scholarly journals The Predictors of In-hospital Mortality in Non-elderly Adult Patients Requiring Emergency Admission for Acute Pancreatitis: Analysis by Generalized Additive Model of 344,120 Patients

2021 ◽  
Vol 233 (5) ◽  
pp. e114-e115
Author(s):  
Matthew McGuirk ◽  
Abbas Smiley ◽  
Rifat Latifi
Keyword(s):  
Acute Pancreatitis ◽  
Hospital Mortality ◽  
Generalized Additive Model ◽  
Additive Model ◽  
Emergency Admission ◽  
Adult Patients ◽  
Elderly Adult

Independent Predictors of In-Hospital Mortality in Elderly and Non-elderly Adult Patients Undergoing Emergency Admission for Hemorrhoids

2022 ◽  
pp. 000313482110604
Author(s):  
Lior Levy ◽  
Abbas Smiley ◽  
Rifat Latifi
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Elderly Patients ◽  
Diagnostic Procedure ◽  
Logistic Regression Model ◽  
Emergency Admission ◽  
Adult Patients ◽  
Elderly Adult ◽  
Multivariable Logistic Regression Model ◽  
Invasive Diagnostic Procedure

Background The study explored determinants of mortality of admitted emergently patients with the primary diagnosis of hemorrhoids, during the years 2005-2014. Methods Demographics, clinical data, and outcomes were obtained from the National Inpatient Sample, 2005-2014, in elderly (65+ years) and non-elderly adult patients (18-64 years) with hemorrhoids who underwent emergency admission. Multivariable logistic regression model with backward elimination was used to identify predictors of mortality. Results 25 808 adult and 26 978 elderly patients were included. Female patients consisted of 42.5% and 59.3% in adult and elderly, respectively. 42 (.2%) adults died, of which 50% were female and 125 (.5%) elderly patients died, of which 60% were female. Mean (SD) age of the adult patients was 47.8 (11) years and in elderly patients was 78.7 (8) years. 82.2% and 85.7% had internal hemorrhoids in adult and elderly patients, respectively. 9326 (36.1%) adult and 7282 (27%) elderly patients underwent an operation. In the final multivariable logistic regression model for adult patients with operation, delayed operation and invasive diagnostic procedures increased the odds of mortality, whereas in elderly patients, delayed operation and frailty index were the risk factors of mortality. In both adults and elderly with no operation, increased hospital length of stay (HLOS) significantly increased the odds of mortality, and undergoing an invasive diagnostic procedure significantly decreased the odds of mortality. Conclusion In all operated patients, increased time to operation and undergoing an invasive diagnostic procedure were the risk factors for mortality. On the other hand, in non-operated emergency hemorrhoids patients, increased age and increased HLOS were the risk factors for mortality while undergoing an invasive diagnostic procedure decreased the odds of mortality.

scholarly journals Predictors of In-Hospital Mortality in Surgical Wards: A Multivariable Retrospective Cohort Analysis of 2,800,069 Hospitalizations

2020 ◽  
Vol 45 (2) ◽  
pp. 480-487
Author(s):  
Magdalena Walicka ◽  
Agnieszka Tuszyńska ◽  
Marcin Chlebus ◽  
Yaroslav Sanchak ◽  
Andrzej Śliwczyński ◽  
...  
Keyword(s):  
Hospital Mortality ◽  
Cohort Analysis ◽  
Emergency Admission ◽  
Male Gender ◽  
Adult Patients ◽  
Weekend Effect ◽  
National Database ◽  
Risk Of Death ◽  
Surgical Units ◽  
Working Day

Abstract Background Identifying prognostic factors that are predictive of in-hospital mortality for patients in surgical units may help in identifying high-risk patients and developing an approach to reduce mortality. This study analyzed mortality predictors based on outcomes obtained from a national database of adult patients. Materials and methods This retrospective study design collected data obtained from the National Health Fund in Poland comprised of 2,800,069 hospitalizations of adult patients in surgical wards during one calendar year. Predictors of mortality which were analyzed included: the patient’s gender and age, diagnosis-related group category assigned to the hospitalization, length of the hospitalization, hospital type, admission type, and day of admission. Results The overall mortality rate was 0.8%, and the highest rate was seen in trauma admissions (24.5%). There was an exponential growth in mortality with respect to the patient’s age, and male gender was associated with a higher risk of death. Compared to elective admissions, the mortality was 6.9-fold and 15.69-fold greater for urgent and emergency admissions (p < 0.0001), respectively. Weekend or bank holiday admissions were associated with a higher risk of death than working day admissions. The “weekend” effect appears to begin on Friday. The highest mortality was observed in less than 1 day emergency cases and with a hospital stay longer than 61 days in any type of admission. Conclusion Age, male gender, emergency admission, and admission on the weekend or a bank holiday are factors associated with greater mortality in surgical units.

