Identifying Patients with Bacteremia in Community-Hospital Emergency Rooms: A Retrospective Cohort Study

<div><p>Objectives</p><p>(1) To develop a clinical prediction rule to identify patients with bacteremia, using only information that is readily available in the emergency room (ER) of community hospitals, and (2) to test the validity of that rule with a separate, independent set of data.</p><p>Design</p><p>Multicenter retrospective cohort study.</p><p>Setting</p><p>To derive the clinical prediction rule we used data from 3 community hospitals in Japan (derivation). We tested the rule using data from one other community hospital (validation), which was not among the three “derivation” hospitals.</p><p>Participants</p><p>Adults (age ≥ 16 years old) who had undergone blood-culture testing while in the ER between April 2011 and March 2012. For the derivation data, n = 1515 (randomly sampled from 7026 patients), and for the validation data n = 467 (from 823 patients).</p><p>Analysis</p><p>We analyzed 28 candidate predictors of bacteremia, including demographic data, signs and symptoms, comorbid conditions, and basic laboratory data. Chi-square tests and multiple logistic regression were used to derive an integer risk score (the “ID-BactER” score). Sensitivity, specificity, likelihood ratios, and the area under the receiver operating characteristic curve (i.e., the AUC) were computed.</p><p>Results</p><p>There were 241 cases of bacteremia in the derivation data. Eleven candidate predictors were used in the ID-BactER score: age, chills, vomiting, mental status, temperature, systolic blood pressure, abdominal sign, white blood-cell count, platelets, blood urea nitrogen, and C-reactive protein. The AUCs was 0.80 (derivation) and 0.74 (validation). For ID-BactER scores ≥ 2, the sensitivities for derivation and validation data were 98% and 97%, and specificities were 20% and 14%, respectively.</p><p>Conclusions</p><p>The ID-BactER score can be computed from information that is readily available in the ERs of community hospitals. Future studies should focus on developing a score with a higher specificity while maintaining the desired sensitivity.</p></div

Percentages of patients with true bacteremia, by ID-BactER score.

<p>Fig 2 shows, for each of five categories defined by ID-BactER score, the percentage of patients in that category who had true bacteremia.</p

Demographic and clinical characteristics.

<p>Demographic and clinical characteristics.</p

Three existing models and our model developed in ER settings.

<p>Three existing models and our model developed in ER settings.</p

Fig 3 shows the ROC curves for the ID-BactER score.

<p><b>ROC curves of ID-BactER score.</b> The ●s indicate results from the derivation set, and the ○s indicate results from the validation set. The areas under the curves are 0.80 (95% CI, 0.77–0.83) for the derivation set and 0.74 (0.68–0.80) for the validation set.</p

Flow diagram of participants.

<p>Fig 1 shows the flow diagram of participants in this study. We began with records of all patients who presented to the ER between April 2011 and March 2012. After the random sampling and exclusions indicated in the Figure, we analysed data from 1515 patients for the derivation set, and data from 467 patients for the validation set.</p

Univariate associations between candidate predictors and true bacteremia in the derivation set,

<p>Univariate associations between candidate predictors and true bacteremia in the derivation set,</p

Multivariate analysis (n = 1288) and scoring.

<p>Multivariate analysis (n = 1288) and scoring.</p