Clinical Risk Assessment Tools Research Articles

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.

Inflammatory risk of albumin combined with C-reactive protein predicts long-term cardiovascular risk in patients with diabetes.

The purpose of this study was to evaluate the predictive value of inflammatory risk as defined by the Glasgow Prognostic Score (GPS) for cardiovascular death in patients with diabetes. This study included 4956 patients (≥18 years old) with diabetes in the National Health and Nutrition Survey from 1999 to 2010. The mortality rate was determined by the correlation with the national death index on December 31, 2019. The GPS was composed of the serum C-reactive protein and the albumin. The primary outcome was cardiovascular death and the secondary outcome was all-cause death. The Cox proportional risk model adjusted for demographic factors and traditional cardiovascular risk factors was used to analyze the cumulative risk of outcomes. Among 4956 diabetes patients with a median follow-up of 10.9 years, 601 cardiovascular deaths and 2187 all-cause deaths were recorded. After adequate model adjustment, compared with the low GPS group, the high GPS group (HR, 1.257 (1.007-1.570), P = 0.043) had a higher cardiovascular mortality. Compared with the low GPS group, the all-cause mortality of the high GPS group (HR, 1.394 (1.245-1.560), P < 0.001) was higher. The results of subgroup analyses were similar with that of the overall cohort. The inflammatory risk as defined by the GPS was closely related to the increased risk of cardiovascular and all-cause death in patients with diabetes. It may be a convenient and efficient clinical practical risk assessment tool for patients with diabetes.

Evaluating cardiovascular disease risk stratification using multiple-polygenic risk scores and pooled cohort equations: insights from a 17-year longitudinal Korean cohort study.

Cardiovascular disease (CVD) remains the leading cause of mortality worldwide, caused by a complex interplay of genetic and environmental factors. This study aimed to evaluate the combined efficacy of multi-polygenic risk scores and pooled cohort equations (PCE) for predicting future CVD risks in the Korean population. In this longitudinal study, 7,612 individuals from the Ansan and Ansung cohorts were analyzed over a 17-year follow-up period. The participants were genotyped using the Korea Biobank Array, and quality-controlled genetic data were subjected to imputation analysis. The weighted sum of the PRSs (wPRSsum) was calculated using PRS-CS with summary statistics from myocardial infarction, ischemic stroke, coronary artery disease, and hypertension genome-wide association studies. The recalibrated PCE was used to assess clinical risk, and the participants were stratified into risk groups based on the wPRSsum and PCE. Associations between these risk scores and incident CVD were evaluated using Cox proportional hazards models and Kaplan-Meier analysis. The wPRSsum approach showed a significant association with incident CVD (HR = 1.15, p = 7.49 × 10-5), and the top 20% high-risk genetic group had an HR of 1.50 (p = 5.04 × 10-4). The recalibrated PCE effectively differentiated between the low and high 10-year CVD risk groups, with a marked difference in survival rates. The predictive models constructed using the wPRSsum and PCE demonstrated a slight improvement in prediction accuracy, particularly among males aged <55 years (C-index = 0.640). We demonstrated that while the integration of wPRSsum with PCE did not significantly outperform the PCE-only model (C-index: 0.703 for combined and 0.704 for PCE-only), it provided enhanced stratification of CVD risk. The highest risk group, identified through the combination of high wPRSsum and PCE scores, exhibited an HR of 4.99 for incident CVD (p = 1.45 × 10-15). These findings highlight the potential of integrating genetic risk assessments with traditional clinical tools for effective CVD risk stratification. Although the addition of wPRSsum to the PCE provided a marginal predictive improvement, it proved valuable in identifying high-risk individuals and supporting personalized treatment strategies. This study reinforces the utility of multi-PRS in conjunction with clinical risk assessment tools, paving the way for more tailored approaches for CVD prevention and management in diverse populations.

Risk Factor Analysis for Developing Major Complications Following Esophageal Surgery-A Two-Center Study.

