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Supplementary Figure S1; Supplementary Tables S1-S3.

Purpose: This study was performed to discover prognostic genomic markers associated with postoperative outcome of stage I to III non–small cell lung cancer (NSCLC) that are reproducible between geographically distant and demographically distinct patient populations.Experimental Design: American patients (n = 27) were stratified on the basis of recurrence and microarray profiling of their tumors was performed to derive a training set of 44 genes. A larger Korean patient validation cohort (n = 138) was also stratified by recurrence and screened for these genes. Four reproducible genes were identified and used to construct genomic and clinicogenomic Cox models for both cohorts.Results: Four genomic markers, DBN1 (drebrin 1), CACNB3 (calcium channel beta 3), FLAD1 (PP591; flavin adenine dinucleotide synthetase), and CCND2 (cyclin D2), exhibited highly significant differential expression in recurrent tumors in the training set (P < 0.001). In the validation set, DBN1, FLAD1 (PP591), and CACNB3 were significant by Cox univariate analysis (P ≤ 0.035), whereas only DBN1 was significant by multivariate analysis. Genomic and clinicogenomic models for recurrence-free survival (RFS) were equally effective for risk stratification of stage I to II or I to III patients (all models P < 0.0001). For stage I to II or I to III patients, 5-year RFS of the low- and high-risk patients was approximately 70% versus 30% for both models. The genomic model for overall survival of stage I to III patients was improved by addition of pT and pN stage (P < 0.0013 vs. 0.010).Conclusion: A 4-gene prognostic model incorporating the multivariate marker DBN1 exhibits potential clinical utility for risk stratification of stage I to III NSCLC patients. Clin Cancer Res; 17(9); 2934–46. ©2011 AACR.

