Factors associated with inappropriateness of antibiotic prescriptions for acutely ill children presenting to ambulatory care in high-income countries: a systematic review and meta-analysis.

BACKGROUND: Acutely ill children are at risk of unwarranted antibiotic prescribing. Data on the appropriateness of antibiotic prescriptions provide insights into potential tailored interventions to promote antibiotic stewardship. OBJECTIVES: To examine factors associated with the inappropriateness of antibiotic prescriptions for acutely ill children presenting to ambulatory care in high-income countries. METHODS: On 8 September 2022, we systematically searched articles published since 2002 in MEDLINE, Embase, CENTRAL, Web of Science, and grey literature databases. We included studies with acutely ill children presenting to ambulatory care settings in high-income countries reporting on the appropriateness of antibiotic prescriptions. The quality of the studies was evaluated using the Appraisal tool for Cross-Sectional Studies and the Newcastle-Ottawa Scale. Pooled ORs were calculated using random-effects models. Meta-regression, sensitivity and subgroup analysis were also performed. RESULTS: We included 40 articles reporting on 30 different factors and their association with inappropriate antibiotic prescribing. 'Appropriateness' covered a wide range of definitions. The following factors were associated with increased inappropriate antibiotic prescribing: acute otitis media diagnosis [pooled OR (95% CI): 2.02 (0.54-7.48)], GP [pooled OR (95% CI) 1.38 (1.00-1.89)] and rural setting [pooled OR (95% CI) 1.47 (1.08-2.02)]. Older patient age and a respiratory tract infection diagnosis have a tendency to be positively associated with inappropriate antibiotic prescribing, but pooling of studies was not possible. CONCLUSIONS: Prioritizing acute otitis media, GPs, rural areas, older children and respiratory tract infections within antimicrobial stewardship programmes plays a vital role in promoting responsible antibiotic prescribing. The implementation of a standardized definition of appropriateness is essential to evaluate such programmes.

Effectiveness of safety netting approaches for acutely ill children: a network meta-analysis.

BACKGROUND: Safety netting advice (SNA) can help in the management of acutely ill children. AIM: Assess the effectiveness of different SNA methods for acutely ill children on antibiotic prescription and consumption. DESIGN AND SETTING: Systematic review and network meta-analysis of randomised controlled trials, non-randomised trials of interventions, and controlled before-after studies in ambulatory care. METHOD: We searched MEDLINE, Embase, Web-Of-Science Core Collection, and Cochrane Central Register of Controlled Trials (22 January 2024). We assessed the risk of bias (RoB) with the Cochrane Tool 2, Revised Cochrane Tool for Cluster-Randomised Trials, and ROBINS-I tool. Certainty of evidence was assessed using the CINeMA approach. We performed sensitivity analyses and network meta-regression. RESULTS: We included 30 studies (20 interventions). Compared to usual care, paper SNA may reduce antibiotic prescribing (OR=0.66 (95%CI: 0.53-0.85), I²=92%, very low certainty; 3 studies, 35,988 participants), especially when combined with oral SNA (OR=0.40 (95%CI: 0.08-2.00), P-score: 0.86), antibiotic consumption (OR=0.39 (95%CI: 0.27-0.58), low RoB; 1 study, 509 participants), and return visits (OR=0.74 , 95%CI 0.63-0.87). Paper SNA without antibiotics may reduce antibiotic consumption compared to paper SNA and delayed antibiotics (OR=0.27 (95%CI: 0.15-0.51, some RoB; 1 study, 206 participants). Video SNA, oral SNA, read-only websites, and web-based modules may increase parental knowledge (ORs 2.23-4.52). Video SNA and web-based modules may improve parental satisfaction (ORs 1.64-4.08). CONCLUSION: Paper SNA (with oral SNA) may reduce antibiotic use and return visits. Video, oral, and online SNA, may improve parental knowledge while video SNA and web-based modules may increase parental satisfaction.

ADNEX risk prediction model for diagnosis of ovarian cancer: systematic review and meta-analysis of external validation studies.

