Validation of the PediBIRN-7 clinical prediction rule for pediatric abusive head trauma.

BACKGROUND: The PediBIRN-7 clinical prediction rule incorporates the (positive or negative) predictive contributions of completed abuse evaluations to estimate abusive head trauma (AHT) probability after abuse evaluation. Applying definitional criteria as proxies for AHT and non-AHT ground truth, it performed with sensitivity 0.73 (95 % CI: 0.66-0.79), specificity 0.87 (95 % CI: 0.82-0.90), and ROC-AUC 0.88 (95 % CI: 0.85-0.92) in its derivation study. OBJECTIVE: To validate the PediBIRN-7's AHT prediction performance in a novel, equivalent, patient population. PARTICIPANTS AND SETTINGS: Consecutive, acutely head-injured children <3 years hospitalized for intensive care across eight sites between 2017 and 2020 with completed skeletal surveys and retinal exams (N = 342). METHODS: Secondary analysis of an existing, cross-sectional, prospective dataset, including assignment of patient-specific estimates of AHT probability, calculation of AHT prediction performance measures (ROC-AUC, sensitivity, specificity, predictive values), and completion of sensitivity analyses to estimate best- and worst-case prediction performances. RESULTS: Applying the same definitional criteria, the PediBIRN-7 performed with sensitivity 0.74 (95 % CI: 0.66-0.81), specificity 0.77 (95 % CI: 0.70-0.83), and ROC-AUC 0.83 (95 % CI: 0.78-0.88). The reduction in ROC-AUC was statistically insignificant (p = .07). Applying physicians' final consensus diagnoses as proxies for AHT and non-AHT ground truth, the PediBIRN-7 performed with sensitivity 0.73 (95 % CI: 0.66-0.79), specificity 0.87 (95 % CI: 0.82-0.90), and ROC-AUC 0.90 (95 % CI: 0.87-0.94). Sensitivity analyses demonstrated minimal changes in rule performance. CONCLUSION: The PediBIRN-7's overall AHT prediction performance has been validated in a novel, equivalent, patient population. Its patient-specific estimates of AHT probability can inform physicians' AHT-related diagnostic reasoning after abuse evaluation.

Performance of Febrile Infant Algorithms by Duration of Fever.

OBJECTIVES: To analyze the performance of commonly used blood tests in febrile infants ≤90 days of age to identify patients at low risk for invasive bacterial infection (bacterial pathogen in blood or cerebrospinal fluid) by duration of fever. METHODS: We conducted a secondary analysis of a prospective single-center registry that includes all consecutive infants ≤90 days of age with fever without a source evaluated at 1 pediatric emergency department between 2008 and 2021. We defined 3 groups based on caregiver-reported hours of fever (<2, 2-12, and ≥12) and analyzed the performance of the biomarkers and Pediatric Emergency Care Applied Research Network, American Academy of Pediatrics, and Step-by-Step clinical decision rules. RESULTS: We included 2411 infants; 76 (3.0%) were diagnosed with an invasive bacterial infection. The median duration of fever was 4 (interquartile range, 2-12) hours, with 633 (26.3%) patients with fever of <2 hours. The area under the curve was significantly lower in patients with <2 hours for absolute neutrophil count (0.562 vs 0.609 and 0.728) and C-reactive protein (0.568 vs 0.760 and 0.812), but not for procalcitonin (0.749 vs 0.780 and 0.773). Among well-appearing infants older than 21 days and negative urine dipstick with <2 hours of fever, procalcitonin ≥0.14 ng/mL showed a better sensitivity (100% with specificity 53.8%) than that of the combination of biomarkers of Step-by-Step (50.0% and 82.2%), and of the American Academy of Pediatrics and Pediatric Emergency Care Applied Research Network rules (83.3% and 58.3%), respectively. CONCLUSIONS: The performance of blood biomarkers, except for procalcitonin, in febrile young infants is lower in fever of very short duration, decreasing the accuracy of the clinical decision rules.

Prospective validity of a clinical prediction rule for response to non-surgical multidisciplinary management of knee osteoarthritis in tertiary care: a multisite prospective longitudinal study.

