1
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Leone MJ, Dashti HS, Coughlin B, Tesh RA, Quadri SA, Bucklin AA, Adra N, Krishnamurthy PV, Ye EM, Hemmige A, Rajan S, Panneerselvam E, Higgins J, Ayub MA, Ganglberger W, Paixao L, Houle TT, Thompson BT, Johnson-Akeju O, Saxena R, Kimchi E, Cash SS, Thomas RJ, Westover MB. Sound and light levels in intensive care units in a large urban hospital in the United States. Chronobiol Int 2023; 40:759-768. [PMID: 37144470 PMCID: PMC10524721 DOI: 10.1080/07420528.2023.2207647] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Revised: 11/18/2022] [Accepted: 04/21/2023] [Indexed: 05/06/2023]
Abstract
Intensive care units (ICUs) may disrupt sleep. Quantitative ICU studies of concurrent and continuous sound and light levels and timings remain sparse in part due to the lack of ICU equipment that monitors sound and light. Here, we describe sound and light levels across three adult ICUs in a large urban United States tertiary care hospital using a novel sensor. The novel sound and light sensor is composed of a Gravity Sound Level Meter for sound level measurements and an Adafruit TSL2561 digital luminosity sensor for light levels. Sound and light levels were continuously monitored in the room of 136 patients (mean age = 67.0 (8.7) years, 44.9% female) enrolled in the Investigation of Sleep in the Intensive Care Unit study (ICU-SLEEP; Clinicaltrials.gov: #NCT03355053), at the Massachusetts General Hospital. The hours of available sound and light data ranged from 24.0 to 72.2 hours. Average sound and light levels oscillated throughout the day and night. On average, the loudest hour was 17:00 and the quietest hour was 02:00. Average light levels were brightest at 09:00 and dimmest at 04:00. For all participants, average nightly sound levels exceeded the WHO guideline of < 35 decibels. Similarly, mean nightly light levels varied across participants (minimum: 1.00 lux, maximum: 577.05 lux). Sound and light events were more frequent between 08:00 and 20:00 than between 20:00 and 08:00 and were largely similar on weekdays and weekend days. Peaks in distinct alarm frequencies (Alarm 1) occurred at 01:00, 06:00, and at 20:00. Alarms at other frequencies (Alarm 2) were relatively consistent throughout the day and night, with a small peak at 20:00. In conclusion, we present a sound and light data collection method and results from a cohort of critically ill patients, demonstrating excess sound and light levels across multiple ICUs in a large tertiary care hospital in the United States. ClinicalTrials.gov, #NCT03355053. Registered 28 November 2017, https://clinicaltrials.gov/ct2/show/NCT03355053.
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Affiliation(s)
- Michael J Leone
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
- Clinical Data Animation Center, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Hassan S Dashti
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
- Brain Data Science Platform, Broad Institute, Cambridge, Massachusetts, USA
- Division of Sleep Medicine, Harvard Medical School, Boston, Massachusetts, USA
| | - Brian Coughlin
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Ryan A Tesh
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
- Clinical Data Animation Center, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Syed A Quadri
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
- Clinical Data Animation Center, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Abigail A Bucklin
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
- Clinical Data Animation Center, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Noor Adra
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
- Clinical Data Animation Center, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Parimala Velpula Krishnamurthy
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
- Clinical Data Animation Center, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Elissa M Ye
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
- Clinical Data Animation Center, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Aashritha Hemmige
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
- Clinical Data Animation Center, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Subapriya Rajan
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
- Clinical Data Animation Center, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Ezhil Panneerselvam
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
- Clinical Data Animation Center, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Jasmine Higgins
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
- Clinical Data Animation Center, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Muhammad Abubakar Ayub
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
- Clinical Data Animation Center, Massachusetts General Hospital, Boston, Massachusetts, USA
- Division of Pulmonary and Critical Care, Department of Medicine, Massachusetts General Hospital, and Harvard Medical School, Boston, Massachusetts, USA
| | - Wolfgang Ganglberger
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
- Clinical Data Animation Center, Massachusetts General Hospital, Boston, Massachusetts, USA
- Sleep & Health Zurich, University of Zurich, Zurich, Switzerland
- Henry and Allison McCance Center for Brain Health, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Luis Paixao
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
- Clinical Data Animation Center, Massachusetts General Hospital, Boston, Massachusetts, USA
- Department of Neurology, Washington University School of Medicine in St. Louis, St. Louis, Missouri, USA
| | - Timothy T Houle
- Sleep & Health Zurich, University of Zurich, Zurich, Switzerland
- Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - B Taylor Thompson
- Division of Pulmonary and Critical Care, Department of Medicine, Massachusetts General Hospital, and Harvard Medical School, Boston, Massachusetts, USA
| | - Oluwaseun Johnson-Akeju
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Richa Saxena
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
- Brain Data Science Platform, Broad Institute, Cambridge, Massachusetts, USA
- Division of Sleep Medicine, Harvard Medical School, Boston, Massachusetts, USA
| | - Eyal Kimchi
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Sydney S Cash
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
- Clinical Data Animation Center, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Robert J Thomas
- Clinical Data Animation Center, Massachusetts General Hospital, Boston, Massachusetts, USA
- Department of Medicine, Division of Pulmonary, Critical Care & Sleep, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
| | - M Brandon Westover
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
- Clinical Data Animation Center, Massachusetts General Hospital, Boston, Massachusetts, USA
- Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
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2
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Bucklin AA, Ganglberger W, Quadri SA, Tesh RA, Adra N, Da Silva Cardoso M, Leone MJ, Krishnamurthy PV, Hemmige A, Rajan S, Panneerselvam E, Paixao L, Higgins J, Ayub MA, Shao YP, Ye EM, Coughlin B, Sun H, Cash SS, Thompson BT, Akeju O, Kuller D, Thomas RJ, Westover MB. High prevalence of sleep-disordered breathing in the intensive care unit - a cross-sectional study. Sleep Breath 2023; 27:1013-1026. [PMID: 35971023 PMCID: PMC9931933 DOI: 10.1007/s11325-022-02698-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Revised: 07/08/2022] [Accepted: 08/08/2022] [Indexed: 01/05/2023]
Abstract
PURPOSE Sleep-disordered breathing may be induced by, exacerbate, or complicate recovery from critical illness. Disordered breathing during sleep, which itself is often fragmented, can go unrecognized in the intensive care unit (ICU). The objective of this study was to investigate the prevalence, severity, and risk factors of sleep-disordered breathing in ICU patients using a single respiratory belt and oxygen saturation signals. METHODS Patients in three ICUs at Massachusetts General Hospital wore a thoracic respiratory effort belt as part of a clinical trial for up to 7 days and nights. Using a previously developed machine learning algorithm, we processed respiratory and oximetry signals to measure the 3% apnea-hypopnea index (AHI) and estimate AH-specific hypoxic burden and periodic breathing. We trained models to predict AHI categories for 12-h segments from risk factors, including admission variables and bio-signals data, available at the start of these segments. RESULTS Of 129 patients, 68% had an AHI ≥ 5; 40% an AHI > 15, and 19% had an AHI > 30 while critically ill. Median [interquartile range] hypoxic burden was 2.8 [0.5, 9.8] at night and 4.2 [1.0, 13.7] %min/h during the day. Of patients with AHI ≥ 5, 26% had periodic breathing. Performance of predicting AHI-categories from risk factors was poor. CONCLUSIONS Sleep-disordered breathing and sleep apnea events while in the ICU are common and are associated with substantial burden of hypoxia and periodic breathing. Detection is feasible using limited bio-signals, such as respiratory effort and SpO2 signals, while risk factors were insufficient to predict AHI severity.
