scholarly journals Predicting the Risk for Hospital-Onset Clostridium difficile Infection (HO-CDI) at the Time of Inpatient Admission: HO-CDI Risk Score

2015 ◽  
Vol 36 (6) ◽  
pp. 695-701 ◽  
Author(s):  
Ying P. Tabak ◽  
Richard S. Johannes ◽  
Xiaowu Sun ◽  
Carlos M. Nunez ◽  
L. Clifford McDonald
Keyword(s):  
Logistic Regression ◽  
Clostridium Difficile ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Predictive Ability ◽  
Risk Identification ◽  
Inpatient Admission ◽  
Risk Scores ◽  
Retrospective Data Analysis ◽  
Time Of Admission

OBJECTIVETo predict the likelihood of hospital-onset Clostridium difficile infection (HO-CDI) based on patient clinical presentations at admissionDESIGNRetrospective data analysisSETTINGSix US acute care hospitalsPATIENTSAdult inpatientsMETHODSWe used clinical data collected at the time of admission in electronic health record (EHR) systems to develop and validate a HO-CDI predictive model. The outcome measure was HO-CDI cases identified by a nonduplicate positive C. difficile toxin assay result with stool specimens collected >48 hours after inpatient admission. We fit a logistic regression model to predict the risk of HO-CDI. We validated the model using 1,000 bootstrap simulations.RESULTSAmong 78,080 adult admissions, 323 HO-CDI cases were identified (ie, a rate of 4.1 per 1,000 admissions). The logistic regression model yielded 14 independent predictors, including hospital community onset CDI pressure, patient age ≥65, previous healthcare exposures, CDI in previous admission, admission to the intensive care unit, albumin ≤3 g/dL, creatinine >2.0 mg/dL, bands >32%, platelets ≤150 or >420 109/L, and white blood cell count >11,000 mm3. The model had a c-statistic of 0.78 (95% confidence interval [CI], 0.76–0.81) with good calibration. Among 79% of patients with risk scores of 0–7, 19 HO-CDIs occurred per 10,000 admissions; for patients with risk scores >20, 623 HO-CDIs occurred per 10,000 admissions (P<.0001).CONCLUSIONUsing clinical parameters available at the time of admission, this HO-CDI model demonstrated good predictive ability, and it may have utility as an early risk identification tool for HO-CDI preventive interventions and outcome comparisons.Infect Control Hosp Epidemiol 2015;00(0):1–7

Attributable Cost of Clostridium difficile Infection in Pediatric Patients

2017 ◽  
Vol 38 (12) ◽  
pp. 1472-1477 ◽  
Author(s):  
Preeti Mehrotra ◽  
Jisun Jang ◽  
Courtney Gidengil ◽  
Thomas J. Sandora
Keyword(s):  
Logistic Regression ◽  
Clostridium Difficile ◽  
Propensity Score ◽  
Regression Model ◽  
Generalized Linear Model ◽  
Logistic Regression Model ◽  
Propensity Scores ◽  
Healthcare Research ◽  
Design And Methods ◽  
Inpatient Database

OBJECTIVESThe attributable cost of Clostridium difficile infection (CDI) in children is unknown. We sought to determine a national estimate of attributable cost and length of stay (LOS) of CDI occurring during hospitalization in children.DESIGN AND METHODSWe analyzed discharge records of patients between 2 and 18 years of age from the Agency for Healthcare Research and Quality (AHRQ) Kids’ Inpatient Database. We created a logistic regression model to predict CDI during hospitalization based on demographic and clinical characteristics. Predicted probabilities from the logistic regression model were then used as propensity scores to match 1:2 CDI to non-CDI cases. Charges were converted to costs and compared between patients with CDI and propensity-score–matched controls. In a sensitivity analysis, we adjusted for LOS as a confounder by including it in both the propensity score and a generalized linear model predicting cost.RESULTSWe identified 8,527 pediatric hospitalizations (0.53%) with a diagnosis of CDI and 1,597,513 discharges without CDI. In our matched cohorts, the attributable cost of CDI occurring during a hospitalization ranged from $1,917 to $8,317, depending on whether model was adjusted for LOS. When not adjusting for LOS, CDI-associated hospitalizations cost 1.6 times more than non-CDI associated hospitalizations. Attributable LOS of CDI was approximately 4 days.CONCLUSIONSClostridium difficile infection in hospitalized children is associated with an economic burden similar to adult estimates. This finding supports a continued focus on preventing CDI in children as a priority. Pediatric CDI cost analyses should account for LOS as an important confounder of cost.Infect Control Hosp Epidemiol 2017;38:1472–1477

