Clinical Prediction Rule Research Articles

Overuse of Computed Tomography Pulmonary Angiography and Low Utilization of Clinical Prediction Rules in Suspected Pulmonary Embolism Patients at a Regional Australian Hospital.

A pulmonary embolism (PE) is an obstruction in the pulmonary arterial system and may include non-specific signs and symptoms. Clinical prediction rules (CPRs) assess the pretest probability (PTP) of a PE to prevent the overuse of computed tomography pulmonary angiography (CTPA). CTPA overuse results in patient harm and health system waste. This study aimed to evaluate CTPA usage in an Australian regional hospital through analyzing CTPA encounters. A retrospective chart analysis was undertaken of 100 CTPAs conducted at an Australian regional hospital from April to May 2023. Analysis was undertaken for parameters including risk factors, signs and symptoms, investigations, and the use of CPRs. Overall, 86% of patients had signs and/or symptoms of a PE within a week of examination, and 6% of the population had signs of deep vein thrombosis. More than half of the population had no risk factors, while the most prevalent risk factors were a recent history of immobilization/trauma and/or having surgery that required general anesthesia in the last 4 weeks. The most common co-morbidity was chronic lung disease (11%). For the pre-test diagnostic workup, the ECG was the most ordered investigation. The Wells' score was used at 10%, while most patients did not have any CPRs applied. The prevalence of PEs discovered on CTPAs was 9%. CPRs were under-utilized in this Australian regional hospital. The D-dimers for ruling out subjects with low PTP derived from CPRs were also underused. This led to the inappropriate overordering of CTPAs, resulting in negative implications for patients and unnecessary costs to the health system.

Abstract B013: A quest for deeper understanding of BRCA2 Variants of Uncertain Significance (VUS)

Abstract Deleterious mutations in homology-directed repair pathway genes impact both future cancer risk and therapeutic intervention options for cancer diagnosis and prognosis. Homology-directed repair pathway is a high-fidelity DNA repair pathway for resolving double-strand breaks (DSBs) throughout the human genome. BRCA2, as a homology-directed repair factor, is essential to ensure genome integrity through an error-free process. Deleterious BRCA2 mutations increase the lifetime risk of breast, ovarian, melanoma, prostate, and pancreatic cancers. Deleterious BRCA2 mutations cover only 25% of the total BRCA2 mutations identified in the population by genetic testing whereas 60% are mutations with unknown connection to the disease called Variants of Uncertain Significance (VUS). The majority of BRCA2 VUS are missense mutations with one point mutation which could potentially alter BRCA2 cellular localization and/or nuclear functions. Thus, evaluating BRCA2 cellular localization and function in BRCA2 missense carriers and tumors is urgently needed to provide clinical guidance regarding cancer risk and treatment options. We have identified a paradoxical relationship between BRCA2 nuclear import and treatment. Deleterious missense mutations in the DNA binding domain of BRCA2 direct the protein to the cytosol which increase sensitivity to PARP inhibitors (PARPi) and platinum drugs. Therefore, although BRCA2 mislocalization might be a cause of cancer, keeping it out of the nucleus allows for much better treatment outcomes. Here, we present how BRCA2 cancer-driver missense mutations lead to cytosolic mislocalization and how BRCA2 localization can be used as a diagnostic tool to stratify patients. Our robust multi-step molecular approach will guide future clinical decision rules surrounding BRCA2 VUS. By providing reliable and accurate functional assays to correctly evaluate the pathogenicity of BRCA2 VUS, our goal is to improve personalized medicine by providing actionable information to patients and their physicians. Citation Format: Cole Niedmann, Judit Jimenez Sainz. A quest for deeper understanding of BRCA2 Variants of Uncertain Significance (VUS) [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: DNA Damage Repair: From Basic Science to Future Clinical Application; 2024 Jan 9-11; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2024;84(1 Suppl):Abstract nr B013.

Are triage tools effective for detecting cervical spine injury in paediatric trauma patients? - A Cochrane Review summary with commentary.

