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Older women with HIV (WWH) confront significant biopsychosocial challenges that may be exacerbated by the COVID-19 pandemic. Between May 2020 and April 2021, following a resiliency intervention conducted as part of a randomized parent trial, 24 cisgender WWH (M = 58 years old) completed quantitative assessments and qualitative interviews exploring the impact of COVID-19 on mental health. Qualitative data were analyzed via rapid analysis. Most participants were Black (62.5%) and non-Hispanic or Latina (87.5%). Emergent themes included (1) increased anxiety and depression; (2) a loss of social connectedness; (3) fear of unknown interactions among COVID-19, HIV, and other comorbidities; and (4) the use of largely adaptive strategies to cope with these issues. Findings suggest that older WWH face significant COVID-19-related mental health challenges, compounding existing stressors. As the pandemic persists, it will be important to assess the impact of these stressors on wellbeing, identify effective coping strategies, and provide increased support to mitigate COVID-19-related mental health issues over time. Trial Registration: ClinicalTrials.gov identifier: NCT03071887.

Half of persons with HIV in the United States (US), many of whom are women, are over age 50. Aging women with HIV (WWH) face unique biopsychosocial challenges, including stigma, the physiological effects of aging, and illness-associated stressors. Resilience interventions can build awareness of such stressors and aid in facilitating the relaxation response; however, no existing interventions specifically cater to the needs of older WWH. The content of the Relaxation Response Resiliency Program, which teaches positive psychology strategies, relaxation techniques, and cognitive behavioral skills, was adapted for older WWH. Thirteen WWH over 50 participated in an open pilot of the adapted intervention to iteratively refine the program and its procedures. Participants attended either 8 or 10 weekly group sessions; three groups were conducted in total. Pre- and post-intervention assessments and qualitative exit interviews were conducted. Among completers, an increase in resilience was observed. Though significance testing was not conducted, social support also increased, and depression, anxiety, and HIV stigma decreased from pre- to post-intervention. Over half of eligible women enrolled; completers reported high satisfaction with the program. However, retention was difficult; six participants withdrew or were lost to follow-up. Mean number of sessions attended was 3.5 in the 8-session group and 5 in the 10-session groups. In this small sample, the adapted intervention led to a clinically meaningful increase in resilience, though recruitment and retention were challenging. Further refinements to the intervention are needed to minimize attrition and increase acceptability before additional testing is initiated.

Background Increased renin angiotensin aldosterone system(RAAS) activity may contribute to excess cardiovascular disease in people with HIV(PWH). We investigated how RAAS blockade may improve myocardial perfusion, injury and function among well-treated PWH. Methods 40 PWH, on stable ART, without known heart disease were randomized to eplerenone 50 mg PO BID(n=20) or identical placebo(n=20) for 12 months. The primary endpoints were: 1) myocardial perfusion assessed by coronary flow reserve(CFR) on cardiac PET or stress myocardial blood flow(sMBF) on cardiac MRI or 2) myocardial inflammation by extracellular mass index(ECMi) on cardiac MRI. Results Beneficial effects on myocardial perfusion were seen for sMBF by cardiac MRI[mean(SD) 0.09(0.56) vs. -0.53(0.68)mL/min/g, P=.03], but not CFR by cardiac PET[0.01(0.64) vs. -0.07(0.48), P=.72, eplerenone vs. placebo]. Eplerenone improved parameters of myocardial function on cardiac MRI including left ventricular end diastolic volume[-13(28) vs. 10(26)mL, P=.03] and global circumferential strain[median (GCS, median(interquartile range 25th-75th), -1.3(-2.9-1.0) vs. 2.3(-0.4-4.1)%, P=.03], eplerenone vs. placebo respectively. On cardiac MRI, improvement in sMBF related to improvement in GCS(ρ=-0.65, P=.057) among those treated with eplerenone. Selecting for those with impaired myocardial perfusion(CFR Conclusions RAAS blockade with eplerenone benefitted key indices and prevented worsening of myocardial perfusion, injury and function among PWH with subclinical cardiac disease when compared to placebo.

Background Human T-lymphotropic virus type 1 (HTLV-1)-associated myelopathy/tropical spastic paraparesis (HAM/TSP) is a chronic neurological condition characterized by progressive myelopathic symptoms including spasticity, pain, weakness, and urinary symptoms, without proven treatments. Mogamulizumab (MOG) is a monoclonal antibody that binds CCR4 and leads to the clearance of HTLV-1-infected CCR4+ cells. A phase 1-2a study in Japan evaluated MOG for the treatment of HAM/TSP and reported decreases in HTLV-1 proviral load and neuroinflammatory markers, with clinical improvement in some participants. Methods We administered MOG 0.1 mg/kg every 8 weeks to individuals with HAM/TSP as a compassionate and palliative treatment. Patients who received MOG had: 1) a positive peripheral HTLV-1 antibody, 2) progressive myelopathic symptoms, and 3) a diagnosis of HAM/TSP. Results Four female patients, ages 45-68, received MOG (range, 2-6 infusions) between November 1, 2019 and November 30, 2022. Two patients with 7 years of symptoms, had Osame scores >5. One patient, with 6 total treatments, received dose-reduced MOG after she developed a rash at the initial dose. The 2 patients with milder baseline disease reported symptomatic improvement and saw reductions in Osame and/or modified Ashworth scale scores during follow-up. The other 2 patients showed no improvement. All 4 developed rashes after receiving MOG – a treatment-limiting event in some cases. Conclusions Clinical trials are needed including diverse patient populations to assess the potential role of MOG for HAM/TSP. Our findings may help inform the development of these trials.

