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Background: Patient factors associated with urinary tract cancer can be used to risk stratify patients referred with haematuria, prioritising those with a higher risk of cancer for prompt investigation., Objective: To develop a prediction model for urinary tract cancer in patients referred with haematuria., Design, Setting, and Participants: A prospective observational study was conducted in 10 282 patients from 110 hospitals across 26 countries, aged ≥16 yr and referred to secondary care with haematuria. Patients with a known or previous urological malignancy were excluded., Outcome Measurements and Statistical Analysis: The primary outcomes were the presence or absence of urinary tract cancer (bladder cancer, upper tract urothelial cancer [UTUC], and renal cancer). Mixed-effect multivariable logistic regression was performed with site and country as random effects and clinically important patient-level candidate predictors, chosen a priori, as fixed effects. Predictors were selected primarily using clinical reasoning, in addition to backward stepwise selection. Calibration and discrimination were calculated, and bootstrap validation was performed to calculate optimism., Results and Limitations: The unadjusted prevalence was 17.2% (n = 1763) for bladder cancer, 1.20% (n = 123) for UTUC, and 1.00% (n = 103) for renal cancer. The final model included predictors of increased risk (visible haematuria, age, smoking history, male sex, and family history) and reduced risk (previous haematuria investigations, urinary tract infection, dysuria/suprapubic pain, anticoagulation, catheter use, and previous pelvic radiotherapy). The area under the receiver operating characteristic curve of the final model was 0.86 (95% confidence interval 0.85-0.87). The model is limited to patients without previous urological malignancy., Conclusions: This cancer prediction model is the first to consider established and novel urinary tract cancer diagnostic markers. It can be used in secondary care for risk stratifying patients and aid the clinician's decision-making process in prioritising patients for investigation., Patient Summary: We have developed a tool that uses a person's characteristics to determine the risk of cancer if that person develops blood in the urine (haematuria). This can be used to help prioritise patients for further investigation., (Copyright © 2022 The Authors. Published by Elsevier B.V. All rights reserved.)

Purpose: The likelihood of a stoma following ileocolic resection (ICR) for Crohn's disease (CD) is an important consideration. This study aims to identify the factors associated with an increased likelihood of a stoma and develop a predictive scoring system (SS)., Methods: Patient data were collected from St. Marks Hospital, London, UK and Humanitas Clinical and Research Center, Milan, Italy, on all patients who underwent an ICR for CD from 2005 to 2017. A logistic regression analysis was used for multivariate analysis. The SS was developed from the logistic regression model. The performance of the SS was evaluated using receiver operating characteristics area under the curve (AUROC)., Results: A total of 628 surgeries were included in the analysis. Sixty-nine surgeries were excluded due to missing data. The remaining 559 were divided into two cohorts for the scoring system's development (n = 434) and validation (n = 125). The regression model was statistically significant (p < 0.0001). The statistically significant independent variables included sex, preoperative albumin and haemoglobin levels, surgical access and simultaneous colonic resection. The AUROC for the development and validation cohorts were 0.803 and 0.905, respectively (p < 0.0001). Youden's index suggested the cut-off score of - 95.9, with a sensitivity of 87.6% and a specificity of 62.9%., Conclusions: Male sex, low preoperative albumin, anaemia, laparoscopic conversion and simultaneous colonic resection were associated with an increased likelihood of requiring a stoma and were used to develop an SS. The calculator is available online at https://rebrand.ly/CrohnsStoma ., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Introduction: Unplanned transfer of trauma patients to the intensive care unit (ICU) carries an associated increase in mortality, hospital length of stay, and cost. Trauma teams need to determine which patients necessitate ICU admission on presentation rather than waiting to intervene on deteriorating patients. This study sought to develop a novel Clinical Risk of Acute ICU Status during Hospitalization (CRASH) score to predict the risk of unplanned ICU admission., Methods: The 2017 Trauma Quality Improvement Program database was queried for patients admitted to nonICU locations. The group was randomly divided into two equal sets (derivation and validation). Multiple logistic regression models were created to determine the risk of unplanned ICU admission using patient demographics, comorbidities, and injuries. The weighted average and relative impact of each independent predictor were used to derive a CRASH score. The score was validated using area under the curve., Results: A total of 624,786 trauma patients were admitted to nonICU locations. From 312,393 patients in the derivation-set, 3769 (1.2%) had an unplanned ICU admission. A total of 24 independent predictors of unplanned ICU admission were identified and the CRASH score was derived with scores ranging from 0 to 32. The unplanned ICU admission rate increased steadily from 0.1% to 3.9% then 12.9% at scores of 0, 6, and 14, respectively. The area under the curve for was 0.78., Conclusions: The CRASH score is a novel and validated tool to predict unplanned ICU admission for trauma patients. This tool may help providers admit patients to the appropriate level of care or identify patients at-risk for decompensation., (Copyright © 2022 The Author(s). Published by Elsevier Inc. All rights reserved.)

