Central line–associated bloodstream infections are among the leading causes of in-hospital deaths in the United States and are a significant factor for increased morbidity, mortality, and healthcare costs. This study integrates several hospital data systems into a case-controlled database to use data analytics for the identification of significant central line–associated bloodstream infection risk factors and develop time-varying patient risk scores for central line–associated bloodstream infections. A case-control study was performed utilizing patient data collected from various sources then gathered and organi zed into a case-controlled dataset for analysis examining various patient-specific attributes for central line–associated bloodstream infections. Training and testing sets were created, and multivariate logistic regressions were used to identify risk factors for central line–associated bloodstream infection. Furthermore, the Cox proportional hazards model was used to infer the hazard rate and risk score for central line–associated bloodstream infections for each individual patient during hospitalization. Significant attributes for central line–associated bloodstream infection cases were the ICU location (P = .008), time from insertion (P ≤ .001), number of surgeries (P = .003), and number of central line manipulations (P = .003). Real-time data analytics and point of care at the bedside can facilitate precision care for patients with an elevated central line–associated bloodstream infection risk, subsequently changing the way healthcare prevents hospital-acquired infecti ons.
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