Sepsis accounts for approximately 20% of global mortality and a substantial healthcare cost. Delayed or missed diagnoses of sepsis and poorly targeted treatment of sepsis-causing bloodstream infections are key contributors to this high mortality. Clinical diagnosis and monitoring of sepsis is based on several host-response measures related to organ system dysfunction. Owing to the low concentration of pathogens in bloodstream infections, identification of the causative pathogen generally requires blood culture over the course of hours to days. This necessitates the use of broad-spectrum antibiotics before culture results are available, which is a major risk factor for the development of antimicrobial resistance. In this Review, we describe the current standards for diagnosis of sepsis and summarize the technologies in use for detection of pathogens and host-response biomarkers in blood. We then identify key challenges in rapid diagnosis and discuss how ideal diagnostics for bloodstream infections could be achieved. Sepsis causes approximately 20% of global mortality and targeted treatment of causative bloodstream infection is limited by slow and insensitive pathogen detection, leading to delayed diagnosis and the overuse of broad-spectrum antibiotics that fuel antimicrobial resistance. This Review describes the measurement of host-response biomarkers for the detection and diagnosis of sepsis and addresses the need for sensitive and scalable approaches to bypass time-consuming blood culture.

Timely identification of bacteria in bloodstream infections is critical for guiding appropriate antibiotic treatment. However, current clinical workflows entail blood culture (1-5 days), followed by Gram staining, PCR, and antibiotic susceptibility testing. These steps delay actionable results, often leading clinicians to prescribe broad-spectrum antibiotics without results from the above tests, contributing to the rising threat of antimicrobial resistance. Specifically, rapid information of even presence of Gram-positive and/or Gram-negative bacteria would help clinicians choose a specific antibiotic regimen after bacteremia is suspected. Here, we developed a rapid, culture-free method that identifies bacterial Gram type within a panel of 6 bacteria from whole blood at a sensitivity of 1-5 CFU/μL within 1.5 h. The assay features a duplex probe-based detection of amplification by release of quenching (DARQ) loop-mediated isothermal amplification (LAMP) system targeting six of the most common bloodstream pathogens in blood cultures in published hospital reports. The two DARQ probes distinguish a panel of four Gram-negative bacteria (E. coli, S. marcescens, P. mirabilis, and K. pneumoniae) from two Gram-positive bacteria (methicillin-susceptible S. aureus/methicillin-resistant S. aureus and S. epidermidis). Coupled with our "biphasic" sample preparation technique (reported earlier) in a 4 μL sample volume, the assay could eliminate the need for blood culture, extraction & purification, providing Gram type information to guide clinical treatments.

Peripheral artery disease (PAD) is a significant worldwide health burden, yet it remains woefully under-diagnosed and under-treated. By the time PAD manifests symptomatically, patients may already suffer from critical limb ischemia (CLI) and require invasive surgical intervention. This can be attributed in part to the heterogeneity of PAD pathogenesis as well as shortcomings in current clinical assessment techniques and standards of care. To address these challenges, clinicians need new approaches to prevent, identify, and treat patients at risk for PAD. In this article, we present three clinical cases that exemplify different needs faced by PAD patients and their providers. These include the need for improved methods of disease detection, more efficient long-term care, and less-invasive treatment strategies. The case studies drive our subsequent review and discussion of key areas of translational research that may address each of these needs. The primary objective of this review is to evaluate the current limitations of clinical practice and survey the landscape of emerging research that will hopefully allow clinicians in the near future to better identify and treat patients with PAD. This research encompasses both technological advances in the diagnosis and treatment of the disease, as well as the development of new strategies for optimizing patient management and care delivery. When combined, these different areas of research will address some of the most pressing challenges posed by PAD, and will help usher in a new era of holistic, patient-centric care.

