Critical care transfer in an English critical care network: Analysis of 1124 transfers delivered by an ad-hoc system.

BACKGROUND: Critical care transfers between hospitals are time critical high-risk episodes for unstable patients who often require urgent lifesaving intervention. This study aimed to establish the scale, nature and safety of current transfer practice in the South West Critical Care Network (SWCCN) in England. METHODS: The SWCCN database contains prospectively collected data in accordance with national guidelines. It was interrogated for all adult (>15 years of age) patients from January 2012 to November 2017. RESULTS: A total of 1124 inter-hospital transfers were recorded, with the majority (935, 83.2%) made for specialist treatment. The transferring team included a doctor in 998 (88.8%) and nurse in 935 (93.7%) transfers. In 204 (18.1%) transfers, delays occurred, with the commonest cause being availability of transport. Critical incidents occurred in 77 (6.9%). CONCLUSIONS: This is the first published data on the transfer activity of a UK adult critical care network. It demonstrates that current ad-hoc provision is not meeting the longstanding expectations of national guidelines in terms of training, clinical experience and timeliness. The authors hope that this study may inform national conversation regarding the development of National Health Service commissioned inter-hospital transfer services for adult patients in England.

Towards a decision support tool for intensive care discharge: machine learning algorithm development using electronic healthcare data from MIMIC-III and Bristol, UK.

OBJECTIVE: The primary objective is to develop an automated method for detecting patients that are ready for discharge from intensive care. DESIGN: We used two datasets of routinely collected patient data to test and improve on a set of previously proposed discharge criteria. SETTING: Bristol Royal Infirmary general intensive care unit (GICU). PATIENTS: Two cohorts derived from historical datasets: 1870 intensive care patients from GICU in Bristol, and 7592 from Medical Information Mart for Intensive Care (MIMIC)-III. RESULTS: In both cohorts few successfully discharged patients met all of the discharge criteria. Both a random forest and a logistic classifier, trained using multiple-source cross-validation, demonstrated improved performance over the original criteria and generalised well between the cohorts. The classifiers showed good agreement on which features were most predictive of readiness-for-discharge, and these were generally consistent with clinical experience. By weighting the discharge criteria according to feature importance from the logistic model we showed improved performance over the original criteria, while retaining good interpretability. CONCLUSIONS: Our findings indicate the feasibility of the proposed approach to ready-for-discharge classification, which could complement other risk models of specific adverse outcomes in a future decision support system. Avenues for improvement to produce a clinically useful tool are identified.

Increasing compliance with low tidal volume ventilation in the ICU with two nudge-based interventions: evaluation through intervention time-series analyses.

OBJECTIVES: Low tidal volume (TVe) ventilation improves outcomes for ventilated patients, and the majority of clinicians state they implement it. Unfortunately, most patients never receive low TVes. 'Nudges' influence decision-making with subtle cognitive mechanisms and are effective in many contexts. There have been few studies examining their impact on clinical decision-making. We investigated the impact of 2 interventions designed using principles from behavioural science on the deployment of low TVe ventilation in the intensive care unit (ICU). SETTING: University Hospitals Bristol, a tertiary, mixed medical and surgical ICU with 20 beds, admitting over 1300 patients per year. PARTICIPANTS: Data were collected from 2144 consecutive patients receiving controlled mechanical ventilation for more than 1 hour between October 2010 and September 2014. Patients on controlled mechanical ventilation for more than 20 hours were included in the final analysis. INTERVENTIONS: (1) Default ventilator settings were adjusted to comply with low TVe targets from the initiation of ventilation unless actively changed by a clinician. (2) A large dashboard was deployed displaying TVes in the format mL/kg ideal body weight (IBW) with alerts when TVes were excessive. PRIMARY OUTCOME MEASURE: TVe in mL/kg IBW. FINDINGS: TVe was significantly lower in the defaults group. In the dashboard intervention, TVe fell more quickly and by a greater amount after a TVe of 8 mL/kg IBW was breached when compared with controls. This effect improved in each subsequent year for 3 years. CONCLUSIONS: This study has demonstrated that adjustment of default ventilator settings and a dashboard with alerts for excessive TVe can significantly influence clinical decision-making. This offers a promising strategy to improve compliance with low TVe ventilation, and suggests that using insights from behavioural science has potential to improve the translation of evidence into practice.

Using 'nudge' principles for order set design: a before and after evaluation of an electronic prescribing template in critical care.

OBJECTIVE: Computerised order sets have the potential to reduce clinical variation and improve patient safety but the effect is variable. We sought to evaluate the impact of changes to the design of an order set on the delivery of chlorhexidine mouthwash and hydroxyethyl starch (HES) to patients in the intensive care unit. METHODS: The study was conducted at University Hospitals Bristol NHS Foundation Trust, UK. Our intensive care unit uses a clinical information system (CIS). All drugs and fluids are prescribed with the CIS and drug and fluid charts are stored within a database. Chlorhexidine mouthwash was added as a default prescription to the prescribing template in January 2010. HES was removed from the prescribing template in April 2009. Both interventions were available to prescribe manually throughout the study period. We conducted a database review of all patients eligible for each intervention before and after changes to the configuration of choices within the prescribing system. RESULTS: 2231 ventilated patients were identified as appropriate for treatment with chlorhexidine, 591 before the intervention and 1640 after. 55.3% were prescribed chlorhexidine before the change and 90.4% after (p<0.001). 6199 patients were considered in the HES intervention, 2177 before the intervention and 4022 after. The mean volume of HES infused per patient fell from 630 mL to 20 mL after the change (p<0.001) and the percentage of patients receiving HES fell from 54.1% to 3.1% (p<0.001). These results were well sustained with time. CONCLUSIONS: The presentation of choices within an electronic prescribing system influenced the delivery of evidence-based interventions in a predictable way and the effect was well sustained. This approach has the potential to enhance the effectiveness of computerised order sets.