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Factors associated with the presence of headache in hospitalized COVID-19 patients and impact on prognosis: a retrospective cohort study.

ABSTRACT: INTRODUCTION:Headache is one of the most frequent neurologic manifestations in COVID-19. We aimed to analyze which symptoms and laboratory abnormalities were associated with the presence of headache and to evaluate if patients with headache had a higher adjusted in-hospital risk of mortality. METHODS:Retrospective cohort study. We included all consecutive patients admitted to the Hospital with confirmed SARS-CoV-2 infection between March 8th and April 11th, 2020. We collected demographic data, clinical variables and laboratory abnormalities. We used multivariate regression analysis. RESULTS:During the study period, 576 patients were included, aged 67.2 (SD: 14.7), and 250/576 (43.3%) being female. Presence of headache was described by 137 (23.7%) patients. The all-cause in-hospital mortality rate was 127/576 (20.0%). In the multivariate analysis, patients with headache had a lower risk of mortality (OR: 0.39, 95% CI: 0.17-0.88, p?=?0.007). After adjusting for multiple comparisons in a multivariate analysis, variables that were independently associated with a higher odds of having headache in COVID-19 patients were anosmia, myalgia, female sex and fever; variables that were associated with a lower odds of having headache were younger age, lower score on modified Rankin scale, and, regarding laboratory variables on admission, increased C-reactive protein, abnormal platelet values, lymphopenia and increased D-dimer. CONCLUSION:Headache is a frequent symptom in COVID-19 patients and its presence is an independent predictor of lower risk of mortality in COVID-19 hospitalized patients.

SUBMITTER: Trigo J

PROVIDER: S-EPMC7388434 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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Factors associated with the presence of headache in hospitalized COVID-19 patients and impact on prognosis: a retrospective cohort study.

Trigo Javier J García-Azorín David D Planchuelo-Gómez Álvaro Á Martínez-Pías Enrique E Talavera Blanca B Hernández-Pérez Isabel I Valle-Peñacoba Gonzalo G Simón-Campo Paula P de Lera Mercedes M Chavarría-Miranda Alba A López-Sanz Cristina C Gutiérrez-Sánchez María M Martínez-Velasco Elena E Pedraza María M Sierra Álvaro Á Gómez-Vicente Beatriz B Arenillas Juan Francisco JF Guerrero Ángel L ÁL

The journal of headache and pain 20200729 1

<h4>Introduction</h4>Headache is one of the most frequent neurologic manifestations in COVID-19. We aimed to analyze which symptoms and laboratory abnormalities were associated with the presence of headache and to evaluate if patients with headache had a higher adjusted in-hospital risk of mortality.<h4>Methods</h4>Retrospective cohort study. We included all consecutive patients admitted to the Hospital with confirmed SARS-CoV-2 infection between March 8th and April 11th, 2020. We collected demo ...[more]

PMID: 32727345

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Project description:ObjectivesStudies have shown that acute kidney injury (AKI) occurrence post SARS-CoV-2 infection is complex and has a poor prognosis. Therefore, more studies are needed to understand the rate and the predications of AKI involvement among hospitalized COVID-19 patients and AKI's impact on prognosis while under different types of medications.Patients and methodsThis study is a retrospective observational cohort study conducted at Bahrain Defence Force (BDF) Royal Medical Services. Medical records of COVID-19 patients admitted to BDF hospital, treated, and followed up from April 2020 to October 2020 were retrieved. Data were analyzed using univariate and multivariate logistic regression with covariate adjustment, and the odds ratio (OR) and 95% confidence (95% CI) interval were reported.ResultsAmong 353 patients admitted with COVID-19, 47.6% developed AKI. Overall, 51.8% of patients with AKI died compared to 2.2% of patients who did not develop AKI (p< 0.001 with OR 48.6 and 95% CI 17.2-136.9). Besides, deaths in patients classified with AKI staging were positively correlated and multivariate regression analysis revealed that moderate to severe hypoalbuminemia (<32 g/L) was independently correlated to death in AKI patients with an OR of 10.99 (CI 95% 4.1-29.3, p<0.001). In addition, 78.2% of the dead patients were on mechanical ventilation. Besides age as a predictor of AKI development, diabetes and hypertension were the major risk factors of AKI development (OR 2.04, p<0.01, and 0.05 for diabetes and hypertension, respectively). Also, two or more comorbidities substantially increased the risk of AKI development in COVID-19 patients. Furthermore, high levels upon hospital admission of D-Dimer, Troponin I, and ProBNP and low serum albumin were associated with AKI development. Lastly, patients taking ACEI/ARBs had less chance to develop AKI stage II/III with OR of 0.19-0.27 (p<0.05-0.01).ConclusionsThe incidence of AKI in hospitalized COVID-19 patients and the mortality rate among AKI patients were high and correlated with AKI staging. Furthermore, laboratory testing for serum albumin, hypercoagulability and cardiac injury markers maybe indicative for AKI development. Therefore, clinicians should be mandated to perform such tests on admission and follow-up in hospitalized patients.

