Prediction of Prehypertenison and Hypertension Based on Anthropometry, Blood Parameters, and Spirometry

Hypertension and prehypertension are risk factors for cardiovascular diseases. However, the associations of both prehypertension and hypertension with anthropometry, blood parameters, and spirometry have not been investigated. The purpose of this study was to identify the risk factors for prehypertension and hypertension in middle-aged Korean adults and to study prediction models of prehypertension and hypertension combined with anthropometry, blood parameters, and spirometry. Binary logistic regression analysis was performed to assess the statistical significance of prehypertension and hypertension, and prediction models were developed using logistic regression, naïve Bayes, and decision trees. Among all risk factors for prehypertension, body mass index (BMI) was identified as the best indicator in both men [odds ratio (OR) = 1.429, 95% confidence interval (CI) = 1.304–1.462)] and women (OR = 1.428, 95% CI = 1.204–1.453). In contrast, among all risk factors for hypertension, BMI (OR = 1.993, 95% CI = 1.818–2.186) was found to be the best indicator in men, whereas the waist-to-height ratio (OR = 2.071, 95% CI = 1.884–2.276) was the best indicator in women. In the prehypertension prediction model, men exhibited an area under the receiver operating characteristic curve (AUC) of 0.635, and women exhibited a predictive power with an AUC of 0.777. In the hypertension prediction model, men exhibited an AUC of 0.700, and women exhibited an AUC of 0.845. This study proposes various risk factors for prehypertension and hypertension, and our findings can be used as a large-scale screening tool for controlling and managing hypertension.

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Predicting Burn Mortality Using a Simple Novel Prediction Model

Indian Journal of Plastic Surgery ◽

10.1055/s-0040-1721867 ◽

2021 ◽

Author(s):

Sneha Sharma ◽

Raman Tandon

Keyword(s):

Logistic Regression ◽

Regression Analysis ◽

Prediction Model ◽

Receiver Operating Characteristic ◽

Logistic Regression Analysis ◽

Operating Characteristic ◽

Binary Logistic Regression ◽

Apache Ii ◽

Apache Ii Score ◽

Binary Logistic Regression Analysis

Abstract Background Prediction of outcome for burn patients allows appropriate allocation of resources and prognostication. There is a paucity of simple to use burn-specific mortality prediction models which consider both endogenous and exogenous factors. Our objective was to create such a model. Methods A prospective observational study was performed on consecutive eligible consenting burns patients. Demographic data, total burn surface area (TBSA), results of complete blood count, kidney function test, and arterial blood gas analysis were collected. The quantitative variables were compared using the unpaired student t-test/nonparametric Mann Whitney U-test. Qualitative variables were compared using the ⊠2-test/Fischer exact test. Binary logistic regression analysis was done and a logit score was derived and simplified. The discrimination of these models was tested using the receiver operating characteristic curve; calibration was checked using the Hosmer—Lemeshow goodness of fit statistic, and the probability of death calculated. Validation was done using the bootstrapping technique in 5,000 samples. A p-value of <0.05 was considered significant. Results On univariate analysis TBSA (p <0.001) and Acute Physiology and Chronic Health Evaluation II (APACHE II) score (p = 0.004) were found to be independent predictors of mortality. TBSA (odds ratio [OR] 1.094, 95% confidence interval [CI] 1.037–1.155, p = 0.001) and APACHE II (OR 1.166, 95% CI 1.034–1.313, p = 0.012) retained significance on binary logistic regression analysis. The prediction model devised performed well (area under the receiver operating characteristic 0.778, 95% CI 0.681–0.875). Conclusion The prediction of mortality can be done accurately at the bedside using TBSA and APACHE II score.

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Abstract 2701: Multicenter Study For Prediction And Validation Of Intracranial Hemorrhage Risk In The Natural History Of Brain Arteriovenous Malformation Patients

Stroke ◽

10.1161/str.43.suppl_1.a2701 ◽

2012 ◽

Vol 43 (suppl_1) ◽

Author(s):

Helen Kim ◽

Tony Pourmohamad ◽

Charles E McCulloch ◽

Michael T Lawton ◽

Jay P Mohr ◽

...

