No clinical benefit in mortality associated with hydroxychloroquine treatment in patients with COVID-19.

BACKGROUND: The use of hydroxychloroquine (HCQ), with or without concurrent administration of azithromycin (AZM), for treatment of COVID-19 has received considerable attention. The purpose of this study was to determine whether HCQ administration is associated with improved mortality in COVID-19 patients. METHODS: We conducted a retrospective analysis of data collected during the care process for COVID-19 positive patients discharged from facilities affiliated with a large healthcare system in the United States as of April 27, 2020. Patients were categorized by treatment with HCQ (in addition to standard supportive therapy) or receipt of supportive therapy with no HCQ. Patient outcomes were evaluated for in-hospital mortality. Patient demographics and clinical characteristics were accounted for through a multivariable regression analysis. RESULTS: A total of 1669 patients were evaluated (no HCQ, n = 696; HCQ, n = 973). When adjusting for patient characteristics, receipt of AZM, and severity of disease at admission, there was no beneficial effect of receipt of HCQ on the risk of death. In this population, there was an 81% increase in the risk of mortality among patients who received HCQ at any time during their hospital stay versus no HCQ exposure (OR: 1.81, 95% CI: 1.20-2.77, p = 0.01). CONCLUSIONS: In this retrospective analysis, we found that there was no benefit of administration of HCQ on mortality in COVID-19 patients. These results support recent changes to clinical trials that discourage the use of HCQ in COVID-19 patients.

Patient characteristics and admitting vital signs associated with coronavirus disease 2019 (COVID-19)-related mortality among patients admitted with noncritical illness.

OBJECTIVE: To determine risk factors for mortality among COVID-19 patients admitted to a system of community hospitals in the United States. DESIGN: Retrospective analysis of patient data collected from the routine care of COVID-19 patients. SETTING: System of >180 acute-care facilities in the United States. PARTICIPANTS: All admitted patients with positive identification of COVID-19 and a documented discharge as of May 12, 2020. METHODS: Determination of demographic characteristics, vital signs at admission, patient comorbidities and recorded discharge disposition in this population to construct a logistic regression estimating the odds of mortality, particular for those patients characterized as not being critically ill at admission. RESULTS: In total, 6,180 COVID-19+ patients were identified as of May 12, 2020. Most COVID-19+ patients (4,808, 77.8%) were admitted directly to a medical-surgical unit with no documented critical care or mechanical ventilation within 8 hours of admission. After adjusting for demographic characteristics, comorbidities, and vital signs at admission in this subgroup, the largest driver of the odds of mortality was patient age (OR, 1.07; 95% CI, 1.06-1.08; P < .001). Decreased oxygen saturation at admission was associated with increased odds of mortality (OR, 1.09; 95% CI, 1.06-1.12; P < .001) as was diabetes (OR, 1.57; 95% CI, 1.21-2.03; P < .001). CONCLUSIONS: The identification of factors observable at admission that are associated with mortality in COVID-19 patients who are initially admitted to non-critical care units may help care providers, hospital epidemiologists, and hospital safety experts better plan for the care of these patients.

Changes in hospitalized coronavirus disease 2019 (COVID-19) patient characteristics and resource use in a system of community hospitals in the United States.

Coronavirus disease 2019 (COVID-19) has migrated to regions that were initially spared, and it is likely that different populations are currently at risk for illness. Herein, we present our observations of the change in characteristics and resource use of COVID-19 patients over time in a national system of community hospitals to help inform those managing surge planning, operational management, and future policy decisions.

External Validation of the Oakland Score to Assess Safe Hospital Discharge Among Adult Patients With Acute Lower Gastrointestinal Bleeding in the US.

