Development and Testing of a Risk Assessment Model for Venous Thrombosis in Medical Inpatients: the Medical Inpatients and Thrombosis (MITH) Study Score

Blood ◽  
2011 ◽  
Vol 118 (21) ◽  
pp. 173-173
Author(s):  
Neil Zakai ◽  
Peter Callas ◽  
Allen Repp ◽  
Mary Cushman
Keyword(s):  
Risk Factors ◽  
Risk Assessment ◽  
Risk Factor ◽  
High Risk ◽  
Venous Thrombosis ◽  
Medical Record ◽  
The United States ◽  
Assessment Model ◽  
C Statistic ◽  
Medical Inpatients

Abstract Abstract 173FN2 Introduction: Multiple government organizations (i.e. the Joint Commission in the United States and the National Institute for Health and Clinical Excellence in the United Kingdom) mandate venous thrombosis (VT) risk assessment for hospitalized patients and provision of VT prophylaxis, however there are no validated VT risk assessment models (RAM) available for use in medical inpatients. Methods: Between January 2002 and June 2009 all cases of VT complicating medical admissions were identified using ICD-9 codes and confirmed by medical record review at a 500 bed teaching hospital. Two controls without VT were frequency matched to each case by admission service (medicine, cardiology, and oncology) and admission year. VT required positive imaging or autopsy. Medical history, presenting conditions, and use of VT prophylaxis in cases and controls were assessed by chart review. Weighted logistic regression was used to calculate odds ratios (OR) and the Taylor series method for 95% confidence intervals (CI) accounting for VT prophylaxis use (both mechanical and pharmacologic). A RAM was developed using clinical judgment and sequentially adding risk factors into a multivariable model. A point value was assigned for each risk factor by dividing the b coefficients' by the lowest b coefficient value and rounding to the nearest integer. To validate the model, the 95% CI for the C-statistic was calculated using bootstrapping with 1000 replicate samples. Results: 299 cases of VT and 601 matched controls were reviewed. The rate of VT per 1000 admissions (95% CI) was 4.6 (3.9, 5.4). Table 1 presents the RAM with the point value for each risk factor. The c-statistic for the model was 0.73 (95% CI 0.70, 0.76). Using a cut-off of ≥2 points as high risk, 79% of cases and 39% of controls were classified as high risk. The probability of VT in the absence of VT prophylaxis for a score <2 was 1.5 (95% CI 1.0, 2.3) per 1000 admissions and for a score ≥2 was 8.8 (95% CI 4.1, 18.8) per 1000 admissions. To evaluate a score assessed by clinical characteristics only, we assessed a score with the same risk factors but removing platelet count and white cell count from the model. The C-statistic was 0.71 (95% CI 0.68, 0.74) and 74% of cases and 39% of controls were high risk. Stratification by admission service or admission to an intensive care unit did not affect interpretation of the results. Conclusion: We present an internally validated RAM that assesses the risk of VT complicating medical admission. The score is simple, relies only on information easily known at the time of admission, and could be incorporated into an electronic medical record. It will allow clinicians to assess VT risk at admission for medical inpatients and weigh the risks and benefits of pharmacologic VT prophylaxis. The RAM will enable further studies to determine optimal VT prevention strategies in medical inpatients. Disclosures: No relevant conflicts of interest to declare.

Clostridium Difficile As a Risk Factor For Hospital-Acquired Venous Thrombosis In Medical Inpatients

Blood ◽  
2013 ◽  
Vol 122 (21) ◽  
pp. 1682-1682
Author(s):  
Samuel A Merrill ◽  
Michael Desarno ◽  
Damon Houghton ◽  
John P. Winters ◽  
Christopher Huston ◽  
...  
Keyword(s):  
Risk Factors ◽  
Risk Assessment ◽  
Risk Factor ◽  
Venous Thrombosis ◽  
Chart Review ◽  
Validation Cohort ◽  
Conflicts Of Interest ◽  
Derivation Cohort ◽  
Medical Inpatients ◽  
Hospital Acquired

