The Development of Statistical Models for Predicting Surgical Site Infections in Japan: Toward a Statistical Model–Based Standardized Infection Ratio

2015 ◽  
Vol 37 (3) ◽  
pp. 260-271 ◽  
Author(s):  
Haruhisa Fukuda ◽  
Manabu Kuroki
Keyword(s):  
Prediction Model ◽  
Statistical Models ◽  
Prediction Models ◽  
Risk Index ◽  
Surgical Site Infections ◽  
Hospital Performance ◽  
Surveillance Data ◽  
Risk Patients ◽  
Observational Cohort ◽  
Retrospective Observational Cohort Study

OBJECTIVETo develop and internally validate a surgical site infection (SSI) prediction model for Japan.DESIGNRetrospective observational cohort study.METHODSWe analyzed surveillance data submitted to the Japan Nosocomial Infections Surveillance system for patients who had undergone target surgical procedures from January 1, 2010, through December 31, 2012. Logistic regression analyses were used to develop statistical models for predicting SSIs. An SSI prediction model was constructed for each of the procedure categories by statistically selecting the appropriate risk factors from among the collected surveillance data and determining their optimal categorization. Standard bootstrapping techniques were applied to assess potential overfitting. The C-index was used to compare the predictive performances of the new statistical models with those of models based on conventional risk index variables.RESULTSThe study sample comprised 349,987 cases from 428 participant hospitals throughout Japan, and the overall SSI incidence was 7.0%. The C-indices of the new statistical models were significantly higher than those of the conventional risk index models in 21 (67.7%) of the 31 procedure categories (P<.05). No significant overfitting was detected.CONCLUSIONSJapan-specific SSI prediction models were shown to generally have higher accuracy than conventional risk index models. These new models may have applications in assessing hospital performance and identifying high-risk patients in specific procedure categories.Infect. Control Hosp. Epidemiol. 2016;37(3):260–271

scholarly journals The Impact Prediction Models of Neoplasia for Lung Nodules in High-Risk Patients

2020 ◽  
Vol 3 (3) ◽  
pp. 138-146
Author(s):  
Camilla Matos Pedreira ◽  
José Alves Barros Filho ◽  
Carolina Pereira ◽  
Thamine Lessa Andrade ◽  
Ricardo Mingarini Terra ◽  
...  
Keyword(s):  
High Risk ◽  
Prediction Model ◽  
Prediction Models ◽  
Lung Nodule ◽  
Smoking History ◽  
Predictive Values ◽  
Impact Prediction ◽  
High Risk Patients ◽  
Risk Patients ◽  
The Impact

Objectives: This study aims to evaluate the impact of using three predictive models of lung nodule malignancy in a population of patients at high-risk for neoplasia according to previous analysis by physicians, as well as evaluate the clinical and radiological malignancy-predictors of the images. Material and Methods: This is a retrospective cohort study, with 135 patients, undergone surgical in the period from 01/07/2013 to 10/05/2016. The study included nodules with dimensions between 5mm and 30mm, excluding multiple nodules, alveolar consolidation, pleural effusion, and lymph node enlargement. The main variables analyzed were age, sex, smoking history, extrathoracic cancer, diameter, location, and presence of spiculation. The calculation of the area under the ROC curve assessed the accuracy of each prediction model. Results: The study analyzed 135 individuals, of which 96 (71.1%) had malignant nodules. The areas under the ROC curves for each prediction model were: Swensen 0.657; Brock 0.662; and Herder 0.633. The models Swensen, Brock, and Herder presented positive predictive values in high-risk patients, corresponding to 83.3%, 81.8%, and 82.9%, respectively. Patients with the intermediate and low-risk presented a high malignant nodule rate, ranging from 69.3-72.5% and 42.8-52.6%, respectively. Conclusion: None of the three quantitative models analyzed in this study was considered satisfactory (AUC> 0.7) and should be used with caution after specialized evaluation to avoid underestimation of the risk of neoplasia. The pretest calculations might not contemplate other factors than those predicted in the regressions, that could present a role in the clinical decision of resection.

