COVID-19 waves in an urban setting 2020-2022: an electronic medical record analysis.

Background: Global and national surveillance efforts have tracked COVID-19 incidence and clinical outcomes, but few studies have compared comorbid conditions and clinical outcomes across each wave of the pandemic. We analyzed data from the COVID-19 registry of a large urban healthcare system to determine the associations between presenting comorbidities and clinical outcomes during the pandemic. Methods: We analyzed registry data for all inpatients and outpatients with COVID-19 from March 2020 through September 2022 (N = 44,499). Clinical outcomes were death, hospitalization, and intensive care unit (ICU) admission. Demographic and clinical outcomes data were analyzed overall and for each wave. Unadjusted and multivariable logistic regressions were performed to explore the associations between age, sex, race, ethnicity, comorbidities, and mortality. Results: Waves 2 and 3 (Alpha and Delta variants) were associated with greater hospitalizations, ICU admissions, and mortality than other variants. Chronic pulmonary disease was the most common comorbid condition across all age groups and waves. Mortality rates were higher in older patients but decreased across all age groups in later waves. In every wave, mortality was associated with renal disease, congestive heart failure, cerebrovascular disease, diabetes, and chronic pulmonary disease. Multivariable analysis found that liver disease and renal disease were significantly associated with mortality, hospitalization, and ICU admission, and diabetes was significantly associated with hospitalization and ICU admission. Conclusion: The COVID-19 registry is a valuable resource to identify risk factors for clinical outcomes. Our findings may inform risk stratification and care planning for patients with COVID-19 based on age and comorbid conditions.

Validation of the novel GLAS algorithm as an aid in the detection of liver fibrosis and cirrhosis based on GP73, LG2m, age, and sex.

BACKGROUND: Diagnosis of liver disease at earlier stages can improve outcomes and reduce the risk of progression to malignancy. Liver biopsy is the gold standard for diagnosis of liver disease, but is invasive and sample acquisition errors are common. Serum biomarkers for liver function and fibrosis, combined with patient factors, may allow for noninvasive detection of liver disease. In this pilot study, we tested and validated the performance of an algorithm that combines GP73 and LG2m serum biomarkers with age and sex (GLAS) to differentiate between patients with liver disease and healthy individuals in two independent cohorts. METHODS: To develop the algorithm, prototype immunoassays were used to measure GP73 and LG2m in residual serum samples collected between 2003 and 2016 from patients with staged fibrosis and cirrhosis of viral or non-viral etiology (n = 260) and healthy subjects (n = 133). The performance of five predictive models using combinations of age, sex, GP73, and/or LG2m from the development cohort were tested. Residual samples from a separate cohort with liver disease (fibrosis, cirrhosis, or chronic liver disease; n = 395) and healthy subjects (n = 106) were used to validate the best performing model. RESULTS: GP73 and LG2m concentrations were higher in patients with liver disease than healthy controls and higher in those with cirrhosis than fibrosis in both the development and validation cohorts. The best performing model included both GP73 and LG2m plus age and sex (GLAS algorithm), which had an AUC of 0.92 (95% CI: 0.90-0.95), a sensitivity of 88.8%, and a specificity of 75.9%. In the validation cohort, the GLAS algorithm had an estimated an AUC of 0.93 (95% CI: 0.90-0.95), a sensitivity of 91.1%, and a specificity of 80.2%. In both cohorts, the GLAS algorithm had high predictive probability for distinguishing between patients with liver disease versus healthy controls. CONCLUSIONS: GP73 and LG2m serum biomarkers, when combined with age and sex (GLAS algorithm), showed high sensitivity and specificity for detection of liver disease in two independent cohorts. The GLAS algorithm will need to be validated and refined in larger cohorts and tested in longitudinal studies for differentiating between stable versus advancing liver disease over time.

Kinetics of SARS-CoV-2 Serum Antibodies Through the Alpha, Delta, and Omicron Surges Among Vaccinated Health Care Workers at a Boston Hospital.