OBJECTIVES: We tested a previously developed clinical prediction tool-a nomogram consisting of four patient measures (lower patient-expected benefit, lower patient-reported knee function, greater knee varus angle and severe medial knee radiological degeneration) that were related to poor response to non-surgical management of knee osteoarthritis. This study sought to prospectively evaluate the predictive validity of this nomogram to identify patients most likely to respond poorly to non-surgical management of knee osteoarthritis. DESIGN: Multisite prospective longitudinal study. SETTING: Advanced practice physiotherapist-led multidisciplinary service across six tertiary hospitals. PARTICIPANTS: Participants with knee osteoarthritis deemed appropriate for trial of non-surgical management following an initial assessment from an advanced practice physiotherapist were eligible for inclusion. INTERVENTIONS: Baseline clinical nomogram scores were collected before a trial of individualised non-surgical management commenced. PRIMARY OUTCOME MEASURE: Clinical outcome (Global Rating of Change) was collected 6 months following commencement of non-surgical management and dichotomised to responder (a little better to a very great deal better) or poor responder (almost the same to a very great deal worse). Clinical nomogram accuracy was evaluated from receiver operating characteristics curve analysis and area under the curve, and sensitivity/specificity and positive/negative likelihood ratios were calculated. RESULTS: A total of 242 participants enrolled. Follow-up scores were obtained from 210 participants (87% response rate). The clinical nomogram demonstrated an area under the curve of 0.70 (p<0.001), with greatest combined sensitivity 0.65 and specificity 0.64. The positive likelihood ratio was 1.81 (95% CI 1.32 to 2.36) and negative likelihood ratio 0.55 (95% CI 0.41 to 0.75). CONCLUSIONS: The knee osteoarthritis clinical nomogram prediction tool may have capacity to identify patients at risk of poor response to non-surgical management. Further work is required to determine the implications for service delivery, feasibility and impact of implementing the nomogram in clinical practice.

Evaluating Clinical Prediction Rules for Bacteremia Detection in the Emergency Department: A Retrospective Review.

BACKGROUND: Bacteremia is a major cause of morbidity. Blood cultures are the gold standard for diagnosing bacteremia. OBJECTIVE: To compare previously published clinical decision rules for predicting a true positive blood culture (bacteremia) in the emergency department. METHODS: Retrospective analysis of medical records of patients who had a blood culture performed in a tertiary hospital emergency department in 2020 (12 months). Positive blood cultures were compared with randomly selected negative blood cultures (1:4 ratio). Blood cultures were analyzed per patient presentation. Clinical data from patient presentations were extracted and appraised against the modified-Shapiro (mShapiro) rule and systemic inflammatory response syndrome (SIRS) criteria to calculate diagnostic accuracy to detect bacteremia. RESULTS: During the study period, 3870 blood cultures were taken from 2921 patients: 476 (12.3%) cultures were positive for bacterial growth, from 421 individual patient presentations (10 excluded as incomplete data). Of included patients, 338 were true positives and 73 contaminates, these were compared with 1446 patients with negative blood culture presentations. Evaluating mShapiro's rule and SIRS criteria to detect bacteremia vs. no bacteremia (negative + contaminated cultures) had a sensitivity of 94.4% (95% confidence interval [CI] 91.4-96.4%) and 84.9% (95% CI 80.7-88.3%), respectively, and a specificity of 37.9% (95% CI 35.5-40.1%) and 33.8% (95% CI 31.5-36.3%), respectively. Both had a high negative predictive value for bacteremia of 96.8% (95% CI 95.1-98.0) and 91.0% (95% CI 88.3-93.1) for mShapiro's rule and SIRS criteria, respectively. CONCLUSIONS: In this cohort, mShapiro's rule performed better than the SIRS criteria at predicting bacteremia.

Are Clinical Prediction Rules Used in Spinal Cord Injury Care? A Survey of Practice.

