Clinical Risk Calculators Research Articles

Abstract 17308: Patient and Primary Care Provider Experiences From an Implementation Study of a Risk Prediction Tool Integrating a Polygenic Risk Score and the QRISK2 Cardiovascular Risk Calculator

Introduction: We have previously shown that combining a polygenic risk score (PRS) for cardiovascular disease (CVD), with standard clinical risk calculators such as QRISK®2, results in improved CVD risk prediction via an integrated risk tool (CVD-IRT). Research Question: The objective of this study was to explore the implementation of CVD-IRT within routine practice in the UK National Health Service (NHS), through participant and healthcare provider (HCP) surveys and interviews. Methods: The Healthcare Evaluation of Absolute Risk Testing Study (HEART) (NCT05294419), a prospective, single-arm pragmatic trial, enrolled 836 participants undergoing health checks across 12 NHS general practices. QRISK2 and CVD-IRT scores were returned to participants via HCPs. The primary outcome of the study was feasibility of CVD-IRT implementation. This was assessed by a mixed methods approach (quantitative and qualitative methods). Results: After the results were reported and discussed, 520 surveys were completed by participants and 824 surveys were completed by HCPs. Subsequently, 21 participants were interviewed and 13 HCPs attended focus groups or interviews to explore their experiences. 23 HCPs completed a final questionnaire (34.8% physician, 21.7% research nurse, 43.5% other). For 90.7% of reports, HCPs indicated that the CVD-IRT could be incorporated into routine primary care in a straightforward manner. 80% of HCPs agreed that having these tests could lead to better health outcomes for patients, and 68.4% believed that the CVD-IRT could help them manage their patients through a shared decision making process covering lifestyle and treatment options. Participants found the report personally useful (98.5%) and easy to understand (94.3%); agreed that genetic measures are important to identify the risks of developing CVD (85.2%); thought that the test should be widely made available (interview summaries); and would recommend to friends and family (86.8%). Conclusion: The implementation of the CVD-IRT into routine health-checks was recommendable, feasible and well received by both HCPs and participants, who felt that the information was useful, could support clinical decisions, and easy to understand.

Prostate-derived circulating microRNAs add prognostic value to prostate cancer risk calculators.

Prostate cancer is the second leading cause of malignancy-related deaths among American men. Active surveillance is a safe option for many men with less aggressive disease, yet definitively determining low-risk cancer is challenging with biopsy alone. Herein, we sought to identify prostate-derived microRNAs in patient sera and serum extracellular vesicles, and determine if those microRNAs improve upon the current clinical risk calculators for prostate cancer prognosis before and after biopsy. Prostate-derived intracellular and extracellular vesicle-contained microRNAs were identified by small RNA sequencing of prostate cancer patient explants and primary cells. Abundant microRNAs were included in a custom microRNA PCR panel that was queried in whole serum and serum extracellular vesicles from a diverse cohort of men diagnosed with prostate cancer. The levels of these circulating microRNAs significantly differed between indolent and aggressive disease and improved the area under the curve for pretreatment nomograms of prostate cancer disease risk. The microRNAs within the extracellular vesicles were the most informative and improved the AUC to 0.739 compared to the existing nomogram alone, which has an AUC of 0.561. The microRNAs in the whole serum improved it to AUC 0.675. In summary, quantifying microRNAs circulating in extracellular vesicles is a clinically feasible assay that may provide additional information for assessing prostate cancer risk stratification.

Vertebral CT attenuation outperforms standard clinical fracture risk prediction tools in detecting osteoporotic disease in lung cancer screening participants.