scholarly journals The Palliative Performance Scale predicts mortality in hospitalized patients with COVID-19

2020 ◽  
Vol 34 (9) ◽  
pp. 1228-1234 ◽  
Author(s):  
Michele Fiorentino ◽  
Sri Ram Pentakota ◽  
Anne C Mosenthal ◽  
Nina E Glass
Keyword(s):  
Palliative Care ◽  
Scale Score ◽  
Public Hospital ◽  
The Elderly ◽  
Hospitalized Patients ◽  
Risk Patients ◽  
Observational Cohort ◽  
Retrospective Observational Cohort Study ◽  
Performance Scale ◽  
Overall Mortality

Background: Coronavirus disease 2019 (COVID-19) has a substantial mortality risk with increased rates in the elderly. We hypothesized that age is not sufficient, and that frailty measured by preadmission Palliative Performance Scale would be a predictor of outcomes. Improved ability to identify high-risk patients will improve clinicians’ ability to provide appropriate palliative care, including engaging in shared decision-making about life-sustaining therapies. Aim: To evaluate whether preadmission Palliative Performance Scale predicts mortality in hospitalized patients with COVID-19. Design: Retrospective observational cohort study of patients admitted with COVID-19. Palliative Performance Scale was calculated from the chart. Using logistic regression, Palliative Performance Scale was assessed as a predictor of mortality controlling for demographics, comorbidities, palliative care measures and socioeconomic status. Setting/participants: Patients older than 18 years of age admitted with COVID-19 to a single urban public hospital in New Jersey, USA. Results: Of 443 admitted patients, we determined the Palliative Performance Scale score for 374. Overall mortality was 31% and 81% in intubated patients. In all, 36% (134) of patients had a low Palliative Performance Scale score. Compared with patients with a high score, patients with a low score were more likely to die, have do not intubate orders and be discharged to a facility. Palliative Performance Scale independently predicts mortality (odds ratio 2.89; 95% confidence interval 1.42–5.85). Conclusions: Preadmission Palliative Performance Scale independently predicts mortality in patients hospitalized with COVID-19. Improved predictors of mortality can help clinicians caring for patients with COVID-19 to discuss prognosis and provide appropriate palliative care including decisions about life-sustaining therapy.

Incidence and Predictors of Surgical-Site Infections in Vietnam

2001 ◽  
Vol 22 (08) ◽  
pp. 485-492 ◽  
Author(s):  
Dao Nguyen ◽  
William Bruce MacLeod ◽  
Dae Cam Phung ◽  
Quyet Thang Cong ◽  
Viet Hung Nguyen ◽  
...  
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Infection Control ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Wound Care ◽  
Risk Index ◽  
Surgical Site Infections ◽  
Risk Patients ◽  
Wound Class

AbstractObjective:To determine the incidence of, and risk factors for, surgical-site infections (SSIs).Design:Prospective observational study of all patients undergoing surgery during a 3-month period.Setting:Two urban hospitals in Hanoi, Vietnam.Patients:All 697 patients admitted for emergent and elective surgery.Methods:Data were collected on all patients undergoing surgery during a 3-month period at each hospital. We stratified the data by type of surgery, wound class, and Study on the Efficacy of Nosocomial Infection Control (SENIC) risk index. The analysis was done with the data sets from each hospital separately and with the combined data. The risk factors for SSI were identified using a logistic-regression model.Results:During the period of observation, 10.9% of 697 patients had SSI. The SSI rate was 8.3% for clean wounds, 8.6% for clean-contaminated, 12.2% for contaminated, and 43.9% for dirty wounds. The lowest rate of SSI (2.4%) was found in obstetric-gynecologic procedures and the highest rate (33.3%) in cardiothoracic operations. Using the SENIC risk index, the incidence of SSI in low-risk patients was 5.1%; for medium-risk patients, 13.5%, and high-risk patients, 24.2%. In a logistic-regression model, abdominal surgery (odds ratio [OR], 4.46;P&lt;.01) and wound class IV (OR, 5.67;P&lt;.01) were significant predictors of SSI. All patients were treated with prolonged courses of perioperative antibiotics. Overall infection control practices were poor as a result of deficient facilities, limited surgical instruments, and a lack of proper supplies for wound care and personal hygiene.Conclusions:There was a higher incidence of SSI in low-risk patients in Vietnam compared with developed countries. Excessive reliance on antimicrobial therapy as a means to limit SSI places patients at higher risk of adverse effects from treatment and also may contribute to worsening problems with antimicrobial resistance. Establishment of an infection control program with guidelines for antimicrobial use should improve the use of prophylactic antibiotics and attention to proper surgical and wound-care techniques. These interventions also should reduce the incidence of SSI and its associated morbidity and costs.

