Examining emotional and behavioural trajectories in siblings of children with life-limiting conditions.

BACKGROUND: Healthy siblings of children with life-limiting conditions often experience emotional and behavioural struggles over the course of the ill child's condition(s). Resources to support these siblings are limited due to a lack of understanding about their needs. Therefore, this study was designed to characterize the emotional and behavioural trajectories among siblings of children with progressive, life-limiting genetic, metabolic, or neurological conditions over a 12-month observation period. METHODS: Seventy siblings were recruited from a large-survey based study (Charting the Territory) that examined the bio-psychosocial health outcomes of parents and siblings. Linear mixed effect models were used to assess the association between siblings' emotions and behaviour trajectories and selected demographic variables. Siblings' emotions and behaviour were measured with Child Behaviour Checklist (CBCL). RESULTS: Siblings' mean age was 11.2 years at baseline and Internalizing, Externalizing, and Total Behaviour Problems mean scores were within normal ranges across time. However, 7-25% of siblings had scores within the clinical range. Brothers had higher levels of Internalizing Problems than sisters, whereas sisters had higher levels of Externalizing Problems than brothers. When treatment was first sought for the ill child less than a year prior to study participation, siblings had higher levels of Internalizing and Externalizing Problems compared with siblings who participated more than one year after treatment was sought. CONCLUSION: Healthy siblings experience emotional and behavioural problems early in the child's disease trajectory. Although these problems improve with time, our findings show that brothers and sisters experience different types of challenges. Therefore, timely support for siblings is important as they navigate through the uncertainties and challenges.

Implementing AI in Hospitals to Achieve a Learning Health System: Systematic Review of Current Enablers and Barriers.

BACKGROUND: Efforts are underway to capitalize on the computational power of the data collected in electronic medical records (EMRs) to achieve a learning health system (LHS). Artificial intelligence (AI) in health care has promised to improve clinical outcomes, and many researchers are developing AI algorithms on retrospective data sets. Integrating these algorithms with real-time EMR data is rare. There is a poor understanding of the current enablers and barriers to empower this shift from data set-based use to real-time implementation of AI in health systems. Exploring these factors holds promise for uncovering actionable insights toward the successful integration of AI into clinical workflows. OBJECTIVE: The first objective was to conduct a systematic literature review to identify the evidence of enablers and barriers regarding the real-world implementation of AI in hospital settings. The second objective was to map the identified enablers and barriers to a 3-horizon framework to enable the successful digital health transformation of hospitals to achieve an LHS. METHODS: The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines were adhered to. PubMed, Scopus, Web of Science, and IEEE Xplore were searched for studies published between January 2010 and January 2022. Articles with case studies and guidelines on the implementation of AI analytics in hospital settings using EMR data were included. We excluded studies conducted in primary and community care settings. Quality assessment of the identified papers was conducted using the Mixed Methods Appraisal Tool and ADAPTE frameworks. We coded evidence from the included studies that related to enablers of and barriers to AI implementation. The findings were mapped to the 3-horizon framework to provide a road map for hospitals to integrate AI analytics. RESULTS: Of the 1247 studies screened, 26 (2.09%) met the inclusion criteria. In total, 65% (17/26) of the studies implemented AI analytics for enhancing the care of hospitalized patients, whereas the remaining 35% (9/26) provided implementation guidelines. Of the final 26 papers, the quality of 21 (81%) was assessed as poor. A total of 28 enablers was identified; 8 (29%) were new in this study. A total of 18 barriers was identified; 5 (28%) were newly found. Most of these newly identified factors were related to information and technology. Actionable recommendations for the implementation of AI toward achieving an LHS were provided by mapping the findings to a 3-horizon framework. CONCLUSIONS: Significant issues exist in implementing AI in health care. Shifting from validating data sets to working with live data is challenging. This review incorporated the identified enablers and barriers into a 3-horizon framework, offering actionable recommendations for implementing AI analytics to achieve an LHS. The findings of this study can assist hospitals in steering their strategic planning toward successful adoption of AI.

Population-level 5-year event-free survival for children with cancer in Australia.

