Comparing broad and narrow phenotype algorithms: differences in performance characteristics and immortal time incurred.

Introduction: When developing phenotype algorithms for observational research, there is usually a trade-off between definitions that are sensitive or specific. The objective of this study was to estimate the performance characteristics of phenotype algorithms designed for increasing specificity and to estimate the immortal time associated with each algorithm. Materials and methods: We examined algorithms for 11 chronic health conditions. The analyses were from data from five databases. For each health condition, we created five algorithms to examine performance (sensitivity and positive predictive value (PPV)) differences: one broad algorithm using a single code for the health condition and four narrow algorithms where a second diagnosis code was required 1-30 days, 1-90 days, 1-365 days, or 1- all days in a subject's continuous observation period after the first code. We also examined the proportion of immortal time relative to time-at-risk (TAR) for four outcomes. The TAR's were: 0-30 days after the first condition occurrence (the index date), 0-90 days post-index, 0-365 days post-index, and 0-1,095 days post-index. Performance of algorithms for chronic health conditions was estimated using PheValuator (V2.1.4) from the OHDSI toolstack. Immortal time was calculated as the time from the index date until the first of the following: 1) the outcome; 2) the end of the outcome TAR; 3) the occurrence of the second code for the chronic health condition. Results: In the first analysis, the narrow phenotype algorithms, i.e., those requiring a second condition code, produced higher estimates for PPV and lower estimates for sensitivity compared to the single code algorithm. In all conditions, increasing the time to the required second code increased the sensitivity of the algorithm. In the second analysis, the amount of immortal time increased as the window used to identify the second diagnosis code increased. The proportion of TAR that was immortal was highest in the 30 days TAR analyses compared to the 1,095 days TAR analyses. Conclusion: Attempting to increase the specificity of a health condition algorithm by adding a second code is a potentially valid approach to increase specificity, albeit at the cost of incurring immortal time.

PheValuator 2.0: Methodological improvements for the PheValuator approach to semi-automated phenotype algorithm evaluation.

PURPOSE: Phenotype algorithms are central to performing analyses using observational data. These algorithms translate the clinical idea of a health condition into an executable set of rules allowing for queries of data elements from a database. PheValuator, a software package in the Observational Health Data Sciences and Informatics (OHDSI) tool stack, provides a method to assess the performance characteristics of these algorithms, namely, sensitivity, specificity, and positive and negative predictive value. It uses machine learning to develop predictive models for determining a probabilistic gold standard of subjects for assessment of cases and non-cases of health conditions. PheValuator was developed to complement or even replace the traditional approach of algorithm validation, i.e., by expert assessment of subject records through chart review. Results in our first PheValuator paper suggest a systematic underestimation of the PPV compared to previous results using chart review. In this paper we evaluate modifications made to the method designed to improve its performance. METHODS: The major changes to PheValuator included allowing all diagnostic conditions, clinical observations, drug prescriptions, and laboratory measurements to be included as predictors within the modeling process whereas in the prior version there were significant restrictions on the included predictors. We also have allowed for the inclusion of the temporal relationships of the predictors in the model. To evaluate the performance of the new method, we compared the results from the new and original methods against results found from the literature using traditional validation of algorithms for 19 phenotypes. We performed these tests using data from five commercial databases. RESULTS: In the assessment aggregating all phenotype algorithms, the median difference between the PheValuator estimate and the gold standard estimate for PPV was reduced from -21 (IQR -34, -3) in Version 1.0 to 4 (IQR -3, 15) using Version 2.0. We found a median difference in specificity of 3 (IQR 1, 4.25) for Version 1.0 and 3 (IQR 1, 4) for Version 2.0. The median difference between the two versions of PheValuator and the gold standard for estimates of sensitivity was reduced from -39 (-51, -20) to -16 (-34, -6). CONCLUSION: PheValuator 2.0 produces estimates for the performance characteristics for phenotype algorithms that are significantly closer to estimates from traditional validation through chart review compared to version 1.0. With this tool in researcher's toolkits, methods, such as quantitative bias analysis, may now be used to improve the reliability and reproducibility of research studies using observational data.

