Impact of ICD-10-CM Transition on Selected Cardiovascular-Related Events in the Sentinel System
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1 Impact of ICD-10-CM Transition on Selected Cardiovascular-Related Events in the Sentinel System Tiffany Siu Woodworth, MPH, Catherine A. Panozzo, PhD, Emily C. Welch, MPH, Ting-Ying Jane Huang, PhD, BSPharm, Qoua L. Her, PharmD, MSc, Catherine Rogers, MPH, Max Ehrmann, MPH, Talia Menzin, Nicole Haug, MPH, Katherine Freitas, Sengwee Darren Toh, ScD Harvard Pilgrim Health Care Institute, Boston, MA, USA 1
2 Disclosures This work was supported by the FDA through the Department of Health and Human Services (HHS) Contract Number HHSF I and Task Order HHSF T The views expressed are the authors and not necessarily those of the Food and Drug Administration, or the Department of Health and Human Services 2
3 Transitioning to ICD-10-CM in the U.S ~14,000 codes 3 to 5 digits Category Etiology, Anatomical Site, Manifestation Oct 1, st digit 2 nd digit ~69,000 codes 3 rd digit 4 th digit 5 th digit 3 to 7 digits, with allowing for increased specificity Category Etiology, Anatomical Site, Severity Extension today 1 st digit 2 nd digit 3 rd digit 4 th digit 5 th digit 6 th digit 7 th digit 3
4 Objective To characterize the impact of the ICD-10-CM transition on the identification selected Health Outcomes of Interest (HOIs) Assess feasibility of using validated algorithms from international studies Assess the feasibility of using algorithms defined through mapping techniques 4
5 Approach to defining conditions Four definitions used to identify each condition ICD-9-CM algorithm from medical literature or previous Sentinel work ICD-10-CM algorithm derived from international validation studies ICD-10-CM Simple Forward Mapping (SFM) conversion using General Equivalence Mappings (GEMs)* ICD-10-CM Forward-Backward Mapping (FBM) conversion using GEMs* Selected health conditions Acute myocardial infarction (AMI), ischemic stroke, angioedema *Fung et al, egems 2016;4(1):Article 4 5
6 Example: Algorithms from literature Ischemic stroke algorithm (total 119 ICD-10 codes): Any of the following ICD-10 codes in the principal position on an inpatient hospital claim: I63.* (cerebral infarction) H34.1* (retinal artery occlusion) Derived algorithm based on literature review Two international studies validated similar algorithm including inpatient and other settings Kokotailo (Canada) Kokotailo, R. A., and M. D. Hill. "Coding of Stroke and Stroke Risk Factors Using International Classification of Diseases, Revisions 9 and 10." Stroke 36.8 (2005): Tolonen (Finland) H, Tolonen, Salomaa V, Torppa J, Sivenius J, Immonen-Räihä P, and Lehtonen A. "The Validation of the Finnish Hospital Discharge Register and Causes of Death Register Data on Stroke Diagnoses." Eur J Cardiovasc Prev Rehabil Jun 14(3):
7 ICD-9-CM Example: Simple-forward mapping Ischemic Stroke algorithm* (total 12 codes): Utilize GEMs forward mapping files SFM: Translation using ICD-9-CM to ICD-10-CM dictionary I I I I I I I I63.20 ICD-10-CM *partial ischemic stroke algorithm 7
