The National Hospital Care Survey Is a Unique Source of Data on Rare Diseases.

OBJECTIVES: This study aims to demonstrate the usefulness of the National Hospital Care Survey (NHCS) for studying rare diseases. METHODS: NHCS contains data on millions of hospital patients from participating US hospitals, including diagnoses coded using 10th revision of International Classification of Diseases, Clinical Modification, making it likely that some of the patients have a diagnosed rare disease. The data for 2016 are unweighted and are not nationally representative. The Orphanet Nomenclature Pack lists 877 10th revision of the International Classification of Diseases codes that correspond to 536 rare diseases. Using Orphanet Nomenclature Pack, we identified NHCS patients with diagnosed rare diseases. We demonstrate the usefulness of NHCS for studying rare diseases by reporting, for each rare disease, the number of patients in NHCS with the disease, the average number of hospital encounters per patient, the average length of hospital stay, and the percent of patients who died either in-hospital or within 90 days after discharge. RESULTS: In just 1 year of NHCS, we identified hundreds of rare diseases with ≥30 patients each (313 rare diseases in the inpatient setting and 273 in the emergency department setting). Although 10th revision of the International Classification of Diseases, Clinical Modification codes identify a small percent of known rare diseases, 12.9% of inpatient patients and 3.4% of emergency department patients had a diagnosed rare disease. CONCLUSIONS: NHCS is a rich source of administrative and electronic health record data on hospital patients with rare diseases, providing unique variables and observations on many patients. Although the percent of patients with each rare disease is low, a large percent of hospital patients has a rare disease.

First assessment of the validity of the only diagnostic criteria for postorgasmic illness syndrome (POIS).

Postorgasmic illness syndrome (POIS) is a rare condition that affects men and about which little is known. According to Waldinger and colleagues, men with POIS fulfill three or more of five preliminary diagnostic criteria regarding symptoms, time to onset, setting, duration, and spontaneous disappearance. We conducted a self-report study to assess, for the first time, the validity of these criteria. One hundred and twenty-seven men with self-reported POIS have completed the survey, making this the largest study of such men to date. Almost all respondents fulfill a majority of the criteria for POIS; a large minority fulfills all five criteria. Almost all respondents always experience symptoms after ejaculating in at least one ejaculatory setting (sex, masturbation, or nocturnal emission), though only a small majority fulfill the criterion that symptoms occur after all ejaculations because a large minority always experience symptoms in one setting but not always in another. The most common symptom cluster from the criteria, involving fatigue, irritation, and concentration difficulties, is always experienced by 80% of respondents. Median symptom severity is 8 on a 0-10 scale. While almost all men with POIS fulfill a majority of the preliminary diagnostic criteria, there is room for refining some of the criteria.

Is seizure frequency variance a predictable quantity?

Background: There is currently no formal method for predicting the range expected in an individual's seizure counts. Having access to such a prediction would be of benefit for developing more efficient clinical trials, but also for improving clinical care in the outpatient setting. Methods: Using three independently collected patient diary datasets, we explored the predictability of seizure frequency. Three independent seizure diary databases were explored: SeizureTracker (n = 3016), Human Epilepsy Project (n = 93), and NeuroVista (n = 15). First, the relationship between mean and standard deviation in seizure frequency was assessed. Using that relationship, a prediction for the range of possible seizure frequencies was compared with a traditional prediction scheme commonly used in clinical trials. A validation dataset was obtained from a separate data export of SeizureTracker to further verify the predictions. Results: A consistent mathematical relationship was observed across datasets. The logarithm of the average seizure count was linearly related to the logarithm of the standard deviation with a high correlation (R2 > 0.83). The three datasets showed high predictive accuracy for this log-log relationship of 94%, compared with a predictive accuracy of 77% for a traditional prediction scheme. The independent validation set showed that the log-log predicted 94% of the correct ranges while the RR50 predicted 77%. Conclusion: Reliably predicting seizure frequency variability is straightforward based on knowledge of mean seizure frequency, across several datasets. With further study, this may help to increase the power of RCTs, and guide clinical practice.

Evaluation of Transition From ICD-9-CM to ICD-10-CM Diagnosis Coding System in the National Ambulatory Medical Care Survey.