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Affiliation(s)
- Abigail A Bucklin
- Department of Neurology, Massachusetts General Hospital (MGH), 55 Fruit Street, Boston, MA, 02114, USA
- Clinical Data Animation Center (CDAC), MGH, Boston, MA, USA
| | - Wolfgang Ganglberger
- Department of Neurology, Massachusetts General Hospital (MGH), 55 Fruit Street, Boston, MA, 02114, USA
- Clinical Data Animation Center (CDAC), MGH, Boston, MA, USA
- Sleep & Health Zurich, University of Zurich, Zurich, Switzerland
- Henry and Allison McCance Center for Brain Health, MGH, Boston, MA, USA
| | - Syed A Quadri
- Clinical Data Animation Center (CDAC), MGH, Boston, MA, USA
| | - Ryan A Tesh
- Department of Neurology, Massachusetts General Hospital (MGH), 55 Fruit Street, Boston, MA, 02114, USA
- Clinical Data Animation Center (CDAC), MGH, Boston, MA, USA
| | - Noor Adra
- Department of Neurology, Massachusetts General Hospital (MGH), 55 Fruit Street, Boston, MA, 02114, USA
- Clinical Data Animation Center (CDAC), MGH, Boston, MA, USA
| | - Madalena Da Silva Cardoso
- Department of Neurology, Massachusetts General Hospital (MGH), 55 Fruit Street, Boston, MA, 02114, USA
- Clinical Data Animation Center (CDAC), MGH, Boston, MA, USA
| | - Michael J Leone
- Department of Neurology, Massachusetts General Hospital (MGH), 55 Fruit Street, Boston, MA, 02114, USA
- Clinical Data Animation Center (CDAC), MGH, Boston, MA, USA
| | - Parimala Velpula Krishnamurthy
- Department of Neurology, Massachusetts General Hospital (MGH), 55 Fruit Street, Boston, MA, 02114, USA
- Clinical Data Animation Center (CDAC), MGH, Boston, MA, USA
| | - Aashritha Hemmige
- Department of Neurology, Massachusetts General Hospital (MGH), 55 Fruit Street, Boston, MA, 02114, USA
- Clinical Data Animation Center (CDAC), MGH, Boston, MA, USA
| | - Subapriya Rajan
- Department of Neurology, Massachusetts General Hospital (MGH), 55 Fruit Street, Boston, MA, 02114, USA
- Clinical Data Animation Center (CDAC), MGH, Boston, MA, USA
| | - Ezhil Panneerselvam
- Department of Neurology, Massachusetts General Hospital (MGH), 55 Fruit Street, Boston, MA, 02114, USA
- Clinical Data Animation Center (CDAC), MGH, Boston, MA, USA
| | - Luis Paixao
- Department of Neurology, Massachusetts General Hospital (MGH), 55 Fruit Street, Boston, MA, 02114, USA
- Clinical Data Animation Center (CDAC), MGH, Boston, MA, USA
| | - Jasmine Higgins
- Department of Neurology, Massachusetts General Hospital (MGH), 55 Fruit Street, Boston, MA, 02114, USA
- Clinical Data Animation Center (CDAC), MGH, Boston, MA, USA
| | - Muhammad Abubakar Ayub
- Department of Neurology, Massachusetts General Hospital (MGH), 55 Fruit Street, Boston, MA, 02114, USA
- Clinical Data Animation Center (CDAC), MGH, Boston, MA, USA
| | - Yu-Ping Shao
- Department of Neurology, Massachusetts General Hospital (MGH), 55 Fruit Street, Boston, MA, 02114, USA
- Clinical Data Animation Center (CDAC), MGH, Boston, MA, USA
| | - Elissa M Ye
- Department of Neurology, Massachusetts General Hospital (MGH), 55 Fruit Street, Boston, MA, 02114, USA
- Clinical Data Animation Center (CDAC), MGH, Boston, MA, USA
| | - Brian Coughlin
- Department of Neurology, Massachusetts General Hospital (MGH), 55 Fruit Street, Boston, MA, 02114, USA
| | - Haoqi Sun
- Department of Neurology, Massachusetts General Hospital (MGH), 55 Fruit Street, Boston, MA, 02114, USA
- Clinical Data Animation Center (CDAC), MGH, Boston, MA, USA
- Henry and Allison McCance Center for Brain Health, MGH, Boston, MA, USA
| | - Sydney S Cash
- Department of Neurology, Massachusetts General Hospital (MGH), 55 Fruit Street, Boston, MA, 02114, USA
- Clinical Data Animation Center (CDAC), MGH, Boston, MA, USA
| | | | - Oluwaseun Akeju
- Henry and Allison McCance Center for Brain Health, MGH, Boston, MA, USA
- Department of Anesthesia, Critical Care and Pain Medicine, MGH, Boston, MA, USA
| | | | - Robert J Thomas
- Clinical Data Animation Center (CDAC), MGH, Boston, MA, USA
- Department of Medicine, Division of Pulmonary, Critical Care & Sleep, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - M Brandon Westover
- Department of Neurology, Massachusetts General Hospital (MGH), 55 Fruit Street, Boston, MA, 02114, USA.
- Clinical Data Animation Center (CDAC), MGH, Boston, MA, USA.
- Henry and Allison McCance Center for Brain Health, MGH, Boston, MA, USA.
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3
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Ganglberger W, Krishnamurthy PV, Quadri SA, Tesh RA, Bucklin AA, Adra N, Da Silva Cardoso M, Leone MJ, Hemmige A, Rajan S, Panneerselvam E, Paixao L, Higgins J, Ayub MA, Shao YP, Coughlin B, Sun H, Ye EM, Cash SS, Thompson BT, Akeju O, Kuller D, Thomas RJ, Westover MB. Sleep staging in the ICU with heart rate variability and breathing signals. An exploratory cross-sectional study using deep neural networks. Front Netw Physiol 2023; 3:1120390. [PMID: 36926545 PMCID: PMC10013021 DOI: 10.3389/fnetp.2023.1120390] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Accepted: 02/13/2023] [Indexed: 03/02/2023]
Abstract
Introduction: To measure sleep in the intensive care unit (ICU), full polysomnography is impractical, while activity monitoring and subjective assessments are severely confounded. However, sleep is an intensely networked state, and reflected in numerous signals. Here, we explore the feasibility of estimating conventional sleep indices in the ICU with heart rate variability (HRV) and respiration signals using artificial intelligence methods Methods: We used deep learning models to stage sleep with HRV (through electrocardiogram) and respiratory effort (through a wearable belt) signals in critically ill adult patients admitted to surgical and medical ICUs, and in age and sex-matched sleep laboratory patients Results: We studied 102 adult patients in the ICU across multiple days and nights, and 220 patients in a clinical sleep laboratory. We found that sleep stages predicted by HRV- and breathing-based models showed agreement in 60% of the ICU data and in 81% of the sleep laboratory data. In the ICU, deep NREM (N2 + N3) proportion of total sleep duration was reduced (ICU 39%, sleep laboratory 57%, p < 0.01), REM proportion showed heavy-tailed distribution, and the number of wake transitions per hour of sleep (median 3.6) was comparable to sleep laboratory patients with sleep-disordered breathing (median 3.9). Sleep in the ICU was also fragmented, with 38% of sleep occurring during daytime hours. Finally, patients in the ICU showed faster and less variable breathing patterns compared to sleep laboratory patients Conclusion: The cardiovascular and respiratory networks encode sleep state information, which, together with artificial intelligence methods, can be utilized to measure sleep state in the ICU.
