123 related articles for article (PubMed ID: 19085957)
1. Predicting in-hospital mortality in patients with cirrhosis: results differ across risk adjustment methods.
Myers RP; Quan H; Hubbard JN; Shaheen AA; Kaplan GG
Hepatology; 2009 Feb; 49(2):568-77. PubMed ID: 19085957
[TBL] [Abstract][Full Text] [Related]
2. Predicting in-hospital mortality in patients undergoing complex gastrointestinal surgery: determining the optimal risk adjustment method.
Grendar J; Shaheen AA; Myers RP; Parker R; Vollmer CM; Ball CG; Quan ML; Kaplan GG; Al-Manasra T; Dixon E
Arch Surg; 2012 Feb; 147(2):126-35. PubMed ID: 22006854
[TBL] [Abstract][Full Text] [Related]
3. Hospital performance reports based on severity adjusted mortality rates in patients with cirrhosis depend on the method of risk adjustment .
Myers RP; Hubbard JN; Shaheen AA; Dixon E; Kaplan GG
Ann Hepatol; 2012; 11(4):526-35. PubMed ID: 22700635
[TBL] [Abstract][Full Text] [Related]
4. Comparison of comorbidity classification methods for predicting outcomes in a population-based cohort of adults with human immunodeficiency virus infection.
Antoniou T; Ng R; Glazier RH; Kopp A; Austin PC
Ann Epidemiol; 2014 Jul; 24(7):532-7. PubMed ID: 24837611
[TBL] [Abstract][Full Text] [Related]
5. Risk adjustment performance of Charlson and Elixhauser comorbidities in ICD-9 and ICD-10 administrative databases.
Li B; Evans D; Faris P; Dean S; Quan H
BMC Health Serv Res; 2008 Jan; 8():12. PubMed ID: 18194561
[TBL] [Abstract][Full Text] [Related]
6. Risk-adjusting acute myocardial infarction mortality: are APR-DRGs the right tool?
Romano PS; Chan BK
Health Serv Res; 2000 Mar; 34(7):1469-89. PubMed ID: 10737448
[TBL] [Abstract][Full Text] [Related]
7. Comparison of risk adjustment methods in patients with liver disease using electronic medical record data.
Xu Y; Li N; Lu M; Dixon E; Myers RP; Jolley RJ; Quan H
BMC Gastroenterol; 2017 Jan; 17(1):5. PubMed ID: 28061757
[TBL] [Abstract][Full Text] [Related]
8. Development and testing of a systemic lupus-specific risk adjustment index for in-hospital mortality.
Ward MM
J Rheumatol; 2000 Jun; 27(6):1408-13. PubMed ID: 10852262
[TBL] [Abstract][Full Text] [Related]
9. Stroke: the Elixhauser Index for comorbidity adjustment of in-hospital case fatality.
Zhu H; Hill MD
Neurology; 2008 Jul; 71(4):283-7. PubMed ID: 18645167
[TBL] [Abstract][Full Text] [Related]
10. Development and initial validation of a risk score for predicting in-hospital and 1-year mortality in patients with hip fractures.
Jiang HX; Majumdar SR; Dick DA; Moreau M; Raso J; Otto DD; Johnston DW
J Bone Miner Res; 2005 Mar; 20(3):494-500. PubMed ID: 15746995
[TBL] [Abstract][Full Text] [Related]
11. The Kaiser Permanente inpatient risk adjustment methodology was valid in an external patient population.
van Walraven C; Escobar GJ; Greene JD; Forster AJ
J Clin Epidemiol; 2010 Jul; 63(7):798-803. PubMed ID: 20004550
[TBL] [Abstract][Full Text] [Related]
12. Comparison of the Elixhauser and Charlson/Deyo methods of comorbidity measurement in administrative data.
Southern DA; Quan H; Ghali WA
Med Care; 2004 Apr; 42(4):355-60. PubMed ID: 15076812
[TBL] [Abstract][Full Text] [Related]
13. Categorized diagnoses and procedure records in an administrative database improved mortality prediction.
Yamana H; Matsui H; Sasabuchi Y; Fushimi K; Yasunaga H
J Clin Epidemiol; 2015 Sep; 68(9):1028-35. PubMed ID: 25596112
[TBL] [Abstract][Full Text] [Related]
14. [Quality of data or quality of care? Comparison of diverse standarization methods by clinical severity, based on the discharge form, in the analysis of hospital mortality].
Ciccone G; Bertero D; Bruno A; Canavese C; Ciccarelli E; Ivaldi C; Pacitti A; Rosato R; Arione R
Epidemiol Prev; 1999; 23(4):286-93. PubMed ID: 10730469
[TBL] [Abstract][Full Text] [Related]
15. Disease-Specific Trends of Comorbidity Coding and Implications for Risk Adjustment in Hospital Administrative Data.
Nimptsch U
Health Serv Res; 2016 Jun; 51(3):981-1001. PubMed ID: 26741707
[TBL] [Abstract][Full Text] [Related]
16. Development and validation of a comorbidity scoring system for patients with cirrhosis.
Jepsen P; Vilstrup H; Lash TL
Gastroenterology; 2014 Jan; 146(1):147-56; quiz e15-6. PubMed ID: 24055278
[TBL] [Abstract][Full Text] [Related]
17. Benchmarking trauma centers on mortality alone does not reflect quality of care: implications for pay-for-performance.
Hashmi ZG; Schneider EB; Castillo R; Haut ER; Zafar SN; Cornwell EE; Mackenzie EJ; Latif A; Haider AH
J Trauma Acute Care Surg; 2014 May; 76(5):1184-91. PubMed ID: 24747447
[TBL] [Abstract][Full Text] [Related]
18. [Evaluation of the capacity of the APR-DRG classification system to predict hospital mortality].
De Marco MF; Lorenzoni L; Addari P; Nante N
Epidemiol Prev; 2002; 26(4):183-90. PubMed ID: 12408005
[TBL] [Abstract][Full Text] [Related]
19. Predicting who dies depends on how severity is measured: implications for evaluating patient outcomes.
Iezzoni LI; Ash AS; Shwartz M; Daley J; Hughes JS; Mackiernan YD
Ann Intern Med; 1995 Nov; 123(10):763-70. PubMed ID: 7574194
[TBL] [Abstract][Full Text] [Related]
20. Clinical redesign using all patient refined diagnosis related groups.
Sedman AB; Bahl V; Bunting E; Bandy K; Jones S; Nasr SZ; Schulz K; Campbell DA
Pediatrics; 2004 Oct; 114(4):965-9. PubMed ID: 15466092
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
