Appropriateness of ICD-coded Diagnostic Inpatient Hospital Discharge Data for Medical Practice Assessment^[*]

Summary

Objectives: We performed a systematic review to investigate the quality of diagnostic hospital discharge data (DHDD) in order to gain insight in the usefulness of these data for medical practice assessment. We investigated the methods used to evaluate data quality, factors that determine data quality and its consequences for medical practice assessment.

Methods: We selected studies in which both completeness (or sensitivity: SENS) and correctness (or positive predictive value: PPV) were measured. We used the random-effects model to calculate mean SENS and PPV and to explore the effect of a number of covariates.

Results: The 101 included studies were very heterogeneous. We distinguished six typical study designs. We found a mean SENS of 0.67 (95%CI: 0.62– 0.73) and PPV of 0.76 (95%CI: 0.73– 0.79); SENS was significantly lower for comorbidity and complication studies than for some single disease studies. PPV was significantly higher for Scandinavian countries than for other countries. Recoding compared to re-abstracting of the medical record as a gold standard gave a significantly lower PPV. Diagnostic data were considered appropriate by the authors of the studies for quality of care purposes when both SENS and PPV were at least 0.85. Only 13% of the studies fulfilled this criterion.

Conclusions: Variability in quality of care between settings can easily be overshadowed by variability in data quality. However, the use of DHDD by physicians to evaluate their own medical practice may be useful. But only if physicians are willing to critically interpret the meaning of the information for their medical practice assessment.

Keywords

ICD - diagnostic data - discharge data - sensitivity - positive predictive value

^* Supplementary online material published on our website www.methods-online.com

• References

• 1 De Coster C, Quan H, Finlayson A, Gao M, Halfon P, Humphries KH. et al Identifying priorities in methodological research using ICD-9-CM and ICD-10 administrative data: report from an international consortium. BMC Health Serv Res 2006; 6: 77
• 2 National Center for Health Statistics. International Classification of Diseases, 9th Revision, Clinical Modification. 6th edition. Hayattsville, Maryland: NCHS; 2003.
• 3 World Health Organization. International Statistical Classification of Diseases and Related Health Problems; Tenth Revision. Geneva: World Health Organization; 1992.
• 4 Cimino JJ. High-quality, standard, controlled healthcare terminologies come of age. Methods Inf Med 2011; 50 (02) 101-104.
• 5 Demlo LK, Campbell PM, Brown SS. Reliability of information abstracted from patients’ medical records. Med Care 1978; 16 (12) 995-1005.
• 6 Prins H, Kruisinga FH, Buller HA, Zwetsloot-Schonk JH. Availability and usability of data for medical practice assessment. Int J Qual Health Care 2002; 14 (02) 127-137.
• 7 McKee M. Routine data: a resource for clinical audit?. Quality in Health Care 1993; [phone].
• 8 Safran C, Chute CG. Exploration and exploitation of clinical databases. International Journal of Bio-Medical Computing 1995; 39 (01) 151-156.
• 9 Wyatt J. Acquisition and use of clinical data for audit and research. Journal of Evaluation in Clinical Practice 1995; 1 (01) 15-27.
• 10 Iezzoni LI. Assessing quality using administrative data. Annals of Internal Medicine 1997; 127 (08) Pt 2 666-674.
• 11 Coory M, Thompson B, Baade P, Fritschi L. Utility of routine data sources for feedback on the quality of cancer care: an assessment based on clinical practice guidelines. BMC Health Serv Res 2009; 9: 84
• 12 Romano PS. Can administrative data be used to compare the quality of health care?. Med Care Rev 1993; 50 (04) 451-477.
• 13 Prins H, Buller HA, Zwetsloot-Schonk JH. Effect of discharge letter-linked diagnosis registration on data quality. Int J Qual Health Care 2000; 12 (01) 47-57.
• 14 Hogan WR, Wagner MM. Accuracy of data in computer-based patient records. J Am Med Inform Assoc 1997; 4 (05) 342-355.
