Cumulative Illness Rating Scale

Linn, Bernard S.; Linn, Margaret W.; Gurel, Lee

doi:10.1111/j.1532-5415.1968.tb02103.x

Cited by 2,030 publications

(1,231 citation statements)

References 8 publications

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“…The CIRS is one of the longest standing comorbidity indices in use (23). It has been validated for use with primary care patients (24), converges with coroner’s pathology reports (25), and is a strong predictor of health-related quality of life (26) and survival (27–29).…”

Section: Methodsmentioning

confidence: 99%

Personality and Physician-Assessed Illness Burden in Older Primary Care Patients Over 4 Years

Chapman

Roberts

Lyness

et al. 2013

The American Journal of Geriatric Psychiatry

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Background Personality traits have been associated with physician-rated illness burden cross-sectionally, but longitudinal associations between personality and objective medical morbidity remain unclear. Purpose To examine associations between personality and physician-rated illness burden 4 years prospectively in older primary care patients. Method At baseline, patients (average age = 75, SD = 6.6, 62% female) completed the NEO-Five Factor Inventory. At baseline and 4 yearly follow-ups, a physician completed the Cumulative Illness Rating Scale based on medical records. Results Linear mixed effects models revealed that higher neuroticism and lower conscientiousness predicted worse average illness burden longitudinally. Relatively disagreeable persons (25th percentile) accumulated morbidity at a 33% faster rate than agreeable (75th percentile) peers. At the final follow-up, a person at the 75th percentile of neuroticism and the 25th percentile of conscientiousness and agreeableness showed morbidity comparable to a peer of average personality but 10 years older. An individual at the 25th percentile of neuroticism and 75th percentile of conscientiousness and agreeableness showed end-point illness burden comparable to a peer of average personality but 10 years younger. 21% of the morbidity associated with neuroticism was explained by total cholesterol. History of hypertension, smoking, alcohol/drug abuse, and affective symptoms of depression each explained 10% or less of the other observed personality effects. Conclusion Personality plays a non-trivial role in healthy aging among older persons. Brief personality assessment may identify at-risk older persons for closer monitoring, enhance the accuracy of medical prognosis, and provide clues for clinical interventions to promote better health.

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Section: Methodsmentioning

confidence: 99%

Personality and Physician-Assessed Illness Burden in Older Primary Care Patients Over 4 Years

Chapman

Roberts

Lyness

et al. 2013

The American Journal of Geriatric Psychiatry

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show abstract

“…[16][17][18] It is generally accepted, however, that multimorbidity measures should-when feasible-consider disease severity and the burden of physiological dysfunction resulting from multiple conditions and affected systems. 18,19 Several multimorbidity indices, including the Charlson score 20,21 and the Index of Coexisting Disease, 22 incorporate disease severity. Although these indices tend to underrepresent some conditions, they have been shown to reliably predict outcomes such as disability and mortality, 18 and can be of value when controlling for differences across patient populations.…”

Section: Definition Of Multimorbiditymentioning

confidence: 99%

Quality of Care for Patients with Multiple Chronic Conditions: The Role of Comorbidity Interrelatedness

et al. 2013

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Multimorbidity-the presence of multiple chronic conditions in a patient-has a profound impact on health, health care utilization, and associated costs. Definitions of multimorbidity in clinical care and research have evolved over time, initially focusing on a patient's number of comorbidities and the associated magnitude of required care processes, and later recognizing the potential influence of comorbidity characteristics on patient care and outcomes. In this article, we review the relationship between multimorbidity and quality of care, and discuss how this relationship may be mediated by the degree to which conditions interact with one another to generate clinical complexity (comorbidity interrelatedness). Drawing on established theoretical frameworks from cognitive engineering and biomedical informatics, we describe how interactions among conditions result in clinical complexity and may affect quality of care. We discuss how this comorbidity interrelatedness influences the value of existing quality guidelines and performance metrics, and describe opportunities to quantify this construct using data widely available through electronic health records. Incorporating comorbidity interrelatedness into conceptualizations of multimorbidity has the potential to enhance clinical and research efforts that aim to improve care for patients with multiple chronic conditions.

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“…Exclusion criteria for the present study included the following: Dementia, central nervous system diseases, unstable medical illnesses, other Axis I disorders (including bipolar disorder and psychotic disorders), drug or alcohol dependence, head trauma or an MMSE score of less than 24. Health status and medical comorbidities were assessed using the Cumulative Illness Rating Scale for Geriatrics (CIRS-G, Linn et al, 1968) and the Cerebrovascular Risk Factor Scale (CVRF, D 'Agostino et al, 1994). To determine glycemic control, hemoglobin A1c (Hgb A1c) levels were measured for all subjects.…”

Section: Subjectsmentioning

confidence: 99%

Measurement of Brain Metabolites in Patients with type 2 Diabetes and Major Depression Using Proton Magnetic Resonance Spectroscopy

Ajilore¹,

Haroon²,

Kumaran³

et al. 2006

Neuropsychopharmacol

109

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Type 2 diabetes and major depression are disorders that are mutual risk factors and may share similar pathophysiological mechanisms. To further understand these shared mechanisms, the purpose of our study was to examine the biochemical basis of depression in patients with type 2 diabetes using proton MRS. Patients with type 2 diabetes and major depression (n ¼ 20) were scanned along with patients with diabetes alone (n ¼ 24) and healthy controls (n ¼ 21) on a 1.5 T MRI/MRS scanner. Voxels were placed bilaterally in dorsolateral white matter and the subcortical nuclei region, both areas important in the circuitry of late-life depression. Absolute values of myoinositol, creatine, N-acetyl aspartate, glutamate, glutamine, and choline corrected for CSF were measured using the LC-Model algorithm. Glutamine and glutamate concentrations in depressed diabetic patients were significantly lower (po0.001) in the subcortical regions as compared to healthy and diabetic control subjects. Myo-inositol concentrations were significantly increased (po0.05) in diabetic control subjects and depressed diabetic patients in frontal white matter as compared to healthy controls. These findings have broad implications and suggest that alterations in glutamate and glutamine levels in subcortical regions along with white matter changes in myo-inositol provide important neurobiological substrates of mood disorders.

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Cumulative Illness Rating Scale

Cited by 2,030 publications

References 8 publications

Personality and Physician-Assessed Illness Burden in Older Primary Care Patients Over 4 Years

Personality and Physician-Assessed Illness Burden in Older Primary Care Patients Over 4 Years

Quality of Care for Patients with Multiple Chronic Conditions: The Role of Comorbidity Interrelatedness

Measurement of Brain Metabolites in Patients with type 2 Diabetes and Major Depression Using Proton Magnetic Resonance Spectroscopy

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