How do patients with diabetes report their comorbidity? Comparison with administrative data

Hoffmann, Jonas; Haastert, Burkhard; Brüne, Manuela; Kaltheuner, Matthias; Begun, Alexander; Chernyak, Nadja; Icks, Andrea

doi:10.2147/clep.s135872

Cited by 10 publications

(8 citation statements)

References 27 publications

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“…Six studies have analysed comorbidity concordance between patient self-reports and administrative data [16][17][18][19][20][21].. As with our study, most found high level of concordance for diabetes (four of six studies had kappa scores ranging from 0.70 and 0.83) [16][17][18]20]. There was a variable level of agreement found for both myocardial infarct (kappa values between 0.14 and 0.75) [17][18][19] and asthma (kappa values between 0.11 and 0.66) [17,18,20]. Two out of the six studies which compared concordance of heart disease demonstrated only fair concordance (kappa values of 0.36 and 0.38) [16,20].…”

Section: Discussionmentioning

confidence: 99%

(Dis)concordance of comorbidity data and cancer status across administrative datasets, medical charts, and self-reports

Sheriffdeen

Millar

Martin

et al. 2020

BMC Health Serv Res

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Background Benchmarking outcomes across settings commonly requires risk-adjustment for co-morbidities that must be derived from extant sources that were designed for other purposes. A question arises as to the extent to which differing available sources for health data will be concordant when inferring the type and severity of co-morbidities, how close are these to the “truth”. We studied the level of concordance for same-patient comorbidity data extracted from administrative data (coded from International Classification of Diseases, Australian modification,10th edition [ICD-10 AM]), from the medical chart audit, and data self-reported by men with prostate cancer who had undergone a radical prostatectomy. Methods We included six hospitals (5 public and 1 private) contributing to the Prostate Cancer Outcomes Registry-Victoria (PCOR-Vic) in the study. Eligible patients from the PCOR-Vic underwent a radical prostatectomy between January 2017 and April 2018.Health Information Manager’s in each hospital, provided each patient’s associated administrative ICD-10 AM comorbidity codes. Medical charts were reviewed to extract comorbidity data. The self-reported comorbidity questionnaire (SCQ) was distributed through PCOR-Vic to eligible men. Results The percentage agreement between the administrative data, medical charts and self-reports ranged from 92 to 99% in the 122 patients from the 217 eligible participants who responded to the questionnaire. The presence of comorbidities showed a poor level of agreement between data sources. Conclusion Relying on a single data source to generate comorbidity indices for risk-modelling purposes may fail to capture the reality of a patient’s disease profile. There does not appear to be a ‘gold-standard’ data source for the collection of data on comorbidities.

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Section: Discussionmentioning

confidence: 99%

(Dis)concordance of comorbidity data and cancer status across administrative datasets, medical charts, and self-reports

Sheriffdeen

Millar

Martin

et al. 2020

BMC Health Serv Res

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“…As the FRAGILE-HF study focused on older patients with heart failure, the patient population in this study included older patients, and age is a strong predictor of disagreement between patient reports and medical records regarding the presence or absence of comorbidities. 9,11,12,28 However, the cohorts evaluated in some previous studies were the same age or older than the patients included in this study. [1][2][3] Therefore, heart failure itself might have contributed to the poor agreement observed in this study.…”

Section: Discussionmentioning

confidence: 99%

“…While 38% of the patients in this study had cognitive dysfunction, previous studies have either included very few patients with cognitive dysfunction, 2 excluded patients with cognitive dysfunction, 3 or did not report the presence or absence of cognitive dysfunction. 1,[8][9][10][11][12][13][14]28 The number of comorbidities may also be associated with disagreement. Patients with heart failure often have a high number of comorbidities.…”

