Citations

AbstractObjectiveThe Longitudinal Evaluation of Familial Frontotemporal Dementia Subjects evaluates familial frontotemporal lobar degeneration (FTLD) kindreds with MAPT, GRN, or C9orf72 mutations. Objectives were to examine whether health‐related quality of life (HRQoL) correlates with clinical symptoms and caregiver burden, and whether self‐rated and informant‐rated HRQoL would correlate with each other.MethodsIndividuals were classified using the Clinical Dementia Rating (CDR®) Scale plus National Alzheimer's Coordinating Center (NACC) FTLD. HRQoL was measured with DEMQOL and DEMQOL‐proxy; caregiver burden with the Zarit Burden Interview (ZBI). For analysis, Pearson correlations and weighted kappa statistics were calculated.ResultsThe cohort of 312 individuals included symptomatic and asymptomatic individuals. CDR® plus NACC FTLD was negatively correlated with DEMQOL (r = −0.20, P = .001), as were ZBI and DEMQOL (r = −0.22, P = .0009). There was fair agreement between subject and informant DEMQOL (κ = 0.36, P <.0001).ConclusionLower HRQoL was associated with higher cognitive/behavior impairment and higher caregiver burden. These findings demonstrate the negative impact of FTLD on individuals and caregivers.

Background: Financial distress from medical treatment is an increasing concern. Healthcare organizations may have different levels of organizational commitment, existing programs, and expected outcomes of screening and management of patient financial distress. Patients and Methods: In November 2018, representatives from 17 (63%) of the 27 existing NCCN Member Institutions completed an online survey. The survey focused on screening and management practices for patient financial distress, perceived barriers in implementation, and leadership attitudes about such practices. Due to the lack of a validated questionnaire in this area, survey questions were generated after a comprehensive literature search and discussions among the study team, including NCCN Best Practices Committee representatives. Results: Responses showed that 76% of centers routinely screened for financial distress, mostly with social worker assessment (94%), and that 56% screened patients multiple times. All centers offered programs to help with drug costs, meal or gas vouchers, and payment plans. Charity care was provided by 100% of the large centers (≥10,000 unique annual patients) but none of the small centers that responded (<10,000 unique annual patients; P =.008). Metrics to evaluate the impact of financial advocacy services included number of patients assisted, bad debt/charity write-offs, or patient satisfaction surveys. The effectiveness of institutional practices for screening and management of financial distress was reported as poor/very poor by 6% of respondents. Inadequate staffing and resources, limited budget, and lack of reimbursement were potential barriers in the provision of these services. A total of 94% agreed with the need for better integration of financial advocacy into oncology practice. Conclusions: Three-fourths of NCCN Member Institutions reported screening and management programs for financial distress, although the actual practices and range of services vary. Information from this study can help centers benchmark their performance relative to similar programs and identify best practices in this area.

Buffet meal intake was inversely related to the ability to resist the urge to overeat. Factors associated with ingestive behavior significantly correlated with satiety and gastric accommodation. Gastric capacity during fasting is associated with calorie intake to the point of comfortable fullness; factors associated with ingestive behavior are associated with satiety and gastric accommodation.

AbstractIntroductionElectronic health record (EHR)‐driven phenotyping is a critical first step in generating biomedical knowledge from EHR data. Despite recent progress, current phenotyping approaches are manual, time‐consuming, error‐prone, and platform‐specific. This results in duplication of effort and highly variable results across systems and institutions, and is not scalable or portable. In this work, we investigate how the nascent Clinical Quality Language (CQL) can address these issues and enable high‐throughput, cross‐platform phenotyping.MethodsWe selected a clinically validated heart failure (HF) phenotype definition and translated it into CQL, then developed a CQL execution engine to integrate with the Observational Health Data Sciences and Informatics (OHDSI) platform. We executed the phenotype definition at two large academic medical centers, Northwestern Medicine and Weill Cornell Medicine, and conducted results verification (n = 100) to determine precision and recall. We additionally executed the same phenotype definition against two different data platforms, OHDSI and Fast Healthcare Interoperability Resources (FHIR), using the same underlying dataset and compared the results.ResultsCQL is expressive enough to represent the HF phenotype definition, including Boolean and aggregate operators, and temporal relationships between data elements. The language design also enabled the implementation of a custom execution engine with relative ease, and results verification at both sites revealed that precision and recall were both 100%. Cross‐platform execution resulted in identical patient cohorts generated by both data platforms.ConclusionsCQL supports the representation of arbitrarily complex phenotype definitions, and our execution engine implementation demonstrated cross‐platform execution against two widely used clinical data platforms. The language thus has the potential to help address current limitations with portability in EHR‐driven phenotyping and scale in learning health systems.

OBJECTIVE Limited information is available about glycemic outcomes with a closed-loop control (CLC) system compared with a predictive low-glucose suspend (PLGS) system. RESEARCH DESIGN AND METHODS After 6 months of use of a CLC system in a randomized trial, 109 participants with type 1 diabetes (age range, 14–72 years; mean HbA1c, 7.1% [54 mmol/mol]) were randomly assigned to CLC (N = 54, Control-IQ) or PLGS (N = 55, Basal-IQ) groups for 3 months. The primary outcome was continuous glucose monitor (CGM)-measured time in range (TIR) for 70–180 mg/dL. Baseline CGM metrics were computed from the last 3 months of the preceding study. RESULTS All 109 participants completed the study. Mean ± SD TIR was 71.1 ± 11.2% at baseline and 67.6 ± 12.6% using intention-to-treat analysis (69.1 ± 12.2% using per-protocol analysis excluding periods of study-wide suspension of device use) over 13 weeks on CLC vs. 70.0 ± 13.6% and 60.4 ± 17.1% on PLGS (difference = 5.9%; 95% CI 3.6%, 8.3%; P &lt; 0.001). Time &gt;180 mg/dL was lower in the CLC group than PLGS group (difference = −6.0%; 95% CI −8.4%, −3.7%; P &lt; 0.001) while time &lt;54 mg/dL was similar (0.04%; 95% CI −0.05%, 0.13%; P = 0.41). HbA1c after 13 weeks was lower on CLC than PLGS (7.2% [55 mmol/mol] vs. 7.5% [56 mmol/mol], difference −0.34% [−3.7 mmol/mol]; 95% CI −0.57% [−6.2 mmol/mol], −0.11% [1.2 mmol/mol]; P = 0.0035). CONCLUSIONS Following 6 months of CLC, switching to PLGS reduced TIR and increased HbA1c toward their pre-CLC values, while hypoglycemia remained similarly reduced with both CLC and PLGS.