Prevalence of prescription opioid use during pregnancy in eight US health plans during 2001-2014.

PURPOSE: To estimate prevalence of prescription opioid use during pregnancy in eight US health plans during 2001-2014. METHODS: We conducted a cohort study of singleton live birth deliveries. Maternal characteristics were ascertained from health plan and/or birth certificate data and opioids dispensed during pregnancy from health plan pharmacy records. Prevalence of prescription opioid use during pregnancy was calculated for any use, cumulative days of use, and number of dispensings. RESULTS: We examined prevalence of prescription opioid use during pregnancy in each health plan. Tennessee Medicaid had appreciably greater prevalence of use compared to the seven other health plans. Thus, results for the two groups were reported separately. In the seven health plans (n = 587 093 deliveries), prevalence of use during pregnancy was relatively stable at 9%-11% throughout 2001-2014. In Tennessee Medicaid (n = 256 724 deliveries), prevalence increased from 29% in 2001 to a peak of 36%-37% in 2004-2010, and then declined to 28% in 2014. Use for ≥30 days during pregnancy was stable at 1% in the seven health plans and increased from 2% to 7% in Tennessee Medicaid during 2001-2014. Receipt of ≥5 opioid dispensings during pregnancy increased in the seven health plans (0.3%-0.6%) and Tennessee Medicaid (3%-5%) during 2001-2014. CONCLUSION: During 2001-2014, prescription opioid use during pregnancy was more common in Tennessee Medicaid (peak prevalence in late 2000s) compared to the seven health plans (relatively stable prevalence). Although a small percentage of women had opioid use during pregnancy for ≥30 days or ≥ 5 dispensings, they represent thousands of women during 2001-2014.

Adults With Asthma Experience No Increase in Asthma-related Exacerbations When Digital Communication Technology Tools Are Employed to Offset Provider Workload: A Pragmatic Randomized Trial.

BACKGROUND: Challenges to health care efficiency are increasingly addressed with the help of digital communication technology tools (DCTs). OBJECTIVE: The objective of this study was to test whether DCT, compared with Usual Care, can reduce health care clinician burden without increasing asthma-related exacerbations among patients with asthma in a large integrated health care system. RESEARCH DESIGN: The (Breathewell) program was a pragmatic, randomized trial at (Kaiser Permanente Colorado), where asthma nurses screen patients for poor symptom control when beta2-agonist refill requests came within 60 days of previous fill or in the absence of a controller medication fill within 4 months (beta2-agonist overfill). A total of 14,978 adults with asthma were randomized to Usual Care or 1 of 2 DCT intervention groups (Text/Phone call or Email). SUBJECTS: Participants included adults 18 and older with an asthma diagnosis at the time of randomization and no history of chronic obstructive pulmonary disease. MEASURES: Primary outcome measures included asthma-related health care resource utilization (eg, asthma nurse contacts), medication use, and exacerbations. RESULTS: A total of 1933 patients had 4337 events which met beta2-agonist overfill criteria. Of the 2874 events in the intervention arm, 1188 (41%) were resolved by DCT contact and did not require additional clinician contact. Asthma medication use and exacerbations over 12 months did not differ among the 3 groups. CONCLUSIONS: DCT tools can successfully contact adult asthma patients to screen for symptoms and facilitate intervention. The absence of differences in medication fills and health care utilization indicates that the strategic replacement of nursing interventions by digital outreach did not reduce treatment adherence or compromise health care outcomes.

Validity of diagnosis and procedure codes for identifying neural tube defects in infants.

