Development of the “chronic condition measurement guide”: a new tool to measure chronic conditions in older people based on ICD-10 and ATC-codes

Juul‐Larsen, Helle Gybel; Christensen, Line Due; Andersen, Ove; Bandholm, Thomas; Kaae, Susanne; Petersen, Janne

doi:10.1007/s41999-019-00188-y

Cited by 8 publications

(36 citation statements)

References 36 publications

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“…We conducted a longitudinal study based on data from national registries in a cohort of acutely hospitalized patients aged 65 and older. We studied patterns of multimorbidity based on the Chronic Condition Measurement Guide 25 using latent class analysis. Healthcare utilization and healthcare complexity were based on data from national health registers in the year leading up to the index admission in 2011.…”

Section: Methodsmentioning

confidence: 99%

“…To assess multimorbidity in the study population, we used the Chronic Condition Measurement Guide to define chronic conditions based on ICD10-and ATC-codes. 25 Data on ICD10-and ATC-codes was extracted with 10 years of history as previous work suggest this to be an optimal follow-up time as 5 years is too short and 15 years do not add any more information when using administrative data. 25,35 The Chronic Condition Measurement Guide is a guide of 83 chronic conditions based on registry data derived from persons aged 65 years and older.…”

Section: Measures Of Multimorbiditymentioning

confidence: 99%

“…25 Data on ICD10-and ATC-codes was extracted with 10 years of history as previous work suggest this to be an optimal follow-up time as 5 years is too short and 15 years do not add any more information when using administrative data. 25,35 The Chronic Condition Measurement Guide is a guide of 83 chronic conditions based on registry data derived from persons aged 65 years and older. The 83 chronic conditions were compiled according to their pathophysiology by a specialist in internal medicine (OA), which constituted 38 chronic conditions in the list, e.g.…”

Section: Measures Of Multimorbiditymentioning

confidence: 99%

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<p>Patterns of Multimorbidity and Differences in Healthcare Utilization and Complexity Among Acutely Hospitalized Medical Patients (≥65 Years) – A Latent Class Approach</p>

Juul‐Larsen¹,

Christensen²,

Bandholm³

et al. 2020

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The majority of acutely admitted older medical patients are multimorbid, receive multiple drugs, and experience a complex treatment regime. To be able to optimize treatment and care, we need more knowledge of the association between different patterns of multimorbidity and healthcare utilization and the complexity thereof. The purpose was therefore to investigate patterns of multimorbidity in a Danish national cohort of acutely hospitalized medical patients aged 65 and older and to determine the association between these multimorbid patterns with the healthcare utilization and complexity. Patients and Methods: Longitudinal cohort study of 129,900 (53% women) patients. Latent class analysis (LCA) was used to develop patterns of multimorbidity based on 22 chronic conditions ascertained from Danish national registers. A latent class regression was used to test for differences in healthcare utilization and healthcare complexity among the patterns measured in the year leading up to the index admission. Results: LCA identified eight distinct multimorbid patterns. Patients belonging to multimorbid patterns including the major chronic conditions; diabetes and chronic obstructive pulmonary disease was associated with higher odds of healthcare utilization and complexity than the reference pattern ("Minimal chronic conditions"). The pattern with the highest number of chronic conditions did not show the highest healthcare utilization nor complexity. Conclusion: Our study showed that chronic conditions cluster together and that these patterns differ in healthcare utilization and complexity. Patterns of multimorbidity have the potential to be used in epidemiological studies of healthcare planning but should be confirmed in other population-based studies.

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Section: Methodsmentioning

confidence: 99%

Section: Measures Of Multimorbiditymentioning

confidence: 99%

Section: Measures Of Multimorbiditymentioning

confidence: 99%

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<p>Patterns of Multimorbidity and Differences in Healthcare Utilization and Complexity Among Acutely Hospitalized Medical Patients (≥65 Years) – A Latent Class Approach</p>

Juul‐Larsen¹,

Christensen²,

Bandholm³

et al. 2020

CLEP

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“…Patient demographic information as physical parameters including weight and height were measured during this time. Data of cardiovascular disease and diabetes were identified by ICD-10 diagnosis codes or ATC medication codes in each patient’s medical record within 10 years before inclusion in the study as described in Juul-Larsen et al 2019 [ 44 ] Data regarding length of stay and 30-days mortality were obtained from the patient’s electronic health records. Patients’ frailty index (FI-OutRef) representing cumulative organ dysfunction, calculated as number of laboratory results outside of reference interval for 17 standard biomarkers, collected at admission: C-reactive protein (CRP), leucocytes, neutrophils, haemoglobin, mean corpuscular haemoglobin concentration (MCHC), mean corpuscular volume (MCV), thrombocytes, creatinine, blood urea nitrogen (BUN), sodium, potassium, albumin, alanine aminotransferase (ALAT), alkaline phosphatase, lactate dehydrogenase, (LDH), bilirubin and factors II, VII and X [ 45 , 46 ].…”