scholarly journals The Relationship Between Tactical Positioning and the Race Outcome in 800-M Running at the 2016 Olympic Games and 2017 IAAF World Championship

2020 ◽  
Vol 71 (1) ◽  
pp. 299-305
Author(s):  
Fernando González-Mohíno ◽  
Jesús Santos del Cerro ◽  
Andrew Renfree ◽  
Inmaculada Yustres ◽  
José Mª González-Ravé
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Olympic Games ◽  
Area Under The Curve ◽  
Predictive Ability ◽  
Significance Test ◽  
Final Position ◽  
The Difference ◽  
The Relationship

AbstractThe purpose of this analysis was to quantify the probability of achieving a top-3 finishing position during 800-m races at a global championship, based on dispersion of the runners during the first and second laps and the difference in split times between laps. Overall race times, intermediate and finishing positions and 400 m split times were obtained for 43 races over 800 m (21 men’s and 22 women’s) comprising 334 individual performances, 128 of which resulted in higher positions (top-3) and 206 the remaining positions. Intermediate and final positions along with times, the dispersion of the runners during the intermediate and final splits (SS1 and SS2), as well as differences between the two split times (Dsplits) were calculated. A logistic regression model was created to determine the influence of these factors in achieving a top-3 position. The final position was most strongly associated with SS2, but also with SS1 and Dsplits. The Global Significance Test showed that the model was significant (p < 0.001) with a predictive ability of 91.08% and an area under the curve coefficient of 0.9598. The values of sensitivity and specificity were 96.8% and 82.5%, respectively. The model demonstrated that SS1, SS2 and Dplits explained the finishing position in the 800-m event in global championships.

scholarly journals Effectiveness of Mid-Regional Pro-Adrenomedullin (MR-proADM) as Prognostic Marker in COVID-19 Critically Ill Patients: an Observational Prospective Study

2020 ◽  
Author(s):  
Giorgia Montrucchio ◽  
Gabriele Sales ◽  
Francesca Rumbolo ◽  
Filippo Palmesino ◽  
Vito Fanelli ◽  
...  
Keyword(s):  
Cardiovascular Disease ◽  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Predictive Ability ◽  
Rank Test ◽  
Operating Characteristics ◽  
Icu Patients ◽  
D Dimer ◽  
Over Time

