Elucidating the need for prostate cancer risk calculators in conjunction with mpMRI in initial risk assessment before prostate biopsy at a tertiary prostate cancer center

OBJECTIVE

Utilizing personalized risk assessment for clinically significant prostate cancer (csPCa) incorporating multiparametric magnetic resonance imaging (mpMRI) reduces biopsies and overdiagnosis. We validated both multi- and univariate risk models in biopsy-naïve men, with and without the inclusion of mpMRI data for csPCa detection.

METHODS

N = 565 men underwent mpMRI-targeted prostate biopsy, and the diagnostic performance of risk calculators (RCs), mpMRI alone, and clinical measures were compared using receiver operating characteristic curve (ROC) analysis and decision curve analysis (DCA). Subgroups were stratified based on mpMRI findings and quality.

RESULTS

csPCa was detected in 56.3%. PI-RADS score achieved the highest area under the curve (AUC) when comparing univariate risk models (AUC 0.82, p < 0.001). Multivariate RCs showed only marginal improvement in csPCa detection compared to PI-RADS score alone, with just one of four RCs showing significant superiority. In mpMRI-negative cases, the non-MRI-based RC performed best (AUC 0.80, p = 0.016), with the potential to spare biopsies for 23%. PSA-density and multivariate RCs demonstrated comparable performance for PI-RADS 3 constellation (AUC 0.65 vs. 0.60-0.65, p > 0.5; saved biopsies 16%). In men with suspicious mpMRI, both mpMRI-based RCs and the PI-RADS score predicted csPCa excellently (AUC 0.82-0.79 vs. 0.80, p > 0.05), highlighting superior performance compared to non-MRI-based models (all p < 0.002). Quality-assured imaging consistently improved csPCa risk stratification across all subgroups.

CONCLUSION

In tertiary centers serving a high-risk population, high-quality mpMRI provides a simple yet effective way to assess the risk of csPCa. Using multivariate RCs reduces multiple biopsies, especially in mpMRI-negative and PI-RADS 3 constellation.

The association between inflammation, arterial stiffness, oxidized LDL and cardiovascular disease in Finnish men with metabolic syndrome - a 15-year follow-up study

BACKGROUND

All-cause mortality and cardiovascular disease are increased in subjects with metabolic syndrome (MetS). Risk scores are used to predict individual risk of heart disease. We performed a long-term follow-up study to investigate whether risk scores and cardiovascular risk factors such as arterial stiffness, high-sensitive C-reactive protein (hs-CRP) and oxidized LDL (OxLDL) can be used to predict cardiovascular events in Finnish men with MetS.

METHODS

After baseline measurements we followed 105 Finnish men aged 30 to 65 years with MetS for a mean period of 16.4 years. The primary outcome of the study was a composite of myocardial infarction, stroke, symptomatic vascular disease diagnosed with invasive angiography, coronary or peripheral revascularization, amputation due to peripheral vascular disease, cardiovascular death and non-cardiovascular death. The endpoints were retrieved from electronic medical records.

RESULTS

The number of acute myocardial infarctions and strokes during the first 10 years was lower than estimated by FINRISK score but SCORE predicted cardiovascular death correctly. During the whole follow-up period, 27 of 105 participants (25.8%) had 30 endpoint events. The incidence of the primary composite outcome was significantly lower in subjects with hs-CRP < 1.0 mg/L than in subjects with hs-CRP ≥ 1.0 mg/L (6 of 41 subjects [14.6%] vs. 21 of 64 subjects [32.8%]; p = 0.036). The incidence of the primary composite outcome was higher among subjects with large artery elasticity classified as borderline compared to subjects with normal large artery elasticity (5 of 10 subjects [50%] vs. 22 of 93 subjects [24%]; p = 0.05). There was no difference in the incidence of primary composite outcome in groups with different degrees of small artery elasticity or different level of oxLDL.

CONCLUSIONS

Men with MetS who had hs-CRP ≥ 1.0 mg/L had higher risk for CVD and all-cause mortality than those with hs-CRP of < 1.0 mg/L. This also applies to subjects with borderline decreased large artery elasticity. The amount of OxLDL had no predictive value on the incidence of CVD and all-cause mortality. Men with MetS participating in the Hämeenlinna Metabolic Syndrome Research Program without lifestyle or drug intervention had better outcome for myocardial infarction or stroke than estimated by the FINRISK score.

TRIAL REGISTRATION

ClinicalTrials.gov NCT01119404 retrospectively registered 07/05/2010.

