Sepsis is a series of clinical syndromes caused by the immunological response to infection. The clinical evidence for sepsis could typically attribute to bacterial infection or bacterial endotoxins, but infections due to viruses, fungi or parasites could also lead to sepsis. Regardless of the etiology, rapid clinical deterioration, prolonged stay in intensive care units and high risk for mortality correlate with the incidence of sepsis. Despite its prevalence and morbidity, improvement in sepsis outcomes has remained limited. In this comprehensive review, we summarize the current landscape of risk estimation, diagnosis, treatment and prognosis strategies in the setting of sepsis and discuss future challenges. We argue that the advent of modern technologies such as in-depth molecular profiling, biomedical big data and machine intelligence methods will augment the treatment and prevention of sepsis. The volume, variety, veracity and velocity of heterogeneous data generated as part of healthcare delivery and recent advances in biotechnology-driven therapeutics and companion diagnostics may provide a new wave of approaches to identify the most at-risk sepsis patients and reduce the symptom burden in patients within shorter turnaround times. Developing novel therapies by leveraging modern drug discovery strategies including computational drug repositioning, cell and gene-therapy, clustered regularly interspaced short palindromic repeats -based genetic editing systems, immunotherapy, microbiome restoration, nanomaterial-based therapy and phage therapy may help to develop treatments to target sepsis. We also provide empirical evidence for potential new sepsis targets including FER and STARD3NL. Implementing data-driven methods that use real-time collection and analysis of clinical variables to trace, track and treat sepsis-related adverse outcomes will be key. Understanding the root and route of sepsis and its comorbid conditions that complicate treatment outcomes and lead to organ dysfunction may help to facilitate identification of most at-risk patients and prevent further deterioration. To conclude, leveraging the advances in precision medicine, biomedical data science and translational bioinformatics approaches may help to develop better strategies to diagnose and treat sepsis in the next decade.

Background Successful clinical decision support (CDS) tools can help use evidence-based medicine to effectively improve patient outcomes. However, the impact of these tools has been limited by low provider adoption due to overtriggering, leading to alert fatigue. We developed a tracking mechanism for monitoring trigger (percent of total visits for which the tool triggers) and adoption (percent of completed tools) rates of a complex CDS tool based on the Wells criteria for pulmonary embolism (PE). Objective We aimed to monitor and evaluate the adoption and trigger rates of the tool and assess whether ongoing tool modifications would improve adoption rates. Methods As part of a larger clinical trial, a CDS tool was developed using the Wells criteria to calculate pretest probability for PE at 2 tertiary centers’ emergency departments (EDs). The tool had multiple triggers: any order for D-dimer, computed tomography (CT) of the chest with intravenous contrast, CT pulmonary angiography (CTPA), ventilation-perfusion scan, or lower extremity Doppler ultrasound. A tracking dashboard was developed using Tableau to monitor real-time trigger and adoption rates. Based on initial low provider adoption rates of the tool, we conducted small focus groups with key ED providers to elicit barriers to tool use. We identified overtriggering of the tool for non-PE-related evaluations and inability to order CT testing for intermediate-risk patients. Thus, the tool was modified to allow CT testing for the intermediate-risk group and not to trigger for CT chest with intravenous contrast orders. A dialogue box, “Are you considering PE for this patient?” was added before the tool triggered to account for CTPAs ordered for aortic dissection evaluation. Results In the ED of tertiary center 1, 95,295 patients visited during the academic year. The tool triggered for an average of 509 patients per month (average trigger rate 2036/30,234, 6.73%) before the modifications, reducing to 423 patients per month (average trigger rate 1629/31,361, 5.22%). In the ED of tertiary center 2, 88,956 patients visited during the academic year, with the tool triggering for about 473 patients per month (average trigger rate 1892/29,706, 6.37%) before the modifications and for about 400 per month (average trigger rate 1534/30,006, 5.12%) afterward. The modifications resulted in a significant 4.5- and 3-fold increase in provider adoption rates in tertiary centers 1 and 2, respectively. The modifications increased the average monthly adoption rate from 23.20/360 (6.5%) tools to 81.60/280.20 (29.3%) tools and 46.60/318.80 (14.7%) tools to 111.20/263.40 (42.6%) tools in centers 1 and 2, respectively. Conclusions Close postimplementation monitoring of CDS tools may help improve provider adoption. Adaptive modifications based on user feedback may increase targeted CDS with lower trigger rates, reducing alert fatigue and increasing provider ...

Non-steroidal anti-inflammatory drugs are not only potent analgesics and antipyretics but also nephrotoxins, and may cause electrolyte disarray. In addition to the commonly expected effects, including hyperkalemia, hyponatremia, acute renal injury, renal cortical necrosis, and volume retention, glomerular disease with or without nephrotic syndrome or nephritis can occur as well including after years of seemingly safe administration. Minimal change disease, secondary membranous glomerulonephritis, and acute interstitial nephritis are all reported glomerular lesions seen with non-steroidal anti-inflammatory use. We report a patient who used non-steroidal anti-inflammatory drugs for years without diabetes, chronic kidney disease, or proteinuria; he then developed severe nephrotic range proteinuria with 7 g of daily urinary protein excretion. Renal biopsy showed minimal change nephropathy, a likely secondary membranous glomerulonephritis, and acute interstitial nephritis present simultaneously in one biopsy. Cessation of non-steroidal anti-inflammatory drug use along with steroid treatment resulted in a moderate improvement in renal function, though residual impairment remained. Urine heavy metal screen returned with elevated levels of urine copper, but with normal ceruloplasmin level. Workup suggested that the elevated copper levels were due to cirrhosis from non-alcoholic fatty liver disease. The membranous glomerulonephritis is possibly linked to non-steroidal anti-inflammatory drug exposure, and possibly to heavy metal exposure, and is clinically and pathologically much less likely to be a primary membranous glomerulonephritis with negative serological markers.

Background: Successful clinical decision support (CDS) tools can bring evidence-based medicine to the point-of-care to effectively improve patient outcomes. However, the impact of these tools has been limited by low provider adoption due to over-triggering, leading to alert fatigue. We have developed a tracking mechanism for monitoring trigger rate (percent of total visits for which the tool triggers) and adoption rate (percent of completed tools) of a complex CDS tool based on the Wells' Criteria for pulmonary embolism (PE).