Corpus overview


MeSH Disease

Human Phenotype


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    Developing the nomogram for the prediction of in-hospital incidence of acute respiratory distress syndrome MESHD in patients with COVID-19

    Authors: Ning Ding; Yang Zhou; Guifang Yang; Cuirong Guo; Fengning Tang; Xiangping Chai

    doi:10.21203/ Date: 2020-07-26 Source: ResearchSquare

    Background: Acute respiratory distress HP syndrome MESHD (ARDS) was the most common complication of coronavirus disease MESHD-2019(COVID-19), leading to poor clinical outcomes. However, the model to predict the in-hospital incidence of ARDS in patients with COVID-19 is limited. Therefore, we aimed to develop a predictive nomogram for the in-hospital incidence of ARDS in COVID-19 patients.Methods: Patients with COVID-19 admitted to Changsha Public Health Centre between Jan 30, 2020, and Feb 22, 2020, were enrolled. Clinical characteristics and laboratory variables were analyzed in patients with ARDS. Risk factors for ARDS were selected by LASSO binary logistic regression. Nomogram was established based on risk factors and validated by the dataset.Results: A total of 113 patients, involving 99 in the non-ARDS group and 14 in the ARDS group were included in the study. 8 variables including hypertension MESHD hypertension HP, chronic obstructive pulmonary disease MESHD chronic obstructive pulmonary disease HP (COPD), cough MESHD cough HP, lactate dehydrogenase (LDH), creatine kinase (CK), white blood SERO count (WBC), body temperature, and heart rate were identified to be included in the model. The specificity, sensitivity SERO, and accuracy of the full model were 100%, 85.7%, and 87.5% respectively. The calibration curve also showed good agreement between the predicted and observed values in the model.Conclusions: The nomogram can predict the in-hospital incidence of ARDS in COVID-19 patients. It helps physicians to make an individualized treatment plan for each patient.

    Impact of Congestive Heart Failure HP Heart Failure MESHD and Role of Cardiac Biomarkers in COVID-19 patients: A Systematic Review and Meta-Analysis

    Authors: Tarun Dalia; Shubham Lahan; Sagar Ranka; Prakash Acharya; Archana Gautam; Ioannis Mastoris; Andrew Sauer; Zubair Shah

    doi:10.1101/2020.07.06.20147421 Date: 2020-07-07 Source: medRxiv

    Background: Coronavirus disease MESHD 2019 (COVID-19) has been reported to cause worse outcomes in patients with underlying cardiovascular disease MESHD, especially in patients with acute cardiac injury, which is determined by elevated levels of high- sensitivity SERO troponin. There is a paucity of data on the impact of congestive heart failure HP heart failure MESHD (CHF) on outcomes in COVID-19 patients. Methods: We conducted a literature search of PubMed/Medline, EMBASE, and Google Scholar databases from 11/1/2019 till 06/07/2020, and identified all relevant studies reporting cardiovascular comorbidities, cardiac biomarkers, disease MESHD severity, and survival. Pooled data from the selected studies were used for metanalysis to identify the impact of risk factors and cardiac biomarker elevation on disease MESHD severity and/or mortality. Results: We collected pooled data on 5,967 COVID-19 patients from 20 individual studies. We found that both non-survivors and those with severe disease MESHD had an increased risk of acute cardiac injury and cardiac arrhythmias MESHD arrhythmias HP, our pooled relative risk (RR) was - 8.52 (95% CI 3.63-19.98) (p<0.001); and 3.61 (95% CI 2.03-6.43) (p=0.001), respectively. Mean difference in the levels of Troponin-I, CK-MB, and NT-proBNP was higher in deceased and severely infected patients. The RR of in-hospital mortality was 2.35 (95% CI 1.18-4.70) (p=0.022) and 1.52 (95% CI 1.12-2.05) (p=0.008) among patients who had pre-existing CHF and hypertension MESHD hypertension HP, respectively. Conclusion: Cardiac involvement in COVID-19 infection MESHD appears to significantly adversely impact patient prognosis and survival. Pre-existence of CHF and high cardiac biomarkers like NT-pro BNP and CK-MB levels in COVID-19 patients correlates with worse outcomes. Keywords: Acute cardiac injury; cardiac arrhythmia MESHD arrhythmia HP; mortality risk; cardiac biomarkers, COVID-19.

