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.

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.

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.

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.

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.

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.

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 Yield, Radiation Exposure, and the Role of Clinical Decision Rules to Limit Computed Tomographic Pulmonary Angiography-Associated Complications.

OBJECTIVES: Computed tomographic pulmonary angiography (CT-PA) is associated with significant cost, contrast, and radiation exposure. Clinical decision rules (CDRs) reduce the need for diagnostic imaging; however, their utility in the medical intensive care unit (MICU) remains unknown. We explored the diagnostic yield and complications associated with CT-PA (radiation exposure and contrast-induced acute kidney injury [AKI]) while investigating the efficacy of CDRs to reduce unnecessary testing. METHODS: All CT-PAs performed in an academic MICU for 4 years were retrospectively reviewed. The Wells and revised Geneva scores (CDRs) and radiation dose per CT-PA were calculated, and the incidence of post-CT-PA AKI was recorded. RESULTS: A total of 439 studies were analyzed; the diagnostic yield was 11% (48 PEs). Positive CT-PAs were associated with a higher Wells score (5.8 versus 3.2, P < 0.001), but similar revised Geneva scores (6.4 versus 6.0, P = 0.32). A Wells score of ≥4 had a positive likelihood ratio of 2.1 with a negative predictive value of 98.2. More than half (88.9%) of patients with a Wells score of ≤4 developed an AKI, with 55.6% of those having recovery of renal function. CONCLUSIONS: There is overutilization of CT-PA in the MICU. The Wells score retains its negative predictive value in critically ill adult patients and may aid to limit radiation exposure and contrast-induced AKI in MICU.

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.

Influence of research evidence on the use of cardiovascular clinical prediction rules in primary care: an exploratory qualitative interview study.

BACKGROUND: Cardiovascular clinical prediction rules (CPRs) are widely used in primary care. They accumulate research evidence through derivation, external validation, and impact studies. However, existing knowledge about the influence of research evidence on the use of CPRs is limited. Therefore, we explored how primary care clinicians' perceptions of and experiences with research influence their use of cardiovascular CPRs. METHODS: We conducted an exploratory qualitative interview study with thematic analysis. Primary care clinicians were recruited from the WWAMI (Washington, Wyoming, Alaska, Montana and Idaho) region Practice and Research Network (WPRN). We used purposeful sampling to ensure maximum variation within the participant group. Data were collected by conducting semi-structured online interviews. We analyzed data using inductive thematic analysis to identify commonalities and differences within themes. RESULTS: Of 29 primary care clinicians who completed the questionnaire, 15 participated in the interview. We identified two main themes relating to the influence of clinicians' perceptions of and experiences with cardiovascular CPR research on their decisions about using cardiovascular CPRs: "Seek and judge" and "be acquainted and assume." When clinicians are familiar with, trust, and feel confident in using research evidence, they might actively search and assess the evidence, which may then influence their decisions about using cardiovascular CPRs. However, clinicians, who are unfamiliar with, distrust, or find it challenging to use research evidence, might be passively acquainted with evidence but do not make their own judgment on the trustworthiness of such evidence. Therefore, these clinicians might not rely on research evidence when making decisions about using cardiovascular CPRs. CONCLUSIONS: Clinicians' perceptions and experiences could influence how they use research evidence in decisions about using cardiovascular CPRs. This implies, when promoting evidence-based decisions, it might be useful to target clinicians' unfamiliarity, distrust, and challenges regarding the use of research evidence rather than focusing only on their knowledge and skills. Further, because clinicians often rely on evidence-unrelated factors, guideline developers and policymakers should recommend cardiovascular CPRs supported by high-quality evidence.

Use of Patient-Reported Symptom Data in Clinical Decision Rules for Predicting Influenza in a Telemedicine Setting.

INTRODUCTION: Increased use of telemedicine could potentially streamline influenza diagnosis and reduce transmission. However, telemedicine diagnoses are dependent on accurate symptom reporting by patients. If patients disagree with clinicians on symptoms, previously derived diagnostic rules may be inaccurate. METHODS: We performed a secondary data analysis of a prospective, nonrandomized cohort study at a university student health center. Patients who reported an upper respiratory complaint were required to report symptoms, and their clinician was required to report the same list of symptoms. We examined the performance of 5 previously developed clinical decision rules (CDRs) for influenza on both symptom reports. These predictions were compared against PCR diagnoses. We analyzed the agreement between symptom reports, and we built new predictive models using both sets of data. RESULTS: CDR performance was always lower for the patient-reported symptom data, compared with clinician-reported symptom data. CDRs often resulted in different predictions for the same individual, driven by disagreement in symptom reporting. We were able to fit new models to the patient-reported data, which performed slightly worse than previously derived CDRs. These models and models built on clinician-reported data both suffered from calibration issues. DISCUSSION: Patients and clinicians frequently disagree about symptom presence, which leads to reduced accuracy when CDRs built with clinician data are applied to patient-reported symptoms. Predictive models using patient-reported symptom data performed worse than models using clinician-reported data and prior results in the literature. However, the differences are minor, and developing new models with more data may be possible.

