Zika Virus Persistence in the Central Nervous System and Lymph Nodes of Rhesus Monkeys.

Zika virus (ZIKV) is associated with severe neuropathology in neonates as well as Guillain-Barré syndrome and other neurologic disorders in adults. Prolonged viral shedding has been reported in semen, suggesting the presence of anatomic viral reservoirs. Here we show that ZIKV can persist in cerebrospinal fluid (CSF) and lymph nodes (LN) of infected rhesus monkeys for weeks after virus has been cleared from peripheral blood, urine, and mucosal secretions. ZIKV-specific neutralizing antibodies correlated with rapid clearance of virus in peripheral blood but remained undetectable in CSF for the duration of the study. Viral persistence in both CSF and LN correlated with upregulation of mechanistic target of rapamycin (mTOR), proinflammatory, and anti-apoptotic signaling pathways, as well as downregulation of extracellular matrix signaling pathways. These data raise the possibility that persistent or occult neurologic and lymphoid disease may occur following clearance of peripheral virus in ZIKV-infected individuals.

Pseudomonas guariconensis Necrotizing Fasciitis, United Kingdom.

We describe a case of necrotizing fasciitis in the United Kingdom in which Pseudomonas guariconensis was isolated from multiple blood culture and tissue samples. The organism carried a Verona integron-encoded metallo-ß-lactamase gene and evidence of decreased susceptibility to ß-lactam antimicrobial agents. Clinicians should use caution when treating infection caused by this rare pathogen.

Antibody and TLR7 agonist delay viral rebound in SHIV-infected monkeys.

The latent viral reservoir is the critical barrier for the development of a cure for HIV-1 infection. Previous studies have shown direct antiviral activity of potent HIV-1 Env-specific broadly neutralizing antibodies (bNAbs) administered when antiretroviral therapy (ART) was discontinued, but it remains unclear whether bNAbs can target the viral reservoir during ART. Here we show that administration of the V3 glycan-dependent bNAb PGT121 together with the Toll-like receptor 7 (TLR7) agonist vesatolimod (GS-9620) during ART delayed viral rebound following discontinuation of ART in simian-human immunodeficiency virus (SHIV)-SF162P3-infected rhesus monkeys in which ART was initiated during early acute infection. Moreover, in the subset of monkeys that were treated with both PGT121 and GS-9620 and that did not show viral rebound after discontinuation of ART, adoptive transfer studies and CD8-depletion studies also did not reveal virus. These data demonstrate the potential of bNAb administration together with innate immune stimulation as a possible strategy for targeting the viral reservoir.

Publisher Correction: Antibody and TLR7 agonist delay viral rebound in SHIV-infected monkeys.

In Fig. 4b of this Article, the x-axis labels 'PGT121' and 'GS-9620' were inadvertently swapped in both graphs. In Fig. 5a, b, 'TLR7' should have been 'GS-9620'. These figures have been corrected online.

Vaccine protection against Zika virus from Brazil.

Zika virus (ZIKV) is a flavivirus that is responsible for the current epidemic in Brazil and the Americas. ZIKV has been causally associated with fetal microcephaly, intrauterine growth restriction, and other birth defects in both humans and mice. The rapid development of a safe and effective ZIKV vaccine is a global health priority, but very little is currently known about ZIKV immunology and mechanisms of immune protection. Here we show that a single immunization with a plasmid DNA vaccine or a purified inactivated virus vaccine provides complete protection in susceptible mice against challenge with a strain of ZIKV involved in the outbreak in northeast Brazil. This ZIKV strain has recently been shown to cross the placenta and to induce fetal microcephaly and other congenital malformations in mice. We produced DNA vaccines expressing ZIKV pre-membrane and envelope (prM-Env), as well as a series of deletion mutants. The prM-Env DNA vaccine, but not the deletion mutants, afforded complete protection against ZIKV, as measured by absence of detectable viraemia following challenge, and protective efficacy correlated with Env-specific antibody titers. Adoptive transfer of purified IgG from vaccinated mice conferred passive protection, and depletion of CD4 and CD8 T lymphocytes in vaccinated mice did not abrogate this protection. These data demonstrate that protection against ZIKV challenge can be achieved by single-shot subunit and inactivated virus vaccines in mice and that Env-specific antibody titers represent key immunologic correlates of protection. Our findings suggest that the development of a ZIKV vaccine for humans is likely to be achievable.