Keyword(s):

Risk Factors ◽

High Risk ◽

Natural History ◽

Prediction Model ◽

Intracranial Hemorrhage ◽

Prediction Models ◽

Statistical Significance ◽

Venous Drainage ◽

Cox Proportional Hazards ◽

Natural Course

Background: BAVM is an important cause of intracranial hemorrhage (ICH) in younger persons. Accurate and reliable prediction models for determining ICH risk in the natural history course of BAVM patients are needed to help guide management. The purpose of this study was to develop a prediction model of ICH risk, and validate the performance independently using the Multicenter AVM Research Study (MARS). Methods: We used 3 BAVM cohorts from MARS: the UCSF Brain AVM Study Project (n=726), Columbia AVM Study (COL, n=640), and Scottish Intracranial Vascular Malformation Study (SIVMS, n=218). Cox proportional hazards analysis of time-to-ICH in the natural course after diagnosis was performed, censoring patients at first treatment, death, or last visit, up to 10 years. UCSF served as the model development cohort. We chose a simple model, including known risk factors that are reliably measured across cohorts (age at diagnosis, gender, initial hemorrhagic presentation, and deep venous drainage); variables were included without regard to statistical significance. Tertiles of predicted probabilities corresponding to low, medium, and high risk were obtained from UCSF and risk thresholds were validated in COL and SIVMS using Kaplan-Meier survival curves and log-rank tests (to assess whether the model discriminated between risk categories). Results: Overall, 82 ICH events occurred during the natural course: 28 in UCSF, 41 in COL, and 13 in SIVMS. Effects in the prediction model (estimated from UCSF data) were: age in decades (HR=1.1, 95% CI=0.9-1.4, P=0.41), initial hemorrhagic presentation (HR=3.6, 95% CI=1.5-8.6, P=0.01), male gender (HR=1.1, 95% CI=0.48-2.6; P=0.81), and deep venous drainage (HR=0.8, 95% CI=0.2-2.8 P=0.72). Tertiles of ICH risk are shown in the Figure , demonstrating good separation of curves into low, medium and high risk after 3 years in UCSF (left, log-rank P=0.05). The model validated well in the COL referral cohort with better discrimination of curves (middle, P<0.001). In SIMVS, a population-based study, the model separated curves in the earlier years but a consistent pattern was not observed (right, P=0.51), possibly due to the small number of ICH events. Conclusion: Our current prediction model for predicting ICH risk in the natural history course validates well in another referral population, but not as well in a population cohort. Inclusion of additional cohorts and risk factors after data harmonization may improve overall prediction and discrimination of ICH risk, and provide a generalizable model for clinical application.

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Identification of Metabolic Syndrome Based on Anthropometric, Blood and Spirometric Risk Factors Using Machine Learning

Applied Sciences ◽

10.3390/app10217741 ◽

2020 ◽

Vol 10 (21) ◽

pp. 7741

Author(s):

Sang Yeob Kim ◽

Gyeong Hee Nam ◽

Byeong Mun Heo

Keyword(s):

Machine Learning ◽

Metabolic Syndrome ◽

Logistic Regression ◽

Prediction Model ◽

Prediction Models ◽

Strong Association ◽

Forced Expiratory Volume ◽

Binary Logistic Regression Analysis ◽

Cross Sectional Survey ◽

Cross Sectional

Metabolic syndrome (MS) is an aggregation of coexisting conditions that can indicate an individual’s high risk of major diseases, including cardiovascular disease, stroke, cancer, and type 2 diabetes. We conducted a cross-sectional survey to evaluate potential risk factor indicators by identifying relationships between MS and anthropometric and spirometric factors along with blood parameters among Korean adults. A total of 13,978 subjects were enrolled from the Korea National Health and Nutrition Examination Survey. Statistical analysis was performed using a complex sampling design to represent the entire Korean population. We conducted binary logistic regression analysis to evaluate and compare potential associations of all included factors. We constructed prediction models based on Naïve Bayes and logistic regression algorithms. The performance evaluation of the prediction model improved the accuracy with area under the curve (AUC) and calibration curve. Among all factors, triglyceride exhibited a strong association with MS in both men (odds ratio (OR) = 2.711, 95% confidence interval (CI) [2.328–3.158]) and women (OR = 3.515 [3.042–4.062]). Regarding anthropometric factors, the waist-to-height ratio demonstrated a strong association in men (OR = 1.511 [1.311–1.742]), whereas waist circumference was the strongest indicator in women (OR = 2.847 [2.447–3.313]). Forced expiratory volume in 6s and forced expiratory flow 25–75% strongly associated with MS in both men (OR = 0.822 [0.749–0.903]) and women (OR = 1.150 [1.060–1.246]). Wrapper-based logistic regression prediction model showed the highest predictive power in both men and women (AUC = 0.868 and 0.932, respectively). Our findings revealed that several factors were associated with MS and suggested the potential of employing machine learning models to support the diagnosis of MS.

