Dataset Information

External Validation of the ICU-Venous Thromboembolism Risk Assessment Model in Adult Critically Ill Patients.

ABSTRACT:

Background

Currently, no universally accepted standardized VTE risk assessment model (RAM) is specifically designed for critically ill patients. Although the ICU-venous thromboembolism (ICU-VTE) RAM was initially developed in 2020, it lacks prospective external validation.

Objectives

To evaluate the predictive performance of the ICU-VTE RAM in terms of VTE occurrence in mixed medical-surgical ICU patients.

Methods

We prospectively enrolled adult patients in the ICU. The ICU-VTE score and Caprini or Padua score were calculated at admission, and the incidence of in-hospital VTE was investigated. The performance of the ICU-VTE RAM was evaluated and compared with that of Caprini or Padua RAM using the receiver operating curve.

Results

We included 269 patients (median age: 70 years; 62.5% male). Eighty-three (30.9%) patients experienced inpatient VTE. The AUC of the ICU-VTE RAM was 0.743 (95% CI, 0.682-0.804, P < 0.001) for mixed medical-surgical ICU patients. Comparatively, the performance of the ICU-VTE RAM was superior to that of the Pauda RAM (AUC: 0.727 vs 0.583, P < 0.001) in critically ill medical patients and the Caprini RAM (AUC: 0.774 vs 0.617, P = 0.128) in critically ill surgical patients, although the latter comparison was not statistically significant.

Conclusions

The ICU-VTE RAM may be a practical and valuable tool for identifying and stratifying VTE risk in mixed medical-surgical critically ill patients, aiding in managing and preventing VTE complications.

SUBMITTER: Zhang L

PROVIDER: S-EPMC11367694 | biostudies-literature | 2024 Jan-Dec

REPOSITORIES: biostudies-literature

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Publications

External Validation of the ICU-Venous Thromboembolism Risk Assessment Model in Adult Critically Ill Patients.

Zhang Lijuan L Chen Fuyang F Hu Su S Zhong Yanxia Y Wei Bohua B Wang Xiaopin X Long Ding D

Clinical and applied thrombosis/hemostasis : official journal of the International Academy of Clinical and Applied Thrombosis/Hemostasis 20240101

<h4>Background</h4>Currently, no universally accepted standardized VTE risk assessment model (RAM) is specifically designed for critically ill patients. Although the ICU-venous thromboembolism (ICU-VTE) RAM was initially developed in 2020, it lacks prospective external validation.<h4>Objectives</h4>To evaluate the predictive performance of the ICU-VTE RAM in terms of VTE occurrence in mixed medical-surgical ICU patients.<h4>Methods</h4>We prospectively enrolled adult patients in the ICU. The ICU ...[more]

PMID: 39215513

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Project description:BackgroundCurrent risk assessment models (RAMs) for prediction of venous thromboembolism (VTE) risk in the outpatient cancer population have shown poor predictive value in many of the most common cancers. The Comparison of Methods for Thromboembolic Risk Assessment with Clinical Perceptions and AwareneSS in Real Life Patients-Cancer Associated Thrombosis (COMPASS-CAT) RAM was derived in this patient population and predicted patients at high risk for VTE even after initiation of chemotherapy. We sought to externally validate this RAM.Materials and methodsPatients aged ≥18 years who presented to a tertiary care center between January 1, 2014, and December 31, 2016, with invasive breast, ovarian, lung, or colorectal cancers were included. The COMPASS-CAT RAM was applied using our health system's tumor registry and variables that were identified by International Statistical Classification of Diseases and Related Health Problems-9 and -10 codes of the electronic health record and independent chart review. The primary endpoint at 6-month study follow-up was documented VTE.ResultsA total of 3,814 patients were included. Documented VTE at 6-month follow-up occurred in 5.85% of patients. Patients stratified into low/intermediate- and high-risk groups had VTE rates of 2.27% and 6.31%, respectively. The sensitivity, specificity, and negative and positive predictive value of the RAM were 95%, 12%, 97.73%, and 6.31%, respectively. Diagnostic accuracy via receiver operating characteristic curve was calculated at 0.62 of the area under the curve.ConclusionIn this large retrospective external validation study of the COMPASS-CAT RAM for VTE in patients with cancer undergoing active treatment, model discrimination was moderate and calibration was poor. The model had good negative predictive value. Further prospective validation studies-especially within 6 months of cancer diagnosis-are needed before the model can be implemented into routine clinical practice for primary thromboprophylaxis of high-VTE-risk patients with cancer with solid tumors.Implications for practiceThis study provides further guidance for researchers and clinicians in determining clinical and laboratory risk factors associated with development of venous thromboembolism among the ambulatory population of patients being treated for lung, breast, colorectal, or ovarian cancer. It validates the COMPASS-CAT risk model that was developed in this cancer population and suggests that further prospective validation of the model, with more focus on patients within 6 months of their index cancer diagnosis, would likely enhance the accuracy and usefulness of this model as a clinical prediction tool.