Compare accuracy of vertebral Hounsfield Unit (VHU) attenuation and FRAX and Garvan Fracture Risk Calculators in identifying low bone mineral density (BMD) and prevalent vertebral compression fractures (VF) in lung cancer screening (LCS) participants. Baseline CT scans from a single site of the International Lung Screen Trial were analysed. BMD was measured using VHU (of the most caudally imaged vertebra) and quantitative CT (QCT) (low BMD defined as <110 HU and <120 mg/cm3, respectively). Prevalent VF were classified semi-quantitatively. 10-year FRAX and Garvan fracture risks were calculated using dual energy X-ray absorptiometry (DXA) femoral neck T-score where available. Discrimination was assessed by area under receiver-operating characteristic curves (AUC). 535 LCS participants were included; 41% had low VHU-BMD, 56% had low QCT-BMD and 10% had ≥1 VF with ≥25% vertebral height loss. VHU demonstrated 94% specificity and 70% sensitivity in identifying low QCT-BMD. VHU was superior to fracture risk tools in discriminating low QCT-BMD (AUC: VHU 0.94 vs FRAX 0.67, Garvan 0.64 [p < 0.05]). In 64 participants with recent DXA scans, VHU was superior to FRAXT-score and GarvanT-score in discriminating low QCT-BMD (AUC: VHU 0.99, FRAXT-score 0.71, GarvanT-score 0.71 [p < 0.05]). VHU was non-inferior to FRAXT-score and GarvanT-score in discriminating VF (AUC: VHU 0.65, FRAXT-score 0.53, GarvanT-score 0.61). VHU outperforms clinical risk calculators in detecting low BMD and discriminates prevalent VF equally well as risk calculators with T-scores, yet is significantly simpler to perform. VHU measurement could aid osteoporosis assessment in high-risk smokers undergoing LCS.

Computer-interpretable guidelines: electronic tools to enhance the utility of thyroid nodule clinical practice guidelines and risk stratification tools.

Clinicians seeking guidance for evaluating and managing thyroid nodules currently have several resources. The principal ones are narrative clinical guidelines and clinical risk calculators. This paper will review the strengths and weaknesses of both. The paper will introduce a concept of computer interpretable guideline, a novel way of transforming narrative guidelines in to a clinical decision support tool that can provide patient specific recommendations at the point of care. The paper then describes an experience of developing an interactive web based computer interpretable guideline for thyroid nodule management, called Thyroid Nodule Management App (TNAPP). The advantages of this approach and the potential barriers for widespread adaptation are discussed.

Accurate diagnosis of prostate cancer by combining Proclarix with magnetic resonance imaging.

To assess of the clinical performance of Proclarix® (a novel Conformité Européenne [CE]-marked biomarker test aiding in the identification of clinically significant prostate cancer [csPCa]) alone or in combination with multiparametric magnetic resonance imaging (mpMRI) to predict csPCa (International Society of Urological Pathology Grade Group ≥2). The study included blood samples from 721 men undergoing mpMRI followed by biopsy at University College London, London, and Vall d'Hebron University Hospital, Barcelona. Samples were tested blindly. The Proclarix-MRI model combining prostate volume, Proclarix and mpMRI results was trained using the UCL cohort (n= 159) and validated in the Vall d'Hebron cohort (n= 562). Its diagnostic performance was established in correlation to biopsy outcome and compared to available clinical parameters and risk calculators. Clinical performance of the Proclarix-MRI model in the validation cohort did not significantly differ from the training cohort and resulted in a sensitivity for csPCa of 90%, 90% negative predictive value and 66% positive predictive value. The Proclarix-MRI score's specificity (68%) was significantly (P< 0.001) better than the MRI-European Randomized study of Screening for Prostate Cancer risk score (51%), Proclarix (27%) or mpMRI (28%) alone. In addition, Proclarix by itself was found to be useful in the MRI Prostate Imaging-Reporting and Data System (PI-RADS) score 3 subgroup by outperforming prostate-specific antigen density in terms of specificity (25% vs 13%, P= 0.004) at 100% sensitivity. When combined with mpMRI and prostate volume, Proclarix reliably predicted csPCa and ruled out men with no or indolent cancer. A large reduction of two thirds of unneeded biopsies was achieved. Proclarix can further be used with high confidence to reliably detect csPCa in men with an indeterminate PI-RADS score 3 mpMRI. Despite these encouraging results, further validation is needed.

Clinical risk calculators informing the decision to admit: A methodologic evaluation and assessment of applicability.