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., (© 2022 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.)

Few prognostic models exist for patients hospitalised with chronic obstructive pulmonary disease (COPD); most are based on small cohorts enroled by specialists in academic centres. Electronic health records (EHRs) provide an opportunity to develop more representative models, although they may not record some variables used in existing models. for this retrospective cohort study, using EHRs, we identified 17,973 patients with an unplanned hospitalisation for COPD (in any diagnostic position) in the Glasgow area between 2011 and 2017. Patients with known lung cancer were excluded. EHR were linked to prior admissions, community prescribing and laboratory data. A pragmatic, parsimonious multivariable model was developed to predict 90-day mortality. we identified 12 variables strongly related to prognosis, including age, sex, length of index hospitalisation stay, prior diagnosis of cancer (excluding lung cancer) or dementia, prescription of oxygen or digoxin, neutrophil/lymphocyte ratio and serum chloride, urea, creatinine and albumin. The model achieved excellent calibration with reasonable discrimination (area under the curve: 0.806; 95% CI: 0.792–0.820). A risk-score was developed and an electronic risk-calculator is provided. a small number of variables, including prescriptions and laboratory data obtained from routine EHRs predict 90-day mortality after a hospitalisation for COPD. The risk-calculator provided might prove useful for service-evaluation and audit, to guide clinical management and to risk-stratify and select patients to be invited to participate in clinical research. [Display omitted] [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]

Background: Health risk calculators are widely available on the Internet, including cardiovascular disease (CVD) risk calculators that estimate the probability of a heart attack, stroke, or death over a 5- or 10-year period. Some calculators convert this probability to "heart age", where a heart age older than current age indicates modifiable risk factors. These calculators may impact patient decision making about CVD risk management with or without clinician involvement, but little is known about how patients use them. Previous studies have not investigated patient understanding of heart age compared to 5-year percentage risk, or the best way to present heart age.Objective: This study aimed to investigate patient experiences and understanding of online heart age calculators that use different verbal, numerical, and graphical formats, based on 5- and 10-year Framingham risk equations used in clinical practice guidelines around the world.Methods: General practitioners in New South Wales, Australia, recruited 26 patients with CVD/lifestyle risk factors who were not taking cholesterol or blood pressure-lowering medication in 2012. Participants were asked to "think aloud" while using two heart age calculators in random order, with semi-structured interviews before and after. Transcribed audio recordings were coded and a framework analysis method was used.Results: Risk factor questions were often misinterpreted, reducing the accuracy of the calculators. Participants perceived older heart age as confronting and younger heart age as positive but unrealistic. Unexpected or contradictory results (eg, low percentage risk but older heart age) led participants to question the credibility of the calculators. Reasons to discredit the results included the absence of relevant lifestyle questions and impact of corporate sponsorship. However, the calculators prompted participants to consider lifestyle changes irrespective of whether they received younger, same, or older heart age results.Conclusions: Online heart age calculators can be misunderstood and disregarded if they produce unexpected or contradictory results, but they may still motivate lifestyle changes. Future research should investigate both the benefits and harms of communicating risk in this way, and how to increase the reliability and credibility of online health risk calculators. [ABSTRACT FROM AUTHOR]

Background: There are numerous risk-prediction models applied to acute myocardial infarction-related cardiogenic shock (AMI-CS) patients to determine a more accurate prognosis and to assist in patient triage. There is wide heterogeneity among the risk models including the nature of predictors evaluated and their specific outcome measures. The aim of this analysis was to evaluate the performance of 20 risk-prediction models in AMI-CS patients., Methods: Patients included in our analysis were admitted to a tertiary care cardiac intensive care unit with AMI-CS. Twenty risk-prediction models were computed utilizing vitals assessments, laboratory investigations, hemodynamic markers, and vasopressor, inotropic and mechanical circulatory support available from within the first 24 ​hours of presentation. Receiver operating characteristic curves were used to assess the prediction of 30-day mortality. Calibration was assessed with a Hosmer-Lemeshow test., Results: Seventy patients (median age 63 years, 67% male) were admitted between 2017 and 2021. The models' area under the curve (AUC) ranged from 0.49 to 0.79, with the Simplified Acute Physiology Score II score having the most optimal discrimination of 30-day mortality (AUC: 0.79, 95% confidence interval [CI]: 0.67-0.90), followed by the Acute Physiology and Chronic Health Evaluation-III score (AUC: 0.72, 95% CI: 0.59-0.84) and the Acute Physiology and Chronic Health Evaluation-II score (AUC: 0.67, 95% CI: 0.55-0.80). All 20 risk scores demonstrated adequate calibration ( p > 0.05 for all)., Conclusions: Among the models tested in a data set of patients admitted with AMI-CS, the Simplified Acute Physiology Score II risk score model demonstrated the highest prognostic accuracy. Further investigations are required to improve the discriminative capabilities of these models or to establish new, more streamlined and accurate methods for mortality prognostication in AMI-CS., (© 2022 The Author(s).)

