Scoring System to Evaluate Risk of Nonoperative Management Failure in Children With Intussusception.

INTRODUCTION: Typical first-line management of children with intussusception is enema reduction; however, failure necessitates surgical intervention. The number of attempts varies by clinician, and predictors of failed nonoperative management are not routinely considered in practice. The purpose of this study is to create a scoring system that predicts risk of nonoperative failure and need for surgical intervention. METHODS: Children diagnosed with intussusception upon presentation to the emergency department of a tertiary children's hospital between 2019 and 2022 were retrospectively identified. Univariable logistic regression identified predictors of nonoperative failure used as starting covariates for multivariable logistic regression with final model determined by backwards elimination. Regression coefficients for final predictors were used to create the scoring system and optimal cut-points were delineated. RESULTS: We identified 143 instances of ultrasound-documented intussusception of which 28 (19.6%) required operative intervention. Predictors of failed nonoperative management included age ≥4 y (odds ratio [OR] 32.83, 95% confidence interval [CI]: 1.91-564.23), ≥1 failed enema reduction attempts (OR 189.53, 95% CI: 19.07-1884.11), presenting heart rate ≥128 (OR 3.38, 95% CI: 0.74-15.36), presenting systolic blood pressure ≥115 mmHg (OR 6.59, 95% CI: 0.93-46.66), and trapped fluid between intussuscepted loops on ultrasound (OR 17.54, 95% CI: 0.77-397.51). Employing these factors, a novel risk scoring system was developed (area under the curve 0.96, 95% CI: 0.93-0.99). Scores range from 0 to 8; ≤2 have low (1.1%), 3-4 moderate (50.0%), and ≥5 high (100%) failure risk. CONCLUSIONS: Using known risk factors for enema failure, we produced a risk scoring system with outstanding discriminate ability for children with intussusception necessitating surgical intervention. Prospective validation is warranted prior to clinical integration.

Diagnostic accuracy of 3-item stroke scale for detection of cerebral large vessel occlusion: A systematic review and meta-analysis.

BACKGROUND: Prompt identification of large vessel occlusion (LVO) in acute ischemic stroke (AIS) is crucial for expedited endovascular therapy (EVT) and improved patient outcomes. Prehospital stroke scales, such as the 3-Item Stroke Scale (3I-SS), could be beneficial in detecting LVO in suspected patients. This meta-analysis evaluates the diagnostic accuracy of 3I-SS for LVO detection in AIS. METHODS: A systematic search was conducted in Medline, Embase, Scopus, and Web of Science databases until February 2024 with no time and language restrictions. Prehospital and in-hospital studies reporting diagnostic accuracy were included. Review articles, studies without reported 3I-SS cut-offs, and studies lacking the required data were excluded. Pooled effect sizes, including area under the curve (AUC), sensitivity, specificity, diagnostic odds ratio (DOR), positive and negative likelihood ratios (PLR and NLR) with 95% confidence intervals (CI) were calculated. RESULTS: Twenty-two studies were included in the present meta-analysis. A 3I-SS score of 2 or higher demonstrated sensitivity of 76% (95% CI: 52%-90%) and specificity of 74% (95% CI: 57%-86%) as the optimal cut-off, with an AUC of 0.81 (95% CI: 0.78-0.84). DOR, PLR, and NLR, were 9 (95% CI: 5-15), 2.9 (95% CI: 2.0-4.3) and 0.32 (95% CI: 0.17-0.61), respectively. Sensitivity analysis confirmed the analyses' robustness in suspected to stroke patients, anterior circulation LVO, assessment by paramedics, and pre-hospital settings. Meta-regression analyses pinpointed LVO definition (anterior circulation, posterior circulation) and patient setting (suspected stroke, confirmed stroke) as potential sources of heterogeneity. CONCLUSION: 3I-SS demonstrates good diagnostic accuracy in identifying LVO stroke and may be valuable in the prompt identification of patients for direct transfer to comprehensive stroke centers.

Development and Validation of a Risk Score for Predicting ICU Admission in Adults with New-Onset Encephalitis.