Esophagectomy carries a high risk of morbidity and mortality compared to other major surgeries. With the aim of creating an easy-to-use clinical preoperative risk assessment tool and to validate previously described risk factors for major complications following surgery, esophagectomies at two tertiary medical centers were analyzed. A total of 450 patients who underwent esophagectomy for esophageal carcinoma at the University Medical Centre, Hamburg, or at the Medical Center University Duisburg-Essen, Germany (January 2008 to January 2020) were retrospectively analyzed. Epidemiological and perioperative data were analyzed to identify the risk factors that impact major complication rates. The primary endpoint of this study was to determine the incidence of major complications. The mean age of the patients was 63 years with a bimodal distribution. There was a male predominance across the cohort (81% vs. 19%, respectively). Alcohol abuse (p = 0.0341), chronic obstructive pulmonary disease (p = 0.0264), and cardiac comorbidity (p = 0.0367) were associated with a significantly higher risk of major complications in the multivariate analysis. Neoadjuvant chemotherapy significantly reduced the risk of major postoperative complications (p < 0.0001). Various patient-related risk factors increased the rate of major complications following esophagectomy. Patient-tailored prehabilitation programs before esophagectomy that focus on minimizing these risk factors may lead to better surgical outcomes and should be analyzed in further studies.

Abstract A116: A machine learning-derived transcriptomic-based biomarker identifies low grade pancreatic ductal adenocarcinoma tumors and may help in treatment decision making

Abstract Introduction: Pancreatic ductal adenocarcinoma (PDAC) is a very aggressive and highly lethal malignancy. The majority of cases present with locally advanced unresectable or metastatic disease, which precludes surgical resection and makes cure virtually impossible. However, a subset of tumors with more indolent biology may be candidates for aggressive cytoreduction. Further, synchronous resection of oligometastatic disease is being investigated in prospective trials. We sought to develop a prognostic biomarker that could identify patients with low grade tumors. Methods: The Pancreatic Adenocarcinoma TCGA dataset was analyzed on the cBioPortal platform, comparing 173 patients with non-metastatic PDAC, known tumor grade, and available mRNAseq data. Well-differentiated (G1) tumors were compared to moderately (G2) or poorly differentiated (G3) patients to identify differentially expressed (ΔE) genes. The patients were split into training (n=138) and testing sets (n=35). Low-importance genes were eliminated using a combination of LASSO regression, recursive feature elimination, random forest (RF), and Boruta algorithms. SMOTE was employed to address minority class imbalance. Support vector machine (SVM), RF, and k-nearest neighbors (kNN) were independently trained for the binary classification of G1 vs G2/3 PDAC. The best model and hyperparameters were selected using the mean bootstrapped nested k-fold cross-validation F1-score and evaluated on the held-out test set. Lastly, the model predictions of grade were used to divide patients into two cohorts for Results: There were 1335 ΔE genes between G1 and G2/3 tumors. A panel of 7 genes were unanimously selected by the four low importance gene-elimination algorithms, and used for generation of predictive algorithms. In bootstrapped nested k-fold cross-validation of the training dataset, mean sensitivity and specificity were 0.98 and 0.91 for G1, respectively, and 0.91 and 0.97 for G2/3 PDAC. The kNN model performed best, with a mean F1-score of 0.942 (95% CI: [0.941, 0.943]) on the training set and 0.781 (95% CI: [0.776, 0.786]) for the test set. Conclusion: This study presents a novel machine learning-generated transcriptomic-based tool that predicts PDAC grade with good accuracy, and has the potential to identify less aggressive variants of PDAC that may be more amenable to aggressive cytoreductive approaches. Further testing and validation of the algorithm in larger datasets and using prospectively collected needle biopsy-acquired material is a worthwhile next step, to refine this into a potent clinical risk assessment tool. Citation Format: Daniel Fu, Constantinos P. Zambirinis. A machine learning-derived transcriptomic-based biomarker identifies low grade pancreatic ductal adenocarcinoma tumors and may help in treatment decision making [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Pancreatic Cancer; 2023 Sep 27-30; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(2 Suppl):Abstract nr A116.

Use of Fracture Risk Assessment Tool in clinical practice and Fracture Risk Assessment Tool future directions.