PURPOSE: Blood is an important but limited resource and deferring packed red blood cell (PRBC) transfusions for iron infusion have been shown to be effective and superior to liberal transfusion strategies in several clinical studies.The American Society of Hematology and the American Association of Blood Banks recommend avoiding PRBC transfusions for iron deficiency without hemodynamic instability.The objective of this study was to identify unwarranted PRBC transfusions and replace them with iron infusion to avoid resource shortage.METHODS: Data was collected securely using the Carle REDCAP database, unique identifiers were assigned for patients, further data was analyzed, and outcomes were reported using IBM SPSS Version 26.Tests used for analysis: Comparing means, one-way analysis of variance using Tukey-b, posthoc tests used: Games-Howell.The primary outcome was the appropriateness of PRBC transfusion.Parameters that were used include HgB/HCt, MCV, ferritin, and transferrin saturation, used to stratify the appropriateness of PRBC transfusion in patients with heart failure, CKD, preoperative, inflammatory, and non-inflammatory state.The same data was used to determine if iron infusion could have been used instead of PRBC transfusion. RESULTS:Out of 65 PRBC transfusion events in step-down ICU patients during the COVID-19 pandemic, 43.1% were noted to be indicated, 40% were noted to be unsure, and 16.9% were not indicated.The means for hemoglobin, hematocrit, and MCV were not significantly different across groups.The mean Hgb for the group indicated to receive IV iron was 7.56(SD¼1), while the mean Hgb for the group not indicated was noted to be 7.03(SD¼0.35).Post-hoc testing (Tukey's-b) revealed significant differences between groups for %Saturation(p¼0.02)and ferritin(p¼0.01).CONCLUSIONS: Previous studies have demonstrated restrictive transfusion strategies such as using lower hemoglobin thresholds to be associated with better outcomes.Our study demonstrates no significant differences in HgB/HCt or MCV between groups.Major limitations of the study include a lack of laboratory investigation of anemia characteristics, for a significant number of patients (40%).Data may further be confounded by the retrospective nature of the study and indications for PRBC based on clinician judgment, which may differ significantly among special populations such as acute coronary syndrome or cancer/ chemotherapy patients.CLINICAL IMPLICATIONS: Poor resource utilization with packed red blood cell (RBC) transfusions can lead to increased costs and reduced availability of RBCs for other patients, especially highlighted by widespread shortages noted amongst other health systems amongst others in the COVID-19 pandemic, and potential transfusion risks for patients.Hospital-wide systems designed to monitor, and screen for unnecessary transfusions and a roadmap for iron infusions may be the need of the day.

A 53-year-old male presented to the emergency room with chest pain, shortness of breath, and back pain. He had recently recovered from COVID-19 infection and returned home on room air. Chest imaging showed bilateral hydropneumothoraces that were not present on the imaging performed during his prior admission three weeks ago. The patient was treated with bilateral chest tube drainage and oxygen support and responded well to treatment. This case represents a unique occurrence of spontaneous loculated bilateral hydropneumothoraces in the context of recent clinical recovery from COVID-19 infection requiring inpatient treatment. This case highlights the importance of an awareness of a potential sequela of COVID-19 that may occur even after presumed clinical recovery.

Since the beginning of the COVID-19 pandemic, several mutations of the SARS-CoV-2 virus have emerged. Current gold standard detection methods for detecting the virus and its variants are based on PCR-based diagnostics using complex laboratory protocols and time-consuming steps, such as RNA isolation and purification, and thermal cycling. These steps limit the translation of technology to the point-of-care and limit accessibility to under-resourced regions. While PCR-based assays currently offer the possibility of multiplexed gene detection, and commercial products of single gene PCR and isothermal LAMP at point-of-care are also now available, reports of isothermal assays at the point-of-care with detection of multiple genes are lacking. Here, we present a microfluidic assay and device to detect and differentiate the Alpha variant (B.1.1.7) from the SARS-CoV-2 virus early strains in saliva samples. The detection assay, which is based on isothermal RT-LAMP amplification, takes advantage of the S-gene target failure (SGTF) to differentiate the Alpha variant from the SARS-CoV-2 virus early strains using a binary detection system based on spatial separation of the primers specific to the N- and S-genes. We use additively manufactured plastic cartridges in a low-cost optical reader system to successfully detect the SARS-CoV-2 virus from saliva samples (positive amplification is detected with concentration ≥10 copies per μL) within 30 min. We demonstrate that our platform can discriminate the B.1.1.7 variant (USA/CA_CDC_5574/2020 isolate) from SARS-CoV-2 negative samples, but also from the SARS-CoV-2 USA-WA1/2020 isolate. The reliability of the developed point-of-care device was confirmed by testing 38 clinical saliva samples, including 20 samples positive for Alpha variant (sensitivity > 90%, specificity = 100%). This study highlights the current relevance of binary-based testing, as the new Omicron variant also exhibits S-gene target failure and could be tested by adapting the approach presented here.

