final model were age and BMI, and the assumption of log-linearity was not fulfilled. The thresholds chosen for the categorization of BMI were 25 kg/m2 and 30 kg/m2 according to anthropometric definition (normal/overweight/obese). The threshold for age (70 years) was much more arbitrary and was choose for sample size and powerful of the prediction reasons. The results of the model are expressed by means of odd-ratio together with their 95% confidence intervals computed by Wald’s methods. The performance was assessed by the rate of prediction error, the receiver operating characteristic (ROC) curve and a graphical illustration of the specificity/sensitivity of the model. The area under this curve (AUC) and its 95% confidence interval computed by bootstrap procedure (2000 replicates) indicated the predictive performances of the model. The internal validity of the model was investigated by splitting the database into two cohorts: a learning cohort (65% of the sample size) to create the model and a validation cohort (35% of the sample size) to assess. Individuals were randomly assigned to one of the cohorts. The predictive performance was assessed by the rate of prediction error and ROC curves. Step 2: construction of the score As the variables involved in the model were discrete, it was possible to construct a simplifi

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Countries

France

Outcome results

None listed

Source: ISRCTN (via WHO ICTRP) · Data processed: Feb 4, 2026