UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.

UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.

UNASSIGNED: Postpartum hemorrhage is the major cause of maternal deaths due to childbirth and also responsible for maternal morbidity.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.

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.
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.
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.
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.

BACKGROUND: 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.

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.

The International Commission on Radiological Protection (ICRP) developed effective dose as a quantity related to risk for occupational and public exposure. There was a need for a similar dose quantity linked to risk for making everyday decisions relating to medical procedures. Coefficients were developed to enable the calculation of doses to organs and tissues, and effective doses for procedures in nuclear medicine and radiology during the 1980s and 1990s. Effective dose has provided a valuable tool that is now used in the establishment of guidelines for patient referral and justification of procedures, choice of appropriate imaging techniques, and providing dose data on potential exposure of volunteers for research studies, all of which require the benefits from the procedure to be weighed against the risks. However, the approximations made in the derivation of effective dose are often forgotten, and the uncertainties in calculations of risks are discussed. An ICRP report on protection dose quantities has been prepared that provides more information on the application of effective dose, and concludes that effective dose can be used as an approximate measure of possible risk. A discussion of the way in which it should be used is given here, with applications for which it is considered suitable. Approaches to the evaluation of risk and methods for conveying information on risk are also discussed.

BACKGROUND: Postoperative pancreatic fistula (POPF) after pancreatoduodenectomy greatly influences patients\' postoperative course. Several evaluation methods have been used to assess the risk of clinically relevant POPF (CR-POPF) after pancreatoduodenectomy namely, the original, alternative, and updated alternative fistula risk scores (o-FRS, a-FRS, and ua-FRS, respectively).
METHODS: We enrolled 106/179 patients who underwent pancreatoduodenectomy in our institution between April 2013 and Mar 2018. CR-POPF was defined as grade B and C POPF according to the 2016 definitions of the International Study Group on Pancreatic Surgery.
RESULTS: Pancreatic gland texture was the only significant risk factor for CR-POPF (p = 0.007). The CR-POPF incidence increased significantly according to the risk groups defined by both o-FRS (p = 0.004) and a-FRS (p = 0.004). The area under the curve for o-FRS, a-FRS, and ua-FRS was 0.693, 0.693, and 0.671, respectively.
CONCLUSIONS: o-FRS, a-FRS, and ua-FRS were almost equally useful for risk evaluation for CR-POPF after pancreatoduodenectomy. Further studies, especially for preoperative objective evaluation of pancreatic gland texture, are needed for more useful and accurate risk evaluation.

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.

There is increasing concern about the use of chromated copper arsenate (CCA) treated timber due to the possible leaching of toxic metals or metalloids. CCA-treated timber waste are currently stockpiled across Australia with limited information about their risks to the environment or human health. In this study, the treatment and utilisation of CCA-treated timber waste as garden mulch, garden retaining walls, and soil additive were investigated. Iron materials were used as immobilising agents. The bioavailability of Cr, Cu and As to Spinacia oleracea from CCA-treated timber, before and after treatment, was determined in the context of human health risk assessment. The results showed that the iron-based treatments resulted in significant decreases in the concentrations of Cu and As in spinach grown in CCA-treated timber in soil. Analyses of CCA derived Cu and As in spinach showed that they accumulated in the roots rather than in the leaves. The risks of toxicity to humans varied for different utilisation scenarios and the immobilisation amendments were shown to reduce carcinogenic and non-carcinogenic risks. The information obtained in this study can inform development of utilisation options for CCA-treated timber wastes.