The ability to computationally predict whether a compound treats a disease would improve the economy and success rate of drug approval. This study describes Project Rephetio to systematically model drug efficacy based on 755 existing treatments. First, we constructed Hetionet (neo4j.het.io), an integrative network encoding knowledge from millions of biomedical studies. Hetionet v1.0 consists of 47,031 nodes of 11 types and 2,250,197 relationships of 24 types. Data were integrated from 29 public resources to connect compounds, diseases, genes, anatomies, pathways, biological processes, molecular functions, cellular components, pharmacologic classes, side effects, and symptoms. Next, we identified network patterns that distinguish treatments from non-treatments. Then, we predicted the probability of treatment for 209,168 compound–disease pairs (het.io/repurpose). Our predictions validated on two external sets of treatment and provided pharmacological insights on epilepsy, suggesting they will help prioritize drug repurposing candidates. This study was entirely open and received realtime feedback from 40 community members.

The ability to computationally predict whether a compound treats a disease would improve the economy and success rate of drug approval. This study describes Project Rephetio to systematically model drug efficacy based on 755 existing treatments. First, we constructed Hetionet (neo4j.het.io), an integrative network encoding knowledge from millions of biomedical studies. Hetionet v1.0 consists of 47,031 nodes of 11 types and 2,250,197 relationships of 24 types. Data were integrated from 29 public resources to connect compounds, diseases, genes, anatomies, pathways, biological processes, molecular functions, cellular components, pharmacologic classes, side effects, and symptoms. Next, we identified network patterns that distinguish treatments from non-treatments. Then, we predicted the probability of treatment for 209,168 compound-disease pairs (het.io/repurpose). Our predictions validated on two external sets of treatment and provided pharmacological insights on epilepsy, suggesting they will help prioritize drug repurposing candidates. This study was entirely open and received realtime feedback from 40 community members.

BACKGROUND Risk factors for delirium are well‐described, yet there is no widely used tool to predict the development of delirium upon admission in hospitalized medical patients. OBJECTIVE To develop and validate a tool to predict the likelihood of developing delirium during hospitalization. DESIGN Prospective cohort study with derivation (May 2010–November 2010) and validation (October 2011–March 2012) cohorts. SETTING Two academic medical centers and 1 Veterans Affairs medical center. PATIENTS Consecutive medical inpatients (209 in the derivation and 165 in the validation cohort) over age 50 years without delirium at the time of admission. MEASUREMENTS Delirium assessed daily for up to 6 days using the Confusion Assessment Method. RESULTS The AWOL prediction rule was derived by assigning 1 point to each of 4 items assessed upon enrollment that were independently associated with the development of delirium (Age ≥ 80 years, failure to spell “World” backward, disOrientation to place, and higher nurse‐rated iLlness severity). Higher scores were associated with higher rates of delirium in the derivation and validation cohorts (P for trend < 0.001 and 0.025, respectively). Rates of delirium according to score in the combined population were: 0(1/50, 2%), 1(5/141, 4%), 2(15/107, 14%), 3(10/50, 20%), and 4(7/11, 64%) (P for trend < 0.001). Area under the receiver operating characteristic curve for the derivation and validation cohorts was 0.81 (0.73–0.90) and 0.69 (0.54–0.83) respectively. CONCLUSIONS The AWOL prediction rule characterizes medical patients' risk for delirium at the time of hospital admission and could be used for clinical stratification and in trials of delirium prevention. Journal of Hospital Medicine 2013;8:493–499. © 2013 Society of Hospital Medicine