Abstract Introduction The American College of Chest Physicians and government agencies recommend risk stratification and pharmacologic prophylaxis to prevent venous thromboembolism (VT) in hospitalized individuals. While risk factor assessment is often completed at admission, clinical changes during hospitalization may influence VT risk. Based on clinical observation of coincidence of C. difficile infection and VT, we sought to determine if C. diff or clinical suspicion of C. diff are risk factors for hospital acquired VT in medical inpatients. Methods Derivation and validation cohorts for the Medical Inpatient Venous Thrombosis Risk Assessment Score were used. Patients were admitted to the medical services at Fletcher Allen Health Care, a 500 bed teaching hospital for the University of Vermont. For derivation, all cases of hospital-acquired VT between 2002-2009 were identified using ICD-9 codes and verified by chart review; cases were matched by admission year and medical service to controls; VT risk factors and C. diff testing and Results were confirmed by chart review. For validation, hospital-acquired VT between 2009-2012 were captured by ICD-9 codes with confirmatory imaging; testing and Results for C. diff were ascertained from the microbiology lab database; VT risk factors were ascertained by ICD-9 codes, electronic problem lists, vital sign data, and lab values. Logistic regression, accounting for VT risk factors (Table), was used to determine whether 1) testing for C. diff or 2) a positive confirmation for C. diff was associated with hospital-acquired VT. Results In the derivation analysis 299 cases of hospital-acquired VT were identified from 64,334 medical admissions and matched to 601 controls. In the validation analysis 120 hospital-acquired VT were identified among 20,946 admissions. In the derivation cohort there were 4,793 tests for C. diff and 478 confirmed cases; and in the validation cohort there were 1,708 tests for C. diff and 260 confirmed cases. After accounting for other VT risk factors in the derivation cohort, testing for C. diff was associated with an OR of 2.14 (95% CI 1.28, 3.61) for VT, positive C. diff with an OR of 3.23 (95% CI 1.00, 10.45) for VT, and negative C. diff with an OR of 1.95 (95% CI 1.09-3.46) for VT. These associations were confirmed in the validation cohort (see Table). Conclusions Both testing for, and a diagnosis of C. diff were associated with hospital-acquired VT in medical inpatients. This relationship could be a surrogate of antimicrobial therapy indicating another active infection or a general marker for systemic illness, direct causation appears less likely. These data suggest that VT risk is dynamic during hospitalization and that further studies incorporating dynamic VT risk assessment are warranted in medical inpatients. Disclosures: No relevant conflicts of interest to declare.

Validation Of Medical Inpatient Venous Thrombosis Risk Assessment (MITH) Score

Blood ◽  
2013 ◽  
Vol 122 (21) ◽  
pp. 2931-2931
Author(s):  
Damon E Houghton ◽  
Michael Desarno ◽  
Peter Callas ◽  
Allen B Repp ◽  
Mary Cushman ◽  
...  
Keyword(s):  
Risk Factors ◽  
Risk Assessment ◽  
Risk Factor ◽  
Venous Thrombosis ◽  
Sensitivity And Specificity ◽  
Validation Cohort ◽  
Medical Inpatients ◽  
History Of ◽  
Hospital Acquired ◽  
Present On Admission

Abstract Introduction Governmental agencies recommend risk assessment of venous thrombosis (VT) for medical inpatients at admission and provision of VT prophylaxis for moderate to high risk patients. While several risk factor models for predicting hospital-acquired VT have been proposed, none have been widely accepted and few have been prospectively validated. We sought to validate the recently published MITH VT risk assessment model in an independent cohort of medical inpatients (Zakai et al, Journal of Thrombosis and Haemostasis 2013). Methods Hospital-acquired VT and risk factors present at admission were collected from adult inpatients between June 2009 and April 2012 admitted to the medicine, medical intensive care, hematology/oncology, or cardiology services at Fletcher Allen Hospital (500 bed teaching hospital for the University of Vermont). Hospital-acquired VT was defined using VT discharge ICD-9 codes (flagged as not present on admission) and record of an imaging study that could diagnosis VT (such as duplex ultrasound, computed tomography angiography, or ventilation perfusions scan). Inpatients with VT ICD-9 codes flagged as present on admission were excluded. The sensitivity and specificity of the definition was confirmed by chart review of 30 cases of hospital-acquired VTE and 30 non-cases. Risk factors for hospital-acquired VT were captured using ICD-9 codes from the problem list, discharge codes, vital signs, and laboratory values at admission. The MITH score was calculated for each patient based on the points for each risk factor: history of heart failure = 5 pts, history of rheumatologic disease = 4 pts, history of fracture in past 3 months = 3 pts, history of cancer in past 12 months = 1 pt, tachycardia (HR>100 at admission) = 2pt, respiratory dysfunction (SpO2<90% at admission or intubated on hospital day 1) = 1 pt, white blood cell count >11 = 1 pt, platelet count >350 = 1 pt. The absolute rates of hospital-acquired VT for different cut points of the score were calculated and compared qualitatively to those previously published for the MITH score. Results There were 120 hospital-acquired VT events complicating 20,334 medical admissions (5.9 cases per 1,000 hospital admissions). The sensitivity and specificity of our definition of hospital-acquired VT was 100% and 91%, respectively. The table presents the prevalence of the MITH score at various cut-offs in cases and non-cases as well as the incidence of VT. In the derivation of the MITH score, the rate of VT per 1000 admissions for a score <1, <2, or <3 was 1.0, 1.5, and 2.1 compared with 0.7, 1.8, and 2.2 VT per 1000 admissions for the validation cohort. The incidence of VTE in the derivation of the MITH score for a score ≥1, ≥2, and ≥3 was 6.0, 8.9, and 12.4 per 1000 admissions compared with 7.9, 9.0, and 10.3 per 1000 admissions in the validation cohort. Conclusions We have validated a previously published VT risk score for hospitalized medical patients in an independent population. Determination of a patient's risk of VT at admission using readily available clinical and laboratory data could allow physicians to make informed decisions about risks and benefits of DVT prophylaxis. Further work is required to determine at what level of risk pharmacologic VT prophylaxis is warranted in this patient population. Disclosures: No relevant conflicts of interest to declare.

