Adequate Calibration Research Articles

Multimodal Data-Driven Prognostic Model for Predicting Long-Term Prognosis in Patients With Ischemic Cardiomyopathy and Heart Failure With Preserved Ejection Fraction After Coronary Artery Bypass Grafting: A Multicenter Cohort Study.

Limited data from the literature are available to assess the efficacy of coronary artery bypass grafting in patients with ischemic cardiomyopathy and heart failure with preserved ejection fraction. Therefore, our objective was to use machine learning techniques integrating clinical features, biomarker data, and echocardiography data to enhance comprehension and risk stratification in patients diagnosed with ischemic cardiomyopathy and heart failure with preserved ejection fraction who have undergone coronary artery bypass grafting surgery. For this study, 294 patients with ischemic cardiomyopathy and heart failure with preserved ejection fraction who underwent coronary artery bypass grafting surgery were assigned to the development cohort (n=176) and the independent validation cohort (n=118). A total of 52 clinical variables were extracted for each patient. The principal clinical end point was the incidence of major adverse cardiovascular events, encompassing cardiac mortality, acute myocardial infarction, acute heart failure, and graft failure. From least absolute shrinkage and selection operator regression, 4 predictors were selected for the final prediction nomogram: diabetes, hypertension, the systemic immune-inflammation index, and NT-proBNP (N-terminal pro-B-type natriuretic peptide). The prediction nomogram achieved satisfactory prediction performance in both the development cohort (C index, 0.768 [95% CI, 0.701-0.835]) and independent validation cohort (C index, 0.633 [95% CI, 0.521-0.745]). Adequate calibration was noted for the likelihood of major adverse cardiovascular events in both the development and independent validation cohorts. Decision curve analysis confirmed the clinical usefulness of the established prediction nomogram. A clinically feasible prognostic model, based on preoperative multimodal data, was developed for risk stratification of patients with ischemic heart and heart failure with preserved ejection fraction who receive coronary artery bypass grafting surgery. https://www.chictr.org.cn; Unique identifier: ChiCTR2300074439.

Utility of the 2019 EULAR/ACR SLE classification criteria for predicting mortality and hospitalisation: development and cross-validation of ominosity score

ObjectiveThe 2019 European Alliance of Associations for Rheumatology (EULAR)/American College of Rheumatology (ACR) systemic lupus erythematosus (SLE) classification criteria score (≥20 points) has been associated with poor outcomes. We aimed...

QLTI-08. PREDICTING POOR HIGH-VALUE CARE OUTCOMES FOLLOWING SURGICAL RESECTION OF MULTIFOCAL GLIOBLASTOMA

Abstract Presenting with dismal prognoses, treatment of multifocal GBM (mGBM) requires extensive medical infrastructure. Individualized assessment for patients at risk of extended length of stay (LOS) and nonroutine discharge disposition, described as high-value healthcare outcomes, has not previously been described. To develop predictive models for extended LOS and nonroutine discharge disposition for mGBM patients. All mGBM patients undergoing surgical resection at our institution (January 1, 2009-December 31, 2020) were included. Predictive models for nonroutine discharge disposition (discharge to a non-home environment) and extended LOS [upper quartile of the LOS for all patients included in the present study’s cohorts (>6 days)] were developed using multivariate logistic regression models. Optimism-corrected C-statistics for each model were calculated using 1000 bootstrapped samples in order to correct for potential bias associated with the limited population size. The Hosmer-Lemeshow test was used to evaluate model goodness of fit and calibration. A total of 137 mGBM patients were included in the present study. Most patients were male (40.1%), white (83.2%), not Hispanic/Latino (97.8%), not married (55.5%) and underwent elective surgery (85.4%). The average age among our patient cohort was 55.41 ± 13.17, the average KPS score was 77.23 ± 14.54 and the average mFI-5 was 0.613 ± 0.760. On bivariate analysis, older age (p=0.015) and decreasing Karnofsky Performance Status (KPS) score (p<0.001) were significantly associated with nonroutine discharge disposition while emergency admission (p=0.010) and increasing frailty (mFI-5) score (p=0.003) were significantly associated with extended LOS. On multivariate analysis, increasing age (p=0.031) and decreasing KPS (p<0.001) predicted nonroutine discharge while emergent admission (p=0.023) and greater mFI-5 (p=0.044) predicted extended LOS. Our models for nonroutine discharge and extended LOS had optimism-correct c-statistics of 0.7507 and 0.7309 respectively and displayed adequate calibration. Following external validation, our models may have utility as a risk stratification tool for this multifocal GBM patient populations.

