Design, validation and implementation of an automated e-alert for acute kidney injury: 6-month pilot study shows increased awareness.

Background: Acute kidney injury (AKI) is defined as a sudden episode of kidney failure but is known to be under-recognized by healthcare professionals. The Kidney Disease Improving Global Outcome (KDIGO) guidelines have formulated criteria to facilitate AKI diagnosis by comparing changes in plasma creatinine measurements (PCr). To improve AKI awareness, we implemented these criteria as an electronic alert (e-alert), in our electronic health record (EHR) system.

Added diagnostic value of routinely measured hematology variables in diagnosing immune checkpoint inhibitor mediated toxicity in the emergency department.

Background: Immune checkpoint inhibitors (ICI) show remarkable results in cancer treatment, but at the cost of immune-related adverse events (irAE). irAE can be difficult to differentiate from infections or tumor progression, thereby challenging treatment, especially in the emergency department (ED) where time and clinical information are limited. As infections are traceable in blood, we were interested in the added diagnostic value of routinely measured hematological blood cell characteristics in addition to standard diagnostic practice in the ED to aid irAE assessment.

A machine learning approach using endpoint adjudication committee labels for the identification of sepsis predictors at the emergency department.

Accurate sepsis diagnosis is paramount for treatment decisions, especially at the emergency department (ED). To improve diagnosis, clinical decision support (CDS) tools are being developed with machine learning (ML) algorithms, using a wide range of variable groups. ML models can find patterns in Electronic Health Record (EHR) data that are unseen by the human eye.

A Wolf in Sheep's Clothing: Reuse of Routinely Obtained Laboratory Data in Research.

Electronic health records (EHRs) contain valuable data for reuse in science, quality evaluations, and clinical decision support. Because routinely obtained laboratory data are abundantly present, often numeric, generated by certified laboratories, and stored in a structured way, one may assume that they are immediately fit for (re)use in research. However, behind each test result lies an extensive context of choices and considerations, made by both humans and machines, that introduces hidden patterns in the data.

Transportability and Implementation Challenges of Early Warning Scores for Septic Shock in the ICU: A Perspective on the TREWScore.

The increased use of electronic health records (EHRs) has improved the availability of routine care data for medical research. Combined with machine learning techniques this has spurred the development of early warning scores (EWSs) in hospitals worldwide. EWSs are commonly used in the hospital where they have been developed, yet few have been transported to external settings and/or internationally.

Ambiguous definitions for baseline serum creatinine affect acute kidney diagnosis at the emergency department.

Background: Acute kidney injury (AKI) incidence is increasing, however AKI is often missed at the emergency department (ED). AKI diagnosis depends on changes in kidney function by comparing a serum creatinine (SCr) measurement to a baseline value. However, it remains unclear to what extent different baseline values may affect AKI diagnosis at ED.