The Risk of Developing Disabling Crohn's Disease: Validation of a Clinical Prediction Rule to Improve Treatment Decision Making.

BACKGROUND: Crohn's disease (CD) is characterized by the development of complications over the course of the disease. It is crucial to identify predictive factors of disabling disease, in order to target patients for early intervention. We evaluated risk factors of disabling CD and developed a prognostic model. METHODS: In total, 511 CD patients were retrospectively analyzed. Univariate and multivariate logistic regression analyses were used to identify demographic, clinical, and biological risk factors. A predictive nomogram model was developed in a subgroup of patients with noncomplicated CD (inflammatory pattern and no perianal disease). RESULTS: The rate of disabling CD within 5 years after diagnosis was 74.6%. Disabling disease was associated with gender, location of disease, requirement of steroids for the first flare, and perianal lesions. In the subgroup of patients (310) with noncomplicated CD, the rate of disabling CD was 80%. In the multivariate analysis age at onset <40 years (OR = 3.46, 95% confidence interval [CI] = 1.52-7.90), extensive disease (L3/L4) (OR = 2.67, 95% CI = 1.18-6.06), smoking habit (OR = 2.09, 95% CI = 1.03-4.27), requirement of steroids at the first flare (OR = 2.20, 95% CI = 1.09-4.45), and albumin (OR = 0.59, 95% CI = 0.36-0.96) were associated with development of disabling disease. The developed predictive nomogram based on these factors presented good discrimination, with an area under the receiver operating characteristic curve of 0.723 (95% CI: 0.670-0.830). CONCLUSION: We identified predictive factors of disabling CD and developed an easy-to-use prognostic model that may be used in clinical practice to help identify patients at high risk and address treatment effectively.

Development and validation of a clinical prediction rule for acute appendicitis in children in primary care.

BACKGROUND: Recognising acute appendicitis in children presenting with acute abdominal pain in primary care is challenging. General practitioners (GPs) may benefit from a clinical prediction rule. OBJECTIVES: To develop and validate a clinical prediction rule for acute appendicitis in children presenting with acute abdominal pain in primary care. METHODS: In a historical cohort study data was retrieved from GP electronic health records included in the Integrated Primary Care Information database. We assigned children aged 4-18 years presenting with acute abdominal pain (≤ 7 days) to development (2010-2012) and validation (2013-2016) cohorts, using acute appendicitis within six weeks as the outcome. Multiple logistic regression was used to develop a prediction model based on predictors with > 50% data availability derived from existing rules for secondary care. We performed internal and external temporal validation and derived a point score to stratify risk of appendicitis into three groups, i.e. low-risk, medium-risk and high-risk. RESULTS: The development and validation cohorts included 2,041 and 3,650 children, of whom 95 (4.6%) and 195 (5.3%) had acute appendicitis. The model included male sex, pain duration (<24, 24-48, > 48 h), nausea/vomiting, elevated temperature (≥ 37.3 °C), abnormal bowel sounds, right lower quadrant tenderness, and peritoneal irritation. Internal and temporal validation showed good discrimination (C-statistics: 0.93 and 0.90, respectively) and excellent calibration. In the three groups, the risks of acute appendicitis were 0.5%, 7.5%, and 41%. CONCLUSION: Combined with further testing in the medium-risk group, the prediction rule could improve clinical decision making and outcomes.

Outcome prediction in pediatric fever in neutropenia: Development of clinical decision rules and external validation of published rules based on data from the prospective multicenter SPOG 2015 FN definition study.

BACKGROUND: Fever in neutropenia (FN) remains a serious complication of childhood cancer therapy. Clinical decision rules (CDRs) are recommended to help distinguish between children at high and low risk of severe infection. The aim of this analysis was to develop new CDRs for three different outcomes and to externally validate published CDRs. PROCEDURE: Children undergoing chemotherapy for cancer were observed in a prospective multicenter study. CDRs predicting low from high risk infection regarding three outcomes (bacteremia, serious medical complications (SMC), safety relevant events (SRE)) were developed from multivariable regression models. Their predictive performance was assessed by internal cross-validation. Published CDRs suitable for validation were identified by literature search. Parameters of predictive performance were compared to assess reproducibility. RESULTS: In 158 patients recruited between April 2016 and August 2018, 360 FN episodes were recorded, including 56 (16%) with bacteremia, 30 (8%) with SMC and 72 (20%) with SRE. The CDRs for bacteremia and SRE used four characteristics (type of malignancy, severely reduced general condition, leucocyte count <0.3 G/L, bone marrow involvement), the CDR for SMC two characteristics (severely reduced general condition and platelet count <50 G/L). Eleven published CDRs were analyzed. Six CDRs showed reproducibility, but only one in both sensitivity and specificity. CONCLUSIONS: This analysis developed CDRs predicting bacteremia, SMC or SRE at presentation with FN. In addition, it identified six published CDRs that show some reproducibility. Validation of CDRs is fundamental to find the best balance between sensitivity and specificity, and will help to further improve management of FN.