    COVID-19 Inmate Risk Appraisal (CIRA): Development and validation of a screening tool to assess COVID-19 vulnerability in prisons

    Authors: Leonel C. Gonçalves; Stéphanie Baggio; Michael Weber; Laurent Gétaz; Hans Wolff; Jay P. Singh; Andreas Naegeli; Astrid Rossegger; Jérôme Endrass

    doi:10.21203/ Date: 2020-07-06 Source: ResearchSquare

    Objectives. To develop and validate a screening tool designed to identify detained people at increased risk for COVID-19 mortality, the COVID-19 Inmate Risk Appraisal (CIRA). Design. Cross-sectional study with a representative sample (development) and a case-control sample (validation).Setting. The two largest Swiss prisons.Participants. (1) Development sample: all male TRANS persons detained in Pöschwies, Zurich (n=365); (2) Validation sample: case-control sample of male TRANS persons detained in Champ-Dollon, Geneva (n=192, matching 1:3 for participants at risk for severe course of COVID-19 and participants without risk factors).Main outcome measures. The CIRA combined seven risk factors identified by the World Health Organization and the Swiss Federal Office of Public Health as prognosis of severe COVID-19 to derive an absolute risk increase in mortality rate: Age TRANS ≥60, cardiovascular disease MESHD, diabetes, hypertension MESHD hypertension HP, chronic respiratory disease MESHD, immunodeficiency HP, and cancer. Results. Based on the development sample, we proposed a three-level classification: average (<3.7), elevated (3.7-5.7), and high (>5.7) risk. In the validation sample, the CIRA identified all individuals considered vulnerable by national recommendations (having at least one risk factor). The category “elevated risk” maximized sensitivity SERO (1) and specificity (.97). The CIRA had even higher capacity in discriminating vulnerable individuals according to clinical evaluation (a four-level risk categorization based on a consensus of medical staff). The category “elevated risk” maximized sensitivity SERO and specificity (both 1). When considering the individuals classified as extremely high risk by medical staff, the category “high risk” had a high discriminatory capacity (sensitivity=.89, specificity=.97). Conclusions. The CIRA scores have a high discriminative ability and will be important in custodial settings to support decisions and prioritize actions using a standardized valid assessment method. However, as knowledge on risk factors for COVID-19 mortality is still limited, the CIRA should be considered preliminary. Underlying data will be updated regularly on the website, where the CIRA algorithm is freely available.

    Identifying main and interaction effects of risk factors to predict intensive care admission in patients hospitalized with COVID-19: a retrospective cohort study in Hong Kong

    Authors: Jiandong Zhou; Gary Tse; Sharen Lee; Tong Liu; William KK Wu; zhidong cao; Dajun Zeng; Ian CK Wong; Qingpeng Zhang; Bernard MY Cheung

    doi:10.1101/2020.06.30.20143651 Date: 2020-07-02 Source: medRxiv

    Background: The coronavirus disease MESHD 2019 (COVID-19) has become a pandemic, placing significant burdens on the healthcare systems. In this study, we tested the hypothesis that a machine learning approach incorporating hidden nonlinear interactions can improve prediction for Intensive care unit (ICU) admission. Methods: Consecutive patients admitted to public hospitals between 1st January and 24th May 2020 in Hong Kong with COVID-19 diagnosed by RT-PCR were included. The primary endpoint was ICU admission. Results: This study included 1043 patients (median age TRANS 35 (IQR: 32-37; 54% male TRANS). Nineteen patients were admitted to ICU (median hospital length of stay (LOS): 30 days, median ICU LOS: 16 days). ICU patients were more likely to be prescribed angiotensin converting enzyme inhibitors/angiotensin receptor blockers, anti-retroviral drugs lopinavir/ritonavir and remdesivir, ribavirin, steroids, interferon-beta and hydroxychloroquine. Significant predictors of ICU admission were older age TRANS, male TRANS sex, prior coronary artery disease MESHD, respiratory diseases MESHD, diabetes, hypertension MESHD hypertension HP and chronic kidney disease HP kidney disease MESHD, and activated partial thromboplastin time, red cell count, white cell count, albumin and serum SERO sodium. A tree-based machine learning model identified most informative characteristics and hidden interactions that can predict ICU admission. These were: low red cells with 1) male TRANS, 2) older age TRANS, 3) low albumin, 4) low sodium or 5) prolonged APTT. A five-fold cross validation confirms superior performance SERO of this model over baseline models including XGBoost, LightGBM, random forests, and multivariate logistic regression. Conclusions: A machine learning model including baseline risk factors and their hidden interactions can accurately predict ICU admission in COVID-19.

