External validation of the IMPROVE Bleeding Risk Assessment Model in medical patients

2016 ◽  
Vol 116 (09) ◽  
pp. 530-536 ◽  
Author(s):  
David J. Rosenberg ◽  
Anne Press ◽  
Joanna Fishbein ◽  
Martin Lesser ◽  
Lauren McCullagh ◽  
...  
Keyword(s):  
Risk Assessment ◽  
High Risk ◽  
High Risk Patient ◽  
Assessment Model ◽  
Risk Assessment Model ◽  
Validation Sample ◽  
Medical Patients ◽  
Medically Ill ◽  
Major Bleed ◽  
Hospitalised Patients

SummaryThe IMPROVE Bleed Risk Assessment Model (RAM) remains the only bleed RAM in hospitalised medical patients using 11 clinical and laboratory factors. The aim of our study was to externally validate the IMPROVE Bleed RAM. A retrospective chart review was conducted between October 1, 2012 and July 31, 2014. We applied the point scoring system to compute risk scores for each patient in the validation sample. We then dichotomised the patients into those with a score <7 (low risk) vs ≥ 7 (high risk), as outlined in the original study, and compared the rates of any bleed, non-major bleed, and major bleed. Among the 12,082 subjects, there was an overall 2.6 % rate of any bleed within 14 days of admission. There was a 2.12 % rate of any bleed in those patients with a score of < 7 and a 4.68 % rate in those with a score ≥ 7 [Odds Ratio (OR) 2.3 (95 % CI=1.8–2.9), p<0.0001]. MB rates were 1.5 % in the patients with a score of < 7 and 3.2 % in the patients with a score of ≥ 7, [OR 2.2 (95 % CI=1.6–2.9), p<0.0001]. The ROC curve was 0.63 for the validation sample. This study represents the largest externally validated Bleed RAM in a hospitalised medically ill patient population. A cut-off point score of 7 or above was able to identify a high-risk patient group for MB and any bleed. The IMPROVE Bleed RAM has the potential to allow for more tailored approaches to thromboprophylaxis in medically ill hospitalised patients.Supplementary Material to this article is available online at www.thrombosis-online.com.

Impact of Venous Thromboembolism (VTE) Prophylaxis and Risk Stratification with the Caprini Model in Hospitalized Patients with Cancer

Blood ◽  
2012 ◽  
Vol 120 (21) ◽  
pp. 364-364
Author(s):  
Joshua M Ruch ◽  
Hsou M Hu ◽  
Vinita Bahl ◽  
Suman L. Sood
Keyword(s):  
Risk Assessment ◽  
High Risk ◽  
Conflicts Of Interest ◽  
Assessment Model ◽  
Risk Assessment Model ◽  
Medical Patients ◽  
Vte Prophylaxis ◽  
Patients With Cancer ◽  
Pharmacologic Prophylaxis ◽  
The Impact

