External validation of a risk assessment model for venous thromboembolism in the hospitalised acutely-ill medical patient (VTE-VALOURR)

2014 ◽  
Vol 112 (10) ◽  
pp. 692-699 ◽  
Author(s):  
Charles Mahan ◽  
Yang Liu ◽  
A. Graham Turpie ◽  
Jennifer Vu ◽  
Nancy Heddle ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Validation Cohort ◽  
External Validation ◽  
Medical Patient ◽  
Risk Groups ◽  
Assessment Model ◽  
Medical Patients ◽  
Control Cohort ◽  
Derivation Cohort ◽  
Risk Assessment Models

SummaryVenous thromboembolic (VTE) risk assessment remains an important issue in hospitalised, acutely-ill medical patients, and several VTE risk assessment models (RAM) have been proposed. The purpose of this large retrospective cohort study was to externally validate the IMPROVE RAM using a large database of three acute care hospitals. We studied 41,486 hospitalisations (28,744 unique patients) with 1,240 VTE hospitalisations (1,135 unique patients) in the VTE cohort and 40,246 VTE-free hospitalisations (27,609 unique patients) in the control cohort. After chart review, 139 unique VTE patients were identified and 278 randomly-selected matched patients in the control cohort. Seven independent VTE risk factors as part of the RAM in the derivation cohort were identified. In the validation cohort, the incidence of VTE was 0.20%; 95% confidence interval (CI) 0.18–0.22, 1.04%; 95%CI 0.88–1.25, and 4.15%; 95%CI 2.79–8.12 in the low, moderate, and high VTE risk groups, respectively, which compared to rates of 0.45%, 1.3%, and 4.74% in the three risk categories of the derivation cohort. For the derivation and validation cohorts, the total percentage of patients in low, moderate and high VTE risk occurred in 68.6% vs 63.3%, 24.8% vs 31.1%, and 6.5% vs 5.5%, respectively. Overall, the area under the receiver-operator characteristics curve for the validation cohort was 0.7731. In conclusion, the IMPROVE RAM can accurately identify medical patients at low, moderate, and high VTE risk. This will tailor future thromboprophylactic strategies in this population as well as identify particularly high VTE risk patients in whom multimodal or more intensive prophylaxis may be beneficial.

Venous Thromboembolism Validation Study of the IMPROVE Risk Assessment Models in the Medical Patient (VTE-VALOURR),

Blood ◽  
2011 ◽  
Vol 118 (21) ◽  
pp. 4218-4218 ◽  
Author(s):  
Charles Edward Mahan ◽  
Yang Liu ◽  
James D. Douketis ◽  
Alexander G.G. Turpie ◽  
Undaleeb Dairkee ◽  
...  
Keyword(s):  
Risk Factors ◽  
Risk Assessment ◽  
Venous Thromboembolism ◽  
High Risk ◽  
Validation Study ◽  
Controlled Trials ◽  
Medical Patients ◽  
Control Cohort ◽  
Randomized Controlled ◽  
Risk Assessment Models