Background: Accurate outcome prediction is desirable post spinal cord injury (SCI), reducing uncertainty for patients and supporting personalized treatments. Numerous attempts have been made to create clinical prediction rules that identify patients who are likely to recover function. It is unknown to what extent these rules are routinely used in clinical practice. Objectives: To better understand knowledge of, and attitudes toward, clinical prediction rules amongst SCI clinicians in the United Kingdom. Methods: An online survey was distributed via mailing lists of clinical special interest groups and relevant National Health Service Trusts. Respondents answered questions about their knowledge of existing clinical prediction rules and their general attitudes to using them. They also provided information about their level of experience with SCI patients. Results: One hundred SCI clinicians completed the survey. The majority (71%) were unaware of clinical prediction rules for SCI; only 8% reported using them in clinical practice. Less experienced clinicians were less likely to be aware. Lack of familiarity with prediction rules was reported as being a barrier to their use. The importance of clinical expertise when making prognostic decisions was emphasized. All respondents reported interest in using clinical prediction rules in the future. Conclusion: The results show widespread lack of awareness of clinical prediction rules amongst SCI clinicians in the United Kingdom. However, clinicians were positive about the potential for clinical prediction rules to support decision-making. More focus should be directed toward refining current rules and improving dissemination within the SCI community.

Paediatric meningitis in the conjugate vaccine era and a novel clinical decision model to predict bacterial aetiology.

OBJECTIVES: The aims of this study were to assess aetiology and clinical characteristics in childhood meningitis, and develop clinical decision rules to distinguish bacterial meningitis from other similar clinical syndromes. METHODS: Children aged <16 years hospitalised with suspected meningitis/encephalitis were included, and prospectively recruited at 31 UK hospitals. Meningitis was defined as identification of bacteria/viruses from cerebrospinal fluid (CSF) and/or a raised CSF white blood cell count. New clinical decision rules were developed to distinguish bacterial from viral meningitis and those of alternative aetiology. RESULTS: The cohort included 3002 children (median age 2·4 months); 1101/3002 (36·7%) had meningitis, including 180 bacterial, 423 viral and 280 with no pathogen identified. Enterovirus was the most common pathogen in those aged <6 months and 10-16 years, with Neisseria meningitidis and/or Streptococcus pneumoniae commonest at age 6 months to 9 years. The Bacterial Meningitis Score had a negative predictive value of 95·3%. We developed two clinical decision rules, that could be used either before (sensitivity 82%, specificity 71%) or after lumbar puncture (sensitivity 84%, specificity 93%), to determine risk of bacterial meningitis. CONCLUSIONS: Bacterial meningitis comprised 6% of children with suspected meningitis/encephalitis. Our clinical decision rules provide potential novel approaches to assist with identifying children with bacterial meningitis. FUNDING: This study was funded by the Meningitis Research Foundation, Pfizer and the NIHR Programme Grants for Applied Research.

Clinical decision rules for appendicitis are not used by surgeons in training: A survey.

BACKGROUNDS: We aimed to investigate surgeons in training knowledge of clinical decision rules (CDR) for diagnosing appendicitis and their attitudes toward implementing them. METHODS: We included surgeons in training practicing in East Denmark who independently could decide to perform a diagnostic laparoscopy for suspected appendicitis. The survey was developed in Research Electronic Data Capture and face-validated before use. It consisted of three parts: (1) the characteristics of the surgeons, (2) their diagnostic approach, and (3) their knowledge and attitude toward introducing CDR in the clinic. Data were collected in January 2023. RESULTS: We achieved 83 (90%) responses, and 52% of surgeons in training believed that appendicitis was difficult to diagnose. Their diagnostic approach mostly included symptoms and physical examinations for abdominal pain, and C-reactive protein. A total of 48% knew of at least one clinical decision rule, and 72% had never used a clinical decision rule. Regarding the necessity of CDR in clinical practice, surgeons in training options were divided into thirds: not needed, neither needed nor not needed, and needed. Surgeons in training indicated that CDR needed to be validated and easily applied before they would implement them. CONCLUSION: Approximately 3/4 of surgeons in training had never utilized a clinical decision rule to diagnose appendicitis, and only half knew of their existence. The symptoms and findings incorporated in most CDR aligned with their diagnostic approach. They were conflicted if CDR needed to be implemented in clinical practice.