Clinical prediction and decision tools that generate outcome-based risk stratification and/or intervention recommendations are prevalent. Appropriate use and validity of these tools, especially those that inform complex clinical decisions, remains unclear. The objective of this study was to assess the methodologic quality and applicability of clinical risk scoring tools used to guide hospitalization decision-making. In February 2021, a comprehensive search was performed of a clinical calculator online database (mdcalc.com) that is publicly available and well-known to clinicians. The primary reference for any calculator tool informing outpatient versus inpatient disposition was considered for inclusion. Studies were restricted to the adult, acute care population. Those focused on obstetrics/gynecology or critical care admission were excluded. The Wasson-Laupacis framework of methodologic standards for clinical prediction rules was applied to each study. A total of 22 calculators provided hospital admission recommendations for 9 discrete medical conditions using adverse events (14/22), mortality (6/22), or confirmatory diagnosis (2/22) as outcomes of interest. The most commonly met methodologic standards included mathematical technique description (22/22) and clinical sensibility (22/22) and least commonly met included reproducibility of the rule (1/22) and measurement of effect on clinical use (1/22). Description of the studied population was often lacking, especially patient race/ethnicity (2/22) and mental or behavioral health (0/22). Only one study reported any item related to social determinants of health. Studies commonly do not meet rigorous methodologic standards and often fail to report pertinent details that would guide applicability. These clinical tools focus primarily on specific disease entities and clinical variables, missing the breadth of information necessary to make a disposition determination and raise significant validation and generalizability concerns.

Abstract 10947: A Polygenic Risk Score Added to a QRISK2 Cardiovascular Risk Calculator Demonstrated Robust Clinical Acceptance in the Primary Care Setting

Introduction: We have previously shown that combining a polygenic risk score (PRS) for cardiovascular disease (CVD), a numerical summary of an individual’s genetic predisposition to CVD, with standard clinical risk calculators such as ASCVD-PCE and QRISK results in improved estimates of CVD risk. Implementation of such a cardiovascular integrated risk tool (CVD IRT) into real world clinical practice is a key focus for further study. Hypothesis: We assessed the hypothesis that a CVD IRT can be incorporated into routine primary care. Methods: The Healthcare Evaluation of Absolute Risk Testing Study (NCT05294419) is a prospective trial recruiting up to 1,000 healthy participants undergoing health checks across 12 UK NHS general practices. Both QRISK2 and CVD IRT scores were generated and returned to clinicians, who then communicated the results to participants. The primary outcome of this study is operational success as well as feedback from health care providers (HCPs) and participants. The study also measures the impact of the CVD IRT on clinical decision making. Results: These are interim analyses. As of April 2022, 624 eligible participants (62% female, mean age 55) have been recruited. A total of 371 CVD IRT reports have been generated, with 100% of blood samples generating scores that were all returned within the designated time frame. Among the primary care HCPs, 89% (8/9) agreed that the incorporation of CVD IRT into routine care could be done in a straightforward manner. Among the participants who have completed a survey to date, 93% (125/135) would likely or very likely recommend the CVD IRT to friends and family. Average QRISK2 (6.3%) and CVD IRT (6.6%) risk scores did not differ significantly, but there were broad changes in risk among individual patients, with 5% (19/371) of patients crossing above the risk threshold to treat according to NICE guidelines (10-year risk ≥ 10%) as well as 3% (11/371) of patients reclassified as very high risk (10-year risk ≥ 20%). Conclusions: The rollout of an integrated risk tool combining polygenic risk into a standardized CVD risk calculator within primary care is feasible and well accepted by clinicians and participants. The CVD IRT results suggest clinically actionable changes in a substantial proportion of this population.

Digital Rectal Examination in Stockholm3 Biomarker-based Prostate Cancer Screening

