Clinical Risk Assessment Models Research Articles

Circulating tumor DNA predicts venous thromboembolism in patients with cancers

BackgroundDespite rapid advances in liquid biopsy for circulating tumor DNA (ctDNA), its prognostic value for venous thromboembolism (VTE) in patients with cancer is underexplored, particularly in underserved and minoritized populations. Objectives▪. MethodsWe analyzed data from 1038 cancer patients who underwent ctDNA measurement for oncologic care at a large safety-net hospital system in the United States. We investigated the association between ctDNA and VTE after adjusting for cancer type, stage, treatment, and time from initial diagnosis using Fine–Gray models. We further assessed the discrimination of the genetic, clinical-only, and combined models using the area under the time-dependent receiver operating characteristic curve (AUC). ResultsThe presence of pathogenic ctDNA was independently associated with VTE after adjusting for clinical variables. Independent of tumor type, the number of pathogenic ctDNA mutations was predictive of future VTE risk (adjusted subdistribution hazard ratios of 2.75, 1.94, and 1.38 for ≥3, 2, and 1 pathogenic mutation, respectively, compared with none; P < .0001). The association was primarily driven by mutations in KRAS, PTEN, CDKN2A, NF1, and EGFR genes. Compared with the clinical-only model (AUC, 0.71; 95% CI, 0.64-0.76), the combined clinical and ctDNA model had a numerically higher time-dependent AUC (AUC, 0.74; 95% CI, 0.67-0.80). ConclusionctDNA testing may serve as an adjunctive tool to clinical risk assessment models in cancer patients to improve personalized VTE risk assessment and management.

Abstract PO4-19-02: Genetic risk estimation in breast cancer and assessing health disparities

Abstract Background: Breast Cancer (BC) incidence and the distribution of BC risk factors vary between racial and ethnic groups within the United States. It has been demonstrated that providing education on individualized breast cancer risk will improve patient uptake to recommended BC screening and prevention strategies. Most clinical risk assessment tools, including the validated IBIS model were derived from data using non-Hispanic white women. Consortia have also now identified over 300 common genetic susceptibility loci for BC, summarized by the polygenic risk score (PRS). When combined with clinical risk assessment tools, such as the IBIS or BCRAT models, the PRS can further refine risk estimation for patients. To date, most studies on the use of PRS have been done in non-Hispanic white women. While these PRS still perform well in racial minorities, there has been a recognized need to improve racial and ethnic diversity in genomic research cohorts. Herein, we aim to combine clinical risk assessment models that are already used in routine clinical practice with information derived from PRS testing in women of racial minorities to determine if this can improve risk estimation, patient understanding, and uptake to recommended breast cancer screening and prevention strategies. Design: This is a minimal risk prospective study with a single arm incorporating the PRS into a standard breast cancer risk reduction consultation, followed by annual surveys over 10 years to determine if and how the information provided by the PRS influenced patient decisions regarding recommended BC screening and prevention. Patients will have IBIS and BCRAT risks calculated at baseline visit. A survey of patient perceptions/understanding of risk, intention to undergo screening, and use of preventive medicine is completed after baseline visit prior to receiving the PRS results. Blood sample is obtained at baseline and DNA samples are analyzed for approximately 300 SNPs on a custom-designed, targeted SNP panel from ThermoFisher Scientific by the Mayo Clinic Genomics Laboratory. This PRS result is then combined with the clinical risk assessments (IBIS and BCRAT scores) using the R package Individualized Coherent Absolute Risk Estimators (iCare) tool to provide a new estimate of breast cancer risk for 5year, 10year, and lifetime risk. Patients are seen for follow-up to review results and then complete a second survey to assess their understanding of the results, BC risk, and how the PRS impacted their decision to undergo screening/prevention strategies. Eligibility Criteria: Women who self-identify as African American/Black or Hispanic/Latinx between 30-75 years old with any of the following: 1.)IBIS score of ≥5% for the 10 year risk OR BCRAT score of ≥ 3 % for the 5 year risk. 2.) History of biopsy proven atypical hyperplasia 3.) History of biopsy proven lobular carcinoma in situ. Specific Aims: Aim 1: The primary aim of this study is to explore if the addition of PRS to the BCRAT and IBIS score will improve intentions to undergo recommended breast cancer screening strategies such as mammography, MRI, or molecular breast imaging in women of underserved racial minorities Aim 2: To explore if the addition of the PRS to the BCRAT and IBIS risk score will aid women of racial minorities in deciding whether to take preventative endocrine therapy. Aim 3: To understand how individualized risk assessment and information on PRS may alter perceived risk of breast cancer. Statistical Methods Analysis will be mostly descriptive. Continuous variables will be summarized as mean (standard deviation) or median (range) and categorical variables will be reported as frequency (percentage). Kaplan-Meier method will be used to estimate the long-term cumulative risk of cancer. Current Accrual: 3. Target Accrual: 50 Citation Format: Lauren Cornell, Sandhya Pruthi, Linda Hasadsri, Sarah McLaughlin. Genetic risk estimation in breast cancer and assessing health disparities [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO4-19-02.

