Journal of clinical monitoring and computing
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J Clin Monit Comput · Oct 2022
Multicenter StudyPredicting hypoglycemia in critically Ill patients using machine learning and electronic health records.
Hypoglycemia is a common occurrence in critically ill patients and is associated with significant mortality and morbidity. We developed a machine learning model to predict hypoglycemia by using a multicenter intensive care unit (ICU) electronic health record dataset. Machine learning algorithms were trained and tested on patient data from the publicly available eICU Collaborative Research Database. ⋯ The best-performing predictive model was the eXtreme gradient boosting model (XGBoost), which achieved an area under the received operating curve (AUROC) of 0.85, a sensitivity of 0.76, and a specificity of 0.76. The machine learning model developed has strong discrimination and calibration for the prediction of hypoglycemia in ICU patients. Prospective trials of these models are required to evaluate their clinical utility in averting hypoglycemia within critically ill patient populations.
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J Clin Monit Comput · Oct 2022
Pressure-flow breath representation eases asynchrony identification in mechanically ventilated patients.
Breathing asynchronies are mismatches between the requests of mechanically ventilated subjects and the support provided by mechanical ventilators. The most widespread technique in identifying these pathological conditions is the visual analysis of the intra-tracheal pressure and flow time-trends. This work considers a recently introduced pressure-flow representation technique and investigates whether it can help nurses in the early detection of anomalies that can represent asynchronies. ⋯ The pressure-flow diagram significantly increases sensitivity and decreases the response time of early asynchrony detection performed by nurses. Moreover, the data suggest that operator experience does not affect the identification results. This outcome leads us to believe that, in emergency contexts with a shortage of nurses, intensive care nurses can be supplemented, for the sole identification of possible respiratory asynchronies, by inexperienced staff.
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J Clin Monit Comput · Oct 2022
Lag times to steady state drug delivery by continuous intravenous infusion: direct comparison of peristaltic and syringe pump performance identifies contributions from infusion system dead volume and pump startup characteristics.
Time lags between the initiation of a continuous drug infusion and achievement of a steady state delivery rate present an important safety concern. At least 3 factors contribute to these time lags: (1) dead volume size, (2) the ratio between total system flow and dead volume, and (3) startup delay. While clinicians employ both peristaltic pumps and syringe pumps to propel infusions, there has been no head-to-head comparison of drug delivery between commercially available infusion pumps with these distinct propulsion mechanisms. ⋯ Startup delay and dead volume in carrier-based infusion systems cause substantial time lags to reaching intended delivery rates. Peristaltic and syringe pumps are similarly susceptible to dead volume effects. Startup performance differed between peristaltic and syringe pumps; their relative performance may be dependent on flow rate.
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J Clin Monit Comput · Oct 2022
Randomized Controlled TrialThe effect of different flow levels and concentrations of sevoflurane during the wash-in phase on volatile agent consumption: a randomized controlled trial.
The standard procedure for low-flow anesthesia usually incorporates a high fresh gas flow (FGF) of 4-6 L/minute during the wash-in phase. However, the administration of a high FGF (4-6 L/min) increases the inhaled anesthetic agent consumption. This study was designed to compare the sevoflurane consumption at 2 rates of flow and vaporizer concentration during the wash-in period. ⋯ The anesthetic agent consumption during the wash-in phase was approximately 3 times lower with the administration of sevoflurane at 1 L/minute FGF than the use of 4 L/minute FGF.
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J Clin Monit Comput · Oct 2022
Implementing a Rapid Response System in a tertiary-care hospital. A cost-effectiveness study.
The occurrence of adverse events (AE) in hospitalized patients substancially increases the risk of disability or death, having a major negative clinical and economic impact on public health. For early identification of patients at risk and to establish preventive measures, different healthcare systems have implemented rapid response systems (RRS). The aim of this study was to carry out a cost-effectiveness analysis of implementing a RRS in a tertiary-care hospital. ⋯ The present analysis shows the RRS as a dominant, less costly and more effective structure compared to the non-RRS.