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

BACKGROUND

Recognising acute appendicitis in children presenting with acute abdominal pain in primary care is challenging. General practitioners (GPs) may benefit from a clinical prediction rule.

OBJECTIVES

To develop and validate a clinical prediction rule for acute appendicitis in children presenting with acute abdominal pain in primary care.

METHODS

In a historical cohort study data was retrieved from GP electronic health records included in the Integrated Primary Care Information database. We assigned children aged 4-18 years presenting with acute abdominal pain (≤ 7 days) to development (2010-2012) and validation (2013-2016) cohorts, using acute appendicitis within six weeks as the outcome. Multiple logistic regression was used to develop a prediction model based on predictors with > 50% data availability derived from existing rules for secondary care. We performed internal and external temporal validation and derived a point score to stratify risk of appendicitis into three groups, i.e. low-risk, medium-risk and high-risk.

RESULTS

The development and validation cohorts included 2,041 and 3,650 children, of whom 95 (4.6%) and 195 (5.3%) had acute appendicitis. The model included male sex, pain duration (<24, 24-48, > 48 h), nausea/vomiting, elevated temperature (≥ 37.3 °C), abnormal bowel sounds, right lower quadrant tenderness, and peritoneal irritation. Internal and temporal validation showed good discrimination (C-statistics: 0.93 and 0.90, respectively) and excellent calibration. In the three groups, the risks of acute appendicitis were 0.5%, 7.5%, and 41%.

CONCLUSION

Combined with further testing in the medium-risk group, the prediction rule could improve clinical decision making and outcomes.

A prediction model for cesarean delivery based on the glycemia in the second trimester: a nested case control study from two centers

OBJECTIVE

Maternal glycemia is associated with the risk of cesarean delivery (CD); therefore, our study aims to developed a prediction model based on glucose indicators in the second trimester to earlier identify the risk of CD.

METHODS

This was a nested case-control study, and data were collected from the 5th Central Hospital of Tianjin (training set) and Changzhou Second People's Hospital (testing set) from 2020 to 2021. Variables with significant difference in training set were incorporated to develop the random forest model. Model performance was assessed by calculating the area under the curve (AUC) and Komogorov-Smirnoff (KS), as well as accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV).

RESULTS

A total of 504 eligible women were enrolled; of these, 169 underwent CD. Pre-pregnancy body mass index (BMI), first pregnancy, history of full-term birth, history of livebirth, 1 h plasma glucose (1hPG), glycosylated hemoglobin (HbA1c), fasting plasma glucose (FPG), and 2 h plasma glucose (2hPG) were used to develop the model. The model showed a good performance, with an AUC of 0.852 [95% confidence interval (CI): 0.809-0.895]. The pre-pregnancy BMI, 1hPG, 2hPG, HbA1c, and FPG were identifies as the more significant predictors. External validation confirmed the good performance of our model, with an AUC of 0.734 (95%CI: 0.664-0.804).

CONCLUSIONS

Our model based on glucose indicators in the second trimester performed well to predict the risk of CD, which may reach the earlier identification of CD risk and may be beneficial to make interventions in time to decrease the risk of CD.

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

OBJECTIVES

Computed tomographic pulmonary angiography (CT-PA) is associated with significant cost, contrast, and radiation exposure. Clinical decision rules (CDRs) reduce the need for diagnostic imaging; however, their utility in the medical intensive care unit (MICU) remains unknown. We explored the diagnostic yield and complications associated with CT-PA (radiation exposure and contrast-induced acute kidney injury [AKI]) while investigating the efficacy of CDRs to reduce unnecessary testing.

METHODS

All CT-PAs performed in an academic MICU for 4 years were retrospectively reviewed. The Wells and revised Geneva scores (CDRs) and radiation dose per CT-PA were calculated, and the incidence of post-CT-PA AKI was recorded.

RESULTS

A total of 439 studies were analyzed; the diagnostic yield was 11% (48 PEs). Positive CT-PAs were associated with a higher Wells score (5.8 versus 3.2, P < 0.001), but similar revised Geneva scores (6.4 versus 6.0, P = 0.32). A Wells score of ≥4 had a positive likelihood ratio of 2.1 with a negative predictive value of 98.2. More than half (88.9%) of patients with a Wells score of ≤4 developed an AKI, with 55.6% of those having recovery of renal function.

CONCLUSIONS

There is overutilization of CT-PA in the MICU. The Wells score retains its negative predictive value in critically ill adult patients and may aid to limit radiation exposure and contrast-induced AKI in MICU.

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

BACKGROUND

Low back pain (LBP) is a common complex condition, where specific diagnoses are hard to identify. Diagnostic clinical prediction rules (CPRs) are known to improve clinical decision-making. A review of LBP diagnostic-CPRs by Haskins et al. (2015) identified six diagnostic-CPRs in derivation phases of development, with one tool ready for implementation. Recent progress on these tools is unknown. Therefore, this review aimed to investigate developments in LBP diagnostic-CPRs and evaluate their readiness for implementation.