BACKGROUND: Timely intensive care unit (ICU) admission for patients with encephalitis is associated with better prognosis. Therefore, our aim was to create a risk score predicting ICU admission in adults with encephalitis, which could aid in optimal management and resource allocation. METHODS: We initially identified variables that would be most predictive of ICU admission among 372 patients with encephalitis from two hospital systems in Houston, Texas (cohort 1), who met the International Encephalitis Consortium (IEC) criteria from 2005 to 2023. Subsequently, we used a binary logistic regression model to create a risk score for ICU admission, which we then validated externally using a separate cohort of patients from two hospitals in Baltimore, Maryland (cohort 2), who met the IEC criteria from 2006 to 2022. RESULTS: Of 634 patients with encephalitis, 255 (40%) were admitted to the ICU, including 45 of 113 (39.8%) patients with an autoimmune cause, 100 of 272 (36.7%) with an infectious cause, and 110 of 249 (44.1%) with an unknown cause (p = 0.225). After conducting a multivariate analysis in cohort 1, we found that the presence of focal neurological signs, new-onset seizure, a Full Outline of Unresponsiveness score ≤ 14, leukocytosis, and a history of chronic kidney disease at admission were associated with an increased risk of ICU admission. The resultant clinical score for predicting ICU admission had an area under the receiver operating characteristic curve (AUROC) of 0.77 (95% confidence interval [CI] 0.72-0.82, p < 0.001). Patients were classified into three risk categories for ICU admission: low risk (score 0, 12.5%), intermediate risk (scores 1-5, 49.5%), and high risk (scores 6-8, 87.5%). External validation in cohort 2 yielded an AUROC of 0.76 (95% CI 0.69-0.83, p < 0.001). CONCLUSIONS: ICU admission is common in patients with encephalitis, regardless of etiology. Our risk score, encompassing neurologic and systemic factors, may aid physicians in decisions regarding intensity of care for adult patients with encephalitis upon hospital admission.

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.

Predicting diabetic foot ulceration using routinely collected data in a foot clinic. What level of prognostic accuracy can be achieved?

This study aimed to investigate the efficacy of using routinely collected clinical data in predicting the risk of diabetic foot ulcer (DFU). The first objective was to develop a prognostic model based on the most important risk factors objectively selected from a set of 39 clinical measures. The second objective was to compare the prediction accuracy of the developed model against that of a model based on only the 3 risk factors that were suggested in the systematic review and meta-analyses study (PODUS). In a cohort study, a set of 12 continuous and 27 categorical data from patients (n = 203 M/F:99/104) who attended a specialised diabetic foot clinic were collected at baseline. These patients were then followed-up for 24 months during which 24 (M/F:17/7) patients had DFU. Multivariate logistic regression was used to develop a prognostic model using the identified risk factors that achieved p < 0.2 based on univariate logistic regression. The final prognostic model included 4 risk factors (Adjusted-OR [95% CI]; p) in total. Impaired sensation (116.082 [12.06-1117.287]; p = 0.000) and presence of callus (6.257 [1.312-29.836]; p = 0.021) were significant (p < 0.05), while having dry skin (5.497 [0.866-34.89]; p = 0.071) and Onychomycosis (6.386 [0.856-47.670]; p = 0.071) that stayed in the model were not significant. The accuracy of the model with these 4 risk factors was 92.3%, where sensitivity and specificity were 78.9%, and 94.0% respectively. The 78.9% sensitivity of our prognostic 4-risk factor model was superior to the 50% sensitivity that was achieved when the three risk factors proposed by PODUS were used. Also our proposed model based on the above 4 risk factors showed to predict the DFU with higher overall prognostic accuracy. These findings have implications for developing prognostic models and clinical prediction rules in specific patient populations to more accurately predict DFU.

Symptom-based scoring technique by machine learning to predict COVID-19: a validation study.

