scholarly journals 322. Risk Factors for COVID-19 Disease Severity Using Electronic-Health Records in a Real-World Cohort in the United States

2021 ◽  
Vol 8 (Supplement_1) ◽  
pp. S266-S267
Author(s):  
Shemra Rizzo ◽  
Ryan Gan ◽  
Devika Chawla ◽  
Kelly Zalocusky ◽  
Xin Chen ◽  
...  
Keyword(s):  
Risk Factors ◽  
Electronic Health Records ◽  
Confidence Intervals ◽  
Disease Severity ◽  
Charlson Comorbidity Index ◽  
Severe Disease ◽  
Older Age ◽  
Calendar Time ◽  
Health Records ◽  
Comorbidity Index

Abstract Background Over 32 million cases of COVID-19 have been reported in the US. Outcomes range from mild upper respiratory infection to hospitalization, acute respiratory failure, and death. We assessed risk factors associated with severe disease, defined as hospitalization within 21 days of diagnosis or death, using US electronic health records (EHR). Methods Patients in the Optum de-identified COVID-19 EHR database who were diagnosed with COVID-19 in 2020 were included in the analysis. Regularized multivariable logistic regression was used to identify risk factors for severe disease. Covariates included demographics, comorbidities, history of influenza vaccination, and calendar time. Results Of the 193,454 eligible patients, 36,043 (18.6%) were hospitalized within 21 days of COVID-19 diagnosis, and 6,397 (3.3%) died. Calendar time followed an inverse J-shaped relationship where severe disease rates rapidly declined in the first 25 weeks of the pandemic. BMI followed an asymmetric V-shaped relationship with highest rates of disease severity observed at the extremes. In the multivariable model, older age had the strongest association with disease severity (odds ratios and 95% confidence intervals of significant associations in Figure). Other risk factors were male sex, uninsured status, underweight and obese BMI, higher Charlson Comorbidity Index, and individual comorbidities including hypertension. Asthma and overweight BMI were not associated with disease severity. Blacks, Hispanics, and Asians experienced higher odds of disease severity compared to Whites. Figure. Significant associations (odds ratio and 95% confidence intervals) with COVID-19 severity (hospitalization or death), adjusted for geographical division. Reference and abbreviation categories: Charlson comorbidity index (CCI) = 0; Age = 18-30; Sex = Female; Race/Ethnicity = White; Insurance = Commercial; Body mass index (BMI) = 18.5-25; Calendar time = 0-25 weeks; Chronic obstructive pulmonary disease (COPD). Conclusion Odds of hospitalization or death have decreased since the start of the pandemic, with the steepest decline observed up to mid-August, possibly reflecting changes in both testing and treatment. Older age is the most important predictor of severe COVID-19. Obese and underweight, but not overweight, BMI were associated with increased odds of disease severity when compared to normal weight. Hypertension, despite not being included in many guidelines for vaccine prioritization, is a significant risk factor. Pronounced health disparities remain across race and ethnicity after accounting for comorbidities, with minorities experiencing higher disease severity. Disclosures Shemra Rizzo, PhD, F. Hoffmann-La Roche Ltd. (Shareholder)Genentech, Inc. (Employee) Ryan Gan, PhD, F. Hoffmann-La Roche Ltd (Shareholder)Genentech, Inc. (Employee) Devika Chawla, PhD MSPH, F. Hoffmann-La Roche Ltd. (Shareholder)Genentech, Inc. (Employee) Kelly Zalocusky, PhD, F. Hoffmann-La Roche Ltd. (Shareholder)Genentech, Inc. (Employee) Xin Chen, PhD, F. Hoffmann-La Roche Ltd. (Shareholder)Genentech, Inc. (Employee) Yifeng Chia, PhD, F. Hoffmann-La Roche Ltd (Shareholder)Genentech, Inc. (Employee)

scholarly journals Predicting dementia diagnosis from cognitive footprints in electronic health records: a case–control study protocol