METHODS

A systematic review was performed on five databases (Medline, Amed, Cochrane Library, PsycInfo, and CINAHL) combined with hand-searching and citation-tracking to identify eligible studies. Study and tool quality were appraised for risk of bias (Quality Assessment of Diagnostic Accuracy Studies-2), methodological quality (checklist using accepted CPR methodological standards), and CPR tool appraisal (GRade and ASsess Predictive).

RESULTS

Of 5021 studies screened, 11 diagnostic-CPRs were identified. Of the six previously known, three have been externally validated but not yet undergone impact analysis. Five new tools have been identified since Haskin et al. (2015); all are still in derivation stages. The most validated diagnostic-CPRs include the Lumbar-Spinal-Stenosis-Self-Administered-Self-Reported-History-Questionnaire and Diagnosis-Support-Tool-to-Identify-Lumbar-Spinal-Stenosis, and the StEP-tool which differentiates radicular from axial-LBP.

CONCLUSIONS

This updated review of LBP diagnostic CPRs found five new tools, all in the early stages of development. Three previously known tools have now been externally validated but should be used with caution until impact evaluation studies are undertaken. Future funding should focus on externally validating and assessing the impact of existing CPRs on clinical decision-making.

Performance of the Dutch clinical prediction rule for the ambulation outcome after spinal cord injury in a middle-income country clinical setting: an external validation study in the Thai retrospective cohort

OBJECTIVE

To perform external geographic and domain validation of the clinical prediction rule (CPR) of the ambulation outcome of patients with traumatic spinal cord injury (SCI) originally developed by van Middendorp, et al. (2011) in Thais with traumatic and non-traumatic SCI.

STUDY DESIGN

Retrospective cohort study.

SETTING

A tertiary rehabilitation facility in Chiang Mai, Thailand.

METHODS

A validation data set, including predictive (age and four neurological variables) and outcome (ambulation status) parameters was retrospectively collected from medical records of patients with traumatic and non-traumatic SCI admitted between December 2007 and December 2019. The performance of the original model was evaluated in both discrimination and calibration aspects, using an area under the receiver-operating characteristic curve (auROC) and calibration curves, respectively.

RESULTS

Three hundred and thirty-three patients with SCI were included in the validation set. The prevalence of ambulators was 59% (197 of 333 participants). An auROC of 0.93 (95% CI 0.90-0.96) indicated excellent discrimination whereas the calibration curve demonstrated underestimation, especially in patients with AIS grade D. Performance of the CPR was decreased but acceptable in patients with non-traumatic SCI.

CONCLUSIONS

Our external validation study demonstrated excellent discrimination but slightly underestimated calibration of the CPR of ambulation outcome after SCI. Regardless of the geographic and etiologic background of the population, the Dutch CPR could be applied to predict the ambulation outcome in patients with SCI.

Diagnostic test accuracy of D-dimer with or without a clinical decision rule in peripartum patients with suspected venous thromboembolism: A systematic review and meta-analysis

BACKGROUND

Pregnancy and the peripartum period is a hypercoagulable state increasing the risk of venous thromboembolism (VTE). There may be a role in utilising D-dimer in the peripartum setting.

AIMS

The purpose of this review was to summarise the latest evidence regarding the diagnostic accuracy of D-dimer in the peripartum setting with or without the addition of clinical decision rules.

METHODS

We searched PubMed and CENTRAL databases to identify articles that included studies of women who had suspected VTE, underwent a D-dimer index test to rule out VTE and where radiological imaging or clinical follow-up, to a minimum of 30 days, was used as the reference standard.

RESULTS

We included 11 studies in the systematic review and meta-analysis. The log diagnostic odds ratio (DOR) for identifying VTE using D-dimer was 1.56 (95% confidence interval (CI) 0.59-2.52). The pooled sensitivity was 87% (95% CI 76.8-93%), specificity was 63.2% (95% CI 47.1-76.7%), and the area under receiver operator characteristic (ROC) curves was 0.76. We included four studies evaluating D-dimer combined with YEARS to detect VTE. The log DOR for identifying VTE using D-dimer combined with YEARS was 1.13 (95% CI 0.005-2.25). The pooled sensitivity was 89.8% (95% CI 60.2-98.1%), specificity was 65.7% (95% CI 54.7-75.2%) and the area under ROC for studies included with the YEARS clinical decision rule was 0.49.

CONCLUSION

This review highlighted that D-dimer use in the peripartum period for detection of VTE had a high sensitivity and high DOR but a poor area under ROC, which may limit its use in clinical practice.

