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Risk Prediction of Venous Thromboembolism in Critically Ill

2018-12-19 03:58:13 | BioPortfolio

Summary

Introduction: Venous thromboembolism (VTE), including both deep vein thrombosis and pulmonary embolism, is a frequent cause of morbidity and mortality. The population of critically ill patients is a heterogeneous group of patients with an overall high average risk of developing VTE. No prognostic model has been developed for estimation of this risk specifically in critically ill patients. The aim is to construct and validate a risk assessment model for predicting the risk of in-hospital VTE in critically ill patients.

Methods: In the first phase of the study we will create a prognostic model based on a derivation cohort of critically ill patients who were acutely admitted to the intensive care unit. A point-based clinical prediction model will be created using backward stepwise regression analysis from a selection of predefined candidate predictors. Model performance, discrimination and calibration will be evaluated, and the model will be internally validated by bootstrapping. In the second phase of the study, external validation will be performed in an independent cohort, and additionally model performance will be compared with performance of existing VTE risk prediction models derived from, and applied to, general medical patients.

Dissemination: This protocol will be published online. The results will be reported according to the Transparent Reporting of multivariate prediction models for Individual Prognosis Or Diagnosis (TRIPOD) statement, and submitted to a peer-reviewed journal for publication.

Description

[1-2] Introduction

Background and rationale

Venous thromboembolism (VTE), including both deep vein thrombosis (DVT) and pulmonary embolism (PE), is a frequent cause of morbidity and mortality. The total VTE incidence per year is estimated at 600.000 events in the U.S. and 700.000 events in Europe. Approximately two-thirds of cases are related to current or recent hospitalization. General risk factors include increasing age, active cancer, previous VTE, thrombophilia, reduced mobility, recent trauma or surgery, heart and/or respiratory failure, stroke, sepsis, use of estrogens, and pregnancy. Critically ill patients often have multiple risk factors at the same time which may predispose them to a high risk of VTE.

Administration of pharmacological thromboprophylaxis lowers the incidence of VTE. Current American College of Chest Physician (ACCP) guidelines recommend prophylaxis with low-molecular-weight heparin (LMWH) or unfractionated heparin (UFH) for all critically ill patients. However, a substantial number of patients will still develop VTE despite "adequate" prophylaxis. In a large and well-conducted randomised controlled trial that compared LMWH to UFH, 5.1% of patients receiving LMWH experienced proximal leg DVT and 1.3% PE.

Individual risk of developing VTE likely varies depending on the number of accumulated risk factors, and a more individualized approach to thromboprophylaxis may be appropriate. The 2012 ACCP guidelines recommend to use a risk assessment tool for identifying high-risk patients. More recently, the International Society on Thrombosis and Haemostasis steering committee has urged the medical community to prioritize VTE risk assessment of all hospitalized patients. Several models have been developed and validated for this purpose in general medical, oncological, and surgical patients. No model for estimating VTE risk has been developed in the critically ill population.‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬

The investigators hypothesize that there is important variance in risk of VTE between individual critically ill patients. The aim of this study is to develop and externally validate a risk assessment model for estimating in-hospital VTE risk in critically ill patients. Additionally, the investigators will compare its performance to existing risk assessment models originally developed in and applied to the general medical patient population.

Objectives

- To develop and internally validate a risk assessment model for predicting the risk of in-hospital VTE in critically ill patients (phase 1)

- To externally validate this new model (phase 2)

- To compare the performance of this model to other VTE prediction models originally developed in the general medical patient population (phase 2)

[3-12] Phase 1: derivation and internal validation

The development and validation of a risk assessment model includes three consecutive phases of derivation, external validation and impact analysis.

In this first phase (i.e., the derivation and internal validation phase) the investigators will construct a multivariable prediction model for estimating VTE risk, and convert this model into a risk assessment score. The intention is to construct a simple score which can be used at the bedside. Subsequently, the score will be internally validated. The investigators will report their findings according to the Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) statement.

3 Study design

Prospective cohort study based on the 'Simple Intensive Care Studies' (SICS) registry. Data collection and analysis of this registry is prospective. The majority of variables used for the current study are collected prospectively; some variables will be added retrospectively (described in more detail below). This protocol has been finalized before the data collection was completed. All analyses will be conducted according to, and after publishing of, this protocol.

4 Study setting

Department of critical care of the University Medical Centre Groningen.

5 Study participants

All acutely admitted critically ill patients who fulfill the eligibility/inclusion criteria for the SICS registry will be included provided no exclusion criteria exist.

Inclusion Criteria:

1. Emergency admission

2. Expected stay > 24 hours

Exclusion Criteria:

1. Age < 18 years 2. Planned admission either after surgery or for other reasons 3. Unable to provide informed consent

6 Outcome

The primary outcome will be in-hospital VTE. VTE will be defined as any objectively proven event occurring during initial hospital admission. No screening protocol will be used. DVT will include acute thrombosis of lower-extremity veins (iliac, femoral or popliteal), confirmed by compression ultrasonography, venography, CT, MRI, or autopsy (Table 1). Pulmonary embolism will be defined as acute thrombosis within the pulmonary vasculature as shown by ventilation-perfusion scan, CT angiography, or autopsy (Table 1). Upper extremity DVT or venous thrombosis in another site will be excluded from the model but included in a sensitivity analysis. All VTE events will be adjudicated by the study coordinator before the development of the prediction model.