Midlife women with HIV (WWH) are disproportionately impacted by cardiovascular disease (CVD), yet little is known about perceptions of CVD risk and the factors that influence engagement in heart health behaviors in this population. Few (if any) studies have used a qualitative approach to examine these perceptions, which has important implications for minimizing the negative impact of HIV-related noncommunicable diseases, the risk for which increases after midlife. Eighteen midlife WWH (aged 40-59) in Boston, MA, completed semistructured interviews to explore perceptions of CVD, HIV, and barriers and facilitators to healthy lifestyle behaviors. Interviews were analyzed via thematic analysis. Participants viewed heart health as important but were unaware of HIV-associated CVD risk. Facilitators included family and generational influences, social support, and access to resources. Physical symptoms, menopause, mental health challenges, and limited financial resources were barriers. Midlife WWH may benefit from tailored CVD prevention interventions that target their unique motivations and barriers to healthy behaviors.

BackgroundWith the advent of efficacious oral direct-acting antivirals (DAAs) for hepatitis C virus (HCV), identification of characteristics associated with adherence is critical to treatment success. We examined correlates of sub-optimal adherence to HCV therapy in a single-arm, multinational, clinical trial.MethodsACTG A5360 enrolled HCV treatment-naive persons without decompensated cirrhosis from 5 countries. All participants received a 12-weeks course of sofosbuvir/velpatasvir at entry. In-person visits occurred at initiation and week 24, sustained virologic response (SVR) assessment. Adherence at week 4 was collected remotely and was dichotomized optimal (100%, no missed doses) versus sub-optimal (ResultsIn total, 400 participants enrolled; 399 initiated treatment; 395/397 (99%) reported completing at week 24. Median age was 47 years with 35% female. Among the 368 reporting optimal adherence at week 4 SVR was 96.5% (95% confidence interval [CI] [94.1%, 97.9%]) vs 77.8% (95% CI [59.2%, 89.4%]) P value < .001. In the multivariate model age ConclusionsSelf-reported optimal adherence at week 4 was associated with SVR. Early self-reported adherence could be used to identify those at higher risk of treatment failure and may benefit from additional support. Younger individuals Clinical Trials Registration. NCT03512210.

A Woman with Fever, Sore Throat, and Confusion A 63-year-old woman with hypertension was admitted during the Covid-19 pandemic because of fever, headache, sore throat, and confusion. She also repor...

BACKGROUND Electronic Health Records (EHR) with large sample sizes and rich information offer great potential for dementia research but current methods of phenotyping cognitive status are not scalable. OBJECTIVE To evaluate whether Natural Language Processing (NLP)-powered semi-automated 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 semi-automated 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) Healthcare using NAT or traditional chart reviews. Patient charts contained EHR data from two datasets: (1) Records from January 1, 2017 to December 31, 2018 for 100 Medicare beneficiaries from the MGB Accountable Care Organization (ACO), and (2) Records from 2-years pre-COVID diagnosis to the date of COVID 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 k=0.89 vs. k=0.80) and significant speed up (time difference mean [SD]: 1.4 [1.3] minutes, P < 0.001; ratio median [min, max]: 2.2 [0.4, 20]) over manual chart reviews. There was moderate agreement with manual chart reviews (Cohen k=0.67). In the cases that exhibited disagreement with manual chart review, NAT adjudication was able to produce assessments that had broader clinical consensus due to its integrated view of highlighted relevant information and semi-automated 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 EHR.

Background We sought to develop an automatable score to predict hospitalization, critical illness, or death for patients at risk for coronavirus disease 2019 (COVID-19) presenting for urgent care. Methods We developed the COVID-19 Acuity Score (CoVA) based on a single-center study of adult outpatients seen in respiratory illness clinics or the emergency department. Data were extracted from the Partners Enterprise Data Warehouse, and split into development (n = 9381, 7 March–2 May) and prospective (n = 2205, 3–14 May) cohorts. Outcomes were hospitalization, critical illness (intensive care unit or ventilation), or death within 7 days. Calibration was assessed using the expected-to-observed event ratio (E/O). Discrimination was assessed by area under the receiver operating curve (AUC). Results In the prospective cohort, 26.1%, 6.3%, and 0.5% of patients experienced hospitalization, critical illness, or death, respectively. CoVA showed excellent performance in prospective validation for hospitalization (expected-to-observed ratio [E/O]: 1.01; AUC: 0.76), for critical illness (E/O: 1.03; AUC: 0.79), and for death (E/O: 1.63; AUC: 0.93). Among 30 predictors, the top 5 were age, diastolic blood pressure, blood oxygen saturation, COVID-19 testing status, and respiratory rate. Conclusions CoVA is a prospectively validated automatable score for the outpatient setting to predict adverse events related to COVID-19 infection., A coronavirus disease 2019 (COVID-19) outpatient screening score is developed and validated prospectively in a single-center setting. It predicts 7-day prognosis: hospitalization, intensive care/ventilation, or death, based on 30 predictors including demographics, COVID-19 status, vital signs, radiology report, and medical history.