BACKGROUND: Coronavirus disease 2019 (COVID-19) surges, such as that which occurred when omicron variants emerged, may overwhelm healthcare systems. To function properly, such systems should balance detection and workloads by improving referrals using simple yet precise and sensitive diagnostic predictions. A symptom-based scoring system was developed using machine learning for the general population, but no validation has occurred in healthcare settings. We aimed to validate a COVID-19 scoring system using self-reported symptoms, including loss of smell and taste as major indicators. METHODS: A cross-sectional study was conducted to evaluate medical records of patients admitted to Dr. Sardjito Hospital, Yogyakarta, Indonesia, from March 2020 to December 2021. Outcomes were defined by a reverse-transcription polymerase chain reaction (RT-PCR). We compared the symptom-based scoring system, as the index test, with antigen tests, antibody tests, and clinical judgements by primary care physicians. To validate use of the index test to improve referral, we evaluated positive predictive value (PPV) and sensitivity. RESULTS: After clinical judgement with a PPV of 61% (n = 327/530, 95% confidence interval [CI]: 60% to 62%), confirmation with the index test resulted in the highest PPV of 85% (n = 30/35, 95% CI: 83% to 87%) but the lowest sensitivity (n = 30/180, 17%, 95% CI: 15% to 19%). If this confirmation was defined by either positive predictive scoring or antigen tests, the PPV was 92% (n = 55/60, 95% CI: 90% to 94%). Meanwhile, the sensitivity was 88% (n = 55/62, 95% CI: 87% to 89%), which was higher than that when using only antigen tests (n = 29/41, 71%, 95% CI: 69% to 73%). CONCLUSIONS: The symptom-based COVID-19 predictive score was validated in healthcare settings for its precision and sensitivity. However, an impact study is needed to confirm if this can balance detection and workload for the next COVID-19 surge.

Predictors of poor outcome following lumbar spinal fusion surgery: a prospective observational study to derive two clinical prediction rules using British Spine Registry data.

PURPOSE: Lumbar spinal fusion surgery (LSFS) is common for lumbar degenerative disorders. The objective was to develop clinical prediction rules to identify which patients are likely to have a favourable outcome to inform decisions regarding surgery and rehabilitation. METHODS: A prospective observational study recruited 600 (derivation) and 600 (internal validation) consecutive adult patients undergoing LSFS for degenerative lumbar disorder through the British Spine Registry. Definition of good outcome (6 weeks, 12 months) was reduction in pain intensity (Numerical Rating Scale, 0-10) and disability (Oswestry Disability Index, ODI 0-50) > 1.7 and 14.3, respectively. Linear and logistic regression models were fitted and regression coefficients, Odds ratios and 95% CIs reported. RESULTS: Lower BMI, higher ODI and higher leg pain pre-operatively were predictive of good disability outcome, higher back pain was predictive of good back pain outcome, and no previous surgery and higher leg pain were predictive of good leg pain outcome; all at 6 weeks. Working and higher leg pain were predictive of good ODI and leg pain outcomes, higher back pain was predictive of good back pain outcome, and higher leg pain was predictive of good leg pain outcome at 12 months. Model performance demonstrated reasonable to good calibration and adequate/very good discrimination. CONCLUSIONS: BMI, ODI, leg and back pain and previous surgery are important considerations pre-operatively to inform decisions for surgery. Pre-operative leg and back pain and work status are important considerations to inform decisions for management following surgery. Findings may inform clinical decision making regarding LSFS and associated rehabilitation.

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.

BACKGROUND: Overprescribing 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. METHODS: Following 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. DISCUSSION: This 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 REGISTRATION: ClinicalTrials.gov Identifier: NCT04255303, Registered February 5 2020, https://clinicaltrials.gov/ct2/show/NCT04255303 .

External Validation of Risk Prediction Models to Improve Selection of Patients for Carotid Endarterectomy.

