Journal of clinical monitoring and computing
-
We evaluated the accuracy and precision of a novel non-invasive monitoring device in comparison with conventional monitoring methods used in intensive care units (ICU). The study device was developed to measure blood pressure, pulse rate, respiratory rate, and oxygen saturation, continuously with a single sensor using the photoplethysmographic technique. Patients who were monitored with arterial pressure lines in the ICU were enrolled. ⋯ Percent errors for systolic, diastolic and mean blood pressures were 2.4% and 6.7% and 6.5%, respectively. Percent errors for pulse rate, respiratory rate and oxygen saturation were 3.4%, 5.6% and 1.4%, respectively. The non-invasive, continuous, multi-parameter monitoring device presented high level of agreement with the invasive arterial blood pressure monitoring, along with sufficient accuracy and precision in the measurements of pulse rate, respiratory rate, and oxygen saturation.
-
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.
-
J Clin Monit Comput · Aug 2019
Cerebral arterial time constant calculated from the middle and posterior cerebral arteries in healthy subjects.
The cerebral arterial blood volume changes (∆CaBV) during a single cardiac cycle can be estimated using transcranial Doppler ultrasonography (TCD) by assuming pulsatile blood inflow, constant, and pulsatile flow forward from large cerebral arteries to resistive arterioles [continuous flow forward (CFF) and pulsatile flow forward (PFF)]. In this way, two alternative methods of cerebral arterial compliance (Ca) estimation are possible. Recently, we proposed a TCD-derived index, named the time constant of the cerebral arterial bed (τ), which is a product of Ca and cerebrovascular resistance and is independent of the diameter of the insonated vessel. ⋯ No difference was found in the τ when calculated using the CFF model. Longer τ from the MCA might be related to the higher Ca of the MCA than that of the PCA. Our results demonstrate MCA-PCA differences in the τ, but only when the PFF model was applied.
-
J Clin Monit Comput · Aug 2019
Comparative Study Observational StudyComparison of ability of pulse pressure variation to predict fluid responsiveness in prone and supine position: an observational study.
We aimed to compare the ability of pulse pressure variation (PPV) to predict fluid responsiveness in prone and supine positions and investigate effect of body mass index (BMI), intraabdominal pressure (IAP) and static respiratory compliance (CS) on PPV. A total of 88 patients undergoing neurosurgery were included. After standardized anesthesia induction, patients' PPV, stroke volume index (SVI), CS and IAP values were recorded in supine (T1) and prone (T2) positions and after fluid loading (T3). ⋯ When all patients were examined for predicting fluid responsiveness, area under curves (AUC) of PPVT2 (0.790, 95%CI 0.690-0.870) was significantly lower than AUC of PPVT1 (0.937, 95%CI 0.878-0.997) with ROC analysis (p = 0.002). When patients whose CST2 was < 31 ml/cmH2O and whose BMI was > 30 kg/m2 were excluded from analysis separately, AUC of PPVT2 became similar to PPVT1. PPV in the prone can predict fluid responsiveness as good as PPV in the supine, only if BMI is < 30 kg/m2 and CS value at prone is > 31 ml/cmH2O.