Comparing the Centers for Medicare and Medicaid Services (CMS) enrollment data death dates to the National Death Index (NDI).

PURPOSE: In the United States, the National Death Index (NDI) is the most complete source of death information, while epidemiologic studies with mortality outcomes often rely on U.S. Medicare data for outcome ascertainment. The purpose of this study was to assess the agreement of death information between the Centers for Medicare & Medicaid Services (CMS) Medicare enrolment data and NDI. METHODS: Using Medicare and NDI data from 1999 through 2016, we identified Medicare beneficiaries who were reported dead in the CMS Medicare enrolment database (EDB) and Common Medicare Environment (CME), linked these beneficiaries to the NDI using CMS Health Insurance Claim number, and compared death dates between the two data sources. To assess agreement between our data sources, we calculated kappa scores; where a kappa of 1 indicates perfect agreement and a kappa of 0 indicates agreement equivalent to chance. We also examined CMS to NDI linkage and death date matching for stability over time. RESULTS: Of the 36 785 640, Medicare beneficiaries reported dead in CMS enrollment data from 1999 to 2016, 97.5% were linked to the NDI. A kappa score of 0.98 showed a near perfect agreement between NDI and CMS reported deaths. The percentage of linked cases exactly matching on death dates increased from 94.8% in 1999 to 99.4% in 2016. CONCLUSIONS: Our findings suggest strong concordance between death dates as recorded by CMS enrollment data and the NDI in the entire Medicare population.

Risk of hospitalized depression and intentional self-harm with brand and authorized generic sertraline.

BACKGROUND: Recent suggestions of therapeutic inequivalence of brand and generic sertraline have raised concerns about disproportionately higher adverse events among generic users. OBJECTIVE: To assess the impact of confounding in a comparison of the risks of worsening depression and intentional self-harm (ISH) between users of brand name sertraline and its pharmaceutically equivalent authorized generic (AG). METHODS: Using a retrospective new-user cohort design, we identified patients with a diagnosis code for depression aged ≥12 years who were continuously enrolled in a Sentinel Data Partner health plan for ≥180 days before their first sertraline dispensing between June 30, 2006 and September 30, 2015. New use was defined as no evidence of sertraline dispensing in the 180 days before index date. We matched each brand name user to up to 10 AG users using propensity scores (PS) and conducted case-centered logistic regression to assess the risks of hospitalized depression and ISH. RESULTS: Before PS matching, brand name users were significantly less likely to be hospitalized for depression [Hazard Ratio (HR) = 0.70 (95% confidence interval (CI): 0.53-0.94)]. However, in the matched analysis, we observed no statistical difference between brand and AG users [HR = 0.84 (95% CI: 0.59-1.21)]. The risk of ISH did not significantly differ between the exposure groups in unmatched (HR = 0.99 (95% CI: 0.60-1.62) and matched analyses [HR = 0.91 (95% CI: 0.49-1.70). CONCLUSION: In depressed patients receiving brand versus AG sertraline, patient characteristics confounded the association with hospitalization. Baseline differences were ameliorated by PS matching resulting in no statistical difference between brand and AG sertraline users.

Use of Sodium-Glucose Cotransporter 2 Inhibitors in Patients With Type 1 Diabetes and Rates of Diabetic Ketoacidosis.

OBJECTIVE: To estimate real-world off-label use of sodium-glucose cotransporter 2 (SGLT2) inhibitors in patients with type 1 diabetes, estimate rates of diabetic ketoacidosis (DKA), and compare them with DKA rates observed in sotagliflozin clinical trials. RESEARCH DESIGN AND METHODS: We identified initiators of SGLT2 inhibitors in the Sentinel System from March 2013 to June 2018, determined the prevalence of type 1 diabetes using a narrow and a broad definition, and measured rates of DKA using administrative claims data. Standardized incidence ratios (SIRs) were calculated using age- and sex-specific follow-up time in Sentinel and age- and sex-specific DKA rates from sotagliflozin trials 309, 310, and 312. RESULTS: Among 475,527 initiators of SGLT2 inhibitors, 0.50% and 0.92% met narrow and broad criteria for type 1 diabetes, respectively. Rates of DKA in the narrow and broad groups were 7.3/100 person-years and 4.5/100 person-years, respectively. Among patients who met narrow criteria for type 1 diabetes, rates of DKA were highest for patients aged 25-44 years, especially females aged 25-44 years (19.7/100 person-years). More DKA events were observed during off-label use of SGLT2 inhibitors in Sentinel than would be expected based on sotagliflozin clinical trials (SIR = 1.83; 95% CI 1.45-2.28). CONCLUSIONS: Real-world off-label use of SGLT2 inhibitors among patients with type 1 diabetes accounted for a small proportion of overall SGLT2 inhibitor use. However, the risk for DKA during off-label use was notable, especially among young, female patients. Although real-word rates of DKA exceeded the expectation based on clinical trials, results should be interpreted with caution due to differences in study methods, patient samples, and study drugs.