BACKGROUND AND PURPOSE: The net benefit of carotid endarterectomy (CEA) is determined partly by the risk of procedural stroke or death. Current guidelines recommend CEA if 30-day risks are <6% for symptomatic stenosis and <3% for asymptomatic stenosis. We aimed to identify prediction models for procedural stroke or death after CEA and to externally validate these models in a large registry of patients from the United States. METHODS: We conducted a systematic search in MEDLINE and EMBASE for prediction models of procedural outcomes after CEA. We validated these models with data from patients who underwent CEA in the American College of Surgeons National Surgical Quality Improvement Program (2011-2017). We assessed discrimination using C statistics and calibration graphically. We determined the number of patients with predicted risks that exceeded recommended thresholds of procedural risks to perform CEA. RESULTS: After screening 788 reports, 15 studies describing 17 prediction models were included. Nine were developed in populations including both asymptomatic and symptomatic patients, 2 in symptomatic and 5 in asymptomatic populations. In the external validation cohort of 26 293 patients who underwent CEA, 702 (2.7%) developed a stroke or died within 30-days. C statistics varied between 0.52 and 0.64 using all patients, between 0.51 and 0.59 using symptomatic patients, and between 0.49 to 0.58 using asymptomatic patients. The Ontario Carotid Endarterectomy Registry model that included symptomatic status, diabetes, heart failure, and contralateral occlusion as predictors, had C statistic of 0.64 and the best concordance between predicted and observed risks. This model identified 4.5% of symptomatic and 2.1% of asymptomatic patients with procedural risks that exceeded recommended thresholds. CONCLUSIONS: Of the 17 externally validated prediction models, the Ontario Carotid Endarterectomy Registry risk model had most reliable predictions of procedural stroke or death after CEA and can inform patients about procedural hazards and help focus CEA toward patients who would benefit most from it.

Derivation and external validation of a simple prediction rule for the development of respiratory failure in hospitalized patients with influenza.

BACKGROUND: Influenza viruses cause seasonal epidemics worldwide with a significant morbimortality burden. Clinical spectrum of Influenza is wide, being respiratory failure (RF) one of its most severe complications. This study aims to elaborate a clinical prediction rule of RF in hospitalized Influenza patients. METHODS: A prospective cohort study was conducted during two consecutive Influenza seasons (December 2016-March 2017 and December 2017-April 2018) including hospitalized adults with confirmed A or B Influenza infection. A prediction rule was derived using logistic regression and recursive partitioning, followed by internal cross-validation. External validation was performed on a retrospective cohort in a different hospital between December 2018 and May 2019. RESULTS: Overall, 707 patients were included in the derivation cohort and 285 in the validation cohort. RF rate was 6.8% and 11.6%, respectively. Chronic obstructive pulmonary disease, immunosuppression, radiological abnormalities, respiratory rate, lymphopenia, lactate dehydrogenase and C-reactive protein at admission were associated with RF. A four category-grouped seven point-score was derived including radiological abnormalities, lymphopenia, respiratory rate and lactate dehydrogenase. Final model area under the curve was 0.796 (0.714-0.877) in the derivation cohort and 0.773 (0.687-0.859) in the validation cohort (p < 0.001 in both cases). The predicted model showed an adequate fit with the observed results (Fisher's test p > 0.43). CONCLUSION: we present a simple, discriminating, well-calibrated rule for an early prediction of the development of RF in hospitalized Influenza patients, with proper performance in an external validation cohort. This tool can be helpful in patient's stratification during seasonal Influenza epidemics.

Risk-stratification in febrile infants 29 to 60 days old: a cost-effectiveness analysis.