Blood stream infections (BSIs) cause high mortality, and their rapid detection remains a significant diagnostic challenge. Timely and informed administration of antibiotics can significantly improve patient outcomes. However, blood culture, which takes up to 5 d for a negative result, followed by PCR remains the gold standard in diagnosing BSI. Here, we introduce a new approach to blood-based diagnostics where large blood volumes can be rapidly dried, resulting in inactivation of the inhibitory components in blood. Further thermal treatments then generate a physical microscale and nanoscale fluidic network inside the dried matrix to allow access to target nucleic acid. The amplification enzymes and primers initiate the reaction within the dried blood matrix through these networks, precluding any need for conventional nucleic acid purification. High heme background is confined to the solid phase, while amplicons are enriched in the clear supernatant (liquid phase), giving fluorescence change comparable to purified DNA reactions. We demonstrate single-molecule sensitivity using a loop-mediated isothermal amplification reaction in our platform and detect a broad spectrum of pathogens, including gram-positive methicillin-resistant and methicillin-susceptible Staphylococcus aureus bacteria, gram-negative Escherichia coli bacteria, and Candida albicans (fungus) from whole blood with a limit of detection (LOD) of 1.2 colony-forming units (CFU)/mL from 0.8 to 1 mL of starting blood volume. We validated our assay using 63 clinical samples (100% sensitivity and specificity) and significantly reduced sample-to-result time from over 20 h to <2.5 h. The reduction in instrumentation complexity and costs compared to blood culture and alternate molecular diagnostic platforms can have broad applications in healthcare systems in developed world and resource-limited settings.

Sepsis is a major cause of mortality among hospitalized patients worldwide. Shorter time to administration of broad-spectrum antibiotics is associated with improved outcomes, but early recognition of sepsis remains a major challenge. In a two-center cohort study with prospective sample collection from 1400 adult patients in emergency departments suspected of sepsis, we sought to determine the diagnostic and prognostic capabilities of a machine-learning algorithm based on clinical data and a set of uncommonly measured biomarkers. Specifically, we demonstrate that a machine-learning model developed using this dataset outputs a score with not only diagnostic capability but also prognostic power with respect to hospital length of stay (LOS), 30-day mortality, and 3-day inpatient re-admission both in our entire testing cohort and various subpopulations. The area under the receiver operating curve (AUROC) for diagnosis of sepsis was 0.83. Predicted risk scores for patients with septic shock were higher compared with patients with sepsis but without shock (p < 0.0001). Scores for patients with infection and organ dysfunction were higher compared with those without either condition (p < 0.0001). Stratification based on predicted scores of the patients into low, medium, and high-risk groups showed significant differences in LOS (p < 0.0001), 30-day mortality (p < 0.0001), and 30-day inpatient readmission (p < 0.0001). In conclusion, a machine-learning algorithm based on electronic medical record (EMR) data and three nonroutinely measured biomarkers demonstrated good diagnostic and prognostic capability at the time of initial blood culture.