Background: Longitudinal serology studies can assist in analyzing the kinetics of antibodies to SARS-CoV-2, helping to inform public health decision making. Our study aims to characterize circulating antibody trends over 18 months in vaccinated participants with and without evidence of COVID-19 infection. Methods: A cohort of health care workers employed at Boston Medical Center was followed to collect serum samples and survey data over 6 time points from July 2020 through December 2021 (N = 527). History of SARS-CoV-2 infection, vaccination, and booster status were confirmed, where possible, through electronic medical records. Serum was assessed for the qualitative and semiquantitative detection of IgG antibody levels (anti-nucleoprotein [anti-N] and anti-spike [anti-S], respectively). Piecewise regression models were utilized to characterize antibody kinetics over time. Results: Anti-S IgG titers remained above the positivity threshold following infection and/or vaccination throughout the 18-month follow-up. Among participants with no evidence of COVID-19 infection, titers declined significantly faster in the initial 90 days after full vaccination (ß = -0.056) from December 2020 to March 2021 as compared with the decline observed following booster dose uptake (ß = -0.023, P < 0.001). Additionally, COVID-19 infection prior to vaccination significantly attenuated the decline of anti-S IgG when compared with no infection following vaccine uptake (P < 0.001). Lastly, fewer participants contracted Omicron when boosted (12.7%) compared to fully vaccinated (17.6%). Regardless of vaccination status, participants who were Omicron positive had lower anti-S IgG titers than those who did not test positive, but this difference was not significant. Conclusions: These findings provide novel 18-month kinetics of anti-S IgG antibodies and highlight the durability of hybrid immunity, underlining the strong humoral response stimulated by combined infection and vaccination.

Optimizing Screening for Colorectal Cancer: An Algorithm Combining Fecal Immunochemical Test, Blood-Based Cancer-Associated Proteins and Demographics to Reduce Colonoscopy Burden.

BACKGROUND: Fecal Immunochemical Test (FIT) is widely used in population-based screening for colorectal cancer (CRC). This had led to major challenges regarding colonoscopy capacity. Methods to maintain high sensitivity without compromising the colonoscopy capacity are needed. This study investigates an algorithm that combines FIT result, blood-based biomarkers associated with CRC, and individual demographics, to triage subjects sent for colonoscopy among a FIT positive (FIT+) screening population and thereby reduce the colonoscopy burden. MATERIALS AND METHODS: From the Danish National Colorectal Cancer Screening Program, 4048 FIT+ (≥100 ng/mL Hemoglobin) subjects were included and analyzed for a panel of 9 cancer-associated biomarkers using the ARCHITECT i2000. Two algorithms were developed: 1) a predefined algorithm based on clinically available biomarkers: FIT, age, CEA, hsCRP and Ferritin; and 2) an exploratory algorithm adding additional biomarkers: TIMP-1, Pepsinogen-2, HE4, CyFra21-1, Galectin-3, B2M and sex to the predefined algorithm. The diagnostic performances for discriminating subjects with or without CRC in the 2 models were benchmarked against the FIT alone using logistic regression modeling. RESULTS: The discrimination of CRC showed an area under the curve (AUC) of 73.7 (70.5-76.9) for the predefined model, 75.3 (72.1-78.4) for the exploratory model, and 68.9 (65.5-72.2) for FIT alone. Both models performed significantly better (P < .001) than the FIT model. The models were benchmarked vs. FIT at cutoffs of 100, 200, 300, 400, and 500 ng/mL Hemoglobin using corresponding numbers of true positives and false positives. All performance metrics were improved at all cutoffs. CONCLUSION: A screening algorithm including a combination of FIT result, blood-based biomarkers and demographics outperforms FIT in discriminating subjects with or without CRC in a screening population with FIT results above 100 ng/mL Hemoglobin.

Challenges to Using Big Data in Cancer.

