Vitamin C Can Shorten the Length of Stay in the ICU: A Meta-Analysis.
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ABSTRACT:
SUBMITTER: Hemila H
PROVIDER: S-EPMC6521194 | biostudies-other | 2019 Mar
REPOSITORIES: biostudies-other
Publications
Vitamin C Can Shorten the Length of Stay in the ICU: A Meta-Analysis.
Hemilä Harri H Chalker Elizabeth E
Nutrients 20190327 4
A number of controlled trials have previously found that in some contexts, vitamin C can have beneficial effects on blood pressure, infections, bronchoconstriction, atrial fibrillation, and acute kidney injury. However, the practical significance of these effects is not clear. The purpose of this meta-analysis was to evaluate whether vitamin C has an effect on the practical outcomes: length of stay in the intensive care unit (ICU) and duration of mechanical ventilation. We identified 18 relevant ...[more]
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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.
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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.
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Project description:ObjectiveHypokalaemia and hyperkalaemia ('dyskalaemia') are commonly seen in patients requiring emergency hospital admission. The adverse effect of dyskalaemia on mortality is well described but there are few data for the effect on hospital length of stay. We sought to determine the association of serum potassium concentration with in-hospital length of stay.DesignSystematic review and meta-analysis.Data sourcesA structured search of MEDLINE, PubMed and SCOPUS databases to 19 March 2021.Eligibility criteriaObservational cohort studies defining exposure of interest as serum potassium levels (at admission or within the first 72 hours) and with outcome of interest as length of hospital stay. Studies had to provide estimates of length of stay as a comparison between normokalaemia and defined ranges of hyperkalaemia or hypokalaemia.Data extraction and synthesisWe identified 39 articles published to March 2021 that met the inclusion and exclusion criteria. Study selection, data extraction and quality assessment were carried out by two reviewers working independently and in duplicate, to assessed eligibility and risk of bias, and extract data from eligible studies. Random effects models were used to pool estimates across the included studies. Meta-analyses were performed using Cochrane-RevMan.ResultsFive studies were included in the meta-analysis. Compared with the reference group (3.5-5.0 mmol/L), the pooled raw differences of medians were 4.45 (95% CI 2.71 to 6.91), 1.99 (95% CI 0.03 to 3.94), 0.98 (95% CI 0.91 to 1.05), 1.51 (95% CI 1.03 to 2.0), 1 (95% CI 0.75 to 1.25) and 2.76 (95% CI 1.24 to 4.29) for patients with potassium levels of <2.5, 2.5 to <3.0, 3.0 to <3.5, <5 to 5.5, <5.5 to 6 and >6.0 mmol/L, respectively.ConclusionHospital length of stay follows a U-shaped distribution, with duration of admission being twofold greater at the extremes of the potassium range.
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Project description:BackgroundAppropriate antibiotic (AB) therapy remains a challenge in the intensive care unit (ICU). Procalcitonin (PCT)-guided AB stewardship could help optimize AB treatment and decrease AB-related adverse effects, but firm evidence is still lacking. Our aim was to compare the effects of PCT-guided AB therapy with standard of care (SOC) in critically ill patients.MethodsWe searched databases CENTRAL, Embase and Medline. We included randomized controlled trials (RCTs) comparing PCT-guided AB therapy (PCT group) with SOC reporting on length of AB therapy, mortality, recurrent and secondary infection, ICU length of stay (LOS), hospital LOS or healthcare costs. Due to recent changes in sepsis definitions, subgroup analyses were performed in studies applying the Sepsis-3 definition. In the statistical analysis, a random-effects model was used to pool effect sizes.ResultsWe included 26 RCTs (n = 9048 patients) in the quantitative analysis. In comparison with SOC, length of AB therapy was significantly shorter in the PCT group (MD - 1.79 days, 95% CI: -2.65, - 0.92) and was associated with a significantly lower 28-day mortality (OR 0.84, 95% CI: 0.74, 0.95). In Sepsis-3 patients, mortality benefit was more pronounced (OR 0.46 95% CI: 0.27, 0.79). Odds of recurrent infection were significantly higher in the PCT group (OR 1.36, 95% CI: 1.10, 1.68), but there was no significant difference in the odds of secondary infection (OR 0.81, 95% CI: 0.54, 1.21), ICU and hospital length of stay (MD - 0.67 days 95% CI: - 1.76, 0.41 and MD - 1.23 days, 95% CI: - 3.13, 0.67, respectively).ConclusionsPCT-guided AB therapy may be associated with reduced AB use, lower 28-day mortality but higher infection recurrence, with similar ICU and hospital length of stay. Our results render the need for better designed studies investigating the role of PCT-guided AB stewardship in critically ill patients.
