Risk Calculator Research Articles

Research on Ship Collision Risk Calculation in Port Navigation Waters Based on Ising Model and AIS Data

Research on Ship Collision Risk Calculation in Port Navigation Waters Based on Ising Model and AIS Data

Update on fracture risk assessment in osteoporosis.

The assessment of fracture risk is playing an ever-increasing role in osteoporosis clinical management and informing international guidelines for osteoporosis. FRAX, a fracture risk calculator that provides individualized 10-year probabilities of hip and major osteoporotic fracture, has been widely used since 2008. In this review, we recap the development and limitations of intervention thresholds and the role of absolute fracture risk. There is an increasing awareness of disparities and inequities in the setting of intervention thresholds in osteoporosis. The limitations of the simple use of prior fracture or the DXA-derived BMD T-score threshold are increasingly being discussed; one solution is to use fracture risk or probabilities in the setting of such thresholds. This approach also permits more objective assessment of high and very high fracture risk to enable physicians to make choices not just about the need to treat but what agents to use in individual patients. Like all clinical tools, FRAX has limitations that need to be considered, but the use of fracture risk in deciding who to treat, when to treat and what agent to use is a mechanism to target treatment equitably to those at an increased risk of fracture.

Short- and long-term performance of risk calculation tools for mortality in patients with acute coronary syndrome

BackgroundThe 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.MethodsThis 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.ResultsSixty-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.ConclusionsThe 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.

Impact of an Online Risk Calculator for Sentinel Node Positivity on Management of Patients with T1 and T2 Melanomas.

Predicting which patients with American Joint Committee on Cancer (AJCC) T1-T2 melanomas will have a positive sentinel lymph node (SLN) is challenging. Melanoma Institute Australia (MIA) developed an internationally validated SLN metastatic risk calculator. This study evaluated the nomogram's impact on T1-T2 melanoma patient management at MIA. SLN biopsy (SLNB) rates were compared for the pre- and post-nomogram periods of 1 July 2018-30 June 2019 and 1 August 2020-31 July 2021, respectively. Overall, 850 patients were identified (pre-nomogram, 383; post-nomogram, 467). SLNB was performed in 29.0% of patients in the pre-nomogram group and 34.5% in the post-nomogram group (p=0.091). The overall positivity rate was 16.2% in the pre-nomogram group and 14.9% in the post-nomogram group (p=0.223). SLNB was performed less frequently in T1a melanoma patients in the pre-nomogram group (1.1%, n=2/177) than in the post-nomogram group (8.6%, n=17/198) [p≤0.001]. This increase was particularly for melanomas with a risk score ≥ 5%, with an SLN positivity rate of 11.8% in the post-nomogram group (p=0.004) compared with zero. For T1b melanomas with a risk score of > 10%, the SLNB rate was 40.0% (8/20) pre-nomogram and 75.0% (12/16) post-nomogram (p=0.049). In this specialized center, the SLN risk calculator appears to influence practice for melanomas previously considered low risk for metastasis, with increased use of SLNB for T1a and higher-risk T1b melanomas. Further evaluation is required across broader practice settings. Melanoma management guidelines could be updated to incorporate the availability of nomograms to better select patients for SLNB than previous criteria.

Single landslide risk assessment considering rainfall‐induced landslide hazard and the vulnerability of disaster‐bearing body