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A clinical prediction model for estimating the risk of developing uveitis in patients with juvenile idiopathic arthritis

Rheumatology ◽

10.1093/rheumatology/keaa733 ◽

2020 ◽

Author(s):

Joeri W van Straalen ◽

Gabriella Giancane ◽

Yasmine Amazrhar ◽

Nikolay Tzaribachev ◽

Calin Lazar ◽

...

Keyword(s):

Risk Factors ◽

Prediction Model ◽

Protective Factor ◽

Prediction Models ◽

Characteristic Curve ◽

Multivariable Analysis ◽

Information Criterion ◽

Logistic Regression Models ◽

Adjusted Risk ◽

Independent Risk Factors

Abstract Objective To build a prediction model for uveitis in children with JIA for use in current clinical practice. Methods Data from the international observational Pharmachild registry were used. Adjusted risk factors as well as predictors for JIA-associated uveitis (JIA-U) were determined using multivariable logistic regression models. The prediction model was selected based on the Akaike information criterion. Bootstrap resampling was used to adjust the final prediction model for optimism. Results JIA-U occurred in 1102 of 5529 JIA patients (19.9%). The majority of patients that developed JIA-U were female (74.1%), ANA positive (66.0%) and had oligoarthritis (59.9%). JIA-U was rarely seen in patients with systemic arthritis (0.5%) and RF positive polyarthritis (0.2%). Independent risk factors for JIA-U were ANA positivity [odds ratio (OR): 1.88 (95% CI: 1.54, 2.30)] and HLA-B27 positivity [OR: 1.48 (95% CI: 1.12, 1.95)] while older age at JIA onset was an independent protective factor [OR: 0.84 (9%% CI: 0.81, 0.87)]. On multivariable analysis, the combination of age at JIA onset [OR: 0.84 (95% CI: 0.82, 0.86)], JIA category and ANA positivity [OR: 2.02 (95% CI: 1.73, 2.36)] had the highest discriminative power among the prediction models considered (optimism-adjusted area under the receiver operating characteristic curve = 0.75). Conclusion We developed an easy to read model for individual patients with JIA to inform patients/parents on the probability of developing uveitis.

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Serum markers change for intraocular metastasis in renal cell carcinoma

Bioscience Reports ◽

10.1042/bsr20203116 ◽

2021 ◽

Author(s):

Tie Sun ◽

Jing Tang ◽

Yi-Cong Pan ◽

Chen-Yu Yu ◽

Biao Li ◽

...

Keyword(s):

Risk Factors ◽

Renal Cell Carcinoma ◽

Logistic Regression ◽

Regression Analysis ◽

Risk Factor ◽

Cell Carcinoma ◽

Renal Cell ◽

Binary Logistic Regression ◽

Binary Logistic Regression Analysis ◽

Intraocular Metastasis

Objective: Intraocular metastasis(IOM) of renal cell carcinoma is rare. In this study, we studied the relationship between different biochemical indicators and the occurrence of IOM in renal cancer patients, and identified the potential risk factors. Methods: A retrospective analysis of the clinical data of 214 patients with renal cell carcinoma from October 2001 to August 2016. Analyze the difference and correlation of various indicators between the two groups with or without IOM, and use binary logistic regression analysis to explore the risk factors of IOM in renal cancer patients. Calculate the diagnostic value of each independent related factor according to the receiver operating curve (ROC). Results: The level of neuron specific enolase （NSE） in renal cell carcinoma patients with IOM was significantly higher than that in patients without IOM (P < 0.05). There was no significant difference in ALP, Hb, serum calcium concentration, AFP, CEA, CA-125 etc. between IOM group and non-intraocular metastasis (NIOM) group (P > 0.05). Binary logistic regression analysis showed that NSE was an independent risk factor for IOM in renal cell carcinoma patients (P < 0.05). ROC curve shows that the factor has high accuracy in predicting IOM, and the area under the curve is 0.774. The cut-off value of NSE was 49.5U/L, the sensitivity was 72.2%, and the specificity was 80.1%. Conclusion：NSE concentration is a risk factor for IOM in patients with renal cell cancer. If the concentration of NSE in the patient's body is ≥49.5U/L, disease monitoring and eye scans should be strengthened.