BACKGROUND: Multiple clinical prediction rules have been published to risk-stratify febrile infants ≤60 days of age for serious bacterial infections (SBI), which is present in 8-13% of infants. We evaluate the cost-effectiveness of strategies to identify infants with SBI in the emergency department. METHODS: We developed a Markov decision model to estimate outcomes in well-appearing, febrile term infants, using the following strategies: Boston, Rochester, Philadelphia, Modified Philadelphia, Pediatric Emergency Care Applied Research Network (PECARN), Step-by-Step, Aronson, and clinical suspicion. Infants were categorized as low risk or not low risk using each strategy. Simulated cohorts were followed for 1 year from a healthcare perspective. Our primary model focused on bacteremia, with secondary models for urinary tract infection and bacterial meningitis. One-way, structural, and probabilistic sensitivity analyses were performed. The main outcomes were SBI correctly diagnosed and incremental cost per quality-adjusted life-year (QALY) gained. RESULTS: In the bacteremia model, the PECARN strategy was the least expensive strategy ($3671, 0.779 QALYs). The Boston strategy was the most cost-effective strategy and cost $9799/QALY gained. All other strategies were less effective and more costly. Despite low initial costs, clinical suspicion was among the most expensive and least effective strategies. Results were sensitive to the specificity of selected strategies. In probabilistic sensitivity analyses, the Boston strategy was most likely to be favored at a willingness-to-pay threshold of $100,000/QALY. In the urinary tract infection model, PECARN was preferred compared to other strategies and the Boston strategy was preferred in the bacterial meningitis model. CONCLUSIONS: The Boston clinical prediction rule offers an economically reasonable strategy compared to alternatives for identification of SBI.

Mild abusive head injury: diagnosis and pitfalls.

Clinicians often miss making the diagnosis of abusive head injury in infants and toddlers who present with mild, non-specific symptoms such as vomiting, fussiness, irritability, trouble sleeping and eating, and seizure. If abusive head injury is missed, the child is likely to go on to experience more severe injury. An extensive review of the medical literature was done to summarize what is known about missed abusive head injury and about how these injuries can be recognized and appropriately evaluated. The following issues will be addressed: the definition of mild head injury, problems encountered when clinicians evaluated mildly ill young children with non-specific symptoms, the risk of missing the diagnosis of mild abusive head trauma, the risks involved in subjecting infants and young children to radiation and/or sedation required for neuroimaging studies, imaging options for suspected neurotrauma in children, clinical prediction rules for evaluating mild head injury in children, laboratory tests than can be helpful in diagnosing mild abusive head injury, history and physical examination when diagnosing or ruling out mild abusive head injury, social and family factors that could be associated with abusive injuries, and interventions that could improve our recognition of mild abusive head injuries. Relevant literature is described and evaluated. The conclusion is that abusive head trauma remains a difficult diagnosis to identify in mildly symptomatic young children.

Predicting the success of multimodal rehabilitation in chronic ankle instability based on patient-reported outcomes.

BACKGROUND: The aim of this study was to identify potential indicators to predict the success of multimodal rehabilitation in chronic ankle instability (CAI) patients based on patient-reported outcomes. METHODS: Sixty patients with self-reported CAI participated. Their demographic information, injury history, and symptoms were recorded. Physical examinations and dynamic posture control tests were performed. The participants underwent sixteen 30-min treatment sessions of multimodal rehabilitation over 8 weeks. Fifty-one patients (85.0%) were available for follow-up after 8 weeks of the intervention. Treatment success was defined based on the participants' perceived recovery using the global rating of change (GRC). Potential predictor variables were entered into a stepwise logistic regression model to identify variables for the prediction of treatment success. RESULTS: Forty of 51 participants (78.4%) were considered to have a successful outcome. Of the variables assessed, time since last sprain ≤ 8 months was a predictor of treatment success (p < 0.05). If a patient met the criteria, there was an 88.03% probability of successful multimodal rehabilitation. CONCLUSION: A time since the last sprain ≤ 8 months may predict successful patient-reported outcomes after multimodal rehabilitation in CAI patients. LEVEL OF EVIDENCE: Prospective study, Level 2.

Development and validation of prediction model for incident overactive bladder: The Nagahama study.

