Hazards Model Research Articles

Development of a novel risk score for the prediction of critical illness amongst COVID-19 patients.

ObjectivesCoronavirus disease‐19 (COVID‐19) is associated with various clinical manifestations, ranging from asymptomatic infection to critical illness. The aim of this study is to evaluate the clinical and laboratory characteristics of hospitalised COVID‐19 patients and construct a predictive model for the discrimination of patients at risk of disease progression.MethodsA single‐centre cohort study was conducted including consecutively patients with COVID‐19. Demographic, clinical and laboratory findings were prospectively collected at admission. The primary outcome of interest was the intensive care unit admission. A risk model was constructed by applying a Cox's proportional hazard's model with elastic net penalty. Its diagnostic performance was assessed by receiver operating characteristic analysis and was compared with conventional pneumonia severity scores.ResultsFrom a total of 67 patients 15 progressed to critical illness. The risk score included patients’ gender, presence of hypertension and diabetes mellitus, fever, shortness of breath, serum glucose, aspartate aminotransferase, lactate dehydrogenase, C‐reactive protein and fibrinogen. Its predictive accuracy was estimated to be high (area under the curve: 97.1%), performing better than CURB‐65, CRB‐65 and PSI/PORT scores. Its sensitivity and specificity were estimated to be 92.3% and 93.3%, respectively, at the optimal threshold of 1.6.ConclusionsA10‐variable risk score was constructed based on clinical and laboratory characteristics in order to predict critical illness amongst hospitalised COVID‐19 patients, achieving better discrimination compared with traditional pneumonia severity scores. The proposed risk model should be externally validated in independent cohorts in order to ensure its prognostic efficacy.

Parametric Survival Models of Hemodialysis Patients in Relation with Patient-Related Factors

Background: Survival analysis refers to analyzing of statistical data for which the outcome variable of interest is time until an event occurs. This research aimed at comparing different models of parametric Proportional Hazards (PH) models (Weibull, exponential, Gompertz) in patients with hemodialysis to determine the best model for assessing the survival of patient. Study consists of 325 hemodialysis patients who referred to public hospitals in Khartoum state in the period from December 2005 to December 2015. Data was used to estimate the survival function with view to identify risk factors influencing among end-stage renal disease (ESRD) population. Based on the Cox-Snell Residuals and AIC, BIC, and Gompertz (PH) model is an efficient model than other when the values of (AIC=662.21), (BIC=703.83) and (R2=0.211) where maintained Study assessed that the variables dealing with univariate models were significant but had a significant effect on hemodialysis survival. The Gompertz model had the smallest AIC and BIC value; therefore; it was selected as the most appropriate model. In multivariable analysis, the BIC had the lowest value and the highest value in each analysis. The study assessed that diabetes mellitus and hypertension, regular, and hospital, had a. significant effect.

Diet quality, weight loss, and diabetes incidence in the Diabetes Prevention Program (DPP)

BackgroundWe evaluated whether diet quality is a predictor of weight loss and reduced diabetes risk, independent of caloric intake in the Diabetes Prevention Program (DPP) cohort, a randomized clinical trial of adults at risk for diabetes.MethodsThis secondary analysis included 2914 participants with available data (964 intensive lifestyle (ILS), 977 metformin, 973 placebo). Dietary intake was assessed using a 117-item food frequency questionnaire. Diet quality was quantified using the Alternative Healthy Eating Index 2010 (AHEI). AHEI ranges from 0 to 110, with higher scores corresponding to higher quality diets. ILS participants had greater improvement (p < 0.001) in AHEI over 1-year (4.2 ± 9.0) compared to metformin (1.2 ± 8.5) and placebo (1.4 ± 8.4). We examined the association between AHEI change and weight change from baseline to 1-year using linear regression, and that between 1-year AHEI change and incident diabetes, using hazard models over an average 3 years follow-up. Models were evaluated within treatment group and adjusted for relevant characteristics including caloric intake, physical activity, BMI and AHEI. Models testing incident diabetes were further adjusted for baseline fasting and 2 h glucose.ResultsAn increase in AHEI score was associated with weight loss in ILS [β per 10-point increase (SE) -1.2 kg (0.3, p < 0.001)], metformin [− 0. 90 kg (0.2, p < 0.001)] and placebo [− 0.55 kg (0.2, p = 0.01)]. However, AHEI change was not associated with incident diabetes in any group before or after adjustment for weight change.ConclusionsControlling for weight, diet quality was not associated with diabetes incidence but helps achieve weight loss, an important factor in diabetes prevention.