BackgroundIncidence of long COVID in the elderly is difficult to estimate and can be under-reported. While long COVID is sometimes considered a novel disease, many viral or bacterial infections have been known to cause prolonged illnesses. We postulate that some influenza patients might develop residual symptoms that would satisfy the diagnostic criteria for long COVID, a condition we call “long Flu”. In this study, we estimate the incidence of long COVID and long Flu among Medicare patients using the World Health Organization (WHO) consensus definition. We compare the incidence, symptomatology, and healthcare utilization between long COVID and long Flu patients.Methods and FindingsThis is a cohort study of Medicare (the U.S. federal health insurance program) beneficiaries over 65. ICD-10-CM codes were used to capture COVID-19, influenza and residual symptoms. Long COVID was identified by a) the designated long COVID-19 code B94.8 (code-based definition), or b) any of 11 symptoms identified in the WHO definition (symptom-based definition), from one to 3 months post infection. A symptom would be excluded if it occurred in the year prior to infection. Long Flu was identified in influenza patients from the combined 2018 and 2019 Flu seasons by the same symptom-based definition for long COVID. Long COVID and long Flu were compared in four outcome measures: a) hospitalization (any cause), b) hospitalization (for long COVID symptom), c) emergency department (ED) visit (for long COVID symptom), and d) number of outpatient encounters (for long COVID symptom), adjusted for age, sex, race, region, Medicare-Medicaid dual eligibility status, prior-year hospitalization, and chronic comorbidities. Among 2,071,532 COVID-19 patients diagnosed between April 2020 and June 2021, symptom-based definition identified long COVID in 16.6% (246,154/1,479,183) and 29.2% (61,631/210,765) of outpatients and inpatients respectively. The designated code gave much lower estimates (outpatients 0.49% (7,213/1,479,183), inpatients 2.6% (5,521/210,765)). Among 933,877 influenza patients, 17.0% (138,951/817,336) of outpatients and 24.6% (18,824/76,390) of inpatients fit the long Flu definition. Long COVID patients had higher incidence of dyspnea, fatigue, palpitations, loss of taste/smell and neurocognitive symptoms compared to long Flu. Long COVID outpatients were more likely to have any-cause hospitalization (31.9% (74,854/234,688) vs. 26.8% (33,140/123,736), odds ratio 1.06 (95% CI 1.05-1.08, pConclusionsRelying on specific long COVID diagnostic codes results in significant under-reporting. We observed that about 30% of hospitalized COVID-19 patients developed long COVID. In a similar proportion of patients, long COVID-like symptoms (long Flu) can be observed after influenza, but there are notable differences in symptomatology between long COVID and long Flu. The impact of long COVID on healthcare utilization is higher than long Flu.Author summaryWhy was this study done?The quoted incidence of long COVID varies widely because of differences in definition and measurement method. Long COVID in the elderly is likely to be under-reported because they are less likely to respond to surveys, and symptoms may be confused with other chronic diseases. We describe a method of identifying long COVID in the elderly using a standard definition.Lingering ill health after infections is not limited to COVID-19. We postulate that some patients may fit the diagnostic criteria of long COVID after a bout of influenza. We call this condition “long Flu”. Comparing and contrasting long COVID and long Flu may shed light on the understanding of long COVID, a disease still shrouded in mystery.What did the researchers do and find?We applied the World Health Organization consensus clinical definition to identify long COVID among 2 million Medicare patients who were diagnosed with COVID-19 between April 2020 and June 2021.We applied the symptom-based long COVID definition to almost 900,000 influenza patients during the 2018 and 2019 Flu seasons to identify long Flu.Long COVID occurred in 16.6% of outpatients and 29.2% of inpatients. The corresponding rates for long Flu were 17% and 24.6%. If one had relied solely on the designated diagnostic code to identify long COVID, the estimated rates of long COVID would be 0.5% and 2.6% among outpatients and inpatients, way below the reported rates in most studies.Despite the similar overall incidence rates, long COVID patients suffered more often from difficulty in breathing, fatigue, palpitations, loss of taste/smell, memory problems, cognitive impairment and sleep disturbance than long Flu patients.Long COVID patients were also more likely to be admitted to hospital and had more outpatient visits on average than long Flu patients.What do these findings mean?The use of designated long COVID diagnostic codes alone is likely to result in gross under-reporting.A similar proportion of influenza patients suffer from a prolonged illness resembling long COVID, but there are notable differences in the incidence of individual symptoms between long COVID and long Flu.Long COVID is associated with higher level of healthcare utilization than long Flu. This means that long COVID is likely to have a bigger impact on the individual’s health as well as on society as a whole.

Objectives To access the accuracy of the Logical Observation Identifiers Names and Codes (LOINC) mapping to local laboratory test codes that is crucial to data integration across time and healthcare systems. Materials and Methods We used software tools and manual reviews to estimate the rate of LOINC mapping errors among 179 million mapped test results from 2 DataMarts in PCORnet. We separately reported unweighted and weighted mapping error rates, overall and by parts of the LOINC term. Results Of included 179 537 986 mapped results for 3029 quantitative tests, 95.4% were mapped correctly implying an 4.6% mapping error rate. Error rates were less than 5% for the more common tests with at least 100 000 mapped test results. Mapping errors varied across different LOINC classes. Error rates in chemistry and hematology classes, which together accounted for 92.0% of the mapped test results, were 0.4% and 7.5%, respectively. About 50% of mapping errors were due to errors in the property part of the LOINC name. Discussions Mapping errors could be detected automatically through inconsistencies in (1) qualifiers of the analyte, (2) specimen type, (3) property, and (4) method. Among quantitative test results, which are the large majority of reported tests, application of automatic error detection and correction algorithm could reduce the mapping errors further. Conclusions Overall, the mapping error rate within the PCORnet data was 4.6%. This is nontrivial but less than other published error rates of 20%–40%. Such error rate decreased substantially to 0.1% after the application of automatic detection and correction algorithm.

