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Background: The absence of consensus for prophylaxis of venous thromboembolism (VTE) in spine surgery underscores the importance of identifying patients at risk. This study incorporated machine learning (ML) models to assess key risk factors of VTE in patients who underwent posterior spinal instrumented fusion. Methods: Data was collected from the IBM MarketScan Database [2009-2021] for patients ≥18 years old who underwent spinal posterior instrumentation (3-6 levels), excluding traumas, malignancies, and infections. VTE incidence (deep vein thrombosis and pulmonary embolism) was recorded 90-day post-surgery. Risk factors for VTE were investigated and compared through several ML models including logistic regression, linear support vector machine (LSVM), random forest, XGBoost, and neural networks. Results: Among the 141,697 patients who underwent spinal fusion with posterior instrumentation (3-6 levels), the overall 90-day VTE rate was 3.81%. The LSVM model demonstrated the best prediction with an area under the curve (AUC) of 0.68. The most important features for prediction of VTE included remote history of VTE, diagnosis of chronic hypercoagulability, metastatic cancer, hemiplegia, and chronic renal disease. Patients who did not have these five key risk factors had a 90-day VTE rate of 2.95%. Patients who had an increasing number of key risk factors had subsequently higher risks of postoperative VTE. Conclusions: The analysis of the data with different ML models identified 5 key variables that are most closely associated with VTE. Using these variables, we have developed a simple risk model with additive odds ratio ranging from 2.80 (1 risk factor) to 46.92 (4 risk factors) over 90 days after posterior spinal fusion surgery. These findings can help surgeons risk-stratify their patients for VTE risk, and potentially guide subsequent chemoprophylaxis.

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Background: The mortality rate of acute coronary syndrome (ACS) remains high. Therefore, patients with ACS should undergo early risk stratification, for which various risk calculation tools are available. However, it remains uncertain whether the predictive performance varies over time between risk calculation tools for different target periods. This study aimed to compare the predictive performance of risk calculation tools in estimating short- and long-term mortality risks in patients with ACS, while considering different observation periods using time-dependent receiver operating characteristic (ROC) analysis. Methods: This study included 404 consecutive patients with ACS who underwent coronary angiography at our hospital from March 2017 to January 2021. The ACTION and GRACE scores for short-term risk stratification purposes and CRUSADE scores for long-term risk stratification purposes were calculated for all participants. The participants were followed up for 36 months to assess mortality. Using time-dependent ROC analysis, we evaluated the area under the curve (AUC) of the ACTION, CRUSADE, and GRACE scores at 1, 6, 12, 24, and 36 months. Results: Sixty-six patients died during the observation periods. The AUCs at 1, 6, 12, 24, and 36 months of the ACTION score were 0.942, 0.925, 0.889, 0.856, and 0.832; those of the CRUSADE score were 0.881, 0.883, 0.862, 0.876, and 0.862; and those of the GRACE score 0.949, 0.928, 0.888, 0.875, and 0.860, respectively. Conclusions: The ACTION and GRACE scores were excellent risk stratification tools for mortality in the short term. The prognostic performance of each risk score was almost similar in the long term, but the CRUSADE score might be a superior risk stratification tool in the longer term than 3 years.