Using the Kaiser Score as a clinical decision rule for breast lesion classification: Does computer-assisted curve type analysis improve diagnosis?

PURPOSE: We aimed to investigate the effect of using visual or automatic enhancement curve type assessment on the diagnostic performance of the Kaiser Score (KS), a clinical decision rule for breast MRI. METHOD: This IRB-approved retrospective study analyzed consecutive conventional BI-RADS 0, 4 or 5 patients who underwent biopsy after 1.5T breast MRI according to EUSOBI recommendations between 2013 and 2015. The KS includes five criteria (spiculations; signal intensity (SI)-time curve type; margins of the lesion; internal enhancement; and presence of edema) resulting in scores from 1 (=lowest) to 11 (=highest risk of breast cancer). Enhancement curve types (Persistent, Plateau or Wash-out) were assessed by two radiologists independently visually and using a pixel-wise color-coded computed parametric map of curve types. KS diagnostic performance differences between readings were compared by ROC analysis. RESULTS: In total 220 lesions (147 benign, 73 malignant) including mass (n = 148) and non-mass lesions (n = 72) were analyzed. KS reading performance in distinguishing benign from malignant lesions did not differ between visual analysis and parametric map (P = 0.119; visual: AUC 0.875, sensitivity 95 %, specificity 63 %; and map: AUC 0.901, sensitivity 97 %, specificity 65 %). Additionally, analyzing mass and non-mass lesions separately, showed no difference between parametric map based and visual curve type-based KS analysis as well (P = 0.130 and P = 0.787). CONCLUSIONS: The performance of the Kaiser Score is largely independent of the curve type assessment methodology, confirming its robustness as a clinical decision rule for breast MRI in any type of breast lesion in clinical routine.

Diagnostic performance of clinical prediction rules to detect group A beta-haemolytic streptococci in people with acute pharyngitis: a systematic review.

OBJECTIVE: To assess and compare the diagnostic performance of Clinical Prediction Rules (CPRs) developed to detect group A Beta-haemolytic streptococci in people with acute pharyngitis (or sore throat). STUDY DESIGN: A systematic review. METHODS: We searched PubMed, Embase and Web of Science (inception-September 2022) for studies deriving and/or validating CPRs comprised of ≥2 predictors from an individual's history or physical examination. Two authors independently screened articles, extracted data and assessed risk of bias in included studies. A meta-analysis was not possible due to heterogeneity. Instead we compared the performance of CPRs when they were validated in the same study population (head-to-head comparisons). We used a modified grading of recommendations, assessment, development, and evaluations (GRADE) approach to assess certainty of the evidence. RESULTS: We included 63 studies, all judged at high risk of bias. Of 24 derived CPRs, 7 were externally validated (in 46 external validations). Five validation studies provided data for head-to-head comparison of four pairs of CPRs. Very low certainty evidence favoured the Centor CPR over the McIsaac (2 studies) and FeverPain CPRs (1 study) and found the Centor CPR was equivalent to the Walsh CPR (1 study). The AbuReesh and Steinhoff 2005 CPRs had a similar poor discriminative ability (1 study). Within and between study comparisons suggested the performance of the Centor CPR may be better in adults (>18 years). CONCLUSION: Very low certainty evidence suggests a better performance of the Centor CPR. When deciding about antibiotic prescribing for pharyngitis patients, involving patients in a shared decision making discussion about the likely benefits and harms, including antibiotic resistance, is recommended. Further research of higher rigour, which compares CPRs across multiple settings, is needed.

Accommodating misclassification effects on optimizing dynamic treatment regimes with Q-learning.

Research on dynamic treatment regimes has enticed extensive interest. Many methods have been proposed in the literature, which, however, are vulnerable to the presence of misclassification in covariates. In particular, although Q-learning has received considerable attention, its applicability to data with misclassified covariates is unclear. In this article, we investigate how ignoring misclassification in binary covariates can impact the determination of optimal decision rules in randomized treatment settings, and demonstrate its deleterious effects on Q-learning through empirical studies. We present two correction methods to address misclassification effects on Q-learning. Numerical studies reveal that misclassification in covariates induces non-negligible estimation bias and that the correction methods successfully ameliorate bias in parameter estimation.