Project description:ObjectivesTo create a risk model for hospital-acquired venous thromboembolism in critically ill children upon admission to an ICU.DesignCase-control study.SettingICUs from eight children's hospitals throughout the United States.SubjectsCritically ill children with hospital-acquired venous thromboembolism (cases) 0-21 years old and similar children without hospital-acquired venous thromboembolism (controls) from January 2012 to December 2016. Children with a recent cardiac surgery, asymptomatic venous thromboembolism, or a venous thromboembolism diagnosed before ICU admission were excluded.InterventionsNone.Measurements and main resultsThe multi-institutional Children's Hospital-Acquired Thrombosis registry was used to identify cases and controls. Multivariable logistic regression was used to determine the association between hospital-acquired venous thromboembolism and putative risk factors present at or within 24 hours of ICU admission to develop the final model. A total of 548 hospital-acquired venous thromboembolism cases (median age, 0.8 yr; interquartile range, 0.1-10.2) and 187 controls (median age, 2.4 yr; interquartile range, 0.2-8.3) were analyzed. In the multivariable model, recent central venous catheter placement (odds ratio, 4.4; 95% CI, 2.7-7.1), immobility (odds ratio 3.6, 95% CI, 2.1-6.2), congenital heart disease (odds ratio 2.9, 95% CI, 1.7-4.7), length of hospital stay prior to ICU admission greater than or equal to 3 days (odds ratio, 2.5; 95% CI, 1.1-5.6), and history of autoimmune/inflammatory condition or current infection (odds ratio, 2.1; 95% CI, 1.2-3.4) were each independently associated with hospital-acquired venous thromboembolism. The risk model had an area under the receiver operating characteristic curve of 0.79 (95% CI, 0.73-0.84).ConclusionsUsing the multicenter Children's Hospital-Acquired Thrombosis registry, we identified five independent risk factors for hospital-acquired venous thromboembolism in critically ill children, deriving a new hospital-acquired venous thromboembolism risk assessment model. A prospective validation study is underway to define a high-risk group for risk-stratified interventional trials investigating the efficacy and safety of prophylactic anticoagulation in critically ill children.

Project description:BackgroundRisk prediction models allow clinicians to forecast which individuals are at a higher risk for developing a particular outcome. We developed and internally validated a delirium prediction model for incident delirium parameterized to patient ICU admission acuity.MethodsThis retrospective, observational, fourteen medical-surgical ICU cohort study evaluated consecutive delirium-free adults surviving hospital stay with ICU length of stay (LOS) greater than or equal to 24 hours with both an admission APACHE II score and an admission type (e.g., elective post-surgery, emergency post-surgery, non-surgical) in whom delirium was assessed using the Intensive Care Delirium Screening Checklist (ICDSC). Risk factors included in the model were readily available in electric medical records. Least absolute shrinkage and selection operator logistic (LASSO) regression was used for model development. Discrimination was determined using area under the receiver operating characteristic curve (AUC). Internal validation was performed by cross-validation. Predictive performance was determined using measures of accuracy and clinical utility was assessed by decision-curve analysis.ResultsA total of 8,878 patients were included. Delirium incidence was 49.9% (n = 4,431). The delirium prediction model was parameterized to seven patient cohorts, admission type (3 cohorts) or mean quartile APACHE II score (4 cohorts). All parameterized cohort models were well calibrated. The AUC ranged from 0.67 to 0.78 (95% confidence intervals [CI] ranged from 0.63 to 0.79). Model accuracy varied across admission types; sensitivity ranged from 53.2% to 63.9% while specificity ranged from 69.0% to 74.6%. Across mean quartile APACHE II scores, sensitivity ranged from 58.2% to 59.7% while specificity ranged from 70.1% to 73.6%. The clinical utility of the parameterized cohort prediction model to predict and prevent incident delirium was greater than preventing incident delirium by treating all or none of the patients.ConclusionsOur results support external validation of a prediction model parameterized to patient ICU admission acuity to predict a patients' risk for ICU delirium. Classification of patients' risk for ICU delirium by admission acuity may allow for efficient initiation of prevention measures based on individual risk profiles.