scholarly journals The HAS-RISC Score for Venous Thromboembolism (VTE) Risk Stratification in Newly Diagnosed Multiple Myeloma Patients Starting Immunomodulatory Drugs (IMID)

Blood ◽  
2018 ◽  
Vol 132 (Supplement 1) ◽  
pp. 144-144 ◽  
Author(s):  
Ang Li ◽  
Qian V. Wu ◽  
Greg Warnick ◽  
Neil A Zakai ◽  
Edward N. Libby ◽  
...  
Keyword(s):  
Risk Factors ◽  
Risk Assessment ◽  
High Risk ◽  
Research Funding ◽  
Risk Group ◽  
High Risk Group ◽  
Assessment Model ◽  
Risk Assessment Model ◽  
Newly Diagnosed ◽  
Very High

Abstract Introduction: Patients with newly diagnosed multiple myeloma (MM) have high risk of venous thromboembolism (VTE) when starting initial treatment that contains immunomodulatory drugs (IMID) such as lenalidomide or thalidomide. The National Comprehensive Cancer Network (NCCN) guideline recommends primary anticoagulant thromboprophylaxis for the high-risk patients. However, it is challenging to risk-stratify patients without a validated risk model. We have conducted a retrospective cohort study using the SEER-Medicare (Surveillance, Epidemiology, and End Results) database to derive a new VTE risk assessment model. Methods: We selected all patients 66 or older with newly diagnosed MM 2007 to 2013. Patients were included if they had a prescription of IMID within twelve months of diagnosis and complete enrollment for fee-for-service and prescription drug coverage. We ascertained baseline demographics and VTE risk factors from the current NCCN guideline using validated codes. The VTE outcome was defined as either one inpatient or two outpatient claims at least 30 days apart in combination with an anticoagulant prescription within 90 days. All patients were followed from the date of IMID initiation until first VTE occurrence or death and were censored for disenrollment from Medicare, discontinuation of IMID (after a grace period of 90 days), autologous transplantation, or the end of claims data (12/31/2014). Cause specific Cox regression models were used for time to VTE analysis. For variable selection, all risk factors with p-value <0.10 were considered candidates for inclusion in the final multivariable regression model. VTE history, recent surgery, and anticoagulant exposure were forced into the model, regardless of significance testing. Integer points were assigned according to the beta coefficients and subsequent risk groups were created. The model's discrimination was validated internally by the bias-corrected Harrell's c statistic and the 95% confidence interval was estimated from 200 bootstrap samples. Results: We identified 2397 MM patients on IMID that met the study criteria. The median time on IMID treatment was 116 days (IQR 28-279). The mean age of patients was 74, 49% were female, 80% were White, 13% were Black, 6.5% were Asian. Only 13% of patients had concurrent anticoagulant exposure (11% warfarin, 2% LMWH, 1% DOAC) with a median duration of 116 days (IQR 42-315 days). In the multivariable model built from candidate covariates, we identified history of VTE, recent surgery, cytotoxic (non-bortezomib) chemotherapy, higher dose dexamethasone, older age, and Black race, as important risk factors. Asian race and LMWH/DOAC use were associated with lower VTE risk (Table 1). We derived a risk assessment model that stratified patients into 2 prognostic risk groups (Table 1): 25% (n=581) in the very high-risk group (score 2 to 7), 75% (n=1816) in the standard-risk group (score -3 to 1). The incidence of VTE at 3 months and 6 months were 9.5% and 16.3% in the very high-risk group compared to 3.7% and 6.3% in the standard-risk group with a resulting hazard ratio of 2.73 (p<0.001) (Figure 1). The bias-corrected Harrell's c statistic for the product index was 0.63 (0.59-0.68). Conclusions: We have derived a VTE risk assessment model specifically for patients with MM starting IMID therapy. The HAS-RiSC score combines 7 clinical risk factors - History of VTE, Age 80+, Surgery within last 90 days, Race Black, race Asian, Steroid use, and Chemotherapy - into a simplified VTE risk assessment model that identifies a subgroup of patients at very high risk for VTE. External validation of this risk assessment model is currently in progress. Disclosures Garcia: Daiichi Sankyo: Research Funding; Incyte: Research Funding; Janssen: Consultancy, Research Funding; Pfizer: Consultancy; Retham Technologies LLC: Consultancy; Shingoi: Consultancy; Portola: Research Funding; Bristol Meyers Squibb: Consultancy; Boehringer Ingelheim: Consultancy. Lyman:Amgen: Other: Research support; Generex Biotechnology: Membership on an entity's Board of Directors or advisory committees; Halozyme; G1 Therapeutics; Coherus Biosciences: Consultancy.