PURPOSE: The use of validated criteria to identify birth defects in electronic healthcare databases can avoid the cost and time-intensive efforts required to conduct chart reviews to confirm outcomes. This study evaluated the validity of various case-finding methodologies to identify neural tube defects (NTDs) in infants using an electronic healthcare database. METHODS: This analysis used data generated from a study whose primary aim was to evaluate the association between first-trimester maternal prescription opioid use and NTDs. The study was conducted within the Medication Exposure in Pregnancy Risk Evaluation Program. A broad approach was used to identify potential NTDs including diagnosis and procedure codes from inpatient and outpatient settings, death certificates and birth defect flags in birth certificates. Potential NTD cases were chart abstracted and confirmed by clinical experts. Positive predictive values (PPVs) and 95% confidence intervals (95% CI) are reported. RESULTS: The cohort included 113 168 singleton live-born infants: 55 960 infants with opioid exposure in pregnancy and 57 208 infants unexposed in pregnancy. Seventy-three potential NTD cases were available for the validation analysis. The overall PPV was 41% using all diagnosis and procedure codes plus birth certificates. Restricting approaches to codes recorded in the infants' medical record or to birth certificate flags increased the PPVs (72% and 80%, respectively) but missed a substantial proportion of confirmed NTDs. CONCLUSIONS: Codes in electronic healthcare data did not accurately identify confirmed NTDs. These results indicate that chart review with adjudication of outcomes is important when conducting observational studies of NTDs using electronic healthcare data.

Validity of Privacy-Protecting Analytical Methods That Use Only Aggregate-Level Information to Conduct Multivariable-Adjusted Analysis in Distributed Data Networks.

Distributed data networks enable large-scale epidemiologic studies, but protecting privacy while adequately adjusting for a large number of covariates continues to pose methodological challenges. Using 2 empirical examples within a 3-site distributed data network, we tested combinations of 3 aggregate-level data-sharing approaches (risk-set, summary-table, and effect-estimate), 4 confounding adjustment methods (matching, stratification, inverse probability weighting, and matching weighting), and 2 summary scores (propensity score and disease risk score) for binary and time-to-event outcomes. We assessed the performance of combinations of these data-sharing and adjustment methods by comparing their results with results from the corresponding pooled individual-level data analysis (reference analysis). For both types of outcomes, the method combinations examined yielded results identical or comparable to the reference results in most scenarios. Within each data-sharing approach, comparability between aggregate- and individual-level data analysis depended on adjustment method; for example, risk-set data-sharing with matched or stratified analysis of summary scores produced identical results, while weighted analysis showed some discrepancies. Across the adjustment methods examined, risk-set data-sharing generally performed better, while summary-table and effect-estimate data-sharing more often produced discrepancies in settings with rare outcomes and small sample sizes. Valid multivariable-adjusted analysis can be performed in distributed data networks without sharing of individual-level data.

Factors Associated With Prevalence and Treatment of Primary Biliary Cholangitis in United States Health Systems.

BACKGROUND & AIMS: Reported prevalence of primary biliary cholangitis (PBC) varies widely. Demographic features and treatment patterns are not well characterized in the United States (US). We analyzed data from the Fibrotic Liver Disease (FOLD) Consortium, drawn from 11 geographically diverse health systems, to investigate epidemiologic factors and treatment of PBC in the US. METHODS: We developed a validated electronic health record-based classification model to identify patients with PBC in the FOLD database from 2003 through 2014. We used multivariable modeling to assess the effects of factors associated with PBC prevalence and treatment with ursodeoxycholic acid (UDCA). RESULTS: We identified 4241 PBC cases among over 14.5 million patients in FOLD health systems; median follow-up was 5 years. Accuracy of the classification model was excellent, with an area under the receiver operating characteristic curve value of 93%, 94% sensitivity, and 87% specificity. The average patient age at diagnosis was 60 years; 21% were Hispanic, 8% were African American, and 7% were Asian American/American Indian/Pacific Islander. Half of the cohort (49%) had elevated levels of alkaline phosphatase, and overall, 70% were treated with UDCA. The estimated 12-year prevalence of PBC was 29.3 per 100,000 persons. Adjusted prevalence values were highest among women (42.8 per 100,000), White patients (29.6 per 100,000), and patients 60-70 years old (44.7 per 100,000). Prevalence was significantly lower among men and African Americans (10.7 and 19.7 per 100,000, respectively) than women and whites; men and African Americans were also less likely to receive UDCA treatment (odds ratios, 0.6 and 0.5, respectively; P < .05). CONCLUSIONS: In an analysis of a large cohort of patients with PBC receiving routine clinical care, we observed significant differences in PBC prevalence and treatment by gender, race, and age.

Near Real-time Surveillance for Consequences of Health Policies Using Sequential Analysis.