Background: Radical cystectomy (RC) for muscle invasive bladder cancer (MIBC) remains the historical gold standard for treatment despite significant perioperative morbidity and subsequent quality of life concerns. Trimodal therapy (TMT) is gaining acceptance as an alternative bladder preserving approach. We aim to identify patients for whom TMT may be the optimal approach by constructing risk calculators of morbidity and mortality associated with RC., Methods: Using the American College of Surgeons National Surgical Quality Improvement Program database, we selected patients diagnosed with MIBC undergoing RC, with a total of 10,642 patients identified. The primary outcome was mortality and secondary outcome was morbidity within 30 days of the procedure. We conducted multivariate logistic regression to obtain the best fit model for each outcome on 70% of the sample. Validation of the models was then performed on the remaining 30% of the sample. Model performance was assessed using discrimination and calibration abilities and a risk calculator was constructed for pre-operative counselling., Results: Of the full cohort, 199 patients (1.9%) died and 2,328 patients (21.9%) experienced morbidity. Variables selected for the model predicting mortality included age, frailty, the American Society of Anesthesiologists status and preoperative creatinine. For the mortality model, the area under the curve was 72% with a Hosmer-Lemeshow statistic of 0.722. For the morbidity model, the area under the curve was 60% with a Hosmer-Lemeshow statistic of 0.287. Variables significant in the model included continent diversion, smoking and frailty., Conclusion: We have constructed statistically significant and clinically relevant models using readily available health indicators to be used in multi-disciplinary discussion to provide high-risk patients with individualised risks of morbidity and mortality from RC, allowing for counselling for alternative treatments such as TMT., Competing Interests: The authors declare no conflicts of interest., (© the authors; licensee ecancermedicalscience.)

A predictive model including age, PCa family history, biopsy status (initial vs repeat), DRE (normal vs abnormal), serum prostate-specific antigen (PSA), and DRE prostate volume ca-tegory was developed to stratify initial PCa suspicion in 1486 men with PSA > 3 ng/mL and/or abnormal DRE, in whom mpMRI followed; 2- to 4-core TRUS-guided biopsies where Prostate Imaging Report and Data System (PI-RADS) > 3 lesions and/or 12-core TRUS systematic biopsies were performed in one academic institution between 1 January 2016−31 December 2019. The csPCa detection rate, defined as International Society of Uro-Pathology grade group 2 or higher, was 36.9%. An external validation of designed BCN-RC 1 was carried out on 946 men from two other institutions in the same metropolitan area, using the same criteria of PCa suspicion and diagnostic approach, yielded a csPCa detection rate of 40.8%. The areas under the receiver operating characteristic curves of BCN-RC 1 were 0.823 (95% CI: 0.800−0.846) in the development cohort and 0.837 (95% CI: 0.811−0.863) in the validation cohort (p = 0.447). In both cohorts, BCN-RC 1 exhibited net benefit over performing mpMRI in all men from 8 and 12% risk thresholds, respectively. At 0.95 sensitivity of csPCa, the specificities of BCN-RC 1 were 0.24 (95% CI: 0.22−0.26) in the development cohort and 0.34 (95% CI: 0.31−0.37) in the validation cohort (p < 0.001). The percentages of avoided mpMRI scans were 17.2% in the development cohort and 22.3% in the validation cohort, missing between 1.8% and 2% of csPCa among men at risk of PCa. In summary, BCN-RC 1 can stratify initial PCa suspicion, reducing the demand of mpMRI, with an acceptable loss of csPCa.