PheValuator: Development and evaluation of a phenotype algorithm evaluator.

BACKGROUND: The primary approach for defining disease in observational healthcare databases is to construct phenotype algorithms (PAs), rule-based heuristics predicated on the presence, absence, and temporal logic of clinical observations. However, a complete evaluation of PAs, i.e., determining sensitivity, specificity, and positive predictive value (PPV), is rarely performed. In this study, we propose a tool (PheValuator) to efficiently estimate a complete PA evaluation. METHODS: We used 4 administrative claims datasets: OptumInsight's de-identified Clinformatics™ Datamart (Eden Prairie,MN); IBM MarketScan Multi-State Medicaid); IBM MarketScan Medicare Supplemental Beneficiaries; and IBM MarketScan Commercial Claims and Encounters from 2000 to 2017. Using PheValuator involves (1) creating a diagnostic predictive model for the phenotype, (2) applying the model to a large set of randomly selected subjects, and (3) comparing each subject's predicted probability for the phenotype to inclusion/exclusion in PAs. We used the predictions as a 'probabilistic gold standard' measure to classify positive/negative cases. We examined 4 phenotypes: myocardial infarction, cerebral infarction, chronic kidney disease, and atrial fibrillation. We examined several PAs for each phenotype including 1-time (1X) occurrence of the diagnosis code in the subject's record and 1-time occurrence of the diagnosis in an inpatient setting with the diagnosis code as the primary reason for admission (1X-IP-1stPos). RESULTS: Across phenotypes, the 1X PA showed the highest sensitivity/lowest PPV among all PAs. 1X-IP-1stPos yielded the highest PPV/lowest sensitivity. Specificity was very high across algorithms. We found similar results between algorithms across datasets. CONCLUSION: PheValuator appears to show promise as a tool to estimate PA performance characteristics.

Gender disparity in the use of drug-eluting stents during percutaneous coronary intervention for acute myocardial infarction.

OBJECTIVE: We examined gender disparity in the use of drug-eluting stents (DES) versus bare metal stents (BMS) during percutaneous coronary intervention (PCI) for acute myocardial infarction (AMI), and gender disparity in all-cause mortality after coronary stent implantation for AMI. BACKGROUND: Gender disparities in AMI managements have been well documented, but it is unclear whether these disparities are seen in the type of coronary stent implantation for AMI and outcomes. METHODS: Hospital discharge data from January 1, 2003 through December 31, 2010 in New Jersey from the Myocardial Infarction Data Acquisition System were used to identify 40,215 patients (12,878 women and 27,337 men) with coronary stent implantation for AMI. The in-hospital, short term (30 days) and long term (1 and 5 year) all-cause mortality rates, unadjusted and adjusted for demographics and comorbidities, were determined. RESULTS: Women were older than men and had a higher prevalence of co-morbidities. Men had higher prevalence of prior coronary revascularizations. After adjustment for co-morbidities, there was no significant gender difference in the use of DES versus BMS for AMI, except in 2003 and 2006 where women were found to be more likely to receive a DES versus a BMS. After adjustment, women had higher odds of in-hospital deaths but no difference in short and long-term all-cause mortality rates. CONCLUSIONS: There was no significant gender difference in the proportion of DES implantation versus BMS for AMI in contemporary years. Women treated with either BMS or DES for AMI had higher in-hospital death than men.

Using a data-driven approach for the development and evaluation of phenotype algorithms for systemic lupus erythematosus.