scholarly journals Aggressive Versus Conservative Fluid Treatment for Acute Pancreatitis in Adult Patients: A Meta-Analysis of 3,127 Cases

2021 ◽  
Author(s):  
Jiyang Liao ◽  
Yang Zhan ◽  
Huachu Wu ◽  
Zhijun Yao ◽  
Xian Peng ◽  
...  
Keyword(s):  
Renal Failure ◽  
Acute Pancreatitis ◽  
Respiratory Failure ◽  
Hospital Mortality ◽  
Organ Failure ◽  
Pancreatic Necrosis ◽  
Meta Analysis ◽  
Adult Patients ◽  
Cochrane Library ◽  
Persistent Organ Failure

Abstract Background: The advantages of aggressive fluid treatment (AFT) compared to conservative fluid treatment (CFT) within 24 h for acute pancreatitis (AP) remain controversial in adult patients. A meta-analysis was undertaken to investigate whether aggressive strategies are more beneficial.Methods: We searched (on February 1, 2021) PubMed, Embase, and the Cochrane Library for eligible trials that assessed the two therapies and performed a meta-analysis. The primary endpoint was in-hospital mortality. Secondary outcomes were adverse events (e.g., renal failure and pancreatic necrosis) within 24 h of treatment.Results: Five randomized controlled trials (RCTs) and 8 observational studies involving 3,127 patients were identified. There was a significant difference in in-hospital mortality for AFT compared to CFT (OR, 1.66; P = 0.0001). The incidences of renal failure (OR, 2.38; P < 0.00001) and pancreatic necrosis (OR, 2.34; P < 0.0001) were similar and significantly different between the two groups. Patients aged > 50 years had a potentially higher utilization of mechanical ventilation and incidence of respiratory failure (OR, 4.88; P < 0.00001). Persistent organ failure, systemic inflammatory response syndrome (SIRS) and length of hospital stay did not differ significantly between the two groups. Sensitivity analysis identified two significant changes: one in persistent SIRS (OR, 2.37; P = 0.02) in patients aged > 50 years and one in the overall incidence of persistent organ failure (OR, 1.81; P = 0.02).Conclusions: Compared to CFT, AFT increases in-hospital mortality and the incidence of renal failure, pancreatic necrosis and respiratory failure with relatively strong evidence.

scholarly journals Assessing the Performance of Random Forests for Modeling Claim Severity in Collision Car Insurance

Risks ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 53
Author(s):  
Yves Staudt ◽  
Joël Wagner
Keyword(s):  
Random Forests ◽  
Goodness Of Fit ◽  
Generalized Additive Model ◽  
Mean Squared Error ◽  
Additive Model ◽  
Continuous Variables ◽  
Insurance Portfolio ◽  
Normal Transformation ◽  
Car Insurance ◽  
Log Normal

For calculating non-life insurance premiums, actuaries traditionally rely on separate severity and frequency models using covariates to explain the claims loss exposure. In this paper, we focus on the claim severity. First, we build two reference models, a generalized linear model and a generalized additive model, relying on a log-normal distribution of the severity and including the most significant factors. Thereby, we relate the continuous variables to the response in a nonlinear way. In the second step, we tune two random forest models, one for the claim severity and one for the log-transformed claim severity, where the latter requires a transformation of the predicted results. We compare the prediction performance of the different models using the relative error, the root mean squared error and the goodness-of-lift statistics in combination with goodness-of-fit statistics. In our application, we rely on a dataset of a Swiss collision insurance portfolio covering the loss exposure of the period from 2011 to 2015, and including observations from 81 309 settled claims with a total amount of CHF 184 mio. In the analysis, we use the data from 2011 to 2014 for training and from 2015 for testing. Our results indicate that the use of a log-normal transformation of the severity is not leading to performance gains with random forests. However, random forests with a log-normal transformation are the favorite choice for explaining right-skewed claims. Finally, when considering all indicators, we conclude that the generalized additive model has the best overall performance.

scholarly journals Early prediction of in-hospital mortality in acute pancreatitis: a retrospective observational cohort study based on a large multicentre critical care database

BMJ Open ◽  
2020 ◽  
Vol 10 (12) ◽  
pp. e041893
Author(s):  
Caifeng Li ◽  
Qian Ren ◽  
Zhiqiang Wang ◽  
Guolin Wang
Keyword(s):  
Risk Factors ◽  
Acute Pancreatitis ◽  
Multivariate Analysis ◽  
Hospital Mortality ◽  
Observational Cohort Study ◽  
Calibration Plot ◽  
Observational Cohort ◽  
Care Database ◽  
Retrospective Observational Cohort Study ◽  
Apache Iv

