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Background: The diagnosis of (significant) prostate cancer ((s)PC) is impeded by overdiagnosis and unnecessary biopsy. Risk calculators (RC) have been developed to mitigate these issues. Contemporary RCs integrate clinical characteristics with mpMRI findings. Objective: To validate two of these models—the MRI-ERSPC-RC-3/4 and the risk model of van Leeuwen. Methods: 265 men with clinical suspicion of PC were enrolled. Every patient received a prebiopsy mpMRI, which was reported according to PI-RADS v2.1, followed by MRI/TRUS fusion-biopsy. Cancers with ISUP grade ≥ 2 were classified as sPC. Outcome measurements and statistical analysis: Statistical analysis was performed by comparing discrimination, calibration, and clinical utility Results: There was no significant difference in discrimination between the RCs. The MRI-ERSPC-RC-3/4-RC showed a nearly ideal calibration-slope (0.94; 95% CI 0.68–1.20) than the van Leeuwen model (0.70; 95% CI 0.52–0.88). Within a threshold range up to 9% for a sPC, the MRI-ERSPC-RC-3/4-RC shows a greater net benefit than the van Leeuwen model. From 10 to 15%, the van Leeuwen model showed a higher net benefit compared to the MRI-ERSP-3/4-RC. For a risk threshold of 15%, the van Leeuwen model would avoid 24% vs. 14% compared to the MRI-ERSPC-RC-3/4 model; 6% vs. 5% sPC would be overlooked, respectively. Conclusion: Both risk models supply accurate results and reduce the number of biopsies and basically no sPC were overlooked. The van Leeuwen model suggests a better balance between unnecessary biopsies and overlooked sPC at thresholds range of 10–15%. The MRI-ERSPC-RC-3/4 risk model provides better overall calibration. [ABSTRACT FROM AUTHOR]

Abstracts Background The number of elderly patients with gastric cancer has been increasing. Most elderly patients have associated reduced physiologic functions that can sometimes become an obstacle to safe surgical treatment. The National Clinical Database Risk Calculator, which based on a large Japanese surgical database, provides predicted mortality and morbidity in each case as the surgical-related risks. The purpose of this study was to investigate the clinical significance of the risk for operative mortality (NRC-mortality), as calculated by the National Clinical Database Risk Calculator, during long-term follow-up after gastrectomy for elderly patients with gastric cancer. Methods We enrolled 73 patients aged ≥ 80 years and underwent gastrectomy at our institution. Their surgical risk was evaluated based on the NRC-mortality. Several clinicopathologic factors, including NRC-mortality, were selected and analyzed as the possible prognostic factors for elderly patients who have undergone gastrectomy for gastric cancer. Statistical analysis was performed using the log-rank test and Cox proportional hazard model. Results NRC-mortality ranged from 0.5 to 10.6%, and the median value was 1.7%. Dividing the patients according to mortality, the overall survival was significantly worse in the high mortality group (≥ 1.7%, n = 38) than in the low mortality group (< 1.7%, n = 35), whereas disease-specific survival was not different between the two groups. In the Cox proportional hazard model, multivariate analysis revealed NRC-mortality, performance status, and surgical procedure as the independent prognostic factors for overall survival. For disease-specific survival, the independent prognostic factors were performance status and pathological stage but not NRC-mortality. Conclusion The NRC-mortality might be clinically useful for predicting both surgical mortality and overall survival after gastrectomy in elderly patients with gastric cancer.

Abstract Background Prostate cancer (PCa) represents a significant healthcare problem. The critical clinical question is the need for a biopsy. Accurate risk stratification of patients before a biopsy can allow for individualised risk stratification thus improving clinical decision making. This study aims to build a risk calculator to inform the need for a prostate biopsy. Methods Using the clinical information of 4801 patients an Irish Prostate Cancer Risk Calculator (IPRC) for diagnosis of PCa and high grade (Gleason ≥7) was created using a binary regression model including age, digital rectal examination, family history of PCa, negative prior biopsy and Prostate-specific antigen (PSA) level as risk factors. The discrimination ability of the risk calculator is internally validated using cross validation to reduce overfitting, and its performance compared with PSA and the American risk calculator (PCPT), Prostate Biopsy Collaborative Group (PBCG) and European risk calculator (ERSPC) using various performance outcome summaries. In a subgroup of 2970 patients, prostate volume was included. Separate risk calculators including the prostate volume (IPRCv) for the diagnosis of PCa (and high-grade PCa) was created. Results IPRC area under the curve (AUC) for the prediction of PCa and high-grade PCa was 0.6741 (95% CI, 0.6591 to 0.6890) and 0.7214 (95% CI, 0.7018 to 0.7409) respectively. This significantly outperforms the predictive ability of cancer detection for PSA (0.5948), PCPT (0.6304), PBCG (0.6528) and ERSPC (0.6502) risk calculators; and also, for detecting high-grade cancer for PSA (0.6623) and PCPT (0.6804) but there was no significant improvement for PBCG (0.7185) and ERSPC (0.7140). The inclusion of prostate volume into the risk calculator significantly improved the AUC for cancer detection (AUC = 0.7298; 95% CI, 0.7119 to 0.7478), but not for high-grade cancer (AUC = 0.7256; 95% CI, 0.7017 to 0.7495). The risk calculator also demonstrated an increased net benefit on decision curve analysis. Conclusion The risk calculator developed has advantages over prior risk stratification of prostate cancer patients before the biopsy. It will reduce the number of men requiring a biopsy and their exposure to its side effects. The interactive tools developed are beneficial to translate the risk calculator into practice and allows for clarity in the clinical recommendations.

Background and Objective: Post-operative respiratory failure (PRF) is one of the serious and common complications associated with poor outcomes, prolonged hospital stay and increased health care expenditure. Objective identification of factors leading to PRF would help optimization of patients and improve outcomes. The aim of the study was to assess the risk of PRF in a cohort of patients undergoing surgery using a validated risk calculator score. Methods: The risk calculator score developed by H Gupta et al was used to predict the chances of PRF in patients undergoing elective/emergency surgery at our hospital from January to June 2016. The primary end point was discharge from hospital or death. Results: 100 patients (Male: 74; Female: 26) undergoing elective/emergency surgery were analyzed. Four patients (4%) died and eleven (11%) developed respiratory failure. A receiver operating characteristic (ROC) curve with a risk calculator score of 6.30 had 92% sensitivity and 82% specificity for predicting PRF. A higher risk score had good correlation with longer hospital stay ( p=0.001). Interpretation and Conclusion: PRF risk calculator score is a valuable tool in predicting respiratory failure in patients, undergoing elective/emergency surgery. It can be useful in identifying high risk patients, allowing one to make an informed choice prior to surgery and may also help optimise such patients pre-operatively.

Risk calculator (RC) combining PSA with other clinical information can help to better select patients at risk of prostate cancer (PCa) for prostate biopsy. The present study aimed to develop a new Pca RC, including MRI and bladder outlet obstruction parameters (BOOP). The ability of these parameters in predicting PCa and clinically significant PCa (csPCa: ISUP GG ≥ 2) was assessed by binary logistic regression. A total of 728 patients were included from two institutions. Of these, 395 (54.3%) had negative biopsies and 161 (22.11%) and 172 (23.6%) had a diagnosis of ISUP GG1 PCa and csPCa. The two RC ultimately included age, PSA, DRE, prostate volume (pVol), post-voided residual urinary volume (PVR), and PIRADS score. Regarding BOOP, higher prostate volumes (csPCa: OR 0.98, CI 0.97,0.99) and PVR ≥ 50 mL (csPCa: OR 0.27, CI 0.15, 0.47) were protective factors for the diagnosis of any PCa and csPCa. AUCs after internal validation were 0.78 (0.75, 0.82) and 0.82 (0.79, 0.86), respectively. Finally, decision curves analysis demonstrated higher benefit compared to the first-generation calculator and MRI alone. These novel RC based on MRI and BOOP may help to better select patient for prostate biopsy after prostate MRI.