Assessing surgical risk calculators on hernia repair candidates with cirrhosis

PURPOSE

Several risk calculators have been developed and deployed to help surgeons estimate the mortality risk that comes with performing hernia repair surgery on patient with severe liver disease. This study seeks to evaluate the accuracy of these risk calculators on patients with cirrhosis and identify the most suitable population of patient to use these calculators on.

METHODS

The American College of Surgeons National Surgery Quality Improvement Program (NSQIP) 2013-2021 datasets were queried for patients who underwent hernia repair surgery. Mayo Clinic's "Post-operative Mortality Risk in Patients with Cirrhosis" risk calculator, Model for End-Stage Liver Disease (MELD) calculator, NSQIP's Surgical Risk Calculator, and a surgical 5-item modified frailty index were assessed to determine whether they accurately predict mortality following abdominal hernia repair.

RESULTS

In total, 1368 patients met inclusion criteria. Receiver operating characteristic (ROC) curve analysis of the 4 mortality risk calculators resulted in the following: NSQIP Surgical Risk Calculator = 0.803 (p < 0.001); "Post-operative Mortality Risk in Patients with Cirrhosis" with an etiology of "Alcoholic or Cholestatic" yielded an AUC = 0.722 (p < 0.001); MELD score yielded an AUC = 0.709 (p < 0.001); and the modified 5-item frailty index yielded an AUC = 0.583 (p = 0.04).

CONCLUSION

The NSQIP Surgical Risk Calculator more accurately predicts 30-day mortality in patients with ascites undergoing hernia repair. However, if the patient is missing one of the 21 input variables required by this calculator, Mayo Clinic's 30-day mortality calculator should be consulted before the more widely used MELD score.

Accuracy of Risk Estimation for Surgeons Versus Risk Calculators in Emergency General Surgery

INTRODUCTION

Surgical risk calculators have expanded in both number and sophistication of their predictive approach. These calculators are gaining popularity as validated tools to help surgeons estimate mortality and complications following emergency general surgery (EGS). However, the accuracy of risk estimates generated by these calculators compared to risk estimation by practicing surgeons has not been explored.

METHODS

Acute care surgeons at a quaternary care center prospectively estimated 30-d mortality and complications for adult EGS patients (2019-2021). Surgeon predictions were compared to Predictive OpTimal Trees in Emergency Surgery Risk (POTTER) and NSQIP estimates. Observed-to-expected (O:E) ratios of median aggregate estimates were calculated. C-statistics for surgeon and calculator estimations were utilized to quantify predictive accuracy.

RESULTS

Among 150 patients (median 61 y, 45% male), 30-d mortality was 15% (n = 23). Observed rates of prolonged mechanical ventilation and acute renal failures were 30% and 10%, respectively. Overall, surgeon predictions were similar to risk calculator estimates for mortality (c-statistics 0.843 [surgeon] versus 0.848 [POTTER] and 0.815 [NSQIP]) and need for prolonged ventilation (c-statistics 0.801 versus 0.722 and 0.689, respectively). Surgeons tended to overestimate complication risks. Surgeon experience was not significantly associated with mortality prediction in an adjusted model.

CONCLUSIONS

Acute care surgeons at a quaternary care center predicted postoperative mortality and complications with similar discrimination when compared to surgical risk calculators. Surgeon expertise should be utilized in conjunction with risk calculators when counseling EGS patients regarding anticipated postoperative outcomes. Surgeons should be cognizant of patterns in overestimation or underestimation of complications.

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.

External validation of two mpMRI-risk calculators predicting risk of prostate cancer before biopsy

Purpose

Risk calculators (RC) aim to improve prebiopsy risk stratification. Their latest versions now include multiparametric magnetic resonance imaging (mpMRI) findings. For their implementation into clinical practice, critical external validations are needed.

Methods

We retrospectively analyzed the patient data of 554 men who underwent ultrasound-guided targeted and systematic prostate biopsies at 2 centers. We validated the mpMRI-RCs of Radtke et al. (RC-R) and Alberts et al. (RC-A), previously shown to predict prostate cancer (PCa) and clinically significant PCa (csPCa). We assessed these RCs’ prediction accuracy by analyzing the receiver-operating characteristics (ROC) curve and evaluated their clinical utility using Decision Curve Analysis (DCA), including Net-Benefit and Net-Reduction curves.

