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.

Clinical Prediction Rules for In-Hospital Mortality Outcome in Melioidosis Patients.

BACKGROUND: Melioidosis, a disease induced by Burkholderia pseudomallei, poses a significant health threat in tropical areas where it is endemic. Despite the availability of effective treatments, mortality rates remain notably elevated. Many risk factors are associated with mortality. This study aims to develop a scoring system for predicting the in-hospital mortality from melioidosis using readily available clinical data. METHODS: The data were collected from Surin Hospital, Surin, Thailand, during the period from April 2014 to March 2017. We included patients aged 15 years and above who had cultures that tested positive for Burkholderia pseudomallei. The clinical prediction rules were developed using significant risk factors from the multivariable analysis. RESULTS: A total of 282 patients with melioidosis were included in this study. In the final analysis model, 251 patients were used for identifying the significant risk factors of in-hospital fatal melioidosis. Five factors were identified and used for developing the clinical prediction rules, and the factors were as follows: qSOFA ≥ 2 (odds ratio [OR] = 2.39, p= 0.025), abnormal chest X-ray findings (OR = 5.86, p < 0.001), creatinine ≥ 1.5 mg/dL (OR = 2.80, p = 0.004), aspartate aminotransferase ≥50 U/L (OR = 4.032, p < 0.001), and bicarbonate ≤ 20 mEq/L (OR = 2.96, p = 0.002). The prediction scores ranged from 0 to 7. Patients with high scores (4-7) exhibited a significantly elevated mortality rate exceeding 65.0% (likelihood ratio [LR+] 2.18, p < 0.001) compared to the low-risk group (scores 0-3) with a lower mortality rate (LR + 0.18, p < 0.001). The area under the receiver operating characteristic curve (AUC) was 0.84, indicating good model performance. CONCLUSIONS: This study presents a simple scoring system based on easily obtainable clinical parameters to predict in-hospital mortality in melioidosis patients. This tool may facilitate the early identification of high-risk patients who could benefit from more aggressive treatment strategies, potentially improving clinical decision-making and patient outcomes.

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.

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.

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

BACKGROUND: 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. OBJECTIVE: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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. TRIAL REGISTRATION: ClinicalTrials.gov NCT04255303; https://clinicaltrials.gov/ct2/show/NCT04255303.

Performance of the IMPACT and CRASH prognostic models for traumatic brain injury in a contemporary multicenter cohort: a TRACK-TBI study.