Big data in healthcare can enable unprecedented understanding of diseases and their treatment, particularly in oncology. These data may include electronic health records, medical imaging, genomic sequencing, payor records, and data from pharmaceutical research, wearables, and medical devices. The ability to combine datasets and use data across many analyses is critical to the successful use of big data and is a concern for those who generate and use the data. Interoperability and data quality continue to be major challenges when working with different healthcare datasets. Mapping terminology across datasets, missing and incorrect data, and varying data structures make combining data an onerous and largely manual undertaking. Data privacy is another concern addressed by the Health Insurance Portability and Accountability Act, the Common Rule, and the General Data Protection Regulation. The use of big data is now included in the planning and activities of the FDA and the European Medicines Agency. The willingness of organizations to share data in a precompetitive fashion, agreements on data quality standards, and institution of universal and practical tenets on data privacy will be crucial to fully realizing the potential for big data in medicine.

Case Studies for Overcoming Challenges in Using Big Data in Cancer.

The analysis of big healthcare data has enormous potential as a tool for advancing oncology drug development and patient treatment, particularly in the context of precision medicine. However, there are challenges in organizing, sharing, integrating, and making these data readily accessible to the research community. This review presents five case studies illustrating various successful approaches to addressing such challenges. These efforts are CancerLinQ, the American Association for Cancer Research Project GENIE, Project Data Sphere, the National Cancer Institute Genomic Data Commons, and the Veterans Health Administration Clinical Data Initiative. Critical factors in the development of these systems include attention to the use of robust pipelines for data aggregation, common data models, data deidentification to enable multiple uses, integration of data collection into physician workflows, terminology standardization and attention to interoperability, extensive quality assurance and quality control activity, incorporation of multiple data types, and understanding how data resources can be best applied. By describing some of the emerging resources, we hope to inspire consideration of the secondary use of such data at the earliest possible step to ensure the proper sharing of data in order to generate insights that advance the understanding and the treatment of cancer.

Development of exploratory algorithms to aid in risk of malignancy prediction of indeterminate pulmonary nodules.

Early detection of lung cancer allows for earlier stage treatment initiation and improved patient prognosis. This report focuses on utilization of combining patient demographic information with non-invasive biomarkers and their potential ability to predict risk of malignancy of nodules. A pilot study cohort of 141 subjects with IPNs (105 stage I cancer and 36 benign nodules) were collected by RUMC. The demographic variables of gender, age, sex, race, ethnicity, nodule size (mm), and smoking pack years, as well as the plasma levels of CA-125, SCC, CEA, HE4, ProGRP, NSE, Cyfra 21-1, hs-CRP, Ferritin, IgG, IgG1, IgG2, IgG3, IgG4, IgE, IgM, IgA, KFLC, and LFLC, were assessed for this cohort. Multivariable analyses of the previously aforementioned biomarkers and demographic variables yielded a reduced algorithm consisting of CA-125, total IgG, IgA, IgM, IgE, LFLC, nodule size, and smoking pack years with improved performance (AUC 0.82, 95 %CI 0.74-0.90) over the same analysis of the demographic variables (age, nodule size, and smoking pack years) alone (AUC 0.70, 95 %CI 0.61-0.78). This reduced algorithm of biomarkers and demographic variables may aid in assessing the risk of IPN malignancy which could be a useful stratification tool in early detection of lung cancer in high-risk subjects.

Biomarker prognostication across Universal Definition of Heart Failure stages.