Unveiling a novel clinical risk assessment model for identifying non-suicidal self-injury risks in depressed Chinese adolescents amidst the COVID-19 pandemic: insights from low self-esteem, internet use, and sleep disturbance.

Non-suicidal self-injury (NSSI) is a highly prevalent behavioral problem among depression adolescent patients that can result in numerous adverse outcomes. This study endeavors to bridge this knowledge gap by creating a comprehensive model that incorporates multiple aspects of NSSI to accurately evaluate its risk in adolescents with depression, thereby enhancing our ability to prevent and address this challenging issue. Using a cross-sectional design, we recruited 302 adolescents with depressive disorders who visited or were hospitalized at Shandong Mental Health Center from December 2021 to June 2022. The participants completed several self-report questionnaires, including the Chinese version of the Internet Addiction Test, the Pittsburgh Sleep Quality Index questionnaire, the Defeat Scale, the Social Avoidance and Distress Scale and the Children's Depression Inventory. Logistic regression analysis was performed to identify the diagnostic factors, which were further used to establish clinical risk assessment models. A receiver operating characteristic curve (ROC) to identify the best model. An external validating team was introduced to verify the assessing efficiency. Based on a logistic regression analysis, three variables have been identified as significant risk factors. Specifically, adolescents with depression who experience low self-esteem, internet use, or suffer from sleep disturbance face an increased risk of NSSI. An integrated risk index for NSSI exhibits excellent accuracy in identifying depressed adolescents at risk of NSSI (area under the curve = 0.86, sensitivity = 0.88, specificity = 0.69). In the validation cohort, the identification performance remains strong (area under the curve = 0.84, sensitivity = 0.72, specificity = 0.81). This study highlighted the role of self-esteem, internet use and sleep disturbance in the development of NSSI. The risk index diagnosing NSSI onset may help to guide the design and application of novel interventions to minimize this risky behavior in future depressed adolescents.

Risk of repeat self-harm among individuals presenting to healthcare services: development and validation of a clinical risk assessment model (OxSET)

BackgroundA self-harm episode is a major risk factor for repeat self-harm. Existing tools to assess and predict repeat self-harm have major methodological limitations, and few are externally validated.ObjectiveTo develop and...

Screening of Associated Factors for Erectile Dysfunction after Radical Prostatectomy and Construction of a Clinical Risk Assessment Model: A Retrospective Study.

In this article, the associated factors for erectile dysfunction (ED) after radical prostatectomy (RP) were explored, and a clinical risk assessment model was constructed. A total of 155 patients who underwent RP in People's Hospital of Hunan Province from November 2020, to November 2021, were selected as the study group. In accordance with the results of International Index of Erectile Function (IIEF-5) at 6 months after surgery, 88 patients were included in the ED group (IIEF-5 <22), and 67 patients were included in the non-ED group (IIEF-5 ≥22). Univariate and multivariate logistic regression analyses were conducted to screen the risk factors for ED after RP, and a risk model was constructed on this basis. In addition, 43 patients with ED after RP and 41 patients with non-ED after RP from January 2022, to January 2023, were included in the test group to evaluate the predictive efficacy of the clinical risk assessment model on the basis of the receiver operating characteristic curve. The study group had a lower postoperative IIEF-5 score than before surgery (p < 0.001). The incidence of ED after RP in the study group was 56.77% (88/155). Multivariate analysis showed that advanced age (odds ratio (OR) = 1.155), large prostate volume (OR = 1.077), smoking (OR = 5.676), drinking (OR = 3.495), hypertension (OR = 8.079), diabetes (OR = 6.082), low preoperative serum testosterone (T) level (OR = 0.684) and high preoperative serum endothelin-1 (ET-1) level (OR = 1.192) were risk factors for ED after RP (p < 0.05). A risk model was constructed as follows: Z = 0.144 × (age) + 0.074 × (prostate volume) + 1.736 × (smoking) + 1.251 × (drinking) + 2.089 × (hypertension) + 1.805 × (diabetes) - 0.380 × (preoperative serum T) + 0.175 × (preoperative serum ET-1). The area under curve (AUC), sensitivity, specificity and 95% CI of this model were 0.906, 97.70%, 73.20%, and 0.848-0.964, respectively (p < 0.001). The clinical risk assessment model constructed on the basis of the above factors provides some references for the scientific prevention and treatment of ED after RP.