8 ICD-9-CM Example: Forward-backward mapping Ischemic Stroke algorithm* (total 91 codes): Utilize both forward mapping and backward mapping files FBM: Translation using ICD-10 to ICD-9 dictionary + SFM codes I63.22 I63.02 I I I I I I I I I I I I ICD-10-CM *partial ischemic stroke algorithm 8
9 Analysis specifications Leveraged publicly available Sentinel tools For each outcome algorithm: Detect trends: incidence over time Coding-era comparison: incidence Indicates study period Indicates washout period only 9
10 Summary of results 11 health plans participating in Sentinel Mix of national and regional health insurers, integrated delivery systems At a minimum, included data through March 31, 2016 Data included: Analysis type Members Member-years Trend analysis: October 1, 2010 August 31, 2016 Coding-era analysis: October 1, 2014 March 31, 2015 October 1, 2015 March 31, million 319 million 57 million 13 million 10
11 Oct-10 Dec-10 Feb-11 Apr-11 Jun-11 Aug-11 Oct-11 Dec-11 Feb-12 Apr-12 Jun-12 Aug-12 Oct-12 Dec-12 Feb-13 Apr-13 Jun-13 Aug-13 Oct-13 Dec-13 Feb-14 Apr-14 Jun-14 Aug-14 Oct-14 Dec-14 Feb-15 Apr-15 Jun-15 Aug-15 Oct-15 Dec-15 Feb-16 Apr-16 Jun-16 Aug-16 Incidence/1K Eligible Members AMI: trend analysis Incidence per 1,000 Eligible Members of Acute Myocardial Infarction (AMI) between October August 2016, by Outcome Definition ICD-9-CM codes from an Algorithm, 183 day washout ICD-10-CM codes from an Algorithm, 183 day washout ICD-10-CM codes from SFM, 183 day washout ICD-10-CM codes from FBM, 183 day washout 11
12 Incidence/1000 eligible members AMI: coding-era analysis Incidence of various AMI definitions per 1000 eligible members using a 90 day washout, Jan-Mar 2015 vs. Jan-Mar Definition ICD-9-CM Code Count ICD-10-CM Code Count Algorithm* SFM 20 6 FBM Each algorithm identified ~16.5K events ICD-9-CM (literature) (0.284, 0.293) ICD-10-CM (literature) (0.288, 0.297) ICD-10-CM (SFM) (0.281, 0.290) ICD-10-CM (FBM) (0.288, 0.297) ICD-10-CM codes identified by the three approaches all included the most frequently used codes *Cutrona et al
13 Oct-10 Dec-10 Feb-11 Apr-11 Jun-11 Aug-11 Oct-11 Dec-11 Feb-12 Apr-12 Jun-12 Aug-12 Oct-12 Dec-12 Feb-13 Apr-13 Jun-13 Aug-13 Oct-13 Dec-13 Feb-14 Apr-14 Jun-14 Aug-14 Oct-14 Dec-14 Feb-15 Apr-15 Jun-15 Aug-15 Oct-15 Dec-15 Feb-16 Apr-16 Jun-16 Aug-16 Incidence/1K Eligible Members Ischemic stroke: trend analysis Incidence per 1,000 Eligible Members of Ischemic Stroke between October August 2016, by Outcome Definition ICD-9-CM codes from an Algorithm, 183 day washout ICD-10-CM codes from an Algorithm, 183 day washout ICD-10-CM codes from SFM, 183 day washout 0.02 SFM
14 Incidence/1000 Eligible Members Ischemic stroke: coding-era analysis Incidence of various AMI definitions per 1000 eligible members using a 90 day washout, Jan-Mar 2015 vs. Jan-Mar Definition ICD-9-CM Code Count ICD-10-CM Code Count Algorithm* SFM FBM Most common code: ICD-9-CM (literature) (0.188, 0.195) ICD-10-CM (literature) (0.189, 0.196) ICD-10-CM (SFM) (0.013, 0.015) ICD-10-CM (FBM) (0.186, 0.193) I63.9: cerebral infarction unspecified Not included in SFM (800 events) Included in Algorithm & FBM: (11K events)
15 Oct-10 Dec-10 Feb-11 Apr-11 Jun-11 Aug-11 Oct-11 Dec-11 Feb-12 Apr-12 Jun-12 Aug-12 Oct-12 Dec-12 Feb-13 Apr-13 Jun-13 Aug-13 Oct-13 Dec-13 Feb-14 Apr-14 Jun-14 Aug-14 Oct-14 Dec-14 Feb-15 Apr-15 Jun-15 Aug-15 Oct-15 Dec-15 Feb-16 Apr-16 Jun-16 Aug-16 Incidence/1K Eligible Members Angioedema: trend analysis Incidence per 1,000 Eligible Members of Angioedema between October August 2016, by Outcome Definition ICD-9-CM codes from an Algorithm, 183 day washout ICD-10-CM codes from an Algorithm, 183 day washout ICD-10-CM codes from SFM and FBM, 183 day washout
16 Incidence/1000 eligible members Angioedema: coding-era analysis Incidence of various angioedema definitions per 1000 eligible members using a 90 day washout, Jan-Mar 2015 vs. Jan-Mar Definition ICD-9-CM Code Count ICD-10-CM Code Count Algorithm* 1 1 Washout 1 3 SFM 1 1 FBM 1 1 One ICD-9-CM code (19K events) 995.1: Angioneurotic edema not elsewhere classified ICD-9-CM (literature) (0.333, 0.343) ICD-10-CM (literature) (0.252, 0.260) ICD-10-CM (SFM) (0.257, 0.266) ICD-10-CM (FBM) (0.257, 0.266) Three ICD-10-CM codes (15K events) T78.3XXA: Angioneurotic edema, initial encounter T78.3XXD: Angioneurotic edema, subsequent encounter T78.3XXS: Angioneurotic edema, sequela *Toh S et al, Johnsen SP et al, Gupta R et al 16
17 Other findings Algorithms that included commonly reported diagnosis codes generally performed similarly In general, FBM performed better than SFM Rates consistent in both eras across 11 diverse, participating health plans 17
18 Strengths and limitations Strengths: Variety and size of the Sentinel distributed database Reproducibility Limitations: Analysis only includes selected HOIs Coding practices may change over time Investigation focused on conditions as outcomes 18
19 Recommendations Continue to monitor trends as ICD-10-CM data accumulate Assess trend analysis for inferential analyses within cohorts Consider sensitivity analyses for outcome definitions Forward-backward mapping may be reasonable substitute Internationally validated algorithms should be tested Clinical expertise should be leveraged If possible, chart validation is ideal 19
20 Acknowledgements Co-authors Catherine Panozzo, Emily Welch, Jane Huang, Qoua Her, Catherine Rogers, Max Ehrmann, Talia Menzin, Nicole Haug, Katherine Freitas, Darren Toh Sentinel Applied Surveillance Core University of Iowa: Ryan Carnahan, Elizabeth Chrischilles University of Pennsylvania: Sean Hennessy, Charles Leonard Many thanks are due to the Data Partners who provided data used in this analysis 20
21 Thank you! 21
22 Extra Slides 22
23 ICD-10-CM vs. ICD-9-CM 23
24 ICD-10-CM Example 1 of >300 ICD-10-CM codes for gout Note: In contrast, only 15 ICD-9-CM codes for gout (e.g., ) Example courtesy of Kevin Haynes, HealthCore 24
25 How do GEMs work? 25
26 Defining clinical concepts with ICD-10-CM Published algorithms International studies, includes other ICD-10 variants (e.g., ICD-10-CA vs ICD-10-CM) ICD-9-CM ICD-10-CM translations using General Equivalence Mappings (GEMs) Simple forward mapping (SFM) Forward-backward mapping (FBM) Fung et al, egems 2016;4(1):Article 4 26
27 Summary of results Outcome definition Acute myocardial infarction Number of incident events Number of eligible members Number of member-years Incidence per 1,000 eligible members (95% CI) Incidence per 1,000 member-years ICD-9-CM algorithm 16,629 57,668,750 13,555, (0.284, 0.293) (1.208, 1.245) ICD-10-CM algorithm 16,780 57,453,227 13,778, (0.288, 0.297) (1.2, 1.236) ICD-10-CM SFM 16,391 57,453,627 13,778, (0.281, 0.290) (1.172, 1.208) ICD-10-CM FBM 16,781 57,452,861 13,777, (0.288, 0.297) (1.2, 1.237) Angioedema ICD-9-CM algorithm 19,486 57,677,701 13,565, (0.333, 0.343) (1.416, 1.457) ICD-10-CM algorithm 14,719 57,464,145 13,789, (0.252, 0.260) (1.05, 1.085) ICD-10-CM SFM 15,020 57,465,350 13,790, (0.257, 0.266) (1.072, 1.107) ICD-10-CM FBM 15,020 57,465,350 13,790, (0.257, 0.266) (1.072, 1.107) Ischemic stroke ICD-9-CM algorithm 11,011 57,634,007 13,535, (0.188, 0.195) (0.798, 0.829) ICD-10-CM algorithm 11,058 57,430,577 13,763, (0.189, 0.196) (0.789, 0.819) ICD-10-CM SFM ,447,546 13,777, (0.013, 0.015) (0.054, 0.062) ICD-10-CM FBM 10,898 57,421,718 13,758, (0.186, 0.193) (0.777, 0.807) 27
28 Selected results: AMI (2) Incidence of various AMI definitions per 1000 eligible members using a 90 day washout, Jan-Mar 2015 vs. Jan-Mar 2016 Outcome definition ICD-9-CM algorithm from literature ICD-10-CM algorithm from literature Number of incident events Number of eligible members Number of memberyears Incidence per 1,000 eligible members (95% CI) 16,629 57,668,750 13,555, (0.284, 0.293) 16,780 57,453,227 13,778, (0.288, 0.297) ICD-10-CM SFM 16,391 57,453,627 13,778, (0.281, 0.290) ICD-10-CM FBM 16,781 57,452,861 13,777, (0.288, 0.297) 28
29 Selected results: angioedema (2) Incidence of various angioedema definitions per 1000 eligible members using a 90 day washout, Jan-Mar 2015 vs. Jan-Mar 2016 Outcome definition ICD-9-CM algorithm from literature ICD-10-CM algorithm from literature Number of incident events Number of eligible members Number of memberyears Incidence per 1,000 eligible members (95% CI) 19,486 57,677,701 13,565, (0.333, 0.343) 14,719 57,464,145 13,789, (0.252, 0.260) ICD-10-CM SFM 15,020 57,465,350 13,790, (0.257, 0.266) ICD-10-CM FBM 15,020 57,465,350 13,790, (0.257, 0.266) 29
30 Selected results: ischemic stroke findings * I63.9, cerebral infarction unspecified, NOT captured by the SFM definition This code represented 24-38% of the ischemic stroke codes submitted by data partners The Algorithm (n=119 codes) and FBM (n=91 codes) identified I63.9 as well as many additional codes *Andrade et al, Tirschwell et al, Kokotailo et al, Tolonen et al 30
31 Selected results: ischemic stroke (2) Incidence of various ischemic stroke definitions per 1000 eligible members using a 90 day washout, Jan-Mar 2015 vs. Jan-Mar 2016 Outcome definition ICD-9-CM algorithm from literature ICD-10-CM algorithm from literature Number of incident events Number of eligible members Number of memberyears Incidence per 1,000 eligible members (95% CI) 11,011 57,634,007 13,535, (0.188, 0.195) 11,058 57,430,577 13,763, (0.189, 0.196) ICD-10-CM SFM ,447,546 13,777, (0.013, 0.015) ICD-10-CM FBM 10,898 57,421,718 13,758, (0.186, 0.193) 31
32 Selected results: angioedema findings One ICD-9-CM code 995.1: Angioneurotic edema not elsewhere classified Three ICD-10-CM codes T78.3XXA: Angioneurotic edema, initial encounter T78.3XXD: Angioneurotic edema, subsequent encounter T78.3XXS: Angioneurotic edema, sequela * Lower incidence in ICD-10-CM era vs. ICD-9-CM era currently unexplained *Toh S et al, Johnsen SP et al, Gupta R et al 32
33 Hypertension results Checking Expectations 1. Is the prevalence steady across the ICD-9 and ICD-10 eras? Yes 2. Is there a sharp decrease of ICD-9-CM and sharp increase of ICD-10-CM codes in October 2015? Yes 3. Are there differences between how the ICD-10-CM definitions performed? No 33
34 Number of Incident Diagnoses /1K Eligible Members Oct-10 Dec-10 Feb-11 Apr-11 Jun-11 Aug-11 Oct-11 Dec-11 Feb-12 Apr-12 Jun-12 Aug-12 Oct-12 Dec-12 Feb-13 Apr-13 Jun-13 Aug-13 Oct-13 Dec-13 Feb-14 Apr-14 Jun-14 Aug-14 Oct-14 Dec-14 Feb-15 Apr-15 Jun-15 Aug-15 Oct-15 Dec-15 Feb-16 Apr-16 Jun-16 Aug Hypertension results Incidence per 1,000 Eligible Members of Hypertension Definition, October August Month - Year ICD-9-CM codes from an Algorithm, 0 day washout ICD-10-CM codes from an Algorithm, 0 day washout ICD-9-CM codes from SFM and FBM, 0 day washout ICD-10-CM codes from SFM, 0 day washout Checking Expectations 1. Is the prevalence steady across the ICD-9 and ICD-10 eras? Yes 2. Is there a sharp decrease of ICD-9-CM and sharp increase of ICD-10-CM codes in October 2015? Yes 3. Are there differences between how the ICD-10-CM definitions performed? No 34
35 Condition #1: hypertension findings Definition * ICD-9-CM Code Count ICD-10-CM Code Count Algorithm SFM FBM I10, essential primary hypertension, included in all three ICD- 10-CM definitions This code represented 83-95% of the hypertension codes submitted by data partners May explain the similar performance of the three definitions *Based on Quan H et al 35
36 Selected results: AMI findings Definition ICD-9-CM Code Count ICD-10-CM Code Count Algorithm* SFM 20 6 FBM ICD-10-CM codes identified by the three approaches all included the most frequently used codes Thus, SFM (n=6 codes) performed as well as the Algorithm (n=12 codes) and FBM (n=14 codes) *Cutrona et al
37 Condition #2: diabetes results FBM SFM Algorithm 37
38 Oct-10 Dec-10 Feb-11 Apr-11 Jun-11 Aug-11 Oct-11 Dec-11 Feb-12 Apr-12 Jun-12 Aug-12 Oct-12 Dec-12 Feb-13 Apr-13 Jun-13 Aug-13 Oct-13 Dec-13 Feb-14 Apr-14 Jun-14 Aug-14 Oct-14 Dec-14 Feb-15 Apr-15 Jun-15 Aug-15 Oct-15 Dec-15 Feb-16 Apr-16 Jun-16 Aug-16 Number of Incident Diagnoses /1K Eligible Members Condition #2: diabetes results Incidence per 1,000 Eligible Members of Diabetes Definition, October August 2016 FBM SFM Algorithm 0.00 Month - Year ICD-9-CM codes from Algorithm, SFM, and FBM, 0 day washout ICD-10-CM codes from SFM, 0 day washout ICD-10-CM codes from FBM, 0 day washout ICD-10-CM codes from an Algorithm, 0 day washout ICD-10-CM codes from SFM with cluster logic, 0 day washout 38
39 Condition #2: diabetes findings Definition ICD-9-CM Code Count ICD-10-CM Code Count Algorithm SFM, no clusters SFM, clusters FBM SFM and FBM performed similarly even though FBM identified 5x as many codes (254 vs. 51) Many of the additional codes pulled by FBM were highly specific, 7-digit codes that may not be used in practice E.g., E10.37X1, Type I DM with diabetic macular edema, resolved following treatment, right eye Only two clusters which may explain the minimal impact of adding them E.g., E11.65 AND E11.21, Type II DM with hyperglycemia AND Type II DM with diabetic nephropathy 39
40 Condition #5: syncope results 40