Abstract Abstract 4218 Introduction. Venous Thromboembolism (VTE) remains the most common cause of preventable death in hospitalized patients despite more than 25 guidelines and over 5 decades of data on VTE prevention. American College of Chest Physicians (ACCP) and International Union of Angiology (IUA) guideline recommendations are primarily based off of risk factors utilized for entry into randomized controlled trials (RCT) or post-hoc analysis of these RCTs. These guidelines recommend a group-based, as opposed to an individualized risk assessment, approach. It is currently unknown how these risk factors interact in a quantitative manner. There are currently no weighted, validated, VTE risk assessment models (RAM) that are data-derived in medical patients. A retrospective VTE RAM (IMPACT ILL) was recently derived from the multinational IMPROVE registry in hospitalized medical patients. (Table 1) The “VTE-VALOURR ” is a retrospective, multi-center, case control, validation study of this RAM. The VTE-VALOURR is also assessing other VTE and bleeding risk factors. Methods. ICD-10 reports and the McMaster Transfusion Registry for Utilization Surveillance and Tracking (TRUST) database, which contains demographics, transfusion data, and approximately 50 clinical variables including thrombotic outcomes of inpatients, were used as the data source at 3 hospitals. Inclusion criteria were hospitalized medical patients ≥ 18 years with ≥ 3 days length of stay (LOS). Exclusion criteria were patients with pregnancy, mental health disorders, atrial fibrillation/ flutter, trauma, spinal cord injury, surgery within 90 days, VTE within 24 hours of admission, treatment dose anticoagulants (including warfarin) within 48 hours of admission, or transferred from a non-McMaster acute care facility. Lower extremity deep vein thrombosis (DVT) and pulmonary embolism out to 90 days post admission were the thrombotic outcomes of interest and verified by chart review. Upper extremity DVT was excluded. Descriptive statistics (proportions and frequencies) were used to summarize binary variables. Results. From January 1st, 2005 to February 28th, 2011, 247,241 hospitalizations occurred at 3 McMaster hospitals. After exclusionary criteria were applied, 779 VTE events were identified. (Figure 1) Of these, 419 were excluded because they were VTE events not related to a previous hospitalization (i.e. community-acquired). Of the remaining 360 patients, 240 have been reviewed with 93 confirmed, included, VTE events having occurred, 147 events being further excluded, and another 120 patients still requiring review. We present an interim analysis of the 93 currently included patients. Of the included patients, 68 (73%) received some form of prophylaxis during their hospital stay while 35 (38%) received appropriate type, dose and duration of prophylaxis. Fifty-eight (62%) of VTE events were therefore “preventable.” Number of risk factors per patient and risk scores for the 93 patients are listed in tables 2 and 3. Conclusions. Validation of this VTE RAM will identify medical patients at risk of VTE that do not readily fit into group-specific VTE risk categories. Additionally, validation will identify subsets of patients at especially high risk of VTE and focus future randomized controlled trials. Other VTE risk factors may be identified with the study. Review of the 120 VTE cohort patients needs to be completed as well as review of a comparator control cohort. Approximately 80% of the current VTE cohort appears to have a score of 2 or above and be at moderate to high risk of VTE. Final results of approximately 150 VTE patients will be presented along with the control cohort as well as if the model is valid. Disclosures: Turpie: Astellas Pharma Europe: Consultancy; Bayer HealthCare AG: Consultancy; Portola Pharma: Consultancy; sanofi-aventis: Consultancy.

scholarly journals The NIV Outcomes (NIVO) Score: prediction of in-hospital mortality in exacerbations of COPD requiring assisted ventilation

2021 ◽  
pp. 2004042
Author(s):  
Tom Hartley ◽  
Nicholas D. Lane ◽  
John Steer ◽  
Mark W. Elliott ◽  
Milind P. Sovani ◽  
...  
Keyword(s):  
Hospital Mortality ◽  
Validation Cohort ◽  
A Priori ◽  
External Validation ◽  
Risk Groups ◽  
Assisted Ventilation ◽  
Hospital Death ◽  
Prediction Tool ◽  
Trial Methodology ◽  
Derivation Cohort

