Receipt of high-frequency ventilation is associated with acute kidney injury in very preterm neonates.

BACKGROUND: High-frequency ventilation (HFV) is frequently used in critically ill preterm neonates. We aimed to determine the incidence of acute kidney injury (AKI) in neonates less than 29 weeks gestation who received HFV in the first week of life and to determine if the rates of AKI differed in those who received other forms of respiratory support. METHODS: This retrospective cohort study of 24 international, level III/IV neonatal intensive care units (NICUs) included neonates less than 29 weeks gestation from the AWAKEN study database. Exclusion criteria included the following: no intravenous fluids ≥ 48 h, admission ≥ 14 days of life, congenital heart disease requiring surgical repair at < 7 days of life, lethal chromosomal anomaly, death within 48 h, severe congenital kidney abnormalities, inability to determine AKI status, insufficient data on ventilation, and when the diagnosis of early AKI was unable to be made. Subjects were grouped into three groups based on ventilation modes (CPAP/no ventilation, conventional ventilation, and HFV). RESULTS: The incidence of AKI was highest in the CPAP/no ventilation group, followed by HFV, followed by conventional ventilation (CPAP/no ventilation 48.5% vs. HFV 42.6% vs. conventional ventilation 28.4% (p = 0.009). An increased risk for AKI was found for those on HFV compared to CPAP/no ventilation (HR = 2.65; 95% CI:1.22-5.73). CONCLUSIONS: HFV is associated with AKI in the first week of life. Neonates on HFV should be screened for AKI. The reasons for this association are not clear. Further studies should evaluate the relationship between ventilator strategies and AKI in premature neonates. A higher resolution version of the Graphical abstract is available as Supplementary information.

Linking Individual Data From the Spinal Cord Injury Model Systems Center and Local Trauma Registry: Development and Validation of Probabilistic Matching Algorithm.

BACKGROUND: Linking records from the National Spinal Cord Injury Model Systems (SCIMS) database to the National Trauma Data Bank (NTDB) provides a unique opportunity to study early variables in predicting long-term outcomes after traumatic spinal cord injury (SCI). The public use data sets of SCIMS and NTDB are stripped of protected health information, including dates and zip code. OBJECTIVES: To develop and validate a probabilistic algorithm linking data from an SCIMS center and its affiliated trauma registry. METHOD: Data on SCI admissions 2011-2018 were retrieved from an SCIMS center (n = 302) and trauma registry (n = 723), of which 202 records had the same medical record number. The SCIMS records were divided equally into two data sets for algorithm development and validation, respectively. We used a two-step approach: blocking and weight generation for linking variables (race, insurance, height, and weight). RESULTS: In the development set, 257 SCIMS-trauma pairs shared the same sex, age, and injury year across 129 clusters, of which 91 records were true-match. The probabilistic algorithm identified 65 of the 91 true-match records (sensitivity, 71.4%) with a positive predictive value (PPV) of 80.2%. The algorithm was validated over 282 SCIMS-trauma pairs across 127 clusters and had a sensitivity of 73.7% and PPV of 81.1%. Post hoc analysis shows the addition of injury date and zip code improved the specificity from 57.9% to 94.7%. CONCLUSION: We demonstrate the feasibility of probabilistic linkage between SCIMS and trauma records, which needs further refinement and validation. Gaining access to injury date and zip code would improve record linkage significantly.