Application of Bayesian approach and probability theory for calculating territorial risk and zoning on urban area

The article describes a new algorithm for assessing territorial risk. The risk in the algorithm is calculated as the ratio of the prior and posterior probabilities of an unfavorable (risk) event. An event is an excess of the threshold content of toxicants in a certain area of space, taking into account how often this event occurred in all other areas of space. This can be used for zoning an urbanized area. We used a probabilistic safety assessment (PSA) apparatus based on hazard modelling and a scenario-based approach. Such an apparatus does not analyse certain integral characteristics of the process, but all possible states in a given time section at the same time, forming a probabilistic model. The probabilistic approach ensures the accuracy and realism of assessments of the state of the environment of an urbanized area that are unattainable for deterministic methods and, in addition, allows you to take into account the cumulative effect of multiple sources of uncertainty. Using the probabilistic method and assessing the level of risks allows you to broadcast complex scientific information in an accessible format to people making environmental decisions. Risk is not an absolute but a relative category. It is heterogeneous in both time and space. Therefore, it can be used when zoning the environment, especially since the parameters that are used to calculate risk very often form the same data arrays necessary for classifying and zoning spatial non-uniformity of the system in general. After analyzing various approaches, we came to the conclusion that for a given time period the probability of an adverse event will be calculated more accurately if we calculate not using the classical formula (the number of cases per time interval), but in the form of the ratio of the posterior and prior probabilities. This takes into account the risk in a certain area of space and correlates with the frequency of the same event in all other areas. To assess the likelihood of polymetallic pollution of environmental objects in an urbanized area with its subsequent zoning, we considered the variability of the metal content in environmental objects. Based on the probabilities that metal concentrations exceeded their threshold values, the territorial relative risk was calculated and zoning of the urbanized territory was carried out. Testing of the considered approaches was carried out in the territory of Kazan on the basis of an array of data of experimental measurements on the content of metals in environmental objects. For sampling, 170 points were selected across the territory of the city of Kazan, which are under the influence of stationary and mobile sources of the influx of metals into the urbanized territory. The probability distribution of exceeding the threshold level of metal content in environmental objects in the territory of Kazan is shown cartographically.

Abstract 15192: Effectiveness and Safety of Reduced Dose of Dabigatran for High Bleeding Risk Patient Subgroups With Atrial Fibrillation

Off-label reduced dosing of direct oral anticoagulants is common among patients at high bleeding risk with atrial fibrillation (AF), however, outcomes data of reduced dosing are limited. We evaluated the effectiveness and safety of reduced dose of dabigatran [110 mg twice daily (BID)] vs. standard dose [150 mg BID] in high bleeding risk subgroups with AF. We identified adult patients with AF who initiated dabigatran (index date) with creatinine clearance (CrCl) ≥30 mL/min between 2012-2018 from Kaiser Permanente Southern California. Three high bleeding risk subgroups were identified: 1) patients aged ≥80 years; 2) patients with moderate renal impairment (CrCl 30-49 mL/min) regardless of age; or 3) patients with bleeding within 12 months prior to the index date or HAS-BLED score ≥3. Patients were followed through December 2018 for stroke or systemic embolism, major bleeding requiring hospitalization, and all-cause mortality. Cause-specific hazard regression models were used to investigate associations between dabigatran dose and clinical outcomes adjusting for covariates and considering competing risks of death. Among high bleeding risk patients with AF [3,749 aged ≥80 years; 1,716 with moderate renal impairment; 3,051 with recent bleed or high HAS-BLED score], 34% received reduced dose of dabigatran. Patients who received reduced dose were older, had reduced renal function, and had more comorbidities. Compared with standard dose, reduced dose of dabigatran was not associated with an increased risk of stroke or systemic embolism but was associated with lower risk of major bleed in patients aged ≥80 years ( Table ). Reduced dose of dabigatran was also associated with lower risk of mortality among patients aged ≥80 years and patients with moderate renal impairment. A lower risk of bleeds and mortality associated with reduced vs standard dose of dabigatran in high-risk patients with AF suggest a better dosing strategy for these risk groups.

