Summary Background Since December, 2019, Wuhan, China, has experienced an outbreak of coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Epidemiological and clinical characteristics of patients with COVID-19 have been reported but risk factors for mortality and a detailed clinical course of illness, including viral shedding, have not been well described. Methods In this retrospective, multicentre cohort study, we included all adult inpatients (≥18 years old) with laboratory-confirmed COVID-19 from Jinyintan Hospital and Wuhan Pulmonary Hospital (Wuhan, China) who had been discharged or had died by Jan 31, 2020. Demographic, clinical, treatment, and laboratory data, including serial samples for viral RNA detection, were extracted from electronic medical records and compared between survivors and non-survivors. We used univariable and multivariable logistic regression methods to explore the risk factors associated with in-hospital death. Findings 191 patients (135 from Jinyintan Hospital and 56 from Wuhan Pulmonary Hospital) were included in this study, of whom 137 were discharged and 54 died in hospital. 91 (48%) patients had a comorbidity, with hypertension being the most common (58 [30%] patients), followed by diabetes (36 [19%] patients) and coronary heart disease (15 [8%] patients). Multivariable regression showed increasing odds of in-hospital death associated with older age (odds ratio 1·10, 95% CI 1·03–1·17, per year increase; p=0·0043), higher Sequential Organ Failure Assessment (SOFA) score (5·65, 2·61–12·23; p Interpretation The potential risk factors of older age, high SOFA score, and d-dimer greater than 1 μg/mL could help clinicians to identify patients with poor prognosis at an early stage. Prolonged viral shedding provides the rationale for a strategy of isolation of infected patients and optimal antiviral interventions in the future. Funding Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences; National Science Grant for Distinguished Young Scholars; National Key Research and Development Program of China; The Beijing Science and Technology Project; and Major Projects of National Science and Technology on New Drug Creation and Development.

Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study

BACKGROUND
Although the vast majority of children with acute infections are managed at home, this is one of the most common problems encountered in children attending emergency departments (EDs) and primary care. Distinguishing children with serious infection from those with minor or self-limiting infection is difficult. This can result in misdiagnosis of children with serious infections, which results in a poorer health outcome, or a tendency to refer or admit children as a precaution; thus, inappropriately utilising secondary-care resources.


OBJECTIVES
We systematically identified clinical features and laboratory tests which identify serious infection in children attending the ED and primary care. We also identified clinical prediction rules and validated those using existing data sets.


DATA SOURCES
We searched MEDLINE, Medion, EMBASE, Cumulative Index to Nursing and Allied Health Literature and Database of Abstracts of Reviews of Effects in October 2008, with an update in June 2009, using search terms that included terms related to five components: serious infections, children, clinical history and examination, laboratory tests and ambulatory care settings. We also searched references of included studies, clinical content experts, and relevant National Institute for Health and Clinical Excellence guidelines to identify relevant studies. There were no language restrictions. Studies were eligible for inclusion if they were based in ambulatory settings in economically developed countries.


REVIEW METHODS
Literature searching, selection and data extraction were carried out by two reviewers. We assessed quality using the quality assessment of diagnostic accuracy studies (QUADAS) instrument, and used spectrum bias and validity of the reference standard as exclusion criteria. We calculated the positive likelihood ratio (LR+) and negative likelihood ratio (LR-) of each feature along with the pre- and post-test probabilities of the outcome. Meta-analysis was performed using the bivariate method when appropriate. We externally validated clinical prediction rules identified from the systematic review using existing data from children attending ED or primary care.