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

INTRODUCTION

Increased use of telemedicine could potentially streamline influenza diagnosis and reduce transmission. However, telemedicine diagnoses are dependent on accurate symptom reporting by patients. If patients disagree with clinicians on symptoms, previously derived diagnostic rules may be inaccurate.

METHODS

We performed a secondary data analysis of a prospective, nonrandomized cohort study at a university student health center. Patients who reported an upper respiratory complaint were required to report symptoms, and their clinician was required to report the same list of symptoms. We examined the performance of 5 previously developed clinical decision rules (CDRs) for influenza on both symptom reports. These predictions were compared against PCR diagnoses. We analyzed the agreement between symptom reports, and we built new predictive models using both sets of data.

RESULTS

CDR performance was always lower for the patient-reported symptom data, compared with clinician-reported symptom data. CDRs often resulted in different predictions for the same individual, driven by disagreement in symptom reporting. We were able to fit new models to the patient-reported data, which performed slightly worse than previously derived CDRs. These models and models built on clinician-reported data both suffered from calibration issues.

DISCUSSION

Patients and clinicians frequently disagree about symptom presence, which leads to reduced accuracy when CDRs built with clinician data are applied to patient-reported symptoms. Predictive models using patient-reported symptom data performed worse than models using clinician-reported data and prior results in the literature. However, the differences are minor, and developing new models with more data may be possible.

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

BACKGROUND

Cardiovascular clinical prediction rules (CPRs) are widely used in primary care. They accumulate research evidence through derivation, external validation, and impact studies. However, existing knowledge about the influence of research evidence on the use of CPRs is limited. Therefore, we explored how primary care clinicians' perceptions of and experiences with research influence their use of cardiovascular CPRs.

METHODS

We conducted an exploratory qualitative interview study with thematic analysis. Primary care clinicians were recruited from the WWAMI (Washington, Wyoming, Alaska, Montana and Idaho) region Practice and Research Network (WPRN). We used purposeful sampling to ensure maximum variation within the participant group. Data were collected by conducting semi-structured online interviews. We analyzed data using inductive thematic analysis to identify commonalities and differences within themes.

RESULTS

Of 29 primary care clinicians who completed the questionnaire, 15 participated in the interview. We identified two main themes relating to the influence of clinicians' perceptions of and experiences with cardiovascular CPR research on their decisions about using cardiovascular CPRs: "Seek and judge" and "be acquainted and assume." When clinicians are familiar with, trust, and feel confident in using research evidence, they might actively search and assess the evidence, which may then influence their decisions about using cardiovascular CPRs. However, clinicians, who are unfamiliar with, distrust, or find it challenging to use research evidence, might be passively acquainted with evidence but do not make their own judgment on the trustworthiness of such evidence. Therefore, these clinicians might not rely on research evidence when making decisions about using cardiovascular CPRs.

CONCLUSIONS

Clinicians' perceptions and experiences could influence how they use research evidence in decisions about using cardiovascular CPRs. This implies, when promoting evidence-based decisions, it might be useful to target clinicians' unfamiliarity, distrust, and challenges regarding the use of research evidence rather than focusing only on their knowledge and skills. Further, because clinicians often rely on evidence-unrelated factors, guideline developers and policymakers should recommend cardiovascular CPRs supported by high-quality evidence.

Efficacy of a Clinical Decision Rule to Enable Direct Oral Challenge in Patients With Low-Risk Penicillin Allergy: The PALACE Randomized Clinical Trial

Importance

Fewer than 5% of patients labeled with a penicillin allergy are truly allergic. The standard of care to remove the penicillin allergy label in adults is specialized testing involving prick and intradermal skin testing followed by an oral challenge with penicillin. Skin testing is resource intensive, limits practice to specialist-trained physicians, and restricts the global population who could undergo penicillin allergy delabeling.

Objective

To determine whether a direct oral penicillin challenge is noninferior to the standard of care of penicillin skin testing followed by an oral challenge in patients with a low-risk penicillin allergy.

Design, Setting, and Participants

This parallel, 2-arm, noninferiority, open-label, multicenter, international randomized clinical trial occurred in 6 specialized centers, 3 in North America (US and Canada) and 3 in Australia, from June 18, 2021, to December 2, 2022. Eligible adults had a PEN-FAST score lower than 3. PEN-FAST is a prospectively derived and internationally validated clinical decision rule that enables point-of-care risk assessment for adults reporting penicillin allergies.

Interventions

Patients were randomly assigned to either direct oral challenge with penicillin (intervention arm) or a standard-of-care arm of penicillin skin testing followed by oral challenge with penicillin (control arm).

Main Outcome and Measure

The primary outcome was a physician-verified positive immune-mediated oral penicillin challenge within 1 hour postintervention in the intention-to-treat population. Noninferiority was achieved if a 1-sided 95% CI of the risk difference (RD) did not exceed 5 percentage points (pp).