Comparison of Classical and Inverse Calibration Equations in Chemical Analysis.

Chemical analysis adopts a calibration curve to establish the relationship between the measuring technique's response and the target analyte's standard concentration. The calibration equation is established using regression analysis to verify the response of a chemical instrument to the known properties of materials that served as standard values. An adequate calibration equation ensures the performance of these instruments. There are two kinds of calibration equations: classical equations and inverse equations. For the classical equation, the standard values are independent, and the instrument's response is dependent. The inverse equation is the opposite: the instrument's response is the independent value. For the new response value, the calculation of the new measurement by the classical equation must be transformed into a complex form to calculate the measurement values. However, the measurement values of the inverse equation could be computed directly. Different forms of calibration equations besides the linear equation could be used for the inverse calibration equation. This study used measurement data sets from two kinds of humidity sensors and nine data sets from the literature to evaluate the predictive performance of two calibration equations. Four criteria were proposed to evaluate the predictive ability of two calibration equations. The study found that the inverse calibration equation could be an effective tool for complex calibration equations in chemical analysis. The precision of the instrument's response is essential to ensure predictive performance. The inverse calibration equation could be embedded into the measurement device, and then intelligent instruments could be enhanced.

External validation and calibration of the HoLISTIC Consortium's advanced-stage Hodgkin lymphoma international prognostic index (A-HIPI) in the Brazilian Hodgkin lymphoma registry.

The Hodgkin lymphoma International Study for Individual Care (HoLISTIC) Consortium's A-HIPI model, developed in 2022 for advanced-stage classical Hodgkin lymphoma (cHL), predicts survival within 5 years amongst newly diagnosed patients. This study validates its performance in the Brazilian Hodgkin lymphoma registry. By 2022, the Brazilian HL registry included 1357 cHL patients, with a median 5-year follow-up. Probabilities for 5-year progression-free survival (PFS) and overall survival (OS) were calculated using A-HIPI-model equations. Discrimination (Harrell C-statistic/Uno C-statistic) and calibration measures assessed external validation and calibration. Lab values beyond the allowed range were excluded, mirroring the initial A-HIPI analysis. A total of 694 advanced-stage cHL patients met the original inclusion criteria (age 18-65 years, Stage IIB-IV). Median age was 31 years; 46.3% were females. Stage distribution was IIB (33.1%), III (27.4%), IV (39.5%). Bulky disease in 32.6%. Five-year PFS and OS were 68.4% and 86.0%, respectively. Harrell C-statistics were 0.60 for PFS and 0.69 for OS, and Uno C-statistics were 0.63 for PFS and 0.72 for OS. Calibration plots demonstrated well-calibrated predictions with calibration slopes of 0.91 and 1.03 for 5-year OS and PFS, respectively. Despite differing patient, clinical characteristics, and socioeconomic factors, the baseline prediction tool performed well in the Brazilian cohort, demonstrating adequate discrimination and calibration. This supports its reliability in diverse settings.

QUALITY OF CHIPS PRODUCED FOR ENERGY PURPOSES IN SOUTHWESTERN PARANÁ

Quality of wood chips produced for energy purposes in the southeast region of Paraná. To characterize and monitor the moisture content of the energy biomass used by a food industry in southwestern Paraná-Brazil, the materials of five different providers were analyzed, four were from Eucalyptus spp. and one from Pinus sp. The chips were characterized according to the moisture content on a wet basis, apparent density, calorific value and immediate chemical analysis was performed. With the collected data, calibration curves were generated to adjust the moisture content measurement equipment, MUG M75, used by the company. The calibration of the M75 was made based on the linear regression model, and all chips presented an adequate calibration curve with a coefficient of determination (R²) above 0.9. The chips had a moisture content of 35 to 45% in the humid base. The highest value of apparent density was 207.67 kg.m³. The superior calorific value was statistically equal for all materials, with an average of 4,384.4 kcal/kg. Regarding the immediate chemical analysis, volatiles were between 80 and 84%, for fixed carbon the highest value obtained was 19.16%. The studied chips had low ash contents, with a general average of 0.59%. All calibration curves showed high accuracy (> 90%), showing that the method used can replace oven drying after the recipe is calibrated for suppliers.