A Single Dermatome Clinical Prediction Rule for Independent Walking 1 Year After Spinal Cord Injury.

OBJECTIVE: To derive and validate a simple, accurate CPR to predict future independent walking ability after SCI at the bedside that does not rely on motor scores and is predictive for those initially classified in the middle of the SCI severity spectrum. DESIGN: Retrospective cohort study. Binary variables were derived, indicating degrees of sensation to evaluate predictive value of pinprick and light touch variables across dermatomes. The optimal single sensory modality and dermatome was used to derive our CPR, which was validated on an independent dataset. SETTING: Analysis of SCI Model Systems dataset. PARTICIPANTS: Individuals with traumatic SCI. The data of 3679 participants (N=3679) were included with 623 participants comprising the derivation dataset and 3056 comprising the validation dataset. INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURES: Self-reported ability to walk both indoors and outdoors. RESULTS: Pinprick testing at S1 over lateral heels, within 31 days of SCI, accurately identified future independent walkers 1 year after SCI. Normal pinprick in both lateral heels provided good prognosis, any pinprick sensation in either lateral heel provided fair prognosis, and no sensation provided poor prognosis. This CPR performed satisfactorily in the middle SCI severity subgroup. CONCLUSIONS: In this large multi-site study, we derived and validated a simple, accurate CPR using only pinprick sensory testing at lateral heels that predicts future independent walking after SCI.

Clinical prediction rules for multi surfaces walking independence using the Berg Balance Scale or Mini-Balance Evaluation Systems Test in persons with stroke.

BACKGROUND: A Clinical prediction rule (CPR) for determining multi surfaces walking independence in persons with stroke has not been established. OBJECTIVES: To develop a CPR for determining multi surfaces walking independence in persons with stroke. METHODS: This was a multicenter retrospective analysis of 419 persons with stroke. We developed a Berg Balance Scale (BBS)-model CPR combining the BBS, comfortable walking speed (CWS) and cognitive impairment, and a Mini-Balance Evaluation Systems Test (Mini-BESTest)-model CPR combining the Mini-BESTest, CWS, and cognitive impairment. A logistic regression analysis was conducted with multi surfaces walking independence as the dependent variable and each factor as an independent variable. The identified factors were scored (0, 1) based on reported cutoff values. The CPR's accuracy was verified by the area under the curve (AUC). We used a bootstrap method internal validation and calculated the CPR's posttest probability. RESULTS: The logistic regression analysis showed that the BBS, CWS, and cognitive impairment were factors in the BBS model, and the Mini-BESTest was a factor in the Mini-BESTest model. The CPRs were 0-3 points for the BBS model and 0-1 points for the Mini-BESTest model. The AUCs (bootstrap mean AUC) of the CPR score were 0.89 (0.90) for the BBS model and 0.72 (0.72) for the Mini-BESTest model. The negative predictive value (negative likelihood ratio) was 97% (0.054) for CPR scores < 2 for the BBS model and 94% (0.060) for CPR scores < 1 for the Mini-BESTest model. CONCLUSIONS: The CPR developed herein is useful for determining multi surfaces walking independence.

Real-time artificial intelligence predicts adverse outcomes in acute pancreatitis in the emergency department: Comparison with clinical decision rule.

OBJECTIVE: Artificial intelligence (AI) prediction is increasingly used for decision making in health care, but its application for adverse outcomes in emergency department (ED) patients with acute pancreatitis (AP) is not well understood. This study aimed to clarify this aspect. METHODS: Data from 8274 ED patients with AP in three hospitals from 2009 to 2018 were analyzed. Demographic data, comorbidities, laboratory results, and adverse outcomes were included. Six algorithms were evaluated, and the one with the highest area under the curve (AUC) was implemented into the hospital information system (HIS) for real-time prediction. Predictive accuracy was compared between the AI model and Bedside Index for Severity in Acute Pancreatitis (BISAP). RESULTS: The mean ± SD age was 56.1 ± 16.7 years, with 67.7% being male. The AI model was successfully implemented in the HIS, with Light Gradient Boosting Machine (LightGBM) showing the highest AUC for sepsis (AUC 0.961) and intensive care unit (ICU) admission (AUC 0.973), and eXtreme Gradient Boosting (XGBoost) showing the highest AUC for mortality (AUC 0.975). Compared to BISAP, the AI model had superior AUC for sepsis (BISAP 0.785), ICU admission (BISAP 0.778), and mortality (BISAP 0.817). CONCLUSIONS: The first real-time AI prediction model implemented in the HIS for predicting adverse outcomes in ED patients with AP shows favorable initial results. However, further external validation is needed to ensure its reliability and accuracy.