BACKGROUND: New health policies may have intended and unintended consequences. Active surveillance of population-level data may provide initial signals of policy effects for further rigorous evaluation soon after policy implementation. OBJECTIVE: This study evaluated the utility of sequential analysis for prospectively assessing signals of health policy impacts. As a policy example, we studied the consequences of the widely publicized Food and Drug Administration's warnings cautioning that antidepressant use could increase suicidal risk in youth. METHOD: This was a retrospective, longitudinal study, modeling prospective surveillance, using the maximized sequential probability ratio test. We used historical data (2000-2010) from 11 health systems in the US Mental Health Research Network. The study cohort included adolescents (ages 10-17 y) and young adults (ages 18-29 y), who were targeted by the warnings, and adults (ages 30-64 y) as a comparison group. Outcome measures were observed and expected events of 2 possible unintended policy outcomes: psychotropic drug poisonings (as a proxy for suicide attempts) and completed suicides. RESULTS: We detected statistically significant (P<0.05) signals of excess risk for suicidal behavior in adolescents and young adults within 5-7 quarters of the warnings. The excess risk in psychotropic drug poisonings was consistent with results from a previous, more rigorous interrupted time series analysis but use of the maximized sequential probability ratio test method allows timely detection. While we also detected signals of increased risk of completed suicide in these younger age groups, on its own it should not be taken as conclusive evidence that the policy caused the signal. A statistical signal indicates the need for further scrutiny using rigorous quasi-experimental studies to investigate the possibility of a cause-and-effect relationship. CONCLUSIONS: This was a proof-of-concept study. Prospective, periodic evaluation of administrative health care data using sequential analysis can provide timely population-based signals of effects of health policies. This method may be useful to use as new policies are introduced.

Validation of an algorithm for identifying type 1 diabetes in adults based on electronic health record data.

PURPOSE: Algorithms using information from electronic health records to identify adults with type 1 diabetes have not been well studied. Such algorithms would have applications in pharmacoepidemiology, drug safety research, clinical trials, surveillance, and quality improvement. Our main objectives were to determine the positive predictive value for identifying type 1 diabetes in adults using a published algorithm (developed by Klompas et al) and to compare it to a simple requirement that the majority of diabetes diagnosis codes be type 1. METHODS: We applied the Klompas algorithm and the diagnosis code criterion to a cohort of 66 690 adult Kaiser Permanente Colorado members with diabetes. We reviewed 220 charts of those identified as having type 1 diabetes and calculated positive predictive values. RESULTS: The Klompas algorithm identified 3286 (4.9% of 66 690) adults with diabetes as having type 1 diabetes. Based on chart reviews, the overall positive predictive value was 94.5%. The requirement that the majority of diabetes diagnosis codes be type 1 identified 3000 (4.5%) as having type 1 diabetes and had a positive predictive value of 96.4%. However, the algorithm criterion involving dispensing of urine acetone test strips performed poorly, with a positive predictive value of 20.0%. CONCLUSIONS: Data from electronic health records can be used to accurately identify adults with type 1 diabetes. When identifying adults with type 1 diabetes, we recommend either a modified version of the Klompas algorithm without the urine acetone test strips criterion or the requirement that the majority of diabetes diagnosis codes be type 1 codes.

Diagnosis-based cohort augmentation using laboratory results data: The case of chronic kidney disease.

PURPOSE: In this report, we use data from FDA's Sentinel System to focus on how augmenting a diagnosis-based chronic kidney disease cohort with patients identified through laboratory results impacts cohort characteristics and outcomes. METHODS: We used data from 2 Data Partners. Patients were eligible if they were health plan members on January 1, 2012. We classified chronic kidney disease patients into mutually exclusive categories according to the hierarchy of (1) ICD-9-CM diagnosis (DXGroup), or (2) two estimated glomerular filtration rates <60 mL/min/1.73m2 , separated by at least 90 days (2-LabGroup), or (3) a single estimated glomerular filtration rates <60 mL/min/1.73m2 (1-LabGroup). We compared the groups on demographic, clinical, and health care utilization characteristics using pairwise standardized differences. We used Cox regression to compare the groups on mortality, adjusting for baseline covariates. RESULTS: We identified 209 864 patients: 107 607 in DxGroup (51%) and 102 257 (49%) from laboratory data alone. For every characteristic, the DxGroup was the sickest, followed by the 2-LabGroup and then the 1-LabGroup. The DxGroup was more likely to die than 2-LabGroup (hazard ratio [HR], 1.47; 95% CI, 1.22-1.77) at Site 1; that effect was observed, but attenuated, at Site 2 (HR, 1.16; 95% CI, 1.07-1.25). The DxGroup was more likely to die than the 1-LabGroup at Site 1 (HR, 1.36; 95% CI, 1.20-1.55), but not at Site 2 (HR, 0.94; 95% CI, 0.89-1.00). CONCLUSIONS: We suggest that drug safety researchers consider whether the method of cohort identification contributes to generalizability of safety findings.