Development and Validation of Prognostic Models for Bleeding and Ischemia in Elderly Patients With Comorbid Acute Coronary Syndrome and Atrial Fibrillation.

Acute coronary syndrome and atrial fibrillation are common cardiovascular diseases in elderly individuals. Patients with comorbidities face increased risks of bleeding and ischemia; however, there is a lack of prognostic models for quantifying these risks in this special population. In this retrospective cohort study, 1851 patients (≥65 years old) with acute coronary syndrome and atrial fibrillation from 2 hospitals in China were included in the development cohort (1252 individuals) and 2 external validation cohorts (284 and 315 individuals). During 1-year follow-up, 96 Bleeding Academic Research Consortium type 3 or 5 bleeding events and 245 thromboembolic events were observed. In the development cohort, the concordance indexes for bleeding at 3, 6, and 12 mo ranged from 0.737 to 0.845 and for ischemia ranged from 0.723 to 0.777. The calibration curve and decision curve analysis indicated adequate calibration and clinical practicability. The concordance indexes varied from 0.679 to 0.809 in the validation cohorts. Subgroup analyses focusing on anticoagulant drugs and antithrombotic therapy were conducted, revealing similar discrimination and calibration. Kaplan-Meier curves demonstrated significant differences (log-rank P<0.001). Additionally, the models outperformed conventional models in concordance indexes, integrated discrimination improvement, and net reclassification improvement. Our study provides 2 robust prognostic models with easily available clinical factors for predicting bleeding and ischemia in elderly patients with acute coronary syndrome and atrial fibrillation. Furthermore, we provide online calculators to facilitate individualized risk evaluation and clinical decision-making. URL: www.chictr.org.cn/. Unique Identifier: ChiCTR2200067185.

Validation of the HAS-BLED scale for the assessment of bleeding risk in patients on anticoagulation therapy with a diagnosis of venous thromboembolic disease.

Several models have been developed to assess bleeding risk in patients with venous thromboembolism, such as HAS-BLED, but their external validity has not been adequately assessed. The objective of the study was to evaluate the discriminative ability and calibration of the HAS-BLED scale for predicting 1-month bleeding risk in patient's anticoagulated for venous thromboembolism. External validation study of a prediction model based on a retrospective cohort of patients with venous thromboembolism treated between November 2019 and January 2022. Calibration of the HAS-BLED scale was evaluated using the Hosmer-Lemeshow test and the ratio of observed to expect events within each risk category. Discriminatory ability was assessed using the area under the curve (AUC) of a receiver operating characteristic curve. We included 735 patients (median age 64 years, female sex 55.2%), pulmonary embolism was diagnosed in most patients (60.7%), and 4.9% presented bleeding events. Regarding calibration, the HAS-BLED scale systematically underestimates the risk both in the general population (ROE 3.76, p < 0.001) and in cancer patients (ROE 4.16). The Hosmer-Lemeshow test rejected the hypothesis of adequate calibration (p < 0.001). Discriminatory ability was limited both in the general population (AUC = 0.57, 95% confidence interval [CI]: 0.48-0.66) and in the subgroup with active cancer (AUC = 0.53, 95% CI: 0.36-0.69). The HAS-BLED scale in patients with venous thromboembolism underestimates the risk of bleeding at 1 month and has a low ability to discriminate high-risk patients. Cautious interpretation of the scale is recommended until additional evidence is available.

Sensitivity analysis of activity scheduling parameters with a parameter optimization framework

Transportation-related activity scheduling is becoming more complex due to the growing number of potential locations and extensive opportunities to visit various places. Throughout the years, in the field of transportation several attempts were made to optimize travelers’ activity chains with different parameters to set, but there is a lack of comprehensive solutions. In this research, the activity chain optimization algorithm is applied, which requires high computational efforts. To provide an adequate calibration of the parameters, a sensitivity analysis is conducted. The aim of the analysis is to reveal how changes in the attribute values modify the final outcomes. The relevant parameters, activity chains, transport modes, optimization algorithms, and fitness functions, are identified and considered. For each parameter, an investigation is conducted to reveal its behavior throughout the runs. For example, changes in the population size and crossover function lead to more reliable results, while alteration in the number of generations and the mutation function have no effects on the outcomes. The analysis presents a peculiar behavior of the parameters related to the activity chains. The results can be useful for transportation planners and service providers in the adaptation of the existing network and transportation services to the travelers’ mobility patterns.