Background People with human immunodeficiency virus (PWH) face increased risks for heart failure and adverse heart failure outcomes. Myocardial steatosis predisposes to diastolic dysfunction, a heart failure precursor. We aimed to characterize myocardial steatosis and associated potential risk factors among a subset of the Randomized Trial to Prevent Vascular Events in HIV (REPRIEVE) participants. Methods Eighty-two PWH without known heart failure successfully underwent cardiovascular magnetic resonance spectroscopy, yielding data on intramyocardial triglyceride (IMTG) content (a continuous marker for myocardial steatosis extent). Logistic regression models were applied to investigate associations between select clinical characteristics and odds of increased or markedly increased IMTG content. Results Median (Q1, Q3) IMTG content was 0.59% (0.28%, 1.15%). IMTG content was increased (> 0.5%) among 52% and markedly increased (> 1.5%) among 22% of participants. Parameters associated with increased IMTG content included age (P = .013), body mass index (BMI) ≥ 25 kg/m2 (P = .055), history of intravenous drug use (IVDU) (P = .033), and nadir CD4 count Conclusions A substantial proportion of antiretroviral therapy–treated PWH exhibited myocardial steatosis. Age, BMI ≥ 25 kg/m2, low nadir CD4 count, and history of IVDU emerged as possible risk factors for myocardial steatosis in this group. Clinical Trials Registration NCT02344290; NCT03238755.

Non-alcoholic fatty liver disease (NAFLD) is the most common liver disease in persons with HIV (PWH) (HIV-NAFLD). It is unknown if HIV-NAFLD is associated with impairment in health-related quality of life (HRQOL). We examined HRQOL in PWH with and without NAFLD, compared HRQOL in HIV- versus primary NAFLD, and determined factors associated with HRQOL in these groups. Prospectively enrolled 200 PWH and 474 participants with primary NAFLD completed the Rand SF-36 assessment which measures 8 domains of HRQOL. Individual domain scores were used to create composite physical and mental component summary scores. Univariate and multivariate analyses determined variables associated with HRQOL in PWH and in HIV- and primary NAFLD. In PWH, 48% had HIV-NAFLD, 10.2% had clinically significant fibrosis, 99.5% were on antiretroviral therapy, and 96.5% had HIV RNA

BACKGROUND Delirium in hospitalized patients is a syndrome of acute brain dysfunction. Diagnostic (International Classification of Diseases [ICD]) codes are often used in studies using electronic health records (EHRs), but they are inaccurate. OBJECTIVE We sought to develop a more accurate method using natural language processing (NLP) to detect delirium episodes on the basis of unstructured clinical notes. METHODS We collected 1.5 million notes from >10,000 patients from among 9 hospitals. Seven experts iteratively labeled 200,471 sentences. Using these, we trained three NLP classifiers: Support Vector Machine, Recurrent Neural Networks, and Transformer. Testing was performed using an external data set. We also evaluated associations with delirium billing (ICD) codes, medications, orders for restraints and sitters, direct assessments (Confusion Assessment Method [CAM] scores), and in-hospital mortality. F1 scores, confusion matrices, and areas under the receiver operating characteristic curve (AUCs) were used to compare NLP models. We used the φ coefficient to measure associations with other delirium indicators. RESULTS The transformer NLP performed best on the following parameters: micro F1=0.978, macro F1=0.918, positive AUC=0.984, and negative AUC=0.992. NLP detections exhibited higher correlations (φ) than ICD codes with deliriogenic medications (0.194 vs 0.073 for ICD codes), restraints and sitter orders (0.358 vs 0.177), mortality (0.216 vs 0.000), and CAM scores (0.256 vs –0.028). CONCLUSIONS Clinical notes are an attractive alternative to ICD codes for EHR delirium studies but require automated methods. Our NLP model detects delirium with high accuracy, similar to manual chart review. Our NLP approach can provide more accurate determination of delirium for large-scale EHR-based studies regarding delirium, quality improvement, and clinical trails.

A Woman with Abdominal Pain and Fever A 38-year-old woman with a history of Crohn’s disease was admitted to the hospital because of abdominal pain and fever. She reported 7 months of progressive sy...

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 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.

BackgroundObjective 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 questionCan a transparent deep learning (DL) model predict the need for MV in hospitalized patients and those with COVID-19 up to 24h in advance?Study design and methodsWe 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.ResultsWe 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 vs0.882, 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.InterpretationA 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.

As a type of relatively new methodology, the transcriptome-wide association study (TWAS) has gained interest due to capacity for gene-level association testing. However, the development of TWAS has outpaced statistical evaluation of TWAS gene prioritization performance. Current TWAS methods vary in underlying biological assumptions about tissue specificity of transcriptional regulatory mechanisms. In a previous study from our group, this may have affected whether TWAS methods better identified associations in single tissues versus multiple tissues. We therefore designed simulation analyses to examine how the interplay between particular TWAS methods and tissue specificity of gene expression affects power and type I error rates for gene prioritization. We found that cross-tissue identification of expression quantitative trait loci (eQTLs) improved TWAS power. Single-tissue TWAS (i.e., PrediXcan) had robust power to identify genes expressed in single tissues, but, often found significant associations in the wrong tissues as well (therefore had high false positive rates). Cross-tissue TWAS (i.e., UTMOST) had overall equal or greater power and controlled type I error rates for genes expressed in multiple tissues. Based on these simulation results, we applied a tissue specificity-aware TWAS (TSA-TWAS) analytic framework to look for gene-based associations with pre-treatment laboratory values from AIDS Clinical Trial Group (ACTG) studies. We replicated several proof-of-concept transcriptionally regulated gene-trait associations, including UGT1A1 (encoding bilirubin uridine diphosphate glucuronosyltransferase enzyme) and total bilirubin levels (p = 3.59×10−12), and CETP (cholesteryl ester transfer protein) with high-density lipoprotein cholesterol (p = 4.49×10−12). We also identified several novel genes associated with metabolic and virologic traits, as well as pleiotropic genes that linked plasma viral load, absolute basophil count, and/or triglyceride levels. By highlighting the advantages of different TWAS methods, our simulation study promotes a tissue specificity-aware TWAS analytic framework that revealed novel aspects of HIV-related traits., Author summary Transcriptome-wide association studies (TWAS) are a type of bioinformatics methodology for identifying complex trait-associated genes. There have been various TWAS methods, each developed under distinct biological assumptions of how genes contribute to complex traits. It is unclear, however, how powerful different TWAS methods are under a variety of biological scenarios. Here, we design an unbiased simulation strategy to evaluate the performance of multiple representative TWAS methods. We find that no one method fits all. Different TWAS methods are advantageous at dealing with different biological scenarios and answering different research questions. Thus, we propose a novel TWAS analytic framework that integrates and maximizes the performance of multiple TWAS methods, and validate its capability using a well-studied real-world dataset. In summary, our study provides quantitative evaluation of method performance to aid future TWAS experimental design and understanding of genes underlying complex human traits. The TWAS evaluation tool is made publicly available.