Derivation of a clinical decision rule to exclude cerebral venous sinus thrombosis in emergency department patients: A retrospective cohort study.

OBJECTIVE: To derive a clinical decision rule to exclude cerebral venous sinus thrombosis (CVST) in the ED. A secondary aim was to derive a rule that incorporated clinical parameters and the non-contrast CT brain. METHODS: Single-centre, retrospective cohort study. Patients suspected of CVST were identified from the radiology database for CT/MR venograms. Clinical features included in the rule were determined by literature review. The presence of these features in participants was determined by chart review. Variables were tested for univariate association with CVST using logistic regression. Variable selection was accomplished using a forward-stepwise process, calculating the sensitivity/specificity of a rule containing the variable of most significance, then repeating the process after adding the next most significant variable. RESULTS: Forty-five out of 912 participants had confirmed CVST. The primary clinical rule was answering 'no' to all the following: any prothrombotic risk factor, age ≥54 years, confusion: sensitivity 95.6% (95% confidence interval [CI] 84.9-99.5%), specificity 40.9% (95% CI 37.6-44.2%), negative predictive value 99.4% (95% CI 97.9-99.9%) and positive predictive value 7.7% (95% CI 7.1-8.3%). The rule classified 39.5% of participants as CVST ruled out. The rule incorporating the non-contrast CT brain was answering 'no' to all the following: abnormal non-contrast CT brain, any prothrombotic risk-factor, age ≥54 years, confusion: sensitivity 100.0% (95% CI 91.6-100.0%), specificity 42.0% (95% CI 38.7-45.4%), negative predictive value 100.0% (95% CI not calculated) and positive predictive value 7.8% (95% CI 7.4-8.2%). The rule classified 40.0% of participants as CVST ruled out. CONCLUSIONS: A clinical decision rule was derived to rule out CVST. These results require validation before adoption into clinical practice.

A clinical decision rule to rule out bloodstream infection in the emergency department: retrospective multicentric observational cohort study.

BACKGROUND: We aimed to identify patients at low risk of bloodstream infection (BSI) in the ED. METHODS: We derived and validated a prediction model to rule out BSI in the ED without the need for laboratory testing by determining variables associated with a positive blood culture (BC) and assigned points according to regression coefficients. This retrospective study included adult patients suspected of having BSI (defined by at least one BC collection) from two European ED between 1 January 2017 and 31 December 2019. The primary end point was the BSI rate in the validation cohort for patients with a negative Bacteremia Rule Out Criteria (BAROC) score. The effect of adding laboratory variables to the model was evaluated as a second step in a two-step diagnostic strategy. RESULTS: We analysed 2580 patients with a mean age of 64 years±21, of whom 46.1% were women. The derived BAROC score comprises 12 categorical clinical variables. In the validation cohort, it safely ruled out BSI without BCs in 9% (58/648) of patients with a sensitivity of 100% (95% CI 95% to 100%), a specificity of 10% (95% CI 8% to 13%) and a negative predictive value of 100% (95% CI 94% to 100%). Adding laboratory variables (creatinine ≥177 µmol/L (2.0 mg/dL), platelet count ≤150 000/mm3 and neutrophil count ≥12 000/mm3) to the model, ruled out BSI in 10.2% (58/570) of remaining patients who had been positive on the BAROC score. The BAROC score with laboratory results had a sensitivity of 100% (95% CI 94% to 100%), specificity of 11% (95% CI 9% to 14%) and negative predictive value of 100% (95% CI 94 to 100%). In the validation cohort, there was no evidence of a difference in discrimination between the area under the receiver operating characteristic for BAROC score with versus without laboratory testing (p=0.6). CONCLUSION: The BAROC score safely identified patients at low risk of BSI and may reduce BC collection in the ED without the need for laboratory testing.