Beyond open big data: addressing unreliable research.

The National Institute of Health invests US $30.9 billion annually in medical research. However, the subsequent impact of this research output on society and the economy is amplified dramatically as a result of the actual medical treatments, biomedical innovations, and various commercial enterprises that emanate from and depend on these findings. It is therefore a great concern to discover that much of published research is unreliable. We propose extending the open data concept to the culture of the scientific research community. By dialing down unproductive features of secrecy and competition, while ramping up cooperation and transparency, we make a case that what is published would then be less susceptible to the sometimes corrupting and confounding pressures to be first or journalistically attractive, which can compromise the more fundamental need to be robustly correct.

Forms or Free-Text? Measuring Advance Care Planning Activity Using Electronic Health Records.

Advance care planning (ACP) discussions seek to guide future serious illness care. These discussions may be recorded in the electronic health record by documentation in clinical notes, structured forms and directives, and physician orders. Yet, most studies of ACP prevalence have only examined structured electronic health record elements and ignored data existing in notes. We sought to investigate the relative comprehensiveness and accuracy of ACP documentation from structured and unstructured electronic health record data sources. We evaluated structured and unstructured ACP documentation present in the electronic health records of 435 patients with cancer drawn from three separate healthcare systems. We extracted structured ACP documentation by manually annotating written documents and forms scanned into the electronic health record. We coded unstructured ACP documentation using a rule-based natural language processing software that identified ACP keywords within clinical notes and was subsequently reviewed for accuracy. The unstructured approach identified more instances of ACP documentation (238, 54.7% of patients) than the structured ACP approach (187, 42.9% of patients). Additionally, 16.6% of all patients with structured ACP documentation only had documents that were judged as misclassified, incomplete, blank, unavailable, or a duplicate of a previously entered erroneous document. ACP documents scanned into electronic health records represent a limited view of ACP activity. Research and measures of clinical practice with ACP should incorporate information from unstructured data.

Video Intervention and Goals-of-Care Documentation in Hospitalized Older Adults: The VIDEO-PCE Randomized Clinical Trial.

Importance: Despite the benefits of goals-of-care (GOC) communication, many hospitalized individuals never communicate their goals or preferences to clinicians. Objective: To assess whether a GOC video intervention delivered by palliative care educators (PCEs) increased the rate of GOC documentation. Design, Setting, and Participants: This pragmatic, stepped-wedge cluster randomized clinical trial included patients aged 65 years or older admitted to 1 of 14 units at 2 urban hospitals in New York and Boston from July 1, 2021, to October 31, 2022. Intervention: The intervention involved PCEs (social workers and nurses trained in GOC communication) facilitating GOC conversations with patients and/or their decision-makers using a library of brief, certified video decision aids available in 29 languages. Patients in the control period received usual care. Main Outcome and Measures: The primary outcome was GOC documentation, which included any documentation of a goals conversation, limitation of life-sustaining treatment, palliative care, hospice, or time-limited trials and was obtained by natural language processing. Results: A total of 10 802 patients (mean [SD] age, 78 [8] years; 51.6% male) were admitted to 1 of 14 hospital units. Goals-of-care documentation during the intervention phase occurred among 3744 of 6023 patients (62.2%) compared with 2396 of 4779 patients (50.1%) in the usual care phase (P < .001). Proportions of documented GOC discussions for Black or African American individuals (865 of 1376 [62.9%] vs 596 of 1125 [53.0%]), Hispanic or Latino individuals (311 of 548 [56.8%] vs 218 of 451 [48.3%]), non-English speakers (586 of 1059 [55.3%] vs 405 of 863 [46.9%]), and people living with Alzheimer disease and related dementias (520 of 681 [76.4%] vs 355 of 570 [62.3%]) were greater during the intervention phase compared with the usual care phase. Conclusions and Relevance: In this stepped-wedge cluster randomized clinical trial of older adults, a GOC video intervention delivered by PCEs resulted in higher rates of GOC documentation compared with usual care, including among Black or African American individuals, Hispanic or Latino individuals, non-English speakers, and people living with Alzheimer disease and related dementias. The findings suggest that this form of patient-centered care delivery may be a beneficial decision support tool. Trial Registration: ClinicalTrials.gov Identifier: NCT04857060.