Fracture Risk Assessment Tool is a free, online fracture risk calculator which can be used to predict 10-year fracture risk for women and men over age 50 years. It incorporates seven clinical risk factors and bone density to give a 10-year risk of major osteoporotic fracture and hip fracture. This dynamic tool can be used with patients at the bedside to help guide treatment decisions. There are some limitations to Fracture Risk Assessment Tool, with the most central limitation being the fact that inputs are binary. Much research has been done to try to refine Fracture Risk Assessment Tool to allow for more accurate risk prediction, and this article describes the data for adjusting Fracture Risk Assessment Tool depending on the clinical scenario such as the dose of glucocorticoid use, presence of diabetes and others. Recently, the new FRAXplus tool has been developed to address many of these concerns and will likely replace the old Fracture Risk Assessment Tool in the future. At the current time, it is available in beta form.

Clinimetrics of the Lanarkshire Oximetry Index for patients with leg ulcers: A systematic review and meta-analysis.

Ankle Brachial Pressure Index (ABPI) measurement has long been considered the gold standard of vascular assessment for people with lower limb ulceration. Despite this, only around 15% of patients in the United Kingdom who require an ABPI measurement undergo the assessment. The Lanarkshire Oximetry Index (LOI) is a cheaper and arguably more accessible approach to vascular assessment and was initially proposed as an alternative to the ABPI in 2000. No synthesis of evidence related to the LOI has been performed since its introduction into the literature. Primary studies were sought to determine the clinimetric properties of the LOI and its level of agreement with ABPI assessments. Systematic searches of MEDLINE, CINAHL, Cochrane Central Register of Controlled Trials, BNI, ProQuest Health and Medicine, Science Direct, Google Scholar and the British Library (online search) were conducted. Reference lists of identified studies were also reviewed to identify additional studies. Three primary studies met the inclusion criteria, reporting data from 307 patients and 584 limbs assessed using both the LOI and ABPI. All three studies reported fair to moderate kappa values for interrater reliability (κ = 0.290-0.747) and statistically significant positive correlation coefficients (r = 0.37, p < 0.001 in two studies) between the LOI and ABPI. The combined data from the three studies indicated a sensitivity of 52% (41.78-62.1, 95% confidence interval [CI]) and specificity of 96.08% (93.4-97.9, 95% CI) for the LOI using the ABPI as a reference. Additional data are required to indicate the safety of the LOI in practice. Data are also required to determine if the LOI is more acceptable to clinicians compared to the ABPI and whether there are any barriers/enablers to its implementation in practice. Given the relatively low specificity of the LOI, it may be beneficial to combine measurement of the LOI with a subjective clinical risk assessment tool to improve the sensitivity of this alternative approach to vascular assessment.

Safe discharge for patients admitted for lower gastrointestinal bleeding (LGITB): derivation and validation of a novel scoring system

AimBleeding from the lower gastrointestinal tract (LGITB) is a common clinical presentation. Recent guidelines have recommended for incorporation of clinical risk assessment tools in the management for LGITB. We derived and validated a novel clinical scoring system to predict safe discharge after LGITB admission, and compared it to other published scoring systems in current literature.MethodsA retrospective cohort of 798 patients with LGITB from August 2018 to March 2021 was included in the derivation cohort. Multivariate binary logistic regression was performed to identify significant clinical variables predictive of safe discharge. A clinical scoring system was developed based on the results, and validated on a prospective cohort of 312 consecutive patients with LGITB from April 2021 to March 2022. The performance of the novel scoring system was compared to other LGITB clinical risk assessment scores via area under the receiver operating characteristics curve (AUROC) analysis.ResultsVariables predictive of safe discharge included the following; absence of previous LGITB admission, absence of ischemic heart disease, absence of blood on digital rectal examination, absence of dizziness or syncope at presentation and the systolic blood pressure and haemoglobin levels at presentation. The novel score had an AUROC of 0.907. A cut-off point of 4 provided a sensitivity of 41.9%, specificity of 97.5%, positive predictive value of 96.4% and negative predictive value of 51.5% for prediction of safe discharge. The score performs comparably to the Oakland score.ConclusionThe novel LGITB clinical risk score has good predictive performance for safe discharge in patients admitted for LGITB.

