Technology caregiver intervention for Alzheimer's disease (I-CARE): Feasibility and preliminary efficacy of Brain CareNotes.

BACKGROUND: The primary aim of the current pilot study was to examine enrollment rate, data completion, usability, acceptance and use of a mobile telehealth application, Brain CareNotes. A secondary aim was to estimate the application's effect in reducing caregiver burden and behavioral and psychological symptoms related to dementia (BPSD). METHODS: Patient-caregiver dyads (n = 53) were recruited and randomized to intervention and control groups. Assessment of usability, acceptance, BPSD symptoms, and caregiver burden were collected at baseline, 3- and 6-month follow-up. RESULTS: The enrollment rate was acceptable despite pandemic related challenges (53/60 target recruitment sample). Among randomized individuals, there was a retention rate of 85% and data completion was attained for 81.5% of those allocated to usual care and 88.5% of those allocated to Brain CareNotes. Mean caregiver-reported app usability at 6 months was 72.5 (IQR 70.0-90.0) on the System Usability Scale-considered "Good to Excellent"-and user acceptance was reasonable as indicated by 85%-90% of caregivers reporting they would intend to use the app to some degree in the next 6 months, if able. Regarding intervention effect, although differences in outcome measures between the groups were not statistically significant, compared to baseline, we found a reduction of caregiver burden (NPI-Caregiver Distress) of 1.0 at 3 months and 0.7 at 6 months for those in the intervention group. BPSD (NPI Total Score) was also reduced from baseline by 4.0 at 3 months and by 0.5 at 6 months. CONCLUSIONS: Brain CareNotes is a highly scalable, usable and acceptable mobile caregiver intervention. Future studies should focus on testing Brain CareNotes on a larger sample size to examine efficacy of reducing caregiver burden and BPSD.

Impact and recognition of cognitive impairment among hospitalized elders.

BACKGROUND: Older adults are predisposed to developing cognitive deficits. This increases their vulnerability for adverse health outcomes when hospitalized. OBJECTIVE: To determine the prevalence and impact of cognitive impairment (CI) among hospitalized elders based on recognition by lCD-coding versus screening done on admission. DESIGN: Observational cohort study. SETTING: Urban public hospital in Indianapolis. PATIENTS: 997 patients age 65 and older admitted to medical services between July 2006 and March 2008. MEASUREMENTS: Impact of CI in terms of length of stay, survival, quality of care and prescribing practices. Cognition was assessed by the Short Portable Mental Status Questionnaire (SPMSQ). RESULTS: 424 patients (43%) were cognitively impaired. Of those 424 patients with CI, 61% had not been recognized by ICD-9 coding. Those unrecognized were younger (mean age 76.1 vs. 79.1, P <0.001); had more comorbidity (mean Charlson index of 2.3 vs.1.9, P = 0.03), had less cognitive deficit (mean SPMSQ 6.3 vs. 3.4, P < 0.001). Among elders with CI, 163 (38%) had at least one day of delirium during their hospital course. Patients with delirium stayed longer in the hospital (9.2 days vs. 5.9, P < 0.001); were more likely to be discharged into institutional settings (75% vs. 31%, P < 0.001) and more likely to receive tethers during their care (89% vs. 69%, P < 0.001), and had higher mortality (9% vs. 4%, P = 0.09). CONCLUSION: Cognitive impairment, while common in hospitalized elders, is under-recognized, impacts care, and increases risk for adverse health outcomes.

A multilevel model for continuous time population estimation.

Statistical methods have been developed and applied to estimating populations that are difficult or too costly to enumerate. Known as multilist methods in epidemiological settings, individuals are matched across lists and estimation of population size proceeds by modeling counts in incomplete multidimensional contingency tables (based on patterns of presence/absence on lists). As multilist methods typically assume that lists are compiled instantaneously, there are few options available for estimating the unknown size of a closed population based on continuously (longitudinally) compiled lists. However, in epidemiological settings, continuous time lists are a routine byproduct of administrative functions. Existing methods are based on time-to-event analyses with a second step of estimating population size. We propose an alternative approach to address the twofold epidemiological problem of estimating population size and of identifying patient factors related to duration (in days) between visits to a health care facility. A Bayesian framework is proposed to model interval lengths because, for many patients, the data are sparse; many patients were observed only once or twice. The proposed method is applied to the motivating data to illustrate the methods' applicability. Then, a small simulation study explores the performance of the estimator under a variety of conditions. Finally, a small discussion section suggests opportunities for continued methodological development for continuous time population estimation.

Nonparametric estimation of ROC curves in the absence of a gold standard.

In the evaluation of diagnostic accuracy of tests, a gold standard on the disease status is required. However, in many complex diseases, it is impossible or unethical to obtain such a gold standard. If an imperfect standard is used, the estimated accuracy of the tests would be biased. This type of bias is called imperfect gold standard bias. In this article we develop a nonparametric maximum likelihood method for estimating ROC curves and their areas of ordinal-scale tests in the absence of a gold standard. Our simulation study shows that the proposed estimators for the ROC curve areas have good finite-sample properties in terms of bias and mean squared error. Further simulation studies show that our nonparametric approach is comparable to the binormal parametric method, and is easier to implement. Finally, we illustrate the application of the proposed method in a real clinical study on assessing the accuracy of seven specific pathologists in detecting carcinoma in situ of the uterine cervix.

Adjusting for non-ignorable verification bias in clinical studies for Alzheimer's disease.

A common problem for comparing the relative accuracy of two screening tests for Alzheimer's disease (AD) in a two-stage design study is verification bias. If the verification bias can be assumed to be ignorable, Zhou and Higgs have proposed a maximum likelihood approach to compare the relative accuracy of screening tests in a two-stage design study. However, if the verification mechanism also depends on the unobserved disease status, the ignorable assumption does not hold. In this paper, we discuss how to use a profile likelihood approach to compare the relative accuracy of two screening tests for AD without assuming the ignorable verification bias mechanism.