The evolving SARS-CoV-2 epidemic in Africa: Insights from rapidly expanding genomic surveillance

INTRODUCTION Investment in Africa over the past year with regard to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sequencing has led to a massive increase in the number of sequences, which, to date, exceeds 100,000 sequences generated to track the pandemic on the continent. These sequences have profoundly affected how public health officials in Africa have navigated the COVID-19 pandemic. RATIONALE We demonstrate how the first 100,000 SARS-CoV-2 sequences from Africa have helped monitor the epidemic on the continent, how genomic surveillance expanded over the course of the pandemic, and how we adapted our sequencing methods to deal with an evolving virus. Finally, we also examine how viral lineages have spread across the continent in a phylogeographic framework to gain insights into the underlying temporal and spatial transmission dynamics for several variants of concern (VOCs). RESULTS Our results indicate that the number of countries in Africa that can sequence the virus within their own borders is growing and that this is coupled with a shorter turnaround time from the time of sampling to sequence submission. Ongoing evolution necessitated the continual updating of primer sets, and, as a result, eight primer sets were designed in tandem with viral evolution and used to ensure effective sequencing of the virus. The pandemic unfolded through multiple waves of infection that were each driven by distinct genetic lineages, with B.1-like ancestral strains associated with the first pandemic wave of infections in 2020. Successive waves on the continent were fueled by different VOCs, with Alpha and Beta cocirculating in distinct spatial patterns during the second wave and Delta and Omicron affecting the whole continent during the third and fourth waves, respectively. Phylogeographic reconstruction points toward distinct differences in viral importation and exportation patterns associated with the Alpha, Beta, Delta, and Omicron variants and subvariants, when considering both Africa versus the rest of the world and viral dissemination within the continent. Our epidemiological and phylogenetic inferences therefore underscore the heterogeneous nature of the pandemic on the continent and highlight key insights and challenges, for instance, recognizing the limitations of low testing proportions. We also highlight the early warning capacity that genomic surveillance in Africa has had for the rest of the world with the detection of new lineages and variants, the most recent being the characterization of various Omicron subvariants. CONCLUSION Sustained investment for diagnostics and genomic surveillance in Africa is needed as the virus continues to evolve. This is important not only to help combat SARS-CoV-2 on the continent but also because it can be used as a platform to help address the many emerging and reemerging infectious disease threats in Africa. In particular, capacity building for local sequencing within countries or within the continent should be prioritized because this is generally associated with shorter turnaround times, providing the most benefit to local public health authorities tasked with pandemic response and mitigation and allowing for the fastest reaction to localized outbreaks. These investments are crucial for pandemic preparedness and response and will serve the health of the continent well into the 21st century

“Difficult to Sedate”: Successful Implementation of a Benzodiazepine-Sparing Analgosedation-Protocol in Mechanically Ventilated Children

We sought to evaluate the success rate of a benzodiazepine-sparing analgosedation protocol (ASP) in mechanically ventilated children and determine the effect of compliance with ASP on in-hospital outcome measures. In this single center study from a quaternary pediatric intensive care unit, our objective was to evaluate the ASP protocol, which included opiate and dexmedetomidine infusions and was used as first-line sedation for all intubated patients. In this study we included 424 patients. Sixty-nine percent (n = 293) were successfully sedated with the ASP. Thirty-one percent (n = 131) deviated from the ASP and received benzodiazepine infusions. Children sedated with the ASP had decrease in opiate withdrawal (OR 0.16, 0.08–0.32), decreased duration of mechanical ventilation (adjusted mean duration 1.81 vs. 3.39 days, p = 0.018), and decreased PICU length of stay (adjusted mean 3.15 vs. 4.7 days, p = 0.011), when compared to the cohort of children who received continuous benzodiazepine infusions. Using ASP, we report that 69% of mechanically ventilated children were successfully managed with no requirement for continuous benzodiazepine infusions. The 69% who were successfully managed with ASP included infants, severely ill patients, and children with chromosomal disorders and developmental disabilities. Use of ASP was associated with decreased need for methadone use, decreased duration of mechanical ventilation, and decreased ICU and hospital length of stay

Use of an Electronic Medical Record (EMR) to Identify Glycemic Intensification Strategies in Type 2 Diabetes.

