Concordance Between Registry and Administrative Data in the Determination of Comorbidity: A Multi-institutional Study.


Journal

Annals of surgery
ISSN: 1528-1140
Titre abrégé: Ann Surg
Pays: United States
ID NLM: 0372354

Informations de publication

Date de publication:
12 2020
Historique:
pubmed: 1 3 2019
medline: 15 12 2020
entrez: 1 3 2019
Statut: ppublish

Résumé

To characterize agreement between administrative and registry data in the determination of patient-level comorbidities. Previous research finds poor agreement between these 2 types of data in the determination of outcomes. We hypothesized that concordance between administrative and registry data would also be poor. A cohort of inpatient operations (length of stay 1 day or greater) was obtained from a consortium of 8 hospitals. Within each hospital, National Surgical Quality Improvement Program (NSQIP) data were merged with intra-institutional inpatient administrative data. Twelve different comorbidities (diabetes, hypertension, congestive heart failure, hemodialysis-dependence, cancer diagnosis, chronic obstructive pulmonary disease, ascites, sepsis, smoking, steroid, congestive heart failure, acute renal failure, and dyspnea) were analyzed in terms of agreement between administrative and NSQIP data. Forty-one thousand four hundred thirty-two inpatient surgical hospitalizations were analyzed in this study. Concordance (Cohen Kappa value) between the 2 data sources varied from 0.79 (diabetes) to 0.02 (dyspnea). Hospital variation in concordance (intersite variation) was quantified using a test of homogeneity. This test found significant intersite variation at a level of P < 0.001 for each of the comorbidities except for dialysis (P = 0.07) and acute renal failure (P = 0.19). These findings imply significant differences between hospitals in their generation of comorbidity data. This study finds significant differences in how administrative versus registry data assess patient-level comorbidity. These differences are of concern to patients, payers, and providers, each of which had a stake in the integrity of these data. Standardized definitions of comorbidity and periodic audits are necessary to ensure data accuracy and minimize bias.

Sections du résumé

OBJECTIVE
To characterize agreement between administrative and registry data in the determination of patient-level comorbidities.
BACKGROUND
Previous research finds poor agreement between these 2 types of data in the determination of outcomes. We hypothesized that concordance between administrative and registry data would also be poor.
METHODS
A cohort of inpatient operations (length of stay 1 day or greater) was obtained from a consortium of 8 hospitals. Within each hospital, National Surgical Quality Improvement Program (NSQIP) data were merged with intra-institutional inpatient administrative data. Twelve different comorbidities (diabetes, hypertension, congestive heart failure, hemodialysis-dependence, cancer diagnosis, chronic obstructive pulmonary disease, ascites, sepsis, smoking, steroid, congestive heart failure, acute renal failure, and dyspnea) were analyzed in terms of agreement between administrative and NSQIP data.
RESULTS
Forty-one thousand four hundred thirty-two inpatient surgical hospitalizations were analyzed in this study. Concordance (Cohen Kappa value) between the 2 data sources varied from 0.79 (diabetes) to 0.02 (dyspnea). Hospital variation in concordance (intersite variation) was quantified using a test of homogeneity. This test found significant intersite variation at a level of P < 0.001 for each of the comorbidities except for dialysis (P = 0.07) and acute renal failure (P = 0.19). These findings imply significant differences between hospitals in their generation of comorbidity data.
CONCLUSION
This study finds significant differences in how administrative versus registry data assess patient-level comorbidity. These differences are of concern to patients, payers, and providers, each of which had a stake in the integrity of these data. Standardized definitions of comorbidity and periodic audits are necessary to ensure data accuracy and minimize bias.

Identifiants

pubmed: 30817356
doi: 10.1097/SLA.0000000000003247
pii: 00000658-202012000-00023
doi:

Types de publication

Journal Article Multicenter Study

Langues

eng

Sous-ensembles de citation

IM

Pagination

1006-1011

Références

Available at: https://www.cdc.gov/nchs/data/hus/hus16.pdf#093. Accessed October 16, 2017.
Etzioni DA, Lessow CL, Lucas HD, et al. Infectious surgical complications are not dichotomous: characterizing discordance between administrative data and registry data. Ann Surg 2018; 267:81–87.
Koch CG, Li L, Hixson E, et al. What are the real rates of postoperative complications: elucidating inconsistencies between administrative and clinical data sources. J Am Coll Surg 2012; 214:798–805.
Lawson EH, Louie R, Zingmond DS, et al. A comparison of clinical registry versus administrative claims data for reporting of 30-day surgical complications. Ann Surg 2012; 256:973–981.
Ingraham AM, Richards KE, Hall BL, et al. Quality improvement in surgery: the American College of Surgeons National Surgical Quality Improvement Program approach. Adv Surg 2010; 44:251–267.
Available at: https://www.facs.org/∼/media/files/quality%20programs/nsqip/nsqip_puf_userguide_2014.ashx. Accessed October 13, 2017.
Available at: https://riskcalculator.facs.org/RiskCalculator/PatientInfo.jsp. Accessed October 13, 2017.
Donner A, Eliasziw M, Klar N. Testing the homogeneity of kappa statistics. Biometrics 1996; 52:176–183.
Rhee C, Dantes R, Epstein L, et al. Incidence and trends of sepsis in US hospitals using clinical vs claims data. JAMA 2017; 318:1241–1249.
Nouraei SA, Hudovsky A, Frampton AE, et al. A study of clinical coding accuracy in surgery: implications for the use of administrative big data for outcomes management. Ann Surg 2015; 261:1096–1107.
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Available at: https://data.medicare.gov/Hospital-Compare/NSQIP-HOSPITAL-COMPARE-DATA/c635-s3cy. Accessed March 8, 2017.
Available at: https://www.medicare.gov/hospitalcompare/search.html. Accessed March 8, 2017.

Auteurs

David A Etzioni (DA)

Department of Surgery, Mayo Clinic Arizona, Phoenix, Arizona.
Mayo Clinic Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Surgical Outcomes Program, Rochester, Minnesota.

Cynthia Lessow (C)

Department of Surgery, Mayo Clinic Arizona, Phoenix, Arizona.
Mayo Clinic Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Surgical Outcomes Program, Rochester, Minnesota.

Liliana G Bordeianou (LG)

Department of Surgery, Massachusetts General Hospital, Boston, Massachusetts.

Hiroko Kunitake (H)

Department of Surgery, Massachusetts General Hospital, Boston, Massachusetts.

Sarah E Deery (SE)

Department of Surgery, Massachusetts General Hospital, Boston, Massachusetts.

Evie Carchman (E)

Department of Surgery, University of Wisconsin, Madison, Wisconsin.

Christina M Papageorge (CM)

Department of Surgery, University of Wisconsin, Madison, Wisconsin.

George Fuhrman (G)

Department of Surgery, Ochsner, New Orleans, Louisiana.

Rachel L Seiler (RL)

University of Queensland Medical Center, Brisbane, Queensland, Australia.

James Ogilvie (J)

Department of Surgery, Spectrum Health Medical Center, Grand Rapids, Michigan.

Elizabeth B Habermann (EB)

Mayo Clinic Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Surgical Outcomes Program, Rochester, Minnesota.

Samuel R Money (SR)

Department of Surgery, Mayo Clinic Arizona, Phoenix, Arizona.

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