Advertisement

Journal of Medical Systems

, 44:54 | Cite as

Web-Based Dashboard for the Interactive Visualization and Analysis of National Risk-Standardized Mortality Rates of Sepsis in the US

  • Meng-Tse Lee
  • Fong-Ci Lin
  • Szu-Ta Chen
  • Wan-Ting Hsu
  • Samuel Lin
  • Tzer-Shyong Chen
  • Feipei Lai
  • Chien-Chang LeeEmail author
Systems-Level Quality Improvement
  • 54 Downloads
Part of the following topical collections:
  1. Systems-Level Quality Improvement

Abstract

Sepsis mortality is heavily influenced by the quality of care in hospitals. Comparing risk-standardized mortality rate (RSMR) of sepsis patients in different states in the United States has potentially important clinical and policy implications. In the current study, we aimed to compare national sepsis RSMR using an interactive web-based dashboard. We analyzed sepsis mortality using the National Inpatient Sample Database of the US. The RSMR was calculated by the hierarchical logistic regression model. We wrote the interactive web-based dashboard using the Shiny framework, an R package that integrates R-based statistics computation and graphics generation. Visual summarizations (e.g., heat map, and time series chart), and interactive tools (e.g., year selection, automatic year play, map zoom, copy or print data, ranking data by name or value, and data search) were implemented to enhance user experience. The web-based dashboard (https://sepsismap.shinyapps.io/index2/) is cross-platform and publicly available to anyone with interest in sepsis outcomes, health inequality, and administration of state/federal healthcare. After extrapolation to the national level, approximately 35 million hospitalizations were analyzed for sepsis mortality each year. Eight years of sepsis mortality data were summarized into four easy to understand dimensions: Sepsis Identification Criteria; Sepsis Mortality Predictors; RSMR Map; RSMR Trend. Substantial variation in RSMR was observed for different states in the US. This web-based dashboard allows anyone to visualize the substantial variation in RSMR across the whole US. Our work has the potential to support healthcare transparency, information diffusion, health decision-making, and the formulation of new public policies.

Keywords

Dashboard Sepsis Risk standardized mortality rate And visualization 

Abbreviations

AHRQ

Agency for Healthcare Research and Quality

AMI

Acute Myocardial Infarction

CCS

Clinical Classification Software codes

CMS

Centers for Medicare & Medicaid Services

HF

Heart Failure

HLM

Hierarchical linear modeling

HHS

Health and Human Services

ICD-9- CM

International Classification of Diseases, Ninth Revision, Clinical Modification

NIS

National Inpatient Sample

NQF

National Quality Forum

RSMRs

Risk Standardized Mortality Rates

United States

US

Notes

Acknowledgements

We thank the staff of the Core Labs, the Department of Medical Research, and National Taiwan University Hospital for technical support. Medical wisdom consulting group for technical assistance in statistical analysis.

Funding Information

This study is supported by the Taiwan National Science Foundation Grant NSC 102–2314-B-002 -131 -MY3; Taiwan National Ministry of Science and Technology Grants MOST 104–2314-B-002 -039 -MY3, and MOST 105–2811-B-002-031. No funding bodies had any role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Compliance with Ethical Standards

Financial Disclosure and Conflict of Interest

None declared.

Conflict of Interest

No Conflicts.

Supplementary material

10916_2019_1509_MOESM1_ESM.docx (176 kb)
ESM 1 (DOCX 175 kb)

References

  1. 1.
    Fleischmann, C., Scherag, A., Adhikari, N. K., Hartog, C. S., Tsaganos, T., Schlattmann, P., Angus, D. C., Reinhart, K., and International Forum of Acute Care T, Assessment of global incidence and mortality of hospital-treated Sepsis. Current estimates and limitations. Am. J. Respir. Crit. Care Med. 193(3):259–272, 2016.  https://doi.org/10.1164/rccm.201504-0781OC.CrossRefPubMedGoogle Scholar
  2. 2.
    Gaieski, D. F., Edwards, J. M., Kallan, M. J., and Carr, B. G., Benchmarking the incidence and mortality of severe sepsis in the United States. Crit. Care Med. 41(5):1167–1174, 2013.  https://doi.org/10.1097/CCM.0b013e31827c09f8.CrossRefPubMedGoogle Scholar
  3. 3.
    Bouza, C., Lopez-Cuadrado, T., Saz-Parkinson, Z., and Amate-Blanco, J. M., Epidemiology and recent trends of severe sepsis in Spain: A nationwide population-based analysis (2006-2011). BMC Infect. Dis. 14:3863, 2014.  https://doi.org/10.1186/s12879-014-0717-7.CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    Jones, S. L., Ashton, C. M., Kiehne, L., Gigliotti, E., Bell-Gordon, C., Disbot, M., Masud, F., Shirkey, B. A., and Wray, N. P., Reductions in sepsis mortality and costs after design and implementation of a nurse-based early recognition and response program. Jt. Comm. J. Qual. Patient Saf. 41(11):483–AP483, 2015.CrossRefGoogle Scholar
  5. 5.
    Kortgen, A., Niederprüm, P., and Bauer, M., Implementation of an evidence-based “standard operating procedure” and outcome in septic shock. Crit. Care Med. 34(4):943–949, 2006.CrossRefGoogle Scholar
  6. 6.
    Kumar, A., Ellis, P., Arabi, Y., Roberts, D., Light, B., Parrillo, J. E., Dodek, P., Wood, G., Kumar, A., and Simon, D., Initiation of inappropriate antimicrobial therapy results in a fivefold reduction of survival in human septic shock. Chest. J. 136(5):1237–1248, 2009.CrossRefGoogle Scholar
  7. 7.
    Kumar, A., Roberts, D., Wood, K. E., Light, B., Parrillo, J. E., Sharma, S., Suppes, R., Feinstein, D., Zanotti, S., and Taiberg, L., Duration of hypotension before initiation of effective antimicrobial therapy is the critical determinant of survival in human septic shock. Crit. Care Med. 34(6):1589–1596, 2006.CrossRefGoogle Scholar
  8. 8.
    Micek, S. T., Roubinian, N., Heuring, T., Bode, M., Williams, J., Harrison, C., Murphy, T., Prentice, D., Ruoff, B. E., and Kollef, M. H., Before–after study of a standardized hospital order set for the management of septic shock. Crit. Care Med. 34(11):2707–2713, 2006.CrossRefGoogle Scholar
  9. 9.
    Moore, L. J., Jones, S. L., Kreiner, L. A., McKinley, B., Sucher, J. F., Todd, S. R., Turner, K. L., Valdivia, A., and Moore, F. A., Validation of a screening tool for the early identification of sepsis. J. Trauma Acute Care Surg. 66(6):1539–1547, 2009.CrossRefGoogle Scholar
  10. 10.
    Rivers, E., Nguyen, B., Havstad, S., Ressler, J., Muzzin, A., Knoblich, B., Peterson, E., and Tomlanovich, M., Early goal-directed therapy in the treatment of severe sepsis and septic shock. N. Engl. J. Med. 345(19):1368–1377, 2001.CrossRefGoogle Scholar
  11. 11.
    Zubrow, M. T., Sweeney, T. A., Fulda, G. J., Seckel, M. A., Ellicott, A. C., Mahoney, D. D., Fasano-Piectrazak, P. M., and Farraj, M. B., Improving care of the sepsis patient. Jt. Comm. J. Qual. Patient Saf. 34(4):187–191, 2008.CrossRefGoogle Scholar
  12. 12.
    Peled, A., When transparency and collaboration collide: The USA open data program. J. Assoc. Inf. Sci. Technol. 62(11):2085–2094, 2011.CrossRefGoogle Scholar
  13. 13.
    Coglianese, C., The transparency president? The Obama administration and open government. Governance 22(4):529–544, 2009.CrossRefGoogle Scholar
  14. 14.
    Drye, E. E., Normand, S.-L. T., Wang, Y., Ross, J. S., Schreiner, G. C., Han, L., Rapp, M., and Krumholz, H. M., Comparison of hospital risk-standardized mortality rates calculated by using in-hospital and 30-day models: An observational study with implications for hospital profiling. Ann. Intern. Med. 156(1_Part_1):19–26, 2012.CrossRefGoogle Scholar
  15. 15.
    Barbash, I. J., Zhang, H., Angus, D. C., Reis, S. E., Chang, C.-C. H., Pike, F. R., and Kahn, J. M., Differences in hospital risk-standardized mortality rates for acute myocardial infarction when assessed using transferred and nontransferred patients. Med. Care 55(5):476–482, 2017.CrossRefGoogle Scholar
  16. 16.
    Angus, D. C., Linde-Zwirble, W. T., Lidicker, J., Clermont, G., Carcillo, J., and Pinsky, M. R., Epidemiology of severe sepsis in the United States: Analysis of incidence, outcome, and associated costs of care. Crit. Care Med. 29(7):1303–1310, 2001.CrossRefGoogle Scholar
  17. 17.
    Martin, G. S., Mannino, D. M., Eaton, S., and Moss, M., The epidemiology of sepsis in the United States from 1979 through 2000. N. Engl. J. Med. 348(16):1546–1554, 2003.CrossRefGoogle Scholar
  18. 18.
    Elixhauser A, Steiner C, Palmer L (2014) Clinical Classifications Software (CCS). US Agency for Healthcare Research and Quality, 2014.Google Scholar
  19. 19.
    Chang W, Cheng J, Allaire J, Xie Y, McPherson J (2015) Shiny: Web application framework for R. R package version 011 1Google Scholar
  20. 20.
    Mohorovičić S Implementing responsive web design for enhanced web presence. In: Information & Communication Technology Electronics & Microelectronics (MIPRO), 2013 36th International Convention on, 2013. IEEE, pp 1206–1210Google Scholar
  21. 21.
    Franklin, A., Gantela, S., Shifarraw, S., Johnson, T. R., Robinson, D. J., King, B. R., Mehta, A. M., Maddow, C. L., Hoot, N. R., Nguyen, V., Rubio, A., Zhang, J., and Okafor, N. G., Dashboard visualizations: Supporting real-time throughput decision-making. J. Biomed. Inform. 71:211–221, 2017.  https://doi.org/10.1016/j.jbi.2017.05.024.CrossRefPubMedGoogle Scholar
  22. 22.
    Dowding, D., Randell, R., Gardner, P., Fitzpatrick, G., Dykes, P., Favela, J., Hamer, S., Whitewood-Moores, Z., Hardiker, N., Borycki, E., and Currie, L., Dashboards for improving patient care: Review of the literature. Int. J. Med. Inform. 84(2):87–100, 2015.  https://doi.org/10.1016/j.ijmedinf.2014.10.001.CrossRefPubMedGoogle Scholar
  23. 23.
    Stadler, J. G., Donlon, K., Siewert, J. D., Franken, T., and Lewis, N. E., Improving the efficiency and ease of healthcare analysis through use of data visualization dashboards. Big Data 4(2):129–135, 2016.CrossRefGoogle Scholar
  24. 24.
    Raban, M. S., Bamford, C., Joolay, Y., and Harrison, M. C., Impact of an educational intervention and clinical performance dashboard on neonatal bloodstream infections. S. Afr. Med. J. 105(7):564–566, 2015.  https://doi.org/10.7196/SAMJnew.7764.CrossRefPubMedGoogle Scholar
  25. 25.
    Lindenauer, P. K., Grosso, L. M., Wang, C., Wang, Y., Krishnan, J. A., Lee, T. A., Au, D. H., Mularski, R. A., Bernheim, S. M., and Drye, E. E., Development, validation, and results of a risk-standardized measure of hospital 30-day mortality for patients with exacerbation of chronic obstructive pulmonary disease. J. Hosp. Med. 8(8):428–435, 2013.  https://doi.org/10.1002/jhm.2066.CrossRefPubMedGoogle Scholar
  26. 26.
    Bernheim, S. M., Grady, J. N., Lin, Z., Wang, Y., Wang, Y., Savage, S. V., Bhat, K. R., Ross, J. S., Desai, M. M., Merrill, A. R., Han, L. F., Rapp, M. T., Drye, E. E., Normand, S. L., and Krumholz, H. M., National patterns of risk-standardized mortality and readmission for acute myocardial infarction and heart failure. Update on publicly reported outcomes measures based on the 2010 release. Circ. Cardiovasc. Qual. Outcomes 3(5):459–467, 2010.  https://doi.org/10.1161/CIRCOUTCOMES.110.957613.CrossRefPubMedPubMedCentralGoogle Scholar
  27. 27.
    Horwitz, L. I., Wang, Y., Desai, M. M., Curry, L. A., Bradley, E. H., Drye, E. E., and Krumholz, H. M., Correlations among risk-standardized mortality rates and among risk-standardized readmission rates within hospitals. J. Hosp. Med. 7(9):690–696, 2012.CrossRefGoogle Scholar
  28. 28.
    Lichtman, J. H., Leifheit-Limson, E. C., Jones, S. B., Wang, Y., and Goldstein, L. B., 30-day risk-standardized mortality and readmission rates after ischemic stroke in critical access hospitals. Stroke 43(10):2741–2747, 2012.  https://doi.org/10.1161/STROKEAHA.112.665646.CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2020

Authors and Affiliations

  • Meng-Tse Lee
    • 1
  • Fong-Ci Lin
    • 2
  • Szu-Ta Chen
    • 3
    • 4
    • 5
    • 6
  • Wan-Ting Hsu
    • 3
  • Samuel Lin
    • 7
  • Tzer-Shyong Chen
    • 8
  • Feipei Lai
    • 1
    • 9
    • 10
  • Chien-Chang Lee
    • 2
    • 11
    Email author
  1. 1.Department of Emergency MedicineNational Taiwan University HospitalTaipeiTaiwan
  2. 2.Graduate Institute of Biomedical Electronics and BioinformaticsNational Taiwan UniversityTaipeiTaiwan
  3. 3.Department of EpidemiologyHarvard T.H. Chan School of Public HealthBostonUSA
  4. 4.Department of PediatricsNational Taiwan University Hospital Yun-Lin BranchYunlin CountyTaiwan
  5. 5.Department of PediatricsNational Taiwan University and College of MedicineTaipeiTaiwan
  6. 6.Graduate Institute of ToxicologyCollege of Medicine, National Taiwan UniversityTaipeiTaiwan
  7. 7.Department of Data SciencesUniversity of CaliforniaBerkeleyUSA
  8. 8.Department of Information ManagementTunghai UniversityTaichungTaiwan
  9. 9.Department of Computer Science & Information EngineeringNational Taiwan UniversityTaipeiTaiwan
  10. 10.Department of Electrical EngineeringNational Taiwan UniversityTaipeiTaiwan
  11. 11.Health Economic Outcomes Research Group and Department of Emergency MedicineNational Taiwan University HospitalTaipeiTaiwan

Personalised recommendations