Use of menopausal hormone therapy beyond age 65 years and its effects on women's health outcomes by types, routes, and doses.

OBJECTIVES: The study aims to assess the use of menopausal hormone therapy beyond age 65 years and its health implications by types of estrogen/progestogen, routes of administration, and dose strengths. METHODS: Using prescription drug and encounter records of 10 million senior Medicare women from 2007-2020 and Cox regression analyses adjusted for time-varying characteristics of the women, we examined the effects of different preparations of menopausal hormone therapy on all-cause mortality, five cancers, six cardiovascular diseases, and dementia. RESULTS: Compared with never use or discontinuation of menopausal hormone therapy after age 65 years, the use of estrogen monotherapy beyond age 65 years was associated with significant risk reductions in mortality (19% or adjusted hazards ratio, 0.81; 95% CI, 0.79-0.82), breast cancer (16%), lung cancer (13%), colorectal cancer (12%), congestive heart failure (CHF) (5%), venous thromboembolism (3%), atrial fibrillation (4%), acute myocardial infarction (11%), and dementia (2%). For the use of estrogen and progestogen combo-therapy, both E+ progestin and E+ progesterone were associated with increased risk of breast cancer by 10%-19%, but such risk can be mitigated using low dose of transdermal or vaginal E+ progestin. Moreover, E+ progestin exhibited significant risk reductions in endometrial cancer (45% or adjusted hazards ratio, 0.55; 95% CI, 0.50-0.60), ovarian cancer (21%), ischemic heart disease (5%), CHF (5%), and venous thromboembolism (5%), whereas E+ progesterone exhibited risk reduction only in CHF (4%). CONCLUSIONS: Among senior Medicare women, the implications of menopausal hormone therapy use beyond age 65 years vary by types, routes, and strengths. In general, risk reductions appear to be greater with low rather than medium or high doses, vaginal or transdermal rather than oral preparations, and with E2 rather than conjugated estrogen.

Trends in Racial Disparities in Healthcare Expenditures Among Senior Medicare Fee-for-service Enrollees in 2007-2020.

Despite the universal healthcare coverages, racial disparities in healthcare expenditures among senior Medicare beneficiaries exist. A few studies explored how racial disparities in healthcare expenditures changed over past decades and how it affected differently across 4 minoritized races, by type of Medicare and poverty levels. We conducted a longitudinal study of 21 healthcare expenditures from senior Medicare fee-for-service enrollees to determine overall and secular trends in racial disparities in healthcare expenditures between 2007 and 2020, during which the Affordable Care Act (ACA) came into full force and the COVID-19 pandemic had begun. We found important disparities in healthcare expenditures across 4 minoritized races compared to Whites, even after adjusting for possible confounders for such disparities. Disparities between Hispanics/Asians and Whites were much greater than disparities between Blacks and Whites, in all Parts A, B, and D expenditures. This reality has not been sufficiently emphasized in the literature. Importantly, Black-White disparities in total Part B expenditure gradually worsened between 2007 and 2020, and Hispanic-White and Asian-White disparities worsened greatly during that time window. Health planners need to focus on these large disparities and develop methods to shrink them.

Nirmatrelvir and Molnupiravir and Post-COVID-19 Condition in Older Patients.

This observational cohort study assesses the occurrence of post­COVID-19 condition symptoms in Medicare enrollees prescribed nirmatrelvir and molnupiravir.

Prevalence and characteristics of long COVID in elderly patients: An observational cohort study of over 2 million adults in the US.

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.

Mis-mappings between a producer's quantitative test codes and LOINC codes and an algorithm for correcting them.

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.

The U.S. National Library of Medicine and standards for electronic health records: One thing led to another.

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.

Effect of common maintenance drugs on the risk and severity of COVID-19 in elderly patients.