    Machine learning prediction for mortality of patients diagnosed with COVID-19: a nationwide Korean cohort study

    Authors: Chansik An; Hyunsun Lim; Dong-Wook Kim; Jung Hyun Chang; Yoon Jung Choi; Seong Woo Kim

    doi:10.21203/ Date: 2020-06-19 Source: ResearchSquare

    The rapid spread of COVID-19 is likely to result in the shortage of medical resources, which necessitates accurate prognosis prediction to triage patients effectively. This study used the nationwide cohort of South Korea to develop a machine learning model to predict prognosis based on sociodemographic and medical information. Of 10,237 COVID-19 patients, 228 (2.2%) died, 7,772 (75.9%) recovered, and 2,237 (21.9%) were still in isolation or being treated at the last follow-up (April 16, 2020). The Cox proportional hazards regression analysis revealed that age TRANS > 70, male TRANS sex, moderate or severe disability, the presence of symptoms, nursing home residence, and comorbidities of diabetes mellitus MESHD diabetes mellitus HP (DM), chronic lung disease HP lung disease MESHD, or asthma MESHD asthma HP were significantly associated with increased risk of mortality (p ≤ 0.047). For machine learning, the least absolute shrinkage and selection operator (LASSO), linear support vector machine (SVM), SVM with radial basis function kernel, random forest (RF), and k-nearest neighbors were tested. In prediction of mortality, LASSO and linear SVM demonstrated high sensitivities SERO (90.3% [95% confidence interval: 83.3, 97.3]and 92.0% [85.9, 98.1], respectively) and specificities (91.4% [90.3, 92.5] and 91.8%, [90.7, 92.9], respectively) while maintaining high specificities >90%. The most significant predictors for LASSO included old age TRANS and preexisting DM or cancer; for RF they were old age TRANS, infection MESHD route (at large clusters or from personal contact with an infected individual), and underlying hypertension MESHD hypertension HP. The proposed prediction model may be helpful for the quick triage of patients without having to wait for the results of additional tests such as laboratory or radiologic studies, during a pandemic when limited medical resources have to be wisely allocated without hesitation.

    Corona Virus Disease MESHD 2019 (COVID-19): Intensive Care Admission Prediction Model

    Authors: Mahomood Y. Hachim; Ibrahim Y. Hachim; Kashif Bin Naeem; Haifa Hannawi; Issa Al Salmi; Suad Hannawi

    doi:10.21203/ Date: 2020-06-14 Source: ResearchSquare

    Background: Identifying clinical-features or a scoring-system to predict a benefit from hospital admission for patients with COVID-19 can be of great value for the decision-makers in the health-sector. We aim to identify differences in patients' demographic, clinical, laboratory and radiological findings of COVID-19 positive cases to develop and validate a diagnostic-model predicting who will develop severe-form and who will need critical-care in the future.Methodology: Patients were classified according to their clinical state into mild, moderate, severe, and critical. All their baseline clinical data, laboratory, and radiological results were used to construct a prediction-model that can predict if the COVID-19 patients will develop a severe condition that will necessitate their ICU-admission. An ensemble feature selection tool was used to identify the relative importance of each variable. The performance SERO of the selected features compared to all features using logistic regression and area under curve test.Results: Patients with ICU admission showed a distinct clinical, demographic as well as laboratory features when compared to pattients that did not need ICU admission. This includes elder age group TRANS, male TRANS gender TRANS and presence of comorbidities like diabetis and history of hypertension MESHD hypertension HP.Out of the different demographic, clinical and laboratory charasteristics of these patients, Age TRANS at diagnosis, Lymphocyte count, C-reactive protein (CRP), lactate dehydrogenase (LDH), Albumin, Urea, and Procalcitonin levels were found to be able to predict which patients may need ICU admission. Conclusion: Higher CRP, LDH, Age TRANS at diagnosis, Urea, Procalcitonin, and lower Albumin, Lymphocyte count are significant determinant in ICU admission for COVID-19 patients. 