Section: Discussionmentioning

confidence: 99%

Inaccurate recognition of own comorbidities is associated with poor prognosis in elderly patients with heart failure

et al. 2022

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Aims A patient's understanding of his or her own comorbidities is part of the recommended patient education for those with heart failure. The accuracy of patients' understanding of their comorbidities and its prognostic impact have not been reported. Methods and resultsPatients hospitalized for heart failure (n = 1234) aged ≥65 years (mean age: 80.1 ± 7.7 years; 531 females) completed a questionnaire regarding their diagnoses of diabetes, malignancy, stroke, hypertension, chronic obstructive pulmonary disease (COPD), and coronary artery disease (CAD). The patients were categorized into three groups based on the number of agreements between self-reported comorbidities and provider-reported comorbidities: low (1-2, n = 19); fair (3-4, n = 376); and high (5-6, n = 839) agreement groups. The primary outcome was a composite of all-cause mortality or heart failure rehospitalization at 1 year. The low agreement group had more comorbidities and a higher prevalence of a history of heart failure. The agreement was good for diabetes (κ = 0.73), moderate for malignancy (κ = 0.56) and stroke (κ = 0.50), and poor-to-fair for hypertension (κ = 0.33), COPD (κ = 0.25), and CAD (κ = 0.30). The fair and low agreement groups had poorer outcomes than the good agreement group [fair agreement group: hazard ratio (HR): 1.25; 95% confidence interval (CI): 1.01-1.56; P = 0.041; low agreement group: HR: 2.74: 95% CI: 1.40-5.35; P = 0.003]. Conclusions The ability to recognize their own comorbidities among older patients with heart failure was low. Patients with less accurate recognition of their comorbidities may be at higher risk for a composite of all-cause mortality or heart failure rehospitalization.

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“…Several observational studies demonstrated lower prevalence of certain CCs at least partly attributable to coding practices, physician and patient-reported bias and acute conditions prioritisation bias. [18][19][20][21] Observational studies demonstrated that certain CCs were underreported while others were accurately reported. 19 22 Ascertainment of mortality All deaths occurring from admission to censoring date were abstracted from Mayo Clinic electronic medical records.…”

Section: Ascertainment Of Ccsmentioning

confidence: 99%

Association of hyperlipidaemia with 5-year survival after hospitalisation for acute myocardial infarction: a propensity score matched analysis

et al. 2020

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ObjectivesThe primary objective was to examine the association between hyperlipidaemia (HLP) and 5-year survival after incident acute myocardial infarction (AMI). The secondary objectives were to assess the effect of HLP on survival to discharge across patient subgroups, and the impact of statin prescription, intensity and long-term statin adherence on 5-year survival.MethodsRetrospective cohort study of 7071 patients hospitalised for AMI at Mayo Clinic from 2001 through 2011. Of these, 2091 patients with HLP (age (mean±SD) 69.7±13.5) were propensity score matched to 2091 patients without HLP (age 70.6±14.2).ResultsIn matched patients, HLP was associated with higher rate of survival to discharge than no HLP (95% vs 91%; log-rank <0.0001). At year 5, the adjusted HR for all-cause mortality in patients with HLP versus no HLP was 0.66 (95% CI 0.58–0.74), and patients with prescription statin versus no statin was 0.24 (95% CI 0.21 to 0.28). The mean survival was 0.35 year greater in patients with HLP than in those with no HLP (95% CI 0.25 to 0.46). Patients with HLP gained on an average 0.17 life year and those treated with statin 0.67 life year at 5 years after AMI. The benefit of concurrent HLP was consistent across study subgroups.ConclusionsIn patients with AMI, concomitant HLP was associated with increased survival and a net gain in life years, independent of survival benefit from statin therapy. The results also reaffirm the role of statin prescription, intensity and adherence in reducing the mortality after incident AMI.

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How do patients with diabetes report their comorbidity? Comparison with administrative data

Cited by 10 publications

References 27 publications

(Dis)concordance of comorbidity data and cancer status across administrative datasets, medical charts, and self-reports

(Dis)concordance of comorbidity data and cancer status across administrative datasets, medical charts, and self-reports

Inaccurate recognition of own comorbidities is associated with poor prognosis in elderly patients with heart failure

Association of hyperlipidaemia with 5-year survival after hospitalisation for acute myocardial infarction: a propensity score matched analysis

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