Purpose: To analyze reticular pseudodrusen (RPD) as an independent risk factor for progression to late age-related macular degeneration (AMD), alongside traditional macular risk factors (soft drusen and pigmentary abnormalities) considered simultaneously., Design: Post hoc analysis of 2 clinical trial cohorts: Age-Related Eye Disease Study (AREDS) and AREDS2., Participants: Eyes with no late AMD at baseline in AREDS (6959 eyes, 3780 participants) and AREDS2 (3355 eyes, 2056 participants)., Methods: Color fundus photographs (CFPs) from annual visits were graded for soft drusen, pigmentary abnormalities, and late AMD. Presence of RPD was from grading of fundus autofluorescence images (AREDS2) and deep learning grading of CFPs (AREDS). Proportional hazards regression analyses were performed, considering AREDS AMD severity scales (modified simplified severity scale [person] and 9-step scale [eye]) and RPD presence simultaneously., Main Outcome Measures: Progression to late AMD, geographic atrophy (GA), and neovascular AMD., Results: In AREDS, for late AMD analyses by person, in a model considering the simplified severity scale simultaneously, RPD presence was associated with a higher risk of progression: hazard ratio (HR), 2.15 (95% confidence interval [CI], 1.75-2.64). However, the risk associated with RPD presence differed at different severity scale levels: HR, 3.23 (95% CI, 1.60-6.51), HR, 3.81 (95% CI, 2.38-6.10), HR, 2.28 (95% CI, 1.59-3.27), and HR, 1.64 (95% CI, 1.20-2.24), at levels 0-1, 2, 3, and 4, respectively. Considering the 9-step scale (by eye), RPD presence was associated with higher risk: HR, 2.54 (95% CI, 2.07-3.13). The HRs were 5.11 (95% CI, 3.93-6.66) at levels 1-6 and 1.78 (95% CI, 1.43-2.22) at levels 7 and 8. In AREDS2, by person, RPD presence was not associated with higher risk: HR, 1.18 (95% CI, 0.90-1.56); by eye, it was HR, 1.57 (95% CI, 1.31-1.89). In both cohorts, RPD presence carried a higher risk for GA than neovascular AMD., Conclusions: Reticular pseudodrusen represent an important risk factor for progression to late AMD, particularly GA. However, the added risk varies markedly by severity level, with highly increased risk at lower/moderate levels and less increased risk at higher levels. Reticular pseudodrusen status should be included in updated AMD classification systems, risk calculators, and clinical trials., (Published by Elsevier Inc.)

Background: C5 palsy is a common postoperative complication after cervical fusion and is associated with increased health care costs and diminished quality of life. Accurate prediction of C5 palsy may allow for appropriate preoperative counseling and risk stratification. We primarily aim to develop an algorithm for the prediction of C5 palsy after instrumented cervical fusion and identify novel features for risk prediction. Additionally, we aim to build a risk calculator to provide the risk of C5 palsy., Methods: We identified adult patients who underwent instrumented cervical fusion at a tertiary care medical center between 2013 and 2020. The primary outcome was postoperative C5 palsy. We developed ensemble machine learning, standard machine learning, and logistic regression models predicting the risk of C5 palsy-assessing discrimination and calibration. Additionally, a web-based risk calculator was built with the best-performing model., Results: A total of 1024 patients were included, with 52 cases of C5 palsy. The ensemble model was well-calibrated and demonstrated excellent discrimination with an area under the receiver-operating characteristic curve of 0.773. The following features were the most important for ensemble model performance: diabetes mellitus, bipolar disorder, C5 or C4 level, surgical approach, preoperative non-motor neurologic symptoms, degenerative disease, number of fused levels, and age., Conclusions: We report a risk calculator that generates patient-specific C5 palsy risk after instrumented cervical fusion. Individualized risk prediction for patients may facilitate improved preoperative patient counseling and risk stratification as well as potential intraoperative mitigating measures. This tool may also aid in addressing potentially modifiable risk factors such as diabetes and obesity., (Copyright © 2022 Elsevier Inc. All rights reserved.)

Background: Assessment of risks of illnesses has been an important part of medicine for decades. We now have hundreds of 'risk calculators' for illnesses, including brain disorders, and these calculators are continually improving as more diverse measures are collected on larger samples., Methods: We first replicated an existing psychosis risk calculator and then used our own sample to develop a similar calculator for use in recruiting 'psychosis risk' enriched community samples. We assessed 632 participants age 8-21 (52% female; 48% Black) from a community sample with longitudinal data on neurocognitive, clinical, medical, and environmental variables. We used this information to predict psychosis spectrum (PS) status in the future. We selected variables based on lasso, random forest, and statistical inference relief; and predicted future PS using ridge regression, random forest, and support vector machines., Results: Cross-validated prediction diagnostics were obtained by building and testing models in randomly selected sub-samples of the data, resulting in a distribution of the diagnostics; we report the mean. The strongest predictors of later PS status were the Children's Global Assessment Scale; delusions of predicting the future or having one's thoughts/actions controlled; and the percent married in one's neighborhood. Random forest followed by ridge regression was most accurate, with a cross-validated area under the curve (AUC) of 0.67. Adjustment of the model including only six variables reached an AUC of 0.70., Conclusions: Results support the potential application of risk calculators for screening and identification of at-risk community youth in prospective investigations of developmental trajectories of the PS.