7 Candidate predictors

Candidate predictors have been selected based on the following criteria:

1. established or suggested association with VTE (based on literature)

2. or incorporation in another VTE risk assessment model;

3. and readily available and easy to obtain in daily clinical practice.

The investigators will explore the following candidate predictors: active cancer, acute infection, acute renal failure, cardiovascular failure, central venous access, elderly age, estrogen therapy, sex, major surgery, mechanical ventilation, multiple trauma, obesity, previous VTE, reduced mobility, respiratory failure, stroke, thrombophilic disorder, and vasopressor use. A complete list of all candidate predictors including their definitions and units of measurement, is displayed in table 2.

Two variables will be evaluated for their prognostic ability, but will not be included in the final model. The first variable is cardiovascular failure, defined as low cardiac output measured by transthoracic echocardiography (Table 2), which is likely to be associated with risk of VTE, but may not be available in all hospitals within 24 hours. The investigators will assess its predictive abilities in a sensitivity analysis since critical care ultrasonography is increasingly used in critical care and likely to be available in all patients in the near future. The second variable is immobilization: in practice, all acutely admitted critically ill patients are immobilized and so this variable will not contribute any information to the model.

8 Data collection methods

The SICS registry consists of two cohorts: SICS-I and SICS-II. All data are prospectively collected within SICS-II but some have not been registered within SICS-I, including antiplatelet and anticoagulant medication, VTE outcome data, active cancer, estrogen use, major surgery, multiple trauma, previous VTE, and thrombophilic disorder. These variables will be retrospectively registered for the patients included in the SICS-I cohort (Table 1 and 2).

9 Data management

Data will be recorded using electronic case report forms (eCRF) in OpenClinica and transferred for analysis. After transfer from OpenClinica, data will be managed in a database created using STATA version 14.0 or newer (StataCorp, College Station, TX). All data will be handled in compliance with national and institutional data regulatory laws.

10 Statistical analysis

Patient characteristics will be presented as means (with standard deviations; SD) or medians (with interquartile ranges; IQR) depending on distributions. Categorical data will be presented as proportions. Normality of the data will be assessed using P-P plots and histograms. Linearity will be assessed using scatter plots. Differences between continuous variables will be assessed using Student's t-tests or Mann-Whitney-U test where appropriate. All analyses will be tested two-sided with statistical significance defined as a two-sided p-value of <0.05. Statistical analysis will performed using STATA version 14.0 or newer (StataCorp, College Station, TX).

The investigators will construct the model using the following steps:

1. Candidate predictor selection criteria were described above. Definitions are displayed in table 2.

2. Missing variables (<25%) will be imputed using multiple imputations. Missing variables (>25%) will be excluded. Multiple imputations for missing outcome data will not be performed and patients with missing VTE data will be excluded from all analyses.

3. The investigators will construct a binary logistic regression model using in-hospital VTE as dependent outcome and the candidate predictors as independent variables. Continuous variables will not be converted to categorical variables. Regression analysis will be conducted using a backward stepwise elimination model. The aim is to include as few variables as reasonably possible to increase simplicity and enhance clinical applicability. The investigators will therefore not use a prespecified significance threshold for elimination. Results will be presented as adjusted Odds ratios (OR) with 95% confidence intervals (CI) and regression coefficients (β-values).

4. The logistic model will be converted to a clinically usable risk assessment model using methods previously described in the Framingham Heart Study.

5. Several tests for evaluation of model performance will be used. Overall predictive performance will be tested using Nagelkerke's R2. Discrimination, which is the ability to distinguish patients with and without VTE, will be quantified using the concordance (C), and is identical to the area under the curve in a receiver operating characteristic curve. Calibration, which is the agreement between predicted and observed frequency, will be tested by a calibration plot, by modeling a regression line with intercept (α) and slope (β), and by using the Hosmer and Lemeshow goodness of fit test.

6. Internal validation (or reproducibility) will be performed using bootstrapping.

External validation is described in more detail below (phase 2 of this protocol).

11 Sample size

Calculation of the total sample size required for developing a prediction model is difficult as this depends heavily on the effective sample size (i.e. total numbers of VTE events). As a rule of thumb there should be a minimum of ten outcome events for each screened candidate predictor included in the multivariable logistic regression model to prevent over-fitting of the model. Assuming a baseline risk of symptomatic VTE of 5% in the study sample implicates that the investigators need to include 3.400 patients to register 170 events for evaluation of seventeen candidate predictor variables.

12 Ethics

The local institutional review board (Medisch Ethische Toetsingscommissie (METc) of the UMCG has previously approved the SICS main study (M15.168207) as well as sub-studies (METc M11.104639 and M16.193856).