BMJ Open ◽  
2020 ◽  
Vol 10 (11) ◽  
pp. e043487
Author(s):  
Hao Luo ◽  
Kui Kai Lau ◽  
Gloria H Y Wong ◽  
Wai-Chi Chan ◽  
Henry K F Mak ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Hong Kong ◽  
Electronic Health Records ◽  
Case Control Study ◽  
Case Control ◽  
Dementia Diagnosis ◽  
Health Records ◽  
Electronic Health ◽  
Control Study

IntroductionDementia is a group of disabling disorders that can be devastating for persons living with it and for their families. Data-informed decision-making strategies to identify individuals at high risk of dementia are essential to facilitate large-scale prevention and early intervention. This population-based case–control study aims to develop and validate a clinical algorithm for predicting dementia diagnosis, based on the cognitive footprint in personal and medical history.Methods and analysisWe will use territory-wide electronic health records from the Clinical Data Analysis and Reporting System (CDARS) in Hong Kong between 1 January 2001 and 31 December 2018. All individuals who were at least 65 years old by the end of 2018 will be identified from CDARS. A random sample of control individuals who did not receive any diagnosis of dementia will be matched with those who did receive such a diagnosis by age, gender and index date with 1:1 ratio. Exposure to potential protective/risk factors will be included in both conventional logistic regression and machine-learning models. Established risk factors of interest will include diabetes mellitus, midlife hypertension, midlife obesity, depression, head injuries and low education. Exploratory risk factors will include vascular disease, infectious disease and medication. The prediction accuracy of several state-of-the-art machine-learning algorithms will be compared.Ethics and disseminationThis study was approved by Institutional Review Board of The University of Hong Kong/Hospital Authority Hong Kong West Cluster (UW 18-225). Patients’ records are anonymised to protect privacy. Study results will be disseminated through peer-reviewed publications. Codes of the resulted dementia risk prediction algorithm will be made publicly available at the website of the Tools to Inform Policy: Chinese Communities’ Action in Response to Dementia project (https://www.tip-card.hku.hk/).

scholarly journals 1665. Using Electronic Health Records to Describe TB in Community Health Settings: a Cohort Analysis in a Large Safety-Net Population

2020 ◽  
Vol 7 (Supplement_1) ◽  
pp. S819-S820
Author(s):  
Jonathan Todd ◽  
Jon Puro ◽  
Matthew Jones ◽  
Jee Oakley ◽  
Laura A Vonnahme ◽  
...  
Keyword(s):  
United States ◽  
Risk Factors ◽  
Electronic Health Records ◽  
Safety Net ◽  
The United States ◽  
Research Network ◽  
Health Records ◽  
Treatment Regimens ◽  
Data Source ◽  
Electronic Health

Abstract Background Over 80% of tuberculosis (TB) cases in the United States are attributed to reactivation of latent TB infection (LTBI). Eliminating TB in the United States requires expanding identification and treatment of LTBI. Centralized electronic health records (EHRs) are an unexplored data source to identify persons with LTBI. We explored EHR data to evaluate TB and LTBI screening and diagnoses within OCHIN, Inc., a U.S. practice-based research network with a high proportion of Federally Qualified Health Centers. Methods From the EHRs of patients who had an encounter at an OCHIN member clinic between January 1, 2012 and December 31, 2016, we extracted demographic variables, TB risk factors, TB screening tests, International Classification of Diseases (ICD) 9 and 10 codes, and treatment regimens. Based on test results, ICD codes, and treatment regimens, we developed a novel algorithm to classify patient records into LTBI categories: definite, probable or possible. We used multivariable logistic regression, with a referent group of all cohort patients not classified as having LTBI or TB, to identify associations between TB risk factors and LTBI. Results Among 2,190,686 patients, 6.9% (n=151,195) had a TB screening test; among those, 8% tested positive. Non-U.S. –born or non-English–speaking persons comprised 24% of our cohort; 11% were tested for TB infection, and 14% had a positive test. Risk factors in the multivariable model significantly associated with being classified as having LTBI included preferring non-English language (adjusted odds ratio [aOR] 4.20, 95% confidence interval [CI] 4.09–4.32); non-Hispanic Asian (aOR 5.17, 95% CI 4.94–5.40), non-Hispanic black (aOR 3.02, 95% CI 2.91–3.13), or Native Hawaiian/other Pacific Islander (aOR 3.35, 95% CI 2.92–3.84) race; and HIV infection (aOR 3.09, 95% CI 2.84–3.35). Conclusion This study demonstrates the utility of EHR data for understanding TB screening practices and as an important data source that can be used to enhance public health surveillance of LTBI prevalence. Increasing screening among high-risk populations remains an important step toward eliminating TB in the United States. These results underscore the importance of offering TB screening in non-U.S.–born populations. Disclosures All Authors: No reported disclosures