Abstract Abstract 364 Introduction: VTE is a common complication in hospitalized medical patients and the role of pharmacologic anticoagulation prophylaxis is well-established. Patients with active malignancy are at higher risk for VTE during hospitalization. However, VTE prophylaxis is underutilized in these patients due to many real and perceived contraindications to prophylaxis. To aid clinicians in determining VTE risk and guide choice of prophylaxis, our institution adopted the Caprini risk assessment model (Ann Surg, 2010; 251[2]:344–50), based on clinical factors such as age, comorbidities, and recent surgery. Our primary objective was to assess adherence to recommended VTE prophylaxis in hospitalized medical patients with solid tumors, hematological malignancies, and bone marrow transplant (BMT) patients in comparison to general medical (GM) patients, and the impact of recommended prophylaxis use on VTE outcomes. Secondary objectives were to evaluate the distribution of Caprini risk scores and the utility of the Caprini risk assessment model for guiding prophylaxis in this population. Methods: Patients admitted to the hematology/oncology (HO; oncology, malignant hematology, and BMT) and GM inpatient services at the University of Michigan between July 1, 2009 to December 31, 2011 were included in the study. After IRB approval, patient information was extracted from the electronic medical record (EMR). A point-scoring method based on the Caprini risk assessment model was used to calculate VTE risk at admission. A score of 3–4 was high risk and ≥ 5 highest risk for VTE. Type of VTE prophylaxis and VTE rate were determined. Recommended prophylaxis was 5000 units TID SQ heparin, 30–40 mg SQ enoxaparin, or 2.5 mg SQ fondaparinux, ± sequential compression devices (SCDs). Pharmacological prophylaxis administration was verified in the EMR. VTE is defined as deep venous thrombosis (DVT) or pulmonary embolism (PE) occurring during hospitalization or within 90 days, confirmed by Doppler, CT or V/Q scan. Adherence was defined as the percentage of patients at high or highest risk for VTE with a length of stay ≥ 2 days who received guideline recommended prophylaxis within 2 days of admission. Patients with a contraindication to prophylaxis were excluded. A retrospective cohort study was performed. Chi-squared test was used to test differences in proportions and Cochran-Armitage test for trends. Results: 4300 patients were admitted to HO and 18,347 to GM services. Compared to GM patients (86.8%), the rate of adherence to recommended VTE prophylaxis was similar for oncology (87.6%), hematology (85.4%), and lower (45.6%) for BMT patients (p<0.0001). The overall VTE rate on HO services was 2.77%. Compared with 1.45% in GM, VTE rate was 3.02% in oncology (p=0.070), 2.01% in hematology (p=0.220), and 3.61% for BMT (p=0.001). Over half (51.3%) of VTE in HO patients occurred in patients who did not receive pharmacologic prophylaxis. In HO patients with a VTE, ordered prophylaxis included 16.0% combined pharmacological and SCD, 32.8% pharmacological alone, 32.8% SCD alone, and 18.5% none. Use of combined or pharmacologic prophylaxis alone was non-significantly increased in the non-VTE HO patients. By the Caprini risk assessment model, 33.3% of all patients on HO services were high and 62.2% highest risk, with less oncology (p=0.0001) and more BMT (p=0.0003) patients classified as high or highest risk. VTE rate in HO patients rose as Caprini risk score increased: score (n, % with VTE) 0–1 (23, 4.35%,); 2 (169, 0.59%); 3–4 (1434, 1.67%); 5–6 (1691, 2.90%); 7–8 (745, 3.76%); and 9 (238, 6.72%), p<0.0001 for trend. Conclusions: Adherence to recommended VTE prophylaxis was high in medical patients with cancer, resulting in low overall rates of VTE during and following discharge. The majority of patients with VTE did not receive recommended pharmacologic prophylaxis. Most VTE occurred in patients at highest risk (Caprini risk assessment score ≥ 5), with a trend to higher VTE rate as individual score increased. These data suggest that the individual Caprini score may provide more detailed VTE risk assessment and may help inform the need for prophylaxis despite perceived relative contraindications in this high risk cancer population. Further study is needed to understand the barriers to ordering VTE prophylaxis in this population and encourage increased prophylaxis use. Disclosures: No relevant conflicts of interest to declare.

scholarly journals Risk Factors for Venous Thromboembolism in Hospitalized Patients in the Chinese Population

2018 ◽  
Vol 13 (1) ◽  
pp. 82-89 ◽  
Author(s):  
Chunling Wang ◽  
Fuping Cui ◽  
Junqiu Li ◽  
Xiangzhi Yuan ◽  
Jia Wang ◽  
...  
Keyword(s):  
Risk Factors ◽  
Risk Assessment ◽  
Venous Thromboembolism ◽  
High Risk ◽  
Chinese Population ◽  
Blood Clot ◽  
Hospitalized Patients ◽  
Assessment Model ◽  
Risk Assessment Model ◽  
Medical Patients

