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Objectives: To compare the performance of currently available biopsy decision support tools incorporating magnetic resonance imaging (MRI) findings in predicting clinically significant prostate cancer (csPCa). Patients and Methods: We retrospectively included men who underwent prostate MRI and subsequent targeted and/or systematic prostate biopsies in two large European centres. Available decision support tools were identified by a PubMed search. Performance was assessed by calibration, discrimination, decision curve analysis (DCA) and numbers of biopsies avoided vs csPCa cases missed, before and after recalibration, at risk thresholds of 5%–20%. Results: A total of 940 men were included, 507 (54%) had csPCa. The median (interquartile range) age, prostate‐specific antigen (PSA) level, and PSA density (PSAD) were 68 (63–72) years, 9 (7–15) ng/mL, and 0.20 (0.13–0.32) ng/mL2, respectively. In all, 18 multivariable risk calculators (MRI‐RCs) and dichotomous biopsy decision strategies based on MRI findings and PSAD thresholds were assessed. The Van Leeuwen model and the Rotterdam Prostate Cancer Risk Calculator (RPCRC) had the best discriminative ability (area under the receiver operating characteristic curve 0.86) of the MRI‐RCs that could be assessed in the whole cohort. DCA showed the highest clinical utility for the Van Leeuwen model, followed by the RPCRC. At the 10% threshold the Van Leeuwen model would avoid 22% of biopsies, missing 1.8% of csPCa, whilst the RPCRC would avoid 20% of biopsies, missing 2.6% of csPCas. These multivariable models outperformed all dichotomous decision strategies based only on MRI‐findings and PSAD. Conclusions: Even in this high‐risk cohort, biopsy decision support tools would avoid many prostate biopsies, whilst missing very few csPCa cases. The Van Leeuwen model had the highest clinical utility, followed by the RPCRC. These multivariable MRI‐RCs outperformed and should be favoured over decision strategies based only on MRI and PSAD. [ABSTRACT FROM AUTHOR]

Objective: To evaluate the performance of risk calculators (RCs) predicting lymph node invasion (LNI) and extraprostatic extension (EPE) in men undergoing transperineal magnetic resonance imaging/transrectal ultrasound (TRUS)‐fusion template saturation biopsy (TTSB) and conventional systematic TRUS‐guided biopsy (SB). Patients and Methods: The RCs were tested in a consecutive cohort of 645 men undergoing radical prostatectomy with extended pelvic LN dissection between 2005 and 2019. TTSB was performed in 230 (35.7%) and SB in 415 (64.3%) men. Risk of LNI and EPE was calculated using the available RCs. Discrimination, calibration, and clinical usefulness stratified by different biopsy techniques were assessed. Results: Lymph node invasion was observed in 23 (10%) and EPE in 73 (31.8%) of cases with TTSB and 53 (12.8%) and 158 (38%) with SB, respectively. RCs showed an excellent discrimination and acceptable calibration for prediction of LNI based on TTSB (area under the curve [AUC]/risk estimation: Memorial Sloan Kettering Cancer Center [MSKCC]‐RC 0.79/−4%, Briganti (2012)‐RC 0.82/−4%, Gandaglia‐RC 0.81/+6%). These were comparable in SB (MSKCC‐RC 0.78/+2%; Briganti (2012)‐RC 0.77/−3%). Decision curve analysis (DCA) revealed a net benefit at threshold probabilities between 3% and 6% when TTSB was used. For prediction of EPE based on TTSB an inferior discrimination and variable calibration were observed (AUC/risk estimation: MSKCC‐RC 0.71/+8% and Martini (2018)‐RC 0.69/+2%) achieving a net benefit on DCA only at risk thresholds of >17%. Performance of RCs for prediction of LNI and EPE based on SB showed comparable results with a better performance in the DCA for LNI (risk thresholds 1–2%) and poorer performance for EPE (risk threshold >20%). This study is limited by its retrospective single‐institution design. Conclusions: The potentially more accurate grading ability of TTSB did not result in improved performance of preoperative RCs. Prediction tools for LNI proved clinical usefulness while RCs for EPE did not. [ABSTRACT FROM AUTHOR]

Background: Emergency general surgery (EGS) patients have an increased risk of mortality and morbidity compared to other surgical patients. Limited risk assessment tools exist for use in both operative and non‐operative EGS patients. We assessed the accuracy of a modified Emergency Surgical Acuity Score (mESAS) in EGS patients at our institution. Methods: A retrospective cohort study from an acute surgical unit at a tertiary referral hospital was performed. Primary endpoints assessed included death before discharge, length of stay (LOS) >5 days and unplanned readmission within 28 days. Operative and non‐operative patients were analysed separately. Validation was performed using the area under the receiver operating characteristic (AUROC), Brier score and Hosmer‐Lemeshow test. Results: A total of 1763 admissions between March 2018 and June 2021 were included for analysis. The mESAS was an accurate predictor of both death before discharge (AUROC 0.979, Brier score 0.007, Hosmer–Lemeshow P = 0.981) and LOS >5 days (0.787, 0.104, and 0.253, respectively). The mESAS was less accurate in predicting readmission within 28 days (0.639, 0.040, and 0.887, respectively). The mESAS retained its predictive ability for death before discharge and LOS >5 days in the split cohort analysis. Conclusion: This study is the first to validate a modified ESAS in a non‐operatively managed EGS population internationally and the first to validate the mESAS in Australia. The mESAS accurately predicts death before discharge and prolonged LOS for all EGS patients, providing a highly useful tool for surgeons and EGS units worldwide. [ABSTRACT FROM AUTHOR]

