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1.
EuropePMC; 2022.
Preprint in English | EuropePMC | ID: ppcovidwho-337850

ABSTRACT

Background: Coronavirus disease 2019 (COVID-19) causes mild symptoms to severe illness in the nervous system. Among the wide-ranged symptomology, headache is a common neurological symptom reported by COVID-19 patients. However, the prevalence, comorbidities, and ethnic susceptibilities of COVID-19-induced headaches are not well-defined. MethodsWe performed a retrospective chart review of patients who tested positive for SARS-CoV2 by reverse transcriptase -polymerase chain reaction (RT-PCR) in March and April 2020 at Massachusetts General Hospital, Boston, Massachusetts, USA. An electronic medical record review was performed to collect demographic data, major COVID-19 symptomology, and results of radiologic and laboratory tests. Data were recorded starting on the disease onset, as identified by the patient’s clinical notes. The demographical trends and prevalence of headache and comorbid symptoms were statistically analyzed. Results We identified 450 patients, 202 (44.9%) male and 248 (55.1%) female, who tested positive for COVID-19 in the study. Common symptoms included cough (66.4%), fever,(58.9%), fatigue (30.9%), and sore throat (29.6%). Forty-six percent of patients had shortness of breath, 32% had evidence of pneumonia, and 26% needed hospitalization. Gender predominance is significant in developing a sore throat, nasal congestion, hypogeusia, headache, ear pain in females, and pneumonia and inpatient hospitalization in males. Younger patients (< 50) were more likely to develop sore throat, fatigue, anosmia, hypogeusia, ear pain, myalgia /arthralgia, and headache. In contrast, older (> 50) patients were prone to develop pneumonia and required hospitalization. Compared to non-Hispanic ethnic populations, a significantly higher number of Hispanic patients reported headaches, nausea/vomiting, nasal congestion, fever, fatigue, anosmia, and myalgia/arthralgia compared to non-Hispanics, suggesting an ethnic susceptibility in COVID-19 symptomatology. Overall, patients who developed headaches were younger and more likely to have mild viral symptoms, pain, and neurological symptoms but not severe respiratory symptoms (shortness of breath and pneumonia) and hospitalization. ConclusionsOur results demonstrated gender, age, and ethnic factors in COVID symptomatology and identified that mild viral, painful, and neurological symptoms are positive predictors for headache development in COVID-19.

2.
Front Neurol ; 12: 642912, 2021.
Article in English | MEDLINE | ID: covidwho-1202073

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.

3.
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
4.
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.

5.
Neurohospitalist ; 11(3): 204-213, 2021 Jul.
Article in English | MEDLINE | ID: covidwho-965426

ABSTRACT

BACKGROUND AND PURPOSE: Reports have suggested that severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes neurologic manifestations including encephalopathy and seizures. However, there has been relatively limited electrophysiology data to contextualize these specific concerns and to understand their associated clinical factors. Our objective was to identify EEG abnormalities present in patients with SARS-CoV-2, and to determine whether they reflect new or preexisting brain pathology. METHODS: We studied a consecutive series of hospitalized patients with SARS-CoV-2 who received an EEG, obtained using tailored safety protocols. Data from EEG reports and clinical records were analyzed to identify EEG abnormalities and possible clinical associations, including neurologic symptoms, new or preexisting brain pathology, and sedation practices. RESULTS: We identified 37 patients with SARS-CoV-2 who underwent EEG, of whom 14 had epileptiform findings (38%). Patients with epileptiform findings were more likely to have preexisting brain pathology (6/14, 43%) than patients without epileptiform findings (2/23, 9%; p = 0.042). There were no clear differences in rates of acute brain pathology. One case of nonconvulsive status epilepticus was captured, but was not clearly a direct consequence of SARS-CoV-2. Abnormalities of background rhythms were common, as may be seen in systemic illness, and in part associated with recent sedation (p = 0.022). CONCLUSIONS: Epileptiform abnormalities were common in patients with SARS-CoV-2 referred for EEG, but particularly in the context of preexisting brain pathology and sedation. These findings suggest that neurologic manifestations during SARS-CoV-2 infection may not solely relate to the infection itself, but rather may also reflect patients' broader, preexisting neurologic vulnerabilities.

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