MRI can identify suspicious lesions, providing the semi-quantitative risk of csPCa through the Prostate Imaging-Report and Data System (PI-RADS). Predictive models of clinical variables that individualise the risk of csPCa have been developed by adding PI-RADS score (MRI-PMs). Our objective is to analyse the current developed MRI-PMs and define their clinical usefulness. A systematic review was performed after a literature search performed by two independent investigators in PubMed, Cochrane, and Web of Science databases, with the Medical Subjects Headings (MESH): predictive model, nomogram, risk model, magnetic resonance imaging, PI-RADS, prostate cancer, and prostate biopsy. This review was made following the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) criteria and studied eligibility based on the Participants, Intervention, Comparator, and Outcomes (PICO) strategy. Among 723 initial identified registers, 18 studies were finally selected. Warp analysis of selected studies was performed with the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. Clinical predictors in addition to the PI-RADS score in developed MRI-PMs were age, PCa family history, digital rectal examination, biopsy status (initial vs. repeat), ethnicity, serum PSA, prostate volume measured by MRI, or calculated PSA density. All MRI-PMs improved the prediction of csPCa made by clinical predictors or imaging alone and achieved most areas under the curve between 0.78 and 0.92. Among 18 developed MRI-PMs, 7 had any external validation, and two RCs were available. The updated PI-RADS version 2 was exclusively used in 11 MRI-PMs. The performance of MRI-PMs according to PI-RADS was only analysed in a single study. We conclude that MRI-PMs improve the selection of candidates for prostate biopsy beyond the PI-RADS category. However, few developed MRI-PMs meet the appropriate requirements in routine clinical practice.

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., (© 2022 American Cancer Society.)

Background: Cardiovascular diseases (CVDs) are the leading cause of morbidity and mortality globally. This review aimed to summarise evidence on the key features, usability and benefits of CVD risk calculators using digital platforms for CVDs prevention and management in populations., Methods: We used search engines and thematic analyses to conduct a scoping review. As the reporting guideline for this review, we used Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR)., Results: A total of 17 studies meeting eligibility criteria were included in the analysis, from which about 70% of the studies have prognostic level I (n = 8) and level II (n = 4) evidence. The review found that various guidelines are recommending different algorithms for CVD risk prediction. The QRISK® was the most accurate CVD risk calculator for several study populations, whereas World Health Organization/International Society of Hypertension (WHO/ISH) risk scores were the least accurate. The key features of CVD risk calculators are variables, predictive accuracy, discrimination index, applicability, understandability, and cost-effectiveness., Conclusion: For the selected risk prediction tool, development and validation research must be done, which considers a mix of stroke-specific risk and CVD risk to establish its usability in the local community and advantages to the particular health-care environment. To get healthcare professionals more involved in preventing and treating CVDs, each healthcare setting should use an online CVD risk assessment tool that is more useful, accurate, and easy to use, based on the population and health system., (© 2022. The Author(s).)

Background and Objectives: Developmental dysplasia of the hip (DDH) is one of the most common musculoskeletal conditions in children. If not treated, it leads to disability, gait abnormalities, limb shortening, and chronic pain. Our study aims to determine the impact of multiple risk factors on the incidence of DDH and to develop an interactive risk assessment tool. Materials and Methods: We conducted a retrospective cohort study in the Outpatient Clinic for Children of the Medical University of Warsaw Hospital. The Graf classification system was used for universal ultrasonographic screening. In total, 3102 infants met the eligibility criteria. Results: The incidence of DDH in the study group was 4.45%. The incidence of DDH in the Warsaw population, Poland, during the study period was 3.73 to 5.17 (95% CI). According to the multivariate analysis, the risk factors for DDH were birth weight (OR = 2.17 (1.41−3.32)), week of delivery (OR = 1.18 (1.00−1.37)), female sex (OR = 8.16 (4.86−13.71)), breech presentation (OR = 5.92 (3.37−10.40)), physical signs of DDH (25.28 (8.77−72.83)) and positive family history in siblings (5.74 (2.68−12.31)). Our results support the recent hypothesis that preterm infants (<37 weeks) have a lower rate of DDH. Conclusions: A multivariate logistic regression predictive model was used to build the risk calculator. The DDH risk calculator will be evaluated in a prospective validation study.

Background: Hypoxemia is a very common issue in patients undergoing heart valve surgery (HVS), related to poor clinical outcomes. However, studies on severe hypoxemia (SH) after HVS have not been reported. The aims of this study were to identify predictors for SH in patients undergoing HVS and to develop and validate a risk prediction model., Methods: Patients undergoing HVS between 2016 and 2019 in a cardiovascular center were enrolled and were assigned to training and validation sets by a 7:3 ratio. Based on whether patients developed SH, they were divided into two groups. By univariate and multivariate analysis, predictors for SH were identified. Based on the predictors and logistic rule, a nomogram and a risk calculator were generated. The model was evaluated using calibration, discrimination and clinical utility., Results: The incidence rates of SH, moderate hypoxemia and mild hypoxemia were respectively 2.4, 23.9, and 58.2%. By multivariate analysis, seven independent risk factors for SH after HVS were identified, including body mass index, chronic obstructive pulmonary disease, renal insufficiency, white blood cell count, serum globulin, cardiopulmonary bypass time, and surgical types. The logistic model demonstrated satisfactory discrimination, calibration and clinical utility in both the training and validation sets. A nomogram and a risk calculator based on the logistic model were generated for easy application. Risk stratification was performed and three risk intervals were defined according to the nomogram and clinical practice. In addition, compared to patients without SH, patients with SH had significantly poorer clinical outcomes., Conclusions: Postoperative hypoxemia was prevalent after HVS, related to poor clinical outcomes. A logistic model including seven independent predictors for SH after HVS were established and validated, which demonstrated satisfactory discrimination, calibration and clinical utility. The results of this study may provide help to individualized risk assessment, early prevention and perioperative management., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Ding, Cheng, Sun, Sun, Wu, Chen, Li, Lei and Su.)

