scholarly journals Mortality Prediction Utilizing Blood Biomarkers to Predict the Severity of COVID-19 Using Machine Learning Technique

Diagnostics ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. 1582
Author(s):  
Tawsifur Rahman ◽  
Fajer A. Al-Ishaq ◽  
Fatima S. Al-Mohannadi ◽  
Reem S. Mubarak ◽  
Maryam H. Al-Hitmi ◽  
...  
Keyword(s):  
High Mortality ◽  
Mortality Risk ◽  
External Validation ◽  
Model Development ◽  
Risk Groups ◽  
Mortality Prediction ◽  
Hospital Death ◽  
High Mortality Risk ◽  
Death Probability ◽  
Improve Patient Management

Healthcare researchers have been working on mortality prediction for COVID-19 patients with differing levels of severity. A rapid and reliable clinical evaluation of disease intensity will assist in the allocation and prioritization of mortality mitigation resources. The novelty of the work proposed in this paper is an early prediction model of high mortality risk for both COVID-19 and non-COVID-19 patients, which provides state-of-the-art performance, in an external validation cohort from a different population. Retrospective research was performed on two separate hospital datasets from two different countries for model development and validation. In the first dataset, COVID-19 and non-COVID-19 patients were admitted to the emergency department in Boston (24 March 2020 to 30 April 2020), and in the second dataset, 375 COVID-19 patients were admitted to Tongji Hospital in China (10 January 2020 to 18 February 2020). The key parameters to predict the risk of mortality for COVID-19 and non-COVID-19 patients were identified and a nomogram-based scoring technique was developed using the top-ranked five parameters. Age, Lymphocyte count, D-dimer, CRP, and Creatinine (ALDCC), information acquired at hospital admission, were identified by the logistic regression model as the primary predictors of hospital death. For the development cohort, and internal and external validation cohorts, the area under the curves (AUCs) were 0.987, 0.999, and 0.992, respectively. All the patients are categorized into three groups using ALDCC score and death probability: Low (probability < 5%), Moderate (5% < probability < 50%), and High (probability > 50%) risk groups. The prognostic model, nomogram, and ALDCC score will be able to assist in the early identification of both COVID-19 and non-COVID-19 patients with high mortality risk, helping physicians to improve patient management.

An improved index for diagnosis and mortality prediction in malignancy associated hemophagocytic lymphohistiocytosis

Blood ◽  
2021 ◽  
Author(s):  
Adi Zoref-Lorenz ◽  
Jun Murakami ◽  
Liron Hofstetter ◽  
Swaminathan P. Iyer ◽  
Ahmad S. Alotaibi ◽  
...  
Keyword(s):  
High Mortality ◽  
Mortality Risk ◽  
Hemophagocytic Lymphohistiocytosis ◽  
Regression Tree ◽  
Hematologic Malignancies ◽  
Mortality Prediction ◽  
Classification And Regression Tree ◽  
Receiver Operating Curve ◽  
High Mortality Risk ◽  
Inflammatory State

Hemophagocytic lymphohistiocytosis (HLH) is a life-threatening inflammatory syndrome that may complicate hematologic malignancies (HM). The appropriateness of current criteria for diagnosing HLH in the context of HMs is unknown because they were developed for children with familial HLH (HLH-2004) or derived from adult patient cohorts in which HMs were underrepresented (HScore). Moreover, many features of these criteria may directly reflect the underlying HM rather than an abnormal inflammatory state. To improve and potentially simplify HLH diagnosis in patients with HMs, we studied an international cohort of 225 adult patients with various HMs both with and without HLH and for whom HLH-2004 criteria were available. We used classification and regression tree and receiver operating curve analysis to identify the most useful diagnostic and prognostic parameters and optimize laboratory cutoff values. Combined elevation of soluble CD25 (&gt;3,900 U/ml) and ferritin (&gt;1,000 ng/ml) best identified HLH-2004 defining features (sensitivity 84%, specificity 81%). Moreover, this combination, which we term the 'optimized HLH inflammatory' (OHI) index, was highly predictive of mortality (hazard ratio 4.3; confidence interval 3.0-6.2) across diverse HMs. Furthermore, the OHI index identified a large group of patients with high mortality risk that were not defined as having HLH by HLH-2004/HScore. Finally, the OHI demonstrates diagnostic and prognostic value when used for routine surveillance of patients with newly diagnosed HMs as well as those with clinically suspected HLH. Thus, we conclude that the OHI index identifies HM patients with an inflammatory state associated with a high mortality risk and warrants further prospective validation.

scholarly journals Development and Validation of a Sepsis Mortality Risk Score for Sepsis-3 Patients in Intensive Care Unit

2021 ◽  
Vol 7 ◽  
Author(s):  
Kai Zhang ◽  
Shufang Zhang ◽  
Wei Cui ◽  
Yucai Hong ◽  
Gensheng Zhang ◽  
...  
Keyword(s):  
Intensive Care ◽  
Mortality Risk ◽  
External Validation ◽  
Model Development ◽  
Mortality Prediction ◽  
Sepsis Mortality ◽  
Severity Scores ◽  
Validation Set ◽  
Development And Validation ◽  
Risk Stratification Score

