scholarly journals VP61.07: Portuguese multicentre external validation of the IOTA Simple Rules, LR2 risk model and ADNEX model (phase I)

2020 ◽  
Vol 56 (S1) ◽  
pp. 332-333
Author(s):  
A. Borges ◽  
M.E. Brito ◽  
P. Pinto ◽  
P. Ambrósio ◽  
M.D. Bernardo ◽  
...  
Keyword(s):  
Phase I ◽  
Risk Model ◽  
External Validation ◽  
Simple Rules ◽  
Model Phase

scholarly journals Sonographic Assessment of Complex Ultrasound Morphology in Pregnant Women with the Use of IOTA Simple Rules Risk and ADNEX Scoring Systems

Diagnostics ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 414
Author(s):  
Artur Czekierdowski ◽  
Norbert Stachowicz ◽  
Agata Smoleń ◽  
Tomasz Kluz ◽  
Tomasz Łoziński ◽  
...  
Keyword(s):  
Pregnant Women ◽  
Risk Model ◽  
Malignant Tumors ◽  
Scoring Systems ◽  
Subjective Assessment ◽  
Risk Level ◽  
Likelihood Ratios ◽  
Predictive Values ◽  
Adnexal Masses ◽  
Simple Rules

Background: To evaluate the accuracy of subjective assessment (SA), the International Ovarian Tumor Analysis (IOTA) group Simple Rules Risk (SRR) and the Assessment of Different NEoplasias in the adneXa (ADNEX) model for the preoperative differentiation of adnexal masses in pregnant women. Methods: The study population comprised 36 pregnant women (median age: 28.5 years old, range: 20–42 years old) with a mean gestation age of 13.5 (range: 8–31) weeks at diagnosis. Tumors were prospectively classified by local sonographers as probably benign or probably malignant using SA. Final tumor histological diagnosis was used as the reference standard in all cases. Logistic regression SRR and ADNEX models were used to obtain a risk score for every case. Serum CA125 and human epidydimis protein 4 (HE4) concentrations were also retrieved and the Risk of Ovarian Malignancy Algorithm (ROMA) value was calculated. The calculated predictive values included positive and negative likelihood ratios of ultrasound and biochemical tests. Results: Final histology confirmed 27 benign and 9 malignant (including 2 borderline) masses. The highest sensitivity (89%) and specificity (70%) were found for the subjective tumor assessment. Although no malignancy was classified as benign using the SRR criteria (sensitivity = 100%), the specificity of this scoring system was only 37%. At the cut-off risk level of >20%, the ADNEX model had a sensitivity of 78% and a specificity of 70%. Serum levels of CA125, HE4 and the ROMA risk model correctly identified adnexal malignant tumors with a sensitivity of 67%, 25% and 25%, respectively. Corresponding specificities were 72%, 100% and 100%, respectively. The highest positive and negative likelihood ratios were found for SA (LR+ = 3.0 and LR− = 0.16, respectively). Overall diagnostic accuracy of all predictive methods used in this study were similar (range: 70–75%) except for SRR (53%). Conclusion: Subjective assessment remains the best predictive method in complex adnexal masses found at prenatal ultrasound in pregnant women. For less experienced sonographers, both the SRR and ADNEX scoring systems may be also used for the characterization of such tumors, while serum tumor markers CA125 and HE4, along with the ROMA algorithm appear to be less accurate.

scholarly journals EXTERNAL VALIDATION OF THE IMPROVING PARTIAL RISK ADJUSTMENT IN SURGERY (PRAIS2) MODEL FOR 30-DAY MORTALITY AFTER PEDIATRIC CARDIAC SURGERY

2020 ◽  
Author(s):  
Lucia Cocomello ◽  
Massimo Caputo ◽  
Rosie Cornish ◽  
Deborah A. Lawlor
Keyword(s):  
Cardiac Surgery ◽  
Clinical Practice ◽  
Risk Adjustment ◽  
Heart Surgery ◽  
Risk Model ◽  
External Validation ◽  
Risk Function ◽  
P Value ◽  
List Type ◽  
Single Centre