Essential principles towards improving clinical risk assessment tools: A conversation with Uri Kartoun, PhD

Uri Kartoun (PhD in robotics, Ben Gurion University of the Negev, Israel) is a Staff Research Scientist and an IBM Master Inventor, co-developer of technologies such as MELD-Plus, EMRBots, Memory-memory (M2) Authentication, and Subpopulation-based Feature Selection. Prior to joining IBM Research in 2016, Kartoun worked at Microsoft Health Solutions Group and at Massachusetts General Hospital. EMWA Guest Editor Daniela Kamir, PhD, interviewed Kartoun about clinical risk assessment tools, organ transplant allocation disparities, and how the Model for End-Stage Liver Disease (MELD) score is used to allocate livers for transplantation. The conversation has been edited for brevity and clarity.

Demystifying the Risk Factors and Preventive Measures for Osteoporosis.

Osteoporosis is a major health problem, globally. It is characterized by structural bone weakness leading to an increased risk of fragility fractures. These fractures commonly affect the spine, hip and wrist bones. Consequently, Osteoporosis related proximal femur and vertebral fractures represent a substantial, growing social and economic burden on healthcare systems worldwide. Indentification of the risk factors, clinical risk assessment, utilization of risk assessment tools and appropriate management that play a crucial role in reducing the burden of Osteoporosis by tackling modifiable risk factors. This chapter explores various risk factors that are associated with Osteoporosis and provides an overview of various clinical and diagnostic risk assessment tools with a particular emphasis on evidence-based strategies for their prevention. The role of emerging technologies such as Artificial Intelligence (AI) and perspectives such as newer diagnostic modalities, monitoring and surveillance approaches in prevention of risk factors in the pathogenesis of Osteoporosis is highlighted.

The novel H2VK-65 clinical risk assessment tool predicts high coronary artery calcium score in symptomatic patients referred for coronary computed tomography angiography.

Coronary computed tomographic angiography (CCTA) has emerged as a powerful imaging modality for the detection and prognostication of individuals with suspected coronary artery disease (CAD). High amounts of coronary artery calcium (CAC) significantly obscure the interpretation of CCTA. Clinical risk assessment tools and data specific to predictors of high CAC in symptomatic patients are limited. Consecutive patients who underwent CAC scan and CCTA to diagnose CAD during 2016-2020 were included. A high CAC score was defined as >400 by Agatston method. Univariate and multivariate analyses were performed to determine the predictors of high CAC. The clinical risk score was derived from factors independently associated with high CAC. The derivation cohort was composed of 465 patients; this score was validated in 98 patients. The mean age was 63 ± 11 years, 53% were female, and 15.9% had high CAC scores. The independent predictors of high CAC scores were age >65 years (odds ratio [OR] 3.02, 95% confidence interval (95%CI) 1.56-5.85, p = 0.001), chronic kidney disease (CKD) (OR 11.09, 95%CI 3.38-36.38, p < 0.001), heart failure (OR 6.52, 95%CI 2.23-19.09, p = 0.001), hypertension (OR 26.44, 95%CI 9.02-77.44, p < 0.001), and vascular diseases, including ischemic stroke/transient ischemic attack and peripheral arterial disease (OR 20.96, 95%CI 4.19-104.86, p < 0.001). The H2VK-65 (Hypertension, Heart failure, Vascular diseases, CKD, and Age > 65) score allocates 1 point for age >65, 2 points for CKD or heart failure, and 3 points for hypertension or vascular diseases. Using a threshold of ≥4 points, the sensitivity and specificity to detect high CAC was 81% and 80%, respectively. The area under the curve was 0.88 and 0.85 in the derivation and validation cohorts, respectively. The novel H2VK-65 score demonstrated good performance for predicting high CAC scores in symptomatic patients referred for CCTA.