Results

A total of 382 adults were randomized, with 377 patients (median [IQR] age, 51 [35-65] years; 247 [65.5%] female) included in the analysis: 187 in the intervention group and 190 in the control group. Most patients had a PEN-FAST score of 0 or 1. The primary outcome occurred in 1 patient (0.5%) in the intervention group and 1 patient (0.5%) in the control group, with an RD of 0.0084 pp (90% CI, -1.22 to 1.24 pp). The 1-sided 95% CI was below the noninferiority margin of 5 pp. In the 5 days following the oral penicillin challenge, 9 immune-mediated adverse events were recorded in the intervention group and 10 in the control group (RD, -0.45 pp; 95% CI, -4.87 to 3.96 pp). No serious adverse events occurred.

Conclusions and Relevance

In this randomized clinical trial, direct oral penicillin challenge in patients with a low-risk penicillin allergy was noninferior compared with standard-of-care skin testing followed by oral challenge. In patients with a low-risk history, direct oral penicillin challenge is a safe procedure to facilitate the removal of a penicillin allergy label.

Trial Registration

ClinicalTrials.gov Identifier: NCT04454229.

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

BACKGROUND

Crohn's disease (CD) is characterized by the development of complications over the course of the disease. It is crucial to identify predictive factors of disabling disease, in order to target patients for early intervention. We evaluated risk factors of disabling CD and developed a prognostic model.

METHODS

In total, 511 CD patients were retrospectively analyzed. Univariate and multivariate logistic regression analyses were used to identify demographic, clinical, and biological risk factors. A predictive nomogram model was developed in a subgroup of patients with noncomplicated CD (inflammatory pattern and no perianal disease).

RESULTS

The rate of disabling CD within 5 years after diagnosis was 74.6%. Disabling disease was associated with gender, location of disease, requirement of steroids for the first flare, and perianal lesions. In the subgroup of patients (310) with noncomplicated CD, the rate of disabling CD was 80%. In the multivariate analysis age at onset <40 years (OR = 3.46, 95% confidence interval [CI] = 1.52-7.90), extensive disease (L3/L4) (OR = 2.67, 95% CI = 1.18-6.06), smoking habit (OR = 2.09, 95% CI = 1.03-4.27), requirement of steroids at the first flare (OR = 2.20, 95% CI = 1.09-4.45), and albumin (OR = 0.59, 95% CI = 0.36-0.96) were associated with development of disabling disease. The developed predictive nomogram based on these factors presented good discrimination, with an area under the receiver operating characteristic curve of 0.723 (95% CI: 0.670-0.830).

CONCLUSION

We identified predictive factors of disabling CD and developed an easy-to-use prognostic model that may be used in clinical practice to help identify patients at high risk and address treatment effectively.

A prediction model for permanent pacemaker implantation after transcatheter aortic valve replacement

BACKGROUND

This study aims to develop a post-procedural risk prediction model for permanent pacemaker implantation (PPMI) in patients treated with transcatheter aortic valve replacement (TAVR).

METHODS

336 patients undergoing TAVR at a single institution were included for model derivation. For primary analysis, multivariate logistic regression model was used to evaluate predictors and a risk score system was devised based on the prediction model. For secondary analysis, a Cox proportion hazard model was performed to assess characteristics associated with the time from TAVR to PPMI. The model was validated internally via bootstrap and externally using an independent cohort.

RESULTS

48 (14.3%) patients in the derivation set had PPMI after TAVR. Prior right bundle branch block (RBBB, OR: 10.46; p < 0.001), pre-procedural aortic valve area (AVA, OR: 1.41; p = 0.004) and post- to pre-procedural AVA ratio (OR: 1.72; p = 0.043) were identified as independent predictors for PPMI. AUC was 0.7 and 0.71 in the derivation and external validation set. Prior RBBB (HR: 5.07; p < 0.001), pre-procedural AVA (HR: 1.33; p = 0.001), post-procedural AVA to prosthetic nominal area ratio (HR: 0.02; p = 0.039) and post- to pre-procedural troponin-T difference (HR: 1.72; p = 0.017) are independently associated with time to PPMI.

CONCLUSIONS

The post-procedural prediction model achieved high discriminative power and accuracy for PPMI. The risk score system was constructed and validated, providing an accessible tool in clinical setting regarding the Chinese population.

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.

Ruling out acute coronary syndrome in primary care with a clinical decision rule and a capillary, high-sensitive troponin I point of care test: study protocol of a diagnostic RCT in the Netherlands (POB HELP)

INTRODUCTION

Chest pain is a common reason for consultation in primary care. To rule out acute coronary syndrome (ACS), general practitioners (GP) refer 40%-70% of patients with chest pain to the emergency department (ED). Only 10%-20% of those referred, are diagnosed with ACS. A clinical decision rule, including a high-sensitive cardiac troponin-I point-of-care test (hs-cTnI-POCT), may safely rule out ACS in primary care. Being able to safely rule out ACS at the GP level reduces referrals and thereby alleviates the burden on the ED. Moreover, prompt feedback to the patients may reduce anxiety and stress.

