Alerting providers to hospitalized persons with dementia using the electronic health record.

BACKGROUND: While patients with dementia entering the hospital have worse outcomes than those without dementia, early detection of dementia in the inpatient setting is less than 50%. We developed and assessed the positive predictive value (PPV) and feasibility of a novel electronic health record (EHR) banner to identify patients with dementia who present to the inpatient setting using data from the medical record. METHODS: We developed and implemented an EHR algorithm to flag hospitalized patients age ≥65 years with potential cognitive impairment in the Epic EHR system using dementia ICD-10 codes, FDA-approved medications, and the use of the term "dementia" in the emergency department physician note. Medical records were reviewed for all patients who were flagged with an EHR banner from October 2022 to May 2023. RESULTS: A total of 344 individuals were identified who had a banner on their chart of which 280 (81.4%) were either diagnosed with dementia or were on an FDA-approved dementia medication. Forty-three individuals who had confirmed dementia were identified by a medication only (15.4%). Of the patients without confirmed dementia, the majority (N = 33, 9.6%) had a diagnosis of altered mental status, cognitive dysfunction, or mild cognitive impairment. Only 31 individuals (9.0%) had no indication of dementia or cognitive decline in their problem list, past medical history, or medication list. CONCLUSIONS: We found that it was feasible to implement an EHR algorithm for prospective dementia identification with a high PPV. These types of algorithms provide an opportunity to accurately identify hospitalized older individuals for inclusion in quality improvement projects, clinical trials, pay-for-performance programs, and other initiatives.

Noninterruptive Clinical Decision Support Decreases Ordering of Respiratory Viral Panels during Influenza Season.

OBJECTIVE: A growing body of evidence suggests that testing for influenza virus alone is more appropriate than multiplex respiratory viral panel (RVP) testing for general populations of patients with respiratory tract infections. We aimed to decrease the proportion of RVPs out of total respiratory viral testing ordered during influenza season. METHODS: We implemented two consecutive interventions: reflex testing for RVPs only after a negative influenza test, and noninterruptive clinical decision support (CDS) including modifications of the computerized physician order entry search behavior and cost display. We conducted an interrupted time series of RVPs and influenza polymerase chain reaction tests pre- and postintervention, and performed a mixed-effects logistic regression analysis with a primary outcome of proportion of RVPs out of total respiratory viral tests. The primary predictor was the intervention period, and covariates included the provider, clinical setting, associated diagnoses, and influenza incidence. RESULTS: From March 2013 to April 2019, there were 24,294 RVPs and 26,012 influenza tests (n = 50,306). Odds of ordering an RVP decreased during the reflex testing period (odds ratio: 0.432, 95% confidence interval: 0.397-0.469), and decreased more dramatically during the noninterruptive CDS period (odds ratio: 0.291, 95% confidence interval: 0.259-0.327). DISCUSSION: The odds of ordering an RVP were 71% less with the noninterruptive CDS intervention, which projected 4,773 fewer RVPs compared with baseline. Assuming a cost equal to Medicare reimbursement rates for RVPs and influenza tests, this would generate an estimated averted cost of $1,259,474 per year. CONCLUSION: Noninterruptive CDS interventions are effective in reducing unnecessary and expensive testing, and avoid typical pitfalls such as alert fatigue.

Evidence for sequenced molecular evolution of IDH1 mutant glioblastoma from a distinct cell of origin.

PURPOSE: Mutation in isocitrate dehydrogenase 1 (IDH1) at R132 (IDH1(R132MUT)) is frequent in low-grade diffuse gliomas and, within glioblastoma (GBM), has been proposed as a marker for GBMs that arise by transformation from lower-grade gliomas, regardless of clinical history. To determine how GBMs arising with IDH1(R132MUT) differ from other GBMs, we undertook a comprehensive comparison of patients presenting clinically with primary GBM as a function of IDH1(R132) mutation status. PATIENTS AND METHODS: In all, 618 treatment-naive primary GBMs and 235 lower-grade diffuse gliomas were sequenced for IDH1(R132) and analyzed for demographic, radiographic, anatomic, histologic, genomic, epigenetic, and transcriptional characteristics. RESULTS: Investigation revealed a constellation of features that distinguishes IDH1(R132MUT) GBMs from other GBMs (including frontal location and lesser extent of contrast enhancement and necrosis), relates them to lower-grade IDH1(R132MUT) gliomas, and supports the concept that IDH1(R132MUT) gliomas arise from a neural precursor population that is spatially and temporally restricted in the brain. The observed patterns of DNA sequence, methylation, and copy number alterations support a model of ordered molecular evolution of IDH1(R132MUT) GBM in which the appearance of mutant IDH1 protein is an initial event, followed by production of p53 mutant protein, and finally by copy number alterations of PTEN and EGFR. CONCLUSION: Although histologically similar, GBMs arising with and without IDH1(R132MUT) appear to represent distinct disease entities that arise from separate cell types of origin as the result of largely nonoverlapping sets of molecular events. Optimal clinical management should account for the distinction between these GBM disease subtypes.

In Silico Enhanced Restriction Enzyme Based Methylation Analysis of the Human Glioblastoma Genome Using Agilent 244K CpG Island Microarrays.

Genome wide methylation profiling of gliomas is likely to provide important clues to improving treatment outcomes. Restriction enzyme based approaches have been widely utilized for methylation profiling of cancer genomes and will continue to have importance in combination with higher density microarrays. With the availability of the human genome sequence and microarray probe sequences, these approaches can be readily characterized and optimized via in silico modeling. We adapted the previously described HpaII/MspI based Methylation Sensitive Restriction Enzyme (MSRE) assay for use with two-color Agilent 244K CpG island microarrays. In this assay, fragmented genomic DNA is digested in separate reactions with isoschizomeric HpaII (methylation-sensitive) and MspI (methylation-insensitive) restriction enzymes. Using in silico hybridization, we found that genomic fragmentation with BfaI was superior to MseI, providing a maximum effective coverage of 22,362 CpG islands in the human genome. In addition, we confirmed the presence of an internal control group of fragments lacking HpaII/MspI sites which enable separation of methylated and unmethylated fragments. We used this method on genomic DNA isolated from normal brain, U87MG cells, and a glioblastoma patient tumor sample and confirmed selected differentially methylated CpG islands using bisulfite sequencing. Along with additional validation points, we performed a receiver operating characteristics (ROC) analysis to determine the optimal threshold (p