ObjectiveTo develop and validate a prediction model for predicting in-hospital mortality in patients with acute pancreatitis (AP).DesignA retrospective observational cohort study based on a large multicentre critical care database.SettingAll subject data were collected from the eICU Collaborative Research Database (eICU-CRD), which covers 200 859 intensive care unit admissions of 139 367 patients in 208 US hospitals between 2014 and 2015.ParticipantsA total of 746 patients with AP were drawn from eICU-CRD. Due to loss to follow-up (four patients) or incomplete data (364 patients), 378 patients were enrolled in the primary cohort to establish a nomogram model and to conduct internal validation.Primary and secondary outcome measuresThe outcome of the prediction model was in-hospital mortality. All risk factors found significant in the univariate analysis were considered for multivariate analysis to adjust for confounding factors. Then a nomogram model was established. The performance of the nomogram model was evaluated by the concordance index (C-index) and the calibration plot. The nomogram model was internally validated using the bootstrap resampling method. The predictive accuracy of the nomogram model was compared with that of Acute Physiology, Age, and Chronic Health Evaluation (APACHE) IV. Decision curve analysis (DCA) was performed to evaluate and compare the potential net benefit using of different predictive models.ResultsThe overall in-hospital mortality rate is 4.447%. Age, BUN (blood urea nitrogen) and lactate (ABL) were the independent risk factors determined by multivariate analysis. The C-index of nomogram model ABL (0.896 (95% CI 0.825 to 0.967)) was similar to that of APACHE IV (p=0.086), showing a comparable discriminating power. Calibration plot demonstrated good agreement between the predicted and the actual in-hospital mortality. DCA showed that the nomogram model ABL was clinically useful.ConclusionsNomogram model ABL, which used readily available data, exhibited high predictive value for predicting in-hospital mortality in AP.

scholarly journals Association of Health Insurance Status with Outcomes of Sepsis in Adult Patients: A Retrospective Cohort Study

2021 ◽  
Vol 18 (11) ◽  
pp. 5777
Author(s):  
Gaon-Sorae Wang ◽  
Kyoung-Min You ◽  
You-Hwan Jo ◽  
Hui-Jai Lee ◽  
Jong-Hwan Shin ◽  
...  
Keyword(s):  
Health Insurance ◽  
Propensity Score ◽  
Hospital Mortality ◽  
Cohort Analysis ◽  
Adult Patients ◽  
Multivariate Logistic Regression Model ◽  
Insurance Status ◽  
Matched Cohort ◽  
Health Insurance Status ◽  
Significant Difference

(1) Background: Sepsis is a life-threatening disease, and various demographic and socioeconomic factors affect outcomes in sepsis. However, little is known regarding the potential association between health insurance status and outcomes of sepsis in Korea. We evaluated the association of health insurance and clinical outcomes in patients with sepsis. (2) Methods: Prospective cohort data of adult patients with sepsis and septic shock from March 2016 to December 2018 in three hospitals were retrospectively analyzed. We categorized patients into two groups according to their health insurance status: National Health Insurance (NHI) and Medical Aid (MA). The primary end point was in-hospital mortality. The multivariate logistic regression model and propensity score matching were used. (3) Results: Of a total of 2526 eligible patients, 2329 (92.2%) were covered by NHI, and 197 (7.8%) were covered by MA. The MA group had fewer males, more chronic kidney disease, more multiple sources of infection, and more patients with initial lactate > 2 mmol/L. In-hospital, 28-day, and 90-day mortality were not significantly different between the two groups and in-hospital mortality was not different in the subgroup analysis. Furthermore, health insurance status was not independently associated with in-hospital mortality in multivariate analysis and was not associated with survival outcomes in the propensity score-matched cohort. (4) Conclusion: Our propensity score-matched cohort analysis demonstrated that there was no significant difference in in-hospital mortality by health insurance status in patients with sepsis.

scholarly journals Generalized additive model with embedded variable selection for bankruptcy prediction: Prediction versus interpretation

2019 ◽  
Vol 7 (1) ◽  
pp. 1597956
Author(s):  
Carlos Valencia ◽  
Sergio Cabrales ◽  
Laura Garcia ◽  
Juan Ramirez ◽  
Diego Calderona ◽  
...  
Keyword(s):  
Variable Selection ◽  
Generalized Additive Model ◽  
Additive Model ◽  
Bankruptcy Prediction ◽  
Selection For
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