A comparison of opioid-involved fatalities captured in the National Poison Data System to data derived from US death certificate literal text.

PURPOSE: The purpose of the study is to describe and compare the number and characteristics of opioid-involved fatal cases captured in the National Poison Data System (NPDS) and in US death certificates. METHODS: NPDS, which collects data on all calls to US poison control centers, and Drug-Involved Mortality (DIM), which combines information from literal text of US death certificates and National Vital Statistics Systems, were queried for opioid-involved fatal cases from 2010 to 2015. Characteristics of the two case series were compared. RESULTS: DIM contained 154 016 opioid-involved overdose deaths, and NPDS contained 2524 fatal opioid exposures, a ratio of 61:1. The number of opioid deaths remained stable in NPDS but increased in DIM over the 6-year period. On average, deaths involving opioids with higher mean dosage strength (in morphine milligram equivalents) per unit among dispensed prescriptions were more likely to be captured in DIM relative to NPDS, as compared with those with a lower mean dosage strength per unit. The increase in fentanyl-related deaths seen in DIM since 2013 was not observed in NPDS. CONCLUSIONS: NPDS is a valuable drug safety surveillance resource due to its timeliness and drug specificity. However, it captures only a small fraction of opioid-involved fatal poisonings, and comparisons with data derived from death certificate literal text indicate that caution is warranted in making inferences about opioid-involved fatality trends over time or comparisons across opioids.

A comparison of two algorithms to identify sudden cardiac deaths in computerized databases.

PURPOSE: Two previously validated algorithms to identify sudden cardiac death using administrative data showed high positive predictive value. We evaluated the agreement between the algorithms using data from a common source population. METHODS: We conducted a cross-sectional study to assess the percent agreement between deaths identified by two sudden cardiac death algorithms using Tennessee Medicaid and death certificate data from 2007 through 2014. The source population included all deceased patients aged 18 to 64 years with Medicaid enrollment in the 6 months prior to death. To identify sudden cardiac deaths, algorithm 1 used only hospital/emergency department (ED) claims from encounters at the time of death, and algorithm 2 required death certificates and used claims data for specific exclusion criteria. RESULTS: We identified 34 107 deaths in the source population over the study period. The two algorithms identified 4372 potential sudden cardiac deaths: Algorithm 1 identified 3117 (71.3%) and algorithm 2 identified 1715 (39.2%), with 460 (10.5%) deaths identified by both algorithms. Of the deaths identified by algorithm 1, 1943 (62.3%) had an underlying cause of death not specified in algorithm 2. Of the deaths identified by algorithm 2, 1053 (61.4%) had no record of a hospital or ED encounter at the time of death, and 202 (11.8%) had a discharge diagnosis code not specified in algorithm 1. CONCLUSIONS: We found low agreement between the two algorithms for identification of sudden cardiac deaths because of differences in sudden cardiac death definitions and data sources.

A systematic review of validated suicide outcome classification in observational studies.

BACKGROUND: Suicidal outcomes, including ideation, attempt, and completed suicide, are an important drug safety issue, though few epidemiological studies address the accuracy of suicidal outcome ascertainment. Our primary objective was to evaluate validated methods for suicidal outcome classification in electronic health care database studies. METHODS: We performed a systematic review of PubMed and EMBASE to identify studies that validated methods for suicidal outcome classification published 1 January 1990 to 15 March 2016. Abstracts and full texts were screened by two reviewers using prespecified criteria. Sensitivity, specificity, and predictive value for suicidal outcomes were extracted by two reviewers. Methods followed PRISMA-P guidelines, PROSPERO Protocol: 2016: CRD42016042794. RESULTS: We identified 2202 citations, of which 34 validated the accuracy of measuring suicidal outcomes using International Classification of Diseases (ICD) codes or algorithms, chart review or vital records. ICD E-codes (E950-9) for suicide attempt had 2-19% sensitivity, and 83-100% positive predictive value (PPV). ICD algorithms that included events with 'uncertain' intent had 4-70% PPV. The three best-performing algorithms had 74-92% PPV, with improved sensitivity compared with E-codes. Read code algorithms had 14-68% sensitivity and 0-56% PPV. Studies estimated 19-80% sensitivity for chart review, and 41-97% sensitivity and 100% PPV for vital records. CONCLUSIONS: Pharmacoepidemiological studies measuring suicidal outcomes often use methodologies with poor sensitivity or predictive value or both, which may result in underestimation of associations between drugs and suicidal behaviour. Studies should validate outcomes or use a previously validated algorithm with high PPV and acceptable sensitivity in an appropriate population and data source.