Walking duration in daily travel: an analysis among males and females using a hazard-based model

This study examines how various travel/built environmental and individual/household characteristics influence the walking durations of males and females in the city of Rasht, Iran, using data from the 2007 Rasht Household Travel Survey (RHTS). Accelerated hazard (AH) modelling, as a new approach in walking-related studies, was used to predict walking durations. The survival curve analysis showed that the ideal walking time was five minutes, but walking up to ten minutes was also acceptable. The AH models indicated that the walking duration of males and females varied under different conditions and contexts. For example, female workers were likely to walk longer trips relative to non-workers; however, this was the opposite among males. Household characteristics such as car ownership were found with greater negative effect on walking duration of males than females. Furthermore, zones with higher land use mix and higher connectivity led to shorter walking duration in both models.

Radiotherapy for Esophageal Cancer in Patients ≥80 years: A Retrospective Study in a Single Institution

There are many countries with aging populations. However, treatment data for patients aged 80 or older are insufficient. The aim of this study was to identify prognostic factors for overall survival (OS) of patients aged 80 years or older who had esophageal cancer and received treated radiotherapy or chemoradiotherapy. We retrospectively reviewed our clinical database. Patients aged 80 years or older who had esophageal cancer and received radiotherapy between 2004 and 2014 were included this study. OS was calculated from the first day of radiotherapy. Pretreatment clinical information (age, gender, histology, clinical stage, performance status, Charlson comorbidity index and blood test results) and treatment methods were reviewed to predict OS. Survival curves were drawn using the Kaplan-Meier method and predictors were analyzed using Cox’s hazards model. Eighty patients were included in this study. The median age of the patients was 83 years (range: 80-94 years). Performance status of 62 patients was 0-1. Twenty-nine patients were treated with concurrent chemoradiotherapy, and 28 patients were treated with involved-field radiotherapy. The median follow-up period was 15.3 months. The 1-year and 3-year OS rates were 85.8% and 61.1%, respectively. In the univariate Cox’s hazards model, clinical stage (hazard rate [HR]: 1.63, 95% confidence interval [95% CI]: 1.05 – 2.59, p = 0.029) and albumin level (HR: 0.37, 95% CI: 0.16 – 0.88, p = 0.025) were significant predictors for OS. However, there was no significant predictor in the multivariate Cox’s hazards model. Clinical stage and blood albumin level were significant predictors in the univariate analysis. These predictors might help decision-making for patients aged 80 years or older who had esophageal cancer. However, further data and analysis are needed.Abstract 3502; TableUnivariate and multivariate Cox’s hazard model for overall survivalUnivariateMultivariateHR95% CIP valueHR95% CIP valueAge0.870.73 – 1.010.073Gender (female vs male)0.400.09 - 1.150.095Histology (SCC vs other)0.550.19 – 2.370.378Stage (I vs IIA vs IIB-III vs IV)1.631.05 – 2.590.0291.470.93 – 2.380.099Performance status1.270.68 – 2.380.450Charlson comorbidity index0.930.72 – 1.120.473Creatinine1.390.12 – 14.20.791Hemoglobin0.860.66 – 1.110.241White blood cell (WBC)1.060.82 – 1.350.94Neutrophil (Neut)1.000.71 – 1.300.997Platelet1.000.99 – 1.010.328Lymphocyte1.580.73 – 3.490.248NLR (WBC/Neut)0.870.56 – 1.150.386Albumin0.370.16 – 0.880.0250.450.19 – 1.110.082SCC0.990.82 – 1.080.821CRP1.150.59 – 1.850.642Elective nodal irradiation vs Involved-field radiotherapy2.030.86 – 5.560.109RT alone vs CRT0.920.42 - 2.020.838Total radiation dose0.990.95 - 1.080.846 Open table in a new tab

Acute Renal Injury in Cardiac Surgery Patients

Background. Acute kidney injury following cardiac surgery remains a common and serious complication.Aim of study. To identify risk factors for the development and morphological features of acute renal injury, to assess the use of renal replacement therapy in patients after cardiac surgery.Material and methods. The study involved 66 patients who were treated in the Department of Cardiac Resuscitation of the N.V. Sklifosovsky Research Institute for Emergency Medicine from 2009 to 2018. Of these, 45 men (68.2%) and 21 women (31.8%). The mean age of the patients was 56.3±13.2 years. Clinical and anatomical analysis of material from 19 deceased patients was carried out. Depending on the use of methods of renal replacement therapy, patients were divided into two groups: Group 1 included 23 patients with acute renal injury requiring the use of renal replacement therapy; Group 2 included 43 patients where methods of renal replacement therapy were not used.Results. Hospital mortality in Group 1 was lower (34.8 and 41.9%, respectively), however, the differences were statistically insignificant (p=0.372). To identify the factors in the development of acute renal damage, a stepwise regression analysis was performed by constructing a regression model of Cox proportional hazards. Age, history of chronic kidney disease, serum creatinine level on the first day after surgery, severity of the condition according to the APACHE-II scale, increased lactate level on day 2 of the postoperative period, decreased urine output on the first day after surgery were statistically significant.Conclusion. Risk factors for the development of ARI after cardiac surgery under cardiopulmonary bypass are advanced age, CKD in history, the severity of the patient’s condition, assessed by the APACHE-II scale, increased serum creatinine on the first day after surgery, increased lactate on day 2 of the postoperative period, a decreased diuresis on day 1 after surgery. The use of RRT in patients after surgery under the conditions of AC was accompanied by a tendency to improve treatment results: in-hospital mortality in the group of patients who underwent RRT was 34.8% versus 41.9% in the group without RRT methods. Morphological and functional features of renal failure in patients with ARI were preceding chronic renal pathological processes of different etiology, mainly affecting the glomeruli, vessels and stroma, as well as acute pathological processes aggravating ARI (dyscirculatory disorder, degenerative changes, necrosis and necrobiosis tubular epithelium).

A dependent Dirichlet process model for survival data with competing risks

In this paper, we first propose a dependent Dirichlet process (DDP) model using a mixture of Weibull models with each mixture component resembling a Cox model for survival data. We then build a Dirichlet process mixture model for competing risks data without regression covariates. Next we extend this model to a DDP model for competing risks regression data by using a multiplicative covariate effect on subdistribution hazards in the mixture components. Though built on proportional hazards (or subdistribution hazards) models, the proposed nonparametric Bayesian regression models do not require the assumption of constant hazard (or subdistribution hazard) ratio. An external time-dependent covariate is also considered in the survival model. After describing the model, we discuss how both cause-specific and subdistribution hazard ratios can be estimated from the same nonparametric Bayesian model for competing risks regression. For use with the regression models proposed, we introduce an omnibus prior that is suitable when little external information is available about covariate effects. Finally we compare the models' performance with existing methods through simulations. We also illustrate the proposed competing risks regression model with data from a breast cancer study. An R package "DPWeibull" implementing all of the proposed methods is available at CRAN.

