Plasma IL-6 levels following corticosteroid therapy as an indicator of ICU length of stay in critically ill COVID-19 patients.
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ABSTRACT:
SUBMITTER: Awasthi S
PROVIDER: S-EPMC7958587 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundPrognostic abilities of medical parameters, which are scoring systems, measurements and biomarkers, are important for stratifying critically ill patients. Indocyanine green plasma disappearance (ICG-PDR) is an established clinical tool for the assessment of liver perfusion and function. Copeptin, MR-proANP and pro-ADM are biomarkers whose prognostic value is still unclear. The goal of this prospective study was to evaluate ICG-PDR, copeptin, MR-proANP and pro-ADM to predict prolonged length of stay (pLOS) in the ICU.MethodsThis study was conducted as a prospective single center study including 110 consecutively admitted ICU patients. Primary endpoint was prolonged length of stay (pLOS) in the ICU, defined as more than three days of stay there.ResultsROC analysis showed an AUC of 0.73 for ICG-PDR, 0.70 for SAPS II, 0.65 for MR-proANP, 0.64 for pro-ADM and 0.54 for copeptin for pLOS in the ICU.ConclusionsThe prediction of pLOS in the ICU might be better by means of ICG-PDR than with the new biomarkers copeptin, MR-proANP or pro-ADM. Nevertheless, there is more need for research to evaluate whether ICG-PDR is an overall prognostic marker for pLOS.Trial registration(ClinicalTrials.gov number, NCT01126554).
| S-EPMC4100565 | biostudies-literature
Project description:BackgroundSepsis is a life-threatening disease with high morbidity and mortality, characterized by an inadequate systemic immune response to an initial stimulus. Whether the use of ondansetron (OND) during intensive care unit (ICU) stay is associated with the prognosis of sepsis patients remains unclear.MethodsCritically ill patients with sepsis were extracted from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. Multivariate logistic regression and Cox regression analyses were used to explore the association between OND use and clinical outcomes after adjusting for confounders. Kaplan-Meier survival curve was used for survival analysis. Propensity score matching (PSM) and subgroup analysis were performed to further confirm the results.ResultsThe OND-medication group showed reduced in-hospital mortality, 28-day and 90-day mortalities. The OR for in-hospital mortality was 0.80 (0.64-0.99) and HRs for 28-day mortality and 90-day mortality were 0.77 (0.64-0.92) and 0.83 (0.70-0.98), respectively. After PSM, the clinical outcomes remained consistent. In-hospital mortality was lower in the OND-medication group (28.1% vs. 35.8%, P= 0.044), as well as 28-day mortality (23.4% vs. 32.1%, P=0.022) and 90-day mortality (27.4% vs. 35.8%, P=0.035). The protective effect of OND in sepsis patients was relatively robust, independent of age, septic shock, vasopressin and mechanical ventilation. Additionally, the OND users had longer lengths of stay in ICU (6.9(3.1-13.2) vs. 5.1(2.5-11.0), P = 0.026) while no statistical differences were found in lengths of stay in hospital (P = 0.333).ConclusionOND exposure might be associated with lower in-hospital, 28-day, and 90-day mortality rates in critically ill patients with sepsis. This study indicated that OND might help improve the prognosis of patients with sepsis.
| S-EPMC10764599 | biostudies-literature
Project description:BackgroundDelirium predicts poor outcomes, however identifying patients with the worst outcomes is challenging. Plasma neurofilament light protein (NfL) is a sensitive indicator of neuronal damage. We undertook an exploratory observational study to determine the association between plasma NfL and delirium in the critically ill.MethodsMoDUS was a randomised placebo-controlled delirium trial of simvastatin done in an UK adult general ICU. We measured NfL levels in plasma samples using a Single molecule array (Simoa) platform. We explored associations between patient's plasma NfL levels and number of delirium days, and clinical outcomes. The control group for baseline NfL were preoperative patients undergoing major surgery.FindingsThe majority of critically ill patients already had a high NfL level on admission. Patients with higher plasma NfL levels at days one and three spent more days in delirium or deep sedation. Patients with zero or one day in delirium or deep sedation had day one mean concentrations of 37.8 pg/ml (SD 32.6) compared with 96.5 pg/ml (SD 106.1)) for patients with two days or more, p-value 0.002 linear mixed effects model. Survivors discharged before 14 days had lower mean plasma NfL concentrations compared to those with longer hospital stays and/or who died within six months. The area under ROC curve for predicting death within six months using day one NfL was 0.81 (0.7,0.9).InterpretationMeasurement of plasma NfL within three days of admission may be useful to identify those patients with worse clinical outcomes, and as an enrichment strategy for future delirium interventional trials in the critically ill.FundingAlzheimer's Society UK, UK Dementia Research Institute.