ABSTRACTObjectiveRisk stratification in paediatric patients undergoing heart surgery remains a challenge. The improving partial risk adjustment in surgery (PRAIS2) is a risk model predicting 30-day mortality which has been recently developed and validated using a UK-based cohort from April 2009-March 2015. We aimed to perform an independent temporal external validation to explore its generalisability and clinical utility.MethodsPRAIS2 validation was carried out using a single centre (Bristol, UK) cohort from April 2004 to March 2009 and April 2015 to July 2019. For each subject PRAIS2 score was calculated according to the original formula. PRAIS2 performance was assessed in terms of discrimination by means of ROC curve analysis and calibration by using the calibration belt method.ResultsA total of 1330 (2004-2009) and 1187 (2015-2019) paediatric cardiac surgical procedures were included in the first and second independent validation, respectively (median age at the procedure 6.0 and 6.9 months). PRAIS2 score showed excellent discrimination for both independent validations (AUC 0.72 (95%CI: 0.65 to 0.80) and 0.87 (95%CI: 0.82 to 0.93), respectively). While PRAIS2 was only marginally calibrated in the first validation, with a tendency to underestimate risk P-value = 0.051), the second validation showed good calibration with 95% confidence belt containing the bisector for predicted mortality (P-value = 0.15); We also observed good performance in the subgroup of patients undergoing non-elective procedures (N = 482; AUC 0.78 (95%CI 0.68 to 0.87); Calibration belt containing the bisector (P-value=0.61).ConclusionsIn a single centre UK-based cohort, PRAIS2 showed excellent discrimination and calibration in predicting 30-day mortality in paediatric cardiac surgery including in those undergoing non-elective procedures. Our results support a wider adoption of PRAIS2 score in the clinical practice.Strengths and limitations of this studyA strength of the present study is that data were prospectively collected as part of the UK National Congenital Heart Disease Audit and as such they undergo continuous and inclusive systematic validation that includes the review of a sample of case notes by external auditors to ensure coding accuracy.We used a recently proposed method (calibration belt) which does not require patients to be categorised according to risk percentile but rather provides a risk function across all risk value with relative uncertainty measure (95% CI)A key limitation of this study is that the sample size is relatively small and considerably smaller than the cohort used to develop PRAIS2Key questionsWhat is already known about this subject? The improving partial risk adjustment in surgery (PRAIS2) is a risk model predicting 30-day mortality which has been recently developed and validated using a UK-wide cohort.What does this study add? The present study reported the first independent external validation of the PRAIS2 using a single centre cohort which confirmed excellent performance of the model and for the first time showed that it also accurately predicts mortality in patients undergoing non-elective proceduresHow might this impact on clinical practice? Our results support a wider adoption of the PRAIS2 in the clinical practice.

Scan-based competing death risk model for reevaluating lung cancer computed tomography screening eligibility

2021 ◽  
pp. 2101613
Author(s):  
Anton Schreuder ◽  
Colin Jacobs ◽  
Nikolas Lessmann ◽  
Mireille JM Broeders ◽  
Mario Silva ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Risk Model ◽  
External Validation ◽  
Area Under The Curve ◽  
Chronic Obstructive ◽  
Quantitative Ct ◽  
Obstructive Pulmonary Disease ◽  
National Lung Screening Trial ◽  
Number Needed To Screen ◽  
Incidence Risk