Summary: Estimating pre‐operative mortality risk may inform clinical decision‐making for peri‐operative care. However, pre‐operative mortality risk prediction models are rarely implemented in routine clinical practice. High predictive accuracy and clinical usability are essential for acceptance and clinical implementation. In this systematic review, we identified and appraised prediction models for 30‐day postoperative mortality in non‐cardiac surgical cohorts. PubMed and Embase were searched up to December 2022 for studies investigating pre‐operative prediction models for 30‐day mortality. We assessed predictive performance in terms of discrimination and calibration. Risk of bias was evaluated using a tool to assess the risk of bias and applicability of prediction model studies. To further inform potential adoption, we also assessed clinical usability for selected models. In all, 15 studies evaluating 10 prediction models were included. Discrimination ranged from a c‐statistic of 0.82 (MySurgeryRisk) to 0.96 (extreme gradient boosting machine learning model). Calibration was reported in only six studies. Model performance was highest for the surgical outcome risk tool (SORT) and its external validations. Clinical usability was highest for the surgical risk pre‐operative assessment system. The SORT and risk quantification index also scored high on clinical usability. We found unclear or high risk of bias in the development of all models. The SORT showed the best combination of predictive performance and clinical usability and has been externally validated in several heterogeneous cohorts. To improve clinical uptake, full integration of reliable models with sufficient face validity within the electronic health record is imperative. [ABSTRACT FROM AUTHOR]

Objectives: To build a one‐year risk calculator (RC) to predict individualized risk for suicide attempt in early‐onset bipolar disorder. Methods: Youth numbering 394 with bipolar disorder who completed ≥2 follow‐up assessments (median follow‐up length = 13.1 years) in the longitudinal Course and Outcome of Bipolar Youth (COBY) study were included. Suicide attempt over follow‐up was assessed via the A‐LIFE Self‐Injurious/Suicidal Behavior scale. Predictors from the literature on suicidal behavior in bipolar disorder that are readily assessed in clinical practice were selected and trichotomized as appropriate (presence past 6 months/lifetime history only/no lifetime history). The RC was trained via boosted multinomial classification trees; predictions were calibrated via Platt scaling. Half of the sample was used to train, and the other half to independently test the RC. Results: There were 249 suicide attempts among 106 individuals. Ten predictors accounted for >90% of the cross‐validated relative influence in the model (AUC = 0.82; in order of relative influence): (1) age of mood disorder onset; (2) non‐suicidal self‐injurious behavior (trichotomized); (3) current age; (4) psychosis (trichotomized); (5) socioeconomic status; (6) most severe depressive symptoms in past 6 months (trichotomized none/subthreshold/threshold); (7) history of suicide attempt (trichotomized); (8) family history of suicidal behavior; (9) substance use disorder (trichotomized); (10) lifetime history of physical/sexual abuse. For all trichotomized variables, presence in the past 6 months reliably predicted higher risk than lifetime history. Conclusions: This RC holds promise as a clinical and research tool for prospective identification of individualized high‐risk periods for suicide attempt in early‐onset bipolar disorder. [ABSTRACT FROM AUTHOR]

Background: Most Prostate Imaging-Reporting and Data System (PI-RADS) 3 lesions do not contain clinically significant prostate cancer (CSPCa; grade group ≥2). This study was aimed at identifying clinical and magnetic resonance imaging (MRI)-derived risk fac- tors that predict CSPCa in men with PI-RADS 3 lesions.Methods: This study analyzed the detection of CSPCa in men who underwent MRI-targeted biopsy for PI-RADS 3 lesions. Multivariable logistic regression models with goodness-of-fit testing were used to identify variables associated with CSPCa. Receiver operating curves and decision curve analyses were used to estimate the clinical utility of a predictive model.Results: Of the 1784 men reviewed, 1537 were included in the training cohort, and 247 were included in the validation cohort. The 309 men with CSPCa (17.3%) were older, had a higher prostate-specific antigen (PSA) density, and had a greater likelihood of an anteriorly located lesion than men without CSPCa (p < .01). Multivariable analysis revealed that PSA density (odds ratio [OR], 1.36; 95% confidence interval [CI], 1.05-1.85; p < .01), age (OR, 1.05; 95% CI, 1.02-1.07; p < .01), and a biopsy-naive status (OR, 1.83; 95% CI, 1.38-2.44) were independently associated with CSPCa. A prior negative biopsy was negatively associated (OR, 0.35; 95% CI, 0.24-0.50; p < .01). The application of the model to the validation cohort resulted in an area under the curve of 0.78. A predicted risk threshold of 12% could have prevented 25% of biopsies while detecting almost 95% of CSPCas with a sensitivity of 94% and a specificity of 34%.Conclusions: For PI-RADS 3 lesions, an elevated PSA density, older age, and a biopsy-naive status were associated with CSPCa, whereas a prior negative biopsy was negatively associated. A predictive model could prevent PI-RADS 3 biopsies while missing few CSPCas.Lay Summary: Among men with an equivocal lesion (Prostate Imaging-Reporting and Data System 3) on multiparametric magnetic resonance imaging (mpMRI), those who are older, those who have a higher prostate-specific antigen density, and those who have never had a biopsy before are at higher risk for having clinically significant prostate cancer (CSPCa) on subsequent biopsy. However, men with at least one negative biopsy have a lower risk of CSPCa. A new predictive model can greatly reduce the need to biopsy equivocal lesions noted on mpMRI while missing only a few cases of CSPCa. [ABSTRACT FROM AUTHOR]