scholarly journals Informing the public health response to COVID-19: a systematic review of risk factors for disease, severity, and mortality

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
M. Flook ◽  
C. Jackson ◽  
E. Vasileiou ◽  
C. R. Simpson ◽  
M. D. Muckian ◽  
...  
Keyword(s):  
Public Health ◽  
Risk Factors ◽  
Systematic Review ◽  
At Risk ◽  
Risk Factor ◽  
Disease Severity ◽  
Severe Disease ◽  
Public Health Response ◽  
Wide Range ◽  
Multivariable Analyses

Abstract Background Severe Acute Respiratory Syndrome coronavirus-2 (SARS-CoV-2) has challenged public health agencies globally. In order to effectively target government responses, it is critical to identify the individuals most at risk of coronavirus disease-19 (COVID-19), developing severe clinical signs, and mortality. We undertook a systematic review of the literature to present the current status of scientific knowledge in these areas and describe the need for unified global approaches, moving forwards, as well as lessons learnt for future pandemics. Methods Medline, Embase and Global Health were searched to the end of April 2020, as well as the Web of Science. Search terms were specific to the SARS-CoV-2 virus and COVID-19. Comparative studies of risk factors from any setting, population group and in any language were included. Titles, abstracts and full texts were screened by two reviewers and extracted in duplicate into a standardised form. Data were extracted on risk factors for COVID-19 disease, severe disease, or death and were narratively and descriptively synthesised. Results One thousand two hundred and thirty-eight papers were identified post-deduplication. Thirty-three met our inclusion criteria, of which 26 were from China. Six assessed the risk of contracting the disease, 20 the risk of having severe disease and ten the risk of dying. Age, gender and co-morbidities were commonly assessed as risk factors. The weight of evidence showed increasing age to be associated with severe disease and mortality, and general comorbidities with mortality. Only seven studies presented multivariable analyses and power was generally limited. A wide range of definitions were used for disease severity. Conclusions The volume of literature generated in the short time since the appearance of SARS-CoV-2 has been considerable. Many studies have sought to document the risk factors for COVID-19 disease, disease severity and mortality; age was the only risk factor based on robust studies and with a consistent body of evidence. Mechanistic studies are required to understand why age is such an important risk factor. At the start of pandemics, large, standardised, studies that use multivariable analyses are urgently needed so that the populations most at risk can be rapidly protected. Registration This review was registered on PROSPERO as CRD42020177714.

scholarly journals Clinical Characteristics, Risk Factors and Predictive Value of COVID-19 Pneumonia: A Retrospective Study of 173 Patients in Wuhan, China

2020 ◽  
Author(s):  
Yang Zhang ◽  
Jun Xue ◽  
Mi Yan ◽  
Jing Chen ◽  
Hai Liu ◽  
...  
Keyword(s):  
Risk Factors ◽  
Predictive Model ◽  
Disease Severity ◽  
Roc Curve ◽  
Predictive Value ◽  
Older Patients ◽  
Clinical Characteristics ◽  
Lactic Dehydrogenase ◽  
Older Age ◽  
Hospital Outcome