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Background: Primary prevention and early detection of hereditary breast cancer has been one of the main topics of breast cancer research in recent decades. The knowledge of risk factors for breast cancer has been increasing continuously just like the recommendations for risk management. Pathogenic germline variants (mutations, class 4/5) of risk genes are significant susceptibility factors in healthy individuals. At the same time, germline mutations serve as biomarkers for targeted therapy in breast cancer treatment. Therefore, management of healthy mutation carriers to enable primary prevention is in the focus as much as the consideration of pathogenic germline variants for therapeutic decisions. Since 1996, the German Consortium has provided quality-assured care for counselees and patients with familial burden of breast and ovarian cancer. Summary: Currently, there are 23 university centers with over 100 cooperating DKG-certified breast and gynecological cancer centers. These centers provide standardized, evidence-based, and knowledge-generating care, which includes aspects of primary as well as secondary and tertiary prevention. An important aspect of quality assurance and development was the inclusion of the HBOC centers in the certification system of the German Cancer Society (GCS). Since 2020, the centers have been regularly audited and their quality standards continuously reviewed according to quality indicators adapted to the current state of research. The standard of care at GC-HBOC' centers involves the evaluation as well as evolution of various aspects of care like inclusion criteria, identification of new risk genes, management of variants of unknown significance (class 3), evaluation of risk-reducing options, intensified surveillance, and communication of risks. Among these, the possibility of intensified surveillance in the GC-HBOC for early detection of breast cancer is an important component of individual risk management for many counselees. As has been shown in recent years, in carriers of pathogenic variants in high-risk genes, this approach enables the detection of breast cancer at very early, more favorable stages although no reduction of mortality has been demonstrated yet. The key component of the intensified surveillance is annual contrast-enhanced breast MRI, supplemented by up to biannual breast ultrasound and mammography usually starting at age 40. Key Messages: Apart from early detection, the central goal of care is the prevention of cancer. By utilizing individualized risk calculation, the optimal timeframe for risk-reducing surgery can be estimated, and counselees can be supported in reaching preference-sensitive decisions.

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BACKGROUND: Osteoporosis poses a growing healthcare challenge owing to its rising prevalence and a significant treatment gap, as patients are widely underdiagnosed and consequently undertreated, leaving them at high risk of osteoporotic fracture. Several tools aim to improve case-finding in osteoporosis. One such tool is the Fracture Risk Evaluation Model (FREM), which in contrast to other tools focuses on imminent fracture risk and holds potential for automation as it relies solely on data that is routinely collected via the Danish healthcare registers. The present article is an analysis protocol for a prediction model that is to be used as a modified version of FREM, with the intention of improving the identification of subjects at high imminent risk of fracture by including pharmacological exposures and using more advanced statistical methods compared to the original FREM. Its main purposes are to document and motivate various aspects and choices of data management and statistical analyses. METHODS: The model will be developed by employing logistic regression with grouped LASSO regularization as the primary statistical approach and gradient-boosted classification trees as a secondary statistical modality. Hyperparameter choices as well as computational considerations on these two approaches are investigated by an unsupervised data review (i.e., blinded to the outcome), which also investigates and handles multicollinarity among the included exposures. Further, we present an unsupervised review of the data and testing of analysis code with respect to speed and robustness on a remote analysis environment. The data review and code tests are used to adjust the analysis plans in a blinded manner, so as not to increase the risk of overfitting in the proposed methods. DISCUSSION: This protocol specifies the planned tool development to ensure transparency in the modeling approach, hence improving the validity of the enhanced tool to be developed. Through an unsupervised data review, it is further documented that the planned statistical approaches are feasible and compatible with the data employed.

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A new innovative methodology system framework for source apportionment and source-specific risk assessment has been proposed and actively applied to identify the contamination characteristics, oriented sources and health risks associated with contamination levels of Heavy metals (HMs) and Polycyclic Aromatic Hydrocarbons (PAHs) in soils, a typical cold agricultural region in Northeastern China. To achieve this meaningful goal, a large-scale dataset including 1780 top soil samples, 10 HMs and 16 priority PAHs has been organized and collected from a typical study area in China. The total concentrations of the 10 selected HMs in study area range from 0.05 to 2147.40 mg/kg, with an average of 549.25 ± 541.37 mg/kg. The average concentrations of PAHs for (3-6)-rings are 16.60 ± 18.90, 26.40 ± 28.20, 9.51 ± 13.00 and 1.99 ± 5.30 ng/g, respectively. On the base of optimized literature source fingerprints for HM and PAH, a widely used receptor model, positive matrix factorization (PMF) has been applied to apportion the contamination sources HMs and PAHs in soils. Then source-specific health risk of soil HMs and PAHs have been assessed using the probabilistic incremental lifetime cancer risk model incorporated with source apportionment results data. Fertilizer residues/coke oven comprise the primary contamination source contributors of HMs and PAHs with corresponding contributions of 32.23 % and 27.93 % for HMs and 37.94 % for PAHs. Fertilizer/pesticide residues contributes most to the risks of soil HMs (28.8 %), followed by fossil fuel combustion (24.6 %), mining activities (20.2 %), traffic and vehicle emission (16.3 %) and electroplating/dyeing (14.1 %). Meanwhile, the ranking of health risks from the five identified contamination sources of soil PAHs are resident discharge, coal-fired boilers, coke oven emission, gasoline combustion and power plant, with the contribution of 27.1 %, 25.3 %, 17.3 %, 15.5 % and 14.8 %. And relatively, source-specific risk assessment demonstrates fossil fuel and coal combustion contribute the greatest impact to the total risk of HMs and PAHs (61.7 % and 56.1 %), respectively. This study provides a good example of how the source specific health risk assessment can be utilized to reduce the contamination in soils.

