Actual Mortality Research Articles

ThP6.15 - 1, 3, and 5-year mortality following emergency laparotomy, can the National Emergency Laparotomy Audit (NELA) or the American College of Surgeons National Quality Improvement Program (ACS-NSQIP) risk calculators predict it? – a retrospective, multi-center, observational, cohort study

Abstract Background Emergency laparotomy (EL) comprises a high-risk group of surgical procedures, associated with a significantly increased risk of death in the following 5 years. EL are performed on a heterogeneous population, of which at least 50% are ≥ 70 years old. Accurate preoperative risk prediction is vital for planning care, patient shared decision, and improving outcomes. To date, no prediction tool can reliably estimate long-term outcomes, including mortality, following an EL. Methods This is a multicenter, retrospective, observational, cohort study that investigated the correlation between actual mortality at 1, 3, and 5 years and predicted 30-day mortality (30-dm) using NELA and ACS-NSQIP risk prediction tools, following EL, in adults aged ≥18, over 2 years, in two UK centers. The sample size was calculated at 385 cases. GraphPad-Prism 10.1.1. was used for statistical analysis. Results 626 cases were analyzed. The male/female ratio was 0.85, age 70 years (55;78); BMI 24.94kg/m² (22;29); preoperative lactate 1.7 mmol/L (1.2;2.6), clinical frailty score 3 (2;4); and Charlson Comorbidity Index 4(2;5) (all data is expressed as median+IQR). The 1, 3, and 5-year survival were 76%, 67%, and 61%, respectively. A perfect positive correlation between NELA and ACS-NSQIP 30-dm and the 1-year actual mortality was found (R²=1). At 3 years only NELA correlated positively to actual mortality (R²=0.23, p<0.05). The 5-year mortality correlated positively with both predictors - NELA: R²=0.61, p<0.001; ACS-NSQIP: R²=0.31, p< 0.05. Conclusion Both NELA and ACS-NSQIP risk calculators can predict mortality at 1,3 and 5 years after an emergency laparotomy, with NELA expressing a stronger correlation.

FP7.8 - Perioperative risk prediction for Emergency Laparotomy in Adults, using the National Emergency Laparotomy Audit (NELA) and the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) risk prediction tools – an Eastern European perspective

Abstract Background Worldwide, emergency laparotomy (EL) is one of the most common procedures. Having an actual 30-day mortality (30-dm) of 10 to 15%, EL has a high mortality rate, particularly in patients >70 years old. Preoperative risk assessment and stratification can improve outcomes. The American-College-of-Surgeons-National Surgical Quality Improvement Program (ACS-NSQIP) and the National Emergency Laparotomy Audit (NELA) risk prediction tools are the most used risk calculators, but both can under or overpredict. Methods This is a single-center, retrospective, cohort study that looked at the correlation between actual and predicted 30-dm using NELA and ACS-NSQIP in adults ≥18, following EL, over 2 years, in a single-tertiary-center from Romania. The primary outcome was defined as the correlation between actual 30-dm and NELA or ACS-NSQIP predicted 30-dm. Statistical analysis was performed using GraphPad-Prism 10.1.1. and Stata18. Results 207 cases were analyzed. Male/female ratio was 1.49; age 65 years (50;77); BMI 26.25kg/m² (23.01;28.35); preoperative lactate 1.8 mmol/L (1.5;3.4); ASA grade 3 (2;4) clinical frailty score 4 (3;4); and Charlson Comorbidity Index 5 (3;8). Actual 30-dm was 31.4% with NELA and ACS-NSQIP predicted 30-dm of 5.78% (1.84;21.48) and 5.5% (1.2;24.2) respectively. NELA and ACS-NSQIP predicted 30-dm expressed a strong positive correlation, R²=0.88; p<0.0001. Both NELA and ACS-NSQIP predicted 30-dm showed a significant positive correlation with actual 30-dm: R²=0.33, p<0.0001; and R²=0.41, p<0.0001 respectively. All data is presented as Median + IQR. Conclusion In a Romanian setting both ACS-NSQIP and NELA risk calculators underpredicted 30-dm, despite expressing a strong positive correlation with actual 30-dm.