Which Procedures Contribute Most to the System-Wide Burden of Postoperative Venous Thromboembolism?

While clinical risk assessment models examine patient-level characteristics that portend morbidity, there is a paucity of literature exploring which procedures contribute most to the system-wide burden of venous thromboembolism (VTE). We aimed to identify highly contributory procedures as potential targets for quality improvement. All patients in the 2020 National Surgical Quality Improvement Program (NSQIP) Public User File were included. Current Procedural Terminology (CPT) codes were analyzed individually and grouped by National Healthcare Safety Network groupings. We counted prevalence of VTE and calculated VTE rate for each CPT and for each grouping. Of 902,968 included patients, 7501 (.83%) sustained postoperative VTE. Of 2748 unique CPT codes, VTE occurred for 762 (28%). Twenty procedure codes (.7%) contributed 39% of the total VTE. VTE rates of these procedures ranged from high-volume procedures with low VTE rates such as laparoscopic cholecystectomy (.25%) and laparoscopic hysterectomy (.32%) to lower volume procedures with high VTE rate such as Hartmann's procedure (4.32%), Whipple procedure (3.85%), and distal pancreatectomy (3.82%). The CPT grouping with the most VTE was colon surgeries (1275/7501). A small number of procedures contributes to the system-wide burden of VTE. High-risk procedures are important targets for standardized prophylaxis protocols. For low-risk procedures, careful attention should be paid to patient-specific factors that may increase VTE risk such as obesity, cancer, or limited mobility, as many common procedures contribute greatly to the systemic burden of VTE. Overall, surveillance can perhaps be targeted on a smaller number of procedures, allowing for more efficient use of quality improvement resources.

Derivation and Validation of a Clinical Risk Assessment Model for Cancer-Associated Thrombosis in Two Unique US Health Care Systems.

Venous thromboembolism (VTE), especially pulmonary embolism (PE) and lower extremity deep vein thrombosis (LE-DVT), is a serious and potentially preventable complication for patients with cancer undergoing systemic therapy. Using retrospective data from patients diagnosed with incident cancer from 2011-2020, we derived a parsimonious risk assessment model (RAM) using least absolute shrinkage and selection operator regression from the Harris Health System (HHS, n = 9,769) and externally validated it using the Veterans Affairs (VA) health care system (n = 79,517). Bootstrapped c statistics and calibration curves were used to assess external model discrimination and fit. Dichotomized risk strata using integer scores were created and compared against the Khorana score (KS). Incident VTE and PE/LE-DVT at 6 months occurred in 590 (6.2%) and 437 (4.6%) patients in HHS and 4,027 (5.1%) and 3,331 (4.2%) patients in the VA health care system. Assessed at the time of systemic therapy initiation, the new RAM included components of the KS with the modified cancer subtype, cancer staging, systemic therapy class, history of VTE, history of paralysis/immobility, recent hospitalization, and Asian/Pacific Islander race. The c statistic was 0.71 in HHS and 0.68 in the VA health care system (compared with 0.65 and 0.60, respectively, for KS). Furthermore, the new RAM appropriately reclassified 28% of patients and increased the proportion of VTEs in the high-risk group from 37% to 68% in the validation data set. The novel RAM stratified patients with cancer into a high-risk group with 8%-10% cumulative incidence of VTE and 7% PE/LE-DVT at 6 months (v 3% and 2%, respectively, in the low-risk group). The model had improved performance over the original KS and doubled the number of VTE events in the high-risk stratum. We encourage additional external validation from prospective studies.[Media: see text].