Background: Many severity scores are widely used for clinical outcome prediction for critically ill patients in the intensive care unit (ICU). However, for patients identified by sepsis-3 criteria, none of these have been developed. This study aimed to develop and validate a risk stratification score for mortality prediction in sepsis-3 patients.Methods: In this retrospective cohort study, we employed the Medical Information Mart for Intensive Care III (MIMIC III) database for model development and the eICU database for external validation. We identified septic patients by sepsis-3 criteria on day 1 of ICU entry. The Least Absolute Shrinkage and Selection Operator (LASSO) technique was performed to select predictive variables. We also developed a sepsis mortality prediction model and associated risk stratification score. We then compared model discrimination and calibration with other traditional severity scores.Results: For model development, we enrolled a total of 5,443 patients fulfilling the sepsis-3 criteria. The 30-day mortality was 16.7%. With 5,658 septic patients in the validation set, there were 1,135 deaths (mortality 20.1%). The score had good discrimination in development and validation sets (area under curve: 0.789 and 0.765). In the validation set, the calibration slope was 0.862, and the Brier value was 0.140. In the development dataset, the score divided patients according to mortality risk of low (3.2%), moderate (12.4%), high (30.7%), and very high (68.1%). The corresponding mortality in the validation dataset was 2.8, 10.5, 21.1, and 51.2%. As shown by the decision curve analysis, the score always had a positive net benefit.Conclusion: We observed moderate discrimination and calibration for the score termed Sepsis Mortality Risk Score (SMRS), allowing stratification of patients according to mortality risk. However, we still require further modification and external validation.

scholarly journals POS0141 ACTIVE SCREENING FOR GOUT IDENTIFIES A LARGER CARDIOVASCULAR POPULATION AT HIGH MORTALITY RISK

2021 ◽  
Vol 80 (Suppl 1) ◽  
pp. 282.2-282
Author(s):  
S. Ruiz-Simón ◽  
I. Calabuig ◽  
M. Gomez-Garberi ◽  
M. Andrés
Keyword(s):  
High Mortality ◽  
Mortality Risk ◽  
Cardiovascular Events ◽  
Cox Regression ◽  
Field Studies ◽  
High Mortality Risk ◽  
Active Screening ◽  
Tertiary Centre ◽  
All Cause Mortality

Background:We have recently revealed by active screening that about a third of gout cases in the cardiovascular population is not registered in records [1], highlighting the value of field studies.Objectives:To assess whether gout screening in patients hospitalized for cardiovascular events may also help identify patients at higher risk of mortality after discharge.Methods:A retrospective cohort field study, carried out in 266 patients admitted for cardiovascular events in the Cardiology, Neurology and Vascular Surgery units of a tertiary centre in Spain. The presence of gout was established by records review and face-to-face interview, according to the 2015 ACR/EULAR criteria. The occurrence of mortality during follow-up and its causes were obtained from electronic medical records. The association between gout and subsequent mortality was tested using Cox regression models. Whether covariates affect the gout-associated mortality was also studied.Results:Of 266 patients recruited at baseline, 17 were excluded due to loss to follow-up (>6mo), leaving a final sample of 249 patients (93.6%). Thirty-six cases (14.5% of the sample) were classified as having gout: twenty-three (63.9%) had a previously registered diagnosis, while 13 (36.1%) had not and was established by the interview.After discharge, the mean follow-up was 19.9 months (SD ±8.6), with a mortality incidence of 21.6 deaths per 100 patient-years, 34.2% by cardiovascular causes.Gout significantly increased the risk of subsequent all-cause mortality, with a hazard ratio (HR) of 2.01 (95%CI 1.13 to 3.58). When the analysis was restricted to gout patients with registered diagnosis, the association remained significant (HR 2.89; 95%CI 1.54 to 5.41).The adjusted HR for all-cause mortality associated with gout was 1.86 (95% CI 1.01-3.40). Regarding the causes of death, both cardiovascular and non-cardiovascular were numerically increased.Secondary variables rising the mortality risk in those with gout were age (HR 1.07; 1.01 to 1.13) and coexistent renal disease (HR 4.70; 1.31 to 16.84), while gender, gout characteristics and traditional risk factors showed no impact.Conclusion:Gout was confirmed an independent predictor of subsequent all-cause mortality in patients admitted for cardiovascular events. Active screening for gout allowed identifying a larger population at high mortality risk, which may help tailor optimal management to minimize the cardiovascular impact.References:[1]Calabuig I, et al. Front Med (Lausanne). 2020 Sep 29;7:560.Disclosure of Interests:Silvia Ruiz-Simón: None declared, Irene Calabuig: None declared, Miguel Gomez-Garberi: None declared, Mariano Andrés Speakers bureau: Grunenthal, Menarini, Consultant of: Grunenthal, Grant/research support from: Grunenthal

scholarly journals Treatment experience of a patient with duodenal trauma

2020 ◽  
pp. 95-96
Author(s):  
A. K. Krekoten ◽  
A. A. Krekoten ◽  
V. N. Mutyl
Keyword(s):  
Small Intestine ◽  
High Mortality ◽  
Mortality Risk ◽  
Postoperative Period ◽  
Pancreatic Ducts ◽  
High Mortality Risk ◽  
Intraoperative Diagnosis ◽  
Short Loop ◽  
The Common