The COVID-19 pandemic has underscored the shortcomings in the deployment of state-of-the-art diagnostics platforms. Although several polymerase chain reaction (PCR)-based techniques have been rapidly developed to meet the growing testing needs, such techniques often need samples collected through a swab, the use of RNA extraction kits, and expensive thermocyclers in order to successfully perform the test. Isothermal amplification-based approaches have also been recently demonstrated for rapid severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) detection by minimizing sample preparation while also reducing the instrumentation and reaction complexity. In addition, there are limited reports of saliva as the sample source, and some of these indicate inferior sensitivity when comparing reverse transcription loop-mediated isothermal amplification (RT-LAMP) with PCR-based techniques. In this paper, we demonstrate an improved sensitivity assay from saliva using a two-step RT-LAMP assay, where a short 10 min RT step is performed with only B3 and backward inner primers before the final reaction. We show that while the one-step RT-LAMP demonstrates satisfactory results, the optimized two-step approach allows detection of only few molecules per reaction and performs significantly better than the one-step RT-LAMP and conventional two-step RT-LAMP approaches with all primers included in the RT step. We show control measurements with RT-PCR, and importantly, we demonstrate RNA extraction-free RT-LAMP-based assays for detection of SARS-CoV-2 from viral transport media and saliva clinical samples.

Point-of-care (POC) detection technologies that enable decentralized, rapid, sensitive, low-cost diagnostics of COVID-19 infection are urgently needed around the world. With many technologies approved for commercialization in the past 10 months, the field of COVID-19 POC diagnostics is rapidly evolving. In this Perspective, we analyze the current state of POC technologies for the diagnosis and monitoring of COVID-19 infection and discuss future challenges in COVID-19 diagnostics. As the COVID-19 pandemic becomes endemic, the advances gained during this past year will likely also be utilized for future prediction of emerging outbreaks and pandemics.

Learning Objectives: Sepsis is a syndrome defined as a lifethreatening organ dysfunction caused by a dysregulated host response triggered by an infection. To date, numerous clinical studies have captured aspects of the host response by measuring biomarkers pertinent to sepsis. Historically, the aim of these studies has been to demonstrate potential diagnostic/prognostic value of a set of biomarkers as opposed to specifically characterizing the degree of dysregulation of the host response. In our study, we sought to quantify the degree of dysregulation of the host response in a clinically relevant population in an intuitive and unbiased manner. Methods: We specifically hypothesized that a multi-dimensional measure of time-dependent abnormality could serve as a useful surrogate for quantifying dysregulation. In a cohort of 252 patients suspected of infection, we measured 4 biomarkers (IL-6, PCT, Pentraxin3, nCD64) spanning various aspects of the host response as a function of time per patient. We quantified multidimensional time-dependent abnormality through the formula sqrt(([x]-[y])[T][S][-1]([x]-[y])) where [x] referred to the 4 biomarker measurements at a given time point for a given patient, [S] referred to the covariance matrix of all biomarker measurements, and [y] referred to the 4 biomarker measurements at discharge for a given patient. We tested the association of relevant features/variables with the following sepsis-related outcomes: ICD9/10 codes (sepsis/severe sepsis/septic shock), the sepsis-3 definition, bacteremia, and length of stay. Results: There were strong associations between the multi-dimensional time-dependent abnormality metric and each of the above sepsis-related outcomes (p-values of 5E-3, 1E-5, 5E-7 and 2E-11 respectively). Overall, there was a greater number of stronger associations between each outcome and our abnormality metric compared to that of any individual biomarker or the SIRS or SOFA score. Furthermore, our abnormality metric yielded stronger associations than any individual biomarker for three out of the four sepsis-related outcomes. Conclusions: This study shows that a multi-dimensional measure of the time-dependent abnormality of biomarkers spanning various aspects of the host response could be a useful metric for physicians to evaluate the progression or resolution of a patient’s septic state.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Renal cell carcinoma is well-known for its propensity to present in unusual ways, and renal cell carcinoma presenting as pleural effusion is extremely rare. Pleural effusion secondary to renal cell carcinoma constitutes only about 1% to 2% of all malignant pleural effusions. We report the case of a 34-year-old man with no significant past medical or surgical history who presented in the Emergency Department with dyspnea. Chest x-ray demonstrated right-sided pleural effusion; computed tomography (CT) reported right-sided effusion in the pleura with suspicious mass in the upper border of left kidney. CT-guided pleural tap was performed and cytology was positive for vimentin and common acute lymphocytic leukemia antigen (CD10), leading to the diagnosis of primary renal cell carcinoma presenting as unilateral pleural effusion. While lungs are the common site of metastasis, the presentation of renal cell carcinoma as pleural effusion or pleural metastasis without lung involvement is rare.