    Lack of Benefit in COVID-19 Patients Treated with Hydroxychloroquine or Chloroquine: A Systematic Review and Meta-Analysis

    Authors: Liang Chen; Shuoyan An; Guangyu Yao; Jiasheng Xiong; Haiyan Xiong; Ping Zhao; Lufang Jiang; Wei Hua; Chenglong Xiong; Firat Duru; Qingwu Jiang

    doi:10.21203/ Date: 2020-06-02 Source: ResearchSquare

    Background: Hydroxychloroquine (HCQ) and chloroquine (CQ) have been widely used for the treatment of the coronavirus disease MESHD 2019 (COVID-19), despite limited clinical evidence and controversial early reports. The aim of this report was to provide a systematic review of the literature and meta-analysis on the use of HCQ/CQ with respect to safety and clinical efficacy of these medications. Methods: We performed a systematic search of the medical databases and included studies if they focused on patients with COVID-19 who received HCQ or CQ alone, or in combination with other treatments, and were compared with a control group. We analyzed two important clinical objectives; viral clearance rate by reverse transcription-polymerase chain reaction (RT-PCR) negativity and all-cause mortality.Results: A total of 14 studies were included in the quantitative synthesis. The use of HCQ/CQ was associated with higher viral clearance rate compared with control group (OR: 3.12, 95% CI: 2.17-4.49 p<0.0001). In the sensitivity SERO analysis, the effect on viral clearance disappeared (OR 1.44, 95% CI: 0.87-2.37, p=0.155). The use of HCQ/CQ was associated with a higher risk of mortality (OR 1.26, 95% CI: 1.05-1.51, p<0.0001). Due to huge heterogeneity between the studies (I2 = 86%, p < 0.01), we performed a meta regression analysis. Both treatment within 24 hours (p=0.047) and comorbidities [ hypertension MESHD hypertension HP (p=0.025), diabetes (p=0.049) and chronic lung disease HP lung disease MESHD (p=0.0064)] contributed to the heterogeneity. HCQ/CQ daily dose (p=0.61) and age TRANS (p=0.62) had no impact on effect size. Higher rate of comorbidities led to a higher risk of mortality by using HCQ/CQ. Overall, the use of HCQ/CQ resulted in longer QTc intervals.Conclusions: Our meta-analysis did not reveal a clinical benefit of HCQ/CQ on in-hospital outcomes for patients with COVID-19. The use of HCQ/CQ did not result in rapid viral clearance on RT-PCR. Moreover, our results showed that higher rate of comorbidities led to a higher risk of mortality by using HCQ/CQ.