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Banks face many intangible hazards that are difficult to calculate. Strategic risk is one of the most critical factors affecting a bank's profitability, financial strength, and commercial success. The impact of risk on profit may be insignificant in the short term. Still, it may become highly significant in the medium and long term, with the potential to cause substantial financial losses and impair bank stability. Hence, strategic risk management is an important endeavor that must be carried out according to the rules set out under the Basel II framework. Analysis of strategic risk is a relatively new research enterprise. The current literature addresses the need to manage this risk and links it to the concept of economic capital, the amount of capital that a company should hold to survive such a risk. However, an action plan has yet to be produced. This paper attempts to address this gap by providing a mathematical analysis of the probability and effect of different strategic risk factors. Specifically, we develop a methodology for calculating a metric of strategic risk in terms of a bank's risk assets. Furthermore, we suggest a way of integrating this metric into the calculation of the capital adequacy ratio.

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Background: Postpartum hemorrhage is the major cause of maternal deaths due to childbirth and also responsible for maternal morbidity. Objectives: In this study we set out to look the incidence of postpartum hemorrhage in our population, to identify the most important risk factors for postpartum hemorrhage and thus develop a predictive risk calculator for postpartum hemorrhage and transfusion. Study design: data was taken from patients who presented vaginal delivery or cesarean section from January 1 to December 31, 2016, the variables taken into account as risk factors were as follows: Gestational age, history of chronic or gestational hypertension, preeclampsia, previous abortions, parity, previous cesarean section, placenta previa, labor time, and postpartum hemorrhage as the event of interest. An objective quantification was performed on a weight scale in grams for the estimation of bleeding, considering postpartum hemorrhage those with >500 ml in vaginal delivery and >1000 ml of blood loss in cesarean section. Subsequently, a predictive risk calculator was developed using the Naïve Bayes algorithm. Results: A success rate of 58% was obtained in the identification of patients at high risk of hemorrhage, and 36% for transfusion, with a sensitivity of 50.7% and specificity of 64.06%, identifying as risk factors for postpartum hemorrhage gestational age between 35 and 40 weeks, hypertension and preeclampsia, previous cesarean section, duration of labor <1 h or more than 10 h, placenta previa and previous history of postpartum hemorrhage. Conclusion: A postpartum hemorrhage risk calculator has been designed, which due to its improved accuracy after incorporation of data becomes a useful tool that will require a larger study population to improve its performance in clinical practice and more similar studies to validate it.

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Lung cancer can be challenging to diagnose in the early stages, where treatment options are optimal. We aimed to develop 1-year prediction models for the individual risk of incident lung cancer for all individuals aged 40 or above living in Denmark on 1 January 2017. The study was conducted using population-based registers on health and sociodemographics from 2007-2016. We applied backward selection on all variables by logistic regression to develop a risk model for lung cancer and applied the models to the validation cohort, calculated receiver-operating characteristic curves, and estimated the corresponding areas under the curve (AUC). In the populations without and with previously confirmed cancer, 4274/2,826,249 (0.15%) and 482/172,513 (0.3%) individuals received a lung cancer diagnosis in 2017, respectively. For both populations, older age was a relevant predictor, and the most complex models, containing variables related to diagnoses, medication, general practitioner, and specialist contacts, as well as baseline sociodemographic characteristics, had the highest AUC. These models achieved a positive predictive value (PPV) of 0.0127 (0.006) and a negative predictive value (NPV) of 0.989 (0.997) with a 1% cut-off in the population without (with) previous cancer. This corresponds to 1.2% of the screened population experiencing a positive prediction, of which 1.3% would be incident with lung cancer. We have developed and tested a prediction model with a reasonable potential to support clinicians and healthcare planners in identifying patients at risk of lung cancer.