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A Machine Learning Model to Predict Intravenous Immunoglobulin-Resistant Kawasaki Disease Patients: A Retrospective Study Based on the Chongqing Population

Frontiers in Pediatrics ◽

10.3389/fped.2021.756095 ◽

2021 ◽

Vol 9 ◽

Author(s):

Jie Liu ◽

Jian Zhang ◽

Haodong Huang ◽

Yunting Wang ◽

Zuyue Zhang ◽

...

Keyword(s):

Machine Learning ◽

Risk Factors ◽

Logistic Regression ◽

Regression Analysis ◽

Retrospective Study ◽

Kawasaki Disease ◽

Prediction Model ◽

Prediction Models ◽

Ivig Resistance ◽

Independent Risk Factors

Objective: We explored the risk factors for intravenous immunoglobulin (IVIG) resistance in children with Kawasaki disease (KD) and constructed a prediction model based on machine learning algorithms.Methods: A retrospective study including 1,398 KD patients hospitalized in 7 affiliated hospitals of Chongqing Medical University from January 2015 to August 2020 was conducted. All patients were divided into IVIG-responsive and IVIG-resistant groups, which were randomly divided into training and validation sets. The independent risk factors were determined using logistic regression analysis. Logistic regression nomograms, support vector machine (SVM), XGBoost and LightGBM prediction models were constructed and compared with the previous models.Results: In total, 1,240 out of 1,398 patients were IVIG responders, while 158 were resistant to IVIG. According to the results of logistic regression analysis of the training set, four independent risk factors were identified, including total bilirubin (TBIL) (OR = 1.115, 95% CI 1.067–1.165), procalcitonin (PCT) (OR = 1.511, 95% CI 1.270–1.798), alanine aminotransferase (ALT) (OR = 1.013, 95% CI 1.008–1.018) and platelet count (PLT) (OR = 0.998, 95% CI 0.996–1). Logistic regression nomogram, SVM, XGBoost, and LightGBM prediction models were constructed based on the above independent risk factors. The sensitivity was 0.617, 0.681, 0.638, and 0.702, the specificity was 0.712, 0.841, 0.967, and 0.903, and the area under curve (AUC) was 0.731, 0.814, 0.804, and 0.874, respectively. Among the prediction models, the LightGBM model displayed the best ability for comprehensive prediction, with an AUC of 0.874, which surpassed the previous classic models of Egami (AUC = 0.581), Kobayashi (AUC = 0.524), Sano (AUC = 0.519), Fu (AUC = 0.578), and Formosa (AUC = 0.575).Conclusion: The machine learning LightGBM prediction model for IVIG-resistant KD patients was superior to previous models. Our findings may help to accomplish early identification of the risk of IVIG resistance and improve their outcomes.

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Establishment of Prediction Models for venous thromboembolism in non-oncological urological inpatients--a single center experience

10.21203/rs.3.rs-35739/v1 ◽

2020 ◽

Author(s):

Kaixuan Li ◽

Haozhen Li ◽

Quan Zhu ◽

Ziqiang Wu ◽

Zhao Wang ◽

...

Keyword(s):