IntroductionAcute exacerbations of COPD (AECOPD) complicated by acute (acidaemic) hypercapnic respiratory failure (AHRF) requiring ventilation are common. When applied appropriately, ventilation substantially reduces mortality. Despite this, there is evidence of poor practice and prognostic pessimism. A clinical prediction tool could improve decision making regarding ventilation, but none is routinely used.MethodsConsecutive patients admitted with AECOPD and AHRF treated with assisted ventilation (principally non-invasive ventilation) were identified in two hospitals serving differing populations. Known and potential prognostic indices were identified a priori. A prediction tool for in-hospital death was derived using multivariable regression analysis. Prospective, external validation was performed in a temporally separate, geographically diverse 10-centre study. The trial methodology adhered to TRIPOD recommendations.ResultsDerivation cohort, n=489, in-hospital mortality 25.4%; validation cohort, n=733, in-hospital mortality 20.1%. Using 6 simple categorised variables; extended Medical Research Council Dyspnoea score (eMRCD)1–4/5a/5b, time from admission to acidaemia >12 h, pH<7.25, presence of atrial fibrillation, Glasgow coma scale ≤14 and chest radiograph consolidation a simple scoring system with strong prediction of in-hospital mortality is achieved. The resultant NIVO score had area under the receiver operated curve of 0.79 and offers good calibration and discrimination across stratified risk groups in its validation cohort.DiscussionThe NIVO score outperformed pre-specified comparator scores. It is validated in a generalisable cohort and works despite the heterogeneity inherent to both this patient group and this intervention. Potential applications include informing discussions with patients and their families, aiding treatment escalation decisions, challenging pessimism, and comparing risk-adjusted outcomes across centres.

The use of weighted and scored risk assessment models for venous thromboembolism

2012 ◽  
Vol 108 (12) ◽  
pp. 1072-1076 ◽  
Author(s):  
Thomas McGinn ◽  
Alok Khorana ◽  
Alex Spyropoulos
Keyword(s):  
Risk Assessment ◽  
Clinical Practice ◽  
Venous Thromboembolism ◽  
Goodness Of Fit ◽  
Statistical Significance ◽  
Univariate Analysis ◽  
External Validation ◽  
Prognostic Index ◽  
Medical Patients ◽  
Risk Assessment Models

SummaryFormalised risk assessment models (RAMs) for venous thromboembolism (VTE) using weighted and scored variables have only recently been widely incorporated into international antithrombotic guidelines.Scored and weighted VTE RAMs have advantages over a simplified group-specific VTE risk approach, with the potential to allow more tailored strategies for thromboprophylaxis and an improved estimation of the risk/benefit profile for a particular patient. The derivation of VTE RAMs should be based on variables that are a priori defined or identified in a univariate analysis and the predictive capability of each variable should be rigorously assessed for both clinical and statistical significance and internal consistency and completeness. The assessment of the RAM should include the goodness of fit of the model and construction of a prognostic index score. Any VTE RAM which has been derived must undergo validation of that model before it can be used in clinical practice. Validation of the model should be performed in a “deliberate”prospective fashion across several diverse clinical sites using pre-defined criteria using basic standards for performing model validation. We discuss the basic concepts in the derivation of recent scored and weighted VTE RAMs in hospitalised surgical and medical patients and cancer outpatients, the mechanisms for accurate external validation of the models, and implications for their use in clinical practice.

scholarly journals A Risk Assessment Model for the Identification of Sickle Cell Disease Patients at Risk for Venous Thromboembolism

Blood ◽  
2018 ◽  
Vol 132 (Supplement 1) ◽  
pp. 3568-3568
Author(s):  
Jifang Zhou ◽  
Gregory Sampang Calip ◽  
Edith A. Nutescu
Keyword(s):  
Risk Assessment ◽  
Venous Thromboembolism ◽  
Risk Score ◽  
Validation Cohort ◽  
Population Based ◽  
Assessment Model ◽  
Risk Assessment Model ◽  
Cell Disease ◽  
Cohort Entry ◽  
Derivation Cohort

