scholarly journals Patient-Reported Outcomes Improves the Prediction of In-patient and Emergency Department Readmission Risks in Coronary Artery Disease

2018 ◽  
Vol 3 (4) ◽  
Author(s):  
Danielle Southern ◽  
Colleen Norris ◽  
Hude Quan ◽  
Maria Santana ◽  
Matthew James ◽  
...  
Keyword(s):  
Emergency Department ◽  
Risk Prediction ◽  
Prediction Models ◽  
Policy Decision ◽  
Patient Reported Outcome Measures ◽  
Baseline Survey ◽  
Post Discharge ◽  
Risk Prediction Models ◽  
Patient Reported ◽  
Artery Disease

IntroductionCoronary Artery Disease (CAD) patients are known to report higher healthcare resource use, such as inpatient [IP] and emergency department [ED] readmissions, than the general population. We investigate if the patient reported outcome measures (PROMs) improve the accuracy of readmissions risk prediction models in CAD. Objectives and ApproachPatients enrolled in the Alberta Provincial Project for Outcomes Assessment in Coronary Heart Disease (APPROACH) registry between 1995 and 2014 who received catheterization (CATH) and completed baseline PROMs were linked to discharge abstract data and national ambulatory data. Logistic regression (LR) was used to develop 30-day and 1-year readmissions risk prediction models adjusting for patients’ demographic, clinical, and self-reported characteristics. PROM was measured using the 19-item Seattle Angina Questionnaire (SAQ). The discriminatory performance of each prediction model was assessed using the Harrel’s c-statistic for LR. ResultsOf the 13,264 patients who completed baseline SAQ, 59 (0.3%) had IP readmissions or ED visits within 30 days, and up to 356 (1.9%) within 1 year of baseline survey. The C-statistics for one-year readmissions risk prediction models that only adjusted for demographic and clinical variables only ranged between 56.4% and 61.2%. The prognostic improvement in the discrimination of these models ranged between 2% to 10% when patient-reported SAQ was included as predictor. The addition of SAQ improves the model discrimination in all types of admission. Conclusion/ImplicationsThe addition of PROMs improves the moderate accuracy of readmissions risk prediction models. These findings highlight the need for routine collection of PROMs in clinical settings and their potential use for aiding clinical and policy decision-making and post-discharge outcomes monitoring in the management of cardiovascular diseases.

scholarly journals Development of risk prediction models to predict urine culture growth for adults with suspected urinary tract infection in the emergency department: protocol for an electronic health record study from a single UK university hospital

2020 ◽  
Vol 4 (1) ◽  
Author(s):  
Patrick Rockenschaub ◽  
Martin J. Gill ◽  
David McNulty ◽  
Orlagh Carroll ◽  
Nick Freemantle ◽  
...  
Keyword(s):  
Emergency Department ◽  
Urinary Tract Infection ◽  
Urinary Tract ◽  
Tract Infection ◽  
Hospital Admission ◽  
Risk Prediction ◽  
Antibiotic Treatment ◽  
Prediction Models ◽  
Risk Prediction Models ◽  
Electronic Health

Abstract Background Urinary tract infection (UTI) is a leading cause of hospital admissions and is diagnosed based on urinary symptoms and microbiological cultures. Due to lags in the availability of culture results of up to 72 h, and the limitations of routine diagnostics, many patients with suspected UTI are started on antibiotic treatment unnecessarily. Predictive models based on routinely collected clinical information may help clinicians to rule out a diagnosis of bacterial UTI in low-risk patients shortly after hospital admission, providing additional evidence to guide antibiotic treatment decisions. Methods Using electronic hospital records from Queen Elizabeth Hospital Birmingham (QEHB) collected between 2011 and 2017, we aim to develop a series of models that estimate the probability of bacterial UTI at presentation in the emergency department (ED) among individuals with suspected UTI syndromes. Predictions will be made during ED attendance and at different time points after hospital admission to assess whether predictive performance may be improved over time as more information becomes available about patient status. All models will be externally validated for expected future performance using QEHB data from 2018/2019. Discussion Risk prediction models using electronic health records offer a new approach to improve antibiotic prescribing decisions, integrating clinical and demographic data with test results to stratify patients according to their probability of bacterial infection. Used in conjunction with expert opinion, they may help clinicians to identify patients that benefit the most from early antibiotic cessation.

scholarly journals Development of risk prediction models to predict urine culture growth for patients with suspected urinary tract infection in the emergency department: protocol for an electronic health record study from a single UK hospital

2020 ◽  
Author(s):  
Patrick Rockenschaub ◽  
Martin J Gill ◽  
David McNulty ◽  
Orlagh Carroll ◽  
Nick Freemantle ◽  
...  
Keyword(s):  
Emergency Department ◽  
Urinary Tract Infection ◽  
Urinary Tract ◽  
Tract Infection ◽  
Hospital Admission ◽  
Risk Prediction ◽  
Antibiotic Treatment ◽  
Prediction Models ◽  
Risk Prediction Models ◽  
Electronic Health