Associations Between Family Member Involvement and Outcomes of Patients Admitted to the Intensive Care Unit: Retrospective Cohort Study.

BACKGROUND: Little is known about family member involvement, by relationship status, for patients treated in the intensive care unit (ICU). OBJECTIVE: Using documentation of family interactions in clinical notes, we examined associations between child and spousal involvement and ICU patient outcomes, including goals of care conversations (GOCCs), limitations in life-sustaining therapy (LLST), and 3-month mortality. METHODS: Using a retrospective cohort design, the study included a total of 858 adult patients treated between 2008 and 2012 in the medical ICU at a tertiary care center in northeastern United States. Clinical notes generated within the first 48 hours of admission to the ICU were used with standard machine learning methods to predict patient outcomes. We used natural language processing methods to identify family-related documentation and abstracted sociodemographic and clinical characteristics of the patients from the medical record. RESULTS: Most of the 858 patients were White (n=650, 75.8%); 437 (50.9%) were male, 479 (55.8%) were married, and the median age was 68.4 (IQR 56.5-79.4) years. Most patients had documented GOCC (n=651, 75.9%). In adjusted regression analyses, child involvement (odds ratio [OR] 0.81; 95% CI 0.49-1.34; P=.41) and child plus spouse involvement (OR 1.28; 95% CI 0.8-2.03; P=.3) were not associated with GOCCs compared to spouse involvement. Child involvement was not associated with LLST when compared to spouse involvement (OR 1.49; 95% CI 0.89-2.52; P=.13). However, child plus spouse involvement was associated with LLST (OR 1.6; 95% CI 1.02-2.52; P=.04). Compared to spouse involvement, there were no significant differences in the 3-month mortality by family member type, including child plus spouse involvement (OR 1.38; 95% CI 0.91-2.09; P=.13) and child involvement (OR 1.47; 95% CI 0.9-2.41; P=.12). CONCLUSIONS: Our findings demonstrate that statistical models derived from text analysis in the first 48 hours of ICU admission can predict patient outcomes. Early child plus spouse involvement was associated with LLST, suggesting that decisions about LLST were more likely to occur when the child and spouse were both involved compared to the involvement of only the spouse. More research is needed to further understand the involvement of different family members in ICU care and its association with patient outcomes.

Natural Language Processing to Identify Advance Care Planning Documentation in a Multisite Pragmatic Clinical Trial.

CONTEXT: Large multisite clinical trials studying decision-making when facing serious illness require an efficient method for abstraction of advance care planning (ACP) documentation from clinical text documents. However, the current gold standard method of manual chart review is time-consuming and unreliable. OBJECTIVES: To evaluate the ability to use natural language processing (NLP) to identify ACP documention in clinical notes from patients participating in a multisite trial. METHODS: Patients with advanced cancer followed in three disease-focused oncology clinics at Duke Health, Mayo Clinic, and Northwell Health were identified using administrative data. All outpatient and inpatient notes from patients meeting inclusion criteria were extracted from electronic health records (EHRs) between March 2018 and March 2019. NLP text identification software with semi-automated chart review was applied to identify documentation of four ACP domains: (1) conversations about goals of care, (2) limitation of life-sustaining treatment, (3) involvement of palliative care, and (4) discussion of hospice. The performance of NLP was compared to gold standard manual chart review. RESULTS: 435 unique patients with 79,797 notes were included in the study. In our validation data set, NLP achieved F1 scores ranging from 0.84 to 0.97 across domains compared to gold standard manual chart review. NLP identified ACP documentation in a fraction of the time required by manual chart review of EHRs (1-5 minutes per patient for NLP, vs. 30-120 minutes for manual abstraction). CONCLUSION: NLP is more efficient and as accurate as manual chart review for identifying ACP documentation in studies with large patient cohorts.