Abstract Background: Venous thromboembolism (VTE) is associated with significant morbidity, functional disability and mortality which leads to annual direct medical costs of 6 to 8 billion U.S. dollars. The incidence of VTE among patients with sickle cell disease (SCD) is significantly higher than in those without SCD, with lifetime risk of up to 25%. The highly variable clinical phenotypes of SCD, in addition to complex pathogenesis of thrombosis in SCD, are challenges to the early identification of high-risk patients and timely initiation of anticoagulant prophylaxis. Objective: To develop a population-based risk assessment model (Predictive AlgoRithm of VTE in SCD, PARViS) for the identification of SCD patients at high-risk of VTE using least absolute shrinkage and selection operator (LASSO) methodology and compare its validity to the Caprini VTE risk assessment model. Method: We conducted a retrospective cohort study using the 2009-2014 Truven Health MarketScan® databases to identify commercially-insured health plan enrollees with VTE and SCD based on International Classification of Diseases (ICD) codes for inpatient and outpatient encounters. Baseline characteristics were assessed over the 6 months period following cohort entry and a risk window for any VTE events starting from day 181 after cohort entry and onwards. The clinical outcomes were defined as occurrence of VTE over the 30-, 90- and 180-day period. The population-based cohort was divided into derivation and validation sets in a 2:1 ratio. The risk score was calculated using LASSO generalized linear regression models and divided into three risk categories for predicting 180-day VTE risk. Kaplan-Meier survivor functions were estimated for VTE rates by estimated risk score and censored for end of continuous enrollment, and end of observation period. The C-statistic was used to assess the prediction performance of the 7-factor risk score, which was compared with the Caprini VTE risk prediction model. Results: Among 11,774 subjects with SCD in the derivation cohort, the mean (SD) age at enrollment was 32.1 (19.8) years and 62.2% were female. From the validation cohort, 5949 SCD subjects were analyzed, participants' mean (SD) age at enrollment was 32.2 (19.7) years, and 62.6% were female. The 30-, 90- and 180-day VTE rates of the overall cohort were 0.6%, 1.3% and 2.0%, respectively. The risk model included age, recent central vein catheter use (<30 day), active cancer, history of VTE, iron overload, osteomyelitis and pulmonary hypertension. Patients with SCD in the validation cohort were stratified into high-, intermediate- and low-risk in 2:3:5 ratio by VTE risk scores. Demographics and distribution of VTE risk factors are listed in Table 1. The rates of VTE at 180-days were 0.47% (95%CI 0.35%-0.64%), 1.38% (95%CI 1.10%-1.73%),6.71% (95%CI 5.94%-7.57%). [Figure 1] In the derivation cohort, C statistics were 0.845 (95%CI 0.818-0.872) for 7-factor RAM in predicting 180-day VTE, 0.883 (95%CI 0.853-0.914) for 90-day VTE, and 0.917 (95%CI 0.875-0.959) for 30-day VTE. In the validation cohort, C statistics were 0.833 (95%CI 0.791-0.875) for 7-factor VTE risk assessment model in predicting 180-day VTE, 0.877 (95%CI 0.831-0.923) for 90-day VTE, and 0.942 (95%CI 0.911-0.972) for 30-day VTE. Using the Caprini VTE risk prediction model, we found statistically significant differences (p<0.0001) with C-statistics for 180-, 90- and 30-day VTE prediction of 0.721 (95%CI 0.672-0.770), 0.775 (95%CI 0.719-0.830), and 0.826 (95%CI 0.759-0.892). [Figure 2] Conclusion: We developed and validated a 7-factor VTE risk assessment model specific to patients with SCD (PARViS). With its straightforward calculation and demonstrated accurate prediction of 6-month VTE rates in patients with SCD, the PARViS model can prove to be a useful prediction tool for clinical practitioners. Disclosures No relevant conflicts of interest to declare.

scholarly journals Development of a machine learning model for predicting pediatric mortality in the early stages of intensive care unit admission

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Bongjin Lee ◽  
Kyunghoon Kim ◽  
Hyejin Hwang ◽  
You Sun Kim ◽  
Eun Hee Chung ◽  
...  
Keyword(s):  
Machine Learning ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Validation Cohort ◽  
External Validation ◽  
Learning Model ◽  
Derivation Cohort ◽  
Icu Admission ◽  
Early Stages ◽  
Machine Learning Model

