Lack of association between patients' measured burden of disease and risk for hospital readmission

Predictors for Unplanned Readmissions within 18 Days after Hospital Discharge: a Retrospective Cohort Study

Since the introduction of the reimbursement system based on diagnosis-related groups (DRG) in Swiss hospitals in 2012, most readmissions occurring within 18 days and appertaining to the same major diagnostic category (MDC) are merged and thus often reimbursed to a lesser extent. While readmissions reflect increased distress for patients and their relatives, the causes are mainly patient-related and difficult to influence. However, it may be possible to identify cases at higher risk for readmission. Therefore, the aim of this study was to find predictors for early readmissions in the same MDC, to identify high-risk index hospitalizations and possibly prevent unnecessary readmissions. The data of all patients admitted to the Clinic of Internal Medicine at the University Hospital of Basel, Switzerland, hospitalized for longer than 24 hours during the pre-DRG period between October 2009 and September 2010 were retrospectively collected. Data were examined for predictors of unplanned readmission within 18 days under the same MDC ('relevant readmission') by means of logistic regression. 7479 patients (median age 67.8 years, 56% male) were admitted to the Clinic of Internal Medicine, with 232 patients (3.1%) being readmitted at least once. Logistic regression revealed male sex (p =0.035) and a high number of prescribed drugs at discharge (p <0.005) as patient-related predictors. The MDCs respiratory system, cardiovascular system, and gastrointestinal/hepatobiliary system were identified as high-risk categories (each p <0.005). Age and length of index hospital stay added no significant explanatory value to the regression model. Unplanned readmissions under the same MDC within 18 days were infrequent and not related to patients' age or length of hospital stay. Overall, multimorbid patients, and hospitalizations regarding the cardiovascular, respiratory, or gastrointestinal system appear to be most at risk and should therefore be specifically targeted in the prevention of early readmissions.

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.

Importance of coding co-morbidities for APR-DRG assignment: Focus on cardiovascular and respiratory diseases

BACKGROUND

The All Patient-Refined Diagnosis-Related Groups (APR-DRGs) system has adjusted the basic DRG structure by incorporating four severity of illness (SOI) levels, which are used for determining hospital payment. A comprehensive report of all relevant diagnoses, namely the patient's underlying co-morbidities, is a key factor for ensuring that SOI determination will be adequate.

OBJECTIVE

In this study, we aimed to characterise the individual impact of co-morbidities on APR-DRG classification and hospital funding in the context of respiratory and cardiovascular diseases.

METHODS

Using 6 years of coded clinical data from a nationwide Portuguese inpatient database and support vector machine (SVM) models, we simulated and explored the APR-DRG classification to understand its response to individual removal of Charlson and Elixhauser co-morbidities. We also estimated the amount of hospital payments that could have been lost when co-morbidities are under-reported.

RESULTS

In our scenario, most Charlson and Elixhauser co-morbidities did considerably influence SOI determination but had little impact on base APR-DRG assignment. The degree of influence of each co-morbidity on SOI was, however, quite specific to the base APR-DRG. Under-coding of all studied co-morbidities led to losses in hospital payments. Furthermore, our results based on the SVM models were consistent with overall APR-DRG grouping logics.

CONCLUSION AND IMPLICATIONS

Comprehensive reporting of pre-existing or newly acquired co-morbidities should be encouraged in hospitals as they have an important influence on SOI assignment and thus on hospital funding. Furthermore, we recommend that future guidelines to be used by medical coders should include specific rules concerning coding of co-morbidities.

