Performance of a Machine Learning Algorithm to Predict Hypotension in Spontaneously Breathing Non-Ventilated Post-Anesthesia and ICU Patients.

Background: Hypotension is common in the post-anesthesia care unit (PACU) and intensive care unit (ICU), and is associated with adverse patient outcomes. The Hypotension Prediction Index (HPI) algorithm has been shown to accurately predict hypotension in mechanically ventilated patients in the OR and ICU and to reduce intraoperative hypotension (IOH). Since positive pressure ventilation significantly affects patient hemodynamics, we performed this validation study to examine the performance of the HPI algorithm in a non-ventilated PACU and ICU population. Materials & Methods: The performance of the HPI algorithm was assessed using prospectively collected blood pressure (BP) and HPI data from a PACU and a mixed ICU population. Recordings with sufficient time (≥3 h) spent without mechanical ventilation were selected using data from the electronic medical record. All HPI values were evaluated for sensitivity, specificity, predictive value, and time-to-event, and a receiver operating characteristic (ROC) curve was constructed. Results: BP and HPI data from 282 patients were eligible for analysis, of which 242 (86%) were ICU patients. The mean age (standard deviation) was 63 (13.5) years, and 186 (66%) of the patients were male. Overall, the HPI predicted hypotension accurately, with an area under the ROC curve of 0.94. The most used HPI threshold cutoff in research and clinical use, 85, showed a sensitivity of 1.00, specificity of 0.79, median time-to-event of 160 s [60-380], PPV of 0.85, and NPV of 1.00. Conclusion: The absence of positive pressure ventilation and the influence thereof on patient hemodynamics does not negatively affect the performance of the HPI algorithm in predicting hypotension in the PACU and ICU. Future research should evaluate the feasibility and influence on hypotension and outcomes following HPI implementation in non-ventilated patients at risk of hypotension.

Hypotension during intensive care stay and mortality and morbidity: a systematic review and meta-analysis.

PURPOSE: The aim of this study is to provide a summary of the existing literature on the association between hypotension during intensive care unit (ICU) stay and mortality and morbidity, and to assess whether there is an exposure-severity relationship between hypotension exposure and patient outcomes. METHODS: CENTRAL, Embase, and PubMed were searched up to October 2022 for articles that reported an association between hypotension during ICU stay and at least one of the 11 predefined outcomes. Two independent reviewers extracted the data and assessed the risk of bias. Results were gathered in a summary table and studies designed to investigate the hypotension-outcome relationship were included in the meta-analyses. RESULTS: A total of 122 studies (176,329 patients) were included, with the number of studies varying per outcome between 0 and 82. The majority of articles reported associations in favor of 'no hypotension' for the outcomes mortality and acute kidney injury (AKI), and the strength of the association was related to the severity of hypotension in the majority of studies. Using meta-analysis, a significant association was found between hypotension and mortality (odds ratio: 1.45; 95% confidence interval (CI) 1.12-1.88; based on 13 studies and 34,829 patients), but not for AKI. CONCLUSION: Exposure to hypotension during ICU stay was associated with increased mortality and AKI in the majority of included studies, and associations for both outcomes increased with increasing hypotension severity. The meta-analysis reinforced the descriptive findings regarding mortality but did not yield similar support for AKI.

Effect of the machine learning-derived Hypotension Prediction Index (HPI) combined with diagnostic guidance versus standard care on depth and duration of intraoperative and postoperative hypotension in elective cardiac surgery patients: HYPE-2 - study protocol of a randomised clinical trial.