Abstract Background Identifying men for a repeat prostate biopsy is a conundrum to urologists. Risk calculators (RCs) such as the European Randomized Study of Screening for Prostate Cancer (ERSPC) RCs have been developed to predict the outcome of prostate biopsies and have been shown to improve diagnostic accuracy compared to PSA alone. However, it was recently shown that the outcome for high-grade prostate cancer (PCa) upon biopsy tended to be underestimated in men with previous negative biopsies using ERSPC RC model 4. For these men, an individualized approach combining the clinical information with the outcome of biomarker-related urine tests may help to make a more informed decision. Case presentation Two men, aged 66 and 69 respectively when presented in the clinic, show the typical dilemma of urologist and patient for electing repeat prostate biopsy. Both men had normal DRE findings, did not have a family history of PCa, presented with serum PSA values between 3 and 10 ng/ml and the first biopsies were negative for disease. The ERSPC RC4 did not indicate a biopsy in these men. The urinary molecular biomarker-based test for HOXC6 and DLX1, combining biomarker-expression profiling with clinical risk factors, resulted in SelectMDx Risk scores for these men that were higher than the cut-off of the test. Based on this outcome, mpMRI was performed with an outcome of PI-RADS ≥4 in both men. Histopathological evaluation of TRUS-guided biopsies confirmed high-grade PCa. Conclusions The urinary molecular biomarker-based risk score played a pivotal role in the diagnosis of clinically significant PCa whereas ERSPC RC4 outcome would not have indicated further diagnostic follow-up in these two cases. The timely diagnosis was shown to be crucial for the curative treatment by radical retropubic prostatectomy and the potential life-years gained for these two vital males.

Abstract Background Predictive models that take race into account like the Prostate Cancer Prevention Trial Risk Calculator 2.0 (PCPT RC) and the new Prostate Biopsy Collaborative Group (PBCG) RC have been developed to equitably mitigate the overdiagnosis of prostate specific antigen (PSA) screening. Few studies have compared the performance of both calculators across racial groups. Methods From 1485 prospectively recruited participants, 954 men were identified undergoing initial prostate biopsy for abnormal PSA or digital rectal examination in five Chicago hospitals between 2009 and 2014. Discrimination, calibration, and frequency of avoided biopsies were calculated to assess the performance of both risk calculators. Results Of 954 participants, 463 (48.5%) were Black, 355 (37.2%) were White, and 136 (14.2%) identified as Other. Biopsy results were as follows: 310 (32.5%) exhibited no cancer, 323 (33.9%) indolent prostate cancer, and 321 (33.6%) clinically significant prostate cancer (csPCa). Differences in area under the curve (AUC)s for the detection of csPCa between PCPT and PBCG were not statistically different across all racial groups. PBCG did not improve calibration plots in Blacks and Others, as it showed higher levels of overprediction at most risk thresholds. PCPT led to an increased number of avoidable biopsies in minorities compared to PBCG at the 30% threshold (68% vs. 28% of all patients) with roughly similar rates of missed csPCa (23% vs. 20%). Conclusion Significant improvements were noticed in PBCG’s calibrations and net benefits in Whites compared to PCPT. Since PBCG’s improvements in Blacks are disputable and potentially biases a greater number of low risk Black and Other men towards unnecessary biopsies, PCPT may lead to better biopsy decisions in racial minority groups. Further comparisons of commonly used risk calculators across racial groups is warranted to minimize excessive biopsies and overdiagnosis in ethnic minorities.

Background: The IDENTIFY study developed a model to predict urinary tract cancer using patient characteristics from a large multicentre, international cohort of patients referred with haematuria. In addition to calculating an individual's cancer risk, it proposes thresholds to stratify them into very-low-risk (<1%), low-risk (1-<5%), intermediate-risk (5-<20%), and high-risk (≥20%) groups., Objective: To externally validate the IDENTIFY haematuria risk calculator and compare traditional regression with machine learning algorithms., Design, Setting, and Participants: Prospective data were collected on patients referred to secondary care with new haematuria. Data were collected for patient variables included in the IDENTIFY risk calculator, cancer outcome, and TNM staging. Machine learning methods were used to evaluate whether better models than those developed with traditional regression methods existed., Outcome Measurements and Statistical Analysis: The area under the receiver operating characteristic curve (AUC) for the detection of urinary tract cancer, calibration coefficient, calibration in the large (CITL), and Brier score were determined., Results and Limitations: There were 3582 patients in the validation cohort. The development and validation cohorts were well matched. The AUC of the IDENTIFY risk calculator on the validation cohort was 0.78. This improved to 0.80 on a subanalysis of urothelial cancer prevalent countries alone, with a calibration slope of 1.04, CITL of 0.24, and Brier score of 0.14. The best machine learning model was Random Forest, which achieved an AUC of 0.76 on the validation cohort. There were no cancers stratified to the very-low-risk group in the validation cohort. Most cancers were stratified to the intermediate- and high-risk groups, with more aggressive cancers in higher-risk groups., Conclusions: The IDENTIFY risk calculator performed well at predicting cancer in patients referred with haematuria on external validation. This tool can be used by urologists to better counsel patients on their cancer risks, to prioritise diagnostic resources on appropriate patients, and to avoid unnecessary invasive procedures in those with a very low risk of cancer., Patient Summary: We previously developed a calculator that predicts patients' risk of cancer when they have blood in their urine, based on their personal characteristics. We have validated this risk calculator, by testing it on a separate group of patients to ensure that it works as expected. Most patients found to have cancer tended to be in the higher-risk groups and had more aggressive types of cancer with a higher risk. This tool can be used by clinicians to fast-track high-risk patients based on the calculator and investigate them more thoroughly., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Background and Objective: The aim of our study was to investigate whether repeat prostate-specific antigen (PSA) testing as currently recommended improves risk stratification for men undergoing magnetic resonance imaging (MRI) and targeted biopsy for suspected prostate cancer (PCa)., Methods: Consecutive men undergoing MRI and prostate biopsy who had at least two PSA tests before prostate biopsy were retrospectively registered and assigned to a development cohort ( n  = 427) or a validation ( n  = 174) cohort. Change in PSA level was assessed as a predictor of clinically significant PCa (csPCa; Gleason score ≥3 + 4, grade group ≥2) by multivariable logistic regression analysis. We developed a multivariable prediction model (MRI-RC) and a dichotomous biopsy decision strategy incorporating the PSA change. The performance of the MRI-RC model and dichotomous decision strategy was assessed in the validation cohort and compared to prediction models and decision strategies not including PSA change in terms of discriminative ability and decision curve analysis., Results: Men who had a decrease on repeat PSA testing had significantly lower risk of csPCa than men without a decrease (odds ratio [OR] 0.3, 95% confidence interval [CI] 0.16-0.54; p  < 0.001). Men with an increased repeat PSA had a significantly higher risk of csPCa than men without an increase (OR 2.97, 95% CI 1.62-5.45; p  < 0.001). Risk stratification using both the MRI-RC model and the dichotomous decision strategy was improved by incorporating change in PSA as a parameter., Conclusions and Clinical Implications: Repeat PSA testing gives predictive information regarding men undergoing MRI and targeted prostate biopsy. Inclusion of PSA change as a parameter in an MRI-RC model and a dichotomous biopsy decision strategy improves their predictive performance and clinical utility without requiring additional investigations., Patient Summary: For men with a suspicion of prostate cancer, repeat PSA (prostate-specific antigen) testing after an MRI (magnetic resonance imaging) scan can help in identifying patients who can safely avoid prostate biopsy., (© 2024 The Author(s).)

Neonatal sepsis, as a significant cause of various complications and adverse outcomes in neonates, remains a serious health burden both domestically and internationally. Strategies such as antibiotic prophylaxis during delivery, the utilization of early-onset sepsis risk calculators, and quality improvement initiatives in neonatal wards are beneficial in alleviating the disease burden of neonatal sepsis. This paper provides a review of the epidemiology, risk factors, and recent advances in clinical management of neonatal sepsis.

