scholarly journals Comparison of mortality risk evaluation tools efficacy in critically ill COVID-19 patients

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Vaidas Vicka ◽  
Elija Januskeviciute ◽  
Sigute Miskinyte ◽  
Donata Ringaitiene ◽  
Mindaugas Serpytis ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Critically Ill ◽  
Mortality Risk ◽  
Multivariate Regression Analysis ◽  
Apache Ii ◽  
Risk Scores ◽  
Icu Mortality ◽  
Icu Patients ◽  
Point Increase ◽  
Number Of Patients

Abstract Background As the COVID-19 pandemic continues, the number of patients admitted to the intensive care unit (ICU) is still increasing. The aim of our article is to estimate which of the conventional ICU mortality risk scores is the most accurate at predicting mortality in COVID-19 patients and to determine how these scores can be used in combination with the 4C Mortality Score. Methods This was a retrospective study of critically ill COVID-19 patients treated in tertiary reference COVID-19 hospitals during the year 2020. The 4C Mortality Score was calculated upon admission to the hospital. The Simplified Acute Physiology Score (SAPS) II, Acute Physiology and Chronic Health Evaluation (APACHE) II, and Sequential Organ Failure Assessment (SOFA) scores were calculated upon admission to the ICU. Patients were divided into two groups: ICU survivors and ICU non-survivors. Results A total of 249 patients were included in the study, of which 63.1% were male. The average age of all patients was 61.32 ± 13.3 years. The all-cause ICU mortality ratio was 41.4% (n = 103). To determine the accuracy of the ICU mortality risk scores a ROC-AUC analysis was performed. The most accurate scale was the APACHE II, with an AUC value of 0.772 (95% CI 0.714–0.830; p < 0.001). All of the ICU risk scores and 4C Mortality Score were significant mortality predictors in the univariate regression analysis. The multivariate regression analysis was completed to elucidate which of the scores can be used in combination with the independent predictive value. In the final model, the APACHE II and 4C Mortality Score prevailed. For each point increase in the APACHE II, mortality risk increased by 1.155 (OR 1.155, 95% CI 1.085–1.229; p < 0.001), and for each point increase in the 4C Mortality Score, mortality risk increased by 1.191 (OR 1.191, 95% CI 1.086–1.306; p < 0.001), demonstrating the best overall calibration of the model. Conclusions The study demonstrated that the APACHE II had the best discrimination of mortality in ICU patients. Both the APACHE II and 4C Mortality Score independently predict mortality risk and can be used concomitantly.

scholarly journals The Effects of Neutrophil-Lymphocyte Ratio, Platelet-Lymphocyte Ratio and Prognostic Markers in Determining the Mortality in Patients Diagnosed With Pneumonia in Intensive Care

2021 ◽  
Author(s):  
Ömer Faruk Altaş ◽  
Mehmet Kızılkaya
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Mortality Risk ◽  
Demographic Data ◽  
Apache Ii ◽  
Prognostic Scores ◽  
Characteristic Analysis ◽  
Neutrophil Lymphocyte Ratio ◽  
Lymphocyte Ratio ◽  
Point Increase

Objective: In this study, we aimed to reveal the level of predicting mortality of the Neutrophil/Lymphocyte (NLR) and Platelet/Lymphocyte Ratios (TLR) calculated in patients hospitalized with the diagnosis of pneumonia in the intensive care unit when compared with other prognostic scores. Method: The hospital records of 112 patients who were admitted to the intensive care unit between January 2015 and January 2018 and met the inclusion criteria were retrospectively reviewed. The patients’ demographic data, the NLR and PLR levels, and the APACHE II (Acute Physiology and Chronic Health Evaluation II) and SOFA (Sequential Organ Failure Assessment) scores were calculated from the patient files. Results: Of the 112 patients examined, 70 were males. The risk analysis showed that the male gender had 2.7 times higher risk of mortality. The NLR, PLR, APACHE II, and SOFA values were found statistically significant in predicting mortality (p<0.001). An evaluation of the risk ratios demonstrated that each one point increase in the NLR increased the mortality risk by 5%, and each one point increase in the SOFA score increased the mortality risk by 13% (p<0.05). In the ROC (receiver operating characteristic) analysis, the NLR assessment proved to be the most powerful, most specific, and sensitive test. The cut-off values were 11.3 for the NLR, 227 for the PLR, 29.8 for the APACHE II scores, and 5.5 for the SOFA scores. Conclusion: We believe that NLR and PLR are strong and independent predictors of mortality that can be easily and cost-effectively tested.

