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1.
Ann Neurol ; 91(3): 367-379, 2022 03.
Article in English | MEDLINE | ID: covidwho-1636023

ABSTRACT

OBJECTIVE: The purpose of this study was to describe cerebrovascular, neuropathic, and autonomic features of post-acute sequelae of coronavirus disease 2019 ((COVID-19) PASC). METHODS: This retrospective study evaluated consecutive patients with chronic fatigue, brain fog, and orthostatic intolerance consistent with PASC. Controls included patients with postural tachycardia syndrome (POTS) and healthy participants. Analyzed data included surveys and autonomic (Valsalva maneuver, deep breathing, sudomotor, and tilt tests), cerebrovascular (cerebral blood flow velocity [CBFv] monitoring in middle cerebral artery), respiratory (capnography monitoring), and neuropathic (skin biopsies for assessment of small fiber neuropathy) testing and inflammatory/autoimmune markers. RESULTS: Nine patients with PASC were evaluated 0.8 ± 0.3 years after a mild COVID-19 infection, and were treated as home observations. Autonomic, pain, brain fog, fatigue, and dyspnea surveys were abnormal in PASC and POTS (n = 10), compared with controls (n = 15). Tilt table test reproduced the majority of PASC symptoms. Orthostatic CBFv declined in PASC (-20.0 ± 13.4%) and POTS (-20.3 ± 15.1%), compared with controls (-3.0 ± 7.5%, p = 0.001) and was independent of end-tidal carbon dioxide in PASC, but caused by hyperventilation in POTS. Reduced orthostatic CBFv in PASC included both subjects without (n = 6) and with (n = 3) orthostatic tachycardia. Dysautonomia was frequent (100% in both PASC and POTS) but was milder in PASC (p = 0.002). PASC and POTS cohorts diverged in frequency of small fiber neuropathy (89% vs 60%) but not in inflammatory markers (67% vs 70%). Supine and orthostatic hypocapnia was observed in PASC. INTERPRETATION: PASC following mild COVID-19 infection is associated with multisystem involvement including: (1) cerebrovascular dysregulation with persistent cerebral arteriolar vasoconstriction; (2) small fiber neuropathy and related dysautonomia; (3) respiratory dysregulation; and (4) chronic inflammation. ANN NEUROL 2022;91:367-379.


Subject(s)
Blood Pressure/physiology , COVID-19/complications , Cerebrovascular Circulation/physiology , Heart Rate/physiology , Inflammation Mediators/blood , Adult , COVID-19/blood , COVID-19/diagnosis , COVID-19/physiopathology , Fatigue/blood , Fatigue/diagnosis , Fatigue/physiopathology , Female , Humans , Male , Middle Aged , Orthostatic Intolerance/blood , Orthostatic Intolerance/diagnosis , Orthostatic Intolerance/physiopathology , Retrospective Studies
2.
J Infect Dis ; 223(1): 38-46, 2021 01 04.
Article in English | MEDLINE | ID: covidwho-1066343

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.


Subject(s)
COVID-19/diagnosis , Severity of Illness Index , Adult , Aged , Critical Illness , Female , Hospitalization , Humans , Intensive Care Units , Male , Middle Aged , Models, Theoretical , Outpatients , Predictive Value of Tests , Prognosis , Prospective Studies , ROC Curve , Sensitivity and Specificity
3.
JMIR Med Inform ; 9(2): e25457, 2021 Feb 10.
Article in English | MEDLINE | ID: covidwho-1032549

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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