Quantitative calculation of single landslide risk has great significance for the prevention and treatment of landslides, through analysing the slope stability under different rainfall recurrence periods. In this study, the rainfall of the past 40 years in Xun'wu County of China is counted and the rainfall during the return periods of 10, 20 and 50 years are calculated to form three different rainfall conditions. Then, the stability of Cheng'nan landslide in Xun'wu County is calculated by the Geo‐Studio 2007 software, and the probability of landslide occurrence is obtained by Monte Carlo theory under these three conditions. Next, the field investigation is employed to obtain the statistical results of the buildings and personnel in the affected area of Cheng'nan landslide. Finally, the risk of economic loss and casualty under the three conditions are calculated. It was demonstrated that: (1) Under the three conditions, the safety factor decreased gradually, the rate of decrease was slower in the first 3 days and faster in the middle period and there was still a downward trend after the end of the rain. (2) The probability of landslide occurrence during the rainfall return periods of 10, 20 and 50 years were 1.77%, 2.97% and 1.61%, respectively. Besides, the risk index of landslide was the highest under the condition of 20‐years rainfall return period. (3) The economic loss risk and casualty risk in the rainfall return periods of 10, 20 and 50 years were 122,700‐yuan and 4.11 people, 205,900‐yuan and 6.89 people, as well as 11,600‐yuan and 3.74 people, respectively.

Osteoporosis - implications of the new guidelines in practice

In September 2023, the guideline on the prophylaxis, diagnosis, and treatment of osteoporosis in postmenopausal women and men was published as a completely revised guideline. The implications for practice include a change in the justifying indication for performing a bone density measurement, the time interval over which the fracture risk is determined, the level and number of therapy thresholds, and the recommendations for the therapeutic approach that are adapted to the individual fracture risk present. Risk assessment for the prediction of spine and hip fractures is essential in the context of osteoporosis diagnostics. In addition to age and gender, there are a total of 33 risk factors to determine the individual risk of fracture. Much more attention is paid to the assessment of the risk of falls and, depending on the result, combined with recommendations for muscle training and protein intake from the age of 65. Risk indicators must also be taken into account when determining the indication for osteoporosis diagnosis, as well as the risk factors of the imminent risk of fracture. The indication for baseline diagnostics has changed from the >20% 10-year fracture risk to diagnostics in postmenopausal women and in men aged 50 years and older, depending on the fracture risk factor profile. This eliminates a specific fracture risk threshold for basic diagnostics. Thus, in the young patient group (50-60 years), the risk factors considered medically relevant for the indication for osteoporosis diagnosis must be taken into account. New thresholds as an indication for initiating therapy is the determination of fracture risk using a risk calculator over 3 years instead of 10 years. The indication for drug therapy should be based on the threshold values of the DVO risk model. The data clearly suggests a significantly faster and more effective fracture risk-reducing effect of anabolic therapy. This is recommended in the first sequence in cases of a very high risk of fracture from 10%/3 years with osteoanabolic active substances (teriparatide or romosozumab). Such a therapy sequence should be initiated directly and not delayed due to upcoming dental procedures. Follow-up therapy to consolidate the reduction of fracture risk should be chosen individually.

#2271 Cardiovascular event prediction in kidney transplant recipients: a new risk score derivation and internal validation

Abstract Background and Aims Kidney transplant recipients (KTRs) have a cardiovascular (CV) risk 3 to 5-fold higher than that of the general population.CV disease represents the main cause of death in KTRs, both due to the presence of traditional risk factors and factors strictly related to transplant. The main CV risk calculators proved to be poorly predictive in the KTR population. We, therefore, aimed to compare the performance of CV risk scores already in use and to develop and validate a CV risk score in our KTRs population. Method Our analysis included 371 adult KTRs in follow-up at the outpatient clinic from 1 January 2015 to 31 December 2016. The composite outcome was the occurrence of MACE. We compared the performance of three main CV risk calculators (Framingham Risk Score-FRS-, INDANA score and PORT score). Thereafter, we built a predictive model selecting study variables by backward stepwise Cox regression. The risk score for each variable was weighted according to the HR of the final multivariable model. Internal and external validation of the prediction score and its discriminative capacity was assessed via area under the curve (AUC) curve, and the calibration via the Hosmer–Lemeshow test. Results After a mean follow-up of 68 months, CV events occurred in 71 (19%) KTRs. FRS, INDANA and PORT were demonstrated to have low predictive power in our population (Fig. 1). The accuracy did not improve after adjustment for immunological and transplant-related variables. Therefore, we derived a model including significative variables at multivariate analysis: age, dialysis vintage, systolic blood pressure, eGFR and 24-hour proteinuria (Akaike information criteria 713). The model discrimination was good (Harrel's c: 0.73). According to the hazard ratio, 3 points have been attributed to age higher than 60 years and 24 h proteinuria higher than 1 g/d, 2 points to dialysis vintage longer than 5 years, systolic blood pressure higher than 140 mmHg and eGFR less than 30 ml/min/1.73 m2. The new score demonstrated good discrimination (AUC 0.70, Fig. 2) and calibration (Hosmer-Lemeshow test 11.34, P = 0.12). The new score was internally validated by 10-fold cross-validation (mean AUC 0.70, 95% CI 0.60-0.77). For each point of the new score, the risk of the event increased by 40% and a score higher than three was associated with a 4-fold increased risk of composite endpoint. Conclusion We confirmed the poor predictive power of the known CV risk assessment tools in our real-world KTRs population. Moreover, their discriminatory power was not improved by adjustment for the occurrence of biopsy-proven rejection, HLA compatibility and donor-specific antibody. A new score for CV event prediction including traditional risk factors, such as age and systolic blood pressure, and variables related to renal function and the effects of previous chronic kidney disease, was developed and internally validated.