OBJECTIVES: We aimed to develop models to predict new-onset overactive bladder in 5 years using a large prospective cohort of the general population. METHODS: This is a secondary analysis of a longitudinal cohort study in Japan. The baseline characteristics were measured between 2008 and 2010, with follow-ups every 5 years. We included subjects without overactive bladder at baseline and with follow-up data 5 years later. Overactive bladder was assessed using the overactive bladder symptom score. Baseline characteristics (demographics, health behaviors, comorbidities, and overactive bladder symptom scores) and blood test data were included as predictors. We developed two competing prediction models for each sex based on logistic regression with penalized likelihood (LASSO). We chose the best model separately for men and women after evaluating models' performance in terms of discrimination and calibration using an internal validation via 200 bootstrap resamples and a temporal validation. RESULTS: We analyzed 7218 participants (male: 2238, female: 4980). The median age was 60 and 55 years, and the number of new-onset overactive bladder was 223 (10.0%) and 288 (5.8%) per 5 years in males and females, respectively. The in-sample estimates for C-statistic, calibration intercept, and slope for the best performing models were 0.77 (95% confidence interval 0.74-0.80), 0.28 and 1.15 for males, and 0.77 (95% confidence interval 0.74-0.80), 0.20 and 1.08 for females. Internal and temporal validation gave broadly similar estimates of performance, indicating low optimism. CONCLUSION: We developed risk prediction models for new-onset overactive bladder among men and women with good predictive ability.

Clinical prediction rules for the diagnosis of neuritis in leprosy.

BACKGROUND: Diagnosing neuritis in leprosy patients with neuropathic pain or chronic neuropathy remains challenging since no specific laboratory or neurophysiological marker is available. METHODS: In a cross-sectional study developed at a leprosy outpatient clinic in Rio de Janeiro, RJ, Brazil, 54 individuals complaining of neural pain (single or multiple sites) were classified into two groups ("neuropathic pain" or "neuritis") by a neurological specialist in leprosy based on anamnesis together with clinical and electrophysiological examinations. A neurologist, blind to the pain diagnoses, interviewed and examined the participants using a standardized form that included clinical predictors, pain features, and neurological symptoms. The association between the clinical predictors and pain classifications was evaluated via the Pearson Chi-Square or Fisher's exact test (p < 0.05). RESULTS: Six clinical algorithms were generated to evaluate sensitivity and specificity, with 95% confidence intervals, for clinical predictors statistically associated with neuritis. The most conclusive clinical algorithm was: pain onset at any time during the previous 90 days, or in association with the initiation of neurological symptoms during the prior 30-day period, necessarily associated with the worsening of pain upon movement and nerve palpation, with 94% of specificity and 35% of sensitivity. CONCLUSION: This algorithm could help physicians confirm neuritis in leprosy patients with neural pain, particularly in primary health care units with no access to neurologists or electrophysiological tests.

Assessing Pretest Clinical Risk of Pulmonary Thromboembolism in the Emergency Department: Proposal of a Simple Modification to the Wells' Score.

BACKGROUND: Clinical scores have been proposed to stratify the risk of pulmonary thromboembolism (PTE), although this approach suffers a low specificity and the unavoidable need for computed tomography pulmonary angiography (CTPA) scans. OBJECTIVE: Our study aimed to investigate a simple modification to the already validated Wells' score to improve its diagnostic accuracy in the emergency department (ED). METHODS: We retrospectively reviewed all CTPA scans performed in the ED setting to rule out PTE over a 1-year (2017) period. Clinical variables potentially associated with PTE were assessed to improve diagnostic accuracy of the Wells' score, thus introducing a modified Wells' score (mWells). RESULTS: Four thousand four hundred thirteen CTPAs were identified, of which 504 were for suspected PTE. The prevalence of PTE was 23.9%. Among clinical data, only peripheral capillary oxygen saturation was consistently correlated with PTE at univariate (odds ratio 2.75 [95% confidence interval 1.61-4.73]) and multivariate (odds ratio 3.78 [95% confidence interval 2.13-6.72]) logistic regression analysis. The mWells' score had a higher area under the receiver operating characteristic curve compared with the original Wells' score: 0.71 (95% confidence interval 0.67-0.75) vs. 0.65 (95% confidence interval 0.61-0.69) (p < .01) and improved diagnostic accuracy. CONCLUSIONS: Current clinical stratification tools for PTE are characterized by low specificity, leading to an overuse of CTPA. mWells', rather than Wells', score showed a better predictive performance of PTE detection. Our results suggest that current diagnostic pathway for PTE may be improved by simple adjustments (i.e., mWells') of clinical prediction scores.