The study aimed to investigate the best- performing of three risk calculators (RCs) for the Turkish population in predicting cancer- free status and high- risk prostate cancer (PCa) in patients undergoing transrectal ultrasound- guided prostate biopsy. The electronic medical records of 527 patients who underwent prostate biopsy for the first time due to PSA of 0.3-- 50 ng/dl and/or cancer suspicion at digital rectal examination (DRE) between January 2017 and December 2020 were retrieved retrospectively. The predictive power of the RCs in the biopsy and the surgical cohort was calculated by two urologists using European Randomised Study of Screening for Prostate Cancer (ERSPC) RC, the North American Prostate Cancer Prevention Trial- RC (PCPT- RC), and the Prostate Biopsy Collaborative Group (PBCG)- RC. All three RCs were successful in predicting PCa and high- risk disease at ROC analysis (p < 0.0001). Of these three nomograms, PBCG- RC outperformed PCPT- RC 2.0 and ERSPC- RH in predicting benign pathology outcomes at biopsy. A better performance of PBCG- RC was also observed in terms of prediction of high- risk disease at biopsy. Using any of the available RCs prior to biopsy is of greater assistance to prostate- specific antigen and DRE than examination alone. The study results show that PBCG- RC performed before biopsy has a higher predictive power than the other two RCs. [ABSTRACT FROM AUTHOR]

Significant technological advances have improved our ability to localize epilepsy and investigate the electrophysiology in patients undergoing preparation for epilepsy surgery. Conversely, our process of decision‐making and outcome prediction has remained essentially restricted to subjective clinical judgment. This may have hindered our ability to improve outcomes. In this review, we highlight the cognitive biases that interfere with medical decision‐making and present data on the use of algorithms and statistical models in general health care, before pivoting to discuss applications in the context of epilepsy. [ABSTRACT FROM AUTHOR]

Objectives: To develop and externally validate a risk calculator for prediction of any cancer recurrence in patients with penile squamous cell carcinoma (pSCC) and inguinal lymph node metastases (ILNM), as to date no validated prognostic tool is available for patients with pSCC and ILNM. Patients and Methods: The development cohort included 234 patients from seven referral centres. The external validation cohort included 273 patients from two additional referral centres. Cox regression identified predictors of any recurrence, which were used to develop a risk calculator. The risk‐calculator grouped the development and the validation cohorts according to the individual risk of any recurrence at 24 months (24m‐R). Adjuvant treatment effects were tested on overall survival (OS) according to the derived tertiles, within the development and validation cohorts. Results: Positive surgical margins, pN3, and ILNM ratio were associated with higher recurrence rate. The 2‐year OS rates were lower for patients with high (>37%) and intermediate (19–37%) compared to low (<19%) 24m‐R risk of recurrence, for both the development (43% and 58% vs 83%, P < 0.001) and validation cohort (44% and 50% vs 85%, P < 0.001). Results were confirmed in the subgroup of patients who did not receive adjuvant treatment (P < 0.001), but not in patients who did receive adjuvant treatments in both the development and validation cohorts (P > 0.1). Conclusion: Adjuvant treatment planning is crucial in patients with pSCC with ILNM, where only weak evidence is available. The current tool proved to successfully stratify patients according to their individual risk, potentially allowing better tailoring of adjuvant treatments. [ABSTRACT FROM AUTHOR]

Risk calculators have been created on the basis of predictive modeling to aid clinicians and patients in deciding whether to pursue prostate biopsy. Ideally, these risk calculators would reduce the diagnosis of low‐risk prostate cancer and increase the diagnosis of clinically significant prostate cancer. However, before these prediction models are to be available for widespread use, they must be carefully validated, discriminated, and calibrated to ensure their performance and applicability. [ABSTRACT FROM AUTHOR]

Background: The American College of Surgeons National Surgical Quality Improvement Program (ACS‐NSQIP) surgical risk calculator provides an estimate preoperatively of operative risks including mortality; however, its utility is not known in Australian emergency general surgical patients. This study sought to determine accuracy of the calculator in predicting outcome of high‐risk patients in an Australian acute surgical unit to establish if this calculator could be a useful tool to identify high‐risk patients in an Australian setting. Methods: Retrospective analysis of patients admitted to the acute surgical unit at a tertiary referral centre between 2018 and 2019 was conducted. High‐risk patients were defined as those who underwent an emergency operation with an ACS‐NSQIP surgical mortality score ≥5%. Post‐operative outcomes assessed included mortality and return to operating theatre, readmission and discharge to nursing home. External validation of the calculator was performed using discrimination and calibration statistics. Results: Over a 14‐month period, 58 patients were high risk, with an average age of 75 years, 93% were classified as functionally independent/partially dependent and 91.4% underwent a laparotomy. Overall 30‐day mortality rate was 20.7%. The ACS‐NSQIP calculator was a reliable predictor of mortality, with c‐statistic of 0.835 (0.654–0.977), Brier score of 0.125 (0.081–0.176) and Hosmer–Lemeshow statistic of 0.389. The calculator was less accurate in its prediction of other outcomes assessed. Conclusion: The ACS‐NSQIP calculator accurately approximated mortality in high‐risk Australian patients requiring emergency surgery. This study has demonstrated that in this patient population, the calculator could reliably be applied in the multidisciplinary care of emergency surgical patients. [ABSTRACT FROM AUTHOR]