Nomogram-based prediction of emergence delirium in elderly patients undergoing laparoscopic surgery

BackgroundA nomogram model has been developed to forecast the incidence of emergence delirium (ED) in elderly patients undergoing laparoscopic surgery. MethodsA secondary analysis was performed on the eMODIPOD trial, which involved elective laparoscopic surgery patients at Xiangya Hospital between October 2017 and October 2019. To assess ED, the Richmond Agitation Sedation Scale (RASS) was combined with the Intensive Care Unit (CAM-ICU) scale, and patients were categorized as ED or non-ED based on the results. The two groups were compared in terms of perioperative information and test results. Logistic regression was utilized to investigate factors contributing to ED occurrence in elderly patients following laparoscopic surgery, and a nomogram was constructed to depict this. The diagnostic performance was evaluated through the utilization of the area under the receiver operating characteristic curve (AUROC) and a calibration plot. Simultaneously, to appraise the clinical value, decision curve analysis (DCA) was employed to assess the clinical value. ResultsOf the 565 patients subject to laparoscopic surgery, 94 were subsequently diagnosed with emergence delirium (ED), yielding a positive rate of 16.6 %. The developed Nomogram model was an amalgamation of several variables, which included preoperative cognitive function, educational level, dezocine, penehyclidine, ASA grading, blood loss, fluid infusion, and Post Anesthesia Care Unit-numerical rating scale (PACU-NRS). The model’s performance was commendable in terms of its discrimination power, as signified by an area under the curve of 0.713(95 % CI = 0.655–0.772). Furthermore, it exhibited adequate calibration, as affirmed by the Hosmer-Lemeshow test (χ2 = 2.243, p = 0.326). Internal validation of the model further emphasized its superior discrimination and calibration capabilities. The decision curve analysis pointed out that when the threshold probability exceeded 0.1, the Nomogram model delivered more substantial benefits compared to the treat-all and treat-none approaches. ConclusionsThis scoring system represents the pioneering Nomogram model utilized for predicting emergence delirium (ED) in geriatric patients undergoing laparoscopic surgery. It exhibits robust efficacy in forecasting the risk of ED, thereby furnishing valuable insights for the prevention, management, and treatment of ED.

Establishment and validation of a nomogram for dropout intention in Chinese early year medical undergraduates

BackgroundThe attrition rate of Chinese medical students is high. This study utilizes a nomogram technique to develop a predictive model for dropout intention among Chinese medical undergraduates based on 19 individual and work-related characteristics.MethodA repeated cross-sectional study was conducted, enrolling 3536 medical undergraduates in T1 (August 2020-April 2021) and 969 participants in T2 (October 2022) through snowball sampling. Demographics (age, sex, study phase, income, relationship status, history of mental illness) and mental health factors (including depression, anxiety, stress, burnout, alcohol use disorder, sleepiness, quality of life, fatigue, history of suicidal attempts (SA), and somatic symptoms), as well as work-related variables (career choice regret and reasons, workplace violence experience, and overall satisfaction with the Chinese healthcare environment), were gathered via questionnaires. Data from T1 was split into a training cohort and an internal validation cohort, while T2 data served as an external validation cohort. The nomogram’s performance was evaluated for discrimination, calibration, clinical applicability, and generalization using receiver operating characteristic curves (ROC), area under the curve (AUC), calibration curves, and decision curve analysis (DCA).ResultFrom 19 individual and work-related factors, five were identified as significant predictors for the construction of the nomogram: history of SA, career choice regret, experience of workplace violence, depressive symptoms, and burnout. The AUC values for the training, internal validation, and external validation cohorts were 0.762, 0.761, and 0.817, respectively. The nomogram demonstrated reliable prediction and discrimination, with adequate calibration and generalization across both the training and validation cohorts.ConclusionThis nomogram exhibits reasonable accuracy in foreseeing dropout intentions among Chinese medical undergraduates. It could guide colleges, hospitals, and policymakers in pinpointing students at risk, thus informing targeted interventions. Addressing underlying factors such as depressive symptoms, burnout, career choice regret, and workplace violence may help reduce the attrition of medical undergraduates.Trial registrationThis is an observational study. There is no Clinical Trial Number associated with this manuscript.

