Artificial intelligence for early sepsis detection and dynamic prognostication in onco-critical care

Table 3 Common clinical variables and biomarkers utilized by machine learning models for sepsis prediction in oncology patients

Data category	Specific variables/features included	Clinical relevance in onco-critical care
Demographics and medical history	Age, gender, primary cancer type, metastatic status, history of solid organ or bone marrow transplant, prior chemotherapy regimens	Captures baseline physiological reserve, specific immunosuppressive states, and the inherent mortality risk associated with the patient’s underlying malignancy[4,27]
High-frequency vital signs	Heart rate, respiratory rate, temperature (max/minute/avg), systolic/diastolic blood pressure, peripheral oxygen saturation	Continuous streams of this data allow algorithms to detect subtle, non-linear trajectories of deterioration hours before a clinical diagnosis of septic shock[14]
Routine laboratory findings	Complete blood count (absolute neutrophil count, platelet count), red cell distribution width, hematocrit	Dynamic shifts in complete blood count parameters, particularly absolute neutrophil count and red cell distribution width, are powerful predictors of bacteremia and mortality in patients with chemotherapy-induced myelosuppression[2]
Metabolic and organ function markers	Minimum/maximum lactate, blood urea nitrogen, serum creatinine, bilirubin, minimum pH, base excess	Highlights critical sepsis-induced organ dysfunction, particularly acute kidney injury which is a major mortality driver in cancer patients receiving nephrotoxic drugs[4,14]
Inflammatory biomarkers	C-reactive protein, procalcitonin, interleukin-6	Aids the model in differentiating between non-infectious tumor fevers (or drug reactions) and true bacterial sepsis[13,28]
Unstructured EHR data	Free-text physician progress notes, nursing assessments, radiology reports	Analyzed via natural language processing, these notes capture nuanced clinical intuition and symptom descriptions that structured numerical data misses[23]

EHR: Electronic health record.