Pediatric cervical spine injuries (CSIs) from blunt trauma carry a high risk of neurological damage. Accurate diagnosis is vital for preventing harm and aiding recovery, yet the diagnostic accuracy of clinical decision rules (CDRs) remains unclear. To assess the effectiveness of triage tools for detecting CSI in pediatric trauma patients. A summary of the Cochrane Review by Tavender et al. (2024), with comments from a rehabilitation perspective. Five studies with 21,379 participants assessed seven CDRs. Direct comparisons showed high sensitivity but low specificity across different CDRs. Indirect comparison studies also demonstrated varying sensitivities and specificities. Insufficient evidence exists to determine the best tools for deciding if imaging is necessary for diagnosing potential CSI in children. Better quality studies are needed to assess the accuracy of CDRs for cervical spine clearance in this population.

Research inefficiencies in external validation studies of the Framingham Wilson coronary heart disease risk rule: A systematic review.

External validation studies create evidence about a clinical prediction rule's (CPR's) generalizability by evaluating and updating the CPR in populations different from those used in the derivation, and also by contributing to estimating its overall performance when meta-analysed in a systematic review. While most cardiovascular CPRs do not have any external validation, some CPRs have been externally validated repeatedly. Hence, we examined whether external validation studies of the Framingham Wilson coronary heart disease (CHD) risk rule contributed to generating evidence to their full potential. A forward citation search of the Framingham Wilson CHD risk rule's derivation study was conducted to identify studies that evaluated the Framingham Wilson CHD risk rule in different populations. For external validation studies of the Framingham Wilson CHD risk rule, we examined whether authors updated the Framingham Wilson CHD risk rule when it performed poorly. We also assessed the contribution of external validation studies to understanding the Predicted/Observed (P/O) event ratio and c statistic of the Framingham Wilson CHD risk rule. We identified 98 studies that evaluated the Framingham Wilson CHD risk rule; 40 of which were external validation studies. Of these 40 studies, 27 (67.5%) concluded the Framingham Wilson CHD risk rule performed poorly but did not update it. Of 23 external validation studies conducted with data that could be included in meta-analyses, 13 (56.5%) could not fully contribute to the meta-analyses of P/O ratio and/or c statistic because these performance measures were neither reported nor could be calculated from provided data. Most external validation studies failed to generate evidence about the Framingham Wilson CHD risk rule's generalizability to their full potential. Researchers might increase the value of external validation studies by presenting all relevant performance measures and by updating the CPR when it performs poorly.

Preliminary Evaluation of the Scandinavian Guidelines for Initial Management of Minimal, Mild, and Moderate Head Injuries with Glial Fibrillary Acidic Protein.

Glial fibrillary acidic protein (GFAP) has become the most promising biomarker for detecting traumatic abnormalities on head computed tomography (CT) in patients with traumatic brain injury (TBI), but most studies have not addressed the potential added value of combining the biomarker with clinical variables that confer risk for intracranial injuries. The Scandinavian Guidelines for Initial Management of Minimal, Mild, and Moderate Head Injuries in Adults were the first clinical decision rules in the field with an incorporated biomarker, the S100 astroglial calcium-binding protein B (S100B), which is used in the Mild (Low Risk) group defined by the guidelines. Our aim was to evaluate the performance of the guidelines when S100B was substituted with GFAP. The sample (N = 296) was recruited from the Tampere University Hospital's emergency department between November 2015 and November 2016, and there were 49 patients with available GFAP results who were stratified in the Mild (Low Risk) group (thus patients undergoing biomarker triaging). A previously reported cutoff of plasma GFAP ≥140 pg/mL was used. Within the Mild (Low Risk) group (n = 49), GFAP sensitivity (with 95% confidence intervals in parentheses) for detecting traumatic CT abnormalities was 1.0 (0.40-1.00), specificity 0.34 (0.19-0.53), the negative predictive value (NPV) 1.0 (0.68-1.00), and the positive predictive value (PPV) 0.16 (0.05-0.37). The sensitivity and specificity of the modified guidelines with GFAP, when applied to all imaged patients (n = 197) in the whole sample, were 0.94 (0.77-0.99) and 0.20 (0.15-0.28), respectively. NPV was 0.94 (0.80-0.99) and PPV 0.18 (0.13-0.25). In the Mild (Low Risk) group, none of the patients with GFAP results below 140 pg/mL had traumatic abnormalities on their head CT. These findings were derived from a small patient subgroup. Future researchers should replicate these findings in larger samples and assess whether GFAP has added or comparable value to S100B in acute TBI management.