A simplified pneumonia severity index (PSI) for clinical outcome prediction in COVID-19.

The Pneumonia Score Index (PSI) was developed to estimate the risk of dying within 30 days of presentation for community-acquired pneumonia patients and is a strong predictor of 30-day mortality after COVID-19. However, three of its required 20 variables (skilled nursing home, altered mental status and pleural effusion) are not discreetly available in the electronic medical record (EMR), resulting in manual chart review for these 3 factors. The goal of this study is to compare a simplified 17-factor version (PSI-17) to the original (denoted PSI-20) in terms of prediction of 30-day mortality in COVID-19. In this retrospective cohort study, the hospitalized patients with confirmed SARS-CoV-2 infection between 2/28/20-5/28/20 were identified to compare the predictive performance between PSI-17 and PSI-20. Correlation was assessed between PSI-17 and PSI-20, and logistic regressions were performed for 30-day mortality. The predictive abilities were compared by discrimination, calibration, and overall performance. Based on 1,138 COVID-19 patients, the correlation between PSI-17 and PSI-20 was 0.95. Univariate logistic regression showed that PSI-17 had performance similar to PSI-20, based on AUC, ICI and Brier Score. After adjusting for confounding variables by multivariable logistic regression, PSI-17 and PSI-20 had AUCs (95% CI) of 0.85 (0.83-0.88) and 0.86 (0.84-0.89), respectively, indicating no significant difference in AUC at significance level of 0.05. PSI-17 and PSI-20 are equally effective predictors of 30-day mortality in terms of several performance metrics. PSI-17 can be obtained without the manual chart review, which allows for automated risk calculations within an EMR. PSI-17 can be easily obtained and may be a comparable alternative to PSI-20.

Medical calculators derived synthetic cohorts: a novel method for generating synthetic patient data

This study shows that we can use synthetic cohorts created from medical risk calculators to gain insights into how risk estimations, clinical reasoning, data-driven subgrouping, and the confidence in risk calculator scores are connected. When prediction variables aren't evenly distributed in these synthetic cohorts, they can be used to group similar cases together, revealing new insights about how cohorts behave. We also found that the confidence in predictions made by these calculators can vary depending on patient characteristics. This suggests that it might be beneficial to include a "normalized confidence" score in future versions of these calculators for healthcare professionals. We plan to explore this idea further in our upcoming research.