• 15 Pine M, Jordan HS, Elixhauser A, Fry DE, Hoaglin DC, Jones B. et al Modifying ICD-9-CM coding of secondary diagnoses to improve risk-adjustment of inpatient mortality rates. Medical Decision Making 2009; 29 (01) 69-81.
• 16 Quan H, Parsons GA, Ghali WA. Assessing accuracy of diagnosis-type indicators for flagging complications in administrative data. J Clin Epidemiol 2004; 57 (04) 366-372.
• 17 Vet de HCW, Eisinga A, Riphagen II, Aertgeerts B, Pewsner D. Searching for Studies. In Deeks JJ, Bossuyt PM, Gatsonis C. editors Cochrane Hand-book for Systematic Reviews of Diagnostic Test Accuracy. 0.4 ed: The Cochrane Collaboration. 2008.
• 18 DerSimonian R, Laird N. Meta-analysis in clinical trials. Control Clin Trials 1986; 7 (03) 177-188.
• 19 Schol AW. Quality of Diagnostic Coding as Part of Routine Discharge Data: A Systematic Review. Master Thesis, Amsterdam: Academic Medical Center at the University of Amsterdam. 2003
• 20 Borenstein M, Hedges L, Rothstein H. Meta-analysis: fixed effect vs. random effects. 2007 (updated 2007; cited May 20, 2012). Available from http://www.sciencedownload.net/demodownload/Fixed%20effect%20vs.%20random%20effects.pdf.
• 21 Madsen M, Davidsen M, Rasmussen S, Abildstrom SZ, Osler M. The validity of the diagnosis of acute myocardial infarction in routine statistics: a comparison of mortality and hospital discharge data with the Danish MONICA registry. J Clin Epidemiol 2003; 56 (02) 124-130.
• 22 Demlo LK, Campbell PM. Improving hospital discharge data: lessons from the National Hospital Discharge Survey. Med Care 1981; 19 (10) 1030-1040.
• 23 Cleary R, Beard R, Coles J, Devlin B, Hopkins A, Schumacher D. et al Comparative hospital databases: value for management and quality. Qual Health Care 1994; 3 (01) 3-10.
• 24 Dixon J, Sanderson C, Elliott P, Walls P, Jones J, Petticrew M. Assessment of the reproducibility of clinical coding in routinely collected hospital activity data: a study in two hospitals. J Public Health Med 1998; 20 (01) 63-69.
• 25 Doremus HD, Michenzi EM. Data quality. An illustration of its potential impact upon a diagnosis-related group’s case mix index and reimbursement. Med Care 1983; 21 (10) 1001-1011.
• 26 Elkin PL, Ruggieri AP, Brown SH, Buntrock J, Bauer BA, Wahner-Roedler D. et al A randomized controlled trial of the accuracy of clinical record retrieval using SNOMED-RT as compared with ICD9-CM. Proc AMIA Symp. 2001: 159-163.
• 27 Fisher ES, Whaley FS, Krushat WM, Malenka DJ, Fleming C, Baron JA. et al The accuracy of Medicare’s hospital claims data: progress has been made, but problems remain. Am J Public Health 1992; 82 (02) 243-248.
• 28 Frohnert BK, Lussky RC, Alms MA, Mendelsohn NJ, Symonik DM, Falken MC. Validity of hospital discharge data for identifying infants with cardiac defects. J Perinatol 2005; 25 (11) 737-742.
• 29 Kashner TM. Agreement between administrative files and written medical records: a case of the Department of Veterans Affairs. Med Care 1998; 36 (09) 1324-1336.
• 30 Mehanni M, Loughman E, Allwright SP, Prichard J. The hospital in-patient enquiry scheme: a study of data accuracy and capture. Ir Med J 1995; 88 (01) 24-26.
• 31 Preen DB, Holman CD, Lawrence DM, Baynham NJ, Semmens JB. Hospital chart review provided more accurate comorbidity information than data from a general practitioner survey or an administrative database. J Clin Epidemiol 2004; 57 (12) 1295-1304.
