scholarly journals Development and Validation of a Clostridium difficile Infection Risk Prediction Model

2011 ◽  
Vol 32 (4) ◽  
pp. 360-366 ◽  
Author(s):  
Erik R. Dubberke ◽  
Yan Yan ◽  
Kimberly A. Reske ◽  
Anne M. Butler ◽  
Joshua Doherty ◽  
...  
Keyword(s):  
High Risk ◽  
Clostridium Difficile ◽  
Prediction Model ◽  
Risk Prediction ◽  
Predictive Accuracy ◽  
Medical Center ◽  
Tertiary Care ◽  
Risk Prediction Model ◽  
Brier Score ◽  
Tertiary Care Medical Center

Objective.To develop and validate a risk prediction model that could identify patients at high risk for Clostridium difficile infection (CDI) before they develop disease.Design and Setting.Retrospective cohort study in a tertiary care medical center.Patients.Patients admitted to the hospital for at least 48 hours during the calendar year 2003.Methods.Data were collected electronically from the hospital's Medical Informatics database and analyzed with logistic regression to determine variables that best predicted patients' risk for development of CDI. Model discrimination and calibration were calculated. The model was bootstrapped 500 times to validate the predictive accuracy. A receiver operating characteristic curve was calculated to evaluate potential risk cutoffs.Results.A total of 35,350 admitted patients, including 329 with CDI, were studied. Variables in the risk prediction model were age, CDI pressure, times admitted to hospital in the previous 60 days, modified Acute Physiology Score, days of treatment with high-risk antibiotics, whether albumin level was low, admission to an intensive care unit, and receipt of laxatives, gastric acid suppressors, or antimotility drugs. The calibration and discrimination of the model were very good to excellent (C index, 0.88; Brier score, 0.009).Conclusions.The CDI risk prediction model performed well. Further study is needed to determine whether it could be used in a clinical setting to prevent CDI-associated outcomes and reduce costs.

scholarly journals A prospective study of acute kidney injury in the intensive care unit: development and validation of a risk prediction model

2019 ◽  
Vol 17 (1) ◽  
Author(s):  
Qi Wang ◽  
Yi Tang ◽  
Jiaojiao Zhou ◽  
Wei Qin
Keyword(s):  
Chronic Kidney Disease ◽  
Acute Kidney Injury ◽  
Intensive Care ◽  
High Risk ◽  
Prediction Model ◽  
Risk Prediction ◽  
Validation Cohort ◽  
Kidney Injury ◽  
Risk Prediction Model ◽  
Risk Patients

Abstract Background Acute kidney injury (AKI) has high morbidity and mortality in intensive care units (ICU). It can also lead to chronic kidney disease (CKD), more costs and longer hospital stay. Early identification of AKI is important. Methods We conducted this monocenter prospective observational study at West China Hospital, Sichuan University, China. We recorded information of each patient in the ICU within 24 h after admission and updated every two days. Patients who reached the primary outcome were accepted into the AKI group. Of all patients, we randomly drew 70% as the development cohort and the remaining 30% as the validation cohort. Using binary logistic regression we got a risk prediction model of the development cohort. In the validation cohort, we validated its discrimination by the area under the receiver operator curve (AUROC) and calibration by a calibration curve. Results There were 656 patients in the development cohorts and 280 in the validation cohort. Independent predictors of AKI in the risk prediction model including hypertension, chronic kidney disease, acute pancreatitis, cardiac failure, shock, pH ≤ 7.30, CK > 1000 U/L, hypoproteinemia, nephrotoxin exposure, and male. In the validation cohort, the AUROC is 0.783 (95% CI 0.730–0.836) and the calibration curve shows good calibration of this prediction model. The optimal cut-off value to distinguish high-risk and low-risk patients is 4.5 points (sensitivity is 78.4%, specificity is 73.2% and Youden’s index is 0.516). Conclusions This risk prediction model can help to identify high-risk patients of AKI in ICU to prevent the development of AKI and treat it at the early stages. Trial registration TCTR, TCTR20170531001. Registered 30 May 2017, http://www.clinicaltrials.in.th/index.php?tp=regtrials&menu=trialsearch&smenu=fulltext&task=search&task2=view1&id=2573

Vaccination Without Documentation: Influenza Immunization Among Medical Residents at a Tertiary-Care Medical Center

2003 ◽  
Vol 24 (8) ◽  
pp. 626-628 ◽  
Author(s):  
Gonzalo Bearman ◽  
Linda Fuentes ◽  
Jaclyn Van Lieu Vorenkamp ◽  
Lewis M. Drusin
Keyword(s):  
High Risk ◽  
Health Service ◽  
Occupational Health ◽  
Medical Care ◽  
Medical Center ◽  
Tertiary Care ◽  
House Staff ◽  
Medical Residents ◽  
Tertiary Care Medical Center ◽  
Risk Patients

AbstractSixty-four percent of medical residents unimmunized by the Occupational Health Service were immunized elsewhere. Those unvaccinated lacked time to comply. An immune staff is critical to prevent transmission to high-risk patients and limit absenteeism. The hospital is implementing a program to deliver medical care to the house staff.