BACKGROUND: Maintenance drugs are used to treat chronic conditions. Several classes of maintenance drugs have attracted attention because of their potential to affect susceptibility to and severity of COVID-19. METHODS: Using claims data on 20% random sample of Part D Medicare enrollees from April to December 2020, we identified patients diagnosed with COVID-19. Using a nested case-control design, non-COVID-19 controls were identified by 1:5 matching on age, race, sex, dual-eligibility status, and geographical region. We identified usage of angiotensin-converting enzyme inhibitors (ACEI), angiotensin-receptor blockers (ARB), statins, warfarin, direct factor Xa inhibitors, P2Y12 inhibitors, famotidine and hydroxychloroquine based on Medicare prescription claims data. Using extended Cox regression models with time-varying propensity score adjustment we examined the independent effect of each study drug on contracting COVID-19. For severity of COVID-19, we performed extended Cox regressions on all COVID-19 patients, using COVID-19-related hospitalization and all-cause mortality as outcomes. Covariates included gender, age, race, geographic region, low-income indicator, and co-morbidities. To compensate for indication bias related to the use of hydroxychloroquine for the prophylaxis or treatment of COVID-19, we censored patients who only started on hydroxychloroquine in 2020. RESULTS: Up to December 2020, our sample contained 374,229 Medicare patients over 65 who were diagnosed with COVID-19. Among the COVID-19 patients, 278,912 (74.6%) were on at least one study drug. The three most common study drugs among COVID-19 patients were statins 187,374 (50.1%), ACEI 97,843 (26.2%) and ARB 83,290 (22.3%). For all three outcomes (diagnosis, hospitalization and death), current users of ACEI, ARB, statins, warfarin, direct factor Xa inhibitors and P2Y12 inhibitors were associated with reduced risks, compared to never users. Famotidine did not show consistent significant effects. Hydroxychloroquine did not show significant effects after censoring of recent starters. CONCLUSION: Maintenance use of ACEI, ARB, warfarin, statins, direct factor Xa inhibitors and P2Y12 inhibitors was associated with reduction in risk of acquiring COVID-19 and dying from it.

The U.S. National Library of Medicine and Standards for Electronic Health Records: One Thing Led to Another.

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.

The Mortality Risk of Proton Pump Inhibitors in 1.9 Million US Seniors: An Extended Cox Survival Analysis.

BACKGROUND & AIMS: Observational studies have linked proton pump inhibitors (PPIs) with increased risk of mortality and other safety outcomes, in contradiction with a recent PPI randomized controlled trial (RCT). Observational studies may be prone to reverse causality, where deaths are attributed to the treatment rather than the conditions that are treated (protopathic bias). METHODS: We analyzed an incident drug user cohort of 1,930,728 elderly Medicare fee-for-service beneficiaries to evaluate the PPI-associated risk of death with a Cox regression analysis with time-varying covariates and propensity score adjustments. To correct for protopathic bias which occurs when a given drug is associated with prodromal signs of death, we implemented a lag-time approach by which any study drug taken during a 90-day look-back window before each death was disregarded. RESULTS: Among 1,930,728 study individuals, 80,972 (4.2%) died during a median 3.8 years of follow-up, yielding an overall unadjusted death rate/1000 person-years of 9.85; 14.31 for PPI users and 7.93 for non- users. With no lag-time, PPI use (vs no use) was associated with 10% increased mortality risk (adjusted HR=1.10; 95% CI 1.08-1.12). However, with a lag-time of 90 days, mortality risk associated with PPI use was near zero (adjusted HR=1.01; 95% CI 0.99-1.02). CONCLUSION: Given the usage patterns of PPIs in patients with conditions that may presage death, protopathic bias may explain the association of PPIs with increased risk of death reported in observational studies.

Using Medicare Data to Assess the Proarrhythmic Risk of Non-Cardiac Treatment Drugs that Prolong the QT Interval in Older Adults: An Observational Cohort Study.

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.

Association between tendon ruptures and use of fluoroquinolone, and other oral antibiotics: a 10-year retrospective study of 1 million US senior Medicare beneficiaries.