Predictors of 30- and 90-day readmission following craniotomy for malignant brain tumors: analysis of nationwide data

Hospital readmissions are a major contributor to increased health care costs and are associated with worse patient outcomes after neurosurgery. We used the newly released Nationwide Readmissions Database (NRD) to describe the association between patient, hospital and payer factors with 30- and 90-day readmission following craniotomy for malignant brain tumor. All adult inpatients undergoing craniotomy for primary and secondary malignant brain tumors in the NRD from 2013 to 2014 were included. We identified all cause readmissions within 30- and 90-days following craniotomy for tumor, excluding scheduled chemotherapeutic procedures. We used univariate and multivariate models to identify patient, hospital and administrative factors associated with readmission. We identified 27,717 admissions for brain tumor craniotomy in 2013-2014, with 3343 (13.2%) 30-day and 5271 (25.7%) 90-day readmissions. In multivariate analysis, patients with Medicaid and Medicare were more likely to be readmitted at 30- and 90-days compared to privately insured patients. Patients with two or more comorbidities were more likely to be readmitted at 30- and 90-days, and patients discharged to skilled nursing facilities or home health care were associated with increased 90-day readmission rates. Finally, hospital procedural volume above the 75th percentile was associated with decreased 90-day readmission rates. Patients treated at high volume hospitals are less likely to be readmitted at 90-days. Insurance type, non-routine discharge and patient comorbidities are predictors of postoperative non-scheduled readmission. Further studies may elucidate potentially modifiable risk factors when attempting to improve outcomes and reduce cost associated with brain tumor surgery.

Association between Prescription Drug Benefit and Hospital Readmission Rates

Purpose

To determine whether primary care medicine clinic (PCMC) patients with a prescription drug benefit were associated with a lower rate of hospital readmissions.

Methods

This study was a retrospective, single-center, cohort study of PCMC patients who had at least 1 hospital readmission in 2011. Eligible patients were divided into 2 groups: patients without prescription drug benefits and patients with prescription drug benefits.

Results

Three hundred fifty-two patients met our inclusion criteria. The number of hospital readmissions for patients with a prescription drug benefit was higher than those with no prescription drug benefit (2.453 ± 2.49 vs 1.88 ± 1.91; P = .052). The length of index admission and the length of hospital readmission in days were higher in patients with no prescription drug benefits (index admission, 5.29 ± 6.38 vs 4.59 ± 4.50; P = .428) (readmission, 5.31 ± 5.90 vs 4.48 ± 4.33, P = .166). The number of days to readmission was higher in those with drug benefits (58.12 ± 63.54 vs 53.39 ± 53.47; P = .316). When patient data were separated by CCI scores, it was noted that patients with pharmacy benefits had significantly more hospital readmissions in each CCI score category except for patients with a CCI of 6.

Conclusion

Although not statistically significant, patients with prescription drug benefits had more hospital readmissions but shorter hospital lengths of stay. Significant data linking hospital readmissions and prescription insurance benefits, if found in future studies, would provide helpful guidance to health care systems.

[The interrater reliability of four common comorbidity indexes used in elderly patients]

OBJECTIVE

To report on the interrater reliability of four common comorbidity indexes used in the hospitalised elderly: Charlson Index (CI), Geriatric Cumulative Illness Rating Scale (CIRS-G), Index of Co-existent Disease (CoD) and Kaplan-Feinstein Index (KFI).

METHOD

Four trained observers, independently reviewed the same 40 medical charts of hospitalised geriatric patients. Scores for the four indexes were calculated, along with the intraclass correlations coefficient (ICC) (quantitative index: CI and CIRS-G) and Kappa coefficient (qualitative index: CoD and KFI). The agreement <0.4 was considered deficient, 0-4-0.75 acceptable and >0.75 excellent.

RESULTS

A total of 40 patients (29 women) of 85.93 (±5.35) years were analysed. Intraclass correlations coefficient: CI: 0.78 (95% CI: 0.67-0.86); CIRS-G (score): 0.66 (95% CI: 0.53-0.78). Kappa coefficient: KFI: 0.51 to 0.76; CoD: 0.44-0.66. The application time was lower for the Charlson index (median of 39seconds [30-45]) and the KFI (42seconds [35-52]) and higher for CIRS-G (score) (128seconds [110-160]) and CoD (102seconds [80-124]).

CONCLUSIONS

Of the four comorbidity indexes used in a hospitalised elderly population, the CI, and CIRS-G (score), are those that have better interrater reliability. The Charlson index and KFI show a lower application time than the CIRS-G (score).