• 32 Quan H, Parsons GA, Ghali WA. Validity of information on comorbidity derived rom ICD-9-CCM administrative data. Med Care 2002; 40 (08) 675-685.
• 33 Ricketts D, Hartley J, Harries W, Hitchin D. Who should code orthopaedic inpatients? A comparison of junior hospital doctors and coding clerks. Ann R Coll Surg Engl 1993; 75 (Suppl. 06) Suppl 203-206.
• 34 Smith MW. Hospital discharge diagnoses: how accurate are they and their international classification of diseases (ICD) codes?. N Z Med J 1989; 102 (876) 507-508.
• 35 van Walraven C, Demers SV. Coding diagnoses and procedures using a high-quality clinical database instead of a medical record review. J Eval Clin Pract 2001; 7 (03) 289-297.
• 36 Yeoh C, Davies H. Clinical coding: completeness and accuracy when doctors take it on. BMJ 1993; 306 6883 972
• 37 Aronsky D, Haug PJ, Lagor C, Dean NC. Accuracy of administrative data for identifying patients with pneumonia. Am J Med Qual 2005; 20 (06) 319-328.
• 38 Beghi E, Logroscino G, Micheli A, Millul A, Perini M, Riva R. et al Validity of hospital discharge diagnoses for the assessment of the prevalence and incidence of amyotrophic lateral sclerosis. Amyotroph Lateral Scler Other Motor Neuron Disord 2001; 2 (02) 99-104.
• 39 Goff Jr. DC, Pandey DK, Chan FA, Ortiz C, Nichaman MZ. Congestive heart failure in the United States: is there more than meets the I(CD code)? The Corpus Christi Heart Project. Arch Intern Med 2000; 160 (02) 197-202.
• 40 Johnson RL, Gabella BA, Gerhart KA, McCray J, Menconi JC, Whiteneck GG. Evaluating sources of traumatic spinal cord injury surveillance data in Colorado. Am J Epidemiol 1997; 146 (03) 266-272.
• 41 Khand AU, Shaw M, Gemmel I, Cleland JG. Do discharge codes underestimate hospitalisation due to heart failure? Validation study of hospital discharge coding for heart failure. Eur J Heart Fail 2005; 7 (05) 792-797.
• 42 Leone MA, Capponi A, Varrasi C, Tarletti R, Monaco F. Accuracy of the ICD-9 codes for identifying TIA and stroke in an Italian automated database. Neurol Sci 2004; 25 (05) 281-288.
• 43 Lofthus CM, Cappelen I, Osnes EK, Falch JA, Kristiansen IS, Medhus AW. et al Local and national electronic databases in Norway demonstrate a varying degree of validity. J Clin Epidemiol 2005; 58 (03) 280-285.
• 44 Mascioli SR, Jacobs Jr. DR, Kottke TE. Diagnostic criteria for hospitalized acute myocardial infarction: the Minnesota experience. Int J Epidemiol 1989; 18 (01) 76-83.
• 45 Movig KL, Leufkens HG, Lenderink AW, Egberts AC. Validity of hospital discharge International Classification of Diseases (ICD) codes for identifying patients with hyponatremia. J Clin Epidemiol 2003; 56 (06) 530-535.
• 46 Pladevall M, Goff DC, Nichaman MZ, Chan F, Ramsey D, Ortiz C. et al An assessment of the validity of ICD Code 410 to identify hospital admissions for myocardial infarction: The Corpus Christi Heart Project. Int J Epidemiol 1996; 25 (05) 948-952.
• 47 Rodella S, Picoco C, Stanzial F, Turazza M, Donati LF. Cancer registration: a feasibility study in northern Italy. The Collaborative Group of Pathologists for Cancer Registration in Verona. Eur J Cancer 1994; 30 A [phone].
• 48 Sapsford RJ, Lawrance RA, Dorsch MF, Das R, Jackson BM, Morrell C. et al Identifying acute myocardial infarction: effects on treatment and mortality, and implications for National Service Framework audit. Qjm 2003; 96 (03) 203-209.