Analysis of time-to-event for observational studies: Guidance to the use of intensity models.

This paper provides guidance for researchers with some mathematical background on the conduct of time-to-event analysis in observational studies based on intensity (hazard) models. Discussions of basic concepts like time axis, event definition and censoring are given. Hazard models are introduced, with special emphasis on the Cox proportional hazards regression model. We provide check lists that may be useful both when fitting the model and assessing its goodness of fit and when interpreting the results. Special attention is paid to how to avoid problems with immortal time bias by introducing time-dependent covariates. We discuss prediction based on hazard models and difficulties when attempting to draw proper causal conclusions from such models. Finally, we present a series of examples where the methods and check lists are exemplified. Computational details and implementation using the freely available R software are documented in Supplementary Material. The paper was prepared as part of the STRATOS initiative.

Relationship between serum B12 concentrations and mortality: experience in NHANES

BackgroundThere is conflicting evidence in the literature on the association between (elevated) serum B12 concentrations and subsequent disease or mortality. We evaluated in the NHANES general population the association of serum B12 concentrations as well as vitamin B12 supplement intake with all-cause, cardiovascular, and cancer-related mortality, while taking into account demographic and lifestyle factors and significant other diseases which are known to be associated with poorer outcome.MethodsThe main outcomes of our study were all-cause mortality, cardiovascular mortality, and cancer-related mortality. Mortality status and cause of death were determined by NHANES-linked National Death Index public access files through December 31, 2015. The association of serum B12 concentrations and vitamin B12 supplement intake with mortality was assessed with Cox proportional hazard (PH) models, with adjustment for a number of relevant demographic and lifestyle factors and comorbidity.ResultsThe final study population of 24,262 participants had a mean age of 48 (SD 19) years; 50.1% were males. The median follow-up duration was 109 months (range 1–201 months). On the census day of December 31, 2015, 3023 participants were determined as deceased (12.5%). The fully adjusted Cox PH model indicated that low serum B12 concentrations < 140 pmol/l were associated with a small increase in all-cause (hazard ratio, HR 1.39, 95% CI 1.08–1.78, p = 0.011) and cardiovascular (HR 1.64, 95% CI 1.08–2.47, p = 0.020) mortality. Similarly, high serum B12 concentrations > 700 pmol/l were associated with an increase in cardiovascular mortality only (HR 1.45, 95% CI 1.01–2.06, p = 0.042). Participants with a diagnosis of hypertension, dyslipidemia, CVD, and cancer more frequently used vitamin B12-containing supplements than those without these diagnoses. We could not demonstrate an association between vitamin B12 supplement intake and mortality, when adjusted for comorbidity.ConclusionsIn the general population of NHANES, low serum B12 concentrations were associated with a moderate increase in all-cause mortality. There was a small but significant increase in cardiovascular mortality in the groups with low or high serum B12. High intake of vitamin B12 in the form of supplements was not associated with any adverse effect on mortality and therefore can be regarded as safe.

Groundwater Arsenic Distribution in India by Machine Learning Geospatial Modeling.

Groundwater is a critical resource in India for the supply of drinking water and for irrigation. Its usage is limited not only by its quantity but also by its quality. Among the most important contaminants of groundwater in India is arsenic, which naturally accumulates in some aquifers. In this study we create a random forest model with over 145,000 arsenic concentration measurements and over two dozen predictor variables of surface environmental parameters to produce hazard and exposure maps of the areas and populations potentially exposed to high arsenic concentrations (>10 µg/L) in groundwater. Statistical relationships found between the predictor variables and arsenic measurements are broadly consistent with major geochemical processes known to mobilize arsenic in aquifers. In addition to known high arsenic areas, such as along the Ganges and Brahmaputra rivers, we have identified several other areas around the country that have hitherto not been identified as potential arsenic hotspots. Based on recent reported rates of household groundwater use for rural and urban areas, we estimate that between about 18–30 million people in India are currently at risk of high exposure to arsenic through their drinking water supply. The hazard models here can be used to inform prioritization of groundwater quality testing and environmental public health tracking programs.

