Protocol for the development and validation of a Polypharmacy Assessment Score.

BACKGROUND: An increasing number of people are using multiple medications each day, named polypharmacy. This is driven by an ageing population, increasing multimorbidity, and single disease-focussed guidelines. Medications carry obvious benefits, yet polypharmacy is also linked to adverse consequences including adverse drug events, drug-drug and drug-disease interactions, poor patient experience and wasted resources. Problematic polypharmacy is 'the prescribing of multiple medicines inappropriately, or where the intended benefits are not realised'. Identifying people with problematic polypharmacy is complex, as multiple medicines can be suitable for people with several chronic conditions requiring more treatment. Hence, polypharmacy is often potentially problematic, rather than always inappropriate, dependent on clinical context and individual benefit vs risk. There is a need to improve how we identify and evaluate these patients by extending beyond simple counts of medicines to include individual factors and long-term conditions. AIM: To produce a Polypharmacy Assessment Score to identify a population with unusual levels of prescribing who may be at risk of potentially problematic polypharmacy. METHODS: Analyses will be performed in three parts: 1. A prediction model will be constructed using observed medications count as the dependent variable, with age, gender and long-term conditions as independent variables. A 'Polypharmacy Assessment Score' will then be constructed through calculating the differences between the observed and expected count of prescribed medications, thereby highlighting people that have unexpected levels of prescribing. Parts 2 and 3 will examine different aspects of validity of the Polypharmacy Assessment Score: 2. To assess 'construct validity', cross-sectional analyses will evaluate high-risk prescribing within populations defined by a range of Polypharmacy Assessment Scores, using both explicit (STOPP/START criteria) and implicit (Medication Appropriateness Index) measures of inappropriate prescribing. 3. To assess 'predictive validity', a retrospective cohort study will explore differences in clinical outcomes (adverse drug reactions, unplanned hospitalisation and all-cause mortality) between differing scores. DISCUSSION: Developing a cross-cutting measure of polypharmacy may allow healthcare professionals to prioritise and risk stratify patients with polypharmacy using unusual levels of prescribing. This would be an improvement from current approaches of either using simple cutoffs or narrow prescribing criteria.

External validation of the barcelona magnetic resonance imaging predictive model for detecting significant prostate cancer including men receiving 5-alpha reductase inhibitors.

PURPOSE: To validate the Barcelona-magnetic resonance imaging predictive model (BCN-MRI PM) for clinically significant prostate cancer (csPCa) in Catalonia, a Spanish region with 7.9 million inhabitants. Additionally, the BCN-MRI PM is validated in men receiving 5-alpha reductase inhibitors (5-ARI). MATERIALS AND METHODS: A population of 2,212 men with prostate-specific antigen serum level > 3.0 ng/ml and/or a suspicious digital rectal examination who underwent multiparametric MRI and targeted and/or systematic biopsies in the year 2022, at ten participant centers of the Catalonian csPCa early detection program, were selected. 120 individuals (5.7%) were identified as receiving 5-ARI treatment for longer than a year. The risk of csPCa was retrospectively assessed with the Barcelona-risk calculator 2 (BCN-RC 2). Men undergoing 5-ARI treatment for less than a year were excluded. CsPCa was defined when the grade group was ≥ 2. RESULTS: The area under the curve of the BCN-MRI PM in 5-ARI naïve men was 0.824 (95% CI 0.783-0.842) and 0.849 (0.806-0.916) in those receiving 5-ARI treatment, p 0.475. Specificities at 100, 97.5, and 95% sensitivity thresholds were to 2.7, 29.3, and 39% in 5-ARI naïve men, while 43.5, 46.4, and 47.8%, respectively in 5-ARI users. The application of BCN-MRI PM would result in a reduction of 23.8% of prostate biopsies missing 5% of csPCa in 5-ARI naïve men, while reducing 25% of prostate biopsies without missing csPCa in 5-ARI users. CONCLUSIONS: The BCN-MRI PM has achieved successful validation in Catalonia and, notably, for the first time, in men undergoing 5-ARI treatment.

