Identification of risk factors of developing pressure injuries among immobile patient, and a risk prediction model establishment: Protocol for a systematic review

Abstract Background Risk prediction models incorporating single nucleotide polymorphisms (SNPs) could lead to individualized prevention of colorectal cancer (CRC). However, the added value of incorporating SNPs into models with only traditional risk factors is still not clear. Hence, our primary aim was to summarize literature on risk prediction models including genetic variants for CRC, while our secondary aim was to evaluate the improvement of discriminatory accuracy when adding SNPs to a prediction model with only traditional risk factors. Methods We conducted a systematic review on prediction models incorporating multiple SNPs for CRC risk prediction. We tested whether a significant trend in the increase of Area Under Curve (AUC) according to the number of SNPs could be observed, and estimated the correlation between AUC improvement and number of SNPs. We estimated pooled AUC improvement for SNP-enhanced models compared with non-SNP-enhanced models using random effects meta-analysis, and conducted meta-regression to investigate the association of specific factors with AUC improvement. Results We included 33 studies, 78.79% using genetic risk scores to combine genetic data. We found no significant trend in AUC improvement according to the number of SNPs (p for trend = 0.774), and no correlation between the number of SNPs and AUC improvement (p = 0.695). Pooled AUC improvement was 0.040 (95% CI: 0.035, 0.045), and the number of cases in the study and the AUC of the starting model were inversely associated with AUC improvement obtained when adding SNPs to a prediction model. In addition, models constructed in Asian individuals achieved better AUC improvement with the incorporation of SNPs compared with those developed among individuals of European ancestry. Conclusions Though not conclusive, our results provide insights on factors influencing discriminatory accuracy of SNP-enhanced models. Genetic variants might be useful to inform stratified CRC screening in the future, but further research is needed.

Download Full-text

A Cox-Based Risk Prediction Model for Early Detection of Cardiovascular Disease: Identification of Key Risk Factors for the Development of a 10-Year CVD Risk Prediction

Advances in Preventive Medicine ◽

10.1155/2019/8392348 ◽

2019 ◽

Vol 2019 ◽

pp. 1-11 ◽

Cited By ~ 4

Author(s):

Xiaona Jia ◽

Mirza Mansoor Baig ◽

Farhaan Mirza ◽

Hamid GholamHosseini

Keyword(s):

Risk Factors ◽

Cardiovascular Disease ◽

Heart Rate ◽

Prediction Model ◽

Risk Prediction ◽

Risk Model ◽

Clinical Laboratory ◽

Risk Prediction Model ◽

Validation Dataset ◽

Cvd Risk

Background and Objective. Current cardiovascular disease (CVD) risk models are typically based on traditional laboratory-based predictors. The objective of this research was to identify key risk factors that affect the CVD risk prediction and to develop a 10-year CVD risk prediction model using the identified risk factors. Methods. A Cox proportional hazard regression method was applied to generate the proposed risk model. We used the dataset from Framingham Original Cohort of 5079 men and women aged 30-62 years, who had no overt symptoms of CVD at the baseline; among the selected cohort 3189 had a CVD event. Results. A 10-year CVD risk model based on multiple risk factors (such as age, sex, body mass index (BMI), hypertension, systolic blood pressure (SBP), cigarettes per day, pulse rate, and diabetes) was developed in which heart rate was identified as one of the novel risk factors. The proposed model achieved a good discrimination and calibration ability with C-index (receiver operating characteristic (ROC)) being 0.71 in the validation dataset. We validated the model via statistical and empirical validation. Conclusion. The proposed CVD risk prediction model is based on standard risk factors, which could help reduce the cost and time required for conducting the clinical/laboratory tests. Healthcare providers, clinicians, and patients can use this tool to see the 10-year risk of CVD for an individual. Heart rate was incorporated as a novel predictor, which extends the predictive ability of the past existing risk equations.

Download Full-text

Multifactorial Screening Tool for Determining Fall Risk in Community-Dwelling Adults Aged 50 Years or Over (FallSensing): Protocol for a Prospective Study (Preprint)

10.2196/preprints.10304 ◽

2018 ◽

Author(s):

Anabela Correia Martins ◽

Juliana Moreira ◽

Catarina Silva ◽

Joana Silva ◽

Cláudia Tonelo ◽

...