Abstract Background Due to the lack of validated biomarkers to predict disease progression and mortality in COVID-19 ICU-patients, we tested the effectiveness of mid-regional pro-adrenomedullin (MR-proADM) in comparison to C-reactive protein (CRP), procalcitonin (PCT), D-dimer, lactate dehydrogenase (LDH) in predicting outcome.Methods All consecutive COVID-19 adult patients admitted between March and June 2020 to the ICU of the ‘Città della Salute e della Scienza’ hospital in Turin (Italy) were enrolled. MR-proADM, clinical and routine laboratory test were measured within 48 hours from ICU admission, on day 3, 7 and 14. Survival curves difference with MR-proADM cut-off set to 1.8 nmol/L were tested using log-rank test. Predictive ability was compared using area under the curve and 95% confidence interval of different receiver-operating characteristics curves. Potential confounding effects were tested using a logistic regression model. Results Fifty-seven patients were enrolled. ICU and overall mortality were 54.4%. Within the first 24 hours, lymphocytopenia was present in 86%; increased D-dimer and CRP levels were found in 84.2% and 87.7% respectively, while PCT values higher than 0.5 μg/L were observed in 47.4%. MR-proADM, CRP and LDH were significantly different between surviving and non-surviving patients and over time, while PCT, D-dimer and NT-pro-BNP did not show any difference between the groups and over time; lymphocytes count was different between surviving and non-surviving patients only.MR-proADM was higher in dying patients (2.65+2.33vs1.18+0.47, p=0.0001) and a higher mortality characterized patients with MR-proADM exceeding 1.8 nmol/L (p=0.0157). The logistic regression model adjusted for age, gender, cardiovascular disease, diabetes mellitus and PCT values confirmed an odds ratio equal to 10.274 (95%CI 1.970-53.578) (p=0.0057) for MR-proADM higher than 1.8 nmol/L and equal to 22.206 (95%CI 1.56-316.960) (p=0.0223) for cardiovascular disease. Overall, MR-proADM was found to have the best predictive ability (AUC=0.846 – 95%CI 0.779-0.899).Conclusions In COVID-19 ICU-patients, MR-proADM seems able to provide a more precise stratification of disease severity and mortality risk than other biomarkers. Repeated MR-proADM measurement may support a rapid and effective decision-making. Further studies are needed to better explain the mechanisms responsible of the increase in MR-proADM observed in COVID-19 patients.

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

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).

Predicting Accidents at Work with Measures of Locus of Control and Job Hazards

1996 ◽  
Vol 78 (1) ◽  
pp. 115-121 ◽  
Author(s):  
Christopher A. Janicak
Keyword(s):  
Logistic Regression ◽  
Locus Of Control ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Predictive Ability ◽  
Workers Compensation ◽  
Control Scale ◽  
Compensation Claims

This study was conducted to assess the predictive ability of measures of locus of control and job hazards in involvement in accidents in the workplace. The locus of control scale consisted of 24 items while the job hazards were a measure of the probability of no involvement in an accident. A logistic regression model was 89% accurate in classifying subjects by involvement in an accident as measured by workers' compensation claims.

scholarly journals A Binary Logistic Regression Model as a Tool to Predict Craft Beer Susceptibility to Microbial Spoilage

Foods ◽  
2021 ◽  
Vol 10 (8) ◽  
pp. 1926
Author(s):  
Magaly Rodríguez-Saavedra ◽  
Karla Pérez-Revelo ◽  
Antonio Valero ◽  
M. Victoria Moreno-Arribas ◽  
Dolores González de Llano
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Goodness Of Fit ◽  
Logistic Regression Model ◽  
External Validation ◽  
Predictive Ability ◽  
Binary Logistic Regression ◽  
Binary Logistic Regression Model ◽  
Microbial Spoilage ◽  
Craft Beer

Beer spoilage caused by microorganisms, which is a major concern for brewers, produces undesirable aromas and flavors in the final product and substantial financial losses. To address this problem, brewers need easy-to-apply tools that inform them of beer susceptibility to the microbial spoilage. In this study, a growth/no growth (G/NG) binary logistic regression model to predict this susceptibility was developed. Values of beer physicochemical parameters such as pH, alcohol content (% ABV), bitterness units (IBU), and yeast-fermentable extract (% YFE) obtained from the analysis of twenty commercially available craft beers were used to prepare 22 adjusted beers at different levels of each parameter studied. These preparations were assigned as a first group of samples, while 17 commercially available beers samples as a second group. The results of G/NG from both groups, after artificially inoculating with one wild yeast and different lactic acid bacteria (LAB) previously adapted to grow in a beer-type beverage, were used to design the model. The developed G/NG model correctly classified 276 of 331 analyzed cases and its predictive ability was 100% in external validation. This G/NG model has good sensitivity and goodness of fit (87% and 83.4%, respectively) and provides the potential to predict craft beer susceptibility to microbial spoilage.

scholarly journals MATHEMATICAL MODELING OF THE DEVELOPMENT OF CHRONIC FLUORINE INTOXICATION IN ALUMINIUM INDUSTRY WORKERS