Repeat Prostate-specific Antigen Testing Improves Risk-based Selection of Men for Prostate Biopsy After Magnetic Resonance Imaging.

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.

Machine Learning and External Validation of the IDENTIFY Risk Calculator for Patients with Haematuria Referred to Secondary Care for Suspected Urinary Tract Cancer.

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.

Predicting Risk of Post-Operative Morbidity and Mortality following Gynaecological Oncology Surgery (PROMEGO): A Global Gynaecological Oncology Surgical Outcomes Collaborative Led Study.

The medical complexity of surgical patients is increasing, and surgical risk calculators are crucial in providing high-value, patient-centered surgical care. However, pre-existing models are not validated to accurately predict risk for major gynecological oncology surgeries, and many are not generalizable to low- and middle-income country settings (LMICs). The international GO SOAR database dataset was used to develop a novel predictive surgical risk calculator for post-operative morbidity and mortality following gynecological surgery. Fifteen candidate features readily available pre-operatively across both high-income countries (HICs) and LMICs were selected. Predictive modeling analyses using machine learning methods and linear regression were performed. The area-under-the-receiver-operating characteristic curve (AUROC) was calculated to assess overall discriminatory performance. Neural networks (AUROC 0.94) significantly outperformed other models (p < 0.001) for evaluating the accuracy of prediction across three groups, i.e., minor morbidity (Clavien-Dindo I-II), major morbidity (Clavien-Dindo III-V), and no morbidity. Logistic-regression modeling outperformed the clinically established SORT model in predicting mortality (AUROC 0.66 versus 0.61, p < 0.001). The GO SOAR surgical risk prediction model is the first that is validated for use in patients undergoing gynecological surgery. Accurate surgical risk predictions are vital within the context of major cytoreduction surgery, where surgery and its associated complications can diminish quality-of-life and affect long-term cancer survival. A model that requires readily available pre-operative data, irrespective of resource setting, is crucial to reducing global surgical disparities.

[Advances in clinical management of neonatal sepsis]. / 新生儿败血症的临床管理进展.

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.

Evaluation of the American College of Surgeons risk calculator in hepatectomy for metastatic colorectal cancer in a Southeast Asian population.

PURPOSE: This study evaluated the accuracy of the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) calculator in predicting outcomes after hepatectomy for colorectal cancer (CRC) liver metastasis in a Southeast Asian population. METHODS: Predicted and actual outcomes were compared for 166 patients undergoing hepatectomy for CRC liver metastasis identified between 2017 and 2022, using receiver operating characteristic curves with area under the curve (AUC) and Brier score. RESULTS: The ACS-NSQIP calculator accurately predicted most postoperative complications (AUC > 0.70), except for surgical site infection (AUC = 0.678, Brier score = 0.045). It also exhibited satisfactory performance for readmission (AUC = 0.818, Brier score = 0.011), reoperation (AUC = 0.945, Brier score = 0.002), and length of stay (LOS, AUC = 0.909). The predicted LOS was close to the actual LOS (5.9 vs. 5.0 days, P = 0.985). CONCLUSION: The ACS-NSQIP calculator demonstrated generally accurate predictions for 30-day postoperative outcomes after hepatectomy for CRC liver metastasis in our patient population.

Development and validation of a 30-day point-scoring risk calculator for small bowel obstruction surgery.