Development of a pig wean-quality score using machine-learning algorithms to characterize and classify groups with high mortality risk under field conditions

Mortality during the post-weaning phase is a critical indicator of swine production system performance, influenced by a complex interaction of multiple factors of the epidemiological triad. This study leveraged retrospective data from 1723 groups of pigs marketed within a US swine production system to develop a Wean-Quality Score (WQS) using machine learning techniques. The study evaluated three machine learning models, Random Forest, Support Vector Machine, and Gradient Boosting Machine, to classify groups having high or low 60-day mortality, where high mortality groups represented 25 % of the groups among the study population with the highest mortality values (n=431; 60-day mortality=9.98 %), and the remaining 75 % of the groups were of low mortality (n=1292; 60-day mortality=2.75 %). The best-performing model, Random Forest (RF), outperformed the other ML models in terms of accuracy (0.90), sensitivity (0.84), and specificity (0.92) metrics, and was then selected for further analysis, which consisted of creating the WQS and ranking the most important factors for classifying groups as high or low mortality. The most important factors ranked through the RF model to classify groups with high mortality were pre-weaning mortality, weaning age, average parity of litters in sow farms, and PRRS status. Additionally, stocking conditions such as stocking density and time to fill the barn were important predictors of high mortality. The WQS was developed and correlated (r = 0.74) with the actual 60-day mortality of the groups, offering a valuable tool for assessing post-weaning survivability in swine production systems before weaning. This study highlights the potential of machine learning and comprehensive data utilization to improve the assessment and management of weaned pig quality in commercial swine production, which producers can utilize to identify and intervene in groups, according to the WQS.

Optimizing BenMAP health impact assessment with meteorological factor driven machine learning models

This study aims to address accuracy challenges in assessing air pollution health impacts using Environmental Benefits Mapping and Analysis Program (BenMap), caused by limited meteorological factor data and missing pollutant data. By employing data increment strategies and multiple machine learning models, this research explores the effects of data volume, time steps, and meteorological factors on model prediction performance using several years of data from Tianjin City as an example. The findings indicate that increasing training data volume enhances the performance of Random Forest Regressor (RF) and Decision Tree Regressor (DT) models, especially for predicting CO, NO2, and PM2.5. The optimal prediction time step varies by pollutant, with the DT model achieving the highest R2 value (0.99) for CO and O3. Combining multiple meteorological factors, such as atmospheric pressure, relative humidity, and dew point temperature, significantly improves model accuracy. When using three meteorological factors, the model achieves an R2 of 0.99 for predicting CO, NO2, PM10, PM2.5, and SO2. Health impact assessments using BenMap demonstrated that the predicted all-cause mortality and specific disease mortalities were highly consistent with actual values, confirming the model's accuracy in assessing health impacts from air pollution. For instance, the predicted and actual all-cause mortality for PM2.5 were both 3120; for cardiovascular disease, both were 1560; and for respiratory disease, both were 780. To validate its generalizability, this method was applied to Chengdu, China, using several years of data for training and prediction of PM2.5, CO, NO2, O3, PM10, and SO2, incorporating atmospheric pressure, relative humidity, and dew point temperature. The model maintained excellent performance, confirming its broad applicability. Overall, we conclude that the machine learning and BenMap-based methods show high accuracy and reliability in predicting air pollutant concentrations and health impacts, providing a valuable reference for air pollution assessment.

Preoperative Modified Frailty Index-11 versus EuroSCORE II in Predicting Postoperative Mortality and Complications in Elderly Patients Who Underwent Elective Open Cardiac Surgery: A Retrospective Cohort Study