OBJECTIVES: To assess the association of fluoroquinolone use with tendon ruptures compared with no fluoroquinolone and that of the four most commonly prescribed non-fluoroquinolone antibiotics in the USA. DESIGN: Retrospective observational study. SETTING: US seniors enrolled in the federal old-age, survivor's insurance programme. PARTICIPANTS: 1 009 925 Medicare fee-for-service beneficiaries and their inpatient, outpatient, prescription drug records were used. INTERVENTIONS: Seven oral antibiotics, fluoroquinolones (ciprofloxacin, levofloxacin, moxifloxacin) and amoxicillin, amoxicillin-clavulanate, azithromycin and cephalexin. PRIMARY AND SECONDARY OUTCOME MEASURES: All tendon ruptures combined, and three types of tendon ruptures by anatomic site, Achilles tendon rupture, rupture of rotator cuff and other tendon ruptures occurred in 2007-2016. RESULTS: Of three fluoroquinolones, only levofloxacin exhibited a significant increased risk of tendon ruptures-16% (HR=1.16; 95% CI 1.06 to 1.28), and 120% (HR=2.20; 95% CI 1.50 to 3.24) for rotator cuff and Achilles tendon rupture, respectively, in the ≤30 days window. Ciprofloxacin (HR=0.96; 95% CI 0.89 to 1.03) and moxifloxacin (HR=0.59; 95% CI 0.37 to 0.93) exhibited no increased risk of tendon ruptures combined.Among the non-fluoroquinolone antibiotics, cephalexin exhibited increased risk of combined tendon ruptures (HR=1.31; 95% CI 1.22 to 1.41) and modest to large risks across all anatomic rupture sites (HRs 1.19-1.93) at ≤30 days window. Notably, the risk of levofloxacin never exceeded the risk of the non-fluoroquinolone, cephalexin in any comparison. CONCLUSIONS: In our study, fluoroquinolones as a class were not associated with the increased risk of tendon ruptures. Neither ciprofloxacin nor moxifloxacin exhibited any risk for tendon ruptures. Levofloxacin did exhibit significant increased risk. Cephalexin with no reported effect on metalloprotease activity had an equal or greater risk than levofloxacin; so we question whether metalloprotease activity has any relevance to observed associations with tendon rupture. Confounding by indication bias may be more relevant and should be given more consideration as explanation for significant associations in observational studies of tendon rupture.

Designing, Conducting, and Reporting Clinical Decision Support Studies: Recommendations and Call to Action.

By enabling more efficient and effective medical decision making, computer-based clinical decision support (CDS) could unlock widespread benefits from the significant investment in electronic health record (EHR) systems in the United States. Evidence from high-quality CDS studies is needed to enable and support this vision of CDS-facilitated care optimization, but limited guidance is available in the literature for designing and reporting CDS studies. To address this research gap, this article provides recommendations for designing, conducting, and reporting CDS studies to: 1) ensure that EHR data to inform the CDS are available; 2) choose decision rules that are consistent with local care processes; 3) target the right users and workflows; 4) make the CDS easy to access and use; 5) minimize the burden placed on users; 6) incorporate CDS success factors identified in the literature, in particular the automatic provision of CDS as a part of clinician workflow; 7) ensure that the CDS rules are adequately tested; 8) select meaningful evaluation measures; 9) use as rigorous a study design as is feasible; 10) think about how to deploy the CDS beyond the original host organization; 11) report the study in context; 12) help the audience understand why the intervention succeeded or failed; and 13) consider the financial implications. If adopted, these recommendations should help advance the vision of more efficient, effective care facilitated by useful and widely available CDS.

Independent effects of 15 commonly prescribed drugs on all-cause mortality among US elderly patients with type 2 diabetes mellitus.