• 49 Segal JB, Powe NR. Accuracy of identification of patients with immune thrombocytopenic purpura through administrative records: a data validation study. Am J Hematol 2004; 75 (01) 12-17.
• 50 Vestergaard M, Obel C, Henriksen TB, Christensen J, Madsen KM, Ostergaard JR. et al The Danish National Hospital Register is a valuable study base for epidemiologic research in febrile seizures. J Clin Epidemiol 2006; 59 (01) 61-66.
• 51 Austin PC, Daly PA, Tu JV. A multicenter study of the coding accuracy of hospital discharge administrative data for patients admitted to cardiac care units in Ontario. Am Heart J 2002; 144 (02) 290-296.
• 52 Bogliun G, Beghi E. Validity of hospital discharge diagnoses for public health surveillance of the Guillain-Barre syndrome. Neurol Sci 2002; 23 (03) 113-117.
• 53 Boyle CA, Dobson AJ. The accuracy of hospital records and death certificates for acute myocardial infarction. Aust N Z J Med 1995; 25 (04) 316-323.
• 54 Chancellor AM, Swingler RJ, Fraser H, Clarke JA, Warlow CP. Utility of Scottish morbidity and mortality data for epidemiological studies of motor neuron disease. J Epidemiol Community Health 1993; 47 (02) 116-120.
• 55 Davenport RJ, Dennis MS, Warlow CP. The accuracy of Scottish Morbidity Record (SMR1) data for identifying hospitalised stroke patients. Health Bull (Edinb) 1996; 54 (05) 402-405.
• 56 Ellekjaer H, Holmen J, Kruger O, Terent A. Identification of incident stroke in Norway: hospital discharge data compared with a population-based stroke register. Stroke 1999; 30 (01) 56-60.
• 57 Graff Stensballe L, Kristensen K, Nielsen J, Aaby P. Diagnosis coding in The Danish National Patient Registry for respiratory syncytial virus infections. Scand J Infect Dis 2005; 37 (10) 747-752.
• 58 Hexter AC, Harris JA, Roeper P, Croen LA, Krueger P, Gant D. Evaluation of the hospital discharge diagnoses index and the birth certificate as sources of information on birth defects. Public Health Rep 1990; 105 (03) 296-307.
• 59 Hunt JP, Baker CC, Fakhry SM, Rutledge RR, Ransohoff D, Meyer AA. Accuracy of administrative data in trauma. Surgery 1999; 126 (02) 191-197.
• 60 Hunt JP, Cherr GS, Hunter C, Wright MJ, Wang YZ, Steeb G. et al Accuracy of administrative data in trauma: splenic injuries as an example. J Trauma 2000; 49 (04) 679-686. discussion 86–88
• 61 Joakimsen RM, Fonnebo V, Sogaard AJ, Tollan A, Stormer J, Magnus JH. The Tromso study: registration of fractures, how good are self-reports, a computerized radiographic register and a discharge register?. Osteoporos Int 2001; 12 (12) 1001-1005.
• 62 Kahn LH, Blustein J, Arons RR, Yee R, Shea S. The validity of hospital administrative data in monitoring variations in breast cancer surgery. Am J Public Health 1996; 86 (02) 243-245.
• 63 Kennedy GT, Stern MP, Crawford MH. Miscoding of hospital discharges as acute myocardial infarction: implications for surveillance programs aimed at elucidating trends in coronary artery disease. Am J Cardiol 1984; 53 (08) 1000-1002.
• 64 Madsen KM, Schonheyder HC, Kristensen B, Nielsen GL, Sorensen HT. Can hospital discharge diagnosis be used for surveillance of bacteremia? A data quality study of a Danish hospital discharge registry. Infect Control Hosp Epidemiol 1998; 19 (03) 175-180.
• 65 Mahonen M, Salomaa V, Brommels M, Molarius A, Miettinen H, Pyorala K. et al The validity of hospital discharge register data on coronary heart disease in Finland. Eur J Epidemiol 1997; 13 (04) 403-415.
