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. 2021 Dec 15;6(6):e481.
doi: 10.1097/pq9.0000000000000481. eCollection 2021 Nov-Dec.

An Evaluation of Antimicrobial Prescribing and Risk-adjusted Mortality

Jonathan H Pelletier  1 , Alicia K Au  1 , Dana Fuhrman  1 , James Zullo  2 , Ann E Thompson  1 , Robert S B Clark  1 , Christopher Horvat  1
Affiliations

An Evaluation of Antimicrobial Prescribing and Risk-adjusted Mortality

Jonathan H Pelletier et al. Pediatr Qual Saf. .

Abstract

The Centers for Disease Control and Prevention recommends tracking risk-adjusted antimicrobial prescribing. Prior studies have used prescribing variation to drive quality improvement initiatives without adjusting for severity of illness. The present study aimed to determine the relationship between antimicrobial prescribing and risk-adjusted ICU mortality in the Pediatric Health Information Systems (PHIS) database, assessed by IBM-Watson risk of mortality. A nested analysis sought to assess an alternative risk model incorporating laboratory data from federated electronic health records.

Methods: Retrospective cohort study of pediatric ICU patients in PHIS between 1/1/2010 and 12/31/2019, excluding patients admitted to a neonatal ICU, and a nested study of PHIS+ from 1/1/2010 to 12/31/2012. Hospital antimicrobial prescription volumes were assessed for association with risk-adjusted mortality.

Results: The cohort included 953,821 ICU encounters (23,851 [2.7%] nonsurvivors). There was 4-fold center-level variability in antimicrobial use. ICU antimicrobial use was not correlated with risk-adjusted mortality assessed using IBM-Watson. A risk model incorporating laboratory data available in PHIS+ significantly outperformed IBM-Watson (c-statistic 0.940 [95% confidence interval 0.933-0.947] versus 0.891 [0.881-0.901]; P < 0.001, area under the precision recall curve 0.561 versus 0.297). Risk-adjusted mortality was inversely associated with antimicrobial prescribing in this smaller cohort using both the PHIS+ and Watson models (P = 0.05 and P < 0.01, respectively).

Conclusions: Antimicrobial prescribing among pediatric ICUs in the PHIS database is variable and not associated with risk-adjusted mortality as assessed by IBM-Watson. Expanding existing administrative databases to include laboratory data can achieve more meaningful insights when assessing multicenter antibiotic prescribing practices.

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Figures

Fig. 1.
Fig. 1.
Watson score performance. A, Receiver operating characteristic curve. B, Precision recall curve. The x axis shows recall (sensitivity). The y axis shows precision (positive predictive value). C, D, GiViTI calibration belt before and after isotonic regression, respectively. D, Calibration belt for full cohort, with more stringent internal calibration methodology applied. The P value is a likelihood ratio test evaluating the deviation of the model from the observed result; a nonsignificant P value is desirable.
Fig. 2.
Fig. 2.
Correlation between antimicrobial prescriptions and risk-adjusted pediatric ICU mortality in PHIS. A, Antimicrobial days of therapy per 1000 pediatric ICU days versus differences from Watson score predicted mortality. B, Antimicrobial days of therapy per 1000 pediatric ICU days versus differences from Watson score predicted mortality after recalibration with isotonic regression. For both panels, each point represents a single hospital, with antimicrobial prescriptions per 1000 ICU days plotted on the x axis, and difference from predicted mortality (% mortality – % predicted mortality) on the y axis. For both panels, the line represents linear regression with 95% confidence interval.
Fig. 3.
Fig. 3.
Antimicrobial prescriptions over time and risk-adjusted pediatric ICU mortality. Each thin line represents 1 PHIS pediatric ICU over time. Antimicrobial days of therapy per 1000 pediatric ICU days are graphed on the y axis against patients’ discharge year on the x axis. Line color is representative of changes in risk-adjusted mortality over time as assessed by linear regression. ICUs with statistically significant reductions in risk-adjusted mortality are graphed in blue. Those with nonsignificant changes are graphed in black. There were no ICUs with significant increases in risk-adjusted mortality. The thick black line represents the line of best fit for the data, or average among the ICUs.
Fig. 4.
Fig. 4.
Comparison of Watson score and Novel Mortality Model Performance on the PHIS+ Subset. A, Receiver operating characteristic curve. The P value is DeLong’s test for related receiver operating characteristic curves. B, Precision recall curve. The x axis shows recall (sensitivity).
Fig. 5.
Fig. 5.
Correlation between antimicrobial prescriptions and risk-adjusted pediatric ICU mortality in the PHIS+ Subset. A, Antimicrobial days of therapy per 1000 pediatric ICU days versus differences from novel PHIS+ score predicted mortality. B, Antimicrobial days of therapy per 1000 pediatric ICU days versus differences from Watson score predicted mortality. For both panels, each point represents a single hospital, with antimicrobial prescriptions per 1000 ICU days plotted on the x axis, and difference from predicted mortality (% mortality – % predicted mortality) on the y axis. For both panels, the line represents linear regression with 95% confidence interval.
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