Project description:BackgroundPeru was one of the countries with the highest COVID-19 mortality worldwide during the first stage of the pandemic. It is then relevant to evaluate the risk factors for mortality in patients hospitalized for COVID-19 in three hospitals in Peru in 2020, from March to May, 2020. Methods: We carried out a retrospective cohort study. The population consisted of patients from three Peruvian hospitals hospitalized for a diagnosis of COVID-19 during the March-May 2020 period. Independent sociodemographic variables, medical history, symptoms, vital functions, laboratory parameters and medical treatment were evaluated. In-hospital mortality was assessed as the outcome. We performed Cox regression models (crude and adjusted) to evaluate risk factors for in-hospital mortality. Hazard ratios (HR) with their respective 95% confidence intervals (95% CI) were calculated. Results: We analyzed 493 hospitalized adults; 72.8% (n=359) were male and the mean age was 63.3 ± 14.4 years. COVID-19 symptoms appeared on average 7.9 ± 4.0 days before admission to the hospital, and the mean oxygen saturation on admission was 82.6 ± 13.8. While 67.6% (n=333) required intensive care unit admission, only 3.3% (n=16) were admitted to this unit, and 60.2% (n=297) of the sample died. In the adjusted regression analysis, it was found that being 60 years old or older (HR=1.57; 95% CI: 1.14-2.15), having two or more comorbidities (HR=1.53; 95% CI: 1.10-2.14), oxygen saturation between 85-80% (HR=2.52; 95% CI: 1.58-4.02), less than 80% (HR=4.59; 95% CI: 3.01-7.00), and being in the middle (HR=1.65; 95% CI: 1.15-2.39) and higher tertile (HR=2.18; 95% CI: 1.51-3.15) of the neutrophil-to-lymphocyte ratio, increased the risk of mortality. Conclusions: The risk factors found agree with what has been described in the literature and allow the identification of vulnerable groups in whom monitoring and early identification of symptoms should be prioritized in order to reduce mortality.

Project description:BackgroundThe novel coronavirus SARS-CoV-2 and its associated disease, COVID-19, have caused worldwide disruption, leading countries to take drastic measures to address the progression of the disease. As SARS-CoV-2 continues to spread, hospitals are struggling to allocate resources to patients who are most at risk. In this context, it has become important to develop models that can accurately predict the severity of infection of hospitalized patients to help guide triage, planning, and resource allocation.ObjectiveThe aim of this study was to develop accurate models to predict the mortality of hospitalized patients with COVID-19 using basic demographics and easily obtainable laboratory data.MethodsWe performed a retrospective study of 375 hospitalized patients with COVID-19 in Wuhan, China. The patients were randomly split into derivation and validation cohorts. Regularized logistic regression and support vector machine classifiers were trained on the derivation cohort, and accuracy metrics (F1 scores) were computed on the validation cohort. Two types of models were developed: the first type used laboratory findings from the entire length of the patient's hospital stay, and the second type used laboratory findings that were obtained no later than 12 hours after admission. The models were further validated on a multicenter external cohort of 542 patients.ResultsOf the 375 patients with COVID-19, 174 (46.4%) died of the infection. The study cohort was composed of 224/375 men (59.7%) and 151/375 women (40.3%), with a mean age of 58.83 years (SD 16.46). The models developed using data from throughout the patients' length of stay demonstrated accuracies as high as 97%, whereas the models with admission laboratory variables possessed accuracies of up to 93%. The latter models predicted patient outcomes an average of 11.5 days in advance. Key variables such as lactate dehydrogenase, high-sensitivity C-reactive protein, and percentage of lymphocytes in the blood were indicated by the models. In line with previous studies, age was also found to be an important variable in predicting mortality. In particular, the mean age of patients who survived COVID-19 infection (50.23 years, SD 15.02) was significantly lower than the mean age of patients who died of the infection (68.75 years, SD 11.83; P<.001).ConclusionsMachine learning models can be successfully employed to accurately predict outcomes of patients with COVID-19. Our models achieved high accuracies and could predict outcomes more than one week in advance; this promising result suggests that these models can be highly useful for resource allocation in hospitals.

