An analysis of the outcomes of Clostridioides difficile occurring in intestinal transplant recipients requiring hospitalization

BACKGROUND

Organ transplantation is a known risk factor for Clostridioides difficile infection (CDI). There is limited published data on the impact of CDI in the intestinal transplant population.

METHODS

We utilized the National Readmission Database (2010-2017) to study the outcomes of CDI in patients having a history of intestinal transplantation. Association of CDI with readmission and hospital resource utilization was computed in multivariable models adjusted for demographics and comorbidities.

RESULTS

During 2010-2017, 8442 hospitalizations with the history of intestinal transplantation had indexed hospital admissions. Of these, 320 (3.8%) had CDI. CDI hospitalization in intestine transplant patients was associated with higher median cost $54 430 (IQR: 27 231, 109 980) as compared to patients who did not have CDI $48 888 (IQR: 22 578, 112 777), (β: 71 814 95% confidence intervals [CI]: 676-142 953, p = .048). The median length of stay was also longer for patients with CDI 7 (IQR: 4, 13) days as compared to 5 (IQR: 3, 11) days in non-CDI (β: 5.51 95% CI: 0.73-10.29, p = .02). The mortality rate, intestinal transplant complications, presence of malnutrition, acute kidney injury, ICU admissions, and sepsis were similar in both groups. CDI was the top cause of 30-day readmission in the intestinal transplant recipients with CDI during the index admission; the number of 30-day readmissions also increased from 2010 to 2017.

CONCLUSION

CDI hospitalization in post-intestine transplant patients occurs commonly and is associated with a longer length of stay and higher costs during hospitalization. The CDI was the most common cause of readmission after the index admission of CDI in these patients.

Demographics and surgery-related complications lead to 30-day readmission rates among knee arthroscopic procedures

PURPOSE

The study objectives were (1) to evaluate risk factors related to 30-day hospital readmissions after arthroscopic knee surgeries and (2) to determine the complications that may arise from surgery.

METHODS

The American College of Surgeons National Surgical Quality Improvement Program database data from 2012 to 2017 were researched. Patients were identified using Current Procedural Terminology codes for knee arthroscopic procedures. Ordinal logistic fit regression and decision tree analysis were used to examine study objectives.

RESULTS

There were 83,083 knee arthroscopic procedures between 2012 and 2017 obtained from the National Surgical Quality Improvement Program database. The overall readmission rate was 0.87%. The complication rates were highest for synovectomy and cartilage procedures, 1.6% and 1.3% respectively. A majority of readmissions were related to the procedure (71.1%) with wound complications being the primary reason (28.2%) followed by pulmonary embolism and deep vein thrombosis, 12.7% and 10.6%, respectively. Gender and body mass index were not significant factors and age over 65 years was an independent risk factor. Wound infection, deep vein thrombosis, and pulmonary embolism were the most prevalent complications.

CONCLUSION

Healthcare professionals have a unique opportunity to modify treatment plans based on patient risk factors. For patients who are at higher risk of inferior surgical outcomes, clinicians should carefully weigh risk factors when considering surgical and non-surgical approaches.

LEVEL OF EVIDENCE

III.

The GeriPACT Initiative to Prevent All-Cause 30-Day Readmission in High Risk Elderly

Background: Suboptimal care transitions increases the risk of adverse events resulting from poor care coordination among providers and healthcare facilities. The National Transition of Care Coalition recommends shifting the discharge paradigm from discharge from the hospital, to transfer with continuous management. The patient centered medical home is a promising model, which improves care coordination and may reduce hospital readmissions. Methods: This is a quality improvement report, the geriatric patient-aligned care team (GeriPACT) at Tennessee Valley Healthcare System (TVHS) participated in ongoing quality improvement (Plan, Do, Study, Act (PDSA)) cycles during teamlet meetings. Post home discharge follow-up for GeriPACT patients was provided by proactive telehealth communication by the Registered Nurse (RN) care manager and nurse practitioner. Periodic operations data obtained from the Data and Statistical Services (DSS) coordinator informed the PDSA cycles and teamlet meetings. Results: at baseline (July 2018–June 2019) the 30-day all-cause readmission for GeriPACT was 21%. From July to December 2019, 30-day all-cause readmissions were 13%. From January to June 2020, 30-day all-cause readmissions were 15%. Conclusion: PDSA cycles with sharing of operations data during GeriPACT teamlet meetings and fostering a shared responsibility for managing high-risk patients contributes to improved outcomes in 30-day all-cause readmissions.