OBJECTIVE: The International Mission on Prognosis and Analysis of Clinical Trials in Traumatic Brain Injury (IMPACT) and Corticosteroid Randomization After Significant Head Injury (CRASH) prognostic models for mortality and outcome after traumatic brain injury (TBI) were developed using data from 1984 to 2004. This study examined IMPACT and CRASH model performances in a contemporary cohort of US patients. METHODS: The prospective 18-center Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) study (enrollment years 2014-2018) enrolled subjects aged ≥ 17 years who presented to level I trauma centers and received head CT within 24 hours of TBI. Data were extracted from the subjects who met the model criteria (for IMPACT, Glasgow Coma Scale [GCS] score 3-12 with 6-month Glasgow Outcome Scale-Extended [GOSE] data [n = 441]; for CRASH, GCS score 3-14 with 2-week mortality data and 6-month GOSE data [n = 831]). Analyses were conducted in the overall cohort and stratified on the basis of TBI severity (severe/moderate/mild TBI defined as GCS score 3-8/9-12/13-14), age (17-64 years or ≥ 65 years), and the 5 top enrolling sites. Unfavorable outcome was defined as GOSE score 1-4. Original IMPACT and CRASH model coefficients were applied, and model performances were assessed by calibration (intercept [< 0 indicated overprediction; > 0 indicated underprediction] and slope) and discrimination (c-statistic). RESULTS: Overall, the IMPACT models overpredicted mortality (intercept -0.79 [95% CI -1.05 to -0.53], slope 1.37 [1.05-1.69]) and acceptably predicted unfavorable outcome (intercept 0.07 [-0.14 to 0.29], slope 1.19 [0.96-1.42]), with good discrimination (c-statistics 0.84 and 0.83, respectively). The CRASH models overpredicted mortality (intercept -1.06 [-1.36 to -0.75], slope 0.96 [0.79-1.14]) and unfavorable outcome (intercept -0.60 [-0.78 to -0.41], slope 1.20 [1.03-1.37]), with good discrimination (c-statistics 0.92 and 0.88, respectively). IMPACT overpredicted mortality and acceptably predicted unfavorable outcome in the severe and moderate TBI subgroups, with good discrimination (c-statistic ≥ 0.81). CRASH overpredicted mortality in the severe and moderate TBI subgroups and acceptably predicted mortality in the mild TBI subgroup, with good discrimination (c-statistic ≥ 0.86); unfavorable outcome was overpredicted in the severe and mild TBI subgroups with adequate discrimination (c-statistic ≥ 0.78), whereas calibration was nonlinear in the moderate TBI subgroup. In subjects ≥ 65 years of age, the models performed variably (IMPACT-mortality, intercept 0.28, slope 0.68, and c-statistic 0.68; CRASH-unfavorable outcome, intercept -0.97, slope 1.32, and c-statistic 0.88; nonlinear calibration for IMPACT-unfavorable outcome and CRASH-mortality). Model performance differences were observed across the top enrolling sites for mortality and unfavorable outcome. CONCLUSIONS: The IMPACT and CRASH models adequately discriminated mortality and unfavorable outcome. Observed overestimations of mortality and unfavorable outcome underscore the need to update prognostic models to incorporate contemporary changes in TBI management and case-mix. Investigations to elucidate the relationships between increased survival, outcome, treatment intensity, and site-specific practices will be relevant to improve models in specific TBI subpopulations (e.g., older adults), which may benefit from the inclusion of blood-based biomarkers, neuroimaging features, and treatment data.

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.

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.

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 .

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.

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.

Using the Y-balance Test as a Predictor Tool for Evaluating Non-contact Injuries in University League Football Players: A Prospective Longitudinal Study.

Background Football is a highly competitive sport, and participants can experience various contact and non-contact sports injuries in the sporting process. In any elite sport, screening players using different scientific tools is an important injury prevention strategy. The Y- Balance test (YBT) was found to be a predictive tool for non-contact injury. However, the use of criteria from these tests to predict injuries has not been substantiated and should be further investigated. Purpose The aim of this study was to determine the predictors for injury among athletes using baseline YBT, number of matches, and minutes of physical activity; the cutoff scores for predictors of injury, including baseline YBT, number of matches, and minutes of physical activity; and the clinical prediction rules for predicting injury in this population. Methods A total of 39 young student football players were included in this study. The mean age was 20.28 years, and the mean body mass index (BMI) was 23.83 kg/m2. A baseline assessment of the participant's characteristics was taken and each participant performed the YBT once before starting the league. After the university league football players had finished their tournament, we asked them questions related to non-contact injuries. Results The results showed that the prevalence of injury was 17.95% among this population. An increase in the YBT score was significantly associated with a decrease in the odds of having an injury [odds ratio (OR) 95% confidence interval (CI): 0.94 (0.88, 0.99), p = 0.047). In addition, the number of matches was significantly associated with an increase in the odds of having an injury p = 0.012. However, the minutes of physical activity were not statistically significant p = 0.065. The highest Youden index was ≤97.89, with a sensitivity of 87.50% and specificity of 71.43%, for the posterior medial reach and ≤92.88, with a sensitivity of 90.62% and specificity of 57.14%, for the posterior lateral reach. The clinical prediction rule was an area under the curve (AUC) of 0.88. Conclusions The results of the study provide evidence for the potential utility of the YBT as a predictor tool for evaluating non-contact injuries in university league football players. By identifying players with lower YBT scores who were at higher risk for injury, targeted interventions could be implemented to address functional movement deficits and potentially reduce injury risk.

