Machine learning forecasting for COVID-19 pandemic-associated effects on paediatric respiratory infections.

OBJECTIVE: The COVID-19 pandemic and subsequent government restrictions have had a major impact on healthcare services and disease transmission, particularly those associated with acute respiratory infection. This study examined non-identifiable routine electronic patient record data from a specialist children's hospital in England, UK, examining the effect of pandemic mitigation measures on seasonal respiratory infection rates compared with forecasts based on open-source, transferable machine learning models. METHODS: We performed a retrospective longitudinal study of respiratory disorder diagnoses between January 2010 and February 2022. All diagnoses were extracted from routine healthcare activity data and diagnosis rates were calculated for several diagnosis groups. To study changes in diagnoses, seasonal forecast models were fit to prerestriction period data and extrapolated. RESULTS: Based on 144 704 diagnoses from 31 002 patients, all but two diagnosis groups saw a marked reduction in diagnosis rates during restrictions. We observed 91%, 89%, 72% and 63% reductions in peak diagnoses of 'respiratory syncytial virus', 'influenza', 'acute nasopharyngitis' and 'acute bronchiolitis', respectively. The machine learning predictive model calculated that total diagnoses were reduced by up to 73% (z-score: -26) versus expected during restrictions and increased by up to 27% (z-score: 8) postrestrictions. CONCLUSIONS: We demonstrate the association between COVID-19 related restrictions and significant reductions in paediatric seasonal respiratory infections. Moreover, while many infection rates have returned to expected levels postrestrictions, others remain supressed or followed atypical winter trends. This study further demonstrates the applicability and efficacy of routine electronic record data and cross-domain time-series forecasting to model, monitor, analyse and address clinically important issues.

The impact of the COVID-19 pandemic on antimicrobial prescribing at a specialist paediatric hospital: an observational study.

BACKGROUND: The COVID-19 pandemic has severely impacted healthcare delivery and there are growing concerns that the pandemic will accelerate antimicrobial resistance. OBJECTIVES: To evaluate the impact of the COVID-19 pandemic on antibiotic prescribing in a tertiary paediatric hospital in London, UK. METHODS: Data on patient characteristics and antimicrobial administration for inpatients treated between 29 April 2019 and Sunday 28 March 2021 were extracted from the electronic health record (EHR). Interrupted time series analysis was used to evaluate antibiotic days of therapy (DOT) and the proportion of prescribed antibiotics from the WHO 'Access' class. RESULTS: A total of 23 292 inpatient admissions were included. Prior to the pandemic there were an average 262 admissions per week compared with 212 during the pandemic period. Patient demographics were similar in the two periods but there was a shift in the specialities that patients had been admitted to. During the pandemic, there was a crude increase in antibiotic DOTs, from 801 weekly DOT before the pandemic to 846. The proportion of Access antibiotics decreased from 44% to 42%. However, after controlling for changes in patient characteristics, there was no evidence for the pandemic having an impact on antibiotic prescribing. CONCLUSIONS: The patient population in a specialist children's hospital was affected by the COVID-19 pandemic, but after adjusting for these changes there was no evidence that antibiotic prescribing was significantly affected by the pandemic. This highlights both the value of routine, high-quality EHR data and importance of appropriate statistical methods that can adjust for underlying changes to populations when evaluating impacts of the pandemic on healthcare.

An exploration of 11 years of seasonality in diagnoses at GOSH

BackgroundWith the extensive impact of the COVID-19 pandemic and subsequent government interventions on the development, diagnosis and treatment of illnesses, building an understanding of ‘typical’ diagnosis trends at GOSH is critical for predicting future demands and potential clinical challenges. Seasonality analysis is an effective method with which one can explore, model and predict the occurrence of events over time when – as with many common diagnoses at GOSH – they generally exhibit a periodic trend over the year.MethodsTo investigate diagnosis seasonality at GOSH, we have extracted all diagnoses recorded in the Legacy and Epic systems, since the year 2010. We have developed an analytics pipeline that uses these data to compute historical rates for any given diagnosis, or group of diagnoses. Based on these diagnosis rates, our pipeline applies a widely used regressive, multiplicative, seasonal decomposition model with integrated model evaluation.ResultsFor the analysis, a total of 3,480,887 diagnosis events were considered across 29,529 patients between receiving a diagnosis between 1stJanuary 2010 and 30th September 2021. This exploration presents data on many of the common diagnoses at GOSH that exhibit a clear seasonal trend in combination with a statistically significant deviation from that trend since March 2020, likely due to the pandemic. In addition, we illustrate how the available data and model allow us to predict the diagnostic shortfall during the same period.