AbstractVenous thromboembolism (VTE) refers to the formation of a blood clot inside veins and has a high risk of inducing medical accidents. An effective risk assessment model will help screen high risk populations and prevent the occurrence of VTE. In this study, 287 VTE cases were collected and analyzed for risk factors in a Chinese population. The risks of VTE were evaluated using the Caprini and Padua models. Our results indicated that the Caprini model was more effective in evaluating VTE risk among hospitalized patients than the Padua model. As well, the Caprini model was more relevant in VTE risk assessment among surgery patients compared with internal medicine patients, while the Padua model showed no significant differences. In our studies, the most frequent risk factors included obesity, medical patients currently at bed rest, and severe lung disease. Our studies provide clinical support on selecting the suitable risk assessment model of VTE in the Chinese population.

scholarly journals An Alternative Risk Assessment Model of Urban Waterlogging: A Case Study of Ningbo City

2021 ◽  
Vol 13 (2) ◽  
pp. 826
Author(s):  
Meiling Zhou ◽  
Xiuli Feng ◽  
Kaikai Liu ◽  
Chi Zhang ◽  
Lijian Xie ◽  
...  
Keyword(s):  
Risk Assessment ◽  
High Risk ◽  
Assessment Model ◽  
Risk Assessments ◽  
Risk Assessment Model ◽  
Risk Area ◽  
The North ◽  
Coastal Cities ◽  
Risk Areas ◽  
The City

Influenced by climate change, extreme weather events occur frequently, and bring huge impacts to urban areas, including urban waterlogging. Conducting risk assessments of urban waterlogging is a critical step to diagnose problems, improve infrastructure and achieve sustainable development facing extreme weathers. This study takes Ningbo, a typical coastal city in the Yangtze River Delta, as an example to conduct a risk assessment of urban waterlogging with high-resolution remote sensing images and high-precision digital elevation models to further analyze the spatial distribution characteristics of waterlogging risk. Results indicate that waterlogging risk in the city proper of Ningbo is mainly low risk, accounting for 36.9%. The higher-risk and medium-risk areas have the same proportions, accounting for 18.7%. They are followed by the lower-risk and high-risk areas, accounting for 15.5% and 9.6%, respectively. In terms of space, waterlogging risk in the city proper of Ningbo is high in the south and low in the north. The high-risk area is mainly located to the west of Jiangdong district and the middle of Haishu district. The low-risk area is mainly distributed in the north of Jiangbei district. These results are consistent with the historical situation of waterlogging in Ningbo, which prove the effectiveness of the risk assessment model and provide an important reference for the government to prevent and mitigate waterlogging. The optimized risk assessment model is also of importance for waterlogging risk assessments in coastal cities. Based on this model, the waterlogging risk of coastal cities can be quickly assessed, combining with local characteristics, which will help improve the city’s capability of responding to waterlogging disasters and reduce socio-economic loss.

A Validated Risk Model for Prediction of Early Readmission in Patients with Hepatic Encephalopathy

2018 ◽  
Vol 17 (5) ◽  
pp. 0-10
Author(s):  
Andrew J. Kruger ◽  
Fasika Aberra ◽  
Sylvester M. Black ◽  
Alice Hinton ◽  
James Hanje ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Hepatic Encephalopathy ◽  
High Risk ◽  
Risk Model ◽  
Cox Regression ◽  
Low Risk ◽  
Assessment Model ◽  
Risk Assessment Model ◽  
Early Readmission ◽  
The Impact