Derivation and External Validation of Clinical Risk Assessment Model for Cancer Associated Thrombosis in Two Unique U.S. Healthcare Systems

Introduction: Venous thromboembolism (VTE), defined as pulmonary embolism (PE) and lower extremity deep vein thrombosis (LE-DVT), is a potentially preventable complication for cancer patients undergoing systemic therapy. The Khorana score (KS) is the most used risk assessment model (RAM) to predict venous thromboembolism (VTE) in ambulatory cancer patients. Potential limitations of the score include modest discrimination and small proportion of people developing VTE in the high-risk subgroup. We aimed to derive and externally validate a more precise yet equally RAM for VTE through two unique healthcare systems. Methods: We derived a parsimonious RAM using the cancer registry-linked electronic health records from the Harris Health System (HHS, N=9,916) and validated it using the Veterans Affairs national healthcare system (VA, N=78,892) from 2011-2020. Patients were included if they had their first-ever newly diagnosed cancer and received first-line systemic therapy within 1 year of diagnosis. Index date was the time systemic therapy and all covariates were extracted on or prior to index date. Incident VTE and PE/LE-DVT were defined using our computable phenotype algorithm (PMID: 35647478). When tested against the gold standard of chart abstractor review in a random sampling of 400 patients at each site, the algorithm had a positive predictive value (PPV) of 98% and sensitivity (Sn) of 96% at HHS; and a PPV of 90% and weighted Sn of 82% at VA. For RAM derivation, 34 candidate predictors of VTE were initially chosen by clinicians and 11 final covariates were selected by the LASSO regression via cross-validated optimal lambda.1se. The original KS components were forced into the final model. For external validation, bootstrapped time-dependent c statistics and calibration curves for 6-month VTE and PE/LE-DVT risk were used to assess model discrimination and fit. Dichotomized risk strata using integer scores were created and compared against the KS. Complete data analysis was performed without imputation. Results: The median follow-up for continuous VTE assessment were 11.5 months (IQR 5.7-21.1) for HHS and 14.9 months (IQR 5.9-31.9) for VA. At 6 months, there were 656 (6.6%) VTE (including 448 (4.5%) PE/LE-DVT) and 1,094 (11.0%) deaths in HHS, and 3,209 (4.1%) VTE (including 2,474 (3.1%) PE/LE-DVT) and 13,792 (17.5%) deaths in VA. Due to differences in healthcare systems, the two cohorts had very different baseline characteristics in demographic, cancer, systemic therapy, and co-morbidity. Patients from HHS were younger (median age 55 vs. 67), more diverse by sex (female 56.6% vs. 4.9%) and race/ethnicity (15% vs. 71% White, 52% vs. 5% Hispanic, 27% vs. 5% Black, 6% vs. 2% Asian/Pacific Islander [API]), compared to the VA. When assessed at the time of systemic therapy initiation, the new RAM in derivation cohort included components of the KS with modified cancer subtype, cancer staging, systemic therapy class, history of VTE, history of paralysis/immobility, recent hospitalization, and API race (Table 1). Due to lower incidence of VTE in VA, re-calibration-in-the-large (intercept only) optimized the overall fit of the model in external validation. The c statistic for the VTE outcome was 0.70 in HHS and 0.66 in VA (compared to 0.64 and 0.59, respectively, for KS). Furthermore, the new RAM appropriately reclassified 27% of patients from the previous risk groups and increased the proportion of VTEs in the high-risk group from 36.7% to 68.0% in the validation dataset (Table 2). Conclusions: Using easily extractable data from two independent healthcare systems, we successfully derived and externally validated a RAM for VTE in a diverse population by sex and race with all cancer types (including hematologic malignancy) and receiving all types of modern systemic therapies (including immune checkpoint inhibitor, targeted therapy, endocrine therapy) in addition to chemotherapy. It stratified approximately 50% of patients with all cancer types receiving all types of modern systemic therapy into a high-risk group with a 6-month VTE risk of 7-11%, and the other half into a low-risk group with a corresponding risk of 3%. The RAM had improved performance over the original KS and doubled the number of VTE events in the high-risk stratum. The incorporation of this modified RAM can lead to improved risk stratification and personalized decision-making for pharmacologic thromboprophylaxis. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

Advances in the Prediction and Risk Assessment of Lung Cancer-Associated Venous Thromboembolism.

