Readmissions of adults within three age groups following hospitalization for pneumonia: Analysis from the Nationwide Readmissions Database

Rates and predictors of 30-day hospital readmissions in adults for drug-induced acute pancreatitis: A retrospective study from the United States National Readmission Database

BACKGROUND AND AIM

Drug-induced acute pancreatitis (DIAP) linked to several medications is a diagnosis of exclusion and is associated with significant morbidity and mortality, contributing to the US healthcare cost burden. Existing studies on DIAP focus on the drug classes that can cause acute pancreatitis. Hence, our retrospective study aims to determine the rates and predictors for 30-day readmissions (30-DR) in patients with index hospitalization for DIAP.

METHODS

From the Nationwide Readmissions Database, we followed adults admitted for DIAP who were discharged alive for 30 days. During 30-DR, we evaluated the rates, predictors, and outcomes of DIAP.

RESULTS

Of the 4457 DIAP patients surviving at discharge, 12.5% were readmitted at 30 days. During readmissions, the predictors of 30-DR for DIAP were young age, the Charlson-Deyo Comorbidity Index of 2 and 3, protein-energy malnutrition, and dyslipidemia. During 30-DR, DIAP had a higher mortality rate (2.4% vs. 0.7%; P < 0.020), extended hospital stays (5.6 days vs. 4 days, 0.000), and higher hospital charges ($12 983.6 vs. $8 255.6; P 0.000).

CONCLUSIONS

DIAP has high 30-DR rates and poorer outcomes.

Machine learning methods to predict 30-day hospital readmission outcome among US adults with pneumonia: analysis of the national readmission database

Background

Hospital readmissions for pneumonia are a growing concern in the US, with significant consequences for costs and quality of care. This study developed the rule-based model and other machine learning (ML) models to predict 30-day readmission risk in patients with pneumonia and compared model performance.

Methods

This population-based study involved patients aged ≥ 18 years hospitalized with pneumonia from January 1, 2016, through November 30, 2016, using the Healthcare Cost and Utilization Project-National Readmission Database (HCUP-NRD). Rule-based algorithms and other ML algorithms, specifically decision trees, random forest, extreme gradient descent boosting (XGBoost), and Least Absolute Shrinkage and Selection Operator (LASSO), were used to model all-cause readmissions 30 days post-discharge from index pneumonia hospitalization. A total of 61 clinically relevant variables were included for ML model development. Models were trained on randomly partitioned 50% of the data and evaluated using the remaining dataset. Model hyperparameters were tuned using the ten-fold cross-validation on the resampled training dataset. The area under the receiver operating curves (AUROC) and area under precision-recall curves (AUPRC) were calculated for the testing set to evaluate the model performance.

Results

Of the 372,293 patients with an index hospital hospitalization for pneumonia, 48,280 (12.97%) were readmitted within 30 days. Judged by AUROC in the testing data, rule-based model (0.6591) significantly outperformed decision tree (0.5783, p value < 0.001), random forest (0.6509, p value < 0.01) and LASSO (0.6087, p value < 0.001), but was less superior than XGBoost (0.6606, p value = 0.015). The AUPRC of the rule-based model in the testing data (0.2146) was higher than the decision tree (0.1560), random forest (0.2052), and LASSO (0.2042), but was similar to XGBoost (0.2147). The top risk-predictive rules captured by the rule-based algorithm were comorbidities, illness severity, disposition locations, payer type, age, and length of stay. These predictive risk factors were also identified by other ML models with high variable importance.

Conclusion

The performance of machine learning models for predicting readmission in pneumonia patients varied. The XGboost was better than the rule-based model based on the AUROC. However, important risk factors for predicting readmission remained consistent across ML models.

Risk factors for hospital readmissions in pneumonia patients: A systematic review and meta-analysis

BACKGROUND

Factors that are associated with the short-term rehospitalization have been investigated previously in numerous studies. However, the majority of these studies have not produced any conclusive results because of their smaller sample sizes, differences in the definition of pneumonia, joint pooling of the in-hospital and post-discharge deaths and lower generalizability.

AIM

To estimate the effect of various risk factors on the rate of hospital readmissions in patients with pneumonia.