• 66 Martin CA, Hobbs MS, Armstrong BK. Identification of non-fatal myocardial infarction through hospital discharge data in Western Australia. J Chronic Dis 1987; 40 (12) 1111-1120.
• 67 McClish DK, Penberthy L, Whittemore M, Newschaffer C, Woolard D, Desch CE. et al Ability of Medicare claims data and cancer registries to identify cancer cases and treatment. Am J Epidemiol 1997; 145 (03) 227-233.
• 68 Nielsen GL, Sorensen HT, Pedersen AB, Sabroe S. Analyses of data quality in registries concerning diabetes mellitus - a comparison between a population based hospital discharge and an insulin prescription registry. J Med Syst 1996; 20 (01) 1-10.
• 69 Norgaard M, Skriver MV, Gregersen H, Pedersen G, Schonheyder HC, Sorensen HT. The data quality of haematological malignancy ICD-10 diagnoses in a population-based hospital discharge registry. Eur J Cancer Prev 2005; 14 (03) 201-206.
• 70 Pajunen P, Koukkunen H, Ketonen M, Jerkkola T, Immonen-Raiha P, Karja-Koskenkari P. et al The validity of the Finnish Hospital Discharge Register and Causes of Death Register data on coronary heart disease. Eur J Cardiovasc Prev Rehabil 2005; 12 (02) 132-137.
• 71 Rosamond WD, Chambless LE, Sorlie PD, Bell EM, Weitzman S, Smith JC. et al Trends in the sensitivity, positive predictive value, false-positive rate, and comparability ratio of hospital discharge diagnosis codes for acute myocardial infarction in four US communities, 1987–2000. Am J Epidemiol 2004; 160 (12) 1137-1146.
• 72 Tetsche MS, Norgaard M, Skriver MV, Andersen ES, Lash TL, Sorensen HT. Accuracy of ovarian cancer ICD-10 diagnosis in a Danish population-based hospital discharge registry. Eur J Gynaecol Oncol 2005; 26 (03) 266-270.
• 73 Toniolo P, Pisani P, Vigano C, Gatta G, Repetto F. Estimating incidence of cancer from a hospital discharge reporting system. Rev Epidemiol Santé Publique 1986; 34 (01) 23-30.
• 74 Warren JL, Feuer E, Potosky AL, Riley GF, Lynch CF. Use of Medicare hospital and physician data to assess breast cancer incidence. Med Care 1999; 37 (05) 445-456.
• 75 Guevara RE, Butler JC, Marston BJ, Plouffe JF, File Jr. TM, Breiman RF. Accuracy of ICD-9-CM codes in detecting community-acquired pneumococcal pneumonia for incidence and vaccine efficacy studies. Am J Epidemiol 1999; 149 (03) 282-289.
• 76 Marrie TJ, Durant H, Sealy E. Pneumonia-the quality of medical records data. Med Care 1987; 25 (01) 20-24.
• 77 McNaughton H, Wadsworth K. Assessing the accuracy of hospital admission and discharge diagnosis of traumatic brain injury in a New Zealand hospital. N Z Med J 2000; 113 1110 184-186.
• 78 Moro ML, Morsillo F. Can hospital discharge diagnoses be used for surveillance of surgical-site infections?. J Hosp Infect 2004; 56 (03) 239-241.
• 79 Pietila K, Tenkanen L, Manttari M, Manninen V. How to define coronary heart disease in register-based follow-up studies: experience from the Helsinki Heart Study. Ann Med 1997; 29 (03) 253-259.
• 80 Porta M, Costafreda S, Malats N, Guarner L, Soler M, Gubern JM. et al Validity of the hospital discharge diagnosis in epidemiologic studies of biliopancreatic pathology. PANKRAS II Study Group. Eur J Epidemiol 2000; 16 (06) 533-541.
