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To analyze the current status of \"pseudo\" unplanned endotracheal extubation in ICU patients in China's tertiary hospitals and to provide a reference for improving the quality of medical care. Through the National Nursing Quality Data Platform, unplanned endotracheal extubation data reported by ICUs in China's tertiary hospitals from 2019 to 2022 were analyzed. The situation of reported hospitals, causes, and the current status of \"pseudo\" unplanned endotracheal extubation in ICU patients was analyzed. The indicator of unplanned endotracheal extubation in ICUs of China’s tertiary hospitals is mainly from first-class tertiary hospitals (74.9%), most of which are self-extractions by patients (74.6%). The proportion of \"pseudo\" unplanned endotracheal extubation is 45.1%. \"Pseudo\" unplanned endotracheal extubation is common in the ICUs of China's tertiary hospitals. As such, management blind spots deserve attention from managers and clinical staff.

Background Self-extubation is a common complication in intubated patients in the intensive care unit (ICU) and is associated with a high rate of reintubation. This study aimed to identify predictors of reintubation following self-extubation (SE) and assess the prognosis of these patients. Methods Data were extracted from the French ICU database, OutcomeRea™. The primary objective was to identify factors associated with reintubation within 48 h after self-extubation. Secondary objectives included evaluating the association between reintubation and mortality, ICU length of stay, and nosocomial pneumonia. Results Between November 1996 and May 2022, 12,917 patients were intubated in the ICU. Among them, 701 patients experienced SE without therapeutic limitations at the time, and 276 (39.4%) required reintubation. In adjusted analyses, the following factors were independently associated with reintubation: a higher non-neurological SOFA score on the day before SE (OR 1.16 [1.01; 1.34]; p = 0.03), duration of invasive mechanical ventilation > 7 days before SE (OR 1.79 [1.04; 3.26]; p = 0.04), enteral nutrition on the day of SE (OR 2.59 [1.75; 3.84]; p < 0.01) and the use of non-invasive ventilation (NIV) within 24 h after SE (OR 0.28 [0.16; 0.5];p < 0.01). Reintubation within 48 h after SE was independently associated with increased 28-day mortality (HR = 3.03 [1.79; 5.12]; p < 0.01) and 90-day mortality (HR = 2.86 [1.86; 4.4]; p < 0.01), a higher risk of nosocomial pneumonia (sdHR, 18.28 [7.70; 43.42]; p < 0.01), and a 13-day increase in ICU length of stay (p < 0.01). Conclusion Enteral nutrition on the day of SE, prolonged mechanical ventilation prior to SE, higher non-neurological SOFA scores, and use of NIV after SE were independently associated with the need for reintubation. Reintubation was also associated with increased mortality, a higher risk of nosocomial pneumonia, and prolonged ICU stay.

Unplanned extubation has been widely recognized as a life-threatening adverse event in intensive care unit patients. To systematically quantify the global prevalence of unplanned extubation among critically ill adults and reintubation rate after unplanned extubation. Systematic review and meta-analysis. We identified original peer-reviewed studies through electronic searches of EMBASE, PubMed, ISI Web of Science, and CINAHL databases involving ventilated adult intensive care unit patients. Primary endpoints were prevalence of overall unplanned extubation rate rate, type of unplanned extubation (self-extubation or accidental extubation) and reintubation rate within 48 hours. Two reviewers independently selected studies and extracted data on the outcomes. Random effect meta-analysis of proportions was used to estimate the pooled prevalence rates. Of 1613 retrieved citations, 38 studies from 18 countries published between 1990 and 2020 were included. The overall methodological quality was low (mean score on Newcastle-Ottawa scale, 2.6/5). The pooled prevalence of unplanned extubation was 6.69% (95% CI, 5.29%-8.24%; 34 studies involving 121,129 subjects) with an incidence density of 1.06 events per 100 ventilator-days (95% CI, 0.7–1.3; 16 studies involving 375,967 ventilation days). The majority of unplanned extubations (84.2%) were self-extubations (95% CI, 79.8%-88.3%; 23 studies involving 2274 unplanned extubations). In addition, 50.2% of subjects with unplanned extubations required reintubation within 48 hours (95% CI, 43.6%-56.9%; 10 studies involving 1564 unplanned extubations). Despite significant heterogeneity between studies, these data showed that 6.7% of intubated adult subjects in the intensive care unit experience unplanned extubation, most of which are self-extubations. Further well-designed studies are required to better understand unplanned extubation among intubated intensive care unitpatient, using standardized methods of data collection and reporting.