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OBJECTIVE: We compare the risk of Down syndrome among five patients carrying a foetus with digynic triploidy and suggest a course of action for these particular serological profiles. METHODS: The concentrations of the different markers used are transformed into multiples of the median by using each of the three software types present on the French market which then determine the risk of Down syndrome. RESULTS: For comparable biochemical and ultrasound profiles, the risk of Down syndrome turns out to be vastly different depending on the type of software employed. The relevance of an immediate diagnostic procedure, of a cell free DNA test or of a basic ultrasound follow-up then arises, leading to a potentially variable care pathway for the patient. CONCLUSIONS: This study confirms that for this type of biochemical profile, the laboratory's advisory service is fundamental, that a control ultrasound is essential and that an invasive procedure must be used almost invariably due to the extremely substantial risk factors.

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PURPOSE: To develop a predictive model based on Danish administrative registers to facilitate automated identification of individuals at risk of any type of cancer. METHODS: A nationwide register-based cohort study covering all individuals in Denmark aged +20 years. The outcome was all-type cancer during 2017 excluding nonmelanoma skin cancer. Diagnoses, medication, and contact with general practitioners in the exposure period (2007-2016) were considered for the predictive model. We applied backward selection to all variables by logistic regression to develop a risk model for cancer. We applied the models to the validation cohort, calculated the receiver operating characteristic curves, and estimated the corresponding areas under the curve (AUC). RESULTS: The study population consisted of 4.2 million persons; 32,447 (0.76%) were diagnosed with cancer in 2017. We identified 39 predictive risk factors in women and 42 in men, with age above 30 as the strongest predictor for cancer. Testing the model for cancer risk showed modest accuracy, with an AUC of 0.82 (95% CI 0.81-0.82) for men and 0.75 (95% CI 0.74-0.75) for women. CONCLUSION: We have developed and tested a model for identifying the individual risk of cancer through the use of administrative data. The models need to be further investigated before being applied to clinical practice.

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Objective: This study aimed to develop multiphase big-data-based prediction models of ovarian hyperstimulation syndrome (OHSS) and a smartphone app for risk calculation and patients' self-monitoring. Methods: Multiphase prediction models were developed from a retrospective cohort database of 21,566 women from January 2017 to December 2020 with controlled ovarian stimulation (COS). There were 17,445 women included in the final data analysis. Women were randomly assigned to either training cohort (n = 12,211) or validation cohort (n = 5,234). Their baseline clinical characteristics, COS-related characteristics, and embryo information were evaluated. The prediction models were divided into four phases: 1) prior to COS, 2) on the day of ovulation trigger, 3) after oocyte retrieval, and 4) prior to embryo transfer. The multiphase prediction models were built with stepwise regression and confirmed with LASSO regression. Internal validations were performed using the validation cohort and were assessed by discrimination and calibration, as well as clinical decision curves. A smartphone-based app "OHSS monitor" was constructed as part of the built-in app of the IVF-aid platform. The app had three modules, risk prediction module, symptom monitoring module, and treatment monitoring module. Results: The multiphase prediction models were developed with acceptable distinguishing ability to identify OHSS at-risk patients. The C-statistics of the first, second, third, and fourth phases in the training cohort were 0.628 (95% CI 0.598-0.658), 0.715 (95% CI 0.688-0.742), 0.792 (95% CI 0.770-0.815), and 0.814 (95% CI 0.793-0.834), respectively. The calibration plot showed the agreement of predictive and observed risks of OHSS, especially at the third- and fourth-phase prediction models in both training and validation cohorts. The net clinical benefits of the multiphase prediction models were also confirmed with a clinical decision curve. A smartphone-based app was constructed as a risk calculator based on the multiphase prediction models, and also as a self-monitoring tool for patients at risk. Conclusions: We have built multiphase prediction models based on big data and constructed a user-friendly smartphone-based app for the personalized management of women at risk of moderate/severe OHSS. The multiphase prediction models and user-friendly app can be readily used in clinical practice for clinical decision-support and self-management of patients.

