Cải thiện việc đo lường đa bệnh bằng cách sử dụng đánh giá tác động chất lượng cuộc sống theo bệnh cụ thể: độ chính xác dự đoán của chỉ số đồng mắc mới

Health and Quality of Life Outcomes - 2022
Mindy L. McEntee1, Barbara Gandek2, John E. Ware2
1College of Health Solutions, Arizona State University, 500 N. 3rd Street, Phoenix, AZ, 85004-0698, USA
2University of Massachusetts Medical School, Worchester, MA, USA

Tóm tắt

Tóm tắt Đặt vấn đề Việc giải thích các kết quả liên quan đến chất lượng cuộc sống (QOL) trong lĩnh vực sức khỏe cần có những phương pháp cải thiện để kiểm soát ảnh hưởng của nhiều điều kiện mãn tính (MCC). Nghiên cứu này so sánh hệ thống các hiệu ứng của phương pháp truyền thống và cải tiến trong việc tổng hợp MCC để nâng cao độ chính xác của dự đoán các kết quả QOL. Phương pháp Các khảo sát trực tuyến đã tiến hành đo lường các kết quả QOL thể chất (PCS) và tinh thần (MCS), chỉ số đồng mắc Charlson (CCI), một danh sách kiểm tra điều kiện mãn tính mở rộng (CCC), và các đánh giá tác động chất lượng cuộc sống theo bệnh cụ thể (QDIS) trong một mẫu phát triển (N = 5490) của người trưởng thành ở Hoa Kỳ. Kiểm soát cho các biến xã hội và nhân khẩu học, các mô hình hồi quy đã so sánh danh sách kiểm tra 12 và 35 điều kiện, trọng số QOL dựa trên tỷ lệ tử vong so với dân số, và trọng số QOL dân số so với cá nhân. Các phân tích đã được kiểm tra lại trên một mẫu độc lập (N = 1220) đại diện cho dân số trưởng thành chung. Các mô hình đã so sánh các ước tính phương sai được giải thích (R2 đã điều chỉnh) và độ khớp của mô hình (AIC) cho PCS và MCS chung qua các phương pháp tổng hợp tại thời điểm ban đầu và theo dõi sau chín tháng. Kết quả So với các mô hình hồi quy chỉ liên quan đến nhân khẩu học (MCS R2 = 0.08, PCS = 0.09) và các mô hình CCI Charlson (MCS R2 = 0.12, PCS = 0.16), độ phương sai tăng lên đã được giải thích bằng việc sử dụng danh sách CCC gồm 35 mục (MCS R2 = 0.22, PCS = 0.31), trọng số QOL MCS/PCS của dân số (R2 = 0.31–0.38, tương ứng) và trọng số QDIS cá nhân hóa (R2 = 0.33 & 0.42). Độ R2 của mô hình và sự khớp được xác nhận trong quá trình kiểm tra chéo. Kết luận Các kết quả thể chất và tinh thần được dự đoán chính xác hơn khi sử dụng danh sách MCC mở rộng, trọng số QOL của dân số thay vì trọng số CCI dựa trên tỷ lệ tử vong, và trọng số QOL cá nhân hóa thay vì trọng số QOL dân số cho từng điều kiện được báo cáo. Sự kết hợp của danh sách CCC và đánh giá QDIS trong 3 phút (QDIS-MCC) cần được kiểm tra thêm nhằm mục đích dự đoán và giải thích các kết quả QOL bị ảnh hưởng bởi MCC.

Từ khóa


Tài liệu tham khảo

Boersma P, Black L, Ward B. Prevalence of multiple chronic conditions among US adults, 2018. Prev Chronic Dis. 2020;17:E106.

Fortin M, Hudon C, Dubois M-F, Almirall J, Lapointe L, Soubhi H. Comparative assessment of three different indices of multimorbidity for studies on health-related quality of life. Health Qual Life Outcomes. 2005;3:1–7.

Gijsen R, Hoeymans N, Schellevis FG, Ruwaard D, Satariano WA, van den Bos GA. Causes and consequences of comorbidity: a review. J Clin Epidemiol. 2001;54:661–74.