Development and validation of algorithms to identify newly diagnosed type 1 and type 2 diabetes in pediatric population using electronic medical records and claims data.

PURPOSE: To develop and validate algorithms to classify diabetes type in newly diagnosed pediatric patients with DM. METHOD: Data from the United States Department of Defense health system were used to identify patients aged 10 to 18 years with incident DM. Two independent sets of 200 children were randomly sampled for algorithm development and validation. Algorithms were developed based on clinical insight, published literature, and quantitative approaches. The actual DM type was ascertained via chart review. Finally, the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were evaluated. RESULTS: Among the 400 patients, mean age was 14.2 (±2.5 years), and 50% were female. The best performing algorithms were based on data available in claims. They consisted of several logical expressions based on one predictor or more, which classified patients by use of glucose-lowering drugs or testing, DM ICD-9 diagnosis codes, and comorbidities. The best performing T2DM and T1DM algorithms achieved 90% and 98% sensitivity, 95% and 95% specificity, 87% and 98% PPV, and 96% and 96% NPV, respectively. CONCLUSIONS: Our results suggest that claims algorithms can accurately identify newly diagnosed T1DM and T2DM pediatric patients, which can facilitate large database studies in children with T1DM and T2DM. However, external validation in other data sources is needed.

Effects of deferasirox dose and decreasing serum ferritin concentrations on kidney function in paediatric patients: an analysis of clinical laboratory data from pooled clinical studies.

BACKGROUND: Serious and fatal deferasirox-induced kidney injury has been reported in paediatric patients. This study aimed to investigate the effects of deferasirox dose and serum ferritin concentrations on kidney function and the effect of impaired kidney function on dose-normalised deferasirox minimum plasma concentration (Cmin). METHODS: We did a case-control analysis using pooled data from ten clinical studies. We identified transfusion-dependent patients with thalassaemia, aged 2-15 years, who were receiving deferasirox and had available baseline and follow-up serum creatinine and ferritin measurements. Cases of acute kidney injury (AKI) were defined according to an estimated glomerular filtration rate (eGFR) threshold of 90 mL/min per 1·73 m2 or less (if baseline eGFR was ≥100 mL/min per 1·73 m2), an eGFR of 60 mL/min per 1·73 m2 or less (if baseline eGFR was <100 mL/min per 1·73 m2), or an eGFR decrease from baseline of at least 25%. Cases were matched to control visits (eGFR ≥120 mL/min per 1·73 m2) on age, sex, study site, and time since drug initiation. We calculated rate ratios for AKI using conditional logistic regression, and evaluated the effect of eGFR changes on Cmin. FINDINGS: Among 1213 deferasirox-treated paediatric patients, 162 cases of AKI and 621 matched control visits were identified. Patients with AKI had a mean 50·2% (SD 15·5) decrease in eGFR from baseline, compared with a 6·9% (29·8) decrease in controls. A significantly increased risk for AKI (rate ratio 1·26, 95% CI 1·08-1·48, p=0·00418) was observed per 5 mg/kg per day increase in deferasirox dispersible tablet dose (equivalent to a 3·5 mg/kg per day dose of film-coated tablets or granules), above the typical starting dose (20 mg/kg per day). An increased risk (1·25, 1·01-1·56, p=0·0400) for AKI was also observed per 250 µg/L decrease in serum ferritin, starting from 1250 µg/L. High-dose deferasirox (dispersible tablet dose >30 mg/kg per day) resulted in an increased risk (4·47, 1·25-15·95, p=0·0209) for AKI when serum ferritin was less than 1000 µg/L. Decreases in eGFR were associated with increased Cmin. INTERPRETATION: Deferasirox can cause AKI in a dose-dependent manner. The increased AKI risk with high-dose deferasirox and lower serum ferritin concentration is consistent with overchelation as a causative factor. Small decreases in eGFR correlate with increased deferasirox Cmin, especially in younger patients. Physicians should closely monitor renal function and serum ferritin, use the lowest effective dose to maintain acceptable body iron burden, and interrupt deferasirox treatment when AKI or volume depletion are suspected. FUNDING: None.

Diagnostic Algorithms for Cardiovascular Death in Administrative Claims Databases: A Systematic Review.

