Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2,3,4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease

From guidelines to practice: improving clinical care through rule-based clinical decision support at the point of care

Healthcare Information Technology (HIT) is a dynamically evolving industry due to continuous advancements in healthcare technologies. This necessitates the availability of highly dynamic applications that accommodate frequent changes in business logic. The automation of Clinical Decision Support (CDS) in particular is most liable to changes in health business logic or rules. In terms of system’s architecture, there is a need to separate business logic and rules from the implementation/functionality of the Electronic Health Record (EHR) application, providing processes and rules as reusable components. We propose an architecture utilizing rule-based technologies to facilitate Decision Support to promptly adapt business logic changes, that are reflected immediately in application behavior. This allows real-time and robust CDS for the physician at point of care. Our rule-based implementation (Business Process Modelling Notation (BPMN)+Rules) was successfully used to emulate Clinical workflows, using as an example, the NICE Lung Cancer Clinical Guideline (CG121) as a test scenario

Clinical correlates of functional status in patients with chronic renal insufficiency

Patients with end-stage renal disease (ESRD) are known to have significantly reduced functional abilities, as measured by the Sickness Impact Profile (SIP). We investigated the clinical correlates with SIP scores in a cohort of patients with lesser degrees of renal dysfunction recruited from an academic general medicine practice (mean calculated creatinine clearance, 25 mL/min). Of 603 eligible patients with chronic renal insufficiency (CRI) defined as a serum creatinine greater than 1.5 mg/dL and a calculated creatinine clearance less than 50 mL/min on two occasions more than 6 months apart, 360 (60%) agreed to participate. These patients were primarily elderly (mean age, 69 years) black (83%), women (69.2%), with an average of 6 years of education and a household income of $400 to$800 per month; 92% had hypertension and 57% had diabetes. The SIP was administered in-home by trained interviewers. Independent variables included demographic data, education, income, and medications (via interviewers), vital signs taken by a renal nurse, and diagnostic test results and diagnoses from patient's computerized records. The total SIP score was the dependent variable, and its physical and psychosocial subscales were also investigated. Variables with univariate correlations with total SIP (P < 0.05) were included in a multiple regression analysis. All variables with a multivariable P value less than 0.10 were included in the final model. The mean SIP score was 24.5 +/- 15.6, higher than that found in patients on dialysis. Significant (P < 0.05) independent correlates with higher SIP scores (greater disability) were lower educational level and income, prior diagnoses of coronary artery disease and stroke, and lower serum albumin

Innovative approaches to application of information technology in disease surveillance and prevention in western Kenya

We describe an electronic injury surveillance system that provides data for improving patient care and monitoring injury incidence and distribution patterns. Patients with injuries visiting a rural Kenyan primary care center were enrolled consecutively over 14 months. Injury information was added onto an existing medical record database that captures data for each patient visit. A new injury data encounter form and entry screen were created that included geographical coordinates of the injury site. These coordinates were obtained using a handheld global positioning system (GPS) device, and data were downloaded to the database and linked to each patient. We created digital maps of injury spatial distribution using geography information systems (GIS) software and correlated injury type and location with patients'clinical data. A computerized medical record system, complemented by GIS technology and an injury-specific component, presents a valuable tool for injury surveillance, epidemiology, prevention and control for communities served by a specific health facility

CHARACTERISTICS OF INJURIES PRESENTING TO A RURAL HEALTH CENTRE IN WESTERN KENYA

ABSTRACTObjectives: To describe characteristics of injuries among patients presenting to a rural healthcentre in western Kenya, and identify the associated risk factors.Design: A retrospective descriptive study.Setting: A primary care Ministry of Health Rural Health Centre in western Kenya.Results: Of the 315 injured patients, 62% were males and 38% females. Young adults aged 15-44 yearswere the most affected, comprising 63.5%. The five most common causes of injury were struck byobject (36.6%), assault (34.4%), falls (11.6%), burns (6.2%) and road traffic accidents (4.7%). Quarrelsand fights were the leading reasons for assaults among males (69.5%) and females (44.4%). Mostinjuries occurred at work (36.2%), when subjects were engaged in vital activities (19.5%) or duringplay/leisure time (19.2%). A third of injured adults aged 15 years and above had consumed alcoholprior to the injury event. Alcohol use was significantly associated with assaults (51.3%) than allother causes of injury (OR=4.51, p&lt;0.0001).Conclusion: The pattern and certain risk factors for non-fatal injuries among patients attending arural health centre, such as place of occurrence, activity and alcohol use, can be identified througha facility-based electronic injury surveillance system. The information can be used to developcontext-specific injury prevention interventions in the community

Implicit Bias among Physicians and its Prediction of Thrombolysis Decisions for Black and White Patients

CONTEXT: Studies documenting racial/ethnic disparities in health care frequently implicate physicians’ unconscious biases. No study to date has measured physicians’ unconscious racial bias to test whether this predicts physicians’ clinical decisions. OBJECTIVE: To test whether physicians show implicit race bias and whether the magnitude of such bias predicts thrombolysis recommendations for black and white patients with acute coronary syndromes. DESIGN, SETTING, AND PARTICIPANTS: An internetbased tool comprising a clinical vignette of a patient presenting to the emergency department with an acute coronary syndrome, followed by a questionnaire and three Implicit Association Tests (IATs). Study invitations were e-mailed to all internal medicine and emergency medicine residents at four academic medical centers in Atlanta and Boston; 287 completed the study, met inclusion criteria, and were randomized to either a black or white vignette patient. MAIN OUTCOME MEASURES: IAT scores (normal continuous variable) measuring physicians’ implicit race preference and perceptions of cooperativeness. Physicians’ attribution of symptoms to coronary artery disease for vignette patients with randomly assigned race, and their decisions about thrombolysis. Assessment of physicians’ explicit racial biases by questionnaire. RESULTS: Physicians reported no explicit preference for white versus black patients or differences in perceived cooperativeness. In contrast, IATs revealed implicit preference favoring white Americans (mean IAT score=0.36, P<.001, one-sample t test) and implicit stereotypes of black Americans as less cooperative with medical procedures (mean IAT score 0.22, P<.001), and less cooperative generally (mean IAT score 0.30, P<.001). As physicians’ prowhite implicit bias increased, so did their likelihood of treating white patients and not treating black patients with thrombolysis (P=.009). CONCLUSIONS: This study represents the first evidence of unconscious (implicit) race bias among physicians, its dissociation from conscious (explicit) bias, and its predictive validity. Results suggest that physicians’ unconscious biases may contribute to racial/ ethnic disparities in use of medical procedures such as thrombolysis for myocardial infarction