OBJECTIVE: Most patients with type 2 diabetes mellitus (T2DM) also have hypertension and hyperlipidemia. Consequently, they are taking medications for all three conditions concurrently and the effect of one drug could be confounded with that of another. This study aimed to determine the independent effects of 15 commonly prescribed medications for three conditions on the risk of all-cause mortality among elderly patients with T2DM. RESEARCH DESIGN AND METHODS: A cohort of 360 437 elderly patients with T2DM from 2007 to 2016 US Medicare data was traced along with cumulative uses of 8 diabetes, 6 hypertension and 1 hyperlipidemia drugs. The relative risk of all-cause mortality for each study drug was estimated using an extended Cox regression analysis adjusting for the concurrent use of other study drugs. RESULTS: Compared with the no use of each study medication, mortality risk declined with use of 3 diabetes drugs, sodium-glucose cotransporter-2 inhibitors (HR=0.73; 95% CI 0.64 to 0.84), glucagon-like peptide-1 receptor agonists (HR=0.75; 95% CI 0.70 to 0.80) and dipeptidyl peptidase-4 inhibitors (HR=0.94; 95% CI 0.91 to 0.98), the use of 3 blood pressure medications, diuretics (HR=0.89; 95% CI 0.87 to 0.92), angiotensin receptor blockers (HR=0.86; 95% CI 0.84 to 0.89), ACE inhibitors (HR=0.98; 95% CI 0.95 to 1.01) as well as statins (HR=0.83; 95% CI 0.80 to 0.85). It increased moderately with insulin (HR=1.55; 95% CI 1.51 to 1.59), sulfonylureas (HR=1.16; 95% CI 1.13 to 1.20), a small inconsistent amount with metformin (HR=1.05), beta-blockers (HR=1.07), dihydropyridine calcium-channel blockers (HR=0.99) and non-dihydropyridine calcium-channel blockers (HR=1.05). The use of thiazolidinedione had no effect. CONCLUSION: Among older patients with diabetes, mortality risk decreased importantly with three new diabetes drugs, 3 blood pressure drugs and statins. It increased moderately with sulfonylurea and insulin. Studies of aggressive use of new T2DM drugs instead of sulfonylureas and insulin are needed. Our statin results empirically validate two national guidelines for using statins in older patients with diabetes. However, 23% of study patients never took a statin, suggesting missed opportunities for prevention.

A method for prioritizing risk groups for early SARS-CoV-2 Vaccination, By the Numbers.

Background: Given the limited supply of two COVID-19 vaccines, it will be important to choose which risk groups to prioritize for vaccination in order to get the most health benefits from that supply. Method: In order to help decide how to get the maximum health yield from this limited supply, we implemented a logistic regression model to predict COVID-19 death risk by age, race, and sex and did the same to predict COVID-19 case risk. Results: Our predictive model ranked all demographic groups by COVID-19 death risk. It was highly concentrated in some demographic groups, e.g. 85+ year old Black, Non-Hispanic patients suffered 1,953 deaths per 100,000. If we vaccinated the 17 demographic groups at highest COVID-19 death ranked by our logistic model, it would require only 3.7% of the vaccine supply needed to vaccinate all the United States, and yet prevent 47% of COVID-19 deaths. Nursing home residents had a higher COVID-19 death risk at 5,200 deaths/100,000, more than our highest demographic risk group. Risk of prison residents and health care workers (HCW) were lower than that of our demographic groups with the highest risks.We saw much less concentration of COVID-19 case risk in any demographic groups compared to the high concentration of COVID-19 death in some such groups. We should prioritize vaccinations with the goal of reducing deaths, not cases, while the vaccine supply is low. Conclusion: SARS-CoV-2 vaccines protect against severe COVID-19 infection and thus against COVID-19 death per vaccine studies. Allocating at least some of the early vaccine supplies to high risk demographic groups could maximize lives saved. Our model, and the risk estimate it produced, could help states define their vaccine allocation rules.

Semantic integration of clinical laboratory tests from electronic health records for deep phenotyping and biomarker discovery.

Electronic Health Record (EHR) systems typically define laboratory test results using the Laboratory Observation Identifier Names and Codes (LOINC) and can transmit them using Fast Healthcare Interoperability Resource (FHIR) standards. LOINC has not yet been semantically integrated with computational resources for phenotype analysis. Here, we provide a method for mapping LOINC-encoded laboratory test results transmitted in FHIR standards to Human Phenotype Ontology (HPO) terms. We annotated the medical implications of 2923 commonly used laboratory tests with HPO terms. Using these annotations, our software assesses laboratory test results and converts each result into an HPO term. We validated our approach with EHR data from 15,681 patients with respiratory complaints and identified known biomarkers for asthma. Finally, we provide a freely available SMART on FHIR application that can be used within EHR systems. Our approach allows readily available laboratory tests in EHR to be reused for deep phenotyping and exploits the hierarchical structure of HPO to integrate distinct tests that have comparable medical interpretations for association studies.