Current treatment guidelines for type 2 diabetes (T2D) recommend individualized intensification of therapy for glycated hemoglobin (A1C) ≥ 7% in most patients. The purpose of this investigation was to explore the ability of an electronic medical record (EMR) to identify glycemic intensification strategies among T2D patients receiving pharmacologic therapy. Patient records between 2005 and 2011 with documentation of A1C and active prescriptions for any diabetes medications were queried to identify potential candidates for intensification based on A1C ≥ 7% while on 1-2 oral diabetes medications (ODM). Patients with follow-up A1C values within 1 year of index A1C were grouped according to intensification with insulin, GLP-1 receptor agonists (GLP-1RA), a new class of ODM, or no intensification. Changes in A1C and continuation of intensification therapy were determined. A total of 4921 patients meeting inclusion criteria were intensified with insulin (n = 416), GLP-1RA (n = 68), ODM (n = 1408), or no additional therapy (n = 3029). Patients receiving insulin had higher baseline (9.3 ± 2.0 vs 8.3 ± 1.2 vs 8.3 ± 1.3 vs 7.6 ± 1.0%, P \u3c .0001) and follow-up A1C (8.1 ± 1.6 vs 7.5 ± 1.2 vs 7.6 ± 1.3 vs 7.2 ± 1.1%, P \u3c .0001) despite experiencing larger absolute A1C reductions (-1.2 ± 2.1 vs -0.8 ± 1.4 vs -0.7 ± 1.4 vs -0.3 ± 1.1%, P \u3c .0001). Patients receiving GLP-1RA were more obese at baseline (BMI: 33.6 ± 7.1 vs 37.7 ± 6.1 vs 33.7 ± 6.8 vs 32.9 ± 7.1 kg/m(2), P \u3c .0001) and follow-up (BMI: 33.9 ± 7.3 vs 36.6 ± 6.1 vs 33.8 ± 7.0 vs 32.4 ± 7.0 kg/m(2), P \u3c .0001) despite experiencing more absolute weight reduction. Insulin was the most and GLP-1RA the least likely therapy to be continued. An EMR allows identification of prescribing practices and compliance with T2D treatment guidelines. Patients receiving intensification of glycemic medications had baseline A1C \u3e8% suggesting that treatment recommendations are not being followed

Use of an electronic health record to identify prevalent and incident cardiovascular disease in type 2 diabetes according to treatment strategy.

BACKGROUND: The increasing use of electronic health records (EHRs) in clinical practice offers the potential to investigate cardiovascular outcomes over time in patients with type 2 diabetes (T2D). OBJECTIVE: To develop a methodology for identifying prevalent and incident cardiovascular disease (CVD) in patients with T2D who are candidates for therapeutic intensification of glucose-lowering therapy. METHODS: Patients with glycated hemoglobin (HbA1c) ≥7% (53 mmol/mol) while receiving 1-2 oral diabetes medications (ODMs) were identified from an EHR (2005-2011) and grouped according to intensification with insulin (INS) (n=372), a different class of ODM (n=833), a glucagon-like peptide receptor 1 agonist (GLP-1RA) (n=59), or no additional therapy (NAT) (n=2017). Baseline prevalence of CVD was defined by documented International Classification of Diseases Ninth Edition (ICD-9) codes for coronary artery disease, cerebrovascular disease, or other CVD with first HbA1c ≥7% (53 mmol/mol). Incident CVD was defined as a new ICD-9 code different from existing codes over 4 years of follow-up. ICD-9 codes were validated by a chart review in a subset of patients. RESULTS: Sensitivity of ICD-9 codes for CVD ranged from 0.83 to 0.89 and specificity from 0.90 to 0.96. Baseline prevalent (INS vs ODM vs GLP-1RA vs NAT: 65% vs 39% vs 54% vs 59%, p CONCLUSIONS: An EHR can be an effective method for identifying prevalent and incident CVD in patients with T2D