According to the most recent data from the National Cancer Center, venous thromboembolism (VTE) has unsurprisingly become one of the most common complications in lung cancer. VTE not only interferes with the equilibrium of the clotting system but it also affects tumor progression and prognosis. For the identification of high-risk patients, many clinical risk assessment models have been developed and validated based on the risk factors found in previous studies. In this review, we will summarize advances in prediction and risk assessment of VTE, with a focus on early diagnosis and therapy, reduction of mortality, and the burden of medical costs in lung cancer patients.

PO-87 Comparison of different clinical risk assessment models (RAMs) in ambulatory cancer patients receiving chemotherapy from the Oncothromb-02 study

PO-87 Comparison of different clinical risk assessment models (RAMs) in ambulatory cancer patients receiving chemotherapy from the Oncothromb-02 study

RISK OF CARDIOVASCULAR DISEASES IN PEOPLE WITH TYPE 1 DIABETES MELLITUS

Objective: People with diabetes mellitus (DM) are at several times higher risk for cardiovascular diseases (CVD). The o objective is to assess the CVD risk in patients with long-lasting Type 1 DM (T1DM) and to compare several risk tools performance. Design and method: A total of T1DM 95 persons, 50 (52.6%) men, at an average age of 41.34 ± 10.3 years were studied. The average duration of T1DM was 25.22 ± 8.7 years, and the median HbA1c 8.15%. All participants completed a demographic questionnaire. Basic anthropometric and hemodynamic parameters were measured applying standard methods. Venous blood was drawn after 12 hours fast. The global CVD risk was assessed by two methods: Steno-2 formula and the recent recommendations of the European Society of Cardiology (ESC). The coronary calcium score (CACS) was evaluated on computer tomography.a Results: Steno-2 method showed gender CVD risk difference - 69.4% of the men were at moderate or high risk while 57.8% of the women were at low risk (p = 0.029). ESC guidelines found that all patients are either at high, 39% and 31% for men and women or at very high risk - 61% and 69% resp. (p = 0.5). The two methods for risk characterization do not correlate well - 44% of people with T1DM who have high and very high risk (ESC) are at low risk according to Steno-2, Cohen's kappa for agreement of the two methods is 0.016, p = 0.78. When CACS is added, a large proportion of patients who are at moderate or high risk by Steno-2 method (20 and 21.3% in total) had CACS-0. Conversely, 10 (10.6%) of all T1DM participants had low Steno-2 risk and CACS &gt; 0, Kendall's tau coefficient b = 0.4, p &lt; 0.0001. The agreement between Steno-2 and CACS is mild (Cronbach's a = 0.501; Intraclass Correlation Coefficient, ICC = 0.44; p = 0.003). The addition of CACS to the clinical risk assessment model improves the diagnostic accuracy, with area under the curve (AUC) 0.72, 95% CI 0.61–0.84, p = 0.001. Conclusions: The T1DM CVD risk assessment methods do not agree well. The addition of CAC score further reclassifies the patients which is important for the timely CVD prevention.

Clinical Risk Assessment Model for Predicting Moderate to Severe Obstructive Sleep Apnea in Adult Thai Patients

Objective: To develop a clinical assessment model for predicting moderate to severe obstructive sleep apnea (OSA). Materials and Methods: All patients suspected of having OSA and undergoing the laboratory polysomnography (PSG) were enrolled. The clinical data, associated factors and PSG finding were reviewed. Significant risk factors for the model were chosen using multivariate logistic regression analysis. The predictive parameters of sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy were calculated. Results: Of the 929 patients, 580 (62.4%) had moderate to severe OSA. Patient ages ranged between 18 and 85 years, with the majority between 30 and 60 years (71.5%). Males were significantly prominent in the moderate to severe OSA group (76.4%). Forty-three percent had a body mass index (BMI) greater than 30 and 52% had a neck circumference (NC) greater than 40. Multivariate analysis showed the male gender, a BMI of 30 or greater, a NC of 40 or greater, a waist to height ratio (WHtR) of 0.5 or greater, the presence of hypertension (HT), and observed apnea were significant factors correlated with moderate to severe OSA. The clinical assessment model was created by using their estimated coefficients. The optimal cutoff points for predicting apnea-hypopnea index (AHI) of 15 or greater was 2, with sensitivity of 85.5% and specificity of 49.6%. Conclusion: The present clinical risk assessment model appears to be a useful practical tool for identifying patients at risk for moderate to severe OSA, with acceptable predictive performance. Keywords: Obstructive sleep apnea, Clinical assessment model, Predicting, Sleep apnea

