Survey of Opioid-Induced Respiratory Depression in Critically Ill Patients Without Mechanical Ventilation and Construction of a Risk Nomogram

Abstract Objectives To investigate the current status of opioid-induced respiratory depression (OIRD) and potential risk factors in critically ill patients without mechanical ventilation in the intensive care unit (ICU) and to construct a risk nomogram to predict OIRD. Methods A total of 103 patients without (or who were weaned from) mechanical ventilation who had stayed for more than 24 h in the ICU between June 1, 2021 and September 31, 2021, were included. Patient data, including respiratory depression events, were recorded. The least absolute shrinkage and selection operator regression model were used to select features that were then used to construct a prediction model by multivariate logistic regression analysis. A nomogram was established for the risk of respiratory depression events in patients without mechanical ventilation. The discriminatory performance and calibration of the nomogram were assessed with Harrell’s concordance index and a calibration plot, respectively, and a bootstrap procedure was used for internal validation. Results Respiratory depression was diagnosed in 49/103 (47.6%) patients. Factors included in the nomogram were cardiopulmonary disease (odds ratio [OR]=5.569, 95% confidence interval [CI]=0.751–118.083), respiratory disease (OR=32.833, 95% CI=4.189–725.164), sepsis (OR=6.898, 95% CI=1.756–33.000), duration of mechanical ventilation (OR=3.019, 95% CI=0.862–11.322), lack of mechanical ventilation (OR=20.757, 95% CI=2.409–502.222), and oxygenation index (OR=7.350, 95% CI=2.483–24.286). The nomogram showed good performance for predicting respiratory depression events in critically ill patients without mechanical ventilation. Conclusion The nomogram can be used to identify ICU patients without mechanical ventilation who are at risk of opioid-induced respiratory depression and may therefore benefit from early intervention.

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Very-low-dose ketamine for the management of pain and sedation in the ICU

Journal of Opioid Management ◽

10.5055/jom.2008.0009 ◽

2018 ◽

Vol 4 (1) ◽

pp. 54 ◽

Cited By ~ 20

Author(s):

Mario De Pinto, MD ◽

Jill Jelacic, MD ◽

William T. Edwards, PhD, MD

Keyword(s):

Mechanical Ventilation ◽

Case Report ◽

Respiratory Depression ◽

Critically Ill ◽

Critically Ill Patients ◽

Low Dose ◽

Anesthetic Agent ◽

Cardiovascular Instability ◽

High Doses ◽

Very High

Management of pain in critically ill patients can be very difficult. In the attempt to provide comfort with adequate levels of opioids and sedatives, respiratory depression and cardiovascular instability may become difficult to control in patients with labile hemodynamics and poor cardiopulmonary reserve. The use of medications like ketamine, an anesthetic agent that in subanesthetic doses has been reported to be effective in preventing opioidinduced tolerance and to have analgesic properties, may be of help, especially in patients who develop tolerance, leading to rapidly escalating doses of opioids and sedatives. The case report presented here shows how a very low dose of ketamine can be helpful for the management of pain and sedation in critically ill patients, especially when they are ready to be weaned from mechanical ventilation, and very high doses of opiods and sedatives do not permit it.

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Prognostic Significance of Thyroid Hormone For Prolonged Mechanical Ventilation In Critically Ill Patients With Cardiac Surgery

10.21203/rs.3.rs-1029226/v1 ◽

2021 ◽

Author(s):

Xiao Shen ◽

Jiakui Sun ◽

Liang Hong ◽

Xiaochun Song ◽

Cui Zhang ◽

...