Risk of Breast Cancer With Long-Term Use of Calcium Channel Blockers or Angiotensin-Converting Enzyme Inhibitors Among Older Women.

Controversy exists about breast cancer risk associated with long-term use of calcium channel blockers (CCBs) or angiotensin-converting enzyme inhibitors (ACEis), respectively. Our objective in this study was to separately evaluate associations between duration of CCB or ACEi use and breast cancer in hypertensive women aged ≥55 years at 3 sites in the Kaiser Permanente health-care system (1997­2012). Exposures included CCB or ACEi use of 1­12 years' duration, determined from pharmacy dispensings. Outcomes included invasive lobular or ductal carcinoma. Statistical methods included discrete-time survival analyses. The cohort included 19,674 (17.9%) CCB users and 90,078 (82.1%) ACEi users. Two percent (n = 397) of CCB users and 1.9% (n = 1,733) of ACEi users developed breast cancer. Compared with 1­<2 years of use, in adjusted analysis, there was no association between CCB use for 2­<12 years and breast cancer: All 95% confidence intervals included 1. Increasing duration of ACEi use was associated with reduced breast cancer risk: Compared with 1­<2 years of use, the adjusted hazard ratio was 0.76 (95% confidence interval: 0.63, 0.92) for 5­<6 years of use and 0.63 (95% confidence interval: 0.43, 0.93) for 9­<10 years of use. We conclude that among older women with hypertension, long-term CCB use does not increase breast cancer risk and long-term treatment with ACEis may confer protection against breast cancer.

Medication Adherence Does Not Explain Black-White Differences in Cardiometabolic Risk Factor Control among Insured Patients with Diabetes.

BACKGROUND: Among patients with diabetes, racial differences in cardiometabolic risk factor control are common. The extent to which differences in medication adherence contribute to such disparities is not known. We examined whether medication adherence, controlling for treatment intensification, could explain differences in risk factor control between black and white patients with diabetes. METHODS: We identified three cohorts of black and white patients treated with oral medications and who had poor risk factor control at baseline (2009): those with glycated hemoglobin (HbA1c) >8 % (n = 37,873), low-density lipoprotein cholesterol (LDL-C) >100 mg/dl (n = 27,954), and systolic blood pressure (SBP) >130 mm Hg (n = 63,641). Subjects included insured adults with diabetes who were receiving care in one of nine U.S. integrated health systems comprising the SUrveillance, PREvention, and ManagEment of Diabetes Mellitus (SUPREME-DM) consortium. Baseline and follow-up risk factor control, sociodemographic, and clinical characteristics were obtained from electronic health records. Pharmacy-dispensing data were used to estimate medication adherence (i.e., medication refill adherence [MRA]) and treatment intensification (i.e., dose increase or addition of new medication class) between baseline and follow-up. County-level income and educational attainment were estimated via geocoding. Logistic regression models were used to test the association between race and follow-up risk factor control. Models were specified with and without medication adherence to evaluate its role as a mediator. RESULTS: We observed poorer medication adherence among black patients than white patients (p < 0.01): 50.6 % of blacks versus 39.7 % of whites were not highly adherent (i.e., MRA <80 %) to HbA1c oral medication(s); 58.4 % of blacks and 46.7 % of whites were not highly adherent to lipid medication(s); and 33.4 % of blacks and 23.7 % of whites were not highly adherent to BP medication(s). Across all cardiometabolic risk factors, blacks were significantly less likely to achieve control (p < 0.01): 41.5 % of blacks and 45.8 % of whites achieved HbA1c <8 %; 52.6 % of blacks and 60.8 % of whites achieved LDL-C <100; and 45.7 % of blacks and 53.6 % of whites achieved SBP <130. Adjusting for medication adherence/treatment intensification did not alter these patterns or model fit statistics. CONCLUSIONS: Medication adherence failed to explain observed racial differences in the achievement of HbA1c, LDL-C, and SBP control among insured patients with diabetes.

