scholarly journals Diabetic Retinopathy Environment-Wide Association Study (EWAS) in NHANES 2005–2008

2020 ◽  
Vol 9 (11) ◽  
pp. 3643
Author(s):  
Kevin Blighe ◽  
Sarega Gurudas ◽  
Ying Lee ◽  
Sobha Sivaprasad
Keyword(s):  
Risk Factors ◽  
Diabetic Retinopathy ◽  
Association Study ◽  
Laboratory Data ◽  
Area Under The Curve ◽  
Principal Component ◽  
Liver Function Tests ◽  
Feature Model ◽  
Laboratory Parameters ◽  
Variable Feature

Several circulating biomarkers are reported to be associated with diabetic retinopathy (DR). However, their relative contributions to DR compared to known risk factors, such as hyperglycaemia, hypertension, and hyperlipidaemia, remain unclear. In this data driven study, we used novel models to evaluate the associations of over 400 laboratory parameters with DR compared to the established risk factors. Methods: we performed an environment-wide association study (EWAS) of laboratory parameters available in National Health and Nutrition Examination Survey (NHANES) 2007–2008 in individuals with diabetes with DR as the outcome (test set). We employed independent variable (feature) selection approaches, including parallelised univariate regression modelling, Principal Component Analysis (PCA), penalised regression, and RandomForest™. These models were replicated in NHANES 2005–2006 (replication set). Our test and replication sets consisted of 1025 and 637 individuals with available DR status and laboratory data respectively. Glycohemoglobin (HbA1c) was the strongest risk factor for DR. Our PCA-based approach produced a model that incorporated 18 principal components (PCs) that had an Area under the Curve (AUC) 0.796 (95% CI 0.761–0.832), while penalised regression identified a 9-feature model with 78.51% accuracy and AUC 0.74 (95% CI 0.72–0.77). RandomForest™ identified a 31-feature model with 78.4% accuracy and AUC 0.71 (95% CI 0.65–0.77). On grouping the selected variables in our RandomForest™, hyperglycaemia alone achieved AUC 0.72 (95% CI 0.68–0.76). The AUC increased to 0.84 (95% CI 0.78–0.9) when the model also included hypertension, hypercholesterolemia, haematocrit, renal, and liver function tests.

scholarly journals Diabetic retinopathy environment-wide association study (EWAS) in NHANES 2005-8.

2020 ◽  
Author(s):  
Kevin Blighe ◽  
Sarega Gurudas ◽  
Ying Lee ◽  
Sobha Sivaprasad
Keyword(s):  
Risk Factors ◽  
Diabetic Retinopathy ◽  
Association Study ◽  
Laboratory Data ◽  
Principal Component ◽  
Penalized Regression ◽  
Liver Function Tests ◽  
Feature Model ◽  
Laboratory Parameters ◽  
Variable Feature

Background: Several circulating biomarkers are reported to be associated with diabetic retinopathy (DR). However, their relative contributions to DR compared to known risk factors, such as hyperglycemia, hypertension, and hyperlipidemia, remain unclear. In this data driven study, we used novel models to evaluate the associations of over 400 laboratory parameters with DR. Methods: We performed an environment-wide association study (EWAS) of laboratory parameters available in National Health and Nutrition Examination Survey (NHANES) 2007-8 in individuals with diabetes with DR as the outcome (test set). We employed independent variable ('feature') selection approaches, including parallelized univariate regression modeling, Principal Component Analysis (PCA), penalized regression, and RandomForest. These models were replicated in NHANES 2005-6 (replication set). Findings: The test and replication set consisted of 1025 and 637 individuals with available DR status and laboratory data respectively. Glycohemoglobin (HbA1c) was the strongest risk factor for DR. Our PCA-based approach produced a model that incorporated 18 principal components (PCs) that had AUC 0.796 (95% CI 0.761-0.832), while penalized regression identified a 9-feature model with 78.51% accuracy and AUC 0.74 (95% CI 0.72-0.77). RandomForest identified a 31-feature model with 78.4% accuracy and AUC 0.71 (95% CI 0.65-0.77). On grouping the selected variables in our RandomForest, hyperglycemia alone achieved AUC 0.72 (95% CI 0.68-0.76). The AUC increased to 0.84 (95% CI 0.78-0.9) when the model also included hypertension, hypercholesterolemia, hematocrit, renal and liver function tests. Interpretation: All models showed that the contributions of established risk factors of DR especially hyperglycemia outweigh other laboratory parameters available in NHANES.