ObjectivesThis study aimed to compare the sensitivity, specificity, receiver operating characteristic (ROC), and area under the curve (AUC) using modified frailty index (mFI), EuroSCORE II, and combined mFI-11 and EuroSCORE II to predict in-hospital mortality and composite morbidities. DesignRetrospective cohort study SettingSongklanagarind Hospital, a tertiary care center in the Southern of Thailand. ParticipantsElderly patients aged ≥ 60 years who underwent elective open-heart surgical procedures on a pump between January 2017 and December 2022 were included. InterventionsROC curves were constructed to evaluate the discriminatory power of EuroSCORE II and mFI-11 for predicting in-hospital mortality and postoperative complications. Measurements and Main ResultsThe actual in-hospital mortality was 2.5 % for all patients. The discriminative accuracy of mFI-11, EuroSCORE II, and combined mFI-11 with EuroSCORE II for predicting in-hospital mortality was good, with respective AUCs of 0.733 (95% CI 0.6157–0.8499), 0.793 (0.6826–0.9026), and 0.78 (0.6686–0.893). The AUC of mFI-11 for predicting postoperative cardiac, respiratory, neurological, and renal complications was 0.558 (95% CI 0.5101–0.6063), 0.606 (0.5542–0.6581), 0.543 (0.4533–0.6337), and 0.652 (0.5859–0.7179), respectively, and that of EuroSCORE II was 0.553 (0.5038–0.6013), 0.631 (0.578–0.6836), 0.619 (0.5306–0.7076) and 0.702 (0.6378–0.7657), respectively. ConclusionFrailty (mFI-11) and EuroSCORE II demonstrated good discrimination in ROC analysis, with EuroSCORE II showing superior predictive accuracy for in-hospital mortality in elderly elective cardiac surgery patients. However, neither score independently predicted mortality in multiple logistic regression, nor did combining them enhance predictive power significantly. Furthermore, both scores were less effective in predicting postoperative complications.

Association of extreme hyperoxemic events and mortality in pediatric critical care: an observational cohort study.

Our aim was to confirm whether extreme hyperoxemic events had been associated with excess mortality in our diverse critical care population. Retrospective analysis of 9 years of data collected in the pediatric and cardiothoracic ICUs in Children's Hospital Los Angeles was performed. The analysis was limited to those mechanically ventilated for at least 24 h, with at least 1 arterial blood gas measurement. An extreme hyperoxemic event was defined as a PaO2 of ≥300 torr. Multivariable logistic regression was used to assess the association of extreme hyperoxemia events and mortality, adjusting for confounding variables. Selected a-priori, these were Pediatric Risk of Mortality III predicted mortality, general or cardiothoracic ICU, number of blood gas measurements, as well as an abnormal blood gas measurements (pH < 7.25, pH > 7.45, and PaO2 < 50 torr). There were 4,003 admissions included with a predicted mortality of 7.1% and an actual mortality of 9.7%. Their care was associated with 75,129 blood gas measurements, in which abnormal measurements were common. With adjustments for these covariates, any hyperoxemic event was associated with excess mortality (p < 0.001). Excess mortality increased with multiple hyperoxemic events (p < 0.046). Additionally, treatment resulting in SpO2 > 98% markedly increased the risk of a hyperoxemic event. Retrospective analysis of critical care admissions showed that extreme hyperoxemic events were associated with higher mortality. Supplemental oxygen levels resulting in SpO2 > 98% should be avoided.

Predictive value of POSSUM scoring system for postoperative complications and mortality in elderly patients with colorectal cancer.

The POSSUM scoring system, widely employed in assessing surgical risks, offers a simplified and objective approach for the prediction of complications and mortality in patient. Despite its effectiveness in various surgical fields, including orthopedics and cardiovascular surgery, yet its utilization in elderly patients undergoing colorectal cancer surgery is infrequent. To analyze the predictive value of POSSUM scoring system for postoperative complications and mortality in elderly with colorectal cancer. 306 elderly colorectal cancer patients were grouped according to the complications and death within 30 days after surgery. Among them, 108 cases in complication group, 198 cases in non-complication group, 16 cases in death group and 290 cases in survival group. POSSUM scores of all subjects were obtained and its predictive value for postoperative complications and mortality of elderly was conducted by ROC curve. No apparent difference were observed in complications and mortality among patients with different disease types, operation types and operation timing (P> 0.05). The R2 in complication group was higher than non-complication group (P< 0.05). The R1 in death group were higher than survival group (P< 0.05). The AUC of R2 for predicting postoperative complications was 0.955 with a sensitivity of 88.89% and a specificity of 94.44% and the AUC of R1 for evaluating postoperative mortality of elderly with colorectal cancer was 0.783 with a sensitivity of 56.25% and a specificity of 82.93%. POSSUM score system has a certain predictive value for postoperative complications and mortality in elderly with colorectal cancer. However, the predicted mortality rate is higher than actual mortality rate.

