Dataset Information

Characteristics of large patient-reported outcomes: Where can one million seizures get us?

ABSTRACT: To analyze data from Seizure Tracker, a large electronic seizure diary, including comparison of seizure characteristics among different etiologies, temporal patterns in seizure fluctuations, and specific triggers.Zero-inflated negative binomial mixed-effects models were used to evaluate temporal patterns of seizure events (during the day or week), as well as group differences in monthly seizure frequency between children and adults and between etiologies. The association of long seizures with seizure triggers was evaluated using a mixed-effects logistic model with subject as the random effect. Incidence rate ratios (IRRs) and odds ratios were reported for analyses involving zero-inflated negative binomial and logistic mixed-effects models, respectively.A total of 1,037,909 seizures were logged by 10,186 subjects (56.7% children) from December 2007 to January 2016. Children had more frequent seizures than adults did (median monthly seizure frequency 3.5 vs. 2.7, IRR 1.26; p < 0.001). Seizures demonstrated a circadian pattern (higher frequency between 07:00 a.m. and 10:00 a.m. and lower overnight), and seizures were reported differentially across the week (seizure rates higher Monday through Friday than Saturday or Sunday). Longer seizures (>5 or >30 min) had a higher proportion of the following triggers when compared with shorter seizures: "Overtired or irregular sleep," "Bright or flashing lights," and "Emotional stress" (p < 0.004).This study explored a large cohort of patients with self-reported seizures; strengths and limitations of large seizure diary databases are discussed. The findings in this study are consistent with those of prior work in smaller validated cohorts, suggesting that patient-recorded databases are a valuable resource for epilepsy research, capable of both replication of results and generation of novel hypotheses.

SUBMITTER: Ferastraoaru V

PROVIDER: S-EPMC6119749 | biostudies-other | 2018 Sep

REPOSITORIES: biostudies-other

ACCESS DATA

Publications

Characteristics of large patient-reported outcomes: Where can one million seizures get us?

Ferastraoaru Victor V Goldenholz Daniel M DM Chiang Sharon S Moss Robert R Theodore William H WH Haut Sheryl R SR

Epilepsia open 20180704 3

<h4>Objective</h4>To analyze data from Seizure Tracker, a large electronic seizure diary, including comparison of seizure characteristics among different etiologies, temporal patterns in seizure fluctuations, and specific triggers.<h4>Methods</h4>Zero-inflated negative binomial mixed-effects models were used to evaluate temporal patterns of seizure events (during the day or week), as well as group differences in monthly seizure frequency between children and adults and between etiologies. The as ...[more]

PMID: 30187007

Similar Datasets

Project description:BackgroundSummaries of product characteristics (SmPCs) are regulatory documents published upon drug approval. They should report all relevant study data and advise how to use drugs safely and effectively. Patient-reported outcomes (PROs) are increasingly used in clinical trials to incorporate the patient perspective-SmPCs should thus adequately report PROs. In Germany, new drugs undergo mandatory early benefit assessment. Pharmaceutical companies submit dossiers containing all evidence; the subsequent dossier assessments focus on patient-relevant outcomes and comprehensively report PROs.ObjectiveThe primary aim was to investigate to what extent PROs recorded as outcomes in clinical trials of new drugs are reported in SmPCs.MethodsWe analysed dossier assessments with randomized controlled trials (RCTs) of new drugs entering the market between 01/2014 and 07/2018 and the corresponding SmPCs, and compared PRO reporting in both document types. For this purpose, we evaluated dossier assessment characteristics (e.g. drug name, indication, disease category) and study characteristics (e.g. evaluable PROs available?). PROs were divided into symptoms and health-related quality of life (HRQoL). SmPCs were screened to identify RCTs. We conducted 3 main evaluation steps: (1) Did the RCT included in the dossier assessment contain evaluable PROs? (2) If yes, was the RCT included in the SmPC? (3) If yes, were the PROs reported in the SmPC? Results are presented descriptively.Results88 dossier assessments including 143 RCTs on 72 drugs were considered: 109 (76.2%) RCTs included evaluable PROs, of which 89 were included in SmPCs. 38 RCTs (42.7%) investigated oncologics, 18 (20.2%) anti-infectives, and 33 (37.1%) other drugs. The RCTs considered symptoms more often than HRQoL (82 vs. 66 RCTs). In SmPCs, PROs were reported for 41 RCTs (46.1%), with a slightly higher reporting rate for RCTs considering HRQoL (43.9%) than for RCTs considering symptoms (41.5%). In oncologic indications, PROs were reported for 36.7% of RCTs considering HRQoL and 33.3% of RCTs considering symptoms. In infectious diseases, the rates were 21.4% (symptoms) and 0% (HRQoL), and for other diseases about 60% (symptoms) to 70% (HRQoL).ConclusionEven though a large amount of PRO data on new drugs is available from clinical trials included in SmPCs, the corresponding results are underreported.