Abstract Background: Urinary tract infection (UTI) is a leading cause of hospital admissions and is diagnosed based on urinary symptoms and microbiological cultures. Due to lags in the availability of culture results of up to 72 hours, and the limitations of routine diagnostics, many patients with suspected UTI are started on antibiotic treatment unnecessarily. Predictive models based on routinely collected clinical information may help clinicians to rule out a diagnosis of bacterial UTI in low-risk patients shortly after hospital admission, providing additional evidence to guide antibiotic treatment decisions.Methods: Using electronic hospital records from Queen Elizabeth Hospital Birmingham (QEHB) collected between 2011 and 2017, we aim to develop a series of models that estimates the probability of bacterial UTI at presentation in the emergency department (ED) among individuals with suspected urinary tract infection syndromes. Predictions will be made during ED attendance and at different time points after hospital admission to assess whether predictive performance may be improved over time as more information becomes available about patient status. All models will be externally validated for expected future performance using QEHB data from 2018/19.Discussion: Risk prediction models using electronic health records offer a new approach to improve antibiotic prescribing decisions, integrating clinical and demographic data with test results to stratify patients according to their probability of bacterial infection. Used in conjunction with expert opinion, they may help clinicians to identify patients that benefit the most from early antibiotic cessation.

scholarly journals Development of risk prediction models to predict urine culture growth for adults with suspected urinary tract infection in the emergency department: protocol for an electronic health record study from a single UK university hospital

2020 ◽  
Author(s):  
Patrick Rockenschaub ◽  
Martin J Gill ◽  
David McNulty ◽  
Orlagh Carroll ◽  
Nick Freemantle ◽  
...  
Keyword(s):  
Emergency Department ◽  
Urinary Tract Infection ◽  
Urinary Tract ◽  
Tract Infection ◽  
Hospital Admission ◽  
Risk Prediction ◽  
Antibiotic Treatment ◽  
Prediction Models ◽  
Risk Prediction Models ◽  
Electronic Health

Abstract Background:Urinary tract infection (UTI) is a leading cause of hospital admissions and is diagnosed based on urinary symptoms and microbiological cultures. Due to lags in the availability of culture results of up to 72 hours, and the limitations of routine diagnostics, many patients with suspected UTI are started on antibiotic treatment unnecessarily. Predictive models based on routinely collected clinical information may help clinicians to rule out a diagnosis of bacterial UTI in low-risk patients shortly after hospital admission, providing additional evidence to guide antibiotic treatment decisions.Methods:Using electronic hospital records from Queen Elizabeth Hospital Birmingham (QEHB) collected between 2011 and 2017, we aim to develop a series of models that estimates the probability of bacterial UTI at presentation in the emergency department (ED) among individuals with suspected UTI syndromes. Predictions will be made during ED attendance and at different time points after hospital admission to assess whether predictive performance may be improved over time as more information becomes available about patient status. All models will be externally validated for expected future performance using QEHB data from 2018/19. Discussion:Risk prediction models using electronic health records offer a new approach to improve antibiotic prescribing decisions, integrating clinical and demographic data with test results to stratify patients according to their probability of bacterial infection. Used in conjunction with expert opinion, they may help clinicians to identify patients that benefit the most from early antibiotic cessation.

scholarly journals Population Bias in Polygenic Risk Prediction Models for Coronary Artery Disease

2020 ◽  
Vol 13 (6) ◽  
Author(s):  
Damian Gola ◽  
Jeanette Erdmann ◽  
Kristi Läll ◽  
Reedik Mägi ◽  
Bertram Müller-Myhsok ◽  
...  
Keyword(s):  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Risk Prediction ◽  
Prediction Models ◽  
Data Sets ◽  
Risk Prediction Models ◽  
Polygenic Risk ◽  
Testing Data ◽  
Artery Disease ◽  
European Populations

Background: Individual risk prediction based on genome-wide polygenic risk scores (PRSs) using millions of genetic variants has attracted much attention. It is under debate whether PRS models can be applied—without loss of precision—to populations of similar ethnic but different geographic background than the one the scores were trained on. Here, we examine how PRS trained in population-specific but European data sets perform in other European subpopulations in distinguishing between coronary artery disease patients and healthy individuals. Methods: We use data from UK and Estonian biobanks (UKB, EB) as well as case-control data from the German population (DE) to develop and evaluate PRS in the same and different populations. Results: PRSs have the highest performance in their corresponding population testing data sets, whereas their performance significantly drops if applied to testing data sets from different European populations. Models trained on DE data revealed area under the curves in independent testing sets in DE: 0.6752, EB: 0.6156, and UKB: 0.5989; trained on EB and tested on EB: 0.6565, DE: 0.5407, and UKB: 0.6043; trained on UKB and tested on UKB: 0.6133, DE: 0.5143, and EB: 0.6049. Conclusions: This result has a direct impact on the clinical usability of PRS for risk prediction models using PRS: a population effect must be kept in mind when applying risk estimation models, which are based on additional genetic information even for individuals from different European populations of the same ethnicity.