Small bowel obstruction (SBO) is one of the most frequent causes of general emergency surgery. The 30-day mortality rate post-surgery ranges widely from 2 to 30%, contingent upon the patient population, which renders risk assessment tools helpful. this study aimed to develop a 30-day point-scoring risk calculator designed for patients undergoing SBO surgery. Patients who underwent SBO surgery were identified in the ACS-NSQIP database from 2005 to 2021. Patients were randomly sampled into an experimental (2/3) and a validation (1/3) group. A weighted point scoring system was developed for the risk of 30-day mortality, utilizing multivariable regression on preoperative risk variables based on Sullivan's method. The risk scores underwent both internal and external validation. Furthermore, the efficacy of the risk score was evaluated in 30-day major surgical complications. A total of 93,517 patients were identified, with 63,521 and 29,996 assigned to the experimental and validation groups, respectively. The risk calculator is structured to assign points based on age (> 85 years, 4 points; 75-85 years, 3 points; 65-75 years, 2 points; 55-65 years, 1 point), disseminated cancer (2 points), American Society of Anesthesiology (ASA) score of 4 or 5 (1 point), preoperative sepsis (1 point), hypoalbuminemia (1 point), and fully dependent functional status (1 point). The risk calculator showed strong discrimination (c-statistic = 0.825, 95% CI 0.818-0.831) and good calibration (Brier score = 0.043) in the experimental group. The point scoring system was successfully translated from individual preoperative variables (c-statistic = 0.840, 95% CI 0.834-0.847) and was externally validated in ACS-NSQIP (c-statistic = 0.827, 95% = CI 0.834-0.847, Brier score = 0.043). The SBO risk score can effectively discriminate major surgical complications including major adverse cardiovascular events (c-statistic = 0.734), cardiac complications (c-statistic = 0.732), stroke (c-statistic = 0.725), pulmonary complications (c-statistic = 0.727), renal complications (c-statistic = 0.692), bleeding (c-statistic 0.674), sepsis (c-statistic = 0.670), with high predictive accuracy (all Brier scores < 0.1). This study developed and validated a concise yet robust 10-point risk scoring system for patients undergoing SBO surgery. It can be informative to determine treatment plans and to prepare for potential perioperative complications in patients undergoing SBO surgery.

Assessing, communicating and managing cardiovascular disease risk: a practical summary of the 2023 guideline.

The outdated cardiovascular disease risk calculator has been reported to overestimate cardiovascular disease risk for a contemporary Australian population, and does not include relevant variables, such as socioeconomic disadvantage, which has been shown to increase the incidence of both heart attack and stroke. The 2023 Australian Guideline for Assessing and Managing Cardiovascular Disease Risk marks a major milestone as the first update to Australia's cardiovascular disease prevention guideline in over a decade. The new guideline may help to refine and recategorise risk estimates, hence improving the discriminatory and predictive value of the new calculator. The new Australian Cardiovascular Disease Risk Calculator expresses risk scores as a percentage estimate of a person's probability of dying or being hospitalised due to cardiovascular disease within the next 5 years. The new calculator expresses risk scores as low (less than 5%), intermediate (5% to less than 10%), or high (10% or higher) risk over 5 years. Reclassification factors built into the new calculator are designed to help clinicians individualise risk estimates. These factors include ethnicity (e.g. First Nations status), family history of premature cardiovascular disease, severe mental illness, kidney disease and coronary artery calcium score. The new calculator also uses optional diabetes-specific variables (supporting a more granular cardiovascular disease risk assessment of people with type 2 diabetes). People who meet the clinically determined high-risk criteria (chronic kidney disease, familial hypercholesterolaemia) should not progress through the Australian Cardiovascular Disease risk calculator, but move straight to management. For a person with a cardiovascular disease risk score recorded from the outdated calculator, clinicians may want to reassess their risk using the new calculator the next time the person attends.

Contemporary Diagnosis of Very Low-risk Prostate Cancer in a Multihospital Health Care System.