BACKGROUND: Event-free survival (EFS) considers other adverse events in addition to mortality. It therefore provides a more complete understanding of the effectiveness and consequences of treatment than standard survival measures, but is rarely reported at the population level for childhood cancer. PROCEDURE: Our study cohort (n = 7067) was obtained from the Australian Childhood Cancer Registry, including children aged under 15 diagnosed with cancer between 2006 and 2015, with follow-up potentially available to 31 December 2020. The events of interest were relapse following remission, progressive disease, diagnosis of a second primary cancer or death from any cause. Five-year EFS and all-cause observed survival were both calculated, stratified by type of childhood cancer, remoteness of residence and stage at diagnosis. Differences in EFS were assessed using multivariable flexible parametric models. RESULTS: Approximately one quarter of patients (n = 1605 of 7067, 23%) experienced at least one of the events of interest within 5 years of diagnosis. Relapse was twice as common for children with metastatic/advanced disease (22%) versus children with localised/limited cancers (11%). Overall 5-year EFS was 75.0% (95% confidence interval [CI]: 73.9%-76.0%), compared to 85.8% observed survival (95% CI: 85.0%-86.6%). Patients with other gliomas had the lowest EFS (35.4%, 95% CI: 27.8%-43.1%). EFS was significantly lower among children with acute myeloid leukaemia in outer regional/remote areas compared to major cities (adjusted hazard ratio [HR] = 1.90, 95% CI: 1.20-3.00). CONCLUSIONS: Reporting EFS at a population level provides further insight on a wider range of impacts apart from mortality alone, contributing towards efforts to improve the management and outcomes of childhood cancer.

What is measured matters: A scoping review of analysis methods used for qualitative patient reported experience measure data.

INTRODUCTION: Hospitals are increasingly turning to patients for valuable feedback regarding their care experience. A common method to collect this information is patient reported experience measures (PREMs) surveys. Health care workers report qualitative PREMs as more interesting, relevant, and informative than quantitative survey responses. However, a major barrier to utilising qualitative PREMs data to drive quality improvements is a lack of resources to analyse the data. This scoping review aimed to review the methods used to analyse qualitative PREMs survey data from routine hospital care. METHODS: We utilised the JBI scoping review methodology, and searched four databases for articles from 2013 to 2023 which analysed qualitative PREMs survey data from routine care in hospitals. Study characteristics were extracted, as well as the analysis method - specifically, whether the study used traditional manual analysis methods in which the researcher reads the text and categorise the data, or automated methods utilising computers and algorithms to read and categorise the data. RESULTS: From 960 unique articles, 123 went through full-text review and 54 were deemed eligible. 75.9 % used only manual content analysis methods to analyse the qualitative responses, 16.7 % of studies used a combination of manual and automated methods, and only 7.4 % used exclusively automated methods. Automated methods were used in 27.5 % of studies published 2019-2023, compared to 14.3 % of studies published 2013-2018. All bar one study using automated methods focused on investigating the validity of the automated methodology or used it to complement manual content analysis. CONCLUSION: The studies included in this review show a transition from traditional time-consuming manual analyses to computerised methods enabling analysis at a larger scale. As the volume of PREMs data collected grows, efficient and effective ways to analyse qualitative PREMs data at scale are required to enable health services to capture the patient voice and drive consumer-centred improvements in care.

Preterm Birth, Family Income, and Intergenerational Income Mobility.

Importance: Preterm birth (PTB) has been associated with lower income in adulthood, but associations with intergenerational income mobility and the role of family socioeconomic status (SES) as modifying factor are unclear. Objectives: To assess whether the association between PTB and income differs according to family SES at birth and to assess the association between PTB and intergenerational income mobility. Design, Setting, and Participants: This study comprised a matched cohort of live births in Canada between January 1, 1990, and December 31, 1996, with follow-up until December 31, 2018. Statistical analysis was performed between May 2023 and March 2024. Exposure: Preterm birth, defined as birth between 24 and 37 weeks' gestational age (with gestational age subcategories of 34-36, 32-33, 28-31, and 24-27 weeks) vs early and full term births (gestational age, 37-41 weeks). Main Outcomes and Measures: Associations between PTB and annual adulthood income in 2018 Canadian dollars were assessed overall (current exhange rate: $1 = CAD $1.37) and stratified by family income quintiles, using generalized estimating equation regression models. Associations between PTB and percentile rank change (ie, difference between the rank of individuals and their parents in the income distribution within their respective generations) and upward or downward mobility (based on income quintile) were assessed using linear and multinomial logistic regressions, respectively. Results: Of 1.6 million included births (51.1% boys and 48.9% girls), 6.9% infants were born preterm (5.4% born at 34-36 weeks, 0.7% born at 32-33 weeks, 0.5% born at 28-31 weeks, and 0.2% born at 24-27 weeks). After matching on baseline characteristics (eg, sex, province of birth, and parental demographics) and adjusting for age and period effects, PTB was associated with lower annual income (mean difference, CAD -$687 [95% CI, -$788 to -$586]; 3% lower per year), and the differences were greater among those belonging to families in the lowest family SES quintile (mean difference, CAD -$807 [95% CI, -$998 to -$617]; 5% lower per year). Preterm birth was also associated with lower upward mobility and higher downward mobility, particularly for those born earlier than 31 weeks' gestational age (24-27 weeks: mean difference in percentile rank change, -8.7 percentile points [95% CI, -10.5 to -6.8 percentile points]). Conclusions and Relevance: In this population-based matched cohort study, PTB was associated with lower adulthood income, lower upward social mobility, and higher downward mobility, with greater differences among those belonging to economically disadvantaged families. Interventions to optimize socioeconomic outcomes of preterm-born individuals would need to define target population considering SES.