AIM: The Universal Definition of Heart Failure (UDHF) provides a framework for staging risk for HF events. It is not clear whether prognostic biomarkers have different meaning across UDHF stages. We sought to evaluate performance of biomarkers to predict HF events among high-risk patients undergoing coronary and/or peripheral angiography categorized into UDHF stages. METHODS: One thousand two hundred thirty-five individuals underwent coronary and/or peripheral angiography were enrolled. Study participants were categorized into UDHF Stage A (at risk), Stage B (pre-HF), and Stage C or D (HF, including end stage) and grouped into Stage A/B and C/D. Biomarkers and clinical variables were used to develop prognostic models. Other measures examined included total HF hospitalizations. RESULTS: Over a median of 3.67 years of follow-up, 155 cardiovascular (CV) deaths occurred, and 299 patients were hospitalized with acute HF. In patients with Stage A/B, galectin-3 (HR = 1.52, P = 0.03), endothelin-1 (HR = 2.16, P = 0.001), and N-terminal pro-B-type natriuretic peptide (NT-proBNP; HR = 1.43, P < 0.001) were associated with incident CV death/HF hospitalization. In Stage C/D, NT-proBNP (HR = 1.26, P = 0.006), soluble urokinase-type plasminogen activator receptor (suPAR; HR = 1.57, P = 0.007) and high-sensitivity C-reactive protein (hs-CRP; HR = 1.15, P = 0.01) were associated with these outcomes. Higher biomarker concentrations were associated with greater total burden of HF events in Stages A/B and C/D. CONCLUSIONS: Among higher risk individuals undergoing angiographic procedures, different biomarkers improve risk stratification in different UDHF stages of HF. More precise prognostication may offer a window of opportunity to initiate targeted preventive measures.

Improving malignancy risk prediction of indeterminate pulmonary nodules with imaging features and biomarkers.

BACKGROUND: Non-invasive biomarkers are needed to improve management of indeterminate pulmonary nodules (IPNs) suspicious for lung cancer. METHODS: Protein biomarkers were quantified in serum samples from patients with 6-30 mm IPNs (n = 338). A previously derived and validated radiomic score based upon nodule shape, size, and texture was calculated from features derived from CT scans. Lung cancer prediction models incorporating biomarkers, radiomics, and clinical factors were developed. Diagnostic performance was compared to the current standard of risk estimation (Mayo). IPN risk reclassification was determined using bias-corrected clinical net reclassification index. RESULTS: Age, radiomic score, CYFRA 21-1, and CEA were identified as the strongest predictors of cancer. These models provided greater diagnostic accuracy compared to Mayo with AUCs of 0.76 (95 % CI 0.70-0.81) using logistic regression and 0.73 (0.67-0.79) using random forest methods. Random forest and logistic regression models demonstrated improved risk reclassification with median cNRI of 0.21 (Q1 0.20, Q3 0.23) and 0.21 (0.19, 0.23) compared to Mayo for malignancy. CONCLUSIONS: A combined biomarker, radiomic, and clinical risk factor model provided greater diagnostic accuracy of IPNs than Mayo. This model demonstrated a strong ability to reclassify malignant IPNs. Integrating a combined approach into the current diagnostic algorithm for IPNs could improve nodule management.

Biomarker Changes in Response to a 12-Week Supplementation of an Oral Nutritional Supplement Enriched with Protein, Vitamin D and HMB in Malnourished Community Dwelling Older Adults with Sarcopenia.

Malnutrition and sarcopenia commonly overlap and contribute to adverse health outcomes. Previously, chronic supplementation with two oral nutritional supplements (ONS), control (CONS) and experimental ONS enriched with protein, vitamin D and ß-hydroxy ß-methylbutyrate (HMB) (EONS), improved muscle strength and quality in malnourished sarcopenic older adults, with EONS demonstrating early strength benefits at 12 weeks. To understand the underlying biological mechanisms contributing to the observed early strength benefits of EONS, we examined serum biomarker changes in response to 12-week supplementation. Serum samples (EONS (n = 90) and CONS (n = 103)) collected at baseline and 12 weeks were analyzed. Biomarkers (n = 243) were measured using multiplexed immunoassay, commercial immunoassays and ELISAs. Sixty markers were excluded with levels below assay detection limits. Sixteen biomarkers significantly changed in response to both interventions including nutritional and metabolic markers. Thirteen biomarkers significantly changed in response to EONS but not CONS. Increases in immunoglobulins, myoglobin, total protein, vitamin E and magnesium were observed with EONS. Inflammation-related ferritin and osteopontin decreased, while soluble receptors for cytokines increased, suggesting decreased inflammation. Sex hormone-binding globulin associated with sarcopenia also decreased with EONS. Biomarkers reflective of multiple biological systems were impacted by nutritional intervention in sarcopenic older adults. Incremental biomarker changes were observed in response to EONS containing HMB that possibly link to improvements in skeletal muscle health.

