Medical management of neurogenic bladder in patients with spina bifida: A scoping review.

INTRODUCTION: Neurogenic bladder is a common source of morbidity in patients with spina bifida and can cause renal damage. Medical management may include imaging, urodynamic studies (UDS), laboratory testing, clean intermittent catheterization (CIC), and medication. There is ongoing debate regarding the optimal management regimen. Approaches are described by two paradigms: proactive and expectant management. In a proactive approach, invasive interventions like CIC and UDS are initiated before the onset of renal abnormalities. In expectant management, UDS, CIC, and medications are started after abnormalities are identified. In this scoping review, we aim to comprehensively review existing literature on outcomes of proactive and expectant management of neurogenic bladder in patients with spina bifida. METHODS: We searched multiple databases and screened articles for inclusion using PRISMA-ScR guidelines. Included studies reported clinical outcomes of any aspect of proactive or expectant neurogenic bladder management in patients with spina bifida. RESULTS: Ultimately, 74 articles were included for review including 67 cohort studies, 4 cross-sectional studies, 2 sequential cohort studies, and 1 randomized control trial. Eleven studies directly compared management strategies. There was substantial heterogeneity in study designs, management protocols, and reported outcomes. Most studies addressed multiple simultaneous aspects of management without specifically analyzing individual aspects. However, some commented on individual aspects of management including UDS (13), CIC (32), imaging (7), and medication (5). Although there was no consensus about optimal management, all direct comparisons of paradigms supported a proactive approach. CONCLUSION: Our review identified a broad body of literature about optimal management of neurogenic bladder. Existing studies vary greatly in terms of treatment protocols, measured outcomes, and management recommendations. Overall, studies that directly compare management are scarce but favor proactive management. Given the implications on clinical outcomes, it is crucial to focus future work on directly comparing management strategies and isolating the effects of different individual management elements.

Predicting postoperative complications in pediatric surgery: A novel pediatric comorbidity index.

INTRODUCTION/BACKGROUND: Comorbidity-driven surgical risk assessment is essential for informed patient counseling, risk-stratification, and outcomes-based health-services research. Existing mortality-focused comorbidity indices have had mixed success at risk-adjustment in children. OBJECTIVE: To develop a new comorbidity-driven multispecialty surgical risk index predicting 30-day postoperative complications in children. STUDY DESIGN: This retrospective cohort study investigated children undergoing surgical procedures across seven specialties in 2014-2015 using the MarketScan® Research databases. The risk index was derived separately for ambulatory and inpatient surgery patients using logistic regression with backward selection. The performance of the novel index in discriminating postoperative complications vis-à-vis three existing comorbidity indices was compared using bootstrapping and area under the receiver operating characteristics curves (AUC). RESULTS: We identified 190,629 ambulatory and 22,633 inpatient patients. The novel index had the best performance for discriminating postoperative complications for inpatients (AUC 0.76, 95% confidence interval [CI] 0.75-0.77) relative to the Charlson Comorbidity Index (CCI, 0.56, 95% CI 0.56-0.57), Van Walraven Index (VWI, 0.60, 95% CI 0.60-0.61), and Rhee Score (RS, 0.69, 95% CI 0.68-0.70). In the ambulatory cohort, the novel index outperformed all three existing indices, though none demonstrated excellent discriminatory ability for complications (novel score 0.68, 95% CI 0.67-0.68; CCI 0.53, 95% CI 0.52-0.53; VWI 0.53, 95% CI 0.52-0.53; RS 0.50, 95% CI 0.49-0.50). DISCUSSION: In both inpatient and ambulatory pediatric settings, our novel comorbidity index demonstrated better performance at predicting postoperative complications than three widely used alternatives. This index will be useful for research and may be adaptable to clinical settings to identify high-risk patients and facilitate perioperative planning. CONCLUSION: We developed a novel pediatric comorbidity index with better performance at predicting postoperative complications than three widely used alternatives.

Financial toxicity among individuals with spina bifida and their families: A qualitative study and conceptual model.