Objectives: Head and neck cancer (HNC) diagnosis through the 2‐week wait, urgent suspicion of cancer (USOC) pathway has failed to increase early cancer detection rates in the UK. A head and neck cancer risk calculator (HaNC‐RC) has previously been designed to aid referral of high‐risk patients to USOC clinics (predictive power: 77%). Our aim was to refine the HaNC‐RC to increase its prediction potential. Design: Following sample size calculation, prospective data collection and statistical analysis of referral criteria and outcomes. Setting: Large tertiary care cancer centre in Scotland. Participants: 3531 new patients seen in routine, urgent and USOC head and neck (HaN) clinics. Main outcome measures: Data collected were as follows: demographics, social history, presenting symptoms and signs and HNC diagnosis. Univariate and multivariate regression analysis were performed to identify significant predictors of HNC. Internal validation was performed using 1000 sample bootstrapping to estimate model diagnostics included the area under the receiver operator curve (AUC), sensitivity and specificity. Results: The updated version of the risk calculator (HaNC‐RC v.2) includes age, gender, unintentional weight loss, smoking, alcohol, positive and negative symptoms and signs of HNC. It has achieved an AUC of 88.6% with two recommended triage referral cut‐offs to USOC (cut‐off: 7.1%; sensitivity: 85%, specificity: 78.3%) or urgent clinics (cut‐off: 2.2%; sensitivity: 97.1%; specificity of 52.9%). This could redistribute cancer detection through USOC clinics from the current 60.9%–85.2%, without affecting total numbers seen in each clinical setting. Conclusions: The use of the HaNC‐RC v.2 has a significant potential in both identifying patients at high risk of HNC early thought USOC clinics but also improving health service delivery practices by reducing the number of inappropriately urgent referrals. [ABSTRACT FROM AUTHOR]

Background: Diagnosis of Parkinson's disease (PD) is typically preceded by nonspecific presentations in primary care.Objectives: The objective of this study was to develop and validate a prediction model for diagnosis of PD based on presentations in primary care.Setting: The settings were general practices providing data for The Health Improvement Network UK primary care database.Methods: Data from 8,166 patients aged older than age 50 years with incident diagnosis of PD and 46,755 controls were analyzed. Likelihood ratios, sensitivity, specificity, and positive and negative predictive values for individual symptoms and combinations of presentations were calculated. An algorithm for risk of diagnosis of PD within 5 years was calculated using multivariate logistic regression analysis. Split sample analysis was used for model validation with a 70% development sample and a 30% validation sample.Results: Presentations independently and significantly associated with later diagnosis of PD in multivariate analysis were tremor, constipation, depression or anxiety, fatigue, dizziness, urinary dysfunction, balance problems, memory problems and cognitive decline, hypotension, rigidity, and hypersalivation. The discrimination and calibration of the risk algorithm were good with an area under the curve of 0.80 (95% confidence interval 0.78-0.81). At a threshold of 5%, 37% of those classified as high risk would be diagnosed with PD within 5 years and 99% of those who were not classified as high risk would not be diagnosed with PD.Conclusion: This risk algorithm applied to routine primary care presentations can identify individuals at increased risk of diagnosis of PD within 5 years to allow for monitoring and earlier diagnosis of PD. © 2019 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society. [ABSTRACT FROM AUTHOR]

Aim: A comprehensive description of morbidity and mortality risk factors for post liver transplant has not been available to date. In this study, we established real‐time risk models of postoperative morbidities and mortality in liver transplant recipients using two Japanese nationwide databases. Methods: Data from two Japanese nationwide databases were combined and used for this study. We developed real‐time prognostic models for morbidity and mortality from a derivation cohort (n = 1472) and validated the findings with an independent cohort (n = 395). Preoperative variables (C1), preoperative and intraoperative variables (C2), and all variables including postoperative morbidities within 30 days (C3) were analyzed to evaluate the independent risk factors for postoperative morbidity and mortality. Results: We established real‐time risk models for morbidity and mortality. Areas under the curve (AUC) of C1 and C2 risk models for mortality were 0.74 (0.63‐0.82) and 0.79 (0.69‐0.86), respectively. Multivariate logistic analysis using C3 showed that hemoglobin <10 g/dL, operative time (hours), and five postoperative morbidities (prolonged ventilation >48 hours, coma >24 hours, renal dysfunction, postoperative systemic sepsis, and serum total bilirubin ≥10 mg/dL) represented independent risk factors for mortality (AUC = 0.87, 95% confidence interval [CI]: 0.78‐0.93). Conclusions: Real‐time risk models of postoperative morbidities and mortality at various perioperative time points in liver transplant recipients were established. These novel approaches may improve postoperative outcomes of liver transplant recipients. Furthermore, these real‐time risk models may be applicable to other surgical procedures. Using two nationwide databases that cover 100% of all transplant surgeries in Japan, we evaluated and created novel risk models of morbidity and mortality. To this end, we used very simple variables at each time point of pre‐, intra‐, and postoperative periods. The results of these analyses could deliver timely feedback to surgeons, nurses, and other medical staff members for better understanding of the conditions of each patient. [ABSTRACT FROM AUTHOR]

