scholarly journals 1166. Development of a Bedside Tool to Predict the Probability of Drug-Resistant Pathogens Among an Adult Population With Gram-Negative Infections

2018 ◽  
Vol 5 (suppl_1) ◽  
pp. S351-S352
Author(s):  
Thomas P Lodise Jr. ◽  
Nicole G Bonine ◽  
J Michael Ye ◽  
Henry J Folse ◽  
Patrick Gillard
Keyword(s):  
Prediction Models ◽  
Adult Population ◽  
Model Development ◽  
Complicated Urinary Tract Infection ◽  
Bloodstream Infections ◽  
Predictive Performance ◽  
Adult Patients ◽  
Gram Negative ◽  
User Friendly ◽  
Hospital Acquired

Abstract Background Identification of infections caused by antimicrobial-resistant microorganisms is critical to administration of early appropriate antibiotic therapy. We developed a clinical bedside tool to estimate the probability of carbapenem-resistant Enterobacteriaceae (CRE), extended spectrum β-lactamase-producing Enterobacteriaceae (ESBL), and multidrug-resistant Pseudomonas aeruginosa (MDRP) among hospitalized adult patients with Gram-negative infections. Methods A retrospective observational study of the Premier Hospital Database (PHD) was conducted. The study included adult hospitalized patients with complicated urinary tract infection (cUTI), complicated intraabdominal infection (cIAI), bloodstream infections (BSI), or hospital-acquired/ventilator-associated pneumonia (HAP/VAP) with a culture-confirmed Gram-negative infection in PHD from 2011 to 2015. Model development steps are shown in Figure 1. The study population was split into training and test cohorts. Prediction models were developed using logistic regression in the training cohort (Figure 1). For each resistant phenotype (CRE, ESBL, and MDRP), a separate model was developed for community-acquired (index culture ≤3 days of admission) and hospital-acquired (index culture >3 days of admission) infections (six models in total). The predictive performance of the models was assessed in the training and test cohorts. Models were converted to a singular user-friendly interface for use at the bedside. Results The most important predictors of antibiotic-resistant Gram-negative bacterial infection were prior number of antibiotics, infection site, prior infection in the last 3 months, hospital prevalence of each resistant pathogen (CRE, ESBL, and MDRP), and age (Figure 2). The predictive performance was highly acceptable for all six models (Figure 3). Conclusion We developed a clinical prediction tool to estimate the probability of CRE, ESBL, and MDRP among hospitalized adult patients with community- and hospital-acquired Gram-negative infections. Our predictive model has been implemented as a user-friendly bedside tool for use by clinicians to predict the probability of resistant infections in individual patients, to guide early appropriate therapy. Disclosures T. P. Lodise Jr., Motif BioSciences: Board Member, Consulting fee. N. G. Bonine, Allergan: Employee, Salary. J. M. Ye, Allergan: Employee, Salary. H. J. Folse, Evidera: Employee, Salary. P. Gillard, Allergan: Employee, Salary.

scholarly journals Models Predicting Hospital Admission of Adult Patients Utilizing Prehospital Data: Systematic Review Using PROBAST and CHARMS

10.2196/30022 ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. e30022
Author(s):  
Ann Corneille Monahan ◽  
Sue S Feldman
Keyword(s):  
Emergency Department ◽  
Hospital Admission ◽  
Real Time ◽  
Prediction Models ◽  
Assessment Tool ◽  
Predictive Performance ◽  
Patient Data ◽  
Adult Patients ◽  
Emergency Department Crowding ◽  
Exit Block