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.

Stretching and muscle-performance exercises for chronic nonspecific neck pain: who may benefit most?

BACKGROUND: Although exercise is the mainstay of treatment for neck pain (NP), uncertainty remains over optimal decision-making concerning who may benefit most from such, particularly in the long term. OBJECTIVE: To identify the subgroup of patients with nonspecific NP most likely to benefit from stretching and muscle-performance exercises. METHODS: This was a secondary analysis of treatment outcomes of 70 patients (10 of whom dropped out) with a primary complaint of nonspecific NP in one treatment arm of a prospective, randomized, controlled trial. All patients performed the exercises, twice weekly for 6 weeks, and a home exercise program. Blinded outcome measurements were collected at baseline, after the 6-week program, and at a 6-month follow-up. Patients rated their perceived recovery on a 15-point global rating of change scale; a rating of "quite a bit better" (+5) or higher was defined as a successful outcome. Clinical predictor variables were developed via logistic regression analysis to classify patients with NP that may benefit from exercise-based treatment. RESULTS: NP duration since onset≤6 months, no cervicogenic headache, and shoulder protraction were independent predictor variables. The pretest probability of success was 47% after the 6-week intervention and 40% at the 6-month follow-up. The corresponding posttest probabilities of success for participants with all three variables were 86% and 71%, respectively; such participants were likely to recover. CONCLUSION: The clinical predictor variables developed in this study may identify patients with nonspecific NP likely to benefit most from stretching and muscle-performance exercises in the short and long terms.

Derivation and internal validation of a clinical prediction score to predict major effect or death in acute metamfetamine toxicity.

OBJECTIVE: Metamfetamine use can cause serious complications or death. We aimed to derive and internally validate a clinical prediction score to predict major effect or death in acute metamfetamine toxicity. METHODS: We performed secondary analysis of 1,225 consecutive cases reported from all local public emergency departments to the Hong Kong Poison Information Centre between 1 January 2010 and 31 December 2019. We split the entire dataset chronologically into derivation (first 70% of cases) and validation (the remaining 30% of cases) cohorts. Univariate analysis was conducted, followed by multivariable logistic regression in the derivation cohort to identify independent predictors of major effect or death. We developed a clinical prediction score based on the regression coefficients of the independent predictors in the regression model and compared its discriminatory performance with five existing early warning scores in the validation cohort. RESULTS: The MASCOT (Male, Age, Shock, Consciousness, Oxygen, Tachycardia) score was derived based on the six independent predictors: male gender (1 point), age (≥35 years, 1 point), shock (mean arterial pressure <65 mmHg, 3 points), consciousness (Glasgow Coma Scale <13, 2 points), need for supplemental oxygen (1 point), and tachycardia (pulse rate >120 beats/min, 1 point). The score ranges from 0-9, with a higher score indicating higher risk. The area under the receiver operating characteristic curve of the MASCOT score was 0.87 (95% CI 0.81-0.93) in the derivation cohort and 0.91 (95% CI 0.81-1.00) in the validation cohort, with a discriminatory performance comparable with existing scores. CONCLUSIONS: The MASCOT score enables quick risk stratification in acute metamfetamine toxicity. Further external validation is warranted before wider adoption.

Predicting in-hospital mortality in pulmonary embolism patients: development and external validation of the PATHOS score.