Improving Cardiovascular Disease Forecasting with Machine Learning and Electronic Medical Record Data Characteristics Within a Local Healthcare Network

The PCE Risk Calculator, developed by the ACC/AHA, is frequently utilized in the United States for the purpose of averting the onset of Atherosclerotic cardiovascular disease (ASCVD) via first-line defense strategies. However, this calculator may not accurately estimate risk for certain populations, potentially leading to either under- or over-estimation of risk. We have created calculator for ASCVD risk specific to a population by leveraging advanced Machine Learning (ML) techniques and Electronic Medical Record (EMR) data. Our study involved comparing predictive accuracy of our calculator with PCE calculator. Between January 1, 2009, and April 30, 2020, data was gathered from 101,110 distinct EMRs of patients who were actively receiving treatment. Patient datasets underwent machine learning techniques containing Longitudinal (LT) and Cross-Sectional (CS) features, or solely CS features, derived from laboratory values and vital statistics. The models' effectiveness was assessed using fresh price metric (Screened Cases Percentage @Sensitivity level). In terms of prediction accuracy, every ML model that was tested performed better than the PCE risk calculator. Area Under Curve (AUC) score of 0.902 was obtained by Random Forest (RF) ML technique when CS and LT characteristics were combined (RF-LTC). Our machine learning model only needed to screen 43% of patients in order to identify 90% of positive ASCVD cases, in contrast to the PCE risk calculator, which required screening 69% of patients. Prediction models created using ML techniques reduce the amount number of tests necessary to forecast ASCVD and increase the accuracy of ASCVD prediction when compared to using PCE calculator alone. The combination of LT and CS features in these ML models leads to a significant enhancement in comparing the ASCVD prediction to utilizing CS features exclusively.

Advances in clinical management of neonatal sepsis

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.

Phthalic acid esters: Are they a big concern for rivers flowing into reservoir with ecological facilities?

The city-river-reservoir system is an important system for safeguarding drinking water. Phthalic acid esters (PAEs) are emerging contaminants in drinking water sources that are gaining attention, and they could pose risks to human health and aquatic organisms. In this study, field studies that lasted four years were conducted to analyze the concentrations, spatial-temporal distribution, and removal effects of six PAEs. The total concentrations of the Σ6PAEs in the water and sediment samples were 0.2–7.4 μg L−1 (mean: 1.3 μg L−1) and 9.2–9594.1 ng g−1 (mean: 847.5 ng g−1), respectively. Di-n-butyl phthalate (DBP) and, bis(2-ethylhexyl) phthalate (DEHP) were the predominant congeners, accounting for 57.2 % in the water samples and 94.1 % in the sediment samples. The urban area contributed 72 % of the PAEs in the system. A significant removal effect of PAEs was observed in the wetland, with a removal rate of 40.2 %. The partitioning of PAEs between the water and sediment was attributed to the removal of dimethyl phthalate and diethyl phthalate that occurred during the water phase, while the removal of DBP and DEHP primarily occurred during the sediment phase. The ecological risk calculation based on the sensitivity distribution model indicated that DBP (HQwater = 0.19, HQsediment = 0.46) and DEHP (HQwater = 0.20, HQsediment = 0.13) possessed moderate risks according to some water and sediment samples. The ecological projects were verified to be effective engineering strategies to reduce ecological risk in the drinking water source.

Impact of Lipoprotein(a) Levels on Cardiovascular Risk Estimation.

A new cardiovascular risk (CVR) calculator that incorporates Lipoprotein(a) [Lp(a)] levels has recently been designed. To estimate CVR using the new score and to identify the reduction in low-density lipoprotein cholesterol (LDL-C) or systolic blood pressure (SBP) necessary to balance the risk attributable to Lp(a). CVR throughout life and at 10 years was estimated with the new score in patients in primary prevention, both considering and not considering the value of Lp(a). When the estimated risk considering Lp(a) levels exceeded the baseline risk, the reduction in LDL-C levels or SBP necessary to balance the risk attributable to Lp(a) was calculated. In total, 671 patients (mean age 54.2 years, 47.2% women) were included. Globally, 22.7% of the population had high Lp(a) values (>50 mg/dL or >125 nmol/L). When calculating CVR throughout life and considering the Lp(a) value, the global risk increased in 66.7% of cases (median 19.3%). Similar results were observed when we assessed the 10-year risk. The risk associated with Lp(a) could be completely compensated by decreasing LDL-C (average 21 mg/dL) or SBP (average 6.3 mmHg) in 79.2% and 74.7% of cases, respectively. When calculating the CVR with the new score, two-thirds and one-third of the population were bidirectionally recategorized as 'up' or 'down,' respectively. The decrease in LDL-C or SBP mitigated the increased risk caused by Lp(a) levels across a substantial proportion of patients.