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Affiliation(s)
- Wolfgang Ganglberger
- Department of Neurology, Massachusetts General Hospital, MGH, Boston, MA, United States.,Clinical Data Animation Center (CDAC), Massachusetts General Hospital, Boston, MA, United States.,Sleep and Health Zurich, University of Zurich, Zurich, Switzerland.,Henry and Allison McCance Center for Brain Health, Massachusetts General Hospital, Boston, MA, United States
| | - Parimala Velpula Krishnamurthy
- Department of Neurology, Massachusetts General Hospital, MGH, Boston, MA, United States.,Clinical Data Animation Center (CDAC), Massachusetts General Hospital, Boston, MA, United States
| | - Syed A Quadri
- Department of Neurology, Massachusetts General Hospital, MGH, Boston, MA, United States.,Clinical Data Animation Center (CDAC), Massachusetts General Hospital, Boston, MA, United States
| | - Ryan A Tesh
- Department of Neurology, Massachusetts General Hospital, MGH, Boston, MA, United States.,Clinical Data Animation Center (CDAC), Massachusetts General Hospital, Boston, MA, United States
| | - Abigail A Bucklin
- Department of Neurology, Massachusetts General Hospital, MGH, Boston, MA, United States.,Clinical Data Animation Center (CDAC), Massachusetts General Hospital, Boston, MA, United States
| | - Noor Adra
- Department of Neurology, Massachusetts General Hospital, MGH, Boston, MA, United States.,Clinical Data Animation Center (CDAC), Massachusetts General Hospital, Boston, MA, United States
| | - Madalena Da Silva Cardoso
- Department of Neurology, Massachusetts General Hospital, MGH, Boston, MA, United States.,Clinical Data Animation Center (CDAC), Massachusetts General Hospital, Boston, MA, United States
| | - Michael J Leone
- Department of Neurology, Massachusetts General Hospital, MGH, Boston, MA, United States.,Clinical Data Animation Center (CDAC), Massachusetts General Hospital, Boston, MA, United States
| | - Aashritha Hemmige
- Department of Neurology, Massachusetts General Hospital, MGH, Boston, MA, United States.,Clinical Data Animation Center (CDAC), Massachusetts General Hospital, Boston, MA, United States
| | - Subapriya Rajan
- Department of Neurology, Massachusetts General Hospital, MGH, Boston, MA, United States.,Clinical Data Animation Center (CDAC), Massachusetts General Hospital, Boston, MA, United States
| | - Ezhil Panneerselvam
- Department of Neurology, Massachusetts General Hospital, MGH, Boston, MA, United States.,Clinical Data Animation Center (CDAC), Massachusetts General Hospital, Boston, MA, United States
| | - Luis Paixao
- Department of Neurology, Massachusetts General Hospital, MGH, Boston, MA, United States.,Clinical Data Animation Center (CDAC), Massachusetts General Hospital, Boston, MA, United States
| | - Jasmine Higgins
- Department of Neurology, Massachusetts General Hospital, MGH, Boston, MA, United States.,Clinical Data Animation Center (CDAC), Massachusetts General Hospital, Boston, MA, United States
| | - Muhammad Abubakar Ayub
- Department of Neurology, Massachusetts General Hospital, MGH, Boston, MA, United States.,Clinical Data Animation Center (CDAC), Massachusetts General Hospital, Boston, MA, United States
| | - Yu-Ping Shao
- Department of Neurology, Massachusetts General Hospital, MGH, Boston, MA, United States.,Clinical Data Animation Center (CDAC), Massachusetts General Hospital, Boston, MA, United States
| | - Brian Coughlin
- Department of Neurology, Massachusetts General Hospital, MGH, Boston, MA, United States
| | - Haoqi Sun
- Department of Neurology, Massachusetts General Hospital, MGH, Boston, MA, United States.,Clinical Data Animation Center (CDAC), Massachusetts General Hospital, Boston, MA, United States.,Harvard Medical School, Boston, MA, United States.,Henry and Allison McCance Center for Brain Health, Massachusetts General Hospital, Boston, MA, United States
| | - Elissa M Ye
- Department of Neurology, Massachusetts General Hospital, MGH, Boston, MA, United States.,Clinical Data Animation Center (CDAC), Massachusetts General Hospital, Boston, MA, United States
| | - Sydney S Cash
- Department of Neurology, Massachusetts General Hospital, MGH, Boston, MA, United States.,Clinical Data Animation Center (CDAC), Massachusetts General Hospital, Boston, MA, United States.,Harvard Medical School, Boston, MA, United States
| | - B Taylor Thompson
- Department of Medicine, Massachusetts General Hospital, Boston, MA, United States
| | - Oluwaseun Akeju
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, MA, United States.,Henry and Allison McCance Center for Brain Health, Massachusetts General Hospital, Boston, MA, United States
| | | | - Robert J Thomas
- Clinical Data Animation Center (CDAC), Massachusetts General Hospital, Boston, MA, United States.,Harvard Medical School, Boston, MA, United States.,Beth Israel Deaconess Medical Center, Department of Medicine, Division of Pulmonary, Critical Care and Sleep, Boston, MA, United States
| | - M Brandon Westover
- Department of Neurology, Massachusetts General Hospital, MGH, Boston, MA, United States.,Clinical Data Animation Center (CDAC), Massachusetts General Hospital, Boston, MA, United States.,Harvard Medical School, Boston, MA, United States.,Henry and Allison McCance Center for Brain Health, Massachusetts General Hospital, Boston, MA, United States
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4
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Piantadosi A, Mukerji SS, Ye S, Leone MJ, Freimark LM, Park D, Adams G, Lemieux J, Kanjilal S, Solomon IH, Ahmed AA, Goldstein R, Ganesh V, Ostrem B, Cummins KC, Thon JM, Kinsella CM, Rosenberg E, Frosch MP, Goldberg MB, Cho TA, Sabeti P. Enhanced Virus Detection and Metagenomic Sequencing in Patients with Meningitis and Encephalitis. mBio 2021; 12:e0114321. [PMID: 34465023 PMCID: PMC8406231 DOI: 10.1128/mbio.01143-21] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Accepted: 07/02/2021] [Indexed: 01/21/2023] Open
Abstract
Meningitis and encephalitis are leading causes of central nervous system (CNS) disease and often result in severe neurological compromise or death. Traditional diagnostic workflows largely rely on pathogen-specific tests, sometimes over days to weeks, whereas metagenomic next-generation sequencing (mNGS) profiles all nucleic acid in a sample. In this single-center, prospective study, 68 hospitalized patients with known (n = 44) or suspected (n = 24) CNS infections underwent mNGS from RNA and DNA to identify potential pathogens and also targeted sequencing of viruses using hybrid capture. Using a computational metagenomic classification pipeline based on KrakenUniq and BLAST, we detected pathogen nucleic acid in cerebrospinal fluid (CSF) from 22 subjects, 3 of whom had no clinical diagnosis by routine workup. Among subjects diagnosed with infection by serology and/or peripheral samples, we demonstrated the utility of mNGS to detect pathogen nucleic acid in CSF, importantly for the Ixodes scapularis tick-borne pathogens Powassan virus, Borrelia burgdorferi, and Anaplasma phagocytophilum. We also evaluated two methods to enhance the detection of viral nucleic acid, hybrid capture and methylated DNA depletion. Hybrid capture nearly universally increased viral read recovery. Although results for methylated DNA depletion were mixed, it allowed the detection of varicella-zoster virus DNA in two samples that were negative by standard mNGS. Overall, mNGS is a promising approach that can test for multiple pathogens simultaneously, with efficacy similar to that of pathogen-specific tests, and can uncover geographically relevant infectious CNS disease, such as tick-borne infections in New England. With further laboratory and computational enhancements, mNGS may become a mainstay of workup for encephalitis and meningitis. IMPORTANCE Meningitis and encephalitis are leading global causes of central nervous system (CNS) disability and mortality. Current diagnostic workflows remain inefficient, requiring costly pathogen-specific assays and sometimes invasive surgical procedures. Despite intensive diagnostic efforts, 40 to 60% of people with meningitis or encephalitis have no clear cause of CNS disease identified. As diagnostic uncertainty often leads to costly inappropriate therapies, the need for novel pathogen detection methods is paramount. Metagenomic next-generation sequencing (mNGS) offers the unique opportunity to circumvent these challenges using unbiased laboratory and computational methods. Here, we performed comprehensive mNGS from 68 prospectively enrolled patients with known (n = 44) or suspected (n = 24) CNS viral infection from a single center in New England and evaluated enhanced methods to improve the detection of CNS pathogens, including those not traditionally identified in the CNS by nucleic acid detection. Overall, our work helps elucidate how mNGS can become integrated into the diagnostic toolkit for CNS infections.