scholarly journals Serum potassium levels and outcomes in critically ill patients in the medical intensive care unit

2018 ◽  
Vol 46 (3) ◽  
pp. 1254-1262 ◽  
Author(s):  
Surat Tongyoo ◽  
Tanuwong Viarasilpa ◽  
Chairat Permpikul
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Critically Ill ◽  
Serum Potassium ◽  
Critically Ill Patients ◽  
Medical Intensive Care Unit ◽  
Apache Ii ◽  
Apache Ii Score ◽  
Icu Mortality ◽  
Medical Intensive Care

Objective To compare the outcomes of patients with and without a mean serum potassium (K+) level within the recommended range (3.5–4.5 mEq/L). Methods This prospective cohort study involved patients admitted to the medical intensive care unit (ICU) of Siriraj Hospital from May 2012 to February 2013. The patients’ baseline characteristics, Acute Physiology and Chronic Health Evaluation II (APACHE II) score, serum K+ level, and hospital outcomes were recorded. Patients with a mean K+ level of 3.5 to 4.5 mEq/L and with all individual K+ values of 3.0 to 5.0 mEq/L were allocated to the normal K+ group. The remaining patients were allocated to the abnormal K+ group. Results In total, 160 patients were included. Their mean age was 59.3±18.3 years, and their mean APACHE II score was 21.8±14.0. The normal K+ group comprised 74 (46.3%) patients. The abnormal K+ group had a significantly higher mean APACHE II score, proportion of coronary artery disease, and rate of vasopressor treatment. An abnormal serum K+ level was associated with significantly higher ICU mortality and incidence of ventricular fibrillation. Conclusion Critically ill patients with abnormal K+ levels had a higher incidence of ventricular arrhythmia and ICU mortality than patients with normal K+ levels.

scholarly journals Association of Serum Albumin Concentration and Mortality Risk in Critically Ill Patients

2002 ◽  
Vol 30 (2) ◽  
pp. 202-207 ◽  
Author(s):  
F. H. Y. Yap ◽  
G. M. Joynt ◽  
T. A. Buckley ◽  
E. L. Y. Wong
Keyword(s):  
Serum Albumin ◽  
Critically Ill ◽  
Mortality Risk ◽  
Critically Ill Patients ◽  
Roc Curves ◽  
Apache Ii ◽  
Surgical Patients ◽  
Apache Ii Score ◽  
Albumin Concentration ◽  
Serum Albumin Concentration

In this study we aimed to examine the association between serum albumin concentration and mortality risk in critically ill patients. We retrospectively studied 1003 patients admitted to our Intensive Care Unit (ICU) over an 18-month period. Serial albumin measurements over 72 hours were compared between survivors and non-survivors, and medical and surgical patients were also compared. Our results showed that serum albumin decreased after ICU admission, most rapidly in the first 24 hours, in both survivors and non-survivors. Serum albumin was lower in non-survivors than in survivors, but albumin concentrations poorly differentiated the two groups. Medical patients had higher admission albumin levels than surgical patients, but both subgroups showed a similar albumin profile over 72 hours. We evaluated the prognostic value of serum albumin using receiver operator characteristic (ROC) curves. We constructed ROC curves for APACHE II score, admission albumin, albumin at 24 and 48 hours. We also combined APACHE II with albumin values and constructed the corresponding ROC curves. Our data showed that serum albumin had low sensitivity and specificity for predicting hospital mortality. Combining APACHE II score with serum albumin concentrations did not improve the accuracy of outcome prediction over that of APACHE II alone.