Survival Analysis of Chronic Kidney Disease Patients with Hemodialysis in West Java. Indonesia, Year 2007 - 2018

The prevalence of chronic kidney disease on dialysis or CKD5D is increasing with a significant impact on disease burden in many countries. Patients are usually listed in the national renal registries, which report demographic data, incidence, prevalence, and outcome. The survival rate is an important outcome measure to characterize the impact of treatment in the CKD5 patient population in the national and international renal registries. Indonesian Society of Nephrology (InaSN) has the Indonesian Renal Registry program to collect data that was endorsed to monitor dialysis treatment quality in Indonesia. IRR releases an annual report, but there is no survival analysis yet. This study aimed to discover the five-year survival rate of CKD5D patients in West Java between 2007–2018 and its factor based on the IRR database. A retrospective cohort study was performed by gaining all patients' data from the IRR database, then data on all of the patients from West Java province who completed a 5-year follow-up on December 31, 2018. Kaplan-Meier analysis and Cox proportional hazard's model were used to analyze the risk factors. There were 3,199 data included in this study. In total, the 1, 2, 3, 4, and 5 year survival rates are 82%, 70%, 62%, 58%, and 55 %, respectively. Patients whose age is above 55 years and with unknown underlying kidney disease have a worse survival rate with a hazard ratio of 1.28 and 1.50, respectively. Further exploration of IRR data will provide better information on dialysis treatment in Indonesia.

Analysis of the Risk Factors for the Recurrence of Ischemic Stroke with Diabetes Mellitus and Establishment of Cox’s Regression Model and the Personal Prognosis Index in Two Years of Follow-Up

Background: Ischemic stroke is a major cause of disability and mortality in patients with type 2 diabetes mellitus (T2DM), and diabetic stroke has a high recurrence rate. Objectives: This prospective cohort study aimed at investigating the risk factors and establishing Cox’s regression model and personal prognosis index for the recurrence of ischemic stroke at a two-year follow-up in T2DM patients. Methods: T2DM patients with ischemic stroke, who were consecutively admitted to the Neurology Department of North China University of Science and Technology Affiliated Hospital between January 1, 2015, and December 31, 2015, were retrospectively reviewed. These cases were followed up since the onset of ischemic stroke for 2 years. Univariate and multivariate Cox’s proportional hazard regression model was used to analyze risk factors associated with the recurrence rate. Thus, a recurrence model and personal prognosis index were set up. Results: During the follow-up period, 44 cases relapsed. Furthermore, the 1-year recurrence rate was 16.48%, while the 2-year recurrence rate was 24.18%. The univariate and multivariate Cox proportional hazard regression model revealed that the independent risk factors associated with recurrence were TOAST criteria (X1) (RR = 1.663; 95% CI = 1.015 - 2.760, P = 0.032), hypertension grade (X2) (RR = 1.897; 95% CI = 1.097 - 3.280, P = 0.022), duration of diabetes mellitus (X3) (RR = 1.151; 95% CI = 1.009 - 1.991, P = 0.039), total cholesterol (X4) (RR = 1.13; 95% CI = 1.006 - 1.876, P = 0.035), and Essen stroke risk score (ESRS) (X5) (RR = 2.055; 95% CI = 1.357 - 3.134, P = 0.001). The personal prognosis index of the recurrence model was as follows: PI = 0.504 X1 + 0.640 X2 + 0.345 X3 + 0.759 X4 + 0.823 X5. Conclusions: TOAST criteria, hypertension grade, duration of diabetes mellitus, total cholesterol, and ESRS were the independent risk factors associated with the recurrence of ischemic stroke with diabetes mellitus. The recurrence model and personal prognosis index equation were successfully established.

