Performance, Revision, and Extension of the National Nosocomial Infections Surveillance System's Risk Index in Brazilian Hospitals

2012 ◽  
Vol 33 (2) ◽  
pp. 124-134 ◽  
Author(s):  
Fernando Martín Biscione ◽  
Renato Camargos Couto ◽  
Tânia M. G. Pedrosa
Keyword(s):  
Nosocomial Infections ◽  
Prediction Models ◽  
Risk Index ◽  
Operative Procedure ◽  
Abdominal Hysterectomy ◽  
Prognostic Scores ◽  
Cutoff Points ◽  
Specific Alternative ◽  
Development And Validation ◽  
Better Than

Objective.To assess the benefit of using procedure-specific alternative cutoff points for National Nosocomial Infections Surveillance (NNIS) risk index variables and of extending surgical site infection (SSI) risk prediction models with a postdischarge surveillance indicator.Design.Open, retrospective, validation cohort study.Setting.Five private, nonuniversity Brazilian hospitals.Patients.Consecutive inpatients operated on between January 1993 and May 2006 (other operations of the genitourinary system [n = 20,723], integumentary system [n = 12,408], or musculoskeletal system [n = 15,714] and abdominal hysterectomy [n = 11,847]).Methods.For each procedure category, development and validation samples were defined nonrandomly. In the development samples, alternative SSI prognostic scores were constructed using logistic regression: (i) alternative NNIS scores used NNIS risk index covariates and cutoff points but locally derived SSI risk strata and rates, (ii) revised scores used procedure-specific alternative cutoff points, and (iii) extended scores expanded revised scores with a postdischarge surveillance indicator. Performances were compared in the validation samples using calibration, discrimination, and overall performance measures.Results.The NNIS risk index showed low discrimination, inadequate calibration, and predictions with high variability. The most consistent advantage of alternative NNIS scores was regarding calibration (prevalence and dispersion components). Revised scores performed slightly better than the NNIS risk index for most procedures and measures, mainly in calibration. Extended scores clearly performed better than the NNIS risk index, irrespective of the measure or operative procedure.Conclusions.Locally derived SSI risk strata and rates improved the NNIS risk index's calibration. Alternative cutoff points further improved the specification of the intrinsic SSI risk component. Controlling for incomplete postdischarge SSI surveillance provided consistently more accurate SSI risk adjustment.Infect Control Hosp Epidemiol 2012;33(2):124-134

Development and validation of prediction models for blood concentrations of dioxins and PCBs using dietary intakes

2012 ◽  
Vol 50 ◽  
pp. 15-21 ◽  
Author(s):  
Helen Engelstad Kvalem ◽  
Anne Lise Brantsæter ◽  
Helle Margrete Meltzer ◽  
Hein Stigum ◽  
Cathrine Thomsen ◽  
...  
Keyword(s):  
Prediction Models ◽  
Dietary Intakes ◽  
Blood Concentrations ◽  
Development And Validation

scholarly journals Development and Validation of a Three-Dimensional Printed Multifunctional Trap for Surveillance of Mosquitoes

2021 ◽  
Vol 37 (2) ◽  
pp. 68-75
Author(s):  
Drew David Reinbold-Wasson ◽  
Michael Hay Reiskind
Keyword(s):  
Mosquito Species ◽  
Three Dimensional ◽  
Cost Effective ◽  
Disease Monitoring ◽  
Life History Stage ◽  
Mosquito Trap ◽  
Host Seeking ◽  
Vector Borne ◽  
Development And Validation ◽  
Better Than

ABSTRACT An essential component of vector-borne disease monitoring programs is mosquito surveillance. Surveillance efforts employ various collection traps depending on mosquito species and targeted life-history stage, i.e., eggs, larvae, host-seeking, resting, or gravid adults. Surveillance activities often use commercial traps, sometimes modified to accept specific mosquito species attractants. The advent of widely available and affordable 3D printing technology allows the construction of novel trap designs and components. The study goal was to develop and assess a cost-effective, multipurpose, 6-volt mosquito trap integrating features of both host-seeking and gravid mosquito traps to collect undamaged live specimens: a multifunctional mosquito trap (MMT). We tested the MMT in comparison to commercial traps, targeting gravid Aedes albopictus, host-seeking Ae. albopictus, and total number of host-seeking mosquitos regardless of species. Field evaluations found the MMT performed as well as or better than comparable commercial traps. This project demonstrates an easy to construct, inexpensive, and versatile mosquito trap, potentially useful for surveying multiple mosquito species and other hematophagous insects by varying attractants into the MMT.