Impact of the COVID-19 pandemic on radiology appointments in a tertiary children's hospital: a retrospective study.

In this retrospective observational study, we evaluated the impact of the COVID-19 pandemic in London on paediatric radiology activity, as a surrogate of overall hospital activity. We showed a large reduction in overall outpatient imaging activity: 49 250 records occurred in the 371 days post COVID-19 period compared with an expected 67 806 records pre COVID-19 period, representing 18 556 'missed' records. Governmental restrictions were associated with reductions in activity, with the largest reduction in activity during tiers 3 and 4 restrictions. Rescheduling such missed outpatients' appointments represents considerable resource planning and the associated clinical impact on paediatric healthcare remains to be determined.

International Analysis of Electronic Health Records of Children and Youth Hospitalized With COVID-19 Infection in 6 Countries.

Importance: Additional sources of pediatric epidemiological and clinical data are needed to efficiently study COVID-19 in children and youth and inform infection prevention and clinical treatment of pediatric patients. Objective: To describe international hospitalization trends and key epidemiological and clinical features of children and youth with COVID-19. Design, Setting, and Participants: This retrospective cohort study included pediatric patients hospitalized between February 2 and October 10, 2020. Patient-level electronic health record (EHR) data were collected across 27 hospitals in France, Germany, Spain, Singapore, the UK, and the US. Patients younger than 21 years who tested positive for COVID-19 and were hospitalized at an institution participating in the Consortium for Clinical Characterization of COVID-19 by EHR were included in the study. Main Outcomes and Measures: Patient characteristics, clinical features, and medication use. Results: There were 347 males (52%; 95% CI, 48.5-55.3) and 324 females (48%; 95% CI, 44.4-51.3) in this study's cohort. There was a bimodal age distribution, with the greatest proportion of patients in the 0- to 2-year (199 patients [30%]) and 12- to 17-year (170 patients [25%]) age range. Trends in hospitalizations for 671 children and youth found discrete surges with variable timing across 6 countries. Data from this cohort mirrored national-level pediatric hospitalization trends for most countries with available data, with peaks in hospitalizations during the initial spring surge occurring within 23 days in the national-level and 4CE data. A total of 27 364 laboratory values for 16 laboratory tests were analyzed, with mean values indicating elevations in markers of inflammation (C-reactive protein, 83 mg/L; 95% CI, 53-112 mg/L; ferritin, 417 ng/mL; 95% CI, 228-607 ng/mL; and procalcitonin, 1.45 ng/mL; 95% CI, 0.13-2.77 ng/mL). Abnormalities in coagulation were also evident (D-dimer, 0.78 ug/mL; 95% CI, 0.35-1.21 ug/mL; and fibrinogen, 477 mg/dL; 95% CI, 385-569 mg/dL). Cardiac troponin, when checked (n = 59), was elevated (0.032 ng/mL; 95% CI, 0.000-0.080 ng/mL). Common complications included cardiac arrhythmias (15.0%; 95% CI, 8.1%-21.7%), viral pneumonia (13.3%; 95% CI, 6.5%-20.1%), and respiratory failure (10.5%; 95% CI, 5.8%-15.3%). Few children were treated with COVID-19-directed medications. Conclusions and Relevance: This study of EHRs of children and youth hospitalized for COVID-19 in 6 countries demonstrated variability in hospitalization trends across countries and identified common complications and laboratory abnormalities in children and youth with COVID-19 infection. Large-scale informatics-based approaches to integrate and analyze data across health care systems complement methods of disease surveillance and advance understanding of epidemiological and clinical features associated with COVID-19 in children and youth.

Experience of SARS-CoV-2 infection in two kidney transplant recipients living with HIV-1 infection.

There is still no consensus on the optimal management of COVID-19 within the general population due to the emerging evidence base. High-risk groups, including kidney transplant recipients living with HIV present unique additional challenges. Here we discuss two kidney transplant recipients living with HIV with SARS-CoV-2 infection and their clinical course, and review the existing literature for this subset of challenging patients.