METHODS

Systematic search was conducted in PubMed Central, EMBASE, MEDLINE, Cochrane library, ScienceDirect and Google Scholar databases and search engines from inception until July 2021. We used the Newcastle Ottawa (NO) scale to assess the quality of published studies. A meta-analysis was carried out with random-effects model and reported pooled odds ratio (OR) with 95% confidence interval (CI).

RESULTS

In total, 17 studies with over 3 million participants were included. Majority of the studies had good to satisfactory quality as per NO scale. Male gender (pooled OR = 1.22; 95%CI: 1.16-1.27), cancer (pooled OR = 1.94; 95%CI: 1.61-2.34), heart failure (pooled OR = 1.28; 95%CI: 1.20-1.37), chronic respiratory disease (pooled OR = 1.37; 95%CI: 1.19-1.58), chronic kidney disease (pooled OR = 1.38; 95%CI: 1.23-1.54) and diabetes mellitus (pooled OR = 1.18; 95%CI: 1.08-1.28) had statistically significant association with the hospital readmission rate among pneumonia patients. Sensitivity analysis showed that there was no significant variation in the magnitude or direction of outcome, indicating lack of influence of a single study on the overall pooled estimate.

CONCLUSION

Male gender and specific chronic comorbid conditions were found to be significant risk factors for hospital readmission among pneumonia patients. These results may allow clinicians and policymakers to develop better intervention strategies for the patients.

Association Rules Mining for Hospital Readmission: A Case Study

As an indicator of healthcare quality and performance, hospital readmission incurs major costs for healthcare systems worldwide. Understanding the relationships between readmission factors, such as input features and readmission length, is challenging following intricate hospital readmission procedures. This study discovered the significant correlation between potential readmission factors (threshold of various settings for readmission length) and basic demographic variables. Association rule mining (ARM), particularly the Apriori algorithm, was utilised to extract the hidden input variable patterns and relationships among admitted patients by generating supervised learning rules. The mined rules were categorised into two outcomes to comprehend readmission data; (i) the rules associated with various readmission length and (ii) several expert-validated variables related to basic demographics (gender, race, and age group). The extracted rules proved useful to facilitate decision-making and resource preparation to minimise patient readmission.

Risk of 30-day hospital readmission associated with medical conditions and drug regimens of polymedicated, older inpatients discharged home: a registry-based cohort study

OBJECTIVES

The present study analysed 4 years of a hospital register (2015-2018) to determine the risk of 30-day hospital readmission associated with the medical conditions and drug regimens of polymedicated, older inpatients discharged home.

DESIGN

Registry-based cohort study.

SETTING

Valais Hospital-a public general hospital centre in the French-speaking part of Switzerland.

PARTICIPANTS

We explored the electronic records of 20 422 inpatient stays by polymedicated, home-dwelling older adults held in the hospital's patient register. We identified 13 802 hospital readmissions involving 8878 separate patients over 64 years old.

OUTCOME MEASURES

Sociodemographic characteristics, medical conditions and drug regimen data associated with risk of readmission within 30 days of discharge.

RESULTS

The overall 30-day hospital readmission rate was 7.8%. Adjusted multivariate analyses revealed increased risk of hospital readmission for patients with longer hospital length of stay (OR=1.014 per additional day; 95% CI 1.006 to 1.021), impaired mobility (OR=1.218; 95% CI 1.039 to 1.427), multimorbidity (OR=1.419 per additional International Classification of Diseases, 10th Revision condition; 95% CI 1.282 to 1.572), tumorous disease (OR=2.538; 95% CI 2.089 to 3.082), polypharmacy (OR=1.043 per additional drug prescribed; 95% CI 1.028 to 1.058), and certain specific drugs, including antiemetics and antinauseants (OR=3.216 per additional drug unit taken; 95% CI 1.842 to 5.617), antihypertensives (OR=1.771; 95% CI 1.287 to 2.438), drugs for functional gastrointestinal disorders (OR=1.424; 95% CI 1.166 to 1.739), systemic hormonal preparations (OR=1.207; 95% CI 1.052 to 1.385) and vitamins (OR=1.201; 95% CI 1.049 to 1.374), as well as concurrent use of beta-blocking agents and drugs for acid-related disorders (OR=1.367; 95% CI 1.046 to 1.788).