Predicting postdischarge hospital‐associated venous thromboembolism among medical patients using a validated mortality risk score derived from common biomarkers

BackgroundDischarged medical patients are at risk for venous thromboembolism (VTE). It is difficult to identify which discharged patients would benefit from extended duration thromboprophylaxis. The Intermountain Risk Score is a prediction score derived from discrete components of the complete blood cell count and basic metabolic panel and is highly predictive of 1‐year mortality. We sought to ascertain if the Intermountain Risk Score might also be predictive of 90‐day postdischarge hospital‐associated VTE (HA‐VTE). MethodsWe applied the Intermountain Risk Score to 60 064 medical patients who survived 90 days after discharge and report predictiveness for HA‐VTE. Area under the receiver operating curve analyses were performed. We then assessed whether the Intermountain Risk Score improved prediction of 2 existing VTE risk assessment models. ResultsThe Intermountain Risk Score poorly predicted HA‐VTE (area under the curve = 0.58; 95% confidence interval [CI], 0.56‐0.60). Each clinical risk assessment model was superior to the Intermountain Risk Score (UTAH area under the curve, 0.63; Kucher area under the curve, 0.62; Intermountain Risk Score area under the curve, 0.58; P < .001 for each comparison). Adding the Intermountain Risk Score to these scores did not substantially improve the performance of either risk assessment model (UTAH + Intermountain Risk Score, 0.65; Kucher + Intermountain Risk Score, 0.64). ConclusionThe Intermountain Risk Score demonstrated poor predictiveness for HA‐VTE when compared to existing risk assessment models. Adding the Intermountain Risk Score to existing risk assessment models did not improve upon either risk assessment model alone to justify the added complexity.

Clinical risk assessment model to predict venous thromboembolism risk after immobilization for lower-limb trauma

BackgroundPatients with lower-limb trauma requiring immobilization have an increased risk of venous thromboembolism (VTE). While thromboprophylaxis for all patients seems not effective, targeted thromboprophylaxis in high risk patients may be an appropriate alternative. Therefore, we aimed to develop and validate a risk assessment model for VTE risk: the TRiP(cast) score (Thrombosis Risk Prediction following cast immobilization). MethodsIn this prediction model study, for development, data were used from the MEGA study (case-control study into the etiology of VTE) and for validation, data from the POT-CAST trial (randomized trial on the effectiveness of thromboprophylaxis following cast immobilization) were used. Model discrimination was calculated by estimating the Area Under the Curve (AUC). For model calibration, observed and predicted risks were assessed. FindingsThe TRiP(cast) score includes 14 items; one item for trauma severity (or type), one for type of immobilization and 12 items related to patients’ characteristics. Validation analyses showed an AUC of 0.74 (95%CI 0.61–0.87) in the complete dataset (n = 1250) and 0.72 (95%CI 0.60–0.84) in the imputed data set (n = 1435). The calibration plot shows the degree of agreement between the observed and predicted risks (intercept 0.0016 and slope 0.933). Using a cut-off score of 7 points in the POT-CAST trial (incidence 1.6%), the sensitivity, specificity, positive and negative predictive values were 76.1%, 51.2%, 2.5%, and 99.2%, respectively. InterpretationThe TRiP(cast) score provides a helpful tool in daily clinical practice to accurately stratify patients in high versus low-risk categories in order to guide thromboprophylaxis prescribing. To accommodate implementation in clinical practice a mobile phone application has been developed. FundingZonMW VIMP grant:17110200011.