Comparison of Various Clinical Risk Assessment Tools in Predicting Major Adverse Cardiac Events in Patients Presenting in Emergency Department with Undifferentiated Cardiac Chest Pain

Objective: To compare the “HEART (History, ECG, Age, Risk factors, Troponin), GRACE (Global Registry of Acute Coronary Events), and TIMI (Thrombolysis in Myocardial Infarction)” scores in predicting major adverse cardiac events (MACE) in patients reporting with undifferentiated cardiac chest pain to the Emergency Department (ED).&#x0D; Study Design: Cross-sectional validation study&#x0D; Duration and place of Study: Emergency Department, Combined Military Hospital, Rawalpindi Pakistan, from Jan to Jun 2021.&#x0D; Methodology: Two hundred and thirty-seven adult patients with atraumatic cardiac-like chest pain and non-diagnostic electrocardiogram (ECG) reporting to the ED were included in the study. HEART, GRACE and TIMI scores were calculated from the data. The number of patients with low risk was identified by each score and compared at a fixed safety level of minimum 95% sensitivity. The potential occurrence of MACE was confirmed using a telephonic follow-up six weeks after the presentation.&#x0D; Results: At an absolute safety level of minimum 95% sensitivity, the HEART score determined 101 patients as “low-risk” with 1.98% MACE missed. The GRACE score identified 49 “low-risk” patients with 4.08% MACE missed, and the TIMI score identified 66 “low-risk” patients with 3.03% MACE missed.&#x0D; Conclusion: Among the three scores under comparison, the HEART score performed better than the GRACE and TIMI scores at the same safety level and surpassed them in differentiating between those with MACE and without MACE.

Addition of Risk-enhancing Factors Improves Risk Assessment of Atherosclerotic Cardiovascular Disease in Middle-aged and Older Chinese Adults: Findings from the Chinese Multi-provincial Cohort Study

Objective: This study aimed to examine whether integrating risk-enhancing factors into the Chinese Society of Cardiology-recommended clinical risk assessment tool (i.e., the CSC model) for atherosclerotic cardiovascular disease (ASCVD) might improve 10-year ASCVD risk stratification in Chinese adults. Methods: A total of 4910 Chinese participants who were 50–79 years of age and free of cardiovascular disease in the 2007–2008 Survey from the Chinese Multi-provincial Cohort Study were included. We assessed the updated model’s clinical utility (i.e., Harrell’s C-index and net reclassification improvement [NRI]) by adding risk-enhancing factors individually or the number of risk-enhancing factors to the CSC model, for all individuals or those at intermediate risk. Risk-enhancing factors, including a family history of CVD, triglycerides ≥2.3 mmol/L, high-sensitivity C-reactive protein ≥2 mg/L, Lipoprotein (a) ≥50 mg/dL, non-high-density lipoprotein cholesterol ≥4.9 mmol/L, overweight/obesity, and central obesity, were evaluated. ASCVD events were defined as a composite endpoint comprising ischemic stroke and acute coronary heart disease events (including nonfatal acute myocardial infarction and all coronary deaths). Results: During a median 10-year follow-up, 449 (9.1%) ASCVD events were recorded. Addition of ≥2 risk-enhancing factors to the CSC model yielded a significant improvement in the C-index (1.0%, 95% confidence interval [CI]: 0.2–1.7%) and a modest improvement in the NRI (2.0%, 95% CI: −1.2–5.4%) in the total population. For intermediate-risk individuals, particularly individuals at high risk of developing ASCVD, significant improvements in NRI were observed after adding ≥2 risk-enhancing factors (17.4%, 95% CI: 5.6–28.5%) to the CSC model. Conclusions: Addition of ≥2 risk-enhancing factors refined 10-year ASCVD risk stratification, particularly for intermediate-risk individuals, supporting their potential in helping tailor targeted interventions in clinical practice.

Reducing Disparities in Postpartum Care Utilization: Development of a Clinical Risk Assessment Tool.