CONCLUSIONS

Thirty-day hospital readmission risk was associated with longer hospital length of stay, health disorders, polypharmacy and drug regimens. The drug regimen patterns increasing the risk of hospital readmission were very heterogeneous. Further research is needed to explore hospital readmissions caused solely by specific drugs and drug-drug interactions.

Readmissions and costs among younger and older adults for targeted conditions during the enactment of the hospital readmission reduction program

Background

The Hospital Readmissions Reduction Program (HRRP) was introduced to reduce readmission rates among Medicare beneficiaries, however little is known about readmissions and costs for HRRP-targeted conditions in younger populations. The primary objective of this study was to examine readmission trends and costs for targeted conditions during policy implementation among younger and older adults in the U.S.

Methods

We analyzed the Nationwide Readmission Database from January 2010 to September 2015 in younger (18–64 years) and older (≥65 years) patients with acute myocardial infarction (AMI), heart failure (HF), pneumonia, and acute exacerbations of chronic obstructive pulmonary disease (AECOPD). Pre- and post-HRRP periods were defined based on implementation of the policy for each condition. Readmission rates were evaluated using an interrupted time series with difference-in-difference analyses and hospital cost differences between early and late readmissions (≤30 vs. > 30 days) were evaluated using generalized linear models.

Results

Overall, this study included 16,884,612 hospitalizations with 3,337,266 readmissions among all age groups and 5,977,177 hospitalizations with 1,104,940 readmissions in those aged 18–64 years. Readmission rates decreased in all conditions. In the HRRP announcement period, readmissions declined significantly for those aged 40–64 years for AMI (p < 0.0001) and HF (p = 0.003). Readmissions decreased significantly in the post-HRRP period for those aged 40–64 years at a slower rate for AMI (p = 0.003) and HF (p = 0.05). Readmission rates among younger patients (18–64 years) varied within all four targeted conditions in HRRP announcement and post-HRRP periods. Adjusted models showed a significantly higher readmission cost in those readmitted within 30 days among younger and older populations for AMI (p < 0.0001), HF (p < 0.0001), pneumonia (p < 0.0001), and AECOPD (p < 0.0001).

Conclusion

Readmissions for targeted conditions decreased in the U.S. during the enactment of the HRRP policy and younger age groups (< 65 years) not targeted by the policy saw a mixed effect. Healthcare expenditures in younger and older populations were significantly higher for early readmissions with all targeted conditions. Further research is necessary evaluating total healthcare utilization including emergency department visits, observation units, and hospital readmissions in order to better understand the extent of the HRRP on U.S. healthcare.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12913-021-06399-z.

Longitudinal course of GDF15 levels before acute hospitalization and death in the general population

Growth differentiation 15 (GDF15) is a potential novel biomarker of biological aging. To separate the effects of chronological age and birth cohort from biological age, longitudinal studies investigating the associations of GDF15 levels with adverse health outcomes are needed. We investigated changes in GDF15 levels over 10 years in an age-stratified sample of the general population and their relation to the risk of acute hospitalization and death. Serum levels of GDF15 were measured three times in 5-year intervals in 2176 participants aged 30, 40, 50, or 60 years from the Danish population-based DAN-MONICA cohort. We assessed the association of single and repeated GDF15 measurements with the risk of non-traumatic acute hospitalizations. We tested whether changes in GDF15 levels over 10 years differed according to the frequency of hospitalizations within 2 years or survival within 20 years, after the last GDF15 measurement. The change in GDF15 levels over time was dependent on age and sex. Higher GDF15 levels and a greater increase in GDF15 levels were associated with an increased risk of acute hospitalization in adjusted Cox regression analyses. Participants with more frequent admissions within 2 years, and those who died within 20 years, after the last GDF15 measurement already had elevated GDF15 levels at baseline and experienced greater increases in GDF15 levels during the study. The change in GDF15 levels was associated with changes in C-reactive protein and biomarkers of kidney, liver, and cardiac function. Monitoring of GDF15 starting in middle-aged could be valuable for the prediction of adverse health outcomes.