Patient safety in the intensive care unit (ICU) is one of the most critical issues, and unplanned extubation (UE) is considered the most adverse event for patient safety. Prevention and early detection of such an event is an essential but difficult component of quality care.BACKGROUNDPatient safety in the intensive care unit (ICU) is one of the most critical issues, and unplanned extubation (UE) is considered the most adverse event for patient safety. Prevention and early detection of such an event is an essential but difficult component of quality care.This study aimed to develop and validate prediction models for UE in ICU patients using machine learning.OBJECTIVEThis study aimed to develop and validate prediction models for UE in ICU patients using machine learning.This study was conducted in an academic tertiary hospital in Seoul, Republic of Korea. The hospital had approximately 2000 inpatient beds and 120 ICU beds. As of January 2019, the hospital had approximately 9000 outpatients on a daily basis. The number of annual ICU admissions was approximately 10,000. We conducted a retrospective study between January 1, 2010, and December 31, 2018. A total of 6914 extubation cases were included. We developed a UE prediction model using machine learning algorithms, which included random forest (RF), logistic regression (LR), artificial neural network (ANN), and support vector machine (SVM). For evaluating the model's performance, we used the area under the receiver operating characteristic curve (AUROC). The sensitivity, specificity, positive predictive value, negative predictive value, and F1 score were also determined for each model. For performance evaluation, we also used a calibration curve, the Brier score, and the integrated calibration index (ICI) to compare different models. The potential clinical usefulness of the best model at the best threshold was assessed through a net benefit approach using a decision curve.METHODSThis study was conducted in an academic tertiary hospital in Seoul, Republic of Korea. The hospital had approximately 2000 inpatient beds and 120 ICU beds. As of January 2019, the hospital had approximately 9000 outpatients on a daily basis. The number of annual ICU admissions was approximately 10,000. We conducted a retrospective study between January 1, 2010, and December 31, 2018. A total of 6914 extubation cases were included. We developed a UE prediction model using machine learning algorithms, which included random forest (RF), logistic regression (LR), artificial neural network (ANN), and support vector machine (SVM). For evaluating the model's performance, we used the area under the receiver operating characteristic curve (AUROC). The sensitivity, specificity, positive predictive value, negative predictive value, and F1 score were also determined for each model. For performance evaluation, we also used a calibration curve, the Brier score, and the integrated calibration index (ICI) to compare different models. The potential clinical usefulness of the best model at the best threshold was assessed through a net benefit approach using a decision curve.Among the 6914 extubation cases, 248 underwent UE. In the UE group, there were more males than females, higher use of physical restraints, and fewer surgeries. The incidence of UE was higher during the night shift as compared to the planned extubation group. The rate of reintubation within 24 hours and hospital mortality were higher in the UE group. The UE prediction algorithm was developed, and the AUROC for RF was 0.787, for LR was 0.762, for ANN was 0.763, and for SVM was 0.740.RESULTSAmong the 6914 extubation cases, 248 underwent UE. In the UE group, there were more males than females, higher use of physical restraints, and fewer surgeries. The incidence of UE was higher during the night shift as compared to the planned extubation group. The rate of reintubation within 24 hours and hospital mortality were higher in the UE group. The UE prediction algorithm was developed, and the AUROC for RF was 0.787, for LR was 0.762, for ANN was 0.763, and for SVM was 0.740.We successfully developed and validated machine learning-based prediction models to predict UE in ICU patients using electronic health record data. The best AUROC was 0.787 and the sensitivity was 0.949, which was obtained using the RF algorithm. The RF model was well-calibrated, and the Brier score and ICI were 0.129 and 0.048, respectively. The proposed prediction model uses widely available variables to limit the additional workload on the clinician. Further, this evaluation suggests that the model holds potential for clinical usefulness.CONCLUSIONSWe successfully developed and validated machine learning-based prediction models to predict UE in ICU patients using electronic health record data. The best AUROC was 0.787 and the sensitivity was 0.949, which was obtained using the RF algorithm. The RF model was well-calibrated, and the Brier score and ICI were 0.129 and 0.048, respectively. The proposed prediction model uses widely available variables to limit the additional workload on the clinician. Further, this evaluation suggests that the model holds potential for clinical usefulness.