Childhood cancer models of survivorship care: a scoping review of elements of care and reported outcomes.

PURPOSE: This study aimed to systematically map elements of care and respective outcomes described in the literature for different models of post-treatment care for survivors of childhood cancer. METHODS: MEDLINE, CINAHL, and Embase were searched with combinations of free text terms, synonyms, and MeSH terms using Boolean operators and are current to January 2024. We included studies that described post-treatment cancer survivorship models of care and reported patient or service level elements of care or outcomes, which we mapped to the Quality of Cancer Survivorship Care Framework domains. RESULTS: Thirty-eight studies with diverse designs were included representing 6101 childhood cancer survivors (or their parent/caregiver) and 14 healthcare professionals. A diverse range of models of care were reported, including paediatric oncologist-led long-term follow-up, multi-disciplinary survivorship clinics, shared-care, and primary care-led follow-up. Elements of care at the individual level most commonly included surveillance for cancer recurrence as well as assessment of physical and psychological effects. At the service level, satisfaction with care was frequently reported but few studies reported how treatment-related-late effects were managed. The evidence does not support one model of care over another. CONCLUSIONS: Gaps in evidence exist regarding distal outcomes such as costs, health care utilization, and mortality, as well as understanding outcomes of managing chronic disease and physical or psychological effects. The findings synthesized in this review provide a valuable reference point for future service planning and evaluation. IMPLICATIONS FOR CANCER SURVIVORS: Decades of research highlight the importance of survivorship care for childhood cancer survivors who are at risk of serious treatment-related late effects. This review emphasizes there is no single, 'one-size fits all' approach for delivering such care to this vulnerable population.

Corrigendum: Second malignant neoplasms within 5 years from first primary diagnosis in pediatric oncology patients in Canada: a population-based retrospective cohort study.

[This corrects the article DOI: 10.3389/fonc.2024.1376652.].

Second malignant neoplasms within 5 years from first primary diagnosis in pediatric oncology patients in Canada: a population-based retrospective cohort study.

Introduction: From the advancement of treatment of pediatric cancer diagnosis, the five-year survival rate has increased significantly. However, the adverse consequence of improved survival rate is the second malignant neoplasm. Although previous studies provided information on the incidence and risk of SMN in long term survivors of childhood cancer, there is still scarce information known for short term (< 5 years) prognosis. This study aims to assess the incidence, characteristics, management, and outcome of children who develop SMN malignancies within 5 years of diagnosis of their initial cancer. Method: This is a retrospective cohort study of early Second Malignant Neoplasms (SMN) in pediatric oncology patients. The Cancer in Young People - Canada (CYP-C) national pediatric cancer registry was used and reviewed pediatric patients diagnosed with their first cancer from 2000-2015. Results: A total of 20,272 pediatric patients with a diagnosis of a first malignancy were analyzed. Of them, 0.7% were diagnosed with a SMN within the first 5 years following their first cancer diagnosis. Development of a SMN impacted survival, shown by an inferior survival rate in the SMN cohort (79.1%) after three years compared to that of the non-SMN cohort (89.7%). Several possible risk factors have been identified in the study including the use of epipodophyllotoxins, exposure to radiation, and hematopoietic stem cell 169 transplant. Discussion: This is the first national study assessing the incidence, 170 characteristics, risk factors and outcome of early SMN in Canadian children 171 from age 0-15 from 2000-2015.