The National Comprehensive Cancer Network (NCCN) very low risk (VLR) category for prostate cancer (PCa) represents clinically insignificant disease, and detection of VLR PCa contributes to overdiagnosis. Greater use of magnetic resonance imaging (MRI) and biomarkers before patient selection for prostate biopsy (PBx) reduces unnecessary biopsies and may reduce the diagnosis of clinically insignificant PCa. We tested a hypothesis that the proportion of VLR diagnoses has decreased with greater use of MRI-informed PBx using data from our 11-hospital system. From 2018 to 2023, 351/3197 (11%) men diagnosed with PCa met the NCCN VLR criteria. The proportion of VLR diagnoses did not change from 2018 to 2023 (p = 0.8) despite an increase in the use of MRI-informed PBx (from 49% to 82%; p < 0.001). Of patients who underwent combined systematic and targeted PBx and were diagnosed with VLR disease, cancer was found in systematic PBx regions in 79% of cases and in targeted PBx regions in 31% of cases. When performing both systematic and targeted PBx, prebiopsy MRI-based risk calculators could limit VLR diagnosis by 41% using a risk threshold of >5% for Gleason grade group ≥3 PCa to recommend biopsy; the reduction would be 77% if performing targeted PBx only. These findings suggest that VLR disease continues to account for a significant minority of PCa diagnoses and could be limited by targeted PBx and risk stratification calculators. PATIENT SUMMARY: We looked at recent trends for the diagnosis of very low-risk (VLR) prostate cancer. We found that VLR cancer still seems to be frequently diagnosed despite the use of MRI (magnetic resonance imaging) scans before biopsy. The use of risk calculators to identify men who could avoid biopsy and/or biopsy only for lesions that are visible on MRI could reduce the overdiagnosis of VLR prostate cancer.

Assessing the generalisability of the psychosis metabolic risk calculator (PsyMetRiC) for young people with first-episode psychosis with validation in a Hong Kong Chinese Han population: a 4-year follow-up study.

Background: Metabolic syndrome (MetS) is common following first-episode psychosis (FEP), contributing to substantial morbidity and mortality. The Psychosis Metabolic Risk Calculator (PsyMetRiC), a risk prediction algorithm for MetS following a FEP diagnosis, was developed in the United Kingdom and has been validated in other European populations. However, the predictive accuracy of PsyMetRiC in Chinese populations is unknown. Methods: FEP patients aged 15-35 y, first presented to the Early Assessment Service for Young People with Early Psychosis (EASY) Programme in Hong Kong (HK) between 2012 and 2021 were included. A binary MetS outcome was determined based on the latest available follow-up clinical information between 1 and 12 years after baseline assessment. The PsyMetRiC Full and Partial algorithms were assessed for discrimination, calibration and clinical utility in the HK sample, and logistic calibration was conducted to account for population differences. Sensitivity analysis was performed in patients aged >35 years and using Chinese MetS criteria. Findings: The main analysis included 416 FEP patients (mean age = 23.8 y, male sex = 40.4%, 22.4% MetS prevalence at follow-up). PsyMetRiC showed adequate discriminative performance (full-model C = 0.76, 95% C.I. = 0.69-0.81; partial-model: C = 0.73, 95% C.I. = 0.65-0.8). Systematic risk underestimation in both models was corrected using logistic calibration to refine PsyMetRiC for HK Chinese FEP population (PsyMetRiC-HK). PsyMetRiC-HK provided a greater net benefit than competing strategies. Results remained robust with a Chinese MetS definition, but worse for the older age group. Interpretation: With good predictive performance for incident MetS, PsyMetRiC-HK presents a step forward for personalized preventative strategies of cardiometabolic morbidity and mortality in young Hong Kong Chinese FEP patients. Funding: This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Comparación entre las calculadoras de riesgo del European Randomized Study for Screening of Prostate Cancer (ERSPC) y Prostate Biopsy Collaborative Group (PBCG): predicción del riesgo de cáncer de próstata clínicamente significativo en una cohorte de pacientes de Argentina / Comparison between the European Randomized Study for Screening of Prostate Cancer (ERSPC) and Prostate Biopsy Collaborative Group (PBCG) risk calculators: Prediction of clinically significant prostate cancer risk in a cohort of patients from Argentina