PurposeA baseline CT scan for lung cancer (LC) screening may reveal information indicating that certain LC screening participants can be screened less, and instead require dedicated early cardiac and respiratory clinical input. We aimed to develop and validate competing death (CD) risk models using CT information to identify participants with a low LC and a high CD risk.MethodsParticipant demographics and quantitative CT measures of LC, cardiovascular disease, and chronic obstructive pulmonary disease were considered for deriving a logistic regression model for predicting five-year CD risk using a sample from the National Lung Screening Trial (n=15 000). Multicentric Italian Lung Detection data was used to perform external validation (n=2287).ResultsOur final CD model outperformed an external pre-scan model (CDRAT) in both the derivation (Area under the curve=0.744 [95% confidence interval=0.727 to 0.761] and 0.677 [0.658 to 0.695], respectively) and validation cohorts (0.744 [0.652 to 0.835] and 0.725 [0.633 to 0.816], respectively). By also taking LC incidence risk into consideration, we suggested a risk threshold where a subgroup (6258/23 096, 27%) was identified with a number needed to screen to detect one LC of 216 (versus 23 in the remainder of the cohort) and ratio of 5.41 CDs per LC case (versus 0.88). The respective values in the validation cohort subgroup (774/2287, 34%) were 129 (versus 29) and 1.67 (versus 0.43).ConclusionsEvaluating both LC and CD risks post-scan may improve the efficiency of LC screening and facilitate the initiation of multidisciplinary trajectories among certain participants.

scholarly journals External Validation and Refinement of Emergency Heart Failure Mortality Risk Grade Risk Model in Patients With Heart Failure in the Emergency Department

CJC Open ◽  
2019 ◽  
Vol 1 (3) ◽  
pp. 123-130
Author(s):  
Nariman Sepehrvand ◽  
Erik Youngson ◽  
Jeffrey A. Bakal ◽  
Finlay A. McAlister ◽  
Brian H. Rowe ◽  
...  
Keyword(s):  
Heart Failure ◽  
Emergency Department ◽  
Mortality Risk ◽  
Risk Model ◽  
External Validation ◽  
Patients With Heart Failure ◽  
Risk Grade

scholarly journals GARFIELD-AF model for prediction of stroke and major bleeding in atrial fibrillation: a Danish nationwide validation study

BMJ Open ◽  
2019 ◽  
Vol 9 (11) ◽  
pp. e033283 ◽  
Author(s):  
Frederik Dalgaard ◽  
Karen Pieper ◽  
Freek Verheugt ◽  
A John Camm ◽  
Keith AA Fox ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Ischaemic Stroke ◽  
Major Bleeding ◽  
Risk Model ◽  
Oral Anticoagulants ◽  
External Validation ◽  
Bleeding Risk ◽  
Risk Scores ◽  
Secondary Outcome

ObjectivesTo externally validate the accuracy of the Global Anticoagulant Registry in the FIELD-Atrial Fibrillation (GARFIELD-AF) model against existing risk scores for stroke and major bleeding risk in patients with non-valvular AF in a population-based cohort.DesignRetrospective cohort study.SettingDanish nationwide registries.Participants90 693 patients with newly diagnosed non-valvular AF were included between 2010 and 2016, with follow-up censored at 1 year.Primary and secondary outcome measuresExternal validation was performed using discrimination and calibration plots. C-statistics were compared with CHA2DS2VASc score for ischaemic stroke/systemic embolism (SE) and HAS-BLED score for major bleeding/haemorrhagic stroke outcomes.ResultsOf the 90 693 included, 51 180 patients received oral anticoagulants (OAC). Overall median age (Q1, Q3) were 75 (66–83) years and 48 486 (53.5%) were male. At 1-year follow-up, a total of 2094 (2.3%) strokes/SE, 2642 (2.9%) major bleedings and 10 915 (12.0%) deaths occurred. The GARFIELD-AF model was well calibrated with the predicted risk for stroke/SE and major bleeding. The discriminatory value of GARFIELD-AF risk model was superior to CHA2DS2VASc for predicting stroke in the overall cohort (C-index: 0.71, 95% CI: 0.70 to 0.72 vs C-index: 0.67, 95% CI: 0.66 to 0.68, p<0.001) as well as in low-risk patients (C-index: 0.64, 95% CI: 0.59 to 0.69 vs C-index: 0.57, 95% CI: 0.53 to 0.61, p=0.007). The GARFIELD-AF model was comparable to HAS-BLED in predicting the risk of major bleeding in patients on OAC therapy (C-index: 0.64, 95% CI: 0.63 to 0.66 vs C-index: 0.64, 95% CI: 0.63 to 0.65, p=0.60).ConclusionIn a nationwide Danish cohort with non-valvular AF, the GARFIELD-AF model adequately predicted the risk of ischaemic stroke/SE and major bleeding. Our external validation confirms that the GARFIELD-AF model was superior to CHA2DS2VASc in predicting stroke/SE and comparable with HAS-BLED for predicting major bleeding.