Physical function patient-reported outcomes among adolescent and young adult cancer survivors: A systematic review.

BACKGROUND: The physical challenges faced by adolescents and young adults (AYA) after a cancer diagnosis may be different from those experienced by paediatric and older adult cancer patients. Patient-reported outcome measures (PROMs) are valuable tools that can be useful in exploring the experiences of AYAs and identifying important issues, recurrent themes and areas to potentially improve quality of life. OBJECTIVE: We compared patient-reported physical function outcomes between AYAs diagnosed with cancer and non-cancer controls. METHOD: This paper builds on a scoping review published in early 2023 and focuses on PROMs related to physical function. RESULTS: This systematic review includes 16 studies that measured and reported on physical function PROMs in AYA cancer survivors compared with their cancer-free peers. Of these studies, 14 found that physical function in AYA survivors was significantly worse. This paper also includes a meta-analysis conducted on 5 studies using the EORTC-QLQ-C30 to measure physical function, which found that physical function score was an average of 7.03 (95% CI: -10.21, -3.86) points lower in the AYA cancer group, compared to their cancer free-peers, a difference that is clinically meaningful. CONCLUSIONS: The results overwhelmingly demonstrate that AYAs post a cancer diagnosis have worse health-related quality of life from a physical function perspective than their cancer-free peers, providing a compelling argument for the need to address this issue. All but one of the studies were cross-sectional, which highlights the need for further assessment of this group longitudinally throughout their cancer journey.

Psychiatric disorders in childhood cancer survivors: A retrospective matched cohort study of inpatient hospitalisations and community-based mental health services utilisation in Western Australia.

OBJECTIVE: We examined the impact of long-term mental health outcomes on healthcare services utilisation among childhood cancer survivors in Western Australia using linked hospitalisations and community-based mental healthcare records from 1987 to 2019. METHOD: The study cohort included 2977 childhood cancer survivors diagnosed with cancer at age < 18 years in Western Australia from 1982 to 2014 and a matched non-cancer control group of 24,994 individuals. Adjusted hazard ratios of recurrent events were estimated using the Andersen-Gill model. The cumulative burden of events over time was assessed using the method of mean cumulative count. The annual percentage change in events was estimated using the negative binomial regression model. RESULTS: The results showed higher community-based service contacts (rate/100 person-years: 30.2, 95% confidence interval = [29.7-30.7] vs 22.8, 95% confidence interval = [22.6-22.9]) and hospitalisations (rate/1000 person-years: 14.8, 95% confidence interval = [13.6-16.0] vs 12.7, 95% confidence interval = [12.3-13.1]) in childhood cancer survivors compared to the control group. Childhood cancer survivors had a significantly higher risk of any event (adjusted hazard ratio = 1.5, 95% confidence interval = [1.1-2.0]). The cumulative burden of events increased with time since diagnosis and across age groups. The annual percentage change for hospitalisations and service contacts significantly increased over time (p < 0.05). Substance abuse was the leading cause of hospitalisations, while mood/affective and anxiety disorders were common causes of service contacts. Risk factors associated with increased service events included cancer diagnosis at age < 5 years, leukaemia diagnosis, high socioeconomic deprivation, and an attained age of < 18 years. CONCLUSIONS: The elevated utilisation of healthcare services observed among childhood cancer survivors emphasises the need for periodic assessment of psychiatric disorders, particularly in high-risk survivors, to facilitate early management and optimise healthcare resources.

Development and validation of thromboembolism diagnostic algorithms in children with cancer from real-world data.

