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Article ; Online: Comparing broad and narrow phenotype algorithms: differences in performance characteristics and immortal time incurred.

Swerdel, Joel N / Conover, Mitchell M

Journal of pharmacy & pharmaceutical sciences : a publication of the Canadian Society for Pharmaceutical Sciences, Societe canadienne des sciences pharmaceutiques

2024 Volume 26, Page(s) 12095

Abstract: Introduction: ...

Abstract	Introduction:
MeSH term(s)	Humans ; Predictive Value of Tests ; Algorithms ; Phenotype ; Databases, Factual ; Upper Extremity Deformities, Congenital
Language	English
Publishing date	2024-01-03
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	1422972-9
ISSN	1482-1826 ; 1482-1826
ISSN (online)	1482-1826
ISSN	1482-1826
DOI	10.3389/jpps.2023.12095
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Article ; Online: Using a data-driven approach for the development and evaluation of phenotype algorithms for systemic lupus erythematosus.

Swerdel, Joel N / Ramcharran, Darmendra / Hardin, Jill

PloS one

2023 Volume 18, Issue 2, Page(s) e0281929

Abstract: Background: Systemic lupus erythematosus (SLE) is a chronic autoimmune disease of unknown origin. The objective of this research was to develop phenotype algorithms for SLE suitable for use in epidemiological studies using empirical evidence from ... ...

Abstract	Background: Systemic lupus erythematosus (SLE) is a chronic autoimmune disease of unknown origin. The objective of this research was to develop phenotype algorithms for SLE suitable for use in epidemiological studies using empirical evidence from observational databases. Methods: We used a process for empirically determining and evaluating phenotype algorithms for health conditions to be analyzed in observational research. The process started with a literature search to discover prior algorithms used for SLE. We then used a set of Observational Health Data Sciences and Informatics (OHDSI) open-source tools to refine and validate the algorithms. These included tools to discover codes for SLE that may have been missed in prior studies and to determine possible low specificity and index date misclassification in algorithms for correction. Results: We developed four algorithms using our process: two algorithms for prevalent SLE and two for incident SLE. The algorithms for both incident and prevalent cases are comprised of a more specific version and a more sensitive version. Each of the algorithms corrects for possible index date misclassification. After validation, we found the highest positive predictive value estimate for the prevalent, specific algorithm (89%). The highest sensitivity estimate was found for the sensitive, prevalent algorithm (77%). Conclusion: We developed phenotype algorithms for SLE using a data-driven approach. The four final algorithms may be used directly in observational studies. The validation of these algorithms provides researchers an added measure of confidence that the algorithms are selecting subjects correctly and allows for the application of quantitative bias analysis.
MeSH term(s)	Humans ; Lupus Erythematosus, Systemic/diagnosis ; Lupus Erythematosus, Systemic/epidemiology ; Predictive Value of Tests ; Algorithms ; Databases, Factual
Language	English
Publishing date	2023-02-16
Publishing country	United States
Document type	Journal Article
ZDB-ID	2267670-3
ISSN	1932-6203 ; 1932-6203
ISSN (online)	1932-6203
ISSN	1932-6203
DOI	10.1371/journal.pone.0281929
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Article ; Online: PheValuator 2.0: Methodological improvements for the PheValuator approach to semi-automated phenotype algorithm evaluation.

Swerdel, Joel N / Schuemie, Martijn / Murray, Gayle / Ryan, Patrick B

Journal of biomedical informatics

2022 Volume 135, Page(s) 104177

Abstract: Purpose: Phenotype algorithms are central to performing analyses using observational data. These algorithms translate the clinical idea of a health condition into an executable set of rules allowing for queries of data elements from a database. ... ...