OBJECTIVE: According to the 2019 European Society of Cardiology (ESC) guidelines on pulmonary embolism (PE), prognosis is calculated using the Pulmonary Embolism Severity Index (PESI), a complex score with debated validity, or simplified PESI (sPESI). We have developed and validated a new risk score for in-hospital mortality (IHM) of patients with PE in the emergency department. METHODS: This retrospective, dual-center cohort study was conducted in the emergency departments of two third-level university hospitals. Patients aged >18 years with a contrast-enhanced computed tomography-confirmed PE were included. Clinical variables and laboratory tests were evaluated blindly to IHM. Multivariable logistic regression was performed to identify the new score's predictors, and the new score was compared with the PESI, sPESI, and shock index. RESULTS: A total of 1,358 patients were included in this study: 586 in the derivation cohort and 772 in the validation cohort, with a global 10.6% of IHM. The PATHOS scores were developed using independent variables to predict mortality: platelet count, age, troponin, heart rate, oxygenation, and systolic blood pressure. The PATHOS score showed good calibration and high discrimination, with an area under the receiver operating characteristics curve of 0.83 (95% confidence interval [CI], 0.77-0.89) in the derivation population and 0.74 (95% CI, 0.68-0.80) in the validation cohort, which is significantly higher than the PESI, sPESI, and shock index in both cohorts (P<0.01 for all comparisons). CONCLUSION: PATHOS is a simple and effective prognostic score for predicting IHM in patients with PE in an emergency setting.

Clinical prediction rules in acute appendicitis: which combination of variables is more effective at predicting?

BACKGROUND: Clinical prediction rules have been designed to reduce variability and improve the diagnostic process. However, there are no unanimous criteria regarding which of them is the most efficient for the diagnosis of acute appendicitis. AIM: The primary aim of this study was to assess the diagnostic efficacy of the most commonly used clinical prediction rules. The second aim was to identify the combination of the smallest number of clinical and analytical variables that would allow a cost-effective diagnostic approach. METHODS: A retrospective observational study was conducted of 458 patients who were evaluated for right iliac fossa pain between January 2010 and December 2016. The scores tested were Alvarado, AIR, RIPASA, and AAS. Univariate and multiple regressions were used for validation. RESULTS: Alvarado one was the most efficient to establish a positive diagnosis of acute appendicitis. However, the most simplified and predictive combination variables included anorexia, white blood cell count > 8275 leukocytes/mL, neutrophilia (> 75%), abdominal pain < 48 h, migrating pain, and temperature out the range of 37-39ºC. CONCLUSIONS: A new and effective CPR (HMC score) for predicting appendicitis in patients presenting with the right iliac fossa pain has been established.

INTRODUCCIÓN: Las escalas de predicción diagnóstica (EPD) se han diseñado con el objetivo de reducir la variabilidad y mejorar el proceso de diagnóstico. Sin embargo, no existen criterios unánimes sobre cuál de ellas es la más el más eficiente para el diagnóstico de apendicitis aguda. OBJETIVO: El objetivo principal de este estudio fue evaluar la eficacia diagnóstica de las escalas de predicción diagnóstica más utilizadas. El segundo objetivo fue identificar la combinación del menor número de variables clínicas y analíticas que permitieran un enfoque diagnóstico más eficiente. MÉTODOS: Se realizó un estudio observacional retrospectivo de 458 pacientes que fueron evaluados por dolor en la fosa ilíaca derecha entre enero de 2010 y diciembre de 2016. Las escalas evaluadas fueron las de Alvarado, AIR, RIPASA y AAS. Se utilizaron la regresion univariada y la múltiple para la validación de los resultados. RESULTADOS: la escala de Alvarado fue la más eficiente para establecer un diagnóstico de apendicitis aguda. No obstante, la combinación de las siguientes variables: anorexia, recuento de leucocitos > 8275 leucocitos/mL, neutrofilia (> 75%), dolor abdominal < 48 horas, dolor migratorio y temperatura fuera del rango de 37-39ºC, demostró ser la más eficiente para establecer un diagnóstico positivo de apendicitis aguda. CONCLUSIONES: Se ha desarrollada una nueva EPD (escala HMDC) para determinar la presencia de apendicitis en pacientes evaluados por dolor en la fosa ilíaca derecha.

