Risk Stratification Model: Lower-Extremity Ultrasonography for Hospitalized Patients with Suspected Deep Vein Thrombosis

Alper, Emily C.; Ip, Ivan K.; Balthazar, Patricia; Piazza, Gregory; Goldhaber, Samuel Z.; Benson, Carol B.; Lacson, Ronilda; Khorasani, Ramin

doi:10.1007/s11606-017-4170-3

Cited by 8 publications

(3 citation statements)

References 18 publications

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“…The vast majority of mortality and readmission predictive models focus on maximizing performance of the models rather than real-world impact on care delivery. In doing so, model developers may use predictors that require data collection from additional workflows [ 5 – 6 ], use variables that may not be fully available or have to mature during the hospitalization [ 6 – 9 ], or may not integrate the model into their electronic health record (EHR) either for real-time prospective validation or for calculation to support clinical workflows [ 6 , 10 – 12 ].…”

Section: Introductionmentioning

confidence: 99%

Clinical Analytics Prediction Engine (CAPE): Development, electronic health record integration and prospective validation of hospital mortality, 180-day mortality and 30-day readmission risk prediction models

et al. 2020

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Background Numerous predictive models in the literature stratify patients by risk of mortality and readmission. Few prediction models have been developed to optimize impact while sustaining sufficient performance. Objective We aimed to derive models for hospital mortality, 180-day mortality and 30-day readmission, implement these models within our electronic health record and prospectively validate these models for use across an entire health system. Materials & methods We developed, integrated into our electronic health record and prospectively validated three predictive models using logistic regression from data collected from patients 18 to 99 years old who had an inpatient or observation admission at NorthShore University HealthSystem, a four-hospital integrated system in the United States, from January 2012 to September 2018. We analyzed the area under the receiver operating characteristic curve (AUC) for model performance. Results Models were derived and validated at three time points: retrospective, prospective at discharge, and prospective at 4 hours after presentation. AUCs of hospital mortality were 0.91, 0.89 and 0.77, respectively. AUCs for 30-day readmission were 0.71, 0.71 and 0.69, respectively. 180-day mortality models were only retrospectively validated with an AUC of 0.85.

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Section: Introductionmentioning

confidence: 99%

Clinical Analytics Prediction Engine (CAPE): Development, electronic health record integration and prospective validation of hospital mortality, 180-day mortality and 30-day readmission risk prediction models

et al. 2020

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“…Yi et al [13] adopted the Padua Prediction Score and Caprini Risk Assessment System to identify high-risk inpatients with VTE, and both had better predictive ability than independent risk factors, such as age, heart failure, varicose veins, and severe lung disease, among others. Furthermore, Khorasani et al [14] developed and validated a DVT risk strati cation model to evaluate the probability of DVT in hospitalized patients using routine clinical data including the following risk factors: previous DVT, active cancer, and hospitalization ≥6 days. All of these risk prediction models tend to provide a risk classi cation to estimate the risk of DVT according to a retrospective analysis of daily electronic medical records.…”

Section: Introductionmentioning

confidence: 99%

Development and Prospective Validation of a Novel Risk Score for Predicting the Risk of Lower Extremity Deep Vein Thrombosis Among Trauma Patients

Peng

Wang

Sun

et al. 2020

Preprint

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Background: Trauma patients have an increased risk of deep vein thrombosis (DVT). Early identification of patients with a high risk of DVT after trauma is crucial for thromboembolism prophylaxis. We aimed to develop and prospectively validate a novel risk score based on a nomogram to predict lower extremity DVT among trauma patients.Methods: Clinical data were collected from 281 trauma patients who were admitted to our trauma center within 24 h of admission from September 2016 to January 2019 to develop a novel DVT risk score. The DVT risk estimates were then calculated prospectively based on the score in a new study cohort from February 2019 to July 2020. The technique of least absolute shrinkage and selection operator (LASSO) was used to select variables for the early prediction of DVT in trauma patients. The DVT risk assessment score (DRAS) was constructed by incorporating related features based on the LASSO analysis and nomogram prediction model. Further, the trauma patients were divided into various risk groups according to the DRAS. The incidence of lower extremity DVT was compared between groups and the discrimination of the DRAS was assessed using the area under the curve (AUC). Results: Based on the LASSO method, eight variables (age, injury severity score, body mass index, D-dimer level, fibrin degradation products, prothrombin time, prealbumin level, and hemoglobin level) were included in the DRAS. A total of 166 trauma patients were enrolled in this prospective study. Increased risk of DVT after trauma was related to higher DRAS. The area under the receiver operating characteristic (ROC) curve for the DRAS was 0.890(0.840–0.939) in the validation cohort. Moreover, the discriminatory capacity of the DRAS was superior to that of each variable independently and the Modified Wells score (P<0.05). Conclusions: We developed and prospectively validated the DRAS to predict the risk of lower extremity DVT among trauma patients, which may facilitate early identification of high-risk patients.