Background Emergency department boarding and hospital exit block are primary causes of emergency department crowding and have been conclusively associated with poor patient outcomes and major threats to patient safety. Boarding occurs when a patient is delayed or blocked from transitioning out of the emergency department because of dysfunctional transition or bed assignment processes. Predictive models for estimating the probability of an occurrence of this type could be useful in reducing or preventing emergency department boarding and hospital exit block, to reduce emergency department crowding. Objective The aim of this study was to identify and appraise the predictive performance, predictor utility, model application, and model utility of hospital admission prediction models that utilized prehospital, adult patient data and aimed to address emergency department crowding. Methods We searched multiple databases for studies, from inception to September 30, 2019, that evaluated models predicting adult patients’ imminent hospital admission, with prehospital patient data and regression analysis. We used PROBAST (Prediction Model Risk of Bias Assessment Tool) and CHARMS (Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies) to critically assess studies. Results Potential biases were found in most studies, which suggested that each model’s predictive performance required further investigation. We found that select prehospital patient data contribute to the identification of patients requiring hospital admission. Biomarker predictors may add superior value and advantages to models. It is, however, important to note that no models had been integrated with an information system or workflow, operated independently as electronic devices, or operated in real time within the care environment. Several models could be used at the site-of-care in real time without digital devices, which would make them suitable for low-technology or no-electricity environments. Conclusions There is incredible potential for prehospital admission prediction models to improve patient care and hospital operations. Patient data can be utilized to act as predictors and as data-driven, actionable tools to identify patients likely to require imminent hospital admission and reduce patient boarding and crowding in emergency departments. Prediction models can be used to justify earlier patient admission and care, to lower morbidity and mortality, and models that utilize biomarker predictors offer additional advantages.

The Influence of Inhomogeneous Input Data from Different Waves on Predictive Model Development for COVID-19 ICU Patients (Preprint)

2021 ◽  
Author(s):  
Sebastian Johannes Fritsch ◽  
Konstantin Sharafutdinov ◽  
Moein Einollahzadeh Samadi ◽  
Gernot Marx ◽  
Andreas Schuppert ◽  
...  
Keyword(s):  
Machine Learning ◽  
Convex Hull ◽  
Prediction Models ◽  
Model Development ◽  
Predictive Performance ◽  
Support Vector ◽  
Good Prediction ◽  
The Impact ◽  
Second Wave ◽  
Over Time

BACKGROUND During the course of the COVID-19 pandemic, a variety of machine learning models were developed to predict different aspects of the disease, such as long-term causes, organ dysfunction or ICU mortality. The number of training datasets used has increased significantly over time. However, these data now come from different waves of the pandemic, not always addressing the same therapeutic approaches over time as well as changing outcomes between two waves. The impact of these changes on model development has not yet been studied. OBJECTIVE The aim of the investigation was to examine the predictive performance of several models trained with data from one wave predicting the second wave´s data and the impact of a pooling of these data sets. Finally, a method for comparison of different datasets for heterogeneity is introduced. METHODS We used two datasets from wave one and two to develop several predictive models for mortality of the patients. Four classification algorithms were used: logistic regression (LR), support vector machine (SVM), random forest classifier (RF) and AdaBoost classifier (ADA). We also performed a mutual prediction on the data of that wave which was not used for training. Then, we compared the performance of models when a pooled dataset from two waves was used. The populations from the different waves were checked for heterogeneity using a convex hull analysis. RESULTS 63 patients from wave one (03-06/2020) and 54 from wave two (08/2020-01/2021) were evaluated. For both waves separately, we found models reaching sufficient accuracies up to 0.79 AUROC (95%-CI 0.76-0.81) for SVM on the first wave and up 0.88 AUROC (95%-CI 0.86-0.89) for RF on the second wave. After the pooling of the data, the AUROC decreased relevantly. In the mutual prediction, models trained on second wave´s data showed, when applied on first wave´s data, a good prediction for non-survivors but an insufficient classification for survivors. The opposite situation (training: first wave, test: second wave) revealed the inverse behaviour with models correctly classifying survivors and incorrectly predicting non-survivors. The convex hull analysis for the first and second wave populations showed a more inhomogeneous distribution of underlying data when compared to randomly selected sets of patients of the same size. CONCLUSIONS Our work demonstrates that a larger dataset is not a universal solution to all machine learning problems in clinical settings. Rather, it shows that inhomogeneous data used to develop models can lead to serious problems. With the convex hull analysis, we offer a solution for this problem. The outcome of such an analysis can raise concerns if the pooling of different datasets would cause inhomogeneous patterns preventing a better predictive performance.