A comparison of comorbidities obtained from hospital administrative data and medical charts in older patients with pneumonia

Background

The use of comorbidities in risk adjustment for health outcomes research is frequently necessary to explain some of the observed variations. Medical charts reviews to obtain information on comorbidities is laborious. Increasingly, electronic health care databases have provided an alternative for health services researchers to obtain comorbidity information. However, the rates obtained from databases may be either over- or under-reported. This study aims to (a) quantify the agreement between administrative data and medical charts review across a set of comorbidities; and (b) examine the factors associated with under- or over-reporting of comorbidities by administrative data.

Methods

This is a retrospective cross-sectional study of patients aged 55 years and above, hospitalized for pneumonia at 3 acute care hospitals. Information on comorbidities were obtained from an electronic administrative database and compared with information from medical charts review. Logistic regression was performed to identify factors that were associated with under- or over-reporting of comorbidities by administrative data.

Results

The prevalence of almost all comorbidities obtained from administrative data was lower than that obtained from medical charts review. Agreement between comorbidities obtained from medical charts and administrative data ranged from poor to very strong (kappa 0.01 to 0.78). Factors associated with over-reporting of comorbidities were increased length of hospital stay, disease severity, and death in hospital. In contrast, those associated with under-reporting were number of comorbidities, age, and hospital admission in the previous 90 days.

Conclusions

The validity of using secondary diagnoses from administrative data as an alternative to medical charts for identification of comorbidities varies with the specific condition in question, and is influenced by factors such as age, number of comorbidities, hospital admission in the previous 90 days, severity of illness, length of hospitalization, and whether inhospital death occurred. These factors need to be taken into account when relying on administrative data for comorbidity information.

Indicators of preclinical disability: women's experiences of an aging body

This paper is derived from a larger multimethod longitudinal study of women's bodily experiences and coping practices before the onset of disability. Twelve women participated in repeated performance measures, in-depth interviews of daily life and physically challenging events, and observations of daily activities conducted over 18 months. Interpretive phenomenological analysis of textual data showed that women's bodies provided multiple indicators or symptoms of preclinical disability. These indicators informed the women that their body was out of synch with their environment; conspicuous during social activities; and vulnerable to becoming dependent on others, technology, or assistive devices to accomplish daily activities. Greater attention to bodily indicators or symptoms may offer a practical method for clinicians to identify preclinical disability.

Hospital admissions among people 65+ related to multimorbidity, municipal and outpatient care

This study aimed at examine the number of planned and acute hospital admissions during 1 year among people 65+ and its relation to municipal care, outpatient care, multimorbidity, age and sex. Four thousand nine hundred and seven individuals having one or more admissions during 2001 were studied. Data were collected from two registers and comparisons were made between those having one, two and three or more hospital stays and between those with and without municipal care and services. Linear regression was used to examine factors predicting number of acute and planned admissions. Fifteen percent of the sample had three or more hospital stays (range 3-15) accounting for 35% of all admissions. This group had significantly more contacts in outpatient care with physician (median number of contacts (md)=15), compared to those with one (md: 8), or two admissions (md: 11). Main predictors for number of admissions were number of diagnosis groups and number of contacts with physician in outpatient care. Those who are frequently admitted to hospital constitute a small group that consume a great deal of inpatient care and also tend to have frequent contacts in outpatient care. Thus interventions focusing on frequent admissions are needed, and this requires collaboration between outpatient and hospital care.

Length of comorbidity lookback period affected regression model performance of administrative health data

BACKGROUND AND OBJECTIVE

The impact of different comorbidity ascertainment lookback periods on modeling posthospitalization mortality and readmission was examined.

METHODS

Index cases comprised medical (n = 326,456) and procedural (n = 349,686) patients with a hospital admission from 1990-1996. Administrative hospital data were extracted for 102 comorbidities, ascertained at index admission and for 1-, 2-, 3-, and 5-year lookback periods. Deaths and readmissions were identified within 12 months and 30 days of separation, respectively. Hierarchically nested and nonnested Cox regressions as well as Receiver Operator Characteristic Area Under the Curve (ROC-AUC) were used to determine model-fit and predictive ability of lookback period models.