Introduction and aim. Hepatic encephalopathy (HE) is a common complication in cirrhotics and is associated with an increased healthcare burden. Our aim was to study independent predictors of 30-day readmission and develop a readmission risk model in patients with HE. Secondary aims included studying readmission rates, cost, and the impact of readmission on mortality. Material and methods. We utilized the 2013 Nationwide Readmission Database (NRD) for hospitalized patients with HE. A risk assessment model based on index hospitalization variables for predicting 30-day readmission was developed using multivariate logistic regression and validated with the 2014 NRD. Patients were stratified into Low Risk and High Risk groups. Cox regression models were fit to identify predictors of calendar-year mortality. Results. Of 24,473 cirrhosis patients hospitalized with HE, 32.4% were readmitted within 30-days. Predictors of readmission included presence of ascites (OR: 1.19; 95% CI: 1.06-1.33), receiving paracentesis (OR: 1.43; 95% CI: 1.26-1.62) and acute kidney injury (OR: 1.11; 95% CI: 1.00-1.22). Our validated model stratified patients into Low Risk and High Risk of 30-day readmissions (29% and 40%, respectively). The cost of the first readmission was higher than index admission in the 30-day readmission cohort ($14,198 vs. $10,386; p-value < 0.001). Thirty-day readmission was the strongest predictor of calendar-year mortality (HR: 4.03; 95% CI: 3.49-4.65). Conclusions. Nearly one-third of patients with HE were readmitted within 30-days, and early readmission adversely impacted healthcare utilization and calendar-year mortality. With our proposed simple risk assessment model, patients at high risk for early readmissions can be identified to potentially avert poor outcomes.

scholarly journals The Application of Gail Model to Predict the Risk of Developing Breast Cancer among Jordanian Women

2020 ◽  
Vol 2020 ◽  
pp. 1-6
Author(s):  
Hikmat Abdel-Razeq ◽  
Luna Zaru ◽  
Ahmed Badheeb ◽  
Shadi Hijjawi
Keyword(s):  
Breast Cancer ◽  
Risk Assessment ◽  
High Risk ◽  
Risk Score ◽  
Assessment Model ◽  
Risk Assessment Model ◽  
Breast Cancer Patients ◽  
Gail Model ◽  
History Of ◽  
The Mean

Background and Objectives. Breast cancer has been the most common cancer affecting women in Jordan. In the process of implementing breast cancer prevention and early detection programs, individualized risk assessment can add to the cost-effectiveness of such interventions. Gail model is a widely used tool to stratify patients into different risk categories. However, concerns about its applicability across different ethnic groups do exist. In this study, we report our experience with the application of a modified version of this model among Jordanian women. Methods. The Gail risk assessment model (RAM) was modified and used to calculate the 5-year and lifetime risk for breast cancer. Patients with known breast cancer were used to test this model. Medical records and hospital database were utilized to collect information on known risk factors. The mean calculated risk score for women tested was 0.65. This number, which corresponds to the Gail original score of 1.66, was used as a cutoff point to categorize patients as high risk. Results. A total of 1786 breast cancer patients with a mean age of 50 (range: 19–93) years were included. The modified version of the Gail RAM was applied on 1213 patients aged 35–59.9 years. The mean estimated risk for developing invasive breast cancer over the following five years was 0.54 (95% CI: 0.52, 0.56), and the lifetime risk was 3.42 (95% CI: 3.30, 3.53). Only 210 (17.3%) women had a risk score >0.65 and thus categorized as high risk. First-degree family history of breast cancer was identified among 120 (57.1%) patients in this high-risk group. Conclusions. Among a group of patients with an established diagnosis of breast cancer, a modified Gail risk assessment model would have been able to stratify only 17% into the high-risk category. The family history of breast cancer contributed the most to the risk score.

scholarly journals The HAS-RISC Score for Venous Thromboembolism (VTE) Risk Stratification in Newly Diagnosed Multiple Myeloma Patients Starting Immunomodulatory Drugs (IMID)

Blood ◽  
2018 ◽  
Vol 132 (Supplement 1) ◽  
pp. 144-144 ◽  
Author(s):  
Ang Li ◽  
Qian V. Wu ◽  
Greg Warnick ◽  
Neil A Zakai ◽  
Edward N. Libby ◽  
...  
Keyword(s):  
Risk Factors ◽  
Risk Assessment ◽  
High Risk ◽  
Research Funding ◽  
Risk Group ◽  
High Risk Group ◽  
Assessment Model ◽  
Risk Assessment Model ◽  
Newly Diagnosed ◽  
Very High