METHODS AND ANALYSIS

The POB HELP study is a clustered randomised controlled diagnostic trial investigating the (cost-)effectiveness and diagnostic accuracy of a primary care decision rule for acute chest pain, consisting of the Marburg Heart Score combined with a hs-cTnI-POCT (limit of detection 1.6 ng/L, 99th percentile 23 ng/L, cut-off value between negative and positive used in this study 3.8 ng/L). General practices are 2:1 randomised to the intervention group (clinical decision rule) or control group (regular care). In total 1500 patients with acute chest pain are planned to be included by GPs in three regions in The Netherlands. Primary endpoints are the number of hospital referrals and the diagnostic accuracy of the decision rule 24 hours, 6 weeks and 6 months after inclusion.

ETHICS AND DISSEMINATION

The medical ethics committee Leiden-Den Haag-Delft (the Netherlands) has approved this trial. Written informed consent will be obtained from all participating patients. The results of this trial will be disseminated in one main paper and additional papers on secondary endpoints and subgroup analyses.

TRIAL REGISTRATION NUMBERS

NL9525 and NCT05827237.

Influencing Factors of Quality of Life and Prediction Model Building in Patients with Primary Nephrotic Syndrome: A Single-Centre Retrospective Study

OBJECTIVE

This study aimed to explore the influencing factors of quality of life (QOL) and establish a prediction model in patients with primary nephrotic syndrome (PNS).

METHODS

A single-centre retrospective study was conducted on 245 patients with PNS admitted to Zibo Central Hospital from August 2020 to August 2022. According to the 36-Item Short-Form Health Survey (SF-36) for QOL evaluation, the patients were divided into the good QOL group (the total score ≥50 points) and poor QOL group (the total score <50 points). Univariate analysis was conducted by collecting clinical data from patients, and multiple logistic regression analysis was carried out on single factors with statistically significant differences to construct a clinical prediction model. The diagnostic efficacy of the prediction model was evaluated using the receiver operating characteristic (ROC) curve.

RESULTS

A total of 245 questionnaires were distributed, and 243 valid questionnaires were recovered, in which 143 cases had good QOL, with an average score of (71.86 ± 10.83) points, and 100 cases had poor QOL, with an average score of (40.03 ± 5.95) points. Statistical differences were observed in age, education level, monthly family average income, payment methods of medical expenses, albumin, 24-hour urinary protein quantification (24 h UPro) and serum uric acid (SUA) in both groups (p < 0.05), whereas no statistical difference was found in gender, body mass index (BMI) and marital status (p > 0.05). The multiple logistic regression analysis showed that age (X1), monthly family average income (X2), payment methods of medical expenses (X3), albumin (X4), 24 h UPro (X5) and SUA (X6) were risk factors for the QOL of patients with PNS, with Y = -12.105 + 0.130X1 + 0.457X2 + 0.448X3 + -0.161X4 + 0.823X5 + 0.025X6 as the regression prediction model. The results of ROC curve showed that the area under the curve (AUC) was 0.987 with standard error of 0.005 (p < 0.001), and 95% CI was 0.976-0.998.

CONCLUSIONS

Age, monthly family average income, payment methods of medical expenses, albumin, 24 h UPro and SUA are risk factors that affect the QOL of patients with PNS, and the construction of prediction model has good evaluation value and can provide a reference for clinical practice.

Validation of a Clinical Decision Rule for Ultrasound Identification of MRSA Skin Abscesses in Children

OBJECTIVE

The aim of this study was to validate an adult-derived clinical decision rule for ultrasound identification of methicillin-resistant Staphylococcus aureus (MRSA) skin abscesses in a pediatric cohort.

METHODS

We conducted a retrospective study of skin and soft tissue infections in patients <21 years presenting to the emergency department who had radiology performed ultrasounds completed and wound cultures obtained. Ultrasound scans were reviewed for edge definition, volume, and shape by 2 pediatric emergency physicians with expertise in point-of-care ultrasound, with approximately 25% of scans reviewed by both experts to evaluate interrater reliability. A third, blinded expert weighed in for discrepancies before analysis. Test performance characteristics were calculated for the clinical decision rule in children.