Presurgical risk model for chronic postsurgical pain based on 6 clinical predictors: a prospective external validation

Pain ◽  
2020 ◽  
Vol 161 (11) ◽  
pp. 2611-2618
Author(s):  
Antonio Montes ◽  
Gisela Roca ◽  
Jordi Cantillo ◽  
Sergi Sabate ◽  
Keyword(s):  
Risk Model ◽  
External Validation ◽  
Clinical Predictors ◽  
Postsurgical Pain ◽  
Chronic Postsurgical Pain

scholarly journals Development and external validation of 1- and 2-year mortality prediction models in cystic fibrosis

2019 ◽  
Vol 54 (3) ◽  
pp. 1900224 ◽  
Author(s):  
Sanja Stanojevic ◽  
Jenna Sykes ◽  
Anne L. Stephenson ◽  
Shawn D. Aaron ◽  
George A. Whitmore
Keyword(s):  
Cystic Fibrosis ◽  
Health Status ◽  
Prediction Models ◽  
Risk Model ◽  
External Validation ◽  
Registry Data ◽  
Receiver Operating Curve ◽  
Clinical Tool ◽  
Risk Of Death ◽  
The Uk

IntroductionWe aimed to develop a clinical tool for predicting 1- and 2-year risk of death for patients with cystic fibrosis (CF). The model considers patients' overall health status as well as risk of intermittent shock events in calculating the risk of death.MethodsCanadian CF Registry data from 1982 to 2015 were used to develop a predictive risk model using threshold regression. A 2-year risk of death estimated conditional probability of surviving the second year given survival for the first year. UK CF Registry data from 2007 to 2013 were used to externally validate the model.ResultsThe combined effect of CF chronic health status and CF intermittent shock risk provided a simple clinical scoring tool for assessing 1-year and 2-year risk of death for an individual CF patient. At a threshold risk of death of ≥20%, the 1-year model had a sensitivity of 74% and specificity of 96%. The area under the receiver operating curve (AUC) for the 2-year mortality model was significantly greater than the AUC for a model that predicted survival based on forced expiratory volume in 1 s <30% predicted (AUC 0.95 versus 0.68 respectively, p<0.001). The Canadian-derived model validated well with the UK data and correctly identified 79% of deaths and 95% of survivors in a single year in the UK.ConclusionsThe prediction models provide an accurate risk of death over a 1- and 2-year time horizon. The models performed equally well when validated in an independent UK CF population.

P0560POST-DISCHARGE FOLLOW UP AFTER AKI. AN EXTERNAL VALIDATION AND DECISION CURVE ANALYSIS OF TWO RISK MODELS

2020 ◽  
Vol 35 (Supplement_3) ◽  
Author(s):  
Simon Sawhney ◽  
Zhi Tan ◽  
Corri Black ◽  
Brenda Hemmelgarn ◽  
Angharad Marks ◽  
...  
Keyword(s):  
Risk Model ◽  
Discharge Planning ◽  
External Validation ◽  
Curve Analysis ◽  
Net Benefit ◽  
Post Discharge ◽  
Decision Curve Analysis ◽  
One Year ◽  
Unplanned Admissions