Pulmonary embolism risk stratification: external validation of the 4-level Clinical Pretest Probability Score (4PEPS)

BackgroundThe 4-level clinical pretest probability score (4PEPS) was recently introduced as a clinical decision rule for the diagnosis of pulmonary embolism (PE). Based on the score, patients are classified into clinical pretest probability categories (c-PTP). The “very low” category aims at excluding PE without further testing; “low” and “moderate” categories require D-dimer testing with specific thresholds, while patients with a “high” pretest directly proceed to imaging. ObjectivesTo provide further external validation of the 4PEPS model. MethodsThe 4PEPS was applied to a previously collected prospective database of 756 patients with clinically suspected PE enrolled from European emergency departments in 2002 to 2003. The safety threshold for the failure rate in our study was calculated at 1.95% based on a 26% prevalence of PE in our study, as per the International Society on Thrombosis and Haemostasis Scientific and Standardization Committee guidance. ResultsPatients were classified as follows: 90 (12%) in the very low c-PTP group, of whom 5 (5.6%; 95% CI, 2.4%-12.4%) had PE; 363 (49%) in the low c-PTP group, of whom 34 had PE (9.4%); 246 (34%) in the moderate c-PTP group, of whom 124 (50%) had PE; and 35 (5%) in the high c-PTP group of whom 30 (86%) had PE. Overall, the failure rate of the 4PEPS was 9/734 (1.2%; 95% CI, 0.59%-2.23%) Overall, 9 out of 734 patients (1.2%; 95% CI, 0.59%-2.23%) were diagnosed with PE despite a negative 4PEPS rule; 5 (5.6%) from the very low c-PTP group, 3 (1.4%) in the low c-PTP group, and 1 (3.2%) in the moderate c-PTP group. ConclusionWe provide external validation data of the 4PEPS. In this high-prevalence cohort (26% prevalence), PE prevalence in the very low-risk group was higher than expected. A prospective validation study is needed before implementing the 4PEPS model in routine clinical practice.

Diagnostic Utility of Cerebrospinal Fluid White Blood Cell Components for the Identification of Bacterial Meningitis in Infants.

To evaluate the diagnostic and predictive utility of cerebrospinal fluid (CSF) white blood cell (WBC) components in the diagnosis of bacterial meningitis in infants discharged from the neonatal intensive care unit (NICU). We identified a cohort of infants discharged from a Pediatrix NICU between 1997 and 2020 who did not have an immunodeficiency, had at least 1 CSF culture collected within the first 120 days of life, and at least 1 CSF laboratory specimen obtained on the day of culture collection. We only included an infant's first CSF culture and excluded cultures from CSF reservoirs and those growing contaminants or nonbacterial organisms. We examined the utility of CSF WBC components to diagnose or predict bacterial meningitis by calculating sensitivity, specificity, positive and negative predictive values, likelihood ratios, and area under the receiver operating curve (AUC) at different cutoff values for each parameter. We performed subgroup analysis excluding infants treated with antibiotics the day before CSF culture collection. Of the 20 756 infants that met the study inclusion criteria, 320 (2%) were diagnosed with bacterial meningitis. We found (AUC [95% CI]) CSF WBC count (0.76 [0.73-0.79]), CSF neutrophil count (0.74 [0.70-0.78]), and CSF neutrophil percent (0.71 [0.67-0.75]) had the highest predictive values for bacterial meningitis, even when excluding infants with early antibiotic administration. No single clinical prediction rule had the optimal discriminatory power for predicting culture-proven bacterial meningitis, and clinicians should be cautious when interpreting CSF WBC parameters in infants with suspected meningitis.

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

PurposeWe 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. MethodThis 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. ResultsIn 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). ConclusionsThe 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.

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

BackgroundBacteremia is a major cause of morbidity. Blood cultures are the gold standard for diagnosing bacteremia. ObjectiveTo compare previously published clinical decision rules for predicting a true positive blood culture (bacteremia) in the emergency department. MethodsRetrospective 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. ResultsDuring 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. ConclusionsIn this cohort, mShapiro's rule performed better than the SIRS criteria at predicting bacteremia.

Internet Tool to Support Self-Assessment and Self-Swabbing of Sore Throat: Development and Feasibility Study.