Aims: To evaluate the risk algorithm by Aspelund et al. for predicting sight-threatening diabetic retinopathy (STDR) in Type 2 diabetes (T2D), and to develop a new STDR prediction model.Methods: The Aspelund et al. algorithm was used to calculate STDR risk from baseline variables in 1012 participants in the Fenofibrate Intervention and Event Lowering in Diabetes (FIELD) ophthalmological substudy, compared to on-trial STDR status, and receiver operating characteristic analysis performed. Using multivariable logistic regression, traditional risk factors and fenofibrate allocation as STDR predictors were evaluated, with bootstrap-based optimism-adjusted estimates of predictive performance calculated.Results: STDR developed in 28 participants. The Aspelund et al. algorithm predicted STDR at 2- and 5-years with area under the curve (AUC) 0.86 (95% CI 0.77-0.94) and 0.86 (0.81-0.92), respectively. In the second model STDR risk factors were any DR at baseline (OR 24.0 [95% CI 5.53-104]), HbA1c (OR 1.95 [1.43-2.64]) and male sex (OR 4.34 [1.32-14.3]), while fenofibrate (OR 0.13 [0.05-0.38]) was protective. This model had excellent discriminatory ability (AUC = 0.89).Conclusions: The algorithm by Aspelund et al. predicts STDR well in the FIELD ophthalmology substudy. Logistic regression analysis found DR at baseline, male sex, and HbA1c were predictive of STDR and, fenofibrate was protective. [ABSTRACT FROM AUTHOR]

Abstract: Objective: To examine when and why women disbelieve tailored information about their risk of developing breast cancer. Methods: 690 women participated in an online program to learn about medications that can reduce the risk of breast cancer. The program presented tailored information about each woman's personal breast cancer risk. Half of women were told how their risk numbers were calculated, whereas the rest were not. Later, they were asked whether they believed that the program was personalized, and whether they believed their risk numbers. If a woman did not believe her risk numbers, she was asked to explain why. Results: Beliefs that the program was personalized were enhanced by explaining the risk calculation methods in more detail. Nonetheless, nearly 20% of women did not believe their personalized risk numbers. The most common reason for rejecting the risk estimate was a belief that it did not fully account for personal and family history. Conclusions: The benefits of tailored risk statistics may be attenuated by a tendency for people to be skeptical that these risk estimates apply to them personally. Practice implications: Decision aids may provide risk information that is not accepted by patients, but addressing the patients’ personal circumstances may lead to greater acceptance. [Copyright &y& Elsevier]

Abstract: Background: Inconclusive test results often occur after prostate-specific antigen (PSA)–based screening for prostate cancer (PCa), leading to uncertainty on whether, how, and when to repeat testing. Objective: To develop and validate a prediction tool for the risk of PCa 4 yr after an initially negative screen. Design, setting, and participants: We analyzed data from 15 791 screen-negative men aged 55–70 yr at the initial screening round of the Rotterdam section of the European Randomized Study of Screening for Prostate Cancer. Outcome measurements and statistical analysis: Follow-up and repeat screening at 4 yr showed either no PCa, low-risk PCa, or potentially high-risk PCa (defined as clinical stage >T2b and/or biopsy Gleason score ≥7 and/or PSA ≥10.0 ng/ml). A multinomial logistic regression analysis included initial screening data on age, PSA, digital rectal examination (DRE), family history, prostate volume, and having had a previous negative biopsy. The 4-yr risk predictions were validated with additional follow-up data up to 8 yr after initial screening. Results and limitations: Positive family history and, especially, PSA level predicted PCa, whereas a previous negative biopsy or a large prostate volume reduced the likelihood of future PCa. The risk of having PCa 4 yr after an initially negative screen was 3.6% (interquartile range: 1.0–4.7%). Additional 8-yr follow-up data confirmed these predictions. Although data were based on sextant biopsies and a strict protocol-based biopsy indication, we suggest that men with a low predicted 4-yr risk (eg, ≤1.0%) could be rescreened at longer intervals or not at all, depending on competing risks, while men with an elevated 4-yr risk (eg, ≥5%) might benefit from immediate retesting. These findings need to be validated externally. Conclusions: This 4-yr future risk calculator, based on age, PSA, DRE, family history, prostate volume, and previous biopsy status, may be a promising tool for reducing uncertainty, unnecessary testing, and overdiagnosis of PCa. [Copyright &y& Elsevier]