Video Images about Decisions for Ethical Outcomes in Kidney Disease (VIDEO-KD): the study protocol for a multi-centre randomised controlled trial.

INTRODUCTION: Older patients with advanced chronic kidney disease (CKD) often are inadequately prepared to make informed decisions about treatments including dialysis and cardiopulmonary resuscitation. Further, evidence shows that patients with advanced CKD do not commonly engage in advance care planning (ACP), may suffer from poor quality of life, and may be exposed to end-of-life care that is not concordant with their goals. We aim to study the effectiveness of a video intervention on ACP, treatment preferences and other patient-reported outcomes. METHODS AND ANALYSIS: The Video Images about Decisions for Ethical Outcomes in Kidney Disease trial is a multi-centre randomised controlled trial that will test the effectiveness of an intervention that includes a CKD-related video decision aid followed by recording personal video declarations about goals of care and treatment preferences in older adults with advancing CKD. We aim to enrol 600 patients over 5 years at 10 sites. ETHICS AND DISSEMINATION: Regulatory and ethical aspects of this trial include a single Institutional Review Board mechanism for approval, data use agreements among sites, and a Data Safety and Monitoring Board. We intend to disseminate findings at national meetings and publish our results. TRIAL REGISTRATION NUMBER: NCT04347629.

Study protocol for Video Images about Decisions to Improve Ethical Outcomes with Palliative Care Educators (VIDEO-PCE): a pragmatic stepped wedge cluster randomised trial of older patients admitted to the hospital

INTRODUCTION: Despite the known benefit to patients and families, discussions about goals, values and preferences for medical care in advancing serious illness often do not occur. Many system and clinician factors, such as patient and clinician reticence and shortage of specialty palliative care teams, contribute to this lack of communication. To address this gap, we designed an intervention to promote goals-of-care conversations and palliative care referrals in the hospital setting by using trained palliative care educators and video decision aids. This paper presents the rationale, design and methods for a trial aimed at addressing barriers to goals-of-care conversations for hospitalised adults aged 65 and older and those with Alzheimer's disease and related Dementias, regardless of age. METHODS AND ANALYSIS: The Video Image about Decisions to Improve Ethical Outcomes with Palliative Care Educators is a pragmatic stepped wedge, cluster randomised controlled trial, which aims to improve and extend goals-of-care conversations in the hospital setting with palliative care educators trained in serious illness communication and video decision aids. The primary outcome is the proportion of patients with goals-of-care documentation in the electronic health record. We estimate that over 9000 patients will be included. ETHICS AND DISSEMINATION: The Institutional Review Board (IRB) at Boston Medical Center will serve as the single IRB of record for all regulatory and ethical aspects of this trial. BMC Protocol Number: H-41482. Findings will be presented at national meetings and in publications. This trial is registered at ClinicalTrials.gov. TRIAL REGISTRATION NUMBER: NCT04857060; ClinicalTrials.gov.

Association of an Advance Care Planning Video and Communication Intervention With Documentation of Advance Care Planning Among Older Adults: A Nonrandomized Controlled Trial.