Risk Factors ◽

Logistic Regression ◽

Venous Thromboembolism ◽

Prediction Model ◽

Regression Model ◽

Logistic Regression Model ◽

Prediction Models ◽

Predictive Ability ◽

Multivariate Logistic Regression Model ◽

Single Center Experience

Abstract Background To establish prediction models for venous thromboembolism (VTE) in non-oncological urological inpatients. Methods A retrospective analysis of 1453 inpatients was carried out and the risk factors for VTE had been clarified our previous studies. Results Risk factors included the following 5 factors: presence of previous VTE (X1), presence of anticoagulants or anti-platelet agents treatment before admission (X2), D-dimer value (≥ 0.89 µg/ml, X3), presence of lower extremity swelling (X4), presence of chest symptoms (X5). The logistic regression model is Logit (P) = − 5.970 + 2.882 * X1 + 2.588 * X2 + 3.141 * X3 + 1.794 * X4 + 3.553 * X5. When widened the p value to not exceeding 0.1 in multivariate logistic regression model, two addition risk factors were enrolled: Caprini score (≥ 5, X6), presence of complications (X7). The prediction model turns into Logit (P) = − 6.433 + 2.696 * X1 + 2.507 * X2 + 2.817 * X3 + 1.597 * X4 + 3.524 * X5 + 0.886 * X6 + 0.963 * X7. Internal verification results suggest both two models have a good predictive ability, but the prediction accuracy turns to be both only 43.0% when taking the additional 291 inpatients’ data in the two models. Conclusion We built two similar novel prediction models to predict VTE in non-oncological urological inpatients. Trial registration: This trial was retrospectively registered at http://www.chictr.org.cn/index.aspx under the public title“The incidence, risk factors and establishment of prediction model for VTE n urological inpatients” with a code ChiCTR1900027180 on November 3, 2019. (Specific URL to the registration web page: http://www.chictr.org.cn/showproj.aspx?proj=44677).

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Changes in Cognitive Function and Risk Factors for Cognitive Impairment of the Elderly in China: 2005–2014

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph16162847 ◽

2019 ◽

Vol 16 (16) ◽

pp. 2847 ◽

Cited By ~ 9

Author(s):

Qilin Zhang ◽

Yanli Wu ◽

Tiankuo Han ◽

Erpeng Liu

Keyword(s):

Risk Factors ◽

Logistic Regression ◽

Regression Analysis ◽

Cognitive Impairment ◽

Cognitive Function ◽

Logistic Regression Analysis ◽

Binary Logistic Regression ◽

The Elderly ◽

Binary Logistic Regression Analysis ◽

Chinese Elderly

Background: The cognitive function of the elderly has become a focus of public health research. Little is known about the changes of cognitive function and the risk factors for cognitive impairment in the Chinese elderly; thus, the purposes of this study are as follows: (1) to describe changes in cognitive function in the Chinese elderly from 2005–2014 and (2) to explore risk factors for cognitive impairment of the Chinese elderly. Design and setting: A total of 2603 participants aged 64 years and above participated in the Chinese Longitudinal Healthy Longevity Survey (CLHLS) and were followed up from 2005 to 2014. Cognitive function and cognitive impairment were assessed using the Chinese version of the Mini-Mental State Examination (MMSE). Binary logistic regression analysis was used to estimate the odds ratio (OR) and 95% confidence intervals (CI) of cognitive impairment. Results: Results revealed that the cognitive function of the Chinese elderly shows diversified changes: deterioration (55.09%), unchanged (17.21%) and improvement (27.70%). In addition, there are significant demographic differences in gender, age, education, marriage and other aspects when it comes to the changes of cognitive function in Chinese elderly. In the binary logistic regression analysis, female, increased age, lower education level, no spouse, less income, worse PWB (psychological well-being), less fresh fruit and vegetable intake, more activities of daily living (ADL) limitations, lower social engagement were significantly associated with higher odds for cognitive impairment. Conclusions: Various interventions should be implemented to maintain cognitive function in Chinese elderly.

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Brain Metastases in Lung Cancer Patients: Clinical Potential Risk Factors of CYFRA21-1 and CEA

10.21203/rs.3.rs-464847/v1 ◽

2021 ◽

Author(s):

Jing Tang ◽

Tie Sun ◽

Qian-Min Ge ◽

Rong-Bin Liang ◽

Ting Su ◽

...