Objective-On October 1, 2015, the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) replaced ICD-9-CM (Ninth Revision) as the diagnosis coding scheme for the U.S. health care system. This study evaluates the impact of this change on the way the National Center for Health Statistics (NCHS) reports diagnosis data for the National Ambulatory Medical Care Survey (NAMCS). Methods-The patient visit records of office-based physicians from the 2014 NAMCS final quarter (n = 20,942) were reviewed. The diagnoses assigned to each record were coded in both ICD-9-CM and ICD-10-CM by professional medical coders. NCHS staff reviewed how well the codes of the primary diagnosis under the two coding systems corresponded to each other. Results-The review showed that 89% of the visit records had compatible ICD-9-CM and ICD-10-CM codes for the primary diagnosis, meaning that the primary diagnosis would be grouped under the same Primary Diagnosis Group (PDG) according to both ICD-9-CM and ICD-10-CM, and it would be correctly assigned to only one PDG. The reasons for mismatches in the ICD-10-CM assignments included coder error (5%), documentation issues such as uncodable diagnoses (3%), and differences between ICD-9-CM and ICD-10-CM (2%).

A multi-dataset time-reversal approach to clinical trial placebo response and the relationship to natural variability in epilepsy.

PURPOSE: Clinical epilepsy drug trials have been measuring increasingly high placebo response rates, up to 40%. This study was designed to examine the relationship between the natural variability in epilepsy, and the placebo response seen in trials. We tested the hypothesis that 'reversing' trial direction, with the baseline period as the treatment observation phase, would reveal effects of natural variability. METHOD: Clinical trial simulations were run with time running forward and in reverse. Data sources were: SeizureTracker.com (patient reported diaries), a randomized sham-controlled TMS trial, and chronically implanted intracranial EEG electrodes. Outcomes were 50%-responder rates (RR50) and median percentage change (MPC). RESULTS: The RR50 results showed evidence that temporal reversal does not prevent large responder rates across datasets. The MPC results negative in the TMS dataset, and positive in the other two. CONCLUSIONS: Typical RR50s of clinical trials can be reproduced using the natural variability of epilepsy as a substrate across multiple datasets. Therefore, the placebo response in epilepsy clinical trials may be attributable almost entirely to this variability, rather than the "placebo effect".

Does accounting for seizure frequency variability increase clinical trial power?

OBJECTIVE: Seizure frequency variability is associated with placebo responses in randomized controlled trials (RCT). Increased variability can result in drug misclassification and, hence, decreased statistical power. We investigated a new method that directly incorporated variability into RCT analysis, ZV. METHODS: Two models were assessed: the traditional 50%-responder rate (RR50), and the variability-corrected score, ZV. Each predicted seizure frequency upper and lower limits using prior seizures. Accuracy was defined as percentage of time-intervals when the observed seizure frequencies were within the predicted limits. First, we tested the ZV method on three datasets (SeizureTracker: n=3016, Human Epilepsy Project: n=107, and NeuroVista: n=15). An additional independent SeizureTracker validation dataset was used to generate a set of 200 simulated trials each for 5 different sample sizes (total N=100 to 500 by 100), assuming 20% dropout and 30% drug efficacy. "Power" was determined as the percentage of trials successfully distinguishing placebo from drug (p<0.05). RESULTS: Prediction accuracy across datasets was, ZV: 91-100%, RR50: 42-80%. Simulated RCT ZV analysis achieved >90% power at N=100 per arm while RR50 required N=200 per arm. SIGNIFICANCE: ZV may increase the statistical power of an RCT relative to the traditional RR50.

Factors associated with substance use treatment completion in residential facilities.

PURPOSE: Individuals in residential treatment often face many challenges, which can include limited education, unstable housing, difficulty participating in the workforce, and severe substance use problems. We analyzed factors associated with substance use treatment completion. We focused on factors that can be influenced by health care system changes resulting from the Affordable Care Act (ACA). DATA AND METHODS: We used the 2010 Treatment Episode Data Set - Discharges (TEDS-D), which is made available by the Substance Abuse and Mental Health Services Administration (SAMHSA). We analyzed factors associated with substance use treatment completion using logistic regression. RESULTS: Individuals in residential treatment were often unemployed or not in the labor force, had prior substance use treatment episodes, used more than one substance, and were uninsured. Factors associated with treatment completion included older age, greater education, employment, criminal justice referral, not being homeless, and private insurance. CONCLUSION: The expansion in private insurance coverage as a result of the ACA may result in more treatment completion in residential settings. Changes to the Medicaid program resulting from the ACA, including coverage of substance use treatment as an essential health benefit and greater support for housing, education, and employment, may also contribute to more residential discharges ending in treatment completion.