OBJECTIVE: To evaluate the accuracy of diagnostic algorithms developed using the International Classification of Diseases (ICD-9-CM and ICD-10-CA) diagnostic codes and physician billing codes for thromboembolism (TE) from health administrative data compared to chart review diagnoses of TE in children with cancer. METHODS: Using data linkage between the Pediatric Oncology Group of Ontario Network Information System (Ontario pediatric cancer registry) and various administrative data housed at ICES, eight algorithms were developed including a single reference to one of the billing codes, multiple references with varying time intervals, and combinations of various billing codes during primary cancer therapy for the whole cohort and, for early (<04/2002) and later (≥04/2002, solely ICD-10 codes) periods. Reference standard was chart review data from prior studies (from 1990 to 2016) among children (≤19 years) with cancer and radiologically confirmed TE. RESULTS: Records of 2056 patients diagnosed with cancer at two participating sites during study period were reviewed; 112 had radiologically confirmed TE. The algorithm with addition of anticoagulation utilization codes was the best performing algorithm (sensitivity = 0.76;specificity = 0.85). With use of ICD-10 only codes, sensitivity of the same algorithm improved to 0.84 with specificity of 0.80. CONCLUSION: This study provides a valid approach for ascertaining pediatric TE using real-world data. IMPACT: Research in pediatric thrombosis, especially cancer-related thrombosis, is limited mainly due to small-sized studies. Real-world data provide ready access to large and diverse populations. However, there are no validated algorithms for identifying thrombosis in real-world data for children. An algorithm based on combination of thrombosis and anticoagulation utilization codes had 76% sensitivity and 85% specificity to identify diagnosis of thrombosis in children in administrative data. This study provides a valid approach for ascertaining pediatric thrombosis using real-world data and offers a good avenue to advance pediatric thrombosis research.

Patient Reported Fatigue Among Adolescent and Young Adult Cancer Patients Compared to Non-Cancer Patients: A Systematic Review and Meta-Analysis.

Adolescent and young adult (AYA) cancer patients and survivors are a growing population due to more frequent diagnoses and improved survival. Fatigue is a common symptom experienced by cancer patients and it is often missed by health care professionals. Patient reported outcome measures (PROMs) can assist in evaluating patient reported fatigue. This systematic review aims to determine if AYA cancer patients report more fatigue than AYAs who have not been diagnosed with cancer. We used a subset of articles from a larger review that searched PubMed, EMBASE, CINAHL, and PsycINFO to determine which PROMs and domains are currently being used to evaluate AYA cancer. This study identified 175 articles related to PROMs in the AYA cancer population. Articles with PROMs reporting on fatigue/vitality were used in this review. From the original 175 articles, we identified 8 fatigue/vitality articles for this review. All eight articles found an increase in fatigue/decrease in vitality in the AYA cancer population compared to healthy controls. A meta-analysis was performed on four articles that used the same PROM tool (EORTC QLQ-C30). This found a statistically significant and clinically meaningful increase in mean fatigue of 12.5 95% confidence interval: 3.3-21.8 points (scale 0-100, higher number indicates more fatigue) in the AYA cancer group compared to healthy noncancer controls. Fatigue in the AYA cancer population is a significant issue, it is often undetected and underreported, and early interventions are needed to prevent the negative subsequent sequelae.

Machine learning clinical prediction models for acute kidney injury: the impact of baseline creatinine on prediction efficacy.

BACKGROUND: There are many Machine Learning (ML) models which predict acute kidney injury (AKI) for hospitalised patients. While a primary goal of these models is to support clinical decision-making, the adoption of inconsistent methods of estimating baseline serum creatinine (sCr) may result in a poor understanding of these models' effectiveness in clinical practice. Until now, the performance of such models with different baselines has not been compared on a single dataset. Additionally, AKI prediction models are known to have a high rate of false positive (FP) events regardless of baseline methods. This warrants further exploration of FP events to provide insight into potential underlying reasons. OBJECTIVE: The first aim of this study was to assess the variance in performance of ML models using three methods of baseline sCr on a retrospective dataset. The second aim was to conduct an error analysis to gain insight into the underlying factors contributing to FP events. MATERIALS AND METHODS: The Intensive Care Unit (ICU) patients of the Medical Information Mart for Intensive Care (MIMIC)-IV dataset was used with the KDIGO (Kidney Disease Improving Global Outcome) definition to identify AKI episodes. Three different methods of estimating baseline sCr were defined as (1) the minimum sCr, (2) the Modification of Diet in Renal Disease (MDRD) equation and the minimum sCr and (3) the MDRD equation and the mean of preadmission sCr. For the first aim of this study, a suite of ML models was developed for each baseline and the performance of the models was assessed. An analysis of variance was performed to assess the significant difference between eXtreme Gradient Boosting (XGB) models across all baselines. To address the second aim, Explainable AI (XAI) methods were used to analyse the XGB errors with Baseline 3. RESULTS: Regarding the first aim, we observed variances in discriminative metrics and calibration errors of ML models when different baseline methods were adopted. Using Baseline 1 resulted in a 14% reduction in the f1 score for both Baseline 2 and Baseline 3. There was no significant difference observed in the results between Baseline 2 and Baseline 3. For the second aim, the FP cohort was analysed using the XAI methods which led to relabelling data with the mean of sCr in 180 to 0 days pre-ICU as the preferred sCr baseline method. The XGB model using this relabelled data achieved an AUC of 0.85, recall of 0.63, precision of 0.54 and f1 score of 0.58. The cohort size was 31,586 admissions, of which 5,473 (17.32%) had AKI. CONCLUSION: In the absence of a widely accepted method of baseline sCr, AKI prediction studies need to consider the impact of different baseline methods on the effectiveness of ML models and their potential implications in real-world implementations. The utilisation of XAI methods can be effective in providing insight into the occurrence of prediction errors. This can potentially augment the success rate of ML implementation in routine care.