Abstract: Aim: The Kidney Disease Improving Global Outcomes (KDIGO) guideline recommends the incorporation of a new risk calculator that quantifies the end‐stage kidney disease (ESKD) risk based on a composite profile of risk factors in living kidney donor candidates (LKDC). We compared the ESKD risk estimates in previously declined versus accepted LKDC to evaluate the predictive capacity and potential impact of this tool. Methods: Baseline 15 year and lifetime ESKD risk estimates without donation were calculated using the risk calculator for LKDC assessed from two centres between 2007 and 2015. LKDC suitability based on the proposed KDIGO and the existing Caring for Australasians with Renal Impairment national guidelines was compared. Results: Median 15 year ESKD risk was 0.14% (IQR 0.09–0.31%) in declined LKDC (n=59) versus 0.10% (0.07–0.14%) in accepted LKDC (n=89) (P<0.001). Lifetime risk was similar: 0.39% (0.23–0.80%) versus 0.35% (0.22–0.56%), respectively; however, declined LKDC had a higher 98% risk percentile value (8.19% vs 1.02%) and were more likely to exceed a 1% ESKD risk threshold (15% vs 1%; P<0.01). The calculator captured reasons for declining donation in only 39% of LKDC; 46.9% of LKDC with Caring for Australasians with Renal Impairment contraindications were reclassified as having an acceptable (≤1%) lifetime risk and no KDIGO contraindications, primarily related to a lower pre‐donation glomerular filtration rate or controlled hypertension with obesity. Conclusion: Declined LKDC had a higher 15 year but similar lifetime ESKD risk. However, the calculator successfully differentiated declined LKDC with a lifetime risk >1%. This risk calculator appears to complement but not replace clinical evaluation. [ABSTRACT FROM AUTHOR]

Background: Risk calculators have traditionally utilized serum prostate-specific antigen (PSA) values in addition to clinical variables to predict the likelihood of prostate cancer (PCa).Purpose: To develop a prebiopsy multiparametric MRI (mpMRI)-based risk score (RS) and a statistical equation for predicting the risk of PCa in biopsy-naive men with serum PSA between 4-10 ng/mL that may help reduce unnecessary biopsies.Study Type: Prospective cross-sectional study.Subjects: In all, 137 consecutive men with PSA between 4-10 ng/mL underwent prebiopsy mpMRI (diffusion-weighted [DW]-MRI and MR spectroscopic imaging [MRSI]) during 2009-2015 were recruited for this study.Field Strength/sequence: 1.5T (Avanto, Siemens Health Care, Erlangen, Germany); T1 -weighted, T2 -weighted, DW-MRI, and MRSI sequences were used.Assessment: All eligible patients underwent mpMRI-directed, cognitive-fusion transrectal ultrasound (TRUS)-guided biopsies.Statistical Tests: An equation model and an RS were developed using receiver operating characteristic (ROC) curve analysis and a multivariable logistic regression approach. A 10-fold crossvalidation and simulation analyses were performed to assess diagnostic performance of various combinations of mpMRI parameters.Results: Of 137 patients, 32 were diagnosed with PCa on biopsy. Multivariable analysis, adjusted with positive pathology, showed apparent diffusion coefficient (ADC), metabolite ratio, and PSA as significant predictors of PCa (P < 0.05). A statistical equation was derived using these predictors. A simple 6-point mpMRI-based RS was derived for calculating the risk of PCa and it showed that it is highly predictive for PCa (odds ratio = 3.74, 95% confidence interval [CI]: 2.24-6.27, area under the curve [AUC] = 0.87). Both models (equation and RS) yielded high predictive performance (AUC ≥0.85) on validation analysis.Data Conclusion: A statistical equation and a simple 6-point mpMRI-based RS can be used as a point-of-care tool to potentially help limit the number of negative biopsies in men with PSA between 4 and 10 ng/mL.Level Of Evidence: 1 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:1227-1236. [ABSTRACT FROM AUTHOR]

Abstract: Objectives: To adapt the Canadian Diabetes Risk Assessment Questionnaire for the Chinese population and to evaluate its psychometric properties. Design and Sample: A cross‐sectional study was conducted with a convenience sample of 194 individuals aged 35–74 years from October 2014 to April 2015. Methods: The Canadian Diabetes Risk Assessment Questionnaire was adapted and translated for the Chinese population. Test–retest reliability was conducted to measure stability. Criterion and convergent validity of the adapted questionnaire were assessed using 2‐hr 75 g oral glucose tolerance tests and the Finnish Diabetes Risk Scores, respectively. Sensitivity and specificity were evaluated to establish its predictive validity. Results: The test–retest reliability was 0.988. Adequate validity of the adapted questionnaire was demonstrated by positive correlations found between the scores and 2‐hr 75 g oral glucose tolerance tests (r = .343, p < .001) and with the Finnish Diabetes Risk Scores (r = .738, p < .001). The area under receiver operating characteristic curve was 0.705 (95% CI .632, .778), demonstrating moderate diagnostic value at a cutoff score of 30. The sensitivity was 73%, with a positive predictive value of 57% and negative predictive value of 78%. Conclusions: Our results provided evidence supporting the translation consistency, content validity, convergent validity, criterion validity, sensitivity, and specificity of the translated Canadian Diabetes Risk Assessment Questionnaire with minor modifications. This paper provides clinical, practical, and methodological information on how to adapt a diabetes risk calculator between cultures for public health nurses. [ABSTRACT FROM AUTHOR]

Objectives To assess the level of agreement between digital rectal examination findings of two urologists and its effect on risk prediction using the digital rectal examination-based Rotterdam Prostate Cancer Risk Calculator. Methods The study sample consisted of a prospective cohort of asymptomatic unscreened men with prostate-specific antigen ≤50.0 ng/mL and transrectal ultrasound volume ≤110 mL who underwent transrectal ultrasound-guided prostate biopsy. Both urologists' digital rectal examination findings were graded normal or abnormal (nodularity and/or induration), and volume classified as 25, 40 or 60 mL, according to the risk calculator algorithm. Interrater agreement analysis using Cohen's kappa (κ) statistic was carried out to determine consistency of digital rectal examination outcome and volume assessment. Receiver operating characteristic curve analysis and calibration plots were constructed to determine the effect of interrater differences. Decision curve analysis was applied to evaluate the clinical usefulness of the model. Results Of the 241 men included in the study, 41% ( n = 98) had prostate cancer (81 were clinically significant, i.e. Gleason ≥3 + 4). There was substantial agreement in the digital rectal examination (abnormal/normal; κ = 0.78; P < 0.001) and volume estimation (κ = 0.79; P < 0.001). Receiver operating characteristic analyses showed good discrimination (0.75-0.78) and were comparable for both urologists. In the high-risk cohort, at a probability threshold of 25%, the risk calculator reduced the prostate biopsy rate by 9%, without missing cancers. Conclusions Slight differences in digital rectal examination findings seem to have very limited impact on the performance of the Rotterdam Prostate Cancer Risk Calculator. Therefore, this can be considered a useful prostate biopsy outcome prediction tool. [ABSTRACT FROM AUTHOR]