Application Lemongrass Extract (Cymbopogon citratus Dc) Against Mortality Death of Termites (Macrotermes gilvus Hagen) With Several Application Techniques

Lemongrass is a plant that contains citronellal, geraniol, and other compounds which are known to increase termite mortality. This research aims to determine the most effective concentration of lemongrass extract on termite mortality using several application techniques. The method used in this research was a Factorial Completely Randomized Design (CRD), with 3 replications where: Treatment factor I used lemongrass extract application. This treatment consists of 10 combinations and in 1 treatment unit there are 30 termites ( Macrotermes gilvus Hagen), so the total is 900 termites. The parameters observed were the percentage of termite mortality (%) and termite activity after application with 3 categories: 1=Alive actively moving; 2=Alive not actively moving; 3=Dead, so the data was analyzed using the Variety Print Analysis Method and Kruskal Wallis Analysis. Significant data analysis results will be followed by the Duncan Mean Range Test (DMRT) at ɑ=5%. The results of the study showed that the application of lemongrass extract (Cymbopogon citrates DC) 100g/l of water was significant in reducing termite (Macrotermes gilvus Hagen) mortality by up to 95% on day 7. The effectiveness of the feeding technique was not significantly different from the spraying technique in reducing actual mortality to 85.44 %. The interaction between the application of lemongrass extract (Cymbopogon citratus DC) 100g/liter of water and the application technique did not significantly reduce the mortality of termites ( Macrotermes gilvus Hagen)

Sun exposure and protection habits: Self-reported attitudes, knowledge and behaviours.

As exposure to UV radiation is the primary modifiable environmental risk factor associated with skin cancer, it remains the principal focus of most prevention strategies. Numerous sun protection campaigns have been implemented worldwide; however, their impact on the actual incidence and mortality rates of skin cancer seems to be limited. To create successful skin cancer prevention campaigns, it is important to have a comprehensive understanding of individuals' attitudes and behaviours regarding sun protection. The aim of the current study was to determine and report on the prevalence of self-reported attitudes, knowledge and behaviours regarding two of the major sun protection recommendations-avoidance of sun exposure and use of sunscreens-in an international representative sample across five continents. This cross-sectional study was conducted in 20 countries using a web-based online survey. A total of 50,552 individuals, comprising 25,388 men (50.22%) and 25,164 women (49.78%), participated in the survey. Among them, 83.2% reported having been voluntarily exposed to the sun (for sun-basking reasons) at least once in the last 12 months, and 47.96% acknowledged being exposed to the sun between the hours of 10 AM and 4 PM. The primary reason for non-adherence was that these hours were the most convenient times (32.28%). Only 24.05% reported applying sunscreen every 2 h when outdoors. Forgetfulness was the primary reason as provided by 27.79% of participants. Males and older age groups were less likely to adopt sun-protective behaviours around the world. Forgetfulness and the challenges posed by time constraints seem to be the biggest barriers to proper adherence. These findings should prompt the collaboration with health authorities and the manufacturers to enhance adherence by setting reasonable sunscreen prices and creating formulations that make their application less burdensome.

Comparative performance of intensive care mortality prediction models based on manually curated versus automatically extracted electronic health record data