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Affiliation(s)
- Anne Piantadosi
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
- Division of Infectious Diseases, Massachusetts General Hospital, Boston, Massachusetts, USA
- Emory University School of Medicine, Atlanta, Georgia, USA
| | - Shibani S. Mukerji
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
| | - Simon Ye
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
- Harvard-MIT Program of Health Sciences and Technology, Cambridge, Massachusetts, USA
| | - Michael J. Leone
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Lisa M. Freimark
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Daniel Park
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Gordon Adams
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
- Division of Infectious Diseases, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Jacob Lemieux
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
- Division of Infectious Diseases, Massachusetts General Hospital, Boston, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
| | - Sanjat Kanjilal
- Division of Infectious Diseases, Massachusetts General Hospital, Boston, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
- Department of Pathology, Brigham and Women’s Hospital, Boston, Massachusetts, USA
| | - Isaac H. Solomon
- Harvard Medical School, Boston, Massachusetts, USA
- Department of Pathology, Brigham and Women’s Hospital, Boston, Massachusetts, USA
| | - Asim A. Ahmed
- Harvard Medical School, Boston, Massachusetts, USA
- Department of Pediatrics, Harvard Medical School, Children’s Hospital, Boston, Massachusetts, USA
| | - Robert Goldstein
- Division of Infectious Diseases, Massachusetts General Hospital, Boston, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
| | - Vijay Ganesh
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
- Department of Neurology, Brigham and Women’s Hospital, Boston, Massachusetts, USA
| | - Bridget Ostrem
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
| | - Kaelyn C. Cummins
- Division of Infectious Diseases, Brigham and Women’s Hospital, Boston, Massachusetts, USA
| | - Jesse M. Thon
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
| | - Cormac M. Kinsella
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, Massachusetts, USA
| | - Eric Rosenberg
- Division of Infectious Diseases, Massachusetts General Hospital, Boston, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
- Department of Pathology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Matthew P. Frosch
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
- Department of Pathology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Marcia B. Goldberg
- Division of Infectious Diseases, Massachusetts General Hospital, Boston, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
| | - Tracey A. Cho
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
- University of Iowa, Department of Neurology, Iowa City, Iowa, USA
| | - Pardis Sabeti
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
- Division of Infectious Diseases, Massachusetts General Hospital, Boston, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, Massachusetts, USA
- Department of Immunology and Infectious Disease, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, USA
- Howard Hughes Medical Institute, Chevy Chase, Maryland, USA
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5
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Sun H, Ganglberger W, Panneerselvam E, Leone MJ, Quadri SA, Goparaju B, Tesh RA, Akeju O, Thomas RJ, Westover MB. Sleep staging from electrocardiography and respiration with deep learning. Sleep 2021; 43:5682785. [PMID: 31863111 DOI: 10.1093/sleep/zsz306] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2019] [Revised: 11/13/2019] [Indexed: 01/08/2023] Open
Abstract
STUDY OBJECTIVES Sleep is reflected not only in the electroencephalogram but also in heart rhythms and breathing patterns. We hypothesized that it is possible to accurately stage sleep based on the electrocardiogram (ECG) and respiratory signals. METHODS Using a dataset including 8682 polysomnograms, we develop deep neural networks to stage sleep from ECG and respiratory signals. Five deep neural networks consisting of convolutional networks and long- and short-term memory networks are trained to stage sleep using heart and breathing, including the timing of R peaks from ECG, abdominal and chest respiratory effort, and the combinations of these signals. RESULTS ECG in combination with the abdominal respiratory effort achieved the best performance for staging all five sleep stages with a Cohen's kappa of 0.585 (95% confidence interval ±0.017); and 0.760 (±0.019) for discriminating awake vs. rapid eye movement vs. nonrapid eye movement sleep. Performance is better for younger ages, whereas it is robust for body mass index, apnea severity, and commonly used outpatient medications. CONCLUSIONS Our results validate that ECG and respiratory effort provide substantial information about sleep stages in a large heterogeneous population. This opens new possibilities in sleep research and applications where electroencephalography is not readily available or may be infeasible.
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Affiliation(s)
- Haoqi Sun
- Department of Neurology, Massachusetts General Hospital, Boston, MA
| | | | | | - Michael J Leone
- Department of Neurology, Massachusetts General Hospital, Boston, MA
| | - Syed A Quadri
- Department of Neurology, Massachusetts General Hospital, Boston, MA
| | - Balaji Goparaju
- Department of Neurology, Massachusetts General Hospital, Boston, MA
| | - Ryan A Tesh
- Department of Neurology, Massachusetts General Hospital, Boston, MA
| | - Oluwaseun Akeju
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, MA
| | - Robert J Thomas
- Division of Pulmonary, Critical Care & Sleep, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA
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6
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Mukerji SS, Das S, Alabsi H, Brenner LN, Jain A, Magdamo C, Collens SI, Ye E, Keller K, Boutros CL, Leone MJ, Newhouse A, Foy B, Li MD, Lang M, Anahtar MN, Shao YP, Ge W, Sun H, Triant VA, Kalpathy-Cramer J, Higgins J, Rosand J, Robbins GK, Westover MB. Prolonged Intubation in Patients With Prior Cerebrovascular Disease and COVID-19. Front Neurol 2021; 12:642912. [PMID: 33897598 PMCID: PMC8062773 DOI: 10.3389/fneur.2021.642912] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Accepted: 03/05/2021] [Indexed: 01/08/2023] Open
Abstract
Objectives: Patients with comorbidities are at increased risk for poor outcomes in COVID-19, yet data on patients with prior neurological disease remains limited. Our objective was to determine the odds of critical illness and duration of mechanical ventilation in patients with prior cerebrovascular disease and COVID-19. Methods: A observational study of 1,128 consecutive adult patients admitted to an academic center in Boston, Massachusetts, and diagnosed with laboratory-confirmed COVID-19. We tested the association between prior cerebrovascular disease and critical illness, defined as mechanical ventilation (MV) or death by day 28, using logistic regression with inverse probability weighting of the propensity score. Among intubated patients, we estimated the cumulative incidence of successful extubation without death over 45 days using competing risk analysis. Results: Of the 1,128 adults with COVID-19, 350 (36%) were critically ill by day 28. The median age of patients was 59 years (SD: 18 years) and 640 (57%) were men. As of June 2nd, 2020, 127 (11%) patients had died. A total of 177 patients (16%) had a prior cerebrovascular disease. Prior cerebrovascular disease was significantly associated with critical illness (OR = 1.54, 95% CI = 1.14-2.07), lower rate of successful extubation (cause-specific HR = 0.57, 95% CI = 0.33-0.98), and increased duration of intubation (restricted mean time difference = 4.02 days, 95% CI = 0.34-10.92) compared to patients without cerebrovascular disease. Interpretation: Prior cerebrovascular disease adversely affects COVID-19 outcomes in hospitalized patients. Further study is required to determine if this subpopulation requires closer monitoring for disease progression during COVID-19.