scholarly journals Clinical Picture and Risk Factors of Severe Respiratory Symptoms in COVID-19 in Children

Viruses ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 2366
Author(s):  
Anna Mania ◽  
Kamil Faltin ◽  
Katarzyna Mazur-Melewska ◽  
Paweł Małecki ◽  
Katarzyna Jończyk-Potoczna ◽  
...  
Keyword(s):  
Risk Factors ◽  
Intensive Care ◽  
Odds Ratio ◽  
Respiratory Symptoms ◽  
Oxygen Therapy ◽  
Clinical Course ◽  
Area Under The Curve ◽  
Clinical Findings ◽  
Laboratory Parameters ◽  
Tract Involvement

Children with COVID-19 develop moderate symptoms in most cases. Thus, a proportion of children requires hospital admission. The study aimed to assess the history, clinical and laboratory parameters in children with COVID-19 concerning the severity of respiratory symptoms. The study included 332 children (median age 57 months) with COVID-19. History data, clinical findings, laboratory parameters, treatment, and outcome, were evaluated. Children were compared in the groups that varied in the severity of symptoms of respiratory tract involvement. Children who required oxygen therapy represented 8.73%, and intensive care 1.5% of the whole cohort. Comorbidities were present in 126 patients (37.95%). Factors increasing the risk of oxygen therapy included comorbidities (odds ratio (OR) = 92.39; 95% confidence interval (95% CI) = (4.19; 2036.90); p < 0.00001), dyspnea (OR = 45.81; 95% CI (4.05; 518.21); p < 0.00001), auscultation abnormalities (OR = 34.33; 95% CI (2.59; 454.64); p < 0.00001). Lactate dehydrogenase (LDH) > 280 IU/L and creatinine kinase > 192 IU/L were parameters with a good area under the curve (0.804-LDH) and a positive predictive value (42.9%-CK). The clinical course of COVID-19 was mild to moderate in most patients. Children with comorbidities, dyspnea, or abnormalities on auscultation are at risk of oxygen therapy. Laboratory parameters potentially useful in patients evaluated for the severe course are LDH > 200 IU/L and CK > 192 IU/L.

scholarly journals Risk-Profile and Feature Selection Comparison in Diabetic Retinopathy

2021 ◽  
Vol 11 (12) ◽  
pp. 1327
Author(s):  
Valeria Maeda-Gutiérrez ◽  
Carlos E. Galván-Tejada ◽  
Miguel Cruz ◽  
Jorge I. Galván-Tejada ◽  
Hamurabi Gamboa-Rosales ◽  
...  
Keyword(s):  
Risk Factors ◽  
Feature Selection ◽  
Diabetic Retinopathy ◽  
Microvascular Complications ◽  
Feature Selection Method ◽  
Area Under The Curve ◽  
Density Lipoprotein ◽  
Clinical Decision ◽  
Machine Learning Techniques ◽  
The Impact

