Dual autoencoders modeling of electronic health records for adverse drug event preventability prediction

Background Elderly patients are at increased risk for Adverse Drug Events (ADEs). Proactively screening elderly people visiting the emergency department for the possibility of their hospital admission being drug-related helps to improve patient care as well as prevent potential unnecessary medical costs. Existing routine ADE assessment heavily relies on a rule-based checking process. Recently, machine learning methods have been shown to be effective in automating the detection of ADEs, however, most approaches used only either structured data or free texts for their feature engineering. How to better exploit all available EHRs data for better predictive modeling remains an important question. On the other hand, automated reasoning for the preventability of ADEs is still a nascent line of research. Methods Clinical information of 714 elderly ED-visit patients with ADE preventability labels was provided as ground truth data by Jeroen Bosch Ziekenhuis hospital, the Netherlands. Methods were developed to address the challenges of applying feature engineering to heterogeneous EHRs data. A Dual Autoencoders (2AE) model was proposed to solve the problem of imbalance embedded in the existing training data. Results Experimental results showed that 2AE can capture the patterns of the minority class without incorporating an extra process for class balancing. 2AE yields adequate performance and outperforms other more mainstream approaches, resulting in an AUPRC score of 0.481. Conclusions We have demonstrated how machine learning can be employed to analyze both structured and unstructured data from electronic health records for the purpose of preventable ADE prediction. The developed algorithm 2AE can be used to effectively learn minority group phenotype from imbalanced data

Hyperoxemia and excess oxygen use in early acute respiratory distress syndrome : Insights from the LUNG SAFE study

Publisher Copyright: © 2020 The Author(s). Copyright: Copyright 2020 Elsevier B.V., All rights reserved.Background: Concerns exist regarding the prevalence and impact of unnecessary oxygen use in patients with acute respiratory distress syndrome (ARDS). We examined this issue in patients with ARDS enrolled in the Large observational study to UNderstand the Global impact of Severe Acute respiratory FailurE (LUNG SAFE) study. Methods: In this secondary analysis of the LUNG SAFE study, we wished to determine the prevalence and the outcomes associated with hyperoxemia on day 1, sustained hyperoxemia, and excessive oxygen use in patients with early ARDS. Patients who fulfilled criteria of ARDS on day 1 and day 2 of acute hypoxemic respiratory failure were categorized based on the presence of hyperoxemia (PaO2 > 100 mmHg) on day 1, sustained (i.e., present on day 1 and day 2) hyperoxemia, or excessive oxygen use (FIO2 ≥ 0.60 during hyperoxemia). Results: Of 2005 patients that met the inclusion criteria, 131 (6.5%) were hypoxemic (PaO2 < 55 mmHg), 607 (30%) had hyperoxemia on day 1, and 250 (12%) had sustained hyperoxemia. Excess FIO2 use occurred in 400 (66%) out of 607 patients with hyperoxemia. Excess FIO2 use decreased from day 1 to day 2 of ARDS, with most hyperoxemic patients on day 2 receiving relatively low FIO2. Multivariate analyses found no independent relationship between day 1 hyperoxemia, sustained hyperoxemia, or excess FIO2 use and adverse clinical outcomes. Mortality was 42% in patients with excess FIO2 use, compared to 39% in a propensity-matched sample of normoxemic (PaO2 55-100 mmHg) patients (P = 0.47). Conclusions: Hyperoxemia and excess oxygen use are both prevalent in early ARDS but are most often non-sustained. No relationship was found between hyperoxemia or excessive oxygen use and patient outcome in this cohort. Trial registration: LUNG-SAFE is registered with ClinicalTrials.gov, NCT02010073publishersversionPeer reviewe

Update on the use of memantine in Alzheimer&amp;rsquo;s disease

Robert J van MarumGeriatric Department, University Medical Center Utrecht, Utrecht, The NetherlandsAbstract: Memantine is a low to moderate affinity N-methyl-D-aspartate receptor (NMDAR) antagonist. The effects of memantine in Alzheimer&amp;rsquo;s disease (AD) have been studied in 7 randomized controlled trials in many post-hoc analyses. Three out of four RCTs in patients with moderate to severe AD (Mini Mental State Examination [MMSE] 14) showed a statistically significant but clinically small positive effect of memantine on cognition, global functioning, activities of daily living (ADL) and neuropsychiatric symptoms. No effects on these outcome measures could be found in the three RCTs studying patients with mild to moderate AD (MMSE 14&amp;ndash;24). Two of these studies evaluated the effect of addition of memantine to donepezil. Only the study in patients with mild to moderate AD showed a positive effect of addition of memantine on cognition, ADL, global functioning and neuropsychiatric functioning. Cost-effectiveness of memantine therapy remains controversial. Post-hoc analyses and observational studies suggest some effects on agitation/aggression, delusions or hallucinations. Side effects of memantine are usually mild and seem to be comparable with placebo. In this review, an oversight of pharmacodynamics and pharmacokinetics of memantine is presented. Also, published data concerning efficacy and safety in patients with AD are presented.Keywords: dementia, Alzheimer&amp;rsquo;s disease, drug therapy, memantin

Dual autoencoders modeling of electronic health records for adverse drug event preventability prediction