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Affiliation(s)
- Shibani S Mukerji
- Department of Neurology, Massachusetts General Hospital, Boston, MA, United States
- Harvard Medical School, Boston, MA, United States
| | - Sudeshna Das
- Department of Neurology, Massachusetts General Hospital, Boston, MA, United States
- Harvard Medical School, Boston, MA, United States
| | - Haitham Alabsi
- Department of Neurology, Massachusetts General Hospital, Boston, MA, United States
- Harvard Medical School, Boston, MA, United States
- Henry and Allison McCance Center for Brain Health, Massachusetts General Hospital, Boston, MA, United States
| | - Laura N Brenner
- Harvard Medical School, Boston, MA, United States
- Division of Pulmonary and Critical Care Medicine, Massachusetts General Hospital, Boston, MA, United States
| | - Aayushee Jain
- Department of Neurology, Massachusetts General Hospital, Boston, MA, United States
- Clinical Data A.I. Center (CDAC), Massachusetts General Hospital, Boston, MA, United States
| | - Colin Magdamo
- Department of Neurology, Massachusetts General Hospital, Boston, MA, United States
| | - Sarah I Collens
- Department of Neurology, Massachusetts General Hospital, Boston, MA, United States
| | - Elissa Ye
- Clinical Data A.I. Center (CDAC), Massachusetts General Hospital, Boston, MA, United States
| | - Kiana Keller
- Department of Neurology, Massachusetts General Hospital, Boston, MA, United States
| | - Christine L Boutros
- Department of Neurology, Massachusetts General Hospital, Boston, MA, United States
| | - Michael J Leone
- Clinical Data A.I. Center (CDAC), Massachusetts General Hospital, Boston, MA, United States
| | - Amy Newhouse
- Harvard Medical School, Boston, MA, United States
- Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA, United States
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, United States
| | - Brody Foy
- Department of Pathology, Massachusetts General Hospital, Boston, MA, United States
- Department of Systems Biology, Harvard Medical School, Boston, MA, United States
| | - Matthew D Li
- Department of Radiology, Massachusetts General Hospital, Boston, MA, United States
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States
| | - Min Lang
- Department of Radiology, Massachusetts General Hospital, Boston, MA, United States
| | - Melis N Anahtar
- Department of Pathology, Massachusetts General Hospital, Boston, MA, United States
| | - Yu-Ping Shao
- Clinical Data A.I. Center (CDAC), Massachusetts General Hospital, Boston, MA, United States
| | - Wendong Ge
- Clinical Data A.I. Center (CDAC), Massachusetts General Hospital, Boston, MA, United States
| | - Haoqi Sun
- Department of Neurology, Massachusetts General Hospital, Boston, MA, United States
- Harvard Medical School, Boston, MA, United States
- Clinical Data A.I. Center (CDAC), Massachusetts General Hospital, Boston, MA, United States
| | - Virginia A Triant
- Harvard Medical School, Boston, MA, United States
- Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA, United States
- Division of Infectious Diseases, Massachusetts General Hospital, Boston, MA, United States
- The Mongan Institute, Massachusetts General Hospital, Boston, MA, United States
| | - Jayashree Kalpathy-Cramer
- Department of Radiology, Massachusetts General Hospital, Boston, MA, United States
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States
| | - John Higgins
- Department of Pathology, Massachusetts General Hospital, Boston, MA, United States
- Department of Systems Biology, Harvard Medical School, Boston, MA, United States
| | - Jonathan Rosand
- Department of Neurology, Massachusetts General Hospital, Boston, MA, United States
- Harvard Medical School, Boston, MA, United States
- Henry and Allison McCance Center for Brain Health, Massachusetts General Hospital, Boston, MA, United States
| | - Gregory K Robbins
- Harvard Medical School, Boston, MA, United States
- Division of Infectious Diseases, Massachusetts General Hospital, Boston, MA, United States
| | - M Brandon Westover
- Department of Neurology, Massachusetts General Hospital, Boston, MA, United States
- Harvard Medical School, Boston, MA, United States
- Henry and Allison McCance Center for Brain Health, Massachusetts General Hospital, Boston, MA, United States
- Clinical Data A.I. Center (CDAC), Massachusetts General Hospital, Boston, MA, United States
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7
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Leone MJ, Sun H, Boutros CL, Liu L, Ye E, Sullivan L, Thomas RJ, Robbins GK, Mukerji SS, Westover MB. HIV Increases Sleep-based Brain Age Despite Antiretroviral Therapy. Sleep 2021; 44:6204183. [PMID: 33783511 DOI: 10.1093/sleep/zsab058] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2020] [Revised: 01/06/2021] [Indexed: 11/12/2022] Open
Abstract
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.
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Affiliation(s)
| | - Haoqi Sun
- Massachusetts General Hospital, Boston, MA, USA
| | | | - Lin Liu
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Elissa Ye
- Massachusetts General Hospital, Boston, MA, USA
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8
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Fernandes M, Sun H, Jain A, Alabsi HS, Brenner LN, Ye E, Ge W, Collens SI, Leone MJ, Das S, Robbins GK, Mukerji SS, Westover MB. Classification of the Disposition of Patients Hospitalized with COVID-19: Reading Discharge Summaries Using Natural Language Processing. JMIR Med Inform 2021; 9:e25457. [PMID: 33449908 PMCID: PMC7879729 DOI: 10.2196/25457] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2020] [Revised: 12/09/2020] [Accepted: 12/12/2020] [Indexed: 01/10/2023] Open
Abstract
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.
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Affiliation(s)
- Marta Fernandes
- Department of Neurology, Massachusetts General Hospital, Boston, MA, United States.,Clinical Data Animation Center, Boston, MA, United States.,Harvard Medical School, Boston, MA, United States
| | - Haoqi Sun
- Department of Neurology, Massachusetts General Hospital, Boston, MA, United States.,Clinical Data Animation Center, Boston, MA, United States.,Harvard Medical School, Boston, MA, United States
| | - Aayushee Jain
- Department of Neurology, Massachusetts General Hospital, Boston, MA, United States.,Clinical Data Animation Center, Boston, MA, United States
| | - Haitham S Alabsi
- Department of Neurology, Massachusetts General Hospital, Boston, MA, United States.,Harvard Medical School, Boston, MA, United States
| | - Laura N Brenner
- Harvard Medical School, Boston, MA, United States.,Division of Pulmonary and Critical Care Medicine, Massachusetts General Hospital, Boston, MA, United States.,Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA, United States
| | - Elissa Ye
- Department of Neurology, Massachusetts General Hospital, Boston, MA, United States.,Clinical Data Animation Center, Boston, MA, United States
| | - Wendong Ge
- Department of Neurology, Massachusetts General Hospital, Boston, MA, United States.,Clinical Data Animation Center, Boston, MA, United States.,Harvard Medical School, Boston, MA, United States
| | - Sarah I Collens
- Department of Neurology, Massachusetts General Hospital, Boston, MA, United States
| | - Michael J Leone
- Department of Neurology, Massachusetts General Hospital, Boston, MA, United States
| | - Sudeshna Das
- Department of Neurology, Massachusetts General Hospital, Boston, MA, United States.,Harvard Medical School, Boston, MA, United States
| | - Gregory K Robbins
- Harvard Medical School, Boston, MA, United States.,Division of Infectious Diseases, Massachusetts General Hospital, Boston, MA, United States
| | - Shibani S Mukerji
- Department of Neurology, Massachusetts General Hospital, Boston, MA, United States.,Harvard Medical School, Boston, MA, United States
| | - M Brandon Westover
- Department of Neurology, Massachusetts General Hospital, Boston, MA, United States.,Clinical Data Animation Center, Boston, MA, United States.,Harvard Medical School, Boston, MA, United States.,McCance Center for Brain Health, Massachusetts General Hospital, Boston, MA, United States
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9
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Sun H, Jain A, Leone MJ, Alabsi HS, Brenner LN, Ye E, Ge W, Shao YP, Boutros CL, Wang R, Tesh RA, Magdamo C, Collens SI, Ganglberger W, Bassett IV, Meigs JB, Kalpathy-Cramer J, Li MD, Chu JT, Dougan ML, Stratton LW, Rosand J, Fischl B, Das S, Mukerji SS, Robbins GK, Westover MB. CoVA: An Acuity Score for Outpatient Screening that Predicts Coronavirus Disease 2019 Prognosis. J Infect Dis 2021; 223:38-46. [PMID: 33098643 PMCID: PMC7665643 DOI: 10.1093/infdis/jiaa663] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2020] [Accepted: 10/19/2020] [Indexed: 01/08/2023] Open
Abstract
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.
