Clinical prediction rules for bacteremia and in‐hospital death based on clinical data at the time of blood withdrawal for culture: an evaluation of their development and use

Nakamura, Tsukasa; Takahashi, Osamu; Matsui, Kunihiko; Shimizu, Shiro; Setoyama, Motoichi; Nakagawa, Mari; Fukui, Tsuguya; Morimoto, Takeshi

doi:10.1111/j.1365-2753.2006.00637.x

Cited by 23 publications

(15 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As in previous studies [33-35], in our study older age was associated with significantly increased rates of mortality (OR, 2.812 [95% CI, 1.039-7.611], p = 0.042). Older patients are often nutritionally or immunologically impaired, making them an easy target for infection and its associated complications [36].…”

Section: Discussionsupporting

confidence: 87%

Predictors of in-hospital mortality in elderly patients with bacteraemia admitted to an Internal Medicine ward

Rebelo¹,

Pereira²,

Lima³

et al. 2011

Int Arch Med

View full text Add to dashboard Cite

BackgroundInfectious diseases are a common cause of increased morbidity and mortality in elderly patients. Bacteraemia in the elderly is a difficult diagnosis and a therapeutic challenge due to age-related vicissitudes and to their comorbidities. The main purpose of the study was to assess independent risk factors for in-hospital mortality among the elderly with bacteraemia admitted to an Internal Medicine Ward.MethodsOverall, a cohort of 135 patients, 65 years of age and older, with bacteraemia were retrospectively studied. Data related to demographic information, comorbidities, clinical parameters on admission, source and type of infection, microorganism isolated in the blood culture, laboratory data and empirical antibiotic treatment was recorded from each patient. Multivariate logistic regression was performed to identify independent predictors of all-cause in-hospital mortality.ResultsOf these 135 patients, 45.9% were women. The most common infections in this group of patients were urinary tract infections (46.7%). The main microorganisms isolated in the blood cultures were Escherichia coli (14.9%), Methicillin-resistant Staphylococcus aureus (MRSA) (12.0%), non-MRSA (11.4%), Klebsiella pneumoniae (9.1%) and Enterococcus faecalis (8.0%). The in-hospital mortality was 22.2%. Independent prognostic factors associated with in-hospital mortality were age ≥ 85 years, chronic renal disease, bacteraemia of unknown focus and cognitive impairment at admission (OR, 2.812 [95% CI, 1.039-7.611; p = 0.042]; OR, 6.179 [95% CI, 1.840-20.748; p = 0.003]; OR, 8.673 [95% CI, 1.557-48.311; p = 0.014] and OR, 3.621 [95% CI, 1.226-10.695; p = 0.020], respectively). By multivariate analysis appropriate antibiotic therapy was not associated with lower odds of mortality.ConclusionBacteraemia in the elderly has a high mortality rate. There are no set of signs or clinical features that can predict bacteraemia in the elderly. However, older age (≥ 85 years), chronic renal disease, bacteraemia of unknown focus and severe cognitive impairment adversely affects the outcome of elderly patients with bacteraemia admitted to an Internal Medicine ward.

show abstract

Section: Discussionsupporting

confidence: 87%

Predictors of in-hospital mortality in elderly patients with bacteraemia admitted to an Internal Medicine ward

Rebelo¹,

Pereira²,

Lima³

et al. 2011

Int Arch Med

View full text Add to dashboard Cite

show abstract

“…al . published a hospital based study with a 19.5% prevalence of bacteraemia and predicting bacteraemia with an ROC-AUC of 0.73 [47]. The prevalence of bacteraemia (19.5%) in this study is higher than generally reported for hospital-based studies and may therefore lack generalizability [10], [11].…”

Section: Discussionmentioning

confidence: 66%

A Risk Prediction Model for Screening Bacteremic Patients: A Cross Sectional Study