Abstract Background: COVID-19 is a globally emerging infectious disease. As the global epidemic continues to spread, the risk of COVID-19 transmission and diffusion in the world will also remain. Currently, several studies describing its clinical characteristics have focused on the initial outbreak, but rarely to the later stage. Here we described clinical characteristics, risk factors for disease severity and in-hospital outcome in patients with COVID-19 pneumonia from Wuhan. Methods: Patients with COVID-19 pneumonia admitted to Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology from February 13 to March 8, 2020, were retrospectively enrolled. Multivariable logistic regression analysis was used to identify risk factors for disease severity and in-hospital outcome and establish predictive models. Receiver operating characteristic (ROC) curve was used to assess the predictive value of above models.Results: 106 (61.3%) of the patients were female. The mean age of study populations was 62.0 years, of whom 73 (42.2%) had underlying comorbidities mainly including hypertension (24.9%). The most common symptoms on admission were fever (67.6%) and cough (60.1%), digestive symptoms (22.0%) was also very common. Older age (OR: 3.420; 95%Cl: 1.415-8.266; P=0.006), diarrhea (OR: 0.143; 95%Cl: 0.033-0.611; P=0.009) and lymphopenia (OR: 4.769; 95%Cl: 2.019-11.266; P=0.000) were associated with severe illness on admission; the area under the ROC curve (AUC) of predictive model were 0.860 (95%CI: 0.802-0.918; P=0.000). Older age (OR: 0.309; 95%Cl: 0.142-0.674; P=0.003), leucopenia (OR: 0.165; 95%Cl: 0.034-0.793; P=0.025), increased lactic dehydrogenase (OR: 0.257; 95%Cl: 0.100-0.659; P=0.005) and interleukins-6 levels (OR: 0.294; 95%Cl: 0.099-0.872; P=0.027) were associated with poor in-hospital outcome; AUC of predictive model were 0.752 (95%CI: 0.681-0.824; P=0.000).Conclusion: Older patients with diarrhea and lymphopenia need early identification and timely intervention to prevent the progression to severe COVID-19 pneumonia. However, older patients with leucopenia, increased lactic dehydrogenase and interleukins-6 levels are at a high risk for poor in-hospital outcome.Trial registration: ChiCTR2000029549

scholarly journals Prognosis of COVID-19 Pneumonia Can Be Early Predicted Combining Age-Adjusted Charlson Comorbidity Index, CRB Score and Baseline Oxygen Saturation.

2021 ◽  
Author(s):  
Pilar Nuevo-Ortega ◽  
Carmen Reina-Artacho ◽  
Francisco Dominguez-Moreno ◽  
Victor Manuel Becerra-Muñoz ◽  
Luis Ruiz-Del-Fresno ◽  
...  
Keyword(s):  
Oxygen Saturation ◽  
Charlson Comorbidity Index ◽  
Severe Disease ◽  
Hospital Environment ◽  
Severe Illness ◽  
Comorbidity Index ◽  
Clinical Tools ◽  
Discharge From Hospital ◽  
Statistical Criteria

Abstract Background: In potentially severe diseases in general and COVID-19 in particular, it is vital to early identify those patients who are going to develop complications. The last update of a recent living systematic review dedicated to predictive models in COVID-19,[1] critically appraises 145 models, 8 of them focused on prediction of severe disease and 23 on mortality. Unfortunately, in all 145 models, they found a risk of bias significant enough to finally "not recommend any for clinical use". Authors suggest concentrating on avoiding biases in sampling and prioritising the study of already identified predictive factors, rather than the identification of new ones that are often dependent on the database. Our objective is to develop a model to predict which patients with COVID-19 pneumonia are at high risk of developing severe illness or dying, using basic and validated clinical tools.Methods: prospective cohort of consecutive patients admitted in a teaching hospital during the “first wave” of the COVID-19 pandemic. Follow-up to discharge from hospital. Multiple logistic regression selecting variables according to clinical and statistical criteria. Results: 404 consecutive patients were evaluated, 392 (97%) completed follow-up. Mean age was 61 years; 59% were men. The median burden of comorbidity was 2 points in the Age-adjusted Charlson Comorbidity Index, CRB was abnormal in 18% of patients and basal oxygen saturation on admission lower than 90% in 18%. A model composed of Age-adjusted Charlson Comorbidity Index, CRB score and basal oxygen saturation can predict unfavorable evolution or death with an area under the ROC curve of 0.85 (95% CI: 0.80-0.89), and 0.90 (95% CI: 0.86 to 0.94), respectively.Conclusion: prognosis of COVID-19 pneumonia can be predicted in the out-of-hospital environment using two classic prognostic scales and a pocket pulse oximeter.