Racial Differences in the Use of Aortic Valve Replacement for Treatment of Symptomatic Severe Aortic Valve Stenosis in the Transcatheter Aortic Valve Replacement Era.

BackgroundAortic valve replacement (AVR) is a life‐saving treatment for patients with symptomatic severe aortic valve stenosis. We sought to determine whether transcatheter AVR has resulted in a more equitable treatment rate by race in the United States.Methods and ResultsA total of 32 853 patients with symptomatic severe aortic valve stenosis were retrospectively identified via Optum’s deidentified electronic health records database (2007–2017). AVR rates in non‐Hispanic Black and White patients were assessed in the year after diagnosis. Multivariate Fine‐Gray hazards models were used to evaluate the likelihood of AVR by race, with adjustment for patient factors and the managing cardiologist. Time‐trend and 1‐year symptomatic severe aortic valve stenosis survival analyses were also performed. From 2011 to 2016, the rate of AVR increased from 20.1% to 37.1%. Overall, Black individuals were less likely than Whites to receive AVR (22.9% versus 31.0%; unadjusted hazard ratio [HR], 0.70; 95% CI, 0.62–0.79; fully adjusted HR, 0.76; 95% CI, 0.67–0.85). Yet, during 2015 to 2016, AVR racial differences were attenuated (29.5% versus 35.2%; adjusted HR, 0.86; 95% CI, 0.74–1.02) because of greater uptake of transcatheter AVR in Blacks than Whites (53.4% of AVRs versus 47.3%; P=0.128). Untreated patients had significantly higher 1‐year mortality than those treated (adjusted HR, 0.57; 95% CI, 0.53–0.61), which was consistent by race (interaction P value=0.52).ConclusionsAlthough transcatheter AVR has increased the use of AVR in the United States, treatment rates remain low. Black patients with symptomatic severe aortic valve stenosis were less likely than White patients to receive AVR, yet these differences have recently narrowed.

Study of hospitalization and mortality in Korean diabetic patients using the diabetes complications severity index

BackgroundThe prevalence of type 2 diabetes mellitus (T2DM) is expected to increase from 7.7% in 2017 to 8.4% in 2045 worldwide. Diabetes complications contribute to morbidity and mortality. To evaluate whether the diabetes complications severity index (DCSI) was associated with increased risks of mortality and hospitalization.MethodsA retrospective cohort study was conducted using the National Health Insurance Database (NHID) sample cohort of 1,102,047 patients (2002–2015) in Korea. Diabetes complications were evaluated at 2 years after the initial diagnosis and during the subsequent follow-up period (mean duration 6.56 ± 2.81 years). The type and severity of complications were evaluated on the basis of the International Classification of Disease Ninth (ICD-9) codes used in DCSI with 7 categories and 55 subcategories of complications. The Cox proportional hazard and Poisson regression models were used to evaluate the mortality and hospitalization rates. The incidence and relative risk of diabetes complications as well as the risk of mortality and hospitalization were the main outcome measures.ResultsA total of 27,871 patients were finally included and grouped by the number of complications present at 2 years. Four hundred ninety patients (5.37%) died without complications, 659 (7.31%) died with one complication and 1153 (11.85%) died with two or more complications. As DCSI at index date increased, the risk of additional new diabetes complications increased by 26% [relative risk (RR) 1.26, 95% CI 1.25–1.27]. The risks of mortality and hospitalization were linearly related to DCSI [hazard ratio 1.13 (95% CI 1.11–1.16), relative risk 1.04 (95% CI 1.03–1.06)].ConclusionsPatients with higher incidence and severity of diabetes complications have increased risks of mortality and hospitalization.

