Dataset Information

Face and content validity of variables associated with the difficult-to-sedate child in the paediatric intensive care unit: A survey of paediatric critical care clinicians.

ABSTRACT: BACKGROUND:Clinicians recognise that some critically ill children are difficult-to-sedate. It may be possible to identify this clinical phenotype for sedation response using statistical modelling techniques adopted from machine learning. This requires identification of a finite number of variables to include in the statistical model. OBJECTIVE:To establish face and content validity for 17 candidate variables identified in the international literature as characteristic of the difficult-to-sedate child phenotype. METHODS:Paediatric critical care clinicians rated the relevance of 17 variables characterising the difficult-to-sedate child using a four-point scale ranging from not (1) to highly relevant (4). Face and content validity of these variables were assessed by calculating a mean score for each item and computing an item-level content validity index. Items with a mean score >1 were rated as having adequate face validity. An item-level content validity index ?0.70 indicated good to excellent content validity. SETTING AND PARTICIPANTS:Web-based survey emailed to members of the Pediatric Acute Lung Injury and Sepsis Investigators Network or the Society of Critical Care Medicine Pediatric Sedation Study Group. RESULTS:Of 411 possible respondents, 121 useable surveys were returned for a response rate of 29%. All items had a mean score >1, indicating adequate face validity. Ten of 17 items scored an item-level content validity index ?0.70. The highest scoring items were requiring three or more sedation classes simultaneously, daily modal sedation score indicating agitation, sedation score indicating agitation for 2 consecutive hours, receiving sedatives at a dose >90th percentile of the usual starting dose, and receiving intermittent paralytic doses for sedation. CONCLUSIONS:Computation of an item-level content validity index validated variables to include in statistical modelling of the difficult-to-sedate phenotype. The results indicate consensus among paediatric critical care clinicians that the majority of candidate variables identified through literature review are characteristic of the difficult-to-sedate child.

SUBMITTER: Lebet RM

PROVIDER: S-EPMC5936660 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Face and content validity of variables associated with the difficult-to-sedate child in the paediatric intensive care unit: A survey of paediatric critical care clinicians.

Lebet Ruth M RM Asaro Lisa A LA Zuppa Athena F AF Curley Martha A Q MAQ

Australian critical care : official journal of the Confederation of Australian Critical Care Nurses 20180319 3

<h4>Background</h4>Clinicians recognise that some critically ill children are difficult-to-sedate. It may be possible to identify this clinical phenotype for sedation response using statistical modelling techniques adopted from machine learning. This requires identification of a finite number of variables to include in the statistical model.<h4>Objective</h4>To establish face and content validity for 17 candidate variables identified in the international literature as characteristic of the diffi ...[more]

PMID: 29567042

Similar Datasets

Project description:The paediatric intensive care unit (PICU) provides care to critically ill neonates, infants and children. These patients are vulnerable and susceptible to the environment surrounding them, yet there is little information available on indoor air quality and factors affecting it within a PICU. To address this gap in knowledge we conducted continuous indoor and outdoor airborne particle concentration measurements over a two-week period at the Royal Children's Hospital PICU in Brisbane, Australia, and we also collected 82 bioaerosol samples to test for the presence of bacterial and viral pathogens. Our results showed that both 24-hour average indoor particle mass (PM10) (0.6-2.2μgm-3, median: 0.9μgm-3) and submicrometer particle number (PN) (0.1-2.8×103pcm-3, median: 0.67×103pcm-3) concentrations were significantly lower (p<0.01) than the outdoor concentrations (6.7-10.2μgm-3, median: 8.0μgm-3 for PM10 and 12.1-22.2×103pcm-3, median: 16.4×103pcm-3 for PN). In general, we found that indoor particle concentrations in the PICU were mainly affected by indoor particle sources, with outdoor particles providing a negligible background. We identified strong indoor particle sources in the PICU, which occasionally increased indoor PN and PM10 concentrations from 0.1×103 to 100×103pcm-3, and from 2μgm-3 to 70μgm-3, respectively. The most substantial indoor particle sources were nebulization therapy, tracheal suction and cleaning activities. The average PM10 and PN emission rates of nebulization therapy ranged from 1.29 to 7.41mgmin-1 and from 1.20 to 3.96pmin-1×1011, respectively. Based on multipoint measurement data, it was found that particles generated at each location could be quickly transported to other locations, even when originating from isolated single-bed rooms. The most commonly isolated bacterial genera from both primary and broth cultures were skin commensals while viruses were rarely identified. Based on the findings from the study, we developed a set of practical recommendations for PICU design, as well as for medical and cleaning staff to mitigate aerosol generation and transmission to minimize infection risk to PICU patients.