scholarly journals Evaluating post-operative venous thromboembolism risk in urology patients using a validated risk assessment model

2021 ◽  
Vol 27 (1) ◽  
Author(s):  
Winston Paul René Padayachee ◽  
Mohamed Haffejee ◽  
Marietha Nel
Keyword(s):  
Risk Factors ◽  
Risk Assessment ◽  
Venous Thromboembolism ◽  
High Risk ◽  
Significant Risk ◽  
Assessment Model ◽  
Risk Category ◽  
Risk Assessment Model ◽  
Surgical Morbidity ◽  
Significant Difference

Abstract Background Venous thromboembolism (VTE) is an important cause of post-surgical morbidity and mortality. This study aimed to apply a validated risk assessment model to evaluate the risk of post-operative VTE in urology patients. Methods This prospective descriptive observational study used the Caprini risk assessment model to evaluate VTE risk in patients planned for elective urology surgery at a tertiary Johannesburg hospital from January to June 2020. Results Two hundred and twenty-six patients with a mean age of 52 years were evaluated for post-operative VTE risk. The population was generally overweight, with a mean BMI of 26.3 kg/m2. The mean Caprini score was 4.42, reflecting a population at high risk for post-operative VTE. There was no statistically significant difference between males and females in this regard. On average, participants had three risk factors for post-operative VTE. Fifteen per cent of all patients were at low risk for VTE, while 40.3% of participants were categorised as moderate risk. The category with the highest percentage of participants (44.7%) was the high-risk category (Caprini score ≥ 5). High-risk patients undergoing oncology surgery comprised 16.8% of the population, and these patients may require extended duration pharmacological thromboprophylaxis to prevent VTE. The most clinically significant risk factors for post-operative VTE included age, obesity, malignancy and HIV infection. Conclusion Venous thromboembolism may be difficult to diagnose, and clinicians may underestimate the risk for it to develop. Risk assessment models, such as the Caprini score, are objective and a practical tool to guide the application of thromboprophylaxis. The application of the Caprini RAM in the elective urological surgery population at Chris Hani Baragwanath Academic Hospital yields similar results to studies performed elsewhere on similar surgical populations. Further research is required to evaluate whether the actual incidence of VTE correlates with the risk assessment in this population. Clinician compliance with the use of RAMs as well as the corresponding recommendations for prophylaxis may need to be evaluated. A validated risk assessment model which accounts for procedure-specific risks in urology may be useful.

Abstract P253: Impact of Hospital-Acquired Venous Thrombosis on Mortality in Medical Inpatients: The Medical Inpatients and Thrombosis (MITH) Study.