scholarly journals Development and Validation of a Dynamic Nomogram to Predict the Risk of Neonatal White Matter Damage

2021 ◽  
Vol 14 ◽  
Author(s):  
Wenjun Cao ◽  
Chenghan Luo ◽  
Mengyuan Lei ◽  
Min Shen ◽  
Wenqian Ding ◽  
...  
Keyword(s):  
Logistic Regression ◽  
White Matter ◽  
Confidence Interval ◽  
Prediction Model ◽  
Risk Prediction ◽  
Predictive Accuracy ◽  
Risk Prediction Model ◽  
Multivariate Logistic Regression ◽  
White Matter Damage ◽  
Web Based

PurposeWhite matter damage (WMD) was defined as the appearance of rough and uneven echo enhancement in the white matter around the ventricle. The aim of this study was to develop and validate a risk prediction model for neonatal WMD.Materials and MethodsWe collected data for 1,733 infants hospitalized at the Department of Neonatology at The First Affiliated Hospital of Zhengzhou University from 2017 to 2020. Infants were randomly assigned to training (n = 1,216) or validation (n = 517) cohorts at a ratio of 7:3. Multivariate logistic regression and least absolute shrinkage and selection operator (LASSO) regression analyses were used to establish a risk prediction model and web-based risk calculator based on the training cohort data. The predictive accuracy of the model was verified in the validation cohort.ResultsWe identified four variables as independent risk factors for brain WMD in neonates by multivariate logistic regression and LASSO analysis, including gestational age, fetal distress, prelabor rupture of membranes, and use of corticosteroids. These were used to establish a risk prediction nomogram and web-based calculator (https://caowenjun.shinyapps.io/dynnomapp/). The C-index of the training and validation sets was 0.898 (95% confidence interval: 0.8745–0.9215) and 0.887 (95% confidence interval: 0.8478–0.9262), respectively. Decision tree analysis showed that the model was highly effective in the threshold range of 1–61%. The sensitivity and specificity of the model were 82.5 and 81.7%, respectively, and the cutoff value was 0.099.ConclusionThis is the first study describing the use of a nomogram and web-based calculator to predict the risk of WMD in neonates. The web-based calculator increases the applicability of the predictive model and is a convenient tool for doctors at primary hospitals and outpatient clinics, family doctors, and even parents to identify high-risk births early on and implementing appropriate interventions while avoiding excessive treatment of low-risk patients.

Development of an Oncology Acute Care Risk Prediction Model

2021 ◽  
pp. 266-271
Author(s):  
Valerie P. Csik ◽  
Michael Li ◽  
Adam F. Binder ◽  
Nathan R. Handley
Keyword(s):  
High Risk ◽  
Prediction Model ◽  
Acute Care ◽  
Risk Prediction ◽  
Risk Index ◽  
Risk Prediction Model ◽  
Discrete Point ◽  
Allocation System ◽  
Ed Visits ◽  
Active Cancer

PURPOSE Acute care utilization (ACU), including emergency department (ED) visits or hospital admissions, is common in patients with cancer and may be preventable. The Center for Medicare & Medicaid Services recently implemented OP-35, a measure in the Hospital Outpatient Quality Reporting Program focused on ED visits and inpatient admissions for 10 potentially preventable conditions that arise within 30 days of chemotherapy. This new measure exemplifies a growing focus on preventing unnecessary ACU. However, identifying patients at high risk of ACU remains a challenge. We developed a real-time clinical prediction model using a discrete point allocation system to assess risk for ACU in patients with active cancer. METHODS We performed a retrospective cohort analysis of patients with active cancer from a large urban academic medical center. The primary outcome, ACU, was evaluated using a multivariate logistic regression model with backward variable selection. We used estimates from the multivariate logistic model to construct a risk index using a discrete point allocation system. RESULTS Eight thousand two hundred forty-six patients were included in the analysis. ED utilization in the last 90 days, history of chronic obstructive pulmonary disease, congestive heart failure or renal failure, and low hemoglobin and low neutrophil count significantly increased risk for ACU. The model produced an overall C-statistic of 0.726. Patients defined as high risk (achieving a score of 2 or higher on the risk index) represented 10% of total patients and 46% of ACU. CONCLUSION We developed an oncology acute care risk prediction model using a risk index–based scoring system, the REDUCE (Reducing ED Utilization in the Cancer Experience) score. Further efforts to evaluate the effectiveness of our model in predicting ACU are ongoing.