Objetivo Comparar el desempeño de las calculadoras de riesgo del European Randomised Study for Screening of Prostate Cancer (ERSPC-RC) y el Prostate Biopsy Collaborative Group (PBCG-RC) en predecir el riesgo de presentar cáncer de próstata clínicamente significativo. Material y métodos Retrospectivamente, se identificó a los pacientes que fueron sometidos a biopsia prostática en el Sanatorio Allende Cerro, Ciudad de Córdoba, Argentina, desde enero de 2018 a diciembre de 2021. Se calculó la probabilidad de tener cáncer de próstata con las dos calculadoras por separado y luego se compararon los resultados para establecer cuál de las dos tuvo mejor desempeño. Para esto, se analizaron áreas bajo la curva (ABC). Resultados Se incluyeron 250 pacientes, 140 (56%) presentaron cáncer de próstata, de los cuales 92 (36,8%) tuvieron cáncer de próstata clínicamente significativo (Score de Gleason ≥7). Los pacientes que presentaron cáncer tenían mayor edad, mayor valor de antígeno prostático específico (PSA) y menor tamaño prostático. El ABC para predecir la probabilidad de tener cáncer de próstata clínicamente significativo fue de 0,79 y 0,73 para PBCG-RC y ERSPC-RC, respectivamente (p=0,0084). Conclusión En esta cohorte de pacientes, ambas calculadoras de riesgo de cáncer de próstata mostraron un buen desempeño para predecir el riesgo de cáncer de próstata clínicamente significativo, si bien el PBCG-RC mostró mejor exactitud. (AU)

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. (AU)

The efficacy of the National Surgical Quality Improvement Program surgical risk calculator in head and neck surgery: A meta-analysis.

BACKGROUND: The National Surgical Quality Improvement Program surgical risk calculator (SRC) estimates the risk for postoperative complications. This meta-analysis assesses the efficacy of the SRC in the field of head and neck surgery. METHODS: A systematic review identified studies comparing the SRC's predictions to observed outcomes following head and neck surgeries. Predictive accuracy was assessed using receiver operating characteristic curves (AUCs) and Brier scoring. RESULTS: Nine studies totaling 1774 patients were included. The SRC underpredicted the risk of all outcomes (including any complication [observed (ob) = 35.9%, predicted (pr) = 21.8%] and serious complication [ob = 28.7%, pr = 17.0%]) except mortality (ob = 0.37%, pr = 1.55%). The observed length of stay was more than twice the predicted length (p < 0.02). Discrimination was acceptable for postoperative pneumonia (AUC = 0.778) and urinary tract infection (AUC = 0.782) only. Predictive accuracy was low for all outcomes (Brier scores ≥0.01) and comparable for patients with and without free-flap reconstructions. CONCLUSION: The SRC is an ineffective instrument for predicting outcomes in head and neck surgery.

Novel clinical risk calculator for improving cancer predictability of mpMRI fusion biopsy in prostates.

PURPOSE: Prostate Imaging-Reporting and Data System (PI-RADS) assists in evaluating lesions on multiparametric magnetic resonance imaging (mpMRI), but there are still ongoing efforts in improving the predictive value for the presence of clinically significant PCa (csPCa) with a Gleason grade group ≥ 2 on Fusion-Biopsy. This pilot study intends to propose an easily implementable method for augmenting predictability of csPCa for PI-RADS. METHODS: A cohort of 151 consecutive patients underwent mpMRI Fusion and random US Biopsy as a result of having at least one PI-RADS lesion grade 3-5 between January 1, 2019 and December 31, 2022. A single radiologist reads all films in this study applying PI-RADS V2. RESULTS: Of the 151 consecutive patients, 49 had a highest lesion of PI-RADS 3, 82 had a highest lesion of PI-RADS 4, and 20 had a highest lesion of PI-RADS 5. For each respective group, 12, 42, and 18 patients had proven csPCa. Two predictive models for csPCa were created by employing a logistical regression with parameters readily available to providers. The models had an AUC of 0.8133 and 0.8206, indicating promising effective models. CONCLUSION: PI-RADS classification has relevant predictability problems for grades 3 and 4. By applying the presented risk calculators, patients with PI-RADS 3 and 4 are better stratified, and thus, a significant number of patients can be spared biopsies with potential complications, such as infection and bleeding. The presented predictive models may be a valuable diagnostic tool, adding additional information in the clinical decision-making process for biopsies.