BACKGROUND: Systemic lupus erythematosus (SLE) is a chronic autoimmune disease of unknown origin. The objective of this research was to develop phenotype algorithms for SLE suitable for use in epidemiological studies using empirical evidence from observational databases. METHODS: We used a process for empirically determining and evaluating phenotype algorithms for health conditions to be analyzed in observational research. The process started with a literature search to discover prior algorithms used for SLE. We then used a set of Observational Health Data Sciences and Informatics (OHDSI) open-source tools to refine and validate the algorithms. These included tools to discover codes for SLE that may have been missed in prior studies and to determine possible low specificity and index date misclassification in algorithms for correction. RESULTS: We developed four algorithms using our process: two algorithms for prevalent SLE and two for incident SLE. The algorithms for both incident and prevalent cases are comprised of a more specific version and a more sensitive version. Each of the algorithms corrects for possible index date misclassification. After validation, we found the highest positive predictive value estimate for the prevalent, specific algorithm (89%). The highest sensitivity estimate was found for the sensitive, prevalent algorithm (77%). CONCLUSION: We developed phenotype algorithms for SLE using a data-driven approach. The four final algorithms may be used directly in observational studies. The validation of these algorithms provides researchers an added measure of confidence that the algorithms are selecting subjects correctly and allows for the application of quantitative bias analysis.

Reproducible variability: assessing investigator discordance across 9 research teams attempting to reproduce the same observational study.

OBJECTIVE: Observational studies can impact patient care but must be robust and reproducible. Nonreproducibility is primarily caused by unclear reporting of design choices and analytic procedures. This study aimed to: (1) assess how the study logic described in an observational study could be interpreted by independent researchers and (2) quantify the impact of interpretations' variability on patient characteristics. MATERIALS AND METHODS: Nine teams of highly qualified researchers reproduced a cohort from a study by Albogami et al. The teams were provided the clinical codes and access to the tools to create cohort definitions such that the only variable part was their logic choices. We executed teams' cohort definitions against the database and compared the number of subjects, patient overlap, and patient characteristics. RESULTS: On average, the teams' interpretations fully aligned with the master implementation in 4 out of 10 inclusion criteria with at least 4 deviations per team. Cohorts' size varied from one-third of the master cohort size to 10 times the cohort size (2159-63 619 subjects compared to 6196 subjects). Median agreement was 9.4% (interquartile range 15.3-16.2%). The teams' cohorts significantly differed from the master implementation by at least 2 baseline characteristics, and most of the teams differed by at least 5. CONCLUSIONS: Independent research teams attempting to reproduce the study based on its free-text description alone produce different implementations that vary in the population size and composition. Sharing analytical code supported by a common data model and open-source tools allows reproducing a study unambiguously thereby preserving initial design choices.

Development and external validation of prediction models for adverse health outcomes in rheumatoid arthritis: A multinational real-world cohort analysis.

BACKGROUND: Identification of rheumatoid arthritis (RA) patients at high risk of adverse health outcomes remains a major challenge. We aimed to develop and validate prediction models for a variety of adverse health outcomes in RA patients initiating first-line methotrexate (MTX) monotherapy. METHODS: Data from 15 claims and electronic health record databases across 9 countries were used. Models were developed and internally validated on Optum® De-identified Clinformatics® Data Mart Database using L1-regularized logistic regression to estimate the risk of adverse health outcomes within 3 months (leukopenia, pancytopenia, infection), 2 years (myocardial infarction (MI) and stroke), and 5 years (cancers [colorectal, breast, uterine] after treatment initiation. Candidate predictors included demographic variables and past medical history. Models were externally validated on all other databases. Performance was assessed using the area under the receiver operator characteristic curve (AUC) and calibration plots. FINDINGS: Models were developed and internally validated on 21,547 RA patients and externally validated on 131,928 RA patients. Models for serious infection (AUC: internal 0.74, external ranging from 0.62 to 0.83), MI (AUC: internal 0.76, external ranging from 0.56 to 0.82), and stroke (AUC: internal 0.77, external ranging from 0.63 to 0.95), showed good discrimination and adequate calibration. Models for the other outcomes showed modest internal discrimination (AUC < 0.65) and were not externally validated. INTERPRETATION: We developed and validated prediction models for a variety of adverse health outcomes in RA patients initiating first-line MTX monotherapy. Final models for serious infection, MI, and stroke demonstrated good performance across multiple databases and can be studied for clinical use. FUNDING: This activity under the European Health Data & Evidence Network (EHDEN) has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No 806968. This Joint Undertaking receives support from the European Union's Horizon 2020 research and innovation programme and EFPIA.