Background Elderly patients are at increased risk for Adverse Drug Events (ADEs). Proactively screening elderly people visiting the emergency department for the possibility of their hospital admission being drug-related helps to improve patient care as well as prevent potential unnecessary medical costs. Existing routine ADE assessment heavily relies on a rule-based checking process. Recently, machine learning methods have been shown to be effective in automating the detection of ADEs, however, most approaches used only either structured data or free texts for their feature engineering. How to better exploit all available EHRs data for better predictive modeling remains an important question. On the other hand, automated reasoning for the preventability of ADEs is still a nascent line of research. Methods Clinical information of 714 elderly ED-visit patients with ADE preventability labels was provided as ground truth data by Jeroen Bosch Ziekenhuis hospital, the Netherlands. Methods were developed to address the challenges of applying feature engineering to heterogeneous EHRs data. A Dual Autoencoders (2AE) model was proposed to solve the problem of imbalance embedded in the existing training data. Results Experimental results showed that 2AE can capture the patterns of the minority class without incorporating an extra process for class balancing. 2AE yields adequate performance and outperforms other more mainstream approaches, resulting in an AUPRC score of 0.481. Conclusions We have demonstrated how machine learning can be employed to analyze both structured and unstructured data from electronic health records for the purpose of preventable ADE prediction. The developed algorithm 2AE can be used to effectively learn minority group phenotype from imbalanced data

Psychotropic drug prescription rates in primary care for people with dementia from recorded diagnosis onwards

Background: Psychotropic drugs are frequently prescribed to people with dementia in nursing homes although severe adverse events and side effects are common. Less is known about the prevalence and types of psychotropic drug prescription in primary care for people with dementia. Objective: This study examined the prevalence of psychotropic drug prescriptions in primary care among persons with dementia from the year of diagnosis onwards. Methods: A longitudinal observational study using electronic health record (EHR) data was conducted. People with dementia were selected from EHR data of 451 general practices in the Netherlands. Age and gender-adjusted psychotropic drug prescription rates were calculated per 1000 person-years from the year the dementia diagnosis was first recorded in general practice up to 8 years after diagnosis. Results: Data of 15,687 patients were analyzed. The prescription rate of psychotropic drugs (not including antidementia drugs) was 420 per 1000 person-years (95% CI 409; 431) in the first year after the recorded dementia diagnosis, which increased to 801 per 1000 person-years (95% CI 649; 989) in the eighth year. The most frequently prescribed drugs were antidepressants, antipsychotics, and antidementia drugs, followed by anxiolytics, hypnotics, and antiepileptics. Conclusions: After a dementia diagnosis is recorded in general practice, the prevalence of psychotropic drug prescriptions is substantial and increases steadily during the disease trajectory of persons with dementia. Although the (in)appropriateness of prescribing was not assessed, these insights may stimulate primary care clinicians to (re)consider their prescription policy of psychotropics for people with dementia more carefully

Psychotropic drug prescription rates in primary care for people with dementia from recorded diagnosis onwards

BACKGROUND: Psychotropic drugs are frequently prescribed to people with dementia in nursing homes although severe adverse events and side effects are common. Less is known about the prevalence and types of psychotropic drug prescription in primary care for people with dementia. OBJECTIVE: This study examined the prevalence of psychotropic drug prescriptions in primary care among persons with dementia from the year of diagnosis onwards. METHODS: A longitudinal observational study using electronic health record (EHR) data was conducted. People with dementia were selected from EHR data of 451 general practices in the Netherlands. Age and gender-adjusted psychotropic drug prescription rates were calculated per 1000 person-years from the year the dementia diagnosis was first recorded in general practice up to 8 years after diagnosis. RESULTS: Data of 15,687 patients were analyzed. The prescription rate of psychotropic drugs (not including antidementia drugs) was 420 per 1000 person-years (95% CI 409; 431) in the first year after the recorded dementia diagnosis, which increased to 801 per 1000 person-years (95% CI 649; 989) in the eighth year. The most frequently prescribed drugs were antidepressants, antipsychotics, and antidementia drugs, followed by anxiolytics, hypnotics, and antiepileptics. CONCLUSIONS: After a dementia diagnosis is recorded in general practice, the prevalence of psychotropic drug prescriptions is substantial and increases steadily during the disease trajectory of persons with dementia. Although the (in)appropriateness of prescribing was not assessed, these insights may stimulate primary care clinicians to (re)consider their prescription policy of psychotropics for people with dementia more carefully

Delirium detection using relative delta power based on 1 minute single-channel EEG : a multicentre study

Background: Delirium is frequently unrecognised. EEG shows slower frequencies (i.e. below 4 Hz) during delirium, which might be useful in improving delirium recognition. We studied the discriminative performance of a brief single-channel EEG recording for delirium detection in an independent cohort of patients. Methods: In this prospective, multicentre study, postoperative patients aged ≥60 yr were included (n=159). Before operation and during the first 3 postoperative days, patients underwent a 5-min EEG recording, followed by a video-recorded standardised cognitive assessment. Two or, in case of disagreement, three delirium experts classified each postoperative day based on the video and chart review. Relative delta power (1–4 Hz) was based on 1-min artifact-free EEG. The diagnostic value of the relative delta power was evaluated by the area under the receiver operating characteristic curve (AUROC), using the expert classification as the gold standard. Results: Experts classified 84 (23.3%) postoperative days as either delirium or possible delirium, and 276 (76.7%) non-delirium days. The AUROC of the relative EEG delta power was 0.75 [95% confidence interval (CI) 0.69–0.82]. Exploratory analysis showed that relative power from 1 to 6 Hz had significantly higher AUROC (0.78, 95% CI 0.72–0.84, P=0.014). Conclusions: Delirium/possible delirium can be detected in older postoperative patients based on a single-channel EEG recording that can be automatically analysed. This objective detection method with a continuous scale instead of a dichotomised outcome is a promising approach for routine detection of delirium. Clinical trial registration: NCT02404181

Effect of Alzheimer disease genetic risk disclosure on dietary supplement use1234

Background: Genetic susceptibility testing for Alzheimer disease (AD) with APOE genotype disclosure is not recommended for clinical use but is available through direct-to-consumer (DTC) genetic testing companies. Little is known about whether APOE genotype disclosure would actually prompt changes in nutrition behaviors among at-risk individuals