Absence of Excess Mortality in Critically Ill Patients With Nosocomial Escherichia coli Bacteremia

2003 ◽  
Vol 24 (12) ◽  
pp. 912-915 ◽  
Author(s):  
Stijn Blot ◽  
Koenraad Vandewoude ◽  
Eric Hoste ◽  
Jan De Waele ◽  
Kathleen Kint ◽  
...  
Keyword(s):  
Escherichia Coli ◽  
Antibiotic Therapy ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Excess Mortality ◽  
Apache Ii ◽  
Icu Patients ◽  
E Coli ◽  
Icu Stay ◽  
And Control

AbstractObjective:To evaluate excess mortality in critically ill patients with Escherichia coli bacteremia after adjustment for severity of illness.Design:Retrospective (1992-2000), pairwise-matched (1:2), risk-adjusted cohort study.Setting:Fifty-four-bed ICU in a university hospital including a medical and surgical ICU, a unit for care after cardiac surgery, and a burns unit.Patients:ICU patients with nosocomial E. coli bacteremia (defined as cases; n = 64) and control-patients without nosocomial bloodstream infection (n = 128).Methods:Case-patients were matched with control-patients on the basis of the Acute Physiology and Chronic Health Evaluation (APACHE) II system: an equal APACHE II score (± 2 points) and diagnostic category. In addition, control-patients were required to have an ICU stay at least as long as that of the respective case-patients prior to onset of the bacteremia.Results:The overall rate of appropriate antibiotic therapy in patients with E. coli bacteremia was high (93%) and such therapy was initiated soon after onset of the bacteremia (0.6 ± 1.0 day). ICU patients with E. coli bacteremia had more acute renal failure. No differences were noted between case-patients and control-patients in incidence of acute respiratory failure, hemodynamic instability, or age. No differences were observed in length of mechanical ventilation or length of ICU stay. In-hospital mortality rates for cases and controls were not different (43.8% and 45.3%, respectively; P = .959).Conclusion:After adjustment for disease severity and acute illness and in the presence of adequate antibiotic therapy, no excess mortality was found in ICU patients with E. coli bacteremia.

Survival in Critically Ill Patients with Acute Kidney Injury Treated with Early Hemodiafiltration

2012 ◽  
Vol 35 (12) ◽  
pp. 1039-1046 ◽  
Author(s):  
Nicolas Boussekey ◽  
Benoit Capron ◽  
Pierre-Yves Delannoy ◽  
Patrick Devos ◽  
Serge Alfandari ◽  
...  
Keyword(s):  
Risk Factors ◽  
Acute Kidney Injury ◽  
Critically Ill ◽  
Mortality Risk ◽  
Critically Ill Patients ◽  
Kidney Injury ◽  
Icu Patients ◽  
Impact On Survival ◽  
Baseline Characteristics ◽  
The Moment

Purpose Early renal replacement therapy (RRT) initiation should theoretically influence many physiological disorders related to acute kidney injury (AKI). Currently, there is no consensus about RRT timing in intensive care unit (ICU) patients. Methods We performed a retrospective analysis of all critically ill patients who received RRT in our ICU during a 3 year-period. Our goal was to identify mortality risk factors and if RRT initiation timing had an impact on survival. RRT timing was calculated from the moment the patient was classified as having acute kidney injury in the RIFLE classification. Results A hundred and ten patients received RRT. We identified four independent mortality risk factors: need for mechanical ventilation (OR = 12.82 (1.305 - 125.868, p = 0.0286); RRT initiation timing >16 h (OR = 5.66 (1.954 - 16.351), p = 0.0014); urine output on admission <500 ml/day (OR = 4.52 (1.666 - 12.251), p = 0.003); and SAPS II on admission >70 (OR = 3.45 (1.216 - 9.815), p = 0.02). The RRT initiation <16 h and RRT initiation >16 h groups presented the same baseline characteristics, except for more severe gravity scores and kidney failure in the early RRT group. Conclusions Early RRT in ICU patients with acute kidney injury or failure was associated with increased survival.

Elevated Red Cell Distribution Width Is Associated With Decreased Ventilator-Free Days in Critically Ill Patients

2016 ◽  
Vol 33 (4) ◽  
pp. 241-247 ◽  
Author(s):  
Tiffany M. N. Otero ◽  
D. Dante Yeh ◽  
Ednan K. Bajwa ◽  
Ruben J. Azocar ◽  
Andrea L. Tsai ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Respiratory Failure ◽  
Critically Ill ◽  
Poisson Regression ◽  
Red Cell ◽  
Cell Distribution ◽  
Normal Range ◽  
Icu Patients ◽  
Distribution Width ◽  
Poisson Regression Analysis