Marengoni A, Angleman S, Melis R, Mangialasche F, Karp A, Garmen A, Meinow B, Fratiglioni L. Aging with multimorbidity: a systematic review of the literature. Ageing Res Rev. 2011;10:430–9.

Salive ME. Multimorbidity in older adults. Epidemiol Rev. 2013;35:75–83.

Phyo AZZ, Freak-Poli R, Craig H, Gasevic D, Stocks NP, Gonzalez-Chica DA, Ryan J. Quality of life and mortality in the general population: a systematic review and meta-analysis. BMC Public Health. 2020;20:1–20.

Fisher KA, Griffith LE, Gruneir A, Upshur R, Perez R, Favotto L, Nguyen F, Markle-Reid M, Ploeg J. Effect of socio-demographic and health factors on the association between multimorbidity and acute care service use: Population-based survey linked to health administrative data. BMC Health Serv Res. 2021;21:1–17.

Fleishman JA, Cohen JW, Manning WG, Kosinski M. Using the SF-12 health status measure to improve predictions of medical expenditures. Med Care. 2006;44:I54–63.

Burgess R, Bishop A, Lewis M, Hill J. Models used for case-mix adjustment of patient reported outcome measures (PROMs) in musculoskeletal healthcare: a systematic review of the literature. Physiotherapy. 2019;105:137–46.

Feinstein AR. An additional basic science for clinical medicine: IV. The development of clinimetrics. Ann Intern Med. 1983;99:843–8.

Carrozzino D, Patierno C, Guidi J, Montiel CB, Cao J, Charlson ME, Christensen KS, Concato J, De las Cuevas C, De Leon J, Clinimetric criteria for patient-reported outcome measures. Psychother Psychosomat 2021;90:222–32.

Tinetti ME, Studenski SA. Comparative effectiveness research and patients with multiple chronic conditions. N Engl J Med. 2011;364:2478–81.

Ahmed S, Berzon RA, Revicki DA, Lenderking WR, Moinpour CM, Basch E, Reeve BB, Wu AW. The use of patient-reported outcomes (PRO) within comparative effectiveness research: Implications for clinical practice and health care policy. Med Care. 2012;50:1060–70.

Maciejewski ML, Bayliss EA. Approaches to comparative effectiveness research in multimorbid populations. Med Care. 2014;52 Suppl 3:S23-30.

Franklin PD, Zheng H, Bond C, Lavallee DC: Translating clinical and patient-reported data to tailored shared decision reports with predictive analytics for knee and hip arthritis. Qual Life Res 2020:1–8.

Rolfson O, Malchau H. The use of patient-reported outcomes after routine arthroplasty: beyond the whys and ifs. Bone Joint J. 2015;97-B:578–81.

Makovski TT, Schmitz S, Zeegers MP, Stranges S, van den Akker M. Multimorbidity and quality of life: systematic literature review and meta-analysis. Ageing Res Rev. 2019;53: 100903.

Bayliss EA, Bonds DE, Boyd CM, Davis MM, Finke B, Fox MH, Glasgow RE, Goodman RA, Heurtin-Roberts S, Lachenmayr S. Understanding the context of health for persons with multiple chronic conditions: moving from what is the matter to what matters. The Ann Fam Med. 2014;12:260–9.

Rojanasopondist P, Galea VP, Connelly JW, Matuszak SJ, Rolfson O, Bragdon CR, Malchau H. What preoperative factors are associated with not achieving a minimum clinically important difference after THA? Findings from an international multicenter study. Clin Orthop Relat Res. 2019;477:1301.

Guyatt GH, Feeny DH, Patrick DL. Measuring health-related quality of life. Ann Intern Med. 1993;118:622–9.

De Vet HC, Terwee CB, Bouter LM. Clinimetrics and psychometrics: two sides of the same coin. J Clin Epidemiol. 2003;56:1146–7.