Sore throat is a common problem and a common reason for the overuse of antibiotics. A web-based tool that helps people assess their sore throat, through the use of clinical prediction rules, taking throat swabs or saliva samples, and taking throat photographs, has the potential to improve self-management and help identify those who are the most and least likely to benefit from antibiotics. We aimed to develop a web-based tool to help patients and parents or carers self-assess sore throat symptoms and take throat photographs, swabs, and saliva samples for diagnostic testing. We then explored the acceptability and feasibility of using the tool in adults and children with sore throats. We used the Person-Based Approach to develop a web-based tool and then recruited adults and children with sore throats who participated in this study by attending general practices or through social media advertising. Participants self-assessed the presence of FeverPAIN and Centor score criteria and attempted to photograph their throat and take throat swabs and saliva tests. Study processes were observed via video call, and participants were interviewed about their views on using the web-based tool. Self-assessed throat inflammation and pus were compared to clinician evaluation of patients' throat photographs. A total of 45 participants (33 adults and 12 children) were recruited. Of these, 35 (78%) and 32 (71%) participants completed all scoring elements for FeverPAIN and Centor scores, respectively, and most (30/45, 67%) of them reported finding self-assessment relatively easy. No valid response was provided for swollen lymph nodes, throat inflammation, and pus on the throat by 11 (24%), 9 (20%), and 13 (29%) participants respectively. A total of 18 (40%) participants provided a throat photograph of adequate quality for clinical assessment. Patient assessment of inflammation had a sensitivity of 100% (3/3) and specificity of 47% (7/15) compared with the clinician-assessed photographs. For pus on the throat, the sensitivity was 100% (3/3) and the specificity was 71% (10/14). A total of 89% (40/45), 93% (42/45), 89% (40/45), and 80% (30/45) of participants provided analyzable bacterial swabs, viral swabs, saliva sponges, and saliva drool samples, respectively. Participants were generally happy and confident in providing samples, with saliva samples rated as slightly more acceptable than swab samples. Most adult and parent participants were able to use a web-based intervention to assess the clinical features of throat infections and generate scores using clinical prediction rules. However, some had difficulties assessing clinical signs, such as lymph nodes, throat pus, and inflammation, and scores were assessed as sensitive but not specific. Many participants had problems taking photographs of adequate quality, but most were able to take throat swabs and saliva samples.

Enabling knowledge translation: implementation of a web-based tool for independent walking prediction after traumatic spinal cord injury.

Several clinical prediction rules (CPRs) have been published, but few are easily accessible or convenient for clinicians to use in practice. We aimed to develop, implement, and describe the process of building a web-based CPR for predicting independent walking 1-year after a traumatic spinal cord injury (TSCI). Using the published and validated CPR, a front-end web application called "Ambulation" was built using HyperText Markup Language (HTML), Cascading Style Sheets (CSS), and JavaScript. A survey was created using QualtricsXM Software to gather insights on the application's usability and user experience. Website activity was monitored using Google Analytics. Ambulation was developed with a core team of seven clinicians and researchers. To refine the app's content, website design, and utility, 20 professionals from different disciplines, including persons with lived experience, were consulted. After 11 revisions, Ambulation was uploaded onto a unique web domain and launched (www.ambulation.ca) as a pilot with 30 clinicians (surgeons, physiatrists, and physiotherapists). The website consists of five web pages: Home, Calculation, Team, Contact, and Privacy Policy. Responses from the user survey (n = 6) were positive and provided insight into the usability of the tool and its clinical utility (e.g., helpful in discharge planning and rehabilitation), and the overall face validity of the CPR. Since its public release on February 7, 2022, to February 28, 2023, Ambulation had 594 total users, 565 (95.1%) new users, 26 (4.4%) returning users, 363 (61.1%) engaged sessions (i.e., the number of sessions that lasted 10 seconds/longer, had one/more conversion events e.g., performing the calculation, or two/more page or screen views), and the majority of the users originating from the United States (39.9%) and Canada (38.2%). Ambulation is a CPR for predicting independent walking 1-year after TSCI and it can assist frontline clinicians with clinical decision-making (e.g., time to surgery or rehabilitation plan), patient education and goal setting soon after injury. This tool is an example of adapting a validated CPR for independent walking into an easily accessible and usable web-based tool for use in clinical practice. This study may help inform how other CPRs can be adopted into clinical practice.

