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Brain natriuretic peptide to predict successful liberation from mechanical ventilation in critically ill patients: a systematic review and meta-analysis.

ABSTRACT: BACKGROUND:Predicting successful liberation from mechanical ventilation (MV) in critically ill patients is challenging. Brain natriuretic peptide (BNP) has been proposed to help guide decision-making for readiness to liberate from MV following a spontaneous breathing trial (SBT). METHODS:We performed a systematic review and meta-analysis of randomized and prospective observational studies that measured BNP levels at the time of SBT in patients receiving MV. The primary endpoint was successful liberation from MV (absence of reintubation or non-invasive ventilation at 48?h). Statistical analyses included bi-variate and Moses-Littenberg models and DerSimonian-Laird pooling of areas under ROC curve (AUROC). RESULTS:A total of 731 articles were screened. Eighteen adult and 2 pediatric studies were fulfilled pre-specified eligibility. The measure of the relative variation of BNP during SBT (?BNP%) after exclusion of SBT failure by clinical criteria in adults yielded a sensitivity and specificity of 0.889 [0.831-0.929] and 0.828 [0.730-0.896] for successful liberation from MV, respectively, with a pooled AUROC of 0.92 [0.88-0.97]. The pooled AUROC for any method of analysis for absolute variation of BNP (?BNP), pre-SBT BNP, and post-SBT BNP were 0.89 [0.83-0.95], 0.77 [0.63-0.91], and 0.85 [0.80-0.90], respectively. CONCLUSION:The relative change in BNP during a SBT has potential value as an incremental tool after successful SBT to predict successful liberation from MV in adults. There is insufficient data to support the use of BNP in children or as an alternate test to clinical indices of SBT, or the use of ?BNP, BNP-pre, and BNP-post as an alternate or incremental test. TRIAL REGISTRATION:PROSPERO CRD42018087474 (6 February 2018).

SUBMITTER: Deschamps J

PROVIDER: S-EPMC7216735 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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Brain natriuretic peptide to predict successful liberation from mechanical ventilation in critically ill patients: a systematic review and meta-analysis.

Deschamps Jean J Andersen Sarah K SK Webber Jordan J Featherstone Robin R Sebastianski Meghan M Vandermeer Ben B Senaratne Janek J Bagshaw Sean M SM

Critical care (London, England) 20200511 1

<h4>Background</h4>Predicting successful liberation from mechanical ventilation (MV) in critically ill patients is challenging. Brain natriuretic peptide (BNP) has been proposed to help guide decision-making for readiness to liberate from MV following a spontaneous breathing trial (SBT).<h4>Methods</h4>We performed a systematic review and meta-analysis of randomized and prospective observational studies that measured BNP levels at the time of SBT in patients receiving MV. The primary endpoint wa ...[more]

PMID: 32393393

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Project description:IntroductionPredicting successful liberation from mechanical ventilation (MV) among critically ill patients receiving MV can be challenging. The current parameters used to predict successful extubation have shown variable predictive value. Brain natriuretic peptide (BNP) has been proposed as a novel biomarker to help guide decision-making in readiness for liberation of MV following a spontaneous breathing trial (SBT). Current evidence on the predictive ability of BNP has been uncertain, and BNP has not been integrated into clinical practice guidelines.Methods and analysisWe will perform a systematic review and meta-analysis to evaluate the value of BNP during SBT to predict success of liberation from MV. A search strategy will be developed in collaboration with a research librarian, and electronic databases (MEDLINE, EMBASE, Cochrane Library, Web of Science) and additional sources will be searched. Search themes will include: (1) BNP and (2) weaning, extubation and/or liberation from MV. Citation screening, selection, quality assessment and data abstraction will be performed in duplicate. The primary outcome will be liberation from MV; secondary outcomes will include time to reintubation, mortality, MV duration, total and postextubation intensive care unit (ICU) stay, hospitalisation duration, tracheostomy rate, ICU-acquired weakness rate and ventilator-free days. Primary statistical analysis will include predictive value of BNP by receiver operating characteristic curve, sensitivity/specificity and likelihood ratios for combination of BNP and SBT parameters for failure of liberation from MV. Secondary statistical analysis will be performed on individual and combinations of extracted metrics.Ethics and disseminationOur review will add knowledge by mapping the current body of evidence on the value of BNP testing for prediction of successful liberation from MV, and describe knowledge gaps and research priorities. Our findings will be disseminated through peer-reviewed publication, presentation at a scientific congress, through regional/national organisations and social media. Research ethics approval is not required.Prospero registration numberCRD42018087474.

