Effects of Physician Volume on Readmission and Mortality in Elderly Patients with Heart Failure: Nationwide Cohort Study

The influence of hospital volume and physician volume on early mortality in acute promyelocytic leukemia patients

Acute promyelocytic leukemia (APL) is a highly curable hematologic malignancy in the era of all-trans retinoic acid (ATRA) combination treatment. However, only a modest change in early mortality rate has been observed despite the wide availability of ATRA. In addition to the clinical characteristics of APL patients, studies on the hospital volume-outcome relationship and the physician volume-outcome relationship remained limited. We aim to evaluate the association between hospital and physician volume and the early mortality rate among APL patients. The patients were collected from Taiwan's National Health Insurance Research Database (NHIRD). Early mortality is defined as death within 30 days of diagnosis. Patients were categorized into four groups according to individual cumulative hospital and physician volume. The risk of all-cause mortality in APL patients with different cumulative volume groups was compared using a Cox proportional hazard model. The probability of overall survival was estimated using the Kaplan-Meier method. All 741 patients were divided into four quartile volume groups. In the multivariate analysis, only physician volume was significantly associated with early mortality rate. The physician volume of the highest quartile was a protective factor for early mortality compared with the physician volume of the lowest quartile (HR 0.10, 95% CI 0.02-0.65). Hospital characteristics were not associated with early mortality. In the sensitivity analyses, the results remained consistent using two other different definitions of early mortality. Higher physician volume was independently associated with lower early mortality, while hospital volume was not. Enhancing the clinical expertise of low-volume physicians may ensure better outcomes.

LACE Index to Predict the High Risk of 30-Day Readmission in Patients With Acute Myocardial Infarction at a University Affiliated Hospital

Background

The LACE index (length of stay, acuity of admission, comorbidity index, and emergency room visit in the past 6 months) has been used to predict the risk of 30-day readmission after hospital discharge in both medical and surgical patients. This study aimed to utilize the LACE index to predict the risk of 30-day readmission in hospitalized patients with acute myocardial infraction (AMI).

Methods

This was a retrospective study. Data were extracted from the hospital's electronic medical records of patients admitted with AMI between 2015 and 2019. LACE index was built on admission patient demographic data, and clinical and laboratory findings during the index of admission. The multivariate logistic regression was performed to determine the association and the risk prediction ability of the LACE index, and 30-day readmission were analyzed by receiver operator characteristic curves with C-statistic.

Results

Of the 3,607 patients included in the study, 5.7% (205) were readmitted within 30 days of discharge from the hospital. The adjusted odds ratio based on logistic regression of all baseline variables showed a statistically significant association with the LACE score and revealed an increased risk of readmission within 30 days of hospital discharge. However, patients with high LACE scores (≥10) had a significantly higher rate of emergency revisits within 30 days from the index discharge than those with low LACE scores. Despite this, analysis of the receiver operating characteristic curve indicated that the LACE index had favorable discrimination ability C-statistic 0.78 (95%CI; 0.75–0.81). The Hosmer–Lemeshow goodness- of-fit test P value was p = 0.920, indicating that the model was well-calibrated to predict risk of the 30-day readmission.

Conclusion

The LACE index demonstrated the good discrimination power to predict the risk of 30-day readmissions for hospitalized patients with AMI. These results can help clinicians to predict the risk of 30-day readmission at the early stage of hospitalization and pay attention during the care of high-risk patients. Future work is to be focused on additional factors to predict the risk of 30-day readmissions; they should be considered to improve the model performance of the LACE index with other acute conditions by using administrative data.

Clinical Characteristics and Factors Associated with Heart Failure Readmission at a Tertiary Hospital in North-Eastern Tanzania

INTRODUCTION

Heart failure (HF) is characterized by frequent episodes of decompensation, leading to a high hospitalization burden. More than 50% of index hospitalizations for HF patients return within 6 months of discharge. Once the patient is readmitted, the risk of further disease progression and the mortality rate are increased. A lot of patients are readmitted due to factors such as poor medication adherence, infections, or worsening comorbidities. The aim of our study was to identify the inpatient burden of HF readmission and to identify the factors associated with early readmission.

METHODS

A hospital-based cross-sectional analytical study was conducted from November 2018 to April 2019 within the medical wards at Kilimanjaro Christian Medical Centre (KCMC), which is a teaching and referral hospital in north-eastern Tanzania. The study population included all patients with HF admitted within the medical ward. Data were collected using questionnaires and blood and radiological investigations, and analysis was done using Statistical Package for Social Science (SPSS) version 25. Chi-square test was used to compare proportions of categorical variables. Logistic regression was used to determine the likelihood for readmission, and p-value of <0.05 was considered to be statistically significant.

RESULTS

A total of 353 patients were identified with HF, of whom 136 (38.5%) had a previous admission. Of the 136 patients analysed, the mean age was 62.8 years (SD 17.1), and 86 (63.2%) were females. Within 30 days after discharge, 34 (25.0%) of the patients were readmissions. Factors for early readmission were unemployment (OR = 2.38, 95% CI = 1.02-5.54, p = 0.043), poor medication adherence (OR = 3.87, 95% CI = 1.67-8.97, p = 0.002), absence of angiotensin-converting enzyme inhibitor (ACEI) or angiotensin receptor blocker (ARB) (OR = 2.40, 95% CI = 1.09-5.31, p = 0.030), and pleural effusion (OR 3.25, 95% CI = 1.44-7.32, p = 0.004).

CONCLUSION

Heart failure is a burden due to a large number of admissions and readmissions. Factors such as poor medication adherence and absence of adequate HF therapy, especially the absence of regimes containing ACEI or ARB, need to be targeted to reduce the number of readmissions. This will help reduce the risk of further decompensations, disease progression, and mortality rate.

An Empirical Investigation of "Physician Congestion" in U.S. University Hospitals

We add a new angle to the debate on whether greater healthcare spending is associated with better outcomes, by focusing on the link between the size of the physician workforce at the ward level and healthcare results. Drawing on standard organization theories, we proposed that due to organizational limitations, the relationship between physician workforce size and medical performance is hump-shaped. Using a sample of 150 U.S. university departments across three specialties that record measures of clinical scores, as well as a rich set of covariates, we found that the relationship was indeed hump-shaped. At the two extremes, departments with an insufficient (excessive) number of physicians may gain a substantial increase in healthcare quality by the addition (dismissal) of a single physician. The marginal elasticity of healthcare quality with respect to the number of physicians, although positive and significant, was much smaller than the marginal contribution of other factors. Moreover, research quality conducted at the ward level was shown to be an important moderator. Our results suggest that studying the relationship between the number of physicians per bed and the quality of healthcare at an aggregate level may lead to bias. Framing the problem at the ward-level may facilitate a better allocation of physicians.