Keyword(s):

Mechanical Ventilation ◽

Cardiac Surgery ◽

Thyroid Hormone ◽

Critically Ill ◽

Critically Ill Patients ◽

Prolonged Mechanical Ventilation ◽

Prognostic Significance ◽

Multivariate Logistic Regression Analysis ◽

Free Triiodothyronine ◽

Total Triiodothyronine

Abstract Background: This study aimed to examine the correlation between thyroid hormone and prolonged mechanical ventilation (MV) in the adult critically ill patients having undergone cardiac surgery. Methods: The present study refers to a retrospective, cohort study that was conducted at Cardiovascular Intensive Care Unit (CVICU) of Nanjing First Hospital from March 2019 to December 2020. Patients receiving cardiac surgery and admitted to the center of the authors in the study period were screen for a potential inclusion. Demographic information, thyroid hormone and other laboratory measurements and outcome variables were recorded for analyses. Prolonged MV was defined as the duration of MV after cardiac surgery longer than 5 days. Thyroid hormones were assessed for the prognostic significance for prolonged MV.Results: On the whole, 118 patients having undergone cardiac surgery were included and analyzed in this study. Patients fell to the control (n=64) and the prolonged MV group (n=54) by complying with the duration of MV after cardiac surgery. The median total triiodothyronine (TT3) and free triiodothyronine (FT3) were 1.03 nmol/L and 3.52 pmol/L in the prolonged MV group before cardiac surgery, significantly lower than 1.23 nmol/L (P=0.005) and 3.87 pmol/L, respectively in the control (P=0.038). multivariate logistic regression analysis indicated that TT3 before surgery (pre-op TT3) had a good prognostic significance for prolonged MV (OR: 0.049, P=0.012). Conclusions: This study concluded that decreased triiodothyronine (T3) could be common in the cardiac patients with prolonged MV, and it would be further reduced after patients undergoing cardiac surgery. Besides, decreased T3 before surgery could act as an effective predictor for prolonged MV after cardiac surgery.

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Prediction of 28-day mortality in critically ill patients with COVID-19: Development and internal validation of a clinical prediction model

PLoS ONE ◽

10.1371/journal.pone.0254550 ◽

2021 ◽

Vol 16 (7) ◽

pp. e0254550

Author(s):

Matteo Luigi Giuseppe Leoni ◽

Luisa Lombardelli ◽

Davide Colombi ◽

Elena Giovanna Bignami ◽

Benedetta Pergolotti ◽

...

Keyword(s):

Prediction Model ◽

Critically Ill ◽

Critically Ill Patients ◽

Goodness Of Fit ◽

Cox Regression ◽

Prognostic Models ◽

Calibration Plot ◽

Internal Validation ◽

Icu Admission ◽

C Statistic

Background COVID-19 pandemic has rapidly required a high demand of hospitalization and an increased number of intensive care units (ICUs) admission. Therefore, it became mandatory to develop prognostic models to evaluate critical COVID-19 patients. Materials and methods We retrospectively evaluate a cohort of consecutive COVID-19 critically ill patients admitted to ICU with a confirmed diagnosis of SARS-CoV-2 pneumonia. A multivariable Cox regression model including demographic, clinical and laboratory findings was developed to assess the predictive value of these variables. Internal validation was performed using the bootstrap resampling technique. The model’s discriminatory ability was assessed with Harrell’s C-statistic and the goodness-of-fit was evaluated with calibration plot. Results 242 patients were included [median age, 64 years (56–71 IQR), 196 (81%) males]. Hypertension was the most common comorbidity (46.7%), followed by diabetes (15.3%) and heart disease (14.5%). Eighty-five patients (35.1%) died within 28 days after ICU admission and the median time from ICU admission to death was 11 days (IQR 6–18). In multivariable model after internal validation, age, obesity, procaltitonin, SOFA score and PaO2/FiO2 resulted as independent predictors of 28-day mortality. The C-statistic of the model showed a very good discriminatory capacity (0.82). Conclusions We present the results of a multivariable prediction model for mortality of critically ill COVID-19 patients admitted to ICU. After adjustment for other factors, age, obesity, procalcitonin, SOFA and PaO2/FiO2 were independently associated with 28-day mortality in critically ill COVID-19 patients. The calibration plot revealed good agreements between the observed and expected probability of death.