Performance of prediction rules and guidelines in detecting serious bacterial infections among Tanzanian febrile children.

BACKGROUND: Health-workers in developing countries rely on clinical algorithms, such as the Integrated Management of Childhood Illnesses (IMCI), for the management of patients, including diagnosis of serious bacterial infections (SBI). The diagnostic accuracy of IMCI in detecting children with SBI is unknown. Prediction rules and guidelines for SBI from well-resourced countries at outpatient level may help to improve current guidelines; however, their diagnostic performance has not been evaluated in resource-limited countries, where clinical conditions, access to care, and diagnostic capacity differ. The aim of this study was to estimate the diagnostic accuracy of existing prediction rules and clinical guidelines in identifying children with SBI in a cohort of febrile children attending outpatient health facilities in Tanzania. METHODS: Structured literature review to identify available prediction rules and guidelines aimed at detecting SBI and retrospective, external validation on a dataset containing 1005 febrile Tanzanian children with acute infections. The reference standard, SBI, was established based on rigorous clinical and microbiological criteria. RESULTS: Four prediction rules and five guidelines, including IMCI, could be validated. All examined rules and guidelines had insufficient diagnostic accuracy for ruling-in or ruling-out SBI with positive and negative likelihood ratios ranging from 1.04-1.87 to 0.47-0.92, respectively. IMCI had a sensitivity of 36.7% (95% CI 29.4-44.6%) at a specificity of 70.3% (67.1-73.4%). Rules that use a combination of clinical and laboratory testing had better performance compared to rules and guidelines using only clinical and or laboratory elements. CONCLUSIONS: Currently applied guidelines for managing children with febrile illness have insufficient diagnostic accuracy in detecting children with SBI. Revised clinical algorithms including simple point-of-care tests with improved accuracy for detecting SBI targeting in tropical resource-poor settings are needed. They should undergo careful external validation against clinical outcome before implementation, given the inherent limitations of gold standards for SBI.

Diagnostic accuracy of clinical tools for assessment of acute stroke: a systematic review.

INTRODUCTION: Recanalisation therapy in acute ischaemic stroke is highly time-sensitive, and requires early identification of eligible patients to ensure better outcomes. Thus, a number of clinical assessment tools have been developed and this review examines their diagnostic capabilities. METHODS: Diagnostic performance of currently available clinical tools for identification of acute ischaemic and haemorrhagic strokes and stroke mimicking conditions was reviewed. A systematic search of the literature published in 2015-2018 was conducted using PubMed, EMBASE, Scopus and The Cochrane Library. Prehospital and in-hospital studies with a minimum sample size of 300 patients reporting diagnostic accuracy were selected. RESULTS: Twenty-five articles were included. Cortical signs (gaze deviation, aphasia and neglect) were shown to be significant indicators of large vessel occlusion (LVO). Sensitivity values for selecting subjects with LVO ranged from 23 to 99% whereas specificity was 24 to 97%. Clinical tools, such as FAST-ED, NIHSS, and RACE incorporating cortical signs as well as motor dysfunction demonstrated the best diagnostic accuracy. Tools for identification of stroke mimics showed sensitivity varying from 44 to 91%, and specificity of 27 to 98% with the best diagnostic performance demonstrated by FABS (90% sensitivity, 91% specificity). Hypertension and younger age predicted intracerebral haemorrhage whereas history of atrial fibrillation and diabetes were associated with ischaemia. There was a variation in approach used to establish the definitive diagnosis. Blinding of the index test assessment was not specified in about 50% of included studies. CONCLUSIONS: A wide range of clinical assessment tools for selecting subjects with acute stroke has been developed in recent years. Assessment of both cortical and motor function using RACE, FAST-ED and NIHSS showed the best diagnostic accuracy values for selecting subjects with LVO. There were limited data on clinical tools that can be used to differentiate between acute ischaemia and haemorrhage. Diagnostic accuracy appeared to be modest for distinguishing between acute stroke and stroke mimics with optimal diagnostic performance demonstrated by the FABS tool. Further prehospital research is required to improve the diagnostic utility of clinical assessments with possible application of a two-step clinical assessment or involvement of simple brain imaging, such as transcranial ultrasonography.

