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.
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.
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.
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.
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 (R
= 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.
Implementation of screening modalities have reduced the burden of colorectal cancer (CRC), but high false positive rates pose a major problem for colonoscopy capacity. We aimed to create a tailored ...screening algorithm that expands the fecal immunochemical test (FIT) with a blood specimen and current age to improve selection of individuals for diagnostic colonoscopy.
In this prospective multi-center study, eight blood-based biomarkers (CEA, Ferritin, hsCRP, HE4, Cyfra21-1, Hepsin, IL-8 and OPG) were investigated in 1,977 FIT positive individuals from the Danish national CRC screening program undergoing follow-up colonoscopy. Specimens were analyzed on ARCHITECT i2000®, ARCHITECT c8000® or Luminex xMAP® machines. FIT analyses and blood-based biomarker data were combined with clinical data (i.e., age and colonoscopy findings) in a cross-validated logistic regression model (algorithm) benchmarked against a model solely using the FIT result (FIT model) applying different cutoffs for FIT positivity.
The cohort included individuals with CRC (n = 240), adenomas (n = 938) or no neoplastic lesions (n = 799). The cross-validated algorithm combining the eight biomarkers, quantitative FIT result and age performed superior to the FIT model in discriminating CRC versus non-CRC individuals (AUC 0.77 versus 0.67, p < 0.001). When discriminating individuals with either CRC or high- or medium-risk adenomas versus low-risk adenomas or clean colorectum, the AUCs were 0.68 versus 0.64 for the algorithm and FIT model, respectively.
The algorithm presented here can improve patient allocation to colonoscopy, reducing colonoscopy burden without compromising cancer and adenomas detection rates or vice versa.
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Challenges to Using Big Data in Cancer Sweeney, Shawn M; Hamadeh, Hisham K; Abrams, Natalie ...
Cancer research (Chicago, Ill.),
04/2023, Letnik:
83, Številka:
8
Journal Article
Recenzirano
Odprti dostop
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.
BACKGROUND:To assess trends in mortality and cause of death for women with HIV, we studied deaths over a 10-year period among participants in the Womenʼs Interagency HIV Study, a representative US ...cohort.
METHODS:Deaths were ascertained by National Death Index Plus match, and causes of death determined by death certificate.
RESULTS:From 1995 through 2004, 710 of 2792 HIV-infected participants died. During this interval, the standardized mortality ratio fell from a high of 24.7 in 1996 to a plateau with a mean of 10.3 from 2001 to 2004. Over the decade, deaths from non-AIDS causes increased and accounted for the majority of deaths by 2001-2004. The most common non-AIDS causes of death were trauma or overdose, liver disease, cardiovascular disease, and malignancy. Independent predictors of mortality besides HIV-associated variables were depressive symptoms and active hepatitis B or C. Women who were overweight or obese were significantly less likely to die of AIDS than women of normal weight.
CONCLUSIONS:In the Womenʼs Interagency HIV Study, the death rate has plateaued in recent years. Although HIV-associated factors predicted AIDS and non-AIDS deaths, other treatable conditions predicted mortality. Further gains in reducing mortality among HIV-infected women may require broader access to therapies for depression, viral hepatitis, and HIV itself.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
We analyzed registry data for all inpatients and outpatients with COVID-19 from March 2020 through September 2022 (
= 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.
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.
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.
•Lean mass loss due to extended bed rest/hospitalization leads to long-term functional decline in older adults.•Blood biomarkers predictors of lean mass loss could increase awareness of lean mass ...loss that occurs during hospitalization.•Two pilot biomarker panels could predict lean mass loss over extended bed rest in older adults.•Panels included TIMP1 and Tenascin C, and Matrix metalloprotease-3 and Apolipoprotein A2.
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.
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.
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.
Panels of blood biomarkers associated with the muscle extracellular matrix may predict the likelihood for LM loss over extended bed rest.
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