Evaluation of hepatitis C virus antibody assay using dried blood spot samples.

Early diagnosis of hepatitis C virus (HCV) infection is essential for prompt initiation of treatment and prevention of transmission, yet several logistical barriers continue to limit access to HCV testing. Dried blood spot (DBS) technology involves a simple fingerstick that eliminates the need for trained personnel, and DBS can be stored and transported at room temperature. We evaluated the use of DBS whole blood samples in the modified Abbott ARCHITECT anti-HCV assay, comparing assay performance against the standard assay run using DBS and venous plasma samples. 144 HCV positive and 104 HCV negative matched venous plasma and whole blood specimens were selected from a retrospective study with convenience sampling in Cameroon. Results obtained using a modified volume DBS assay were highly correlated to the results of the standard assay run with plasma on clinical samples and dilution series (R2 = 0.71 and 0.99 respectively). The ARCHITECT Anti-HCV assay with input volume modification more accurately detects HCV antibodies in DBS whole blood samples with 100% sensitivity and specificity, while the standard assay had 90.97% sensitivity. The use of DBS has the potential to expand access to HCV testing to underserved or marginalized populations with limited access to direct HCV care.

Early detection of colorectal neoplasia: application of a blood-based serological protein test on subjects undergoing population-based screening.

BACKGROUND: Blood-based biomarkers used for colorectal cancer screening need to be developed and validated in appropriate screening populations. We aimed to develop a cancer-associated protein biomarker test for the detection of colorectal cancer in a screening population. METHODS: Participants from the Danish Colorectal Cancer Screening Program were recruited. Blood samples were collected prior to colonoscopy. The cohort was divided into training and validation sets. We present the results of model development using the training set. Age, sex, and the serological proteins CEA, hsCRP, TIMP-1, Pepsinogen-2, HE4, CyFra21-1, Galectin-3, ferritin and B2M were used to develop a signature test to discriminate between participants with colorectal cancer versus all other findings at colonoscopy. RESULTS: The training set included 4048 FIT-positive participants of whom 242 had a colorectal cancer. The final model for discriminating colorectal cancer versus all other findings at colonoscopy had an AUC of 0.70 (95% CI: 0.66-0.74) and included age, sex, CEA, hsCRP, HE4 and ferritin. CONCLUSION: The performance of the biomarker signature in this FIT-positive screening population did not reflect the positive performance of biomarker signatures seen in symptomatic populations. Additional biomarkers are needed if the serological biomarkers are to be used as a frontline screening test.

Instability of corticotropin during long-term storage - myth or reality?

OBJECTIVES: Corticotropin is notorious for its instability. Whereas several studies have investigated its short-term stability in plasma following venous blood sampling, studies on long-term stability are lacking. Here we investigated the long-term storage stability of corticotropin in ethylenediaminetetraacetic acid containing plasma. METHODS: Specimens from healthy volunteers (neat, spiked) were stored in polypropylene microcentrifuge tubes with socket screw-caps at -20 °C and -70 °C for up to one and a half years. Corticotropin in plasma was measured using an Abbott research only immunoassay. Separately, specimens from patients were collected during diagnostic routine testing and stored in polystyrene tubes with push-caps at -20 °C for up to 6 years. In these samples corticotropin hormone was measured using the Diasorin corticotropin immunoassay. RESULTS: Storage of specimens at -20 °C or -70 °C for up to one and a half years showed minimal changes (<11%) in corticotropin levels, while storage of patient samples at -20 °C for up to 6 years showed a significant (54%) reduction in corticotropin levels. CONCLUSIONS: Corticotropin levels are stable in plasma when stored at -20 °C for one and a half years using the Abbott research only assay, but with longer storage time a significant reduction in corticotropin levels can be expected. Once specimens are stored for future corticotropin measurements, one should consider storage time, storage temperature and assay differences.