A Prospective Observational Cohort of Clinical Outcomes in Medical Inpatients prescribed Pharmacological Thromboprophylaxis Using Different Clinical Risk Assessment Models(COMPT RAMs)

The Caprini and Padua venous thromboembolism (VTE) risk assessment models (RAMs) are used to assess VTE risk in surgical and in medical patients respectively. This study aims to compare the proportion of medical inpatients eligible for VTE prophylaxis using the hospital Caprini-based RAM to using the Caprini and Padua RAMs and to assess the associated clinical outcomes. In a prospective observational study, we assessed 297 adult medical inpatients for whom VTE thromboprophylaxis was initiated according to the hospital Caprini-based RAM, referred to as the Lebanese American University Medical Center RAM (LAUMC-RAM). The Padua, Caprini and IMPROVE bleeding risk scores were also assessed for all patients. Bleeding and thromboembolism were evaluated at 14 and 30 days post VTE risk assessment. Pharmacologic thromboprophylaxis was warranted in 97.6%, 99.7%, and 52.9% of patients using the Caprini-based, Caprini, and Padua RAMs respectively. The Caprini-based and Caprini RAMs were highly correlated (r = 0.873 p < 0.001) and were significantly less correlated with the Padua RAM. Major and overall bleeding occurred in 1.4% and 9.2% respectively. VTE was reported in 0.4% with no VTE related mortality. In hospitalized medical patients, the Caprini-based RAM can accurately distinguish low and high VTE risk without resulting in increased risk of bleeding.

Towards a more precise and individualized assessment of breast cancer risk.

Many clinically based models are available for breast cancer risk assessment; however, these models are not particularly useful at the individual level, despite being designed with that intent. There is, therefore, a significant need for improved, precise individualized risk assessment. In this Research Perspective, we highlight commonly used clinical risk assessment models and recent scientific advances to individualize risk assessment using precision biomarkers. Genome-wide association studies have identified >100 single nucleotide polymorphisms (SNPs) associated with breast cancer risk, and polygenic risk scores (PRS) have been developed by several groups using this information. The ability of a PRS to improve risk assessment is promising; however, validation in both genetically and ethnically diverse populations is needed. Additionally, novel classes of biomarkers, such as microRNAs, may capture clinically relevant information based on epigenetic regulation of gene expression. Our group has recently identified a circulating-microRNA signature predictive of long-term breast cancer in a prospective cohort of high-risk women. While progress has been made, the importance of accurate risk assessment cannot be understated. Precision risk assessment will identify those women at greatest risk of developing breast cancer, thus avoiding overtreatment of women at average risk and identifying the most appropriate candidates for chemoprevention or surgical prevention.

Prospective Assessment of Clinical Risk Factors and Biomarkers of Hypercoagulability for the Identification of Patients with Lung Adenocarcinoma at Risk for Cancer-Associated Thrombosis: The Observational ROADMAP-CAT Study.

The aim of this prospective study was to identify the most clinically relevant hypercoagulability biomarkers in lung adenocarcinoma patients for elaboration of an improved risk assessment model (RAM) for venous thromboembolism (VTE). One hundred fifty ambulatory patients with lung adenocarcinoma were prospectively enrolled. Thrombin generation, procoagulant phospholipid-dependent clotting time (Procoag-PPL), tissue factor activity (TFa), factor VIIa (FVIIa), factor V (FV), antithrombin, D-Dimers, P-selectin, and heparanase levels were assessed in platelet-poor plasma at inclusion (baseline) and at the end of the third chemotherapy cycle (third chemotherapy). Cox regression analysis was used to identify independent VTE predictors. At baseline, patients had significantly attenuated thrombin generation, shorter Procoag-PPL, higher levels of TFa, D-Dimers, and heparanase, and lower levels of FVIIa and P-selectin, compared with controls. A significant increase in Procoag-PPL, FV, and FVIIa and a decrease of P-selectin levels were observed between baseline and third chemotherapy. Hospitalization within the last 3 months prior to assessment, time since cancer diagnosis less than 6 months, mean rate index (MRI) of thrombin generation, and Procoag-PPL were independently associated with symptomatic VTE. Accordingly, a prediction model including Procoag-PPL and MRI showed significant discriminating capacity (area under the curve: 0.84). Ambulatory patients with lung adenocarcinoma may display pronounced blood hypercoagulability due to decreased Procoag-PPL, increased endothelial cell activation, and increased degradation of fibrin. Incorporation of Procoag-PPL and MRI of thrombin generation may improve the accuracy of a VTE-RAM in the above setting. The prospective ROADMAP-CAT study identified two biomarkers of hypercoagulability, the procoagulant phospholipid-dependent clotting time (Procoag-PPL) and the mean rate index (MRI) of the propagation phase of thrombin generation assessed with the Calibrated Automated Thrombinoscope, as being clinically relevant for the classification of ambulatory patients with lung adenocarcinoma receiving a maximum of one cycle of chemotherapy into high and intermediate/low risk for venous thromboembolism. Measurement of Procoag-PPL and MRI within 1 month after the administration of the first chemotherapy cycle provides significant accuracy of the assessment. Association of the Procoag-PPL and MRI with the clinical risk assessment model for cancer-associated thrombosis in ambulatory patients with solid tumors (COMPASS-CAT RAM) further improved its accuracy.