Results

We found that the Area Under the ROC Curve (AUC) for predicting PCa was 0.681 [confidence interval (CI) 95% 0.635–0.727] for RC-A. The AUCs for predicting csPCa were 0.635 (CI 95% 0.583–0.686) for RC-A and 0.676 (CI 95% 0.627–0.725) for RC-R. For example, at a risk threshold of 12%, RC-A needs to assess 334 and RC-R 500 patients to detect one additional true positive PCa or csPCa patient, respectively. At the same risk threshold of 12%, RC-A only needs to assess 6 and RC-R 16 patients to detect one additional true negative PCa or csPCa patient.

Conclusion

The mpMRI-RCs, RC-R and RC-A, are robust and valuable tools for patient counseling. Although they do not improve PCa and csPCa detection rates by a clinically meaningful margin, they aid in avoiding unnecessary prostate biopsies. Their implementation could reduce overdiagnosis and reduce PCa screening morbidity.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00345-022-04119-8.

Surgical risk calculators in veterans following lower extremity amputation

OBJECTIVE

To evaluate the accuracy of multiple risk calculators for 30-day mortality on patients undergoing major lower extremity amputation.

METHODS

The actual 30-day mortality at a single Veterans Affairs institution was compared to the predicted outcome from the following risk calculators: ACS-NSQIP, VASQIP, amputation scoring tool (AST), and POTTER elective.

RESULTS

The overall calculated 30-day mortality was similar to the actual mortality with the VASQIP and POTTER elective risk calculators, while the NSQIP and AST over-estimated the 30-day mortality. The predictive accuracy of the POTTER and NSQIP risk calculators were moderate (AUC >0.7), and fair for the VASQIP and AST.

CONCLUSION

Risk assessment tools can provide adjunctive data on predicted 30-day mortality in patients undergoing major lower extremity amputation. In our study, there were differences in predictability of the risk calculators for lower extremity amputation that should be considered when utilizing a risk assessment tool to improve physician-patient shared decision-making.

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

BACKGROUND

Transition 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.

METHODS

We 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.

RESULTS

After 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.

CONCLUSIONS

Clinical-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.

Interdisciplinary assessment and diagnostic algorithm: The role of the diabetologist

In recent years, many studies have revealed the importance of heart failure (HF) development in type 2 diabetes (T2D), which increases the morbidity and mortality during the course of diabetes. In this context, it became important to emphasize the role of both cardiologists and diabetologists in the early diagnosis and further adequate treatment of HF in T2D. While HF appears in two major forms, with reduced or preserved ejection fraction (EF), namely HFrEF and HFpEF, it became important to define the optimal approach to the diagnostics. Regarding HFrEF, the role of cardiological methods remained dominant, while the complexity of early diagnosis requires nowadays more active participation of diabetologists. The absence of abundant symptoms and echocardiographic findings imposed the need for the use of risk markers based on metabolic variables and low-grade inflammation parameters. Following that unmet need, numerous studies have defined the possible relationship between metabolic variables in diabetes and the risk for HF. Moreover, attempts have been made to integrate biochemical and clinical parameters into risk score engines and some of them gave promising results. However, the follow-up studies in T2D subjects are needed to determine the clinical relevance of these new approaches.

Use of noninvasive scores for advanced liver fibrosis can guide the need for hepatic biopsy during bariatric procedures

BACKGROUND

Patients with obesity are at increased risk for nonalcoholic fatty liver disease (NAFLD). The effectiveness of noninvasive screening tests for ruling out advanced fibrosis (stage 3-4) is unknown.

OBJECTIVES

To determine the prevalence of advanced fibrosis in patients undergoing routine liver biopsy during bariatric surgery and assess the effectiveness of existing noninvasive risk calculators.

SETTING

Academic medical center in the United States.

METHODS

Routine liver biopsies were obtained during first-time bariatric surgery (January 2001-December 2017). Patient demographic characteristics, co-morbidities, and preoperative laboratory values were compiled. Sensitivity, specificity, negative predictive value (NPV), and positive predictive value (PPV) were compared between 3 noninvasive risk calculators for advanced fibrosis: the fibrosis-4 index, NAFLD fibrosis score, and aminotransferase-to-platelet ratio index (APRI).

RESULTS

Among 2465 patients, the prevalence of advanced fibrosis (stage 3-4) was 3.4%. The mean age was 45.5 years, and the mean body mass index was 46.8. The sensitivity of noninvasive risk calculators ranged from 85% (NAFLD fibrosis score) to 24% (APRI). The NAFLD fibrosis score performed best in screening out advanced fibrosis, with an NPV of 99%. The PPV ranged from 9% to 65%. In this study cohort, the use of the NALFD fibrosis score correctly ruled out advanced fibrosis in 893 (36%) patients, with 13 false negatives.

