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

    Fortin, Stephen P / Schuemie, Martijn

    Pharmacoepidemiology and drug safety

    2022  Volume 31, Issue 12, Page(s) 1242–1252

    Abstract: 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 ... ...

    Abstract 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 β-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; β-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.
    MeSH term(s) Humans ; Propensity Score ; Cohort Studies ; Bias ; Algorithms ; Databases, Factual
    Language English
    Publishing date 2022-07-20
    Publishing country England
    Document type Journal Article
    ZDB-ID 1099748-9
    ISSN 1099-1557 ; 1053-8569
    ISSN (online) 1099-1557
    ISSN 1053-8569
    DOI 10.1002/pds.5510
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  2. Article ; Online: Correction to: Adaptation and validation of a coding algorithm for the Charlson Comorbidity Index in administrative claims data using the SNOMED CT standardized vocabulary.

    Fortin, Stephen P / Reps, Jenna / Ryan, Patrick

    BMC medical informatics and decision making

    2023  Volume 23, Issue 1, Page(s) 109

    Language English
    Publishing date 2023-06-15
    Publishing country England
    Document type Published Erratum
    ZDB-ID 2046490-3
    ISSN 1472-6947 ; 1472-6947
    ISSN (online) 1472-6947
    ISSN 1472-6947
    DOI 10.1186/s12911-023-02205-4
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  3. Article ; Online: Adaptation and validation of a coding algorithm for the Charlson Comorbidity Index in administrative claims data using the SNOMED CT standardized vocabulary.

    Fortin, Stephen P / Reps, Jenna / Ryan, Patrick

    BMC medical informatics and decision making

    2022  Volume 22, Issue 1, Page(s) 261

    Abstract: 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 ...

    Abstract 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.
    MeSH term(s) Algorithms ; Comorbidity ; Humans ; International Classification of Diseases ; Retrospective Studies ; Systematized Nomenclature of Medicine ; Vocabulary
    Language English
    Publishing date 2022-10-07
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2046490-3
    ISSN 1472-6947 ; 1472-6947
    ISSN (online) 1472-6947
    ISSN 1472-6947
    DOI 10.1186/s12911-022-02006-1
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  4. Article ; Online: Applied comparison of large-scale propensity score matching and cardinality matching for causal inference in observational research.

    Fortin, Stephen P / Johnston, Stephen S / Schuemie, Martijn J

    BMC medical research methodology

    2021  Volume 21, Issue 1, Page(s) 109

    Abstract: 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 ... ...

    Abstract 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.
    MeSH term(s) Causality ; Cohort Studies ; Databases, Factual ; Humans ; Propensity Score
    Language English
    Publishing date 2021-05-24
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ISSN 1471-2288
    ISSN (online) 1471-2288
    DOI 10.1186/s12874-021-01282-1
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  5. Article ; Online: Correction to: Applied comparison of large-scale propensity score matching and cardinality matching for causal inference in observational research.

    Fortin, Stephen P / Johnston, Stephen S / Schuemie, Martijn J

    BMC medical research methodology

    2021  Volume 21, Issue 1, Page(s) 174

    Language English
    Publishing date 2021-08-21
    Publishing country England
    Document type Published Erratum
    ISSN 1471-2288
    ISSN (online) 1471-2288
    DOI 10.1186/s12874-021-01365-z
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  6. Article ; Online: Baseline risk characterization of early versus later adopters of long-acting paliperidone palmitate formulations.

    Fife, Daniel / Fortin, Stephen / Qiu, Hong / Yamazaki, Michiyo / Najarian, Dean / Voss, Erica A

    Neuropsychopharmacology reports

    2022  Volume 42, Issue 3, Page(s) 347–351

    Abstract: 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, ...

    Abstract 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.
    MeSH term(s) Antipsychotic Agents/adverse effects ; Humans ; Japan/epidemiology ; Male ; Paliperidone Palmitate/adverse effects ; Schizophrenia/drug therapy
    Chemical Substances Antipsychotic Agents ; Paliperidone Palmitate (R8P8USM8FR)
    Language English
    Publishing date 2022-06-01
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ISSN 2574-173X
    ISSN (online) 2574-173X
    DOI 10.1002/npr2.12260
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  7. Article ; Online: Performance characteristics of code-based algorithms to identify urinary tract infections in large United States administrative claims databases.

    Fortin, Stephen P / Swerdel, Joel / Sarnecki, Michal / Doua, Joachim / Colasurdo, Jamie / Geurtsen, Jeroen

    Pharmacoepidemiology and drug safety

    2022  Volume 31, Issue 9, Page(s) 953–962

    Abstract: 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 ... ...

    Abstract 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.
    MeSH term(s) Adult ; Algorithms ; Databases, Factual ; Humans ; Observational Studies as Topic ; Reproducibility of Results ; United States/epidemiology ; Urinalysis ; Urinary Tract Infections/diagnosis ; Urinary Tract Infections/drug therapy ; Urinary Tract Infections/epidemiology
    Language English
    Publishing date 2022-07-04
    Publishing country England
    Document type Journal Article ; Review ; Research Support, Non-U.S. Gov't
    ZDB-ID 1099748-9
    ISSN 1099-1557 ; 1053-8569
    ISSN (online) 1099-1557
    ISSN 1053-8569
    DOI 10.1002/pds.5492
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  8. Article ; Online: Enhancement and external validation of algorithms using diagnosis codes to identify invasive

    Hernandez-Pastor, Luis / Geurtsen, Jeroen / El Khoury, Antoine C / Fortin, Stephen P / Gauthier-Loiselle, Marjolaine / Yu, Louise H / Cloutier, Martin

    Current medical research and opinion

    2023  Volume 39, Issue 10, Page(s) 1303–1312

    Abstract: Objective: To assess the predictive accuracy of code-based algorithms for identifying invasive : 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 ... ...

    Abstract Objective: To assess the predictive accuracy of code-based algorithms for identifying invasive
    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
    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
    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.
    MeSH term(s) Humans ; Escherichia coli ; Predictive Value of Tests ; Algorithms ; Sepsis/diagnosis ; Infertility ; Databases, Factual
    Language English
    Publishing date 2023-08-31
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 80296-7
    ISSN 1473-4877 ; 0300-7995
    ISSN (online) 1473-4877
    ISSN 0300-7995
    DOI 10.1080/03007995.2023.2247968
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    Zs.A 955: Show issues Location:
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  9. Article ; Online: Comparison of safety and utilization outcomes in inpatient versus outpatient laparoscopic sleeve gastrectomy: a retrospective, cohort study.

    Fortin, Stephen P / Kalsekar, Iftekhar / Johnston, Stephen / Akincigil, Ayse

    Surgery for obesity and related diseases : official journal of the American Society for Bariatric Surgery

    2020  Volume 16, Issue 11, Page(s) 1661–1671

    Abstract: Background: Laparoscopic sleeve gastrectomy (LSG) is the most common type of bariatric surgery performed in the United States and may be performed on an outpatient basis. Limited literature exists comparing outcomes of outpatient and inpatient LSG, and ... ...

    Abstract Background: Laparoscopic sleeve gastrectomy (LSG) is the most common type of bariatric surgery performed in the United States and may be performed on an outpatient basis. Limited literature exists comparing outcomes of outpatient and inpatient LSG, and study results are conflicting.
    Objectives: To compare safety and utilization outcomes of outpatient versus inpatient LSG.
    Settings: Retrospective, multihospital database study (Optum Pan-Therapeutics Database).
    Methods: Patients 18 years of age and older who underwent LSG between October 1, 2015, and December 31, 2018, were identified from the Optum Pan-Therapeutics Database and classified as having undergone outpatient or inpatient surgery. Nearest neighbor propensity score matching and generalized estimating equations accounting for procedural physician-level clustering were used to compare the following outcomes between outpatient and inpatient LSG: all-cause 30-day patient morbidity, hospital readmission, readmission length of stay, bariatric reoperation. and mortality.
    Results: We identified 22,945 patients (outpatient: 1542; inpatient: 21,403) meeting the study inclusion criteria. After propensity score matching, the inpatient and outpatient groups contained 1542 and 13,903 patients, respectively. Bariatric reoperation (n = 13) and mortality (n = 5) were rare events occurring in <.1% of all cases. Compared with the inpatient group, the outpatient group had a statistically significant lower readmission length of stay (4.63 versus 3.23 days; P = .0057). Otherwise, there was no significant association between procedure setting and 30-day overall morbidity (4.8% versus 5.3%; P = .5775) or hospital readmission (2.6% versus 2.1%; P = .1841).
    Conclusions: Safety and utilization outcomes were similar between outpatient and inpatient LSG, and outpatient LSG was associated with shorter hospital readmission length of stay.
    MeSH term(s) Adolescent ; Adult ; Bariatric Surgery ; Cohort Studies ; Gastrectomy ; Humans ; Inpatients ; Laparoscopy ; Obesity, Morbid/surgery ; Outpatients ; Postoperative Complications/epidemiology ; Retrospective Studies ; Treatment Outcome ; United States/epidemiology
    Language English
    Publishing date 2020-07-21
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2274243-8
    ISSN 1878-7533 ; 1550-7289
    ISSN (online) 1878-7533
    ISSN 1550-7289
    DOI 10.1016/j.soard.2020.07.012
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  10. Article ; Online: Improving visual communication of discriminative accuracy for predictive models: the probability threshold plot.

    Johnston, Stephen S / Fortin, Stephen / Kalsekar, Iftekhar / Reps, Jenna / Coplan, Paul

    JAMIA open

    2021  Volume 4, Issue 1, Page(s) ooab017

    Abstract: 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 (: Materials and methods: We ... ...

    Abstract 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 (
    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
    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.
    Language English
    Publishing date 2021-03-12
    Publishing country United States
    Document type Journal Article
    ISSN 2574-2531
    ISSN (online) 2574-2531
    DOI 10.1093/jamiaopen/ooab017
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