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Affiliation(s)
- Haoqi Sun
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA.,Harvard Medical School, Boston, Massachusetts, USA.,Clinical Data AI Center, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Aayushee Jain
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA.,Clinical Data AI Center, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Michael J Leone
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA.,Clinical Data AI Center, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Haitham S Alabsi
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA.,Harvard Medical School, Boston, Massachusetts, USA
| | - Laura N Brenner
- Harvard Medical School, Boston, Massachusetts, USA.,Division of Pulmonary and Critical Care Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA.,Division of General Internal Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Elissa Ye
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA.,Clinical Data AI Center, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Wendong Ge
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA.,Harvard Medical School, Boston, Massachusetts, USA.,Clinical Data AI Center, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Yu-Ping Shao
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA.,Clinical Data AI Center, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Christine L Boutros
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Ruopeng Wang
- Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA.,Athinoula A. Martinos Center for Biomedical Imaging, Boston, Massachusetts, USA
| | - Ryan A Tesh
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA.,Clinical Data AI Center, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Colin Magdamo
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Sarah I Collens
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Wolfgang Ganglberger
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA.,Clinical Data AI Center, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Ingrid V Bassett
- Harvard Medical School, Boston, Massachusetts, USA.,Division of Infectious Diseases, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - James B Meigs
- Harvard Medical School, Boston, Massachusetts, USA.,Division of General Internal Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Jayashree Kalpathy-Cramer
- Harvard Medical School, Boston, Massachusetts, USA.,Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA.,Athinoula A. Martinos Center for Biomedical Imaging, Boston, Massachusetts, USA
| | - Matthew D Li
- Harvard Medical School, Boston, Massachusetts, USA.,Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA.,Athinoula A. Martinos Center for Biomedical Imaging, Boston, Massachusetts, USA
| | - Jacqueline T Chu
- Harvard Medical School, Boston, Massachusetts, USA.,Division of General Internal Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA.,Division of Infectious Diseases, Massachusetts General Hospital, Boston, Massachusetts, USA.,MGH Chelsea HealthCare Center, Chelsea, Massachusetts, USA
| | - Michael L Dougan
- Harvard Medical School, Boston, Massachusetts, USA.,Division of Gastroenterology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Lawrence W Stratton
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA.,Harvard Medical School, Boston, Massachusetts, USA
| | - Jonathan Rosand
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA.,Harvard Medical School, Boston, Massachusetts, USA
| | - Bruce Fischl
- Harvard Medical School, Boston, Massachusetts, USA.,Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA.,Athinoula A. Martinos Center for Biomedical Imaging, Boston, Massachusetts, USA.,Massachusetts Institute of Technology Health Sciences & Technology Program/Computer Science and Artificial Intelligence Laboratory, Cambridge, Massachusetts, USA
| | - Sudeshna Das
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA.,Harvard Medical School, Boston, Massachusetts, USA
| | - Shibani S Mukerji
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA.,Harvard Medical School, Boston, Massachusetts, USA
| | - Gregory K Robbins
- Harvard Medical School, Boston, Massachusetts, USA.,Division of Infectious Diseases, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - M Brandon Westover
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA.,Harvard Medical School, Boston, Massachusetts, USA.,Clinical Data AI Center, Massachusetts General Hospital, Boston, Massachusetts, USA
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10
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Sun H, Jain A, Leone MJ, Alabsi HS, Brenner LN, Ye E, Ge W, Shao YP, Boutros CL, Wang R, Tesh RA, Magdamo C, Collens SI, Ganglberger W, Bassett IV, Meigs JB, Kalpathy-Cramer J, Li MD, Chu JT, Dougan ML, Stratton LW, Rosand J, Fischl B, Das S, Mukerji SS, Robbins GK, Westover MB. CoVA: An Acuity Score for Outpatient Screening that Predicts Coronavirus Disease 2019 Prognosis. J Infect Dis 2021. [PMID: 33098643 DOI: 10.1093/infdis/jiaa663/5938525] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/03/2023] Open
Abstract
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.
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Affiliation(s)
- Haoqi Sun
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA.,Harvard Medical School, Boston, Massachusetts, USA.,Clinical Data AI Center, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Aayushee Jain
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA.,Clinical Data AI Center, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Michael J Leone
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA.,Clinical Data AI Center, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Haitham S Alabsi
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA.,Harvard Medical School, Boston, Massachusetts, USA
| | - Laura N Brenner
- Harvard Medical School, Boston, Massachusetts, USA.,Division of Pulmonary and Critical Care Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA.,Division of General Internal Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Elissa Ye
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA.,Clinical Data AI Center, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Wendong Ge
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA.,Harvard Medical School, Boston, Massachusetts, USA.,Clinical Data AI Center, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Yu-Ping Shao
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA.,Clinical Data AI Center, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Christine L Boutros
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Ruopeng Wang
- Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA.,Athinoula A. Martinos Center for Biomedical Imaging, Boston, Massachusetts, USA
| | - Ryan A Tesh
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA.,Clinical Data AI Center, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Colin Magdamo
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Sarah I Collens
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Wolfgang Ganglberger
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA.,Clinical Data AI Center, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Ingrid V Bassett
- Harvard Medical School, Boston, Massachusetts, USA.,Division of Infectious Diseases, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - James B Meigs
- Harvard Medical School, Boston, Massachusetts, USA.,Division of General Internal Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Jayashree Kalpathy-Cramer
- Harvard Medical School, Boston, Massachusetts, USA.,Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA.,Athinoula A. Martinos Center for Biomedical Imaging, Boston, Massachusetts, USA
| | - Matthew D Li
- Harvard Medical School, Boston, Massachusetts, USA.,Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA.,Athinoula A. Martinos Center for Biomedical Imaging, Boston, Massachusetts, USA
| | - Jacqueline T Chu
- Harvard Medical School, Boston, Massachusetts, USA.,Division of General Internal Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA.,Division of Infectious Diseases, Massachusetts General Hospital, Boston, Massachusetts, USA.,MGH Chelsea HealthCare Center, Chelsea, Massachusetts, USA
| | - Michael L Dougan
- Harvard Medical School, Boston, Massachusetts, USA.,Division of Gastroenterology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Lawrence W Stratton
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA.,Harvard Medical School, Boston, Massachusetts, USA
| | - Jonathan Rosand
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA.,Harvard Medical School, Boston, Massachusetts, USA
| | - Bruce Fischl
- Harvard Medical School, Boston, Massachusetts, USA.,Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA.,Athinoula A. Martinos Center for Biomedical Imaging, Boston, Massachusetts, USA.,Massachusetts Institute of Technology Health Sciences & Technology Program/Computer Science and Artificial Intelligence Laboratory, Cambridge, Massachusetts, USA
| | - Sudeshna Das
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA.,Harvard Medical School, Boston, Massachusetts, USA
| | - Shibani S Mukerji
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA.,Harvard Medical School, Boston, Massachusetts, USA
| | - Gregory K Robbins
- Harvard Medical School, Boston, Massachusetts, USA.,Division of Infectious Diseases, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - M Brandon Westover
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA.,Harvard Medical School, Boston, Massachusetts, USA.,Clinical Data AI Center, Massachusetts General Hospital, Boston, Massachusetts, USA
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11
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Abstract
IMPORTANCE Dementia is an increasing cause of disability and loss of independence in the elderly population yet remains largely underdiagnosed. A biomarker for dementia that can identify individuals with or at risk for developing dementia may help close this diagnostic gap. OBJECTIVE To investigate the association between a sleep electroencephalography-based brain age index (BAI), the difference between chronological age and brain age estimated using the sleep electroencephalogram, and dementia. DESIGN, SETTING, AND PARTICIPANTS In this retrospective cross-sectional study of 9834 polysomnograms, BAI was computed among individuals with previously determined dementia, mild cognitive impairment (MCI), or cognitive symptoms but no diagnosis of MCI or dementia, and among healthy individuals without dementia from August 22, 2008, to June 4, 2018. Data were analyzed from November 15, 2018, to June 24, 2020. EXPOSURE Dementia, MCI, and dementia-related symptoms, such as cognitive change and memory impairment. MAIN OUTCOMES AND MEASURES The outcome measures were the trend in BAI when moving from groups ranging from healthy, to symptomatic, to MCI, to dementia and pairwise comparisons of BAI among these groups. FINDINGS A total of 5144 sleep studies were included in BAI examinations. Patients in these studies had a median (interquartile range) age of 54 (43-65) years, and 3026 (59%) were men. The patients included 88 with dementia, 44 with MCI, 1075 who were symptomatic, and 2336 without dementia. There was a monotonic increase in mean (SE) BAI from the nondementia group to the dementia group (nondementia: 0.20 [0.42]; symptomatic: 0.58 [0.41]; MCI: 1.65 [1.20]; dementia: 4.18 [1.02]; P < .001). CONCLUSIONS AND RELEVANCE These findings suggest that a sleep-state electroencephalography-based BAI shows promise as a biomarker associated with progressive brain processes that ultimately result in dementia.