Evaluation of propensity scores, disease risk scores, and regression in confounder adjustment for the safety of emerging treatment with group sequential monitoring.

PURPOSE: The objective of this study was to evaluate regression, matching, and stratification on propensity score (PS) or disease risk score (DRS) in a setting of sequential analyses where statistical hypotheses are tested multiple times. METHODS: In a setting of sequential analyses, we simulated incident users and binary outcomes with different confounding strength, outcome incidence, and the adoption rate of treatment. We compared Type I error rate, empirical power, and time to signal using the following confounder adjustments: (i) regression; (ii) treatment matching (1:1 or 1:4) on PS or DRS; and (iii) stratification on PS or DRS. We estimated PS and DRS using lookwise and cumulative methods (all data up to the current look). We applied these confounder adjustments in examining the association between non-steroidal anti-inflammatory drugs and bleeding. RESULTS: Propensity score and DRS methods had similar empirical power and time to signal. However, DRS methods yielded Type I error rates up to 17% for 1:4 matching and 15.3% for stratification methods when treatment and outcome were common and confounding strength with treatment was stronger. When treatment and outcome were not common, stratification on PS and DRS and regression yielded 8-10% Type I error rates and inflated empirical power. However, when outcome and treatment were common, both regression and stratification on PS outperformed other matching methods with Type I error rates close to 5%. CONCLUSIONS: We suggest regression and stratification on PS when the outcomes and/or treatment is common and use of matching on PS with higher ratios when outcome or treatment is rare or moderately rare.

Methods for using clinical laboratory test results as baseline confounders in multi-site observational database studies when missing data are expected.

PURPOSE: Our purpose was to quantify missing baseline laboratory results, assess predictors of missingness, and examine performance of missing data methods. METHODS: Using the Mini-Sentinel Distributed Database from three sites, we selected three exposure-outcome scenarios with laboratory results as baseline confounders. We compared hazard ratios (HRs) or risk differences (RDs) and 95% confidence intervals (CIs) from models that omitted laboratory results, included only available results (complete cases), and included results after applying missing data methods (multiple imputation [MI] regression, MI predictive mean matching [PMM] indicator). RESULTS: Scenario 1 considered glucose among second-generation antipsychotic users and diabetes. Across sites, glucose was available for 27.7-58.9%. Results differed between complete case and missing data models (e.g., olanzapine: HR 0.92 [CI 0.73, 1.12] vs 1.02 [0.90, 1.16]). Across-site models employing different MI approaches provided similar HR and CI; site-specific models provided differing estimates. Scenario 2 evaluated creatinine among individuals starting high versus low dose lisinopril and hyperkalemia. Creatinine availability: 44.5-79.0%. Results differed between complete case and missing data models (e.g., HR 0.84 [CI 0.77, 0.92] vs. 0.88 [0.83, 0.94]). HR and CI were identical across MI methods. Scenario 3 examined international normalized ratio (INR) among warfarin users starting interacting versus noninteracting antimicrobials and bleeding. INR availability: 20.0-92.9%. Results differed between ignoring INR versus including INR using missing data methods (e.g., RD 0.05 [CI -0.03, 0.13] vs 0.09 [0.00, 0.18]). Indicator and PMM methods gave similar estimates. CONCLUSION: Multi-site studies must consider site variability in missing data. Different missing data methods performed similarly. Copyright © 2016 John Wiley & Sons, Ltd.

Trimethoprim-sulfonamide use during the first trimester of pregnancy and the risk of congenital anomalies.