Background: Unplanned extubation (UE) in intensive care units (ICUs) is a significant patient safety concern, associated with increased morbidity and healthcare utilization; the reported rates of UE vary from 1% to 15%. There is sparse data on the effects of multiple performance improvement (PI) strategies to decrease the rate of UE, particularly in inner-city ICU populations. This study evaluates the impact of PI strategies on UE rates and associated patient outcomes in an adult ICU. Objectives: To determine the impact of performance improvement (PI) strategies on rates of unplanned extubation (UE), reintubation, tracheostomy, mortality, and length of hospital stay in ICU patients. Design: Retrospective cohort study Methods: This retrospective observational study included 6,397 mechanically ventilated patients admitted to a single tertiary ICU between 2015 and 2023. Three distinct time periods were compared: Period 1 (2015–2017, pre-PI), Period 2 (2018–2020, early-PI), and Period 3 (2021–2023, sustained-PI). Demographics, sedation practices, UE characteristics, and outcomes were analyzed using logistic regression. Results: UE incidence declined significantly from 3.79% in Period 1 to 2.17% in Period 3 (p = 0.002). Reintubation rates dropped from 45.2% to 26.7% (p = 0.011), and tracheostomy rates from 19.0% to 2.2% (p < 0.001). Multivariate analysis showed reduced odds of reintubation in Periods 2 (OR = 0.219, p = 0.001) and 3 (OR = 0.345, p = 0.021) and reduced odds of tracheostomy in Period 3 (OR = 0.011, p = 0.016). Risk factors for reintubation included the absence of prior intubation history and not undergoing spontaneous breathing trials. Older age (⩾71 years) and positive urine toxicology for opiates were strongly associated with tracheostomy. Conclusion: Implementation of PI strategies significantly reduced rates of unplanned extubation, reintubation, and tracheostomy. These findings support continued quality improvement initiatives in ICU airway management. Plain language summary Challenges in a urban city ICU-unplanned removal of endotracheal tube and strategies to prevent it Endotracheal tubes (ET tubes) are breathing tubes that are used to provide respiratory support to a patient from a ventilator. Since unintentional removal of ET tubes burdens both patients and the patient care team, several studies have been done to look for strategies to prevent it. There are various reasons why a ET tube removal can happen in an unplanned manner. This usually depends on risk factors in the patient which puts them at a higher risk of either self removal of the tube or it could be due to environmental factors such as different methods to secure the tube and different medications used to keep the patient comfortable while breathing with a ventilator. Regardless, every unplanned removal of ET tube carries a risk to the patient and can potentially lead to more complications. Urban city ICUs cater to a especially high risk patient population who are more critically ill and have unique disease pathologies. At our inner city hospital ICU, we explored and refined strategies to prevent these unplanned episodes and studied their effectiveness over almost 10 years. We divided our study period into 3 phases- before any strategy was implemented, after the 1st round of intervention and after the 2nd round of interventions. We found that a systemic way of implementing specific strategies has shown to reduce both the chances of unplanned removal event occurring and the complications occurring from it, specifically the need of replacing a removed ET tube and the need of long term ventilator. We did not find any change in death rate or how long the patients spends in the ICU even after these strategies were implemented. We recommend that a team approach including different levels of medical staff, nurses and respiratory staff to improve outcomes with unplanned extubations. More research is needed to further understand even better strategies in urban centers. We hope our study serves as a direction in which inner city hospital like ours can improve patient outcomes