| S-EPMC9092506 | biostudies-literature
Project description:BackgroundAdvanced predictive analytical techniques are being increasingly applied to clinical risk assessment. This study compared a neural network model to several other models in predicting the length of stay (LOS) in the cardiac surgical intensive care unit (ICU) based on pre-incision patient characteristics.MethodsThirty six variables collected from 185 cardiac surgical patients were analyzed for contribution to ICU LOS. The Automatic Linear Modeling (ALM) module of IBM-SPSS software identified 8 factors with statistically significant associations with ICU LOS; these factors were also analyzed with the Artificial Neural Network (ANN) module of the same software. The weighted contributions of each factor ("trained" data) were then applied to data for a "new" patient to predict ICU LOS for that individual.ResultsFactors identified in the ALM model were: use of an intra-aortic balloon pump; O2 delivery index; age; use of positive cardiac inotropic agents; hematocrit; serum creatinine ≥ 1.3 mg/deciliter; gender; arterial pCO2. The r2 value for ALM prediction of ICU LOS in the initial (training) model was 0.356, p <0.0001. Cross validation in prediction of a "new" patient yielded r2 = 0.200, p <0.0001. The same 8 factors analyzed with ANN yielded a training prediction r2 of 0.535 (p <0.0001) and a cross validation prediction r2 of 0.410, p <0.0001. Two additional predictive algorithms were studied, but they had lower prediction accuracies. Our validated neural network model identified the upper quartile of ICU LOS with an odds ratio of 9.8(p <0.0001).ConclusionsANN demonstrated a 2-fold greater accuracy than ALM in prediction of observed ICU LOS. This greater accuracy would be presumed to result from the capacity of ANN to capture nonlinear effects and higher order interactions. Predictive modeling may be of value in early anticipation of risks of post-operative morbidity and utilization of ICU facilities.
| S-EPMC4692524 | biostudies-literature
Project description:To evaluate the association between length of ICU stay and 1-year mortality for elderly patients who survived to hospital discharge in the United States.Retrospective cohort study of a random sample of Medicare beneficiaries who survived to hospital discharge, with 1- and 3-year follow-up, stratified by the number of days of intensive care and with additional stratification based on receipt of mechanical ventilation.None.The cohort included 34,696 Medicare beneficiaries older than 65 years who received intensive care and survived to hospital discharge in 2005.Among 34,696 patients who survived to hospital discharge, the mean ICU length of stay was 3.4 days (± 4.5 d). Patients (88.9%) were in the ICU for 1-6 days, representing 58.6% of ICU bed-days. Patients (1.3%) were in the ICU for 21 or more days, but these patients used 11.6% of bed-days. The percentage of mechanically ventilated patients increased with increasing length of stay (6.3% for 1-6 d in the ICU and 71.3% for ? 21 d). One-year mortality was 26.6%, ranging from 19.4% for patients in the ICU for 1 day, up to 57.8% for patients in the ICU for 21 or more days. For each day beyond 7 days in the ICU, there was an increased odds of death by 1 year of 1.04 (95% CI, 1.03-1.05) irrespective of the need for mechanical ventilation.Increasing ICU length of stay is associated with higher 1-year mortality for both mechanically ventilated and non-mechanically ventilated patients. No specific cutoff was associated with a clear plateau or sharp increase in long-term risk.
| S-EPMC4792682 | biostudies-literature
Project description:insufficient feeding is frequent in the intensive care unit (ICU), which results in poor outcomes. Little is known about the nutrition pattern of patients requiring prolonged ICU stays. The aims of our study are to describe the demographic, metabolic, and nutritional specificities of chronically critically ill (CCI) patients defined by an ICU stay >2 weeks, and to identify an early risk factor. analysis of consecutive patients prospectively admitted to the CCI program, with the following variables: demographic characteristics, Nutrition Risk Screening (NRS-2002) score, total daily energy from nutritional and non-nutritional sources, protein and glucose intakes, all arterial blood glucose values, length of ICU and hospital stay, and outcome (ICU and 90-day survival). Two phases were considered for the analysis: the first 10 days, and the next 20 days of the ICU stay. parametric and non-parametric tests. 150 patients, aged 60 ± 15 years were prospectively included. Median (Q1, Q3) length of ICU stay was 31 (26, 46) days. The mortality was 18% at ICU discharge and 35.3% at 90 days. Non-survivors were older (p = 0.024), tended to have a higher SAPSII score (p = 0.072), with a significantly higher NRS score (p = 0.033). Enteral nutrition predominated, while combined feeding was minimally used. All patients received energy and protein below the ICU's protocol recommendation. The proportion of days with fasting was 10.8%, being significantly higher in non-survivors (2 versus 3 days; p = 0.038). Higher protein delivery was associated with an increase in prealbumin over time (r2 = 0.19, p = 0.027). High NRS scores may identify patients at highest risk of poor outcome when exposed to underfeeding. Further studies are required to evaluate a nutrition strategy for patients with high NRS, addressing combined parenteral nutrition and protein delivery.