Cardiac risk stratification before noncardiac surgery remains important. Two major areas have been emphasized, namely, cost-effective risk stratification and enhanced identification of high-risk populations. Recent studies have highlighted the lack of quality and affordable medical consultation. The indications for resting preoperative echocardiography merit streamlining, given recent data that failed to demonstrate tangible benefit. Furthermore, noninvasive cardiac stress testing is expensive and unnecessary in low-risk patients. Perioperative troponin determination significantly improves the detection of myocardial infarction, facilitating its early management. The revised cardiac risk index is a standard tool for risk stratification, despite multiple limitations. The first approach has been to recalibrate the traditional risk index to specific high-risk surgical subgroups. The second approach has been to develop new cardiac risk models with more power. Both approaches have yielded risk calculators that outperform the traditional risk model. Furthermore, this latest generation of risk models is available as online calculators that can be accessed at the bedside. Further clinical trials are indicated to test the validity, clinical utility, and cost-effectiveness of these novel risk calculators. It is likely that these powerful instruments will refine the indications for specialized cardiac testing, offering multiple opportunities to reduce perioperative risk and cost simultaneously. [ABSTRACT FROM AUTHOR]

Background: The International Spine Study Group (ISSG) and the European Spine Study Group (ESSG) developed an adult spinal deformity (ASD) risk calculator based on one of the most granular, prospective ASD databases. The calculator utilizes preoperative radiographic, surgical, and patient-specific variables to predict patient-reported outcomes and complication rates at 2 years. Our aim was to assess the ISSG-ESSG risk calculator's usability in a single-institution ASD population., Methods: Frail ([F], 0.3 > 0.5) ASD patients were isolated in a single-center ASD database. Basic demographics were assessed via χ 2 tests. Each F patient was inputted into the ESSG risk calculator to identify individual predictive rates for postoperative 2-year health-related quality of life questions (HRQL) outcomes and major complications. These calculated predicted outcomes were analyzed against those identified from the ASD database in order to validate the calculator's predictability via Brier scores. A score closer to 1 meant the ISSG-ESSG calculator was not predictive of that specific outcome. A score closer to 0 meant the ISSG-ESSG calculator was a predictive tool for that factor.t tests. Each F patient was inputted into the ESSG risk calculator to identify individual predictive rates for postoperative 2-year health-related quality of life questions (HRQL) outcomes and major complications. These calculated predicted outcomes were analyzed against those identified from the ASD database in order to validate the calculator's predictability via Brier scores. A score closer to 1 meant the ISSG-ESSG calculator was not predictive of that specific outcome. A score closer to 0 meant the ISSG-ESSG calculator was a predictive tool for that factor., Results: A total of 631 ASD patients were isolated (55.8 ± 16.8 years, 26.68 kg/m 2 , 0.95 ± 1.3 Charlson Comorbidity Index). Of those patients, 7.8% were frail. Fifty percent of frail patients received an interbody fusion, 58.3% received a decompression, and 79.2% underwent osteotomy. Surgical details were as follows: mean operative time was 342.9 ± 94.3 minutes, mean estimated blood loss was 2131.82 ± 1011 mL, and average length of stay was 7.12 ± 2.5 days. The ISSG-ESSG calculator predicted the likelihood of improvement for the following HRQL's: Oswestry Disability Index (ODI) (86%), Scoliosis Research Society (SRS)-22 mental health (71.1%), SRS-22 total (87.6%), and major complication (53.4%). The single institution had lower percentages of improvement in ODI (24.6%), SRS-22 mental health (21.3%), SRS-22 total (25.1%), and lower presence of major complication (34.8%). The calculated Brier scores identified the calculator's predictability for each factor was as follows: ODI (0.24), SRS-22 mental health (0.21), SRS-22 total (0.25), and major complication (0.28)., Conclusions: All of the variables had low Brier scores, indicating that the ISSG-ESSG calculator can be used as a predictive tool for ASD frail patients., Competing Interests: Declaration of Conflicting Interests: The authors report no conflicts of interest in this work., (This manuscript is generously published free of charge by ISASS, the International Society for the Advancement of Spine Surgery. Copyright © 2022 ISASS. To see more or order reprints or permissions, see http://ijssurgery.com.)

