Prediction of mortality for congestive heart failure patients: results from different wards of an Italian teaching hospital

Analyzing the 20-year declining trend of hospital length-of-stay in European countries with different healthcare systems and reimbursement models

The study aims to investigate the last 20-year (2000-2019) of hospital length of stay (LOS) trends and their association with different healthcare systems (HS) among 25 European countries. A panel dataset was created using secondary data from Eurostat and Global Burden of Disease study databases, with dependent and control variables aggregated at the national level over a period of 20 years. A time trend analysis was conducted using a weighted least squares model for panel data to investigate the association between LOS, HS models [National Health Service (NHS), National Health Insurance, Social Health Insurance (SHI), and Etatist Social Health Insurance], healthcare reimbursement schemes [Prospective Global Budget (PGB), Diagnosis Related Groups (DRG), and Procedure Service Payment (PSP)], and control variables. The study showed a reduction of average LOS from 9.20 days in 2000 to 7.24 in 2019. SHI was associated with a lower LOS compared to NHS (b = - 0.6327, p < 0.05). Both DRG (b = 1.2399, p < 0.05) and PSP (b = 1.1677, p < 0.05) reimbursement models were positively associated with LOS compared to PGB. Our results confirmed the downward trend of LOS in the last 20 years, its multifactorial nature, and the influence of the SHI model of HS. This could be due to the financial incentives present in fee-for-service payment models and the role of competition in creating a market for healthcare services. These results offer insight into the factors influencing healthcare utilization and can inform the design of more effective, efficient, and sustainable HS.

All Patient Refined-Diagnosis Related Groups' (APR-DRGs) Severity of Illness and Risk of Mortality as predictors of in-hospital mortality

The aims of this study were to assess All-Patient Refined Diagnosis-Related Groups' (APR-DRG) Severity of Illness (SOI) and Risk of Mortality (ROM) as predictors of in-hospital mortality, comparing with Charlson Comorbidity Index (CCI) and Elixhauser Comorbidity Index (ECI) scores. We performed a retrospective observational study using mainland Portuguese public hospitalizations of adult patients from 2011 to 2016. Model discrimination (C-statistic/ area under the curve) and goodness-of-fit (R-squared) were calculated. Our results comprised 4,176,142 hospitalizations with 5.9% in-hospital deaths. Compared to the CCI and ECI models, the model considering SOI, age and sex showed a statistically significantly higher discrimination in 49.6% (132 out of 266) of APR-DRGs, while in the model with ROM that happened in 33.5% of APR-DRGs. Between these two models, SOI was the best performer for nearly 20% of APR-DRGs. Some particular APR-DRGs have showed good discrimination (e.g. related to burns, viral meningitis or specific transplants). In conclusion, SOI or ROM, combined with age and sex, perform better than more widely used comorbidity indices. Despite ROM being the only score specifically designed for in-hospital mortality prediction, SOI performed better. These findings can be helpful for hospital or organizational models benchmarking or epidemiological analysis.

Comparison of Measures to Predict Mortality and Length of Stay in Hospitalized Patients

BACKGROUND

Patient risk adjustment is critical for hospital benchmarking and allocation of healthcare resources. However, considerable heterogeneity exists among measures.

OBJECTIVES

The performance of five measures was compared to predict mortality and length of stay (LOS) in hospitalized adults using claims data; these include three comorbidity composite scores (Charlson/Deyo age-comorbidity score, V W Elixhauser comorbidity score, and V W Elixhauser age-comorbidity score), 3 M risk of mortality (3 M ROM), and 3 M severity of illness (3 M SOI) subclasses.

METHODS

Binary logistic and zero-truncated negative binomial regression models were applied to a 2-year retrospective dataset (2013-2014) with 123,641 adult inpatient admissions from a large hospital system in New York City.

RESULTS

All five measures demonstrated good to strong model fit for predicting in-hospital mortality, with C-statistics of 0.74 (95% confidence interval [CI] [0.74, 0.75]), 0.80 (95% CI [0.80, 0.81]), 0.81(95% CI [0.81, 0.82]), 0.94 (95% CI [0.93, 0.94]), and 0.90 (95% CI [0.90, 0.91]) for Charlson/Deyo age-comorbidity score, V W Elixhauser comorbidity score, V W Elixhauser age-comorbidity score, 3 M ROM, and 3 M SOI, respectively. The model fit statistics to predict hospital LOS measured by the likelihood ratio index were 0.3%, 1.2%, 1.1%, 6.2%, and 4.3%, respectively.

DISCUSSION

The measures tested in this study can guide nurse managers in the assignment of nursing care and coordination of needed patient services and administrators to effectively and efficiently support optimal nursing care.

Short-term adjusted outcomes for heart failure

PURPOSE

Heart failure (HF) is recognized as a major problem in industrialized countries. Short-term adjusted outcomes are indicators of quality for care process during/after hospitalization. Our aim is to evaluate, for patients with principal diagnosis of HF, in-hospital mortality and 30-day readmissions for all-causes using two different risk adjustment (RA) tools.

METHODS AND RESULTS

We used data from the hospital discharge abstract (HD) of a retrospective cohort of patients (2002-2007) admitted in Tuscan hospitals, Italy. Considered outcomes were in-hospital mortality and readmission at 30 days. We compared the All-Patients Refined Diagnosis Related Groups (APR-DRG) system and the Elixhauser Index (EI). Logistic regression was performed and models were compared using the C statistic (C). examined records were 58.202. Crude in-hospital mortality was 9.7%. Thirty-day readmission was 5.1%. The APR-DRG class of risk of death (ROD) was a predictive factor for in-hospital mortality; the APR-DRG class of severity was not significantly associated with 30-day readmissions (P>0.05). EI comorbidities which were more strongly associated with outcomes were nonmetastatic cancer for in-hospital mortality (odds ratio, OR 2.25, P<0.001), uncomplicated and complicated diabetes for 30-day hospital readmissions (OR 1.20 and 1.34, P<0.001). The discriminative abilities for in-hospital mortality were sufficient for both models (C 0.67 for EI, C 0.72 for APR-DRG) while they were low for 30-day readmissions rate (C 0.53 and 0.52).

CONCLUSIONS

Age, gender, APR-DRG ROD and some Elixhauser comorbidities are predictive factors of outcomes; only the APR-DRG showed an acceptable ability to predict hospital mortality while none of them was satisfactory in predicting the readmissions within 30 days.