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“…The study by Alper et al in this issue of JGIM puts forth a novel, inpatient-specific DVT risk stratification model. 2 The authors collected data on risk factors for thromboembolism-including age, recent surgery or active cancer-among 2960 inpatients who were evaluated for DVT with lower extremity ultrasound at an academic quaternarycare hospital. They then generated four clinical characteristics that were associated with ultrasound-diagnosed DVT, and assigned each characteristic a point value based on odds ratios: prior DVT (6 points), active cancer (1 point), hospital stay ≥ 6 days (1 point), and age ≥ 46 years (1 point).…”

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confidence: 99%

Refining Risk for Deep Vein Thrombosis in Hospitalized Patients

Parks

Kohlwes

2017

J GEN INTERN MED

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C linicians often assume that the test characteristics for a diagnostic test are fixed. This allows clinicians to smoothly transition from a pre-test to a post-test probability when considering diagnostic or treatment thresholds. Unfortunately, these Bfixed^test characteristics apply best to the derivation group studied and may not always be generalizable to other populations. Applying a diagnostic test outside a derivation population may lead to Bspectrum bias,^or the false assumption that a diagnostic test will work well in any population studied. 1An often-taught example of spectrum bias is the use of the outpatient-derived Wells score for the detection of deep venous thrombosis (DVT) in the inpatient setting. Compared to outpatients, hospitalized patients have a higher prevalence of pro-thrombotic characteristics such as immobility, comorbid illness and recent surgery, which makes the Wells score test characteristics less reliable in the inpatient setting.The potential for spectrum bias leaves clinicians with few evidence-based options for assessing pre-test probability for DVT in hospitalized patients. The study by Alper et al. in this issue of JGIM puts forth a novel, inpatient-specific DVT risk stratification model. 2 The authors collected data on risk factors for thromboembolism-including age, recent surgery or active cancer-among 2960 inpatients who were evaluated for DVT with lower extremity ultrasound at an academic quaternarycare hospital. They then generated four clinical characteristics that were associated with ultrasound-diagnosed DVT, and assigned each characteristic a point value based on odds ratios: prior DVT (6 points), active cancer (1 point), hospital stay ≥ 6 days (1 point), and age ≥ 46 years (1 point). These points were added to generate a CEBI [Center for Evidence-Based Imaging] score that categorized patients as low-risk (0 points) or high-risk (≥ 1 points) for DVT. The CEBI score demonstrated high sensitivity (98.1%) and negative predictive value (97.1) and adequate AUC (0.73) for discerning which hospitalized patients were at risk for proximal DVT.The CEBI scoring system improves substantially on the Wells Score, which performs poorly in inpatients. As the authors rightly point out, its strength is in accurately identifying low-risk patients who do not require further imaging, potentially reducing overuse. Conversely, another key point to emerge from this data is that our threshold for obtaining an ultrasound should be quite low. Just one point on the CEBI score-for example, any patient aged 46 or older-defines a patient as high-risk. More than 90% of patients in this study met that criterion, and over one in five of these high-risk patients had a DVT. Ultrasound is relatively inexpensive, non-invasive and widely available, and does not expose patients to radiation. Moreover, venous thromboembolism is associated with significant morbidity and mortality but can be safely treated in most patients. With all this in mind, we can now feel more confident in deferring ultrasound in truly l...

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Risk Stratification Model: Lower-Extremity Ultrasonography for Hospitalized Patients with Suspected Deep Vein Thrombosis

Abstract: In hospitalized adults, specific factors can help clinicians predict risk of DVT, identifying those with low pre-test probability, in whom ultrasonography can be safely avoided.

Cited by 8 publications

References 18 publications

Clinical Analytics Prediction Engine (CAPE): Development, electronic health record integration and prospective validation of hospital mortality, 180-day mortality and 30-day readmission risk prediction models

Clinical Analytics Prediction Engine (CAPE): Development, electronic health record integration and prospective validation of hospital mortality, 180-day mortality and 30-day readmission risk prediction models

Development and Prospective Validation of a Novel Risk Score for Predicting the Risk of Lower Extremity Deep Vein Thrombosis Among Trauma Patients

Refining Risk for Deep Vein Thrombosis in Hospitalized Patients

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