P–419 On prognosis after unexplained recurrent pregnancy losses (RPL); a systematic review and external validation of clinical prediction models

2021 ◽  
Vol 36 (Supplement_1) ◽  
Author(s):  
A Youssef
Keyword(s):  
Systematic Review ◽  
Supportive Care ◽  
Prediction Model ◽  
Pregnancy Outcome ◽  
Prediction Models ◽  
External Validation ◽  
Model Development ◽  
Predictive Performance ◽  
Pregnancy Rates ◽  
Cochrane Library

Abstract Study question Which models that predict pregnancy outcome in couples with unexplained RPL exist and what is the performance of the most used model? Summary answer We identified seven prediction models; none followed the recommended prediction model development steps. Moreover, the most used model showed poor predictive performance. What is known already RPL remains unexplained in 50–75% of couples For these couples, there is no effective treatment option and clinical management rests on supportive care. Essential part of supportive care consists of counselling on the prognosis of subsequent pregnancies. Indeed, multiple prediction models exist, however the quality and validity of these models varies. In addition, the prediction model developed by Brigham et al is the most widely used model, but has never been externally validated. Study design, size, duration We performed a systematic review to identify prediction models for pregnancy outcome after unexplained RPL. In addition we performed an external validation of the Brigham model in a retrospective cohort, consisting of 668 couples with unexplained RPL that visited our RPL clinic between 2004 and 2019. Participants/materials, setting, methods A systematic search was performed in December 2020 in Pubmed, Embase, Web of Science and Cochrane library to identify relevant studies. Eligible studies were selected and assessed according to the TRIPOD) guidelines, covering topics on model performance and validation statement. The performance of predicting live birth in the Brigham model was evaluated through calibration and discrimination, in which the observed pregnancy rates were compared to the predicted pregnancy rates. Main results and the role of chance Seven models were compared and assessed according to the TRIPOD statement. This resulted in two studies of low, three of moderate and two of above average reporting quality. These studies did not follow the recommended steps for model development and did not calculate a sample size. Furthermore, the predictive performance of neither of these models was internally- or externally validated. We performed an external validation of Brigham model. Calibration showed overestimation of the model and too extreme predictions, with a negative calibration intercept of –0.52 (CI 95% –0.68 – –0.36), with a calibration slope of 0.39 (CI 95% 0.07 – 0.71). The discriminative ability of the model was very low with a concordance statistic of 0.55 (CI 95% 0.50 – 0.59). Limitations, reasons for caution None of the studies are specifically named prediction models, therefore models may have been missed in the selection process. The external validation cohort used a retrospective design, in which only the first pregnancy after intake was registered. Follow-up time was not limited, which is important in counselling unexplained RPL couples. Wider implications of the findings: Currently, there are no suitable models that predict on pregnancy outcome after RPL. Moreover, we are in need of a model with several variables such that prognosis is individualized, and factors from both the female as the male to enable a couple specific prognosis. Trial registration number Not applicable

scholarly journals The Standardization of Hospital-Acquired Infection Rates Using Prediction Models in Iran: Observational Study of National Nosocomial Infection Registry Data

10.2196/33296 ◽  
2021 ◽  
Vol 7 (12) ◽  
pp. e33296
Author(s):  
Neda Izadi ◽  
Koorosh Etemad ◽  
Yadollah Mehrabi ◽  
Babak Eshrati ◽  
Seyed Saeed Hashemi Nazari
Keyword(s):  
Internal Medicine ◽  
Infectious Disease ◽  
Prediction Models ◽  
Scientific Evidence ◽  
Bloodstream Infections ◽  
Surgical Site Infections ◽  
Infection Rates ◽  
Hospital Acquired Infection ◽  
Tract Infections ◽  
Hospital Acquired