Importance: COVID-19 has disproportionately killed older adults and racial and ethnic minority individuals, raising questions about the relevance of advance care planning (ACP) in this population. Video decision aids and communication skills training offer scalable delivery models. Objective: To assess whether ACP video decision aids and a clinician communication intervention improved the rate of ACP documentation during an evolving pandemic, with a focus on African American and Hispanic patients. Design, Setting, and Participants: The Advance Care Planning: Communicating With Outpatients for Vital Informed Decisions trial was a pre-post, open-cohort nonrandomized controlled trial that compared ACP documentation across the baseline pre-COVID-19 period (September 15, 2019, to March 14, 2020), the COVID-19 wave 1 period (March 15, 2020, to September 14, 2020), and an intervention period (December 15, 2020, to June 14, 2021) at a New York metropolitan area ambulatory network of 22 clinics. All patients 65 years or older who had at least 1 clinic or telehealth visit during any of the 3 study periods were included. Main Outcomes and Measures: The primary outcome was ACP documentation. Results: A total of 14 107 patients (mean [SD] age, 81.0 [8.4] years; 8856 [62.8%] female; and 2248 [15.9%] African American or Hispanic) interacted with clinicians during the pre-COVID-19 period; 12 806 (mean [SD] age, 81.2 [8.5] years; 8047 [62.8%] female; and 1992 [15.6%] African American or Hispanic), during wave 1; and 15 106 (mean [SD] 80.9 [8.3] years; 9543 [63.2%] female; and 2535 [16.8%] African American or Hispanic), during the intervention period. Clinicians documented ACP in 3587 patients (23.8%) during the intervention period compared with 2525 (17.9%) during the pre-COVID-19 period (rate difference [RD], 5.8%; 95% CI, 0.9%-7.9%; P = .01) and 1598 (12.5%) during wave 1 (RD, 11.3%; 95% CI, 6.3%-12.1%; P < .001). Advance care planning was documented in 447 African American patients (30.0%) during the intervention period compared with 233 (18.1%) during the pre-COVID-19 period (RD, 11.9%; 95% CI, 4.1%-15.9%; P < .001) and 130 (11.0%) during wave 1 (RD, 19.1%; 95% CI, 11.7%-21.2%; P < .001). Advance care planning was documented for 222 Hispanic patients (21.2%) during the intervention period compared with 127 (13.2%) during the pre-COVID-19 period (RD, 8.0%; 95% CI, 2.1%-10.9%; P = .004) and 82 (10.2%) during wave 1 (RD, 11.1%; 95% CI, 5.5%-14.5%; P < .001). Conclusions and Relevance: This intervention, implemented during the evolving COVID-19 pandemic, was associated with higher rates of ACP documentation, especially for African American and Hispanic patients. Trial Registration: ClinicalTrials.gov Identifier: NCT04660422.

Deep learning to predict long-term mortality in patients requiring 7 days of mechanical ventilation.

BACKGROUND: Among patients with acute respiratory failure requiring prolonged mechanical ventilation, tracheostomies are typically placed after approximately 7 to 10 days. Yet half of patients admitted to the intensive care unit receiving tracheostomy will die within a year, often within three months. Existing mortality prediction models for prolonged mechanical ventilation, such as the ProVent Score, have poor sensitivity and are not applied until after 14 days of mechanical ventilation. We developed a model to predict 3-month mortality in patients requiring more than 7 days of mechanical ventilation using deep learning techniques and compared this to existing mortality models. METHODS: Retrospective cohort study. Setting: The Medical Information Mart for Intensive Care III Database. Patients: All adults requiring ≥ 7 days of mechanical ventilation. Measurements: A neural network model for 3-month mortality was created using process-of-care variables, including demographic, physiologic and clinical data. The area under the receiver operator curve (AUROC) was compared to the ProVent model at predicting 3 and 12-month mortality. Shapley values were used to identify the variables with the greatest contributions to the model. RESULTS: There were 4,334 encounters divided into a development cohort (n = 3467) and a testing cohort (n = 867). The final deep learning model included 250 variables and had an AUROC of 0.74 for predicting 3-month mortality at day 7 of mechanical ventilation versus 0.59 for the ProVent model. Older age and elevated Simplified Acute Physiology Score II (SAPS II) Score on intensive care unit admission had the largest contribution to predicting mortality. DISCUSSION: We developed a deep learning prediction model for 3-month mortality among patients requiring ≥ 7 days of mechanical ventilation using a neural network approach utilizing readily available clinical variables. The model outperforms the ProVent model for predicting mortality among patients requiring ≥ 7 days of mechanical ventilation. This model requires external validation.