Project description:BackgroundRecognition of carbapenem-resistant gram-negative bacteria (CR-GNB) carriage is frequently delayed, which increases the risk of subsequent infection and transmission. Previously, we developed a scoring system to identify CR-GNB carriage upon intensive care unit (ICU) admission. Although the ICU-CARB score showed satisfactory performance, it has not been externally validated. In this study, therefore, we externally validated the ICU-CARB score.MethodsIn the previous article, we introduced a risk-scoring system that incorporated seven key variables: neurological disease, high-risk department history, length of stay ≥ 14 days, ICU history, invasive mechanical ventilation, gastrointestinal tube placement, and carbapenem usage. To externally validate the ICU-CARB score, we conducted a study involving patients admitted to the ICUs of four tertiary hospitals between January 2021 and December 2023. Patients from three hospitals were grouped into Cohort I (n = 815) and those from the fourth hospital into Cohort II (n = 1602). Model calibration, discrimination, and performance were then assessed.ResultsA total of 2417 patients were included, among which 289 (12%) carried CR-GNB upon ICU admission. Neurological disease, high-risk department history and length of stay ≥ 14 days were still 3 most important contributing factors in the scoring system. The ICU-CARB score exhibited high calibration, with an area under the receiver operating characteristic curve of 0.825 (95% confidence interval [CI], 0.778-0.873) for Cohort I and 0.823 (95% CI, 0.791-0.855) for Cohort II. The ICU-CARB score showed a highly positive association with CR-GNB carriage in both cohort I (C = 0.315; P < 0.001) and Cohort II (C = 0.381; P < 0.001).ConclusionsDespite differences in patient population characteristics, the ICU-CARB score for CR-GNB carriage upon ICU admission exhibited good discrimination in external validation, supporting its potential generalizability to other ICU settings.

Project description:ObjectiveVenous thromboembolism (VTE) is a preventable cause of hospitalization-related morbidity and mortality. VTE prevention requires accurate risk stratification. Federal agencies mandated VTE risk assessment for all hospital admissions. We have shown that the widely used Caprini (30 risk factors) and Padua (11 risk factors) VTE risk-assessment models (RAMs) have limited predictive ability for VTE when used for all general hospital admissions. Here, we test whether combining the risk factors from all 23 available VTE RAMs improves VTE risk prediction.MethodsWe analyzed data from the first hospitalizations of 1,282,014 surgical and non-surgical patients admitted to 1298 Veterans Affairs facilities nationwide between January 2016 and December 2021. We used logistic regression to predict VTE within 90 days of admission using risk factors from all 23 available VTE RAMs. Area under the receiver operating characteristic curves (AUC), sensitivity, specificity, and positive (PPV) and negative predictive values (NPV) were used to quantify the predictive power of our models. The metrics were computed at two diagnostic thresholds that maximized (1) the value of sensitivity + specificity-1; and (2) PPV and were compared using McNemar's test. The Delong-Delong test was used to compare AUCs.ResultsAfter excluding those with missing data, 1,185,633 patients (mean age, 66 years; 93% male; and 72% White) were analyzed, of whom 33,253 (2.8%) had a VTE (deep venous thrombosis [DVT], n = 19,218, 1.6%; pulmonary embolism [PE], n = 10,190, 0.9%; PE + DVT, n = 3845, 0.3%). Our composite RAM included 102 risk factors and improved prediction of VTE compared with the Caprini RAM risk factors (AUC composite model: 0.74; AUC Caprini risk-factor model: 0.63; P < .0001). When the sum of sensitivity and specificity-1 was maximized, the composite model demonstrated small improvements in sensitivity, specificity and PPV; NPV was high in both models. When PPV was maximized, the PPV of the composite model was improved but remained low. The nature of the relationship between NPV and PPV precluded any further gain in PPV by sacrificing NPV and sensitivity.ConclusionsUsing a composite of 102 risk factors from all available VTE RAMs, we improved VTE prediction in a large, national cohort of >1 million general hospital admissions. However, neither model has a sensitivity or PPV that permits it to be a reliable predictor of VTE. We demonstrate the limits of currently available VTE risk prediction tools; no available RAM is ready for widespread use in the general hospital population.

Dataset Information

External Validation of the ICU-Venous Thromboembolism Risk Assessment Model in Adult Critically Ill Patients.

Background

Objectives

Methods

Results

Conclusions

Publications

External Validation of the ICU-Venous Thromboembolism Risk Assessment Model in Adult Critically Ill Patients.

Similar Datasets

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