BackgroundPathological digital rectal examination (DRE) is suggestive of prostate cancer but has low sensitivity and specificity. DRE is incorporated in many clinical risk calculators, but there is less evidence on how DRE performs in the setting of blood biomarkers and polygenic risk prediction models other than prostate-specific antigen (PSA) associated with prostate cancer. The Stockholm3 test combines a blood test and clinical variables including DRE. ObjectiveTo assess the predictive performance of DRE for finding clinically significant prostate cancer in systematic biopsy and evaluate its added value to the multivariable diagnostic test Stockholm3. Design, setting, and participantsThis population-based study in the screening by invitation setting included 5543 men aged 50–69 yr with PSA ≥3 ng/ml who were referred for systematic prostate biopsy between 2012 and 2015. The STHLM3 study is registered with ISRCTN.com as ISRCTN84445406. Outcome measurements and statistical analysisPredictive performance was assessed via estimates of sensitivity and specificity and in logistic regression. Clinically significant cancer was defined as International Society of Urological Pathology grade group ≥2 (GG ≥2) cancer on systematic biopsy. Results and limitationsWe found that 11% of men with PSA ≥3 ng/ml had a suspicious DRE. A suspicious DRE was associated with a 3.16-fold higher risk (95% confidence interval [CI] 2.83–3.52) of GG ≥2 cancer and greater length of cancer on biopsy. The risk of nonsignificant cancer was similar regardless of the DRE finding. The risk of GG ≥2 cancer was 46.2% (95% CI 42.2–50.3%) for men with a suspicious DRE versus 14.6% (95% CI 13.7–15.7%) for men with a negative DRE. The elevated risk of GG ≥2 cancer persisted after adjusting for the other Stockholm3 test parameters (odds ratio 2.88, 95% CI 2.32–3.57). For detection of GG ≥2 cancer among men with PSA ≥3 ng/ml, DRE had sensitivity of 27.8% (95% CI 25.1–30.7%) and specificity of 92.8% (95% CI 92.1–93.6%). ConclusionsIn this screening-by-invitation setting we found that for men with PSA ≥3 ng/ml, a suspicious DRE indicates more than threefold higher risk of harboring significant prostate cancer. DRE as a variable adds significant precision to the Stockholm3 prediction model. Men with a suspicious DRE should be referred for further diagnostic workup, including biopsy. Patient summaryWe investigated the ability of digital rectal examination to predict if a patient has clinically significant prostate cancer. We found that digital rectal examination provides valuable information and can help doctors in making an informed decision on whether to recommend prostate biopsy.

Augmenting prostate magnetic resonance imaging reporting to incorporate diagnostic recommendations based upon clinical risk calculators.

Risk calculators have offered a viable tool for clinicians to stratify patients at risk of prostate cancer (PCa) and to mitigate the low sensitivity and specificity of screening prostate specific antigen (PSA). While initially based on clinical and demographic data, incorporation of multiparametric magnetic resonance imaging (MRI) and the validated prostate imaging reporting and data system suspicion scoring system has standardized and improved risk stratification beyond the use of PSA and patient parameters alone. Biopsy-naïve patients with lower risk profiles for harboring clinically significant PCa are often subjected to uncomfortable, invasive, and potentially unnecessary prostate biopsy procedures. Incorporating risk calculator data into prostate MRI reports can broaden the role of radiologists, improve communication with clinicians primarily managing these patients, and help guide clinical care in directing the screening, detection, and risk stratification of PCa.

How Communicating Polygenic and Clinical Risk for Atherosclerotic Cardiovascular Disease Impacts Health Behavior: an Observational Follow-up Study.

Prediction tools that combine polygenic risk scores with clinical factors provide a new opportunity for improved prediction and prevention of atherosclerotic cardiovascular disease, but the clinical utility of polygenic risk score has remained unclear. We collected a prospective cohort of 7342 individuals (64% women, mean age 56 years) and estimated their 10-year risk for atherosclerotic cardiovascular disease both by a traditional risk score and a composite score combining the effect of a polygenic risk score and clinical risk factors. We then tested how returning the personal risk information with an interactive web-tool impacted on the participants' health behavior. When reassessed after 1.5 years by a clinical visit and questionnaires, 20.8% of individuals at high (>10%) 10-year atherosclerotic cardiovascular disease risk had seen a doctor, 12.4% reported weight loss, 14.2% of smokers had quit smoking, and 15.4% had signed up for health coaching online. Altogether, 42.6% of persons at high risk had made one or more health behavioral changes versus 33.5% of persons at low/average risk such that higher baseline risk predicted a favorable change (OR [CI], 1.53 [1.37-1.72] for persons at high risk versus the rest, P<0.001), with both high clinical (P<0.001) and genomic risk (OR [CI], 1.10 [1.03-1.17], P=0.003) contributing independently. Web-based communication of personal atherosclerotic cardiovascular disease risk-data including polygenic risk to middle-aged persons motivates positive changes in health behavior and the propensity to seek care. It supports integration of genomic information into clinical risk calculators as a feasible approach to enhance disease prevention.

Association of lung cancer incidence and COVID-19 case fatality with income level in Ohio.