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Affiliation(s)
- Elissa Ye
- Department of Neurology, Massachusetts General Hospital, Boston
| | - Haoqi Sun
- Department of Neurology, Massachusetts General Hospital, Boston
| | | | - Luis Paixao
- Department of Neurology, Massachusetts General Hospital, Boston
| | - Robert J. Thomas
- Division of Pulmonary, Critical Care and Sleep, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts
| | - Alice D. Lam
- Department of Neurology, Massachusetts General Hospital, Boston
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12
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Sun H, Jain A, Leone MJ, Alabsi HS, Brenner L, Ye E, Ge W, Shao YP, Boutros C, Wang R, Tesh R, Magdamo C, Collens SI, Ganglberger W, Bassett IV, Meigs JB, Kalpathy-Cramer J, Li MD, Chu J, Dougan ML, Stratton L, Rosand J, Fischl B, Das S, Mukerji S, Robbins GK, Westover MB. COVID-19 Outpatient Screening: a Prediction Score for Adverse Events. medRxiv 2020:2020.06.17.20134262. [PMID: 32607523 PMCID: PMC7325189 DOI: 10.1101/2020.06.17.20134262] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
BACKGROUND We 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. METHODS Single-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 event ratio (E/O). Discrimination was assessed by C-statistics (AUC). RESULTS In 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. CONCLUSIONS CoVA is a prospectively validated automatable score to assessing risk for adverse outcomes related to COVID-19 infection in the outpatient setting.
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Affiliation(s)
- Haoqi Sun
- Department of Neurology, Massachusetts General Hospital, Boston, MA
- Harvard Medical School, Boston, MA
- Clinical Data AI Center (CDAC), Massachusetts General Hospital, Boston, MA
| | - Aayushee Jain
- Department of Neurology, Massachusetts General Hospital, Boston, MA
- Clinical Data AI Center (CDAC), Massachusetts General Hospital, Boston, MA
| | - Michael J Leone
- Department of Neurology, Massachusetts General Hospital, Boston, MA
- Clinical Data AI Center (CDAC), Massachusetts General Hospital, Boston, MA
| | - Haitham S Alabsi
- Department of Neurology, Massachusetts General Hospital, Boston, MA
- Harvard Medical School, Boston, MA
| | - Laura Brenner
- Harvard Medical School, Boston, MA
- Division of Pulmonary and Critical Care Medicine, Massachusetts General Hospital, Boston, MA
- Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA
| | - Elissa Ye
- Department of Neurology, Massachusetts General Hospital, Boston, MA
- Clinical Data AI Center (CDAC), Massachusetts General Hospital, Boston, MA
| | - Wendong Ge
- Department of Neurology, Massachusetts General Hospital, Boston, MA
- Harvard Medical School, Boston, MA
- Clinical Data AI Center (CDAC), Massachusetts General Hospital, Boston, MA
| | - Yu-Ping Shao
- Department of Neurology, Massachusetts General Hospital, Boston, MA
- Clinical Data AI Center (CDAC), Massachusetts General Hospital, Boston, MA
| | | | - Ruopeng Wang
- Department of Radiology, Massachusetts General Hospital, Boston, MA
- Athinoula A. Martinos Center for Biomedical Imaging, Boston, MA
| | - Ryan Tesh
- Department of Neurology, Massachusetts General Hospital, Boston, MA
- Clinical Data AI Center (CDAC), Massachusetts General Hospital, Boston, MA
| | - Colin Magdamo
- Department of Neurology, Massachusetts General Hospital, Boston, MA
| | - Sarah I Collens
- Department of Neurology, Massachusetts General Hospital, Boston, MA
| | - Wolfgang Ganglberger
- Department of Neurology, Massachusetts General Hospital, Boston, MA
- Clinical Data AI Center (CDAC), Massachusetts General Hospital, Boston, MA
| | - Ingrid V Bassett
- Harvard Medical School, Boston, MA
- Division of Infectious Diseases, Massachusetts General Hospital, Boston, MA
| | - James B Meigs
- Harvard Medical School, Boston, MA
- Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA
| | - Jayashree Kalpathy-Cramer
- Harvard Medical School, Boston, MA
- Department of Radiology, Massachusetts General Hospital, Boston, MA
- Athinoula A. Martinos Center for Biomedical Imaging, Boston, MA
| | - Matthew D Li
- Harvard Medical School, Boston, MA
- Department of Radiology, Massachusetts General Hospital, Boston, MA
- Athinoula A. Martinos Center for Biomedical Imaging, Boston, MA
| | - Jacqueline Chu
- Harvard Medical School, Boston, MA
- Division of Infectious Diseases, Massachusetts General Hospital, Boston, MA
- Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA
- MGH Chelsea HealthCare Center, Chelsea, MA
| | - Michael L Dougan
- Harvard Medical School, Boston, MA
- Division of Gastroenterology, Massachusetts General Hospital, Boston, MA
| | - Lawrence Stratton
- Department of Neurology, Massachusetts General Hospital, Boston, MA
- Harvard Medical School, Boston, MA
| | - Jonathan Rosand
- Department of Neurology, Massachusetts General Hospital, Boston, MA
- Harvard Medical School, Boston, MA
| | - Bruce Fischl
- Harvard Medical School, Boston, MA
- Department of Radiology, Massachusetts General Hospital, Boston, MA
- Athinoula A. Martinos Center for Biomedical Imaging, Boston, MA
- MIT HST/CSAIL, Cambridge, MA
| | - Sudeshna Das
- Department of Neurology, Massachusetts General Hospital, Boston, MA
- Harvard Medical School, Boston, MA
| | - Shibani Mukerji
- Department of Neurology, Massachusetts General Hospital, Boston, MA
- Harvard Medical School, Boston, MA
| | - Gregory K Robbins
- Harvard Medical School, Boston, MA
- Division of Infectious Diseases, Massachusetts General Hospital, Boston, MA
| | - M Brandon Westover
- Department of Neurology, Massachusetts General Hospital, Boston, MA
- Harvard Medical School, Boston, MA
- Clinical Data AI Center (CDAC), Massachusetts General Hospital, Boston, MA
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13
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Leone MJ, Sun H, Boutros C, Sullivan L, Thomas RJ, Robbins G, Mukerji S, Westover M. 1008 Brain Age Based on Sleep Encephalography is Elevated in HIV+ Adults on ART. Sleep 2020. [DOI: 10.1093/sleep/zsaa056.1004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Abstract
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<0.001), obstructive apnea (47% vs 30%, p=0.03), depression (49% vs 23%, p<0.001), and bipolar disorder (19% vs 4%, p<0.001).