Abstract Introduction: Patients with newly diagnosed multiple myeloma (MM) have high risk of venous thromboembolism (VTE) when starting initial treatment that contains immunomodulatory drugs (IMID) such as lenalidomide or thalidomide. The National Comprehensive Cancer Network (NCCN) guideline recommends primary anticoagulant thromboprophylaxis for the high-risk patients. However, it is challenging to risk-stratify patients without a validated risk model. We have conducted a retrospective cohort study using the SEER-Medicare (Surveillance, Epidemiology, and End Results) database to derive a new VTE risk assessment model. Methods: We selected all patients 66 or older with newly diagnosed MM 2007 to 2013. Patients were included if they had a prescription of IMID within twelve months of diagnosis and complete enrollment for fee-for-service and prescription drug coverage. We ascertained baseline demographics and VTE risk factors from the current NCCN guideline using validated codes. The VTE outcome was defined as either one inpatient or two outpatient claims at least 30 days apart in combination with an anticoagulant prescription within 90 days. All patients were followed from the date of IMID initiation until first VTE occurrence or death and were censored for disenrollment from Medicare, discontinuation of IMID (after a grace period of 90 days), autologous transplantation, or the end of claims data (12/31/2014). Cause specific Cox regression models were used for time to VTE analysis. For variable selection, all risk factors with p-value <0.10 were considered candidates for inclusion in the final multivariable regression model. VTE history, recent surgery, and anticoagulant exposure were forced into the model, regardless of significance testing. Integer points were assigned according to the beta coefficients and subsequent risk groups were created. The model's discrimination was validated internally by the bias-corrected Harrell's c statistic and the 95% confidence interval was estimated from 200 bootstrap samples. Results: We identified 2397 MM patients on IMID that met the study criteria. The median time on IMID treatment was 116 days (IQR 28-279). The mean age of patients was 74, 49% were female, 80% were White, 13% were Black, 6.5% were Asian. Only 13% of patients had concurrent anticoagulant exposure (11% warfarin, 2% LMWH, 1% DOAC) with a median duration of 116 days (IQR 42-315 days). In the multivariable model built from candidate covariates, we identified history of VTE, recent surgery, cytotoxic (non-bortezomib) chemotherapy, higher dose dexamethasone, older age, and Black race, as important risk factors. Asian race and LMWH/DOAC use were associated with lower VTE risk (Table 1). We derived a risk assessment model that stratified patients into 2 prognostic risk groups (Table 1): 25% (n=581) in the very high-risk group (score 2 to 7), 75% (n=1816) in the standard-risk group (score -3 to 1). The incidence of VTE at 3 months and 6 months were 9.5% and 16.3% in the very high-risk group compared to 3.7% and 6.3% in the standard-risk group with a resulting hazard ratio of 2.73 (p<0.001) (Figure 1). The bias-corrected Harrell's c statistic for the product index was 0.63 (0.59-0.68). Conclusions: We have derived a VTE risk assessment model specifically for patients with MM starting IMID therapy. The HAS-RiSC score combines 7 clinical risk factors - History of VTE, Age 80+, Surgery within last 90 days, Race Black, race Asian, Steroid use, and Chemotherapy - into a simplified VTE risk assessment model that identifies a subgroup of patients at very high risk for VTE. External validation of this risk assessment model is currently in progress. Disclosures Garcia: Daiichi Sankyo: Research Funding; Incyte: Research Funding; Janssen: Consultancy, Research Funding; Pfizer: Consultancy; Retham Technologies LLC: Consultancy; Shingoi: Consultancy; Portola: Research Funding; Bristol Meyers Squibb: Consultancy; Boehringer Ingelheim: Consultancy. Lyman:Amgen: Other: Research support; Generex Biotechnology: Membership on an entity's Board of Directors or advisory committees; Halozyme; G1 Therapeutics; Coherus Biosciences: Consultancy.

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