Corporate Default Predictions Using Machine Learning: Literature Review

Corporate default predictions play an essential role in each sector of the economy, as highlighted by the global financial crisis and the increase in credit risk. This study reviews the corporate default prediction literature from the perspectives of financial engineering and machine learning. We define three generations of statistical models: discriminant analyses, binary response models, and hazard models. In addition, we introduce three representative machine learning methodologies: support vector machines, decision trees, and artificial neural network algorithms. For both the statistical models and machine learning methodologies, we identify the key studies used in corporate default prediction. By comparing these methods with findings from the interdisciplinary literature, our review suggests some new tasks in the field of machine learning for predicting corporate defaults. First, a corporate default prediction model should be a multi-period model in which future outcomes are affected by past decisions. Second, the stock price and the corporate value determined by the stock market are important factors to use in default predictions. Finally, a corporate default prediction model should be able to suggest the cause of default.

Towards better combination regimens of cytarabine and FLT3 inhibitors in acute myeloid leukemia.

AML patients with FLT3/ITD mutations have poor response to cytarabine-based chemotherapy. FLT3 inhibitors (FLT3i) may resensitize cells to cytarabine (CYT). Improving treatment outcome of this combination may benefit from a mechanistic extrapolation approach from in vitro data. The effects of CYT and several FLT3i on cell proliferation and cell cycle kinetics were examined in AML cell lines. The effect of FLT3i (quizartinib, midostaurin, sorafenib) on cell proliferation and cell cycle kinetics was assessed in AML cell lines with differing FLT3 status; HEL (negligible expression of wild-type FLT3), EOL1 (wild-type FLT3), MV4-11 (FLT3-ITD resulting in constitutively active isoform). Semi-mechanistic cell cycle models for CYT and FLT3i were developed. Clinical CYT and quizartinib pharmacokinetic dosage regimens were modeled. Survival of AML patients was described via a hazard model. Simulations exploring different CYT/quizartinib regimens were conducted with the goal of improving treatment outcome. FLT3 status was associated with sensitivity to CYT (HEL cells most sensitive > EOL1 > MV4-11 cells). This order of sensitivity is reversed for FLT3i. Cytarabine induced apoptosis in the S-phase while all FLT3i induced apoptosis and cell cycle arrest at G1 phase. Simulations of candidate clinical regimens predict better cell kill upon adding quizartinib simultaneously with or immediately after CYT exposure. Overall survival was predicted to be significantly better with quizartinib 200mg administered every 48h vs every 24h in patients with FLT3 aberrations. Simultaneous administration of quizartinib and CYT every other day is a promising combination regimen for AML patients with FLT3 mutations.

Determining the long-term health burden and risk of sequelae for 14 foodborne infections in British Columbia, Canada: protocol for a retrospective population-based cohort study

IntroductionOver one in eight Canadians is affected by a foodborne infection annually; however, the long-term consequences, including the risks and costs of sequelae, are unclear. We aim to estimate the...

An Adaptive Ground Motion Prediction Equation for Use in Low-to-Moderate Seismicity Regions

ABSTRACT In regions of low-to-moderate seismicity where representative strong motion data is lacking, the modelling of seismic hazard relies on the use of seismological models. This paper presents a set of expressions that can be used as ground motion prediction equations that have been transformed from seismological models which resolve the generation of seismic waves into several components. The feature of this presented set of expressions is that it can be adapted to represent earthquake ground motion behaviour that is defined by a diversity of seismological models. The motivation behind the development of the presented adaptive predictive relationship which is known as the Component Attenuation Model (CAM) was to fast track, and make transparent, the transformation from seismological models to predictions of response spectral values for engineering applications. Thus, CAM can be used to waive away the need of executing any software for undertaking stochastic simulations nor time-history analyses for calculation of the response spectral ordinates. An important and original, the feature of CAM is incorporating the shear wave velocity profile of the bedrock and the associated upper-crustal modification into the model. This article presenting CAM is essentially a contribution to engineering as opposed to seismology. The potential benefits derived from the fast-tracking can be considerable given that the transformation is seldom a one-off process and would need to be repeated for any given targeted area, in view of uncertainties surrounding seismological conditions of the earth crust around the globe.