Section: Methodsmentioning

confidence: 99%

Utility of suPAR and NGAL for AKI Risk Stratification and Early Optimization of Renal Risk Medications among Older Patients in the Emergency Department

et al. 2021

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Diagnosis of acute kidney injury (AKI) based on plasma creatinine often lags behind actual changes in renal function. Here, we investigated early detection of AKI using the plasma soluble urokinase plasminogen activator receptor (suPAR) and neutrophil gelatinase-sssociated lipocalin (NGAL) and observed the impact of early detection on prescribing recommendations for renally-eliminated medications. This study is a secondary analysis of data from the DISABLMENT cohort on acutely admitted older (≥65 years) medical patients (n = 339). Presence of AKI according to kidney disease: improving global outcomes (KDIGO) criteria was identified from inclusion to 48 h after inclusion. Discriminatory power of suPAR and NGAL was determined by receiver-operating characteristic (ROC). Selected medications that are contraindicated in AKI were identified in Renbase®. A total of 33 (9.7%) patients developed AKI. Discriminatory power for suPAR and NGAL was 0.69 and 0.78, respectively, at a cutoff of 4.26 ng/mL and 139.5 ng/mL, respectively. The interaction of suPAR and NGAL yielded a discriminatory power of 0.80, which was significantly higher than for suPAR alone (p = 0.0059). Among patients with AKI, 22 (60.6%) used at least one medication that should be avoided in AKI. Overall, suPAR and NGAL levels were independently associated with incident AKI and their combination yielded excellent discriminatory power for risk determination of AKI.

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“…60 The overall validation involved estimating: (1) the reliability in multimorbidity prevalence between administrative data and another data source (survey) 25,26 ; and (2) the capacity to predict an outcome such as use of health services, 29 socioeconomic status, 38,39 health outcomes [34][35][36][37] and mortality. 22,60 Of note, there was no overall validation of any of the multimorbidity measures against a gold standard.…”

Section: Validation Of Measuresmentioning

confidence: 99%

Multimorbidity measures from health administrative data using ICD system codes: A systematic review

Simard

Rahme

Calfat

et al. 2021

Pharmacoepidemiology and Drug

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Background: We aimed to identify and characterize adult population-based multimorbidity measures using health administrative data and the International Classification of Diseases (ICD) codes for disease identification.Methods: We performed a narrative systematic review of studies using or describing development or validation of multimorbidity measures. We compared the number of diseases included in the measures, the process of data extraction (case definition) and the validation process. We assessed the methodological robustness using eight criteria, five based on general criteria for indicators (AIRE instrument) and three multimorbidity-specific criteria.Results: Twenty-two multimorbidity measures were identified. The number of diseases they included ranged from 5 to 84 (median = 20), with 19 measures including both physical and mental conditions. Diseases were identified using ICD codes extracted from inpatient and outpatient data (18/22) and sometimes including drug claims (10/22). The validation process relied mainly on the capacity of the measures to predict health outcome (5/22), or on the validation of each individual disease against a gold standard (8/22). Six multimorbidity measures met at least six of the eight robustness criteria assessed. Conclusion:There is significant heterogeneity among the measures used to assess multimorbidity in administrative databases, and about a third are of low to moderate quality. A more consensual approach to the number of diseases or groups of diseases included in multimorbidity measures may improve comparison between regions, and potentially provide better control for multimorbidity-related confounding in studies.

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Development of the “chronic condition measurement guide”: a new tool to measure chronic conditions in older people based on ICD-10 and ATC-codes

Cited by 8 publications

References 36 publications

<p>Patterns of Multimorbidity and Differences in Healthcare Utilization and Complexity Among Acutely Hospitalized Medical Patients (≥65 Years) – A Latent Class Approach</p>

<p>Patterns of Multimorbidity and Differences in Healthcare Utilization and Complexity Among Acutely Hospitalized Medical Patients (≥65 Years) – A Latent Class Approach</p>

Utility of suPAR and NGAL for AKI Risk Stratification and Early Optimization of Renal Risk Medications among Older Patients in the Emergency Department

Multimorbidity measures from health administrative data using ICD system codes: A systematic review

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