• 81 Ragnarson Tennvall G, Apelqvist J, Eneroth M. The inpatient care of patients with diabetes mellitus and foot ulcers. A validation study of the correspondence between medical records and the Swedish Inpatient Registry with the consequences for cost estimations. J Intern Med 2000; 248 (05) 397-405.
• 82 Rinaldi R, Vignatelli L, Galeotti M, Azzimondi G, de Carolis P. Accuracy of ICD-9 codes in identifying ischemic stroke in the General Hospital of Lugo di Romagna (Italy). Neurol Sci 2003; 24 (02) 65-69.
• 83 Birman-Deych E, Waterman AD, Yan Y, Nilasena DS, Radford MJ, Gage BF. Accuracy of ICD-9-CM codes for identifying cardiovascular and stroke risk factors. Med Care 2005; 43 (05) 480-485.
• 84 Geraci JM, Ashton CM, Kuykendall DH, Johnson ML, Wu L. International Classification of Diseases, 9th Revision, Clinical Modification codes in discharge abstracts are poor measures of complication occurrence in medical inpatients. Med Care 1997; 35 (06) 589-602.
• 85 Hsu VP, Staat MA, Roberts N, Thieman C, Bernstein I D, Bresee J. et al Use of active surveillance to validate international classification of diseases code estimates of rotavirus hospitalizations in children. Pediatrics 2005; 115 (01) 78-82.
• 86 Kohli HS, Knill-Jones RP. How accurate are SMR1 (Scottish Morbidity Record 1) data?. Health Bull (Edinb) 1992; 50 (01) 14-23. discussion 9–31
• 87 Lee DS, Donovan L, Austin PC, Gong Y, Liu PP, Rouleau JL. et al Comparison of coding of heart failure and comorbidities in administrative and clinical data for use in outcomes research. Med Care 2005; 43 (02) 182-188.
• 88 Lydon-Rochelle MT, Holt VL, Cardenas V, Nelson JC, Easterling TR, Gardella C. et al The reporting of pre-existing maternal medical conditions and complications of pregnancy on birth certificates and in hospital discharge data. Am J Obstet Gynecol 2005; 193 (01) 125-134.
• 89 Mears SC, Bawa M, Pietryak P, Jones LC, Rajadhyaksha AD, Hungerford DS. et al Coding of diagnoses, comorbidities, and complications of total hip arthroplasty. Clin Orthop Relat Res 2002; (402) 164-170.
• 90 Mont MA, Mears SC, Jones LC, Rajadhyaksha AD, Krackow AM, Bawa M. et al Is coding of diagnoses, comorbidities, and complications in total knee arthroplasty accurate?. J Arthroplasty 2002; 17 (06) 767-772.
• 91 Parrish KM, Holt VL, Connell FA, Williams B, LoGerfo JP. Variations in the accuracy of obstetric procedures and diagnoses on birth records in Washington State, 1989. Am J Epidemiol 1993; 138 (02) 119-127.
• 92 Powell H, Lim LL, Heller RF. Accuracy of administrative data to assess comorbidity in patients with heart disease. an Australian perspective. J Clin Epidemiol 2001; 54 (07) 687-693.
• 93 Romano PS, Mark DH. Bias in the coding of hospital discharge data and its implications for quality assessment. Med Care 1994; 32 (01) 81-90.
• 94 Thomas SK, Brooks SE, Mullins CD, Baquet CR, Merchant S. Use of ICD-9 coding as a proxy for stage of disease in lung cancer. Pharmacoepidemiol Drug Saf 2002; 11 (08) 709-713.
• 95 Azimuddin K, Rosen L, Reed 3rd JF. Computerized assessment of complications after colorectal surgery: is it valid?. Dis Colon Rectum 2001; 44 (04) 500-505.
• 96 Hawker GA, Coyte PC, Wright JG, Paul JE, Bombardier C. Accuracy of administrative data for assessing outcomes after knee replacement surgery. J Clin Epidemiol 1997; 50 (03) 265-273.