scholarly journals Poor Physical Capacity in Bronchiectasis Patients Is Correlated with Poor Quality of Life

2021 ◽  
Author(s):  
Jarkko Mäntylä ◽  
Tanja Törölä ◽  
Witold Mazur ◽  
Paula Bergman ◽  
Paula Kauppi
Keyword(s):  
Quality Of Life ◽  
Risk Factors ◽  
Disease Severity ◽  
Bacterial Colonization ◽  
Severe Disease ◽  
Poor Quality ◽  
Physical Capacity ◽  
Bacterial Colonisation ◽  
Cross Sectional

Abstract BackgroundTo study the risk factors associated with quality of life (QoL) in a cohort of Finnish non-cystic fibrosis bronchiectasis (BE) patients. We aimed to evaluate which of the clinical characteristics were risk factors for poor quality of life, how patients with frequent exacerbations differed from those with only few exacerbations and if QoL symptom domains were correlated with dyspnoea or severity of BE.MethodsA cross-sectional study and part of the EMBARC study including questionnaire data and medical record data. Study participants were recruited between August 2016 and March 2018 from three different pulmonary clinics in Helsinki University Central Hospital (HUH) catchment area, Finland. The study included 95 adult patients with (mean age was 69 (SD± 13) years).A Finnish translation of the disease-specific quality of life-bronchiectasis (QoL-B) questionnaire was applied, and scores in the lowest quarter (25%) of the scale were considered to indicate poor QoL. The bronchiectasis severity index (BSI) and FACED (including FEV1, age, pulmonary bacterial colonization, affected lobes and dyspnoea) score were used. The severity of dyspnoea was examined using the modified Medical Research Council (mMRC) dyspnoea scale.ResultsAlmost all (82%) presented with chronic sputum production and exacerbations, with a median rate of 1.7 (SD ±1.6). Exacerbations (OR 1.7, p < 0.01), frequent exacerbations (OR 4.9, p < 0.01), high BSI score (OR 1.3, p < 0.01) and extensive disease (OR 3.7, p = 0.05) were predictive of poor QoL. Frequent exacerbations were associated with bronchial bacterial colonisation, low forced expiratory volume in one second (FEV1) and radiological disease severity. Based on the BSI, 34.1% of our cohort had severe disease, whereas 11.6% were classified as severe according to the FACED score. The mMRC dyspnoea score (r = -0.57) and BSI (r = -0.60) were negatively correlated with physical domain in QoL-B questionnaire. ConclusionFrequent exacerbations, radiological disease severity and high BSI score were predictive of poor QoL. Reduced physical capacity was correlated with dyspnoea and severity of disease. Interventions to reduce bacterial colonisation and to maintain physical functioning should be used to minimize exacerbations and to improve quality of life in BE patients.Study registrationUniversity of Helsinki, faculty of medicine; 148/16.08.2017

scholarly journals Prevalence, incidence, and outcomes across cardiovascular diseases in homeless individuals using national linked electronic health records

2020 ◽  
Vol 41 (41) ◽  
pp. 4011-4020
Author(s):  
Atsunori Nanjo ◽  
Hannah Evans ◽  
Kenan Direk ◽  
Andrew C Hayward ◽  
Alistair Story ◽  
...  
Keyword(s):  
Risk Factors ◽  
Electronic Health Records ◽  
Mortality Risk ◽  
Homeless People ◽  
Arterial Disease ◽  
Population Based ◽  
Health Records ◽  
Coronary Death ◽  
Health And Social Care ◽  
Electronic Health