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Faculty Opinions recommendation of A protocol of no sedation for critically ill patients receiving mechanical ventilation: a randomised trial.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.1855959.3703054 ◽

2010 ◽

Author(s):

Tarek Sharshar

Keyword(s):

Mechanical Ventilation ◽

Critically Ill ◽

Critically Ill Patients ◽

Randomised Trial

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Effect of timing of intubation on clinical outcomes of critically ill patients with COVID-19: a systematic review and meta-analysis of non-randomized cohort studies

Critical Care ◽

10.1186/s13054-021-03540-6 ◽

2021 ◽

Vol 25 (1) ◽

Author(s):

Eleni Papoutsi ◽

Vassilis G. Giannakoulis ◽

Eleni Xourgia ◽

Christina Routsi ◽

Anastasia Kotanidou ◽

...

Keyword(s):

Systematic Review ◽

Mechanical Ventilation ◽

Clinical Outcomes ◽

Critically Ill ◽

Critically Ill Patients ◽

Meta Analysis ◽

Mortality And Morbidity ◽

Icu Admission ◽

All Cause Mortality ◽

Detectable Difference

Abstract Background Although several international guidelines recommend early over late intubation of patients with severe coronavirus disease 2019 (COVID-19), this issue is still controversial. We aimed to investigate the effect (if any) of timing of intubation on clinical outcomes of critically ill patients with COVID-19 by carrying out a systematic review and meta-analysis. Methods PubMed and Scopus were systematically searched, while references and preprint servers were explored, for relevant articles up to December 26, 2020, to identify studies which reported on mortality and/or morbidity of patients with COVID-19 undergoing early versus late intubation. “Early” was defined as intubation within 24 h from intensive care unit (ICU) admission, while “late” as intubation at any time after 24 h of ICU admission. All-cause mortality and duration of mechanical ventilation (MV) were the primary outcomes of the meta-analysis. Pooled risk ratio (RR), pooled mean difference (MD) and 95% confidence intervals (CI) were calculated using a random effects model. The meta-analysis was registered with PROSPERO (CRD42020222147). Results A total of 12 studies, involving 8944 critically ill patients with COVID-19, were included. There was no statistically detectable difference on all-cause mortality between patients undergoing early versus late intubation (3981 deaths; 45.4% versus 39.1%; RR 1.07, 95% CI 0.99–1.15, p = 0.08). This was also the case for duration of MV (1892 patients; MD − 0.58 days, 95% CI − 3.06 to 1.89 days, p = 0.65). In a sensitivity analysis using an alternate definition of early/late intubation, intubation without versus with a prior trial of high-flow nasal cannula or noninvasive mechanical ventilation was still not associated with a statistically detectable difference on all-cause mortality (1128 deaths; 48.9% versus 42.5%; RR 1.11, 95% CI 0.99–1.25, p = 0.08). Conclusions The synthesized evidence suggests that timing of intubation may have no effect on mortality and morbidity of critically ill patients with COVID-19. These results might justify a wait-and-see approach, which may lead to fewer intubations. Relevant guidelines may therefore need to be updated.

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Effectiveness of anaesthesia ventilator use for mechanical ventilation in critically ill patients during the COVID-19 pandemic

British Journal of Anaesthesia ◽

10.1016/j.bja.2021.04.001 ◽

2021 ◽

Author(s):

Aurélie GOUEL-CHERON ◽

Yoann ELMALEH ◽

Camille COUFFIGNAL ◽

Elie KANTOR ◽

Simon MESLIN ◽

...

Keyword(s):

Mechanical Ventilation ◽

Critically Ill ◽

Critically Ill Patients

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Etiology, incidence, and outcomes of patient–ventilator asynchrony in critically-ill patients undergoing invasive mechanical ventilation

Scientific Reports ◽

10.1038/s41598-021-90013-z ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Yongfang Zhou ◽

Steven R. Holets ◽

Man Li ◽

Gustavo A. Cortes-Puentes ◽

Todd J. Meyer ◽

...