Charlson ME, Carrozzino D, Guidi J, Patierno C. Charlson comorbidity index: a critical review of clinimetric properties. Psychother Psychosom. 2022;91:8–35.

Patrick DL, Deyo RA. Generic and disease-specific measures in assessing health status and quality of life. Med Care. 1989;27:S217–32.

Ware JE Jr, Gandek B, Guyer R, Deng N. Standardizing disease-specific quality of life measures across multiple chronic conditions: development and initial evaluation of the QOL Disease Impact Scale (QDIS®). Health Qual Life Outcomes. 2016;14:84.

Ware JE Jr, Harrington M, Guyer R, Boulanger R. A system for integrating generic and disease-specific patient-reported outcome (PRO) measures. Mapi Res Inst Patient Rep Outcomes Newslett. 2012;48:1–4.

Ware JE Jr, Richardson MM, Meyer KB, Gandek B. Improving CKD-specific patient-reported measures of health-related quality of life. J Am Soc Nephrol. 2019;30:664–77.

Ware JE Jr, Gandek B, Allison J. The validity of disease-specific quality of life attributions among adults with multiple chronic conditions. Int J Stat Med Res. 2016;5:17–40.

Ware JE Jr, Guyer R. Validity of QOL impact attributions to specific diseases: a multitrait-multimethod comparison. Value Health. 2013;16:A599-600.

McDowell I. Measuring health: a guide to rating scales and questionnaires. USA: Oxford University Press; 2006.

De Groot V, Beckerman H, Lankhorst GJ, Bouter LM. How to measure comorbidity: a critical review of available methods. J Clin Epidemiol. 2003;56:221–9.

Manning Jr WG, Newhouse JP, Ware Jr JE, The status of health in demand estimation; or, beyond excellent, good, fair, poor. In: Economic aspects of health. University of Chicago Press; 1982: 141–184.

Lash TL, Mor V, Wieland D, Ferrucci L, Satariano W, Silliman RA. Methodology, design, and analytic techniques to address measurement of comorbid disease. J Gerontol A Biol Sci Med Sci. 2007;62:281–5.

Brilleman SL, Gravelle H, Hollinghurst S, Purdy S, Salisbury C, Windmeijer F. Keep it simple? Predicting primary health care costs with clinical morbidity measures. J Health Econ. 2014;35:109–22.

Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Clin Epidemiol. 1987;40:373–83.

Elixhauser A, Steiner C, Harris DR, Coffey RM, Comorbidity measures for use with administrative data. Med care 1998;8–27.

Bjorner JB, Kosinski M, Ware JE Jr. The feasibility of applying item response theory to measures of migraine impact: a re-analysis of three clinical studies. Qual Life Res. 2003;12:887–902.

Deng N, Anatchkova MD, Waring ME, Han KT, Ware JE Jr. Testing item response theory invariance of the standardized Quality-of-life Disease Impact Scale (QDIS®) in acute coronary syndrome patients: differential functioning of items and test. Qual Life Res. 2015;24:1809–22.

Feinstein AR. The pre-therapeutic classification of co-morbidity in chronic disease. J Chronic Dis. 1970;23:455–68.

Harrison C, Fortin M, van den Akker M, Mair F, Calderon-Larranaga A, Boland F, Wallace E, Jani B, Smith S. Comorbidity versus multimorbidity: why it matters. London, England: SAGE Publications Sage UK; 2021.

Ware Jr JE, Guyer R, Harrington M, Strom M, Boulanger R: Evaluation of a more comprehensive survey item bank for standardizing disease-specific impact comparisons across chronic conditions. Qual Life Res. 2012; 27–8.

Ware JE Jr, Kosinski M, Bjorner JB, Bayliss MS, Batenhorst A, Dahlöf CG, Tepper S, Dowson A. Applications of computerized adaptive testing (CAT) to the assessment of headache impact. Qual Life Res. 2003;12:935–52.

Goodman RA, Posner SF, Huang ES, Parekh AK, Koh HK. Measuring chronic conditions: imperatives for research, policy, program, and practice. Prev Chronic Dis. 2013;10:E66.