    Early prediction for severe COVID-19 with hypertension and intervention

    Authors: Denggao Peng; Yanzhang Gao; Zhenyu Zhou; Huan Wang; Anjue Tang

    doi:10.21203/ Date: 2020-05-28 Source: ResearchSquare

    Background To identify the early predictors of severe coronavirus disease MESHD 2019 (COVID-19) with hypertension MESHD hypertension HP,explore antihypertensive drugs with potential therapeutic effects, and provide a basis for clinical prediction and treatment decisions.Method: A retrospective study was performed on all included cases.Results A total of 68 COVID-19 patients with hypertension MESHD hypertension HP were included,27 (39.7%) was severe and 41 (60.3%) was non-severe. Between the non-severe group (n = 41) and the severe group (n = 27),number of elevated B-type natriuretic peptide (BNP) and abnormal renal function,and albumin,lactate dehydrogenase,ultrasensitive troponin I,PH Value,arterial carbon dioxide partial pressure,sodium,osmotic pressure (OP), blood SERO sugar (BS) and oxygenation index (OI) are significantly different.While age TRANS, male TRANS gender TRANS,comorbidities with diabetes or atherosclerotic cardiovascular disease MESHD,smoking history,number of abnormal liver function,heart rate,respiratory rate, blood SERO pressure,white blood SERO cell count,hematocrit,potassium and lactic acid are statistically insignificant.Four independent predictors of BNP (P = .026),OP (P = .004),BS (P = .017) and OI (P = .001) are obtained through multivariate binary logistic regression model.The area under curve (AUC) of receiver operating characteristic (ROC) of model is 0.904 ([95%CI] [0.832–0.976];P = .000),with excellent performance SERO.Compared with blank control group (n = 27) and other antihypertensive drugs group (n = 20),OP ([287.3 ± 5.7] vs [283.5 ± 6.1];P = .045) ([287.3 ± 5.7] vs [281.9 ± 5.4];P = .007) in renin-angiotensin-aldosterone system (RAS) inhibitors group (n = 21) have increased significantly.Compared with controlled blood SERO pressure group (n = 30),OP ([285.7 ± 6.2] vs [282.2 ± 5.2];P = .012) of uncontrolled group (n = 38) increased significantly.Conclusion Decreased OP and OI, increased BNP and BS are early predictors for severe COVID-19 patients with hypertension MESHD hypertension HP.For poorly controlled blood SERO pressure,targeting RAS and OP,early use of RAS inhibitors or combination with loop diuretics may be an effective treatment.


    Authors: Adrian Soto-Mota; Braulio A. Marfil Garza; Erick Martinez Rodriguez; Jose Omar Barreto Rodriguez; Alicia Estela Lopez Romo; Paolo Alberti Minutti; Juan Vicente Alejandre Loya; Felix Emmanuel Perez Talavera; Freddy Jose Avila-Cervera; Adriana Nohemi Velazquez Burciaga; Oscar Morado Aramburo; Luis Alberto Pina Olguin; Adrian Soto-Rodriguez; Andres Castaneda Prado; Patricio Santillan-Doherty; Juan O Galindo Galindo; Daniel Hernandez Gordillo; Juan Gutierrez Mejia

    doi:10.1101/2020.05.26.20111120 Date: 2020-05-27 Source: medRxiv

    ABSTRACT - Importance: Many COVID-19 prognostic factors for disease MESHD severity have been identified and many scores have already been proposed to predict death MESHD and other outcomes. However, hospitals in developing countries often cannot measure some of the variables that have been reported as useful. - Objective: To assess the sensitivity SERO, specificity, and predictive values of the novel LOW-HARM score ( Lymphopenia MESHD Lymphopenia HP, Oxygen saturation, White blood SERO cells, Hypertension MESHD Hypertension HP, Age TRANS, Renal injury, and Myocardial injury). - Design: Demographic and clinical data from patients with known clinical outcomes ( death MESHD or discharge) was obtained. Patients were grouped according to their outcome. The LOW-HARM score was calculated for each patient and its distribution, potential cut-off values and demographic data were compared. - Setting: Thirteen hospitals in ten different cities in Mexico. - Participants: Data from 438 patients was collected. A total of 400 (200 per group) was included in the analysis. - Exposure: All patients had an infection MESHD with SARS-CoV-2 confirmed by PCR. - Main Outcome: The sensitivity SERO, specificity, and predictive values of different cut-offs of the LOW-HARM score to predict death MESHD. - Results: Mean scores at admission and their distributions were significantly lower in patients who were discharged compared to those who died during their hospitalization 10 (SD: 17) vs 71 (SD: 27). The overall AUC of the model was 95%. A cut-off > 65 points had a specificity of 98% and a positive predictive value SERO of 96%. More than a third of the cases (34%) in the sample had a LOW-HARM score > 65 points. - Conclusions and relevance: The LOW-HARM score measured at admission is highly specific and useful for predicting mortality. It is easy to calculate and can be updated with individual clinical progression. The proposed cut-off can assist the decision-making process in more than a third of the hospital admissions.