Development of a risk prediction model of potentially avoidable readmission for patients hospitalised with community-acquired pneumonia: study protocol and population

INTRODUCTION

30-day readmission rate is considered an adverse outcome reflecting suboptimal quality of care during index hospitalisation for community-acquired pneumonia (CAP). However, potentially avoidable readmission would be a more relevant metric than all-cause readmission for tracking quality of hospital care for CAP. The objectives of this study are (1) to estimate potentially avoidable 30-day readmission rate and (2) to develop a risk prediction model intended to identify potentially avoidable readmissions for CAP.

METHODS AND ANALYSIS

The study population consists of consecutive patients admitted in two hospitals from the community or nursing home setting with pneumonia. To qualify for inclusion, patients must have a primary or secondary discharge diagnosis code of pneumonia. Data sources include routinely collected administrative claims data as part of diagnosis-related group prospective payment system and structured chart reviews. The main outcome measure is potentially avoidable readmission within 30 days of discharge from index hospitalisation. The likelihood that a readmission is potentially avoidable will be quantified using latent class analysis based on independent structured reviews performed by four panellists. We will use a two-stage approach to develop a claims data-based model intended to identify potentially avoidable readmissions. The first stage implies deriving a clinical model based on data collected through retrospective chart review only. In the second stage, the predictors comprising the medical record model will be translated into International Classification of Diseases, 10th revision discharge diagnosis codes in order to obtain a claim data-based risk model.The study sample consists of 1150 hospital stays with a diagnosis of CAP. 30-day index hospital readmission rate is 17.5%.

ETHICS AND DISSEMINATION

The protocol was reviewed by the Comité de Protection des Personnes Sud Est V (IRB#6705). Efforts will be made to release the primary study results within 6 months of data collection completion.

TRIAL REGISTRATION NUMBER

ClinicalTrials.gov Registry (NCT02833259).

Prediction of 30-Day Hospital Readmissions for All-Cause Dental Conditions using Machine Learning

Introduction

It is unknown whether patients admitted for all-cause dental conditions (ACDC) are at high risk for hospital readmission, or what are the risk factors for dental hospital readmission.

Objective

We examined the prevalence of, and risk factors associated with, 30-day hospital readmission for patients with an all-cause dental admission. We applied artificial intelligence to develop machine learning (ML) algorithms to predict patients at risk of 30-day hospital readmission.

Methods

This study used data extracted from the 2013 Nationwide Readmissions Database (NRD). There were a total of 11,341 cases for all-cause index admission for dental patients admitted to the hospitals. Descriptive statistics were used to analyze patient characteristics. This study applied five techniques to build risk prediction models and to identify risk factors. Model performance was evaluated using area under the receiver operating characteristic curve (AUC), and accuracy, sensitivity, specificity and precision.

Results

There were 11% of patients admitted for ACDC readmitted within 30 days of hospital discharge. On average, the total charge per patient was $131,004 for those with 30-day readmission (n=1254) and $69,750 for those without readmission (n=10,087). Factors significantly associated with 30-day hospital readmission included total charges, number of diagnoses, age, number of chronic conditions, length of hospital stays, number of procedures, Medicare insurance and Medicaid insurance, and severity of illness. Model performance from all methods was similar with the artificial neural network showing the highest AUC of 0.739.

Conclusion

Our results demonstrate that readmission after hospitalization with ACDC is fairly common. If one-third of the 30-day readmission cases can be avoided, there is a potential annual saving of over $25 million among the twenty-one states represented in the NRD. The ML algorithms can predict hospital readmission in dental patients and should be further tested to aid the reduction of hospital readmission and enhancement of patient-centered care.

Is the placement of jejunostomy tubes in patients with esophageal cancer undergoing esophagectomy associated with increased inpatient healthcare utilization? An analysis of the National Readmissions Database

BACKGROUND

Patients undergoing esophagectomy often receive jejunostomy tubes (j-tubes) for nutritional supplementation. We hypothesized that j-tubes are associated with increased post-esophagectomy readmissions.

STUDY DESIGN

We identified esophagectomies for malignancy with (EWJ) or without (EWOJ) j-tubes using the 2010-2015 Nationwide Readmissions Database. Outcomes include readmission, inpatient mortality, and complications. Outcomes were compared before and after propensity score matching (PSM).