Study Objectives Age-related comorbidities and immune activation raise concern for advanced brain aging in people living with HIV (PLWH). The brain age index (BAI) is a machine learning model that quantifies deviations in brain activity during sleep relative to healthy individuals of the same age. High BAI was previously found to be associated with neurological, psychiatric, cardiometabolic diseases, and reduced life expectancy among people without HIV. Here, we estimated the effect of HIV infection on BAI by comparing PLWH and HIV− controls. Methods Clinical data and sleep EEGs from 43 PLWH on antiretroviral therapy (HIV+) and 3,155 controls (HIV−) were collected from Massachusetts General Hospital. The effect of HIV infection on BAI, and on individual EEG features, was estimated using causal inference. Results The average effect of HIV on BAI was estimated to be +3.35 years (p < 0.01, 95% CI = [0.67, 5.92]) using doubly robust estimation. Compared to HIV− controls, HIV+ participants exhibited a reduction in delta band power during deep sleep and rapid eye movement sleep. Conclusion We provide causal evidence that HIV contributes to advanced brain aging reflected in sleep EEG. A better understanding is greatly needed of potential therapeutic targets to mitigate the effect of HIV on brain health, potentially including sleep disorders and cardiovascular disease

BACKGROUND: Women with HIV (WWH) are at elevated risk for cardiovascular disease (CVD) compared to men with HIV. Lifestyle interventions, like the Diabetes Prevention Program (DPP), may reduce CVD risk, but most fail to address barriers to healthy behaviors facing WWH. OBJECTIVE: To inform the adaptation of the DPP for midlife WWH, pilot the modified intervention, and assess feasibility, acceptability, and implementation barriers. METHODS: Interviews were conducted with cisgender, English-speaking WWH ages 40–59 to assess intervention preferences. The adapted DPP was piloted and evaluated. CVD knowledge, CVD risk perception, quality of life, and physical activity were assessed pre- and post-intervention. RESULTS: Eighteen WWH completed interviews. Adaptations included reducing the number of sessions and adding HIV, CVD, stress, aging, menopause, and smoking content. Of 14 women contacted for the pilot, seven completed a baseline, five attended group sessions, and five completed a post-treatment assessment. Attendance barriers included transportation access and costs. Satisfaction was moderate; informal exit interviews indicated that women would recommend the program. CVD knowledge, perceived risk, and physical activity increased, and fatigue and mental health improved. Content on nutrition, aging, HIV, and stress was seen as most useful; suggested changes included group exercises and additional content on recipes, HIV management, and aging. CONCLUSIONS: Midlife WWH reported benefits from our adapted intervention. Increases in CVD knowledge and perceived CVD risk suggest improved awareness of the impact of lifestyle behaviors. Retention was adequate; socioeconomic barriers were common. Intervention feasibility and acceptability may be improved via remote access and further content customization.

Background Delirium in hospitalized patients is a syndrome of acute brain dysfunction. Diagnostic (International Classification of Diseases [ICD]) codes are often used in studies using electronic health records (EHRs), but they are inaccurate. Objective We sought to develop a more accurate method using natural language processing (NLP) to detect delirium episodes on the basis of unstructured clinical notes. Methods We collected 1.5 million notes from >10,000 patients from among 9 hospitals. Seven experts iteratively labeled 200,471 sentences. Using these, we trained three NLP classifiers: Support Vector Machine, Recurrent Neural Networks, and Transformer. Testing was performed using an external data set. We also evaluated associations with delirium billing (ICD) codes, medications, orders for restraints and sitters, direct assessments (Confusion Assessment Method [CAM] scores), and in-hospital mortality. F1 scores, confusion matrices, and areas under the receiver operating characteristic curve (AUCs) were used to compare NLP models. We used the φ coefficient to measure associations with other delirium indicators. Results The transformer NLP performed best on the following parameters: micro F1=0.978, macro F1=0.918, positive AUC=0.984, and negative AUC=0.992. NLP detections exhibited higher correlations (φ) than ICD codes with deliriogenic medications (0.194 vs 0.073 for ICD codes), restraints and sitter orders (0.358 vs 0.177), mortality (0.216 vs 0.000), and CAM scores (0.256 vs –0.028). Conclusions Clinical notes are an attractive alternative to ICD codes for EHR delirium studies but require automated methods. Our NLP model detects delirium with high accuracy, similar to manual chart review. Our NLP approach can provide more accurate determination of delirium for large-scale EHR-based studies regarding delirium, quality improvement, and clinical trails.