Background It is crucial to identify the risk factors for unplanned extubation (UEX) in thoracoabdominal drainage tubes as early as possible and establish applicable risk prediction model to reduce the incidence of UEX. Methods A retrospective survey of patients who underwent Thoracoabdominal drainage tubes placement at a tertiary hospital was conducted in Zhejiang Province, China, between January 2020 and January 2023. A training set was established to build the predictive model and conduct internal validation, which was assessed for discrimination using ROC curves and for Calibration using the Hosmer–Lemeshow test and Calibration curves. A nomogram was constructed to visually present the results of the logistic regression analysis. An external validation dataset was created for assessing the external validation of the model. Results a total of 2220 patients were enrolled. Multiple logistic regression analysis showed that negative pressure ball drainage, adhesive fixation method, self-care ability (self-care vs. complete dependence), self-care ability (partial dependence vs. complete dependence), and Thoracoabdominal drainage tubes were statistically significant factors associated with UEX ( P  < 0.05).The predictive model equation was as follows: a = 0.95–1.66 × drainage method + 2.45 × fixation method −4.17 × self-care ability (self-care vs. complete dependence) −2.79 × self- care ability (partial dependence vs. complete dependence).In the internal validation, the AUC was 0.897 (95% CI = 0.87–0.92; P  < 0.001), with a sensitivity of 0.75 and specificity of 0.93, indicating a high level of discrimination for the model. The Hosmer–Lemeshow test yielded a chi-square (χ 2 ) value of 2.823 with 8 degrees of freedom and a P -value of 0.945, indicating high accuracy of the model. In the external validation, the AUC was 0.839 (95% CI = 0.75–0.93; P  < 0.001), with a sensitivity of 0.73 and specificity of 0.96. The Hosmer–Lemeshow test yielded a χ 2 value of 12.85 with 8 degrees of freedom and a P -value of 0.117. The DCA plot shows that the DCA curve is consistently higher than the two extreme curves, indicating a good fit of the model. Conclusion The predictive model for the risk of unplanned extubation of thoracoabdominal drainage tubes in postoperative patients demonstrates good discrimination and Calibration. It can provide reference for clinical nursing staff in predicting the risk and early development of personalized preventive strategies for drainage tube UEX.

To explore factors associated with unplanned extubation in Intensive Care Unit for adult patients. A systematic review and meta-analysis were performed of studies identified through Pubmed, CINAHL, Cochrane Library, PsycINFO and Web of Science published from initiation to September 2017. Only articles in English were included. The Newcastle-Ottawa Scale was used to evaluate the quality of the included articles. Ten eligible studies were identified, encompassing a total of 2092 patients (457 in the unplanned extubation group; 1635 in the control group). The subsequent meta-analysis identified significant risk factors for unplanned extubation are male [odds ratio (OR) 1.54, 95% CI 1.12-2.12; P = 0.008], confusion [OR 0.10, 95% CI 0.05-0.17; P < 0.00001], physical restraint [OR 3.10, 95% CI 2.21-4.34; P < 0.00001], higher GCS scores [mean difference (MD) 1.06, 95% CI 0.59-1.52; P < 0.00001] and lower APACHE II scores [MD -2.26, 95% CI -3.35- -1.16; P < 0.0001]. Renal disease is a protective factor for unplanned extubation [OR 0.32, 95% CI 0.15-0.70; P = 0.004]. Patients were male, confused, having physical restraint, with higher GCS and lower APACHE II scores are significant risk factors for unplanned extubation in Intensive Care Unit adult patients.