Medical imaging utilization and associated radiation exposure in children with down syndrome.

OBJECTIVE: To evaluate the frequency of medical imaging or estimated associated radiation exposure in children with Down syndrome. METHODS: This retrospective cohort study included 4,348,226 children enrolled in six U.S. integrated healthcare systems from 1996-2016, 3,095 of whom were diagnosed with Down syndrome. We calculated imaging rates per 100 person years and associated red bone marrow dose (mGy). Relative rates (RR) of imaging in children with versus without Down syndrome were estimated using overdispersed Poisson regression. RESULTS: Compared to other children, children with Down syndrome received imaging using ionizing radiation at 9.5 times (95% confidence interval[CI] = 8.2-10.9) the rate when age <1 year and 2.3 times (95% CI = 2.0-2.5) between ages 1-18 years. Imaging rates by modality in children <1 year with Down syndrome compared with other children were: computed tomography (6.6 vs. 2.0, RR = 3.1[95%CI = 1.8-5.1]), fluoroscopy (37.1 vs. 3.1, RR 11.9[95%CI 9.5-14.8]), angiography (7.6 vs. 0.2, RR = 35.8[95%CI = 20.6-62.2]), nuclear medicine (6.0 vs. 0.6, RR = 8.2[95% CI = 5.3-12.7]), radiography (419.7 vs. 36.9, RR = 11.3[95%CI = 10.0-12.9], magnetic resonance imaging(7.3 vs. 1.5, RR = 4.2[95% CI = 3.1-5.8]), and ultrasound (231.2 vs. 16.4, RR = 12.6[95% CI = 9.9-15.9]). Mean cumulative red bone marrow dose from imaging over a mean of 4.2 years was 2-fold higher in children with Down syndrome compared with other children (4.7 vs. 1.9mGy). CONCLUSIONS: Children with Down syndrome experienced more medical imaging and higher radiation exposure than other children, especially at young ages when they are more vulnerable to radiation. Clinicians should consider incorporating strategic management decisions when imaging this high-risk population.

Mental health patient-reported outcomes among adolescents and young adult cancer survivors: A systematic review.

BACKGROUND: Adolescent and young adult (AYA) cancer patients and survivors face significant mental health challenges throughout their cancer journey that are different to those faced by children and older adults. Patient-reported outcome measures (PROMs) can be used to explore the experiences of AYAs, and to identify important issues and areas for potential improvement in quality of life. OBJECTIVE: We aimed to compare patient reported mental health outcomes between AYAs diagnosed with cancer and non-cancer controls. METHOD: We built on a larger systematic review of AYA cancer PROMs which searched PubMed, EMBASE, CINAHL and PsychINFO. This review identified 175 articles, which were filtered to those reporting on mental health and including a non-cancer control group. RESULTS: We identified 12 eligible studies. Seven studies (58%) found those diagnosed with cancer reported poorer mental health than the non-cancer controls. The remaining five (42%) studies found no significant difference in severity or prevalence of mental health between the AYA cancer cohort and the healthy control group. Most (83%) were cross-sectional studies, highlighting the need for further longitudinal assessment of this group throughout their journey. CONCLUSIONS: The mental health outcomes feature conflicting results and illustrate the need for larger studies to characterise discrepancies.

Rates and predictors of visits to primary care physicians during and after treatment of childhood acute lymphoblastic leukemia: A population-based cohort study.