Background Many factors contribute to the spreading of hospital-acquired infections (HAIs). Objective This study aimed to standardize the HAI rate using prediction models in Iran based on the National Healthcare Safety Network (NHSN) method. Methods In this study, the Iranian nosocomial infections surveillance system (INIS) was used to gather data on patients with HAIs (126,314 infections). In addition, the hospital statistics and information system (AVAB) was used to collect data on hospital characteristics. First, well-performing hospitals, including 357 hospitals from all over the country, were selected. Data were randomly split into training (70%) and testing (30%) sets. Finally, the standardized infection ratio (SIR) and the corrected SIR were calculated for the HAIs. Results The mean age of the 100,110 patients with an HAI was 40.02 (SD 23.56) years. The corrected SIRs based on the observed and predicted infections for respiratory tract infections (RTIs), urinary tract infections (UTIs), surgical site infections (SSIs), and bloodstream infections (BSIs) were 0.03 (95% CI 0-0.09), 1.02 (95% CI 0.95-1.09), 0.93 (95% CI 0.85-1.007), and 0.91 (95% CI 0.54-1.28), respectively. Moreover, the corrected SIRs for RTIs in the infectious disease, burn, obstetrics and gynecology, and internal medicine wards; UTIs in the burn, infectious disease, internal medicine, and intensive care unit wards; SSIs in the burn and infectious disease wards; and BSIs in most wards were >1, indicating that more HAIs were observed than expected. Conclusions The results of this study can help to promote preventive measures based on scientific evidence. They can also lead to the continuous improvement of the monitoring system by collecting and systematically analyzing data on HAIs and encourage the hospitals to better control their infection rates by establishing a benchmarking system.

scholarly journals Development of Institutional Guidelines for Management of Gram-Negative Bloodstream Infections: Incorporating Local Evidence

2017 ◽  
Vol 52 (10) ◽  
pp. 691-697 ◽  
Author(s):  
Elizabeth B. Nimmich ◽  
P. Brandon Bookstaver ◽  
Joseph Kohn ◽  
Julie Ann Justo ◽  
Katie L. Hammer ◽  
...  
Keyword(s):  
Health Care ◽  
Critically Ill ◽  
Antimicrobial Therapy ◽  
Critically Ill Patients ◽  
Severity Of Illness ◽  
Bloodstream Infections ◽  
Evidence Based ◽  
Gram Negative ◽  
Empirical Antimicrobial Therapy ◽  
Hospital Acquired

Background: Appropriate empirical antimicrobial therapy is associated with improved outcomes of patients with Gram-negative bloodstream infections (BSI). Objective: Development of evidence-based institutional management guidelines for empirical antimicrobial therapy of Gram-negative BSI. Methods: Hospitalized adults with Gram-negative BSI in 2011-2012 at Palmetto Health hospitals in Columbia, SC, USA, were identified. Logistic regression was used to examine the association between site of infection acquisition and BSI due to Pseudomonas aeruginosa or chromosomally mediated AmpC-producing Enterobacteriaceae (CAE). Antimicrobial susceptibility rates of bloodstream isolates were stratified by site of acquisition and acute severity of illness. Retained antimicrobial regimens had predefined susceptibility rates ≥90% for noncritically ill and ≥95% for critically ill patients. Results: Among 390 patients, health care–associated (odds ratio [OR]: 3.0, 95% confidence interval [CI]: 1.5-6.3] and hospital-acquired sites of acquisition (OR: 3.7, 95% CI: 1.6-8.4) were identified as risk factors for BSI due to P aeruginosa or CAE, compared with community-acquired BSI (referent). Based on stratified bloodstream antibiogram, ceftriaxone met predefined susceptibility criteria for community-acquired BSI in noncritically ill patients (95%). Cefepime and piperacillin-tazobactam monotherapy achieved predefined susceptibility criteria in noncritically ill (95% both) and critically ill patients with health care–associated and hospital-acquired BSI (96% and 97%, respectively) and critically ill patients with community-acquired BSI (100% both). Conclusions: Incorporation of site of acquisition, local antimicrobial susceptibility rates, and acute severity of illness into institutional guidelines provides objective evidence-based approach for optimizing empirical antimicrobial therapy for Gram-negative BSI. The suggested methodology provides a framework for guideline development in other institutions.