RESULTS

Of 22,429 patients undergoing esophagectomy, 16,829 (75.0%) received j-tubes. Patients were similar in age and gender but EWJ were more likely to receive chemotherapy (24.2% vs. 15.1%, p < 0.01). EWJ was associated with decreased 180-day inpatient mortality (HR 0.72 [0.52-0.99]) but not with higher readmissions at 30- (15.2% vs. 14.0%, p = 0.16; HR 0.9 [0.77-1.05]) or 180 days (25.2% vs. 24.3%, p = 0.37; HR 0.94 [0.79-1.10]) or increased complications (p = 0.37). These results were confirmed in the PSM cohort.

CONCLUSION

J-tubes placed in the setting of esophagectomy do not increase inpatient readmissions or mortality.

Readmission of older acutely admitted medical patients after short-term admissions in Denmark: a nationwide cohort study

Background

Knowledge of unplanned readmission rates and prognostic factors for readmission among older people after early discharge from emergency departments is sparse. The aims of this study were to examine the unplanned readmission rate among older patients after short-term admission, and to examine risk factors for readmission including demographic factors, comorbidity and admission diagnoses.

Methods

This cohort study included all medical patients aged ≥65 years acutely admitted to Danish hospitals between 1 January 2013 and 30 June 2014 and surviving a hospital stay of ≤24 h. Data on readmission within 30 days, comorbidity, demographic factors, discharge diagnoses and mortality were obtained from the Danish National Registry of Patients and the Danish Civil Registration System. We examined risk factors for readmission using a multivariable Cox regression to estimate adjusted hazard ratios (aHR) with 95% confidence intervals (CI) for readmission.

Results

A total of 93,306 patients with a median age of 75 years were acutely admitted and discharged within 24 h, and 18,958 (20.3%; 95% CI 20.1 - 20.6%) were readmitted with a median time to readmission of 8 days (IQR 3 - 16 days). The majority were readmitted with a new diagnosis. Male sex (aHR 1.15; 1.11 - 1.18) and a Charlson Comorbidity Index ≥3 (aHR 2.28; 2.20 - 2.37) were associated with an increased risk of readmission. Discharge diagnoses associated with increased risk of readmission were heart failure (aHR 1.26; 1.12 - 1.41), chronic obstructive pulmonary disease (aHR 1.33; 1.25 - 1.43), dehydration (aHR 1.28; 1.17 - 1.39), constipation (aHR 1.26; 1.14 - 1.39), anemia (aHR 1.45; 1.38 - 1.54), pneumonia (aHR 1.15; 1.06 - 1.25), urinary tract infection (aHR 1.15; 1.07 - 1.24), suspicion of malignancy (aHR 1.51; 1.37 - 1.66), fever (aHR 1.52; 1.33 - 1.73) and abdominal pain (aHR 1.12; 1.05 - 1.19).

Conclusions

One fifth of acutely admitted medical patients aged ≥65 were readmitted within 30 days after early discharge. Male gender, the burden of comorbidity and several primary discharge diagnoses were risk factors for readmission.

Community-acquired pneumonia in critically ill very old patients: a growing problem

Very old (aged ≥80 years) adults constitute an increasing proportion of the global population. Currently, this subgroup of patients represents an important percentage of patients admitted to the intensive care unit. Community-acquired pneumonia (CAP) frequently affects very old adults. However, there are no specific recommendations for the management of critically ill very old CAP patients. Multiple morbidities, polypharmacy, immunosenescence and frailty contribute to an increased risk of pneumonia in this population. CAP in critically ill very old patients is associated with higher short- and long-term mortality; however, because of its uncommon presentation, diagnosis can be very difficult. Management of critically ill very old CAP patients should be guided by their baseline characteristics, clinical presentation and risk factors for multidrug-resistant pathogens. Hospitalisation in intermediate care may be a good option for critical ill very old CAP patients who do not require invasive procedures and for whom intensive care is questionable in terms of benefit.

There is currently no international recommendation for the management of critically ill older patients over 80 years of age with CAP. We report and discuss recent literature in order to help physicians in the decision-making process of these patients.http://bit.ly/2ql0mIz