| S-EPMC6679172 | biostudies-literature
Project description:There is limited evidence for the association between malnutrition and mortality as well as Intensive Care Unit length-of-stay (ICU-LOS) in critically ill patients. We aimed to examine the aforementioned associations by conducting a prospective cohort study in an ICU of a Singapore tertiary hospital. Between August 2015 and October 2016, all adult patients with ? 24 h of ICU-LOS were included. The 7-point Subjective Global Assessment (7-point SGA) was used to determine patients' nutritional status within 48 h of ICU admission. Multivariable regressions were conducted in two ways: (1) presence versus absence of malnutrition, and (2) dose-dependent association for each 1-point decrease in the 7-point SGA. There were 439 patients of which 28.0% were malnourished, and the 28-day mortality rate was 28.0%. Malnutrition was associated with an increased risk of 28-day mortality (adjusted Relative Risk 1.33 (95% Confidence Interval: 1.05-1.69)), and this risk increased with a greater degree of malnutrition (adjusted Relative Risk 1.08 (95% Confidence Interval: 1.00-1.16) for each 1-point decrease in the 7-point SGA). No significant association was found between malnutrition and ICU-LOS. The results of this study indicated a clear association between malnutrition and higher 28-day mortality in critically ill patients. The association between malnutrition and ICU-LOS could not be replicated and hence requires further evaluation.
| S-EPMC5793238 | biostudies-literature
Project description:We report preliminary data from a cohort of adults admitted to COVID-designated intensive care units from March 6 through April 17, 2020 across an academic healthcare system. Among 217 critically ill patients, mortality for those who required mechanical ventilation was 29.7% (49/165), with 8.5% (14/165) of patients still on the ventilator at the time of this report. Overall mortality to date in this critically ill cohort is 25.8% (56/217), and 40.1% (87/217) patients have survived to hospital discharge. Despite multiple reports of mortality rates exceeding 50% among critically ill adults with COVID-19, particularly among those requiring mechanical ventilation, our early experience indicates that many patients survive their critical illness.
| S-EPMC7276026 | biostudies-literature
Project description:PurposePosaconazole is an antifungal drug currently being used for prophylaxis and treatment of invasive fungal infections such as aspergillosis. To date, therapeutic drug monitoring (TDM) of posaconazole is recommended with the use of oral suspension, but the potential need of TDM with the use of IV formulations is rising. Therefore, we aimed to investigate the pharmacokinetics of IV posaconazole in critically ill patients.MethodsIn a prospective study, we analysed 168 consecutivelly collected posaconazole levels from 10 critically ill patients drawn during a 7 day curse. Posaconazole concentrations were measured using a chromatographic method. Demographic and laboratory data were collected, and the data was analysed using descriptive statistics.ResultsWe included 168 posaconazole levels, resulting in a median trough of 0.62 [0.29-1.05] mg/L with 58% not reaching the suggested target of 0.5 mg/L for fungal prophylaxis. Moreover, 74% of the trough levels were under the target of 1 mg/L which is proposed for the treatment of aspergillosis.ConclusionPosaconazole exposure is highly variable in critically ill patients resulting in potentially insufficient drug concentrations in many cases. TDM is highly recommended to identify and avoid underexposure.Trial registration numberNCT05275179, March 11, 2022.
| S-EPMC10665255 | biostudies-literature
Project description:Machine learning can predict outcomes and determine variables contributing to precise prediction, and can thus classify patients with different risk factors of outcomes. This study aimed to investigate the predictive accuracy for mortality and length of stay in intensive care unit (ICU) patients using machine learning, and to identify the variables contributing to the precise prediction or classification of patients. Patients (n = 12,747) admitted to the ICU at Chiba University Hospital were randomly assigned to the training and test cohorts. After learning using the variables on admission in the training cohort, the area under the curve (AUC) was analyzed in the test cohort to evaluate the predictive accuracy of the supervised machine learning classifiers, including random forest (RF) for outcomes (primary outcome, mortality; secondary outcome, length of ICU stay). The rank of the variables that contributed to the machine learning prediction was confirmed, and cluster analysis of the patients with risk factors of mortality was performed to identify the important variables associated with patient outcomes. Machine learning using RF revealed a high predictive value for mortality, with an AUC of 0.945 (95% confidence interval [CI] 0.922-0.977). In addition, RF showed high predictive value for short and long ICU stays, with AUCs of 0.881 (95% CI 0.876-0.908) and 0.889 (95% CI 0.849-0.936), respectively. Lactate dehydrogenase (LDH) was identified as a variable contributing to the precise prediction in machine learning for both mortality and length of ICU stay. LDH was also identified as a contributing variable to classify patients into sub-populations based on different risk factors of mortality. The machine learning algorithm could predict mortality and length of stay in ICU patients with high accuracy. LDH was identified as a contributing variable in mortality and length of ICU stay prediction and could be used to classify patients based on mortality risk.
| S-EPMC9334583 | biostudies-literature