Sensitivity of the Kaiser Permanente early-onset sepsis calculator : A systematic review and meta-analysis

Background: Determining which babies should receive antibiotics for potential early onset sepsis (EOS) is challenging. We performed a meta-analysis quantifying how many EOS cases might be 'missed' using the Kaiser Permanente electronic calculator, compared with National Institute for Health and Care Excellence (NICE) guidelines. Methods: A systematic literature search was carried out for studies citing the article in which the calculator was publicised. Studies were eligible if they presented data evaluating the calculator, either by retrospective case review or prospective cohort study. The primary outcome measure was numbers of culture positive EOS cases where the calculator did not recommend empirical antibiotics, but NICE guidelines would have. Data were pooled using a random effect meta-analysis. A subgroup analysis was performed using data from studies of babies exposed to chorioamnionitis. Findings: Eleven studies were included. There were a total of 75 EOS cases across the studies and a minimum of 14 (best case scenario), and a maximum of 22 (worst case scenario) cases where use of the calculator would have resulted in delayed or missed treatment, compared to if NICE guidelines had been followed. The probability of missed/delayed treatment for an EOS case were best case 0.19 [95% confidence intervals 0.11 - 0.29], worst case 0.31 [95% CI 0.17 - 0.49]. The probability of missing cases was significantly more in babies exposed to chorioamnionitis. Interpretation: A large proportion of EOS cases were 'missed' by the calculator. Further evaluation of the calculator is recommended before it is introduced into UK clinical practice. Funding: None

A Comparison of Administrative and Physiologic Predictive Models in Determining Risk Adjusted Mortality Rates in Critically Ill Patients

Hospitals are increasingly compared based on clinical outcomes adjusted for severity of illness. Multiple methods exist to adjust for differences between patients. The challenge for consumers of this information, both the public and healthcare providers, is interpreting differences in risk adjustment models particularly when models differ in their use of administrative and physiologic data. We set to examine how administrative and physiologic models compare to each when applied to critically ill patients.We prospectively abstracted variables for a physiologic and administrative model of mortality from two intensive care units in the United States. Predicted mortality was compared through the Pearsons Product coefficient and Bland-Altman analysis. A subgroup of patients admitted directly from the emergency department was analyzed to remove potential confounding changes in condition prior to ICU admission.We included 556 patients from two academic medical centers in this analysis. The administrative model and physiologic models predicted mortalities for the combined cohort were 15.3% (95% CI 13.7%, 16.8%) and 24.6% (95% CI 22.7%, 26.5%) (t-test p-value<0.001). The r(2) for these models was 0.297. The Bland-Atlman plot suggests that at low predicted mortality there was good agreement; however, as mortality increased the models diverged. Similar results were found when analyzing a subgroup of patients admitted directly from the emergency department. When comparing the two hospitals, there was a statistical difference when using the administrative model but not the physiologic model. Unexplained mortality, defined as those patients who died who had a predicted mortality less than 10%, was a rare event by either model.In conclusion, while it has been shown that administrative models provide estimates of mortality that are similar to physiologic models in non-critically ill patients with pneumonia, our results suggest this finding can not be applied globally to patients admitted to intensive care units. As patients and providers increasingly use publicly reported information in making health care decisions and referrals, it is critical that the provided information be understood. Our results suggest that severity of illness may influence the mortality index in administrative models. We suggest that when interpreting "report cards" or metrics, health care providers determine how the risk adjustment was made and compares to other risk adjustment models

Limitations and opportunities of whole blood bilirubin measurements by GEM premier 4000®

Abstract Background Neonatal hyperbilirubinemia has traditionally been screened by either total serum bilirubin or transcutaneous bilirubin. Whole blood bilirubin (TwB) by the GEM Premier 4000® blood gas analyzer (GEM) is a relatively new technology and it provides fast bilirubin results with a small sample volume and can measure co-oximetry and other analytes. Our clinical study was to evaluate the reliability of TwB measured by the GEM and identify analytical and clinical factors that may contribute to possible bias. Methods 440 consecutive healthy newborn samples that had plasma bilirubin ordered for neonatal hyperbilirubinemia screening were included. TwB was first measured using the GEM, after which the remainder of the blood was spun and plasma neonatal bilirubin was measured using the VITROS 5600® (VITROS). Results 62 samples (14%) were excluded from analysis due to failure in obtaining GEM results. Passing-Bablok regression suggested that the GEM results were negatively biased at low concentrations of bilirubin and positively biased at higher concentrations relative to the VITROS results (y = 1.43x-61.13). Bland-Altman plots showed an overall negative bias of the GEM bilirubin with a wide range of differences compared to VITROS. Both hemoglobin concentration and hemolysis affected the accuracy of the GEM results. Clinically, male infants had higher mean bilirubin levels, and infants delivered by caesarean section had lower hemoglobin levels. When comparing the number of results below the 40th percentile and above the 95th percentile cut-offs in the Bhutani nomogram which would trigger discharge or treatment, GEM bilirubin exhibited poor sensitivity and poor specificity in contrast to VITROS bilirubin. Conclusions An imperfect correlation was observed between whole blood bilirubin measured on the GEM4000® and plasma bilirubin on the VITROS 5600®. The contributors to the observed differences between the two instruments were specimen hemolysis and the accuracy of hemoglobin measurements, the latter of which affects the calculation of plasma-equivalent bilirubin. Additionally, the lack of standardization of total bilirubin calibration particularly in newborn specimens, may also account for some of the disagreement in results