Translation of clinical prediction rules for febrile children to primary care practice: an observational cohort study

Ierland, Yvette van; Elshout, Gijs; Berger, Marjolein Y.; Vergouwe, Yvonne; Wilde, Marcel de; Lei, Johan van der; Mol, Henriette A.; Oostenbrink, Rianne

doi:10.3399/bjgp15x684373

Cited by 8 publications

(6 citation statements)

References 33 publications

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“…However, the clinical model did not perform equally well when externally validated, yielding AUC of only 0.60 (0.49–0.70) in the validation cohort ( 52 ). An updated version including ill clinical appearance increased the AUC to 0.69 (0.63–0.75) in derivation population and to 0.65 (0.62–0.67) when validated in an external dataset, although in primary care ( 54 ). Similarly, another clinical score developed by Brent et al ( 13 ) based on eight clinical variables showing moderate ability to predict SBI [AUC, 0.77 (0.71–0.83)] did not perform equally well when validated in external datasets ( 29 ).…”

Section: Discussionmentioning

confidence: 99%

Integrating Clinical Signs at Presentation and Clinician's Non-analytical Reasoning in Prediction Models for Serious Bacterial Infection in Febrile Children Presenting to Emergency Department

et al. 2022

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ObjectiveDevelopment and validation of clinical prediction model (CPM) for serious bacterial infections (SBIs) in children presenting to the emergency department (ED) with febrile illness, based on clinical variables, clinician's “gut feeling,” and “sense of reassurance.Materials and MethodsFebrile children presenting to the ED of Children's Clinical University Hospital (CCUH) between April 1, 2017 and December 31, 2018 were enrolled in a prospective observational study. Data on clinical signs and symptoms at presentation, together with clinician's “gut feeling” of something wrong and “sense of reassurance” were collected as candidate variables for CPM. Variable selection for the CPM was performed using stepwise logistic regression (forward, backward, and bidirectional); Akaike information criterion was used to limit the number of parameters and simplify the model. Bootstrapping was applied for internal validation. For external validation, the model was tested in a separate dataset of patients presenting to six regional hospitals between January 1 and March 31, 2019.ResultsThe derivation cohort consisted of 517; 54% (n = 279) were boys, and the median age was 58 months. SBI was diagnosed in 26.7% (n = 138). Validation cohort included 188 patients; the median age was 28 months, and 26.6% (n = 50) developed SBI. Two CPMs were created, namely, CPM1 consisting of six clinical variables and CPM2 with four clinical variables plus “gut feeling” and “sense of reassurance.” The area under the curve (AUC) for receiver operating characteristics (ROC) curve of CPM1 was 0.744 (95% CI, 0.683–0.805) in the derivation cohort and 0.692 (95% CI, 0.604–0.780) in the validation cohort. AUC for CPM2 was 0.783 (0.727–0.839) and 0.752 (0.674–0.830) in derivation and validation cohorts, respectively. AUC of CPM2 in validation population was significantly higher than that of CPM1 [p = 0.037, 95% CI (−0.129; −0.004)]. A clinical evaluation score was derived from CPM2 to stratify patients in “low risk,” “gray area,” and “high risk” for SBI.ConclusionBoth CPMs had moderate ability to predict SBI and acceptable performance in the validation cohort. Adding variables “gut feeling” and “sense of reassurance” in CPM2 improved its ability to predict SBI. More validation studies are needed for the assessment of applicability to all febrile patients presenting to ED.

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Section: Discussionmentioning

confidence: 99%

Integrating Clinical Signs at Presentation and Clinician's Non-analytical Reasoning in Prediction Models for Serious Bacterial Infection in Febrile Children Presenting to Emergency Department

et al. 2022

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“…Clinical decision rules (CDRs) can help clinicians to assess the probability that a patient has a particular condition. 11 They are used widely in medicine to inform decisions about investigation and management. 12 , 13 Mant and colleagues developed a CDR for heart failure by undertaking a systematic review that identified 11 prospective studies set in primary care.…”

Section: Introductionmentioning

confidence: 99%

Primary care REFerral for EchocaRdiogram (REFER) in heart failure: a diagnostic accuracy study

et al. 2016

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BackgroundSymptoms of breathlessness, fatigue, and ankle swelling are common in general practice but deciding which patients are likely to have heart failure is challenging. AimTo evaluate the performance of a clinical decision rule (CDR), with or without N-Terminal pro-B type natriuretic peptide (NT-proBNP) assay, for identifying heart failure. Design and settingProspective, observational, diagnostic validation study of patients aged >55 years, presenting with shortness of breath, lethargy, or ankle oedema, from 28 general practices in England. MethodThe outcome was test performance of the CDR and natriuretic peptide test in determining a diagnosis of heart failure. The reference standard was an expert consensus panel of three cardiologists. ResultsThree hundred and four participants were recruited, with 104 (34.2%; 95% confidence interval [CI] = 28.9 to 39.8) having a confirmed diagnosis of heart failure. The CDR+NT-proBNP had a sensitivity of 90.4% (95% CI = 83.0 to 95.3) and specificity 45.5% (95% CI = 38.5 to 52.7). NT-proBNP level alone with a cut-off <400 pg/ ml had sensitivity 76.9% (95% CI = 67.6 to 84.6) and specificity 91.5% (95% CI = 86.7 to 95.0). At the lower cut-off of NT-proBNP <125 pg/ml, sensitivity was 94.2% (95% CI = 87.9 to 97.9) and specificity 49.0% (95% CI = 41.9 to 56.1). ConclusionAt the low threshold of NT-proBNP <125 pg/ml, natriuretic peptide testing alone was better than a validated CDR+NT-proBNP in determining which patients presenting with symptoms went on to have a diagnosis of heart failure. The higher NT-proBNP threshold of 400 pg/ml may mean more than one in five patients with heart failure are not appropriately referred. Guideline natriuretic peptide thresholds may need to be revised.

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“…5 van Ierland and colleagues assessed the applicability and diagnostic value of published CPRs for identifying serious infections in febrile children in primary care by validating them in 9794 children presenting in Dutch out-of-hours care. 6 They found 794 (8.1%) of children were referred to the emergency department, a reasonable proxy outcome for serious illness in primary care. The CPRs tested performed moderately at best and disappointingly all CPRs had low sensitivity (that is, poor rule out value) although several had high specificity (that is, good rule in value).…”

Section: Identifying Serious Infections In Primary Carementioning

confidence: 99%

Child health care in general practice: priorities for education and practice

Gill

Thompson

2015

Br J Gen Pract

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Translation of clinical prediction rules for febrile children to primary care practice: an observational cohort study

Cited by 8 publications

References 33 publications

Integrating Clinical Signs at Presentation and Clinician's Non-analytical Reasoning in Prediction Models for Serious Bacterial Infection in Febrile Children Presenting to Emergency Department

Integrating Clinical Signs at Presentation and Clinician's Non-analytical Reasoning in Prediction Models for Serious Bacterial Infection in Febrile Children Presenting to Emergency Department

Primary care REFerral for EchocaRdiogram (REFER) in heart failure: a diagnostic accuracy study

Child health care in general practice: priorities for education and practice

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