Introduction Serious cardiac arrhythmias caused by QT-prolonging drugs are difficult to predict based on physiological measurement and pre-approval clinical trials. Post-marketing surveillance and monitoring are important to generate safety data. Objectives To assess whether an observational study using Medicare claims data can detect the arrhythmogenic risk of QT-prolonging drugs. Methods We identified 17 QT-prolonging drugs with known risk of torsades des pointes (TdP) that were not used to treat cardiac arrhythmias. Amoxicillin and four serotonin-norepinephrine reuptake inhibitors (SNRIs) were used as controls. De-identified claims data of 1.2 million Medicare beneficiaries were accessed. Two separate Cox regressions were done for short-term and chronic-use drugs. The primary outcome was a composite of ventricular arrhythmias and/or sudden death, identified by ICD diagnostic codes. We explored the independent effect of each study drug on the outcomes. Other covariates included patient demographics, comorbidities, and known risk factors for drug-induced cardiac arrhythmia. Results We were able to detect increased risk in 14 of 17 study drugs (82.3%), and none of the control drugs. Among the fluoroquinolones, ciprofloxacin was the safest. Azithromycin and clarithromycin were relatively safe compared to erythromycin. Compared to SNRIs, both citalopram and escitalopram had increased risk, more so with escitalopram than citalopram. Comorbidities associated with increased risk included ischemic heart disease, electrolyte imbalance, bradycardia, acute myocardial infarction, heart failure, and chronic kidney and liver disease. Conclusion Medicare data can be utilized for post-marketing surveillance and monitoring of the proarrhythmic risk of QT-prolonging drugs in older adults. Supplementary Information The online version contains supplementary material available at 10.1007/s40801-021-00230-1.

BackgroundThe long-term influence of menopausal hormone therapy remains unanswered due to the termination of randomized clinical trials and discordant findings from observational studies.MethodsFrom 2007-2019 enrollment records of 100% Medicare beneficiaries, we identified 7 million female enrollees aged 65 or more. We identified type, route and strength of estrogen based on their prescription drug utilization records. Using vital status record and encounter records, we defined the first onset of thirteen patient outcomes; all-cause mortality; 5 cancers (breast, lung, endometrial, colorectal, ovarian cancers); 6 CV conditions (ischemic heart diseases, heart failure, venous thromboembolism, stroke, atrial fibrillation, acute myocardial infarction); and dementia. Then, we implemented an extended Cox regression analysis to examine the effects of type, route, and strength of estrogens on each of 13 study outcomes.FindingsEstrogen monotherapy (ET) exhibited a significant, 20% (aHR=0.80; 95% CI 0.78-0.82), relative risk reduction of mortality. The reduction was greater with estradiol (aHR=0.78; 95% CI 0.75-0.80) than conjugated estrogen (aHR=0.86; 95% CI 0.85-0.88), and with vaginal (aHR=0.69; 95% CI 0.65-0.74) than oral (aHR=0.89; 95% CI 0.87-0.90) and transdermal (aHR=0.78; 95% CI 0.75-0.81) preparations. ET also exhibited significant risk reductions for all study cancers, breast (aHR=0.82; 95% CI 0.80-0.84), lung (aHR=0.87; 95% CI 0.84-0.90), endometrial (aHR=0.65; 95% CI 0.62-0.69), colorectal (aHR=0.86; 95% CI 0.82-0.90) and ovarian (aHR=0.83; 95% CI 0.79-0.88). ET slightly increased risks of ischemic heart diseases (aHR=1.03; 95% CI 1.01-1.04). However, such risk was not observed with low dose ET (aHR=0.98; 95% CI 0.97-0.99). Both combination therapy (aHR=1.11; 95% CI 1.08-1.14) and progestogen monotherapy (aHR=1.09; 95% CI 1.05-1.13) exhibited a significantly increased risk of breast cancer. Oral HT exhibited a moderately increased risk of dementia.ConclusionsAmong female Medicare beneficiaries aged ≥65, the effect of menopausal hormone therapy varies by type, route, and strength but overall estrogen seemed beneficial.