The 2021 European Association of Urology recommendations for early prostate cancer detection included a risk-based algorithm. Risk assessment methods are proposed to prevent excessive use of prostate magnetic resonance imaging (MRI) and biopsy, simultaneously reducing overdiagnosis and overtreatment. However, the clinical implications of sequential use of risk assessment tests have not yet been properly assessed. We provide an appraisal of the recommended algorithm and evaluate its outcomes in a contemporary prospective study population of biopsy-naïve men. To increase the effectiveness in cases of limited MRI capacity, we show that use of the Rotterdam Prostate Cancer Risk Calculator-3 for pre-MRI risk stratification could avoid more than one-third of MRI examinations. After prostate MRI, use of either the Prostate Imaging-Reporting and Data System (PI-RADS) score or a risk model including MRI outcome as a variable could avoid six out of ten prostate biopsies while maintaining high sensitivity. However, implementation in health care systems requires due consideration of the access to and quality of diagnostic resources, as well as cost-effectiveness., Patient Summary: We evaluated the European Association of Urology risk-based strategy for early prostate cancer detection. Risk assessment before magnetic resonance imaging (MRI) using a risk calculator or prostate-specific antigen (PSA) density could reduce MRI demands and overdiagnosis of insignificant cancers. Risk assessment using prostate MRI results could avoid 60% of prostate biopsies while maintaining prostate cancer detection rates.The European Association of Urology (EAU) recently published its current position and recommendations on prostate-specific antigen (PSA) testing [1]. On the basis of the literature and expert opinion, a risk-based algorithm for early detection of prostate cancer (PCa) was proposed. The guideline recommends stratifying men with PSA ≥3 ng/ml as either "low risk", for whom magnetic resonance imaging (MRI) can be avoided, or "intermediate and high risk", for whom prostate MRI should be performed as a basis for further diagnostic decisions. Strategies must be developed to use health care resources efficiently and to reduce unnecessary morbidity, anxiety, and costs of diagnostics. However, any paradigm shift inevitably leads to a paucity of research data. As a result, there is still debate regarding which men can safely avoid an initial MRI but are subjected to clinical follow-up, and which men must undergo an immediate MRI. The authors proposed four methods for risk assessment: (1) family history; (2) PSA velocity; (3) PSA density; and (4) risk calculators. It must be stressed that the availability and quality of prostate MRI in each situation should be considered when using these pre-MRI risk assessment tools. We discuss in brief the proposed risk assessment methods including MRI and assess potential outcomes in a contemporary population., (© 2022 The Author(s).)

Background: The European Association of Urology guidelines recommend the use of imaging, biomarkers, and risk calculators in men at risk of prostate cancer. Risk predictive calculators that combine multiparametric magnetic resonance imaging with prebiopsy variables aid as an individualized decision-making tool for patients at risk of prostate cancer, and advanced neural networking increases reliability of these tools., Objective: To develop a comprehensive risk predictive online web-based tool using magnetic resonance imaging (MRI) and clinical data, to predict the risk of any prostate cancer (PCa) and clinically significant PCa (csPCa) applicable to biopsy-naïve men, men with a prior negative biopsy, men with prior positive low-grade cancer, and men with negative MRI., Design Setting and Participants: Institutional review board-approved prospective data of 1902 men undergoing biopsy from October 2013 to September 2021 at Mount Sinai were collected., Outcome Measurements and Statistical Analysis: Univariable and multivariable analyses were used to evaluate clinical variables such as age, race, digital rectal examination, family history, prostate-specific antigen (PSA), biopsy status, Prostate Imaging Reporting and Data System score, and prostate volume, which emerged as predictors for any PCa and csPCa. Binary logistic regression was performed to study the probability. Validation was performed with advanced neural networking (ANN), multi-institutional European cohort (Prostate MRI Outcome Database [PROMOD]), and European Randomized Study of Screening for Prostate Cancer Risk Calculator (ERSPC RC) 3/4., Results and Limitations: Overall, 2363 biopsies had complete clinical information, with 57.98% any cancer and 31.40% csPCa. The prediction model was significantly associated with both any PCa and csPCa having an area under the curve (AUC) of 81.9% including clinical data. The AUC for external validation was calculated in PROMOD, ERSPC RC, and ANN for any PCa (0.82 vs 0.70 vs 0.90) and csPCa (0.82 vs 0.78 vs 0.92), respectively. This study is limited by its retrospective design and overestimation of csPCa in the PROMOD cohort., Conclusions: The Mount Sinai Prebiopsy Risk Calculator combines PSA, imaging and clinical data to predict the risk of any PCa and csPCa for all patient settings. With accurate validation results in a large European cohort, ERSPC RC, and ANN, it exhibits its efficiency and applicability in a more generalized population. This calculator is available online in the form of a free web-based tool that can aid clinicians in better patients counseling and treatment decision-making., Patient Summary: We developed the Mount Sinai Prebiopsy Risk Calculator (MSP-RC) to assess the likelihood of any prostate cancer and clinically significant disease based on a combination of clinical and imaging characteristics. MSP-RC is applicable to all patient settings and accessible online., (Crown Copyright © 2022 Published by Elsevier B.V. on behalf of European Association of Urology.)