41 Oct-10 Dec-10 Feb-11 Apr-11 Jun-11 Aug-11 Oct-11 Dec-11 Feb-12 Apr-12 Jun-12 Aug-12 Oct-12 Dec-12 Feb-13 Apr-13 Jun-13 Aug-13 Oct-13 Dec-13 Feb-14 Apr-14 Jun-14 Aug-14 Oct-14 Dec-14 Feb-15 Apr-15 Jun-15 Aug-15 Oct-15 Dec-15 Feb-16 Apr-16 Jun-16 Aug-16 Number of Diagnoses per 1,000 Eligible Members Condition #5: syncope results 1.60 Incidence per 1,000 Eligible Members by Syncope Definition, 183-Day Washout Month - Year ICD-9-CM codes from Algorithm, SFM, and FBM, 183 day washout ICD-10-CM codes from Algorithm, SFM, and FBM, 183 day washout 41
42 Condition #5: syncope results Incidence of sycope definitions per 1000 eligible members using a 90 day washout, Jan- Mar 2015 vs. Jan-Mar 2016 Outcome definition 2015 ICD-9-CM codes from Algorithm, SFM, and FBM, 90 day washout 2016 ICD-9-CM codes from Algorithm, SFM, and FBM, 90 day washout Number of incident events Number of eligible members Number of memberyears Incidence per 1,000 eligible members (95% CI) 115,757 31,209,998 7,350, ,805 31,955,722 7,612, ICD version used to define Scenario Oct (Nov-10 to Sep- 14) Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr Washout (d) Incidence with respect to Event
43 Condition #5: syncope findings One ICD-9-CM code 780.2: syncope and collapse One ICD-10-CM code R55: syncope and collapse Definition ICD-9-CM Code Count ICD-10-CM Code Count Algorithm* 1 1 SFM 1 1 FBM 1 1 Despite a one-to-one mapping and identical clinical concepts, the incidence in the ICD-10 era was higher than the ICD era Higher incidence in ICD-10 era vs. ICD-9 era currently unexplained *Jette et al
44 Condition #6: drug overdose results 44
45 Oct-10 Dec-10 Feb-11 Apr-11 Jun-11 Aug-11 Oct-11 Dec-11 Feb-12 Apr-12 Jun-12 Aug-12 Oct-12 Dec-12 Feb-13 Apr-13 Jun-13 Aug-13 Oct-13 Dec-13 Feb-14 Apr-14 Jun-14 Aug-14 Oct-14 Dec-14 Feb-15 Apr-15 Jun-15 Aug-15 Oct-15 Dec-15 Feb-16 Apr-16 Jun-16 Aug-16 Number of Diagnoses per 1,000 Eligible Members Condition #6: drug overdose results 0.80 Incidence per 1,000 Eligible Members by Drug Overdose Definition, 183- Day Washout Month - Year ICD-9-CM codes from SFM, 183 day washout ICD-10-CM codes from SFM, 183 day washout ICD-9-CM codes from FBM, 183 day washout ICD-10-CM codes from FBM, 183 day washout 45
46 Condition #6: drug overdose results Incidence of various drug overdose definitions per 1000 eligible members using a 90 day washout, Jan- Mar 2015 vs. Jan-Mar 2016 Outcome definition 2015 ICD-9-CM codes from SFM and FBM, 90 day washout 2016 ICD-9-CM codes from SFM, 90 day washout ICD-9-CM codes from FBM, 90 day washout Number of incident events Number of eligible members Number of memberyears Incidence per 1,000 eligible members (95% CI) 29,200 31,225,534 7,373, ,175 31,974,813 7,638, ,778 31,974,169 7,636,
47 Condition #6: drug overdose findings Definition ICD-9-CM Code Count ICD-10-CM Code Count SFM FBM Same seasonal pattern of drug overdose replicated in ICD-10 era Currently uncertain why seasonality exists Month-year incidence similar between eras, despite >2-fold increase in the number of ICD-10-CM codes used in the SFM and FBM definitions All ICD-10-CM codes had 7-digits 47
48 Condition #6: drug overdose sample codes 48
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