Project description:BackgroundCOVID-19 is an ongoing global pandemic. Changes in haematological characteristics in patients with COVID-19 are emerging as important features of the disease. We aimed to explore the haematological characteristics and related risk factors in patients with COVID-19.MethodsThis retrospective cohort study included patients with COVID-19 admitted to three designated sites of Wuhan Union Hospital (Wuhan, China). Demographic, clinical, laboratory, treatment, and outcome data were extracted from electronic medical records and compared between patients with moderate, severe, and critical disease (defined according to the diagnosis and treatment protocol for novel coronavirus pneumonia, trial version 7, published by the National Health Commission of China). We assessed the risk factors associated with critical illness and poor prognosis. Dynamic haematological and coagulation parameters were investigated with a linear mixed model, and coagulopathy screening with sepsis-induced coagulopathy and International Society of Thrombosis and Hemostasis overt disseminated intravascular coagulation scoring systems was applied.FindingsOf 466 patients admitted to hospital from Jan 23 to Feb 23, 2020, 380 patients with COVID-19 were included in our study. The incidence of thrombocytopenia (platelet count <100 × 109 cells per L) in patients with critical disease (42 [49%] of 86) was significantly higher than in those with severe (20 [14%] of 145) or moderate (nine [6%] of 149) disease (p<0·0001). The numbers of lymphocytes and eosinophils were significantly lower in patients with critical disease than those with severe or moderate disease (p<0·0001), and prothrombin time, D-dimer, and fibrin degradation products significantly increased with increasing disease severity (p<0·0001). In multivariate analyses, death was associated with increased neutrophil to lymphocyte ratio (≥9·13; odds ratio [OR] 5·39 [95% CI 1·70-17·13], p=0·0042), thrombocytopenia (platelet count <100 × 109 per L; OR 8·33 [2·56-27·15], p=0·00045), prolonged prothrombin time (>16 s; OR 4·94 [1·50-16·25], p=0·0094), and increased D-dimer (>2 mg/L; OR 4·41 [1·06-18·30], p=0·041). Thrombotic and haemorrhagic events were common complications in patients who died (19 [35%] of 55). Sepsis-induced coagulopathy and International Society of Thrombosis and Hemostasis overt disseminated intravascular coagulation scores (assessed in 12 patients who survived and eight patients who died) increased over time in patients who died. The onset of sepsis-induced coagulopathy was typically before overt disseminated intravascular coagulation.InterpretationRapid blood tests, including platelet count, prothrombin time, D-dimer, and neutrophil to lymphocyte ratio can help clinicians to assess severity and prognosis of patients with COVID-19. The sepsis-induced coagulopathy scoring system can be used for early assessment and management of patients with critical disease.FundingNational Key Research and Development Program of China.

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Factors associated with the presence of headache in hospitalized COVID-19 patients and impact on prognosis: a retrospective cohort study.

Publications

Factors associated with the presence of headache in hospitalized COVID-19 patients and impact on prognosis: a retrospective cohort study.

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