INTRODUCTION: Spina bifida is the most common permanently disabling birth defect in the United States and requires lifelong, multi-specialty care. The cost of such care has the potential to result in financial toxicity - the 'objective financial burden' and 'subjective financial distress' which can negatively impact clinical outcomes. While this concept has been extensively studied in other areas of medicine, particularly oncology, financial toxicity has not yet been examined in pediatric urology or in individuals with spina bifida and their families/caregivers. OBJECTIVE: To qualitatively explore the presence of financial toxicity in individuals with spina bifida and their caregivers with the objective of identifying themes and creating a conceptual model. MATERIALS AND METHODS: We conducted semi-structured interviews with individuals with spina bifida and/or their caregivers with the aim of eliciting information regarding financial distress associated with spina bifida care. Interviews were transcribed and qualitative thematic analysis was performed to identify recurring themes. These insights were used to create a conceptual model of financial toxicity among individuals with spina bifida. RESULTS: A total of 14 interviews were conducted (total of 6 patients and 13 parents/caregivers). Average patient age was 17.9 years. Five dominant themes were identified: 1) resources (insurance type, community support, etc.), 2) direct costs (copays, deductibles, travel expenses, etc.), 3) indirect costs (lost work time, hindered career advancement, resource navigation burden, etc.), 4) coping (work adjustments, decreased spending, etc.), and 5) affect (lack of control, uncertainty, worry, etc.). These insights were used to create a conceptual model. DISCUSSION: This is the first study to explore financial toxicity in spina bifida and establish a conceptual model. Our findings are corroborated by prior spina bifida literature and are closely mirrored by studies in cancer patients. Given that financial toxicity is associated with negative outcomes in other medical domains, the impact of financial toxicity on health outcomes among individuals with spina bifida warrants further study, particularly in instrument development to better understand and quantify financial toxicity in this group. CONCLUSION: Financial toxicity is a concern among individuals with spina bifida and their caregivers. This concept will need to be investigated further in order to develop validated measurement tools, identify solutions, and provide optimal care; our conceptual model will help guide these future investigations.

Combining adult with pediatric patient data to develop a clinical decision support tool intended for children: leveraging machine learning to model heterogeneity.

BACKGROUND: Clinical decision support (CDS) tools built using adult data do not typically perform well for children. We explored how best to leverage adult data to improve the performance of such tools. This study assesses whether it is better to build CDS tools for children using data from children alone or to use combined data from both adults and children. METHODS: Retrospective cohort using data from 2017 to 2020. Participants include all individuals (adults and children) receiving an elective surgery at a large academic medical center that provides adult and pediatric services. We predicted need for mechanical ventilation or admission to the intensive care unit (ICU). Predictor variables included demographic, clinical, and service utilization factors known prior to surgery. We compared predictive models built using machine learning to regression-based methods that used a pediatric or combined adult-pediatric cohort. We compared model performance based on Area Under the Receiver Operator Characteristic. RESULTS: While we found that adults and children have different risk factors, machine learning methods are able to appropriately model the underlying heterogeneity of each population and produce equally accurate predictive models whether using data only from pediatric patients or combined data from both children and adults. Results from regression-based methods were improved by the use of pediatric-specific data. CONCLUSIONS: CDS tools for children can successfully use combined data from adults and children if the model accounts for underlying heterogeneity, as in machine learning models.

Machine Learning for Urodynamic Detection of Detrusor Overactivity.

OBJECTIVE: To develop a machine learning algorithm that identifies detrusor overactivity (DO) in Urodynamic Studies (UDS) in the spina bifida population. UDS plays a key role in assessment of neurogenic bladder in patients with spina bifida. Due to significant variability in individual interpretations of UDS data, there is a need to standardize UDS interpretation. MATERIALS AND METHODS: Patients who underwent UDS at a single pediatric urology clinic between May 2012 and September 2020 were included. UDS files were analyzed in both time and frequency domains, varying inclusion of vesical, abdominal, and detrusor pressure channels. A machine learning pipeline was constructed using data windowing, dimensionality reduction, and support vector machines. Models were designed to detect clinician identified detrusor overactivity. RESULTS: Data were extracted from 805 UDS testing files from 546 unique patients. The generated models achieved good performance metrics in detecting DO agreement with the clinician, in both time- and frequency-based approaches. Incorporation of multiple channels and data windowing improved performance. The time-based model with all 3 channels had the highest area under the curve (AUC) (91.9 ± 1.3%; sensitivity: 84.2 ± 3.8%; specificity: 86.4 ± 1.3%). The 3-channel frequency-based model had the highest specificity (AUC: 90.5 ± 1.9%; sensitivity: 68.3 ± 5.3%; specificity: 92.9 ± 1.1%). CONCLUSION: We developed a promising proof-of-concept machine learning pipeline that identifies DO in UDS. Machine-learning-based predictive modeling algorithms may be employed to standardize UDS interpretation and could potentially augment shared decision-making and improve patient care.