CONCLUSIONS

The prevalence of advanced fibrosis in individuals undergoing routine first-time bariatric procedures is 3.4%. Use of the NALFD fibrosis score can rule out advanced fibrosis in one-third of this population, and guide surgical decision-making.

[Cardiovascular risk calculators for people living with human immunodeficiency virus]

The increasing access and efficacy of antiretroviral therapy has allowed people living with human immunodeficiency virus to achieve a life expectancy similar to that of the general population. However, this goal may be affected by the increased risk of cardiovascular disease in this group. This risk is multifactorial, involving the high prevalence of traditional risk factors, the development of a pro-inflammatory state related to chronic infection, and the use of antiretroviral drugs with an adverse metabolic profile. In daily practice, in order to estimate this risk and guide medical decision-making, different calculators are available. These are based on data from population cohorts, many of them from human immunodeficiency virusnegative subjects. The main aim of this review is to describe the epidemiology of cardiovascular disease in people living with human immunodeficiency virus, the available risk calculators and their use.

Predicting Patient-Centered Outcomes from Spine Surgery Using Risk Assessment Tools: a Systematic Review

PURPOSE OF REVIEW

The purpose of this systematic review is to evaluate the current literature in patients undergoing spine surgery in the cervical, thoracic, and lumbar spine to determine the available risk assessment tools to predict the patient-centered outcomes of pain, disability, physical function, quality of life, psychological disposition, and return to work after surgery.

RECENT FINDINGS

Risk assessment tools can assist surgeons and other healthcare providers in identifying the benefit-risk ratio of surgical candidates. These tools gather demographic, medical history, and other pertinent patient-reported measures to calculate a probability utilizing regression or machine learning statistical foundations. Currently, much is still unknown about the use of these tools to predict quality of life, disability, and other factors following spine surgery. A systematic review was conducted using PRISMA guidelines that identified risk assessment tools that utilized patient-reported outcome measures as part of the calculation. From 8128 identified studies, 13 articles met inclusion criteria and were accepted into this review. The range of c-index values reported in the studies was between 0.63 and 0.84, indicating fair to excellent model performance. Post-surgical patient-reported outcomes were identified in the following categories (n = total number of predictive models): return to work (n = 3), pain (n = 9), physical functioning and disability (n = 5), quality of life (QOL) (n = 6), and psychosocial disposition (n = 2). Our review has synthesized the available evidence on risk assessment tools for predicting patient-centered outcomes in patients undergoing spine surgery and described their findings and clinical utility.

Comparing the prediction of prostate biopsy outcome using the Chinese Prostate Cancer Consortium (CPCC) Risk Calculator and the Asian adapted Rotterdam European Randomized Study of Screening for Prostate Cancer (ERSPC) Risk Calculator in Chinese and European men

PURPOSE

To externally validate the clinical utility of Chinese Prostate Cancer Consortium Risk Calculator (CPCC-RC) and Asian adapted Rotterdam European Randomized Study of Screening for Prostate Cancer Risk Calculator 3 (A-ERSPC-RC3) for prediction prostate cancer (PCa) and high-grade prostate cancer (HGPCa, Gleason Score ≥ 3 + 4) in both Chinese and European populations.

MATERIALS AND METHODS

The Chinese clinical cohort, the European population-based screening cohort, and the European clinical cohort included 2,508, 3,616 and 617 prostate biopsy-naive men, respectively. The area under the receiver operating characteristic curve (AUC), calibration plot and decision curve analyses were applied in the analysis.

RESULTS

The CPCC-RC's predictive ability for any PCa (AUC 0.77, 95% CI 0.75-0.79) was lower than the A-ERSPC-RC3 (AUC 0.79, 95% CI 0.77-0.81) in the European screening cohort (p < 0.001), but similar for HGPCa (p = 0.24). The CPCC-RC showed lower predictive accuracy for any PCa (AUC 0.65, 95% CI 0.61-0.70), but acceptable predictive accuracy for HGPCa (AUC 0.73, 95% CI 0.69-0.77) in the European clinical cohort. The A-ERSPC-RC3 showed an AUC of 0.74 (95% CI 0.72-0.76) in predicting any PCa, and a similar AUC of 0.74 (95% CI 0.72-0.76) in predicting HGPCa in Chinese cohort. In the Chinese population, decision curve analysis revealed a higher net benefit for CPCC-RC than A-ERSPC-RC3, while in the European screening and clinical cohorts, the net benefit was higher for A-ERSPC-RC3.