IntroductionBenchmarking intensive care units for audit and feedback is frequently based on comparing actual mortality versus predicted mortality. Traditionally, mortality prediction models rely on a limited number of input variables and significant manual data entry and curation. Using automatically extracted electronic health record data may be a promising alternative. However, adequate data on comparative performance between these approaches is currently lacking. MethodsThe AmsterdamUMCdb intensive care database was used to construct a baseline APACHE IV in-hospital mortality model based on data typically available through manual data curation. Subsequently, new in-hospital mortality models were systematically developed and evaluated. New models differed with respect to the extent of automatic variable extraction, classification method, recalibration usage and the size of collection window. ResultsA total of 13 models were developed based on data from 5,077 admissions divided into a train (80%) and test (20%) cohort. Adding variables or extending collection windows only marginally improved discrimination and calibration. An XGBoost model using only automatically extracted variables, and therefore no acute or chronic diagnoses, was the best performing automated model with an AUC of 0.89 and a Brier score of 0.10. DiscussionPerformance of intensive care mortality prediction models based on manually curated versus automatically extracted electronic health record data is similar. Importantly, our results suggest that variables typically requiring manual curation, such as diagnosis at admission and comorbidities, may not be necessary for accurate mortality prediction. These proof-of-concept results require replication using multi-centre data.

METASTATIC DISEASE OF THE PROXIMAL FEMUR: MANAGEMENT AND OUTCOMES IN A REGIONAL CANCER CENTRE

The burden of metastatic disease presenting with axial skeleton lesions is exponentially rising predominantly due to advances in oncological therapies. A large proportion is these lesions are located in the proximal femora, which given its unique biomechanical architecture is problematic.These patients are frequently comorbid and require prompt and concise decision making regarding their orthopaedic care in line with recent British Orthopaedic Association guidelines.We present data detailing the outcomes for patients with proximal femoral metastatic disease referred and treated over a three year period in an Regional Cancer Centre.We retrospectively reviewed a prospectively maintained database of all patients referred for discussion at MDT with axial skeletal metastatic disease. From this we isolated patients with femoral disease. Demographic data along with primary tumour and metastatic disease site were assessed. Treatment regimens were analysed and compared. Finally predicted and actual mortality data was collated.331 patients were referred over the analysed time period, of which 99 had femoral disease. 66% of patients were managed conservatively with serial monitoring while 34% underwent operative treatment. 65% of those received an intramedullary fixation while 35% had arthroplasty performed. There was a 51:49 split male to female with Prostate, Lung and Breast being the predominant primary tumours. Concurrent spinal metastatic disease was noted in 62% of patients while visceral mets were seen in 37%. Mortality rate was 65% with an average prognosis of 388 days (1.06years) while average mortality was noted within 291 days (0.8 years).Proximal femoral metastatic disease accounts for a large volume of the overall mets burden. There is an overall tendency towards conservative management and of those requiring surgery IM nailing was the treatment of choice. The data would indicate that outcomes for these patients are guarded and on average worse than those predicted.

Hygienic aspects of mortality of the population of an industrial city

Introduction. The target indicators of the Federal Project “Clean Air” include a reduction in emissions of chemical pollutants by at least 20%. However, the main socially significant goal remains to improve the medical and demographic situation by reducing environmental and hygienic health risks.&#x0D; The purpose of the study was to preliminary assess the reserves for reducing mortality in the city of Omsk by identifying the most unfavourable territories in terms &#x0D; of atmospheric air quality and identify priority chemicals, presumably causing negative trend in mortality.&#x0D; Materials and methods. To assess the quality of atmospheric air in the city of Omsk, the results of social and hygienic monitoring, environmental monitoring &#x0D; of Federal Service of Russia on Hydrometeorology and Monitoring of the Environment (RosGidromet) for the multi-year period over 2009–2022 were used. &#x0D; An analysis of the mortality rate of the population of Omsk for 2017–2022 was carried out. The data were studied in the context of municipal districts of the city of Omsk and in dynamics. Statistical methods were used in the study: ANOVA, correlation analysis, assessment of relative mortality risks. &#x0D; Results. The relative risk (RR) of mortality for persons living in the most polluted areas of the city is 23–25% higher than in conditionally clean areas. Strong positive associations have been established: between the levels of actual total mortality and the specific gravity of atmospheric air samples exceeding the maximum single limit concentrations (R = 0.86; p &lt; 0.05); between perennial average annual carbon monoxide concentrations and standardized rates of total mortality &#x0D; (R = 0.93; p&lt;0.05) and male mortality rates from all natural causes (R = 0.89; p&lt;0.05).&#x0D; Limitations. The study is limited to the statistical research methods used. &#x0D; Conclusion. The results of the study can become the basis for more efficient use of administrative resources in municipal districts of the city. As additional reserves for reducing mortality, the development of primary prevention measures at the outpatient level is important. It is necessary to optimize methodological approaches in the health control system of the population living in risk areas with developed industry.