RESULTS
We identified 1939 articles, of which 35 were selected for inclusion in the review. There was only a single study from primary care; all others were performed in the ED. The quality of the included studies was modest. We also identified seven data sets (11,045 children) to use for external validation. The most useful clinical features for ruling in serious infection was parental or clinician overall concern that the illness was different from previous illnesses or that something was wrong. In low- or intermediate-prevalence settings, the presence of fever had some diagnostic value. Additional red flag features included cyanosis, poor peripheral circulation, rapid breathing, crackles on auscultation, diminished breath sounds, meningeal irritation, petechial rash, decreased consciousness and seizures. Procalcitonin (LR+ 1.75-2.96, LR- 0.08-0.35) and C-reactive protein (LR+ 2.53-3.79, LR- 0.25-0.61) were superior to white cell counts. The best performing clinical prediction rule was a five-stage decision tree rule, consisting of the physician's gut feeling, dyspnoea, temperature ≥ 40 °C, diarrhoea and age. It was able to decrease the likelihood of serious infections substantially, but on validation it provided good ruling out value only in low-to-intermediate-prevalence settings (LR- 0.11-0.28). We also identified and validated the Yale Observation Scale and prediction rules for pneumonia, meningitis and gastroenteritis.


LIMITATIONS
Only a single study was identified from primary-care settings, therefore results may lack generalisability.


CONCLUSIONS
Several clinical features are useful to increase or decrease the probability that a child has a serious infection. None is sufficient on its own to substantially raise or lower the risk of serious infection. Some are highly specific ('red flags'), so when present should prompt a more thorough or repeated assessment. C-reactive protein and procalcitonin demonstrate similar diagnostic characteristics and are both superior to white cell counts. However, even in children with a serious infection, red flags will occur infrequently, and their absence does not lower the risk. The diagnostic gap is currently filled by using clinical 'gut feeling' and diagnostic safety-netting, which are still not well defined. Although two prediction rules for serious infection and one for meningitis provided some diagnostic value, we do not recommend widespread implementation at this time. Future research is needed to identify predictors of serious infection in children in primary-care settings, to validate prediction rules more widely, and determine the added value of blood tests in primary-care settings.


FUNDING
The National Institute for Health Research Health Technology Assessment programme.

Final report for HTA Project 07/37/05: Systematic review and validation of prediction rules for identifying children with serious infections in emergency departments and urgent-access primary care

During the coronavirus disease 2019 (COVID-19) pandemic, rapid and accurate triage of patients at the emergency department is critical to inform decision-making. We propose a data-driven approach for automatic prediction of deterioration risk using a deep neural network that learns from chest X-ray images and a gradient boosting model that learns from routine clinical variables. Our AI prognosis system, trained using data from 3661 patients, achieves an area under the receiver operating characteristic curve (AUC) of 0.786 (95% CI: 0.745–0.830) when predicting deterioration within 96 hours. The deep neural network extracts informative areas of chest X-ray images to assist clinicians in interpreting the predictions and performs comparably to two radiologists in a reader study. In order to verify performance in a real clinical setting, we silently deployed a preliminary version of the deep neural network at New York University Langone Health during the first wave of the pandemic, which produced accurate predictions in real-time. In summary, our findings demonstrate the potential of the proposed system for assisting front-line physicians in the triage of COVID-19 patients.

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An artificial intelligence system for predicting the deterioration of COVID-19 patients in the emergency department

During the coronavirus disease 2019 (COVID-19) pandemic, rapid and accurate triage of patients at the emergency department is critical to inform decision-making. We propose a data-driven approach for automatic prediction of deterioration risk using a deep neural network that learns from chest X-ray images and a gradient boosting model that learns from routine clinical variables. Our AI prognosis system, trained using data from 3,661 patients, achieves an area under the receiver operating characteristic curve (AUC) of 0.786 (95% CI: 0.745-0.830) when predicting deterioration within 96 hours. The deep neural network extracts informative areas of chest X-ray images to assist clinicians in interpreting the predictions and performs comparably to two radiologists in a reader study. In order to verify performance in a real clinical setting, we silently deployed a preliminary version of the deep neural network at New York University Langone Health during the first wave of the pandemic, which produced accurate predictions in real-time. In summary, our findings demonstrate the potential of the proposed system for assisting front-line physicians in the triage of COVID-19 patients.