• 97 Ballaro A, Oliver S, Emberton M. Do we do what they say we do? coding errors in urology. BJU Int 2000; 85 (04) 389-391.
• 98 Edouard L, Rawson NS. Reliability of the recording of hysterectomy in the Saskatchewan health care system. Br J Obstet Gynaecol 1996; 103 (09) 891-897.
• 99 Best WR, Khuri SF, Phelan M, Hur K, Henderson WG, Demakis JG. et al Identifying patient preoperative risk factors and postoperative adverse events in administrative databases: results from the Department of Veterans Affairs National Surgical Quality Improvement Program. J Am Coll Surg 2002; 194 (03) 257-266.
• 100 Faciszewski T, Broste SK, Fardon D. Quality of data regarding diagnoses of spinal disorders in administrative databases. A multicenter study. J Bone Joint Surg Am 1997; 79 (10) 1481-1488.
• 101 Cadwallader HL, Toohey M, Linton S, Dyson A, Riley TV. A comparison of two methods for identifying surgical site infections following orthopaedic surgery. J Hosp Infect 2001; 48 (04) 261-266.
• 102 Faciszewski T, Johnson L, Noren C, Smith MD. Administrative databases’ complication coding in anterior spinal fusion procedures. What does it mean?. Spine 1995; 20 (16) 1783-1788.
• 103 Flum DR, Koepsell TD. Evaluating diagnostic accuracy in appendicitis using administrative data. J Surg Res 2005; 123 (02) 257-261.
• 104 Fox KM, Reuland M, Hawkes WG, Hebel JR, Hudson J, Zimmerman SI. et al Accuracy of medical records in hip fracture. J Am Geriatr Soc 1998; 46 (06) 745-750.
• 105 Humphries KH, Rankin JM, Carere RG, Buller CE, Kiely FM, Spinelli JJ. Co-morbidity data in outcomes research: are clinical data derived from administrative databases a reliable alternative to chart review?. J Clin Epidemiol 2000; 53 (04) 343-349.
• 106 Jollis JG, Ancukiewicz M, DeLong ER, Pryor DB, Muhlbaier LH, Mark DB. Discordance of databases designed for claims payment versus clinical information systems. Implications for outcomes research. Ann Intern Med 1993; 119 (08) 844-850.
• 107 Kjaergaard J, Clemmensen IH, Thomsen BL, Storm HH. Validity of diagnoses of and operations for nonmalignant gynecological conditions in the Danish National Hospital Registry. J Clin Epidemiol 2002; 55 (02) 137-142.
• 108 Korst LM, Gregory KD, Gornbein JA. Elective primary caesarean delivery: accuracy of administrative data. Paediatr Perinat Epidemiol 2004; 18 (02) 112-119.
• 109 Li J, Morlet N, Semmens J, Gavin A, Ng J. Coding accuracy for endophthalmitis diagnosis and cataract procedures in Western Australia. The Endophthalmitis Population Study of Western Australia (EPSWA): second report. Ophthalmic Epidemiol 2003; 10 (02) 133-145.
• 110 Losina E, Barrett J, Baron JA, Katz JN. Accuracy of Medicare claims data for rheumatologic diagnoses in total hip replacement recipients. J Clin Epidemiol 2003; 56 (06) 515-519.
• 111 Lydon-Rochelle MT, Holt VL, Nelson JC, Cardenas V, Gardella C, Easterling TR. et al Accuracy of reporting maternal in-hospital diagnoses and intrapartum procedures in Washington State linked birth records. Paediatr Perinat Epidemiol 2005; 19 (06) 460-471.
• 112 Malenka DJ, McLerran D, Roos N, Fisher ES, Wennberg JE. Using administrative data to describe casemix: a comparison with the medical record. J Clin Epidemiol 1994; 47 (09) 1027-1032.
• 113 Romano PS, Chan BK, Schembri ME, Rainwater JA. Can administrative data be used to compare postoperative complication rates across hospitals?. Med Care 2002; 40 (10) 856-867.