110 Background: Social determinants of health (SDOH) have an impact of health and quality-of-life. However, most models of clinical disease and risk calculators do not incorporate SDOH. Many diseases have residual, unexplained risk after known risk factors have been incorporated. SDOH is important component of this residual risk and resources should be allocated to study their relation to disease. Here, we investigate the role of SDOH on the incidence and severity of respiratory diseases by evaluating the association of income level with lung cancer incidence and COVID-19 case fatality ratio in the state of Ohio. Methods: Socioeconomic data for the state of Ohio was manually collected. Lung cancer incidence was acquired from the National Cancer Institute’s online portal. COVID-19 case fatality ratios were downloaded from Johns Hopkins University’s GitHub repository. All three datasets contained complete information for each of Ohio’s 88 counties and were merged by county name. The first two datasets are up-to-date as of 2019, while the COVID-19 data was up-to-date as of 22 May 2021. Data manipulation, visualization, and statistical inference was conducted in the R programming language. Results: Median county-level income across Ohio was $52,722 (IQR: 47,273-59,166). Median county-level lung cancer incidence was 68.4 per 100,000 population (IQR: 62.0-80.6) and median COVID-19 case fatality ratio was 1.98% (IQR: 1.65-2.36). Data for counties with highest and lowest median income are shown in table. Lung cancer incidence and COVID-19 case fatality ratio were not significantly correlated ( n = 88, p &gt; 0.05). Median income was significantly correlated with both, lung cancer incidence ( r = -0.49, p &lt; 0.001) and COVID-19 case fatality ratio ( r = -0.41, p &lt; 0.001). This implies that 24% of the variation in lung cancer incidence and 17% of the variation in COVID-19 case fatality ratio in counties across Ohio can be explained by median county income. Conclusions: SDOH is significantly associated with the incidence and severity of infectious and oncologic respiratory diseases. This association is strengthened since the disease variables themselves are uncorrelated, decreasing the likelihood of confounding. COVID-19 incidence was not used because of uncertainty in the mechanism of SDOH affecting transmission. However, theories suggest that oncologic incidence and infectious disease treatment are affected by SDOH. We validate these theories and encourage the incorporation of SDOH into clinical care and public health. Future directions include testing the association between income level and lung cancer severity in clinical datasets from medical institutions in Ohio.[Table: see text]

Toward Generalizable and Transdiagnostic Tools for Psychosis Prediction: An Independent Validation and Improvement of the NAPLS-2 Risk Calculator in the Multisite PRONIA Cohort

BackgroundTransition to psychosis is among the most adverse outcomes of clinical high-risk (CHR) syndromes encompassing ultra-high risk (UHR) and basic symptom states. Clinical risk calculators may facilitate an early and individualized interception of psychosis, but their real-world implementation requires thorough validation across diverse risk populations, including young patients with depressive syndromes. MethodsWe validated the previously described NAPLS-2 (North American Prodrome Longitudinal Study 2) calculator in 334 patients (26 with transition to psychosis) with CHR or recent-onset depression (ROD) drawn from the multisite European PRONIA (Personalised Prognostic Tools for Early Psychosis Management) study. Patients were categorized into three risk enrichment levels, ranging from UHR, over CHR, to a broad-risk population comprising patients with CHR or ROD (CHR|ROD). We assessed how risk enrichment and different predictive algorithms influenced prognostic performance using reciprocal external validation. ResultsAfter calibration, the NAPLS-2 model predicted psychosis with a balanced accuracy (BAC) (sensitivity, specificity) of 68% (73%, 63%) in the PRONIA-UHR cohort, 67% (74%, 60%) in the CHR cohort, and 70% (73%, 66%) in patients with CHR|ROD. Multiple model derivation in PRONIA–CHR|ROD and validation in NAPLS-2–UHR patients confirmed that broader risk definitions produced more accurate risk calculators (CHR|ROD-based vs. UHR-based performance: 67% [68%, 66%] vs. 58% [61%, 56%]). Support vector machines were superior in CHR|ROD (BAC = 71%), while ridge logistic regression and support vector machines performed similarly in CHR (BAC = 67%) and UHR cohorts (BAC = 65%). Attenuated psychotic symptoms predicted psychosis across risk levels, while younger age and reduced processing speed became increasingly relevant for broader risk cohorts. ConclusionsClinical-neurocognitive machine learning models operating in young patients with affective and CHR syndromes facilitate a more precise and generalizable prediction of psychosis. Future studies should investigate their therapeutic utility in large-scale clinical trials.