Background Context: Surgical decompression and stabilization in the setting of spinal metastasis is performed to relieve pain and preserve functional status. These potential benefits must be weighed against the risks of perioperative morbidity and mortality. Accurate prediction of a patient's postoperative survival is a crucial component of patient counseling.Purpose: To externally validate the SORG machine learning algorithms for prediction of 90-day and 1-year mortality after surgery for spinal metastasis.Study Design/setting: Retrospective, cohort study PATIENT SAMPLE: Patients 18 years or older at a tertiary care medical center treated surgically for spinal metastasis OUTCOME MEASURES: Mortality within 90 days of surgery, mortality within 1 year of surgery METHODS: This is a retrospective cohort study of 298 adult patients at a tertiary care medical center treated surgically for spinal metastasis between 2004 and 2020. Baseline characteristics of the validation cohort were compared to the derivation cohort for the SORG algorithms. The following metrics were used to assess the performance of the algorithms: discrimination, calibration, overall model performance, and decision curve analysis.Results: Sixty-one patients died within 90 days of surgery and 133 died within 1 year of surgery. The validation cohort differed significantly from the derivation cohort. The SORG algorithms for 90-day mortality and 1-year mortality performed excellently with respect to discrimination; the algorithm for 1-year mortality was well-calibrated. At both postoperative time points, the SORG algorithms showed greater net benefit than the default strategies of changing management for no patients or for all patients.Conclusions: With an independent, contemporary, and geographically distinct population, we report successful external validation of SORG algorithms for preoperative risk prediction of 90-day and 1-year mortality after surgery for spinal metastasis. By providing accurate prediction of intermediate and long-term mortality risk, these externally validated algorithms may inform shared decision-making with patients in determining management of spinal metastatic disease. [ABSTRACT FROM AUTHOR]

Non-ischemic dilated cardiomyopathy (DCM) can be complicated by sustained ventricular arrhythmias (SVA) and sudden cardiac death (SCD). By now, left-ventricular ejection fraction (LV-EF) is the main guideline criterion for primary prophylactic ICD implantation, potentially leading either to overtreatment or failed detection of patients at risk without severely impaired LV-EF. The aim of the European multi-center study DETECTIN-HF was to establish a clinical risk calculator for individualized risk stratification of DCM patients. 1393 patients (68% male, mean age 50.7 ± 14.3y) from four European countries were included. The outcome was occurrence of first potentially life-threatening ventricular arrhythmia. The model was developed using Cox proportional hazards, and internally validated using cross validation. The model included seven independent and easily accessible clinical parameters sex, history of non-sustained ventricular tachycardia, history of syncope, family history of cardiomyopathy, QRS duration, LV-EF, and history of atrial fibrillation. The model was also expanded to account for presence of LGE as the eight8h parameter for cases with available cMRI and scar information. During a mean follow-up period of 57.0 months, 193 (13.8%) patients experienced an arrhythmic event. The calibration slope of the developed model was 00.97 (95% CI 0.90–1.03) and the C-index was 0.72 (95% CI 0.71–0.73). Compared to current guidelines, the model was able to protect the same number of patients (5-year risk ≥8.5%) with 15% fewer ICD implantations. This DCM-SVA risk model could improve decision making in primary prevention of SCD in non-ischemic DCM using easily accessible clinical information and will likely reduce overtreatment. • A seven-parameter model predicts life-threatening arrhythmia in dilated cardiomyopathy. • MRI delivers additional information and increases model performance. • This model performs well regardless of whether the patients carry CRT or not. • Compared to LV-EF only, this model could significantly reduce ICD implantations. [ABSTRACT FROM AUTHOR]

Background: Superficial surgical site infections (S-SSIs) are common after trauma laparotomy, leading to morbidity, increased costs, and prolonged length of stay (LOS). Opportunities to mitigate S-SSI risks are limited to the intra-operative and post-operative periods. Accurate S-SSI risk stratification is paramount at the time of operation to inform immediate management. We aimed to develop a risk calculator to aid in surgical decision-making at the time of emergency laparotomy. Methods: A retrospective cohort study of patients requiring emergency trauma laparotomy between 2011 and 2017 at a single, level 1 trauma center was performed. Operative factors, skin management strategy, and outcomes were determined by chart review. Bayesian multilevel logistic regression was utilized to create a risk calculator with variables available upon closure of the laparotomy. Models were validated on a 30% test cohort and discrimination reported as an area under the receiver operating characteristics curve (AUROC). Results: Of 1,322 patients, the majority were male (77%) with median age of 33 years, injured by blunt mechanism (54%), and median injury severity score of 19. Eighty-eight (7%) patients developed an S-SSI. Patients who developed S-SSI had higher final lactate, blood loss, transfusion requirements, and wound classification. Patients with S-SSI more frequently had mesenteric or large bowel injury than those without S-SSI. Superficial SSI was associated with increased complications and prolonged length of stay (LOS). The S-SSI predictive model demonstrated moderate discrimination with an AUROC of 0.69 (95% confidence interval [CI], 0.56-0.81). Parameters contributing the most to the model were damage control laparotomy, full-thickness large bowel injury, and large bowel resection. Conclusion: A predictive model for S-SSI was built using factors available to the surgeon upon index emergency trauma laparotomy closure. This calculator may be used to standardize intra- and post-operative care and to identify high-risk patients in whom to test novel preventative strategies and improve overall outcomes for patients requiring emergency trauma laparotomy. [ABSTRACT FROM AUTHOR]

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 (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.

Purpose: In biopsy naïve men suspected for prostate cancer (PCa), it is uncertain how a risk-calculator and bi-parametric (bp) MRI should be combined to decide on prostate biopsy, balancing cancer detection rates and diagnostic burden. Methods: Prospective, single centre cohort study (August 2018–April 2019). All patients referred with serum PSA ≥ 3 ng/ml or abnormal digital rectal examination received bpMRI and risk for PCa was calculated using the ERSPC risk-calculator. Men with either PI-RADS ≥ 3 or calculator risk-score > 20% were recommended to undergo systematic biopsy (SB) and targeted biopsy (TB) of any visible lesion (reference pathway). Eight different derived diagnostic pathways were compared to the reference pathway regarding cancer detection, number of biopsies and bpMRIs performed. Results: Of 496 patients; 233 (47%) had a risk-calculator score of > 20%; 201 (41%) had PI-RADS score ≥ 3. The reference pathway detected PCa in 32.1%, clinically significant (cs) PCa in 19.4%, with 41% avoided biopsies, but 0% avoided bpMRI. Stratification with only risk-calculator: 76% csPCa diagnosed, 53% avoided biopsies and 100% avoided bpMRI. Stratification with only bpMRI: 97% csPCa diagnosed, 59% avoided biopsies, but 0% avoided bpMRI. A pathway with risk-calculator first, followed only with bpMRI when high-risk: 81% csPCa diagnosed, 72% avoided biopsies and 53% avoided bpMRI. Conclusion: Upfront bpMRI as a risk stratification tool outperforms risk-calculator in detecting significant disease. Applying the risk-calculator first to decide on performing an MRI, avoids 1 out of 2 MRIs, but up to 1 out of 5 significant cancers are missed. [ABSTRACT FROM AUTHOR]

Objectives: Selection criteria for palliative limited surgery in patients with non-small cell lung cancer (NSCLC) can vary by institution or surgeon. We retrospectively reviewed outcomes of poor-risk patients who underwent palliative segmentectomy (PS), using the National Clinical Database Risk Calculator (RC). Methods: We retrospectively analyzed medical records of patients with NSCLC tumors ≥ 20 mm and consolidation/tumor ratios ≥ 0.5 on computed tomography, who underwent PS from January 2009 to March 2016. Median follow-up time was 47 months (range 2–102 months). Results: We enrolled 67 patients (median age: 73.0 years), of whom 54 received thoracoscopic surgery and 28 received medial lymph-node dissection. The RC's mean predictive probability rate for perioperative mortality or severe complications was 7.1%. Of the 67 patients, 24 patients (43.0%) suffered post-surgical complications, including 2 (3%) who died in hospital; 17 eventually suffered NSCLC recurrences and/or metastases, 11 eventually died from NSCLC, and 17 died from other diseases. Five-year overall survival (OS) was 59.4%. When the patients were divided into high-risk (HR) and low-risk (LR) groups based on the RC, 5-year OS was significantly less in the HR group (43.9%) than in the LR group (82.2%; P < 0.05). Conclusion: The RC, which was developed primarily to determine perioperative risk, can predict long-term prognosis for compromised patients who undergo PS. [ABSTRACT FROM AUTHOR]