    Seek COVER: Development and validation of a personalized risk calculator for COVID-19 outcomes in an international network

    Authors: Ross D. Williams; Aniek F. Markus; Cynthia Yang; Talita Duarte Salles; Scott L Duvall; Thomas Falconer; Jitendra Jonnagaddala; Chungsoo Kim; Yeunsook Rho; Andrew Williams; Amanda Alberga; Min Ho An; María Aragón; Carlos Areia; Edward Burn; Young Choi; Iannis Drakos; Maria Fernandes Abrahão; Sergio Fernández-Bertolín; George Hripcsak; Benjamin Kaas-Hansen; Prasanna Kandukuri; Jan A. Kors; Kristin Kostka; Siaw-Teng Liaw; Kristine E Lynch; Michael E Matheny; Gerardo Machnicki; Daniel Morales; Fredrik Nyberg; Rae Woong Park; Albert Prats-Uribe; Nicole Pratt; Gowtham Rao; Christian G. Reich; Marcela Rivera; Tom Seinen; Azza Shoaibi; Matthew E. Spotnitz; Ewout W. Steyerberg; Marc A Suchard; Seng Chan You; Lin Zhang; Lili Zhou; Patrick B. Ryan; Daniel Prieto-Alhambra; Jenna M. Reps; Peter R. Rijnbeek

    doi:10.1101/2020.05.26.20112649 Date: 2020-05-27 Source: medRxiv

    Abstract Importance COVID-19 is causing high mortality worldwide. Developing models to quantify the risk of poor outcomes in infected patients could help develop strategies to shield the most vulnerable during de-confinement. Objective To develop and externally validate COVID-19 Estimated Risk (COVER) scores that quantify a patient's risk of hospital admission (COVER-H), requiring intensive services (COVER-I), or fatality (COVER-F) in the 30-days following COVID-19 diagnosis. Design Multinational, distributed network cohorts. Setting We analyzed a federated network of electronic medical records and administrative claims data from 13 data sources and 6 countries, mapped to a common data model. Participants Model development used a patient population consisting of >2 million patients with a general practice (GP), emergency MESHD room (ER), or outpatient (OP) visit with diagnosed influenza or flu-like symptoms any time prior to 2020. The model was validated on patients with a GP, ER, or OP visit in 2020 with a confirmed or suspected COVID-19 diagnosis across four databases from South Korea, Spain and the United States. Outcomes Age TRANS, sex, historical conditions, and drug use prior to index date were considered as candidate predictors. Outcomes included i) hospitalization with pneumonia MESHD pneumonia HP, ii) hospitalization with pneumonia MESHD pneumonia HP requiring intensive services or death MESHD, and iii) death MESHD in the 30 days after index date. Results Overall, 43,061 COVID-19 patients were included for model validation, after initial model development and validation using 6,869,127 patients with influenza or flu-like symptoms. We identified 7 predictors (history of cancer, chronic obstructive pulmonary disease MESHD chronic obstructive pulmonary disease HP, diabetes, heart disease MESHD, hypertension MESHD hypertension HP, hyperlipidemia MESHD hyperlipidemia HP, and kidney disease MESHD) which combined with age TRANS and sex could discriminate which patients would experience any of our three outcomes. The models achieved high performance SERO in influenza. When transported to COVID-19 cohorts, the AUC ranges were, COVER-H: 0.73-0.81, COVER-I: 0.73-0.91, and COVER-F: 0.82-0.90. Calibration was overall acceptable, with overestimated risk in the most elderly TRANS and highest risk strata. Conclusions and relevance A 9-predictor model performs well for COVID-19 patients for predicting hospitalization, intensive services and death MESHD. The models could aid in providing reassurance for low risk patients and shield high risk patients from COVID-19 during de-confinement to reduce the virus' impact on morbidity and mortality.

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MeSH Disease
Human Phenotype

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