Keyword(s):

Risk Factors ◽

Prediction Model ◽

Prospective Study ◽

Risk Prediction ◽

Fall Risk ◽

Screening Tool ◽

Community Dwelling ◽

Risk Prediction Model ◽

Risk Of Falling ◽

Home Hazards

BACKGROUND Falls are a major health problem among older adults. The risk of falling can be increased by polypharmacy, vision impairment, high blood pressure, environmental home hazards, fear of falling, and changes in the function of musculoskeletal and sensory systems that are associated with aging. Moreover, individuals who experienced previous falls are at higher risk. Nevertheless, falls can be prevented by screening for known risk factors. OBJECTIVE The objective of our study was to develop a multifactorial, instrumented, screening tool for fall risk, according to the key risk factors for falls, among Portuguese community-dwelling adults aged 50 years or over and to prospectively validate a risk prediction model for the risk of falling. METHODS This prospective study, following a convenience sample method, will recruit community-dwelling adults aged 50 years or over, who stand and walk independently with or without walking aids in parish councils, physical therapy clinics, senior’s universities, and other facilities in different regions of continental Portugal. The FallSensing screening tool is a technological solution for fall risk screening that includes software, a pressure platform, and 2 inertial sensors. The screening includes questions about demographic and anthropometric data, health and lifestyle behaviors, a detailed explanation about procedures to accomplish 6 functional tests (grip strength, Timed Up and Go, 30 seconds sit to stand, step test, 4-Stage Balance test “modified,” and 10-meter walking speed), 3 questionnaires concerning environmental home hazards, and an activity and participation profile related to mobility and self-efficacy for exercise. RESULTS The enrollment began in June 2016 and we anticipate study completion by the end of 2018. CONCLUSIONS The FallSensing screening tool is a multifactorial and evidence-based assessment which identifies factors that contribute to fall risk. Establishing a risk prediction model will allow preventive strategies to be implemented, potentially decreasing fall rate. REGISTERED REPORT IDENTIFIER RR1-10.2196/10304

Download Full-text

Identification of risk factors for daptomycin-associated creatine phosphokinase elevation and development of a risk prediction model for incidence probability

Open Forum Infectious Diseases ◽

10.1093/ofid/ofab568 ◽

2021 ◽

Author(s):

Masaru Samura ◽

Naoki Hirose ◽

Takenori Kurata ◽

Keisuke Takada ◽

Fumio Nagumo ◽

...

Keyword(s):

Risk Factors ◽

Logistic Regression ◽

Regression Analysis ◽

Prediction Model ◽

Risk Prediction ◽

Risk Score ◽

Logistic Regression Analysis ◽

Creatine Phosphokinase ◽

Risk Prediction Model ◽

Risk Scores

Abstract Background In this study, we investigated the risk factors for daptomycin-associated creatine phosphokinase (CPK) elevation and established a risk score for CPK elevation. Methods Patients who received daptomycin at our hospital were classified into the normal or elevated CPK group based on their peak CPK levels during daptomycin therapy. Univariable and multivariable analyses were performed, and a risk score and prediction model for the incidence probability of CPK elevation were calculated based on logistic regression analysis. Results The normal and elevated CPK groups included 181 and 17 patients, respectively. Logistic regression analysis revealed that concomitant statin use (odds ratio [OR] 4.45, 95% confidence interval [CI] 1.40–14.47, risk score 4), concomitant antihistamine use (OR 5.66, 95% CI 1.58–20.75, risk score 4), and trough concentration (Cmin) between 20 and <30 µg/mL (OR 14.48, 95% CI 2.90–87.13, risk score 5) and ≥30.0 µg/mL (OR 24.64, 95% CI 3.21–204.53, risk score 5) were risk factors for daptomycin-associated CPK elevation. The predicted incidence probabilities of CPK elevation were <10% (low risk), 10%–<25% (moderate risk), and ≥25% (high risk) with the total risk scores of ≤4, 5–6, and ≥8, respectively. The risk prediction model exhibited a good fit (area under the receiving-operating characteristic curve 0.85, 95% CI 0.74–0.95). Conclusions These results suggested that concomitant use of statins with antihistamines and Cmin ≥20 µg/mL were risk factors for daptomycin-associated CPK elevation. Our prediction model might aid in reducing the incidence of daptomycin-associated CPK elevation.

Download Full-text

Abstract 14508: Development of a Risk Prediction Model for 1-year Readmission Among Younger Adults Hospitalized for Acute Myocardial Infarction

Circulation ◽

10.1161/circ.142.suppl_3.14508 ◽

2020 ◽

Vol 142 (Suppl_3) ◽

Author(s):

Rachel P Dreyer ◽

Terrence E Murphy ◽

Valeria Raparelli ◽

Sui Tsang ◽

Gail Onofrio ◽

...