Biomarker-Based Risk Stratification Tool in Pediatric Acute Respiratory Distress Syndrome: Single-Center, Longitudinal Validation in a 2014-2019 Cohort.

The Pediatric Acute Respiratory Distress Syndrome Biomarker Risk Model (PARDSEVERE) used age and three plasma biomarkers measured within 24 hours of pediatric acute respiratory distress syndrome (ARDS) onset to predict mortality in a pilot cohort of 152 patients. However, longitudinal performance of PARDSEVERE has not been evaluated, and it is unclear whether the risk model can be used to prognosticate after day 0. We, therefore, sought to determine the test characteristics of PARDSEVERE model and population over the first 7 days after ARDS onset. Secondary unplanned post hoc analysis of data from a prospective observational cohort study carried out 2014-2019. University-affiliated PICU. Mechanically ventilated children with ARDS. None. Between July 2014 and December 2019, 279 patients with ARDS had plasma collected at day 0, 266 at day 3 (11 nonsurvivors, two discharged between days 0 and 3), and 207 at day 7 (27 nonsurvivors, 45 discharged between days 3 and 7). The actual prevalence of mortality on days 0, 3, and 7, was 23% (64/279), 14% (38/266), and 13% (27/207), respectively. The PARDSEVERE risk model for mortality on days 0, 3, and 7 had area under the receiver operating characteristic curve (AUROC [95% CI]) of 0.76 (0.69-0.82), 0.68 (0.60-0.76), and 0.74 (0.65-0.83), respectively. The AUROC data translate into prevalence thresholds for the PARDSEVERE model for mortality (i.e., using the sensitivity and specificity values) of 37%, 27%, and 24% on days 0, 3, and 7, respectively. Negative predictive value (NPV) was high throughout (0.87-0.90 for all three-time points). In this exploratory analysis of the PARDSEVERE model of mortality risk prediction in a population longitudinal series of data from days 0, 3, and 7 after ARDS diagnosis, the diagnostic performance is in the "acceptable" category. NPV was good. A major limitation is that actual mortality is far below the prevalence threshold for such testing. The model may, therefore, be more useful in cohorts with higher mortality rates (e.g., immunocompromised, other countries), and future enhancements to the model should be explored.

Emerging Insights into Stevens-Johnson Syndrome and Toxic Epidermal Necrolysis Induced by Immune Checkpoint Inhibitor and Tumor-Targeted Therapy.

Anticancer drugs have revolutionized tumor therapy, with cutaneous toxicities such as Stevens-Johnson syndrome (SJS) and toxic epidermal necrolysis (TEN) being common immune-related adverse events. The debate over the efficacy of systemic corticosteroids in treating these conditions persists, while tumor necrosis factor (TNF)-alpha inhibitors show promise. This study aims to evaluate the effectiveness and safety of combination therapy involving the TNF-α inhibitor adalimumab for SJS/TEN induced by anticancer drugs. A literature review of SJS/TEN cases induced by anticancer drugs from 1992 to 2023 was conducted, alongside an analysis of patients admitted to the First Affiliated Hospital of Fujian Medical University during the same period. Clinical characteristics, skin healing time, mortality, and adverse events were evaluated in two treatment groups: SJS/TEN patients treated with targeted anticancer therapies and immunotherapies. Among the 27 patients studied (18 with SJS or SJS-TEN overlapping and 9 with TEN), combination therapy with adalimumab significantly reduced mucocutaneous reepithelization time and healing duration compared to corticosteroid monotherapy. Patients receiving adalimumab combined with corticosteroids had lower actual mortality rates than those on corticosteroid monotherapy. The combination therapy also showed a trend towards reducing standardized mortality rates based on the Score of Toxic Epidermal Necrolysis (SCORTEN). The findings suggest that adalimumab in combination with corticosteroids provides significant clinical benefits and is safer than corticosteroids alone for treating SJS/TEN induced by targeted anticancer therapies and immunotherapies. This study contributes valuable insights into potential treatment strategies for severe cutaneous adverse reactions to anticancer drugs, highlighting the importance of exploring alternative therapies such as TNF-α inhibitors in managing these conditions effectively.