Added value of clinical decision rules for the management of enhancing breast MRI lesions: A systematic comparison of the Kaiser score and the Göttingen score.

PURPOSE: We investigated the added value of two internationally used clinical decision rules in the management of enhancing lesions on breast MRI. METHODS: This retrospective, institutional review board approved study included consecutive patients from two different populations. Patients received breast MRI according to the recommendations of the European Society of Breast Imaging (EUSOBI). Initially, all examinations were assessed by expert readers without using clinical decision rules. All lesions rated as category 4 or 5 according to the Breast Imaging Reporting and Data System were histologically confirmed. These lesions were re-evaluated by an expert reader blinded to the histology. He assigned each lesion a Göttingen score (GS) and a Kaiser score (KS) on different occasions. To provide an estimate on inter-reader agreement, a second fellowship-trained reader assessed a subset of these lesions. Subgroup analyses based on lesion type (mass vs. non-mass), size (>1 cm vs. ≤ 1 cm), menopausal status, and significant background parenchymal enhancement were conducted. The areas under the ROC curves (AUCs) for the GS and KS were compared, and the potential to avoid unnecessary biopsies was determined according to previously established cutoffs (KS > 4, GS > 3) RESULTS: 527 lesions in 506 patients were included (mean age: 51.8 years, inter-quartile-range: 43.0-61.0 years). 131/527 lesions were malignant (24.9 %; 95 %-confidence-interval: 21.3-28.8). In all subgroups, the AUCs of the KS (median = 0.91) were higher than those of the GS (median = 0.83). Except for "premenopausal patients" (p = 0.057), these differences were statistically significant (p ≤ 0.01). Kappa agreement was higher for the KS (0.922) than for the GS (0.358). CONCLUSION: Both the KS and the GS provided added value for the management of enhancing lesions on breast MRI. The KS was superior to the GS in terms of avoiding unnecessary biopsies and showed superior inter-reader agreement; therefore, it may be regarded as the clinical decision rule of choice.

Diagnostic clinical prediction rules for categorising low back pain: A systematic review.

BACKGROUND: Low back pain (LBP) is a common complex condition, where specific diagnoses are hard to identify. Diagnostic clinical prediction rules (CPRs) are known to improve clinical decision-making. A review of LBP diagnostic-CPRs by Haskins et al. (2015) identified six diagnostic-CPRs in derivation phases of development, with one tool ready for implementation. Recent progress on these tools is unknown. Therefore, this review aimed to investigate developments in LBP diagnostic-CPRs and evaluate their readiness for implementation. METHODS: A systematic review was performed on five databases (Medline, Amed, Cochrane Library, PsycInfo, and CINAHL) combined with hand-searching and citation-tracking to identify eligible studies. Study and tool quality were appraised for risk of bias (Quality Assessment of Diagnostic Accuracy Studies-2), methodological quality (checklist using accepted CPR methodological standards), and CPR tool appraisal (GRade and ASsess Predictive). RESULTS: Of 5021 studies screened, 11 diagnostic-CPRs were identified. Of the six previously known, three have been externally validated but not yet undergone impact analysis. Five new tools have been identified since Haskin et al. (2015); all are still in derivation stages. The most validated diagnostic-CPRs include the Lumbar-Spinal-Stenosis-Self-Administered-Self-Reported-History-Questionnaire and Diagnosis-Support-Tool-to-Identify-Lumbar-Spinal-Stenosis, and the StEP-tool which differentiates radicular from axial-LBP. CONCLUSIONS: This updated review of LBP diagnostic CPRs found five new tools, all in the early stages of development. Three previously known tools have now been externally validated but should be used with caution until impact evaluation studies are undertaken. Future funding should focus on externally validating and assessing the impact of existing CPRs on clinical decision-making.