A Predictive Model Facilitates Early Recognition of Spinal Epidural Abscess in Adults

Introduction: Spinal epidural abscess (SEA), a highly morbid and potentially lethal deep tissue infection of the central nervous system has more than tripled in incidence over the past decade. Early recognition at the point of initial clinical presentation may prevent irreversible neurologic injury or other serious, adverse outcomes. To facilitate early recognition of SEA, we developed a predictive scoring model.Methods: Using data from a 10-year, retrospective, case-control study of adults presenting for care at a tertiary-care, regional, academic medical center, we used the Integrated Discrimination Improvement Index (IDI) to identify candidate discriminators and created a multivariable logistic regression model, refined based on p-value significance. We selected a cutpoint that optimized sensitivity and specificity.  Results: The final multivariable logistic regression model based on five characteristics –patient age, fever and/or rigor, antimicrobial use within 30 days, back/neck pain, and injection drug use – shows excellent discrimination (AUC 0.88 [95% confidence interval 0.84, 0.92]). We used the model’s β coefficients to develop a scoring system in which a cutpoint of six correctly identifies cases 89% of the time. Bootstrapped validation measures suggest this model will perform well across samples drawn from this population. Conclusion: Our predictive scoring model appears to reliably discriminate patients who require emergent spinal imaging upon clinical presentation to rule out SEA and should be used in conjunction with clinical judgment

A Predictive Model Facilitates Early Recognition of Spinal Epidural Abscess in Adults

Introduction: Spinal epidural abscess (SEA), a highly morbid and potentially lethal deep tissue infection of the central nervous system has more than tripled in incidence over the past decade. Early recognition at the point of initial clinical presentation may prevent irreversible neurologic injury or other serious, adverse outcomes. To facilitate early recognition of SEA, we developed a predictive scoring model. Methods: Using data from a 10-year, retrospective, case-control study of adults presenting for care at a tertiary-care, regional, academic medical center, we used the Integrated Discrimination Improvement Index (IDI) to identify candidate discriminators and created a multivariable logistic regression model, refined based on p-value significance. We selected a cutpoint that optimized sensitivity and specificity. Results: The final multivariable logistic regression model based on five characteristics –patient age, fever and/or rigor, antimicrobial use within 30 days, back/neck pain, and injection drug use – shows excellent discrimination (AUC 0.88 [95% confidence interval {0.84, 0.92}]). We used the model’s β coefficients to develop a scoring system in which a cutpoint of six correctly identifies cases 89% of the time. Bootstrapped validation measures suggest this model will perform well across samples drawn from this population. Conclusion: Our predictive scoring model appears to reliably discriminate patients who require emergent spinal imaging upon clinical presentation to rule out SEA and should be used in conjunction with clinical judgment

Microdroplet sandwich real-time rt-PCR for detection of pandemic and seasonal influenza subtypes.

As demonstrated by the recent 2012/2013 flu epidemic, the continual emergence of new viral strains highlights the need for accurate medical diagnostics in multiple community settings. If rapid, robust, and sensitive diagnostics for influenza subtyping were available, it would help identify epidemics, facilitate appropriate antiviral usage, decrease inappropriate antibiotic usage, and eliminate the extra cost of unnecessary laboratory testing and treatment. Here, we describe a droplet sandwich platform that can detect influenza subtypes using real-time reverse-transcription polymerase chain reaction (rtRT-PCR). Using clinical samples collected during the 2010/11 season, we effectively differentiate between H1N1p (swine pandemic), H1N1s (seasonal), and H3N2 with an overall assay sensitivity was 96%, with 100% specificity for each subtype. Additionally, we demonstrate the ability to detect viral loads as low as 10(4) copies/mL, which is two orders of magnitude lower than viral loads in typical infected patients. This platform performs diagnostics in a miniaturized format without sacrificing any sensitivity, and can thus be easily developed into devices which are ideal for small clinics and pharmacies

Decreasing Emergency Department Walkout Rate and Boarding Hours by Improving Inpatient Length of Stay