BACKGROUND: Sulfonamide antibacterials are widely used in pregnancy, but evidence about their safety is mixed. The objective of this study was to assess the association between first-trimester sulfonamide exposure and risk of specific congenital malformations. METHODS: Mother-infant pairs were selected from a cohort of 1.2 million live-born deliveries (2001-2008) at 11 US health plans comprising the Medication Exposure in Pregnancy Risk Evaluation Program. Mothers with first-trimester trimethoprim-sulfonamide (TMP-SUL) exposures were randomly matched 1:1 to (i) a primary comparison group (mothers exposed to penicillins and/or cephalosporins) and (ii) a secondary comparison group (mothers with no dispensing of an antibacterial, antiprotozoal, or antimalarial medication during the same time period). The outcomes were cardiovascular abnormalities, cleft palate/lip, clubfoot, and urinary tract abnormalities. RESULTS: We first identified 7615 infants in the TMP-SUL exposure group, of which 7595 (99%) were exposed to a combination of TMP-SUL and the remaining 1% to sulfonamides alone. After matching (1:1) to the comparator groups and only including those with complete data on covariates, there were 20 064 (n = 6688 per group) in the primary analyses. Overall, cardiovascular defects (1.52%) were the most common and cleft lip/palate (0.10%) the least common that were evaluated. Compared with penicillin/cephalosporin exposure, and no antibacterial exposure, TMP-SUL exposure was not associated with statistically significant elevated risks for cardiovascular, cleft lip/palate, clubfoot, or urinary system defects. CONCLUSIONS: First-trimester TMP-SUL exposure was not associated with a higher risk of the congenital anomalies studied, compared with exposure to penicillins and/or cephalosporins, or no exposure to antibacterials.

Trends in diabetes incidence among 7 million insured adults, 2006-2011: the SUPREME-DM project.

An observational cohort analysis was conducted within the Surveillance, Prevention, and Management of Diabetes Mellitus (SUPREME-DM) DataLink, a consortium of 11 integrated health-care delivery systems with electronic health records in 10 US states. Among nearly 7 million adults aged 20 years or older, we estimated annual diabetes incidence per 1,000 persons overall and by age, sex, race/ethnicity, and body mass index. We identified 289,050 incident cases of diabetes. Age- and sex-adjusted population incidence was stable between 2006 and 2010, ranging from 10.3 per 1,000 adults (95% confidence interval (CI): 9.8, 10.7) to 11.3 per 1,000 adults (95% CI: 11.0, 11.7). Adjusted incidence was significantly higher in 2011 (11.5, 95% CI: 10.9, 12.0) than in the 2 years with the lowest incidence. A similar pattern was observed in most prespecified subgroups, but only the differences for persons who were not white were significant. In 2006, 56% of incident cases had a glycated hemoglobin (hemoglobin A1c) test as one of the pair of events identifying diabetes. By 2011, that number was 74%. In conclusion, overall diabetes incidence in this population did not significantly increase between 2006 and 2010, but increases in hemoglobin A1c testing may have contributed to rising diabetes incidence among nonwhites in 2011.

Health care system-level factors associated with performance on Medicare STAR adherence metrics in a large, integrated delivery system.

BACKGROUND: The Centers for Medicare and Medicaid Services provide significant incentives to health plans that score well on Medicare STAR metrics for cardiovascular disease risk factor medication adherence. Information on modifiable health system-level predictors of adherence can help clinicians and health plans develop strategies for improving Medicare STAR scores, and potentially improve cardiovascular disease outcomes. OBJECTIVE: To examine the association of Medicare STAR adherence metrics with system-level factors. RESEARCH DESIGN: A cross-sectional study. SUBJECTS: A total of 129,040 diabetes patients aged 65 years and above in 2010 from 3 Kaiser Permanente regions. MEASURES: Adherence to antihypertensive, antihyperlipidemic, and oral antihyperglycemic medications in 2010, defined by Medicare STAR as the proportion of days covered ≥ 80%. RESULTS: After controlling for individual-level factors, the strongest predictor of achieving STAR-defined medication adherence was a mean prescribed medication days' supply of > 90 days (RR=1.61 for antihypertensives, oral antihyperglycemics, and statins; all P < 0.001). Using mail order pharmacy to fill medications > 50% of the time was independently associated with better adherence with these medications (RR = 1.07, 1.06, 1.07; P < 0.001); mail order use had an increased positive association among black and Hispanic patients. Medication copayments ≤ $10 for 30 days' supply (RR = 1.02, 1.02, 1.02; P < 0.01) and annual individual out-of-pocket maximums ≤ $2000 (RR = 1.02, 1.01, 1.02; P < 0.01) were also significantly associated with higher adherence for all 3 therapeutic groupings. CONCLUSIONS: Greater medication days' supply and mail order pharmacy use, and lower copayments and out-of-pocket maximums, are associated with better Medicare STAR adherence. Initiatives to improve adherence should focus on modifiable health system-level barriers to obtaining evidence-based medications.

