Prevalence of Sleep CALM nocturia factors in a male veteran population.

INTRODUCTION: Nocturia is a complex and multifactorial condition, associated with several genitourinary abnormalities as well as a host of conditions beyond the urinary tract, and thus often poses a significant diagnostic challenge in real-world practice. Sleep Disorders, Comorbidities, Actions, Lower Urinary Tract Dysfunction, and Medications, the so-called "Sleep C.A.L.M." factors, are five common causes of nocturia requiring judicious evaluation according to current consensus guidelines. This study aims to assess the prevalence of the Sleep C.A.L.M. factors in a nocturia clinical population. METHODS: Retrospective analysis of frequency-volume charts from men with ≥2 nocturnal voids as well as concurrent demographic, clinical, and medical history data to identify patients with each of the Sleep C.A.L.M. FACTORS: Comorbidities and medications were classified as a single group. RESULTS: A total of 213 subjects met the criteria for inclusion (median age 68.0 [63.5-75.5] years). The prevalence of 1) sleep disorders, 2) comorbidities and/or medication use, 3) actions (i.e., modifiable behaviors/lifestyle factors), and 4) lower urinary tract dysfunction was 31%, 31%, 19%, and 41%, respectively. Among included participants, 73% were found to have at least 1 Sleep C.A.L.M. factor, and 33% had multiple Sleep C.A.L.M. FACTORS: Results were similar upon stratification by age and nocturnal polyuria status. CONCLUSIONS: The Sleep C.A.L.M. factors are highly common among nocturia patients in the clinical urology setting. Although many of these factors are strongly associated with advanced age in community-based nocturia study samples, they appear common even among younger men in a nocturia patient population; the differential effect of age and individual Sleep C.A.L.M. factors on nocturia pathophysiology requires further investigation.

Development and validation of a predictive model of drug-resistant genetic generalized epilepsy.

OBJECTIVE: To develop and validate a clinical prediction model for antiepileptic drug (AED)-resistant genetic generalized epilepsy (GGE). METHOD: We performed a case-control study of patients with and without drug-resistant GGE, nested within ongoing longitudinal observational studies of AED response at 2 tertiary epilepsy centers. Using a validation dataset, we tested the predictive performance of 3 candidate models, developed from a training dataset. We then tested the candidate models' predictive ability on an external testing dataset. RESULTS: Of 5,189 patients in the ongoing longitudinal study, 121 met criteria for AED-resistant GGE and 468 met criteria for AED-responsive GGE. There were 66 patients with GGE in the external dataset, of whom 17 were cases. Catamenial epilepsy, history of a psychiatric condition, and seizure types were strongly related with drug-resistant GGE case status. Compared to women without catamenial epilepsy, women with catamenial epilepsy had about a fourfold increased risk for AED resistance. The calibration of 3 models, assessing the agreement between observed outcomes and predictions, was adequate. Discriminative ability, as measured with area under the receiver operating characteristic curve (AUC), ranged from 0.58 to 0.65. CONCLUSION: Catamenial epilepsy, history of a psychiatric condition, and the seizure type combination of generalized tonic clonic, myoclonic, and absence seizures are negative prognostic factors of drug-resistant GGE. The AUC of 0.6 is not consistent with truly effective separation of the groups, suggesting other unmeasured variables may need to be considered in future studies to improve predictability.