A case of combined duodenal trauma in the 11-year-old patient is described. Complications of pre-operative and intraoperative diagnosis as well as complicated postoperative period are emphasized to be a cause of high mortality risk in this pathology. The disconnection of proximal and distal parts of duodenum was performed; anastomoses were placed between the common bile and pancreatic ducts and small intestine, and gastroenteroanastomosis was performed on a short loop. Follow-up traced for 10 months: recovery.

scholarly journals Machine Learning for Mortality Prediction in Pediatric Myocarditis

2021 ◽  
Vol 9 ◽  
Author(s):  
Fu-Sheng Chou ◽  
Laxmi V. Ghimire
Keyword(s):  
Machine Learning ◽  
Linear Regression ◽  
Regression Model ◽  
Current Knowledge ◽  
External Validation ◽  
Model Development ◽  
Kidney Injury ◽  
High Specificity ◽  
Mortality Prediction ◽  
Linear Regression Models

Background: Pediatric myocarditis is a rare disease. The etiologies are multiple. Mortality associated with the disease is 5–8%. Prognostic factors were identified with the use of national hospitalization databases. Applying these identified risk factors for mortality prediction has not been reported.Methods: We used the Kids' Inpatient Database for this project. We manually curated fourteen variables as predictors of mortality based on the current knowledge of the disease, and compared performance of mortality prediction between linear regression models and a machine learning (ML) model. For ML, the random forest algorithm was chosen because of the categorical nature of the variables. Based on variable importance scores, a reduced model was also developed for comparison.Results: We identified 4,144 patients from the database for randomization into the primary (for model development) and testing (for external validation) datasets. We found that the conventional logistic regression model had low sensitivity (~50%) despite high specificity (&gt;95%) or overall accuracy. On the other hand, the ML model struck a good balance between sensitivity (89.9%) and specificity (85.8%). The reduced ML model with top five variables (mechanical ventilation, cardiac arrest, ECMO, acute kidney injury, ventricular fibrillation) were sufficient to approximate the prediction performance of the full model.Conclusions: The ML algorithm performs superiorly when compared to the linear regression model for mortality prediction in pediatric myocarditis in this retrospective dataset. Prospective studies are warranted to further validate the applicability of our model in clinical settings.

scholarly journals Relatively high mortality risk in elderly Swedish subjects with low selenium status

2015 ◽  
Vol 70 (1) ◽  
pp. 91-96 ◽  
Author(s):  
U Alehagen ◽  
P Johansson ◽  
M Björnstedt ◽  
A Rosén ◽  
C Post ◽  
...  
Keyword(s):  
High Mortality ◽  
Mortality Risk ◽  
Selenium Status ◽  
High Mortality Risk ◽  
Low Selenium

scholarly journals Wilson disease: more than meets the eye

2018 ◽  
Vol 94 (1112) ◽  
pp. 335.2-347 ◽  
Author(s):  
Claire Kelly ◽  
Marinos Pericleous
Keyword(s):  
Liver Transplantation ◽  
Liver Disease ◽  
Liver Failure ◽  
Acute Liver Failure ◽  
High Mortality ◽  
Mortality Risk ◽  
Wilson Disease ◽  
Copper Metabolism ◽  
High Mortality Risk ◽  
High Index Of Suspicion

Wilson disease is a rare but important disorder of copper metabolism, with a failure to excrete copper appropriately into bile. It is a multisystem condition with presentations across all branches of medicine. Diagnosis can be difficult and requires a high index of suspicion. It should be considered in unexplained liver disease particularly where neuropsychiatric features are also present. Treatments are available for all stages of disease. A particularly important presentation not to overlook is acute liver failure which carries a high mortality risk and may require urgent liver transplantation. Here, we provide an overview of this complex condition.

scholarly journals Positive follow-up blood cultures identify high mortality risk among patients with Gram-negative bacteraemia

2020 ◽  
Vol 26 (7) ◽  
pp. 904-910 ◽  
Author(s):  
S.A. Maskarinec ◽  
L.P. Park ◽  
F. Ruffin ◽  
N.A. Turner ◽  
N. Patel ◽  
...  
Keyword(s):  
High Mortality ◽  
Mortality Risk ◽  
Blood Cultures ◽  
High Mortality Risk ◽  
Gram Negative
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