Knowledge about early risk factors for major depressive disorder (MDD) is critical to identify those who are at high risk. A multivariable model to predict adolescents' individual risk of future MDD has recently been developed however its performance in a UK sample was far from perfect. Given the potential role of air pollution in the aetiology of depression, we investigate whether including childhood exposure to air pollution as an additional predictor in the risk prediction model improves the identification of UK adolescents who are at greatest risk for developing MDD. We used data from the Environmental Risk (E-Risk) Longitudinal Twin Study, a nationally representative UK birth cohort of 2232 children followed to age 18 with 93% retention. Annual exposure to four pollutants – nitrogen dioxide (NO 2), nitrogen oxides (NO X), particulate matter <2.5 μm (PM 2.5) and <10 μm (PM 10) – were estimated at address-level when children were aged 10. MDD was assessed via interviews at age 18. The risk of developing MDD was elevated most for participants with the highest (top quartile) level of annual exposure to NO X (adjusted OR = 1.43, 95% CI = 0.96–2.13) and PM 2.5 (adjusted OR = 1.35, 95% CI = 0.95–1.92). The separate inclusion of these ambient pollution estimates into the risk prediction model improved model specificity but reduced model sensitivity – resulting in minimal net improvement in model performance. Findings indicate a potential role for childhood ambient air pollution exposure in the development of adolescent MDD but suggest that inclusion of risk factors other than this may be important for improving the performance of the risk prediction model. • High resolution air pollution exposure estimates are modelled for a UK birth cohort. • Pollution estimates are added to a risk prediction model for adolescent depression. • High NO X and PM 2.5 exposure improves model specificity but not sensitivity. • Inclusion of other risk factors may increase prediction of adolescent depression. [ABSTRACT FROM AUTHOR]

Venous thromboembolism (VTE) is one of the most common causes of postoperative mortality following bariatric surgery. The majority of VTE events occur after discharge from the hospital. Little consensus exists regarding who should receive extended enoxaparin thromboprophylaxis or how they should be dosed, namely whether to use weight-based or BMI-stratified dosing strategies. Provide an overview of the risk factors associated with VTE in procedures among bariatric patients including the use of predictive tools to stratify risk and the various approaches to enoxaparin chemoprophylaxis in obesity. Multiple centers. A review of the literature identified studies evaluating risk factors for VTE including demographic characteristics, co-morbidities, and operative factors. The use of calculators to stratify patients by risk and approaches to extended thromboprophylaxis in obesity were evaluated as well. VTE was associated with increased age, weight, male sex, and prior history of VTE, all frequently included in risk calculators. Outside of those major risk factors, there is little consensus about the importance of patient diagnoses. Weight-based dosing was often superior to standardized dosing in studies across disciplines in generating target anti-Xa levels however there is no consistent association of reduced risk of VTE with therapeutic anti-Xa levels. Risk calculators may be a valuable tool for identifying patients at high-risk for VTE, but their efficacy depends on the rating algorithm and inclusion of various risk factors and is methodologically limited by prophylactic interventions. Future work should consider if biochemical factors should be included in patient stratification approaches in particular when defining the ideal chemoprophylaxis approach. Transparency and consistency in data collection and reporting is needed to better assess and inform the ideal dosing strategy to prevent VTE following bariatric surgery. • VTE was associated with increased age, weight, male gender, and prior history of VTE; all frequently included in risk calculators. Outside of those major risk factors, there is little consensus about the importance of patient characteristic such as OSA or COPD. • Weight-based dosing was often superior to standardized dosing in studies across disciplines in generating target anti-Xa levels however there is no consistent association of reduced risk of VTE with therapeutic anti-Xa levels. • Risk calculators may be a valuable tool for identifying patient at high-risk for VTE, but their efficacy depends on the rating algorithm and inclusion of various risk factors. [ABSTRACT FROM AUTHOR]

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Background: An accurate prediction model could identify high-risk subjects of incident Overactive bladder (OAB) among the general population and enable early prevention which may save on the related medical costs. However, no efficient model has been developed for predicting incident OAB. In this study, we will develop a model for predicting the onset of OAB at 5-year in the general population setting.Methods: Data will be obtained from the Nagahama Cohort Project, a longitudinal, general population cohort study. The baseline characteristics were measured between Nov 28, 2008 and Nov 28, 2010, and follow-up was performed every 5 years. From the total of 9,764 participants (male: 3,208, female: 6,556) at baseline, we will exclude participants who could not attend the follow-up assessment and those who were defined as having OAB at baseline. The outcome will be incident OAB defined using the Overactive Bladder Symptom Score (OABSS) at follow-up assessment. Baseline questionnaires (demographic, health behavior, comorbidities and OABSS) and blood test data will be included as predictors. We will develop a logistic regression model utilizing shrinkage methods (LASSO penalization method). Model performance will be evaluated by discrimination and calibration. Net benefit will be evaluated by decision curve analysis. We will perform an internal validation and a temporal validation of the model. We will develop a web-based application to visualize the prediction model and facilitate its use in clinical practice.Discussion: This will be the first study to develop a model to predict the incidence of OAB. [ABSTRACT FROM AUTHOR]

Objective: Low patient recruitment into diabetes prevention programs is a challenge. The primary aim of this study was to demonstrate that an increased recruitment rate can be achieved by communicating personalized risk of progression to type 2 diabetes, estimating risk reduction with weight loss, and offering program choice. Secondary aims included program participation rate, weight loss, and short-term decreased diabetes risk.Methods: In this single-arm study, persons with prediabetes from 3 primary care sites received a letter that communicated their personalized risk of progression to diabetes within 3-years, estimated risk reduction with 5, 10, 15 % weight loss, reported in pounds, and offered a choice of 5 free, 6-month, programs. A one-sided test was used to compare the recruitment rate against the maximum expected rate of (10 %).Results: Recruitment response rate was 25.3 % (81/328, 95 % CI=[20.0 %, 29.4 %]) which was significantly higher than expected (p < 0.0001). Overall, 65 % of participants completed >75 % of contacts. BMI, HbA1c, and diabetes risk (all p < 0.0001) improved at 6 months; BMI (p < 0.0001) and HbA1c (p < 0.05) improved at 12 months.Conclusion: Recruitment response rate was better than expected.Practice Implications: Communicating personalized risk and reduction estimates with a choice of programs resulted in favorable outcomes, sustained at 1-year. [ABSTRACT FROM AUTHOR]

Background: Predictive tools are useful adjuncts in surgical planning. They help guide patient selection, candidacy for inpatient vs outpatient surgery, and discharge disposition as well as predict the probability of readmissions and complications after total joint arthroplasty (TJA). Surgeons may find it difficult due to significant variation among risk calculators to decide which tool is best suited for a specific patient for optimal decision-based care. Our aim is to perform a systematic review of the literature to determine the existing post-TJA readmission calculators and compare the specific elements that comprise their formula. Second, we intend to evaluate the pros and cons of each calculator.Methods: Using a Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocols protocol, we conducted a systematic search through 3 major databases for publications addressing TJA risk stratification tools for readmission, discharge disposition, and early complications. We excluded those manuscripts that were not comprehensive for hips and knees, did not list discharge, readmission or complication as the primary outcome, or were published outside the North America.Results: Ten publications met our criteria and were compared on their sourced data, variable types, and overall algorithm quality. Seven of these were generated with single institution data and 3 from large administrative datasets. Three tools determined readmission risk, 5 calculated discharge disposition, and 2 predicted early complications. Only 4 prediction tools were validated by external studies. Seven studies utilized preoperative data points in their risk equations while 3 utilized intraoperative or postsurgical data to delineate risk.Conclusion: The extensive variation among TJA risk calculators underscores the need for tools with more individualized stratification capabilities and verification. The transition to outpatient and same-day discharge TJA may preclude or change the need for many of these calculators. Further studies are needed to develop more streamlined risk calculator tools that predict readmission and surgical complications. [ABSTRACT FROM AUTHOR]