Importance: Lower gastrointestinal bleeding (LGIB), which manifests as blood in the colon or anorectum, is a common reason for hospitalization. In most patients, LGIB stops spontaneously with no in-hospital intervention. A risk score that could identify patients at low risk of experiencing adverse outcomes could help improve the triage process and allow greater numbers of patients to receive outpatient management of LGIB. Objective: To externally validate the Oakland Score, which was previously developed using a score threshold of 8 points to identify patients with LGIB who are at low risk of adverse outcomes. Design, Setting, and Participants: This multicenter prognostic study was conducted in 140 US hospitals in the Hospital Corporation of America network. A total of 46 179 adult patients (aged ≥16 years) admitted to the hospital with a primary diagnosis of LGIB between June 1, 2016, and October 15, 2018, were initially identified using diagnostic codes. Of those, 51 patients were excluded because they were more likely to have upper gastrointestinal bleeding, leaving a study population of 46 128 patients with LGIB. For the statistical analysis of the Oakland Score, an additional 8061 patients were excluded because they were missing data on Oakland Score components or clinical outcomes, resulting in 38 067 patients included in the analysis. The study used area under the receiver operating characteristic curves with 95% CIs for external validation of the model. Sensitivity and specificity were calculated for each score threshold (≤8 points, ≤9 points, and ≤10 points). Data were analyzed from October 16, 2018, to September 4, 2019. Main Outcomes and Measures: Identification of patients who met the criteria for safe discharge from the hospital and comparison of the performance of 2 score thresholds (≤8 points vs ≤10 points). Safe discharge was defined as the absence of blood transfusion, rebleeding, hemostatic intervention, hospital readmission, and death. Results: Among 46 128 adult patients with LGIB, the mean (SD) age was 70.1 (16.5) years; 23 091 patients (50.1%) were female. Of those, 22 074 patients (47.9%) met the criteria for safe discharge from the hospital. In this group, the mean (SD) age was 67.9 (18.1) years, and 11 056 patients (50.1%) were female. In the statistical analysis of the Oakland Score, which included only the 38 067 patients with complete data, the area under the receiver operating characteristic curve for safe discharge was 0.87 (95% CI, 0.87-0.87). An Oakland Score threshold of 8 points or lower identified 3305 patients (8.7%), with a sensitivity and specificity for safe discharge of 98.4% and 16.0%, respectively. Extension of the Oakland Score threshold to 10 points or lower identified 6770 patients (17.8%), with a sensitivity and specificity for safe discharge of 96.0% and 31.9%, respectively. Conclusions and Relevance: In this study, the Oakland Score consistently identified patients with acute LGIB who were at low risk of experiencing adverse outcomes and whose conditions could safely be managed without hospitalization. The score threshold to identify low-risk patients could be extended from 8 points or lower to 10 points or lower to allow identification of a greater proportion of low-risk patients.

2019 John M. Eisenberg Patient Safety and Quality Awards: SPOTting Sepsis to Save Lives: A Nationwide Computer Algorithm for Early Detection of Sepsis: Innovation in Patient Safety and Quality at the National Level (Eisenberg Award).

BACKGROUND: In recognition of the potential of data to drive care and the need for early identification of patients with sepsis, HCA Healthcare developed an automated sepsis detection algorithm-SPOT (Sepsis Prediction and Optimization of Therapy). The algorithm was deployed at scale and served as a mechanism to reduce the time to detection and improve sepsis mortality in 173 hospitals across the United States. METHODS: The SPOT algorithm was designed as a rules-based detection of defined criteria that would interpret available electronic and basic laboratory data in near real time. Working from an organizational recognition of the need to construct a national clinical data warehouse to allow for the aggregation and analysis of data streams, HCA Healthcare designed and deployed SPOT and delivered the alert from the algorithm to the bedside to initiate existing clinical workflows for patients with sepsis. RESULTS: SPOT improved the timeliness of sepsis detection by providing alerts when signals of sepsis become available, triggering initiation of sepsis screens. This gave an advantage of about six hours over the legacy practice of sepsis screening at shift change. When deployed alongside existing sepsis improvement initiatives, SPOT was associated with an acceleration of improvement in mortality-particularly in the not-present-on-admission (NPOA) septic shock population, the patients at greatest risk for mortality. This population had seen little improvement with prior initiatives, but mortality improved 3.9 percentage points from 2018 to 2019. When accounting for seasonal variation, there was a decline in mortality rate following the deployment of SPOT, as compared to the year prior, of 9.9% for NPOA severe sepsis and 5.1% for NPOA septic shock. CONCLUSION: Development of the SPOT algorithm for the detection of sepsis from data available in the electronic health record resulted in more timely recognition, faster initiation of treatment, and improved survival for patients.