Incidence rate of hospitalization and mortality in the first year following initial diagnosis of cardiac amyloidosis in the US claims databases.

OBJECTIVE: This study aimed to determine rates of hospitalization and in-hospital mortality in the first year following amyloidosis diagnosis with cardiac involvement using observational databases. METHODS: Three administrative claims databases, IBM MarketScan® Commercial Claims and Encounters (CCAE), IBM MarketScan® Multi-State Medicare Database (MDCR), and Optum's de-identified Clinformatics® Data Mart Database (Optum) were analyzed. Adults ≥18 years old, with a diagnosis of amyloidosis and evidence of cardiac involvement (i.e. heart failure, heart block, or cardiomyopathy) but no hepatic/renal failure prior to amyloidosis diagnosis were included for analysis. The primary analyses identified patients between 01-01-2010 and 31-12-2017 period. We calculated the rates of hospitalization and in-hospital mortality within 1 year after the initial diagnosis of amyloidosis. A sensitivity analysis was conducted for patients identified in Optum database during 2004-2011 period, which provided additional mortality information. RESULTS: A total of 419, 654, and 922 patients from CCAE, MDCR, and Optum were identified during 2010-2017 period, with mean age of 55.6, 77.8, and 74.2 years, respectively. Within 1 year following initial amyloidosis diagnosis, incidence rates (95% confidence interval [CI]) of hospitalization were 78.4 (66.3, 90.4), 78.6 (69.2, 87.9), and 61.2 (54.4, 68.0) per 100 person-years, rates of in-hospital mortality were 16.5 (11.8, 21.3), 8.4 (5.7, 11.0), and 17.7 (14.5, 21.0) per 100 person-years, in CCAE, MDCR, and Optum, respectively. The mortality rate from the sensitivity analysis among patients identified in Optum 2004-2011 period was higher compared with Optum 2010-2017 period. CONCLUSIONS: The results from this study indicate that amyloidosis with cardiac involvement is a condition with high rates of hospitalization and mortality in the first year after initial diagnosis. Future studies are needed to further evaluate the outcomes within the subtypes of amyloidosis and understand the risk factors associated with poor prognoses.

Evaluation of code-based algorithms to identify pulmonary arterial hypertension and chronic thromboembolic pulmonary hypertension patients in large administrative databases.

Large administrative healthcare (including insurance claims) databases are used for various retrospective real-world evidence studies. However, in pulmonary arterial hypertension and chronic thromboembolic pulmonary hypertension, identifying patients retrospectively based on administrative codes remains challenging, as it relies on code combinations (algorithms) and the accuracy for patient identification of most of them is unknown. This study aimed to assess the performance of various algorithms in correctly identifying patients with pulmonary arterial hypertension or chronic thromboembolic pulmonary hypertension in administrative databases. A systematic literature review was performed to find publications detailing code-based algorithms used to identify pulmonary arterial hypertension and chronic thromboembolic pulmonary hypertension patients. PheValuator, a diagnostic predictive modelling tool, was applied to three US claims databases, yielding models that estimated the probability of a patient having the disease. These models were used to evaluate the performance characteristics of selected pulmonary arterial hypertension and chronic thromboembolic pulmonary hypertension algorithms. With increasing algorithm complexity, average positive predictive value increased (pulmonary arterial hypertension: 13.4-66.0%; chronic thromboembolic pulmonary hypertension: 10.3-75.1%) and average sensitivity decreased (pulmonary arterial hypertension: 61.5-2.7%; chronic thromboembolic pulmonary hypertension: 20.7-0.2%). Specificities and negative predictive values were high (≥97.5%) for all algorithms. Several of the algorithms performed well overall when considering all of these four performance parameters, and all algorithms performed with similar accuracy across the three claims databases studied, even though most were designed for patient identification in a specific database. Therefore, it is the objective of a study that will determine which algorithm may be most suitable; one- or two-component algorithms are most inclusive and three- or four-component algorithms identify most precise pulmonary arterial hypertension or chronic thromboembolic pulmonary hypertension populations, respectively.