Health services research in patients with breast cancer (CAMISS-prospective): study protocol for an observational prospective study.

BACKGROUND: Though breast cancer remains a major health problem, there is a lack of information on health care provided to patients with this disease and associated costs. In addition, there is a need to update and validate risk stratification tools in Spain. Our purpose is to evaluate the health services provided for breast cancer in Spain, from screening and diagnosis to treatment and prognosis. METHODS: Prospective cohort study involving 13 hospitals in Spain with a follow-up period of up to 5 years after diagnostic biopsy. Eligibility criteria: Patients diagnosed with breast cancer between April 2013 and May 2015 that have consented to participate in the study. DATA COLLECTION: Data will be collected on the following: pre-intervention medical history, biological, clinical, and sociodemographic characteristics, mode of cancer detection, hospital admission, treatment, and outcomes up to 5 years after initial treatment. Questionnaires about quality of life (EuroQoL EQ-5D-5 L, the European Organization For Research And Treatment Of Cancer Core Quality Of Life Questionnaire EORTC QLQ-C30 join to the specific breast cancer module (QLQ-BR23), as well as Hospital Anxiety and Depression Scale were completed by the patients before the beginning of the initial treatment and at the end of follow-up period, 2 years later. The end-points of the study were changes in health-related quality of life, recurrence, complications and readmissions at 2 and 5 years after initial treatment. STATISTICAL ANALYSIS: Descriptive statistics will be calculated and multivariate models will be used where appropriate to adjust for potential confounders. In order to create and validate a prediction model, split validation and bootstrapping will be performed. Cost analysis will be carried out from the perspective of a national health system. DISCUSSION: The results of this coordinated project are expected to generate scientifically valid and clinically and socially important information to inform the decision-making of managers and the authorities responsible for ensuring equality in care processes as well in health outcomes. For clinicians, clinical prediction rules will be developed which are expected to serve as the basis for the development of software applications. TRIAL REGISTRATION: NCT02439554 . Date of registration: May 8, 2015 (retrospectively registered) .

Prognostic models for intracerebral hemorrhage: systematic review and meta-analysis.

BACKGROUND: Prognostic tools for intracerebral hemorrhage (ICH) patients are potentially useful for ascertaining prognosis and recommended in guidelines to facilitate streamline assessment and communication between providers. In this systematic review with meta-analysis we identified and characterized all existing prognostic tools for this population, performed a methodological evaluation of the conducting and reporting of such studies and compared different methods of prognostic tool derivation in terms of discrimination for mortality and functional outcome prediction. METHODS: PubMed, ISI, Scopus and CENTRAL were searched up to 15th September 2016, with additional studies identified using reference check. Two reviewers independently extracted data regarding the population studied, process of tool derivation, included predictors and discrimination (c statistic) using a predesignated spreadsheet based in the CHARMS checklist. Disagreements were solved by consensus. C statistics were pooled using robust variance estimation and meta-regression was applied for group comparisons using random effect models. RESULTS: Fifty nine studies were retrieved, including 48,133 patients and reporting on the derivation of 72 prognostic tools. Data on discrimination (c statistic) was available for 53 tools, 38 focusing on mortality and 15 focusing on functional outcome. Discrimination was high for both outcomes, with a pooled c statistic of 0.88 for mortality and 0.87 for functional outcome. Forty three tools were regression based and nine tools were derived using machine learning algorithms, with no differences found between the two methods in terms of discrimination (p = 0.490). Several methodological issues however were identified, relating to handling of missing data, low number of events per variable, insufficient length of follow-up, absence of blinding, infrequent use of internal validation, and underreporting of important model performance measures. CONCLUSIONS: Prognostic tools for ICH discriminated well for mortality and functional outcome in derivation studies but methodological issues require confirmation of these findings in validation studies. Logistic regression based risk scores are particularly promising given their good performance and ease of application.