Mining heterogeneous clinical notes by multi-modal latent topic model.

Latent knowledge can be extracted from the electronic notes that are recorded during patient encounters with the health system. Using these clinical notes to decipher a patient's underlying comorbidites, symptom burdens, and treatment courses is an ongoing challenge. Latent topic model as an efficient Bayesian method can be used to model each patient's clinical notes as "documents" and the words in the notes as "tokens". However, standard latent topic models assume that all of the notes follow the same topic distribution, regardless of the type of note or the domain expertise of the author (such as doctors or nurses). We propose a novel application of latent topic modeling, using multi-note topic model (MNTM) to jointly infer distinct topic distributions of notes of different types. We applied our model to clinical notes from the MIMIC-III dataset to infer distinct topic distributions over the physician and nursing note types. Based on manual assessments made by clinicians, we observed a significant improvement in topic interpretability using MNTM modeling over the baseline single-note topic models that ignore the note types. Moreover, our MNTM model led to a significantly higher prediction accuracy for prolonged mechanical ventilation and mortality using only the first 48 hours of patient data. By correlating the patients' topic mixture with hospital mortality and prolonged mechanical ventilation, we identified several diagnostic topics that are associated with poor outcomes. Because of its elegant and intuitive formation, we envision a broad application of our approach in mining multi-modality text-based healthcare information that goes beyond clinical notes. Code available at https://github.com/li-lab-mcgill/heterogeneous_ehr.

Long-Term Prognosis of Older Adults Who Survive Emergency Mechanical Ventilation.

CONTEXT: Emergent mechanical ventilation represents an important inflection point in seriously ill older adults' illness trajectories. Data are lacking on the long-term prognosis after surviving mechanical ventilation to inform shared decision making in serious illness conversations. OBJECTIVES: Describe the long-term prognosis of older adults who survive emergency mechanical ventilation to inform shared decision making. METHODS: This is a retrospective cohort study from a single-center intensive care unit in an academic, urban, and tertiary care medical center. We included adults aged 75 years and older consecutively admitted with mechanical ventilation between 2008 and 2012 in the Multiparameter Intelligent Monitoring of Intensive Care III database. We excluded patients who were electively admitted. Our primary outcome was the long-term prognosis after leaving the hospital stratified by discharge location. Our secondary outcome was the frequency of documented serious illness conversations within 48 hours of hospitalization recommended by the National Quality Forum. RESULTS: We identified 415 patients (454 hospital admissions) consecutively admitted to the intensive care unit. The median age was 82.6 years, 54.0% were female, 78.2% were white, non-Hispanic, and in-hospital mortality rate was 36.6%. Among the survivors, the median survival after hospital discharge was 163.5 days (interquartile range 37.5-476.8). Only 49.1% of patients had documented serious illness conversations within 48 hours of hospitalization. About 63.4% of patients (59 of 93) who were discharged to long-term acute care hospitals died by six months. CONCLUSION: This study demonstrated the long-term prognosis of older adults who underwent emergent mechanical ventilation. These data could be used to inform shared decision making in serious illness conversations.

Deep Natural Language Processing Identifies Variation in Care Preference Documentation.