Identification of Stevens-Johnson syndrome and toxic epidermal necrolysis in electronic health record databases.

BACKGROUND: Stevens-Johnson syndrome (SJS) and toxic epidermal necrolysis (TEN) carry a high mortality risk. While identifying clinical and genetic risk factors for these conditions has been hindered by their rarity, large electronic health databases hold promise for identifying large numbers of cases for study, especially with the introduction in 2008 of ICD-9 codes more specific for these conditions. OBJECTIVE: The objective of this study is to estimate the validity of ICD-9 codes for ascertaining SJS/TEN in 12 collaborating research units in the USA, covering almost 60 million lives. METHODS: From the electronic databases at each site, we ascertained potential cases of SJS/TEN using ICD-9 codes. At five sites, a subset of medical records was abstracted and standardized criteria applied by board-certified dermatologists to adjudicate diagnoses. Multivariate logistic regression was used to identify factors independently associated with validated SJS/TEN cases. RESULTS: A total of 56 591 potential cases of SJS/TEN were identified. A subset of 276 charts was selected for adjudication and 39 (of the 276) were confirmed as SJS/TEN. Patients with the ICD-9 codes introduced after 2008 were more likely to be confirmed as cases (OR 3.32; 95%CI 0.82, 13.47) than those identified in earlier years. Likelihood of case status increased with length of hospitalization. Applying the probability of case status to the 56 591 potential cases, we estimated 475-875 to be valid SJS/TEN cases. CONCLUSION: Newer ICD-9 codes, along with length of hospitalization, identified patients with a high likelihood of SJS/TEN. This is important for identification of subjects for future pharmacogenomics studies.

Genital herpes and its treatment in relation to preterm delivery.

To examine the risks of genital herpes and antiherpes treatment during pregnancy in relation to preterm delivery (PTD), we conducted a multicenter, member-based cohort study within 4 Kaiser Permanente regions: northern and southern California, Colorado, and Georgia. The study included 662,913 mother-newborn pairs from 1997 to 2010. Pregnant women were classified into 3 groups based on genital herpes diagnosis and treatment: genital herpes without treatment, genital herpes with antiherpes treatment, and no herpes diagnosis or treatment (unexposed controls). After controlling for potential confounders, we found that compared with being unexposed, having untreated genital herpes during first or second trimester was associated with more than double the risk of PTD (odds ratio (OR) = 2.23, 95% confidence interval (CI): 1.80, 2.76). The association was stronger for PTD due to premature rupture of membrane (OR = 3.57, 95% CI: 2.53, 5.06) and for early PTD (≤35 weeks gestation) (OR = 2.87, 95% CI: 2.22, 3.71). In contrast, undergoing antiherpes treatment during pregnancy was associated with a lower risk of PTD compared with not being treated, and the PTD risk was similar to that observed in the unexposed controls (OR = 1.11, 95% CI: 0.89, 1.38). The present study revealed increased risk of PTD associated with genital herpes infection if left untreated and a potential benefit of antiherpes medications in mitigating the effect of genital herpes infection on the risk of PTD.

Estimating the effects of time-varying exposures in observational studies using Cox models with stabilized weights adjustment.