Low Dose Radiation and Solid Tumors Mortality Risk.

US nuclear capable shipyard workers have increased potential for occupational radiation exposure. The aim of the study is to examine solid tumor mortality risks at low doses. 437,937 workers working from 1957 to 2004 at eight US shipyards were studied. Radiation workers with a median life-time dose at 0.82 mSv had a significantly lower solid tumor mortality risk (relative risk [RR]: 0.96, 95% confidence interval [CI]: 0.94-0.98) than nonradiation workers. Among 153,930 radiation workers, the RRs of solid tumors increased with increasing dose categories without statistical significance. The dose category >0-<25 mSv had significantly lower RR (0.95, 95% CI: 0.91-0.99) versus 0 dose and the excess relative risk was 0.05/100 mSv (95% CI: 0.01-0.08). Solid tumor risk might increase with radiation dose, but not linearly at low doses. Actual mortality risk may be dependent on dose received.

Beyond crippling bias: Carcass‐location bias in roadkill studies

AbstractWildlife roadkill studies need to cope with a mismatch among recorded carcasses and actual road mortality, because of the existence of three biases: crippling, carcass‐persistence, and observer bias. Here, we focused on the often overlooked crippling bias, suggesting that it should be called carcass‐location bias and disentangling the related three possible outcomes for affected wildlife: injured animal escaping and dying away from road, animal rebounding off the road after vehicle collision, and animal retained by vehicle. Such outcomes can probably be affected by different species traits, and, in order to make a first evaluation of this hypothesis, we opportunistically collected 150 direct observations on the ultimate fate of roadkilled vertebrates. Approximately one third of them were affected by carcass‐location bias, so extremely difficult to be recorded through typical roadkill surveys, entailing a considerable and overlooked source of error for roadkill studies and mitigation actions based on them.

Pricing longevity bond with affine-jump-diffusion multi-cohort mortality model

Facing increasingly severe longevity problems, traditional longevity risk management methods are no longer the final answer. Longevity risk securitization provides a good hedging method, which transfers the longevity risks to a wider capital market and realizes the cross-market transfer of risks. The pricing process of longevity derivatives depends on the underlying risk factors (stochastic processes of mortality and interest rate). We adopt an affine-jump-diffusion process to model the dynamics of mortality and estimate the model parameters with the actual mortality data in China. The measure implied by the annuity market price is chosen as the risk-neutral pricing measure. We use the Chinese annuity market price to calculate the concrete form of the measure transformation and use the risk-neutral pricing measure thereby to price the longevity bond designed by Denuit et al. (2007).

Population-Based Study of the Incidence and Mortality Rate of Acute Aortic Dissection.

Acute aortic dissection (AAD) has high morbidity and a high fatality rate for a cardiovascular disease. Recent studies suggested that the incidence of AAD is increasing. However, the actual incidence and mortality rates of AAD are not well known. This study investigated the current epidemiology of AAD within the Yatsushiro medical jurisdictional area. A population-based review of patients with AAD was performed in a geographically well-defined area. Data were collected retrospectively from January 2011 to December 2020 for a total of 196 patients with AAD (Stanford Type A, n=126 [64.3%]; Stanford Type B, n=70 [35.7%]). The mean patient age was 74.3 years, and 55.6% (109/196) were women. The crude and age-standardized incidence rates of AAD in our medical jurisdictional area were 13.6 and 11.4 per 100,000 inhabitants per year, respectively. The crude and age-standardized 30-day mortality rates of AAD were 4.9 and 4.0 per 100,000 inhabitants per year, respectively. There were upward tendencies for both the incidence and 30-day mortality rate of AAD with age, with both being significantly higher in patients aged ≥85 years (P<0.001). This population-based study detected a higher incidence of AAD than previous studies, but reported a lower incidence of AAD in men than in women. Increasing age was associated with an increased incidence and mortality rate of AAD.