scholarly journals Regional Collision Risk Prediction System at a Collision Area Considering Spatial Pattern

2021 ◽  
Vol 9 (12) ◽  
pp. 1365
Author(s):  
Ho Namgung ◽  
Joo-Sung Kim
Keyword(s):  
Spatial Pattern ◽  
Risk Prediction ◽  
Fuzzy Inference System ◽  
Prediction Models ◽  
Fuzzy Inference ◽  
Risk Index ◽  
Prediction System ◽  
Collision Risk ◽  
Inference System ◽  
Optimal Decisions

To reduce the risk of collision in territorial sea areas, including trade ports and entry waterways, and to enhance the safety and efficiency of ship passage, the International Maritime Organization requires the governing body of every country to establish and operate a vessel traffic service (VTS). However, previous studies on risk prediction models did not consider the locations of near collisions and actual collisions and only employed a combined collision risk index in surveillance sea areas. In this study, we propose a regional collision risk prediction system for a collision area considering spatial patterns using a density-based spatial clustering of applications with noise (DBSCAN). Furthermore, a fuzzy inference system based on a near collision (FIS-NC) and long short-term memory (LSTM) is adopted to help a vessel traffic service operator (VTSO) make timely optimal decisions. In the local spatial pattern stage, the ship trajectory was determined by identifying the actual-collision and near-collision locations simultaneously. Finally, the system was developed by learning a sequence dataset from the extracted trajectory of the ship when a collision occurred. The proposed system can recommend an action faster than the fuzzy inference system based on the near-collision location. Therefore, using the developed system, a VTSO can quickly predict ship collision risk situations and make timely optimal decisions at dangerous surveillance sea areas.

scholarly journals Safety Risk Assessment of Tourism Management System Based on PSO-BP Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Wenru Guo
Keyword(s):  
Neural Network ◽  
Risk Assessment ◽  
Management System ◽  
Evaluation Model ◽  
Risk Index ◽  
Security Risk ◽  
Bp Network ◽  
Model Based ◽  
Tourism Management ◽  
Better Than

With the development of science and technology, system management is gradually applied to tourism management. How to correctly assess the security risks of the tourism management system has become an important means to maintain passenger information. The security risk index of the travel management system is input into the PSO-BP network as a sample, and the corresponding risk value of the index is used as the network output. The results show that the error results, accuracy (96.53%), training time (216 s), number of iterations (275 times), and convergence speed are all better than traditional BP network. The relative error of PSO-BP network (0.32%) is better than that of BP network, with 300 iterations, and the error is close to 10–5. The average evaluation accuracy of S based on PSO-BP network is 99.72%, and the average time consumed is 2.512 s. It is superior to the evaluation model based on fuzzy set and entropy weight theory and the evaluation model based on gray correlation analysis and radial basis function neural network. In conclusion, the security risk assessment of the tourism management system based on PSO-BP network can effectively assess the security risk of the tourism management system.

Development and validation of prediction models for the risks of diabetes-related hospitalization and in-hospital mortality in patients with type 2 diabetes

Metabolism ◽  
2018 ◽  
Vol 85 ◽  
pp. 38-47 ◽  
Author(s):  
Tsai-Chung Li ◽  
Chia-Ing Li ◽  
Chiu-Shong Liu ◽  
Wen-Yuan Lin ◽  
Chih-Hsueh Lin ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Hospital Mortality ◽  
Prediction Models ◽  
Development And Validation

Abstract W MP37: Novel Prognostic Scores for Early Prediction of Outcome Following Aneurysmal Subarachnoid Hemorrhage