Project description:Rationale: Determining when an intensive care unit (ICU) patient is ready for discharge to the ward is a complex daily challenge for any ICU care team. Patients who experience unplanned readmissions to the ICU have increased mortality, length of stay, and cost compared with those not readmitted during their hospital stay. The accuracy of clinician prediction for ICU readmission is unknown.Objectives: To determine the accuracy of ICU physicians and nurses for predicting ICU readmissionsMethods: We conducted a prospective study in the medical ICU of an academic hospital from October 2015 to September 2017. After daily rounding for patients being transferred to the ward, ICU clinicians (nurses, residents, fellows, and attendings) were asked to report the likelihood of readmission within 48 hours (using a 1-10 scale, with 10 being "extremely likely"). The accuracy of the clinician prediction score (1-10) was assessed for all clinicians and by clinician type using sensitivity, specificity, and area under the curve (AUC) for the receiver operating characteristic curve for predicting the primary outcome, which was ICU readmission within 48 hours of ICU discharge.Results: A total of 2,833 surveys was collected for 938 ICU-to-ward transfers, of which 40 (4%) were readmitted to the ICU within 48 hours of transfer. The median clinician likelihood of readmission score was 3 (interquartile range, 2-4). When physician and nurse likelihood scores were combined, the median clinician likelihood score had an AUC of 0.70 (95% confidence interval [CI], 0.62-0.78) for predicting ICU readmission within 48 hours. Nurses were significantly more accurate than interns at predicting 48-hour ICU readmission (AUC, 0.73 [95% CI, 0.64-0.82] vs. AUC, 0.60 [95% CI, 0.49-0.71]; P = 0.03). All other pairwise comparisons were not significantly different for predicting ICU readmission within 48 hours (P > 0.05 for all comparisons).Conclusions: We found that all clinicians surveyed in our ICU, regardless of the level of experience or clinician type, had only fair accuracy for predicting ICU readmission. Further research is needed to determine if clinical decision support tools would provide prognostic value above and beyond clinical judgment for determining who is ready for ICU discharge.

Project description:BackgroundOptimal sedation and analgesia is a challenge in paediatric intensive care units (PICU) because of difficulties in scoring systems and specific metabolism inducing tolerance and withdrawal. Excessive sedation is associated with prolonged mechanical ventilation and hospitalisation. Adult and paediatric data suggest that goal-directed sedation algorithms reduce the duration of mechanical ventilation. We implemented a nurse-driven sedation protocol in a PICU and evaluated its impact.MethodsWe conducted a before and after protocol implementation study in a population of children aged 0-18 years who required mechanical ventilation for at least 24 h between January 2013 and March 2015. After the protocol implementation in January 2014, nurses managed analgesia and sedation following an algorithm that included the COMFORT behaviour scale (COMFORT-B). Duration of mechanical ventilation was the primary outcome; secondary outcomes were total doses and duration of medications, PICU length of stay, incidence of ventilator-associated pneumonia, and occurrence of withdrawal symptoms. Pre-post analysis followed with segmented regression analysis of interrupted time series was used to assess the effect of protocol.ResultsA total of 200 children were analysed, including 107 before implementation and 93 children after implementation of the protocol. After implementation of the protocol, the total number of COMFORT-B scores per day of mechanical ventilation significantly increased from 3.9 ± 2.5 times during the pre-implementation period to 6.6 ± 3.5 times during the post-implementation period (p < 10-3). Mean duration of mechanical ventilation tended to be lower in the post-implementation period (8.3 ± 7.3 vs 6.6 ± 5.6 days, p = 0.094), but changes in either the trend per trimester from pre-implementation to post-implementation (p = 0.933) or the immediate change after implementation (p = 0.923) were not significant with segmented regression analysis. No significant change between pre- and post-implementation was shown for total dose of sedatives, withdrawal symptoms, agitation episodes, or unplanned endotracheal extubations.ConclusionsThese results were promising and suggested that implementation of a nurse-driven sedation protocol in a PICU was feasible. Evaluation of sedation and analgesia was better after the protocol implementation; duration of mechanical ventilation and occurrence of withdrawal symptoms tended to be reduced.

Dataset Information

Face and content validity of variables associated with the difficult-to-sedate child in the paediatric intensive care unit: A survey of paediatric critical care clinicians.

Publications

Face and content validity of variables associated with the difficult-to-sedate child in the paediatric intensive care unit: A survey of paediatric critical care clinicians.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets