Development and Validation of the Medication-Based Disease Burden Index

Outpatient Parenteral Antimicrobial Therapy in a Safety Net Hospital: Opportunities for Improvement

Background

Outpatient parenteral antimicrobial therapy (OPAT) is a safe and cost-effective transitional care approach administered via different delivery models. No standards exist for appropriate OPAT program staffing. We examined outcomes of patients receiving OPAT via different care models to identify strategies to improve safety while reducing health care overuse.

Methods

Retrospective demographic, clinical, and outcome data of patients discharged with OPAT were reviewed in 2 periods (April-June 2021 and January-March 2022; ie, when staffing changed) and stratified by care model: self-administered OPAT, health care OPAT, and skilled nursing facility OPAT.

Results

Of 342 patients, 186 (54%) received OPAT in 2021 and 156 (46%) in 2022. Hospital length of stay rose from 12.4 days to 14.3 in 2022. In a Cox proportional hazards regression model, visits to the emergency department (ED) within 30 days of OPAT initiation (hazard ratio, 1.76; 95% CI, 1.13-2.73; P = .01) and readmissions (hazard ratio, 2.34; 95% CI, 1.22-4.49; P = .01) increased in 2022 vs 2021, corresponding to decreases in OPAT team staffing. Higher readmissions in the 2022 cohort were for reasons unrelated to OPAT (P = .01) while readmissions related to OPAT did not increase (P = .08).

Conclusions

In a well-established OPAT program, greater health care utilization-length of stay, ED visits, and readmissions-were seen during periods of higher staff turnover and attrition. Rather than blunt metrics such as ED visits and readmissions, which are influenced by multiple factors besides OPAT, our findings suggest the need to develop OPAT-specific outcome measures as a quality assessment tool and to establish optimal OPAT program staffing ratios.

Utility of prescription-based comorbidity indices for predicting mortality among Australian men with prostate cancer

BACKGROUND

Drug prescription registries has become an alternative data source to hospital admission databases for measuring comorbidities. However, the predictive validity of prescription-based comorbidity measures varies based on the population under investigation and outcome of interest. We aimed to determine which prescription-based index of comorbidity has most utility in Australian men with prostate cancer.

METHODS

We studied 25,414 South Australian men diagnosed with prostate cancer between 2003 and 2019 from state-wide administrative linked datasets. The Rx-Risk index, Chronic Disease Score (CDS), Drug Comorbidity Index (DCI) and Pharmaceutical Prescribing Profile (P3) with one year lookback period from prostate cancer diagnosis were evaluated. The predictive ability of each index to determine all-cause deaths within two and five years of prostate cancer diagnosis was compared using the c-statistic from flexible parametric survival models, adjusting for age, socioeconomic status and year of prostate cancer diagnosis.

RESULTS

The Rx-Risk index performed better in predicting two-year (c-statistic = 0.818) and five-year (c-statistic = 0.784) all-cause mortality than P3, CDS and DCI. Including comorbidity measures as continuous scores resulted in a better performance than including them as categories. Grouping scores into four categories (≤0, >0 - ≤1, >1 - ≤2, and >2) resulted in better performance and calibration than using fewer categories.

CONCLUSION

Rx-Risk was validated in Australia and reflects Australian prescribing patterns. It showed better predictive performance for mortality in our study, with a modest improvement over P3, CDS and DCI. For research with prostate cancer populations, we recommend the use of drug-based comorbidity indices that have been validated in a similar population.

Viewpoint: how to measure comorbidities in patients with rheumatoid arthritis - clinical and academic value

Given the high prevalence and the enormous impact on key outcomes, comorbidities are important to consider, especially in patients with RA. Comorbidity indices are tools to quantify the impact of the overall burden of coexisting diseases on a specific outcome of interest. Until now, no gold standard exists on how to measure comorbidities. A large variety of indices have been developed using different settings and therefore leading to conceptual differences. Choosing the right tool clearly depends on the intention (clinical or research purpose) and the specific research question. The current article will address the purpose and challenge of measuring comorbidities in RA patients.

Charlson Comorbidity Index: A Critical Review of Clinimetric Properties

The present critical review was conducted to evaluate the clinimetric properties of the Charlson Comorbidity Index (CCI), an assessment tool designed specifically to predict long-term mortality, with regard to its reliability, concurrent validity, sensitivity, incremental and predictive validity. The original version of the CCI has been adapted for use with different sources of data, ICD-9 and ICD-10 codes. The inter-rater reliability of the CCI was found to be excellent, with extremely high agreement between self-report and medical charts. The CCI has also been shown either to have concurrent validity with a number of other prognostic scales or to result in concordant predictions. Importantly, the clinimetric sensitivity of the CCI has been demonstrated in a variety of medical conditions, with stepwise increases in the CCI associated with stepwise increases in mortality. The CCI is also characterized by the clinimetric property of incremental validity, whereby adding the CCI to other measures increases the overall predictive accuracy. It has been shown to predict long-term mortality in different clinical populations, including medical, surgical, intensive care unit (ICU), trauma, and cancer patients. It may also predict in-hospital mortality, although in some instances, such as ICU or trauma patients, the CCI did not perform as well as other instruments designed specifically for that purpose. The CCI thus appears to be clinically useful not only to provide a valid assessment of the patient's unique clinical situation, but also to demarcate major diagnostic and prognostic differences among subgroups of patients sharing the same medical diagnosis.

Factors influencing prescription and administration of analgesic medication: A longitudinal study of people with dementia living in care homes

OBJECTIVES

To (1) describe the prescription and administration of regular and 'as required' (pro re nata [PRN]) analgesics in English care homes, (2) investigate individual and care home factors associated with analgesic use.

METHODS

We collected data (2014-2016) at 0-, 4-, and 12-months nested in a longitudinal cohort study of 86 English care homes about residents with diagnosed or probable dementia. We describe analgesics prescribed as regular or PRN medication, by class, and PRN administration. We explored individual differences (sociodemographic; dementia severity [Clinical Dementia Rating]), and care home differences (type; ownership; number of beds; dementia-registered/specialist; Care Quality Commission rating) in prescription and administration using multilevel regression models.

RESULTS

Data were available for 1483 residents. At baseline, 967 residents (67.9%) were prescribed analgesics: 426 residents (28.7%) prescribed regular analgesics and 670 (45.2%) prescribed PRN. Paracetamol was the most prescribed analgesic (56.7%), with PRN prescriptions more common than regular (39.7% vs. 16.6%). Across all study visits, 344 residents (mean = 41.9%) with a PRN prescription did not receive any analgesic in the 2 weeks prior to data collection. Male residents and those with severe dementia received fewer analgesics. Care homes differences in PRN administration were not explained by the modelled variables.

CONCLUSIONS

Pain management in English care homes largely relies on PRN paracetamol that is frequently prescribed but infrequently administered. Care homes differ in how often they administer PRN analgesics. Some care home residents particularly those with more severe dementia are likely to have untreated pain.

Examining variation in the measurement of multimorbidity in research: a systematic review of 566 studies

BACKGROUND

A systematic understanding of how multimorbidity has been constructed and measured is unavailable. This review aimed to examine the definition and measurement of multimorbidity in peer-reviewed studies internationally.

METHODS

We systematically reviewed studies on multimorbidity, via a search of nine bibliographic databases (Ovid [PsycINFO, Embase, Global Health, and MEDLINE], Web of Science, the Cochrane Library, CINAHL Plus, Scopus, and ProQuest Dissertations & Theses Global), from inception to Jan 21, 2020. Reference lists and tracked citations of retrieved articles were hand-searched. Eligible studies were full-text articles measuring multimorbidity for any purpose in community, primary care, care home, or hospital populations receiving a non-specialist service. Abstracts, qualitative research, and case series were excluded. Two reviewers independently reviewed the retrieved studies with conflicts resolved by discussion or a third reviewer, and a single researcher extracted data from published papers. To assess our objectives of how multimorbidity has been measured and examine variation in the chronic conditions included (in terms of number and type), we used descriptive analysis (frequencies, cross-tabulation, and negative binomial regression) to summarise the characteristics of multimorbidity studies and measures (study setting, source of morbidity data, study population, primary study purpose, and multimorbidity measure type). This systematic review is registered with PROSPERO, CRD420201724090.

FINDINGS

566 studies were included in our review, of which 206 (36·4%) did not report a reference definition for multimorbidity and 73 (12·9%) did not report the conditions their measure included. The number of conditions included in measures ranged from two to 285 (median 17 [IQR 11-23). 452 (79·9%) studies reported types of condition within a single multimorbidity measure; most included at least one cardiovascular condition (441 [97·6%] of 452 studies), metabolic and endocrine condition (440 [97·3%]), respiratory condition (422 [93·4%]), musculoskeletal condition (396 [87·6%]), or mental health condition (355 [78·5%]) in their measure of multimorbidity. Chronic infections (123 [27·2%]), haematological conditions (110 [24·3%]), ear, nose, and throat conditions (107 [23·7%]), skin conditions (70 [15·5%]), oral conditions (19 [4·2%]), and congenital conditions (14 [3·1%]) were uncommonly included. Only eight individual conditions were included by more than half of studies in the multimorbidity measure used (diabetes, stroke, cancer, chronic obstructive pulmonary disease, hypertension, coronary heart disease, chronic kidney disease, and heart failure), with individual mental health conditions under-represented. Of the 566 studies, 419 were rated to be of moderate risk of bias, 107 of high risk of bias, and 40 of low risk of bias according to the Effective Public Health Practice Project quality assessment tool.

INTERPRETATION

Measurement of multimorbidity is poorly reported and highly variable. Consistent reporting of measure definitions should be required by journals, and consensus studies are needed to define core and study-dependent conditions to include in measures of multimorbidity.

FUNDING

Health Data Research UK.

Comparison between drug therapy-based comorbidity indices and the Charlson Comorbidity Index for the detection of severe multimorbidity in older subjects

BACKGROUND

To know burden disease of a patient is a key point for clinical practice and research, especially in the elderly. Charlson's Comorbidity Index (CCI) is the most widely used rating system, but when diagnoses are not available therapy-based comorbidity indices (TBCI) are an alternative. However, their performance is debated. This study compares the relations between Drug Derived Complexity Index (DDCI), Medicines Comorbidity Index (MCI), Chronic Disease Score (CDS), and severe multimorbidity, according to the CCI classification, in the elderly.

METHODS

Logistic regression and Receiver Operating Characteristic (ROC) analysis were conducted on two samples from Italy: 2579 nursing home residents (Korian sample) and 7505 older adults admitted acutely to geriatric or internal medicine wards (REPOSI sample).

RESULTS

The proportion of subjects with severe comorbidity rose with TBCI score increment, but the Area Under the Curve (AUC) for the CDS (Korian: 0.70, REPOSI: 0.79) and MCI (Korian: 0.69, REPOSI: 0.81) were definitely better than the DDCI (Korian: 0.66, REPOSI: 0.74). All TBCIs showed low Positive Predictive Values (maximum: 0.066 in REPOSI and 0.317 in Korian) for the detection of severe multimorbidity.

CONCLUSION

CDS and MCI were better predictors of severe multimorbidity in older adults than DDCI, according to the CCI classification. A high CCI score was related to a high TBCI. However, the opposite is not necessarily true probably because of non-evidence-based prescriptions or physicians' prescribing attitudes. TBCIs did not appear selective for detecting of severe multimorbidity, though they could be used as a measure of disease burden, in the absence of other solutions.

A narrative review of using prescription drug databases for comorbidity adjustment: A less effective remedy or a prescription for improved model fit?

BACKGROUND

The use of claims data for identifying comorbid conditions in patients for research purposes has been widely explored. Traditional measures of comorbid adjustment included diagnostic data (e.g., ICD-9-CM or ICD-10-CM codes), with the Charlson and Elixhauser methodology being the two most common approaches. Prescription data has also been explored for use in comorbidity adjustment, however early methodologies were disappointing when compared to diagnostic measures.

OBJECTIVE

The objective of this methodological review is to compare results from newer studies using prescription-based data with more traditional diagnostic measures.

METHODS

A review of studies found on PubMed, Medline, Embase or CINAHL published between January 1990 and December 2020 using prescription data for comorbidity adjustment. A total of 50 studies using prescription drug measures for comorbidity adjustment were found.

CONCLUSIONS

Newer prescription-based measures show promise fitting models, as measured by predictive ability, for research, especially when the primary outcomes are utilization or drug expenditure rather than diagnostic measures. More traditional diagnostic-based measures still appear most appropriate if the primary outcome is mortality or inpatient readmissions.

More than two-dozen prescription drug-based risk scores are available for risk adjustment: A systematic review

OBJECTIVE

While several prescription drug-based risk indices have been developed, their design, performance, and application has not previously been synthesized.

STUDY DESIGN AND SETTING

We searched Ovid MEDLINE, CINAHL and Embase from inception through March 3, 2020 and included studies that developed or updated a prescription drug-based risk index. Two reviewers independently performed screening and extracted information on data source, study population, cohort sizes, outcomes, study methodology and performance. Predictive performance was evaluated using C statistics for binary outcomes and R² for continuous outcomes. The PROSPERO ID for this review is CRD42020165498.

RESULTS

Of 19,112 articles that were retrieved, 124 were full-text screened and 25 were included, each of which represented a de novo or updated drug-based index. The indices were customized to varied age groups and clinical populations and most commonly evaluated outcomes including mortality (36%), hospitalization (24%) and healthcare costs (24%). C statistics ranged from 0.62 to 0.92 for mortality and 0.59 to 0.72 for hospitalization, while adjusted R² for healthcare costs ranged from 0.06 to 0.62. Seven of the 25 risk indices included used global drug classification algorithms.

CONCLUSIONS

More than two-dozen prescription drug-based risk indices have been developed and they differ significantly in design, performance and application.

A data-driven medication score predicts 10-year mortality among aging adults

Health differences among the elderly and the role of medical treatments are topical issues in aging societies. We demonstrate the use of modern statistical learning methods to develop a data-driven health measure based on 21 years of pharmacy purchase and mortality data of 12,047 aging individuals. The resulting score was validated with 33,616 individuals from two fully independent datasets and it is strongly associated with all-cause mortality (HR 1.18 per point increase in score; 95% CI 1.14–1.22; p = 2.25e−16). When combined with Charlson comorbidity index, individuals with elevated medication score and comorbidity index had over six times higher risk (HR 6.30; 95% CI 3.84–10.3; AUC = 0.802) compared to individuals with a protective score profile. Alone, the medication score performs similarly to the Charlson comorbidity index and is associated with polygenic risk for coronary heart disease and type 2 diabetes.

Diclofenac reduces the risk of Alzheimer's disease: a pilot analysis of NSAIDs in two US veteran populations

Background:

Our aim was to determine whether specific nonsteroidal anti-inflammatory (NSAID) agents are associated with a decreased frequency of Alzheimer’s disease (AD).

Materials and methods:

Days of drug exposure were determined for diclofenac, etodolac, and naproxen using US Department of Veterans Affairs (VA) pharmacy transaction records, combined from two separate VA sites. AD diagnosis was established by the International Classification of Diseases, ninth revision (ICD-9)/ICD-10 diagnostic codes and the use of AD medications. Cox regression survival analysis was used to evaluate the association between AD frequency and NSAID exposure over time. Age at the end of the study and the medication-based disease burden index (a comorbidity index) were used as covariates.

Results:

Frequency of AD was significantly lower in the diclofenac group (4/1431, 0.28%) compared with etodolac (328/14,646, 2.24%), and naproxen (202/12,203, 1.66%). For regression analyses, naproxen was chosen as the comparator drug, since it has been shown to have no effect on the development of AD. Compared with naproxen, etodolac had no effect on the development of AD, hazard ratio (HR) 1.00 [95% confidence interval (CI): 0.84–1.20, p = 0.95]. In contrast, diclofenac had a significantly lower HR of AD compared with naproxen, HR 0.25 (95% CI: 0.09–0.68, p <0.01). After site effects were controlled for, age at end of the study (HR = 1.08, 95% CI: 1.07–1.09, p <0.001) was also found to influence the development of AD, and the medication-based disease burden index was a strong predictor for AD, HR 5.17 (95% CI: 4.60–5.81) indicating that as comorbidities increase, the risk for AD increases very significantly.

Conclusion:

Diclofenac, which has been shown to have active transport into the central nervous system, and which has been shown to lower amyloid beta and interleukin 1 beta, is associated with a significantly lower frequency of AD compared with etodolac and naproxen. These results are compelling, and parallel animal studies of the closely related fenamate NSAID drug class.

Measuring multimorbidity inequality across Italy through the multisource comorbidity score: a nationwide study

Background

Multimorbidity is a growing concern for healthcare systems, with many countries experiencing demographic transition to older population profiles. A simple multisource comorbidity score (MCS) has been recently developed and validated. A very large real-world investigation was conducted with the aim of measuring inequalities in the MCS distribution across Italy.

Methods

Beneficiaries of the Italian National Health Service aged 50–85 years who in 2018 were resident in one of the 10 participant regions formed the study population (15.7 million of the 24.9 million overall resident in Italy). MCS was assigned to each beneficiary by categorizing the individual sum of the comorbid values (i.e. the weights corresponding to the comorbid conditions of which the individual suffered) into one of the six categories denoting a progressive worsening comorbidity status. MCS distributions in women and men across geographic partitions were compared.

Results

Compared with beneficiaries from northern Italy, those from centre and south showed worse comorbidity profile for both women and men. MCS median age (i.e. the age above which half of the beneficiaries suffered at least one comorbidity) ranged from 60 (centre and south) to 68 years (north) in women and from 63 (centre and south) to 68 years (north) in men. The percentage of comorbid population was lower than 50% for northern population, whereas it was around 60% for central and southern ones.

Conclusion

MCS allowed of capturing geographic variability of multimorbidity prevalence, thus showing up its value for addressing health policy in order to guide national health planning.

Measuring multimorbidity beyond counting diseases: systematic review of community and population studies and guide to index choice

OBJECTIVES

To identify and summarise existing indices for measuring multimorbidity beyond disease counts, to establish which indices include mental health comorbidities or outcomes, and to develop recommendations based on applicability, performance, and usage.

DESIGN

Systematic review.

DATA SOURCES

Seven medical research databases (Medline, Web of Science Core Collection, Cochrane Library, Embase, PsycINFO, Scopus, and CINAHL Plus) from inception to October 2018 and bibliographies and citations of relevant papers. Searches were limited to English language publications.

ELIGIBILITY CRITERIA FOR STUDY SELECTION

Original articles describing a new multimorbidity index including more information than disease counts and not focusing on comorbidity associated with one specific disease. Studies were of adults based in the community or at population level.

RESULTS

Among 7128 search results, 5560 unique titles were identified. After screening against eligibility criteria the review finally included 35 papers. As index components, 25 indices used conditions (weighted or in combination with other parameters), five used diagnostic categories, four used drug use, and one used physiological measures. Predicted outcomes included mortality (18 indices), healthcare use or costs (13), hospital admission (13), and health related quality of life (7). 29 indices considered some aspect of mental health, with most including it as a comorbidity. 12 indices are recommended for use.

CONCLUSIONS

35 multimorbidity indices are available, with differing components and outcomes. Researchers and clinicians should examine existing indices for suitability before creating new ones.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO CRD42017074211.

Multimorbidity and healthcare resource utilization in Switzerland: a multicentre cohort study

BACKGROUND

Multimorbidity is associated with higher healthcare resource utilization, but we lack data on the association of specific combinations of comorbidities with healthcare resource utilization. We aimed to identify the combinations of comorbidities associated with high healthcare resource utilization among multimorbid medical inpatients.

METHODS

We performed a multicentre retrospective cohort study including 33,871 multimorbid (≥2 chronic diseases) medical inpatients discharged from three Swiss hospitals in 2010-2011. Healthcare resource utilization was measured as 30-day potentially avoidable readmission (PAR), prolonged length of stay (LOS) and difference in median LOS. We identified the combinations of chronic comorbidities associated with the highest healthcare resource utilization and quantified this association using regression techniques.

RESULTS

Three-fourths of the combinations with the strongest association with PAR included chronic kidney disease. Acute and unspecified renal failure combined with solid malignancy was most strongly associated with PAR (OR 2.64, 95%CI 1.79;3.90). Miscellaneous mental health disorders combined with mood disorders was the most strongly associated with LOS (difference in median LOS: 17 days) and prolonged LOS (OR 10.77, 95%CI 8.38;13.84). The number of chronic diseases was strongly associated with prolonged LOS (OR 9.07, 95%CI 8.04;10.24 for ≥10 chronic diseases), and to a lesser extent with PAR (OR 2.16, 95%CI 1.75;2.65 for ≥10 chronic diseases).

CONCLUSIONS

Multimorbidity appears to have a higher impact on LOS than on PAR. Combinations of comorbidities most strongly associated with healthcare utilization included kidney disorders for PAR, and mental health disorders for LOS.

Patterns of multimorbidity associated with 30-day readmission: a multinational study

BACKGROUND

Multimorbidity is associated with higher healthcare utilization; however, data exploring its association with readmission are scarce. We aimed to investigate which most important patterns of multimorbidity are associated with 30-day readmission.

METHODS

We used a multinational retrospective cohort of 126,828 medical inpatients with multimorbidity defined as ≥2 chronic diseases. The primary and secondary outcomes were 30-day potentially avoidable readmission (PAR) and 30-day all-cause readmission (ACR), respectively. Only chronic diseases were included in the analyses. We presented the OR for readmission according to the number of diseases or body systems involved, and the combinations of diseases categories with the highest OR for readmission.

RESULTS

Multimorbidity severity, assessed as number of chronic diseases or body systems involved, was strongly associated with PAR, and to a lesser extend with ACR. The strength of association steadily and linearly increased with each additional disease or body system involved. Patients with four body systems involved or nine diseases already had a more than doubled odds for PAR (OR 2.35, 95%CI 2.15-2.57, and OR 2.25, 95%CI 2.05-2.48, respectively). The combinations of diseases categories that were most strongly associated with PAR and ACR were chronic kidney disease with liver disease or chronic ulcer of skin, and hematological malignancy with esophageal disorders or mood disorders, respectively.

CONCLUSIONS

Readmission was associated with the number of chronic diseases or body systems involved and with specific combinations of diseases categories. The number of body systems involved may be a particularly interesting measure of the risk for readmission in multimorbid patients.

Cognitive impairment in older patients undergoing colorectal surgery

Background

With increasing numbers of older people being referred for elective colorectal surgery, cognitive impairment is likely to be present and affect many aspects of the surgical pathway. This study is aimed to determine the prevalence of cognitive impairment and assess it against surgical outcomes.

Methods

The Montreal Cognitive Assessment (MoCA) was carried out in patients aged more than 65 years. We recorded demographic information. Data were collected on length of hospital stay, complications and 30-day mortality.

Results

There were 101 patients assessed, median age was 74 years (interquartile range = 68–80), 54 (53.5%) were women. In total, 58 people (57.4%) ‘failed’ the Montreal Cognitive Assessment test (score ≤ 25). There were two deaths (3.4%) within 30 days of surgery in the abnormal Montreal Cognitive Assessment group and none in the normal group. Twenty-nine (28.7%) people experienced a complication. The percentage of patients with complications was higher in the group with normal Montreal Cognitive Assessment (41.9%) than abnormal Montreal Cognitive Assessment (19.9%) ( p = 0.01) and the severity of those complications were greater (chi-squared for trend p = 0.01). The length of stay was longer in people with an abnormal Montreal Cognitive Assessment (mean 8.1 days vs. 5.8 days, p = 0.03).

Conclusion

Cognitive impairment was common, which has implications for informed consent. Cognitive impairment was associated with less postoperative complications but a longer length of hospital stay.

Predictors of post-colonoscopy emergency department use

BACKGROUND AND AIMS

Unplanned hospital visits within 7 days of colonoscopy were recently proposed as a quality measure. It is unknown whether patient, procedure, or endoscopist characteristics predict post-colonoscopy emergency department (ED) visits. Our aim was to determine the incidence and relatedness of ED visits within 7 days of colonoscopy and to identify predictors of post-colonoscopy ED use.

METHODS

In this retrospective, single-center, cohort study, we evaluated outpatient colonoscopies performed at a tertiary academic medical center or affiliated facility between January 2008 and September 2013. We determined the incidence of ED visits within 7 days of colonoscopy and the relatedness of the ED visit to the procedure. We assessed for independent factors associated with ED use within 7 days using logistic regression analysis.

RESULTS

We reviewed 50,319 colonoscopies performed on 44,082 individuals (47% male, median age 59 years) by 40 endoscopists. There were 382 (0.76%) ED visits after colonoscopy, of which 68% were related to the procedure. On multivariate analysis, recent ED visit (odds ratio [OR], 16.60; 95% confidence interval [CI], 12.83-21.48; P < .001), EMR (OR, 4.69; 95% CI, 2.82-7.79; P < .001), number of medication classes (OR, 1.18; 95% CI, 1.11-1.26; P < .001), endoscopist adenoma detection rate (ADR) (OR, 1.14; 95% CI, 1.01-1.29; P = .029), and white race (OR, 0.77; 95% CI, 0.62-0.97; P = .028) were identified as independent variables associated with ED visits after colonoscopy.

CONCLUSIONS

Increased patient complexity, higher endoscopist ADR, and EMR were associated with increased ED use after colonoscopy. Patients at high risk for an unplanned hospital visit within 7 days should be targeted for quality improvement efforts to reduce adverse events and cost.

Developing and validating a novel multisource comorbidity score from administrative data: a large population-based cohort study from Italy

OBJECTIVE

To develop and validate a novel comorbidity score (multisource comorbidity score (MCS)) predictive of mortality, hospital admissions and healthcare costs using multiple source information from the administrative Italian National Health System (NHS) databases.

METHODS

An index of 34 variables (measured from inpatient diagnoses and outpatient drug prescriptions within 2 years before baseline) independently predicting 1-year mortality in a sample of 500 000 individuals aged 50 years or older randomly selected from the NHS beneficiaries of the Italian region of Lombardy (training set) was developed. The corresponding weights were assigned from the regression coefficients of a Weibull survival model. MCS performance was evaluated by using an internal (ie, another sample of 500 000 NHS beneficiaries from Lombardy) and three external (each consisting of 500 000 NHS beneficiaries from Emilia-Romagna, Lazio and Sicily) validation sets. Discriminant power and net reclassification improvement were used to compare MCS performance with that of other comorbidity scores. MCS ability to predict secondary health outcomes (ie, hospital admissions and costs) was also investigated.

RESULTS

Primary and secondary outcomes progressively increased with increasing MCS value. MCS improved the net 1-year mortality reclassification from 27% (with respect to the Chronic Disease Score) to 69% (with respect to the Elixhauser Index). MCS discrimination performance was similar in the four regions of Italy we tested, the area under the receiver operating characteristic curves (95% CI) being 0.78 (0.77 to 0.79) in Lombardy, 0.78 (0.77 to 0.79) in Emilia-Romagna, 0.77 (0.76 to 0.78) in Lazio and 0.78 (0.77 to 0.79) in Sicily.

CONCLUSION

MCS seems better than conventional scores for predicting health outcomes, at least in the general population from Italy. This may offer an improved tool for risk adjustment, policy planning and identifying patients in need of a focused treatment approach in the everyday medical practice.

Dispensing Patterns of Ranibizumab and Aflibercept for the Treatment of Neovascular Age-Related Macular Degeneration: A Retrospective Cohort Study in Australia

INTRODUCTION

Anti-vascular endothelial growth factor therapy is the standard of care for neovascular age-related macular degeneration (nAMD). The dosage of two licensed agents, ranibizumab and aflibercept, was established through clinical trials; however, it is unclear if either agent is administered as recommended in routine clinical practice. Using pharmacy claims data, we investigated if the dispensing patterns of ranibizumab differ from those of aflibercept 6 and 12 months after treatment initiation.

METHODS

Prescription data retrieved from the Australian IMS^® AUS LRx database were used to identify nAMD patients with one or more claims for ranibizumab or aflibercept between December 1, 2012, and March 31, 2015, with follow-up of at least 6 months. The number of ranibizumab and aflibercept units dispensed was adjusted for baseline patient Medication-Based Disease Burden Index (MBDBI) scores. No difference in the number of ranibizumab versus aflibercept units dispensed was concluded if the 95% confidence interval (CI) limits of the adjusted mean difference between the study cohorts were 1.00 unit or less.

RESULTS

Baseline patient MBDBI scores were similar for the ranibizumab (N = 1235) and aflibercept (N = 959) cohorts. The adjusted mean (standard deviation) number of units dispensed was 5.3 (1.3) versus 5.1 (1.4) at month 6 and 8.9 (2.2) versus 8.9 (2.3) at month 12. The 95% CI limits of the adjusted mean difference did not exceed 1.00 unit dispensed at either time point: 95% CI of 0.09 to 0.32 for an adjusted mean difference of 0.20 at month 6 and -0.23 to 0.30 for an adjusted mean difference of 0.04 at month 12. Mean (standard deviation) dispensing intervals were comparable for both cohorts: 35.3 (19.2) days versus 36.8 (20.0) days at month 6 (adjusted mean difference -1.59 days; 95% CI -2.51 to -0.67 days) and 41.2 (20.9) days versus 41.6 (20.4) days at month 12 (adjusted mean difference -0.40 days; 95% CI -1.70 to 0.91 days).

CONCLUSIONS

Ranibizumab and aflibercept are dispensed in a similar manner by Australian pharmacies during the first year of treatment.

FUNDING

Novartis Pharma AG.

Development of a research tool to document self-reported chronic conditions in primary care

BACKGROUND

Researchers interested in multimorbidity often find themselves in the dilemma of identifying or creating an operational definition in order to generate data. Our team was invited to propose a tool for documenting the presence of chronic conditions in participants recruited for different research studies.

OBJECTIVE

To describe the development of such a tool.

DESIGN

A scoping review in which we identified relevant studies, selected studies, charted the data, and collated and summarized the results. The criteria considered for selecting chronic conditions were: (1) their relevance to primary care services; (2) the impact on affected patients; (3) their prevalence among the primary care users; and (4) how often the conditions were present among the lists retrieved from the scoping review.

RESULTS

Taking into account the predefined criteria, we developed a list of 20 chronic conditions/categories of conditions that could be self-reported. A questionnaire was built using simple instructions and a table including the list of chronic conditions/categories of conditions.

CONCLUSIONS

We developed a questionnaire to document 20 self-reported chronic conditions/categories of conditions intended to be used for research purposes in primary care. Guided by previous literature, the purpose of this questionnaire is to evaluate the self-reported burden of multimorbidity by participants and to encourage comparability among research studies using the same measurement.

Patient-Centered Anesthesia Triage System Predicts ASA Physical Status

BACKGROUND

The purpose of this study was to validate a patient-centered anesthesia triage system (PCATS) by examining its association with, and predictive value of, ASA physical status (PS) classification. ASA PS classification is a widely used indicator of health status and the predictor of risk of perioperative complications. Thus, ASA PS is a good triage point such that healthy surgical patients (ASA PS I and II) undergoing low-complexity surgery are assessed by telephone, whereas less-healthy patients (ASA PS III and IV) or those patients undergoing highly complex surgery are seen in person at a presurgical clinic. However, ASA PS is not commonly available in electronic health records or easily determined by nonanesthesiologists. PCATS criteria, including the number of prescription medications used daily, body mass index (BMI), age, and surgical complexity, are readily available in electronic health records. Nonclinical scheduling personnel can use PCATS to make appropriate preassessment appointments for elective surgical patients before surgery.

METHODS

After getting approval from the University of Florida IRB for an exempt study, 300 consecutive patients scheduled in the presurgical clinic over a 1-week span were retrospectively enrolled. Each of the records was reviewed and collated for study identification number, number of prescription medications, BMI, and ASA PS classification assigned on the day of surgery. In addition, a surgical complexity score was assigned to each procedure (high, moderate, minimal).The association between PCATS and individual PCATS criteria and ASA PS was assessed by χ test. The utility of PCATS to discriminate between ASA PS classifications was assessed using receiver operating characteristic (ROC) curves as well as other indicators of clinical validity: sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and positive clinical utility index ([CIU+] = sensitivity × PPV) and negative CIU ([CIU-] = specificity × PPV).

RESULTS

BMI (P = .002), age (P = .01), surgical complexity (P < .0001), and number of prescriptions (P < .001) were significantly associated with ASA PS. Definitions included as PCATS criteria were BMI > 35, age > 80 years, 5 or more prescriptions, and high surgical complexity. Eighty-seven percent of patients with any PCATS criterion were ASA PS classification III or IV. From ROC curve analysis, PCATS emerged as a significant, and moderately good, predictor of ASA PS class (area under the curve = 0.75, 95% confidence interval [CI], 0.69-0.83). PCATS was highly sensitive (0.88, 95% CI, 0.84-0.92) and specific (0.74; 95% CI, 0.61-0.86), and had excellent utility in confirmation/case finding (CUI+ = 0.83, 95% CI, 0.82-0.84) and moderate utility in screening out cases (CUI- = 0.43, 95% CI, 0.41-0.44).

CONCLUSIONS

PCATS serves as a useful, and valid, predictor of ASA PS classification. Thus, it may also serve as a tool to triage patients to an appropriate venue for preoperative assessment that can be utilized by nonclinical schedulers. Using a simple tool such as PCATS may help streamline the presurgical patient experience and improve clinic staff utilization.

Pre-hospital medications in total hip arthroplasty: risk factors for poor outcomes

INTRODUCTION

The influence of co-morbidities on complication rates and length of hospitalisation after surgery is well recognised. Clinical instruments predicting this influence, are of increasing interest. We sought to determine whether a count of a patient's preoperative pharmaceuticals would be associated to postoperative outcomes.

MATERIAL AND METHODS

In this retrospective, consecutive case series, 668 patients undergoing elective primary total hip arthroplasty (THA) were analysed. Age, gender, BMI, ASA-classification, nicotine or alcohol abuse, and the number and type of medications were documented.

RESULTS

Mean age was 63 years (18-94), 53% were females. A total of 60 (8.9%) local and 19 (2.8%) systemic complications occurred during hospital stay. A total of 11 (1.6%) patients died, while 49 (7.3%) local complications occurred during the first postoperative year. Length of hospital stay, blood transfusions, and morbidity were found to be significantly related to the quantity of medications (p&lt;0.001). While the risk of an extended hospital stay (&gt;7 days) increased by a factor of 1.15 (CI: 1.08-1.22) with each medication, the risk of experiencing a complication within the first postoperative year was 1.19 times (CI: 1.07-1.29) for each additional medication. Type of medication also influenced morbidity: the odds ratio was 1.89 (CI: 1.05-3.41) for platelet inhibiting agents and 4.07 (CI: 1.96-8.42) for oral anticoagulants in early morbidity, which increased to 6.05 (CI:2.92-12.53) in 1-year follow-up.

CONCLUSIONS

The investigation illustrated the significant influence of the number and/or type of medication on complications, morbidity and prolonged hospital stay. This predictive tool may be useful, for physicians and non-health professionals, in estimating particular outcomes after elective THA.

The Drug Derived Complexity Index (DDCI) Predicts Mortality, Unplanned Hospitalization and Hospital Readmissions at the Population Level

OBJECTIVE

to develop and validate the Drug Derived Complexity Index (DDCI), a predictive model derived from drug prescriptions able to stratify the general population according to the risk of death, unplanned hospital admission, and readmission, and to compare the new predictive index with the Charlson Comorbidity Index (CCI).

DESIGN

Population-based cohort study, using a record-linkage analysis of prescription databases, hospital discharge records, and the civil registry. The predictive model was developed based on prescription patterns indicative of chronic diseases, using a random sample of 50% of the population. Multivariate Cox proportional hazards regression was used to assess weights of different prescription patterns and drug classes. The predictive properties of the DDCI were confirmed in the validation cohort, represented by the other half of the population. The performance of DDCI was compared to the CCI in terms of calibration, discrimination and reclassification.

SETTING

6 local health authorities with 2.0 million citizens aged 40 years or above.

RESULTS

One year and overall mortality rates, unplanned hospitalization rates and hospital readmission rates progressively increased with increasing DDCI score. In the overall population, the model including age, gender and DDCI showed a high performance. DDCI predicted 1-year mortality, overall mortality and unplanned hospitalization with an accuracy of 0.851, 0.835, and 0.584, respectively. If compared to CCI, DDCI showed discrimination and reclassification properties very similar to the CCI, and improved prediction when used in combination with the CCI.

CONCLUSIONS AND RELEVANCE

DDCI is a reliable prognostic index, able to stratify the entire population into homogeneous risk groups. DDCI can represent an useful tool for risk-adjustment, policy planning, and the identification of patients needing a focused approach in everyday practice.

Examining the prevalence and patterns of multimorbidity in Canadian primary healthcare: a methodologic protocol using a national electronic medical record database

In many developed countries, the burden of disease has shifted from acute to long-term or chronic diseases – producing new and broader challenges for patients, healthcare providers, and healthcare systems. Multimorbidity, the coexistence of two or more chronic diseases within an individual, is recognized as a significant public health and research priority. This protocol aims to examine the prevalence, characteristics, and changing burden of multimorbidity among adult primary healthcare (PHC) patients using electronic medical record (EMR) data. The objectives are two-fold: (1) to measure the point prevalence and clusters of multimorbidity among adult PHC patients; and (2) to examine the natural history and changing burden of multimorbidity over time among adult PHC patients. Data will be derived from the Canadian Primary Care Sentinel Surveillance Network (CPCSSN). The CPCSSN database contains longitudinal, point-of-care data from EMRs across Canada. To identify adult patients with multimorbidity, a list of 20 chronic disease categories (and corresponding ICD-9 codes) will be used. A computational cluster analysis will be conducted using a customized computer program written in JAVA. A Cox proportional hazards analysis will be used to model time-to-event data, while simultaneously adjusting for provider- and patient-level predictors. All analyses will be conducted using STATA SE 13.1. This research is the first of its kind using a pan-Canadian EMR database, which will provide an opportunity to contribute to the international evidence base. Future work should systematically compare international research using similar robust methodologies to determine international and geographical variations in the epidemiology of multimorbidity.

Comparing co-morbidities in total joint arthroplasty patients using the RxRisk-V, Elixhauser, and Charlson Measures: a cross-sectional evaluation

BACKGROUND

Joint arthroplasty patients have a high prevalence of co-morbidities and this impacts their surgical outcomes. There are different ways to ascertain co-morbidities and appropriate measurement is necessary. The purpose of this study was to: (1) describe the prevalence of co-morbidities in a cohort of total hip arthroplasty (THA) and knee arthroplasty (TKA) patients using two diagnoses-based measures (Charlson and Elixhauser) and one prescription-based measure (RxRisk-V); (2) compare the agreement of co-morbidities amongst the measures.

METHODS

A cross-sectional study of Australian veterans undergoing THAs (n = 11,848) and TKAs (n = 18,972) between 2001 and 2012 was conducted. Seventeen co-morbidities were identified using the Charlson, 30 using the Elixhauser, and 42 using the RxRisk-V measure. Agreement between co-morbidities was calculated using Kappa (κ) statistics.

RESULTS

Combining measures, 64 conditions were identified, of these 28 were only identified using the RxRisk-V, 11 using the Elixhauser, and 2 using the Charlson. The most prevalent conditions was pain treated with anti-inflammatories (58.7% THAs, 55.9% TKAs), pain treated with narcotics (55.0% THAs, 50.9% TKAs), hypertension (56.0% THAs and TKAs), and anticoagulation disorders (53.0% THAs, 48.6% TKAs). Diabetes was the only condition with substantial agreement (all κ > 0.6) amongst all measures. When comparing the diagnoses based algorithms, agreement was high for overlapping conditions (all κ > 0.71).

CONCLUSIONS

Different measures identified different co-morbidities, provided different estimates for the same co-morbidity, and had different levels of agreement for common co-morbidities. This highlights the importance of understanding co-morbidity measures and using them appropriately in studies and case-mix adjustments analyses.

An Evaluation of Health Service Impacts Consequent to Switching from Brand to Generic Venlafaxine in New Zealand under Conditions of Price Neutrality

OBJECTIVE

To study the health impact on adult New Zealand patients who switch from originator brand to generic venlafaxine.

METHODS

The national pharmacy database was used to select patients using venlafaxine for at least 6 months. Switchers and nonswitchers were identified, and switch behavior was compared for a 12-month follow-up period. Change in health service use following switching was also compared between switchers and nonswitchers including use of the emergency department, hospital, and specialist outpatient services over the same period.

RESULTS

Approximately 12% of all originator brand users switched to generic venlafaxine, at least half of whom continued to use the generic throughout the follow-up period to August 1, 2012. Almost 60% of new users of the generic venlafaxine, however, switched to using the originator brand. Aside from a slight reduction in the use of outpatient services among switchers, there were no significant differences in health services use between switchers and nonswitchers for either existing or new venlafaxine users.

CONCLUSIONS

Although both products remain fully subsidized and available, there is little incentive for prescribers, pharmacists, or patients to switch to the less expensive generic brand. If savings to the national New Zealand budget are to be realized, additional policy measures should be implemented to minimize incentives for multiple and reverse switching, and prescribers, as key opinion leaders, could take the lead in promoting generics to their patients.

Comorbidity burden assessment in older people admitted to a Portuguese University Hospital

PURPOSE

To determine the most valuable comorbidity index to apply in a clinical context and its prospective association with 1-year mortality and 3-month readmission. The authors also intend to gauge the evolution of older patients' admission profile over 13 years, in the same clinical setting.

SUBJECTS/MATERIALS AND METHODS

The authors analyzed data from 100 consecutive patients admitted in 2012. The Charlson Comorbidity Index (CCI), the Cumulative Illness Rating Scale for Geriatrics (CIRS-G) and the Medication-Based Disease Burden Index (MDBI) were used to evaluate comorbidity. Length of stay, number of diagnoses and of medications, readmission and mortality were assessed. A p value <0.05 was considered significant.

RESULTS

Mean age was 80.6 years, mean length of stay was 8.8 days, and mean number of diagnosis per patient was 7.9. Mean values of score were of 3.6 for the CCI, 11.3 for the CIRS-G and 0.552 for the MDBI. Three-month readmission and 1-year mortality rates related to higher CCI and CIRS-G scores. No association was found between MDBI and the outcomes evaluated. One-year mortality reached 24 % and 3-month readmission was of 43 %. Comparing the two samples, mean age increased in 2.1 years and the number of diagnosis by 2.2. Length of stay decreased 2 days.

DISCUSSION AND CONCLUSION

CCI was easier to use but the CIRS-G was better at evaluating comorbidity. MDBI did not seem to be a trustworthy tool. Despite an older population with high comorbidity, length of stay decreased over 13 years. However, readmission was high. Introduction of geriatric care standards is required to improve health outcomes for older patients.

Patient-centered care for people living with multimorbidity

PURPOSE OF REVIEW

The purpose of this review is to consider a patient-centred approach to the care of people living with HIV (PLWH) who have multimorbidity, irrespective of the specific conditions.

RECENT FINDINGS

Interdisciplinary care to achieve patient-centred care for people with multimorbidity is recognized as important, but the evaluation of models designed to achieve this goal are needed. Key elements of such approaches include patient preferences, interpretation of the evidence, prognosis as a tool to inform patient-centred care, clinical feasibility and optimization of treatment regimens.

SUMMARY

Developing and evaluating the best models of patient-centred care for PLWH who also have multimorbidity is essential. This challenge represents an opportunity to leverage the lessons learned from the care of people with multimorbidity in general, and vice versa.

The impact on health outcomes and healthcare utilisation of switching to generic medicines consequent to reference pricing: the case of lamotrigine in New Zealand

BACKGROUND

Many countries have implemented generic reference pricing and substitution as methods of containing pharmaceutical expenditure. However, resistance to switching between medicines is apparent, especially in the case of anti-epileptic medicines.

OBJECTIVES

This study sought to exploit a nation-wide policy intervention on generic reference pricing in New Zealand to evaluate the health outcomes of patients switching from originator to generic lamotrigine, an anti-epileptic medicine.

METHODS

A retrospective study using the national health collections and prescription records was conducted comparing patients who switched from originator brand to generic lamotrigine with patients who remained on the originator brand. Primary outcome measures included switch behaviour, changes in utilisation of healthcare services at emergency departments, hospitalisations and use of specialist services, and mortality.

RESULTS

Approximately one-quarter of all patients using the originator brand of lamotrigine switched to generic lamotrigine, half of whom made the switch within 60 days of the policy implementation. Multiple switches (three or more) between generic and brand products were evident for around 10% of switchers. Switch-back rates of 3% were apparent within 30 days post-switch. No difference in heath outcome measures was associated with switching from originator lamotrigine to a generic equivalent and hence no increased costs could be found for switchers.

CONCLUSIONS

Switching from brand to generic lamotrigine is largely devoid of adverse health outcomes; however, creating an incentive to ensure a greater proportion of patients switch to generic lamotrigine is required to achieve maximal financial savings from a policy of generic reference pricing.

Development and validation of the Kilifi Epilepsy Beliefs and Attitude Scale

Epilepsy remains misunderstood, particularly in resource poor countries (RPC). We developed and validated a tool to assess beliefs and attitudes about epilepsy among people with epilepsy (PWE) in Kilifi, Kenya. The 50-item scale was developed through a literature review and qualitative study findings, and its reliability and validity were assessed with 673 PWE. A final scale of 34 items had Cronbach's alpha scores for the five subscales: causes of epilepsy (α=0.71); biomedical treatment of epilepsy (α=0.70); cultural treatment of epilepsy (α=0.75); risk and safety concerns about epilepsy (α=0.56); and negative attitudes about epilepsy (α=0.76) and entire scale (α=0.70). Test-retest reliability was acceptable for all the subscales. The Kilifi Epilepsy Beliefs and Attitude Scale is a reliable and valid tool that measures beliefs and attitudes about epilepsy. It may be useful in other RPC or as a tool to assess the effectiveness of interventions to improve knowledge, beliefs, and attitudes about epilepsy.

Measures of multimorbidity and morbidity burden for use in primary care and community settings: a systematic review and guide

PURPOSE

Many patients consulting in primary care have multiple conditions (multimorbidity). Aims of this review were to identify measures of multimorbidity and morbidity burden suitable for use in research in primary care and community populations, and to investigate their validity in relation to anticipated associations with patient characteristics, process measures, and health outcomes.

METHODS

Studies were identified using searches in MEDLINE and EMBASE from inception to December 2009 and bibliographies.

RESULTS

Included were 194 articles describing 17 different measures. Commonly used measures included disease counts (n = 98), Chronic Disease Score (CDS)/RxRisk (n = 17), Adjusted Clinical Groups (ACG) System (n = 25), the Charlson index (n = 38), the Cumulative Index Illness Rating Scale (CIRS; n = 10) and the Duke Severity of Illness Checklist (DUSOI; n = 6). Studies that compared measures suggest their predictive validity for the same outcome differs only slightly. Evidence is strongest for the ACG System, Charlson index, or disease counts in relation to care utilization; for the ACG System in relation to costs; for Charlson index in relation to mortality; and for disease counts or Charlson index in relation to quality of life. Simple counts of diseases or medications perform almost as well as complex measures in predicting most outcomes. Combining measures can improve validity.

CONCLUSIONS

The measures most commonly used in primary care and community settings are disease counts, Charlson index, ACG System, CIRS, CDS, and DUSOI. Different measures are most appropriate according to the outcome of interest. Choice of measure will also depend on the type of data available. More research is needed to directly compare performance of different measures.

Predicting healthcare utilization using a pharmacy-based metric with the WHO's Anatomic Therapeutic Chemical algorithm

BACKGROUND

Automated pharmacy claim data have been used for risk adjustment on health care utilization. However, most published pharmacy-based morbidity measures incorporate a coding algorithm that requires the medication data to be coded using the US National Drug Codes or the American Hospital Formulary Service drug codes, making studies conducted outside the US operationally cumbersome.

OBJECTIVE

This study aimed to verify that the pharmacy-based metric with the World Health Organization (WHO) Anatomical Therapeutic Chemical (ATC) algorithm can be used to explain the variations in health care utilization.

RESEARCH DESIGN

The Longitudinal Health Insurance Database of Taiwan's National Health Insurance enrollees was used in this study. We chose 2006 as the baseline year to predict the total cost, medication cost, and the number of outpatient visits in 2007. The pharmacy-based metric with 32 classes of chronic conditions was modified from a revised version of the Chronic Disease Score.

RESULTS

The ordinary least squares (OLS) model and log-transformed OLS model adjusted for the pharmacy-based metric had a better R in concurrently predicting total cost compared with the model adjusted for Deyo's Charlson Comorbidity Index and Elixhauser's Index. The pharmacy-based metric models also provided a superior performance in predicting medication cost and number of outpatient visits. For prospectively predicting health care utilization, the pharmacy-based metric models also performed better than the models adjusted by the diagnosis-based indices.

CONCLUSIONS

The pharmacy-based metric with the WHO ATC algorithm and the matching ATC codes were tested and found to be valid for explaining the variation in health care utilization.

The measurement of multiple chronic diseases--a systematic review on existing multimorbidity indices

BACKGROUND

Multimorbidity, defined as the coexistence of 2 or more chronic diseases, is a common phenomenon especially in older people. Numerous efforts to establish a standardized instrument to assess the level of multimorbidity have failed until now, and indices are primarily characterized by their high heterogeneity. Thus, the objective is to provide a comprehensive overview on existing instruments on the basis of a systematic literature review.

METHODS

The review was performed in MedLine. All articles published between January 1, 1960 and August 31, 2009 in German or English language, with the primary focus either on the development of a weighted index or on the effect of multimorbidity on different outcomes, were identified.

RESULTS

A total of 39 articles met the inclusion criteria. In the majority of studies (59.0%), the list of included diseases was presented without any selection criteria. Only the high prevalence of diseases (17.9%), their impact on mortality, function, and health status served as a point of reference. Information on the prevalence of chronic conditions mostly rely on self-reports. On average, the 39 indices included 18.5 diseases, ranging between 4 and 102 different conditions. Most frequently mentioned diseases were diabetes mellitus (in 97.5% of indices), followed by stroke (89.7%), hypertension, and cancer (each 84.6%). Overall, three different weighting methods could be distinguished.

CONCLUSIONS

The systematic literature further emphasis the heterogeneity of existing multimorbidity indices. However, one important similarity is that the focus is on diseases with a high prevalence and a severe impact on affected individuals.

Validity of medication-based co-morbidity indices in the Australian elderly population

OBJECTIVES

To determine the validity of two medication-based co-morbidity indices, the Medicines Disease Burden Index (MDBI) and Rx-Risk-V in the Australian elderly population.

METHODS

In Phase I, the sensitivity and specificity of both indices were determined in 767 respondents from wave 6 of the Australian Longitudinal Study of Ageing (ALSA). Medication-defined index disease categories were compared to self-reported medical conditions. Correlation with self-rated health was examined and Cox proportional hazards models were used to assess the predictive validity for mortality. Phase II verified the predictive ability of Rx-Risk-V in a sample of 213,191 veterans from Australian Department of Veterans' Affairs (DVA) database.

RESULTS

MDBI and Rx-Risk-V scores could be calculated for 28% and 73% of the ALSA sample respectively. Both indices had high specificities and low to moderate sensitivities compared to self-reported medical conditions. Total weighted scores were significantly related to self-rated health (p<0.001). Both indices were predictive of mortality (Hazard Ratio (HR) =3.690 (95% CI 2.264-6.015) for MDBI and HR 1.079 (95% CI 1.045-1.114) for Rx-Risk-V. The predictive validity for mortality of Rx-Risk-V was confirmed using DVA data (HR= 1.090, 95% CI 1.088-1.092).

CONCLUSIONS

Medication-based co-morbidity indices Rx-Risk-V and MDBI are valid measures of co-morbidity. However, Rx-Risk-V detects more comorbidity in the Australian elderly population and is likely to be a more suitable index to use in administrative datasets, particularly where studies include large numbers of outpatients.

Sleep-disordered breathing and frailty in the Cardiovascular Health Study Cohort

Sleep-disordered breathing (SDB) is associated with pathophysiology that may influence the development and progression of frailty. Using data collected in 1995-1996, the authors explored the relation between SDB and components of frailty among 1,042 participants of the Cardiovascular Health Study. Diagnosis of SDB was based on the results of overnight polysomnography, and severe SDB was defined as an apnea-hypopnea index of >30 per hour of sleep. Slow walking speed, low grip strength, exhaustion, low physical activity, and unexplained weight loss were referred to as frailty indicator variables. There were 584 (56%) female and 458 (44%) male participants, and the mean age was 77 (standard deviation, 4) years. There was independent association between severe SDB and 1 or more frailty indicator variables (adjusted odds ratio = 4.85, 95% confidence interval: 1.40, 16.78), slow walking speed (adjusted odds ratio = 2.67, 95% confidence interval: 1.04, 6.84), and low grip strength (adjusted odds ratio = 3.29, 95% confidence interval: 1.36, 7.96) among female study participants. The finding of an independent association between SDB and frailty indicator variables among older women could have important implications in interventions aimed at preventing or delaying the progression of frailty.

Relationship of in-hospital medication modifications of elderly patients to postdischarge medications, adherence, and mortality

BACKGROUND

Medication regimens are constantly modified and updated during a patient's hospitalization. These modifications and those made after discharge might increase the risk for nonadherence, polypharmacy, and poor outcomes among elderly patients.

OBJECTIVES

To investigate the extent of in-hospital modification of medication regimens of elderly patients and its relationship to medication adherence as well as one-month postdischarge drug regimen modifications and to examine the relationship of the modifications, adherence, and polypharmacy to mortality and readmissions 3 months postdischarge.

METHODS

Clinical and demographic data, postdischarge medication modifications, and adherence were prospectively obtained in 212 elderly patients. Inhospital drug regimen modifications were retrospectively recorded.

RESULTS

The average +/- SD in-hospital medication regimen modification rate was 49.8% +/- 28.4. No modifications were found in 9.7% of the patients. Using demographic and clinical parameters, we performed regression analysis and found that patients who were admitted with polypharmacy, discharged home, and cognitively normal experienced fewer medication modifications (p < 0.05). At one month postdischarge, the average medication regimen modification rate was 37.5% +/- 25.4. In- and posthospital modifications were directly correlated (p = 0.047). Three months postdischarge, 17 patients had died and 50 had been readmitted. The independent risk factors for mortality were in-hospital modification rate of 50% or greater (OR 6.4; 95% CI 1.3 to 29.7), impaired cognition (OR 4.2; 95% CI 1.4 to 12.3), and each chronic disease (OR 1.2; 95% CI 1 to 1.5). No relationships were found between in-hospital medication regimen modifications and readmissions or with postdischarge modifications, adherence, and polypharmacy to mortality and readmissions.

CONCLUSIONS

Hospitalization of elderly patients is characterized by extensive medication regimen modifications, which are directly correlated with postdischarge modifications and may indicate an increased risk of mortality.

Trajectories of improvement for six depression-related outcomes

OBJECTIVE

Although depression treatment improves diverse outcomes, it is unclear whether these improvements are comparable in magnitude and timing. The objective was therefore to compare treatment-related improvements in depressive symptoms, work and social functioning, hopefulness, somatic complaints and positive well-being.

METHOD

Secondary analysis of a large clinical trial of selective serotonin reuptake inhibitors for primary care depression. Depressed patients (n=573) from 37 practices from two primary care networks were randomized to fluoxetine, paroxetine or sertraline, and then followed naturalistically. At 1, 3, 6 and 9 months after treatment initiation, assessments were made of depressive symptom severity, social and work functioning, positive well-being, hopefulness beliefs and somatic complaints. Data were analyzed with linear regression modeling.

RESULTS

Although 68% and 88% of total mood improvement occurred by Months 1 and 3, respectively, improvement plateaued sooner for somatic complaints (P=.001 at Month 1), and more gradually for hopefulness [P (Month 1)=.015, P (Month 3)=.036]. Although magnitude of improvement was interrelated across outcomes, timing of mood improvement was unrelated to the timing of improvement in both somatic complaints and hopefulness. Improvement in somatic complaints was primarily attributable to improvements in head, back and stomach pain.

CONCLUSIONS

Work and social functioning, and positive affect improve synchronously with mood. Compared to mood, improvement in pain complaints peaks earlier, whereas improvement in hopefulness is much more linear over time. Because depression treatment response appears to be complex and multidimensional, a broader conceptualization of depression remission may be indicated.