Paraneoplastic cerebellar degeneration is a rare neurological complication triggered by certain underlying malignancies secondary to antibodies against cerebellar antigens. We describe the case of a 71-year-old female who presented with vertigo and ataxic gait. Initial imaging reported schwannoma, but it did not explain the reason for vertigo. Initial lab investigations and MRI Brain were unremarkable. Full body CT scan revealed a large heterogenous pelvic mass. Paraneoplastic panel was positive for anti-Yo antibodies. The patient underwent treatment for her ovarian cancer with surgery and chemotherapy. She had some improvement in her neurological symptoms.

Sepsis is a leading cause of death and is the most expensive condition to treat in U.S. hospitals. Despite targeted efforts to automate earlier detection of sepsis, current techniques rely exclusively on using either standard clinical data or novel biomarker measurements. In this study, we apply machine learning techniques to assess the predictive power of combining multiple biomarker measurements from a single blood sample with electronic medical record data (EMR) for the identification of patients in the early to peak phase of sepsis in a large community hospital setting. Combining biomarkers and EMR data achieved an area under the receiver operating characteristic (ROC) curve (AUC) of 0.81, while EMR data alone achieved an AUC of 0.75. Furthermore, a single measurement of six biomarkers (IL-6, nCD64, IL-1ra, PCT, MCP1, and G-CSF) yielded the same predictive power as collecting an additional 16 hours of EMR data(AUC of 0.80), suggesting that the biomarkers may be useful for identifying these patients earlier. Ultimately, supervised learning using a subset of biomarker and EMR data as features may be capable of identifying patients in the early to peak phase of sepsis in a diverse population and may provide a tool for more timely identification and intervention.

The case highlights an unusual presentation of sweet’s syndrome in a previously healthy male. Acute febrile neutrophilic dermatosis (Sweet’s syndrome) is characterized by pyrexia, neutrophilia, and the abrupt appearance of erythematous, painful, cutaneous plaques, primarily on the upper extremities, head, and neck [1]. Histologically, the salient feature is a dense neutrophilic dermal infiltrate. Approximately 10 to 15 percent of published cases of Sweet’s syndrome occurred in patients with cancer [2]. Response to systemic steroids is dramatic in virtually all patients, regardless of the presence of malignancy [2].

SESSION TYPE: Pleural Student/Resident Case Report Posters

INTRODUCTION:Gluteal compartment syndrome is a rare, often unrecognized syndrome that may manifest as renal failure, sepsis, and death. Delay in diagnosis can result in significant morbidity and possible mortality. We report a case of occult gluteal compartment syndrome causing unresolving rhabdomyolysis.CASE PRESENTATION:A 50-year-old Caucasian American man with history of chronic obstructive pulmonary disease was admitted status post fall and loss of consciousness for an unknown duration. Initial work-up revealed severe rhabdomyolysis, opioid abuse and acute renal failure. Inspite of three days of intensive therapy his condition did not improve and his renal failure worsened. On improvement of his condition three days later, he indicated some discomfort in his right hip. Physical examination was significant for swelling of the right gluteal region, which was tender and firm on palpation. A non-contrast CT scan showed evidence of gluteal compartment syndrome and emergent surgery resulted in significant improvement of his condition.CONCLUSION:Gluteal compartment syndrome most commonly occurs in individuals with altered mental status due to drugs or alcohol, who remain in one position for an extended period of time. This prolonged compression leads to muscle damage, edema, and a full-blown compartment syndrome. Due to its anatomic location and rarity, diagnosis is often missed or delayed, resulting in significant morbidity and possible mortality. The mainstay of treatment is fasciotomy.