Indirect covariate balance and residual confounding: An applied comparison of propensity score matching and cardinality matching.

PURPOSE: Propensity score matching (PSM) is subject to limitations associated with limited degrees of freedom and covariate overlap. Cardinality matching (CM), an optimization algorithm, overcomes these limitations by matching directly on the marginal distribution of covariates. This study compared the performance of PSM and CM. METHODS: Comparative cohort study of new users of angiotensin-converting enzyme inhibitor (ACEI) and ß-blocker monotherapy identified from a large U.S. administrative claims database. One-to-one matching was conducted through PSM using nearest-neighbor matching (caliper = 0.15) and CM permitting a maximum standardized mean difference (SMD) of 0, 0.01, 0.05, and 0.10 between comparison groups. Matching covariates included 37 patient demographic and clinical characteristics. Observed covariates included patient demographics, and all observed prior conditions, drug exposures, and procedures. Residual confounding was assessed based on the expected absolute systematic error of negative control outcome experiments. PSM and CM were compared in terms of post-match patient retention, matching and observed covariate balance, and residual confounding within a 10%, 1%, 0.25% and 0.125% sample group. RESULTS: The eligible study population included 182 235 (ACEI: 129363; ß-blocker: 56872) patients. CM achieved superior patient retention and matching covariate balance in all analyses. After PSM, 1.6% and 28.2% of matching covariates were imbalanced in the 10% and 0.125% sample groups, respectively. No significant difference in observed covariate balance was observed between matching techniques. CM permitting a maximum SMD <0.05 was associated with improved residual bias as compared to PSM. CONCLUSION: We recommend CM with more stringent balance criteria as an alternative to PSM when matching on a set of clinically relevant covariates.

Performance characteristics of code-based algorithms to identify urinary tract infections in large United States administrative claims databases.

BACKGROUND: In real-world evidence research, reliability of coding in healthcare databases dictates the accuracy of code-based algorithms in identifying conditions such as urinary tract infection (UTI). This study evaluates the performance characteristics of code-based algorithms to identify UTI. METHODS: Retrospective observational study of adults contained within three large U.S. administrative claims databases on or after January 1, 2010. A targeted literature review was performed to inform the development of 10 code-based algorithms to identify UTIs consisting of combinations of diagnosis codes, antibiotic exposure for the treatment of UTIs, and/or ordering of a urinalysis or urine culture. For each database, a probabilistic gold standard was developed using PheValuator. The performance characteristics of each code-based algorithm were assessed compared with the probabilistic gold standard. RESULTS: A total of 2 950 641, 1 831 405, and 2 294 929 patients meeting study criteria were identified in each database. Overall, the code-based algorithm requiring a primary UTI diagnosis code achieved the highest positive predictive values (PPV; >93.8%) but the lowest sensitivities (<12.9%). Algorithms requiring three UTI diagnosis codes achieved similar PPV (>0.899%) and improved sensitivity (<41.6%). Algorithms requiring a single UTI diagnosis code in any position achieved the highest sensitivities (>72.1%) alongside a slight reduction in PPVs (<78.3%). All-time prevalence estimates of UTI ranged from 21.6% to 48.6%. CONCLUSIONS: Based on these findings, we recommend use of algorithms requiring a single UTI diagnosis code, which achieved high sensitivity and PPV. In studies where PPV is critical, we recommend code-based algorithms requiring three UTI diagnosis codes rather than a single primary UTI diagnosis code.

Development and performance characteristics of novel code-based algorithms to identify invasive Escherichia coli disease.

PURPOSE: Evaluation of novel code-based algorithms to identify invasive Escherichia coli disease (IED) among patients in healthcare databases. METHODS: Inpatient visits with microbiological evidence of invasive bacterial disease were extracted from the Optum© electronic health record database between January 1, 2016 and June 30, 2020. Six algorithms, derived from diagnosis and drug exposure codes associated to infectious diseases and Escherichia coli, were developed to identify IED. The performance characteristics of algorithms were assessed using a reference standard derived from microbiology data. RESULTS: Among 97 194 eligible records, 25 310 (26.0%) were classified as IED. Algorithm 1 (diagnosis code for infectious invasive disease due to E. coli) had the highest positive predictive value (PPV; 96.0%) and lowest sensitivity (60.4%). Algorithm 2, which additionally included patients with diagnosis codes for infectious invasive disease due to an unspecified organism, had the highest sensitivity (95.5%) and lowest PPV (27.8%). Algorithm 4, which required patients with a diagnosis code for infectious invasive disease due to unspecified organism to have no diagnosis code for non-E. coli infections, achieved the most balanced performance characteristics (PPV, 93.6%; sensitivity, 78.1%; F1 score, 85.1%). Finally, adding exposure to antibiotics in the treatment of E. coli had limited impact on performance algorithms 5 and 6. CONCLUSION: Algorithm 4, which achieved the most balanced performance characteristics, offers a useful tool to identify patients with IED and assess the burden of IED in healthcare databases.

Adaptation and validation of a coding algorithm for the Charlson Comorbidity Index in administrative claims data using the SNOMED CT standardized vocabulary.

OBJECTIVES: The Charlson comorbidity index (CCI), the most ubiquitous comorbid risk score, predicts one-year mortality among hospitalized patients and provides a single aggregate measure of patient comorbidity. The Quan adaptation of the CCI revised the CCI coding algorithm for applications to administrative claims data using the International Classification of Diseases (ICD). The purpose of the current study is to adapt and validate a coding algorithm for the CCI using the SNOMED CT standardized vocabulary, one of the most commonly used vocabularies for data collection in healthcare databases in the U.S. METHODS: The SNOMED CT coding algorithm for the CCI was adapted through the direct translation of the Quan coding algorithms followed by manual curation by clinical experts. The performance of the SNOMED CT and Quan coding algorithms were compared in the context of a retrospective cohort study of inpatient visits occurring during the calendar years of 2013 and 2018 contained in two U.S. administrative claims databases. Differences in the CCI or frequency of individual comorbid conditions were assessed using standardized mean differences (SMD). Performance in predicting one-year mortality among hospitalized patients was measured based on the c-statistic of logistic regression models. RESULTS: For each database and calendar year combination, no significant differences in the CCI or frequency of individual comorbid conditions were observed between vocabularies (SMD ≤ 0.10). Specifically, the difference in CCI measured using the SNOMED CT vs. Quan coding algorithms was highest in MDCD in 2013 (3.75 vs. 3.6; SMD = 0.03) and lowest in DOD in 2018 (3.93 vs. 3.86; SMD = 0.02). Similarly, as indicated by the c-statistic, there was no evidence of a difference in the performance between coding algorithms in predicting one-year mortality (SNOMED CT vs. Quan coding algorithms, range: 0.725-0.789 vs. 0.723-0.787, respectively). A total of 700 of 5,348 (13.1%) ICD code mappings were inconsistent between coding algorithms. The most common cause of discrepant codes was multiple ICD codes mapping to a SNOMED CT code (n = 560) of which 213 were deemed clinically relevant thereby leading to information gain. CONCLUSION: The current study repurposed an important tool for conducting observational research to use the SNOMED CT standardized vocabulary.

Comparative Effectiveness of Famotidine in Hospitalized COVID-19 Patients.

INTRODUCTION: Famotidine has been posited as a potential treatment for coronavirus disease 2019 (COVID-19). We compared the incidence of COVID-19 outcomes (i.e., death and death or intensive services use) among hospitalized famotidine users vs proton pump inhibitors (PPIs) users, hydroxychloroquine users, or famotidine nonusers separately. METHODS: We constructed a retrospective cohort study using data from COVID-19 Premier Hospital electronic health records. The study population was COVID-19 hospitalized patients aged 18 years or older. Famotidine, PPI, and hydroxychloroquine exposure groups were defined as patients dispensed any medication containing 1 of the 3 drugs on the day of admission. The famotidine nonuser group was derived from the same source population with no history of exposure to any drug with famotidine as an active ingredient before or on the day of admission. Time at risk was defined based on the intention-to-treat principle starting 1 day after admission to 30 days after admission. For each study comparison group, we fit a propensity score model through large-scale regularized logistic regression. The outcome was modeled using a survival model. RESULTS: We identified 2,193 users of PPI, 5,950 users of the hydroxychloroquine, 1,816 users of famotidine, and 26,820 nonfamotidine users. After propensity score stratification, the hazard ratios (HRs) for death were as follows: famotidine vs no famotidine HR 1.03 (0.89-1.18), vs PPIs: HR 1.14 (0.94-1.39), and vs hydroxychloroquine: 1.03 (0.85-1.24). Similar results were observed for the risk of death or intensive services use. DISCUSSION: We found no evidence of a reduced risk of COVID-19 outcomes among hospitalized COVID-19 patients who used famotidine compared with those who did not or compared with PPI or hydroxychloroquine users.

Applied comparison of large-scale propensity score matching and cardinality matching for causal inference in observational research.

BACKGROUND: Cardinality matching (CM), a novel matching technique, finds the largest matched sample meeting prespecified balance criteria thereby overcoming limitations of propensity score matching (PSM) associated with limited covariate overlap, which are especially pronounced in studies with small sample sizes. The current study proposes a framework for large-scale CM (LS-CM); and compares large-scale PSM (LS-PSM) and LS-CM in terms of post-match sample size, covariate balance and residual confounding at progressively smaller sample sizes. METHODS: Evaluation of LS-PSM and LS-CM within a comparative cohort study of new users of angiotensin-converting enzyme inhibitor (ACEI) and thiazide or thiazide-like diuretic monotherapy identified from a U.S. insurance claims database. Candidate covariates included patient demographics, and all observed prior conditions, drug exposures and procedures. Propensity scores were calculated using LASSO regression, and candidate covariates with non-zero beta coefficients in the propensity model were defined as matching covariates for use in LS-CM. One-to-one matching was performed using progressively tighter parameter settings. Covariate balance was assessed using standardized mean differences. Hazard ratios for negative control outcomes perceived as unassociated with treatment (i.e., true hazard ratio of 1) were estimated using unconditional Cox models. Residual confounding was assessed using the expected systematic error of the empirical null distribution of negative control effect estimates compared to the ground truth. To simulate diverse research conditions, analyses were repeated within 10 %, 1 and 0.5 % subsample groups with increasingly limited covariate overlap. RESULTS: A total of 172,117 patients (ACEI: 129,078; thiazide: 43,039) met the study criteria. As compared to LS-PSM, LS-CM was associated with increased sample retention. Although LS-PSM achieved balance across all matching covariates within the full study population, substantial matching covariate imbalance was observed within the 1 and 0.5 % subsample groups. Meanwhile, LS-CM achieved matching covariate balance across all analyses. LS-PSM was associated with better candidate covariate balance within the full study population. Otherwise, both matching techniques achieved comparable candidate covariate balance and expected systematic error. CONCLUSIONS: LS-CM found the largest matched sample meeting prespecified balance criteria while achieving comparable candidate covariate balance and residual confounding. We recommend LS-CM as an alternative to LS-PSM in studies with small sample sizes or limited covariate overlap.

Minimizing confounding in comparative observational studies with time-to-event outcomes: An extensive comparison of covariate balancing methods using Monte Carlo simulation.

Observational studies are frequently used in clinical research to estimate the effects of treatments or exposures on outcomes. To reduce the effects of confounding when estimating treatment effects, covariate balancing methods are frequently implemented. This study evaluated, using extensive Monte Carlo simulation, several methods of covariate balancing, and two methods for propensity score estimation, for estimating the average treatment effect on the treated using a hazard ratio from a Cox proportional hazards model. With respect to minimizing bias and maximizing accuracy (as measured by the mean square error) of the treatment effect, the average treatment effect on the treated weighting, fine stratification, and optimal full matching with a conventional logistic regression model for the propensity score performed best across all simulated conditions. Other methods performed well in specific circumstances, such as pair matching when sample sizes were large (n = 5000) and the proportion treated was < 0.25. Statistical power was generally higher for weighting methods than matching methods, and Type I error rates were at or below the nominal level for balancing methods with unbiased treatment effect estimates. There was also a decreasing effective sample size with an increasing number of strata, therefore for stratification-based weighting methods, it may be important to consider fewer strata. Generally, we recommend methods that performed well in our simulations, although the identification of methods that performed well is necessarily limited by the specific features of our simulation. The methods are illustrated using a real-world example comparing beta blockers and angiotensin-converting enzyme inhibitors among hypertensive patients at risk for incident stroke.

Psychotropic drug prescribing before and during the COVID-19 pandemic among people with depressive and anxiety disorders: a multinational network study.

BACKGROUND: People with mental health conditions were potentially more vulnerable than others to the neuropsychiatric effects of the COVID-19 pandemic and the global efforts taken to contain it. The aim of this multinational study was to examine the changes in psychotropic drug prescribing during the pandemic among people with depressive and anxiety disorders. METHODS: This study included electronic medical records and claims data from nine databases in six countries (France, Germany, Italy, the UK, South Korea, and the USA) of patients with a diagnosis of depressive or anxiety disorders between 2016 and 2021. The outcomes were monthly prevalence rates of antidepressant, antipsychotic, and anxiolytic drug prescribing. The associations between the pandemic and psychotropic drug prescribing were examined with interrupted time series analyses for the total sample and stratified by sex and age group. People with lived experience were not involved in the research and writing process. FINDINGS: Between Jan 1, 2016 and Dec 31, 2020, an average of 16 567 914 patients with depressive disorders (10 820 956 females [65·31%] and 5 746 958 males [34·69%]) and 15 988 451 patients with anxiety disorders (10 688 788 females [66·85%] and 5 299 663 males [33·15%]) were identified annually. Most patients with depressive disorders and anxiety disorders were aged 45-64 years. Ethnicity data were not available. Two distinct trends in prescribing rates were identified. The first pattern shows an initial surge at the start of the pandemic (eg, antipsychotics among patients with depressive disorders in MDCD_US (rate ratio [RR] 1·077, 95% CI 1·055-1·100), followed by a gradual decline towards the counterfactual level (RR 0·990, 95% CI 0·988-0·992). The second pattern, observed in four databases for anxiolytics among patients with depressive disorders and two for antipsychotics among patients with anxiety disorders, shows an immediate increase (eg, antipsychotics among patients with anxiety disorders in IQVIA_UK: RR 1·467, 95% CI 1·282-1·675) without a subsequent change in slope (RR 0·985, 95% CI 0·969-1·003). In MDCD_US and IQVIA_US, the anxiolytic prescribing rate continued to increase among patients younger than 25 years for both disorders. INTERPRETATION: The study reveals persistently elevated rates of psychotropic drug prescriptions beyond the initial phase of the pandemic. These findings underscore the importance of enhanced mental health support and emphasise the need for regular review of psychotropic drug use among this patient group in the post-pandemic era. FUNDING: University Grants Committee, Research Grants Council, The Government of the Hong Kong Special Administrative Region.

Incidence of mental health diagnoses during the COVID-19 pandemic: a multinational network study.

AIMS: Population-wide restrictions during the COVID-19 pandemic may create barriers to mental health diagnosis. This study aims to examine changes in the number of incident cases and the incidence rates of mental health diagnoses during the COVID-19 pandemic. METHODS: By using electronic health records from France, Germany, Italy, South Korea and the UK and claims data from the US, this study conducted interrupted time-series analyses to compare the monthly incident cases and the incidence of depressive disorders, anxiety disorders, alcohol misuse or dependence, substance misuse or dependence, bipolar disorders, personality disorders and psychoses diagnoses before (January 2017 to February 2020) and after (April 2020 to the latest available date of each database [up to November 2021]) the introduction of COVID-related restrictions. RESULTS: A total of 629,712,954 individuals were enrolled across nine databases. Following the introduction of restrictions, an immediate decline was observed in the number of incident cases of all mental health diagnoses in the US (rate ratios (RRs) ranged from 0.005 to 0.677) and in the incidence of all conditions in France, Germany, Italy and the US (RRs ranged from 0.002 to 0.422). In the UK, significant reductions were only observed in common mental illnesses. The number of incident cases and the incidence began to return to or exceed pre-pandemic levels in most countries from mid-2020 through 2021. CONCLUSIONS: Healthcare providers should be prepared to deliver service adaptations to mitigate burdens directly or indirectly caused by delays in the diagnosis and treatment of mental health conditions.

Enhancement and external validation of algorithms using diagnosis codes to identify invasive Escherichia coli disease.

OBJECTIVE: To assess the predictive accuracy of code-based algorithms for identifying invasive Escherichia coli (E. coli) disease (IED) among inpatient encounters in US hospitals. METHODS: The PINC AI Healthcare Database (10/01/2015-03/31/2020) was used to assess the performance of six published code-based algorithms to identify IED cases among inpatient encounters. Case-confirmed IEDs were identified based on microbiological confirmation of E. coli in a normally sterile body site (Group 1) or in urine with signs of sepsis (Group 2). Code-based algorithm performance was assessed overall, and separately for Group 1 and Group 2 based on sensitivity, specificity, positive and negative predictive value (PPV and NPV) and F1 score. The improvement in performance of refinements to the best-performing algorithm was also assessed. RESULTS: Among 2,595,983 encounters, 97,453 (3.8%) were case-confirmed IED (Group 1: 60.9%; Group 2: 39.1%). Across algorithms, specificity and NPV were excellent (>97%) for all but one algorithm, but there was a trade-off between sensitivity and PPV. The algorithm with the most balanced performance characteristics included diagnosis codes for: (1) infectious disease due to E. coli OR (2) sepsis/bacteremia/organ dysfunction combined with unspecified E. coli infection and no other concomitant non-E. coli invasive disease (sensitivity: 56.9%; PPV: 56.4%). Across subgroups, the algorithms achieved lower algorithm performance for Group 2 (sensitivity: 9.9%-61.1%; PPV: 3.8%-16.0%). CONCLUSIONS: This study assessed code-based algorithms to identify IED during inpatient encounters in a large US hospital database. Such algorithms could be useful to identify IED in healthcare databases that lack information on microbiology data.

Serially Combining Epidemiological Designs Does Not Improve Overall Signal Detection in Vaccine Safety Surveillance.

INTRODUCTION: Vaccine safety surveillance commonly includes a serial testing approach with a sensitive method for 'signal generation' and specific method for 'signal validation.' The extent to which serial testing in real-world studies improves or hinders overall performance in terms of sensitivity and specificity remains unknown. METHODS: We assessed the overall performance of serial testing using three administrative claims and one electronic health record database. We compared type I and II errors before and after empirical calibration for historical comparator, self-controlled case series (SCCS), and the serial combination of those designs against six vaccine exposure groups with 93 negative control and 279 imputed positive control outcomes. RESULTS: The historical comparator design mostly had fewer type II errors than SCCS. SCCS had fewer type I errors than the historical comparator. Before empirical calibration, the serial combination increased specificity and decreased sensitivity. Type II errors mostly exceeded 50%. After empirical calibration, type I errors returned to nominal; sensitivity was lowest when the methods were combined. CONCLUSION: While serial combination produced fewer false-positive signals compared with the most specific method, it generated more false-negative signals compared with the most sensitive method. Using a historical comparator design followed by an SCCS analysis yielded decreased sensitivity in evaluating safety signals relative to a one-stage SCCS approach. While the current use of serial testing in vaccine surveillance may provide a practical paradigm for signal identification and triage, single epidemiological designs should be explored as valuable approaches to detecting signals.

Rates of Antipsychotic Drug Prescribing Among People Living With Dementia During the COVID-19 Pandemic.

Importance: Concerns have been raised that the use of antipsychotic medication for people living with dementia might have increased during the COVID-19 pandemic. Objective: To examine multinational trends in antipsychotic drug prescribing for people living with dementia before and during the COVID-19 pandemic. Design, Setting, and Participants: This multinational network cohort study used electronic health records and claims data from 8 databases in 6 countries (France, Germany, Italy, South Korea, the UK, and the US) for individuals aged 65 years or older between January 1, 2016, and November 30, 2021. Two databases each were included for South Korea and the US. Exposures: The introduction of population-wide COVID-19 restrictions from April 2020 to the latest available date of each database. Main Outcomes and Measures: The main outcomes were yearly and monthly incidence of dementia diagnosis and prevalence of people living with dementia who were prescribed antipsychotic drugs in each database. Interrupted time series analyses were used to quantify changes in prescribing rates before and after the introduction of population-wide COVID-19 restrictions. Results: A total of 857 238 people with dementia aged 65 years or older (58.0% female) were identified in 2016. Reductions in the incidence of dementia were observed in 7 databases in the early phase of the pandemic (April, May, and June 2020), with the most pronounced reduction observed in 1 of the 2 US databases (rate ratio [RR], 0.30; 95% CI, 0.27-0.32); reductions were also observed in the total number of people with dementia prescribed antipsychotic drugs in France, Italy, South Korea, the UK, and the US. Rates of antipsychotic drug prescribing for people with dementia increased in 6 databases representing all countries. Compared with the corresponding month in 2019, the most pronounced increase in 2020 was observed in May in South Korea (Kangwon National University database) (RR, 2.11; 95% CI, 1.47-3.02) and June in the UK (RR, 1.96; 95% CI, 1.24-3.09). The rates of antipsychotic drug prescribing in these 6 databases remained high in 2021. Interrupted time series analyses revealed immediate increases in the prescribing rate in Italy (RR, 1.31; 95% CI, 1.08-1.58) and in the US Medicare database (RR, 1.43; 95% CI, 1.20-1.71) after the introduction of COVID-19 restrictions. Conclusions and Relevance: This cohort study found converging evidence that the rate of antipsychotic drug prescribing to people with dementia increased in the initial months of the COVID-19 pandemic in the 6 countries studied and did not decrease to prepandemic levels after the acute phase of the pandemic had ended. These findings suggest that the pandemic disrupted the care of people living with dementia and that the development of intervention strategies is needed to ensure the quality of care.

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.

Baseline risk characterization of early versus later adopters of long-acting paliperidone palmitate formulations.

Early Post-Marketing Phase Vigilance (EPPV) is a unique system that encourages reporting of serious adverse reactions for medications newly introduced to Japan. When a once-monthly paliperidone palmitate formulation (PP1M) was introduced in Japan in 2013, EPPV detected a signal of increased mortality, but this signal was not subsequently confirmed. To clarify whether that signal reflected increased adverse event reporting or an atypically high baseline mortality risk among early adopters of PP1M, we evaluated the baseline risk characteristics of early, mid, and later adopters of PP1M in a Japanese database and did a similar evaluation of PP1M and the three-monthly formulation (PP3M) in two US databases. In Japan, early adopters compared with later adopters were older (mean 39.16 vs 33.70 years) but had a lower proportion of male patients (32.0% vs 44.44%), and a lower mean number of antipsychotic medications (distinct active medical substances) other than paliperidone (2.62 vs 2.85). In the United States, the baseline characteristics of early adopters of PP1M and PP3M did not suggest higher mortality risk than later adopters. These results offer no convincing evidence that the unconfirmed early signal of increased mortality with PP1M was due to increased baseline mortality risk among early adopters.

Comparison of Clinical Outcomes of Gripping Surface Technology Staple Reloads versus Standard Staple Reloads Used with Manual Linear Surgical Staplers.

Purpose: Linear surgical staplers reduce rates of surgical adverse events (bleeding, leaks, infections) compared to manual sutures thereby reducing patient risks, surgeon workflow disruption, and healthcare costs. However, further improvements are needed. Ethicon Gripping Surface Technology (GST) reloads, tested and approved by regulatory authorities in combination with powered staplers, may reduce surgical risks through improved tissue grip. While manual staplers are used in some regions due to affordability, clinical data on GST reloads used with manual staplers are unavailable. This study compared surgical adverse event rates of manual staplers with GST vs standard reloads. These data may be used for label changes in China and Latin America. Patients and Methods: Patients undergoing general or thoracic surgery between October 1, 2015 and August 31, 2021 using ECHELON FLEX™ manual staplers with GST or standard reloads were identified from the Premier Healthcare Database. GST reloads were compared to standard reloads for non-inferiority in bleeding and anastomotic leak for general surgery. Secondary outcomes included sepsis for general surgery, and bleeding and prolonged air leak for thoracic surgery. Covariate balancing was performed using stable balancing weights. Results: The general and thoracic surgery cohorts contained 4571 (GST: 2780; standard: 1791) and 814 (GST: 514; standard: 300) patients, respectively. GST reloads were non-inferior to standard reloads for bleeding and anastomotic leak (adjusted cumulative incidence ratio: 1.02 [90% CI: 0.71, 1.45] and 1.03 [90% CI: 0.72, 1.46], respectively) for general surgery. Compared with standard reloads, GST reloads had a similar incidence of sepsis (2.2% vs 2.1%) for general surgery and lower incidences of bleeding (9.5% vs 16.0%) and prolonged air leak (12.6% vs 14.0%,) for thoracic surgery. Conclusion: GST reloads, compared to standard reloads, used with ECHELON FLEX™ manual staplers had comparable perioperative bleeding and anastomotic leak for general surgery, and lower incidences of safety events for thoracic surgery.

Corrigendum: Vaccine safety surveillance using routinely collected healthcare data-An empirical evaluation of epidemiological designs.

[This corrects the article DOI: 10.3389/fphar.2022.893484.].

Vaccine Safety Surveillance Using Routinely Collected Healthcare Data-An Empirical Evaluation of Epidemiological Designs.

Background: Routinely collected healthcare data such as administrative claims and electronic health records (EHR) can complement clinical trials and spontaneous reports to detect previously unknown risks of vaccines, but uncertainty remains about the behavior of alternative epidemiologic designs to detect and declare a true risk early. Methods: Using three claims and one EHR database, we evaluate several variants of the case-control, comparative cohort, historical comparator, and self-controlled designs against historical vaccinations using real negative control outcomes (outcomes with no evidence to suggest that they could be caused by the vaccines) and simulated positive control outcomes. Results: Most methods show large type 1 error, often identifying false positive signals. The cohort method appears either positively or negatively biased, depending on the choice of comparator index date. Empirical calibration using effect-size estimates for negative control outcomes can bring type 1 error closer to nominal, often at the cost of increasing type 2 error. After calibration, the self-controlled case series (SCCS) design most rapidly detects small true effect sizes, while the historical comparator performs well for strong effects. Conclusion: When applying any method for vaccine safety surveillance we recommend considering the potential for systematic error, especially due to confounding, which for many designs appears to be substantial. Adjusting for age and sex alone is likely not sufficient to address differences between vaccinated and unvaccinated, and for the cohort method the choice of index date is important for the comparability of the groups. Analysis of negative control outcomes allows both quantification of the systematic error and, if desired, subsequent empirical calibration to restore type 1 error to its nominal value. In order to detect weaker signals, one may have to accept a higher type 1 error.

Improving visual communication of discriminative accuracy for predictive models: the probability threshold plot.

OBJECTIVES: To propose a visual display-the probability threshold plot (PTP)-that transparently communicates a predictive models' measures of discriminative accuracy along the range of model-based predicted probabilities (Pt). MATERIALS AND METHODS: We illustrate the PTP by replicating a previously-published and validated machine learning-based model to predict antihyperglycemic medication cessation within 1-2 years following metabolic surgery. The visual characteristics of the PTPs for each model were compared to receiver operating characteristic (ROC) curves. RESULTS: A total of 18 887 patients were included for analysis. Whereas during testing each predictive model had nearly identical ROC curves and corresponding area under the curve values (0.672 and 0.673), the visual characteristics of the PTPs revealed substantive between-model differences in sensitivity, specificity, PPV, and NPV across the range of Pt. DISCUSSION AND CONCLUSIONS: The PTP provides improved visual display of a predictive model's discriminative accuracy, which can enhance the practical application of predictive models for medical decision making.