AbstractThe aim of this study was to develop a predictive model of pediatric mortality in the early stages of intensive care unit (ICU) admission using machine learning. Patients less than 18 years old who were admitted to ICUs at four tertiary referral hospitals were enrolled. Three hospitals were designated as the derivation cohort for machine learning model development and internal validation, and the other hospital was designated as the validation cohort for external validation. We developed a random forest (RF) model that predicts pediatric mortality within 72 h of ICU admission, evaluated its performance, and compared it with the Pediatric Index of Mortality 3 (PIM 3). The area under the receiver operating characteristic curve (AUROC) of RF model was 0.942 (95% confidence interval [CI] = 0.912–0.972) in the derivation cohort and 0.906 (95% CI = 0.900–0.912) in the validation cohort. In contrast, the AUROC of PIM 3 was 0.892 (95% CI = 0.878–0.906) in the derivation cohort and 0.845 (95% CI = 0.817–0.873) in the validation cohort. The RF model in our study showed improved predictive performance in terms of both internal and external validation and was superior even when compared to PIM 3.

Evaluation of cohort diversity in development and validation studies of hereditary cancer genetic risk assessment models.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. 10600-10600
Author(s):  
Amanda Gammon ◽  
Ambreen Khan ◽  
Joanne M. Jeter
Keyword(s):  
Risk Assessment ◽  
Validation Studies ◽  
Genetic Risk ◽  
External Validation ◽  
Ashkenazi Jewish ◽  
Genetic Risk Assessment ◽  
Internal Validation ◽  
Risk Assessment Models ◽  
Race Ethnicity ◽  
Development And Validation

10600 Background: Multiple models estimate a person’s chance of harboring a pathogenic variant increasing cancer risk. Some pathogenic variants are more common in individuals from specific ancestries, such as the BRCA1 and BRCA2 founder variants in Ashkenazi Jews. Yet data remains limited on the larger variant spectrum seen among people of different ancestral backgrounds and whether or not the pathogenic variant frequency differs in many populations. Due to this, it is important that genetic risk assessment models be validated in a diverse cohort including Black, Indigenous, People of Color (BIPOC). Methods: A literature search was conducted to identify published development and validation studies for the following genetic risk assessment models: BRCAPRO, MMRPRO, CanRisk/BOADICEA, Tyrer-Cuzick, and PREMM. Validation studies that only evaluated the cancer risk prediction capabilities of the models (and not the genetic variant risk prediction) were excluded. The following participant information was abstracted from each study: total number of participants, gender, race, and ethnicity. Authors were contacted to obtain missing information (if available). Results: 12 development and 12 validation studies of the genetic risk assessment models BRCAPRO, MMRPRO, CanRisk/BOADICEA, Tyrer-Cuzick, and PREMM were abstracted. Of the validation studies, five were internal validation studies conducted by the model developers, and seven were external validation studies. Four external validation studies compared multiple models. 75% (18/24) of papers did not include reporting of participant race or ethnicity information in their published reports. External validation studies (4/7, 57%) more often reported participant race/ethnicity than development (0/12, 0%) or internal validation (2/5, 40%) studies. The external validation studies for BRCAPRO reporting race/ethnicity information involved cohorts that ranged from 50-51% non-Ashkenazi Jewish white, 28% African American, 1% Asian, 2-49% Hispanic, and 19-42% Ashkenazi Jewish. The external validation studies for MMRPRO and PREMM reporting race/ethnicity information involved cohort that ranged from 0-82% white, 4-100% Asian, 7% Black, and 7% Hispanic. Conclusions: Increased reporting of participant ancestry and ethnicity is needed in the development and validation studies of genetic risk assessment models. BRCAPRO’s validation cohorts have included a higher percentage of Hispanic and Black/African American participants, while MMRPRO and PREMM have been validated in a higher percentage of Asian participants. As debate continues about the utility of currently used racial categories in genetics research, it will be important to determine how best to report on participant diversity. These findings highlight the continued need for genetics researchers to engage BIPOC and identify ways to diversify their participant cohorts.