A comparative study of explainable ensemble learning and logistic regression for predicting in-hospital mortality in the emergency department

This study addresses the challenges associated with emergency department (ED) overcrowding and emphasizes the need for efficient risk stratification tools to identify high-risk patients for early intervention. While several scoring systems, often based on logistic regression (LR) models, have been proposed to indicate patient illness severity, this study aims to compare the predictive performance of ensemble learning (EL) models with LR for in-hospital mortality in the ED. A cross-sectional single-center study was conducted at the ED of Imam Reza Hospital in northeast Iran from March 2016 to March 2017. The study included adult patients with one to three levels of emergency severity index. EL models using Bagging, AdaBoost, random forests (RF), Stacking and extreme gradient boosting (XGB) algorithms, along with an LR model, were constructed. The training and validation visits from the ED were randomly divided into 80% and 20%, respectively. After training the proposed models using tenfold cross-validation, their predictive performance was evaluated. Model performance was compared using the Brier score (BS), The area under the receiver operating characteristics curve (AUROC), The area and precision–recall curve (AUCPR), Hosmer–Lemeshow (H–L) goodness-of-fit test, precision, sensitivity, accuracy, F1-score, and Matthews correlation coefficient (MCC). The study included 2025 unique patients admitted to the hospital’s ED, with a total percentage of hospital deaths at approximately 19%. In the training group and the validation group, 274 of 1476 (18.6%) and 152 of 728 (20.8%) patients died during hospitalization, respectively. According to the evaluation of the presented framework, EL models, particularly Bagging, predicted in-hospital mortality with the highest AUROC (0.839, CI (0.802–0.875)) and AUCPR = 0.64 comparable in terms of discrimination power with LR (AUROC (0.826, CI (0.787–0.864)) and AUCPR = 0.61). XGB achieved the highest precision (0.83), sensitivity (0.831), accuracy (0.842), F1-score (0.833), and the highest MCC (0.48). Additionally, the most accurate models in the unbalanced dataset belonged to RF with the lowest BS (0.128). Although all studied models overestimate mortality risk and have insufficient calibration (P > 0.05), stacking demonstrated relatively good agreement between predicted and actual mortality. EL models are not superior to LR in predicting in-hospital mortality in the ED. Both EL and LR models can be considered as screening tools to identify patients at risk of mortality.

Prediction of perioperative complications and mortality in patients with complicated forms of colon cancer

Complicated forms of colon cancer occupy a significant share of emergency operations in general surgical departments with high rates of mortality and complications. Assessment of possible complications and mortality before surgery is an important aspect in choosing treatment tactics for this group of patients. The aim of the study. To analyze the risks of complications and mortality in the perioperative period in patients with complicated forms of colorectal cancer using the prognostic CR-POSSUM scoring system and to determine the critical levels of the CR-POSSUM score using ROC analysis. Materials and methods. The data of 71 (100 %) patients who were treated for complicated forms of colorectal cancer were analyzed. The studied group included 36 (50.71 %) men and 35 (49.29 %) women aged 67.97 ± 12.71 years. In the postoperative period, complications were found in 32 (45.07 %) patients, there were 8 (11.27 %) deaths. Results. The mortality risk at the time of hospitalization was 6.40 (3.50; 19.10) %. The actual mortality rate in the studied group was 8 (11.27 %) patients, which corresponded to the CR-POSSUM theoretically calculated mortality risk, U = 234.0, p = 0.739. Conclusions. Increased level of complications was noted at the critical score ˃20 points, AUC = 0.744, p ˂ 0.001 (sensitivity – 71.0 %, specificity – 67.5 %) and the critical score of increasing the mortality rate was ˃22 points, AUC = 0.781, p = 0.005 (sensitivity – 75.0 %, specificity – 74.6 %) as defined by CR-POSSUM. According to the ROC analysis, the increase in mortality in the studied group did not depend on the character and complexity of surgical interventions (p = 0.159), but on the general patient condition at the time of hospital admission (р ˂ 0.001). At the same time, the increased level of complications in the group depended on both the character and complexity of the surgical intervention (p = 0.028) and on the general patient health at the time of hospitalization to the general surgical department (р ˂ 0.001).