Serum biomarkers that predict lean mass loss over bed rest in older adults: An exploratory study.

BACKGROUND: Lean mass (LM) loss during extended bed rest contributes to long term functional decline in older adults. Identifying blood biomarkers that predict a hospitalized individual's risk of losing LM could allow for timely intervention. METHODS: LM from 19 healthy subjects (age 60-76 y, 4 males, 15 females), who were confined to 10 days of complete bed rest, was measured pre- and post-bed rest. One hundred eighty-seven biomarkers from pre-bed rest fasted serum samples were obtained from all evaluable subjects (n = 18), analyzed using multiplexed immunoassay array and pooled. Decision tree analysis was used to identify pre-bed rest markers that predict LM loss over bed rest. RESULTS: Sixty-three markers were excluded due to being below assay detection limits. One pair of markers, Tissue inhibitor of metalloprotease-1 (TIMP1) and tenascin C (TNC), were found to correlate with percent change in total LM over bed rest: [R2 = 0.71, all subjects; R2 = 0.76, females]. Subjects with pre-bed rest TIMP1 ≥ 141 ng/ml had the highest loss of total LM over bed rest, whereas subjects with pre-bed rest TIMP1 < 141 and TNC ≥ 461 ng/ml maintained total LM over bed rest. An additional marker set was found to correlate with percent change in leg LM loss over bed rest: matrix metalloprotease-3 (MMP3) and apolipoprotein A2 (APOA2) [R2 = 0.59, females]. Females with pre-bed rest MMP3 < 6.93 ng/ml had the highest loss of leg LM over bed rest. Whereas females with pre-bed rest MMP3 ≥ 6.93 and ApoA2 < 276 ng/ml, maintained leg lean mass at the end of bed rest. CONCLUSIONS: Panels of blood biomarkers associated with the muscle extracellular matrix may predict the likelihood for LM loss over extended bed rest.

Evaluation of algorithm development approaches: Development of biomarker panels for early detection of colorectal lesions.

INTRODUCTION: Colorectal cancer (CRC) is the third most common cancer in the U.S. Early detection of CRC can substantially increase survival rates. Test compliance may be improved by offering a blood-based test option. METHODS: Endoscopy II trial specimens were tested for AFP, CA19-9, CEA, hs-CRP, CyFra 21-1, Ferritin, Galectin-3, and TIMP-1 levels. These biomarkers, as well as patient demographic information (e.g., age, gender), were included in algorithm development. Six statistical methods were utilized to develop algorithms that would discriminate cancer vs. noncancers. Statistical methods included logistic regression, adaptive index modeling, partial least-squares discriminant analysis, feature vector (weighted and unweighted), and random forest. The performance of these algorithms was compared against benchmark criteria established for stool-based tests. RESULTS: Using several statistical methods, the presence of CRC and high-risk adenomas was detected with an AUCs of at least 0.65-0.76, with a few of models approaching the stool-based tests benchmark performance. Further, common markers were utilized across the different statistical techniques, with model complexities ranging from 3 to 9 markers. CONCLUSIONS: Predictive models identified subjects with CRC and high-risk adenomas with the similar levels of statistical accuracy. Clinical performance differences were minimal across the statistical techniques, although the intuitive interpretations, model complexity, clinical adoption and implementation varied.

Validation of a novel model for the early detection of hepatocellular carcinoma.