BACKGROUND Medical notes are a rich source of patient data; however, the nature of unstructured text has largely precluded the use of these data for large retrospective analyses. Transforming clinical text into structured data can enable large-scale research studies with electronic health records (EHR) data. Natural language processing (NLP) can be used for text information retrieval, reducing the need for labor-intensive chart review. Here we present an application of NLP to large-scale analysis of medical records at 2 large hospitals for patients hospitalized with COVID-19. OBJECTIVE Our study goal was to develop an NLP pipeline to classify the discharge disposition (home, inpatient rehabilitation, skilled nursing inpatient facility [SNIF], and death) of patients hospitalized with COVID-19 based on hospital discharge summary notes. METHODS Text mining and feature engineering were applied to unstructured text from hospital discharge summaries. The study included patients with COVID-19 discharged from 2 hospitals in the Boston, Massachusetts area (Massachusetts General Hospital and Brigham and Women’s Hospital) between March 10, 2020, and June 30, 2020. The data were divided into a training set (70%) and hold-out test set (30%). Discharge summaries were represented as bags-of-words consisting of single words (unigrams), bigrams, and trigrams. The number of features was reduced during training by excluding n-grams that occurred in fewer than 10% of discharge summaries, and further reduced using least absolute shrinkage and selection operator (LASSO) regularization while training a multiclass logistic regression model. Model performance was evaluated using the hold-out test set. RESULTS The study cohort included 1737 adult patients (median age 61 [SD 18] years; 55% men; 45% White and 16% Black; 14% nonsurvivors and 61% discharged home). The model selected 179 from a vocabulary of 1056 engineered features, consisting of combinations of unigrams, bigrams, and trigrams. The top features contributing most to the classification by the model (for each outcome) were the following: “appointments specialty,” “home health,” and “home care” (home); “intubate” and “ARDS” (inpatient rehabilitation); “service” (SNIF); “brief assessment” and “covid” (death). The model achieved a micro-average area under the receiver operating characteristic curve value of 0.98 (95% CI 0.97-0.98) and average precision of 0.81 (95% CI 0.75-0.84) in the testing set for prediction of discharge disposition. CONCLUSIONS A supervised learning–based NLP approach is able to classify the discharge disposition of patients hospitalized with COVID-19. This approach has the potential to accelerate and increase the scale of research on patients’ discharge disposition that is possible with EHR data.

Background Medical notes are a rich source of patient data; however, the nature of unstructured text has largely precluded the use of these data for large retrospective analyses. Transforming clinical text into structured data can enable large-scale research studies with electronic health records (EHR) data. Natural language processing (NLP) can be used for text information retrieval, reducing the need for labor-intensive chart review. Here we present an application of NLP to large-scale analysis of medical records at 2 large hospitals for patients hospitalized with COVID-19. Objective Our study goal was to develop an NLP pipeline to classify the discharge disposition (home, inpatient rehabilitation, skilled nursing inpatient facility [SNIF], and death) of patients hospitalized with COVID-19 based on hospital discharge summary notes. Methods Text mining and feature engineering were applied to unstructured text from hospital discharge summaries. The study included patients with COVID-19 discharged from 2 hospitals in the Boston, Massachusetts area (Massachusetts General Hospital and Brigham and Women’s Hospital) between March 10, 2020, and June 30, 2020. The data were divided into a training set (70%) and hold-out test set (30%). Discharge summaries were represented as bags-of-words consisting of single words (unigrams), bigrams, and trigrams. The number of features was reduced during training by excluding n-grams that occurred in fewer than 10% of discharge summaries, and further reduced using least absolute shrinkage and selection operator (LASSO) regularization while training a multiclass logistic regression model. Model performance was evaluated using the hold-out test set. Results The study cohort included 1737 adult patients (median age 61 [SD 18] years; 55% men; 45% White and 16% Black; 14% nonsurvivors and 61% discharged home). The model selected 179 from a vocabulary of 1056 engineered features, consisting of combinations of unigrams, bigrams, and trigrams. The top features contributing most to the classification by the model (for each outcome) were the following: “appointments specialty,” “home health,” and “home care” (home); “intubate” and “ARDS” (inpatient rehabilitation); “service” (SNIF); “brief assessment” and “covid” (death). The model achieved a micro-average area under the receiver operating characteristic curve value of 0.98 (95% CI 0.97-0.98) and average precision of 0.81 (95% CI 0.75-0.84) in the testing set for prediction of discharge disposition. Conclusions A supervised learning–based NLP approach is able to classify the discharge disposition of patients hospitalized with COVID-19. This approach has the potential to accelerate and increase the scale of research on patients’ discharge disposition that is possible with EHR data.

The NOD-like receptor protein family pyrin domain containing 3 (NLRP3) inflammasome, activated in the setting of HIV, contributes to pro-atherogenic inflammation. Among antriretroviral therapy–naïve people with HIV (vs controls), levels of caspase-1—a key component of the NLRP3 inflammasome—were significantly increased. Six months of elvitegravir/cobicistat/emtricitabine/tenofovir disoproxil fumarate significantly decreased caspase-1 levels in association with CD4+/CD8+ ratio recovery. Trial registration. ClinicalTrials.gov NCT 01766726.