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Project description:IntroductionPostoperative atrial fibrillation (POAF) is a common complication following cardiac surgery associated with increased morbidity and mortality. Although sustained hyperglycemia is a known risk factor of AF and poor ICU outcomes, emerging in-vitro studies reveal acute glycemic fluctuations to be an additional independent predictor of AF. The effect of acute glycemic fluctuations on the incidence of POAF in the clinical setting remains unclear. We aim to investigate the effect of the magnitude of acute perioperative glycemic fluctuations on the incidence of POAF in a multi-ethnic Southeast-Asian population.MethodsWe obtained data from1743 patients who underwent elective CABG in a tertiary heart centre from 2009-2011. Patients were kept to a tight baseline glycemic control in accordance with hospital protocol. The magnitude of the difference between the highest and lowest perioperative glucose levels up till the first 48 postoperative hours was employed as a measure of glycemic fluctuation. Patients were divided into 4 groups for analysis based on the magnitude of glycemic fluctuation:A)0-2mmol/L(N = 147); B)>2-4mmol/L(N = 426); C)>4-6mmol/L(N = 513); D)>6mmol/L(N = 657).Our primary outcome was the incidence of POAF. Secondary outcomes included ICU and 30-day mortality and length of stay.ResultsThe overall incidence of POAF was 14.7%. This increased as the magnitude of glycemic fluctuation increased, and was statistically highest in Group D(16.4%) as compared with the other 3 sub-groups. Multivariate logistic regression revealed the magnitude of perioperative glycemic fluctuation to be an independent risk factor of POAF(O.R.1.06, 95% C.I.1.01-1.11, p = 0.014).ICU length of stay was statistically highest in Group D(63.1 hours, p = < .001). However, ICU and 30 day mortality rates were similar among the 4 groups.ConclusionIncreased magnitudes of acute perioperative glycemic fluctuations are associated with a significantly increased risk of POAF and length of ICU stay; and should therefore be minimised but balanced against the risks of hypoglycemia so as to avoid POAF and optimise patient outcomes.
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Project description:PurposeCOVID-19 pandemic has multifaceted presentations with rising evidence of immune-mediated mechanisms underplay. We sought to explore the outcomes of severe COVID-19 patients treated with a multi-mechanism approach (MMA) in addition to standard-of-care (SC) versus patients who only received SC treatment.Materials and methodsData were collected retrospectively for patients admitted to the intensive care unit (ICU). This observational cohort study was performed at five institutions, 3 in the United States and 2 in Honduras. Patients were stratified for MMA vs. SC treatment during ICU stay. MMA treatment consists of widely available medications started immediately upon hospitalization. These interventions target immunomodulation, anticoagulation, viral suppression, and oxygenation. Primary outcomes included in-hospital mortality and length of stay (LOS) for the index hospitalization and were measured using logistic regression.ResultsOf 86 patients admitted, 65 (76%) who had severe COVID-19 were included in the study; 30 (46%) patients were in SC group, compared with 35 (54%) patients treated with MMA group. Twelve (40%) patients in the SC group died, compared with 5 (14%) in the MMA group (p-value = 0.01, Chi squared test). After adjustment for gender, age, treatment group, Q-SOFA score, the MMA group had a mean length of stay 8.15 days, when compared with SC group with 13.55 days. ICU length of stay was reduced by a mean of 5.4 days (adjusted for a mean age of 54 years, p-value 0.03) and up to 9 days (unadjusted for mean age), with no significant reduction in overall adjusted mortality rate, where the strongest predictor of mortality was the use of mechanical ventilation.ConclusionThe finding that MMA decreases the average ICU length of stay by 5.4 days and up to 9 days in older patients suggests that implementation of this treatment protocol could allow a healthcare system to manage 60% more COVID-19 patients with the same number of ICU beds.
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