From intensive care monitors to cloud environments: a structured data pipeline for advanced clinical decision support.

Background: Clinical decision-making is increasingly shifting towards data-driven approaches and requires large databases to develop state-of-the-art algorithms for diagnosing, detecting and predicting diseases. The intensive care unit (ICU), a data-rich setting, faces challenges with high-frequency, unstructured monitor data. Here, we showcase a successful example of a data pipeline to efficiently move patient data to the cloud environment for structured storage.

Systematic review and meta-analysis of goal-directed haemodynamic therapy algorithms during surgery for the prevention of surgical site infection.

Background: Surgical site infection (SSI) is the most common postoperative complication. Goal-directed haemodynamic therapy (GDHT) may help to prevent SSI, but recommendations for its use initially have been set at conditional because of low-certainty evidence at the time. An updated systematic review with SSI as the primary endpoint has not been performed since 2011, and important new evidence has emerged.

Effect of a Machine Learning-Derived Early Warning Tool With Treatment Protocol on Hypotension During Cardiac Surgery and ICU Stay: The Hypotension Prediction 2 (HYPE-2) Randomized Clinical Trial.

Objectives: Cardiac surgery is associated with perioperative complications, some of which might be attributable to hypotension. The Hypotension Prediction Index (HPI), a machine-learning-derived early warning tool for hypotension, has only been evaluated in noncardiac surgery. We investigated whether using HPI with diagnostic guidance reduced hypotension during cardiac surgery and in the ICU.

The influence of anesthetic drug strategy on the incidence of post-induction hypotension in elective, non-cardiac surgery - A prospective observational cohort study.

Study Objectives: To identify the influence of modifiable factors in anesthesia induction strategy on post-induction hypotension (PIH), specifically the type, dosage and speed of administration of induction agents. A secondary aim was to identify patient related non-modifiable factors associated with PIH.

Design: Single-center, prospective observational cohort study.

Defining Postinduction Hemodynamic Instability With an Automated Classification Model.

Background: Postinduction hypotension (PIH) may be associated with increased morbidity and mortality. In earlier studies, the definition of PIH is solely based on different absolute or relative thresholds. However, the time-course (eg, how fast blood pressure drops during induction) is rarely incorporated, whereas it might represent the hemodynamic instability of a patient.