Bioactivity Prediction Based on Matched Molecular Pair and Matched Molecular Series Methods

2020 ◽  
Vol 26 (33) ◽  
pp. 4195-4205
Author(s):  
Xiaoyu Ding ◽  
Chen Cui ◽  
Dingyan Wang ◽  
Jihui Zhao ◽  
Mingyue Zheng ◽  
...  
Keyword(s):  
Prediction Model ◽  
Large Scale ◽  
Prediction Models ◽  
Predictive Accuracy ◽  
Lead Optimization ◽  
Consensus Method ◽  
Molecular Pair ◽  
Bioactivity Prediction ◽  
Compound Synthesis ◽  
Consensus Modeling

Background: Enhancing a compound’s biological activity is the central task for lead optimization in small molecules drug discovery. However, it is laborious to perform many iterative rounds of compound synthesis and bioactivity tests. To address the issue, it is highly demanding to develop high quality in silico bioactivity prediction approaches, to prioritize such more active compound derivatives and reduce the trial-and-error process. Methods: Two kinds of bioactivity prediction models based on a large-scale structure-activity relationship (SAR) database were constructed. The first one is based on the similarity of substituents and realized by matched molecular pair analysis, including SA, SA_BR, SR, and SR_BR. The second one is based on SAR transferability and realized by matched molecular series analysis, including Single MMS pair, Full MMS series, and Multi single MMS pairs. Moreover, we also defined the application domain of models by using the distance-based threshold. Results: Among seven individual models, Multi single MMS pairs bioactivity prediction model showed the best performance (R2 = 0.828, MAE = 0.406, RMSE = 0.591), and the baseline model (SA) produced the most lower prediction accuracy (R2 = 0.798, MAE = 0.446, RMSE = 0.637). The predictive accuracy could further be improved by consensus modeling (R2 = 0.842, MAE = 0.397 and RMSE = 0.563). Conclusion: An accurate prediction model for bioactivity was built with a consensus method, which was superior to all individual models. Our model should be a valuable tool for lead optimization.

Fire modelling in Tasmanian buttongrass moorlands. III. Dead fuel moisture

2001 ◽  
Vol 10 (2) ◽  
pp. 241 ◽  
Author(s):  
Jon B. Marsden-Smedley ◽  
Wendy R. Catchpole
Keyword(s):  
Prediction Model ◽  
Regression Model ◽  
Fire Management ◽  
Prediction Models ◽  
Dew Point ◽  
Seasonal Effects ◽  
Experimental Program ◽  
Fuel Moisture ◽  
Fire Behaviour ◽  
Fire Modelling

An experimental program was carried out in Tasmanian buttongrass moorlands to develop fire behaviour prediction models for improving fire management. This paper describes the results of the fuel moisture modelling section of this project. A range of previously developed fuel moisture prediction models are examined and three empirical dead fuel moisture prediction models are developed. McArthur’s grassland fuel moisture model gave equally good predictions as a linear regression model using humidity and dew-point temperature. The regression model was preferred as a prediction model as it is inherently more robust. A prediction model based on hazard sticks was found to have strong seasonal effects which need further investigation before hazard sticks can be used operationally.

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