Implementation of the Symptom Based Risk Calculator for Head and Neck Cancer HaNC-RC v.2 Post COVID-19 Pandemic. A Single Centre Audit.

Background: As the incidence of head and neck cancer continues to rise, the volume of referrals to our urgent suspected cancer clinics continues to rise with it. Cancer referral and review time targets are not being met within the UK, and our centre has experienced an increase in volume of referrals which cannot be met by available clinic slots. We proposed a pathway to the North East London Cancer Alliance to safely triage these patients using the Head and Neck Cancer Risk Calculator version 2 (HaNC-RCv2). Methods: All 2-week-wait referrals to our unit in June 2023 were initially triaged in a telephone consultation by a specialty registrar working in the department. A brief history would be taken, and a risk score calculated. Those scoring < 5% were moved to routine or less urgent follow up. Results: 120 patients were referred to our department. We were able to safely triage 48.7% patients off the urgent suspected cancer pathway and to routine follow up. A total of 3 patients were found to have a head and neck malignancy and all were treated within the 62 day window. Conclusion: As trusts work to cut the waiting times following the COVID-19 pandemic, there is an evident need for more efficient practices. The use of validated, safe triaging methods such as this can play a central role.

Preoperative risk calculator for the probability of completing nephron sparing for kidney cancer.

PURPOSE: In absence of predictive models, preoperative estimation of the probability of completing partial (PN) relative to radical nephrectomy (RN) is invariably inaccurate and subjective. We aimed to develop an evidence-based model to assess objectively the probability of PN completion based on patients' characteristics, tumor's complexity, urologist expertise and surgical approach. DESIGN, SETTING AND PARTICIPANTS: 675 patients treated with PN or RN for cT1-2 cN0 cM0 renal mass by seven surgeons at one single experienced centre from 2000 to 2019. OUTCOMES MEASUREMENTS AND STATISTICAL ANALYSES: The outcome of the study was PN completion. We used a multivariable logistic regression (MVA) model to investigate predictors of PN completion. We used SPARE score to assess tumor complexity. We used a bootstrap validation to compute the model's predictive accuracy. We investigated the relationship between the outcomes and specific predictors of interest such as tumor's complexity, approach and experience. RESULTS: Of 675 patients, 360 (53%) were treated with PN vs. 315 (47%) with RN. Smaller tumors [Odds ratio (OR): 0.52, 95%CI 0.44-0.61; P < 0.001], lower SPARE score (OR: 0.67, 95%CI 0.47-0.94; P = 0.02), more experienced surgeons (OR: 1.01, 95%CI 1.00-1.02; P < 0.01), robotic (OR: 10; P < 0.001) and open (OR: 36; P < 0.001) compared to laparoscopic approach resulted associated with higher probability of PN completion. Predictive accuracy of the model was 0.94 (95% CI 0.93-0.95). CONCLUSIONS: The probability of PN completion can be preoperatively assessed, with optimal accuracy relaying on routinely available clinical information. The proposed model might be useful in preoperative decision-making, patient consensus, or during preoperative counselling. PATIENT SUMMARY: In patients with a renal mass the probability of completing a partial nephrectomy varies considerably and without a predictive model is invariably inaccurate and subjective. In this study we build-up a risk calculator based on easily available preoperative variables that can predict with optimal accuracy the probability of not removing the entire kidney.

The Gail Model and Its Use in Preventive Screening: A Comparison of the Corbelli Study.

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.

External Validation of an Online Wound Infection and Wound Reoperation Risk Calculator After Metastatic Spinal Tumor Surgery.

