scholarly journals P8 A single centre evaluation of risk prediction models and imaging modalities in acute appendicitis

BJS Open ◽  
2021 ◽  
Vol 5 (Supplement_1) ◽  
Author(s):  
James Ashcroft ◽  
Aminder A Singh ◽  
Siobhan Rooney ◽  
John Bennett ◽  
Richard Justin Davies ◽  
...  
Keyword(s):  
High Risk ◽  
Risk Prediction ◽  
Prediction Models ◽  
Ct Imaging ◽  
Low Risk ◽  
Imaging Modalities ◽  
Risk Prediction Models ◽  
Suspected Appendicitis ◽  
High Risk Women ◽  
Low Risk Women

Abstract Objective Patients with suspected appendicitis remain a diagnostic challenge. This study aims to validate risk prediction models and to investigate diagnostic accuracy of ultrasonography (US) and computed tomography (CT) in adults undergoing an appendicectomy. Materials and Methods A retrospective case review of patients aged 16-45 undergoing an appendicectomy between January 2019 to January 2020 at a tertiary referral centre was performed. Primary outcomes were the accuracy of a high-risk appendicitis risk score and US and CT imaging modalities when compared to histological reports following appendicectomy. Results A total of 206 patients (107/205, 51.9% women) were included. Removal of histologically normal appendix was equally likely in men and women (13.1 versus 11.2%, relative risk 1.17, 95% c.i. 0.56 to 2.44; P =0.67). A high-risk appendicitis score correctly identified 84.0% (79/94) of cases in men and 85.9% (67/78) of cases in women. US was reported as equivocal in 85.7% (18/21) of low-risk women and 59.0% (23/39) of high-risk women. CT in low-risk women resulted in 25.0% (2/8) equivocal results whilst correctly diagnosing (5/6) or excluding (1/2) appendicitis in 75.0% of the total cohort (6/8). In high-risk women CT resulted in 3.8% (1/26) equivocal results whilst correctly detecting (22/23) or excluding (1/3) appendicitis in 88.5% of total high-risk patients (23/26). Conclusions This study suggests that risk prediction models may be useful in both women and men to identify appendicitis. US imaging gave high rates of equivocal results and should not be relied upon for the diagnosis of appendicitis but may be useful to exclude other differential diagnoses. CT imaging is a highly accurate diagnostic tool and could be considered in those at low-risk where clinical suspicion remains to reduce negative appendicectomy rates.

scholarly journals Breast cancer risk prediction accuracy in Jewish Israeli high-risk women using the BOADICEA and IBIS risk models

2013 ◽  
Vol 95 (6) ◽  
pp. 174-177 ◽  
Author(s):  
YAEL LAITMAN ◽  
MONICA SIMEONOV ◽  
LITAL KEINAN-BOKER ◽  
IRENA LIPHSHITZ ◽  
EITAN FRIEDMAN
Keyword(s):  
Breast Cancer ◽  
High Risk ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Risk Prediction ◽  
Predictive Value ◽  
Prediction Models ◽  
Medical Center ◽  
Risk Prediction Models ◽  
High Risk Women

SummarySeveral breast cancer risk prediction models have been validated in ethnically diverse populations, but none in Israeli high-risk women. To validate the accuracy of the IBIS and BOADICEA risk prediction models in Israeli high-risk women, the 10-year and lifetime risk for developing breast cancer were calculated using both BOADICEA and IBIS models for high-risk, cancer-free women, counselled at the Sheba Medical Center from 1 June 1996–31 May 2000. Women diagnosed with breast cancer by 31 May 2011 were identified from the Israeli National Cancer Registry. The observed to expected breast cancer ratios were calculated to evaluate the predictive value of both algorithms. Overall, 358 mostly (N = 205, 57·2%) Ashkenazi women, were eligible, age range at counselling was 20–75 years (mean 46·76 ± 9·8 years). Over 13·6 ± 1·45 years (range 11–16 years), 15 women (4·19%) were diagnosed with breast cancer, at a mean age of 57 ± 8·6 years. The 10-year risks assigned by BOADICEA and IBIS ranged from 0·2 to 12·6% and 0·89 to 21·7%, respectively. The observed:expected breast cancer ratio was 15/18·6 (0·8–95% CI 0·48–1·33) and 15/28·6 (0·52–95% CI 0·32–0·87), using both models, respectively. In Jewish Israeli high-risk women the BOADICEA model has a better predictive value and accuracy in determining 10-year breast cancer risk than the IBIS model.