Abstract Aims The risk and burden of cardiovascular disease (CVD) are higher in homeless than in housed individuals but population-based analyses are lacking. The aim of this study was to investigate prevalence, incidence and outcomes across a range of specific CVDs among homeless individuals. Methods and results  Using linked UK primary care electronic health records (EHRs) and validated phenotypes, we identified homeless individuals aged ≥16 years between 1998 and 2019, and age- and sex-matched housed controls in a 1:5 ratio. For 12 CVDs (stable angina; unstable angina; myocardial infarction; sudden cardiac death or cardiac arrest; unheralded coronary death; heart failure; transient ischaemic attack; ischaemic stroke; subarachnoid haemorrhage; intracerebral haemorrhage; peripheral arterial disease; abdominal aortic aneurysm), we estimated prevalence, incidence, and 1-year mortality post-diagnosis, comparing homeless and housed groups. We identified 8492 homeless individuals (32 134 matched housed individuals). Comorbidities and risk factors were more prevalent in homeless people, e.g. smoking: 78.1% vs. 48.3% and atrial fibrillation: 9.9% vs. 8.6%, P &lt; 0.001. CVD prevalence (11.6% vs. 6.5%), incidence (14.7 vs. 8.1 per 1000 person-years), and 1-year mortality risk [adjusted hazard ratio 1.64, 95% confidence interval (CI) 1.29–2.08, P &lt; 0.001] were higher, and onset was earlier (difference 4.6, 95% CI 2.8–6.3 years, P &lt; 0.001), in homeless, compared with housed people. Homeless individuals had higher CVD incidence in all three arterial territories than housed people. Conclusion  CVD in homeless individuals has high prevalence, incidence, and 1-year mortality risk post-diagnosis with earlier onset, and high burden of risk factors. Inclusion health and social care strategies should reflect this high preventable and treatable burden, which is increasingly important in the current COVID-19 context.

scholarly journals Prognostic Risk Factors for Complications Associated with Below the Knee Amputations

2018 ◽  
Vol 3 (3) ◽  
pp. 2473011418S0037
Author(s):  
Jason Ni ◽  
Eric Lukosius ◽  
Kaitlin Saloky ◽  
Kempland Walley ◽  
Leanne Ludwick ◽  
...  
Keyword(s):  
Risk Factors ◽  
Postoperative Complications ◽  
Charlson Comorbidity Index ◽  
High Rate ◽  
Tourniquet Time ◽  
Ambulatory Status ◽  
Asa Classification ◽  
Below The Knee ◽  
Comorbidity Index ◽  
Prognostic Risk Factors

Category: Other Introduction/Purpose: Below the knee amputation (BKA) is an effective surgical procedure for individuals with severe injury or infection to their lower extremities. However, patients who receive these procedures are subject to significant morbidity and a high rate of postoperative complications due to the presence of multiple concomitant comorbidities. Despite the wide practice of this intervention, prognostic risk factors aiding in predicting surgical outcomes in these patients are poorly understood. The purpose of this study is to evaluate risk factors that may contribute to the outcomes of BKA procedures. Methods: The clinical and radiographic outcomes for 89 patients ages 19-90 who underwent BKA were retrospectively evaluated from 2012-2017. Postoperative complications of mortality, infection, and reoperation were evaluated with patient and surgical variables. Patient variables included: age, ambulatory status, obesity, diabetes, HbA1C2 levels, neuropathy, smoking, Charlson Comorbidity Index (CCI), and American Society of Anesthesiologists (ASA) classification. Surgical variables evaluated included: presence of pre-op infection, pre-op ambulatory status, tourniquet time, tourniquet pressure, and usage of prophylactic antibiotics. Results: Of the patients evaluated there was an overall complication rate of 49% (44/89) and a mortality rate of 19% (17/89). Patients with diabetes (p=.035), a greater score on the Charlson Comorbidity Index (p=.001), and an ASA classification =3 (p=.005) were associated with a greater risk of mortality. Operative values (i.e. tourniquet time, tourniquet pressure etc.) did not affect patient mortality rates in a significant way, but there was a higher incidence of complications (i.e. mortality, post-op infections, and reoperations) with patients with pre-operative infections. Conclusion: Diabetes, a higher CCI score and a greater ASA value were found to be significant predictors of patient mortality after BKA (p<0.05). Future perioperative optimization in these patients identified as high risk may improve patient outcomes in the future.

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