Interventions to Influence Consulting and Antibiotic Use for Acute Respiratory Tract Infections in Children: A Systematic Review and Meta-Analysis

BACKGROUND: Respiratory tract infections (RTIs) are common in children and generally self-limiting, yet often result in consultations to primary care. Frequent consultations divert resources from care for potentially more serious conditions and increase the opportunity for antibiotic overuse. Overuse of antibiotics is associated with adverse effects and antimicrobial resistance, and has been shown to influence how patients seek care in ensuing illness episodes. METHODOLOGY/PRINCIPAL FINDINGS: We conducted a systematic review and meta-analysis to assess the effectiveness of interventions directed towards parents or caregivers which were designed to influence consulting and antibiotic use for respiratory tract infections (RTIs) in children in primary care. Main outcomes were parental consulting rate, parental knowledge, and proportion of children subsequently consuming antibiotics. Of 5,714 references, 23 studies (representing 20 interventions) met inclusion criteria. Materials designed to engage children in addition to parents were effective in modifying parental knowledge and behaviour, resulting in reductions in consulting rates ranging from 13 to 40%. Providing parents with delayed prescriptions significantly decreased reported antibiotic use (Risk Ratio (RR) 0.46 (0.40, 0.54); moreover, a delayed or no prescribing approach did not diminish parental satisfaction. CONCLUSIONS: IN ORDER TO BE MOST EFFECTIVE, INTERVENTIONS TO INFLUENCE PARENTAL CONSULTING AND ANTIBIOTIC USE SHOULD: engage children, occur prior to an illness episode, employ delayed prescribing, and provide guidance on specific symptoms. These results support the wider implementation of interventions to reduce inappropriate antibiotic use in children

Selection Bias in Lung Allocation: Influence on Lung Allocation Score and Physician Decision-Making

In the U.S., donor lungs are allocated to recipients based on a lung allocation score (LAS). While the statistical models used to construct the LAS control for patients’ demographic and clinical values, they do not account for selection bias, which arises because: (1) individuals are removed from the waitlist once they receive transplant (dependent censoring), and (2) in order to receive transplant, individuals must survive on the waitlist long enough for a suitable lung to become available (survivor bias). Failure to account for selection bias can lead to inaccurate predicted probabilities and suboptimal organ allocation. The goal of this dissertation is to improve the predictive accuracy of the LAS by mitigating selection bias so that lungs are allocated to the appropriate patients in the appropriate order. This goal was accomplished via three aims. First, we proposed a weighted estimation strategy to mitigate selection bias in the pre- and post-transplant LAS models, constructed a modified LAS score using these weights, and compared its performance to that of the existing LAS. Second, we examined the clinical impact of our modified LAS in both observed data and through simulations. Third, we conducted qualitative semi-structured interviews with lung transplant surgeons and pulmonologists throughout the U.S. to examine respondents’ understanding of selection bias and how it may affect the LAS and organ distribution. We found that our modified LAS exhibited better discrimination and calibration than the existing LAS and led to changes in patient prioritization. Diagnosis group, six-minute walk distance, continuous mechanical ventilation, functional status, and age exhibited the largest impact on prioritization changes. Simulations suggest that one-year waitlist survival may improve under the modified LAS, while one-year post-transplant and overall survival remain comparable to that under the existing LAS. Finally, our qualitative study demonstrates that selection bias can arise at several points along the transplantation pathway. To address such bias, transplant centers must consider both patient health and program health within constraints imposed by donor organ scarcity. We hope that this work can inform future revisions of the LAS and other prediction models in organ transplantation to ensure more equitable allocation of donor organs

Dynamic prediction modeling of postoperative mortality among patients undergoing surgical aortic valve replacement in a statewide cohort over a 12-year periodCentral MessagePerspective