One of the main microvascular complications presented in the Mexican population is diabetic retinopathy which affects 27.50% of individuals with type 2 diabetes. Therefore, the purpose of this study is to construct a predictive model to find out the risk factors of this complication. The dataset contained a total of 298 subjects, including clinical and paraclinical features. An analysis was constructed using machine learning techniques including Boruta as a feature selection method, and random forest as classification algorithm. The model was evaluated through a statistical test based on sensitivity, specificity, area under the curve (AUC), and receiving operating characteristic (ROC) curve. The results present significant values obtained by the model obtaining 69% of AUC. Moreover, a risk evaluation was incorporated to evaluate the impact of the predictors. The proposed method identifies creatinine, lipid treatment, glomerular filtration rate, waist hip ratio, total cholesterol, and high density lipoprotein as risk factors in Mexican subjects. The odds ratio increases by 3.5916 times for control patients which have high levels of cholesterol. It is possible to conclude that this proposed methodology is a preliminary computer-aided diagnosis tool for clinical decision-helping to identify the diagnosis of DR.

scholarly journals Identification of Risk Factors Associated With Mortality Among Patients With COVID-19 Using Random Forest Model: A Historical Cohort Study

2021 ◽  
Author(s):  
Fatemeh Moghaddam-Tabrizi ◽  
Tahereh Omidi ◽  
Masoomeh Mahdi-Akhgar ◽  
Robabeh Bahadori ◽  
Rohollah Valizadeh ◽  
...  
Keyword(s):  
Risk Factors ◽  
Cohort Study ◽  
Random Forest ◽  
Early Stage ◽  
Laboratory Data ◽  
Area Under The Curve ◽  
Nasal Airway ◽  
Historical Cohort ◽  
Testing Sample ◽  
Factors Associated

There is conflicting evidence about factors associated with Clinical course and risk factors for mortality of adult inpatients. We aimed to identify the demographic, clinical, treatment, and laboratory data factors associated with mortality in the Khoy district. We performed a retrospective cohort study including COVID-19 infected patients who were admitted to Qamar-Bani Hashim hospital from 2 November 2020 to 4 December 2020. We used random forest methods to explore the risk factors associated with death. The applied method was evaluated using sensitivity, specificity, accuracy, and the area under the curve. Age, pulmonary symptoms, patients need a ventilator, brain symptoms, nasal airway, job were the most important risk factors for mortality of COVID-19 in the random forest (RF) method. The RF method showed the highest accuracy, 82.9 and 79.3, for training and testing samples, respectively. However, this method resulted in the highest specificity (89.5% for training and 95.7% for testing sample) and the highest sensitivity (91.9% for training and 94.5% for testing sample). The potential risk factors consisting of older age, pulmonary symptoms, the use of a ventilator, brain symptoms, nasal airway, and the job could help clinicians to identify patients with poor prognosis at an early stage.

scholarly journals Z-score is a possible predictor of the risk of coronary artery lesion development in patients with Kawasaki disease in Japan

2021 ◽  
Author(s):  
Takayuki Suzuki ◽  
Nobuyuki Kakimoto ◽  
Tomoya Tsuchihashi ◽  
Tomohiro Suenaga ◽  
Takashi Takeuchi ◽  
...  
Keyword(s):  
Risk Factors ◽  
Coronary Artery ◽  
Kawasaki Disease ◽  
Characteristic Curve ◽  
Laboratory Data ◽  
Delayed Diagnosis ◽  
Area Under The Curve ◽  
Coronary Artery Lesion ◽  
Z Score ◽  
Male Sex

Abstract ABSTRACT Risk factors for coronary artery lesion (CAL) development in patients with Kawasaki disease (KD) include male sex, age <12 months, intravenous immunoglobulin (IVIG) resistance, and delayed diagnosis. We aimed to explore the relationship between CAL development and Z-score. We enrolled 281 patients with KD who were treated with our protocol. Echocardiography was performed in three phases: pre-treatment (P1), post-treatment (P2), and 4 weeks after onset (P3). The highest Z-score of the right, left main, left anterior descending, and left circumflex coronary arteries was expressed as Zmax at each phase. P3-Zmax ≥2.5 represented CAL development. Clinical parameters, such as laboratory data and Z-scores, were retrospectively compared between patients with and without CAL development. Sixty-seven patients (23.8%) showed a P1-Zmax ≥2.0, and CAL development occurred in 21 patients (7.5%). Independent risk factors associated with CAL development were P1-Zmax, a ΔZmax (P2-Zmax − P1-Zmax) ≥1, male sex, <12 months of age, and resistant to the first IVIG administration (adjusted odds ratio [95% confidence interval]: 1.98 [1.01–3.92], 4.04 [1.11-14.7], 6.62 [1.33–33.04], 4.71 [1.51–14.68], 5.26 [1.62–17.13], respectively). Using receiver operating characteristic curve analysis, a P1-Zmax ≥1.43 detected CAL development with an area under the curve of 0.64 (sensitivity = 81.0%; specificity = 48.1%). Conclusions : Our results suggest that P1-Zmax and a ΔZmax (P2-Zmax − P1-Zmax) ≥1 may predict CAL development.