Sangha O, Stucki G, Liang MH, Fossel AH, Katz JN. The self-administered comorbidity questionnaire: a new method to assess comorbidity for clinical and health services research. Arthritis Care Res: Off J Am College Rheumatol. 2003;49:156–63.

Bayliss EA, Ellis JL, Steiner JF. Subjective assessments of comorbidity correlate with quality of life health outcomes: Initial validation of a comorbidity assessment instrument. Health Qual Life Outcomes. 2005;3:51.

Poitras M-E, Fortin M, Hudon C, Haggerty J, Almirall J. Validation of the disease burden morbidity assessment by self-report in a French-speaking population. BMC Health Serv Res. 2012;12:1–6.

Byles JE, D’Este C, Parkinson L, O’Connell R, Treloar C. Single index of multimorbidity did not predict multiple outcomes. J Clin Epidemiol. 2005;58:997–1005.

Ware JE Jr, Kosinski M, Dewey JE, Gandek B. How to score and interpret single-item health status measures: a manual for users of the SF-8 health survey. Lincoln, RI: QualityMetric Incorporated. 2001;15:5.

Ware Jr JE, Kosinski M, Bayliss MS, McHorney CA, Rogers WH, Raczek A, Comparison of methods for the scoring and statistical analysis of SF-36 health profile and summary measures: summary of results from the Medical Outcomes Study. Med Care 1995:AS264–79.

Ware JE Jr, Sherbourne CD. The MOS 36-item short-form health survey (SF-36): I. Conceptual framework and item selection. Med Care. 1992;30:473–83.

Ware JE Jr. SF-36 health survey update. Spine. 2000;25:3130–9.

Parsons VL, Design and estimation for the national health interview survey, 2006–2015. US Department of Health and Human Services, Centers for Disease Control and …; 2014.

United States Department of Health and Human Services Center for Medicare and Medicaid Services: Medicare Health Outcomes Survey (HOS), 2016;1998–2014.

Ware Jr JE, Kosinski M, Bjorner J, Turner-Bowker D, Gandek B, Maruish M,User's Manual for the SF-36v2 Health Survey. Lincoln, RI: QualityMetric Incorporated; 2007.

Ware JE Jr, Bayliss MS, Rogers WH, Kosinski M, Tarlov AR. Differences in 4-year health outcomes for elderly and poor, chronically iII patients treated in HMO and fee-for-service systems: results from the Medical Outcomes Study. JAMA. 1996;276:1039–47.

Alonso J, Ferrer M, Gandek B, Ware JE, Aaronson NK, Mosconi P, Rasmussen NK, Bullinger M, Fukuhara S, Kaasa S. Health-related quality of life associated with chronic conditions in eight countries: results from the International Quality of Life Assessment (IQOLA) Project. Qual Life Res. 2004;13:283–98.

Sechrest L. Incremental validity. In: Jackson DN, Messick S, editors. Problems in human assessment. New York: McGraw-Hill; 1967. p. 368–71.

Haynes SN, Lench HC. Incremental validity of new clinical assessment measures. Psychol Assess. 2003;15:456–66.

Chaudhry S, Jin L, Meltzer D, Use of a self-report-generated Charlson Comorbidity Index for predicting mortality. Med Care 2005;607–15.

Zhang JX, Iwashyna TJ, Christakis NA, The performance of different lookback periods and sources of information for Charlson comorbidity adjustment in Medicare claims. Med Care 1999;1128–39.

CKD-EPI Creatinine Equation [https://www.kidney.org/professionals/KDOQI/gfr_calculator].

Akaike H. A new look at the statistical model identification. IEEE Trans Autom Control. 1974;19:716–23.

Burnham KP, Anderson DR, A practical information-theoretic approach. Model selection and multimodel inference, 2nd ed Springer, New York 2002, 2.

Burnham KP, Anderson DR. Multimodel inference: understanding AIC and BIC in model selection. Sociol Methods Res. 2004;33:261–304.