Project description:BackgroundProne positioning (PP) has been used to improve oxygenation in patients affected by the SARS-CoV-2 disease (COVID-19). Several mechanisms, including lung recruitment and better lung ventilation/perfusion matching, make a relevant rational for using PP. However, not all patients maintain the oxygenation improvement after returning to supine position. Nevertheless, no evidence exists that a sustained oxygenation response after PP is associated to outcome in mechanically ventilated COVID-19 patients. We analyzed data from 191 patients affected by COVID-19-related acute respiratory distress syndrome undergoing PP for clinical reasons. Clinical history, severity scores and respiratory mechanics were analyzed. Patients were classified as responders (≥ median PaO2/FiO2 variation) or non-responders (< median PaO2/FiO2 variation) based on the PaO2/FiO2 percentage change between pre-proning and 1 to 3 h after re-supination in the first prone positioning session. Differences among the groups in physiological variables, complication rates and outcome were evaluated. A competing risk regression analysis was conducted to evaluate if PaO2/FiO2 response after the first pronation cycle was associated to liberation from mechanical ventilation.ResultsThe median PaO2/FiO2 variation after the first PP cycle was 49 [19-100%] and no differences were found in demographics, comorbidities, ventilatory treatment and PaO2/FiO2 before PP between responders (96/191) and non-responders (95/191). Despite no differences in ICU length of stay, non-responders had a higher rate of tracheostomy (70.5% vs 47.9, P = 0.008) and mortality (53.7% vs 33.3%, P = 0.006), as compared to responders. Moreover, oxygenation response after the first PP was independently associated to liberation from mechanical ventilation at 28 days and was increasingly higher being higher the oxygenation response to PP.ConclusionsSustained oxygenation improvement after first PP session is independently associated to improved survival and reduced duration of mechanical ventilation in critically ill COVID-19 patients.

Project description:In hypoxemic patients at risk for developing respiratory failure, the decision to initiate invasive mechanical ventilation (IMV) may be extremely difficult, even more so among patients suffering from COVID-19. Delayed recognition of respiratory failure may translate into poor outcomes, emphasizing the need for stronger predictive models for IMV necessity. We developed a two-step model; the first step was to train a machine learning predictive model on a large dataset of non-COVID-19 critically ill hypoxemic patients from the United States (MIMIC-III). The second step was to apply transfer learning and adapt the model to a smaller COVID-19 cohort. An XGBoost algorithm was trained on data from the MIMIC-III database to predict if a patient would require IMV within the next 6, 12, 18 or 24 h. Patients' datasets were used to construct the model as time series of dynamic measurements and laboratory results obtained during the previous 6 h with additional static variables, applying a sliding time-window once every hour. We validated the adaptation algorithm on a cohort of 1061 COVID-19 patients from a single center in Israel, of whom 160 later deteriorated and required IMV. The new XGBoost model for the prediction of the IMV onset was trained and tested on MIMIC-III data and proved to be predictive, with an AUC of 0.83 on a shortened set of features, excluding the clinician's settings, and an AUC of 0.91 when the clinician settings were included. Applying these models "as is" (no adaptation applied) on the dataset of COVID-19 patients degraded the prediction results to AUCs of 0.78 and 0.80, without and with the clinician's settings, respectively. Applying the adaptation on the COVID-19 dataset increased the prediction power to an AUC of 0.94 and 0.97, respectively. Good AUC results get worse with low overall precision. We show that precision of the prediction increased as prediction probability was higher. Our model was successfully trained on a specific dataset, and after adaptation it showed promise in predicting outcome on a completely different dataset. This two-step model successfully predicted the need for invasive mechanical ventilation 6, 12, 18 or 24 h in advance in both general ICU population and COVID-19 patients. Using the prediction probability as an indicator of the precision carries the potential to aid the decision-making process in patients with hypoxemic respiratory failure despite the low overall precision.

Project description:Studying interhospital transfer of critically ill patients with coronavirus disease 2019 pneumonia in the spring 2020 surge may help inform future pandemic management.ObjectivesTo compare outcomes for mechanically ventilated patients with coronavirus disease 2019 transferred to a tertiary referral center with increased surge capacity with patients admitted from the emergency department.Design setting participantsObservational cohort study of single center urban academic medical center ICUs. All patients admitted and discharged with coronavirus disease 2019 pneumonia who received invasive ventilation between March 17, 2020, and October 14, 2020.Main outcome and measuresDemographic and clinical variables were obtained from the electronic medical record. Patients were classified as emergency department admits or interhospital transfers. Regression models tested the association between transfer status and survival, adjusting for demographics and presentation severity.ResultsIn total, 298 patients with coronavirus disease 2019 pneumonia were admitted to the ICU and received mechanical ventilation. Of these, 117 were transferred from another facility and 181 were admitted through the emergency department. Patients were primarily male (64%) and Black (38%) or Hispanic (45%). Transfer patients differed from emergency department admits in having English as a preferred language (71% vs 56%; p = 0.008) and younger age (median 57 vs 61 yr; p < 0.001). There were no differences in race/ethnicity or primary payor. Transfers were more likely to receive extracorporeal membrane oxygenation (12% vs 3%; p = 0.004). Overall, 50 (43%) transferred patients and 78 (43%) emergency department admits died prior to discharge. There was no significant difference in hospital mortality or days from intubation to discharge between the two groups.Conclusions and relevanceIn a single-center retrospective cohort, no significant differences in hospital mortality or length of stay between interhospital transfers and emergency department admits were found. While more study is needed, this suggests that interhospital transfer of critically ill patients with coronavirus disease 2019 can be done safely and effectively.

Dataset Information

Brain natriuretic peptide to predict successful liberation from mechanical ventilation in critically ill patients: a systematic review and meta-analysis.

Publications

Brain natriuretic peptide to predict successful liberation from mechanical ventilation in critically ill patients: a systematic review and meta-analysis.

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