BackgroundWe sought to develop an automatable score to predict hospitalization, critical illness, or death in patients at risk for COVID-19 presenting for urgent care during the Massachusetts outbreak.MethodsSingle-center study of adult outpatients seen in respiratory illness clinics (RICs) or the emergency department (ED), including development (n = 9381, March 7-May 2) and prospective (n = 2205, May 3-14) cohorts. Data was queried from Partners Enterprise Data Warehouse. Outcomes were hospitalization, critical illness or death within 7 days. We developed the COVID-19 Acuity Score (CoVA) using automatically extracted data from the electronic medical record and learning-to-rank ordinal logistic regression modeling. Calibration was assessed using predicted-to-observed ratio (E/O). Discrimination was assessed by C-statistics (AUC).ResultsIn the development cohort, 27.3%, 7.2%, and 1.1% of patients experienced hospitalization, critical illness, or death, respectively; and in the prospective cohort, 26.1%, 6.3%, and 0.5%. CoVA showed excellent performance in the development cohort (concurrent validation) for hospitalization (E/O: 1.00, AUC: 0.80); for critical illness (E/O: 1.00, AUC: 0.82); and for death (E/O: 1.00, AUC: 0.87). Performance in the prospective cohort (prospective validation) was similar for hospitalization (E/O: 1.01, AUC: 0.76); for critical illness (E/O 1.03, AUC: 0.79); and for death (E/O: 1.63, AUC=0.93). Among 30 predictors, the top five were age, diastolic blood pressure, blood oxygen saturation, COVID-19 testing status, and respiratory rate.ConclusionsCoVA is a prospectively validated automatable score to assessing risk for adverse outcomes related to COVID-19 infection in the outpatient setting.

IMPORTANCEObjective and early identification of hospitalized patients, and particularly those with novel coronavirus disease 2019 (COVID-19), who may require mechanical ventilation is of great importance and may aid in delivering timely treatment.OBJECTIVETo develop, externally validate and prospectively test a transparent deep learning algorithm for predicting 24 hours in advance the need for mechanical ventilation in hospitalized patients and those with COVID-19.DESIGNObservational cohort studySETTINGTwo academic medical centers from January 01, 2016 to December 31, 2019 (Retrospective cohorts) and February 10, 2020 to May 4, 2020 (Prospective cohorts).PARTICIPANTSOver 31,000 admissions to the intensive care units (ICUs) at two hospitals. Additionally, 777 patients with COVID-19 patients were used for prospective validation. Patients who were placed on mechanical ventilation within four hours of their admission were excluded.MAIN OUTCOME(S) and MEASURE(S)Electronic health record (EHR) data were extracted on an hourly basis, and a set of 40 features were calculated and passed to an interpretable deep-learning algorithm to predict the future need for mechanical ventilation 24 hours in advance. Additionally, commonly used clinical criteria (based on heart rate, oxygen saturation, respiratory rate, FiO2and pH) was used to assess future need for mechanical ventilation. Performance of the algorithms were evaluated using the area under receiver-operating characteristic curve (AUC), sensitivity, specificity and positive predictive value.RESULTSAfter applying exclusion criteria, the external validation cohort included 3,888 general ICU and 402 COVID-19 patients. The performance of the model (AUC) with a 24-hour prediction horizon at the validation site was 0.882 for the general ICU population and 0.918 for patients with COVID-19. In comparison, commonly used clinical criteria and the ROX score achieved AUCs in the range of 0.773 – 0.782 and 0.768 – 0.810 for the general ICU population and patients with COVID-19, respectively.CONCLUSIONS and RELEVANCEA generalizable and transparent deep-learning algorithm improves on traditional clinical criteria to predict the need for mechanical ventilation in hospitalized patients, including those with COVID-19. Such an algorithm may help clinicians with optimizing timing of tracheal intubation, better allocation of mechanical ventilation resources and staff, and improve patient care.

ObjectivePeople with HIV (PWH) continue to experience sensory neuropathy and neuropathic pain in the combination antiretroviral therapy (cART) era for unclear reasons. This study evaluated the role of iron in a previously reported association of iron-loading hemochromatosis (HFE) gene variants with reduced risk of neuropathy in PWH who received more neurotoxic cART, since an iron-related mechanism also might be relevant to neuropathic symptoms in PWH living in low-resource settings today.DesignThis time-to-event analysis addressed the impact of systemic iron levels on the rapidity of neuropathy onset in PWH who initiated cART.MethodsSoluble transferrin receptor (sTFR), the sTFR-ferritin index of iron stores, and high-sensitivity C-reactive protein (hsCRP) levels were determined in stored baseline sera from participants of known HFE genotype from AIDS Clinical Trials Group (ACTG) Study 384, a multicenter randomized clinical trial that evaluated cART strategies. Associations with incident neuropathy were evaluated in proportional-hazards, time-to-event regression models, adjusting for potential confounders.ResultsOf 151 eligible participants with stored serum who were included in the original genetic study, 43 had cART-associated neuropathy; 108 had sufficient serum for analysis, including 30 neuropathy cases. Carriers of HFE variants had higher systemic iron (lower sTFR and sTFR-ferritin index) and lower hsCRP levels than non-carriers (all pG variant, and lower baseline hsCRP were associated with significantly delayed neuropathy in self-reported whites (n = 28; all p-valuesConclusionsHigher iron stores, the HFE 187C>G variant, and lower hsCRP predicted delayed onset of neuropathy among self-reported white individuals initating cART. These findings require confirmation but may have implications for cART in HIV+ populations in areas with high endemic iron deficiency, especially those PWH in whom older, more neurotoxic antiretroviral drugs are occasionally still used.