Prediction of significant prostate cancer in biopsy-naïve men: Validation of a novel risk model combining MRI and clinical parameters and comparison to an ERSPC risk calculator and PI-RADS.

BackgroundRisk models (RM) need external validation to assess their value beyond the setting in which they were developed. We validated a RM combining mpMRI and clinical parameters for the probability of harboring significant prostate cancer (sPC, Gleason Score ≥ 3+4) for biopsy-naïve men.Material and methodsThe original RM was based on data of 670 biopsy-naïve men from Heidelberg University Hospital who underwent mpMRI with PI-RADS scoring prior to MRI/TRUS-fusion biopsy 2012–2015. Validity was tested by a consecutive cohort of biopsy-naïve men from Heidelberg (n = 160) and externally by a cohort of 133 men from University College London Hospital (UCLH). Assessment of validity was performed at fusion-biopsy by calibration plots, receiver operating characteristics curve and decision curve analyses. The RM`s performance was compared to ERSPC-RC3, ERSPC-RC3+PI-RADSv1.0 and PI-RADSv1.0 alone.ResultsSPC was detected in 76 men (48%) at Heidelberg and 38 men (29%) at UCLH. The areas under the curve (AUC) were 0.86 for the RM in both cohorts. For ERSPC-RC3+PI-RADSv1.0 the AUC was 0.84 in Heidelberg and 0.82 at UCLH, for ERSPC-RC3 0.76 at Heidelberg and 0.77 at UCLH and for PI-RADSv1.0 0.79 in Heidelberg and 0.82 at UCLH. Calibration curves suggest that prevalence of sPC needs to be adjusted to local circumstances, as the RM overestimated the risk of harboring sPC in the UCLH cohort. After prevalence-adjustment with respect to the prevalence underlying ERSPC-RC3 to ensure a generalizable comparison, not only between the Heidelberg and die UCLH subgroup, the RM`s Net benefit was superior over the ERSPC`s and the mpMRI`s for threshold probabilities above 0.1 in both cohorts.ConclusionsThe RM discriminated well between men with and without sPC at initial MRI-targeted biopsy but overestimated the sPC-risk at UCLH. Taking prevalence into account, the model demonstrated benefit compared with clinical risk calculators and PI-RADSv1.0 in making the decision to biopsy men at suspicion of PC. However, prevalence differences must be taken into account when using or validating the presented risk model.

Promise and Implementation of Proteomic Prostate Cancer Biomarkers.

Prostate cancer is one of the most commonly diagnosed malignancy and the fifth leading cause of cancer mortality in men. Despite the broad use of prostate-specific antigen test that resulted in an increase in number of diagnosed cases, disease management needs to be improved. Proteomic biomarkers alone and or in combination with clinical and pathological risk calculators are expected to improve on decreasing the unnecessary biopsies, stratify low risk patients, and predict response to treatment. To this end, significant efforts have been undertaken to identify novel biomarkers that can accurately discriminate between indolent and aggressive cancer forms and indicate those men at high risk for developing prostate cancer that require immediate treatment. In the era of “big data” and “personalized medicine” proteomics-based biomarkers hold great promise to provide clinically applicable tools, as proteins regulate all biological functions, and integrate genomic information with the environmental impact. In this review article, we aim to provide a critical assessment of the current proteomics-based biomarkers for prostate cancer and their actual clinical applicability. For that purpose, a systematic review of the literature published within the last 10 years was performed using the Web of Science Database. We specifically discuss the potential and prospects of use for diagnostic, prognostic and predictive proteomics-based biomarkers, including both body fluid- and tissue-based markers.

How to better identify patients at high risk of postoperative complications?

Preoperative risk assessment and perioperative factors may help identify patients at increased risk of postoperative complications and allow postoperative management strategies that improve patient outcomes. This review summarizes historical and more recent scoring systems for predicting patients with increased morbidity and mortality in the postoperative period. Most prediction scores predict postoperative mortality with, at best, moderate accuracy. Scores that incorporate surgery-specific and intraoperative covariates may improve the accuracy of traditional scores. Traditional risk factors including increased ASA physical status score, emergent surgery, intraoperative blood loss and hemodynamic instability are consistently associated with increased mortality using most scoring systems. Preoperative clinical risk indices and risk calculators estimate surgical risk with moderate accuracy. Surgery-specific risk calculators are helpful in identifying patients at increased risk of 30-day mortality. Particular attention should be paid to intraoperative hemodynamic instability, blood loss, extent of surgical excision and volume of resection.