scholarly journals 1274. Activity of Ceftolozane/Tazobactam, Imipenem/Relebactam, and Comparators Against Pseudomonas aeruginosa Isolates from Patients with Bloodstream and Other Infection Types—SMART United States/Canada 2018-2019

2021 ◽  
Vol 8 (Supplement_1) ◽  
pp. S725-S726
Author(s):  
Sibylle Lob ◽  
Meredith Hackel ◽  
C Andrew DeRyke ◽  
Kelly Harris ◽  
Katherine Young ◽  
...  
Keyword(s):  
Treatment Options ◽  
Bloodstream Infections ◽  
Independent Contractor ◽  
Gram Negative ◽  
The Us ◽  
Gram Negative Organisms ◽  
Infection Types ◽  
Tract Infections ◽  
Hospital Acquired ◽  
Intraabdominal Infections

Abstract Background Ceftolozane/tazobactam (C/T), an antipseudomonal cephalosporin combined with a β-lactamase inhibitor, was approved for treatment of complicated urinary tract (cUTI) and intraabdominal infections (cIAI), and hospital-acquired/ventilator-associated bacterial pneumonia (HAP/VAP). Imipenem/relebactam (IMI/REL) is a combination of imipenem/cilastatin with relebactam, an inhibitor of class A and C β-lactamases. IMI/REL was approved for HAP/VAP and for infections due to aerobic gram-negative organisms in adults with limited treatment options (e.g., cUTI, cIAI). We compared the activity of C/T and IMI/REL against P. aeruginosa from bloodstream infections (BSI) to those from other infection types. Methods As part of the SMART program, 24 hospitals in the US and 8 in Canada each collected up to 250 consecutive gram-negative isolates per year in 2018-2019 from patients with BSI, lower respiratory tract infections (LRTI), IAI, and UTI. A total of 2351 Pa isolates were collected. MICs were determined using CLSI broth microdilution and breakpoints. Results Pa isolates from BSI tended to show higher susceptibility than IAI, UTI, and especially LRTI isolates (Table). Susceptibility to the tested comparator β-lactams was 11-12 percentage points lower among LRTI than BSI isolates, while C/T and IMI/REL susceptibility was only 2-5% lower. Even among BSI isolates, the comparator β-lactams were active against only 75-88% of isolates, while C/T and IMI/REL were active against >95%. Only amikacin showed higher activity. Analyzing coverage by either C/T or IMI/REL, 98.7% of Pa isolates from BSI were susceptible to one or both agents. C/T and IMI/REL maintained activity against 89% and 69% of meropenem-nonsusceptible (MEM-NS) Pa isolates from BSI (n=36), respectively, and 87% and 76% of piperacillin/tazobactam (P/T)-NS Pa (n=38). Results Table Conclusion Even among BSI isolates, which were generally more susceptible than those from other infection types, Pa susceptibility to commonly used β-lactams like MEM and P/T was < 90%, 7-23% lower than C/T and IMI/REL. Given the desirability of β-lactams among clinicians and the >98% coverage by either C/T or IMI/REL of Pa isolates from BSI, both agents represent important options in the treatment of patients with BSI. Disclosures Sibylle Lob, PhD, IHMA (Employee)Pfizer, Inc. (Independent Contractor) Meredith Hackel, PhD MPH, IHMA (Employee)Pfizer, Inc. (Independent Contractor) C. Andrew DeRyke, PharmD, Merck & Co., Inc. (Employee, Shareholder) Kelly Harris, PharmD, BCPS, Merck & Co. Inc (Employee) Katherine Young, MS, Merck (Employee) Mary Motyl, PhD, Merck & Co., Inc. (Employee, Shareholder) Daniel F. Sahm, PhD, IHMA (Employee)Pfizer, Inc. (Independent Contractor)

scholarly journals Sample size for binary logistic prediction models: Beyond events per variable criteria