Mesoscale modelling and simulation of irradiation-induced expansion in concrete

The effects of irradiation on concrete is a topic of concern in nuclear applications, where for some specific members the material may be exposed at long-term to significant levels of neutron radiation. In this study, the effects of radiation-induced volumetric expansion (RIVE) of aggregates on concrete behaviour are investigated numerically at the mesoscale. Cylindrical concrete specimens constituted of 3 phases, namely mortar matrix, polyhedral aggregates and interfaces between both of these phases, are generated in 3D. Besides RIVE of aggregates, temperature increase due to the energy deposited during the irradiation, drying and damage development are considered in the finite element simulations. A phase-field approach and a cohesive-zone model are applied to describe damage in the matrix and interfaces, respectively. The simulation results are compared to experimental data of concrete specimens exposed to significant neutron fluences exceeding 6 × 10–19 n/cm2, in terms of residual Young modulus, compressive strength, temperatures evolution and overall expansion of the specimens. Globally, with an adequate calibration of parameters and in particular those related to the damage evolution laws, the simulation results of residual mechanical properties are in relatively good agreement with the experimental data. In addition, specimen expansions are well reproduced, providing that RIVE effects are also considered for the mortar phase, which is justified by the significant volume fraction of embedded sand grains. Mortar RIVE is modelled with the same approach as aggregate RIVE by applying a proportionality parameter, whose effect is discussed. Finally, simulations predict an intense drying of the specimens due to temperature increase.

Risk of new-onset diabetes and efficacy of pharmacological weight loss therapy.

To develop a clinical risk model to identify individuals at higher risk of developing new-onset diabetes and who might benefit more from weight loss pharmacotherapy. A total of 21 143 patients without type 2 diabetes at baseline from two TIMI clinical trials of stable cardiovascular patients were divided into a derivation (~2/3) and validation (~1/3) cohort. The primary outcome was new-onset diabetes. Twenty-seven candidate risk variables were considered, and variable selection was performed using multivariable Cox regression. The final model was evaluated for discrimination and calibration, and for its ability to identify patients who experienced a larger benefit from the weight loss medication lorcaserin in terms of risk of new-onset diabetes. During a median (interquartile range) follow-up of 2.3 (1.8-2.7) years, new-onset diabetes occurred in 1013 patients (7.7%). The final model included five independent predictors (glycated haemoglobin, fasting glucose, age, body mass index, and triglycerides/high-density lipoprotein). The clinical risk model showed good discrimination (Harrell's C-indices 0.802, 95% confidence interval [CI] 0.788-0.817 and 0.807, 95% CI 0.788-0.826) in the derivation and validation cohorts. The calibration plot demonstrated adequate calibration (2.5-year area under the curve was 81.2 [79.1-83.5]). While hazard ratios for new-onset diabetes with a weight-loss therapy were comparable across risk groups (annual risks of <1%, 1%-5%, and >5%), there was a sixfold gradient in absolute risk reduction from lowest to highest risk group (p = 0.027). The developed clinical risk model effectively predicts new-onset diabetes, with potential implications for personalized patient care and therapeutic decision making.

External Validation of a Model for Persistent Perfusion Deficit in Patients With Incomplete Reperfusion After Thrombectomy: EXTEND-PROCEED.