Delirium in critically ill children: an international point prevalence study∗

Objectives: To determine prevalence of delirium in critically ill children and explore associated risk factors. Design: Multi-institutional point prevalence study. Setting: Twenty-five pediatric critical care units in the United States, the Netherlands, New Zealand, Australia, and Saudi Arabia. Patients: All children admitted to the pediatric critical care units on designated study days (n = 994). Intervention: Children were screened for delirium using the Cornell Assessment of Pediatric Delirium by the bedside nurse. Demographic and treatment-related variables were collected. Measurements and Main Results: Primary study outcome measure was prevalence of delirium. In 159 children, a final determination of mental status could not be ascertained. Of the 835 remaining subjects, 25% screened positive for delirium, 13% were classified as comatose, and 62% were delirium-free and coma-free. Delirium prevalence rates varied significantly with reason for ICU admission, with highest delirium rates found in children admitted with an infectious or inflammatory disorder. For children who were in the PICU for 6 or more days, delirium prevalence rate was 38%. In a multivariate model, risk factors independently associated with development of delirium included age less than 2 years, mechanical ventilation, benzodiazepines, narcotics, use of physical restraints, and exposure to vasopressors and antiepileptics. Conclusions: Delirium is a prevalent complication of critical illness in children, with identifiable risk factors. Further multi-institutional, longitudinal studies are required to investigate effect of delirium on long-term outcomes and possible preventive and treatment measures. Universal delirium screening is practical and can be implemented in pediatric critical care units

The Burden of Critical Illness in Hospitalized Children in Low- and Middle-Income Countries: Protocol for a Systematic Review and Meta-Analysis

BackgroundThe majority of childhood deaths occur in low- and middle-income countries (LMICs). Many of these deaths are avoidable with basic critical care interventions. Quantifying the burden of pediatric critical illness in LMICs is essential for targeting interventions to reduce childhood mortality.ObjectiveTo determine the burden of hospitalization and mortality associated with acute pediatric critical illness in LMICs through a systematic review and meta-analysis of the literature.Data sources and search strategyWe will identify eligible studies by searching MEDLINE, EMBASE, CINAHL, and LILACS using MeSH terms and keywords. Results will be limited to infants or children (ages >28 days to 12 years) hospitalized in LMICs and publications in English, Spanish, or French. Publications with non-original data (e.g., comments, editorials, letters, notes, conference materials) will be excluded.Study selectionWe will include observational studies published since January 1, 2005, that meet all eligibility criteria and for which a full text can be located.Data extractionData extraction will include information related to study characteristics, hospital characteristics, underlying population characteristics, patient population characteristics, and outcomes.Data synthesisWe will extract and report data on study, hospital, and patient characteristics; outcomes; and risk of bias. We will report the causes of admission and mortality by region, country income level, and age. We will report or calculate the case fatality rate (CFR) for each diagnosis when data allow.ConclusionsBy understanding the burden of pediatric critical illness in LMICs, we can advocate for resources and inform resource allocation and investment decisions to improve the management and outcomes of children with acute pediatric critical illness in LMICs

The Burden of Critical Illness in Hospitalized Children in Low- and Middle-Income Countries: Protocol for a Systematic Review and Meta-Analysis.

Background The majority of childhood deaths occur in low- and middle-income countries (LMICs). Many of these deaths are avoidable with basic critical care interventions. Quantifying the burden of pediatric critical illness in LMICs is essential for targeting interventions to reduce childhood mortality. Objective To determine the burden of hospitalization and mortality associated with acute pediatric critical illness in LMICs through a systematic review and meta-analysis of the literature. Data Sources and Search Strategy We will identify eligible studies by searching MEDLINE, EMBASE, CINAHL, and LILACS using MeSH terms and keywords. Results will be limited to infants or children (ages >28 days to 12 years) hospitalized in LMICs and publications in English, Spanish, or French. Publications with non-original data (e.g., comments, editorials, letters, notes, conference materials) will be excluded. Study Selection We will include observational studies published since January 1, 2005, that meet all eligibility criteria and for which a full text can be located. Data Extraction Data extraction will include information related to study characteristics, hospital characteristics, underlying population characteristics, patient population characteristics, and outcomes. Data Synthesis We will extract and report data on study, hospital, and patient characteristics; outcomes; and risk of bias. We will report the causes of admission and mortality by region, country income level, and age. We will report or calculate the case fatality rate (CFR) for each diagnosis when data allow. Conclusions By understanding the burden of pediatric critical illness in LMICs, we can advocate for resources and inform resource allocation and investment decisions to improve the management and outcomes of children with acute pediatric critical illness in LMICs