Journal of clinical monitoring and computing
-
J Clin Monit Comput · Aug 2024
Estimation of the transpulmonary pressure from the central venous pressure in mechanically ventilated patients.
Transpulmonary pressure (PL) calculation requires esophageal pressure (PES) as a surrogate of pleural pressure (Ppl), but its calibration is a cumbersome technique. Central venous pressure (CVP) swings may reflect tidal variations in Ppl and could be used instead of PES, but the interpretation of CVP waveforms could be difficult due to superposition of heartbeat-induced pressure changes. Thus, we developed a digital filter able to remove the cardiac noise to obtain a filtered CVP (f-CVP). ⋯ Both PLf-CVP and PLCVP correlated well with PLPES (r = 0.98, p < 0.001 vs. r = 0.94, p < 0.001), again with a lower bias in Bland Altman analysis in favor of PLf-CVP (0.15, LoA - 0.95, 1.26 cmH2O vs. 0.80, LoA - 1.51, 3.12, cmH2O). PLf-CVP discriminated high PL value with an area under the receiver operating characteristic curve 0.99 (standard deviation, SD, 0.02) (AUC difference = 0.01 [-0.024; 0.05], p = 0.48). In mechanically ventilated patients with acute respiratory failure, the digital filtered CVP estimated ΔPES and PL obtained from digital filtered CVP represented a reliable value of standard PL measured with the esophageal method and could identify patients with non-protective ventilation settings.
-
J Clin Monit Comput · Aug 2024
Perfusion tomography in early follow-up of acute traumatic subdural hematoma: a case series.
Perfusion Computed Tomography (PCT) is an alternative tool to assess cerebral hemodynamics during trauma. As acute traumatic subdural hematomas (ASH) is a severe primary injury associated with poor outcomes, the aim of this study was to evaluate the cerebral hemodynamics in this context. Five adult patients with moderate and severe traumatic brain injury (TBI) and ASH were included. ⋯ One patient died with the highest preoperative MTT (9.97 s) and CBV (4.51 ml/100 g). CBF seems to increase after surgery, especially when evaluated together with the MTT values. It is suggested that the improvement in postoperative brain hemodynamics correlates to favorable outcome.
-
J Clin Monit Comput · Aug 2024
Electronic health record data is unable to effectively characterize measurement error from pulse oximetry: a simulation study.
Large data sets from electronic health records (EHR) have been used in journal articles to demonstrate race-based imprecision in pulse oximetry (SpO2) measurements. These articles do not appear to recognize the impact of the variability of the SpO2 values with respect to time ("deviation time"). This manuscript seeks to demonstrate that due to this variability, EHR data should not be used to quantify SpO2 error. Using the MIMIC-IV Waveform dataset, SpO2 values are sampled from 198 patients admitted to an intensive care unit and used as reference samples. ⋯ Each analysis was repeated to evaluate whether the measurement errors were affected by increasing the deviation time. All error values increased linearly with respect to the logarithm of the time deviation. At 10 min, the ARMS error increased from a baseline of 2% to over 4%. EHR data cannot be reliably used to quantify SpO2 error. Caution should be used in interpreting prior manuscripts that rely on EHR data.
-
J Clin Monit Comput · Aug 2024
LetterCan the values of the venous-to-arterial PCO2 difference (pCO2 gap) be negative?
In this manuscript, we discussed if it is physiologically sound that the difference between venous-to-arterial carbon dioxide partial pressure difference (pCO2 gap) can yield negative values.
-
J Clin Monit Comput · Aug 2024
Observational StudyA machine learning algorithm for detecting abnormal patterns in continuous capnography and pulse oximetry monitoring.
Continuous capnography monitors patient ventilation but can be susceptible to artifact, resulting in alarm fatigue. Development of smart algorithms may facilitate accurate detection of abnormal ventilation, allowing intervention before patient deterioration. The objective of this analysis was to use machine learning (ML) to classify combined waveforms of continuous capnography and pulse oximetry as normal or abnormal. ⋯ This study presents a promising advancement in respiratory monitoring, focusing on reducing false alarms and enhancing accuracy of alarm systems. Our algorithm reliably distinguishes normal from abnormal waveforms. More research is needed to define patterns to distinguish abnormal ventilation from artifacts.