CONTEXT: Documentation of care preferences within 48 hours of admission to an intensive care unit (ICU) is a National Quality Forum-endorsed quality metric for older adults. Care preferences are poorly captured by administrative data. OBJECTIVES: Using deep natural language processing, our aim was to determine the rate of care preference documentation in free-text notes and to assess associated patient factors. METHODS: Retrospective review of notes by clinicians using a deep natural language processing to identify care preference documentation, including goals-of-care and treatment limitations, within 48 hours of ICU admission within five ICUs (medical, cardiac, surgery, trauma surgery, and cardiac surgery) for adults 75 years and older. Covariates included demographics, ICU type, sequential organ failure assessment score, and need for mechanical ventilation. RESULTS: Deep natural language processing reviewed 11,575 clinician notes for 1350 ICU admissions. Median patient age was 84.0 years (interquartile range 78.0-88.4). Overall, 64.7% had documentation of care preferences. Patients with documentation were older (85 vs. 83 years; P < 0.001) and more often female (53.8% vs. 43.4%; P < 0.001). In adjusted analysis, rates of care preference documentation were higher for older patients, females, nonelective admissions, and admissions to the medical vs. the cardiac or surgical ICUs (all P ≤ 0.01). CONCLUSION: Care preference documentation within 48 hours was absent in more than one-third of ICU admissions among patients aged 75 years and older and was more likely to occur in medical vs. cardiac or surgical ICUs.

Sustained maternal antibody and cellular immune responses in pregnant women infected with Zika virus and mother to infant transfer of Zika-specific antibodies.

PROBLEM: Evaluation of Zika virus (ZIKV)-specific humoral and cellular immune response in pregnant women exposed to ZIKV. METHOD OF STUDY: In this observational, prospective cohort study, we recruited pregnant women presenting for prenatal ultrasound for ZIKV exposure at a single academic teaching hospital in Boston, MA from November 2016 to December 2018. We collected blood, urine, and cervicovaginal swabs antepartum, intrapartum, and postpartum; and cord blood and placenta at delivery. We used experimental assays to calculate quantitative viral loads, ZIKV-specific immunoglobulin titers, and ZIKV-specific T-cell responses. RESULTS: We enrolled 22 participants, three of which had serologic-confirmed ZIKV infection. No participants demonstrated sustained ZIKV shedding. ZIKV-specific IgG/IgM antibody was sustained throughout pregnancy and postpartum. ZIKV envelope and capsid-specific T-cell responses were also observed, albeit inconsistent. No newborns in this cohort had congenital Zika syndrome. Infant cord blood of infected mothers exhibited ZIKV-specific IgG, but not IgM antibodies. CONCLUSION: We detected a robust, prolonged maternal humoral immune response to ZIKV during pregnancy and postpartum. We also demonstrated evidence for efficient transplacental antibody transfer from mother to infant at birth, supporting the importance of neonatal passive immunity to ZIKV. Maternal T-cell responses were less consistent among pregnant women infected with ZIKV.

Deep Natural Language Processing to Identify Symptom Documentation in Clinical Notes for Patients With Heart Failure Undergoing Cardiac Resynchronization Therapy.

CONTEXT: Clinicians lack reliable methods to predict which patients with congestive heart failure (CHF) will benefit from cardiac resynchronization therapy (CRT). Symptom burden may help to predict response, but this information is buried in free-text clinical notes. Natural language processing (NLP) may identify symptoms recorded in the electronic health record and thereby enable this information to inform clinical decisions about the appropriateness of CRT. OBJECTIVES: To develop, train, and test a deep NLP model that identifies documented symptoms in patients with CHF receiving CRT. METHODS: We identified a random sample of clinical notes from a cohort of patients with CHF who later received CRT. Investigators labeled documented symptoms as present, absent, and context dependent (pathologic depending on the clinical situation). The algorithm was trained on 80% and fine-tuned parameters on 10% of the notes. We tested the model on the remaining 10%. We compared the model's performance to investigators' annotations using accuracy, precision (positive predictive value), recall (sensitivity), and F1 score (a combined measure of precision and recall). RESULTS: Investigators annotated 154 notes (352,157 words) and identified 1340 present, 1300 absent, and 221 context-dependent symptoms. In the test set of 15 notes (35,467 words), the model's accuracy was 99.4% and recall was 66.8%. Precision was 77.6%, and overall F1 score was 71.8. F1 scores for present (70.8) and absent (74.7) symptoms were higher than that for context-dependent symptoms (48.3). CONCLUSION: A deep NLP algorithm can be trained to capture symptoms in patients with CHF who received CRT with promising precision and recall.