Impact of HFNC application on mortality and intensive care length of stay in acute respiratory failure secondary to COVID-19 pneumonia.
Ontology highlight
ABSTRACT: Background
In Covid-19 pneumonia, high mortality rates reported in intubated patients have raised non-invasive methods of respiratory support.Objective
We aimed to evaluate the impact of HFNC application on intubation requirement, intensive care length of stay, and short-term mortality in patients with COVID-19 pneumonia.Material-method
Patients receiving oxygen by reservoir mask or HFNC therapy in our intensive care units due to COVID-19 pneumonia were included in the study. Group H consisted of patients who received HFNC, and Group K consisted of patients who received conventional oxygen therapy (COT). The number of patients intubated, duration of intensive care stay and short-term mortality were recorded.Results
43 patients were included. The short-term mortality and the number of patients with intubation need was lower in Group H. There was no significant difference between the Groups in the length of intensive care stay.Conclusion
Administration of HFNC in respiratory failure secondary to COVID-19 pneumonia decreases the need for intubation and mortality.
SUBMITTER: Sayan I
PROVIDER: S-EPMC7874976 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Hospital length of stay (LOS) is important to administrators and families of neonates admitted to the neonatal intensive care unit (NICU). A prediction model for NICU LOS was developed using predictors birth weight, gestational age and two severity of illness tools, the score for neonatal acute physiology, perinatal extension (SNAPPE) and the morbidity assessment index for newborns (MAIN).Consecutive admissions (n=293) to a New England regional level III NICU were retrospectively collected. Multiple predictive models were compared for complexity and goodness-of-fit, coefficient of determination (R (2)) and predictive error. The optimal model was validated prospectively with consecutive admissions (n=615). Observed and expected LOS was compared.The MAIN models had best Akaike's information criterion, highest R (2) (0.786) and lowest predictive error. The best SNAPPE model underestimated LOS, with substantial variability, yet was fairly well calibrated by birthweight category. LOS was longer in the prospective cohort than the retrospective cohort, without differences in birth weight, gestational age, MAIN or SNAPPE.LOS prediction is improved by accounting for severity of illness in the first week of life, beyond factors known at birth. Prospective validation of both MAIN and SNAPPE models is warranted.
| S-EPMC4073289 | biostudies-literature
Project description:BackgroundAdverse events (AEs) are patient injuries caused by medical care. Previous studies have reported increased mortality rates and prolonged hospital length of stay in patients having an AE. However, these studies have not adequately accounted for potential biases which might influence these associations. We performed this study to measure the independent influence of intensive care unit (ICU) based AEs on in-hospital mortality and hospital length of stay.MethodsProspective cohort study in an academic tertiary-care ICU. Patients were monitored daily for adverse clinical occurrences. Data about adverse clinical occurrences were reviewed by a multidisciplinary team who rated whether they were AEs and whether they were preventable. We determined the association of AEs in the ICU with time to death and time to hospital discharge using multivariable survival analysis models.ResultsWe evaluated 207 critically ill patients (81% required mechanical ventilation, median Glasgow Coma Scale = 8, median predicted mortality = 31%). Observed mortality rate and hospital length of stay were 25% (95% CI 19%-31%) and 15 days (IQR 8-34 days), respectively. ICU-based AEs and preventable AEs occurred in 40 patients (19%, 95% CI 15%-25%) and 21 patients (10%, 95% CI 7%-15%), respectively. ICU-based AEs and preventable AEs were not significantly associated with time to in-hospital death (HR = 0.93, 95% CI 0.44-1.98 and HR = 0.72 95% CI 0.25-2.04, respectively). ICU-based AEs and preventable AEs were independently associated with time to hospital discharge ((HR = 0.50, 95% CI 0.31-0.81 and HR = 0.46 95% CI 0.23-0.91, respectively)). ICU-based AEs were associated with an average increase in hospital length of stay of 31 days.ConclusionThe impact of AEs on hospital length of stay was clinically relevant. Larger studies are needed to conclusively measure the association between preventable AEs and patient outcomes.
| S-EPMC2621200 | biostudies-literature
Project description:ObjectiveSeveral studies show that chronic opioid dependence leads to higher in-hospital mortality, increased risk of hospital readmissions, and worse outcomes in trauma cases. However, the association of outpatient prescription opioid use on morbidity and mortality has not been adequately evaluated in a critical care setting. The purpose of this study was to determine if there is an association between chronic opioid use and mortality after an ICU admission.DesignA single-center, longitudinal retrospective cohort study of all Intensive Care Unit (ICU) patients admitted to a tertiary-care academic medical center from 2001 to 2012 using the MIMIC-III database.SettingMedical Information Mart for Intensive Care III database based in the United States.PatientsAdult patients 18 years and older were included. Exclusion criteria comprised of patients who expired during their hospital stay or presented with overdose; patients with cancer, anoxic brain injury, non-prescription opioid use; or if an accurate medication reconciliation was unable to be obtained. Patients prescribed chronic opioids were compared with those who had not been prescribed opioids in the outpatient setting.InterventionsNone.Measurements and main resultsThe final sample included a total of 22,385 patients, with 2,621 (11.7%) in the opioid group and 19,764 (88.3%) in the control group. After proceeding with bivariate analyses, statistically significant and clinically relevant differences were identified between opioid and non-opioid users in sex, length of hospital stay, and comorbidities. Opioid use was associated with increased mortality in both the 30-day and 1-year windows with a respective odds ratios of 1.81 (95% CI, 1.63-2.01; p<0.001) and 1.88 (95% CI, 1.77-1.99; p<0.001), respectively.ConclusionsChronic opioid usage was associated with increased hospital length of stay and increased mortality at both 30 days and 1 year after ICU admission. Knowledge of this will help providers make better choices in patient care and have a more informed risk-benefits discussion when prescribing opioids for chronic usage.