Increased alpha-defensins as a blood marker for schizophrenia susceptibility.

Schizophrenia is a severe psychotic illness affecting 1% of the general population. There are no consistent pathological features, and the disorder is defined by a complex symptomatology, which overlaps with other psychiatric illnesses. Diagnosis is based on a clinical interview, relying on the patient meeting criteria according to diagnosis manuals, including Diagnostic and Statistical Manual of Mental Disorders, 4th Ed. and International Statistical Classification of Diseases, 10th Revision. Because of the ambiguous symptoms, the diagnostic process can take many months and often years. Rapid and effective treatment has been shown to impact positively on disease progression and outcome, and it is therefore important to identify disease-associated biomarkers allowing early diagnosis. Reliable biomarkers can be used for the development of diagnostic tests and may also help us understand the underlying pathology of this disorder. In the present study, proteins from anti-CD3 stimulated and unstimulated peripheral blood T cell lysates from 15 minimally medicated and unmedicated patients and 15 age-, sex-, race-, and smoking-matched controls were profiled on cation exchange (CM10) chips using SELDI-TOF. Partial least squares discriminate analysis was used to separate patient and control groups according to the expression of 108 detected peaks, and two peaks of 3,374 and 3,450 Da, corresponding to alpha-defensins based on masses and cationic properties, were found to contribute significantly to the separation of patient and control groups. Reduction of T cell lysates with DTT resulted in a 6-Da shift in the mass of these peaks consistent with the presence of three cysteine bonds in the structure, confirming them as alpha-defensins. Quantification of alpha-defensins in T cell lysates from six patients and 18 healthy controls was carried out by ELISA, which also showed that alpha-defensin levels were significantly increased in patient lysates when compared with matched controls (p = 0.0197). Plasma from 21 monozygotic twins discordant for schizophrenia and eight healthy unaffected twin pairs was also analyzed for the expression of alpha-defensins by ELISA. Notably both affected and unaffected twins were found to have significantly elevated alpha-defensin levels compared with healthy control twin pairs (p = 0.0014 and p = 0.0115, respectively). Increased expression of alpha-defensins in unaffected as well as affected discordant monozygotic twins is of particular interest as monozygotic twins share genes and usually environmental upbringing. The unaffected twin therefore represents the biological and environmental risk of developing schizophrenia in the absence of overt symptomatology and therapeutic medication. These findings suggest that alpha-defensins could be an important early indicator of the risk of schizophrenia.

A sequential Monte Carlo EM approach to the transcription factor binding site identification problem.

MOTIVATION: A significant and stubbornly intractable problem in genome sequence analysis has been the de novo identification of transcription factor binding sites in promoter regions. Although theoretically pleasing, probabilistic methods have faced difficulties due to model mismatch and the nature of the biological sequence. These problems result in inference in a high dimensional, highly multimodal space, and consequently often display only local convergence and hence unsatisfactory performance. ALGORITHM: In this article, we derive and demonstrate a novel method utilizing a sequential Monte Carlo-based expectation-maximization (EM) optimization to improve performance in this scenario. The Monte Carlo element should increase the robustness of the algorithm compared to classical EM. Furthermore, the parallel nature of the sequential Monte Carlo algorithm should be more robust than Gibbs sampling approaches to multimodality problems. RESULTS: We demonstrate the superior performance of this algorithm on both semi-synthetic and real data from Escherichia coli. AVAILABILITY: http://sigproc-eng.cam.ac.uk/ approximately ej230/smc_em_tfbsid.tar.gz

A microarray data analysis framework for postmortem tissues.

This paper will give a complete methodological approach to the processing of oligonucleotide microarray data from postmortem tissue, particularly brain matter. Attention will be drawn to each of the important stages in the process; specifically the quality control, gene expression value calculation, multiple hypothesis testing and correlation analyses. We shall initially discuss the theoretical foundations of each individual method and subsequently apply the ensemble to a sample data set to illustrate and visualise important points.