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.
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Affiliation(s)
- M J Leone
- Massachusetts General Hospital, Boston, MA
| | - H Sun
- Massachusetts General Hospital, Boston, MA
| | - C Boutros
- Massachusetts General Hospital, Boston, MA
| | - L Sullivan
- Massachusetts General Hospital, Boston, MA
| | - R J Thomas
- Massachusetts General Hospital, Boston, MA
| | - G Robbins
- Massachusetts General Hospital, Boston, MA
| | - S Mukerji
- Massachusetts General Hospital, Boston, MA
| | - M Westover
- Massachusetts General Hospital, Boston, MA
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14
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Zafar A, Fiani B, Hadi H, Arshad M, Cathel A, Naeem M, Parsons MS, Farooqui M, Bucklin AA, Leone MJ, Baig A, Quadri SA. Cerebral vascular malformations and their imaging modalities. Neurol Sci 2020; 41:2407-2421. [DOI: 10.1007/s10072-020-04415-4] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2020] [Accepted: 04/13/2020] [Indexed: 12/19/2022]
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15
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Piantadosi A, Kanjilal S, Ganesh V, Khanna A, Hyle EP, Rosand J, Bold T, Metsky HC, Lemieux J, Leone MJ, Freimark L, Matranga CB, Adams G, McGrath G, Zamirpour S, Telford S, Rosenberg E, Cho T, Frosch MP, Goldberg MB, Mukerji SS, Sabeti PC. Rapid Detection of Powassan Virus in a Patient With Encephalitis by Metagenomic Sequencing. Clin Infect Dis 2019; 66:789-792. [PMID: 29020227 PMCID: PMC5850433 DOI: 10.1093/cid/cix792] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2017] [Accepted: 09/06/2017] [Indexed: 11/13/2022] Open
Abstract
We describe a patient with severe and progressive encephalitis of unknown etiology. We performed rapid metagenomic sequencing from cerebrospinal fluid and identified Powassan virus, an emerging tick-borne flavivirus that has been increasingly detected in the United States.
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Affiliation(s)
- Anne Piantadosi
- Division of Infectious Diseases, Massachusetts General Hospital.,Harvard Medical School, Boston.,Broad Institute of Massachusetts Institute of Technology (MIT) and Harvard, Cambridge
| | - Sanjat Kanjilal
- Division of Infectious Diseases, Massachusetts General Hospital.,Harvard Medical School, Boston
| | - Vijay Ganesh
- Department of Neurology, Massachusetts General Hospital, Boston
| | - Arjun Khanna
- Department of Neurology, Massachusetts General Hospital, Boston
| | - Emily P Hyle
- Division of Infectious Diseases, Massachusetts General Hospital.,Harvard Medical School, Boston
| | - Jonathan Rosand
- Department of Neurology, Massachusetts General Hospital, Boston
| | - Tyler Bold
- Division of Infectious Diseases, Massachusetts General Hospital
| | - Hayden C Metsky
- Broad Institute of Massachusetts Institute of Technology (MIT) and Harvard, Cambridge.,Department of Electrical Engineering and Computer Science, MIT, Cambridge
| | - Jacob Lemieux
- Broad Institute of Massachusetts Institute of Technology (MIT) and Harvard, Cambridge.,Department of Medicine, Massachusetts General Hospital, Boston
| | - Michael J Leone
- Department of Neurology, Massachusetts General Hospital, Boston
| | - Lisa Freimark
- Broad Institute of Massachusetts Institute of Technology (MIT) and Harvard, Cambridge
| | - Christian B Matranga
- Broad Institute of Massachusetts Institute of Technology (MIT) and Harvard, Cambridge
| | - Gordon Adams
- Division of Infectious Diseases, Massachusetts General Hospital
| | - Graham McGrath
- Division of Infectious Diseases, Massachusetts General Hospital
| | | | - Sam Telford
- Tufts School of Veterinary Medicine, North Grafton
| | - Eric Rosenberg
- Division of Infectious Diseases, Massachusetts General Hospital.,Harvard Medical School, Boston
| | - Tracey Cho
- Harvard Medical School, Boston.,Department of Neurology, Massachusetts General Hospital, Boston
| | - Matthew P Frosch
- Harvard Medical School, Boston.,Division of Neuropathology, Massachusetts General Hospital
| | - Marcia B Goldberg
- Division of Infectious Diseases, Massachusetts General Hospital.,Broad Institute of Massachusetts Institute of Technology (MIT) and Harvard, Cambridge.,Department of Microbiology and Immunobiology, Harvard Medical School
| | - Shibani S Mukerji
- Harvard Medical School, Boston.,Department of Neurology, Massachusetts General Hospital, Boston.,Department of Cancer Immunology and Virology, Dana-Farber Cancer Institute, Boston
| | - Pardis C Sabeti
- Broad Institute of Massachusetts Institute of Technology (MIT) and Harvard, Cambridge.,FAS Center for Systems Biology, Department of Organismic and Evolutionary Biology, Harvard University, Cambridge.,Department of Immunology and Infectious Disease, Harvard School of Public Health, Boston, Massachusetts.,Howard Hughes Medical Institute, Chevy Chase, Maryland
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16
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Affiliation(s)
- Mahmoud A AbdelRazek
- From the Department of Neurology, Massachusetts General Hospital, Harvard University, Boston.
| | - Michael J Leone
- From the Department of Neurology, Massachusetts General Hospital, Harvard University, Boston
| | - Nagagopal Venna
- From the Department of Neurology, Massachusetts General Hospital, Harvard University, Boston
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17
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Leone MJ, Schurter BN, Letson B, Booth V, Zochowski M, Fink CG. Synchronization properties of heterogeneous neuronal networks with mixed excitability type. Phys Rev E Stat Nonlin Soft Matter Phys 2015; 91:032813. [PMID: 25871163 PMCID: PMC4899572 DOI: 10.1103/physreve.91.032813] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/31/2014] [Indexed: 06/04/2023]
Abstract
We study the synchronization of neuronal networks with dynamical heterogeneity, showing that network structures with the same propensity for synchronization (as quantified by master stability function analysis) may develop dramatically different synchronization properties when heterogeneity is introduced with respect to neuronal excitability type. Specifically, we investigate networks composed of neurons with different types of phase response curves (PRCs), which characterize how oscillating neurons respond to excitatory perturbations. Neurons exhibiting type 1 PRC respond exclusively with phase advances, while neurons exhibiting type 2 PRC respond with either phase delays or phase advances, depending on when the perturbation occurs. We find that Watts-Strogatz small world networks transition to synchronization gradually as the proportion of type 2 neurons increases, whereas scale-free networks may transition gradually or rapidly, depending upon local correlations between node degree and excitability type. Random placement of type 2 neurons results in gradual transition to synchronization, whereas placement of type 2 neurons as hubs leads to a much more rapid transition, showing that type 2 hub cells easily "hijack" neuronal networks to synchronization. These results underscore the fact that the degree of synchronization observed in neuronal networks is determined by a complex interplay between network structure and the dynamical properties of individual neurons, indicating that efforts to recover structural connectivity from dynamical correlations must in general take both factors into account.
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Affiliation(s)
- Michael J. Leone
- Mathematics Department, New College of Florida, Sarasota, Florida 34243, USA
- Program in Neural Computation, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA
| | | | - Benjamin Letson
- Mathematics Department, Ohio Wesleyan University, Delaware, Ohio 43015, USA
- Mathematics Department, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, USA
| | - Victoria Booth
- Mathematics Department and Anesthesiology Department, University of Michigan, Ann Arbor, Michigan 48109, USA
| | - Michal Zochowski
- Physics Department and Biophysics Program, University of Michigan, Ann Arbor, Michigan 48109, USA
| | - Christian G. Fink
- Physics Department and Neuroscience Program, Ohio Wesleyan University, Delaware, Ohio 43015, USA
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