Validation of Prediction Rules for Computed Tomography Use in Children With Blunt Abdominal or Blunt Head Trauma: Protocol for a Prospective Multicenter Observational Cohort Study.

BACKGROUND: Traumatic brain injuries (TBIs) and intra-abdominal injuries (IAIs) are 2 leading causes of traumatic death and disability in children. To avoid missed or delayed diagnoses leading to increased morbidity, computed tomography (CT) is used liberally. However, the overuse of CT leads to inefficient care and radiation-induced malignancies. Therefore, to maximize precision and minimize the overuse of CT, the Pediatric Emergency Care Applied Research Network (PECARN) previously derived clinical prediction rules for identifying children at high risk and very low risk for IAIs undergoing acute intervention and clinically important TBIs after blunt trauma in large cohorts of children who are injured. OBJECTIVE: This study aimed to validate the IAI and age-based TBI clinical prediction rules for identifying children at high risk and very low risk for IAIs undergoing acute intervention and clinically important TBIs after blunt trauma. METHODS: This was a prospective 6-center observational study of children aged <18 years with blunt torso or head trauma. Consistent with the original derivation studies, enrolled children underwent routine history and physical examinations, and the treating clinicians completed case report forms prior to knowledge of CT results (if performed). Medical records were reviewed to determine clinical courses and outcomes for all patients, and for those who were discharged from the emergency department, a follow-up survey via a telephone call or SMS text message was performed to identify any patients with missed IAIs or TBIs. The primary outcomes were IAI undergoing acute intervention (therapeutic laparotomy, angiographic embolization, blood transfusion, or intravenous fluid for ≥2 days for pancreatic or gastrointestinal injuries) and clinically important TBI (death from TBI, neurosurgical procedure, intubation for >24 hours for TBI, or hospital admission of ≥2 nights due to a TBI on CT). Prediction rule accuracy was assessed by measuring rule classification performance, using standard point and 95% CI estimates of the operational characteristics of each prediction rule (sensitivity, specificity, positive and negative predictive values, and diagnostic likelihood ratios). RESULTS: The project was funded in 2016, and enrollment was completed on September 1, 2021. Data analyses are expected to be completed by December 2022, and the primary study results are expected to be submitted for publication in 2023. CONCLUSIONS: This study will attempt to validate previously derived clinical prediction rules to accurately identify children at high and very low risk for clinically important IAIs and TBIs. Assuming successful validation, widespread implementation is then indicated, which will optimize the care of children who are injured by better aligning CT use with need. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR1-10.2196/43027.

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.

Predicting In-Hospital Mortality in Severe COVID-19: A Systematic Review and External Validation of Clinical Prediction Rules.

Multiple prediction models for risk of in-hospital mortality from COVID-19 have been developed, but not applied, to patient cohorts different to those from which they were derived. The MEDLINE, EMBASE, Scopus, and Web of Science (WOS) databases were searched. Risk of bias and applicability were assessed with PROBAST. Nomograms, whose variables were available in a well-defined cohort of 444 patients from our site, were externally validated. Overall, 71 studies, which derived a clinical prediction rule for mortality outcome from COVID-19, were identified. Predictive variables consisted of combinations of patients' age, chronic conditions, dyspnea/taquipnea, radiographic chest alteration, and analytical values (LDH, CRP, lymphocytes, D-dimer); and markers of respiratory, renal, liver, and myocardial damage, which were mayor predictors in several nomograms. Twenty-five models could be externally validated. Areas under receiver operator curve (AUROC) in predicting mortality ranged from 0.71 to 1 in derivation cohorts; C-index values ranged from 0.823 to 0.970. Overall, 37/71 models provided very-good-to-outstanding test performance. Externally validated nomograms provided lower predictive performances for mortality in their respective derivation cohorts, with the AUROC being 0.654 to 0.806 (poor to acceptable performance). We can conclude that available nomograms were limited in predicting mortality when applied to different populations from which they were derived.