Cancer indeed represents a significant public health challenge, and unplanned extubation of peripherally inserted central catheter (PICC-UE) is a critical concern in patient safety. Identifying independent risk factors and implementing high-quality assessment tools for early detection in high-risk populations can play a crucial role in reducing the incidence of PICC-UE among patients with cancer. Precise prevention and treatment strategies are essential to improve patient outcomes and safety in clinical settings. This study aims to identify the independent risk factors associated with PICC-UE in patients with cancer and to construct a predictive model tailored to this group, offering a theoretical framework for anticipating and preventing PICC-UE in these patients. Independent risk factors for PICC-UE in patients with cancer were identified, including impaired physical mobility (odds ratio [OR] 2.775, 95% CI 1.951-3.946), diabetes (OR 1.754, 95% CI 1.134-2.712), surgical history (OR 1.734, 95% CI 1.313-2.290), elevated D-dimer concentration (OR 2.376, 95% CI 1.778-3.176), targeted therapy (OR 1.441, 95% CI 1.104-1.881), surgical treatment (OR 1.543, 95% CI 1.152-2.066), and more than 1 catheter puncture (OR 1.715, 95% CI 1.121-2.624). Protective factors were normal BMI (OR 0.449, 95% CI 0.342-0.590), polyurethane catheter material (OR 0.305, 95% CI 0.228-0.408), and valved catheter (OR 0.639, 95% CI 0.480-0.851). The TOPSIS synthesis analysis results showed that in the train set, the composite index (Ci) values were 0.00 for the logistic model, 0.82 for the support vector machine model, and 0.85 for the random forest model. In the test set, the Ci values were 0.00 for the logistic model, 1.00 for the support vector machine model, and 0.81 for the random forest model. The optimal model, constructed based on the support vector machine, was obtained and validated externally. The ROC curve, calibration curve, and DCA curve demonstrated that the model exhibited excellent accuracy, stability, generalizability, and clinical applicability. In summary, this study identified 10 independent risk factors for PICC-UE in patients with cancer. The predictive model developed using the support vector machine algorithm demonstrated excellent clinical applicability and was validated externally, providing valuable support for the early prediction of PICC-UE in patients with cancer.

Aims This study was performed to identify and summarize systematic reviews focusing on the prevention of unplanned endotracheal extubation in the intensive care unit. Design Overview of systematic reviews. Methods This overview was conducted according to the Preferred Reporting Items for Overviews of Systematic Reviews, including the harms checklist. A literature search of PubMed, the Cochrane Library, CINAH, Embase, Web of Science, SINOMED and PROSPERO was performed from January 1, 2005–June 1, 2021. A systematic review focusing on unplanned extubation was included, resulting in an evidence summary. Results Thirteen systematic reviews were included. A summary of evidence on unplanned endotracheal extubation was developed, and the main contents were risk factors, preventive measures and prognosis. The most important nursing measures were restraint, fixation of the tracheal tube, continuous quality improvement, psychological care and use of a root cause analysis for the occurrence of unplanned endotracheal extubation. Conclusions This overview re‐evaluated risk factors and preventive measures for unplanned endotracheal extubation in the intensive care unit, resulting in a summary of evidence for preventing unplanned endotracheal extubation and providing direction for future research. Trial registration details The study was registered on the PROSPERO website.

Purpose Unplanned extubation (UE) is an important paediatric intensive care unit (PICU) quality indicator. Studies on UE have been modest in size, with accurate UE rate calculation potentially hampered by ventilation episodes recorded in calendar days. We wished to document UE rates, outcomes, associated factors and quantify error when calendar days rather than exact timings are used. Methods We recorded prospectively all UE episodes and potential associated factors in our 20-bed PICU for 12,533 admissions (2000–2013). Ventilation episodes were recorded to the minute, with non-invasive and tracheostomy ventilation excluded. Analysis utilised multilevel mixed-effects Poisson regression, adjusting for multiple ventilation episodes in the same patient. Results Overall, 243 UEs occurred within 14,141 ventilation episodes (31,564 intubated days), giving a UE rate of 0.77 (95 % CI 0.67–0.87) episodes per 100 intubated days. If calendar ventilation days were used, the yearly UE rate was underestimated by 27–35 %. UE rates decreased with time, by approximately 0.05/100 intubated days each year. Associations with UE incidence rate included patient age, source of admission, disease severity and diagnostic category, with nasal tubes decreasing the risk. Although UE versus planned extubation was associated with a higher re-intubation rate (43 versus 8 %) and longer median PICU stay (4.6 versus 2.6 days, p  < 0.001), mortality between the two groups did not differ (3.0 versus 5.1 %, p  = 0.18). Conclusions This study provides contemporaneous UE rates for benchmarking. Recording ventilation in calendar days underestimates UE rate. Several factors associated with UE may serve as a focus of quality improvement.