Accepted Manuscript. The Challenges of Big Data in Dermatology. Megan H. Noe, MD, MPH, Arash Mostaghimi, MD, MPA, MPH

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1 Accepted Manuscript The Challenges of Big Data in Dermatology Megan H. Noe, MD, MPH, Arash Mostaghimi, MD, MPA, MPH PII: S (18) DOI: /j.jaad Reference: YMJD To appear in: Journal of the American Academy of Dermatology Received Date: 13 March 2018 Accepted Date: 18 March 2018 Please cite this article as: Noe MH, Mostaghimi A, The Challenges of Big Data in Dermatology, Journal of the American Academy of Dermatology (2018), doi: /j.jaad This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

2 The Challenges of Big Data in Dermatology Megan H. Noe 1, MD, MPH and Arash Mostaghimi 2, MD, MPA, MPH 1 Department of Dermatology, University of Pennsylvania, Philadelphia, PA 2 Department of Dermatology, Brigham and Women s Hospital, Harvard Medical School, Boston, MA Word Count: 481 Funding Sources: None Conflict of Interest: The authors have no conflict of interest to declare Corresponding Author: Megan Noe, MD, MPH 3400 Civic Center Blvd Perelman Center for Advanced Medicine 7 South Philadelphia, PA P: F: megan.noe@uphs.upenn.edu 25

3 To the Editor: We read with interest the article by Kaffenberger et al. highlighting the comorbid disease associations and impact of inpatient procedures on patients hospitalized with pyoderma gangrenosum (PG). 1 We applaud the authors for recognizing the prognosis of inpatients with PG is a subject that has not been well described and deserves further research. The results this study were based on data from the Nationwide Inpatient Sample (NIS), a 20% stratified sample of all discharges from US community hospitals, including individuals covered by Medicare, Medicaid, private insurance and those who are uninsured. 2 All adult patients with an ICD-9 code for PG (686.01) as the primary or secondary discharge diagnosis were included in the analysis. While the research question is important, we have concerns about the validity of ICD-9 based identification of PG cases. Clinically, PG is a challenging diagnosis with a high misdiagnosis rate. 3 The use of ICD-9 codes for the diagnosis of PG was recently validated in electronic medical records and only 45.3% of patients with at least one ICD-9 code for PG were found to have PG after review of the medical record. 4 This suggests that more than half of the patients included in this analysis may not actually have PG. In the validation study, the positive predictive value increased significantly when patients had more than one code (69.2%) or when it was a dermatologist-rendered code (84.6%) 4, suggesting it may be possible to design an algorithm to identify PG within large, claims-based datasets. The NIS is unfortunately not linked to medical records, and neither these advanced algorithms nor a database-specific validation can be performed. While we encourage dermatologists to continue epidemiology and outcomes research for rare diseases within the field of dermatology, it is paramount that our research aims are held to the highest standards. Ideally, validation of each database should be done independently using primary data. When this is not possible, as with the NIS, studies using administrative claims data should employ validated algorithms to identify the primary outcome and related comorbidities. This is especially critical when using large databases to identify rare diseases that are based on clinical evaluation. Correct dermatologic diagnoses are made through the careful integration of the patient history, clinical exam and review of skin

4 pathology by an experienced clinician and not an objective value like blood pressure or HgA1C. Because of this intellectual complexity, these diagnoses can be difficult to correctly identify in datasets If these guidelines are followed, the use of large administrative databases has the potential to play a major role in advancing our understanding of rare skin diseases. If non-validated approaches are taken, these studies may undermine progress by presenting systematically biased and incorrect data. "Big Data" has the potential to make connections and find novel associations that are otherwise impossible to identify in smaller patient cohorts; the peril is that without proper methods these insights may be erroneous. Conflict of Interest: The authors have no conflict of interest to declare.

5 References: 1. Kaffenberger BH, Hinton A, Krishna S. The impact of underlying disease state on outcomes in patients with pyoderma gangrenosum: A national survey. J Am Acad Dermatol. 2018, doi: /j.jaad Overview of the National Inpatient Sample. Available at Accessed on March 7, Weening RH, Davis MDP, Dahl PR, Su DWP. Skin ulcers misdiagnosed as pyoderma gangrenosum. N Engl J Med. 2002; 347: Lockwood SJ, Li DG, Butler D, Tsiaras W, Joyce C and Mostaghimi A. The Validity of the Diagnostic Code for Pyoderma Gangrenosum in an Electronic Database. Br J Dermatol. Accepted Author Manuscript. 2018, doi: /bjd