RESULTS

The 1-year lookback period provided the best model-fit for both patient groups when modeling mortality. A similar model-fit was seen at index admission for procedural but not medical patients. The superior readmission model employed 5 years of lookback for both patient groups. With one exception, all lookback period models were superior to those abstracting comorbidity from index admission only. Similar results were evident from ROC-AUC, although greater predictive ability was seen with modeling of mortality (0.847-0.923) compared with readmission (0.593-0.681).

CONCLUSION

The explanatory power of regression models, when adjusting for comorbidity, is influenced by length of lookback, outcome investigated and clinical subgroup. Shorter periods (approximately 1 year) appear appropriate for modeling posthospitalization mortality, whereas longer lookback periods are superior for readmission outcomes.

Interrater reliability of measurements of comorbid illness should be reported

OBJECTIVE

Comorbidity indices are commonly used to stratify patients to control for treatment selection bias. The objectives here were to review the reporting of interrater reliability when studies use comorbidity indices in clinical research publications and to report the interrater reliability of four common indices in a particular research setting.

STUDY DESIGN AND SETTING

Four trained abstractors reviewed the same 40 charts of patients with squamous cell carcinoma of the head and neck from a regional cancer center. Scores for the Charlson Index, the Index of Co-existent Disease, the Cumulative Illness Rating Scale, and the Kaplan-Feinstein Classification were calculated, and the intraclass correlation coefficient was used to assess interrater reliability.

RESULTS

The details on the training of abstractors and the results of interrater reliability tests are not commonly reported. In our study setting, the Charlson Index had excellent reliability and the others had acceptable reliability.

CONCLUSION

If the quality of a study using an index or scale is to be assessed, the reliability and interrater reliability of the score assignment process should be reported.

Primary Intensive Care: Pilot Study of a Primary Care–Based Intervention for High-Utilizing Patients

This pilot study was conducted to determine whether primary care patients with perceived inappropriate high healthcare utilization would require fewer emergency or inpatient services while enrolled in a weekly multidisciplinary clinic. Seventeen high-utilizing or difficult management patients of a primary care center were referred for the special intervention, Primary Intensive Care (PIC). Although not selected for the presence of psychopathology, 16 patients had comorbid psychiatric diagnoses. Patients followed in the PIC Clinic had significantly lower inpatient and emergency department use during their enrollment in the intervention when compared to the matched pre-enrollment time period, although the total hospital cost differences did not reach statistical significance. Patient and staff satisfaction was high, although the intervention was very difficult for the providers.

Posthospital care transitions: patterns, complications, and risk identification

OBJECTIVES

To (1) describe patterns of posthospital care transitions; (2) characterize these patterns as uncomplicated or complicated; (3) identify those at greatest risk for complicated transitions.

DATA SOURCES/STUDY SETTING

The Medicare Current Beneficiary Survey was used to identify beneficiaries aged 65 and older who were discharged from an acute care hospital in 1997-1998.

STUDY DESIGN

Patterns of posthospital transfers were described over a 30-day time period following initial hospital discharge. Uncomplicated posthospital care patterns were defined as a sequence of transfers from higher-to lower-intensity care environments without recidivism, while complicated posthospital care patterns were defined as the opposite sequence of events. Indices were developed to identify patients at risk for complicated transitions.

PRINCIPAL FINDINGS

Forty-six distinct types of care patterns were observed during the 30 days following hospital discharge. Among these patterns, 444 episodes (61.2 percent) were limited to a single transfer, 130 episodes (17.9 percent) included two transfers, 62 episodes (8.5 percent) involved three transfers, and 31 episodes (4.3 percent) involved four or more transfers. Fifty-nine episodes (8.1 percent) resulted in death. Between 13.4 percent and 25.0 percent of posthospital care patterns in the 1998 sample were classified as complicated. The area under the receiver operating curve was 0.771 for a predictive index that utilized administrative data and 0.833 for an index that used a combination of administrative and self-reported data.

CONCLUSIONS

Posthospital care transitions are common among Medicare beneficiaries and patterns of care vary greatly. A significant number of beneficiaries experienced complicated care transitions-a finding that has important implications for both patient safety and cost-containment efforts. Patients at risk for complicated care patterns can be identified using data available at the time of hospital discharge.

Physical performance measures in the clinical setting

OBJECTIVES

To assess the ability of gait speed alone and a three-item lower extremity performance battery to predict 12-month rates of hospitalization, decline in health, and decline in function in primary care settings serving older adults.

DESIGN

Prospective cohort study.

SETTING

Primary care programs of a Medicare health maintenance organization (HMO) and Veterans Affairs (VA) system.

PARTICIPANTS

Four hundred eighty-seven persons aged 65 and older.

MEASUREMENTS

Lower extremity performance Established Population for Epidemiologic Studies of the Elderly (EPESE) battery including gait speed, chair stands, and tandem balance tests; demographics; health care use; health status; functional status; probability of repeated admission scale (Pra); and primary physician's hospitalization risk estimate.

RESULTS

Veterans had poorer health and higher use than HMO members. Gait speed alone and the EPESE battery predicted hospitalization; 41% (21/51) of slow walkers (gait speed <0.6 m/s) were hospitalized at least once, compared with 26% (70/266) of intermediate walkers (0.6-1.0 m/s) and 11% (15/136) of fast walkers (>1.0 m/s) (P <.0001). The relationship was stronger in the HMO than in the VA. Both performance measures remained independent predictors after accounting for Pra. The EPESE battery was superior to gait speed when both Pra and primary physician's risk estimate were included. Both performance measures predicted decline in function and health status in both health systems. Performance measures, alone or in combination with self-report measures, were more able to predict outcomes than self-report alone.

CONCLUSION

Gait speed and a physical performance battery are brief, quantitative estimates of future risk for hospitalization and decline in health and function in clinical populations of older adults. Physical performance measures might serve as easily accessible "vital signs" to screen older adults in clinical settings.

Survey of major chronic iIlnesses and hospital admissions via the emergency department in a randomized older population in Randwick, Australia

OBJECTIVE

To find out if patients with chronic illnesses living in the community are at risk of unplanned hospital admissions through emergency departments; what types of chronic illnesses may be putative risk factors; and if an increase in the number of chronic illnesses may be associated with increased risk.

METHODS

The survey included the completion of a standardized questionnaire for medical illnesses in a random sample of older people dwelling in the community and analysis of admission records to our hospital. The principal diagnoses for admissions were recorded. The risk factors for admissions were analysed.

RESULTS

Five hundred and twenty-six (239 men and 287 women) people aged 55 years and over were interviewed. Musculoskeletal disorders, hypertension, gastrointestinal disorders and ischaemic heart disease were the most frequently reported of the chronic illnesses surveyed. A total number of 70 people from the survey group with a total of 115 admissions through emergency departments were recorded. Using logistic regression model, hypertension, ischaemic heart disease and age were found to be risk factors for emergency admissions amongst this group of community-dwelling residents. The ratios were 2.03 (95% confidence interval (CI): 1.2-3.44), 2.02 (95% CI: 1.16-3.49) and 1.05 (95% CI: 1.02-1.09), respectively. Furthermore, multiple (three or more) chronic illnesses were found to be a strong predictor of hospital admission via emergency department (chi-square = 16.647, DF = 1, P-value < 0.001).

CONCLUSION

We conclude that there was significant association between multiple chronic diseases and emergency admissions for older people. Of these, hypertension and ischaemic heart disease were found to be significant predictors. Age per se was found to be of borderline significance.

Measuring comorbidity in patients with head and neck cancer

BACKGROUND

Comorbidities are diseases or conditions that coexist with a disease of interest. The importance of comorbidities is that they can alter treatment decisions, change resource utilization, and confound the results of survival analysis.

OBJECTIVE

The objective of this study was to determine the best comorbidity index to use in survival analysis of patients with squamous cell carcinoma of the head and neck.

METHOD

Four validated indexes, with very different methodologies (i.e., the Charlson Index, the Cumulative Illness Rating Scale, the Kaplan-Feinstein Classification, the Index of Co-existent Disease), were tested using data from 379 unselected consecutive patients with complete 3-year follow-up from the Kingston Regional Cancer Center. Kaplan-Meier analysis and Cox Proportional Hazards Regression were used to stratify patients into three levels of increasing severity of comorbidity for each index. The Proportion of Variance Explained and Receiver Operating Characteristics curves were used to compare the performance of the indexes.