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Analysis of the effectiveness of preventative phacoemulsification (PE) in anatomically short eyes with an increased risk of an acute glaucoma attack in thickened lens can help in choosing the optimal management tactics for these patients. PURPOSE: To study the changes in morphometric parameters of the anterior segment of anatomically short eyes with an increased risk of acute glaucoma attack before and after preventative PE. MATERIAL AND METHODS: The study included 70 patients (70 eyes) with a high risk of acute glaucoma attack in short anatomical axial length and thickened lens, with posterior and middle positions of the ciliary body. They were divided into 2 groups: the main group consisted of 45 patients who agreed to undergo PE; the comparison group consisted of 25 patients who refused to undergo PE due to good visual functions and no complaints about vision. B-mode ultrasound examination of the anterior segment of the eye was used to evaluate the cross-sectional surface area of the lens (CSAL) and the cross-sectional surface area of the anterior chamber (CSAAC). RESULTS: In the main group, on day 2-3 after PE the anterior chamber depth increased in all eyes, the CSAAC index increased, the anterior chamber angle (ACA) widened to Schaffer grade to II-III. After 1-3 years and 4-5 years, the anterior chamber depth, the CSAAC index and the width of the ACA remained stable. In the comparison group, after 1-3 years the anterior chamber depth and the CSAAC index significantly decreased, the ACA narrowed, and the intraocular pressure increased; after 4-5 years, there was a progressive increase in the CSAL index, the ACA was completely closed (0°). CONCLUSION: In all cases of timely performed preventative PE, already on days 2-3 after surgery there was a statistically significant improvement in the main morphometric parameters of the eye, which remained stable for 4-5 years.

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The Charlson Comorbidity Index (CCI) may be applicable for predicting fracture risk since several diagnoses from the index are predictors of fracture. Main results were that the CCI was updated to predict risk of hip fracture with fair precision and that the index could be useful in detecting high-risk individuals. PURPOSE: Several of the Charlson Comorbidity Index (CCI) diagnoses are validated predictors of fracture. The purpose of this study was to evaluate the performance of the CCI 1987 by Charlson et al. and of the CCI 2011 by Quan et al. in predicting major osteoporotic fracture (MOF) and hip fracture (HF). Furthermore, it was examined whether the index could be modified to improve fracture risk prediction. METHODS: The study population included the entire Danish population aged 45 + years as per January 1, 2018. The cohort was split randomly 50/50 into a development and a validation cohort. CCI diagnoses and fracture outcomes were identified from hospital diagnoses. The weighting of diagnoses was updated in a new Charlson Fracture Index (CFI) using multivariable logistic regression. Predictive capabilities of the CCI 1987, the updated CCI 2011 and the new Charlson Fracture index were evaluated in the validation cohort by receiver operating characteristics (ROC) curves and area under the curve (AUC). RESULTS: In the validation cohort, the 1987 and 2011 CCIs resulted in AUCs below or around 0.7 in prediction of MOF and HF in both sexes. The CFI resulted in AUCs < 0.7 in prediction of MOF in both sexes. In prediction of HF, the CFI resulted in AUC of 0.755 (95% CI 0.749; 0.761) in women and 0.782 (95% CI 0.772; 0.793) in men. CONCLUSION: The 1987 and 2011 CCIs showed overall poor accuracy in fracture risk prediction. The CFI showed fair accuracy in prediction of HF in women and in men.

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In the wake of the almost quarter of a century since the conceptualization of ultra-high-risk (UHR) states for psychosis, empirical evidences in the field are constantly scrutinized and re-assessed through meta-analytic lens. Briefly, such scrutiny converges on three major evidences: pretest risk enrichment, risk hierarchy within UHR states, and declining transition rates. While the former two are intuitive, the dilution effect remains elusive and might be rather symptomatic of unsolved issues in the field. Those include the heterogeneously reported antipsychotic (AP) exposure in UHR samples and the almost univocal focus on purely psychometric transition to psychosis. Both issues lead to the neglect of functional equivalents of transition, i.e. that of a mental state at immediate need for AP medication, and might have a cascading confounding effect on the predictive value of contemporary risk calculators centered on criterial transition as a unique outcome.