We recently developed a model (PROCEED) that predicts the occurrence of persistent perfusion deficit (PPD) at 24 hours in patients with incomplete angiographic reperfusion after thrombectomy. This study aims to externally validate the PROCEED model using prospectively acquired multicenter data. Individual patient data for external validation were obtained from the Endovascular Therapy for Ischemic Stroke with Perfusion-Imaging Selection, Tenecteplase versus Alteplase Before Endovascular Therapy for Ischemic Stroke part 1 and 2 trials, and a prospective cohort of the Medical University of Graz. The model's primary outcome was the occurrence of PPD, defined as a focal, wedge-shaped perfusion delay on 24-hour follow-up perfusion imaging that corresponds to the capillary phase deficit on last angiographic series in patients with <Thrombolysis in Cerebral Infarction 3 reperfusion after thrombectomy. The model's performance was evaluated with discrimination, calibration accuracy, and clinical decision curves. We included 371 patients (38% with PPD). The externally validated model had good discrimination (C-statistic 0.81, 95% CI 0.77-0.86) and adequate calibration (intercept 0.25, 95% CI 0.21-0.29 and slope 0.98, 95% CI 0.90-1.12). Across a wide range of probability thresholds (i.e., depending on the physicians' preferences on how the model should be used), the model shows net benefit on clinical decision curves, informing physicians on the likelihood of PPD. If a physician's attitude toward false-positive and false-negative ratings is equal, the model would reduce 13 in 100 unnecessary interventions by correctly predicting complete delayed reperfusion, without missing a patient with PPD. The externally validated model had adequate predictive accuracy and discrimination. Depending on the acceptable threshold probability, the model accurately predicts persistent incomplete reperfusion and may advise physicians whether additional reperfusion attempts should be performed.

GEMA-Na and MELD 3.0 severity scores to address sex disparities for accessing liver transplantation: a nationwide retrospective cohort study

The Gender-Equity Model for liver Allocation corrected by serum sodium (GEMA-Na) and the Model for End-stage Liver Disease 3.0 (MELD 3.0) could amend sex disparities for accessing liver transplantation (LT). We aimed to assess these inequities in Spain and to compare the performance of GEMA-Na and MELD 3.0. Nationwide cohort study including adult patients listed for a first elective LT (January 2016-December 2021). The primary outcome was mortality or delisting for sickness within the first 90 days. Independent predictors of the primary outcome were evaluated using multivariate Cox's regression with adjusted relative risks (RR) and 95% confidence intervals (95% CI). The discrimination of GEMA-Na and MELD 3.0was assessed using Harrell c-statistics (Hc). The study included 6071 patients (4697 men and 1374 women). Mortality or delisting for clinical deterioration occurred in 286 patients at 90 days (4.7%). Women had reduced access to LT (83.7% vs. 85.9%; p=0.037) and increased risk of mortality or delisting for sickness at 90 days (adjusted RR=1.57 [95% CI 1.09-2.28]; p=0.017). Female sex remained as an independent risk factor when using MELD or MELD-Na but lost its significance in the presence of GEMA-Na or MELD 3.0. Among patients included for reasons other than tumours (n=3606; 59.4%), GEMA-Na had Hc=0.753 (95% CI 0.715-0.792), which was higher than MELD 3.0 (Hc=0.726 [95% CI 0.686-0.767; p=0.001), showing both models adequate calibration. GEMA-Na and MELD 3.0 might correct sex disparities for accessing LT, but GEMA-Na provides more accurate predictions of waiting list outcomes and could be considered the standard of care for waiting list prioritization. Instituto de Salud Carlos III, Agencia Estatal de Investigación (Spain), and European Union.

Development and Validation of the Hospital Medicine Safety Sepsis Initiative Mortality Model

When comparing outcomes after sepsis, it is essential to account for patient case mix to make fair comparisons. We developed a model to assess risk-adjusted 30-day mortality in the Michigan Hospital Medicine Safety sepsis initiative (HMS-Sepsis). Can HMS-Sepsis registry data adequately predict risk of 30-day mortality? Do performance assessments using adjusted vsunadjusted data differ? Retrospective cohort of community-onset sepsis hospitalizations in the HMS-Sepsis registry (April 2022-September 2023), with split derivation (70%) and validation (30%) cohorts. We fit a risk-adjustment model (HMS-Sepsis mortality model) incorporating acute physiologic, demographic, and baseline health data and assessed model performance using concordance (C) statistics, Brier scores, and comparisons of predicted vsobserved mortality by deciles of risk. We compared hospital performance (first quintile, middle quintiles, fifth quintile) using observed vsadjusted mortality to understand the extent to which risk adjustment impacted hospital performance assessment. Among 17,514 hospitalizations from 66 hospitals during the study period, 12,260 hospitalizations (70%) were used for model derivation and 5,254 hospitalizations (30%) were used for model validation. Thirty-day mortality for the total cohort was 19.4%. The final model included 13 physiologic variables, two physiologic interactions, and 16 demographic and chronic health variables. The most significant variables were age, metastatic solid tumor, temperature, altered mental status, and platelet count. The model C statistic was 0.82 for the derivation cohort, 0.81 for the validation cohort, and≥ 0.78 for all subgroups assessed. Overall calibration error was 0.0%, and mean calibration error across deciles of risk was 1.5%. Standardized mortality ratios yielded different assessments than observed mortality for 33.9%of hospitals. The HMS-Sepsis mortality model showed strong discrimination and adequate calibration and reclassified one-third of hospitals to a different performance category from unadjusted mortality. Based on its strong performance, the HMS-Sepsis mortality model may aid in fair hospital benchmarking, assessment of temporal changes, and observational causal inference analysis.