| S-EPMC8061930 | biostudies-literature
Project description:Intensive care units (ICUs) are increasingly interested in assessing and improving their performance. ICU Length of Stay (LoS) could be seen as an indicator for efficiency of care. However, little consensus exists on which prognostic method should be used to adjust ICU LoS for case-mix factors. This study compared the performance of different regression models when predicting ICU LoS. We included data from 32,667 unplanned ICU admissions to ICUs participating in the Dutch National Intensive Care Evaluation (NICE) in the year 2011. We predicted ICU LoS using eight regression models: ordinary least squares regression on untransformed ICU LoS,LoS truncated at 30 days and log-transformed LoS; a generalized linear model with a Gaussian distribution and a logarithmic link function; Poisson regression; negative binomial regression; Gamma regression with a logarithmic link function; and the original and recalibrated APACHE IV model, for all patients together and for survivors and non-survivors separately. We assessed the predictive performance of the models using bootstrapping and the squared Pearson correlation coefficient (R2), root mean squared prediction error (RMSPE), mean absolute prediction error (MAPE) and bias. The distribution of ICU LoS was skewed to the right with a median of 1.7 days (interquartile range 0.8 to 4.0) and a mean of 4.2 days (standard deviation 7.9). The predictive performance of the models was between 0.09 and 0.20 for R2, between 7.28 and 8.74 days for RMSPE, between 3.00 and 4.42 days for MAPE and between -2.99 and 1.64 days for bias. The predictive performance was slightly better for survivors than for non-survivors. We were disappointed in the predictive performance of the regression models and conclude that it is difficult to predict LoS of unplanned ICU admissions using patient characteristics at admission time only.
| S-EPMC4215850 | biostudies-literature
Project description:IntroductionEffective planning of elective surgical procedures requiring postoperative intensive care is important in preventing cancellations and empty intensive care unit (ICU) beds. To improve planning, we constructed, validated and tested three models designed to predict length of stay (LOS) in the ICU in individual patients.MethodsRetrospective data were collected from 518 consecutive patients who underwent oesophagectomy with reconstruction for carcinoma between January 1997 and April 2005. Three multivariable linear regression models for LOS, namely preoperative, postoperative and intra-ICU, were constructed using these data. Internal validation was assessed using bootstrap sampling in order to obtain validated estimates of the explained variance (r2). To determine the potential gain of the best performing model in day-to-day clinical practice, prospective data from a second cohort of 65 consecutive patients undergoing oesophagectomy between May 2005 and April 2006 were used in the model, and the predictive performance of the model was compared with prediction based on mean LOS.ResultsThe intra-ICU model had an r2 of 45% after internal validation. Important prognostic variables for LOS included greater patient age, comorbidity, type of surgical approach, intraoperative respiratory minute volume and complications occurring within 72 hours in the ICU. The potential gain of the best model in day-to-day clinical practice was determined relative to mean LOS. Use of the model reduced the deficit number (underestimation) of ICU days by 65 and increased the excess number (overestimation) of ICU days by 23 for the cohort of 65 patients. A conservative analysis conducted in the second, prospective cohort of patients revealed that 7% more oesophagectomies could have been accommodated, and 15% of cancelled procedures could have been prevented.ConclusionPatient characteristics can be used to create models that will help in predicting LOS in the ICU. This will result in more efficient use of ICU beds and fewer cancellations.
| S-EPMC2206463 | 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:ImportanceLaboratory data are frequently collected throughout the care of critically ill patients. Currently, these data are interpreted by comparison with values from healthy outpatient volunteers. Whether this is the most useful comparison has yet to be demonstrated.ObjectivesTo understand how the distribution of intensive care unit (ICU) laboratory values differs from the reference range, and how these distributions are related to patient outcomes.Design, setting, and participantsCross-sectional study of a large critical care database, the Medical Information Mart for Intensive Care database, from January 1, 2001, to October 31, 2012. The database is collected from ICU data from a large tertiary medical center in Boston, Massachusetts. The data are collected from medical, cardiac, neurologic, and surgical ICUs. All patients in the database from all ICUs for 2001 to 2012 were included. Common laboratory measurements over the time window of interest were sampled. The analysis was conducted from March to June 2017.Main outcomes and measuresThe overlapping coefficient and Cohen standardized mean difference between distributions were calculated, and kernel density estimate visualizations for the association between laboratory values and the probability of death or quartile of ICU length of stay were created.ResultsAmong 38?605 patients in the ICU (21?852 [56.6%] male; mean [SD] age, 74.5 [55.1] years), 8878 (23%) had the best outcome (ICU survival, shortest quartile length of stay) and 3090 (8%) had the worst outcome (ICU nonsurvival). Distribution curves based on ICU data differed significantly from the hospital standard range (mean [SD] overlapping coefficient, 0.51 [0.32-0.69]). All laboratory values for the best outcome group differed significantly from those in the worst outcome group. Both the best and worst outcome group curves revealed little overlap with and marked divergence from the reference range.Conclusions and relevanceThe standard reference ranges obtained from healthy volunteers differ from the analogous range generated from data from patients in intensive care. Laboratory data interpretation may benefit from greater consideration of clinically contextual and outcomes-related factors.