Nonhome discharge (NHD) has significant implications for patient counseling and discharge planning and is frequently required following fenestrated-branched endovascular aortic repair (FB-EVAR) of complex abdominal aortic aneurysms (CAAA) and thoracoabdominal aortic aneurysms (TAAA). We aimed to identify preoperative predictors of NHD after elective FB-EVAR for CAAA and TAAA and develop a risk calculator able to predict NHD. A retrospective review of prospectively collected data on all patients undergoing FB-EVAR between January 2007 and December 2021 at a single institution was performed. Exclusion criteria were admission from a nonhome setting, emergency and repeat FB-EVAR, and discharge to an unknown destination. The cohort was randomly split into separate development (70% of patients) and validation (30%) cohorts to develop a predictive calculator for NHD. Independent variables associated with NHD were assessed in a series of logistic regression analyses from 100 bootstrapped samples of the development set, and a model was developed using the most predictive variables. Resulting parameter estimates were applied to data in the validation set to assess model discrimination and calibration. From the initial cohort of 712 FB-EVAR patients, 644 were included in the study (74% male; mean age, 75.4 ± 7.6 years), including 452 with CAAA (70%) and 192 with TAAA (30%). Early mortality occurred in eight patients (1.2%; 5 in CAAA and 3 in TAAA) and the median hospital stay was 5 days (4 for CAAA and 7 for TAAA). Ninety-seven patients (15%) had a NHD. On multivariable analysis, older age (per year, odds ratio [OR], 1.08; P <.001), female gender (OR, 3.03; P <.001), smoking (OR, 2.86; P =.01), congestive heart failure (OR, 3.05; P =.004), peripheral artery disease (OR, 1.81; P =.07), and extent I (OR, 3.17), II (OR, 2.84), and III (OR, 2.52; all P =.08) TAAAs were associated with an increased likelihood of NHD in the development set. Based on these factors, the risk calculator was developed which accurately predicts NHD in the validation set with an area under the curve of 0.7. Older, female smokers with congestive heart failure and peripheral artery disease and more extensive aneurysms are at highest risk of NHD after FB-EVAR. Using only preoperative factors, our risk calculator can predict accurately who will have a NHD, allowing enhanced preoperative patient counselling and accelerated hospital discharge. [ABSTRACT FROM AUTHOR]

Abstract Background A key barrier to controlling esophageal adenocarcinoma (EAC) is identifying those most likely to benefit from screening and surveillance. We aimed to develop an online educational tool, termed IC-RISC™, for providers and patients to estimate more precisely their absolute risk of developing EAC, interpret this estimate in the context of risk of dying from other causes, and aid in decision-making. Results U.S. incidence and mortality data and published relative risk estimates from observational studies and clinical trials were used to calculate absolute risk of EAC over 10 years adjusting for competing risks. These input parameters varied depending on presence of the key precursor, Barrett’s esophagus. The open source application works across common devices to gather risk factor data and graphically illustrate estimated risk on a single page. Changes to input data are immediately reflected in the colored graphs. We used the calculator to compare the risk distribution between EAC cases and controls from six population-based studies to gain insight into the discrimination metrics of current practice guidelines for screening, observing that current guidelines sacrifice a significant amount of specificity to identify 78–86% of eventual cases in the US population. Conclusions This educational tool provides a simple and rapid means to graphically communicate risk of EAC in the context of other health risks, facilitates “what-if” scenarios regarding potential preventative actions, and can inform discussions regarding screening, surveillance and treatment options. Its generic architecture lends itself to being easily extended to other cancers with distinct pathways and/or intermediate stages, such as hepatocellular cancer. IC-RISC™ extends current qualitative clinical practice guidelines into a quantitative assessment, which brings the possibility of preventative actions being offered to persons not currently targeted for screening and, conversely, reducing unnecessary procedures in those at low risk. Prospective validation and application to existing well-characterized cohort studies are needed.

Risk prediction models including the Prostate Health Index (phi) for prostate cancer have been well established and evaluated in the Western population. The aim of this study is to build phi-based risk calculators in a prostate biopsy population and evaluate their performance in predicting prostate cancer (PCa) and high-grade PCa (Gleason score ≥7) in the Chinese population. We developed risk calculators based on 635 men who underwent initial prostate biopsy. Then, we validated the performance of prostate-specific antigen (PSA), phi, and the risk calculators in an additional observational cohort of 1045 men. We observed that the phi-based risk calculators (risk calculators 2 and 4) outperformed the PSA-based risk calculator for predicting PCa and high-grade PCa in the training cohort. In the validation study, the area under the receiver operating characteristic curve (AUC) for risk calculators 2 and 4 reached 0.91 and 0.92, respectively, for predicting PCa and high-grade PCa, respectively; the AUC values were better than those for risk calculator 1 (PSA-based model with an AUC of 0.81 and 0.82, respectively) (all P < 0.001). Such superiority was also observed in the stratified population with PSA ranging from 2.0 ng ml−1to 10.0 ng ml−1. Decision curves confirmed that a considerable proportion of unnecessary biopsies could be avoided while applying phi-based risk calculators. In this study, we showed that, compared to risk calculators without phi, phi-based risk calculators exhibited superior discrimination and calibration for PCa in the Chinese biopsy population. Applying these risk calculators also considerably reduced the number of unnecessary biopsies for PCa.

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]

Purpose: To develop a novel Taiwanese prostate cancer (PCa) risk model for predicting PCa, comparing its predictive performance with that of two well-established PCa risk calculator apps. Methods: 1545 men undergoing prostate biopsies in a Taiwanese tertiary medical center between 2012 and 2019 were identified retrospectively. A five-fold cross-validated logistic regression risk model was created to calculate the probabilities of PCa and high-grade PCa (Gleason score ≧ 7), to compare those of the Rotterdam and Coral apps. Discrimination was analyzed using the area under the receiver operator characteristic curve (AUC). Calibration was graphically evaluated with the goodness-of-fit test. Decision-curve analysis was performed for clinical utility. At different risk thresholds to biopsy, the proportion of biopsies saved versus low- and high-grade PCa missed were presented. Results: Overall, 278/1309 (21.2%) patients were diagnosed with PCa, and 181 out of 278 (65.1%) patients had high-grade PCa. Both our model and the Rotterdam app demonstrated better discriminative ability than the Coral app for detection of PCa (AUC: 0.795 vs 0.792 vs 0.697, DeLong's method: P < 0.001) and high-grade PCa (AUC: 0.869 vs 0.873 vs 0.767, P < 0.001). Using a ≥ 10% risk threshold for high-grade PCa to biopsy, our model could save 67.2% of total biopsies; among these saved biopsies, only 3.4% high-grade PCa would be missed. Conclusion: Our new logistic regression model, similar to the Rotterdam app, outperformed the Coral app in the prediction of PCa and high-grade PCa. Additionally, our model could save unnecessary biopsies and avoid missing clinically significant PCa in the Taiwanese population. [ABSTRACT FROM AUTHOR]

The burden of adolescent depression is high in low- and middle-income countries (LMICs), yet research into prevention is lacking. Development and validation of models to predict individualized risk of depression among adolescents in LMICs is rare but crucial to ensure appropriate targeting of preventive interventions. We assessed the ability of a model developed in Brazil, a middle-income country, to predict depression in an existing culturally different adolescent cohort from Nepal, a low-income country with a large youth population with high rates of depression. Data were utilized from the longitudinal study of 258 former child soldiers matched with 258 war-affected civilian adolescents in Nepal. Prediction modelling techniques were employed to predict individualized risk of depression at age 18 or older in the Nepali cohort using a penalized logistic regression model. Following a priori exclusions for prior depression and age, 55 child soldiers and 71 war-affected civilians were included in the final analysis. The model was well calibrated, had good overall performance, and achieved good discrimination between depressed and non-depressed individuals with an area under the curve (AUC) of 0.73 (bootstrap-corrected 95% confidence interval 0.62–0.83). The Brazilian model comprising seven matching sociodemographic predictors, was able to stratify individualized risk of depression in a Nepali adolescent cohort. Further testing of the model's performance in larger socio-culturally diverse samples in other geographical regions should be attempted to test the model's wider generalizability. [ABSTRACT FROM AUTHOR]