Objective To investigate biopsy complications and hospital admissions that could be reduced by the use of European Randomized Study of Screening for Prostate Cancer ( ERSPC) risk calculators. Materials and Methods All biopsies performed in the Rotterdam section of the ERSPC between 1993 and 2015 were included. Biopsy complications and hospital admission data were prospectively recorded in questionnaires that were completed 2 weeks after biopsy. The ERSPC risk calculators 3 ( RC3) and 4 ( RC4) were applied to men attending the first and subsequent rounds of screening, respectively. Applying the predefined RC3/4 probability thresholds for prostate cancer ( PCa) risk of ≥12.5% and high-grade PCa risk ≥3%, we assessed the number of complications, admissions and costs that could be reduced by avoiding biopsies in men below these thresholds. Results A total of 10 747 biopsies with complete questionnaires were included. For these biopsies a complication rate of 67.9% (7294/10 747), a post-biopsy fever rate of 3.9% (424/10747) and a hospital admission rate of 0.9% (92/10747) were recorded. The fever rate was found to be static over the years, but the hospital admission rate tripled from 0.6% (1993-1996) to 2.1% (2009-2015). Among 7704 biopsies which fit the criteria for RC3 or RC4, 35.8% of biopsies (2757/7704), 37.4% of complications (1972/5268), 39.4% of fever events (128/325) and 42.3% of admissions (30/71) could have been avoided by using one of the risk calculators. More complications could have been avoided if RC4 had been used and for more recent biopsies (2009-2015). Our findings show that 35.9% of the total cost of biopsies and complication treatment could have been avoided. Conclusion A significant proportion of biopsy complications, hospital admissions and costs could be reduced if biopsy decisions were based on ERSPC risk calculators instead of PSA only. This effect was most prominent in more recent biopsies and in men with repeated biopsies or screening. [ABSTRACT FROM AUTHOR]

Background The morbidity rate after pancreaticoduodenectomy remains high. The objectives of this retrospective cohort study were to clarify the risk factors associated with serious morbidity (Clavien-Dindo classification grades IV-V), and create complication risk calculators using the Japanese National Clinical Database. Methods Between 2011 and 2012, data from 17,564 patients who underwent pancreaticoduodenectomy at 1,311 institutions in Japan were recorded in this database. The morbidity rate and associated risk factors were analyzed. Results The overall and serious morbidity rates were 41.6% and 4.5%, respectively. A pancreatic fistula ( PF) with an International Study Group of Pancreatic Fistula ( ISGPF) grade C was significantly associated with serious morbidity ( P < 0.001). Twenty-one variables were considered statistically significant predictors of serious complications, and 15 of them overlapped with those of a PF with ISGPF grade C. The predictors included age, sex, obesity, functional status, smoking status, the presence of a comorbidity, non-pancreatic cancer, combined vascular resection, and several abnormal laboratory results. C-indices of the risk models for serious morbidity and grade C PF were 0.708 and 0.700, respectively. Conclusions Preventing a PF grade C is important for decreasing the serious morbidity rate and these risk calculations contribute to adequate patient selection. [ABSTRACT FROM AUTHOR]

Using both simulated and real datasets, we compared two approaches for estimating absolute risk from nested case-control (NCC) data and demonstrated the feasibility of using the NCC design for estimating absolute risk. In contrast to previously published results, we successfully demonstrated not only that data from a matched NCC study can be used to unbiasedly estimate absolute risk but also that matched studies give better statistical efficiency and classify subjects into more appropriate risk categories. Our result has implications for studies that aim to develop or validate risk prediction models. In addition to the traditional full cohort study and case-cohort study, researchers designing these studies now have the option of performing a NCC study with huge potential savings in cost and resources. Detailed explanations on how to obtain the absolute risk estimates under the proposed approach are given. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Chronic hepatitis, cirrhosis and hepatocellular carcinoma (HCC) are considered to be sequential adverse outcomes in patients with persistent hepatitis B virus (HBV) infection. HBV infection is endemic in Taiwan and most HBV carriers acquire the virus early in life. The impact of HBV factors on the natural course of patients with chronic HBV infection has been investigated in three cohort studies. The first Risk Evaluation of Viral Load Elevation and Associated Liver Disease/Cancer-Hepatitis B Virus (REVEAL-HBV) cohort study revealed that HBV viral load is a strong predictive factor for the risk of cirrhosis and HCC and baseline serum HBV DNA levels >2000 IU/ml may increase the risk of cirrhosis and HCC in adult HBV carriers. In the second Study of E Antigen seRoClearance of Hepatitis B (SEARCH-B), HBsAg level <100 IU/ml at 1-year post HBeAg seroconversion was shown to be a predictor of HBsAg seroclearance over time. Recently, the third Elucidation of Risk Factors for Disease Control or Advancement in Taiwanese Hepatitis B Carriers (ERADICATE-B) cohort study also suggested that HBsAg levels were a complementary predictive risk factor to HBV DNA levels for predicting HBV-related adverse events in patients with low viral load (HBV DNA level <2000 IU/ml). An HBsAg level >1000 IU/ml in HBeAg-negative patients with low viral load, is associated with higher risks of HCC, cirrhosis, and HBeAg-negative hepatitis. Based on results of the REVEAL-HBV cohort study, a risk calculator to predict HCC in non-cirrhotic patients was developed and validated by independent international cohorts (REACH-B). In the recent update of the REVEAL-HBV study, HBsAg level was incorporated into the HCC risk prediction model with excellent accuracy. In conclusion, evidence from these HBV clinical cohorts confirms the progression and integration of viral biomarkers for the prediction of the prognosis of Asian chronic hepatitis B (CHB) patients. If the predictive power of the HCC risk calculator could be validated in non-Asian populations, it could be used in clinical practice to individualize the management of HBV carriers with different levels of HCC risk. [ABSTRACT FROM AUTHOR]

