Development and Evaluation of a Natural Language Processing Annotation Tool to Facilitate Phenotyping of Cognitive Status in Electronic Health Records: Diagnostic Study.

BACKGROUND: Electronic health records (EHRs) with large sample sizes and rich information offer great potential for dementia research, but current methods of phenotyping cognitive status are not scalable. OBJECTIVE: The aim of this study was to evaluate whether natural language processing (NLP)-powered semiautomated annotation can improve the speed and interrater reliability of chart reviews for phenotyping cognitive status. METHODS: In this diagnostic study, we developed and evaluated a semiautomated NLP-powered annotation tool (NAT) to facilitate phenotyping of cognitive status. Clinical experts adjudicated the cognitive status of 627 patients at Mass General Brigham (MGB) health care, using NAT or traditional chart reviews. Patient charts contained EHR data from two data sets: (1) records from January 1, 2017, to December 31, 2018, for 100 Medicare beneficiaries from the MGB Accountable Care Organization and (2) records from 2 years prior to COVID-19 diagnosis to the date of COVID-19 diagnosis for 527 MGB patients. All EHR data from the relevant period were extracted; diagnosis codes, medications, and laboratory test values were processed and summarized; clinical notes were processed through an NLP pipeline; and a web tool was developed to present an integrated view of all data. Cognitive status was rated as cognitively normal, cognitively impaired, or undetermined. Assessment time and interrater agreement of NAT compared to manual chart reviews for cognitive status phenotyping was evaluated. RESULTS: NAT adjudication provided higher interrater agreement (Cohen κ=0.89 vs κ=0.80) and significant speed up (time difference mean 1.4, SD 1.3 minutes; P<.001; ratio median 2.2, min-max 0.4-20) over manual chart reviews. There was moderate agreement with manual chart reviews (Cohen κ=0.67). In the cases that exhibited disagreement with manual chart reviews, NAT adjudication was able to produce assessments that had broader clinical consensus due to its integrated view of highlighted relevant information and semiautomated NLP features. CONCLUSIONS: NAT adjudication improves the speed and interrater reliability for phenotyping cognitive status compared to manual chart reviews. This study underscores the potential of an NLP-based clinically adjudicated method to build large-scale dementia research cohorts from EHRs.

Development and Prospective Validation of a Deep Learning Algorithm for Predicting Need for Mechanical Ventilation.

BACKGROUND: Objective and early identification of hospitalized patients, and particularly those with novel coronavirus disease 2019 (COVID-19), who may require mechanical ventilation (MV) may aid in delivering timely treatment. RESEARCH QUESTION: Can a transparent deep learning (DL) model predict the need for MV in hospitalized patients and those with COVID-19 up to 24 h in advance? STUDY DESIGN AND METHODS: We trained and externally validated a transparent DL algorithm to predict the future need for MV in hospitalized patients, including those with COVID-19, using commonly available data in electronic health records. Additionally, commonly used clinical criteria (heart rate, oxygen saturation, respiratory rate, Fio2, and pH) were used to assess future need for MV. Performance of the algorithm was evaluated using the area under receiver operating characteristic curve (AUC), sensitivity, specificity, and positive predictive value. RESULTS: We obtained data from more than 30,000 ICU patients (including more than 700 patients with COVID-19) from two academic medical centers. The performance of the model with a 24-h prediction horizon at the development and validation sites was comparable (AUC, 0.895 vs 0.882, respectively), providing significant improvement over traditional clinical criteria (P < .001). Prospective validation of the algorithm among patients with COVID-19 yielded AUCs in the range of 0.918 to 0.943. INTERPRETATION: A transparent deep learning algorithm improves on traditional clinical criteria to predict the need for MV in hospitalized patients, including in those with COVID-19. Such an algorithm may help clinicians to optimize timing of tracheal intubation, to allocate resources and staff better, and to improve patient care.

Cognitive behavioural therapy for adherence and depression in patients with HIV: a three-arm randomised controlled trial.

BACKGROUND: Depression is highly prevalent in people with HIV and has consistently been associated with poor antiretroviral therapy (ART) adherence. Integrating cognitive behavioural therapy (CBT) for depression with adherence counselling using the Life-Steps approach (CBT-AD) has an emerging evidence base. The aim of this study was to test the efficacy of CBT-AD. METHODS: In this three-arm randomised controlled trial in HIV-positive adults with depression, we compared CBT-AD with information and supportive psychotherapy plus adherence counselling using the Life-Steps approach (ISP-AD), and with enhanced treatment as usual (ETAU) including Life-Steps adherence counselling only. Participants were recruited from three sites in New England, USA (two hospital settings and one community health centre). Patients were randomly assigned (2:2:1) to receive CBT-AD (one Life-Steps session plus 11 weekly integrated sessions lasting up to 1 h each), ISP-AD (one Life-Steps session plus 11 weekly integrated sessions lasting up to 1 h each), or ETAU (one Life-Steps session and five assessment visits roughly every 2 weeks), randomisation was done with allocation software, in pairs, and stratified by three variables: study site, whether or not participants had been prescribed antidepressant medication, and whether or not participants had a history of injection drug use. The primary outcome was ART adherence at the end of treatment (4 month assessment) assessed via electronic pill caps (Medication Event Monitoring System [MEMS]) with correction for pocketed doses, analysed by intention to treat. FINDINGS: Patients were recruited from Feb 26, 2009, to June 21, 2012. Patients who were assigned to CBT-AD (94 randomly assigned, 83 completed assessment) had greater improvements in adherence (estimated difference 1·00 percentage point per visit, 95% CI 0·34 to 1·66, p=0·003) and depression (Center for Epidemiological Studies depression [CESD] score estimated difference -0·41, -0·66 to -0·16, p=0·001; Montgomery-Asberg depression rating scale [MADRS] score -4·69, -8·09 to -1·28, p=0·007; clinical global impression [CGI] score -0·66, -1·11 to -0·21, p=0·005) than did patients who had ETAU (49 assigned, 46 completed assessment) after treatment (4 months). No significant differences in adherence were noted between CBT-AD and ISP-AD (97 assigned, 87 completed assessment). No study-related adverse events were reported. INTERPRETATION: Integrating evidenced-based treatment for depression with evidenced-based adherence counselling is helpful for individuals living with HIV/AIDS and depression. Future efforts should examine how to best disseminate effective psychosocial depression treatments such as CBT-AD to people living with HIV/AIDS and examine the cost-effectiveness of such approaches. FUNDING: National Institute of Mental Health, National Institute of Allergy and Infectious Diseases.