Abstract Background and Aims There is limited evidence to inform which people should receive follow up after AKI and for what reasons. Here we report the external validation (geographical and temporal) and potential clinical utility of two complementary models for predicting different post-discharge outcomes after AKI. We used decision curve analysis, a technique that enables visualisation of the trade-off (net benefit) between identifying true positives and avoiding false positives across a range of potential risk thresholds for a risk model. Based on decision curve analysis we compared model guided approaches to follow up after AKI with alternative strategies of standardised follow up – e.g. follow up of all people with AKI, severe AKI, or a discharge eGFR&lt;30. Method The Alberta AKI risk model predicts the risk of stage G4 CKD at one year after AKI among those with a baseline GFR&gt;=45 and at least 90 days survival (2004-2014, n=9973). A trial is now underway using this tool at a 10% threshold to identify high risk people who may benefit from specialist nephrology follow up. The Aberdeen AKI risk model provides complementary predictions of early mortality or unplanned readmissions within 90 days of discharge (2003, n=16453), aimed at supporting non-specialists in discharge planning, with a threshold of 20-40% considered clinically appropriate in the study. For the Alberta model we externally validated using Grampian residents with hospital AKI in 2011-2013 (n=9382). For the Aberdeen model we externally validated using all people admitted to hospital in Grampian in 2012 (n=26575). Analysis code was shared between the sites to maximise reproducibility. Results Both models discriminated well in the external validation cohorts (AUC 0.855 for CKD G4, and AUC 0.774 for death and readmissions model), but as both models overpredicted risks, recalibration was performed. For both models, decision curve analysis showed that prioritisation of patients based on the presence or severity of AKI would be inferior to a model guided approach. For predicting CKD G4 progression at one year, a strategy guided by discharge eGFR&lt;30 was similar to a model guided approach at the prespecified 10% threshold (figure 1). In contrast for early unplanned admissions and mortality, model guided approaches were superior at the prespecified 20-40% threshold (figure 2). Conclusion In conclusion, prioritising AKI follow up is complex and standardised recommendations for all people may be an inefficient and inadequate way of guiding clinical follow-up. Guidelines for AKI follow up should consider suggesting an individualised approach both with respect to purpose and prioritisation.

scholarly journals Identification of the high-risk patient in primary percutaneous coronary intervention: development and validation of a novel predictive index

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
S Blake ◽  
C Proscia ◽  
A Eleuteri ◽  
D Groves ◽  
R.H Stables
Keyword(s):  
High Risk ◽  
Risk Model ◽  
External Validation ◽  
Coronary Intervention ◽  
Lv Function ◽  
Composite Outcome ◽  
C Statistic ◽  
Percutaneous Coronary ◽  
Risk Patients ◽  
Myocardial Blush Grade

Abstract Background Primary percutaneous coronary intervention (PPCI) is the best treatment for patients with ST elevation myocardial infarction (STEMI). Several risk scores have been created to help risk-stratify these patients but few of these can be calculated in-lab, during the acute event. Development of a score that could be applied during PPCI could aid operators' decisions regarding adjunctive therapies and post-procedural surveillance which could improve patient outcomes. This study aimed to develop a simple, practical risk model that could be applied during PPCI to identify high-risk patients. Methods Demographic, clinical and outcome data were collected for all patients, as part of the HEAT-PPCI trial, who underwent PPCI for suspected STEMI between February 2012 and November 2013 at our hospital. Independent predictors of the composite outcome of 28-day mortality or severe impairment of LV function (ejection fraction ≤35%) were identified using multiple logistic regression. A risk model was fitted and internal validation was performed by bootstrapping. External validation was performed on a separate cohort of patients with STEMI. Results The derivation cohort included 1271 patients, with 131/1271 = 10.3% experiencing the composite outcome of 28-day mortality or poor LV function. Three variables were required to predict the outcome: age (OR:2.07, 95% CI 1.55 to 2.78), location of the culprit artery (OR:6.16, 95% CI 4.00 to 9.47), myocardial blush grade post-PPCI (OR:2.32, 95% CI 1.39 to 3.88). External validation was performed on 324 patients undergoing PPCI from a different centre. The model showed good discrimination on ROC-curve analysis (c statistic 0.79, 95% CI 0.75 to 0.83) and performed well on external validation (c statistic 0.87, 95% CI 0.72 to 0.95). Accuracy of the risk model on the validation data was improved by simple recalibration. The model was used to create a risk prediction chart that can be used in-lab during PPCI (Figure 1). Conclusions We have developed a risk model that accurately predicts 28-day mortality or poor LV function following STEMI using age, culprit location and myocardial blush grade. The model can assist operators in identifying high-risk patients during PPCI. Funding Acknowledgement Type of funding source: Public hospital(s). Main funding source(s): National Health Service, UK

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