Circulation ◽  
2012 ◽  
Vol 125 (suppl_10) ◽  
Author(s):  
Catherine R Mygatt ◽  
Peter W Callas ◽  
Mary Cushman ◽  
Allen B Repp ◽  
Neil A Zakai
Keyword(s):  
Risk Factors ◽  
Blood Pressure ◽  
Venous Thrombosis ◽  
Medical Record ◽  
Odds Ratio ◽  
Medical Patients ◽  
Risk Of Death ◽  
Medical Inpatients ◽  
Record Review ◽  
Hospital Acquired

Introduction: The Joint Commission mandates assessing risk and providing venous thrombosis (VT) prophylaxis in hospital inpatients. Pharmacologic VT prophylaxis reduces VT among medical inpatients, but the impact on survival is unknown. We studied the association of hospital acquired VT and VT prophylaxis with risk of inpatient mortality. Methods: We identified all cases of VT complicating medical admissions at a 500 bed teaching hospital in Burlington, Vermont from January 2002 to June 2009. VT cases were identified using ICD-9 codes, confirmed by medical record review, and frequency matched 1:2 to patients without VT by admission year and service (oncology, general medicine or cardiology). Death from VT was determined by medical record review and standardized criteria. We calculated odds ratios (OR) of death for hospital-acquired VT and VT prophylaxis using weighted multivariable logistic regression and 95% confidence intervals (CI) using the Taylor series method. Results: Of 64,334 admissions, 299 patients had hospital-acquired VT. 56 of these died from any cause and 24 died due to their VT. For every 1000 admissions, 87 ended in death and 4.6 had a hospital-acquired VT. Hospital-acquired VT was associated with increased odds of death, but this was attenuated by adjustment for other risk factors for death ( Table ). VT prophylaxis was inversely associated with odds of death when similarly adjusted ( Table ). Conclusions: One in 235 deaths in medical patients was attributable to hospital-acquired VT, a potentially preventable event. Occurrence of VT after admission in medical patients was associated with risk of death, but this was mediated by other patient characteristics. Findings suggest providing VT prophylaxis reduces risk of death in hospital, but this requires confirmation due to the low number of deaths in this study. Table Multivariable Model of Risk Factors for In-Hospital Mortality Risk Factor Unadjusted Odds Ratio (95% CI) Adjusted Odds Ratio (95% CI) * VT occurring during admission 2.41 (1.54, 3.77) 1.21 (0.58, 2.53) VT prophylaxis provided throughout admission ** 1.15 (0.51, 2.62) 0.48 (0.16, 1.45) * Model adjusted for the following (measured on admission unless indicated): sex, age, systolic blood pressure, diastolic blood pressure, heart rate, hypoxic or ventilated, pneumonia, metastatic cancer, history of myocardial infarction, dementia, use of full anticoagulation, VT prophylaxis stopped during admission, admitted or transferred to intensive care. ** Reference group: no prophylaxis or anticoagulation during admission.

scholarly journals Whom Should We Test for COVID-19? Performance of a Symptom and Risk Factor Questionnaire on COVID-19 Test Results and Patient Outcomes in an Immediate Care Setting

2020 ◽  
Vol 11 ◽  
pp. 215013272098129
Author(s):  
Lauren Oshman ◽  
Amanda Caplan ◽  
Raabiah Ali ◽  
Lavisha Singh ◽  
Rabeeya Khalid ◽  
...  
Keyword(s):  
Risk Factors ◽  
Risk Assessment ◽  
Risk Factor ◽  
High Risk ◽  
Effect Size ◽  
Care Setting ◽  
Risk Group ◽  
Low Risk ◽  
Risk Patients ◽  
Assessment Questionnaire

Introduction: The CDC and Illinois Department of Public Health disseminated risk factor criteria for COVID-19 testing early in the pandemic. The objective of this study is to assess the effectiveness of risk stratifying patients for COVID-19 testing and to identify which risk factors and which other clinical variables were associated with SARS-CoV-2 PCR test positivity. Methods: We conducted an observational cohort study on a sample of symptomatic patients evaluated at an immediate care setting. A risk assessment questionnaire was administered to every patient before clinician evaluation. High-risk patients received SARS-CoV-2 test and low-risk patients were evaluated by a clinician and selectively tested based on clinician judgment. Multivariate analyses tested whether risk factors and additional variables were associated with test positivity. Results: The adjusted odds ratio of testing positive was associated with COVID-19-positive or suspect close contact (aOR 1.56, 95% CI 1.15-2.10), large gathering attendance with a COVID-19-positive individual (aOR 1.92, 95% CI 1.10-3.34), and, with the largest effect size, decreased taste/smell (aOR 2.83, 95% CI 2.01-3.99). Testing positive was associated with ages 45-64 and ≥65 (aOR 1.75, 95% CI 1.25-2.44, and aOR 2.78, 95% CI 1.49-5.16), systolic blood pressures ≤120 (aOR 1.64, 95% CI 1.20-2.24), and, with the largest effect size, temperatures ≥99.0°F (aOR 3.06, 95% CI 2.23-4.20). The rate of positive SARS-CoV-2 test was similar between high-risk and low risk patients (225 [22.2%] vs 50 [19.8%]; P = .41). Discussion: The risk assessment questionnaire was not effective at stratifying patients for testing. Although individual risk factors were associated with SARS-CoV-2 test positivity, the low-risk group had similar positivity rates to the high-risk group. Our observations underscore the need for clinicians to develop clinical experience and share best practices and for systems and payors to support policies, funding, and resources to test all symptomatic patients.