Enlightenment from the COVID-19 Pandemic: The Roles of Environmental Factors in Future Public Health Emergency Response.

In medicine, we examine our errors closely. Since the publication of “To Err is Human” by the Institute of Medicine at the end of the last century, patient safety and quality are priorities ( 1 ). One core principle is that we cannot improve care if we do not examine our errors and use them to change our processes. Errors are destined to be repeated and risk to patients further magnified when we do not learn from mistakes.

The last few months have tragically left us with ample opportunities to improve. The COVID-19 pandemic has amplified pre-existing deficiencies and inequities of our healthcare system. US healthcare is incentivized to react to sickness rather than proactively focus on health maintenance. As an emergency physician, I witness the impact of this approach daily. Far more money and effort are expended on minimally impactful interventions than addressing social determinants of health such as housing, food security and safety from violence. Collectively, these have a greater impact on healthcare outcomes than any pill. Procedures to manage illness are well-compensated, but public health systems to improve population health are underfunded and understaffed. On any given day, emergency departments (EDs) operate near or over capacity. A lack of inpatient beds forces EDs to hold admitted patients until space is available. ED care is then shunted to suboptimal conditions, leaving us to care for patients in waiting rooms, chairs, and hallways ( 2 ). Such reactionary systems fail spectacularly in the face of time-sensitive emergencies, because they lack the plasticity to respond quickly ( 3 ). Our healthcare and public health systems have faltered in the face of the COVID-19 pandemic. The question remains, will we learn from our mistakes?

COVID-19 is a slow-moving mass casualty incident (MCI). An MCI occurs when the available …

/pdf/point-of-no-return-covid-19-and-the-u-s-healthcare-system-an-3qc38k9wup.pdf

Point of no return: COVID-19 and the U.S. healthcare system: An emergency physician's perspective.

Evidence-based crisis standards of care for out-of-hospital cardiac arrests in a pandemic.

The emergency department (ED) is a unique practice environment that functions simultaneously as a place for high-acuity care of life-threatening illness and injury and also as a safety net for patients with chronic untreated disease. Patient presentations reflect not only pathophysiological derangements in individuals but also the consequences of social dysfunction as well as of healthcare itself, the latter related to the contrasting harms of poor access (in many cases) and excessive intervention (in many others). As changes in the larger healthcare system lead to more frequent use of the ED, clinicians have less time to spend with increasingly sick patients, resulting in more testing and less listening,1 as well as burnout among providers and medical harm and financial cost for patients.1–3

In attempting to address overuse in medicine, the Choosing Wisely campaign asked medical specialty societies to develop lists of diagnostic and therapeutic interventions that are being undertaken too frequently, leading to waste and harm.4 While different individuals and groups might not agree on every item identified, the ‘top 5’ lists that emerged from this process reflected in part an attempt to avoid controversy and left some important items—indeed some critical ‘elephants in the room’—unmentioned. While specialty societies do undertake advocacy work to address the health needs of the public, they also have a fundamental duty to advocate for and protect the interests of their specialty. Furthermore, healthcare dollars that are ‘wasted’ are of course not actually thrown away but rather end up in someone’s pocket; thus, there is clearly a conflict of interest when specialty societies address the overuse of extremely lucrative medical procedures that provide substantial income to their members.

The Right Care Alliance (RCA) is a US-based collaborative effort of healthcare practitioners and patients to address systemic issues of both overuse and underuse …

https://emj.bmj.com/content/emermed/early/2019/12/24/emermed-2019-209031.full.pdf

Maia Dorsett

Papers

Point of no return: COVID-19 and the U.S. healthcare system: An emergency physician's perspective.

Evidence-based crisis standards of care for out-of-hospital cardiac arrests in a pandemic.

Bringing value, balance and humanity to the emergency department: The Right Care Top 10 for emergency medicine

EMS Curriculum Should Educate Beyond a Technical Scope of Practice: Position Statement and Resource Document.