Keyword(s):

Risk Factors ◽

Lung Cancer ◽

Logistic Regression ◽

Regression Analysis ◽

Brain Metastasis ◽

Brain Metastases ◽

Cancer Patients ◽

Binary Logistic Regression ◽

Binary Logistic Regression Analysis ◽

Lung Cancer Patients

Abstract Background At present, little is known about the specific risk factors of brain metastasis in patients with lung cancer. This study aims to explore the risk factors of brain metastasis. Methods From April 1999 to July 2017, a total of 1,615 lung cancer patients were included in this retrospective study. The patients were divided into two groups, namely brain metastasis group and non-brain metastasis group. Student's t test, non-parametric rank sum test and chi-square test were used to describe whether there is a significant difference between the two groups. We compared the serum biomarkers of the two groups of patients, including alkaline phosphatase (ALP), Calcium, calcium hemoglobin (HB), alpha fetoprotein (AFP), cancer embryonic antigen (CEA), CA-125, CA-199, CA- 153, CA-724, cytokeratin fragment 19 (CYFRA 21 − 1), total prostate specific antigen (TPSA), squamous cell carcinoma antigen (SCC-Ag) ,and neuron specific enolase (NSE). Binary logistic regression analysis was used to determine its risk factors, and receiver operating curve (ROC) analysis was used to evaluate its diagnostic value for brain metastases in patients with lung cancer. Results In the analysis of brain metastases in patients with lung cancer, binary logistic regression analysis showed that CYFRA21-1 and CEA are independent risk factors for brain metastases in patients with lung cancer (both P < 0.001). The sensitivity and specificity of diagnosing brain metastasis were CYFRA21-1, 38.0% and 87.4%, respectively; CEA was 39.7% and 79.3%, respectively. Conclusion Serum CYFRA21-1 and CEA have predictive value in the diagnosis of brain metastases in patients with lung cancer.

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Asphyxia and Neonatal Respiratory Distress Syndrome Are Independent Predictors of the Non-response to Inhaled Nitric Oxide in the Newborns With PPHN

Frontiers in Pediatrics ◽

10.3389/fped.2021.665830 ◽

2021 ◽

Vol 9 ◽

Author(s):

Yuwei Zhao ◽

Lei Liang ◽

Guanghui Liu ◽

Hong Zheng ◽

Liying Dai ◽

...

Keyword(s):

Nitric Oxide ◽

Risk Factors ◽

Pulmonary Hypertension ◽

Logistic Regression ◽

Regression Analysis ◽

Distress Syndrome ◽

Binary Logistic Regression ◽

Binary Logistic Regression Analysis ◽

Neonatal Respiratory Distress ◽

Independent Risk Factors

Aim: Not all the neonates respond with improvement in oxygenation following inhaled nitric oxide treatment (iNO) treatment. The aim of this study was to assess the independent risk factors associated with non-response to iNO during the 2 weeks of postnatal treatment in neonates diagnosed with persistent pulmonary hypertension (PPHN).Materials and Methods: This retrospective cohort study included all newborns with PPHN who received iNO treatment for more than 24 h. Demographic, obstetric, perinatal data and clinical complications were extracted from the hospitalization records. Subjects were divided into two groups according to their response to iNO inspiration during the first 24 h of iNO treatment. No response was defined as an increase in SpO2 < 5% or the inability to sustain saturation levels in the first 24 h of iNO treatment. For descriptive statistics, χ2 and t-test analysis were used to compare categorical and continuous variables between the two groups. To evaluate independent risk factors of non-responsiveness to iNO treatment, binary logistic regression analysis were performed.Results: A total of 75 newborns were included in the study. Sixty-two cases were in the responders group, and 13 cases were in the non-responders group. Univariate analysis showed that asphyxia, neonatal respiratory distress syndrome (NRDS), pulmonary surfactant administration, meconium aspiration syndrome (MAS), the severity of pulmonary hypertension (PH), and high-frequency oscillatory ventilation (HFOV) therapy were the high-risk factors affecting the response to iNO treatment in the newborns with PPHN. The binary logistic regression analysis indicated that asphyxia and NRDS incidence were independent predictors of non-responsiveness to iNO treatment [asphyxia: OR 4.193, 95% CI 1.104–15.927, P = 0.035; NRDS: OR 0.154, 95% CI 0.036–0.647, P = 0.011]. The patients in the non-responders group had shorter iNO inspiration followed by MV duration, supplemental oxygen and hospital stay, and higher mortality. There were no significant differences in IVH, PVL, and BPD between two groups.Conclusion: In the newborns with PPHN, asphyxia and NRDS resulted as the independent risk factors of non-responsiveness to iNO therapy. Asphyxia in the newborns with PPHN is detrimental to the response to iNO treatment, while NRDS is beneficial.

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