Objective: Clinical prediction models for surgical aortic valve replacement mortality, are valuable decision tools but are often limited in their ability to account for changes in medical practice, patient selection, and the risk of outcomes over time. Recent research has identified methods to update models as new data accrue, but their effect on model performance has not been rigorously tested. Methods: The study population included 44,546 adults who underwent an isolated surgical aortic valve replacement from January 1, 1999, to December 31, 2018, statewide in Pennsylvania. After chronologically splitting the data into training and validation sets, we compared calibration, discrimination, and accuracy measures amongst a nonupdating model to 2 methods of model updating: calibration regression and the novel dynamic logistic state space model. Results: The risk of mortality decreased significantly during the validation period (P < .01) and the nonupdating model demonstrated poor calibration and reduced accuracy over time. Both updating models maintained better calibration (Hosmer-Lemeshow χ2 statistic) than the nonupdating model: nonupdating (156.5), calibration regression (4.9), and dynamic logistic state space model (8.0). Overall accuracy (Brier score) was consistently better across both updating models: dynamic logistic state space model (0.0252), calibration regression (0.0253), and nonupdating (0.0256). Discrimination improved with the dynamic logistic state space model (area under the curve, 0.696) compared with the nonupdating model (area under the curve, 0.685) and calibration regression method (area under the curve, 0.687). Conclusions: Dynamic model updating can improve model accuracy, discrimination, and calibration. The decision as to which method to use may depend on which measure is most important in each clinical context. Because competing therapies have emerged for valve replacement models, updating may guide clinical decision making

Decision-making Among Hepatitis C Virus-negative Transplant Candidates Offered Organs from Donors with HCV Infection

Background. Historically, many organs from deceased donors with hepatitis C virus (HCV) were discarded. The advent of highly curative direct-acting antiviral (DAA) therapies motivated transplant centers to conduct trials of transplanting HCV-viremic organs (nucleic acid amplification test positive) into HCV-negative recipients, followed by DAA treatment. However, the factors that influence candidates' decisions regarding acceptance of transplant with HCV-viremic organs are not well understood. Methods. To explore patient-level perceptions, influences, and experiences that inform candidate decision-making regarding transplant with organs from HCV-viremic donors, we conducted a qualitative semi-structured interview study embedded within 3 clinical trials investigating the safety and efficacy of transplanting lungs and kidneys from HCV-viremic donors into HCV-negative recipients. The study was conducted from June 2019 to March 2021. Results. Among 44 HCV-negative patients listed for organ transplant who were approached for enrollment in the applicable clinical trial, 3 approaches to decision-making emerged: positivist, risk analyses, and instinctual response. Perceptions of risk contributed to conceptualizations of factors influencing decisions. Moreover, most participants relied on multiple decision-making approaches, either simultaneously or sequentially. Conclusions. Understanding how different decisional models influence patients' choices regarding transplant with organs from HCV-viremic donors may promote shared decision-making among transplant patients and providers

Decision-making Among Hepatitis C Virus-negative Transplant Candidates Offered Organs from Donors with HCV Infection

Background. Historically, many organs from deceased donors with hepatitis C virus (HCV) were discarded. The advent of highly curative direct-acting antiviral (DAA) therapies motivated transplant centers to conduct trials of transplanting HCV-viremic organs (nucleic acid amplification test positive) into HCV-negative recipients, followed by DAA treatment. However, the factors that influence candidates’ decisions regarding acceptance of transplant with HCV-viremic organs are not well understood. Methods. To explore patient-level perceptions, influences, and experiences that inform candidate decision-making regarding transplant with organs from HCV-viremic donors, we conducted a qualitative semistructured interview study embedded within 3 clinical trials investigating the safety and efficacy of transplanting lungs and kidneys from HCV-viremic donors into HCV-negative recipients. The study was conducted from June 2019 to March 2021. Results. Among 44 HCV-negative patients listed for organ transplant who were approached for enrollment in the applicable clinical trial, 3 approaches to decision-making emerged: positivist, risk analyses, and instinctual response. Perceptions of risk contributed to conceptualizations of factors influencing decisions. Moreover, most participants relied on multiple decision-making approaches, either simultaneously or sequentially. Conclusions. Understanding how different decisional models influence patients’ choices regarding transplant with organs from HCV-viremic donors may promote shared decision-making among transplant patients and providers