Journal of clinical monitoring and computing
-
J Clin Monit Comput · Aug 2019
Identify and monitor clinical variation using machine intelligence: a pilot in colorectal surgery.
Standardized clinical pathways are useful tool to reduce variation in clinical management and may improve quality of care. However the evidence supporting a specific clinical pathway for a patient or patient population is often imperfect limiting adoption and efficacy of clinical pathway. Machine intelligence can potentially identify clinical variation and may provide useful insights to create and optimize clinical pathways. ⋯ Multiple sub-groups were easily created and analyzed. Adherence reporting tools were easy to use enabling almost real time monitoring. Machine intelligence provided useful insights to create and monitor care pathways with several advantages over traditional analytic approaches including: (1) analysis across disparate data sets, (2) unsupervised discovery, (3) speed and auto-generation of clinical pathways, (4) ease of use by team members, and (5) adherence reporting.
-
Hospital noise levels regularly exceed those recommended by the World Health Organization (WHO). It is uncertain whether high noise levels have adverse effects on patient health. High levels of noise increase patient sleep loss, anxiety levels, length of hospital stay, and morbidity rates. ⋯ The Hospital Consumer Assessment of Healthcare Providers and Systems survey shows a slight improvement in overall hospital noise levels in the United States, indicating a minor reduction in noise levels. Alarm ambiguity, alarm masking and inefficient alarm design contributes to a large portion of sounds that exceed the environmental noise level in the hospital. Improving the hospital soundscape can begin by training staff in noise reduction, enforcing noise reduction programs, reworking alarm design and encouraging research to evaluate the relative effects of noise producing stimuli on the hospital soundscape.
-
J Clin Monit Comput · Aug 2019
Machine learning based framework to predict cardiac arrests in a paediatric intensive care unit : Prediction of cardiac arrests.
A cardiac arrest is a life-threatening event, often fatal. Whilst clinicians classify some of the cardiac arrests as potentially predictable, the majority are difficult to identify even in a post-incident analysis. Changes in some patients' physiology when analysed in detail can however be predictive of acute deterioration leading to cardiac or respiratory arrests. ⋯ A positive predictive value of 11% and negative predictive value of 98% was obtained with a prevalence of 5% by our method of prediction. While clinicians predicted 4 out of the 69 cardiac arrests (6%), the prediction system predicted 63 (91%) cardiac arrests. Prospective validation of the automated system remains.
-
J Clin Monit Comput · Aug 2019
Comparative Study Observational StudyA comparison of propofol-to-BIS post-operative intensive care sedation by means of target controlled infusion, Bayesian-based and predictive control methods: an observational, open-label pilot study.
We evaluated the feasibility and robustness of three methods for propofol-to-bispectral index (BIS) post-operative intensive care sedation, a manually-adapted target controlled infusion protocol (HUMAN), a computer-controlled predictive control strategy (EPSAC) and a computer-controlled Bayesian rule-based optimized control strategy (BAYES). ⋯ Both computer-based control systems are feasible to be used during ICU sedation with overall tighter control than HUMAN and even with lower required CePROP. EPSAC control required higher CeREMI than BAYES or HUMAN to maintain stable control. Clinical trial number: NCT00735631.
-
J Clin Monit Comput · Aug 2019
Selection of cuffed endotracheal tube for children with congenital heart disease based on an ultrasound-based linear regression formula.
It remains to be discovered whether a formula predicting the subglottic transverse diameter measured by ultrasound (SGDformula) for the selection of an appropriate endotracheal tube (ETT) for children without congenital heart disease (CHD) is useful for children with CHD. A formula for predicting SGD was established after assessing 60 children ≤ 8 years without CHD and validated on 60 children with CHD. We selected the cuffed ETT size based on the SGD by ultrasound (SGDultra). ⋯ And the mean bias (SGDformula-ETT size and SGDultra-ETT size) was 0.21 mm (95% confidence interval, - 0.59 to 1.01 mm) and 0.00 mm (- 0.79 to 0.84 mm). For the CHD group, the ultrasound-based method yielded a 78% success rate of ETT size choice, while the formula-based method permitted an appropriate ETT size in only 32% of subjects (P < 0.001). Our analysis showed that measuring the SGDultra was more accurate in predicting the correct OD of the ETT in children with CHD undergoing cardiovascular surgery, based on the correlation and agreement with ETT OD.