The American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) calculator is meant to provide an estimation of perioperative risk. Our goal was to determine the clinical applicability of the calculator in major head and neck surgery. A retrospective chart review was completed for major head and neck operations performed at 1 institution from 2013 to 2014. The calculated perioperative complication risks from the ACS NSQIP calculator were compared with observed complication rates. Overall, the ACS NSQIP calculator had little predictive value for pneumonia, surgical site infection, 30-day return to operating room, or length of stay within this cohort (P > .05). The calculator appears to have some value predicting total numbers of complications but has poor performance predicting an individual’s risk of suffering a perioperative complication. In conclusion, in our small cohort of patients, the ACS NSQIP calculator was a poor predictor of perioperative complications following major head and neck operations. [ABSTRACT FROM AUTHOR]

BACKGROUND The Prostate Cancer Prevention Trial risk calculator for high-grade (PCPTHG) prostate cancer (CaP) was developed to improve the detection of clinically significant CaP. In this study, the authors compared the performance of the PCPTHG against multiparametric magnetic resonance imaging (MP-MRI) in predicting men at risk of CaP. METHODS Men with an abnormal prostate-specific antigen (PSA) level or digital rectal examination (DRE) and a suspicious lesion on a 3-Tesla MP-MRI were enrolled prospectively. Three radiologists reviewed and graded all lesions on a 5-point Likert scale. Biopsy of suspicious lesion(s) was performed using a proprietary MRI/transrectal ultrasound fusion-guided prostate biopsy system, after which 12-core biopsy was performed. A genitourinary pathologist reviewed all pathology slides. The performance of PCPTHG was compared with that of MP-MRI in predicting clinically significant CaP. RESULTS Of 175 men who were eligible for analysis, 64.6% (113 of 175 men) were diagnosed with CaP, including 93 of 113 men (82.3%) who had clinically significant disease. Age, abnormal DRE, PSA, PSA density, prostate size, extraprostatic extension on MRI, apparent diffusion coefficient value, and MRI lesion size were identified as significant predictors of high-grade CaP (all P < .05). The area under the receiver operating characteristic curve of PCPTHG for predicting high-grade CaP was 0.676 (95% confidence interval [CI], 0.592-0.751). By using a risk cutoff of ≥15% for biopsy as, proposed previously for high-grade CaP, sensitivity was 96.4%, specificity was 7.6%, and the false-positive rate was 51.1%. In contrast, the area under the receiver operating characteristic curve of MP-MRI for high-grade CaP was 0.769 (95% CI, 0.703-0.834), and it was 0.812 (95% CI, 0.754-0.869) for clinically significant CaP. CONCLUSIONS MP-MRI outperforms PCPTHG in predicting clinically significant CaP, and its application may help select patients who will benefit from CaP diagnosis and treatment. Cancer 2014;120:2876-2882. © 2014 American Cancer Society. [ABSTRACT FROM AUTHOR]

Study Type - Prognosis (cohort series) Level of Evidence 2a What's known on the subject? and What does the study add? The present study is one of the first to investigate urologists' and patients' compliance with recommendations based on a risk calculator that calculates the probability of indolent prostate cancer. A threshold was set for a recommendation of active surveillance vs active treatment. Active surveillance recommendations based on a prostate cancer risk calculator were followed by most patients, but 30% with active treatment recommendations chose active surveillance instead. This indicates that the threshold may be too high for urologists and patients. OBJECTIVES To assess urologists' and patients' compliance with treatment recommendations based on a prostate cancer risk calculator (RC) and the reasons for non-compliance., To assess the difference between patients who were compliant and non-compliant with recommendations based on this RC., PATIENTS AND METHODS Eight urologists from five Dutch hospitals included 240 patients with prostate cancer (PCa), aged 55-75 years, from December 2008 to February 2011., The urologists used the European Randomized Study of Screening for Prostate Cancer RC which predicts the probability of potentially indolent PCa (P[indolent]), using serum prostate-specific antigen (PSA), prostate volume and pathological findings on biopsy., Inclusion criteria were PSA <20 ng/mL, clinical stage T1 or T2a-c disease, <50% positive sextant biopsy cores, ≤20 mm cancer tissue, ≥40 mm benign tissue and Gleason ≤3 + 3. If the P(indolent) was >70%, active surveillance (AS) was recommended, and active treatment (AT) otherwise., After the treatment decision, patients completed a questionnaire about their treatment choice, related (dis)advantages, and validated measurements of other factors, e.g. anxiety., RESULTS Most patients (45/55, 82%) were compliant with an AS recommendation. Another 54 chose AS despite an AT recommendation (54/185, 29%)., The most common reason for non-compliance with AT recommendations by urologists was the patient's preference for AS ( n= 30). These patients most often reported the delay of physical side effects of AT as the main advantage ( n= 19)., Those who complied with AT recommendations had higher mean PSA levels (8 vs 7 ng/mL, P= 0.02), higher mean amount of cancer tissue (7 vs 3 mm, P < 0.001), lower mean P(indolent) (36% vs 55%, P < 0.001), and higher mean generic anxiety scores (42 vs 38, P= 0.03) than those who did not comply., CONCLUSIONS AS recommendations were followed by most patients, while 29% with AT recommendations chose AS instead., Although further research is needed to validate the RC threshold, the current version is already useful in treatment decision-making in men with localized PCa. [ABSTRACT FROM AUTHOR]

Study Type - Diagnostic (cohort) Level of Evidence 2b What's known on the subject? and What does the study add? So far, few publications have shown that a prediction model influences the behaviour of both physicians and patients. To our knowledge, it was unknown whether urologists and patients are compliant with the recommendations of a prostate cancer risk calculator and their reasons for non-compliance. Recommendations of the European Randomized study of Screening for Prostate Cancer risk calculator (ERSPC RC) about the need of a prostate biopsy were followed in most patients. In most cases of non-compliance with 'no biopsy' recommendations, a PSA level ≥3 ng/mL was decisive to opt for biopsy. Before implementation of the ERSPC RC in urological practices at a large scale, it is important to obtain insight into the use of guidelines that might counteract the adoption of the use of the RC as a result of opposing recommendations. OBJECTIVES To assess both urologist and patient compliance with a 'no biopsy' or 'biopsy' recommendation of the European Randomized study of Screening for Prostate Cancer (ERSPC) Risk Calculator (RC), as well as their reasons for non-compliance., To assess determinants of patient compliance., PATIENTS AND METHODS The ERSPC RC calculates the probability on a positive sextant prostate biopsy ( Pposb) using serum prostate-specific antigen (PSA) level, outcomes of digital rectal examination and transrectal ultrasonography, and ultrasonographically assessed prostate volume. A biopsy was recommended if Pposb≥20%., Between 2008 and 2011, eight urologists from five Dutch hospitals included 443 patients (aged 55-75 years) after a PSA test with no previous biopsy., Urologists calculated the Pposb using the RC in the presence of patients and completed a questionnaire about compliance., Patients completed a questionnaire about prostate cancer knowledge, attitude towards prostate biopsy, self-rated health (12-Item Short Form Health Survey), anxiety (State Trait Anxiety Inventory-6, Memorial Anxiety Scale for Prostate Cancer) and decision-making measures (Decisional Conflict Scale)., RESULTS Both urologists and patients complied with the RC recommendation in 368 of 443 (83%) cases., If a biopsy was recommended, almost all patients (96%; 257/269) complied, although 63 of the 174 (36%) patients were biopsied against the recommendation of the RC., Compliers with a 'no biopsy' recommendation had a lower mean Pposb than non-compliers (9% vs 14%; P < 0.001)., Urologists opted for biopsies against the recommendations of the RC because of an elevated PSA level (≥3 ng/mL) (78%; 49/63) and patients because they wanted certainty (60%; 38/63)., CONCLUSIONS Recommendations of the ERSPC RC on prostate biopsy were followed in most patients., The RC hence may be a promising tool for supporting clinical decision-making. [ABSTRACT FROM AUTHOR]

Chronic hepatitis, cirrhosis and hepatocellular carcinoma (HCC) are considered to be sequential adverse outcomes in patients with persistent hepatitis B virus (HBV) infection. HBV infection is endemic in Taiwan and most HBV carriers acquire the virus early in life. The impact of HBV factors on the natural course of patients with chronic HBV infection has been investigated in three cohort studies. The first Risk Evaluation of Viral Load Elevation and Associated Liver Disease/Cancer-Hepatitis B Virus (REVEAL-HBV) cohort study revealed that HBV viral load is a strong predictive factor for the risk of cirrhosis and HCC and baseline serum HBV DNA levels >2000 IU/ml may increase the risk of cirrhosis and HCC in adult HBV carriers. In the second Study of E Antigen seRoClearance of Hepatitis B (SEARCH-B), HBsAg level <100 IU/ml at 1-year post HBeAg seroconversion was shown to be a predictor of HBsAg seroclearance over time. Recently, the third Elucidation of Risk Factors for Disease Control or Advancement in Taiwanese Hepatitis B Carriers (ERADICATE-B) cohort study also suggested that HBsAg levels were a complementary predictive risk factor to HBV DNA levels for predicting HBV-related adverse events in patients with low viral load (HBV DNA level <2000 IU/ml). An HBsAg level >1000 IU/ml in HBeAg-negative patients with low viral load, is associated with higher risks of HCC, cirrhosis, and HBeAg-negative hepatitis. Based on results of the REVEAL-HBV cohort study, a risk calculator to predict HCC in non-cirrhotic patients was developed and validated by independent international cohorts (REACH-B). In the recent update of the REVEAL-HBV study, HBsAg level was incorporated into the HCC risk prediction model with excellent accuracy. In conclusion, evidence from these HBV clinical cohorts confirms the progression and integration of viral biomarkers for the prediction of the prognosis of Asian chronic hepatitis B (CHB) patients. If the predictive power of the HCC risk calculator could be validated in non-Asian populations, it could be used in clinical practice to individualize the management of HBV carriers with different levels of HCC risk., (© 2014 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.)