Combined Clinical Parameters and Multiparametric Magnetic Resonance Imaging for Advanced Risk Modeling of Prostate Cancer—Patient-tailored Risk Stratification Can Reduce Unnecessary Biopsies

BackgroundMultiparametric magnetic resonance imaging (mpMRI) is gaining widespread acceptance in prostate cancer (PC) diagnosis and improves significant PC (sPC; Gleason score≥3+4) detection. Decision making based on European Randomised Study of Screening for PC (ERSPC) risk-calculator (RC) parameters may overcome prostate-specific antigen (PSA) limitations. ObjectiveWe added pre-biopsy mpMRI to ERSPC-RC parameters and developed risk models (RMs) to predict individual sPC risk for biopsy-naïve men and men after previous biopsy. Design, setting, and participantsWe retrospectively analyzed clinical parameters of 1159 men who underwent mpMRI prior to MRI/transrectal ultrasound fusion biopsy between 2012 and 2015. Outcome measurements and statistical analysisMultivariate regression analyses were used to determine significant sPC predictors for RM development. The prediction performance was compared with ERSPC-RCs, RCs refitted on our cohort, Prostate Imaging Reporting and Data System (PI-RADS) v1.0, and ERSPC-RC plus PI-RADSv1.0 using receiver-operating characteristics (ROCs). Discrimination and calibration of the RM, as well as net decision and reduction curve analyses were evaluated based on resampling methods. Results and limitationsPSA, prostate volume, digital-rectal examination, and PI-RADS were significant sPC predictors and included in the RMs together with age. The ROC area under the curve of the RM for biopsy-naïve men was comparable with ERSPC-RC3 plus PI-RADSv1.0 (0.83 vs 0.84) but larger compared with ERSPC-RC3 (0.81), refitted RC3 (0.80), and PI-RADS (0.76). For postbiopsy men, the novel RM's discrimination (0.81) was higher, compared with PI-RADS (0.78), ERSPC-RC4 (0.66), refitted RC4 (0.76), and ERSPC-RC4 plus PI-RADSv1.0 (0.78). Both RM benefits exceeded those of ERSPC-RCs and PI-RADS in the decision regarding which patient to receive biopsy and enabled the highest reduction rate of unnecessary biopsies. Limitations include a monocentric design and a lack of PI-RADSv2.0. ConclusionsThe novel RMs, incorporating clinical parameters and PI-RADS, performed significantly better compared with RMs without PI-RADS and provided measurable benefit in making the decision to biopsy men at a suspicion of PC. For biopsy-naïve patients, both our RM and ERSPC-RC3 plus PI-RADSv1.0 exceeded the prediction performance compared with clinical parameters alone. Patient summaryCombined risk models including clinical and imaging parameters predict clinically relevant prostate cancer significantly better than clinical risk calculators and multiparametric magnetic resonance imaging alone. The risk models demonstrate a benefit in making a decision about which patient needs a biopsy and concurrently help avoid unnecessary biopsies.

Racial Variation in the Utility of Urinary Biomarkers PCA3 and T2ERG in a Large Multicenter Study