Development of a clinical prediction rule for mobility status at discharge in patients with total knee arthroplasty: Using a decision tree model

BackgroundTotal knee arthroplasty (TKA) is an effective treatment to improve mobility in patients with severe knee osteoarthritis. However, some patients continue to have poor mobility after surgery. The preoperative identification of patients with poor mobility after TKA allows for better treatment selection and appropriate goal setting. The purpose of this study was to develop a clinical prediction rule (CPR) to predict mobility after TKA. MethodsThis study included patients undergoing primary TKA. Predictors of outcome included patient characteristics, physical function, and psychological factors, which were measured preoperatively. The outcome measure was the Timed Up and Go test, which was measured at discharge. Patients with a score of ≥11 s were considered having a low-level of mobility. The classification and regression tree methodology of decision tree analysis was used for developing a CPR. ResultsOf the 101 cases (mean age, 72.2 years; 71.3 % female), 26 (25.7 %) were classified as low-mobility. Predictors were the modified Gait Efficacy Scale, age, knee pain on the operated side, knee extension range of motion on the non-operated side, and Somatic Focus, a subscale of the Tampa Scale for Kinesiophobia (short version). The model had a sensitivity of 50.0 %, a specificity of 98.7 %, a positive predictive value of 92.9 %, a positive likelihood ratio of 37.5, and an area under the receiver operating characteristic curve of 0.853. ConclusionWe have developed a CPR that, with some accuracy, predicts the mobility outcomes of patients after TKA. This CPR may be useful for predicting postoperative mobility and clinical goal setting.

The utility of the digital clock drawing test as an accurate early screening tool for those with preclinical Alzheimer’s disease

AbstractBackgroundWith new drug treatments emerging, the need for early Alzheimer’s disease (AD) detection methods that can be done in a primary care setting has become urgent. Significant advances have been made in validating blood‐based AD biomarkers, but the absence of a validated, non‐invasive clinical indicator remains a barrier for determining those who might be optimal candidates for treatment. The digital clock drawing test (dCDT) has been previously shown to be related to AD PET biomarkers. However, it is unknown whether and the extent to which dCDT would predict AD as compared to blood‐based AD biomarkers. The current study used an interpretable machine learning approach to determine whether the dCDT, independently and in combination with blood‐based AD biomarkers (e.g., amyloid‐β 42 (Aβ42); total tau (T‐tau)), was predictive of incident AD.MethodThis study included participants from the Framingham Heart Study Generation 2 cohort (Gen 2; n = 527) who had dCDT measures, plasma Aβ42, and T‐tau. The fast interpretable greedy‐tree sums method was used to identify clinical decision rules for AD prediction. Synthetic minority oversampling technique was employed to handle the class imbalance. The predictive ability of plasma Aβ42, T‐tau and dCDT measures was evaluated by both area under the curve (AUC) and standardized partial AUC. The permutation feature importance method was used to show the importance of each measure.ResultInterpretable clinical decision rules were generated as multivariate biomarker signatures for AD prediction (Figure 1). The dCDT (AUC 0.859) was better at incident AD prediction than AD blood‐based biomarkers (AUC 0.839). The combination of dCDT, Aβ42, and T‐tau best predicted incident AD (AUC 0.884) (Figure 2). The combination of dCDT and blood‐based biomarkers also has higher partial AUC than individual ones (Table 1). The total score and information processing scores were the most important contributing dCDT measures.ConclusionThese results highlight the efficiency of detecting early AD through the integration of digital measures as a clinical indicator combined with AD specific biomarkers. The interpretable decision rules have the potential to provide automated results that can be used in practical clinical settings, although external validation is warranted.

The Application of Immersive Virtual Reality and Machine Learning for the Assessment of Unilateral Spatial Neglect

Abstract Even subtle forms of hemispatial neglect after stroke negatively affect the performance of daily life tasks, increase the risk of injury, and are associated with poor rehabilitation outcomes. Conventional paper-and-pencil tests, however, often underestimate the symptoms. We aimed to identify relevant neglect-specific measures and clinical decision rules based on machine learning techniques on behavioral data generated in a new Virtual Reality (VR) application, the immersive virtual road-crossing task. In total, 59 participants were included in our study: two right-hemispheric stroke groups with left neglect (N = 20) or no neglect (N = 19), classified based on conventional tests and medical diagnosis, and healthy controls (N = 20). A neuropsychological test battery and the VR task were administered to all participants. We applied decision trees and random forest models to predict the respective groups based on the results of the VR task. Our feature selection procedure yielded six features as suitable predictors, most of which involved lateral time-related measures, particularly reaction times, and head movements. Our model achieved a high training accuracy of 96.6% and estimated test accuracy of 76.8%. These results confirm previous reports that temporal behavioral patterns are key to detecting subtle neglect in patients with chronic stroke. Our results indicate that VR combined with machine learning has the potential to achieve higher test accuracies while being highly applicable to clinical practice.