BACKGROUND: The biomarkers alpha-fetoprotein (AFP) and protein induced by vitamin K absence/antagonist-II (PIVKA-II) may be useful for detecting early-stage hepatocellular carcinoma (HCC). We evaluated the performance of AFP and PIVKA-II levels, alone and in combination with clinical factors, for the early detection of HCC. METHODS: In a case-control study, serum AFP and PIVKA-II were measured using the ARCHITECT immunoassay analyzer system in a cohort of 119 patients with HCC, 215 patients with non-malignant liver disease, and 34 healthy subjects. Five predictive models for detecting HCC were developed based on age, gender, AFP, and/or PIVKA-II levels; the best model was validated in an independent cohort of 416 patients with HCC and 412 control subjects with cirrhosis. RESULTS: In both cohorts, AFP and PIVKA-II concentrations were higher in patients with HCC compared to healthy controls and patients with non-malignant liver disease. The model that combined AFP and PIVKA-II, age, and gender had the highest AUC of 0.95 (0.95, 95% CI 0.93-0.98), with a sensitivity of 93% and a specificity of 84% in the development cohort, and an AUC of 0.87 (95% CI 0.85-0.90), sensitivity of 74%, and specificity of 85% in the validation cohort. When limiting the validation cohort to only early-stage HCC, the AUC was 0.85 (95% CI 0.81-0.88), sensitivity was 70%, and specificity was 86%. CONCLUSIONS: Compared to each biomarker alone, the combination of AFP and PIVKA-II with age and gender improved the accuracy of detecting HCC and differentiating HCC from non-malignant liver disease.

Clean Colorectum at Diagnostic Colonoscopy: Subsequent Detection of Extracolonic Malignancies by Plasma Protein Biomarkers?

INTRODUCTION: Most of the subjects undergoing diagnostic colonoscopy do not have neoplastic bowel lesions. Potentially, some of the symptoms may therefore be caused by extracolonic malignancy, and subjects with persisting symptoms may need subsequent examinations. Blood-based, cancer-associated biomarkers may aid in directing the examinations for other specific malignant diseases. METHODS: EDTA plasma samples available from a previous prospective study of subjects undergoing diagnostic colonoscopy were used for analysis of 18 protein biomarkers. The study population of 3732 subjects included 400 patients with colorectal cancer (CRC) and 177 patients with extracolonic malignancies. Univariable analysis of the association of specific biomarkers and extracolonic cancers included those with 10 or more cases. Subsequently, reduced models of 4 or 6 biomarkers, respectively, were established by choosing those with the highest likelihood; age and sex were included as well. RESULTS: Univariable analyses showed that CyFra21-1 had an area under curve (AUC) of 0.87 for lung cancers (n = 33), CA19-9 had an AUC of 0.85 for pancreatic cancer (n = 22), CA125 had an AUC of 0.95 for ovary cancer (n = 16), B2M had an AUC of 0.81 for non-Hodgkin lymphoma (n = 12), and total prostate-specific antigen had an AUC of 0.99 for prostate cancer (n = 10). The multivariable analysis of 4 or 6 biomarkers plus age and sex as explanatory variables showed AUCs of 0.82 to 0.85 both for extracolonic cancers and CRC. The 4 biomarkers included in the model for detection of extracolonic cancers were CA125, hsCRP, CA19-9, and CyFra21-1; the 2 additional for the 6 biomarkers model were CEA and Galectin-3. Similarly, the 4 biomarkers included in the model for detection of CRC were CEA, CyFra21-1, Ferritin, and HE4; the two additional for the 6 biomarkers model were hsCRP and Pepsinogen 2. CONCLUSIONS: Results of this study indicate that it may be possible to detect subjects that have an increased risk of extracolonic cancer following a colonoscopy without findings of neoplastic lesions. Combinations of various protein biomarkers may direct subsequent examination after colonoscopy with clean colorectum. The results, although preliminary, may form the basis for additional research directed both for primary examinations of subjects with symptoms of malignancy and subsequent examinations after colonoscopy.

Incidence and Prevalence of Achalasia in Central Chicago, 2004-2014, Since the Widespread Use of High-Resolution Manometry.