Keyword(s):

Risk Factors ◽

Myocardial Infarction ◽

Acute Myocardial Infarction ◽

Prediction Model ◽

Risk Prediction ◽

Bayesian Model Averaging ◽

Risk Model ◽

Young Patients ◽

Risk Prediction Model ◽

Younger Adults

Introduction: Although readmission over the first year following hospitalization for acute myocardial infarction (AMI) is common among younger adults (18-55 yrs), there is no available risk prediction model for this age group. Existing risk models have been developed in older populations, have modest predictive ability, and exhibit methodological drawbacks. We developed a risk prediction model that considered a broad range of demographic, clinical, and psychosocial factors for readmission within 1-year of hospitalization for AMI among young adults. Methods: Young AMI adults (18-55 yrs) were enrolled from the prospective observational VIRGO study (2008-2012) of 3,572 patients. Data were obtained from medical record abstraction, interviews, and adjudicated hospitalization records. The outcome was all-cause readmission within 1-year. We used a two-stage selection process (LASSO followed by Bayesian Model Averaging) to develop a risk model. Results: The median age was 48 years (IQR: 44,52), 67.1% were women, and 20.1% were Non-white or Hispanic. Within 1-year, 906 patients (25.3%) were readmitted. Patients who were readmitted were more likely to be female, black, and had a clustering of adverse risk factors and co-morbidities. From 61 original variables considered, the final multivariable model of readmission within 1-year of discharge consisted of 14 predictors (Figure) . The model was well calibrated (Hosmer-Lemeshow P >0.05) with moderate discrimination (C statistic over 33 imputations: 0.69 development cohort). Conclusion: Adverse clinical risk factors such as diabetes, hypertension and prior AMI, but also female sex, access to specialist care, and major depression were associated with a higher risk of readmission at 1-year post AMI. This information is important to inform the development of interventions to reduce readmissions in young patients with AMI.

Download Full-text

Development and validation of an incidence risk prediction model for early foot ulcer in diabetes based on a high evidence systematic review and meta-analysis

Diabetes Research and Clinical Practice ◽

10.1016/j.diabres.2021.109040 ◽

2021 ◽

pp. 109040

Author(s):

Dong Chen ◽

Meijun Wang ◽

Xin Shang ◽

Xixi Liu ◽

Xinbang Liu ◽

...

Keyword(s):

Systematic Review ◽

Prediction Model ◽

Risk Prediction ◽

Meta Analysis ◽

Foot Ulcer ◽

Risk Prediction Model ◽

Incidence Risk ◽

Development And Validation

Download Full-text

Critical assessment of new risk factors for breast cancer: considerations for development of an improved risk prediction model

Endocrine Related Cancer ◽

10.1677/erc-06-0045 ◽

2007 ◽

Vol 14 (2) ◽

pp. 169-187 ◽

Cited By ~ 131

Author(s):

Richard J Santen ◽

Norman F Boyd ◽

Rowan T Chlebowski ◽

Steven Cummings ◽

Jack Cuzick ◽

...

Keyword(s):

Breast Cancer ◽

Risk Factors ◽

Prediction Model ◽

Cancer Prevention ◽

Risk Prediction ◽

Breast Cancer Prevention ◽

Risk Prediction Model ◽

Clinical Material ◽

Collaborative Group ◽

Density State

The majority of candidates for breast cancer prevention have not accepted tamoxifen because of the perception of an unfavorable risk/benefit ratio and the acceptance of raloxifene remains to be determined. One means of improving this ratio is to identify women at very high risk of breast cancer. Family history, age, atypia in a benign biopsy, and reproductive factors are the main parameters currently used to determine risk. The most powerful risk factor, mammographic density, is not presently employed routinely. Other potentially important factors are plasma estrogen and androgen levels, bone density, weight gain, age of menopause, and fracture history, which are also not currently used in a comprehensive risk prediction model because of lack of prospective validation. The Breast Cancer Prevention Collaborative Group (BCPCG) met to critically examine and prioritize risk factors that might be selected for further testing by multivariate analysis using existing clinical material. The BCPCG reached a consensus that quantitative breast density, state of the art plasma estrogen and androgen measurements, history of fracture and height loss, BMI, and waist–hip ratio had sufficient priority for further testing. As a practical approach, these parameters could be added to the existing Tyrer–Cuzick model which encompasses factors included in both the Claus and Gail models. The BCPCG analyzed potentially available clinical material from previous prospective studies and determined that a large case/control study to evaluate these new factors might be feasible at this time.

Download Full-text

Establishment of Risk Prediction Model of Postoperative Pancreatic Fistula after Pancreatoduodenectomy: 2016 Edition of Definition and Grading System of Pancreatic Fistula

10.21203/rs.3.rs-142949/v1 ◽

2021 ◽

Author(s):

Jun Yu ◽

Chao-yi Ren ◽

Jun Wang ◽

Wei Cui ◽

Jin-juan Zhang ◽

...