Comparative Performance of Clinical Risk Assessment Models for Hospital-Acquired Venous Thromboembolism in Medical Patients

Comparative Performance of Clinical Risk Assessment Models for Hospital-Acquired Venous Thromboembolism in Medical Patients

Characterization of Venous Thromboembolism Risk in Medical Inpatients Using Different Clinical Risk Assessment Models.

Symptomatic venous thromboembolism (VTE) occurs in about 1% of patients within 3 months after admission to a medical unit. Recent evidence for thromboprophylaxis in an unselected medical inpatient population has suggested only a modest net benefit. Consequently, guidelines recommend careful risk stratification to guide thromboprophylaxis. To compare candidacy for thromboprophylaxis according to 4 risk stratification models: a regional preprinted order (PPO) set used in the study institution, the Padua Prediction Score, and the IMPROVE predictive and associative risk assessment models. A retrospective review of health records was undertaken for patients with no contraindication to pharmacologic thromboprophylaxis who were admitted to the internal medicine service of a teaching hospital between April and July 2013. Of the 298 patients in the study cohort, 238 (80.0%) received pharmacologic thromboprophylaxis on admission, ordered according to the regional PPO. However, according to the Padua and the IMPROVE predictive risk assessment models, only 64 (21.5%) and 21 (7.0%) of the patients, respectively, were eligible for thromboprophylaxis at the time of admission. On the basis of risk factors identified during the subsequent hospital stay, 54 (18.1%) of the patients were eligible for thromboprophylaxis according to the IMPROVE associative model. Chance-corrected agreement between the PPO and the published risk assessment models was generally poor, with kappa coefficients of 0.109 for the PPO compared with the Padua Prediction Score and 0.013 for the PPO compared with the IMPROVE predictive model. These data suggest that quantitative models such as the Padua Prediction Score and the IMPROVE models identify more patients at low risk of venous thromboembolism than do in-hospital qualitative risk assessment models. Adoption of these guideline-based risk assessment models for predicting thromboembolic risk in medical inpatients could reduce the use of pharmacologic thromboprophylaxis from 80% to as low as 7%. Further external prognostic validation of risk assessment models and impact analysis studies may show improvements in safety and resource utilization.

Clinical risk assessment model for pediatric obstructive sleep apnea.

To develop a clinical risk prediction model that identifies children with obstructive sleep apnea (OSA) in a clinical setting by examining the symptoms, physical status, and OSA-18 questionnaire results. Single institutional, cross-sectional study. Children aged 2 to 18 years with symptoms of OSA were enrolled. Pediatric OSA was diagnosed through full-night polysomnography. Clinical data, namely demographics, symptoms, OSA-18 survey results, tonsil and adenoid sizes, and the weight of each child, were examined for constructing a simple point-based clinical model for OSA prediction. Variables for the risk model were selected using multivariable logistic regression analyses. Of the 310 participants (mean age, 7.6 ± 3.7 years; boys, 67%), 170 (55%) experienced OSA. Modeling variables were determined using several univariate logistic regression analyses, followed by multivariable logistic regression analyses. A point-based clinical model incorporating the age, tonsil size (5 points maximum), adenoid size (5 and 20 points for age > 6 years and < 6 years, respectively), obesity (5 points for age > 6 years), and breathing pauses (5 points) was developed (area under the curve = 0.832). Moreover, the optimal cutoff points for predicting the apnea-hypopnea index of > 1 and > 5 were 10 (sensitivity, 72.9%; specificity, 65.0%) and 12 (sensitivity, 77.5%; specificity, 56.9%), respectively. Internal validation using the bootstrap method revealed no apparent overfitting problem. A novel clinical prediction model was developed for determining the risk of pediatric OSA; the model can be useful in identifying high-risk patients among those with sleep disturbances. 4. Laryngoscope, 126:2403-2409, 2016.