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BackgroundMedical 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. ObjectiveOur 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. MethodsText 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. ResultsThe 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. ConclusionsA 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.

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 24 h 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 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.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.

BackgroundPeople 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.MethodsEighty-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.ResultsMedian (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

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.

ObjectivesThe introduction of two new non-nucleoside reverse transcriptase inhibitors (NNRTIs) in the past 5 years and the identification of novel NNRTI-associated mutations have made it necessary to reassess the extent of phenotypic NNRTI cross-resistance.MethodsWe analysed a dataset containing 1975, 1967, 519 and 187 genotype-phenotype correlations for nevirapine, efavirenz, etravirine and rilpivirine, respectively. We used linear regression to estimate the effects of RT mutations on susceptibility to each of these NNRTIs.ResultsSixteen mutations at 10 positions were significantly associated with the greatest contribution to reduced phenotypic susceptibility (≥10-fold) to one or more NNRTIs, including: 14 mutations at six positions for nevirapine (K101P, K103N/S, V106A/M, Y181C/I/V, Y188C/L and G190A/E/Q/S); 10 mutations at six positions for efavirenz (L100I, K101P, K103N, V106M, Y188C/L and G190A/E/Q/S); 5 mutations at four positions for etravirine (K101P, Y181I/V, G190E and F227C); and 6 mutations at five positions for rilpivirine (L100I, K101P, Y181I/V, G190E and F227C). G190E, a mutation that causes high-level nevirapine and efavirenz resistance, also markedly reduced susceptibility to etravirine and rilpivirine. K101H, E138G, V179F and M230L mutations, associated with reduced susceptibility to etravirine and rilpivirine, were also associated with reduced susceptibility to nevirapine and/or efavirenz.ConclusionsThe identification of novel cross-resistance patterns among approved NNRTIs illustrates the need for a systematic approach for testing novel NNRTIs against clinical virus isolates with major NNRTI-resistance mutations and for testing older NNRTIs against virus isolates with mutations identified during the evaluation of a novel NNRTI.

BackgroundEffective and easy to implement interventions to improve adherence to antiretroviral therapy are needed.ObjectiveTo compare site nurse-initiated adherence and symptom support telephone calls for HIV-positive individuals starting antiretroviral therapy to the study site's standard of care.MethodsA randomized controlled trial of site nurse-initiated adherence and symptom support telephone calls for HIV-positive individuals starting antiretroviral therapy. Subjects were randomized to receive site nurse-initiated telephone calls (intervention) or no additional calls to the site's standard of care (control). Subjects received calls 1 to 3 days after initiating antiretrovirals, on weeks 1, 2, 3, 6, 10, 14, 18, 22, and 26, and every 8 weeks thereafter. Self-reported adherence was captured during study visits.ResultsA total of 333 subjects starting antiretrovirals as part of ACTG 384 were co-enrolled into ACTG 5031. Subjects were followed for up to 160 weeks and were contacted for 74% of scheduled calls. There was no significant difference in proportion of patients with ≯95% mean total adherence (87.9% and 91.2%; P = .34) and mean self-reported total adherence (97.9% and 98.4%) in the intervention and control groups, respectively, or in symptom distress and clinical endpoints.ConclusionsIn the context of a clinical trial where self-reported adherence was exceptionally high, the site nurse-initiated telephone calls did not further improve self-reported adherence, symptom distress, or clinical outcomes.

Determining the phenotypic impacts of reverse transcriptase (RT) mutations on individual nucleoside RT inhibitors (NRTIs) has remained a statistical challenge because clinical NRTI-resistant HIV-1 isolates usually contain multiple mutations, often in complex patterns, complicating the task of determining the relative contribution of each mutation to HIV drug resistance. Furthermore, the NRTIs have highly variable dynamic susceptibility ranges, making it difficult to determine the relative effect of an RT mutation on susceptibility to different NRTIs. In this study, we analyzed 1,273 genotyped HIV-1 isolates for which phenotypic results were obtained using the PhenoSense assay (Monogram, South San Francisco, CA). We used a parsimonious feature selection algorithm, LASSO, to assess the possible contributions of 177 mutations that occurred in 10 or more isolates in our data set. We then used least-squares regression to quantify the impact of each LASSO-selected mutation on each NRTI. Our study provides a comprehensive view of the most common NRTI resistance mutations. Because our results were standardized, the study provides the first analysis that quantifies the relative phenotypic effects of NRTI resistance mutations on each of the NRTIs. In addition, the study contains new findings on the relative impacts of thymidine analog mutations (TAMs) on susceptibility to abacavir and tenofovir; the impacts of several known but incompletely characterized mutations, including E40F, V75T, Y115F, and K219R; and a tentative role in reduced NRTI susceptibility for K64H, a novel NRTI resistance mutation.

Immune responses induced during the early stages of chronic viral infections are thought to influence disease outcome. Using HIV as a model, we examined virus-specific cytotoxic T lymphocytes (CTLs), T helper cells, and viral genetic diversity in relation to duration of infection and subsequent response to antiviral therapy. Individuals with acute HIV-1 infection treated before seroconversion had weaker CTL responses directed at fewer epitopes than persons who were treated after seroconversion. However, treatment-induced control of viremia was associated with the development of strong T helper cell responses in both groups. After 1 yr of antiviral treatment initiated in acute or early infection, all epitope-specific CTL responses persisted despite undetectable viral loads. The breadth and magnitude of CTL responses remained significantly less in treated acute infection than in treated chronic infection, but viral diversity was also significantly less with immediate therapy. We conclude that early treatment of acute HIV infection leads to a more narrowly directed CTL response, stronger T helper cell responses, and a less diverse virus population. Given the need for T helper cells to maintain effective CTL responses and the ability of virus diversification to accommodate immune escape, we hypothesize that early therapy of primary infection may be beneficial despite induction of less robust CTL responses. These data also provide rationale for therapeutic immunization aimed at broadening CTL responses in treated primary HIV infection.

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 Forty 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 (ECM i) 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 (GCS; median [interquartile range 25th–75th]: −1.3% [−2.9%–1.0%] vs 2.3% [−0.4%–4.1%]; P =.03), eplerenone versus placebo respectively. On cardiac MRI, improvement in sMBF related to improvement in global circumferential strain (ρ = −0.65, P =.057) among those treated with eplerenone. Selecting for those with impaired myocardial perfusion (CFR <2.5 and/or sMBF <1.8), there was a treatment effect of eplerenone versus placebo to improve CFR (0.28 [0.27] vs −0.05 [0.36]; P =.04). Eplerenone prevented a small increase in troponin (0.00 [−0.13–0.00] vs 0.00 [0.00–0.74] ng/L; P =.03) without effects on ECM i (0.9 [−2.3–4.3] vs −0.7 [−2.2–−0.1] g/m2; P =.38). CD4+ T-cell count (127 [−38–286] vs −6 [−168–53] cells/μL; P =.02) increased in the eplerenone- versus placebo-treated groups. 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 with placebo. Clinical Trials Registration NCT02740179 (https://clinicaltrials.gov/ct2/show/NCT02740179?term=NCT02740179&draw=2&rank=1). [ABSTRACT FROM AUTHOR]

In 2014, a total of 30.8 million US residents traveled internationally, and 39% of trips were to the Caribbean, Central America, and South America (the Americas) (1). Travelers to this [...]

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. [ABSTRACT FROM AUTHOR]