Project description:Importance:The collection of patient-reported outcomes (PROs) has garnered intense interest, but dissemination of PRO programs has been limited, as have analyses of the factors associated with successful programs. Objective:To identify factors associated with improving PRO collection rates within a large health care system using a centralized PRO infrastructure. Design, Setting, and Participants:This cohort study included 205 medical and surgical clinics in the Partners Healthcare system in Massachusetts that implemented a PRO program between March 15, 2014, and December 31, 2018, using a standardized centralized infrastructure. Data were analyzed from March to April 2019. Exposures:Relevant clinical characteristics were recorded for each clinic launching a PRO program. Main Outcomes and Measures:The primary outcome was the mean PRO collection rate during each clinic's most recent 6 months of collection prior to January 2019. Data were analyzed using a linear regression model with the 6-month PRO collection rate as the dependent variable and clinic characteristics as independent variables. Secondary analysis used a logistic regression model to assess clinical factors associated with successful clinics, defined as those that collected PROs at a rate greater than 50%. Results:Between March 2014 and December 2018, 205 Partners Healthcare clinics were available for analysis, and 4?061?205 PRO measures from 745?028 encounters were collected. Among these, 103 clinics (50.2%) collected at a rate greater than 50%. Increased collection rates were associated with more than 50% of physicians in a clinic trained on PROs (change, 19.6% [95% CI, 9.9%-29.4%]; P?<?.001), routine administrative oversight of collection rates (change, 16.0% [95% CI, 6.6%-25.5%]; P?=?.001), previous collection of PROs on paper (change, 12.5% [95% CI, 4.7%-20.3%]; P?=?.002), presence of a clinical champion (change, 11.2% [95% CI, 2.5%-20.0%]; P?=?.01) and payer incentive (change, 10.5% [95% CI, 2.0%-18.9%]; P?=?.02). Conclusions and Relevance:These findings suggest that training physicians on the use of PROs, administrative surveillance of collection rates, and the presence of a local clinical champion may be promising interventions for increasing PRO collection. Clinics that have previously collected PROs may have greater success in increasing collections. Payer incentive for collection was associated with improved collections, but not associated with successful programs.

Project description:BackgroundAutosomal dominant polycystic kidney disease (ADPKD) has been associated with metabolic disturbances characterized by downregulation of AMP-activated protein kinase (AMPK), a critical sensor of the cellular energy status. Therapeutic activation of AMPK by metformin could inhibit cyst enlargement by inhibition of both the mammalian target of rapamycin pathway and fluid secretion via the CFTR chloride channel.MethodsWe designed a phase-2, randomized, placebo-controlled, clinical trial to assess the safety, tolerability, and efficacy of metformin on total kidney volume in adults without diabetes (age 18-60 years) with ADPKD and eGFR of ≥50 ml/min per 1.73 m2. There were no eligibility criteria relating to kidney volume. In addition to demographics and clinical/family history, baseline parameters included eGFR, total kidney and liver volumes measured by MRI, and patient-reported outcomes were ascertained by the Medical Outcomes Study Short Form-36, the Gastrointestinal Safety Rating Scale, and the HALT-PKD pain questionnaire.ResultsWe successfully randomized 97 participants recruited from two university-based clinical sites in Baltimore and Boston. The mean age of participants was 41.9 years, 72% were female, and 94% of participants were White. The majority of study participants had early stage disease, with a mean eGFR of 86.8±19.0 ml/min per 1.73 m2. Approximately half of the study participants (48%) were classified as high risk for progression (Mayo imaging classes 1C, 1D, or 1E). There was no correlation between kidney and/or liver size and health-related quality of life (HRQoL) or gastrointestinal symptom severity.ConclusionsWe report successful recruitment in this ongoing, novel, clinical trial of metformin in ADPKD, with a study sample comprising patients with early stage disease and nearly a half of participants considered at high estimated risk for progression. Participants reported a low gastrointestinal symptom burden at baseline, and HRQoL similar to that of the general population, with no differences in symptoms or HRQoL related to organomegaly.Clinical trial registry name and registration numberMetformin as a Novel Therapy for Autosomal Dominant Polycystic Kidney Disease (TAME), NCT02656017.

Project description:BackgroundPatient-reported outcome measures (PROMs) are commonly-used surrogates for clinical outcomes in cancer research. When researching severe diseases such as cancer, it is difficult to avoid the problem of incomplete questionnaires from drop-outs or missing data from patients who pass away during the observation period. The aim of this exploratory study was to explore patient characteristics and the patient-reported outcomes associated with the time-to-dropout.MethodsIn an Oncological Social Care Project (OSCAR) study, the condition of the participants was assessed four times within 12 months (t0: baseline, t1: 3 months, t2: 6 months, and t3: 12 months) by validated PROMs. We performed competing-risk regressions based on Fine and Gray's proportional sub-distribution hazards model for exploring factors associated with time-to-dropout. Death was considered a competing risk.ResultsThree hundred sixty-two participants were analyzed in the study. 193 (53.3%) completed a follow-up after 12 months, 67 (18.5%) patients dropped out, and 102 patients (28.2%) died during the study period. Poor subjective social support was related to a higher risk of drop-out (SHR = 2.10; 95%CI: 1.01-4.35). Lower values in health-related quality of life were related to drop-out and death. The sub-scales global health status/QoL, role functioning, physical functioning, and fatigue symptom in the EORTC QLQ-C30 were key characteristics of early drop-out.ConclusionSeverely affected cancer patients with poor social support and poor quality of life seem more likely to drop out of studies than patients with higher levels of social support and a better quality of life. This should be considered when planning studies to assess advanced cancer patients. Methods of close continued monitoring should be actively used when patient experiences a substantial deterioration in their health-related quality of life and symptoms during the study. Results for such studies have to be interpreted with caution in light of specific drop-out mechanisms.Trial registrationOSCAR study was registered to the German Clinical Trials Register (DRKS-ID: DRKS00013640 ). Registered 29 December 2017.

Dataset Information

Characteristics of large patient-reported outcomes: Where can one million seizures get us?

Publications

Characteristics of large patient-reported outcomes: Where can one million seizures get us?

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure