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Background: Long-acting injectable (LAI) antipsychotics (APs) each have an oral equivalent formulation, while aripiprazole, olanzapine, and ziprasidone each also have a short-acting injectable (SAI) equivalent formulation. Inpatient prescribing patterns of LAIs and their oral/SAI equivalents are less characterized in populations other than Medicaid, Medicare, and Veterans Affairs populations. Mapping out inpatient prescribing patterns remains an important first step to ensure appropriate use of antipsychotics during this critical juncture of patient care prior to discharge. This study determined inpatient prescribing patterns of first- (FGA) and second-generation antipsychotic (SGA) LAIs and their oral/SAI formulations. Methods: This was a large retrospective study using the Cerner Health Facts® database. Hospital admissions due to schizophrenia, schizoaffective disorder, or bipolar disorder from 2010 to 2016 were identified. AP utilization was defined as the proportion of inpatient stays during which at least 1 AP was administered to the total number of inpatient visits over the observed period. Descriptive analyses were used to determine prescribing patterns for APs. Chi-square tests were used to determine utilization differences across years. Results: 94,989 encounters were identified. Encounters during which oral/SAI of SGA LAIs were administered were most common (n = 38,621, 41%). Encounters during which FGA LAIs or SGA LAIs were administered were the least common (n = 1,047, 1.1%). Prescribing patterns differed across years (p < 0.05) within the SGA LAI subgroup analysis (N = 6,014). Paliperidone palmitate (63%, N = 3,799) and risperidone (31%, N = 1,859) were the most frequently administered. Paliperidone palmitate utilization increased from 30% to 72% (p < 0.001), while risperidone utilization decreased from 70% to 18% (p < 0.001). Conclusions: Compared with their oral or SAI formulations, LAIs were underutilized from 2010 to 2016. Among SGA LAIs, the prescribing patterns of paliperidone palmitate and risperidone changed significantly. [ABSTRACT FROM AUTHOR]

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Importance: Sacral neuromodulation (SNM) requires removal for infectious complications in 3-11%., Objective: The objective of this study was to examine the effect of preoperative antibiotic choice on all-cause SNM device removal rates., Study Design: This was a retrospective cohort analysis, using the Health Facts Database, representing more than 750 hospitals. We included female patients undergoing SNM implantation from 2010 to 2018. Univariate and multivariate logistic regression identified factors associated with removal. Thirty-five comorbidities were evaluated. Those with P < 0.2 on univariate analysis were included in the multivariate analysis. We decided a priori to include prophylactic antibiotic choice in the final model., Results: Of 1,433 patients, 170 (11.9%) had device removal. Patients were 63.0 ± 14.9 years old, predominantly Caucasian (90.0%), treated in urban hospitals (94.1%), and married (54.2%). A total of 11.8% were obese, and 18.0% smoked. Those in the removal cohort were more likely from the Northeastern United States; 52.3% received first-gen cephalosporins (CPSN), 7.4% second- or third-generation CPSNs, 9.1% vancomycin, 13.4% aminoglycosides, 4.6% clindamycin, and 13.3% fluoroquinolones. Compared to vancomycin, more removals were associated with first-generation CPSNs (odds ratio [OR] = 3.1, 95% confidence interval [1.4, 6.8]); clindamycin (OR = 3.2, [1.2, 8.4]); second/third-generation CPSNs (OR = 3.1, [1.3, 7.6]); and aminoglycosides (OR = 3.1, [1.3, 7.4]). Additionally, patients treated in the Northeast were more likely to undergo removal (OR = 1.9, [1.0, 3.7])., Conclusions: Vancomycin as a prophylactic antibiotic was associated with fewer device removals compared to most antibiotics in this retrospective cohort analysis. While prospective trials could confirm this benefit, low removal rates may make this impractical., Competing Interests: Dr. Sutkin has research funding from the Intuitive Foundation and from the Agency for Healthcare Research and Quality. That funding and research is unrelated to the work described in this article. The other authors have declared they have no conflicts of interest., (Copyright © 2024 American Urogynecologic Society. All rights reserved.)

Importance: Few studies compare the link between hemogobin A1c (HbA1c) and urogynecologic surgical complications., Objective: The objective of this study was to determine the association between HbA1c and reoperation in women undergoing surgery for stress urinary incontinence (SUI) or pelvic organ prolapse (POP)., Study Design: We performed 2 separate retrospective cohort analyses using Cerner's HealthFacts Database (750 hospitals; 519,000,000 patient encounters from January 1, 2010, to November 30, 2018). We included women undergoing surgery for (1) SUI or (2) apical POP by International Classification of Diseases coding who had HbA1c at the initial procedure. Each analysis compared those undergoing reoperation for complications or recurrence and those who did not. Multivariable logistic regression assessed the association between reoperation and HbA1c both as a continuous variable and comparing the commonly accepted cutoff ≥8., Results: Of 30,180 SUI surgical procedures and 26,389 POP surgical procedures, 1,625 (5.4%) and 805 (3.1%) had HbA1c. Median (interquartile range) HbA1c in grams per deciliter was similar by reoperation status (SUI: 6.0 [5.6-6.8] vs 6.1 [5.6-6.9], P = 0.35; POP: 6.2 [5.6-6.6] vs 6.1 [5.7-6.8], P = 0.60). Reoperation was also similar using the HbA1c ≥8% cutoff (SUI: 6.9% vs 7.4%, P = 0.79; POP: 6.3% vs 5.4%, P = 0.77). On multivariate analysis, HbA1c value was not a significant predictor of reoperation either as a continuous (SUI: odds ratio [OR] = 0.966, 95% CI = 0.833-1.119; POP: OR = 1.040, 95% CI = 0.801-1.350) or dichotomous variable ≥8 (SUI: OR = 0.767, 95% CI = 0.407-1.446; POP: OR = 0.988, 95% CI = 0.331-2.951). Mean follow-up was 4.28-5.13 years., Conclusion: Although other studies have shown a link between diabetes and complications, we were unable to show an association between HbA1c values and rates of reoperation., Competing Interests: Conflicts of interest: Dr Sutkin has research funding from the Intuitive Foundation. That funding and research are unrelated to the work described in this manuscript. The other authors have no relevant financial or nonfinancial interests to disclose., (Copyright © 2023 American Urogynecologic Society. All rights reserved.)

Background: Electronic health record (EHR) data have many quality problems that may affect the outcome of research results and decision support systems. Many methods have been used to evaluate EHR data quality. However, there has yet to be a consensus on the best practice. We used a rule-based approach to assess the variability of EHR data quality across multiple healthcare systems., Methods: To quantify data quality concerns across healthcare systems in a PCORnet Clinical Research Network, we used a previously tested rule-based framework tailored to the PCORnet Common Data Model to perform data quality assessment at 13 clinical sites across eight states. Results were compared with the current PCORnet data curation process to explore the differences between both methods. Additional analyses of testosterone therapy prescribing were used to explore clinical care variability and quality., Results: The framework detected discrepancies across sites, revealing evident data quality variability between sites. The detailed requirements encoded the rules captured additional data errors with a specificity that aids in remediation of technical errors compared to the current PCORnet data curation process. Other rules designed to detect logical and clinical inconsistencies may also support clinical care variability and quality programs., Conclusion: Rule-based EHR data quality methods quantify significant discrepancies across all sites. Medication and laboratory sources are causes of data errors., Competing Interests: The authors have no conflicts of interest to declare., (© The Author(s) 2023.)

Individual researchers and research networks have developed and applied different approaches to assess the data quality of electronic health record (EHR) data. A previously published rules-based method to evaluate the data quality of EHR data provides deeper levels of data quality analysis. To examine the effectiveness and generalizability of the rule-based framework, we reprogrammed and translated published rule templates to operate against the PCORnet Common Data Model and executed them against a database for a single center of the Greater Plains Collaborative (GPC) PCORnet Clinical Research Network. The framework detected additional data errors and logical inconsistencies not revealed by current data quality procedures. Laboratory and medication data were more vulnerable to errors. Hemolyzed samples in the emergency department and metformin prescribing in ambulatory clinics are further described to illustrate application of specific rule-based findings by researchers to engage their health systems in evaluating healthcare delivery and clinical quality concerns., (©2022 AMIA - All rights reserved.)