Barriers to Implementing Registered Nurse-Driven Clinical Decision Support for Antibiotic Stewardship: Retrospective Case Study.

Up to 50% of antibiotic prescriptions for upper respiratory infections (URIs) are inappropriate. Clinical decision support (CDS) systems to mitigate unnecessary antibiotic prescriptions have been implemented into electronic health records, but their use by providers has been limited. As a delegation protocol, we adapted a validated electronic health record-integrated clinical prediction rule (iCPR) CDS-based intervention for registered nurses (RNs), consisting of triage to identify patients with low-acuity URI followed by CDS-guided RN visits. It was implemented in February 2022 as a randomized controlled stepped-wedge trial in 43 primary and urgent care practices within 4 academic health systems in New York, Wisconsin, and Utah. While issues were pragmatically addressed as they arose, a systematic assessment of the barriers to implementation is needed to better understand and address these barriers. We performed a retrospective case study, collecting quantitative and qualitative data regarding clinical workflows and triage-template use from expert interviews, study surveys, routine check-ins with practice personnel, and chart reviews over the first year of implementation of the iCPR intervention. Guided by the updated CFIR (Consolidated Framework for Implementation Research), we characterized the initial barriers to implementing a URI iCPR intervention for RNs in ambulatory care. CFIR constructs were coded as missing, neutral, weak, or strong implementation factors. Barriers were identified within all implementation domains. The strongest barriers were found in the outer setting, with those factors trickling down to impact the inner setting. Local conditions driven by COVID-19 served as one of the strongest barriers, impacting attitudes among practice staff and ultimately contributing to a work infrastructure characterized by staff changes, RN shortages and turnover, and competing responsibilities. Policies and laws regarding scope of practice of RNs varied by state and institutional application of those laws, with some allowing more clinical autonomy for RNs. This necessitated different study procedures at each study site to meet practice requirements, increasing innovation complexity. Similarly, institutional policies led to varying levels of compatibility with existing triage, rooming, and documentation workflows. These workflow conflicts were compounded by limited available resources, as well as an implementation climate of optional participation, few participation incentives, and thus low relative priority compared to other clinical duties. Both between and within health care systems, significant variability existed in workflows for patient intake and triage. Even in a relatively straightforward clinical workflow, workflow and cultural differences appreciably impacted intervention adoption. Takeaways from this study can be applied to other RN delegation protocol implementations of new and innovative CDS tools within existing workflows to support integration and improve uptake. When implementing a system-wide clinical care intervention, considerations must be made for variability in culture and workflows at the state, health system, practice, and individual levels. ClinicalTrials.gov NCT04255303; https://clinicaltrials.gov/ct2/show/NCT04255303.

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

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. 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. 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. A clinical decision rule was derived to rule out CVST. These results require validation before adoption into clinical practice.

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

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. 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). 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). 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.

Minor head injury in anticoagulated patients: Outcomes and analysis of clinical predictors. A prospective study

BackgroundThe optimal management of patients taking oral anticoagulants who experience minor head injury (MHI) is unclear. The availability of validated protocols and reliable predictors of prognosis would be of great benefit. We investigated clinical factors as predictors of clinical outcomes and intracranial injury (ICI). MethodsWe conducted a single-cohort, prospective, observational study in an ED. Our structured clinical pathway included a first head CT scan, 24 h observation and a second CT scan. The primary outcome was the occurrence of MHI-related death or re-admission to ED at day +30. The secondary outcome was the rate of delayed ICI (dICI), defined as second positive CT scan after a first negative CT scan. We assessed some clinical predictors derived from guidelines and clinical prediction rules as potential risk factors for the outcomes. Results450 patients with a negative first CT scan who underwent a second CT scan composed our ‘study population’. The rate of the primary outcome was 4%. The rate of the secondary outcome was 4.7%. Upon univariate and multivariate analysis no statistically significant predictors for the outcomes were found. ConclusionsPrevious retrospective studies showed a lot of negative predictive factors for anticoagulated patients suffering a minor head injury. In our prospective study no clinical factors emerged as predictors of poor clinical outcomes and dICI. So, even if we confirmed a low rate of adverse outcomes, the best management of these patients in ED remains not so clear and future trials are needed.