scholarly journals Risk prediction models for atherosclerotic cardiovascular disease: A systematic assessment with particular reference to Qatar

2021 ◽  
Vol 2021 (2) ◽  
Author(s):  
Aziz Sheikh ◽  
Ulugbek Nurmatov ◽  
Huda Amer Al-Katheeri ◽  
Rasmeh Ali Al Huneiti
Keyword(s):  
Risk Prediction ◽  
Prediction Models ◽  
Ethnically Diverse ◽  
Risk Of Bias ◽  
Low Risk ◽  
Atherosclerotic Cardiovascular Disease ◽  
Eligibility Criteria ◽  
Risk Prediction Models ◽  
Ascvd Risk

Background: Atherosclerotic cardiovascular disease (ASCVD) is a common disease in the State of Qatar and results in considerable morbidity, impairment of quality of life and mortality. The American College of Cardiology/American Heart Association Pooled Cohort Equations (PCE) is currently used in Qatar to identify those at high risk of ASCVD. However, it is unclear if this is the optimal ASCVD risk prediction model for use in Qatar's ethnically diverse population. Aims: This systematic review aimed to identify, assess the methodological quality of and compare the properties of established ASCVD risk prediction models for the Qatari population. Methods: Two reviewers performed head-to-head comparisons of established ASCVD risk calculators systematically. Studies were independently screened according to predefined eligibility criteria and critically appraised using Prediction Model Risk Of Bias Assessment Tool. Data were descriptively summarized and narratively synthesized with reporting of key statistical properties of the models. Results: We identified 20,487 studies, of which 41 studies met our eligibility criteria. We identified 16 unique risk prediction models. Overall, 50% (n = 8) of the risk prediction models were judged to be at low risk of bias. Only 13% of the studies (n = 2) were judged at low risk of bias for applicability, namely, PREDICT and QRISK3.Only the PREDICT risk calculator scored low risk in both domains. Conclusions: There is no existing ASCVD risk calculator particularly well suited for use in Qatar's ethnically diverse population. Of the available models, PREDICT and QRISK3 appear most appropriate because of their inclusion of ethnicity. In the absence of a locally derived ASCVD for Qatar, there is merit in a formal head-to-head comparison between PCE, which is currently in use, and PREDICT and QRISK3.

Algorithms for Individual Prediction of COVID-19 infection and Hospitalization among Symptomatic patients based on health data (Preprint)

2021 ◽  
Author(s):  
Rossella Murtas ◽  
Nuccia Morici ◽  
Chiara Cogliati ◽  
Massimo Puoti ◽  
Barbara Omazzi ◽  
...  
Keyword(s):  
High Risk ◽  
Prediction Models ◽  
Hospital Admissions ◽  
Health Data ◽  
Low Risk ◽  
Risk Prediction Models ◽  
Predictive Algorithms ◽  
Medium Risk ◽  
Risk Patients ◽  
At Home