CONCLUSIONS

The A-ERSPC-RC3 accurately predict the prostate biopsy in a contemporary Chinese multi-center clinical cohort. The CPCC-RC can predict accurately in a population-based screening cohort, but not in the European clinical cohort.

Cardiovascular Risk Assessment and Therapeutic Implications in Rheumatoid Arthritis

Patients with rheumatoid arthritis (RA) suffer from a magnitude of excess cardiovascular risk. A paradoxical lipid pattern has been observed in rheumatoid arthritis patients where low levels of total cholesterol and low-density lipoprotein are associated with a higher risk of cardiovascular disease. This paper aims to break down the evidence explaining why patients with low to normal LDL, and total cholesterol have such excess cardiovascular risk. A component of the enhanced cardiovascular risk is systemic inflammation and the subsequent pro-atherogenic dyslipidemia patterns. Due to this "lipid paradox," current risk algorithms and guidelines designed for the general population may underestimate cardiovascular risk in patients with rheumatoid arthritis. The purpose of this paper is to critically evaluate some of the discrepancies and layers of cardiovascular risk in RA patients, the role RA medication may have in mitigating or increasing cardiovascular risk, and the possible role of statin therapy.

Shared Decision-Making

Shared decision-making is not a new concept but has been gaining traction in recent years as a means of engaging patients in their health care choices. Decision aid development is being encouraged in order to further enhance patient discussions with clinicians. As shared decision-making engages the patient's core values and goals, clinicians are better able to guide patients with their health care choices. Shared decision-making results in less utilization of health care resources, engages more patient autonomy, and is being advocated for all patients as value-based care.

Favourable understandability, but poor actionability: An evaluation of online type 2 diabetes risk calculators

OBJECTIVE

The study aim was to identify all freely available online diabetes risk calculators and to evaluate their suitability for patients with low health literacy.

METHODS

Online diabetes risk calculators were identified by an environmental scan. The Patient Education Material Assessment Tool for Printable Materials was used to determine understandability and actionability scores. A high-risk profile was used to compare the risk results obtained with each calculator.

RESULTS

Thirty-five risk calculators were identified; 51% had no described model, 23% reported absolute risk and 31% used visual aids. The estimated risk for the same profile ranged from low to very high. The mean understandability score was 79% (SD = 19%) and the mean actionability score was 42% (SD = 30%).

CONCLUSIONS

Online diabetes risk calculators are generally understandable, but not very actionable, and may not be completely suitable for use by patients with low health literacy. The estimated risk is highly variable depending on the underlying model used for the calculation.

PRACTICE IMPLICATIONS

Patients and healthcare providers need to exercise caution when selecting a diabetes risk calculator.

Prostate Cancer Markers

Diagnostic biomarkers derived from blood, urine, or prostate tissue provide additional information beyond clinical calculators to determine the risk of detecting high-grade prostate cancer. Once diagnosed, multiple markers leverage prostate cancer biopsy tissue to prognosticate clinical outcomes, including adverse pathology at radical prostatectomy, disease recurrence, and prostate cancer mortality; however the clinical utility of some outcomes to patient decision making is unclear. Markers using tissue from radical prostatectomy specimens provide additional information about the risk of biochemical recurrence, development of metastatic disease, and subsequent mortality beyond existing multivariable clinical calculators (the use of a marker to simply sub-stratify risk groups such as the NCCN groups is of minimal value). No biomarkers currently available for prostate cancer have been prospectively validated to be predict an improved clinical outcome for a specific therapy based on the test result; however, further research and development of these tests may produce a truly predictive biomarker for prostate cancer treatment.

Medical Device Integrated Vital Signs Monitoring Application with Real-Time Clinical Decision Support

This research involves the design and development of a novel Android smartphone application for real-time vital signs monitoring and decision support. The proposed application integrates market available, wireless and Bluetooth connected medical devices for collecting vital signs. The medical device data collected by the app includes heart rate, oxygen saturation and electrocardiograph (ECG). The collated data is streamed/displayed on the smartphone in real-time. This application was designed by adopting six screens approach (6S) mobile development framework and focused on user-centered approach and considered clinicians-as-a-user. The clinical engagement, consultations, feedback and usability of the application in the everyday practices were considered critical from the initial phase of the design and development. Furthermore, the proposed application is capable to deliver rich clinical decision support in real-time using the integrated medical device data.