Reducing prescribing of antibiotics for acute respiratory infections using a frontline nurse-led EHR-Integrated clinical decision support tool: protocol for a stepped wedge randomized control trial

BackgroundOverprescribing of antibiotics for acute respiratory infections (ARIs) remains a major issue in outpatient settings. Use of clinical prediction rules (CPRs) can reduce inappropriate antibiotic prescribing but they remain underutilized by physicians and advanced practice providers. A registered nurse (RN)-led model of an electronic health record-integrated CPR (iCPR) for low-acuity ARIs may be an effective alternative to address the barriers to a physician-driven model.MethodsFollowing qualitative usability testing, we will conduct a stepped-wedge practice-level cluster randomized controlled trial (RCT) examining the effect of iCPR-guided RN care for low acuity patients with ARI. The primary hypothesis to be tested is: Implementation of RN-led iCPR tools will reduce antibiotic prescribing across diverse primary care settings. Specifically, this study aims to: (1) determine the impact of iCPRs on rapid strep test and chest x-ray ordering and antibiotic prescribing rates when used by RNs; (2) examine resource use patterns and cost-effectiveness of RN visits across diverse clinical settings; (3) determine the impact of iCPR-guided care on patient satisfaction; and (4) ascertain the effect of the intervention on RN and physician burnout.DiscussionThis study represents an innovative approach to using an iCPR model led by RNs and specifically designed to address inappropriate antibiotic prescribing. This study has the potential to provide guidance on the effectiveness of delegating care of low-acuity patients with ARIs to RNs to increase use of iCPRs and reduce antibiotic overprescribing for ARIs in outpatient settings.Trial registrationClinicalTrials.gov Identifier: NCT04255303, Registered February 5 2020, https://clinicaltrials.gov/ct2/show/NCT04255303.

Guidelines for neuroprognostication in adults with traumatic spinal cord injury

BackgroundTraumatic spinal cord injury (tSCI) impacts patients and their families acutely and often for the long term. The ability of clinicians to share prognostic information about mortality and functional outcomes allows patients and their surrogates to engage in decision-making and plan for the future. These guidelines provide recommendations on the reliability of acute-phase clinical predictors to inform neuroprognostication and guide clinicians in counseling adult patients with tSCI or their surrogates.MethodsA narrative systematic review was completed using Grading of Recommendations Assessment, Development, and Evaluation methodology. Candidate predictors, including clinical variables and prediction models, were selected based on clinical relevance and presence of an appropriate body of evidence. The Population/Intervention/Comparator/Outcome/Timing/Setting question was framed as “When counseling patients or surrogates of critically ill patients with traumatic spinal cord injury, should < predictor, with time of assessment if appropriate > be considered a reliable predictor of < outcome, with time frame of assessment >?” Additional full-text screening criteria were used to exclude small and lower quality studies. Following construction of an evidence profile and summary of findings, recommendations were based on four Grading of Recommendations Assessment, Development, and Evaluation criteria: quality of evidence, balance of desirable and undesirable consequences, values and preferences, and resource use. Good practice recommendations addressed essential principles of neuroprognostication that could not be framed in the Population/Intervention/Comparator/Outcome/Timing/Setting format. Throughout the guideline development process, an individual living with tSCI provided perspective on patient-centered priorities.ResultsSix candidate clinical variables and one prediction model were selected. Out of 11,132 articles screened, 369 met inclusion criteria for full-text review and 35 articles met eligibility criteria to guide recommendations. We recommend pathologic findings on magnetic resonance imaging, neurological level of injury, and severity of injury as moderately reliable predictors of American Spinal Cord Injury Impairment Scale improvement and the Dutch Clinical Prediction Rule as a moderately reliable prediction model of independent ambulation at 1 year after injury. No other reliable or moderately reliable predictors of mortality or functional outcome were identified. Good practice recommendations include considering the complete clinical condition as opposed to a single variable and communicating the challenges of likely functional deficits as well as potential for improvement and for long-term quality of life with SCI-related deficits to patients and surrogates.ConclusionsThese guidelines provide recommendations about the reliability of acute-phase predictors of mortality, functional outcome, American Spinal Injury Association Impairment Scale grade conversion, and recovery of independent ambulation for consideration when counseling patients with tSCI or their surrogates and suggest broad principles of neuroprognostication in this context.