Patients receiving care at Federally Qualified Health Centers (FQHCs) have low postpartum care attendance. Perinatal morbidity and mortality disproportionately affect patients with low-income and are potentially preventable. The purpose of this study was to develop a clinical decision support tool to identify FQHC patients less likely to return for postpartum care. To accomplish this purpose, we evaluated established predictors and novel risk factors in our patient population. This is a retrospective, secondary data analysis of 50,022 patients who received prenatal care past 24 weeks' gestation in FQHCs between 2012 and 2017. The postpartum visit was defined using Healthcare Effectiveness Data and Information Set measures as early care (birth to 21 days) and later care (21-84 days). Anderson's Behavioral Model for Access to Healthcare guided inclusion of potentially predictive factors. We stratified data by postpartum care attendance, and a final predictive model was selected by model fit statistics and clinical relevance. In our sample, 64% of birthing persons attended postpartum care at FQHCs. Of those who returned for care, 38% returned within 21 days postbirth and 62% returned between 21 and 84 days, with 28% returning for both early and later care. Predictors for postpartum care attendance included maternal age, parity, gestational age at first visit, and number of prenatal care visits. A clinical decision support tool for identifying patients less likely to return for care was created. An easy to implement clinical decision support tool can help identify FQHC patients at risk for postpartum nonattendance. Future interventions to improve adequacy of prenatal care can encourage early entry into prenatal care and sufficient prenatal visits. These efforts may improve postpartum care attendance and maternal health.

Lung cancer prediction using machine learning on data from a symptom e-questionnaire for never smokers, formers smokers and current smokers.

PurposeThe aim of the present study was to investigate the predictive ability for lung cancer of symptoms reported in an adaptive e-questionnaire, separately for never smokers, former smokers, and current smokers.Patients and methodsConsecutive patients referred for suspected lung cancer were recruited between September 2014 and November 2015 from the lung clinic at the Karolinska University Hospital, Stockholm, Sweden. A total of 504 patients were later diagnosed with lung cancer (n = 310) or no cancer (n = 194). All participants answered an adaptive e-questionnaire with a maximum of 342 items, covering background variables and symptoms/sensations suspected to be associated with lung cancer. Stochastic gradient boosting, stratified on smoking status, was used to train and test a model for predicting the presence of lung cancer.ResultsAmong never smokers, 17 predictors contributed to predicting lung cancer with 82% of the patients being correctly classified, compared with 26 predictors with an accuracy of 77% among current smokers and 36 predictors with an accuracy of 63% among former smokers. Age, sex, and education level were the most important predictors in all models.ConclusionMethods or tools to assess the likelihood of lung cancer based on smoking status and to prioritize investigative and treatment measures among all patients seeking care with diffuse symptoms are much needed. Our study presents risk assessment models for patients with different smoking status that may be developed into clinical risk assessment tools that can help clinicians in assessing a patient’s risk of having lung cancer.

Which predictive variables are emphasized when violence risk assessments are performed in Norway?

Introduction Violence is considered both a societal issue and a public health issue. Due to the high economic, societal, and individual cost associated with exposure to violence, clinical risk assessment-tools are now being implemented in the public health care system as well as outside of it. To ensure early identification and prevention, various professional groups perform structured risk assessments in Norway, including police, doctors, and psychologists.ObjectivesThere is a need to examine competence and organizational factors, which may affect the ability to make accurate assessments in different levels of the health service, as well as in the police who often are involved in early identification and action-taking concerning violent individuals. Based on variation in risk assessment competencies, and characteristics of different work environments, our project aims to investigate whether some factors seem to be more important than others in clinical assessments when comparing different professional groups with or without a professional background in health care.Methods In our study, we will be able to tell if there is a significant difference in how different professional groups emphasize different risk factors, and in which way individual factors such as formal competencies, years of experience, and personal and professional attitudes to violence affect the risk violence assessments performed.ResultsWe hypothesize that retrospective, clinical, and dynamic risk-factors are interpreted differently by different professional groups, and therefore entail significant variations in assessments, and the health care provided.ConclusionsIn this planned study, we will examine variations in the practice of violence risk assessment in Norway.DisclosureNo significant relationships.