Review: The Role of Dual-Energy Computed Tomography in Detecting Monosodium Urate Deposits in Vascular Tissues.

To highlight novel findings in the detection of monosodium urate deposits in vessels using dual energy computed tomography, and to discuss the potential clinical implications for gout and hyperuricemia patients. Gout is an independent risk factor for cardiovascular disease. However, classical risk calculators do not take into account these hazards, and parameters to identify patients at risk are lacking. Monosodium urate measured by dual energy computed tomography is a well-established technology for the detection and quantification of monosodium urate deposits in peripheral joints and tendons. Recent findings also suggest its applicability to identify vascular urate deposits. Dual energy computed tomography is a promising tool for detection of cardiovascular monosodium urate deposits in gout patients, to better delineate individuals at increased risk for cardiovascular disease.

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 (> 85years, 4 points; 75-85years, 3 points; 65-75years, 2 points; 55-65years, 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.

The Public's Intended Uptake of Hypothetical Esophageal Adenocarcinoma Screening Scenarios: A Nationwide Survey.

Screening for early esophageal adenocarcinoma (EAC) may potentially reduce EAC-related mortality and morbidity. This study aimed to examine the Dutch population's intended uptake of 3 hypothetical EAC screening test scenarios and preferences for potential future organization. A total of 8,350 Dutch individuals aged 45-75 years were invited, of whom 2,258 completed a web-based survey. Participants were randomly assigned to 1 of 3 hypothetical screening test scenarios (i.e., transnasal endoscopy, ingestible cell collection device, or breath analysis). The primary outcome was intended uptake. Secondary outcomes included acceptance of screening eligibility criteria and preferences regarding invitation, counseling, and diagnostic follow-up. We performed exploratory univariable and multivariable regression analyses to assess which determinants were associated with EAC screening intent. Intended uptake of screening was highest in the breath analysis scenario (95%), followed by conventional upper endoscopy (78%), an ingestible cell collection device (75%), and transnasal endoscopy (68%) ( P < 0.001). Anticipating discomfort was most strongly associated with decreased intention to undergo transnasal endoscopy (odds ratio 0.18, 95% confidence interval 0.11-0.29) or swallow a cell collection device (odds ratio 0.20, 95% confidence interval 0.13-0.32). Cancer worry and high acceptance of test sensitivity/specificity were consistently associated with a positive intention to participate in screening. Inviting persons for screening based on gastroesophageal reflux disease symptoms, age, or the output of a risk prediction model was acceptable to 74%, 69%, and 66%, respectively. Inviting only men was acceptable for only 41% of women. The majority (58%) preferred to be invited by a public health organization, and 32% of the participants preferred to discuss their decision to participate with a healthcare professional. Participants in this study self-selected through a web-based survey, potentially introducing selection bias. Participants generally intended to participate in EAC screening, although the level of intent depended on the discomfort and performance associated with the offered screening test. Determining eligibility based on gastroesophageal reflux disease symptoms, age, or a risk calculator, but not sex, would be acceptable to most individuals.

Predicting Mortality Prior to Interhospital Hospital for "Unseen" General Surgery Patients: Development, Validation and Feasibility Trial of a Mortality Risk Calculator.