Introduction: Elevated red cell distribution width (RDW) is associated with mortality in a variety of respiratory conditions. Recent data also suggest that RDW is associated with mortality in intensive care unit (ICU) patients. Although respiratory failure is common in the ICU, the relationship between RDW and pulmonary outcomes in the ICU has not been previously explored. Therefore, our goal was to investigate the association of admission RDW with 30-day ventilator-free days (VFDs) in ICU patients. Methods: We performed a retrospective analysis from an ongoing prospective, observational study. Patients were recruited from medical and surgical ICUs of a large teaching hospital in Boston, Massachusetts. The RDW was assessed within 1 hour of ICU admission. Poisson regression analysis was used to investigate the association of RDW (normal: 11.5%-14.5% vs elevated: >14.5%) with 30-day VFD, while controlling for age, sex, race, body mass index, Nutrition Risk in the Critically Ill score, the presence of chronic lung disease, Pao2/Fio2 ratio, and admission levels of hemoglobin, mean corpuscular volume, phosphate, albumin, C-reactive protein, and creatinine. Results: A total of 637 patients comprised the analytic cohort. Mean RDW was 15 (standard deviation 4%), with 53% of patients in the normal range and 47% with elevated levels. Median VFD was 16 (interquartile range: 6-25) days. Poisson regression analysis demonstrated that ICU patients with elevated admission RDW were likely to have 32% lower 30-day VFDs compared to their counterparts with RDW in the normal range (incidence rate ratio: 0.68; 95% confidence interval: 0.55-0.83: P < .001). Conclusions: We observed an inverse association of RDW and 30-day VFD, despite controlling for demographics, nutritional factors, and severity of illness. This supports the need for future studies to validate our findings, understand the physiologic processes that lead to elevated RDW in patients with respiratory failure, and determine whether changes in RDW may be used to support clinical decision-making.

scholarly journals Deploying unsupervised clustering analysis to derive clinical phenotypes and risk factors associated with mortality risk in 2022 critically ill patients with COVID-19 in Spain

Critical Care ◽  
2021 ◽  
Vol 25 (1) ◽  
Author(s):  
Alejandro Rodríguez ◽  
◽  
Manuel Ruiz-Botella ◽  
Ignacio Martín-Loeches ◽  
María Jimenez Herrera ◽  
...  
Keyword(s):  
Risk Factors ◽  
Critically Ill ◽  
Mortality Risk ◽  
Critically Ill Patients ◽  
Clustering Analysis ◽  
Unsupervised Clustering ◽  
Older Age ◽  
Icu Mortality ◽  
Factors Associated ◽  
Clinical Phenotypes

Abstract Background The identification of factors associated with Intensive Care Unit (ICU) mortality and derived clinical phenotypes in COVID-19 patients could help for a more tailored approach to clinical decision-making that improves prognostic outcomes. Methods Prospective, multicenter, observational study of critically ill patients with confirmed COVID-19 disease and acute respiratory failure admitted from 63 ICUs in Spain. The objective was to utilize an unsupervised clustering analysis to derive clinical COVID-19 phenotypes and to analyze patient’s factors associated with mortality risk. Patient features including demographics and clinical data at ICU admission were analyzed. Generalized linear models were used to determine ICU morality risk factors. The prognostic models were validated and their performance was measured using accuracy test, sensitivity, specificity and ROC curves. Results The database included a total of 2022 patients (mean age 64 [IQR 5–71] years, 1423 (70.4%) male, median APACHE II score (13 [IQR 10–17]) and SOFA score (5 [IQR 3–7]) points. The ICU mortality rate was 32.6%. Of the 3 derived phenotypes, the A (mild) phenotype (537; 26.7%) included older age (< 65 years), fewer abnormal laboratory values and less development of complications, B (moderate) phenotype (623, 30.8%) had similar characteristics of A phenotype but were more likely to present shock. The C (severe) phenotype was the most common (857; 42.5%) and was characterized by the interplay of older age (> 65 years), high severity of illness and a higher likelihood of development shock. Crude ICU mortality was 20.3%, 25% and 45.4% for A, B and C phenotype respectively. The ICU mortality risk factors and model performance differed between whole population and phenotype classifications. Conclusion The presented machine learning model identified three clinical phenotypes that significantly correlated with host-response patterns and ICU mortality. Different risk factors across the whole population and clinical phenotypes were observed which may limit the application of a “one-size-fits-all” model in practice.

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