Implementing a mobile diagnostic unit to increase access to imaging and laboratory services in western Kenya.

Access to basic imaging and laboratory services remains a major challenge in rural, resource-limited settings in sub-Saharan Africa. In 2016, the Academic Model Providing Access to Healthcare programme in western Kenya implemented a mobile diagnostic unit (MDU) outfitted with a generator-powered X-ray machine and basic laboratory tests to address the lack of these services at rural, low-resource, public health facilities. The objective of this paper is to describe the design, implementation, preliminary impact and operational challenges of the MDU in western Kenya. Since implementing the MDU at seven rural health facilities serving a catchment of over half a million people, over 4500 chest radiographs have been performed, with one or more abnormalities detected in approximately 30% of radiographs. We observed favorable feedback and uptake of MDU services by healthcare workers and patients. However, various operational challenges in the design and construction of the MDU and the transmission and reporting of radiographs in remote areas were encountered. Our experience supports the feasibility of deploying an MDU to increase access to basic radiology and laboratory services in rural, resource-limited settings.

Use of Electronic Health Record Data to Evaluate the Impact of Race on 30-Day Mortality in Patients Admitted to the Intensive Care Unit.

INTRODUCTION: The current body of literature examining the impact of race upon outcomes for patients admitted to the intensive care unit (ICU) is limited. The primary objective of our study was to explore this question using a large cohort drawn from an electronic health record (EHR)-based data source. METHODS: We conducted a retrospective cohort study using Multiparameter Intelligent Monitoring in Intensive Care (MIMIC-II), an EHR-derived database encompassing ICU admissions to an academic medical center in Boston, Massachusetts, between 2001 and 2008. Adults admitted to a medical or surgical ICU were assessed for the primary outcome of 30-day mortality and secondary outcomes of in-hospital mortality and hospital length-of-stay. Multivariate logistic regression was used to determine the association between race and the primary outcome. RESULTS: The study cohort consisted of 14,684 adult ICU patients-10,562 White, 1311 Black, 363 Asian, 868 "Other," and 1580 without known race. Thirty-day mortality rates experienced by Black and Asian individuals were significantly lower than mortality among those identified as White, with odds ratios of 0.62 (95 % CI 0.50-0.77) and 0.64 (95 % CI 0.44-0.93), respectively. Patients without known race experienced the highest crude mortality overall (27.4 %) and twice the adjusted odds of mortality compared with the White group. CONCLUSIONS: In a large, racially diverse cohort of general ICU patients, White patients experienced significantly higher mortality than non-White patients. Our results are consistent with findings from other studies that indicate that the non-White race does not appear to negatively impact short-term survival following ICU admission.

Preparing a collection of radiology examinations for distribution and retrieval.

OBJECTIVE: Clinical documents made available for secondary use play an increasingly important role in discovery of clinical knowledge, development of research methods, and education. An important step in facilitating secondary use of clinical document collections is easy access to descriptions and samples that represent the content of the collections. This paper presents an approach to developing a collection of radiology examinations, including both the images and radiologist narrative reports, and making them publicly available in a searchable database. MATERIALS AND METHODS: The authors collected 3996 radiology reports from the Indiana Network for Patient Care and 8121 associated images from the hospitals' picture archiving systems. The images and reports were de-identified automatically and then the automatic de-identification was manually verified. The authors coded the key findings of the reports and empirically assessed the benefits of manual coding on retrieval. RESULTS: The automatic de-identification of the narrative was aggressive and achieved 100% precision at the cost of rendering a few findings uninterpretable. Automatic de-identification of images was not quite as perfect. Images for two of 3996 patients (0.05%) showed protected health information. Manual encoding of findings improved retrieval precision. CONCLUSION: Stringent de-identification methods can remove all identifiers from text radiology reports. DICOM de-identification of images does not remove all identifying information and needs special attention to images scanned from film. Adding manual coding to the radiologist narrative reports significantly improved relevancy of the retrieved clinical documents. The de-identified Indiana chest X-ray collection is available for searching and downloading from the National Library of Medicine (http://openi.nlm.nih.gov/).