Keyword(s):

Risk Factors ◽

Logistic Regression ◽

Prediction Model ◽

Pancreatic Fistula ◽

Pancreatic Duct ◽

Risk Prediction ◽

Main Pancreatic Duct ◽

Postoperative Pancreatic Fistula ◽

Risk Prediction Model ◽

Validation Set

Abstract ObjectiveTo establish a risk prediction model for pancreatic fistula according to the pancreatic fistula standards of the 2016 edition.MethodsClinical data from 182 patients with PD admitted to Tianjin Third Central Hospital from January 2016 to February 2020 were retrospectively analyzed. Patients were divided into modeling (01/2016 to 12/2018) and validation (01/2019 to 02/2020) sets according to the time of admission. The risk factors for postoperative pancreatic fistula (POPF) were screened by univariate and multivariate logistic regression analyses, and a risk prediction model for POPF was established in the modeling set. This score was tested in the validation set.ResultsLogistic regression analysis showed that the main pancreatic duct index and CT value were independent risk factors according to the 2016 pancreatic fistula grading standard, based on which a risk prediction model for POPF was established. Receiver operating characteristic curve analysis showed that the area under the curve was 0.788 in the modeling set and 0.824 in the validation set.ConclusionThe main pancreatic duct index and CT value of the pancreas are closely related to the occurrence of pancreatic fistula after PD, and the established risk prediction model for pancreatic fistula has good prediction accuracy.

Download Full-text

Real-world data-driven risk prediction of hospitalization for heart failure in non-diabetic CKD

European Heart Journal - Digital Health ◽

10.1093/ehjdh/ztab104.3057 ◽

2021 ◽

Vol 2 (4) ◽

Author(s):

F Kleinjung ◽

J Schuchhardt ◽

C Bauer ◽

S Lindemann ◽

M Brinker ◽

...

Keyword(s):

Risk Factors ◽

Heart Failure ◽

Prediction Model ◽

Risk Prediction ◽

Selection Criteria ◽

Baseline Period ◽

Risk Prediction Model ◽

Data Driven ◽

Kaplan Meier ◽

Artery Disease

Abstract Background Heart failure (HF) is a major cause of cardiovascular morbidity and mortality. Despite recent advances in diagnosis and management of HF, the prognosis remains poor. HF and chronic kidney disease (CKD) are interlinked chronic health conditions. The availability of large volume of patient data and modern analytic techniques opens new opportunities for identification of individuals at elevated risk of HF. Purpose Develop risk prediction model for HF hospitalizations (HHF) in patients with non-diabetic CKD by applying data-driven computational intelligence techniques to a US population-based administrative claims database. Methods Individual-level data from the US Optum Clinformatics Data Mart for years 2008–2018 were analysed. To be eligible for inclusion, adult individuals were required to have non-diabetic CKD stage 3 or 4 (index event) and one year continuous insurance coverage prior to the index date (baseline period). Selection criteria and the main clinical outcome, hospitalisation for heart failure (HHF), were identified by using laboratory tests results and/or specific codes from common clinical coding systems. Risk prediction model for HHF was built on patient data in the baseline period composed to more than 6,000 variables. Computational intelligence method based on ant colony optimization was used to develop a time-to-first-event risk prediction model for HHF. Results Of the 64 million individuals in the database, 504,924 satisfied the selection criteria. Median age was 75 years, 60% were female. Among most common baseline comorbidities were hypertension (85%) and hyperlipidaemia (68%). Coronary artery disease, HF, atrial fibrillation and peripheral artery disease were recorded in 24%, 16%, 15% and 14% of individuals. Over a median follow-up of 744 days, 53,282 (11%) patients had recorded HHF, the corresponding incidence rate was 3.95 events/100 patient-years. The developed risk prediction model for HHF in non-diabetic CKD contained 20 risk factors. The five strongest risk factors were history of HF, intake of loop diuretics, severely increased albuminuria, atrial fibrillation or flutter and CKD 4 as observed “yes/no” in the baseline period. Fig. 1 depicts the final risk prediction model. To assess model performance, all patients in the cohort were stratified into five HHF risk groups. For each group, a Kaplan-Meier curve was built based on the HHF outcome data in the database. Fig. 2 shows clear separation between the curves, demonstrating high performance of the developed risk prediction model. Conclusion Despite many existing scores to predict HHF, their use is limited. Some scores rely on availability of rarely collected information, some are applicable for specific patient populations only. Risk prediction model for HHF in non-diabetic CKD is presented, which contains risk factors routinely collected by healthcare providers. Therefore, it might be applicable for HHF risk estimation in various settings. Funding Acknowledgement Type of funding sources: Other. Main funding source(s): Bayer AG Forest plot of HHF risk prediction model Kaplan-Meier plot of risk strata

Download Full-text