• 114 Romano PS, Schembri ME, Rainwater JA. Can administrative data be used to ascertain clinically significant postoperative complications?. Am J Med Qual 2002; 17 (04) 145-154.
• 115 Romano PS, Yasmeen S, Schembri ME, Keyzer JM, Gilbert WM. Coding of perineal lacerations and other complications of obstetric care in hospital discharge data. Obstet Gynecol 2005; 106 (04) 717-725.
• 116 Roos LL, Sharp SM, Cohen MM. Comparing clinical information with claims data: some similarities and differences. J Clin Epidemiol 1991; 44 (09) 881-888.
• 117 Taylor LK, Travis S, Pym M, Olive E, Henderson-Smart DJ. How useful are hospital morbidity data for monitoring conditions occurring in the perinatal period?. Aust N Z J Obstet Gynaecol 2005; 45 (01) 36-41.
• 118 Weintraub WS, Deaton C, Shaw L, Mahoney E, Morris DC, Saunders C. et al Can cardiovascular clinical characteristics be identified and outcome models be developed from an in-patient claims database?. Am J Cardiol 1999; 84 (02) 166-169.
• 119 Mikolajczyk K, Szabatin M, Rudnicki P, Grodzki M, Burger C. A JAVA environment for medical image data analysis: initial application for brain PET quantitation. Med Inform (Lond) 1998; 23 (03) 207-214.
• 120 Albarran JW, Clarke BA, Crawford J. ’It was not chest pain really, I can’t explain it!’ An exploratory study on the nature of symptoms experienced by women during their myocardial infarction. J Clin Nurs 2007; 16 (07) 1292-1301.
• 121 Correa-de-Araujo R, Stevens B, Moy E, Nilasena D, Chesley F, McDermott K. Gender differences across racial and ethnic groups in the quality of care for acute myocardial infarction and heart failure associated with comorbidities. Womens Health Issues 2006; 16 (02) 44-55.
• 122 Macaskill P, Gatsonis C, Deeks JJ, Harbord RM, Takwoingi Y. Analysing and Presenting Results. In Deeks JJ, Bossuyt PM, Gatsonis C. editors Cochrane Handbook for Systematic Reviews of Diagnostic Test Accuracy: The Cochrane Collaboration. 2010.
• 123 DerSimonian R, Kacker R. Random-effects model for meta-analysis of clinical trials: an update. Contemp Clin Trials 2007; 28 (02) 105-114.
• 124 Glas AS, Lijmer JG, Prins MH, Bonsel GJ, Bossuyt PM. The diagnostic odds ratio: a single indicator of test performance. J Clin Epidemiol 2003; 56 (11) 1129-1135.
• 125 Reitsma JB, Glas AS, Rutjes AW, Scholten RJ, Bossuyt PM, Zwinderman AH. Bivariate analysis of sensitivity and specificity produces informative summary measures in diagnostic reviews. J Clin Epidemiol 2005; 58 (10) 982-990.
• 126 Reitsma JB, Rutjes AWS, Whiting P, Vlassov VV, Leeflang MMG, Deeks JJ. Assessing methodological quality. In Deeks JJ, Bossuyt PM, Gatsonis C. editors Cochrane Handbook for Systematic Reviews of Diagnostic Test Accuracy. 1.0.0 ed: The Cochrane Collaboration. 2009.
• 127 Leibson CL, Naessens JM, Brown RD, Whisnant JP. Accuracy of hospital discharge abstracts for identifying stroke. Stroke 1994; 25 (12) 2348-2355.
• 128 Quan H, Li B, Saunders LD, Parsons GA, Nilsson CI, Alibhai A. et al Assessing validity of ICD-9-CM and ICD-10 administrative data in recording clinical conditions in a unique dually coded database. Health Services Research 2008; 43 (04) 1424-1441.
• 129 Lijmer JG, Mol BW, Heisterkamp S, Bonsel GJ, Prins MH, van der Meulen JH. et al Empirical evidence of design-related bias in studies of diagnostic tests. JAMA 1999; 282 (11) 1061-1066.