[13-21] Phase 2: external validation

Phase two, the external validation of the newly constructed risk assessment model, will be conducted in an independent sample of critically ill patients in other hospitals. For this purpose, the investigators will create a multicenter cohort based on prospectively collected data derived from the Dutch National Intensive Care Evaluation (NICE) registry.

13 Study design

Multicenter cohort study based on prospectively collected data within the National Intensive Care Evaluation registry (from now on referred to as: NICE cohort).

14 Study setting

Two Intensive Care Units (ICUs) in hospitals in the Northern part of the Netherlands.

15 Study participants

All acutely admitted critically ill patients who fulfill the eligibility criteria and none of the exclusion criteria will be included. Because of the retrospective design of this cohort, eligibility criteria depart minimally from the criteria the investigators applied to the derivation cohort as these data were derived from a prospective study.

Inclusion Criteria:

1. Emergency admission

Exclusion Criteria:

2. Age < 18 years

3. Planned admission either after surgery or for other reasons

16 Outcome and candidate predictors

Outcomes in the external validation cohort are defined identical as in the derivation cohort (Table 1). Candidate predictor definitions are provided in table 2.

17 Data collection methods

The investigators will request data from the Netherlands National Intensive Care Evaluation (NICE) registry. The NICE registry has been developed for quality improvement, for comparing outcomes between different ICUs, and for research purposes. Its dataset contains 96 items for each patient admitted to one of the participating ICUs. Data collection occurs either manually or automatically. Quality of data in this registry has previously been assessed as 'good'. Data on all but five candidate predictors (active cancer, central venous access, exogenous estrogen, previous venous thromboembolism, thrombophilic disorder) are routinely collected in this registry. VTE outcome data, use of prophylactic or therapeutic anticoagulation, and the five remaining candidate predictor variables will be collected retrospectively from patient files in the participating hospitals (Table 1 and 2). In each participating ICU inclusion will start with the most recently admitted patient for whom complete outcome data (i.e. one 'complete hospital stay' with or without VTE) are available. The investigators will then sequentially include all eligible patients, going back in time until a total sample of 1.000 patients per ICU has been reached.

18 Data management

Data will be recorded using eCRFs in OpenClinica and transferred for analysis. After transfer from OpenClinica, all data will be managed in a database created using STATA version 14.0 or newer (StataCorp, College Station, TX). All data will be handled in compliance with national and institutional data regulatory laws.

19 Statistical analysis

Descriptive statistics will be conducted following the same methods as described in 'phase 1' of this protocol. For external validation, the investigators will test overall model predictive performance, calibration and discrimination and compare this to the derivation sample. Overall predictive performance will be tested using Nagelkerke's R2. Discrimination, which is the ability to distinguish patients with and without VTE, will be quantified using the concordance (C), and is identical to the area under the curve in a receiver operating characteristic curve. Calibration, which is the agreement between predicted and observed frequency, will be tested by a calibration plot, by modeling a regression line with intercept (α) and slope (β), and by using the Hosmer and Lemeshow goodness of fit test.

The investigators will compare performance of the newly developed model to two existing VTE risk assessment models (IMPROVE VTE and Padua prediction score) originally developed in acutely ill medical patients using the same measures of overall predictive performance, discrimination, and calibration as described above.

20 Sample size

For assessing model performance in an external validation sample at least 100 events and 100 non-events are required as a rule of thumb. The investigators therefore expect a total sample size of 2.000 patients (assuming a baseline VTE risk of 5%) or more is required. The investigators intend to include 1.000 patients in each participating ICU.

21 Ethics

Due to the observational nature of the investigations, the WMO is not applicable and formal ethical review is not required. A waiver for informed consent for collecton of data will be requested from the local institutional review board (Medisch Ethische Toetsingscommissie; METc) of the participating hospitals.

[22] Phase 3: implementation and impact analysis

The third and last phase comprises implementation of the model and impact analysis.

22 Implementation and impact analysis

The investigators have not yet planned an impact analysis in this very early phase.

Study Design

Conditions

Venous Thrombosis

Location

University Medical Center Groningen
Groningen
Netherlands
9713GZ

Status

Recruiting

Source

University Medical Center Groningen

Results (where available)

View Results

Links

Published on BioPortfolio: 2018-12-19T03:58:13-0500

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Impaired venous blood flow or venous return (venous stasis), usually caused by inadequate venous valves. Venous insufficiency often occurs in the legs, and is associated with EDEMA and sometimes with VENOUS STASIS ULCERS at the ankle.

A platelet-specific protein which is released when platelets aggregate. Elevated plasma levels have been reported after deep venous thrombosis, pre-eclampsia, myocardial infarction with mural thrombosis, and myeloproliferative disorders. Measurement of beta-thromboglobulin in biological fluids by radioimmunoassay is used for the diagnosis and assessment of progress of thromboembolic disorders.

The formation or presence of a blood clot (THROMBUS) within a vein.

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