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The Fracture Risk Evaluation Model (FREM) identifies individuals at high imminent risk of major osteoporotic fractures. We validated FREM on 74,828 individuals from Manitoba, Canada, and found significant fracture risk stratification for all FREM scores. FREM performed better than age alone but not as well as FRAX® with BMD. INTRODUCTION: The FREM is a tool developed from Danish public health registers (hospital diagnoses) to identify individuals over age 45 years at high imminent risk of major osteoporotic fractures (MOF) and hip fracture (HF). In this study, our aim was to examine the ability of FREM to identify individuals at high imminent fracture risk in women and men from Manitoba, Canada. METHODS: We used the population-based Manitoba Bone Mineral Density (BMD) Program registry, and identified women and men aged 45 years or older undergoing baseline BMD assessment with 2 years of follow-up data. From linked population-based data sources, we constructed FREM scores using up to 10 years of prior healthcare information. RESULTS: The study population comprised 74,828 subjects, and during the 2 years of observation, 1612 incident MOF and 299 incident HF occurred. We found significant fracture risk stratification for all FREM scores, with AUC estimates of 0.63-0.66 for MOF for both sexes and 0.84 for women and 0.65-0.67 for men for HF. FREM performed better than age alone but not as well as FRAX® with BMD. The inclusion of physician claims data gave slightly better performance than hospitalization data alone. Overall calibration for 1-year MOF prediction was reasonable, but HF prediction was overestimated. CONCLUSION: In conclusion, the FREM algorithm shows significant fracture risk stratification when applied to an independent clinical population from Manitoba, Canada. Overall calibration for MOF prediction was good, but hip fracture risk was systematically overestimated indicating the need for recalibration.

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INTRODUCTION: This study aimed to investigate the number of cores needed in a systematic biopsy (SB) in men with clinical suspicion of prostate cancer (PCa) but negative prebiopsy multiparametric magnetic resonance imaging and to test prostate-specific antigen (PSA) density as an indicator for reduced SB. METHODS: Two hundred and seventy-four patients were analyzed, extracted from an institutional database. Detection rates of any PCa and clinically significant (CS) PCa for different reduced biopsy protocols were compared by using Fisher's exact test. RESULTS: In total, 12-core SB revealed PCa in 103 (37.6%) men. Detection rates of reduced biopsy protocols were 74 (27%, 6-core) and 82 (29.9%, 8-core). Regarding CSPCa, 12-core SB revealed a detection rate of 26 (9.5%). Reduced biopsy protocols detected less CSPCa: 15 (5.5%) and 18 (6.6%), respectively. All differences were statistically significant, p < 0.05. PSA density ≥0.15 did not help to filter out men in whom a reduced biopsy may be sufficient. CONCLUSIONS: Twelve-core SB still has the highest detection rate of any PCa and CSPCa compared to reduced biopsy protocols. If the investigator and patient agree - based on individual risk calculation - to perform a biopsy, this SB should contain at least 12 cores regardless of PSA density.