Background Interventions that target anxiety/depressive symptoms in the context of smoking treatment have shown promise irrespective of psychiatric diagnosis. Yet, these tailored treatments are largely absent for persons who smoke and are living with HIV (SLWH). Objective To evaluate a novel, smoking cessation intervention that addresses anxiety/depression and HIV-related health (QUIT) against a time-matched control (TMC) and a standard of care (SOC) condition. Methods SLWH (N = 180) will be recruited and enrolled from 3 medical clinics in Boston, MA, and Houston, TX. The trial will consist of a baseline assessment, a 10-week intervention/assessment period, and follow-up assessments, accounting for a total study duration of approximately 8 months. All participants will complete a baseline visit and a pre-randomization standardized psychoeducation visit, and will then be randomized to one of three conditions: QUIT, TMC, or SOC. QUIT and TMC will consist of nine 60-min, cognitive behavioral therapy-based, individual weekly counseling sessions using standard smoking cessation counseling; additionally, QUIT will target anxiety and depressive symptoms by addressing underlying mechanisms related to mood and quit difficulty. SOC participants will complete weekly self-report surveys for nine weeks. All participants will be encouraged to quit at Session 7 and will be offered nicotine replacement therapy to help. Conclusions QUIT is designed to improve smoking cessation in SLWH by addressing anxiety and depression and HIV-related health issues. If successful, the QUIT intervention would be ready for implementation and dissemination into “real-world” behavioral health and social service settings consistent with the four objectives outlined in NIDA's Strategic Plan.

Zika virus has recently been introduced to the Americas and is spreading rapidly. We evaluated the characteristics of US travelers to Zika virus–affected countries who were seen at Global TravEpiNet sites during March 2015–October 2016. Nearly three quarters of travelers were men or women of reproductive age.

PURPOSE OF REVIEW: Sexual well-being and intimacy are critical to overall quality of life and retain a high degree of significance for aging individuals, even though these considerations are often overlooked in older populations. Sexual health may be particularly impacted in older individuals living with HIV, especially women, as a result of both physical and psychosocial disease-specific factors. Despite this, sexuality research related to HIV has traditionally focused on risk reduction, rather than on other elements of sexual wellness. In this review, we examine several aspects of sexual well-being that may be important to older women living with HIV (OWLH). RECENT FINDINGS: This review summarizes existing literature on sexuality in OWLH over the age of 50 and explores five themes related to sexual health: physical and emotional intimacy, desire/interest, satisfaction/pleasure, frequency of sexual activity, and abstinence. Reduced intimacy among OWLH was reported across most studies, due to stigma and disclosure concerns, lack of opportunity for relationships, and difficulty communicating sexual preferences. Data on sexual desire/interest and satisfaction/pleasure among OWLH were mixed. Frequency of sexual activity varied widely across studies, and abstinence emerged as both an intentional and inadvertent decision for OWLH. Factors related to menopause as it relates to sexuality and HIV are also discussed. SUMMARY: Sexual health and well-being are important to women living with HIV over 50, though key components such as intimacy, desire, and pleasure remain poorly understood. As this population continues to grow, comprehensive and age-specific interventions are needed to examine positive aspects of sexuality and promote sexual wellness among OWLH.

Introduction Sleep EEG is a promising tool to measure brain aging in vulnerable populations such as people with HIV, who are high risk of brain aging due to co-morbidities, increased inflammation, and antiretroviral neurotoxicity. Our lab previously developed a machine learning model that estimates age from sleep EEG (brain age, BA), which reliably predicts chronological age (CA) in healthy adults. The difference between BA and CA, the brain age index (BAI), independently predicts mortality, and is increased by cardiovascular co-morbidities. Here, we assessed BAI in HIV+ compared to matched HIV- adults. Methods Sleep EEGs from 43 treated HIV+ adults were gathered and matched to controls (HIV-, n=284) by age, gender, race, alcoholism, smoking and substance use history. We compared BAI between groups and used additional causal interference methods to ensure robustness. Individual EEG features that underlie BA prediction were also compared. We performed a sub-analysis of BAI between HIV+ with or without a history of AIDS. Results After matching, mean CA of HIV+ vs HIV- adults were 49 and 48 years, respectively (n.s.). The mean HIV+ BAI was 3.04 years higher than HIV- (4.4 vs 1.4 yr; p=0.048). We found consistent and significant results with alternative causal inference methods. Several EEG features predictive of BA were different in the HIV+ and HIV- cohorts. Most notably, non-REM stage 2 sleep (N2) delta power (1-4Hz) was decreased in HIV+ vs. HIV- adults, while theta (4-8Hz) and alpha (8-12Hz) power were increased. Those with AIDS (n=19, BAI=4.40) did not have significantly different BAI than HIV+ without AIDS (n=23, BAI=5.22). HIV+ subjects had higher rates of insomnia (56% vs 29%, p Conclusion HIV+ individuals on ART have excess sleep-EEG based brain age compared to matched controls. This excess brain age is partially due to reduction in delta power during N2, suggesting decreased sleep depth. These results suggest sleep EEG could be a valuable brain aging biomarker for the HIV population. Support This research is supported by the Harvard Center for AIDS Research HU CFAR NIH/NIAID 5P30AI060354-16.