BACKGROUND The coronavirus disease 2019 (COVID-19) pandemic has generated a huge strain on the health care system worldwide. The metropolitan area of Milan, Italy was one of the most hit area in the world. OBJECTIVE Robust risk prediction models are needed to stratify individual patient risk for public health purposes METHODS Two predictive algorithms were implemented in order to foresee the probability of being a COVID-19 patient and the risk of being hospitalized. The predictive model for COVID-19 positivity was developed in 61.956 symptomatic patients, whereas the model for COVID-19 hospitalization was developed in 36.834 COVID-19 positive patients. Exposures considered were age, gender, comorbidities and symptoms associated with COVID-19 (vomiting, cough, fever, diarrhoea, myalgia, asthenia, headache, anosmia, ageusia, and dyspnoea). RESULTS The predictive models showed a good fit for predicting COVID-19 disease [AUC 72.6% (95% CI 71.6%-73.5%)] and hospitalization [AUC 79.8% (95% CI 78.6%-81%)]. Using these results, 118,804 patients with COVID-19 from October 25 to December 11, 2020 were stratified into low, medium and high risk for COVID-19 severity. Among the overall population, 67.030 (56%) were classified as low-risk, 43.886 (37%) medium-risk, and 7.888 (7%) high-risk, with 89% of the overall population being assisted at home, 9% hospitalized, and 2% dead. Among those assisted at home, most people (60%) were classified as low risk, whereas only 4% were classified at high risk. According to ordinal logistic regression, the OR of being hospitalised or dead was 5.0 (95% CI 4.6-5.4) in high-risk patients and 2.7 (95% CI 2.6-2.9) in medium-risk patients, as compared to low-risk patients. CONCLUSIONS A simple monitoring system, based on primary care datasets with linkage to COVID-19 testing results, hospital admissions data and death records may assist in proper planning and allocation of patients and resources during the ongoing COVID-19 pandemic.

scholarly journals Risk Prediction Models for Liver Cancer in Adults: A Systematic Review and Meta-Analysis

2021 ◽  
Author(s):  
Maomao Cao ◽  
He Li ◽  
Dianqin Sun ◽  
Siyi He ◽  
Yadi Zheng ◽  
...  
Keyword(s):  
Systematic Review ◽  
High Risk ◽  
Liver Cancer ◽  
Risk Prediction ◽  
Prediction Models ◽  
Meta Analysis ◽  
Risk Of Bias ◽  
Risk Models ◽  
Risk Prediction Models ◽  
Prediction Risk

Abstract Background Prediction of liver cancer risk is beneficial to define high-risk population of liver cancer and guide clinical decisions. We aimed to review and critically appraise the quality of existing risk-prediction models for liver cancer. Methods This systematic review followed the guidelines of CHARMS (Checklist for Critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies) and Preferred Reporting Items for Systematic Reviews and Meta (PRISMA). We searched for PubMed, Embase, Web of Science, and the Cochrane Library from inception to July 2020. Prediction model Risk Of Bias Assessment Tool was used to assess the risk of bias of all potential articles. A narrative description and meta-analysis were conducted. Results After removal irrespective and duplicated citations, 20 risk prediction publications were finally included. Within the 20 studies, 15 studies performed model derivation and validation process, three publications only conducted developed procedure without validation and two articles were used to validate existing models. Discrimination was expressed as area under curve or C statistic, which was acceptable for most models, ranging from 0.64 to 0.96. Calibration of the predictions model were rarely assessed. All models were graded at high risk of bias. The risk bias of applicability in 13 studies was considered low. Conclusions This systematic review gives an overall review of the prediction risk models for liver cancer, pointing out several methodological issues in their development. No prediction risk models were recommended due to the high risk of bias.Systematic review registration: This systematic has been registered in PROSPERO (International Prospective Register of Systemic Review: CRD42020203244).

scholarly journals 1227Colorectal cancer risk prediction models incorporating lifestyle and biomarker data: Results from the EPIC cohort

2021 ◽  
Vol 50 (Supplement_1) ◽  
Author(s):  
Krasimira Aleksandrova ◽  
Robin Reichmann ◽  
Mazda Jenab ◽  
Sabina Rinaldi ◽  
Rudolf Kaaks ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
High Risk ◽  
Risk Prediction ◽  
Prediction Models ◽  
Lifestyle Factors ◽  
Health Concern ◽  
Incidence Density ◽  
Processed Meat ◽  
Risk Prediction Models ◽  
Biomarker Data