Risk Assessment of Gravity Dam with Machine Properties

2013 ◽  
Vol 454 ◽  
pp. 43-47
Author(s):  
Fen Hua Li ◽  
Hui Ma
Keyword(s):  
Risk Factors ◽  
Risk Assessment ◽  
Risk Factor ◽  
Comprehensive Evaluation ◽  
Fuzzy Comprehensive Evaluation ◽  
Assessment Model ◽  
Gravity Dam ◽  
Risk Assessment Model ◽  
Investigation Method ◽  
Water Conservancy

In recent years, water conservancy cause has been developing rapidly in China, the gravity dam risk assessment model has a great significance for the study. Firstly, the paper introduces risk and risk assessment. Then, the thesis uses expert investigation method to recognize the main risk factors of the project, analyze the probability and loss of risk factors of the gravity dam ; and based on the R = P × C model and fuzzy comprehensive evaluation method.Finally, we obtain the level of each risk factor, providing the basis for the prevention of the risks.

Predisposing factors for first and recurrent venous thrombosis

ESC CardioMed ◽  
2018 ◽  
pp. 2751-2755
Author(s):  
Willem M. Lijfering ◽  
Suzanne C. Cannegieter
Keyword(s):  
Risk Factors ◽  
Risk Factor ◽  
High Risk ◽  
Venous Thrombosis ◽  
Case Fatality ◽  
Predisposing Factors ◽  
Medical Illness ◽  
Vein Thrombosis ◽  
Patient Will ◽  
Recurrent Venous Thrombosis

Venous thrombosis, which mainly manifests as deep vein thrombosis of the leg or pulmonary embolism, is a major contributor to global disease burden. With a recurrence rate of approximately 25% in 5 years, and a 30-day case fatality rate of 5–10%, identification of predisposing factors for venous thrombosis is imperative. Dozens of risk factors for first venous thrombosis are known today, which can be grouped into three categories: first venous thrombosis ‘provoked by a transient risk factor’, ‘provoked by a persistent risk factor’, or ‘unprovoked’. This chapter comments on how risk factors known today can be classified into these categories, how this classification determines recurrence risk, and how knowledge on predisposing risk factors should be interpreted and integrated for optimal clinical use. The chapter proposes that predisposing factors for venous thrombosis are not the same for each high-risk situation. This is important to consider when one wants to identify high-risk groups in, for example, cancer patients, surgical patients, in patients with a medical illness, or in patients at risk for recurrent venous thrombosis. This way it will be possible to expose only those patients at unacceptably high risk of thrombosis to the risks and burden of anticoagulant treatment. In conclusion, the knowledge on predisposing factors for venous thrombosis is extensive, but the patient will benefit most when this knowledge is properly integrated, depending on the clinical situation.

The Health Check-Up Data-Analysis for Risk Assessment of Chronic Kidney Disease (CKD) in Taiwan

2021 ◽  
Author(s):  
Ming-Shu Chen ◽  
Mao-Jhen Jhou ◽  
Chi-Jie Lu ◽  
Chung-Chih Hung
Keyword(s):  
Risk Factors ◽  
Cardiovascular Disease ◽  
Risk Assessment ◽  
Chronic Kidney Disease ◽  
Risk Factor ◽  
High Risk ◽  
Kidney Disease ◽  
High Risk Population ◽  
Healthy Population ◽  
High Risk Factors

Early detection of chronic kidney disease (CKD) for high-risk population adults is very important. It has a common risk factor and causal relationship with chronic diseases such as diabetes, hypertension and cardiovascular disease etc. The results of this study provide that for early high-risk factors detection in CKD healthy population can be used by home care to recommend adjuvant treatment.

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