Introduction: Radical cystectomy (RC) with urinary diversion is associated with significant perioperative morbidity and mortality, varying between 30% and 70% and between 0.3% and 10.6%, respectively. Risk calculators have been extensively studied in the general surgery literature to predict 30- and 90-day postoperative morbidity and mortality but have not been widely accepted in the RC literature.Materials and Methods: We performed a search of MEDLINE and Embase databases during May 2020 to identify all relevant studies using the following keywords: radical cystectomy, surgical complication predictive model, surgical complication predictive equation, surgical complication predictive nomogram, surgical risk calculator, morbidity, and mortality. We determined the existing surgical predictive nomograms, calculators, and indices and their accuracy in predicting morbidity, mortality, and major complications after RC.Results: National Surgical Quality Improvement Program had poor accuracy at predicting 30-day morbidity at mortality (AUC 0.5-0.6). LACE index showed good discrimination at predicting 90-day mortality (AUC 0.7). The various frailty and sarcopenia indices have shown poor to fair accuracy at predicting (AUC 0.5-0.7). The Isbarn and Aziz nomograms have equivalent accuracy at predicting 90-day mortality (AUC 0.7) but are limited by inclusion of tumor histology and presence of metastatic disease as variables. POSSUM and P-POSSUM have poor ability at predicting morbidity and mortality (AUC 0.5) and are cumbersome calculators. The surgical Apgar score has been able to predict 30-day morbidity and mortality but can only be used in the postoperative setting.Discussion: The currently available surgical risk calculators have either poor accuracy at predicting post-RC morbidity and mortality or are limited by types of variables included. An ideal risk calculator would be comprised of preoperative factors only and have a high accuracy to serve as a tool for preoperative patient counseling prior to surgery.Conclusion: There exists a strong need to develop a comprehensive and accurate preoperative risk calculator that predicts morbidity and mortality after RC. [ABSTRACT FROM AUTHOR]

Background: A recent systematic review demonstrated that reinfection rates following eradication of hip and knee periprosthetic joint infection (PJI) may be as high as 29%. This study aimed to develop a preoperative risk calculator for assessing patient's individual risk associated with reinfection following treatment of PJI in total joint arthroplasty (TJA).Methods: A total of 1081 consecutive patients who underwent revision TJA for PJI were evaluated. In total, 293 patients were diagnosed with TJA reinfection. A total of 56 risk factors, including patient characteristics and surgical variables, were evaluated with multivariate regression analysis. Analysis of the area under the receiver operating characteristics curve was performed to evaluate the strength of the predictive model.Results: Of the 56 risk factors studied, 19 were found to have a significant effect as risk factor for TJA reinfection. The strongest predictors for TJA reinfection included previous PJI treatment techniques such as irrigation and debridement, the number of previous surgical interventions, medical comorbidities such as obesity, drug abuse, depression and smoking, as well as microbiology including the presence of Enterococcus species. The combined area under the receiver operating characteristics curve of the risk calculator for periprosthetic hip and knee joint reinfection was 0.75.Conclusions: The study findings demonstrate that surgical factors, including previous PJI surgical treatment techniques as well as the number of previous surgeries, alongside microbiology including the presence of Enterococcus species have the strongest effect on the risk for periprosthetic THA and TKA joint reinfection, suggesting the limited applicability of the existing risk calculators for the development of PJI following primary TJA in predicting the risk of periprosthetic joint reinfection. [ABSTRACT FROM AUTHOR]

BackgroundEuropean Association of Urology (EAU) guidelines recommend using risk-calculators (RCs), imaging or additional biomarkers in asymptomatic men at risk of prostate cancer (PCa).ObjectivesTo compare the performance of mpMRI, a RC we recently developed and two commonly used RC not including mpMRI in predicting the risk of PCa, as well as the added value of mpMRI to each RC.Design, Setting, and ParticipantsSingle-center retrospective study evaluating 221 biopsy-naïve patients who underwent prebiopsy mpMRI.Outcome Measurements and Statistical AnalysisPatients’ probabilities of any PCa and clinically significant PCa (csPC, defined as Gleason-Score ≥3 + 4) were computed according to mpMRI, European Randomized Study of Screening for Prostate Cancer RC (ERSPC-RC), the Prostate Biopsy Collaborative Group RC (PBCG-RC) and the Foggia Prostate Cancer RC (FPC-RC). Logistic regression, AUC, and Decision curve analysis (DCA) were used to assess the accuracy of tested models.Results and LimitationThe FPC-RC outperformed mpMRI in diagnosing both any PCa (AUC 0.76 vs 0.69) and csPCa (AUC 0.80 vs 0.75). Conversely mpMRI showed a higher accuracy in predicting any PCa compared to the PBCG-RC and the ERSPC-RC but similar performances in predicting csPCa. At multivariable analysis predicting csPCa and any PCa, the addition of mpMRI findings improved the accuracy of each calculator. DCA showed that the FPC-RC provided a greater net benefit than mpMRI and the other RCs. The addition of mpMRI findings improved the net benefit provided by each calculator.ConclusionsmpMRI was outperformed by the novel FPC-RC and showed similar performances compared to the PBCG and ERSPC RCs in predicting csPCa. The addition of mpMRI findings improved the diagnostic accuracy of each of these calculators

Background: European Association of Urology (EAU) guidelines recommend using risk-calculators (RCs), imaging or additional biomarkers in asymptomatic men at risk of prostate cancer (PCa). Objectives: To compare the performance of mpMRI, a RC we recently developed and two commonly used RC not including mpMRI in predicting the risk of PCa, as well as the added value of mpMRI to each RC. Design, Setting, and Participants: Single-center retrospective study evaluating 221 biopsy-naïve patients who underwent prebiopsy mpMRI. Outcome Measurements and Statistical Analysis: Patients' probabilities of any PCa and clinically significant PCa (csPC, defined as Gleason-Score ≥3 + 4) were computed according to mpMRI, European Randomized Study of Screening for Prostate Cancer RC (ERSPC-RC), the Prostate Biopsy Collaborative Group RC (PBCG-RC) and the Foggia Prostate Cancer RC (FPC-RC). Logistic regression, AUC, and Decision curve analysis (DCA) were used to assess the accuracy of tested models. Results and Limitation: The FPC-RC outperformed mpMRI in diagnosing both any PCa (AUC 0.76 vs 0.69) and csPCa (AUC 0.80 vs 0.75). Conversely mpMRI showed a higher accuracy in predicting any PCa compared to the PBCG-RC and the ERSPC-RC but similar performances in predicting csPCa. At multivariable analysis predicting csPCa and any PCa, the addition of mpMRI findings improved the accuracy of each calculator. DCA showed that the FPC-RC provided a greater net benefit than mpMRI and the other RCs. The addition of mpMRI findings improved the net benefit provided by each calculator. Conclusions: mpMRI was outperformed by the novel FPC-RC and showed similar performances compared to the PBCG and ERSPC RCs in predicting csPCa. The addition of mpMRI findings improved the diagnostic accuracy of each of these calculators [ABSTRACT FROM AUTHOR]

Background: Risk calculators are useful tools that can help clinicians and researchers better understand an individual's risk of conversion to psychosis. The North American Prodrome Longitudinal Study (NAPLS2) Individualized Risk Calculator has good predictive accuracy but could be potentially improved by the inclusion of a biomarker. Baseline cortisol, a measure of hypothalamic-pituitary-adrenal (HPA) axis functioning that is impacted by biological vulnerability to stress and exposure to environmental stressors, has been shown to be higher among individuals at clinical high-risk for psychosis (CHRP) who eventually convert to psychosis than those who do not. We sought to determine whether the addition of baseline cortisol to the NAPLS2 risk calculator improved the performance of the risk calculator.Methods: Participants were drawn from the NAPLS2 study. A subset of NAPLS2 participants provided salivary cortisol samples. A multivariate Cox proportional hazards regression evaluated the likelihood of an individual's eventual conversion to psychosis based on demographic and clinical variables in addition to baseline cortisol levels.Results: A total of 417 NAPLS2 participants provided salivary cortisol and were included in the analysis. Higher levels of cortisol were predictive of conversion to psychosis in a univariate model (C-index = 0.59, HR = 21.5, p-value = 0.004). The inclusion of cortisol in the risk calculator model resulted in a statistically significant improvement in performance from the original risk calculator model (C-index = 0.78, SE = 0.028).Conclusions: Salivary cortisol is an inexpensive and non-invasive biomarker that could improve individual predictions about conversion to psychosis and treatment decisions for CHR-P individuals. [ABSTRACT FROM AUTHOR]