Keyword(s):

Risk Factors ◽

Mechanical Ventilation ◽

Critically Ill ◽

Critically Ill Patients ◽

Invasive Mechanical Ventilation ◽

Surgical Intensive Care ◽

Mechanically Ventilated ◽

History Of ◽

Training Background ◽

The Relationship

AbstractPatient–ventilator asynchrony (PVA) is commonly encountered during mechanical ventilation of critically ill patients. Estimates of PVA incidence vary widely. Type, risk factors, and consequences of PVA remain unclear. We aimed to measure the incidence and identify types of PVA, characterize risk factors for development, and explore the relationship between PVA and outcome among critically ill, mechanically ventilated adult patients admitted to medical, surgical, and medical-surgical intensive care units in a large academic institution staffed with varying provider training background. A single center, retrospective cohort study of all adult critically ill patients undergoing invasive mechanical ventilation for ≥ 12 h. A total of 676 patients who underwent 696 episodes of mechanical ventilation were included. Overall PVA occurred in 170 (24%) episodes. Double triggering 92(13%) was most common, followed by flow starvation 73(10%). A history of smoking, and pneumonia, sepsis, or ARDS were risk factors for overall PVA and double triggering (all P < 0.05). Compared with volume targeted ventilation, pressure targeted ventilation decreased the occurrence of events (all P < 0.01). During volume controlled synchronized intermittent mandatory ventilation and pressure targeted ventilation, ventilator settings were associated with the incidence of overall PVA. The number of overall PVA, as well as double triggering and flow starvation specifically, were associated with worse outcomes and fewer hospital-free days (all P < 0.01). Double triggering and flow starvation are the most common PVA among critically ill, mechanically ventilated patients. Overall incidence as well as double triggering and flow starvation PVA specifically, portend worse outcome.

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Selection strategy for sedation depth in critically ill patients on mechanical ventilation

BMC Medical Informatics and Decision Making ◽

10.1186/s12911-021-01452-7 ◽

2021 ◽

Vol 21 (S2) ◽

Author(s):

Longxiang Su ◽

Chun Liu ◽

Fengxiang Chang ◽

Bo Tang ◽

Lin Han ◽

...

Keyword(s):

Mechanical Ventilation ◽

Dimensionality Reduction ◽

Critically Ill ◽

Critically Ill Patients ◽

Latent Profile Analysis ◽

Profile Analysis ◽

Mechanically Ventilated ◽

Mechanically Ventilated Patients ◽

Sedation And Analgesia ◽

Ventilated Patients

Abstract Background Analgesia and sedation therapy are commonly used for critically ill patients, especially mechanically ventilated patients. From the initial nonsedation programs to deep sedation and then to on-demand sedation, the understanding of sedation therapy continues to deepen. However, according to different patient’s condition, understanding the individual patient’s depth of sedation needs remains unclear. Methods The public open source critical illness database Medical Information Mart for Intensive Care III was used in this study. Latent profile analysis was used as a clustering method to classify mechanically ventilated patients based on 36 variables. Principal component analysis dimensionality reduction was used to select the most influential variables. The ROC curve was used to evaluate the classification accuracy of the model. Results Based on 36 characteristic variables, we divided patients undergoing mechanical ventilation and sedation and analgesia into two categories with different mortality rates, then further reduced the dimensionality of the data and obtained the 9 variables that had the greatest impact on classification, most of which were ventilator parameters. According to the Richmond-ASS scores, the two phenotypes of patients had different degrees of sedation and analgesia, and the corresponding ventilator parameters were also significantly different. We divided the validation cohort into three different levels of sedation, revealing that patients with high ventilator conditions needed a deeper level of sedation, while patients with low ventilator conditions required reduction in the depth of sedation as soon as possible to promote recovery and avoid reinjury. Conclusion Through latent profile analysis and dimensionality reduction, we divided patients treated with mechanical ventilation and sedation and analgesia into two categories with different mortalities and obtained 9 variables that had the greatest impact on classification, which revealed that the depth of sedation was limited by the condition of the respiratory system.

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