Background Myriad infectious and noninfectious causes of encephalomyelitis (EM) have similar clinical manifestations, presenting serious challenges to diagnosis and treatment. Metabolomics of cerebrospinal fluid (CSF) was explored as a method of differentiating among neurological diseases causing EM using a single CSF sample. Methodology/Principal findings 1H NMR metabolomics was applied to CSF samples from 27 patients with a laboratory-confirmed disease, including Lyme disease or West Nile Virus meningoencephalitis, multiple sclerosis, rabies, or Histoplasma meningitis, and 25 controls. Cluster analyses distinguished samples by infection status and moderately by pathogen, with shared and differentiating metabolite patterns observed among diseases. CART analysis predicted infection status with 100% sensitivity and 93% specificity. Conclusions/Significance These preliminary results suggest the potential utility of CSF metabolomics as a rapid screening test to enhance diagnostic accuracies and improve patient outcomes., Author summary Inflammation of the brain and spinal cord, known as encephalomyelitis, is a dangerous condition that can be caused by a wide range of pathogens, such as viruses and bacteria, and other medical conditions including autoimmunity or drug intoxications. Given the many possible causes, it is often difficult for clinicians treating patients with encephalomyelitis to identify the underlying cause, which in turn determines the appropriate treatment. Infections and other diseases causing neurological inflammation work by distinct biological mechanisms and, consequently, can cause unique biochemical changes that can be observed in cerebrospinal fluid of affected individuals. The researchers used a metabolomics technique to measure a range of small molecules in cerebrospinal fluid and examine biochemical differences in patients with encephalomyelitis caused by Lyme disease, West Nile Virus, multiple sclerosis, rabies, or fungal infection. The researchers found distinct differences in the biochemical profiles of patients whose encephalomyelitis was caused by infections versus patients with no infection, and also identified different patterns among the individual diseases. This study showed that metabolomics may be useful in improving diagnosis and treatment of diseases affecting the central nervous system by enhancing understanding of their unique effects on metabolism.

A Woman with Chronic Abdominal Pain A 66-year-old woman from Guatemala presented to a community health center for treatment of latent tuberculosis infection. She reported a 2-month history of abdom...

Background Opioid use disorder poses a significant public health risk. Persons who inject drugs (PWID) suffer from high mortality and morbidity secondary to serious infectious diseases, often requiring prolonged courses of outpatient parenteral antibiotics. The goal of this study was to determine the outcomes of PWID discharged to home or to a skilled nursing or rehabilitation facility (SNF/rehab) with parenteral antibiotic treatment under an outpatient parenteral antimicrobial therapy (OPAT) program. Methods This is a retrospective observational study. The study population was identified via hospital and OPAT databases using substance use disorder diagnoses and confirmed through chart review. The study population included hospitalized PWID with injection drug use in the preceding 2 years who were discharged between 2010 and 2015 to complete at least 2 weeks of parenteral antibiotics and monitored by the OPAT program. Retrospective chart review was used to describe patient characteristics and outcomes. Results Fifty-two patients met inclusion criteria, 21 of whom were discharged to home and 31 were discharged to a SNF/rehab. Of the patients discharged to home, 17 (81%) completed their planned antibiotic courses without complication. Twenty (64%) patients discharged to a SNF/rehab completed the antibiotic courses without complication. Six (11%) patients had line infections, 6 (11%) had injection drug use relapse, and 12 (23%) required readmission. Conclusions Persons who inject drugs discharged home were not more likely to have complications than those discharged to a SNF/rehab. Home OPAT may be a safe discharge option in carefully selected patients.

A public health authority contacted the hospital about a 32-year-old man with fever, headache, and myalgias that had developed 8 days after he had traveled from Liberia during an epidemic of Ebola virus disease. Management decisions were made.

Summary Background Adherence is key to the success of antiretroviral therapy. Enhanced partner support might benefit patients with previous treatment failure. We aimed to assess whether an enhanced partner-based support intervention with modified directly observed therapy would improve outcomes with second-line therapy in HIV-infected patients for whom first-line therapy had failed. Methods We did a multicentre, international, randomised clinical trial at nine sites in Botswana, Brazil, Haiti, Peru, South Africa, Uganda, Zambia, and Zimbabwe. Participants aged 18 years or older for whom first-line therapy had failed, with HIV RNA concentrations greater than 1000 copies per mL and with a willing partner, were randomly assigned (1:1), via computer-generated randomisation, to receive partner-based modified directly observed therapy or standard of care. Randomisation was stratified by screening HIV RNA concentration (≤10 000 copies per mL vs >10 000 copies per mL). Participants and site investigators were not masked to group assignment. Primary outcome was confirmed virological failure (viral load >400 copies per mL) by week 48. Analysis was by intention to treat. This trial is registered with ClinicalTrials.gov, number NCT00608569. Findings Between April 23, 2009, and Sept 29, 2011, we randomly assigned 259 participants to the modified directly observed therapy group (n=129) or the standard-of-care group (n=130). 34 (26%) participants in the modified directly observed therapy group achieved the primary endpoint of virological failure by week 48 compared with 23 (18%) participants in the standard-of-care group. The Kaplan-Meier estimated cumulative probability of virological failure by week 48 was 25·1% (95% CI 17·7–32·4) in the modified directly observed therapy group and 17·3% (10·8–23·7) in the standard-of-care group, for a weighted difference in standard of care versus modified directly observed therapy of −6·6% (95% CI −16·5% to 3·2%; p=0·19). 36 (14%) participants reported at least one grade 3 or higher adverse event or laboratory abnormality (n=21 in the modified directly observed therapy group and n=15 in the standard-of-care group). Interpretation Partner-based training with modified directly observed therapy had no effect on virological suppression. The intervention does not therefore seem to be a promising strategy to increase adherence. Intensive follow-up with clinic staff might be a viable approach in this setting. Funding AIDS Clinical Trials Group and the National Institute of Allergy and Infectious Diseases, US National Institutes of Health.