Abstract	Purpose: Phenotype algorithms are central to performing analyses using observational data. These algorithms translate the clinical idea of a health condition into an executable set of rules allowing for queries of data elements from a database. PheValuator, a software package in the Observational Health Data Sciences and Informatics (OHDSI) tool stack, provides a method to assess the performance characteristics of these algorithms, namely, sensitivity, specificity, and positive and negative predictive value. It uses machine learning to develop predictive models for determining a probabilistic gold standard of subjects for assessment of cases and non-cases of health conditions. PheValuator was developed to complement or even replace the traditional approach of algorithm validation, i.e., by expert assessment of subject records through chart review. Results in our first PheValuator paper suggest a systematic underestimation of the PPV compared to previous results using chart review. In this paper we evaluate modifications made to the method designed to improve its performance. Methods: The major changes to PheValuator included allowing all diagnostic conditions, clinical observations, drug prescriptions, and laboratory measurements to be included as predictors within the modeling process whereas in the prior version there were significant restrictions on the included predictors. We also have allowed for the inclusion of the temporal relationships of the predictors in the model. To evaluate the performance of the new method, we compared the results from the new and original methods against results found from the literature using traditional validation of algorithms for 19 phenotypes. We performed these tests using data from five commercial databases. Results: In the assessment aggregating all phenotype algorithms, the median difference between the PheValuator estimate and the gold standard estimate for PPV was reduced from -21 (IQR -34, -3) in Version 1.0 to 4 (IQR -3, 15) using Version 2.0. We found a median difference in specificity of 3 (IQR 1, 4.25) for Version 1.0 and 3 (IQR 1, 4) for Version 2.0. The median difference between the two versions of PheValuator and the gold standard for estimates of sensitivity was reduced from -39 (-51, -20) to -16 (-34, -6). Conclusion: PheValuator 2.0 produces estimates for the performance characteristics for phenotype algorithms that are significantly closer to estimates from traditional validation through chart review compared to version 1.0. With this tool in researcher's toolkits, methods, such as quantitative bias analysis, may now be used to improve the reliability and reproducibility of research studies using observational data.
MeSH term(s)	Reproducibility of Results ; Algorithms ; Databases, Factual ; Machine Learning ; Phenotype
Language	English
Publishing date	2022-08-19
Publishing country	United States
Document type	Journal Article
ZDB-ID	2057141-0
ISSN	1532-0480 ; 1532-0464
ISSN (online)	1532-0480
ISSN	1532-0464
DOI	10.1016/j.jbi.2022.104177
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Article ; Online: PheValuator: Development and evaluation of a phenotype algorithm evaluator.

Swerdel, Joel N / Hripcsak, George / Ryan, Patrick B

Journal of biomedical informatics

2019 Volume 97, Page(s) 103258

Abstract: Background: The primary approach for defining disease in observational healthcare databases is to construct phenotype algorithms (PAs), rule-based heuristics predicated on the presence, absence, and temporal logic of clinical observations. However, a ... ...

Abstract	Background: The primary approach for defining disease in observational healthcare databases is to construct phenotype algorithms (PAs), rule-based heuristics predicated on the presence, absence, and temporal logic of clinical observations. However, a complete evaluation of PAs, i.e., determining sensitivity, specificity, and positive predictive value (PPV), is rarely performed. In this study, we propose a tool (PheValuator) to efficiently estimate a complete PA evaluation. Methods: We used 4 administrative claims datasets: OptumInsight's de-identified Clinformatics™ Datamart (Eden Prairie,MN); IBM MarketScan Multi-State Medicaid); IBM MarketScan Medicare Supplemental Beneficiaries; and IBM MarketScan Commercial Claims and Encounters from 2000 to 2017. Using PheValuator involves (1) creating a diagnostic predictive model for the phenotype, (2) applying the model to a large set of randomly selected subjects, and (3) comparing each subject's predicted probability for the phenotype to inclusion/exclusion in PAs. We used the predictions as a 'probabilistic gold standard' measure to classify positive/negative cases. We examined 4 phenotypes: myocardial infarction, cerebral infarction, chronic kidney disease, and atrial fibrillation. We examined several PAs for each phenotype including 1-time (1X) occurrence of the diagnosis code in the subject's record and 1-time occurrence of the diagnosis in an inpatient setting with the diagnosis code as the primary reason for admission (1X-IP-1stPos). Results: Across phenotypes, the 1X PA showed the highest sensitivity/lowest PPV among all PAs. 1X-IP-1stPos yielded the highest PPV/lowest sensitivity. Specificity was very high across algorithms. We found similar results between algorithms across datasets. Conclusion: PheValuator appears to show promise as a tool to estimate PA performance characteristics.
MeSH term(s)	Algorithms ; Atrial Fibrillation/diagnosis ; Cerebral Infarction/diagnosis ; Computational Biology ; Current Procedural Terminology ; Databases, Factual/statistics & numerical data ; Diagnosis, Computer-Assisted/statistics & numerical data ; Diagnostic Errors/statistics & numerical data ; Humans ; Models, Statistical ; Myocardial Infarction/diagnosis ; Phenotype ; Predictive Value of Tests ; Probability ; Renal Insufficiency, Chronic/diagnosis ; Sensitivity and Specificity
Language	English
Publishing date	2019-07-29
Publishing country	United States
Document type	Evaluation Study ; Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	2057141-0
ISSN	1532-0480 ; 1532-0464
ISSN (online)	1532-0480
ISSN	1532-0464
DOI	10.1016/j.jbi.2019.103258
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Article: Evaluation of code-based algorithms to identify pulmonary arterial hypertension and chronic thromboembolic pulmonary hypertension patients in large administrative databases.

Sprecher, Viviane P / Didden, Eva-Maria / Swerdel, Joel N / Muller, Audrey

Pulmonary circulation

2020 Volume 10, Issue 4, Page(s) 2045894020961713

Abstract: Large administrative healthcare (including insurance claims) databases are used for various retrospective real-world evidence studies. However, in pulmonary arterial hypertension and chronic thromboembolic pulmonary hypertension, identifying patients ... ...

Abstract	Large administrative healthcare (including insurance claims) databases are used for various retrospective real-world evidence studies. However, in pulmonary arterial hypertension and chronic thromboembolic pulmonary hypertension, identifying patients retrospectively based on administrative codes remains challenging, as it relies on code combinations (algorithms) and the accuracy for patient identification of most of them is unknown. This study aimed to assess the performance of various algorithms in correctly identifying patients with pulmonary arterial hypertension or chronic thromboembolic pulmonary hypertension in administrative databases. A systematic literature review was performed to find publications detailing code-based algorithms used to identify pulmonary arterial hypertension and chronic thromboembolic pulmonary hypertension patients. PheValuator, a diagnostic predictive modelling tool, was applied to three US claims databases, yielding models that estimated the probability of a patient having the disease. These models were used to evaluate the performance characteristics of selected pulmonary arterial hypertension and chronic thromboembolic pulmonary hypertension algorithms. With increasing algorithm complexity, average positive predictive value increased (pulmonary arterial hypertension: 13.4-66.0%; chronic thromboembolic pulmonary hypertension: 10.3-75.1%) and average sensitivity decreased (pulmonary arterial hypertension: 61.5-2.7%; chronic thromboembolic pulmonary hypertension: 20.7-0.2%). Specificities and negative predictive values were high (≥97.5%) for all algorithms. Several of the algorithms performed well overall when considering all of these four performance parameters, and all algorithms performed with similar accuracy across the three claims databases studied, even though most were designed for patient identification in a specific database. Therefore, it is the objective of a study that will determine which algorithm may be most suitable; one- or two-component algorithms are most inclusive and three- or four-component algorithms identify most precise pulmonary arterial hypertension or chronic thromboembolic pulmonary hypertension populations, respectively.
Language	English
Publishing date	2020-11-10
Publishing country	United States
Document type	Journal Article
ZDB-ID	2638089-4
ISSN	2045-8940 ; 2045-8932
ISSN (online)	2045-8940
ISSN	2045-8932
DOI	10.1177/2045894020961713
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Article ; Online: Developing Predictive Models to Determine Patients in End-of-Life Care in Administrative Datasets.

Swerdel, Joel N / Reps, Jenna M / Fife, Daniel / Ryan, Patrick B

Drug safety

2020 Volume 43, Issue 5, Page(s) 447–455

Abstract: Introduction: In observational studies with mortality endpoints, one needs to consider how to account for subjects whose interventions appear to be part of 'end-of-life' care.: Objective: The objective of this study was to develop a diagnostic ... ...

Abstract	Introduction: In observational studies with mortality endpoints, one needs to consider how to account for subjects whose interventions appear to be part of 'end-of-life' care. Objective: The objective of this study was to develop a diagnostic predictive model to identify those in end-of-life care at the time of a drug exposure. Methods: We used data from four administrative claims datasets from 2000 to 2017. The index date was the date of the first prescription for the last new drug subjects received during their observation period. The outcome of end-of-life care was determined by the presence of one or more codes indicating terminal or hospice care. Models were developed using regularized logistic regression. Internal validation was through examination of the area under the receiver operating characteristic curve (AUC) and through model calibration in a 25% subset of the data held back from model training. External validation was through examination of the AUC after applying the model learned on one dataset to the three other datasets. Results: The models showed excellent performance characteristics. Internal validation resulted in AUCs ranging from 0.918 (95% confidence interval [CI] 0.905-0.930) to 0.983 (95% CI 0.978-0.987) for the four different datasets. Calibration results were also very good, with slopes near unity. External validation also produced very good to excellent performance metrics, with AUCs ranging from 0.840 (95% CI 0.834-0.846) to 0.956 (95% CI 0.952-0.960). Conclusion: These results show that developing diagnostic predictive models for determining subjects in end-of-life care at the time of a drug treatment is possible and may improve the validity of the risk profile for those treatments.
MeSH term(s)	Adult ; Aged ; Aged, 80 and over ; Databases, Factual ; Female ; Humans ; Male ; Middle Aged ; Models, Theoretical ; Terminal Care
Language	English
Publishing date	2020-01-14
Publishing country	New Zealand
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	1018059-x
ISSN	1179-1942 ; 0114-5916
ISSN (online)	1179-1942
ISSN	0114-5916
DOI	10.1007/s40264-020-00906-7
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Article ; Online: Cancer death and antihypertensive drug treatment-response.

Swerdel, Joel N / Kostis, John B

Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology

2014 Volume 23, Issue 11, Page(s) 2608

MeSH term(s)	Antihypertensive Agents/therapeutic use ; Female ; Humans ; Hypertension/drug therapy ; Male ; Neoplasms/mortality
Chemical Substances	Antihypertensive Agents
Language	English
Publishing date	2014-11
Publishing country	United States
Document type	Comment ; Letter ; Research Support, Non-U.S. Gov't
ZDB-ID	1153420-5
ISSN	1538-7755 ; 1055-9965
ISSN (online)	1538-7755
ISSN	1055-9965
DOI	10.1158/1055-9965.EPI-14-0864
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Article ; Online: Incidence rate of hospitalization and mortality in the first year following initial diagnosis of cardiac amyloidosis in the US claims databases.

Wang, Lu / Swerdel, Joel N / Weaver, James / Weiss, Brendan / Pan, Guohua / Yuan, Zhong / DiBattiste, Peter M

Current medical research and opinion

2021 Volume 37, Issue 8, Page(s) 1275–1281

Abstract: Objective: This study aimed to determine rates of hospitalization and in-hospital mortality in the first year following amyloidosis diagnosis with cardiac involvement using observational databases.: Methods: Three administrative claims databases, IBM ...

Abstract	Objective: This study aimed to determine rates of hospitalization and in-hospital mortality in the first year following amyloidosis diagnosis with cardiac involvement using observational databases. Methods: Three administrative claims databases, IBM MarketScan Results: A total of 419, 654, and 922 patients from CCAE, MDCR, and Optum were identified during 2010-2017 period, with mean age of 55.6, 77.8, and 74.2 years, respectively. Within 1 year following initial amyloidosis diagnosis, incidence rates (95% confidence interval [CI]) of hospitalization were 78.4 (66.3, 90.4), 78.6 (69.2, 87.9), and 61.2 (54.4, 68.0) per 100 person-years, rates of in-hospital mortality were 16.5 (11.8, 21.3), 8.4 (5.7, 11.0), and 17.7 (14.5, 21.0) per 100 person-years, in CCAE, MDCR, and Optum, respectively. The mortality rate from the sensitivity analysis among patients identified in Optum 2004-2011 period was higher compared with Optum 2010-2017 period. Conclusions: The results from this study indicate that amyloidosis with cardiac involvement is a condition with high rates of hospitalization and mortality in the first year after initial diagnosis. Future studies are needed to further evaluate the outcomes within the subtypes of amyloidosis and understand the risk factors associated with poor prognoses.
MeSH term(s)	Aged ; Amyloidosis/diagnosis ; Amyloidosis/epidemiology ; Databases, Factual ; Hospitalization ; Humans ; Incidence ; Infant, Newborn ; Medicare ; Middle Aged ; Retrospective Studies ; United States/epidemiology
Language	English
Publishing date	2021-04-23
Publishing country	England
Document type	Journal Article ; Observational Study ; Research Support, Non-U.S. Gov't
ZDB-ID	80296-7
ISSN	1473-4877 ; 0300-7995
ISSN (online)	1473-4877
ISSN	0300-7995
DOI	10.1080/03007995.2021.1913109
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Article ; Online: Rates of Hospitalization for Dehydration Following Hurricane Sandy in New Jersey.

Swerdel, Joel N / Rhoads, George G / Cosgrove, Nora M / Kostis, John B

Disaster medicine and public health preparedness

2016 Volume 10, Issue 2, Page(s) 188–192

Abstract: Objective: Hurricane Sandy, one of the most destructive natural disasters in New Jersey history, made landfall on October 29, 2012. Prolonged loss of electrical power and extensive infrastructure damage restricted access for many to food and water. We ... ...

Abstract	Objective: Hurricane Sandy, one of the most destructive natural disasters in New Jersey history, made landfall on October 29, 2012. Prolonged loss of electrical power and extensive infrastructure damage restricted access for many to food and water. We examined the rate of dehydration in New Jersey residents after Hurricane Sandy. Methods: We obtained data from 2008 to 2012 from the Myocardial Infarction Data Acquisition System (MIDAS), a repository of in-patient records from nonfederal New Jersey hospitals (N=517,355). Patients with dehydration had ICD-9-CM discharge diagnosis codes for dehydration, volume depletion, and/or hypovolemia. We used log-linear modeling to estimate the change in in-patient hospitalizations for dehydration comparing 2 weeks after Sandy with the same period in the previous 4 years (2008-2011). Results: In-patient hospitalizations for dehydration were 66% higher after Sandy than in 2008-2011 (rate ratio [RR]: 1.66; 95% confidence interval [CI]: 1.50, 1.84). Hospitalizations for dehydration in patients over 65 years of age increased by nearly 80% after Sandy compared with 2008-2011 (RR: 1.79; 95% CI: 1.58, 2.02). Conclusion: Sandy was associated with a marked increase in hospitalizations for dehydration. Reducing the rate of dehydration following extreme weather events is an important public health concern that needs to be addressed, especially in those over 65 years of age.
MeSH term(s)	Aged ; Aged, 80 and over ; Cyclonic Storms/statistics & numerical data ; Dehydration/epidemiology ; Dehydration/therapy ; Female ; Hospitalization/statistics & numerical data ; Humans ; Male ; Myocardial Infarction/epidemiology ; Myocardial Infarction/mortality ; Myocardial Infarction/therapy ; New Jersey ; Public Health/methods
Language	English
Publishing date	2016-04
Publishing country	United States
Document type	Journal Article
ZDB-ID	2375268-3
ISSN	1938-744X ; 1935-7893
ISSN (online)	1938-744X
ISSN	1935-7893
DOI	10.1017/dmp.2015.169
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Article ; Online: The effect of Hurricane Sandy on cardiovascular events in New Jersey.

Swerdel, Joel N / Janevic, Teresa M / Cosgrove, Nora M / Kostis, John B

Journal of the American Heart Association

2014 Volume 3, Issue 6, Page(s) e001354

Abstract: Background: Hurricane Sandy made landfall in New Jersey (NJ) on October 29, 2012. We studied the impact of this extreme weather event on the incidence of, and 30-day mortality from, cardiovascular (CV) events (CVEs), including myocardial infarctions (MI) ...

Abstract	Background: Hurricane Sandy made landfall in New Jersey (NJ) on October 29, 2012. We studied the impact of this extreme weather event on the incidence of, and 30-day mortality from, cardiovascular (CV) events (CVEs), including myocardial infarctions (MI) and strokes, in NJ. Methods and results: Data were obtained from the MI data acquisition system (MIDAS), a database of all inpatient hospital discharges with CV diagnoses in NJ, including death certificates. Patients were grouped by their county of residence, and each county was categorized as either high- (41.5% of the NJ population) or low-impact area based on data from the Federal Emergency Management Agency and other sources. We utilized Poisson regression comparing the 2 weeks following Sandy landfall with the same weeks from the 5 previous years. In addition, we used CVE data from the 2 weeks previous in each year as to adjust for yearly changes. In the high-impact area, MI incidence increased by 22%, compared to previous years (attributable rate ratio [ARR], 1.22; 95% confidence interval [CI], 1.16, 1.28), with a 31% increase in 30-day mortality (ARR, 1.31; 95% CI, 1.22, 1.41). The incidence of stroke increased by 7% (ARR, 1.07; 95% CI, 1.03, 1.11), with no significant change in 30-day stroke mortality. There were no changes in incidence or 30-day mortality of MI or stroke in the low-impact area. Conclusion: In the 2 weeks following Hurricane Sandy, there were increases in the incidence of, and 30-day mortality from, MI and in the incidence of stroke.
MeSH term(s)	Aged ; Aged, 80 and over ; Cause of Death ; Cyclonic Storms ; Databases, Factual ; Disasters ; Female ; Humans ; Incidence ; Male ; Middle Aged ; Myocardial Infarction/diagnosis ; Myocardial Infarction/epidemiology ; Myocardial Infarction/mortality ; New Jersey/epidemiology ; Odds Ratio ; Residence Characteristics ; Risk Assessment ; Risk Factors ; Stroke/diagnosis ; Stroke/epidemiology ; Stroke/mortality ; Time Factors
Language	English
Publishing date	2014-12-08
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2653953-6
ISSN	2047-9980 ; 2047-9980
ISSN (online)	2047-9980
ISSN	2047-9980
DOI	10.1161/JAHA.114.001354
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