ImportanceIn patients with severe aortic valve stenosis at intermediate surgical risk, transcatheter aortic valve replacement (TAVR) with a self-expanding supra-annular valve was noninferior to surgery for all-cause mortality or disabling stroke at 2 years. Comparisons of longer-term clinical and hemodynamic outcomes in these patients are limited.ObjectiveTo report prespecified secondary 5-year outcomes from the Symptomatic Aortic Stenosis in Intermediate Risk Subjects Who Need Aortic Valve Replacement (SURTAVI) randomized clinical trial.Design, Setting, and ParticipantsSURTAVI is a prospective randomized, unblinded clinical trial. Randomization was stratified by investigational site and need for revascularization determined by the local heart teams. Patients with severe aortic valve stenosis deemed to be at intermediate risk of 30-day surgical mortality were enrolled at 87 centers from June 19, 2012, to June 30, 2016, in Europe and North America. Analysis took place between August and October 2021.InterventionPatients were randomized to TAVR with a self-expanding, supra-annular transcatheter or a surgical bioprosthesis.Main Outcomes and MeasuresThe prespecified secondary end points of death or disabling stroke and other adverse events and hemodynamic findings at 5 years. An independent clinical event committee adjudicated all serious adverse events and an independent echocardiographic core laboratory evaluated all echocardiograms at 5 years.ResultsA total of 1660 individuals underwent an attempted TAVR (n = 864) or surgical (n = 796) procedure. The mean (SD) age was 79.8 (6.2) years, 724 (43.6%) were female, and the mean (SD) Society of Thoracic Surgery Predicted Risk of Mortality score was 4.5% (1.6%). At 5 years, the rates of death or disabling stroke were similar (TAVR, 31.3% vs surgery, 30.8%; hazard ratio, 1.02 [95% CI, 0.85-1.22]; P = .85). Transprosthetic gradients remained lower (mean [SD], 8.6 [5.5] mm Hg vs 11.2 [6.0] mm Hg; P &amp;lt; .001) and aortic valve areas were higher (mean [SD], 2.2 [0.7] cm2 vs 1.8 [0.6] cm2; P &amp;lt; .001) with TAVR vs surgery. More patients had moderate/severe paravalvular leak with TAVR than surgery (11 [3.0%] vs 2 [0.7%]; risk difference, 2.37% [95% CI, 0.17%- 4.85%]; P = .05). New pacemaker implantation rates were higher for TAVR than surgery at 5 years (289 [39.1%] vs 94 [15.1%]; hazard ratio, 3.30 [95% CI, 2.61-4.17]; log-rank P &amp;lt; .001), as were valve reintervention rates (27 [3.5%] vs 11 [1.9%]; hazard ratio, 2.21 [95% CI, 1.10-4.45]; log-rank P = .02), although between 2 and 5 years only 6 patients who underwent TAVR and 7 who underwent surgery required a reintervention.Conclusions and RelevanceAmong intermediate-risk patients with symptomatic severe aortic stenosis, major clinical outcomes at 5 years were similar for TAVR and surgery. TAVR was associated with superior hemodynamic valve performance but also with more paravalvular leak and valve reinterventions.

Chronic meningitis of unknown etiology is a vexing illness for patients and clinicians. Identification of the correct pathogen can be challenging and time consuming, leading to delays in appropriate treatment. Although Sporothrix schenckii is a recognized and treatable cause of chronic meningitis, neurologists and infectious diseases physicians may not regularly evaluate for Sporothrix infection. We describe an immunocompetent patient with chronic meningitis who partially responded to empiric fluconazole. Prompted by a recent culture-confirmed case of meningeal sporotrichosis, we tested for S schenckii antibodies from the cerebrospinal fluid, which were positive. His clinical and functional status improved, and the S schenckii antibody titer decreased with itraconazole therapy. Clinicians should consider S schenckii in the differential diagnosis for chronic meningitis, even in immunocompetent patients, particularly when the clinical picture does not respond to standard empiric therapy.

No abstract

Appreciative inquiry was developed to initiate and animate change. As implementation science gains a foothold in practice settings to bridge theory, evidence, and practice, appreciative inquiry takes on new meaning as a leadership intervention and training tool. J Contin Educ Nurs. 2016;47(5):207-209.

No abstract