STUDY DESIGN: This was a single-institutional retrospective cohort study. OBJECTIVE: Wound infections are common following spine metastasis surgery and can result in unplanned reoperations. A recent study published an online wound complication risk calculator but has not yet undergone external validation. Our aim was to evaluate the accuracy of this risk calculator in predicting 30-day wound infections and 30-day wound reoperations using our operative spine metastasis population. METHODS: An internal operative database was used to identify patients between 2012 and 2022. The primary outcomes were 1) any surgical site infection and 2) wound-related revision surgery within 30 days following surgery. Patient details were manually collected from electronic medical records and entered into the calculator to determine predicted complication risk percentages. Predicted risks were compared to observed outcomes using receiver operator characteristic (ROC) curves with areas under the curve (AUC). RESULTS: A total of 153 patients were included. The observed 30-day postoperative wound infection incidence was 5% while the predicted wound infection incidence was 6%. In ROC analysis, good discrimination was found for the wound infection model (AUC = 0.737; P = 0.024). The observed wound reoperation rate was 5% and the predicted wound reoperation rate was 6%. ROC analysis demonstrated poor discrimination for wound reoperations (AUC = 0.559; P = 0.597). CONCLUSIONS: The online wound-related risk calculator was found to accurately predict wound infections but not wound reoperations within our metastatic spine surgery cohort. We suggest that the model may be clinically useful despite underlying population differences, but further work must be done to generate and validate accurate prediction tools.

Optimal extent of lymph node dissection for high-risk gastric cancer stratified by a national clinical database risk calculator.

BACKGROUND: For patients with gastric cancer, a well-balanced treatment that considers both oncological aspects and surgical risk is demanded. This study aimed to explore the optimal extent of lymph node dissection (LND) for patients with gastric cancer according to surgical risk, stratified by the risk calculator system produced by the Japan National Clinical Database (NCD). PATIENTS AND METHODS: We retrospectively evaluated 187 patients who underwent radical gastrectomy for gastric cancer. Using the median predicted anastomotic leak rate obtained by the NCD risk calculator as the cutoff value, we classified 97 and 90 patients as having high and low risks, respectively. RESULTS: In low-risk patients, although limited LND reduced the postoperative intraabdominal infectious complications (IAIC), multivariate analysis revealed standard LND as an independent prognostic factor that improved Relapse-free survival (RFS). In high-risk patients, the rates of postoperative IAIC and RFS were similar between standard and limited LND. Pancreatic fistula was not observed in the limited dissection group. CONCLUSION: Limited LND might be the optimal treatment strategy for patients with gastric cancer with high surgical risk.

Investigating Efficient Risk-Stratified Pathways for the Early Detection of Clinically Significant Prostate Cancer.

Risk-stratified pathways (RSPs) are recommended by the European Association of Uro-logy (EAU) to improve the early detection of clinically significant prostate cancer (csPCa). RSPs can reduce magnetic resonance imaging (MRI) demand, prostate biopsies, and the over-detection of insignificant PCa (iPCa). Our goal is to analyze the efficacy and cost-effectiveness of several RSPs by using sequential stratifications from the serum prostate-specific antigen level and digital rectal examination, the Barcelona risk calculators (BCN-RCs), MRI, and Proclarix™. In a cohort of 567 men with a serum PSA level above 3.0 ng/mL who underwent multiparametric MRI (mpMRI) and targeted and/or systematic biopsies, the risk of csPCa was retrospectively assessed using Proclarix™ and BCN-RCs 1 and 2. Six RSPs were compared with those recommended by the EAU that, stratifying men from MRI, avoided 16.7% of prostate biopsies with a prostate imaging-reporting and data system score of <3, with 2.6% of csPCa cases remaining undetected. The most effective RSP avoided mpMRI exams in men with a serum PSA level of >10 ng/mL and suspicious DRE, following stratifications from BCN-RC 1, mpMRI, and Proclarix™. The demand for mpMRI decreased by 19.9%, prostate biopsies by 19.8%, and over-detection of iPCa by 22.7%, while 2.6% of csPCa remained undetected as in the recommended RSP. Cost-effectiveness remained when the Proclarix™ price was assumed to be below EUR 200.