PURPOSE: Assessing the safety and effectiveness of medical products with observational electronic medical record data is challenging when the treatment is time-varying. The objective of this paper is to develop a Cox model stratified by event times with stabilized weights (SWs) adjustment to examine the effect of time-varying treatment in observational studies. METHODS: Time-varying SWs are calculated at unique event times and are used in a Cox model stratified by event times to estimate the effect of time-varying treatment. We applied this method in examining the effect of an antiplatelet agent, clopidogrel, on events, including bleeding, myocardial infarction, and death after a drug-eluting stent was implanted in coronary artery. Clopidogrel use may change over time on the basis of patients' behavior (e.g., non-adherence) and physicians' recommendations (e.g., end of duration of therapy). We also compared the results with those from a Cox model for counting processes adjusting for all covariates used in creating SWs. RESULTS: We demonstrate that the (i) results from the stratified Cox model without SWs adjustment and the Cox model for counting processes without covariate adjustment are identical in analyzing the clopidogrel data; and (ii) the effects of clopidogrel on bleeding, myocardial infarction, and death are larger in the stratified Cox model with SWs adjustment compared with those from the Cox model for counting processes with covariate adjustment. CONCLUSIONS: The Cox model stratified by event times with time-varying SWs adjustment is useful in estimating the effect of time-varying treatments in observational studies while balancing for known confounders.

Chronic opioid use emerging after bariatric surgery.

PURPOSE: Little is known about opioid use after bariatric surgery among patients who did not use opioids chronically before surgery. Our purpose was to determine opioid use the year after bariatric surgery among patients who did not use opioids chronically pre-surgery and to identify pre-surgery characteristics associated with chronic opioid use after surgery. METHODS: This retrospective cohort study across nine US health systems included 10 643 patients aged 21 years or older who underwent bariatric surgery and who were not chronic opioid users pre-surgery. The main outcome was chronic opioid use the post-surgery year (excluding 30 post-operative days) defined as ≥10 dispensings over ≥90 days or ≥120 total days' supply. RESULTS: Overall, 4.0% (n = 421) of patients became chronic opioid users the post-surgery year. Pre-surgery opioid total days' supply was strongly associated with chronic use post-surgery (1-29 days adjusted odds ratio [OR] 1.89 [95%CI, 1.24-2.88]; 90-119 days OR, 14.29 [95%CI, 6.94-29.42] compared with no days). Other factors associated with increased likelihood of post-surgery chronic use included pre-surgery use of non-narcotic analgesics (OR, 2.22 [95%CI, 1.39-3.54]), antianxiety agents (OR, 1.67 [95%CI, 1.12-2.50]), and tobacco (OR, 1.44 [95%CI, 1.03-2.02]). Older age (OR, 0.84 [95%CI, 0.73-0.97] each decade) and a laparoscopic band procedure (OR, 0.42 [95%CI, 0.25-0.70] vs. laparoscopic bypass) were associated with decreased likelihood of chronic opioid use post-surgery. CONCLUSIONS: Most patients who became chronic opioid users the year after bariatric surgery used opioids intermittently before surgery.

Electronic clinical laboratory test results data tables: lessons from Mini-Sentinel.

PURPOSE: Developing electronic clinical data into a common data model posed substantial challenges unique from those encountered with administrative data. We present here the design, implementation, and use of the Mini-Sentinel Distributed Database laboratory results table (LRT). METHODS: We developed the LRT and guided Mini-Sentinel data partners (DPs) in populating it from their source data. Data sources included electronic health records and internal and contracted clinical laboratory systems databases. We employed the Logical Observation Identifiers, Names, and Codes (LOINC®) results reporting standards. We evaluated transformed results data using data checks and an iterative, ongoing characterization and harmonization process. RESULTS: Key LRT variables included test name, subcategory, specimen source, LOINC, patient location, specimen date and time, result unit, and unique person identifier. Selected blood and urine chemistry, hematology, coagulation, and influenza tests were included. Twelve DPs with outpatient test results participated; four also contributed inpatient test results. As of September 2013, the LRT included 385,516,239 laboratory test results; data are refreshed at least quarterly. LOINC availability and use varied across DP. Multiple data quality and content issues were identified and addressed. CONCLUSION: Developing the LRT brought together disparate data sources with no common coding structure. Clinical laboratory test results obtained during routine healthcare delivery are neither uniformly coded nor documented in a standardized manner. Applying a systematic approach with data harmonization efforts and ongoing oversight and management is necessary for a clinical laboratory results data table to remain valid and useful.