Background: Risk estimation models integrated into Electronic Health Records (EHRs) can deliver innovative approaches in psychiatry, but clinicians' endorsement and their real-world usability are unknown. This study aimed to investigate the real-world feasibility of implementing an individualised, transdiagnostic risk calculator to automatically screen EHRs and detect individuals at-risk for psychosis.Methods: Feasibility implementation study encompassing an in-vitro phase (March 2018 to May 2018) and in-vivo phase (May 2018 to April 2019). The in-vitro phase addressed implementation barriers and embedded the risk calculator (predictors: age, gender, ethnicity, index cluster diagnosis, age*gender) into the local EHR. The in-vivo phase investigated the real-world feasibility of screening individuals accessing secondary mental healthcare at the South London and Maudsley NHS Trust. The primary outcome was adherence of clinicians to automatic EHR screening, defined by the proportion of clinicians who responded to alerts from the risk calculator, over those contacted.Results: In-vitro phase: implementation barriers were identified/overcome with clinician and service user engagement, and the calculator was successfully integrated into the local EHR through the CogStack platform. In-vivo phase: 3722 individuals were automatically screened and 115 were detected. Clinician adherence was 74% without outreach and 85% with outreach. One-third of clinicians responded to the first email (37.1%) or phone calls (33.7%). Among those detected, cumulative risk of developing psychosis was 12% at six-month follow-up.Conclusion: This is the first implementation study suggesting that combining precision psychiatry and EHR methods to improve detection of individuals with emerging psychosis is feasible. Future psychiatric implementation research is urgently needed. [ABSTRACT FROM AUTHOR]

The aim of this review was to identify the different risk assessment tools that stratify the individual's risk of four of the eight leading causes of death: stroke, ischaemic heart diseases, type 2 diabetes mellitus, and dementia. It follows part I, which summarized the risk assessment tools for the other four leading causes of death (breast cancer, lung cancer, colorectal cancer and osteoporosis). As in part I, the different tools were compared by their variables and validation criteria and an overview table was designed for each illness. The tables facilitate the choice of the adequate risk assessment tool for the individual patient in order to estimate the risk of developing an NCD. This could guide treating physicians in the decision-making process about completing diagnostics for early detection and, if necessary, treatment, such that the patient's quality of life can be preserved and costs to the health care system are minimal. [ABSTRACT FROM AUTHOR]

Objective: To compare the performance of the risk calculators of the European Randomized Study for Screening of Prostate Cancer (ERSPC) and the Prostate Biopsy Collaborative Group (PBCG) in predicting the risk of presenting clinically significant prostate cancer., Material and Methods: Retrospectively, patients who underwent prostate biopsy at Sanatorio Allende Cerro, Ciudad de Córdoba, Argentina, were identified from January 2018 to December 2021. The probability of having prostate cancer was calculated with the two calculators separately and then the results were compared to establish which of the two performed better. For this, areas under the curve (AUC) were analyzed., Results: 250 patients were included, 140 (56%) presented prostate cancer, of which 92 (65.71%) had clinically significant prostate cancer (Gleason score ≥7). The patients who presented cancer were older, had a higher prostate-specific antigen (PSA) value, and had a smaller prostate size. The AUC to predict the probability of having clinically significant prostate cancer was 0.79 and 0.73 for PBCG-RC and ERSPC-RC respectively (P=0.0084)., Conclusion: In this cohort of patients, both prostate cancer risk calculators performed well in predicting clinically significant prostate cancer risk, although the PBCG-RC showed better accuracy., (Copyright © 2023 AEU. Published by Elsevier España, S.L.U. All rights reserved.)

Background This study aims to determine the usage of the Gail model in screening for breast cancer during physical examinations of women by sampling primary care physicians in two regions of Texas - Hidalgo County and Johnson County. A Gail score of 1.66% or higher indicates increased breast cancer risk. Three specialties are surveyed: internal medicine (IM), family medicine (FM), and gynecology (GYN). The null hypothesis for this study is that primary care physicians do not use the Gail model in screening for breast cancer during physical examinations of women. Methods A survey was distributed to 100 physicians with specialties in IM, FM, and GYN from May 2022 to July 2022. The survey assessed the physician's frequency of use of the Gail model and chemoprevention. Data were collected by distributing survey questionnaires to physicians in person. Descriptive statistics were used for response distributions. Fisher's exact probability test was used for comparisons across specialties. Results The response rate was 34% (34/100). Thirty-eight percent of the physicians surveyed reported using the Gail model in their practice (IM 46%, FM 23%, and GYN 31%). All 13 of the physicians using the Gail model were open to using chemoprevention. Conclusions Only 38% of the physicians surveyed responded that they use the Gail model in their practice. The study concluded that a minority of primary care physicians used the Gail model to decrease breast cancer risk. Further research would help to define better the Gail model and its use in preventing breast cancer in women. The Gail model appears to be beneficial to breast cancer risk reduction; however, risk reduction medication side effects need to be minimized., Competing Interests: The authors have declared that no competing interests exist., (Copyright © 2024, Pruitt et al.)

Purpose: We sought to develop and validate a prostate biopsy risk calculator for Black men and compare it with the Prostate Cancer Prevention Trial version 2.0, Prostate Biopsy Collaborative Group, and Kaiser Permanente Prostate Cancer Risk Calculators for the detection of Gleason Grade Group (GG) ≥ 2 prostate cancer (PCa)., Materials and Methods: We prospectively recruited 2 cohorts of men undergoing prostate biopsy from 5 facilities in Chicago. The first cohort was split into development (70%) and internal validation (30%) groups. The second was used for external validation. Iterative logistic regression was used to develop 3 models for predicting GG ≥ 2 PCa. Models were compared for discrimination using the C statistics, calibration curves, and net benefit curves. The frequency of unnecessary biopsies and missed PCas was compared at 10% and 30% risk thresholds., Results: The 2 cohorts included 393 and 292 Black men, respectively. Our first model, Mistry-Sun 1, used serum PSA and prior negative biopsy. Mistry-Sun 2 added abnormal digital rectal exam (DRE) and an interaction term with abnormal DRE and PSA to Mistry-Sun 1. Mistry-Sun 3 added prostate volume, abnormal DRE, and age to Mistry-Sun 1. The C statistics were 0.74, 0.74, and 0.78, respectively, and were similar to or higher than established calculators. At the 10% and 30% risk thresholds our models had the fewest unnecessary biopsies and an appropriate proportion of missed GG ≥ 2 PCas., Conclusions: Tailoring a risk calculator to detect clinically significant PCa in Black men may improve biopsy decision-making and outcomes compared to tools developed in non-Black populations.