Extended anticoagulation for venous thromboembolism: A survey of the American Venous Forum and the European Venous Forum

ObjectiveExtended anticoagulation therapy should always be considered after standard treatment of an unprovoked episode of venous thromboembolism (VTE). It can also be considered for selected patients with provoked VTE. However, the evidence-based protocols suggested by some clinical guidelines and risk assessment tools to guide this practice are limited and ambiguous. The goal of the present survey research was to analyze current practices in applying extended anticoagulation therapy for patients with VTE among members of the American Venous Forum (AVF) and European Venous Forum (EVF). MethodsAn online survey was created by the AVF Research Committee. The survey consisted of 16 questions to identify the country of practice, specialty, experience of the participating physicians, and their clinical practice patterns in applying extended anticoagulation therapy for VTE patients. The survey was distributed via e-mail to the members of the AVF and EVF. ResultsA total of 144 practitioners, 48 AVF members (33%) and 96 EVF members (66%), participated in the survey. Most of the respondents identified themselves as vascular specialists with primary certification in vascular surgery (70%), vascular medicine or angiology (9%), and venous disease or phlebology (3%). Of the 144 respondents, 72% believed that the risk of VTE recurrence will generally overweigh the risk of bleeding for patients with unprovoked VTE. Extended anticoagulation therapy might be used by 97% of providers. Different patterns in real world clinical practice were identified. More than one half of the practitioners estimated the VTE recurrence and bleeding risk subjectively. The antithrombotic drugs most commonly used for secondary prophylaxis were rivaroxaban, apixaban, warfarin, dabigatran, and aspirin, in decreasing order of frequency. Among the reasons selected for not regularly considering extended anticoagulation therapy were the lack of specific clinical practice guidelines (24%), lack of reported evidence (9%), and absence of valid VTE and/or bleeding risk prediction calculators (8%). Twelve participants (8%) stated that extended anticoagulation therapy would not be beneficial for most patients with VTE. Ten participants (7%) indicated that prescribing extended anticoagulation therapy was outside the scope of their specialty. ConclusionsDifferent practice patterns exist regarding extending anticoagulation therapy beyond the standard treatment for patients with VTE. Major gaps in knowledge remain a serious challenge at least partially explaining the inaccuracy and inconsistency in long-term VTE management. Appropriately designed studies are needed to evaluate risk stratification tools when contemporary best medical therapy is used, accurately predict VTE recurrence and its long-term outcomes, and tailor safe and effective secondary prophylaxis.

Clinical usefulness of prediction tools to identify adult hospitalized patients at risk of drug-related problems: A systematic review of clinical prediction models and risk assessment tools.

This study aimed to review systematically all available prediction tools identifying adult hospitalized patients at risk of drug-related problems, and to synthesize the evidence on performance and clinical usefulness. PubMed, Scopus, Web of Science, Embase, and CINAHL databases were searched for relevant studies. Titles, abstracts and full-text studies were sequentially screened for inclusion by two independent reviewers. The Prediction Model Risk of Bias Assessment Tool (PROBAST) and the Revised Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) checklists were used to assess risk of bias and applicability of prediction tools. A narrative synthesis was performed. A total of 21 studies were included, 14 of which described the development of new prediction tools (four risk assessment tools and ten clinical prediction models) and six studies were validation based and one an impact study. There were variations in tool development processes, outcome measures and included predictors. Overall, tool performance had limitations in reporting and consistency, with the discriminatory ability based on area under the curve receiver operating characteristics (AUROC) ranging from poor to good (0.62-0.81), sensitivity and specificity ranging from 57.0% to 89.9% and 30.2% to 88.0%, respectively. The Medicines Optimisation Assessment tool and Assessment of Risk tool were prediction tools with the lowest risk of bias and low concern for applicability. Studies reporting external validation and impact on patient outcomes were scarce. Most prediction tools have limitations in development and validation processes, as well as scarce evidence of clinical usefulness. Future studies should attempt to either refine currently available tools or apply a rigorous process capturing evidence of acceptance, usefulness, performance and outcomes.