Prediction of Isolated Local Recurrence After Resection of Pancreatic Ductal Adenocarcinoma: A Nationwide Study

BackgroundDistinguishing postoperative fibrosis from isolated local recurrence (ILR) after resection of pancreatic ductal adenocarcinoma (PDAC) is challenging. A prognostic model that helps to identify patients at risk of ILR can assist clinicians when evaluating patients’ postoperative imaging. This nationwide study aimed to develop a clinically applicable prognostic model for ILR after PDAC resection.Patients and MethodsAn observational cohort study was performed, including all patients who underwent PDAC resection in the Netherlands (2014–2019; NCT04605237). On the basis of recurrence location (ILR, systemic, or both), multivariable cause-specific Cox-proportional hazard analysis was conducted to identify predictors for ILR and presented as hazard ratios (HRs) with 95% confidence intervals (CIs). A predictive model was developed using Akaike’s Information Criterion, and bootstrapped discrimination and calibration indices were assessed.ResultsAmong 1194/1693 patients (71%) with recurrence, 252 patients (21%) developed ILR. Independent predictors for ILR were resectability status (borderline versus resectable, HR 1.42; 95% CI 1.03–1.96; P = 0.03, and locally advanced versus resectable, HR 1.11; 95% CI 0.68–1.82; P = 0.66), tumor location (head versus body/tail, HR 1.50; 95% CI 1.00–2.25; P = 0.05), vascular resection (HR 1.86; 95% CI 1.41–2.45; P < 0.001), perineural invasion (HR 1.47; 95% CI 1.01–2.13; P = 0.02), number of positive lymph nodes (HR 1.04; 95% CI 1.01–1.08; P = 0.02), and resection margin status (R1 < 1 mm versus R0 ≥ 1 mm, HR 1.64; 95% CI 1.25–2.14; P < 0.001). Moderate performance (concordance index 0.66) with adequate calibration (slope 0.99) was achieved.ConclusionsThis nationwide study identified factors predictive of ILR after PDAC resection. Our prognostic model, available through www.pancreascalculator.com, can be utilized to identify patients with a higher a priori risk of developing ILR, providing important information in patient evaluation and prognostication.

External validation and comparison of magnetic resonance imaging-based risk prediction models for prostate biopsy stratification.

Magnetic resonance imaging (MRI) is a promising tool for risk assessment, potentially reducing the burden of unnecessary prostate biopsies. Risk prediction models that incorporate MRI data have gained attention, but their external validation and comparison are essential for guiding clinical practice. The aim is to externally validate and compare risk prediction models for the diagnosis of clinically significant prostate cancer (csPCa). A cohort of 4606 patients across fifteen European tertiary referral centers were identified from a prospective maintained database between January 2016 and April 2023. Transrectal or transperineal image-fusion MRI-targeted and systematic biopsies for PI-RADS score of ≥ 3 or ≥ 2 depending on patient characteristics and physician preferences. Probabilities for csPCa, defined as International Society of Urological Pathology (ISUP) grade ≥ 2, were calculated for each patients using eight models. Performance was characterized by area under the receiver operating characteristic curve (AUC), calibration, and net benefit. Subgroup analyses were performed across various clinically relevant subgroups. Overall, csPCa was detected in 2154 (47%) patients. The models exhibited satisfactory performance, demonstrating good discrimination (AUC ranging from 0.75 to 0.78, p < 0.001), adequate calibration, and high net benefit. The model described by Alberts showed the highest clinical utility for threshold probabilities between 10 and 20%. Subgroup analyses highlighted variations in models' performance, particularly when stratified according to PSA level, biopsy technique and PI-RADS version. We report a comprehensive external validation of risk prediction models for csPCa diagnosis in patients who underwent MRI-targeted and systematic biopsies. The model by Alberts demonstrated superior clinical utility and should be favored when determining the need for a prostate biopsy.