Pre-biopsy multiparametric MRI is now recommended by multiple guidelines, not only for men with persistent suspicion of prostate cancer after prior negative systematic biopsy, but also at initial screening before the first biopsy. The major benefit of pre-biopsy MRI in the diagnostic work-up is to promote individualized risk-adapted approaches for biopsy-decision management. Multiple MRI-directed diagnostic pathways can be conceived, with each approach having net-benefit trade-offs between benefits and harms, based on improved diagnostic yields of significant cancers and reduced biopsy testing and reduced detection of indolent prostate cancer. In this paper, we illustrate how clinical benefits can be maximized in men with MRI-negative and MRI-positive results, using the PI-RADS Multiparametric MRI and MRI-directed biopsy pathway. From a practice perspective, we emphasize five golden rules: (1) that multiparametric MRI approach including targeted biopsies be reserved for men likely to benefit from early detection and treatment of prostate cancer; (2) that there is a need to carefully assess risk of significant disease using PSA and clinical parameters before and after MRI; (3) do not offer immediate biopsy if the MRI is negative, unless other high-risk factors are present; (4) accept that not all significant cancers are found immediately and have robust 'safety nets' for men with negative MRI scans who avoid immediate biopsy and for positive MRI patients with negative or non-explanatory histology; and (5) use MRI-directed biopsy methods that minimize overdiagnosis and improve risk stratification. [ABSTRACT FROM AUTHOR]

Risk calculators for prediction of conversion of Clinical High-Risk (CHR) individuals to syndromal psychosis have recently been developed and have generated considerable clinical use and research interest. Predictor variables in these calculators have been clinical rather than biological, and our goal was to incorporate a neurochemical imaging measure into this framework and assess its impact on prediction. We combined striatal glutamate 1H MRS data with the SIPS symptoms identified by the Columbia Risk Calculator as having the greatest predictive value in order to develop an imaging-based risk calculator for conversion to psychosis. We evaluated the calculator in 19 CHR individuals, 7 (36.84%) of whom converted to syndromal psychosis during the 2-year follow up. The receiver operating characteristic (ROC) curve for the logistic model including only striatal glutamate and visual perceptual abnormalities showed an AUC = 0.869 (95% CI = [0.667, 1.000]) and AUCoa = 0.823, with sensitivity of 0.714, specificity of 0.917, positive predictive value of 0.833, and negative predictive value of 0.846. These results represent modest improvements over each of the individual ROC curves based on either striatal glutamate or visual perceptual abnormalities alone. The preliminary model building and evaluation presented here in a small CHR sample suggests that the approach of incorporating predictive imaging measures into risk classification is not only feasible but offers the potential of enhancing risk assessment. [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]

The American College of Surgeons (ACS) National Surgical Quality Improvement Program (NSQIP) calculator is a useful tool used by physicians to better inform patients on the surgical risk of postoperative complications. It makes use of the NSQIP database to derive the chance for several adverse outcomes to occur postoperatively given certain patient's factors. The aim of this study was to assess its applicability in a series of patients undergoing an Ivor Lewis esophagectomy. Data from 100 consecutive patients who underwent an Ivor Lewis esophagectomy between September 2013 and November 2017 at our institution were reviewed. Each patient was assessed using the ACS NSQIP surgical risk calculator. Actual events in this group were compared with their particular NSQIP-assessed risk. Logistic regression models were used to compare surgical risk calculator estimates binary outcomes such as incidence of postoperative complications such as cardiac events, renal events, surgical site infection, and death. Mixed linear model was used for length of stay (LOS) duration versus observed LOS. C-statistic was for predictive accuracy each binary outcome and intraclass correlation was used for LOS. C-statistic values were higher than the cutoff (0.75) for surgical site infection and death. The ACS NSQIP risk calculator was poorly predictive of other reported outcomes by the calculator such as cardiac or renal complications. Corroboration between observed LOS and predicted LOS was weak (8 d versus 11 d, respectively, intraclass coefficient 0.04). This study suggests that the risk calculator is useful for identifying risk of death or surgical site infection but poor at discriminating likelihood of other reported outcomes occurring, such as pneumonia, acute renal failure and cardiac complications for patients who underwent an Ivor Lewis esophagectomy. Estimations for procedure-specific complications for esophagectomy may need reevaluated. [ABSTRACT FROM AUTHOR]

Background: Guidelines recommend surgical excision of atypical ductal hyperplasia (ADH) due to the concern of undersampling a potential malignancy on core needle biopsy (CNB). The purpose of this study was to determine clinical, radiological and pathological variables associated with ADH upstaging to cancer and to develop a predictive risk calculator capable of identifying women who have a low oncological risk of upstaging. Methods: A prospectively collected database from a tertiary breast referral center was analyzed for women diagnosed with ADH on CNB between January 2013 to December 2017 who underwent surgical excision. CNB and surgical pathology reports were examined to determine rate of upstaging. The association between clinical, radiological and pathological variables were evaluated using regression analysis to determine predictors of ADH upstaging to cancer. Significant variables (p ≤ 0.05) identified on univariate analysis were assigned a score of "1" and were included in the ADH upstaging risk calculator. Results: A total of 1986 patients underwent surgery for a high-risk lesion. We identified 318 (16.0%) patients who had ADH identified on their CNB who underwent surgery—of which 290 were included in our study. The upstage rate was 24.8%. Five variables were associated with upstaging and included in our calculator: (1) lesion > 5 mm on ultrasound; (2) lesion > 5 mm on mammogram; (3) one or more "high-risk" lesion(s) on CNB; (4) pathological suspicion for cancer and; (5) incomplete removal of calcifications on CNB. Patients with a score of 0 had a 2% risk of being upstaged to cancer and were deemed low risk with 17.2% of patients falling within this category. Conclusions: Patients with ADH on CNB can be stratified into a low oncological cohort who have a 2% risk of being upstaged to carcinoma. In the future, these select patients may be counselled and potentially offered observation as an alternative to surgery. [ABSTRACT FROM AUTHOR]

Abstract Background The American College of Surgeons National Surgical Quality Improvement Program (NSQIP®) developed a surgical risk calculator using data from 1.4 million patients and including 1557 unique Current Procedural Terminology (CPT) codes. Although this calculator demonstrated excellent performance in predicting postoperative mortality, morbidity, and six surgical complications, it was not developed specifically for use in older surgical patients who have worse surgical outcomes and additional unique risk factors compared to younger adults. We aimed to test the ability of a simple self-reported mobility tool to predict postoperative outcomes in the older surgical population compared to the NSQIP. Methods We used data from a prospective cohort study that enrolled 197 older surgical patients (≥ 69 years) undergoing various elective surgeries and assessed 30-day surgical outcomes. Statistical models included data from the Mobility Assessment Tool-short form (MAT-sf) alone, covariates alone, and MAT-sf data and covariates. We used leave-one-out (LOO) cross-validation of the models within our cohort and compared their performance for predicting postoperative outcomes against the NSQIP calculator based on receiver operating characteristic area under the curve (ROC AUC). Results Patients with poor self-reported mobility experienced higher rates of postoperative complications and nursing home placement. There was no difference in performance between any of our models and the NSQIP calculator (p > 0.1), with AUC between 0.604 and 0.697 for predicting postoperative complications and 0.653 and 0.760 for predicting nursing home placement. All models also predicted a length of stay (LOS) similar to the actual LOS. Conclusion Mobility assessment alone using MAT-sf can predict postoperative complications, nursing home placement, and LOS for older surgical patients, with accuracy comparable to that of the NSQIP calculator. The simplicity of this noninvasive risk assessment tool makes it an attractive alternative to the NSQIP calculator that requires 20 patient predictors and the planned procedure, or CPT code to predict the chance that patients will have 15 different adverse outcomes following surgery.

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]

Objective: With each recurrence the prognosis of bipolar disorder (BD) worsens, indicating the need to identify the factors associated with increased recurrence risk. The course of BD is heterogenous and although risk factors for recurrence for the group as a whole have been reported in the literature, identification of risk factors for a specific individual are crucial for developing personalized treatments.Method: A total of 363 recovered BD youths/young adults from the Course and Outcome of Bipolar Youth (COBY) study were included. Participants were evaluated on average every 7 months for a median of 12.5 years and interviewed with standard instruments. Risk factors of recurrence from the literature were used to build a risk calculator (RC) to predict recurrence risk at different time intervals.Results: Approximately 80% of participants had at least one syndromal recurrence and 60% had ≥2 recurrences, particularly depressions. The 6-month and 1-, 2-, 3-, and 5-year RC showed an accuracy between 72% and 82% for predicting any mood recurrences, and up to 80% for depression and 89% for hypo/mania (sensitivity/specificity both 0.74). The most influential recurrence risk factors were shorter recovery lengths, younger age at assessment, earlier mood onset, and more severe prior depression. Although important, other factors associated with recurrence risk, such as interepisodic subsyndromal mood symptoms and comorbidities, did not influence the RC score beyond factors noted above.Conclusion: The RC provides a useful tool for predicting an individual's recurrence risk of depression and/or hypo/mania in BD youths and for developing personalized interventions and informing research. Replication studies are warranted. [ABSTRACT FROM AUTHOR]