scholarly journals Development of screening tools to predict the risk of recurrence and related complications following anal fistula surgery: protocol for a prospective cohort study

BMJ Open ◽  
2020 ◽  
Vol 10 (3) ◽  
pp. e035134
Author(s):  
Zubing Mei ◽  
Yue Li ◽  
Zhijun Zhang ◽  
Haikun Zhou ◽  
Suzhi Liu ◽  
...  
Keyword(s):  
Cohort Study ◽  
Risk Prediction ◽  
Anal Fistula ◽  
Prediction Models ◽  
Clinical Laboratory ◽  
Perioperative Complications ◽  
Screening Tools ◽  
Early Screening ◽  
Risk Prediction Models ◽  
Patient Reported

IntroductionPostoperative recurrence and related complications are common and related to poor outcomes in patients with anal fistula (AF). Due to being associated with short-term and long-term cure rates, perioperative complications have received widespread attention following AF surgery. This study aims to identify a set of predictive factors to develop risk prediction models for recurrence and related complications following AF surgery. We plan to develop and validate risk prediction models, using information collected through a WeChat patient-reported questionnaire system combined with clinical, laboratory and imaging findings from the perioperative period until 3–6 months following AF surgery.Methods and analysisThis is a prospective hospital-based cohort study using a linked database of collected health data as well as the follow-up outcomes for all adult patients who suffered from AF at a tertiary referral hospital in Shanghai, China. We will perform logistic regression models to predict anal fistula recurrence (AFR) as well as related complications (eg, wound haemorrhage, faecal impaction, urinary retention, delayed wound healing and unplanned hospitalisation) during and after AF surgery, and machine learning approaches will also be applied to develop risk prediction models. This prospective study aims to develop the first risk prediction models for AFR and related complications using multidimensional variables. These tools can be used to warn, motivate and empower patients to avoid some modifiable risk factors to prevent postoperative complications early. This study will also provide alternative tools for the early screening of high-risk patients with AFR and related complications, helping surgeons better understand the aetiology and outcomes of AF in an earlier stage.Ethics and disseminationThe study was approved by the Institutional Review Board of Shuguang Hospital affiliated with Shanghai University of Traditional Chinese Medicine (approval number: 2019-699-54-01). The results of this study will be submitted to international scientific peer-reviewed journals or conferences in surgery, anorectal surgery or anorectal diseases.Trial registration numberChiCTR1900025069; Pre-results.

scholarly journals Development of risk prediction models to predict urine culture growth for adults with suspected urinary tract infection in the emergency department: protocol for an electronic health record study from a single UK university hospital

2020 ◽  
Author(s):  
Patrick Rockenschaub ◽  
Martin J Gill ◽  
David McNulty ◽  
Orlagh Carroll ◽  
Nick Freemantle ◽  
...  
Keyword(s):  
Emergency Department ◽  
Urinary Tract Infection ◽  
Urinary Tract ◽  
Tract Infection ◽  
Hospital Admission ◽  
Risk Prediction ◽  
Antibiotic Treatment ◽  
Prediction Models ◽  
Risk Prediction Models ◽  
Electronic Health

Abstract Background: Urinary tract infection (UTI) is a leading cause of hospital admissions and is diagnosed based on urinary symptoms and microbiological cultures. Due to lags in the availability of culture results of up to 72 hours, and the limitations of routine diagnostics, many patients with suspected UTI are started on antibiotic treatment unnecessarily. Predictive models based on routinely collected clinical information may help clinicians to rule out a diagnosis of bacterial UTI in low-risk patients shortly after hospital admission, providing additional evidence to guide antibiotic treatment decisions.Methods: Using electronic hospital records from Queen Elizabeth Hospital Birmingham (QEHB) collected between 2011 and 2017, we aim to develop a series of models that estimates the probability of bacterial UTI at presentation in the emergency department (ED) among individuals with suspected UTI syndromes. Predictions will be made during ED attendance and at different time points after hospital admission to assess whether predictive performance may be improved over time as more information becomes available about patient status. All models will be externally validated for expected future performance using QEHB data from 2018/19. Discussion: Risk prediction models using electronic health records offer a new approach to improve antibiotic prescribing decisions, integrating clinical and demographic data with test results to stratify patients according to their probability of bacterial infection. Used in conjunction with expert opinion, they may help clinicians to identify patients that benefit the most from early antibiotic cessation.

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