Using machine learning to predict 30-day readmissions after posterior lumbar fusion: an NSQIP study involving 23,264 patients

OBJECTIVE

Unplanned preventable hospital readmissions within 30 days are a great burden to patients and the healthcare system. With an estimated $41.3 billion spent yearly, reducing such readmission rates is of the utmost importance. With the widespread adoption of big data and machine learning, clinicians can use these analytical tools to understand these complex relationships and find predictive factors that can be generalized to future patients. The object of this study was to assess the efficacy of a machine learning algorithm in the prediction of 30-day hospital readmission after posterior spinal fusion surgery.

METHODS

The authors analyzed the distribution of National Surgical Quality Improvement Program (NSQIP) posterior lumbar fusions from 2011 to 2016 by using machine learning techniques to create a model predictive of hospital readmissions. A deep neural network was trained using 177 unique input variables. The model was trained and tested using cross-validation, in which the data were randomly partitioned into training (n = 17,448 [75%]) and testing (n = 5816 [25%]) data sets. In training, the 17,448 training cases were fed through a series of 7 layers, each with varying degrees of forward and backward communicating nodes (neurons).

RESULTS

Mean and median positive predictive values were 78.5% and 78.0%, respectively. Mean and median negative predictive values were both 97%, respectively. Mean and median areas under the curve for the model were 0.812 and 0.810, respectively. The five most heavily weighted inputs were (in order of importance) return to the operating room, septic shock, superficial surgical site infection, sepsis, and being on a ventilator for > 48 hours.

CONCLUSIONS

Machine learning and artificial intelligence are powerful tools with the ability to improve understanding of predictive metrics in clinical spine surgery. The authors' model was able to predict those patients who would not require readmission. Similarly, the majority of predicted readmissions (up to 60%) were predicted by the model while retaining a 0% false-positive rate. Such findings suggest a possible need for reevaluation of the current Hospital Readmissions Reduction Program penalties in spine surgery.

Impact of the Medicare hospital readmissions reduction program on vulnerable populations

BACKGROUND

The Hospital Readmissions Reduction Program (HRRP) was established by the 2010 Patient Protection and Affordable Care Act (ACA) in an effort to reduce excess hospital readmissions, lower health care costs, and improve patient safety and outcomes. Although studies have examined the policy's overall impacts and differences by hospital types, research is limited on its effects for different types of vulnerable populations. The aim of this study was to analyze the impact of the HRRP on readmissions for three targeted conditions (acute myocardial infarction, heart failure, and pneumonia) among four types of vulnerable populations, including low-income patients, patients served by hospitals that serve a high percentage of low-income or Medicaid patients, and high-risk patients at the highest quartile of the Elixhauser comorbidity index score.

METHODS

Data on patient and hospital information came from the Nationwide Readmission Database (NRD), which contained all discharges from community hospitals in 27 states during 2010-2014. Using difference-in-difference (DD) models, linear probability regressions were conducted for the entire sample and sub-samples of patients and hospitals in order to isolate the effect of the HRRP on vulnerable populations. Multiple combinations of treatment and control groups and triple difference (DDD) methods were used for testing the robustness of the results. All models controlled for the patient and hospital characteristics.

RESULTS

There have been statistically significant reductions in readmission rates overall as well as for vulnerable populations, especially for acute myocardial infarction patients in hospitals serving the largest percentage of low-income patients and high-risk patients. There is also evidence of spillover effects for non-targeted conditions among Medicare patients compared to privately insured patients.

CONCLUSIONS

The HRRP appears to have created the right incentives for reducing readmissions not only overall but also for vulnerable populations, accruing societal benefits in addition to previously found reductions in costs. As the reduction in the rate of readmissions is not consistent across patient and hospital groups, there could be benefits to adjusting the policy according to the socioeconomic status of a hospital's patients and neighborhood.