Objectives: To conduct a systematic review of studies externally validating the ADNEX (Assessment of Different Neoplasias in the adnexa) model for diagnosis of ovarian cancer and to present a meta-analysis of its performance. Design: Systematic review and meta-analysis of external validation studies. Data sources: Medline, Embase, Web of Science, Scopus, and Europe PMC, from 15 October 2014 to 15 May 2023. Eligibility criteria for selecting studies: All external validation studies of the performance of ADNEX, with any study design and any study population of patients with an adnexal mass. Two independent reviewers extracted the data. Disagreements were resolved by discussion. Reporting quality of the studies was scored with the TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis) reporting guideline, and methodological conduct and risk of bias with PROBAST (Prediction model Risk Of Bias Assessment Tool). Random effects meta-analysis of the area under the receiver operating characteristic curve (AUC), sensitivity and specificity at the 10% risk of malignancy threshold, and net benefit and relative utility at the 10% risk of malignancy threshold were performed. Results: 47 studies (17 007 tumours) were included, with a median study sample size of 261 (range 24-4905). On average, 61% of TRIPOD items were reported. Handling of missing data, justification of sample size, and model calibration were rarely described. 91% of validations were at high risk of bias, mainly because of the unexplained exclusion of incomplete cases, small sample size, or no assessment of calibration. The summary AUC to distinguish benign from malignant tumours in patients who underwent surgery was 0.93 (95% confidence interval 0.92 to 0.94, 95% prediction interval 0.85 to 0.98) for ADNEX with the serum biomarker, cancer antigen 125 (CA125), as a predictor (9202 tumours, 43 centres, 18 countries, and 21 studies) and 0.93 (95% confidence interval 0.91 to 0.94, 95% prediction interval 0.85 to 0.98) for ADNEX without CA125 (6309 tumours, 31 centres, 13 countries, and 12 studies). The estimated probability that the model has use clinically in a new centre was 95% (with CA125) and 91% (without CA125). When restricting analysis to studies with a low risk of bias, summary AUC values were 0.93 (with CA125) and 0.91 (without CA125), and estimated probabilities that the model has use clinically were 89% (with CA125) and 87% (without CA125). Conclusions: The results of the meta-analysis indicated that ADNEX performed well in distinguishing between benign and malignant tumours in populations from different countries and settings, regardless of whether the serum biomarker, CA125, was used as a predictor. A key limitation was that calibration was rarely assessed. Systematic review registration: PROSPERO CRD42022373182.

Development and validation of a risk prediction model for hospital admission in COVID-19 patients presenting to primary care.

BACKGROUND: There is a paucity of prognostic models for COVID-19 that are usable for in-office patient assessment in general practice (GP). OBJECTIVES: To develop and validate a risk prediction model for hospital admission with readily available predictors. METHODS: A retrospective cohort study linking GP records from 8 COVID-19 centres and 55 general practices in the Netherlands to hospital admission records. The development cohort spanned March to June 2020, the validation cohort March to June 2021. The primary outcome was hospital admission within 14 days. We used geographic leave-region-out cross-validation in the development cohort and temporal validation in the validation cohort. RESULTS: In the development cohort, 4,806 adult patients with COVID-19 consulted their GP (median age 56, 56% female); in the validation cohort 830 patients did (median age 56, 52% female). In the development and validation cohort respectively, 292 (6.1%) and 126 (15.2%) were admitted to the hospital within 14 days, respectively. A logistic regression model based on sex, smoking, symptoms, vital signs and comorbidities predicted hospital admission with a c-index of 0.84 (95% CI 0.83 to 0.86) at geographic cross-validation and 0.79 (95% CI 0.74 to 0.83) at temporal validation, and was reasonably well calibrated (intercept -0.08, 95% CI -0.98 to 0.52, slope 0.89, 95% CI 0.71 to 1.07 at geographic cross-validation and intercept 0.02, 95% CI -0.21 to 0.24, slope 0.82, 95% CI 0.64 to 1.00 at temporal validation). CONCLUSION: We derived a risk model using readily available variables at GP assessment to predict hospital admission for COVID-19. It performed accurately across regions and waves. Further validation on cohorts with acquired immunity and newer SARS-CoV-2 variants is recommended.

A general practice prediction model based on signs and symptoms of COVID-19 patients reliably predicted hospitalisation.The model performed well in second-wave data with other dominant variants and changed testing and vaccination policies.In an emerging pandemic, GP data can be leveraged to develop prognostic models for decision support and to predict hospitalisation rates.

Table 0; documenting the steps to go from clinical database to research dataset.

OBJECTIVES: Data-driven decision support tools have been increasingly recognized to transform health care. However, such tools are often developed on predefined research datasets without adequate knowledge of the origin of this data and how it was selected. How a dataset is extracted from a clinical database can profoundly impact the validity, interpretability and interoperability of the dataset, and downstream analyses, yet is rarely reported. Therefore, we present a case study illustrating how a definitive patient list was extracted from a clinical source database and how this can be reported. STUDY DESIGN AND SETTING: A single-center observational study was performed at an academic hospital in the Netherlands to illustrate the impact of selecting a definitive patient list for research from a clinical source database, and the importance of documenting this process. All admissions from the critical care database admitted between January 1, 2013, and January 1, 2023, were used. RESULTS: An interdisciplinary team collaborated to identify and address potential sources of data insufficiency and uncertainty. We demonstrate a stepwise data preparation process, reducing the clinical source database of 54,218 admissions to a definitive patient list of 21,553 admissions. Transparent documentation of the data preparation process improves the quality of the definitive patient list before analysis of the corresponding patient data. This study generated seven important recommendations for preparing observational health-care data for research purposes. CONCLUSION: Documenting data preparation is essential for understanding a research dataset originating from a clinical source database before analyzing health-care data. The findings contribute to establishing data standards and offer insights into the complexities of preparing health-care data for scientific investigation. Meticulous data preparation and documentation thereof will improve research validity and advance critical care.