scholarly journals Risk-assessment models for VTE and bleeding in hospitalized medical patients: an overview of systematic reviews

2020 ◽  
Vol 4 (19) ◽  
pp. 4929-4944
Author(s):  
Andrea J. Darzi ◽  
Allen B. Repp ◽  
Frederick A. Spencer ◽  
Rami Z. Morsi ◽  
Rana Charide ◽  
...  
Keyword(s):  
Risk Factors ◽  
Risk Assessment ◽  
Systematic Reviews ◽  
Risk Of Bias ◽  
Clinical Expertise ◽  
Medical Patients ◽  
Vte Prophylaxis ◽  
Risk Assessment Models ◽  
Medical Inpatients ◽  
Overview Of Systematic Reviews

Abstract Multiple risk-assessment models (RAMs) for venous thromboembolism (VTE) in hospitalized medical patients have been developed. To inform the 2018 American Society of Hematology (ASH) guidelines on VTE, we conducted an overview of systematic reviews to identify and summarize evidence related to RAMs for VTE and bleeding in medical inpatients. We searched Epistemonikos, the Cochrane Database, Medline, and Embase from 2005 through June 2017 and then updated the search in January 2020 to identify systematic reviews that included RAMs for VTE and bleeding in medical inpatients. We conducted study selection, data abstraction and quality assessment (using the Risk of Bias in Systematic Reviews [ROBIS] tool) independently and in duplicate. We described the characteristics of the reviews and their included studies, and compared the identified RAMs using narrative synthesis. Of 15 348 citations, we included 2 systematic reviews, of which 1 had low risk of bias. The reviews included 19 unique studies reporting on 15 RAMs. Seven of the RAMs were derived using individual patient data in which risk factors were included based on their predictive ability in a regression analysis. The other 8 RAMs were empirically developed using consensus approaches, risk factors identified from a literature review, and clinical expertise. The RAMs that have been externally validated include the Caprini, Geneva, IMPROVE, Kucher, and Padua RAMs. The Padua, Geneva, and Kucher RAMs have been evaluated in impact studies that reported an increase in appropriate VTE prophylaxis rates. Our findings informed the ASH guidelines. They also aim to guide health care practitioners in their decision-making processes regarding appropriate individual prophylactic management.

scholarly journals Potential Invasion Risk of Pet Traded Lizards, Snakes, Crocodiles, and Tuatara in the EU on the Basis of a Risk Assessment Model (RAM) and Aquatic Species Invasiveness Screening Kit (AS-ISK)

Diversity ◽  
2019 ◽  
Vol 11 (9) ◽  
pp. 164 ◽  
Author(s):  
Oldřich Kopecký ◽  
Anna Bílková ◽  
Veronika Hamatová ◽  
Dominika Kňazovická ◽  
Lucie Konrádová ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Common Species ◽  
Assessment Model ◽  
Thamnophis Sirtalis ◽  
Indigenous Species ◽  
The European Union ◽  
Risk Assessment Models ◽  
Species Invasiveness ◽  
Establishment Potential ◽  
Negative Impacts

Because biological invasions can cause many negative impacts, accurate predictions are necessary for implementing effective restrictions aimed at specific high-risk taxa. The pet trade in recent years became the most important pathway for the introduction of non-indigenous species of reptiles worldwide. Therefore, we decided to determine the most common species of lizards, snakes, and crocodiles traded as pets on the basis of market surveys in the Czech Republic, which is an export hub for ornamental animals in the European Union (EU). Subsequently, the establishment and invasion potential for the entire EU was determined for 308 species using proven risk assessment models (RAM, AS-ISK). Species with high establishment potential (determined by RAM) and at the same time with high potential to significantly harm native ecosystems (determined by AS-ISK) included the snakes Thamnophis sirtalis (Colubridae), Morelia spilota (Pythonidae) and also the lizards Tiliqua scincoides (Scincidae) and Intellagama lesueurii (Agamidae).

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