Things We Do for No Reason - The "48 Hour Rule-out" for Well-Appearing Febrile Infants

FeBRILe3: Safety Evaluation of Febrile Infant Guidelines Through Prospective Bayesian Monitoring

OBJECTIVES

Despite evidence supporting earlier discharge of well-appearing febrile infants at low risk of serious bacterial infection (SBI), admissions for ≥48 hours remain common. Prospective safety monitoring may support broader guideline implementation.

METHODS

A sequential Bayesian safety monitoring framework was used to evaluate a new hospital guideline recommending early discharge of low-risk infants. Hospital readmissions within 7 days of discharge were regularly assessed against safety thresholds, derived from historic rates and expert opinion, and specified a priori (8 per 100 infants). Infants aged under 3 months admitted to 2 Western Australian metropolitan hospitals for management of fever without source were enrolled (August 2019-December 2021), to a prespecified maximum 500 enrolments.

RESULTS

Readmission rates remained below the prespecified threshold at all scheduled analyses. Median corrected age was 34 days, and 14% met low-risk criteria (n = 71). SBI was diagnosed in 159 infants (32%), including urinary tract infection (n = 140) and bacteraemia (n = 18). Discharge occurred before 48 hours for 192 infants (38%), including 52% deemed low-risk. At study completion, 1 of 37 low-risk infants discharged before 48 hours had been readmitted (3%), for issues unrelated to SBI diagnosis. In total, 20 readmissions were identified (4 per 100 infants; 95% credible interval 3, 6), with >0.99 posterior probability of being below the prespecified noninferiority threshold, indicating acceptable safety.

CONCLUSIONS

A Bayesian monitoring approach supported safe early discharge for many infants, without increased risk of readmission. This framework may be used to embed safety evaluations within future guideline implementation programs to further reduce low-value care.

Time to Blood Culture Positivity by Pathogen and Primary Service

OBJECTIVES

Initiation and continuation of empirical antimicrobial agents for a 48-72-hour observation period is routine practice in the diagnosis and treatment of infants and children with concern for bacteremia. We examined blood cultures at a freestanding pediatric hospital over a 6-year period to determine the time to positivity.

METHODS

Data were extracted for all patients who were hospitalized and had blood cultures drawn between January 2013 and December 2018. Time to positivity was calculated on the basis of date and time culture was collected compared with date and time growth was first reported.

RESULTS

Over a 6-year period, 89 663 blood cultures were obtained, of which 6184 had positive results. After exclusions, a total of 2121 positive blood culture results remained, including 1454 (69%) pathogens and 667 contaminants (31%). For all positive blood culture results, the number and percentage positive at 24, 36, and 48 hours were 1441 of 2121 (68%), 1845 of 2121 (87%) and 1970 of 2121 (93%), respectively. One hundred twenty-five (66 pathogens, 59 contaminants) of the 89 663 cultures (0.14%) yielded positive results between 36 and 48 hours, indicating that 719 patients would need to be treated for 48 hours rather than 36 hours to prevent 1 case of antibiotic termination before positive result. Median times to positive result by pathogen and service line are presented.

CONCLUSIONS

This study reveals that ≤36 hours may be a sufficient period of observation for infants and children started on empirical antimicrobial agents for concern for bacteremia. These findings highlight opportunities for antimicrobial stewardship to limit antimicrobial .

Trends and Variation in Length of Stay Among Hospitalized Febrile Infants ≤60 Days Old

OBJECTIVES

Researchers in recent studies suggest that hospitalized febrile infants aged ≤60 days may be safely discharged if bacterial cultures are negative after 24-36 hours of incubation. We aimed to describe trends and variation in length of stay (LOS) for hospitalized febrile infants across children's hospitals.

METHODS

We conducted a multicenter retrospective cohort study of febrile infants aged ≤60 days hospitalized from 2016 to 2019 at 39 hospitals in the Pediatric Health Information System database. We excluded infants with complex chronic conditions, bacterial infections, lower respiratory tract viral infections, and those who required ICU admission. The primary outcomes were trends in LOS overall and for individual hospitals, adjusted for patient demographics and clinical characteristics. We also evaluated the hospital-level association between LOS and 30-day readmissions.

RESULTS

We identified 11 868 eligible febrile infant encounters. The adjusted mean LOS for the study cohort decreased from 44.0 hours in 2016 to 41.9 hours in 2019 (P < .001). There was substantial variation in adjusted mean LOS across children's hospitals, range 33.5-77.9 hours in 2016 and 30.4-100.0 hours in 2019. The change from 2016 to 2019 in adjusted mean LOS across individual hospitals also varied widely (-23.9 to +26.7 hours; median change -1.8 hours, interquartile range: -5.4 to 0.3). There was no association between hospital-level LOS and readmission rates (P = .70).

CONCLUSIONS

The LOS for hospitalized febrile infants decreased marginally between 2016 and 2019, although overall LOS and change in LOS varied substantially across children's hospitals. Continued quality improvement efforts are needed to reduce LOS for hospitalized febrile infants.

A Feasibility Study to Attribute Patients to Primary Interns on Inpatient Ward Teams Using Electronic Health Record Data

PURPOSE

To inform graduate medical education (GME) outcomes at the individual resident level, this study sought a method for attributing care for individual patients to individual interns based on "footprints" in the electronic health record (EHR).

METHOD

Primary interns caring for patients on an internal medicine inpatient service were recorded daily by five attending physicians of record at University of Cincinnati Medical Center in August 2017 and January 2018. These records were considered gold standard identification of primary interns. The following EHR variables were explored to determine representation of primary intern involvement in care: postgraduate year, progress note author, discharge summary author, physician order placement, and logging clicks in the patient record. These variables were turned into quantitative attributes (e.g., progress note author: yes/no), and informative attributes were selected and modeled using a decision tree algorithm.

RESULTS

A total of 1,511 access records were generated; 116 were marked as having a primary intern assigned. All variables except discharge summary author displayed at least some level of importance in the models. The best model achieved 78.95% sensitivity, 97.61% specificity, and an area under the receiver-operator curve of approximately 91%.

CONCLUSIONS

This study successfully predicted primary interns caring for patients on inpatient teams using EHR data with excellent model performance. This provides a foundation for attributing patients to primary interns for the purposes of determining patient diagnoses and complexity the interns see as well as supporting continuous quality improvement efforts in GME.