#2801 Which patients may benefit most from hemodiafiltration compared with hemodialysis: prediction of the individualized treatment effect

Abstract Background and Aims Haemodiafiltration (HDF) with an appropriate dosing reduces death from any cause based on an individual participant data meta-analysis and aggregated data meta-analyses of available randomized trials. No differences in treatment effect are reported for specific patient populations when defined on a single characteristic in subgroup analyses. Yet, it is unclear to what extent individual patients, characterized by a combination of clinical characteristics, may benefit from HDF. This study aimed to develop and internally validate a treatment effect prediction model to determine which individual patients would benefit most from well dosed HDF, compared with haemodialysis (HD), in terms of survival. Method Individual participant data from five randomized controlled trials (CONTRAST, ESHOL, Turkish HDF study, French HDF study, CONVINCE) comparing HDF with HD on all-cause mortality were used to derive a Royston-Parmar model for exploring the prediction of absolute treatment effect of HDF based on pre-specified patient and disease characteristics, notably age, sex, body mass index, diabetes mellitus, history of cardiovascular disease, creatinine levels, and c-reactive protein levels. Internal validation of the model was performed using internal-external cross validation. Results Among 4153 participants, with a median follow-up of 30 months (Q1-Q3: 24-36), death from any cause occurred in 558 patients (27.2%). The median predicted survival benefit of HDF compared with HD was 6.9 (Q1–Q3: 5.6–9.0) months, with a range of 2 to 42 months. Patients who were predicted to benefit most from HDF were younger, less likely to have diabetes or a cardiovascular history and had higher serum creatinine levels. Internal-external cross validation showed adequate discrimination and calibration. Conclusion All-cause mortality is reduced by HDF compared with HD in ESKD patients. Yet, the absolute survival benefit seems to considerably differ between individual patients. Our results suggest that the effects of HDF on absolute survival can be predicted accurately using a combination of readily available patient and disease characteristics. This prediction model approach exemplifies the potential of prediction algorithms in supporting clinical decision-making for the choice of treatment options. Validation and potential updates to the presented algorithm are necessary, along with a preferable randomized evaluation of its clinical impact before widespread implementation.

The impact of prostate volume estimation on the risk-adapted biopsy decision based on prostate-specific antigen density and magnetic resonance imaging score.

Utility of prostate-specific antigen density (PSAd) for risk-stratification to avoid unnecessary biopsy remains unclear due to the lack of standardization of prostate volume estimation. We evaluated the impact of ellipsoidal formula using multiparametric magnetic resonance (MRI) and semi-automated segmentation using tridimensional ultrasound (3D-US) on prostate volume and PSAd estimations as well as the distribution of patients in a risk-adapted table of clinically significant prostate cancer (csPCa). In a prospectively maintained database of 4841 patients who underwent MRI-targeted and systematic biopsies, 971 met inclusions criteria. Correlation of volume estimation was assessed by Kendall's correlation coefficient and graphically represented by scatter and Bland-Altman plots. Distribution of csPCa was presented using the Schoots risk-adapted table based on PSAd and PI-RADS score. The model was evaluated using discrimination, calibration plots and decision curve analysis (DCA). Median prostate volume estimation using 3D-US was higher compared to MRI (49cc[IQR 37-68] vs 47cc[IQR 35-66], p < 0.001). Significant correlation between imaging modalities was observed (τ = 0.73[CI 0.7-0.75], p < 0.001). Bland-Altman plot emphasizes the differences in prostate volume estimation. Using the Schoots risk-adapted table, a high risk of csPCa was observed in PI-RADS 2 combined with high PSAd, and in all PI-RADS 4-5. The risk of csPCa was proportional to the PSAd for PI-RADS 3 patients. Good accuracy (AUC of 0.69 and 0.68 using 3D-US and MRI, respectively), adequate calibration and a higher net benefit when using 3D-US for probability thresholds above 25% on DCA. Prostate volume estimation with semi-automated segmentation using 3D-US should be preferred to the ellipsoidal formula (MRI) when evaluating PSAd and the risk of csPCa.