Development of a Preoperative Clinical Risk Assessment Tool for Postoperative Complications After Hysterectomy

To develop a preoperative risk assessment tool that quantifies the risk of postoperative complications within 30 days of hysterectomy. Retrospective analysis. Michigan Surgical Quality Collaborative hospitals. Women who underwent hysterectomy for gynecologic indications. Development of a nomogram to create a clinical risk assessment tool. Postoperative complications within 30 days were the primary outcome. Bivariate analysis was performed comparing women who had a complication and those who did not. The patient registry was randomly divided. A logistic regression model developed and validated from the Collaborative database was externally validated with hysterectomy cases from the National Surgical Quality Improvement Program, and a nomogram was developed to create a clinical risk assessment tool. Of the 41,147 included women, the overall postoperative complication rate was 3.98% (n=1638). Preoperative factors associated with postoperative complications were sepsis (odds ratio [OR] 7.98; confidence interval [CI], 1.98-32.20), abdominal approach (OR 2.27; 95% CI, 1.70-3.05), dependent functional status (OR 2.20; 95% CI, 1.34-3.62), bleeding disorder (OR 2.10; 95% CI, 1.37-3.21), diabetes with HbA1c ≥9% (OR 1.93; 95% CI, 1.16-3.24), gynecologic cancer (OR 1.86; 95% CI, 1.49-2.31), blood transfusion (OR 1.84; 95% CI, 1.15-2.96), American Society of Anesthesiologists Physical Status Classification System class ≥3 (OR 1.46; 95% CI, 1.24-1.73), government insurance (OR 1.3; 95% CI, 1.40-1.90), and body mass index ≥40 (OR 1.25; 95% CI, 1.04-1.50). Model discrimination was consistent in the derivation, internal validation, and external validation cohorts (C-statistics 0.68, 0.69, 0.68, respectively). We validated a preoperative clinical risk assessment tool to predict postoperative complications within 30 days of hysterectomy. Modifiable risk factors identified were preoperative blood transfusion, poor glycemic control, and open abdominal surgery.

Age, personal and family history are independently associated with venous thromboembolism following acute Achilles tendon rupture

PurposeThe primary aim was to determine independent patient, injury and management-related factors associated with symptomatic venous thromboembolism (VTE) following acute Achilles tendon rupture (ATR). The secondary aim was to suggest a clinical VTE risk assessment tool for patients with acute ATR. MethodsFrom 2010–2018, 984 consecutive adults (median age 47yrs, 73% [n = 714/984] male) sustaining an acute ATR were retrospectively identified. Ninety-five percent (n = 939/984) were managed non-operatively in a below-knee cast (52%, n = 507/984) or walking boot (44%, n = 432/984), with 5% (n = 45/984) undergoing primary operative repair (<6wks post-injury). VTE was diagnosed using local medical records and national imaging archives, reviewed at a mean 5yrs (range 1–10) post-injury. Multivariate logistic regression was performed to determine independent factors associated with VTE. ResultsThe incidence of VTE within 90 days of ATR was 3.6% (n = 35/984; deep vein thrombosis 2.1% [n = 21/984], pulmonary embolism 1.9% [n = 19/984]), and the median time to VTE was 24 days (interquartile range 15–44). Age ≥50yrs (adjusted OR [aOR] 2.3, p = 0.027), personal history of VTE/thrombophilia (aOR 6.1, p = 0.009) and family history of VTE (aOR 20.9, p<0.001) were independently associated with VTE following ATR. These non-modifiable risk factors were incorporated into a VTE risk assessment tool. Only 23% of patients developing VTE (n = 8/35) had a relevant personal or family history, but incorporating age ≥50yrs into the VTE risk assessment tool (alongside personal and family history) identified 69% of patients with VTE (n = 24/35). Non weight-bearing for ≥2wks after ATR was also independently associated with VTE (aOR 3.2, p = 0.026). ConclusionsAge ≥50 years, personal history of VTE/thrombophilia and a positive family history were independently associated with VTE following ATR. Incorporating age into our suggested VTE risk assessment tool enhanced its sensitivity in identifying at-risk patients. Early weight-bearing in an appropriate orthosis may be beneficial to all patients in VTE risk reduction.