INTRODUCTION: Valid algorithms for identification of cardiovascular (CV) deaths allow researchers to reliably assess the CV safety of medications, which is of importance to regulatory science, patient safety, and public health. OBJECTIVE: The aim was to conduct a systematic review of algorithms to identify CV death in administrative health plan claims databases. METHODS: We searched MEDLINE, EMBASE, and Cochrane Library for English-language studies published between January 1, 2012 and October 17, 2017. We examined references in systematic reviews to identify earlier studies. Selection included any observational study using electronic health care data to evaluate the sensitivity, specificity, positive predictive value (PPV), or negative predictive value (NPV) of algorithms for CV death (sudden cardiac death [SCD], myocardial infarction [MI]-related death, or stroke-related death) among adults aged ≥ 18 years in the United States. Data were extracted by two independent reviewers, with disagreements resolved through further discussion and consensus. The Quality Assessment of Diagnostic Accuracy Studies-2 instrument was used to assess the risk of bias. RESULTS: Five studies (n = 4 on SCD, n = 1 on MI- and stroke-related death) were included after a review of 2053 citations. All studies reported algorithm PPVs, with incomplete reporting on other accuracy parameters. One study was at low risk of bias, three studies were at moderate risk of bias, and one study was at unclear risk of bias. Two studies identified community-occurring SCD: one identified events using International Classification of Disease, Ninth Revision (ICD-9) codes on death certificates and other criteria from medical claims (PPV = 86.8%) and the other identified events resulting in hospital presentation using first-listed ICD-9 codes on emergency department or inpatient medical claims (PPV = 92.3%). Two studies used death certificates alone to identify SCD (PPV = 27% and 32%, respectively). One study used medical claims to identify CV death (PPV = 36.4%), coronary heart disease mortality (PPV = 28.3%), and stroke mortality (PPV = 34.5%). CONCLUSION: Two existing algorithms based on medical claims diagnoses with or without death certificates can accurately identify SCD to support pharmacoepidemiologic studies. Developing valid algorithms identifying MI- and stroke-related death should be a research priority. PROSPERO 2017 CRD42017078745.

Overall and cause-specific mortality in the Sentinel system: A power analysis.

PURPOSE: Mortality data within the Sentinel Death Tables remain generally uncharacterized. Assessment of mortality data within Sentinel will help inform its utility for medical product safety studies. METHODS: To determine if Sentinel contains sufficient all-cause and cause-specific mortality events to power postmarketing safety studies. We calculated crude rates of all-cause mortality and suicide and proportional mortality from suicide from 2004 to 2012 in seven Sentinel data partners. Results were stratified by data partner, sex, age group, and calendar year and compared with national estimates from Centers for Disease Control and Prevention Wide-ranging ONline Data for Epidemiologic Research. We performed sample size estimations for all-cause mortality and 10 leading causes of death. RESULTS: We observed 479 694 deaths, including 5811 suicides, during 68 million person-years of follow-up. Pooled mean death and suicide rates in the data partners were 710 and 8.6 per 100 000 person-years, respectively (vs 810 and 11.8 nationally). The mean proportional mortality from suicide among the data partners was 1.2%, compared with 1.5% nationally. National trends of decreasing overall mortality and increasing proportional mortality for suicide were reflected within Sentinel. We estimated that detecting hazard ratios of 1.25 and 3 would require 16 442 and 460 exposed patients, respectively, for overall mortality, and 1.3 million and 37 411, respectively, for suicide. CONCLUSIONS: This was the first study to investigate mortality data in the Sentinel death tables. We found that all-cause mortality appeared well powered for use as a safety outcome and cause-specific mortality outcomes may be adequately powered in certain circumstances. Further investigation into the quality of the Sentinel death data is needed.

Clinical epidemiology and treatment patterns of patients with multicentric Castleman disease: results from two US treatment centres.

Multicentric Castleman disease (MCD) is a rare lymphoproliferative disease with little known about its epidemiology or treatment modalities. Clinical and demographic data of MCD patients identified between 2000 and 2009 were collected from medical records at two United States (US) MCD referral centres. ZIP codes identified patient residences; prevalence and incidence were estimated based on catchment areas. Patient clinical, demographic, and biochemical characteristics, drug therapies and medical utilization were descriptively reported. MCD patients (n = 59) were 61% male, mean age of 53 years (median = 55 years) and 68% Caucasian. Of those with known human immunodeficiency virus (HIV) status (n = 41), 85% (n = 35) were negative, 15% (n = 6) were positive. Most frequent physician-reported symptoms (n = 33) were fatigue (49%, n = 16), fever (39%, n = 13), and night sweats (30%, n = 10). The estimated US 10-year prevalence was 2·4 per million. During first year of follow-up after study entry, the top two systemic therapies (n = 27) were monotherapies: prednisone (33%, n = 9) and rituximab (19%, n = 5). After a follow-up of 2 years, 92% of patients were alive. This study provides new information on MCD population demographics, treatment patterns, and medical utilization; a minimal US period prevalence rate is proposed. Study replication is needed to improve external validity.