2018 ◽  
Vol 28 (8) ◽  
pp. 2455-2474 ◽  
Author(s):  
Maarten van Smeden ◽  
Karel GM Moons ◽  
Joris AH de Groot ◽  
Gary S Collins ◽  
Douglas G Altman ◽  
...  
Keyword(s):  
Sample Size ◽  
Prediction Models ◽  
Model Development ◽  
Binary Logistic Regression ◽  
Predictive Performance ◽  
Total Sample ◽  
Sample Size Determination ◽  
Minimal Sample ◽  
Out Of Sample ◽  
Before And After

Binary logistic regression is one of the most frequently applied statistical approaches for developing clinical prediction models. Developers of such models often rely on an Events Per Variable criterion (EPV), notably EPV ≥10, to determine the minimal sample size required and the maximum number of candidate predictors that can be examined. We present an extensive simulation study in which we studied the influence of EPV, events fraction, number of candidate predictors, the correlations and distributions of candidate predictor variables, area under the ROC curve, and predictor effects on out-of-sample predictive performance of prediction models. The out-of-sample performance (calibration, discrimination and probability prediction error) of developed prediction models was studied before and after regression shrinkage and variable selection. The results indicate that EPV does not have a strong relation with metrics of predictive performance, and is not an appropriate criterion for (binary) prediction model development studies. We show that out-of-sample predictive performance can better be approximated by considering the number of predictors, the total sample size and the events fraction. We propose that the development of new sample size criteria for prediction models should be based on these three parameters, and provide suggestions for improving sample size determination.

scholarly journals Pathogen-focused Clinical Development to Address Unmet Medical Need: Cefiderocol Targeting Carbapenem Resistance

2019 ◽  
Vol 69 (Supplement_7) ◽  
pp. S559-S564 ◽  
Author(s):  
Roger Echols ◽  
Mari Ariyasu ◽  
Tsutae Den Nagata
Keyword(s):  
Complicated Urinary Tract Infection ◽  
Development Program ◽  
Bloodstream Infections ◽  
The United States ◽  
Clinical Development ◽  
European Medicines Agency ◽  
Open Label ◽  
Site Specific ◽  
Gram Negative ◽  
Carbapenem Resistant

AbstractHistorically, the regulatory requirements of the US Food and Drug Administration (FDA) and the European Medicines Agency (EMA) for developing new antibiotics have not addressed pathogen-focused indications for drug approval. The design of the necessary randomized controlled trials traditionally involves the enrollment of patients with site-specific infections caused by susceptible as well as resistant pathogens. Cefiderocol has undergone a streamlined clinical development program to address serious carbapenem-resistant infections. The regulatory approach, and the pivotal clinical trials, differed between the FDA and EMA. In the United States, the APEKS-cUTI (Acinetobacter, Pseudomonas, Escherichia coli, Klebsiella, Stenotrophomonas–complicated urinary tract infection) study was conducted to provide the basis for FDA approval of a site-specific cUTI indication. The EMA, however, preferred the CREDIBLE-CR (A MultiCenter, RandomizED, Open-label ClInical Study of S-649266 or Best AvailabLE Therapy for the Treatment of Severe Infections Caused by Carbapenem-Resistant Gram-negative Pathogens) study, in which patients with nosocomial pneumonia, bloodstream infections, or cUTIs were enrolled if they had a carbapenem-resistant pathogen. The resulting European label will be pathogen focused rather than infection site specific (ie, treatment of gram-negative infection in patients with limited treatment options). The implications and limitations of these different regulatory processes are discussed.

Sign in / Sign up

Export Citation Format

Share Document