CONCLUSION

The Kaplan-Feinstein Classification was the most successful in stratifying patients in this population.

[A new model for medical care to multi-admitted patients]

BACKGROUND AND OBJECTIVE

Patients with chronic conditions are often readmitted to hospital. A study was designed to improve health care to these patients.

PATIENTS AND METHODS

Patients attended at the Internal Medicine Department of a hospital area who were admitted to hospital at least three times in a year were included in the study. Within a prospective study, patients were attended by means of specific consultation with care upon request and telephone care, with the possibility of programmed admission.

RESULTS

Patients were followed for 32 months, with a 45% decrease in the admission rate, 50% in visits to the Emergency Department, and 26% in hospital stay days.

CONCLUSIONS

The proposed care model decrease the attending needs for these patients.

Accuracy of administrative data to assess comorbidity in patients with heart disease. an Australian perspective

The objective of this study was to determine the accuracy of administrative data (by use of hospital discharge codes) for measuring comorbidity in patients with heart disease. One thousand seven hundred and sixty-five medical records of subjects admitted to hospital for AMI, unstable angina, angina pectoris, chronic IHD or heart failure were reviewed. The number and types of comorbidities were determined from the medical records (regarded as the "gold standard"). These were compared with the 10 discharge codes obtained from the hospital administrative records (referred to as the "administrative data"). The rate of false-negative and false-positive comorbidity diagnoses were determined. Twenty of the 21 comorbidities studied were underreported in the administrative data. For these 20 comorbidities, the median false-negative rate was 49.5% and ranged from 11% for diabetes to 100% for dementia. False-positive rates were low, less than 1.5%, except for chronic arrythmia (4.8%) and hypertension (4.2%). Mean percent agreement was high, ranging from 88% for hypertension to 100% for AIDS/HIV. Administrative data based on hospital discharge codes consistently underestimate the presence of comorbid conditions in our population. This has implications for administrators when estimating mortality, length of stay and disability. Researchers also need to be aware when using administrative data based on hospital discharge codes to assess subject's comorbidities that they may be widely underreported.

Measurement and impact of comorbidity in older cancer patients

As the world population ages, oncologists are increasingly confronted with the problem of comorbidity in cancer patients. This has stemmed an increasing interest into approaching comorbidity in a systematic way, in order to integrate it in treatment decisions. So far, data on the subject have been widely scattered through the medical literature. This article is aimed at reviewing the available data on the interaction of comorbidity and prognosis. This overview should provide an accessible source of references for oncological investigators developing research in the field. Various methods have been used to sum comorbidity. However, a major effort remains to be done to analyze how various diseases combine in influencing prognosis. The main end-point explored so far is mortality, with which comorbidity globally is reliably correlated. A largely open challenge remains to correlate comorbidity with treatment tolerance, and functional and quality of life outcomes, as well as to integrate it in clinical decision-making.

Measuring comorbidity in older cancer patients

The aim of this article was to provide oncology researchers with adequate tools and practical advice to integrate comorbidity into clinical studies. Open research questions are also discussed. Commonly used comorbidity indexes were identified and a detailed literature search was done by MEDLINE and cross-referencing. Expert opinion was sought on each index. A common scheme exploring the description of the index, clinical experience, metrological performance, easiness of use, cross-compatibility and preservation of data was followed. The actual indexes are included in the Appendix. Four commonly used indexes were identified: the Charlson Comorbidity Index (Charlson), the Cumulative Illness Rating Scale (CIRS), the Index of Coexistent Disease (ICED), and the Kaplan-Feinstein index. The Charlson is the most commonly used whereas the performance of the first two indexes is best characterised. Most studies are retrospective and focus on mortality as an outcome and a base of grading. All indexes are easy to use and require a maximum of 10 min to be filled. Inter-rater and test-retest reliability is generally good. Little is known about other outcomes and the way various diseases cumulate in influencing prognosis. Thus, several reliable indexes are available to measure comorbidity in cancer patients. They show that globally comorbidity is a strong predictor of outcome. Since little is still known about the importance of individual comorbidities for various outcomes and the way comorbidity cumulates in influencing cancer treatment, a wide integration of comorbidity in prospective studies is essential.

[Chronic comorbidity and homogeneity in diagnostic related groups]

OBJECTIVE

[corrected] One of the ways to compare the efficiency of different hospitals and services is to evaluate Diagnostic Related Groups (DRGs), with the hypothesis that patients in the same RDG will present homogeneous behavior with respect to length of stay. The object of this study was to evaluate in the context os the National Health System the internal variability of specific DRGs in terms of the patients' comorbidity.

METHODS

On the basis of various comorbidity scores measured with the Charlson index (ChI), we analyzed length of stay, inhospital mortality and emergency readmissions at 30 and 365 days in 106.673 hospitalizations (excluding subjects younger than 17 years of age, and obstetrics and psychiatric patients) in 12 hospitals, and in 17 DRGs selected on the basis of their greater frequency and comorbidity.

RESULTS

In the aggregated analysis, length of stay (from 8.5 days in patients with no comorbidity to 17.0 days in patients with scores higher than 4) and inhospital mortality rates (from 3.7% in patients with no comorbidity to 17.6% in patients with highest score) increased significantly with each level of the Charlson index. The readmission rate at 30 days rose from 4.7% to 10.9% also in step with increases in comorbidity scores. Readmissions at one year varied from 14.8% in patients with scores of 0 to 35.2% in patients with scores of 3-4, and dropped to 27.9% in patients with scores higher than 4. When analysing different DRGs, 8 of the 17 groups studied showed a significantly higher length of stay with increased comorbidity scores. Some DRGs also showed intra-group variability with respect to mortality and readmission, particularly at 365 days.

CONCLUSIONS

Some DRGs show significant internal variability in terms of comorbidity that may be generating a false worse evaluation of the efficiency of hospitals that treat patients with higher comorbidity.

Chronic comorbidity and outcomes of hospital care: length of stay, mortality, and readmission at 30 and 365 days

This article evaluates the behavior of an adaptation of the Charlson Index (CHI) applied to administrative databases to measure the relationship between chronic comorbidity and the hospital care outcomes of length of stay (LOS), in-hospital mortality, and emergency readmissions at 30 and 365 days. These outcomes were analyzed in 106,673 hospitalization episodes whose records are registered in a minimum basic data set maintained by the public health authorities of the community of Valencia, Spain. The highest comorbidity measured by the CHI was associated with greater LOS and in-hospital mortality and increased readmission at 30 and 365 days. The rate of readmissions at 1 year dropped, however, in the group with the greatest comorbidity, probably owing to an increase in mortality after hospitalization. While comorbidity does appear to increase the risk of adverse outcomes in general and mortality and readmission specifically, the second outcome is only possible if the first has not occurred. For this reason, information and selection biases derived from administrative databases, or from the CHI itself, should be taken into account when using and interpreting the index.

Do disease specific characteristics add to the explanation of mobility limitations in patients with different chronic diseases? A study in The Netherlands

STUDY OBJECTIVES

To determine whether disease specific characteristics, reflecting clinical disease severity, add to the explanation of mobility limitations in patients with specific chronic diseases.

DESIGN AND SETTING

Cross sectional study of survey data from community dwelling elderly people, aged 55-85 years, in the Netherlands.

PARTICIPANTS AND METHODS

The additional explanation of mobility limitations by disease specific characteristics was examined by logistic regression analyses on data from 2830 community dwelling elderly people.

MAIN RESULTS

In the total sample, chronic non-specific lung disease, cardiac disease, peripheral atherosclerosis, diabetes mellitus, stroke, arthritis and cancer (the index diseases), were all independently associated with mobility limitations. Adjusted for age, sex, comorbidity, and medical treatment disease specific characteristics that explain the association between disease and mobility mostly reflect decreased endurance capacity (shortness of breath and disturbed night rest in chronic non-specific lung disease, angina pectoris and congestive heart failure in cardiac disease), or are directly related to mobility function (stiffness and lower body complaints in arthritis). For atherosclerosis and diabetes mellitus, disease specific characteristics did not add to the explanation of mobility limitations.

CONCLUSIONS

The results provide evidence that, to obtain more detailed information about the differential impact of chronic diseases on mobility, disease specific characteristics are important to take into account.

Reliability of a comorbidity measure: the Index of Co-Existent Disease (ICED)

The reliability of an established comorbidity index (the Index of Co-Existent Disease) was tested using retrospective data from the case notes of elderly patients who had undergone total hip replacement. Inter-rater reliability was examined twice, first with two raters (n = 39) and then with three (n = 49). Intra-rater reliability was assessed using one rater (n = 45). Reasons for any lack of reliability were explored. The inter-rater reliability of the ICED was moderate (kappa 0.5-0.6). While the Functional Severity index performed well (kappa 0.6-1.0), the Index of Disease Severity subindex was less reliable (kappa 0.4-0.5). Differences between raters had an impact on the observed association between comorbidity and serious post-operative complications. Intra-rater reliability was excellent (kappa 0.9). Several reasons why inter-rater reliability was only moderate were identified, mostly related to uncertainties in applying the ICED. The reliability of the ICED needs to be improved before it is used more widely with retrospective data. This might be achieved by further clarification of the instructions for its use.

Case-mix adjustment using administrative databases: a paradigm to guide future research

One of the most persistent problems in the field of quality assessment remains how to remove the confounding effect of different institutions providing care to patients with dissimilar severity of illness and case complexity. The authors review the literature to determine whether risk adjustment systems based on administrative data are inherently inferior to systems that depend on primary data collection and conclude that they are not. In light of the potential competence of risk adjustment systems based on administrative data, the authors identify those systems that are best supported by theory and evidence. Data elements that have been found most explanatory of medical outcomes are also identified. On the basis of an evaluation of the performance of various risk adjustment approaches, the authors propose a paradigm that could serve to unify and direct future studies.

Risk factors for nonelective hospital readmissions

We previously reported a predictive model that identified potentially modifiable risk factors for nonelective readmission to a county hospital. The objectives of this study were to determine if those risk factors were generalizable to a different population. We found that the previously reported risk factors were generalizable, and other potentially modifiable risk factors were identified in this population of veterans. However, further research is needed to establish whether or not the risk factors can be modified and whether or not modification improves outcomes.

Does increased access to primary care reduce hospital readmissions? Veterans Affairs Cooperative Study Group on Primary Care and Hospital Readmission

BACKGROUND

For chronically ill patients, readmission to the hospital can be frequent and costly. We studied the effect of an intervention designed to increase access to primary care after discharge from the hospital, with the goals of reducing readmissions and emergency department visits and increasing patients' quality of life and satisfaction with care.

METHODS

In a multicenter randomized, controlled trial at nine Veterans Affairs Medical Centers, we randomly assigned 1396 veterans hospitalized with diabetes, chronic obstructive pulmonary disease, or congestive heart failure to receive either usual care or an intensive primary care intervention. The intervention involved close follow-up by a nurse and a primary care physician, beginning before discharge and continuing for the next six months.

RESULTS

The patients were severely ill. Half of those with congestive heart failure (504 patients) had disease in New York Heart Association class III or IV; 30 percent of those with diabetes (751 patients) had end-organ damage; and a quarter of those with chronic obstructive pulmonary disease (583 patients) required home oxygen treatment or oral corticosteroids. The patients had extremely poor quality-of-life scores. Although they received more intensive primary care than the controls, the patients in the intervention group had significantly higher rates of readmission (0.19 vs 0.14 per month, P = 0.005) and more days of rehospitalization (10.2 vs 8.8, P = 0.041). The patients in the intervention group were more satisfied with their care (P < 0.001), but there was no difference between the study groups in quality-of-life scores, which remained very low (P = 0.53).

CONCLUSIONS

For veterans discharged from Veterans Affairs hospitals, the primary care intervention we studied increased rather than decreased the rate of rehospitalization, although patients in the intervention group were more satisfied with their care.