Developing Predictive Models to Determine Patients in End-of-Life Care in Administrative Datasets.

INTRODUCTION: In observational studies with mortality endpoints, one needs to consider how to account for subjects whose interventions appear to be part of 'end-of-life' care. OBJECTIVE: The objective of this study was to develop a diagnostic predictive model to identify those in end-of-life care at the time of a drug exposure. METHODS: We used data from four administrative claims datasets from 2000 to 2017. The index date was the date of the first prescription for the last new drug subjects received during their observation period. The outcome of end-of-life care was determined by the presence of one or more codes indicating terminal or hospice care. Models were developed using regularized logistic regression. Internal validation was through examination of the area under the receiver operating characteristic curve (AUC) and through model calibration in a 25% subset of the data held back from model training. External validation was through examination of the AUC after applying the model learned on one dataset to the three other datasets. RESULTS: The models showed excellent performance characteristics. Internal validation resulted in AUCs ranging from 0.918 (95% confidence interval [CI] 0.905-0.930) to 0.983 (95% CI 0.978-0.987) for the four different datasets. Calibration results were also very good, with slopes near unity. External validation also produced very good to excellent performance metrics, with AUCs ranging from 0.840 (95% CI 0.834-0.846) to 0.956 (95% CI 0.952-0.960). CONCLUSION: These results show that developing diagnostic predictive models for determining subjects in end-of-life care at the time of a drug treatment is possible and may improve the validity of the risk profile for those treatments.

Comparison of First-Line Dual Combination Treatments in Hypertension: Real-World Evidence from Multinational Heterogeneous Cohorts.

BACKGROUND AND OBJECTIVES: 2018 ESC/ESH Hypertension guideline recommends 2-drug combination as initial anti-hypertensive therapy. However, real-world evidence for effectiveness of recommended regimens remains limited. We aimed to compare the effectiveness of first-line anti-hypertensive treatment combining 2 out of the following classes: angiotensin-converting enzyme (ACE) inhibitors/angiotensin-receptor blocker (A), calcium channel blocker (C), and thiazide-type diuretics (D). METHODS: Treatment-naïve hypertensive adults without cardiovascular disease (CVD) who initiated dual anti-hypertensive medications were identified in 5 databases from US and Korea. The patients were matched for each comparison set by large-scale propensity score matching. Primary endpoint was all-cause mortality. Myocardial infarction, heart failure, stroke, and major adverse cardiac and cerebrovascular events as a composite outcome comprised the secondary measure. RESULTS: A total of 987,983 patients met the eligibility criteria. After matching, 222,686, 32,344, and 38,513 patients were allocated to A+C vs. A+D, C+D vs. A+C, and C+D vs. A+D comparison, respectively. There was no significant difference in the mortality during total of 1,806,077 person-years: A+C vs. A+D (hazard ratio [HR], 1.08; 95% confidence interval [CI], 0.97-1.20; p=0.127), C+D vs. A+C (HR, 0.93; 95% CI, 0.87-1.01; p=0.067), and C+D vs. A+D (HR, 1.18; 95% CI, 0.95-1.47; p=0.104). A+C was associated with a slightly higher risk of heart failure (HR, 1.09; 95% CI, 1.01-1.18; p=0.040) and stroke (HR, 1.08; 95% CI, 1.01-1.17; p=0.040) than A+D. CONCLUSIONS: There was no significant difference in mortality among A+C, A+D, and C+D combination treatment in patients without previous CVD. This finding was consistent across multi-national heterogeneous cohorts in real-world practice.

Acute pancreatitis risk in type 2 diabetes patients treated with canagliflozin versus other antihyperglycemic agents: an observational claims database study.