Stroke ◽  
2015 ◽  
Vol 46 (suppl_1) ◽  
Author(s):  
Blessing Jaja ◽  
Hester Lingsma ◽  
Ewout Steyerberg ◽  
R. Loch Macdonald ◽  
Keyword(s):  
Subarachnoid Hemorrhage ◽  
Cross Validation ◽  
Prediction Models ◽  
Aneurysmal Subarachnoid Hemorrhage ◽  
Model Performance ◽  
Predictor Variable ◽  
Predictive Ability ◽  
Prognostic Scores ◽  
Operating Characteristics ◽  
Fisher Grade

Background: Aneurysmal subarachnoid hemorrhage (SAH) is a cerebrovascular emergency. Currently, clinicians have limited tools to estimate outcomes early after hospitalization. We aimed to develop novel prognostic scores using large cohorts of patients reflecting experience from different settings. Methods: Logistic regression analysis was used to develop prediction models for mortality and unfavorable outcomes according to 3-month Glasgow outcome score after SAH based on readily obtained parameters at hospital admission. The development cohort was derived from 10 prospective studies involving 10936 patients in the Subarachnoid Hemorrhage International Trialists (SAHIT) repository. Model performance was assessed by bootstrap internal validation and by cross validation by omission of each of the 10 studies, using R2 statistic, Area under the receiver operating characteristics curve (AUC), and calibration plots. Prognostic scores were developed from the regression coefficients. Results: Predictor variable with the strongest prognostic strength was neurologic status (partial R2 = 12.03%), followed by age (1.91%), treatment modality (1.25%), Fisher grade of CT clot burden (0.65%), history of hypertension (0.37%), aneurysm size (0.12%) and aneurysm location (0.06%). These predictors were combined to develop 3 sets of hierarchical scores based on the coefficients of the regression models. The AUC at bootstrap validation was 0.79-0.80, and at cross validation was 0.64-0.85. Calibration plots demonstrated satisfactory agreement between predicted and observed probabilities of the outcomes. Conclusions: The novel prognostic scores have good predictive ability and potential for broad application as they have been developed from prospective cohorts reflecting experience from different centers globally.

scholarly journals Evaluating Modeling and Validation Strategies for Tooth Loss

2019 ◽  
Vol 98 (10) ◽  
pp. 1088-1095 ◽  
Author(s):  
J. Krois ◽  
C. Graetz ◽  
B. Holtfreter ◽  
P. Brinkmann ◽  
T. Kocher ◽  
...  
Keyword(s):  
Tooth Loss ◽  
Predictive Power ◽  
Prediction Models ◽  
Recursive Partitioning ◽  
External Validation ◽  
Model Development ◽  
Gradient Boosting ◽  
Extreme Gradient Boosting ◽  
Complex Models ◽  
Development And Validation

Prediction models learn patterns from available data (training) and are then validated on new data (testing). Prediction modeling is increasingly common in dental research. We aimed to evaluate how different model development and validation steps affect the predictive performance of tooth loss prediction models of patients with periodontitis. Two independent cohorts (627 patients, 11,651 teeth) were followed over a mean ± SD 18.2 ± 5.6 y (Kiel cohort) and 6.6 ± 2.9 y (Greifswald cohort). Tooth loss and 10 patient- and tooth-level predictors were recorded. The impact of different model development and validation steps was evaluated: 1) model complexity (logistic regression, recursive partitioning, random forest, extreme gradient boosting), 2) sample size (full data set or 10%, 25%, or 75% of cases dropped at random), 3) prediction periods (maximum 10, 15, or 20 y or uncensored), and 4) validation schemes (internal or external by centers/time). Tooth loss was generally a rare event (880 teeth were lost). All models showed limited sensitivity but high specificity. Patients’ age and tooth loss at baseline as well as probing pocket depths showed high variable importance. More complex models (random forest, extreme gradient boosting) had no consistent advantages over simpler ones (logistic regression, recursive partitioning). Internal validation (in sample) overestimated the predictive power (area under the curve up to 0.90), while external validation (out of sample) found lower areas under the curve (range 0.62 to 0.82). Reducing the sample size decreased the predictive power, particularly for more complex models. Censoring the prediction period had only limited impact. When the model was trained in one period and tested in another, model outcomes were similar to the base case, indicating temporal validation as a valid option. No model showed higher accuracy than the no-information rate. In conclusion, none of the developed models would be useful in a clinical setting, despite high accuracy. During modeling, rigorous development and external validation should be applied and reported accordingly.

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