scholarly journals 312. Clinical Attributes and Risk Factors for In- Hospital Mortality among Covid-19 Patients in a Community Hospital Setting: A Propensity Matched Analysis

2021 ◽  
Vol 8 (Supplement_1) ◽  
pp. S262-S262
Author(s):  
Karthik Seetharam ◽  
Premila Bhat ◽  
Kelash Kumar ◽  
Thinzar Wai ◽  
Vamsi Yenugadhati ◽  
...  
Keyword(s):  
Risk Factors ◽  
Hospital Mortality ◽  
Community Hospital ◽  
Laboratory Data ◽  
Hospital Setting ◽  
Case Series ◽  
Laboratory Parameters ◽  
Presenting Symptoms ◽  
Discharged Patients ◽  
The Impact

Abstract Background New York City emerged as the Epicenter for Covid-19 due to novel Coronavirus SARS-CoV-2 soon after it was declared a Global Pandemic in early 2020 by the WHO. Covid-19 presents with a wide spectrum of illness from asymptomatic to severe respiratory failure, shock, multiorgan failure and death. Although the overall fatality rate is low, there is significant mortality among hospitalized patients. There is limited information exploring the impact of Covid-19 in community hospital settings in ethnically diverse populations. We aimed to identify risk factors for Covid-19 mortality in our institution. Methods We conducted a retrospective cohort study of hospitalized in our institution for Covid 19 from March 1st to June 21st 2020. It comprised of 425 discharged patients and 245 expired patients. Information was extracted from our EMR which included demographics, presenting symptoms, and laboratory data. We propensity matched 245 expired patients with a concurrent cohort of discharged patients. Statistically significant covariates were applied in matching, which included age, gender, race, body mass index (BMI), diabetes mellitus, and hypertension. The admission clinical attributes and laboratory parameters and outcomes were analyzed. Results The mean age of the matched cohort was 66.9 years. Expired patients had a higher incidence of dyspnea (P &lt; 0.001) and headache (0.031). In addition, expired patients had elevated CRP- hs (mg/dl) ≥ 123 (&lt; .0001), SGOT or AST (IU/L) ≥ 54 (p &lt; 0.001), SGPT or ALT (IU/L) ≥ 41 (p &lt; 0.001), and creatinine (mg/dl) ≥ 1.135 (0.001), lower WBC counts (k/ul) ≥ 8.42 (0.009). Furthermore, on multivariate logistic regression, dyspnea (OR = 2.56, P &lt; 0.001), creatinine ≥ 1.135 (OR = 1.79, P = 0.007), LDH(U/L) &gt; 465 (OR = 2.18, P = 0.001), systolic blood pressure &lt; 90 mm Hg (OR = 4.28, p = .02), respiratory rate &gt; 24 (OR = 2.88, p = .001), absolute lymphocyte percent (≤ 12%) (OR = 1.68, p = .001) and procalcitonin (ng/ml) ≥ 0.305 (OR = 1.71, P = .027) predicted in- hospital mortality in all matched patients. Conclusion Our case series provides admission clinical characteristics and laboratory parameters that predict in- hospital mortality in propensity Covid 19 matched patients with a large Hispanic population. These risk factors will require further validation. Disclosures All Authors: No reported disclosures