2020 ◽  
Vol 99 (1) ◽  
pp. 115-119
Author(s):  
L. N. Budkar ◽  
Tatyana Yu. Obukhova ◽  
S. I. Solodushkin ◽  
A. A. Fedoruk ◽  
O. G. Shmonina ◽  
...  
Keyword(s):  
Mathematical Modeling ◽  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Aluminum Industry ◽  
Univariate Analysis ◽  
Predictive Ability ◽  
Occupational Exposures ◽  
Exposure Limits ◽  
X Ray

Introduction. Chronic fluorine intoxication prevails among the newly discovered occupational diseases in aluminum industry workers. Mathematical modeling is one of the helpful tools in ensuring better risk management with respect to the development of occupational fluorosis. Objective. Developing a logistic regression model predicting a probability of occupational fluorosis development in an occupational staff of aluminum plants in order to suggest adequate prophylactic strategies. Material and methods. A logistic regression model predicting a probability of the development of occupational fluorosis in aluminum industry workers of the Sverdlovsk region was constructed. The model embraced the results of a univariate analysis conducted with respect to major occupational exposures and health characteristics of 201 workers. Results. Six major factors were identified as being predictive of occupational fluorosis development in aluminum industry workers: age (fluorosis risk increases with age); type 2 diabetes mellitus; atrophic gastritis; kidney cysts; X-ray examination data (fluorosis risk increases with the stage as determined by X-ray); the hydro fluoride concentration increases by more than 2 occupational exposure limits. The developed model was verified by clinical cases and showed a high predictive ability (86.2 %). Both sensitivity (true positive rate) and specificity (true negative rate) of the model amounted to 86.2 %. Conclusion. By multivariate analysis the significant, mutually independent factors were identified, their combination being associated with chronic fluorine intoxication in an occupational staff of aluminum plants. The developed mathematical model has a high predictive ability and can be recommended as a sure tool to forecast the course of occupational fluorosis development in the workers at the aluminum industry.

Predicting Clostridium difficile Toxin in Hospitalized Patients With Antibiotic-Associated Diarrhea

2007 ◽  
Vol 28 (4) ◽  
pp. 377-381 ◽  
Author(s):  
Nir Peled ◽  
Silvio Pitlik ◽  
Zmira Samra ◽  
Arkadi Kazakov ◽  
Yoram Bloch ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Clostridium Difficile ◽  
Positive Result ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Hospitalized Patients ◽  
Watery Diarrhea ◽  
Toxin A ◽  
Antibiotic Associated Diarrhea ◽  
Assay Result

Objective.Clostridium difficile infection is implicated in 20%-30% of cases of antibiotic-associated diarrhea. Studying hospitalized patients who received antibiotic therapy and developed diarrhea, our objective was to compare the clinical characteristics of patients who developed C. difficile–associated diarrhea (CDAD) with those of patients with a negative result of a stool assay for C. difficile toxin.Methods.A prospective study was done with a cohort of 217 hospitalized patients who had received antibiotics and developed diarrhea. Patients with CDAD were defined as patients who had diarrhea and a positive result for C. difficile toxin A/B by an enzyme immunoassay of stool. The variables that yielded a significant difference on univariate analysis between patients with a positive assay result and patients with a negative assay result were entered into a logistic regression model for prediction of C. difficile toxin.Setting.A 900-bed tertiary care medical center.Results.Of 217 patients, 52 (24%) had a positive result of assay for C. difficile toxin A/B in their stool. The logistic regression model included impaired functional capacity, watery diarrhea, use of a proton pump inhibitor, use of a histamine receptor blocker, leukocytosis, and hypoalbuminemia. The area under the receiver operating characteristic curve for the model as a predictor of a positive result for the stool toxin assay was 0.896 (95% confidence interval, 0.661-1.000; P<.001), with 95% specificity and 68% sensitivity.Conclusions.Our results may help clinicians to predict the risk of CDAD in hospitalized patients with antibiotic-associated diarrhea, to guide careful, specific empirical therapy, and to direct early attention to infection control issues.

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