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After the nuclear accident in Fukushima, the public interest in radiation related cancer-risk assessment increased. However, interpretations of results from epidemiological studies and comprehension of cancer risk assessment methods can be unclear and involve questions about correctness and validity of the approaches. To shed some light on this potential lack of clarity, valid versus invalid radiation cancer risk assessments methods are illustrated here using Swiss population data. This involves a comparison of the cancer risk assessment method based on collective dose and the cumulative risk assessment method, where the latter is recommended with regard to uncertainties and risk of misinterpretation. Further, risk assessment in different dose ranges is discussed and it is concluded that below 100 mSv it cannot be appropriately stated that an adequate strength of evidence of a causal relationship between cancer and radiation is provided, because of the large uncertainties in this dose range. However, the linear non-threshold (LNT) model can be used to model the dose response, because it represents a prudent and parsimonious model, that fits the data well and lies within the given uncertainties. Additionally, treatments of uncertainties in the risk models are illustrated. The EU-project CONFIDENCE software is applied here to obtain example radiation related lifetime cancer risks for exposures of 20 mSv and 5 mSv. Furthermore, the impact of different dosimetry errors on the uncertainties in the cancer lifetime risk calculation is analysed, by including different standard deviations (SD) and by comparing the sampling of the doses from a normal and a lognormal distribution. Using the normal distribution, for females exposed to 20 mSv, the 95% confidence interval (CI) on the cancer lifetime risk increases, when compared to using a SD of 4 mSv, by a factor of 1.5 using a SD of 8 mSv and by a factor of 1.7 using a SD of 10 mSv. The corresponding factors for males for the same exposure are 1.3 and 1.5 respectively. For exposure to 5 mSv, the 95% CIs on the risk increase by a factor of 1.2 for females and 1.4 for men for a SD of 2 mSv using the normal distribution compared to the lognormal distribution and by a factor of 1.5 and 1.8 for a SD of 3 mSv compared to a SD of 1 mSv respectively. Furthermore, differences in the resulting 95% CI on the risk, using different distributions for the dose sampling are visible.

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BACKGROUND: The American College of Surgeon (ACS) Surgical Risk Calculator is an online tool that helps surgeons estimate the risk of postoperative complications for numerous surgical procedures across several surgical specialties. METHODS: We evaluated the predictive performance of the calculator in 385 cancer patients undergoing breast surgery. Calculator-predicted complication rates were compared with observed complication rates; calculator performance was evaluated using calibration and discrimination analyses. RESULTS: The mean calculator-predicted rates for any complication (4.1%) and serious complication (3.2%) were significantly lower than the observed rates (11.2% and 5.2%, respectively). The area under the curve was 0.617 for any complication and 0.682 for serious complications. p Values for the Hosmer-Lemeshow test were significant (<.05) for both outcomes. Brier scores were 0.102 for any complication and 0.048 for serious complication. CONCLUSIONS: The ACS risk calculator is not an ideal tool for predicting individual risk of complications following breast surgery in a Mexican cohort. The most valuable use of the calculator may reside in its role as an aid for patient-led surgery planning. The possibility of introducing breast surgery-specific data could improve the performance of the calculator. Furthermore, a disease-specific calculator could provide more accurate predictions and include complications more frequently found in breast cancer surgery.

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BACKGROUND: Postoperative pancreatic fistula (POPF) after pancreatoduodenectomy (PD) can be associated with severe postoperative morbidity. This study aims to develop a preoperative POPF risk calculator that can be easily implemented in clinical routine. METHODS: Patients undergoing PD were identified from a prospectively-maintained database. A total of 11 preoperative baseline and CT-based radiological parameters were used in a binominal logistic regression model. Parameters remaining predictive for grade B/C POPF were entered into the risk calculator and diagnostic accuracy measures and ROC curves were calculated for a training and a test patient cohort. The risk calculator was transformed into a simple nomogram. RESULTS: A total of 242 patients undergoing PD in the period from 2012 to 2018 were included. CT-imaging-based maximum main pancreatic duct (MPD) diameter (p = 0.047), CT-imaging-based pancreatic gland diameter at the anticipated resection margin (p = 0.002) and gender (p = 0.058) were the parameters most predictive for grade B/C POPF. Based on these parameters, a risk calculator was developed to identify patients at high risk of developing grade B/C POPF. In a training cohort of PD patients this risk calculator was associated with an AUC of 0.808 (95%CI 0.726-0.874) and an AUC of 0.756 (95%CI 0.669-0-830) in the independent test cohort. A nomogram applicable as a visual risk scale for quick assessment of POPF grade B/C risk was developed. CONCLUSION: The preoperative POPF risk calculator provides a simple tool to stratify patients planned for PD according to the risk of developing postoperative grade B/C POPF. The nomogram visual risk scale can be easily integrated into clinical routine and may be a valuable model to select patients for POPF-preventive therapy or as a stratification tool for clinical trials.

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