RESULTS

Two hundred nine patients were enrolled, with mean age of 9.8 (±6.7) years; 87 (42%) were male. Sixty-nine (33%) patients had a wound culture positive for MRSA. The clinical decision rule had a sensitivity of 86% (95% confidence interval [CI], 75%-93%), specificity of 32% (95% CI, 25%-41%), positive predictive value of 38% (95% CI, 35%-42%), negative predictive value of 82% (95% CI, 71%-89%), positive likelihood ratio of 1.26 (95% CI, 1.08-1.46), negative likelihood ratio of 0.45 (95% CI, 0.24-0.84), and an odds ratio of 2.8 (95% CI, 1.31-5.97).

CONCLUSIONS

This clinical decision rule for ultrasound identification of MRSA abscesses had moderately high sensitivity and negative predictive value in pediatric patients, with similar sensitivity compared with the original adult validation group. Ultrasound may help identify MRSA abscesses, allowing for improved antibiotic choices and outcomes for children with MRSA abscesses.

Development and external validation of a prognostic tool for nonsevere COVID-19 inpatients

To develop a machine learning model and nomogram to predict the probability of persistent virus shedding (PVS) in hospitalized patients with coronavirus disease 2019 (COVID-19), the clinical symptoms and signs, laboratory parameters, cytokines, and immune cell data of 429 patients with nonsevere COVID-19 were retrospectively reviewed. Two models were developed using the Akaike information criterion (AIC). The performance of these two models was analyzed and compared by the receiver operating characteristic (ROC) curve, calibration curve, net reclassification index (NRI), and integrated discrimination improvement (IDI). The final model included the following independent predictors of PVS: sex, C-reactive protein (CRP) level, interleukin-6 (IL-6) level, the neutrophil-lymphocyte ratio (NLR), monocyte count (MC), albumin (ALB) level, and serum potassium level. The model performed well in both the internal validation (corrected C-statistic = 0.748, corrected Brier score = 0.201) and external validation datasets (corrected C-statistic = 0.793, corrected Brier score = 0.190). The internal calibration was very good (corrected slope = 0.910). The model developed in this study showed high discriminant performance in predicting PVS in nonsevere COVID-19 patients. Because of the availability and accessibility of the model, the nomogram designed in this study could provide a useful prognostic tool for clinicians and medical decision-makers.

A New Clinical Prediction Rule for Infective Endocarditis in Emergency Department Patients With Fever: Definition and First Validation of the CREED Score

Background Infective endocarditis (IE) could be suspected in any febrile patients admitted to the emergency department (ED). This study was aimed at assessing clinical criteria predictive of IE and identifying and prospectively validating a sensible and easy-to-use clinical prediction score for the diagnosis of IE in the ED. Methods and Results We conducted a retrospective observational study, enrolling consecutive patients with fever admitted to the ED between January 2015 and December 2019 and subsequently hospitalized. Several clinical and anamnestic standardized variables were collected and evaluated for the association with IE diagnosis. We derived a multivariate prediction model by logistic regression analysis. The identified predictors were assigned a score point value to obtain the Clinical Rule for Infective Endocarditis in the Emergency Department (CREED) score. To validate the CREED score we conducted a prospective observational study between January 2020 and December 2021, enrolling consecutive febrile patients hospitalized after the ED visit, and evaluating the association between the CREED score values and the IE diagnosis. A total of 15 689 patients (median age, 71 [56-81] years; 54.1% men) were enrolled in the retrospective cohort, and IE was diagnosed in 267 (1.7%). The CREED score included 12 variables: male sex, anemia, dialysis, pacemaker, recent hospitalization, recent stroke, chest pain, specific infective diagnosis, valvular heart disease, valvular prosthesis, previous endocarditis, and clinical signs of suspect endocarditis. The CREED score identified 4 risk groups for IE diagnosis, with an area under the receiver operating characteristic curve of 0.874 (0.849-0.899). The prospective cohort included 13 163 patients, with 130 (1.0%) IE diagnoses. The CREED score had an area under the receiver operating characteristic curve of 0.881 (0.848-0.913) in the validation cohort, not significantly different from the one calculated in the retrospective cohort (P=0.578). Conclusions In this study, we propose and prospectively validate the CREED score, a clinical prediction rule for the diagnosis of IE in patients with fever admitted to the ED. Our data reflect the difficulty of creating a meaningful tool able to identify patients with IE among this general and heterogeneous population because of the complexity of the disease and its low prevalence in the ED setting.

Risk of death by suicide following self-harm presentations to healthcare: development and validation of a multivariable clinical prediction rule (OxSATS)

BACKGROUND

Assessment of suicide risk in individuals who have self-harmed is common in emergency departments, but is often based on tools developed for other purposes.

OBJECTIVE

We developed and validated a predictive model for suicide following self-harm.

METHODS

We used data from Swedish population-based registers. A cohort of 53 172 individuals aged 10+ years, with healthcare episodes of self-harm, was split into development (37 523 individuals, of whom 391 died from suicide within 12 months) and validation (15 649 individuals, 178 suicides within 12 months) samples. We fitted a multivariable accelerated failure time model for the association between risk factors and time to suicide. The final model contains 11 factors: age, sex, and variables related to substance misuse, mental health and treatment, and history of self-harm. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis guidelines were followed for the design and reporting of this work.

