Dataset Information

Multidisciplinary interventions and continuous quality improvement to reduce unplanned extubation in adult intensive care units: A 15-year experience.

ABSTRACT: We conduct a retrospective study of patients with unplanned extubation (UE) in adult intensive care units (ICU) at a medical center. In 2001, a multidisciplinary team of intensivists, senior residents, nurses, and respiratory therapists was established at Chi Mei Medical Center. The improvement interventions, implemented between 2001 and 2015, were organized around 8 key areas: standardizing procedures, improving communication skills, revising sedation and weaning protocols, changing strategies for restraints, establishing a task force for identifying and managing high-risk patients, using new quality-improvement models as breakthrough series and team resource management, using the strategy of accountability without assigning blame, and changing a new method to secure endotracheal tube. We measured the outcome as the annual event and the rate of UE. During this 15-year period, there were 1404 episodes of UE, with 44,015 episodes of mechanical ventilation (MV) (319,158 ventilator-days). The overall rate of UE was 3.19/100 ventilated patients (4.40/1000 ventilator-days). In 2001, there were 188 episodes of UE and the rate of UE was 6.82/100 ventilated patients or 9.0/1000 ventilator-days. After this continue quality improvement project had been implemented, the annual number of episodes of UE declined to 27, and the rate fell to 0.95/100 ventilated patients or 1.36/1000 ventilator-days in 2015. Overall, the trend analysis showed the change was significant with P?

SUBMITTER: Chao CM

PROVIDER: S-EPMC5502132 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature

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Multidisciplinary interventions and continuous quality improvement to reduce unplanned extubation in adult intensive care units: A 15-year experience.

Chao Chien-Ming CM Lai Chih-Cheng CC Chan Khee-Siang KS Cheng Kuo-Chen KC Ho Chung-Han CH Chen Chin-Ming CM Chou Willy W

Medicine 20170701 27

We conduct a retrospective study of patients with unplanned extubation (UE) in adult intensive care units (ICU) at a medical center. In 2001, a multidisciplinary team of intensivists, senior residents, nurses, and respiratory therapists was established at Chi Mei Medical Center. The improvement interventions, implemented between 2001 and 2015, were organized around 8 key areas: standardizing procedures, improving communication skills, revising sedation and weaning protocols, changing strategies ...[more]

PMID: 28682859

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Project description:ImportanceUnplanned extubations (UEs) in children contribute to significant morbidity and mortality, with an arbitrary benchmark target of less than 1 UE per 100 ventilator days. However, there have been no multicenter initiatives to reduce these events.ObjectiveTo determine if a multicenter quality improvement initiative targeting all intubated neonatal and pediatric patients is associated with a reduction in UEs and morbidity associated with UE events.Design, setting, and participantsThis multicenter quality improvement initiative enrolled patients from pediatric, neonatal, and cardiac intensive care units (ICUs) in 43 participating children's hospitals from March 2016 to December 2018. All patients with an endotracheal tube requiring mechanical ventilation were included in the study.InterventionsParticipating hospitals implemented a quality improvement bundle to reduce UEs, which included standardized anatomic reference points and securement methods, protocol for high-risk situations, and multidisciplinary apparent cause analyses.Main outcomes and measuresThe main outcome measures for this study included bundle compliance with each factor tested and UE rates on the center level and on the cohort level.ResultsAmong the 43 children's hospitals, the quality improvement initiative was associated with an aggregate 24.1% reduction in UE events, from a baseline rate of 1.135 UEs per 100 ventilator days to 0.862 UEs per 100 ventilator days. Across ICU settings studied, the pediatric ICU and neonatal ICU demonstrated centerline shifts, with an absolute reduction in events of 20.6% (from a baseline rate of 0.729 UEs per 100 ventilator days to 0.579 UEs per 100 ventilator days) and 17.6% (from a baseline rate of 1.555 UEs per 100 ventilator days to 1.282 UEs per 100 ventilator days), respectively. Most UEs required reintubation within 1 hour (mean of 120 of 206 events per month [58.3%]), followed by UEs that did not require reintubation (mean of 78 of 206 events per month [37.9%]) and UEs that resulted in cardiovascular collapse (mean of 8 of 206 events per month [3.9%]). Cardiovascular collapse events represented the most significant consequence of UE studied, and the collaborative reduced these UE events by 36.6%, from a study baseline rate of 0.041 UEs per 100 ventilator days to 0.026 UEs per 100 ventilator days.Conclusions and relevanceThis multicenter quality improvement initiative was associated with a reduction in UEs across different pediatric populations in diverse settings. A significant reduction in event rate and rate of harm (cardiovascular collapse) was observed, which was sustained over the time course of the intervention. This quality improvement process and UE bundle may be considered standard of care for pediatric hospitals in the future.

Project description: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.ObjectiveThis study aimed to develop and validate prediction models for UE in ICU patients using machine learning.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.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.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.

Dataset Information

Multidisciplinary interventions and continuous quality improvement to reduce unplanned extubation in adult intensive care units: A 15-year experience.

Publications

Multidisciplinary interventions and continuous quality improvement to reduce unplanned extubation in adult intensive care units: A 15-year experience.

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