Construction and validation of an eight-gene risk prediction model for stage II-IIIA lung adenocarcinoma.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e20553-e20553
Author(s):  
Jianchun Duan ◽  
Hua Bai ◽  
Yiting Sun ◽  
Fei Gai ◽  
Shenya Tian ◽  
...  
Keyword(s):  
High Risk ◽  
Prediction Model ◽  
Lung Adenocarcinoma ◽  
Risk Prediction ◽  
Risk Group ◽  
Survival Outcome ◽  
Risk Prediction Model ◽  
Low Risk ◽  
Gene Set

e20553 Background: Clinical characters cannot precisely evaluate long-term survival of patients with resectable lung adenocarcinoma. Genomics studies of lung adenocarcinoma (LUAD) have advanced our understanding of LUAD's biology. Thus, genomics-based robust models predicting survival outcome for patients with operatable LUAD needs to be investigated. Here, we aimed to identify new gene signatures to construct a risk prediction model via integrating Omics data from The Cancer Genome Atlas (TCGA) to better evaluate the long-term clinical outcome of LUAD patients. Methods: A cohort of one hundred and eighty-nine stage II-IIIA lung adenocarcinoma cases receiving tumor resection were screened out and downloaded from TCGA database. Tumor samples without survival information and genes with low or no expression were removed. Genes associated with cancer and immune were further narrowed down using a Master Panel Gene Set (Amoydx). Lasso-Cox regression analysis was used to screen gene-survival outcome, and then a risk prediction model was established. LUAD cases were divided into high-risk or low-risk groups as per the scores, to assess differential expressed genes and pathways. Results: A total of 8 most survival outcome related genes (CLEC7A, PAX5, XCR1, KRT7, PLCG1, DKK1, CLEC10A, IKZF3) were identified after Lasso-Cox regression analysis and used for model construction. The overall survival (OS), progression-free survival (PFS) and disease-free survival (DFS) from the subgroups within the high- and low-risk groups were assessed and showed significant prolonged in low-risk group, the hazard ratio (HR) of OS was 2.72 (95%CI: 2.04-3.61, P = 5.91e-12) in high-risk group. Hierarchical clustering analysis, gene ontology (GO) analysis, gene set enrichment analysis (GSEA), and gene set variation analysis (GSVA) revealed that genes involved in immune responses were significantly suppressed in high-risk group, while as genes involved in antioxidative metabolism were activated, which gave us a hint that immune-metabolism interaction might play a vital role in determining the distal survival outcome of LUAD. Conclusions: Our risk prediction model enables precise evaluation of long-term survival for patients with LUAD. Further, it provides a novel and comprehensive understanding of biological impacts on LUAD prognosis, which offers new insights for future development of precise diagnostic and therapeutic approaches.[Table: see text]

Prospective Validation of a Risk Prediction Model for Severe Sepsis in Children With Cancer and High-risk Febrile Neutropenia

2013 ◽  
Vol 32 (12) ◽  
pp. 1318-1323 ◽  
Author(s):  
María E. Santolaya ◽  
Ana M. Alvarez ◽  
Carmen L. Avilés ◽  
Ana Becker ◽  
Marcela Venegas ◽  
...  
Keyword(s):  
Severe Sepsis ◽  
High Risk ◽  
Febrile Neutropenia ◽  
Prediction Model ◽  
Risk Prediction ◽  
Risk Prediction Model ◽  
Children With Cancer

scholarly journals Prediction of infection risk associated with the dynamic behavior of aerosol particles during dental aerosol generating procedures in dental clinics in the context of the COVID-19 pandemic

2021 ◽  
Author(s):  
Paula Alejandra Baldion ◽  
Camilo Alejandro Guerrero ◽  
Alberto Carlos Cruz ◽  
Henry Oliveros Rodríguez ◽  
Diego Enrique Betancourt
Keyword(s):  
High Risk ◽  
Prediction Model ◽  
Dynamic Behavior ◽  
Risk Prediction ◽  
Aerosol Particles ◽  
Infection Risk ◽  
Risk Prediction Model ◽  
Dental Procedures ◽  
Risk Of Exposure ◽  
Phantom Head

Abstract Background: The health emergency declaration owing to severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has drawn attention toward nosocomial transmission. The transmission of the disease varies depending on the environmental conditions. Saliva is a recognized SARS-CoV-2 reservoir in infected individuals. Therefore, exposure to fluids during dental procedures leads to a high risk of contagion. Objective: This study aimed to develop an infection risk prediction model for COVID-19 based on an analysis of the dynamic behavior of aerosol particles generated during dental procedures. Materials and methods: The dispersion of aerosol particles during dental aerosol-generating procedures (AGPs) performed on phantoms was evaluated using colored saliva. The gravity-deposited aerosol particles were registered using filter paper within the perimeter of the phantom head and the settled particles were recorded in standardized photographs. Digital images were processed to analyze the stained area and the drops dimensions. A logistic regression model was built with the variables ventilation, distance from the mouth, instrument used, area of the mouth treated, and location within the perimeter area. Results: The largest percentage of aerosol particles ranged from 1 to 5 µm. The maximum settlement range from the mouth of the phantom head was 320 cm, with a high-risk cut-off distance of 78 cm. Ventilation, distance, instrument used, area of the mouth being treated, and location within the perimeter showed association with the amount of aerosol particles. These variables were used for constructing a scale to determine the risk of exposure to aerosol particles in dentistry within an infection risk prediction model. Conclusion: Contamination by disseminated aerosol particles represents a risk for the dental staff. Thus, it is advisable to improve ventilation and use biosafety measures. The need to implement new clinical and educational strategies was evident. This model is useful for predicting the risk of exposure to COVID-19 in dental practice.

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