FINDINGS

An 11-item risk model to predict suicide was developed using sociodemographic and clinical risk factors, and showed good discrimination (c-index 0.77, 95% CI 0.75 to 0.78) and calibration in external validation. For risk of suicide within 12 months, using a 1% cut-off, sensitivity was 82% (75% to 87%) and specificity was 54% (53% to 55%). A web-based risk calculator is available (Oxford Suicide Assessment Tool for Self-harm or OxSATS).

CONCLUSIONS

OxSATS accurately predicts 12-month risk of suicide. Further validations and linkage to effective interventions are required to examine clinical utility.

CLINICAL IMPLICATIONS

Using a clinical prediction score may assist clinical decision-making and resource allocation.

Assessment and prediction of glioblastoma therapy response: challenges and opportunities

Glioblastoma is the most aggressive type of primary adult brain tumour. The median survival of patients with glioblastoma remains approximately 15 months, and the 5-year survival rate is <10%. Current treatment options are limited, and the standard of care has remained relatively constant since 2011. Over the last decade, a range of different treatment regimens have been investigated with very limited success. Tumour recurrence is almost inevitable with the current treatment strategies, as glioblastoma tumours are highly heterogeneous and invasive. Additionally, another challenging issue facing patients with glioblastoma is how to distinguish between tumour progression and treatment effects, especially when relying on routine diagnostic imaging techniques in the clinic. The specificity of routine imaging for identifying tumour progression early or in a timely manner is poor due to the appearance similarity of post-treatment effects. Here, we concisely describe the current status and challenges in the assessment and early prediction of therapy response and the early detection of tumour progression or recurrence. We also summarize and discuss studies of advanced approaches such as quantitative imaging, liquid biomarker discovery and machine intelligence that hold exceptional potential to aid in the therapy monitoring of this malignancy and early prediction of therapy response, which may decisively transform the conventional detection methods in the era of precision medicine.

Measuring the performance of prediction models to personalize treatment choice

When data are available from individual patients receiving either a treatment or a control intervention in a randomized trial, various statistical and machine learning methods can be used to develop models for predicting future outcomes under the two conditions, and thus to predict treatment effect at the patient level. These predictions can subsequently guide personalized treatment choices. Although several methods for validating prediction models are available, little attention has been given to measuring the performance of predictions of personalized treatment effect. In this article, we propose a range of measures that can be used to this end. We start by defining two dimensions of model accuracy for treatment effects, for a single outcome: discrimination for benefit and calibration for benefit. We then amalgamate these two dimensions into an additional concept, decision accuracy, which quantifies the model's ability to identify patients for whom the benefit from treatment exceeds a given threshold. Subsequently, we propose a series of performance measures related to these dimensions and discuss estimating procedures, focusing on randomized data. Our methods are applicable for continuous or binary outcomes, for any type of prediction model, as long as it uses baseline covariates to predict outcomes under treatment and control. We illustrate all methods using two simulated datasets and a real dataset from a trial in depression. We implement all methods in the R package predieval. Results suggest that the proposed measures can be useful in evaluating and comparing the performance of competing models in predicting individualized treatment effect.

Performance of Vibration-Controlled Transient Elastography and Clinical Prediction Models In Liver Transplant Recipients

Hepatic fibrosis is a strong predictor of clinical outcomes following liver transplantation (LT).1 Despite the centrality of hepatic fibrosis in clinical outcomes, the published literature with noninvasive fibrosis assessment in LT recipients is limited and liver biopsy, despite its invasive nature, remains the reference standard. Vibration-controlled transient elastography (VCTE) and clinical prediction models (CPM) are point-of-care tests that can provide noninvasive assessment of hepatic fibrosis2-4; however, the data comparing the diagnostic performance of VCTE and CPM in LT recipients are lacking. The current study evaluated the diagnostic performance of VCTE and CPM in LT recipients using best practices in regulatory sciences for biomarker development.

Cost-effectiveness of running a paediatric oncology unit in Ethiopia

OBJECTIVE

To estimate the cost-effectiveness of running a paediatric oncology unit in Ethiopia to inform the revision of the Ethiopia Essential Health Service Package (EEHSP), which ranks the treatment of childhood cancers at a low and medium priority.