Develop and validate a mortality risk calculator that could be utilized at the time of transfer, leveraging routinely collected variables that could be obtained by trained non-clinical transfer personnel. There are no objective tools to predict mortality at the time of inter-hospital transfer for Emergency General Surgery (EGS) patients that are "unseen" by the accepting system. Patients transferred to general or colorectal surgery services from January 2016 through August 2022 were retrospectively identified and randomly divided into training and validation cohorts (3:1 ratio). The primary outcome was admission-related mortality, defined as death during the index admission or within 30 days post-discharge. Multiple predictive models were developed and validated. Among 4,664 transferred patients, 280 (6.0%) experienced mortality. Predictive models were generated utilizing 19 routinely collected variables; the penalized regression model was selected over other models due to excellent performance using only 12 variables. The model performance on the validating set resulted in an area under the receiver operating characteristic curve, sensitivity, specificity, and balanced accuracy of 0.851, 0.90, 0.67, and 0.79, respectively. After bias correction, Brier score was 0.04, indicating a strong association between the assigned risk and the observed frequency of mortality. A risk calculator using twelve variables has excellent predictive ability for mortality at the time of interhospital transfer among "unseen" EGS patients. Quantifying a patient's mortality risk at the time of transfer could improve patient triage, bed and resource allocation, and standardize care.

Probabilistic Performance-based Fire Design of Structures: A Hazard-Centred and Consequence-Oriented Perspective

AbstractRisk-based design and assessment methods are gaining popularity in performance-based structural fire engineering. These methods usually start by defining a set of hazard scenarios to use as analysis inputs. This approach, proven highly effective for other hazard types such as earthquakes, may not be optimal for fire safety design. Indeed, the strong coupling between the fire phenomenon and structural features enables an ad-hoc design variable selection (and/or optimisation) to reduce fire intensity, making fire scenarios additional design outputs. In addition, such a coupling effect implies that fire scenarios maximising consequences are structure specific. Building on these considerations, this paper discusses the limitations that arise at different analysis steps (i.e., fire-scenario and intensity treatment, identifying fire intensity measures, probabilistic fire hazard analysis, developing fire fragility models, and risk calculation) when using conventional risk-based approaches for design purposes. Furthermore, it compares such approaches with a fire safety design methodology (the Consequence-oriented Fire intensity Optimisation, CFO, approach) that addresses the identified limitations. The potential benefits of integrating the two approaches are also discussed. Finally, the fire design of a simplified steel-girder bridge is introduced as an illustrative example, comparing the consequence metrics and design updating strategies resulting from the two approaches.

Beyond Stages: Predicting Individual Time Dependent Risk for Type 1 Diabetes.

Essentially all individuals with multiple autoantibodies will develop clinical type 1 diabetes. Multiple AABs and normal glucose tolerance define Stage 1 diabetes; abnormal glucose tolerance defines Stage 2. However, the rate of progression within these stages is heterogeneous, necessitating personalized risk calculators to improve clinical implementation. We developed 3 models using TrialNet's Pathway to Prevention data to accommodate the reality that not all risk variables are clinically available. The Small model included AAB status, fasting glucose, HbA1c and age, while the Medium and Large models added predictors of disease progression measured via oral glucose tolerance testing. All models markedly improved granularity regarding personalized risk missing from current categories of stages of T1D. Model derived risk calculations are consistent with the expected reduction of risk with increasing age and increase in risk with higher glucose and lower insulin secretion, illustrating the suitability of the models. Adding glucose and insulin secretion data altered model predicted probabilities within Stages. In those with high 2-hour glucose, a high C-peptide markedly decreased predicted risk; lower C-peptide obviated the age-dependent risk of 2-hour glucose alone, providing a more nuanced estimate of rate of disease progression within Stage 2. While essentially all those with multiple AABs will develop type 1 diabetes, the rate of progression is heterogeneous and not explained by any individual single risk variable. The model-based probabilities developed here provide an adaptable personalized risk calculator to better inform decisions about how and when to monitor disease progression in clinical practice.

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

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. 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. 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. 5.0days, P = 0.985). The ACS-NSQIP calculator demonstrated generally accurate predictions for 30-day postoperative outcomes after hepatectomy for CRC liver metastasis in our patient population.