| S-EPMC6324400 | biostudies-literature
Project description:Background: Clinical assessment of disease severity is an important part of medical practice for prediction of mortality and morbidity in Intensive Care Unit (ICU). A disease severity scoring system can be used as guidance for clinicians for objective assessment of disease outcomes and estimation of the chance of recovery. This study aimed to evaluate the hypothesis that the mortality and length of stay in emergency ICUs predicted by APACHE-IV is different to the real rates of mortality and length of stay observed in our emergency ICU in Iran. Methods: This was a retrospective cohort study conducted on the data of 839 consecutive patients admitted to the emergency ICU of Nemazi Hospital, Shiraz, Iran, during 2012-2015. The relevant variables were used to calculate APACHE-IV. Length of stay and death or discharge, Glasgow coma score, and acute physiology score were also evaluated. Moreover, the accuracy of APACHE-IV for mortality was assessed using area under the Receiver Operator Characteristic (ROC) curve. Results: Of the studied patients, 157 died and 682 were discharged (non-survivors and survivors, respectively). The length of stay in the ICU was 10.98±14.60, 10.22 ± 14.21 and 14.30±15.80 days for all patients, survivors, and non-survivors, respectively. The results showed that APACHE-IV model underestimated length of stay in our emergency ICU (p<0.001). In addition, the overall observed mortality was 17.8%, while the predicted mortality by APACHE-IV model was 21%. Therefore, there was an overestimation of predicted mortality by APACHE-IV model, with an absolute difference of 3.2% (p=0.036). Conclusion: The findings showed that APACHE-IV was a poor predictor of length of stay and mortality rate in emergency ICU. Therefore, specific models based on big sample sizes of Iranian patients are required to improve accuracy of predictions.
| S-EPMC5710303 | biostudies-literature
Project description:Clinical endpoints measured in terms of duration, such as intensive care unit (ICU) length of stay (LOS), are widely used in randomized clinical trials (RCTs) and observational research. In analyses of patient-level data from a recent RCT, in which ICU LOS was the primary endpoint, and in administrative data, we showed that additional ICU time is often accrued by patients after they are deemed ready for discharge. This "immutable" time (which cannot plausibly be altered by interventions under study) varies by day, week, and year, adding on average one-third of a day to total LOS. We then used statistical simulations, informed by the administrative data and RCT, to assess the impact of immutable time on the measurement and statistical comparison of patients' ICU LOS. These simulations demonstrated that immutable time combines with clinically necessary ICU time (neither of which is likely to be normally distributed) to produce overall LOS distributions that might either mask true treatment effects or suggest false treatment effects relative to analyses of time to discharge readiness. The extent and direction of bias were complex functions of the statistical method used, mortality rates and distributions, and the magnitude of immutable time relative to intervention-associated reductions in LOS.
| S-EPMC5860585 | biostudies-literature
Project description:RationaleThe COVID-19 pandemic induces considerable strain on intensive care unit resources.ObjectivesWe aim to provide early predictions of individual patients' intensive care unit length of stay, which might improve resource allocation and patient care during the on-going pandemic.MethodsWe developed a new semiparametric distributional index model depending on covariates which are available within 24h after intensive care unit admission. The model was trained on a large cohort of acute respiratory distress syndrome patients out of the Minimal Dataset of the Swiss Society of Intensive Care Medicine. Then, we predict individual length of stay of patients in the RISC-19-ICU registry.MeasurementsThe RISC-19-ICU Investigators for Switzerland collected data of 557 critically ill patients with COVID-19.Main resultsThe model gives probabilistically and marginally calibrated predictions which are more informative than the empirical length of stay distribution of the training data. However, marginal calibration was worse after approximately 20 days in the whole cohort and in different subgroups. Long staying COVID-19 patients have shorter length of stay than regular acute respiratory distress syndrome patients. We found differences in LoS with respect to age categories and gender but not in regions of Switzerland with different stress of intensive care unit resources.ConclusionA new probabilistic model permits calibrated and informative probabilistic prediction of LoS of individual patients with COVID-19. Long staying patients could be discovered early. The model may be the basis to simulate stochastic models for bed occupation in intensive care units under different casemix scenarios.
| S-EPMC7894868 | biostudies-literature