METHODS

We built a decision analytical model-a decision tree-to estimate the cost-effectiveness of running a paediatric oncology unit compared with a do-nothing scenario (no paediatric oncology care) from a healthcare provider perspective. We used the recently (2018-2019) conducted costing estimate for running the paediatric oncology unit at Tikur Anbessa Specialized Hospital (TASH) and employed a mixed costing approach (top-down and bottom-up). We used data on health outcomes from other studies in similar settings to estimate the disability-adjusted life years (DALYs) averted of running a paediatric oncology unit compared with a do-nothing scenario over a lifetime horizon. Both costs and effects were discounted (3%) to the present value. The primary outcome was incremental cost in US dollars (USDs) per DALY averted, and we used a willingness-to-pay (WTP) threshold of 50% of the Ethiopian gross domestic product per capita (USD 477 in 2019). Uncertainty was tested using one-way and probabilistic sensitivity analyses.

RESULTS

The incremental cost and DALYs averted per child treated in the paediatric oncology unit at TASH were USD 876 and 2.4, respectively, compared with no paediatric oncology care. The incremental cost-effectiveness ratio of running a paediatric oncology unit was USD 361 per DALY averted, and it was cost-effective in 90% of 100 000 Monte Carlo iterations at a USD 477 WTP threshold.

CONCLUSIONS

The provision of paediatric cancer services using a specialised oncology unit is most likely cost-effective in Ethiopia, at least for easily treatable cancer types in centres with minimal to moderate capability. We recommend reassessing the priority-level decision of childhood cancer treatment in the current EEHSP.

Development and validation of a predictive model for major complications after extracorporeal shockwave lithotripsy in patients with ureteral stones: based on a large prospective cohort

The risk factors of complications after SWL are not well characterized. Therefore, based on a large prospective cohort, we aimed to develop and validate a nomogram for predicting major complications after extracorporeal shockwave lithotripsy (SWL) in patients with ureteral stones. The development cohort included 1522 patients with ureteral stones who underwent SWL between June 2020 and August 2021 in our hospital. Five hundred and fifty-three patients with ureteral stones participated in the validation cohort from September 2020 to April 2022. The data were prospectively recorded. Backward stepwise selection was applied using the likelihood ratio test with Akaike's information criterion as the stopping rule. The efficacy of this predictive model was assessed concerning its clinical usefulness, calibration, and discrimination. Finally, 7.2% (110/1522) of patients in the development cohort and 8.7% (48/553) of those in the validation cohort suffered from major complications. We identified five predictive factors for major complications: age, gender, stone size, Hounsfield unit of stone, and hydronephrosis. This model showed good discrimination with an area under the receiver operating characteristic curves of 0.885 (0.872-0.940) and good calibration (P = 0.139). The decision curve analysis showed that the model was clinically valuable. In this large prospective cohort, we found that older age, female gender, higher Hounsfield unit, size, and grade of hydronephrosis were risk predictors of major complications after SWL. This nomogram will be helpful in preoperative risk stratification to provide individualized treatment recommendations for each patient. Furthermore, early identification and appropriate management of high-risk patients may decrease postoperative morbidity.

Comparing GPs' antibiotic prescribing decisions to a clinical prediction rule: an online vignette study

BACKGROUND

The 'STARWAVe' clinical prediction rule (CPR) uses seven factors to guide risk assessment and antibiotic prescribing in children with cough (Short illness duration, Temperature, Age, Recession, Wheeze, Asthma, Vomiting).

AIM

To assess the influence of STARWAVe factors on GPs' unaided risk assessments and prescribing decisions.

DESIGN AND SETTING

Clinical vignettes administered to 188 UK GPs online.

METHOD

GPs were randomly assigned to view 32 (out of a possible 64) vignettes online depicting children with cough. The vignettes comprised the seven STARWAVe factors, which were varied systematically. For each vignette, GPs assessed risk of deterioration in one of two ways (sliding-scale versus risk-category selection) and indicated whether they would prescribe antibiotics. Finally, GPs saw an additional vignette, suggesting that the parent was concerned. Mixed-effects regressions were used to measure the influence of STARWAVe factors, risk-elicitation method, and parental concern on GPs' assessments and decisions.

RESULTS

Six STARWAVe risk factors correctly increased GPs' risk assessments (bssliding-scale≥0.66, odds ratios [ORs]category-selection≥1.75, Ps≤0.001), whereas one incorrectly reduced them (short illness duration: b sliding-scale -0.30, ORcategory-selection 0.80, P≤0.039). Conversely, one STARWAVe factor increased prescribing odds (temperature: OR 5.22, P<0.001), whereas the rest either reduced them (short illness duration, age, and recession: ORs≤0.70, Ps<0.001) or had no significant impact (wheeze, asthma, and vomiting: Ps≥0.065). Parental concern increased risk assessments (b sliding-scale 1.29, ORcategory-selection 2.82, P≤0.003) but not prescribing odds (P = 0.378).

CONCLUSION

GPs use some, but not all, STARWAVe factors when making unaided risk assessments and prescribing decisions. Such discrepancies must be considered when introducing CPRs to clinical practice.