Objective: Observational evidence suggests that patients with type 2 diabetes mellitus (T2DM) are at increased risk for acute pancreatitis (AP) versus those without T2DM. A small number of AP events were reported in clinical trials of the sodium glucose co-transporter 2 inhibitor canagliflozin, though no imbalances were observed between treatment groups. This observational study evaluated risk of AP among new users of canagliflozin compared with new users of six classes of other antihyperglycemic agents (AHAs).Methods: Three US claims databases were analyzed based on a prespecified protocol approved by the European Medicines Agency. Propensity score adjustment controlled for imbalances in baseline covariates. Cox regression models estimated the hazard ratio of AP with canagliflozin compared with other AHAs using on-treatment (primary) and intent-to-treat approaches. Sensitivity analyses assessed robustness of findings.Results: Across the three databases, there were between 12,023-80,986 new users of canagliflozin; the unadjusted incidence rates of AP (per 1000 person-years) were between 1.5-2.2 for canagliflozin and 1.1-6.6 for other AHAs. The risk of AP was generally similar for new users of canagliflozin compared with new users of glucagon-like peptide-1 receptor agonists, dipeptidyl peptidase-4 inhibitors, sulfonylureas, thiazolidinediones, insulin, and other AHAs, with no consistent between-treatment differences observed across databases. Intent-to-treat and sensitivity analysis findings were qualitatively consistent with on-treatment findings.Conclusions: In this large observational study, incidence rates of AP in patients with T2DM treated with canagliflozin or other AHAs were generally similar, with no evidence suggesting that canagliflozin is associated with increased risk of AP compared with other AHAs.

Association Between Dehydration and Short-Term Risk of Ischemic Stroke in Patients with Atrial Fibrillation.

Previous cross-sectional studies have demonstrated a higher incidence of dehydration in patients admitted for stroke suggesting a possible association. However, the temporality of the association has not been well established. We examined whether dehydration increases the risk of ischemic stroke in patients with a recent hospitalization for atrial fibrillation (AF). Data was from 1994 to 2012 from the Myocardial Infarction Data Acquisition System (MIDAS), a repository of in-patient records New Jersey hospitals, for AF hospitalizations (n = 1,282,787). Estimates for the association between AF hospitalization with/without dehydration and ischemic stroke within 30 days post-AF discharge were determined using log-linear multivariable modeling adjusting for socio-demographic factors and comorbid conditions. Within 10 days of discharge for AF, patients 18-80 years old (YO) with comorbid dehydration had a 60 % higher risk of ischemic stroke compared to AF patients without comorbid dehydration (adjusted risk ratio (ARR) 1.60, 95 % confidence interval (CI) 1.28-2.00). Eighteen- to 80-YO patients had a 34 % higher risk of ischemic stroke in days 11-20 post-AF discharge (ARR 1.34, 95 % CI 1.04, 1.74). There was no difference in the risk of stroke in 18-80-YO patients with or without prior dehydration during days 21-30 post-AF discharge. We also found no difference in the risk of ischemic stroke during any time period in patients over 80 YO. Dehydration may be a significant risk factor for ischemic stroke in patients 18-80 YO with AF.

Frequency of Stroke After Percutaneous Coronary Intervention or Coronary Artery Bypass Grafting (from an Eleven-Year Statewide Analysis).

We compared stroke rates associated with coronary artery bypass grafting (CABG), both on-pump and off-pump, and percutaneous coronary intervention (PCI) with both drug-eluting stent (DES) and bare-metal stent (BMS) and the impact on 30-day and 1-year all-cause mortality. The Myocardial Infarction Data Acquisition System database was used to study patients who had on-pump CABG (n = 47,254), off-pump CABG (n = 19,118), and PCI with BMS (n = 46,641), and DES (n = 115,942) in New Jersey from 2002 to 2012. Multiple logistic and Cox proportional hazard models were used to compare the risk of stroke and mortality. Adjustments were made for demographics, year of hospitalization, and co-morbidities. The rate of postprocedural stroke was lowest with DES (0.5%), followed by BMS (0.6%), off-pump CABG (1.3%), and on-pump CABG (1.8%). After adjustment, on-pump CABG had a higher risk of stroke compared with off-pump (odds ratio 1.36, 95% CI 1.18 to 1.56, p <0.0001). DES had lower risk of stroke compared with off-pump CABG (odds ratio 0.64, 95% CI 0.55 to 0.74, p <0.0001). There was a significant excess risk of 1-year mortality due to the interaction between stroke and procedure type (on-pump vs off-pump CABG and PCI with DES vs BMS; p value for interaction = 0.02). In conclusion, in this retrospective analysis of nonrandomized data from a statewide database, PCI with DES was associated with the lowest rate of postprocedural stroke, and off-pump CABG had a lower rate of postprocedural stroke than on-pump CABG; there was an excess 1-year mortality risk with on-pump versus off-pump CABG and with DES versus BMS in patients with stroke.