Mehta HB, Sura SD, Adhikari D, Andersen CR, Williams SB, Senagore AJ, Kuo YF, Goodwin JS. Adapting the Elixhauser comorbidity index for cancer patients. Cancer. 2018;124:2018–25.

Rundell SD, Resnik L, Heagerty PJ, Kumar A, Jarvik JG. Comparing the performance of comorbidity indices in predicting functional status, health-related quality of life, and total health care use in older adults with back pain. J Orthop Sports Phys Ther. 2020;50:143–8.

Dominick KL, Dudley TK, Coffman CJ, Bosworth HB. Comparison of three comorbidity measures for predicting health service use in patients with osteoarthritis. Arthritis Care Res. 2005;53:666–72.

Norman GR, Sloan JA, Wyrwich KW. Interpretation of changes in health-related quality of life: the remarkable universality of half a standard deviation. Med Care. 2003;41:582–92.

Safford MM, Allison JJ, Kiefe CI. Patient complexity: more than comorbidity. The vector model of complexity. J Gen Intern Med. 2007;22:382–90.

Huntley AL, Johnson R, Purdy S, Valderas JM, Salisbury C. Measures of multimorbidity and morbidity burden for use in primary care and community settings: a systematic review and guide. The Ann Family Med. 2012;10:134–41.

Boyd CM, Darer J, Boult C, Fried LP, Boult L, Wu AW. Clinical practice guidelines and quality of care for older patients with multiple comorbid diseases: implications for pay for performance. JAMA. 2005;294:716–24.

Ware JE Jr. Conceptualization and measurement of health-related quality of life: comments on an evolving field. Arch Phys Med Rehabil. 2003;84:S43-51.

Fava GA, Belaise C. A discussion on the role of clinimetrics and the misleading effects of psychometric theory. J Clin Epidemiol. 2005;58:753–6.

Fava GA, Ruini C, Rafanelli C. Psychometric theory is an obstacle to the progress of clinical research. Psychother Psychosom. 2004;73:145–8.

Zyzanski SJ, Perloff E. Clinimetrics and psychometrics work hand in hand. Arch Intern Med. 1999;159:1811–7.

Streiner DL. Clinimetrics vs. psychometrics: an unnecessary distinction. J Clin Epidemiol. 2003;56:1142–5.

Gerteis J, Izrael D, Deitz D, LeRoy L, Ricciardi R, Miller T, Basu J, Multiple chronic conditions chartbook. AHRQ Publications No, Q14–0038. Rockville, MD: Agency for Healthcare Research and Quality 2014, 4:2014.

Ward BW, Schiller JS, Peer reviewed: prevalence of multiple chronic conditions among US adults: estimates from the National Health Interview Survey, 2010. Preventing chronic disease 2013; 10.

Ware Jr JE, Kosinski M, Turner-Bowker DM, Gandeck B, User's Manual for the SF-12v2TM Health Survey. Lincoln, RI: QualityMetric Incorporated 2007.

Guerra SG, Berbiche D, Vasiliadis H-M. Measuring multimorbidity in older adults: comparing different data sources. BMC Geriatr. 2019;19:166.

Corser W, Sikorskii A, Olomu A, Stommel M, Proden C, Holmes-Rovner M. Concordance between comorbidity data from patient self-report interviews and medical record documentation. BMC Health Serv Res. 2008;8:1–9.

Olomu AB, Corser WD, Stommel M, Xie Y, Holmes-Rovner M. Do self-report and medical record comorbidity data predict longitudinal functional capacity and quality of life health outcomes similarly? BMC Health Serv Res. 2012;12:1–9.

van Walraven C, Austin PC, Jennings A, Quan H, Forster AJ, A modification of the Elixhauser comorbidity measures into a point system for hospital death using administrative data. Med Care 2009;626–33.

Ware JE Jr, Gandek B, Franklin P, Lemay C. Cutting edge solutions to improving the efficiency of PRO measurement: from real-data simulations to pilot testing before and after total joint replacement in a national registry. Qual Life Res. 2015;24:60.

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Health and Quality of Life Outcomes

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