Journal of Clinical Monitoring and Computing最新文献

This retrospective cohort study aimed to investigate the association between intraoperative hypotension (IOH) and postoperative acute kidney injury (AKI) among patients who underwent emergent critical cesarean delivery. We analyzed electronic health records from January 2019 to August 2024. IOH was defined as a mean arterial pressure (MAP) less than 65 mmHg. It was quantified using four metrics: hypotensive event count, cumulative duration, area under the threshold (AUC), and time-weighted average (TWA). Postoperative AKI was defined according to the Kidney Disease: Improving Global Outcomes (KDIGO) clinical practice guidelines, based on serum creatinine levels. We employed multivariable logistic regression to assess the independent association between the primary IOH metric (cumulative duration) and postoperative AKI, adjusting for clinically relevant covariates. Sensitivity analyses were conducted using alternative IOH metrics. Postoperative AKI was diagnosed in 69 of the 508 patients (13.58%). Multivariable logistic regression analysis revealed that all four measures of intraoperative hypotension were independently associated with an increased risk of AKI: hypotensive event count (adjusted OR 2.098, 95%CI [1.180-3.732]; P = 0.012), cumulative duration (adjusted OR 1.036, 95%CI [1.013-1.060]; P = 0.002), AUC (adjusted OR 1.004, 95%CI [1.001-1.007]; P = 0.009), and TWA (adjusted OR 1.557, 95%CI [1.058-2.291]; P = 0.025). Our findings demonstrate that IOH was independently associated with a higher incidence of postoperative AKI in patients who underwent an emergent critical cesarean delivery.

Patient-ventilator asynchrony (PVA) is a common and critically import clinical problem in patients receiving mechanical ventilation. However, PVAs are often underrecognized, underestimated and delayed, and there has been minimal success in automating their detection. In this study, we develop an efficient and fast end-to-end model to recognize PVAs on ventilator waveforms: running the model costs 106.5ms on CPUs and 7.8ms on GPUs. We propose label striping and stripe-mask encoding for efficient multi-class multi-target detecting. The model innovatively integrates causal convolutional, depth-wise separable convolutional, and recurrent neural networks to memorize long short-term causal features. With 60s waveform segments, our model performs a cross-validation mean average precision (mAP) of 88.1% and a testing mAP of 65.7% for comprehensive PVA detection. Our approach might be implemented as a monitoring tool to automatically identify PVAs for improving bedside and remote care and prioritizing patient comfort.

Purpose: Preoperative anesthesia evaluation is a crucial step in ensuring patient safety and optimizing perioperative care. A heterogenous patient population requiring varying levels of assessment often leads to inefficiencies and additional resource allocation. This study proposes using pre-trained language models to assist in triaging the appropriate degree of preoperative anesthesia evaluation for surgical patients.

Methods: Retrospective institutional data were obtained from surgical patients evaluated at a single center preoperative anesthesia care clinic. The performance of four pre-trained language models (RoBERTa, BERT, ClinicalBERT, and PubMedBERT) in the classification of which patients would be appropriate for a nursing preoperative phone call versus in-person clinician evaluation was assessed using F1-score, area under the receiver operating characteristics curve (AUC), specificity, sensitivity, and average precision. For each pre-trained language model, three different data input combinations were assessed: (1) diagnosis codes (D); (2) clinical text data (N); and (3) diagnosis codes and clinical text (D + N). The data were split into training (75%) and test set (25%).

Results: There were 1,761 unique patients, with an average of 12 notes per patient and a total of 46,922 clinical documents, included in the analysis. The AUC range between the four language models was highest in the D + N analyses (0.70 - 0.74), lower in the N analyses (0.58 - 0.73) and lowest in the D analyses (0.57 - 0.62). RoBERTa had the highest score compared to the other language models for all data types.

Conclusions: Automating integrated analysis using pre-trained language models to aid in preoperative triaging could enhance accuracy and efficiency at scale, reducing manual review and provider burden.

Cardiac index (CI) is a key physiologic indicator correlated with end-organ perfusion in cardiac surgical patients, yet it is not routinely measured in all cases. This study evaluated the accuracy of estimating CI using routinely available physiologic monitor data, adjusted for relevant patient, physiologic, and procedural factors documented in perioperative anesthesia records. We analyzed anesthesia records from adult cardiac surgical patients with thermodilution-based CI measurements across seven US hospitals from 2014 to 2022. Four published formulas-based on intraoperative blood pressure and heart rate-were used to estimate CI in generalized linear models, with adjustment for perioperative patient and procedure characteristics. Bland-Altman analysis compared adjusted CI estimates to reference thermodilution CI values. The ability of each estimator to classify patients with low CI (< 2.2 L/min/m²) was assessed for concordance. In a cohort of 5,989 patients, the median (IQR = interquartile range) thermodilution-based CIs were 2.1 (1.8-2.6) and 2.4 (2.0-2.9) L/min/m² before and after cardiopulmonary bypass, respectively. The best-performing formula, Liljestrand and Zander, achieved mean absolute errors of 0.45 and 0.47 L/min/m² before and after bypass, respectively. However, its reliability in classifying low CI was limited (Cohen's kappa = 0.26 pre-bypass, 0.20 post-bypass). Routinely collected physiologic and patient data can be used to generate population-level cardiac index estimates in adult cardiac surgery patients when appropriately adjusted, though individual-level discrimination of low CI is limited. These findings inform future large-scale perioperative hemodynamic research.

Cerebrovascular autoregulation maintains stable cerebral blood flow by counteracting slow changes in cerebral perfusion pressure (termed "slow waves"). Conventional assessment involves invasive techniques using intracranial pressure (ICP) or technically challenging cerebral blood flow velocity (FV) measurements. Near-infrared spectroscopy (NIRS) has emerged as a non-invasive alternative; however, its ability to accurately capture the slow-wave oscillations fundamental to cerebrovascular autoregulation remains uncertain. 412 h of simultaneous ICP, FV, NIRS, and arterial blood pressure (ABP) monitoring from 35 traumatic brain injury patients were explored. Coherence, gain, and Granger causality analyses were employed to assess whether NIRS adequately reflects slow waves in ABP, FV, or ICP to investigate whether NIRS is a suitable alternative for assessing the state of cerebrovascular autoregulation In this single-centre observational cohort study, 89 recordings from 35 moderate to severe traumatic brain injury (TBI) patients (totalling 412 h of artefact-free data) were analysed. Simultaneous high-resolution recordings of NIRS, ICP, FV, and arterial blood pressure (ABP) were acquired. Coherence and gain were computed across defined frequency bands (0.001-0.5 Hz), with a focus on the range most relevant to cerebrovascular autoregulation (0.005-0.05 Hz). Granger causality was used to explore directional relationships between physiological inputs (ABP, FV, ICP) and NIRS outputs (rSO2 and haemoglobin metrics). Haemoglobin-based NIRS metrics (total, oxy-, deoxy-, and delta haemoglobin) demonstrated significantly higher coherence and Granger causality with FV and ICP compared to rSO2 (p < 0.001, large effect sizes) capturing the slow-wave oscillations central to cerebrovascular autoregulation. In contrast, rSO₂ exhibited poor coherence and low causality, especially with ABP, likely due to device-specific post-processing and resolution limitations. NIRS derived haemoglobin metrics reliably capture slow-wave dynamics reflective of cerebrovascular autoregulation and reactivity, offering a non-invasive alternative to traditional methods. Conversely, rSO2 lacks sufficient temporal fidelity to detect these fluctuations under routine clinical conditions, limiting its utility for cerebrovascular autoregulation assessment.