Rates of Hospitalization for Dehydration Following Hurricane Sandy in New Jersey.

OBJECTIVE: Hurricane Sandy, one of the most destructive natural disasters in New Jersey history, made landfall on October 29, 2012. Prolonged loss of electrical power and extensive infrastructure damage restricted access for many to food and water. We examined the rate of dehydration in New Jersey residents after Hurricane Sandy. METHODS: We obtained data from 2008 to 2012 from the Myocardial Infarction Data Acquisition System (MIDAS), a repository of in-patient records from nonfederal New Jersey hospitals (N=517,355). Patients with dehydration had ICD-9-CM discharge diagnosis codes for dehydration, volume depletion, and/or hypovolemia. We used log-linear modeling to estimate the change in in-patient hospitalizations for dehydration comparing 2 weeks after Sandy with the same period in the previous 4 years (2008-2011). RESULTS: In-patient hospitalizations for dehydration were 66% higher after Sandy than in 2008-2011 (rate ratio [RR]: 1.66; 95% confidence interval [CI]: 1.50, 1.84). Hospitalizations for dehydration in patients over 65 years of age increased by nearly 80% after Sandy compared with 2008-2011 (RR: 1.79; 95% CI: 1.58, 2.02). CONCLUSION: Sandy was associated with a marked increase in hospitalizations for dehydration. Reducing the rate of dehydration following extreme weather events is an important public health concern that needs to be addressed, especially in those over 65 years of age.

Ischemic Stroke Rate Increases in Young Adults: Evidence for a Generational Effect?

BACKGROUND: The incidence rates of ischemic stroke and ST-segment elevation myocardial infarction (STEMI) have decreased significantly in the United States since 1950. However, there is evidence of flattening of this trend or increasing rates for stroke in patients younger than 50 years. The objective of this study was to examine the changes in incidence rates of stroke and STEMI using an age-period-cohort model with statewide data from New Jersey. METHODS AND RESULTS: We obtained stroke and STEMI data for the years 1995-2014 from the Myocardial Infarction Data Acquisition System, a database of hospital discharges in New Jersey. Rates by age for the time periods 1994-1999, 2000-2004, 2005-2009, and 2010-2014 were obtained using census estimates as denominators for each age group and period. The rate of stroke more than doubled in patients aged 35 to 39 years from 1995-1999 to 2010-2014 (rate ratio [RR], 2.47; 95% CI, 2.07-2.96 [P<0.0001]). We also found increased rates of stroke in those aged 40 to 44, 45 to 49, and 50 to 54 years. Strokes rates in those older than 55 years decreased during these time periods. Those born from 1945-1954 had lower age-adjusted rates of stroke than those born both in the prior 20 years and in the following 20 years. STEMI rates, in contrast, decreased in all age groups and in each successive birth cohort. CONCLUSIONS: There appears to be a significant birth cohort effect in the risk of stroke, where patients born from 1945-1954 have lower age-adjusted rates of stroke compared with those born in earlier and later years.

The effect of Hurricane Sandy on cardiovascular events in New Jersey.