Abstract Background Colorectal cancer represents a major public health concern and there is a worrying tendency of increasing incidence rates among younger people in the last decades. Risk stratification of high-risk individuals may aid targeted disease prevention. We therefore aimed to evaluate the predictive value of a wide range of lifestyle and biomarker variables using data within the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort. Methods A range of lifestyle, anthropometric and dietary variables in 329,885 participants in the EPIC cohort were evaluated as potential predictors for risk of colorectal cancer over 10 years. Biomarker measurements of 41 parameters were available for 1,320 CRC cases and 1,320 controls selected using incidence density matching. Best sets of predictors were selected using elastic net regularization with bootstrapping. Random survival forest was applied as a novel technique to validate the set of selected predictors taking variable interactions into account. Results The results suggested a set of lifestyle factors including age, waist circumference, height, smoking, alcohol consumption, physical activity, vegetables, dairy products, processed meat, and sugar and confectionary that showed good discrimination (Harrell's C-index: 0.710) and excellent calibration. The analyses further revealed a set of biomarkers that increased the predictive performance beyond age, sex and lifestyle factors. Conclusions Risk prediction models based on lifestyle and biomarker data may prove useful in the identification of individuals at high risk for colorectal cancer. Key messages Risk prediction models incorporating lifestyle and biomarker data could contribute to developing strategies for targeted colorectal cancer prevention.

scholarly journals Prospective Implementation of a Risk Prediction Model for Bloodstream Infection Safely Reduces Antibiotic Usage in Febrile Pediatric Cancer Patients Without Severe Neutropenia

2020 ◽  
Vol 38 (27) ◽  
pp. 3150-3160
Author(s):  
Adam J. Esbenshade ◽  
Zhiguo Zhao ◽  
Alaina Baird ◽  
Emily A. Holmes ◽  
Daniel E. Dulek ◽  
...  
Keyword(s):  
High Risk ◽  
Bloodstream Infection ◽  
Risk Prediction ◽  
Pediatric Patients ◽  
Prediction Models ◽  
Low Risk ◽  
Severe Neutropenia ◽  
Patients With Cancer ◽  
Intravenous Antibiotics ◽  
Pediatric Cancer Patients

PURPOSE Management of febrile pediatric patients with cancer with an absolute neutrophil count of 500/µL or greater is unclear. The Esbenshade Vanderbilt (EsVan) risk prediction models have been shown to predict bloodstream infection (BSI) likelihood in this population, and this study sought to prospectively validate and implement these models in clinical practice. METHODS Data were prospectively collected on febrile pediatric patients with cancer with a central venous catheter from April 2015 to August 2019 at a single site, at which the models (EsVan: 2015 to 2017; EsVan2: October 2017 to 2019) were initially developed and subsequently implemented for clinical management in well-appearing nonseverely neutropenic individuals. It was recommended that patients with low BSI risk (< 10%) be discharged home without antibiotics, those with intermediate BSI risk (10%-39.9%) be administered an antibiotic before discharge, and those with high BSI risk (> 40%) be admitted on broad-spectrum antibiotics. Seven-day outcomes were then collected and EsVan models were prospectively validated and C-statistics estimated. RESULTS In 937 febrile, nonsevere neutropenia episodes, frequencies of low-, intermediate-, and high-risk episodes were 88.9%, 8.6%, and 2.3% respectively. BSI incidence was 4.2% (39 of 937). Within risk groups, low-risk BSI incidence was 1.9% (16 of 834) with BSI incidence of 13.6% and 54.5% for intermediate- and high-risk episodes, respectively. Empirical intravenous antibiotics were administered in 21.1% of low-risk episodes at presentation and at 7 days postpresentation, 72.3% of episodes never required intravenous antibiotics. There were no deaths or clinical decompensations attributable to antibiotic delay. For BSI detection, EsVan and EsVan2 models applied to the new cohort achieved C-statistics of 0.802 and 0.824, respectively. CONCLUSION Prospective, real-time clinical utilization of the EsVan models accurately predicts BSI risk and safely reduces unnecessary antibiotic use in febrile, nonseverely neutropenic pediatric patients with cancer.

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