Introduction: Patient progress, the movement of patients through a hospital system from admission todischarge, is a foundational component of operational effectiveness in healthcare institutions. Optimalpatient progress is a key to delivering safe, high-quality and high-value clinical care. The Baystate PatientProgress Initiative (BPPI), a cross-disciplinary, multifaceted quality and process improvement project, waslaunched on March 1, 2014, with the primary goal of optimizing patient progress for adult patients.Methods: The BPPI was implemented at our system’s tertiary care, academic medical center, a highvolume,high-acuity hospital that serves as a regional referral center for western Massachusetts. TheBPPI was structured as a 24-month initiative with an oversight group that ensured collaborative goalalignment and communication of operational teams. It was organized to address critical aspects ofa patient’s progress through his hospital stay and to create additional inpatient capacity. The specificgoal of the BPPI was to decrease length of stay (LOS) on the inpatient adult Hospital Medicine serviceby optimizing an interdisciplinary plan of care and promoting earlier departure of discharged patients.Concurrently, we measured the effects on emergency department (ED) boarding hours per patient andwalkout rates.Results: The BPPI engaged over 300 employed clinicians and non-clinicians in the work. We createdincreased inpatient capacity by implementing daily interdisciplinary bedside rounds to proactively addresspatient progress; during the 24 months, this resulted in a sustained rate of discharge orders writtenbefore noon of more than 50% and a decrease in inpatient LOS of 0.30 days (coefficient: -0.014, 95%CI [-0.023, -0.005] P< 0.005). Despite the increase in ED patient volumes and severity of illness overthe same time period, ED boarding hours per patient decreased by approximately 2.1 hours (coefficient:-0.09; 95% CI [-0.15, -0.02] P = 0.007). Concurrently, ED walkout rates decreased by nearly 32% to amonthly mean of 0.4 patients (coefficient: 0.4; 95% CI [-0.7, -0.1] P= 0.01).Conclusion: The BPPI realized significant gains in patient progress for adult patients by promotingearlier discharges before noon and decreasing overall inpatient LOS. Concurrently, ED boarding hoursper patient and walkout rates decreased

Inter-Alpha-Inhibitor Proteins Are Endogenous Furin Inhibitors and Provide Protection against Experimental Anthrax Intoxication

Inter-alpha-inhibitor protein (IαIp) functions as an endogenous serine protease inhibitor in human plasma, and IαIp levels diminish rapidly during acute inflammatory states. One potential target for IαIp is furin, a cell-associated serine endopeptidase essential for the activation of protective antigen and the formation of anthrax lethal toxin (LT). IαIp blocks furin activity in vitro and provides significant protection against cytotoxicity for murine peritoneal macrophages exposed to up to 500 ng/ml LT. A monoclonal antibody (MAb), 69.31, that specifically blocks the enzymatic activity of IαIp eliminates its protective effect against LT-induced cytotoxicity. IαIp (30 mg/kg of body weight) administered to BALB/c mice 1 hour prior to an intravenous LT challenge resulted in 71% survival after 7 days compared with no survivors among the control animals (P < 0.001). We conclude that human IαIp may be an effective preventative or therapeutic agent against anthrax intoxication

Droplet sandwich platform.

<p>a: Drawings of platform: 3D drawing of the droplet sandwich platform displaying the ITO coated glass (a) with a compound droplet (b) surrounded by a spacer (c) and covered with a coverslip (d), which is fully assembled to sandwich the compound droplet in a reaction chamber (e). The ITO surface heats radially, as displayed the modeled heating profile for the ITO glass when 15 V is applied to the resistive surface as generated by COMSOL Multiphysics (f). The dimension of the slide is 40 mm×40 mm and the compound droplet is approximately 2.8 mm in diameter. b: Workflow and representative data: Sample isolation is done from nasopharygeal swabs and the rtRT-PCR mix is transferred to the droplet sandwich platform for thermal cycling. Temperature cycling occurs at the center of the radial profile as displayed by the plot where the black line represents the controlled surface temperature and the red line is the calibrated droplet temperature. Fluorescence is collected in real-time during the extension phase of PCR, with DNA amplification of positive samples displayed in green, negative samples with no change over time in blue and calculated threshold in black.</p