Background Incidence of long COVID in the elderly is difficult to estimate and can be underreported. While long COVID is sometimes considered a novel disease, many viral or bacterial infections have been known to cause prolonged illnesses. We postulate that some influenza patients might develop residual symptoms that would satisfy the diagnostic criteria for long COVID, a condition we call “long Flu.” In this study, we estimate the incidence of long COVID and long Flu among Medicare patients using the World Health Organization (WHO) consensus definition. We compare the incidence, symptomatology, and healthcare utilization between long COVID and long Flu patients. Methods and findings This is a cohort study of Medicare (the US federal health insurance program) beneficiaries over 65. ICD-10-CM codes were used to capture COVID-19, influenza, and residual symptoms. Long COVID was identified by (a) the designated long COVID code B94.8 (code-based definition), or (b) any of 11 symptoms identified in the WHO definition (symptom-based definition), from 1 to 3 months post-infection. A symptom would be excluded if it occurred in the year prior to infection. Long Flu was identified in influenza patients from the combined 2018 and 2019 Flu seasons by the same symptom-based definition for long COVID. Long COVID and long Flu were compared in 4 outcome measures: (a) hospitalization (any cause); (b) hospitalization (for long COVID symptom); (c) emergency department (ED) visit (for long COVID symptom); and (d) number of outpatient encounters (for long COVID symptom), adjusted for age, sex, race, region, Medicare-Medicaid dual eligibility status, prior-year hospitalization, and chronic comorbidities. Among 2,071,532 COVID-19 patients diagnosed between April 2020 and June 2021, symptom-based definition identified long COVID in 16.6% (246,154/1,479,183) and 29.2% (61,631/210,765) of outpatients and inpatients, respectively. The designated code gave much lower estimates (outpatients 0.49% (7,213/1,479,183), inpatients 2.6% (5,521/210,765)). Among 933,877 influenza patients, 17.0% (138,951/817,336) of outpatients and 24.6% (18,824/76,390) of inpatients fit the long Flu definition. Long COVID patients had higher incidence of dyspnea, fatigue, palpitations, loss of taste/smell, and neurocognitive symptoms compared to long Flu. Long COVID outpatients were more likely to have any-cause hospitalization (31.9% (74,854/234,688) versus 26.8% (33,140/123,736), odds ratio 1.06 (95% CI 1.05 to 1.08, p < 0.001)), and more outpatient visits than long Flu outpatients (mean 2.9(SD 3.4) versus 2.5(SD 2.7) visits, incidence rate ratio 1.09 (95% CI 1.08 to 1.10, p < 0.001)). There were less ED visits in long COVID patients, probably because of reduction in ED usage during the pandemic. The main limitation of our study is that the diagnosis of long COVID in is not independently verified. Conclusions Relying on specific long COVID diagnostic codes results in significant underreporting. We observed that about 30% of hospitalized COVID-19 patients developed long COVID. In a similar proportion of patients, long COVID-like symptoms (long Flu) can be observed after influenza, but there are notable differences in symptomatology between long COVID and long Flu. The impact of long COVID on healthcare utilization is higher than long Flu.

When Donald A.B. Lindberg M.D. became Director in 1984, the U.S. National Library of Medicine (NLM) was a leader in the development and use of information standards for published literature but had no involvement with standards for clinical data. When Dr. Lindberg retired in 2015, NLM was the Central Coordinating Body for Clinical Terminology Standards within the U.S. Department of Health and Human Services, a major funder of ongoing maintenance and free dissemination of clinical terminology standards required for use in U.S. electronic health records (EHRs), and the provider of many services and tools to support the use of terminology standards in health care, public health, and research. This chapter describes key factors in the transformation of NLM into a significant player in the establishment of U.S. terminology standards for electronic health records.