BACKGROUND: Hurricane Sandy made landfall in New Jersey (NJ) on October 29, 2012. We studied the impact of this extreme weather event on the incidence of, and 30-day mortality from, cardiovascular (CV) events (CVEs), including myocardial infarctions (MI) and strokes, in NJ. METHODS AND RESULTS: Data were obtained from the MI data acquisition system (MIDAS), a database of all inpatient hospital discharges with CV diagnoses in NJ, including death certificates. Patients were grouped by their county of residence, and each county was categorized as either high- (41.5% of the NJ population) or low-impact area based on data from the Federal Emergency Management Agency and other sources. We utilized Poisson regression comparing the 2 weeks following Sandy landfall with the same weeks from the 5 previous years. In addition, we used CVE data from the 2 weeks previous in each year as to adjust for yearly changes. In the high-impact area, MI incidence increased by 22%, compared to previous years (attributable rate ratio [ARR], 1.22; 95% confidence interval [CI], 1.16, 1.28), with a 31% increase in 30-day mortality (ARR, 1.31; 95% CI, 1.22, 1.41). The incidence of stroke increased by 7% (ARR, 1.07; 95% CI, 1.03, 1.11), with no significant change in 30-day stroke mortality. There were no changes in incidence or 30-day mortality of MI or stroke in the low-impact area. CONCLUSION: In the 2 weeks following Hurricane Sandy, there were increases in the incidence of, and 30-day mortality from, MI and in the incidence of stroke.

Rapid decreases in blood pressure from antihypertensive treatment were associated with increased cancer mortality in the Systolic Hypertension in the Elderly Program.

BACKGROUND: Research on the association between antihypertensive drug treatment (HTDT) and cancer is equivocal. We tested the hypothesis that large, rapid decreases in blood pressure following HTDT are associated with higher cancer mortality. METHODS: Data from the Systolic Hypertension in the Elderly Program (SHEP) with 15-year cause-specific follow-up for mortality were used. We used changes from baseline in seated and standing systolic blood pressure (SBP) measurements at 3, 6, 9, and 12 months after the initiation of HTDT. Hazard ratios adjusted for demographics, comorbidities, and competing risk of non-cancer-related deaths were estimated to determine the association between SBP change, as a continuous or time-dependent measure, and cancer-related death. RESULTS: SHEP participants taking antihypertensive medication who exhibited a decrease in seated SBP of 29 mm Hg or more (50th percentile and above) at 3 months were at a 58% greater risk of cancer-related death during a 15-year follow-up compared with those with no decrease in SBP (P = 0.007, 42% increased risk P = 0.02 for standing SBP). Those participants whose maximal seated SBP change occurred in the first 3 months of treatment had 2.6-times greater risk of cancer mortality compared with those whose maximal seated SBP change occurred at 12 months (P = 0.004). CONCLUSIONS: Large SBP decreases early in HTDT were associated with an increased risk of cancer-related death during a 15-year follow-up. Further studies are needed to confirm and explore the potential mechanisms for this association. IMPACT: Rapid decreases in blood pressure following HTDT may be a risk factor for cancer. Cancer Epidemiol Biomarkers Prev; 23(8); 1589-97. ©2014 AACR.

Competing cardiovascular and noncardiovascular risks and longevity in the systolic hypertension in the elderly program.

We examined the effect of chlorthalidone-based stepped care on the competing risks of cardiovascular (CV) versus non-CV death in the Systolic Hypertension in the Elderly Program (SHEP). Participants were randomly assigned to chlorthalidone-based stepped-care therapy (n = 2,365) or placebo (n = 2,371) for 4.5 years, and all participants were advised to take active therapy thereafter. At the 22-year follow-up, the gain in life expectancy free from CV death in the active treatment group was 145 days (95% confidence interval [CI] 23 to 260, p = 0.012). The gain in overall life expectancy was smaller (105 days, 95% CI -39 to 242, p = 0.073) because of a 40-day (95% CI -87 to 161) decrease in survival from non-CV death. Compared with an age- and gender-matched cohort, participants had markedly higher overall life expectancy (Wilcoxon p = 0.00001) and greater chance of reaching the ages of 80 (81.3% vs 57.6%), 85 (58.1% vs 37.4%), 90 (30.5% vs 22.0%), 95 (11.9% vs 8.8%), and 100 years (3.7% vs 2.8%). In conclusion, Systolic Hypertension in the Elderly Program participants had higher overall life expectancy than actuarial controls and those randomized to active therapy had longer life expectancy free from CV death but had a small increase in the competing risk of non-CV death.