INTRODUCTION: Patient re-engagement with primary care physicians (PCPs) after cancer treatment is essential to facilitate survivorship care and to meet non-oncology primary care needs. We identified rates and predictors of PCP visits both during and after treatment among a population-based cohort of children with acute lymphoblastic leukemia (ALL). METHODS: Children of age less than 18 years at ALL diagnosis in Ontario between 2002 and 2012 were linked to administrative data and matched to controls without cancer. PCPs at diagnosis were identified and PCP visit rates during treatment compared between patients and controls. Post-treatment PCP visit rates were also calculated. Predictors included demographic-, disease-, and PCP-related variables. RESULTS: A total of 743/793 (94%) patients and 3112/3947 (79%) controls had a PCP at diagnosis. Almost half of patients (361/743, 45%) did not visit their PCP during treatment. Visit rate during treatment was 0.64 per person per year (PPPY) versus 1.4 PPPY among controls (adjusted rate ratio [aRR] 0.47, 95th confidence interval [95CI]: 0.40-0.54; p < .0001). No disease- or PCP-related factors were associated with visit rates. Total 711 patients completed frontline therapy; 287 (40.4%) did not have a PCP visit after treatment. Nonetheless, survivors overall visited PCPs post treatment more often than controls (aRR 1.4, 95CI: 1.2-1.6; p < .0001). Survivors who saw their PCP during treatment had post-treatment visit rates twice that of other survivors (aRR 2.0, 95CI: 1.6-2.5; p < .0001). CONCLUSIONS: Only a portion of children with ALL see their PCPs during treatment and return to PCP care following treatment completion. Post-treatment engagement with PCPs may be improved by PCP involvement during ALL treatment.

The impact of transition to a digital hospital on medication errors (TIME study).

Digital transformation in healthcare improves the safety of health systems. Within our health service, a new digital hospital has been established and two wards from a neighbouring paper-based hospital transitioned into the new digital hospital. This created an opportunity to evaluate the impact of complete digital transformation on medication safety. Here we discuss the impact of transition from a paper-based to digital hospital on voluntarily reported medication incidents and prescribing errors. This study utilises an interrupted time-series design and takes place across two wards as they transition from a paper to a digital hospital. Two data sources are used to assess impacts on medication incidents and prescribing errors: (1) voluntarily reported medication incidents and 2) a chart audit of medications prescribed on the study wards. The chart audit collects data on procedural, dosing and therapeutic prescribing errors. There are 588 errors extracted from incident reporting software during the study period. The average monthly number of errors reduces from 12.5 pre- to 7.5 post-transition (p < 0.001). In the chart audit, 5072 medication orders are reviewed pre-transition and 3699 reviewed post-transition. The rates of orders with one or more error reduces significantly after transition (52.8% pre- vs. 15.7% post-, p < 0.001). There are significant reductions in procedural (32.1% pre- vs. 1.3% post-, p < 0.001), and dosing errors (32.3% pre- vs. 14% post-, p < 0.001), but not therapeutic errors (0.6% pre- vs. 0.7% post-, p = 0.478). Transition to a digital hospital is associated with reductions in voluntarily reported medication incidents and prescribing errors.

Association of Country Income Level With the Characteristics and Outcomes of Critically Ill Patients Hospitalized With Acute Kidney Injury and COVID-19.

Introduction: Acute kidney injury (AKI) has been identified as one of the most common and significant problems in hospitalized patients with COVID-19. However, studies examining the relationship between COVID-19 and AKI in low- and low-middle income countries (LLMIC) are lacking. Given that AKI is known to carry a higher mortality rate in these countries, it is important to understand differences in this population. Methods: This prospective, observational study examines the AKI incidence and characteristics of 32,210 patients with COVID-19 from 49 countries across all income levels who were admitted to an intensive care unit during their hospital stay. Results: Among patients with COVID-19 admitted to the intensive care unit, AKI incidence was highest in patients in LLMIC, followed by patients in upper-middle income countries (UMIC) and high-income countries (HIC) (53%, 38%, and 30%, respectively), whereas dialysis rates were lowest among patients with AKI from LLMIC and highest among those from HIC (27% vs. 45%). Patients with AKI in LLMIC had the largest proportion of community-acquired AKI (CA-AKI) and highest rate of in-hospital death (79% vs. 54% in HIC and 66% in UMIC). The association between AKI, being from LLMIC and in-hospital death persisted even after adjusting for disease severity. Conclusions: AKI is a particularly devastating complication of COVID-19 among patients from poorer nations where the gaps in accessibility and quality of healthcare delivery have a major impact on patient outcomes.