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Clinical and biochemical predictors of intensive care unit admission among patients with diabetic ketoacidosis

World Journal of Diabetes 2023年第3期14卷 271-278页

作者： Adeel Ahmad Khan Fateen Ata Phool Iqbal Mohammed Bashir Anand Kartha Department of Endocrinology Hamad Medical CorporationDoha 00000Qatar Department of Medicine Metropolitan Hospital CenterNew YorkNY 10595United States Department of Medicine Hamad Medical CorporationDoha 00000Qatar

BACKGROUND Diabetic ketoacidosis(DKA)contributes to 94%of diabetes-related hospital admissions,and its incidence is *** to the complexity of its management and the need for rigorous monitoring,many DKA patients are managed in the intensive care unit(ICU).However,studies comparing DKA patients managed in ICU to non-ICU settings show an increase in healthcare costs without significantly affecting patient *** is,therefore,essential to identify suitable candidates for ICU care in DKA *** To evaluate factors that predict the requirement for ICU care in DKA *** This retrospective study included consecutive patients with index DKA episodes who presented to the emergency department of four general hospitals of Hamad Medical Corporation,Doha,Qatar,between January 2015 and March *** adult patients(>14 years)fulfilling the American Diabetes Association criteria for DKA diagnosis were *** We included 922 patients with DKA in the final analysis,of which 229(25%)were managed in the *** to non-ICU patients,patients admitted to ICU were older[mean(SD)age of 40.4±13.7 years vs 34.5±14.6 years;P<0.001],had a higher body mass index[median(IQR)of 24.6(21.5-28.4)kg/m2 vs 23.7(20.3-27.9)kg/m2;P<0.030],had T2DM(61.6%)and were predominantly males(69%vs 31%;P<0.020).ICU patients had a higher white blood cell count[median(IQR)of 15.1(10.2-21.2)×103/uL vs 11.2(7.9-15.7)×103/uL,P<0.001],urea[median(IQR)of 6.5(4.6-10.3)mmol/L vs 5.6(4.0-8.0)mmol/L;P<0.001],creatinine[median(IQR)of 99(75-144)mmol/L vs 82(63-144)mmol/L;P<0.001],C-reactive protein[median(IQR)of 27(9-83)mg/L vs 14(5-33)mg/L;P<0.001]and anion gap[median(IQR)of 24.0(19.2-29.0)mEq/L vs 22(17-27)mEq/L;P<0.001];while a lower venous pH[mean(SD)of 7.10±0.15 vs 7.20±0.13;P<0.001]and bicarbonate level[mean(SD)of 9.2±4.1 mmol/L vs 11.6±4.3 mmol/L;P<0.001]at admission than those not requiring ICU management of DKA(P<0.001).Patients in the ICU group had a longer LOS[median(IQR)of

关键词： Diabetic ketoacidosis Type 1 diabetes mellitus Type 2 diabetes mellitus Intensive care unit critical care outcomes Length of stay

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Prediction of hospital mortality in intensive care unit patients from clinical and laboratory data: A machine learning approach

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World Journal of critical care Medicine 2022年第5期11卷 317-329页

作者： Elena Caires Silveira Soraya Mattos Pretti Bruna Almeida Santos Caio Fellipe Santos Correa Leonardo Madureira Silva Fabricio Freire de Melo Multidisciplinary Institute of Health Federal University of BahiaVitória da Conquista 45-029094Brazil

BACKGROUND Intensive care unit(ICU)patients demand continuous monitoring of several clinical and laboratory parameters that directly influence their medical progress and the staff’s *** data are vital in the assistance of these patients,being already used by several scoring *** this context,machine learning approaches have been used for medical predictions based on clinical data,which includes patient *** To develop a binary classifier for the outcome of death in ICU patients based on clinical and laboratory parameters,a set formed by 1087 instances and 50 variables from ICU patients admitted to the emergency department was obtained in the“WiDS(Women in Data Science)Datathon 2020:ICU Mortality Prediction”*** For categorical variables,frequencies and risk ratios were *** variables were computed as means and standard deviations and Mann-Whitney U tests were *** then divided the data into a training(80%)and test(20%)*** training set was used to train a predictive model based on the Random Forest algorithm and the test set was used to evaluate the predictive effectiveness of the *** A statistically significant association was identified between need for intubation,as well predominant systemic cardiovascular involvement,and hospital death.A number of the numerical variables analyzed(for instance Glasgow Coma Score punctuations,mean arterial pressure,temperature,pH,and lactate,creatinine,albumin and bilirubin values)were also significantly associated with death *** proposed binary Random Forest classifier obtained on the test set(n=218)had an accuracy of 80.28%,sensitivity of 81.82%,specificity of 79.43%,positive predictive value of 73.26%,negative predictive value of 84.85%,F1 score of 0.74,and area under the curve score of *** predictive variables of the greatest importance were the maximum and minimum lactate values,adding up to a predictive importance of 15.54%.CONCLUSIO

关键词： Hospital mortality Machine learning Patient outcome assessment Routinely collected health data Intensive care units critical care outcomes

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COVID-19: Lymphocyte Subpopulations Monitoring in critically Ill Patients

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International Journal of Clinical Medicine 2020年第8期11卷 465-473页

作者： Amra Ziadi Abdelhamid Hachimi Raja Hazime Imane Brahim Brahim Admou Fouzia Douirek Ahmed R. El Adib Said Younous Abdenasser M. Samkaoui Polyvalent Intensive care Unit Arrazi Hospital Mohammed VIth University Centre Cadi Ayyad University Marrakech Morocco Medical Intensive care Unit Arrazi Hospital Mohammed VIth University Centre Cadi Ayyad University Marrakech Morocco Laboratory of Immunology Centre of Clinical Research Mohammed VIth University Centre Cadi Ayyad University Marrakech Mo-rocco Polyvalent Intensive care Unit Child and Mother Hospital Mohammed VIth University Centre Cadi Ayyad University Marrakech Morocco

Background: The alteration of lymphocyte subpopulations can help to predict the severity and the prognosis of severe Coronavirus disease 2019 (COVID-19). Our goal was to describe the kinetics of lymphocyte subsets, and their impact on the severity and mortality in critically ill COVID-19 patients. Methods: We collected demographic data, comorbidities, clinical signs on admission, laboratory findings on admission then a follow-up during hospitalization. Lymphocyte subsets including CD3+ T cells, CD4+ T cells, CD8+ T cells, B cells, and natural killer (NK) cells were counted by flow cytometer. Results: On admission, we observed lymphopenia in 57% of cases, decreased CD3+ T cells in 76% of cases, decreased CD4+ T cells in 81% of cases, decreased CD8+ T cells in 62% of cases, decreased B cells in 52% of cases, and decreased natural killer (NK) cells in 33% of cases. After treatment, decreased CD3+ T cells, decreased CD4+ T cells, decreased CD8+ T cells, and decreased natural killer cells were predictor factors of mortality, in the univariable analysis. Conclusion: CD3+ T cells, CD4+ T cells, CD8+ T cells, and natural killer cells were predictor factors of severity, ICU mortality, and also a useful tool for predicting disease progression.

关键词： SARS-CoV-2 Coronavirus Disease 2019 Lymphocyte Subsets critical care outcomes

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