BACKGROUND & AIMS: Reported global incidence and prevalence values for achalasia vary widely, from 0.03 to 1.63 per 100,000 persons per year and from 1.8 to 12.6 per 100,000 persons per year, respectively. This study aimed to reconcile these low values with findings from a major referral center, in central Chicago (which began using high-resolution manometry in 2004 and used it in all clinical studies since 2005), and has determined the incidence and prevalence of achalasia to be much greater. METHODS: We collected data from the Northwestern Medicine Enterprise Data Warehouse database (tertiary care setting) of adults residing in Chicago with an encounter diagnosis of achalasia from 2004 through 2014. Patient files were reviewed to confirm diagnosis and residential address. US Census Bureau population data were used as the population denominator. We assumed that we encountered every incident case in the city to calculate incidence and prevalence estimates. Data were analyzed for the city at large and for the 13 zip codes surrounding the Northwestern Memorial Hospital (NMH), the NMH neighborhood. RESULTS: We identified 379 cases (50.9% female) that met the full inclusion criteria; of these, 246 were incident cases. Among these, 132 patients resided in the NMH neighborhood, 89 of which were incident cases. Estimated yearly incidences were stable over the study period, ranging from 0.77 to 1.35 per 100,000 citywide (average, 1.07 per 100,000) and from 1.41 to 4.60 per 100,000 in the NMH neighborhood (average, 2.92 per 100,000). The corresponding prevalence values increased progressively, from 4.68 to 14.42 per 100,000 citywide and from 15.64 to 32.58 per 100,000 in the NMH neighborhood. CONCLUSIONS: The incidence and prevalence of achalasia in central Chicago diagnosed using state-of-the-art technology and diagnostic criteria are at least 2- to 3-fold greater than previous estimates. Additional studies are needed to determine the generalizability of these data to other regions.

Detection of colorectal neoplasia: Combination of eight blood-based, cancer-associated protein biomarkers.

Serological biomarkers may be an option for early detection of colorectal cancer (CRC). The present study assessed eight cancer-associated protein biomarkers in plasma from subjects undergoing first time ever colonoscopy due to symptoms attributable to colorectal neoplasia. Plasma AFP, CA19-9, CEA, hs-CRP, CyFra21-1, Ferritin, Galectin-3 and TIMP-1 were determined in EDTA-plasma using the Abbott ARCHITECT® automated immunoassay platform. Primary endpoints were detection of (i) CRC and high-risk adenoma and (ii) CRC. Logistic regression was performed. Final reduced models were constructed selecting the four biomarkers with the highest likelihood scores. Subjects (N = 4,698) were consecutively included during 2010-2012. Colonoscopy detected 512 CRC patients, 319 colonic cancer and 193 rectal cancer. Extra colonic malignancies were detected in 177 patients, 689 had adenomas of which 399 were high-risk, 1,342 had nonneoplastic bowell disease and 1,978 subjects had 'clean' colorectum. Univariable analysis demonstrated that all biomarkers were statistically significant. Multivariate logistic regression demonstrated that the blood-based biomarkers in combination significantly predicted the endpoints. The reduced model resulted in the selection of CEA, hs-CRP, CyFra21-1 and Ferritin for the two endpoints; AUCs were 0.76 and 0.84, respectively. The postive predictive value at 90% sensitivity was 25% for endpoint 1 and the negative predictive value was 93%. For endpoint 2, the postive predictive value was 18% and the negative predictive value was 97%. Combinations of serological protein biomarkers provided a significant identification of subjects with high risk of the presence of colorectal neoplasia. The present set of biomarkers could become important adjunct in early detection of CRC.

BioFire FilmArray Respiratory Panel for Detection of Enterovirus D68.

During the enterovirus D68 (EV-D68) outbreak of 2014, the BioFire FilmArray (FA) respiratory panel was used to detect rhinovirus/enterovirus in respiratory specimens; suspected EV-D68-positive specimens were sent to CDC for confirmation. Positive rhinovirus/enterovirus FA targets revealed patterns loosely associated with EV-D68 that may be useful for confirmation triaging.