INTRODUCTION: Hypotension is common during cardiac surgery and often persists postoperatively in the intensive care unit (ICU). Still, treatment is mainly reactive, causing a delay in its management. The Hypotension Prediction Index (HPI) can predict hypotension with high accuracy. Using the HPI combined with a guidance protocol resulted in a significant reduction in the severity of hypotension in four non-cardiac surgery trials. This randomised trial aims to evaluate the effectiveness of the HPI in combination with a diagnostic guidance protocol on reducing the occurrence and severity of hypotension during coronary artery bypass grafting (CABG) surgery and subsequent ICU admission. METHODS AND ANALYSIS: This is a single-centre, randomised clinical trial in adult patients undergoing elective on-pump CABG surgery with a target mean arterial pressure of 65 mm Hg. One hundred and thirty patients will be randomly allocated in a 1:1 ratio to either the intervention or control group. In both groups, a HemoSphere patient monitor with embedded HPI software will be connected to the arterial line. In the intervention group, HPI values of 75 or above will initiate the diagnostic guidance protocol, both intraoperatively and postoperatively in the ICU during mechanical ventilation. In the control group, the HemoSphere patient monitor will be covered and silenced. The primary outcome is the time-weighted average of hypotension during the combined study phases. ETHICS AND DISSEMINATION: The medical research ethics committee and the institutional review board of the Amsterdam UMC, location AMC, the Netherlands, approved the trial protocol (NL76236.018.21). No publication restrictions apply, and the study results will be disseminated through a peer-reviewed journal. TRIAL REGISTRATION NUMBER: The Netherlands Trial Register (NL9449), ClinicalTrials.gov (NCT05821647).

Machine learning methods for perioperative anesthetic management in cardiac surgery patients: a scoping review.

BACKGROUND: Machine learning (ML) is developing fast with promising prospects within medicine and already has several applications in perioperative care. We conducted a scoping review to examine the extent and potential limitations of ML implementation in perioperative anesthetic care, specifically in cardiac surgery patients. METHODS: We mapped the current literature by searching three databases: MEDLINE (Ovid), EMBASE (Ovid), and Cochrane Library. Articles were eligible if they reported on perioperative ML use in the field of cardiac surgery with relevance to anesthetic practices. Data on the applicability of ML and comparability to conventional statistical methods were extracted. RESULTS: Forty-six articles on ML relevant to the work of the anesthesiologist in cardiac surgery were identified. Three main categories emerged: (I) event and risk prediction, (II) hemodynamic monitoring, and (III) automation of echocardiography. Prediction models based on ML tend to behave similarly to conventional statistical methods. Using dynamic hemodynamic or ultrasound data in ML models, however, shifts the potential to promising results. CONCLUSIONS: ML in cardiac surgery is increasingly used in perioperative anesthetic management. The majority is used for prediction purposes similar to conventional clinical scores. Remarkable ML model performances are achieved when using real-time dynamic parameters. However, beneficial clinical outcomes of ML integration have yet to be determined. Nonetheless, the first steps introducing ML in perioperative anesthetic care for cardiac surgery have been taken.

Erythropoiesis-stimulating agents as replacement therapy for blood transfusions in critically ill patients with anaemia: A systematic review with meta-analysis.

OBJECTIVES: The primary objectives of this meta-analysis in critically ill adult patients admitted to the intensive care unit (ICU) were to analyse whether erythropoiesis-stimulating agents (ESAs) reduced the number of patients receiving red blood cell (RBC) transfusion and resulted in a change in haemoglobin (Hb) concentration. Our secondary objectives were adverse events and mortality. BACKGROUND: Anaemia is common in ICU patients, and currently, the standard therapy is RBC transfusion, which is known to be associated with adverse events. ESA could potentially reduce the need for RBC transfusion. METHODS: EMBASE, Cochrane and PubMed were searched up to January 2020. RESULTS: A total of 1357 articles were identified, of which 18 articles met the inclusion criteria for the qualitative synthesis. Eight of these studies were used in the meta-analyses. Comparing ESA vs control group, there was a small reduction in the proportion of patients who received one or more RBC transfusions (relative risk [RR] 0.88; confidence interval [CI] 0.78-1.00, moderate certainty). The change in Hb concentration was trivial (mean difference -0.31 g/dL; CI -0.51 to -0.05, high certainty). The number of serious adverse events (RR 1.02; 0.90-1.15, low certainty) and the overall short-term mortality were similar (RR 0.80; CI 0.61-1.05, low certainty) between the groups. CONCLUSION: ESA resulted in a small reduction in the proportion of patients transfused and a trivial increase in haemoglobin concentration, both of questionable clinical relevance, without impacting adverse events or mortality. These results do not support the routine use of ESA to treat anaemia in critically ill adults.