Application of Principal Component Analysis and Logistic Regression Model in Lupus Nephritis Patients with Clinical Hypothyroidism

2020 ◽  
Author(s):  
Ting Huang ◽  
Jiarong Li ◽  
Weiru Zhang
Keyword(s):  
Risk Factors ◽  
Principal Component Analysis ◽  
Logistic Regression ◽  
Lupus Nephritis ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Area Under The Curve ◽  
Principal Component ◽  
Component Analysis ◽  
Cross Sectional

Abstract Background : Previous studies indicate that the prevalence of hypothyroidism is much higher in patients with lupus nephritis (LN) than in the general population, and is associated with LN’s activity. Principal component analysis (PCA) and logistic regression can help determine relevant risk factors and identify LN patients at high risk of hypothyroidism; as such, these tools may prove useful in managing this disease. Methods: We carried out a cross-sectional study of 143 LN patients diagnosed by renal biopsy, all of whom had been admitted to Xiangya Hospital of Central South University in Changsha, China, between June 2012 and December 2016. The PCA–logistic regression model was used to determine the influential principal components for LN patients who have hypothyroidism. Results : Our PCA–logistic regression analysis results demonstrated that serum creatinine, blood urea nitrogen, blood uric acid, total protein, albumin, and anti-ribonucleoprotein antibody were important clinical variables for LN patients with hypothyroidism. The area under the curve of this model was 0.855. Conclusion : The PCA–logistic regression model performed well in identifying important risk factors for certain clinical outcomes, and promoting clinical research on other diseases will be beneficial. Using this model, clinicians can identify at-risk subjects and either implement preventative strategies or manage current treatments.

Chemometric Monitoring of Designed Composting Processes Using Laboratory Measurements and near Infrared Spectroscopy

2009 ◽  
Vol 17 (5) ◽  
pp. 275-287 ◽  
Author(s):  
Tom Lillhonga ◽  
Julia Grünwald ◽  
Paul Geladi
Keyword(s):  
Near Infrared ◽  
Laboratory Data ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Response Surfaces ◽  
Ammonium Concentration ◽  
Laboratory Measurements ◽  
Yard Waste ◽  
Laboratory Parameters ◽  
Physical Laboratory

Fox manure, food waste and yard waste were used as pure composts to construct a laboratory-scale designed (simplex mixture) experiment with repeated centre points that was monitored over 31 calendar days by near infrared (NIR) spectroscopy (900–1700 nm) and additionally by sampling for wet chemical and physical laboratory measurements: pH, energy, moisture content, NH3/NH4+ (simply called ammonium) concentration, and temperature. Three methods of data analysis were tested on the resulting data matrices: (1) modelling of the mixture design by Scheffé models, (2) principal component analysis (PCA) with interpretation of score and loading plots for the laboratory parameters and the NIR spectra separately and (3) partial least squares (PLS) regression modelling between NIR spectra and laboratory parameters. Significant Scheffé models were obtained and these could be used to make response surfaces of the parameters as a function of time. The PCA scores for the laboratory data reproduced the mixture triangles at the start of the experiment and showed that all the aerobic composts evolved to a common endpoint region. The PCA analysis of the NIR data indicated that score plots are a useful tool for monitoring the decomposition process of composts. Each of the composts could be followed over time by observing directions in the score space and changes in the step distances in score space. PLS models were built for each of the laboratory parameters and, additionally, composting time against NIR spectra, where spectra from the three centre points were used as an independent test set. The parameters pH, temperature, ammonium concentration and composting time all gave RER values above 10 and RPD values above 3 were obtained for temperature, pH and composting time.

Diabetic retinopathy: Prevalence and risk factors

2019 ◽  
Author(s):  
Siham Elmir ◽  
Siham Rouf ◽  
Khadija Boujtat ◽  
Hanane Latrech
Keyword(s):  
Risk Factors ◽  
Diabetic Retinopathy
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