To our knowledge it is unknown whether urinary biomarkers for prostate cancer have added utility to clinical risk calculators in different racial groups. We examined the utility of urinary biomarkers added to clinical risk calculators for predicting prostate cancer in African American and nonAfrican American men. Demographics, PCPT (Prostate Cancer Prevention Trial) risk scores, data on the biomarkers data PCA3 (prostate cancer antigen 3) and T2ERG (transmembrane protease serine 2 and v-ets erythroblastosis virus E26 oncogene homolog gene fusion), and biopsy pathology features were prospectively collected on 718 men as part of EDRN (Early Detection Research Network). Utility was determined by generating ROC curves and comparing AUC values for the baseline multivariable PCPT model and for models containing biomarker scores. PCA3 and T2ERG added utility for the prediction of prostate cancer and clinically significant prostate cancer when combined with the PCPT Risk Calculator. This utility was seen in nonAfrican American men only for PCA3 (AUC 0.64 increased to 0.75 for prostate cancer and to 0.69-0.77 for clinically significant prostate cancer, both p <0.001) and for T2ERG (AUC 0.64-0.74 for prostate cancer, p <0.001, and 0.69-0.73 for clinically significant prostate cancer, p = 0.029). African American men did not have an added benefit with the addition ofbiomarkers, including PCA3 (AUC 0.75-0.77, p = 0.64, and 0.65-0.66, p=0.74)and T2ERG (AUC 0.75-0.74, p = 0.74, and 0.65-0.64, p = 0.88), forprostate cancer and clinically significant prostate cancer, respectively. Limitations include the small number of African American men (72). The post hoc subgroup analysis nature of the study limited findings to being hypothesis generating. As novel biomarkers are discovered, clinical utility should be established across demographically diverse cohorts.

Bone mineral density: testing for osteoporosis.

Primary osteoporosis is related to bone loss from ageing. Secondary osteoporosis results from specific conditions that may be reversible. A thoracolumbar X-ray is useful in identifying vertebral fractures, and dual energy X-ray absorptiometry is the preferred method of calculating bone mineral density. The density of the total hip is the best predictor for a hip fracture, while the lumbar spine is the best site for monitoring the effect of treatment. The T-score is a comparison of the patient's bone density with healthy, young individuals of the same sex. A negative T-score of -2.5 or less at the femoral neck defines osteoporosis. The Z-score is a comparison with the bone density of people of the same age and sex as the patient. A negative Z-score of -2.5 or less should raise suspicion of a secondary cause of osteoporosis. Clinical risk calculators can be used to predict the 10-year probability of a hip or major osteoporotic fracture. A probability of more than 5% for the hip or more than 20% for any fracture is abnormal and treatment may be warranted.

How can we identify the high-risk patient?

Purpose of reviewAccurate and early identification of high-risk surgical patients allows for targeted use of perioperative monitoring and interventions that may improve their outcomes. This review summarizes current evidence on how information from the preoperative, operative, and immediate postoperative periods can help identify such individuals.Recent findingsSimple risk indices, such as the Revised Cardiac Risk Index or American Society of Anesthesiologists Physical Status scale, and online calculators allow risk to be estimated with moderate accuracy using readily available preoperative clinical information. Both specific specialized tests (i.e., cardiopulmonary exercise testing and cardiac stress testing) and promising novel biomarkers (i.e., troponins and natriuretic peptides) can help refine these risk estimates before surgery. Estimates of perioperative risk can be further informed by information acquired during the operative and immediate postoperative periods, such as risk indices (i.e., surgical Apgar score), individual risk factors (i.e., intraoperative hypotension), or postoperative biomarkers (i.e., troponins and natriuretic peptides).SummaryPreoperative clinical risk indices and risk calculators estimate surgical risk with moderate accuracy. Although novel biomarkers, specialized preoperative testing, and immediate postoperative risk indices show promise as methods to refine these risk estimates, more research is needed on how best to integrate risk information from these different sources.

Improving patient prostate cancer risk assessment: Moving from static, globally-applied to dynamic, practice-specific risk calculators

Clinical risk calculators are now widely available but have generally been implemented in a static and one-size-fits-all fashion. The objective of this study was to challenge these notions and show via a case study concerning risk-based screening for prostate cancer how calculators can be dynamically and locally tailored to improve on-site patient accuracy. Yearly data from five international prostate biopsy cohorts (3 in the US, 1 in Austria, 1 in England) were used to compare 6 methods for annual risk prediction: static use of the online US-developed Prostate Cancer Prevention Trial Risk Calculator (PCPTRC); recalibration of the PCPTRC; revision of the PCPTRC; building a new model each year using logistic regression, Bayesian prior-to-posterior updating, or random forests. All methods performed similarly with respect to discrimination, except for random forests, which were worse. All methods except for random forests greatly improved calibration over the static PCPTRC in all cohorts except for Austria, where the PCPTRC had the best calibration followed closely by recalibration. The case study shows that a simple annual recalibration of a general online risk tool for prostate cancer can improve its accuracy with respect to the local patient practice at hand.