et al. 2014

View full text Add to dashboard Cite

BackgroundBacteraemia is a frequent and severe condition with a high mortality rate. Despite profound knowledge about the pre-test probability of bacteraemia, blood culture analysis often results in low rates of pathogen detection and therefore increasing diagnostic costs. To improve the cost-effectiveness of blood culture sampling, we computed a risk prediction model based on highly standardizable variables, with the ultimate goal to identify via an automated decision support tool patients with very low risk for bacteraemia.MethodsIn this retrospective hospital-wide cohort study evaluating 15,985 patients with suspected bacteraemia, 51 variables were assessed for their diagnostic potency. A derivation cohort (n = 14.699) was used for feature and model selection as well as for cut-off specification. Models were established using the A2DE classifier, a supervised Bayesian classifier. Two internally validated models were further evaluated by a validation cohort (n = 1,286).ResultsThe proportion of neutrophile leukocytes in differential blood count was the best individual variable to predict bacteraemia (ROC-AUC: 0.694). Applying the A2DE classifier, two models, model 1 (20 variables) and model 2 (10 variables) were established with an area under the receiver operating characteristic curve (ROC-AUC) of 0.767 and 0.759, respectively. In the validation cohort, ROC-AUCs of 0.800 and 0.786 were achieved. Using predefined cut-off points, 16% and 12% of patients were allocated to the low risk group with a negative predictive value of more than 98.8%.ConclusionApplying the proposed models, more than ten percent of patients with suspected blood stream infection were identified having minimal risk for bacteraemia. Based on these data the application of this model as an automated decision support tool for physicians is conceivable leading to a potential increase in the cost-effectiveness of blood culture sampling. External prospective validation of the model's generalizability is needed for further appreciation of the usefulness of this tool.

show abstract

“…11,16 Although ordering blood cultures is a routine clinical behavior when there is concern for an infection, a clinician's ability to subjectively predict who has a BSI only improves the likelihood 2-fold. 6 Despite the availability of multiple scoring systems to aid the clinicians, 10,21,22 our study found that over 50% of cultures were ordered in the setting of fever or leukocytosis, potentially demonstrating a triggered response to an event, rather than a decision based on probabilities. This common clinician instinct to "culture if spikes" is an ineffective practice if not coupled with additional clinical information.…”

Section: Discussionmentioning

confidence: 78%

Culture if spikes? Indications and yield of blood cultures in hospitalized medical patients

Linsenmeyer

Strymish

Dhanani

et al. 2016

Journal of Hospital Medicine

View full text Add to dashboard Cite

BACKGROUND Although optimal utilization of blood cultures has been studied in populations, including emergency room and intensive care patients, less is known about the use of blood cultures in populations consisting exclusively of patients on a medical service. OBJECTIVE To identify the physician‐selected indication and yield of blood cultures ordered after hospitalization to an acute medical service and to identify populations in which blood cultures may not be necessary. DESIGN, SETTING, AND PATIENTS A prospective cohort study was performed at a single Veterans Affairs Medical Center from October 1, 2014 through April 15, 2015. Participants included all hospitalized patients on a medical service for whom a blood culture was ordered. MEASUREMENTS The main outcomes were the rate of true positive blood cultures and the predictors of true positive cultures. RESULTS The true positive rate was 3.6% per order. The most common physician‐selected indications were fever and leukocytosis, neither of which alone was highly predictive of true positive blood cultures. The only indication significantly associated with a true positive blood culture was “follow‐up previous positive” (likelihood ratio [LR]+ 3.4, 95% confidence interval [CI]: 1.8‐6.5). The only clinical predictors were a working diagnosis of bacteremia/endocarditis (LR+ 3.7, 95% CI: 2.5‐5.7) and absence of antibiotic exposure within 72 hours of the culture (LR+ 2.4, 95% CI: 1.2‐4.9). CONCLUSIONS The rate of true positive blood cultures among patients on a medical service was lower than previously studied. Using objective and easily obtainable clinical characteristics, including antibiotic exposure and working diagnosis, may improve the likelihood of true positive blood cultures. Journal of Hospital Medicine 2016;11:336–340. © 2016 Society of Hospital Medicine

show abstract

Clinical prediction rules for bacteremia and in‐hospital death based on clinical data at the time of blood withdrawal for culture: an evaluation of their development and use

Abstract: The clinical prediction rules are helpful for improved clinical decision making for bacteremia patients.

Cited by 23 publications

References 29 publications

Predictors of in-hospital mortality in elderly patients with bacteraemia admitted to an Internal Medicine ward

Predictors of in-hospital mortality in elderly patients with bacteraemia admitted to an Internal Medicine ward

A Risk Prediction Model for Screening Bacteremic Patients: A Cross Sectional Study

Culture if spikes? Indications and yield of blood cultures in hospitalized medical patients

Contact Info

Product

Resources

About