Risk factors for 30-day hospital readmission in patients with diabetic foot

INTRODUCTION

Diabetic foot (DF) is part of the natural history of diabetes mellitus, ulceration being a severe complication with a prevalence of approximately 6.3 %, which confers a significant economic burden. Hospital readmission in the first thirty (30) days is considered a measure of quality of healthcare and it's been identified that the most preventable causes are the ones that occur in this period. This study seeks to identify the risk factors associated with readmission of patients with DF.

METHODS

A case-control study was done by performing a secondary analysis of a database. Descriptive statistics were used for all variables of interest, bivariate analysis to identify statistically significant variables, and a logistic regression model for multivariate analysis.

RESULTS

575 cases were analyzed (113 cases, 462 controls). A 20 % incidence rate of 30-day readmission was identified. Statistically significant differences were found in relation to the institution of attention (Hospital Universitario de la Samaritana: OR 1.9, p value < 0.01, 95 % CI 1.2-3.0; Hospital Universitario San Ignacio: OR 0.5, p value < 0.01, 95 % CI 0.3-0.8) and the reasons for readmission before 30 days, especially due to surgical site infection (SSI) (OR 7.1, p value < 0.01, 95 % CI 4.1-12.4), sepsis (OR 8.4, p value 0.02, 95 % CI 1.2-94.0), dehiscence in amputation stump (OR 16.4, p value < 0.01, 95 % CI 4.2-93.1) and decompensation of other pathologies (OR 3.5, p value < 0.01, 95 % CI 2.1-5.7).

CONCLUSION

The hospital readmission rate before 30 days for our population compares to current literature. Our results were consistent with exacerbation of chronic pathologies, but other relevant variables not mentioned in other studies were the hospital in which patients were taken care of, the presence of SSI, sepsis, and dehiscence of the amputation stump. We consider thoughtful and close screening of patients at risk in an outpatient setting might identify possible readmissions.

Impact of Bariatric Surgery on Unplanned Hospital Admissions for Infection

Purpose

Both obesity and type 2 diabetes are associated with an increased risk of skin and soft tissue (SSTI), urinary tract, and lower respiratory tract infections but it is not clear whether the incidence of such infections is reduced after bariatric surgery.

Materials and Methods

In people accepted onto our publicly funded bariatric program, we recorded unplanned admissions to public hospitals over a median follow-up of 4.5 years in those successfully undergoing surgery and in those who withdrew from the program. Rates of admission for the composite outcome (SSTI, urinary tract, or lower respiratory infection) were compared.

Results

Of 774 people accepted onto the program, 49% underwent surgery. Infections accounted for 27% of unplanned admissions in those not completing surgery and 13% of those who underwent surgery (p < 0.001). The rate of admission was 60% lower in people who underwent surgery than those who did not: 4.3 vs 12.2 per 100 patient-years (P < 0.002), a difference maintained across 8 years’ follow-up. The impact of surgery was independent of enrolment age, BMI, or diabetes and smoking status. Of the three types of infection in the composite outcome, SSTI were the most prevalent and showed the greatest reduction (p < 0.0001). The median day stay for infection was 0.5 day less in those who underwent surgery (p < 0.01).

Conclusions

Hospitalization for these three infectious diseases in people undergoing bariatric surgery was lower than that in people enrolled in the bariatric program but not completing surgery. The effect was greatest for SSTI, and sustained to at least 8 years.

Graphical abstract

Supplementary Information

The online version contains supplementary material available at 10.1007/s11695-022-05975-4.

What's new in the epidemiology of skin and soft tissue infections in 2018?

PURPOSE OF REVIEW

Skin and soft tissue infections (SSTIs) are among the most common infections in outpatients and the most frequent infectious cause of referrals to emergency departments in developed world, contributing to significant morbidity and healthcare expenditures. We sought to review recent literature covering epidemiology of SSTIs.

RECENT FINDINGS

Staphylococcus aureus and streptococci predominate and methicillin-resistant S. aureus (MRSA) poses additional challenges; community-acquired-MRSA in some areas is superseding methicillin-susceptible S. aureus and multidrug resistance is evolving. Incidence data of SSTIs from United States show a decreasing trend, whereas trends of hospitalization rates were increasing. Despite low mortality associated with SSTIs, high rates of treatment failure and relapses are of concern. Diagnosis and management decisions in the emergency department (ED) lack validated tools for prediction of clinical response particularly among elderly, immunocompromised, obese, and patients with comorbidities. A variety of modifiable and nonmodifiable risk factors of the host and data from local epidemiology should be considered to prevent recurrence and treatment failure.

SUMMARY

An evolving epidemiology of SSTIs make microbiologic documentation and surveillance of local data imperative. New assessment algorithms with potential use in the ED are a priority. The universal applicability of international guidelines is questioned in this setting.

A Dashboard Prototype for Tracking the Impact of Diabetes on Hospital Readmissions Using a National Administrative Database

Background

Over the past several decades, diabetes mellitus has contributed to a significant disease burden in the general population. Evidence suggests that patients with a coexisting diabetes diagnosis consume more hospital resources, and have higher readmission rates compared to those who do not. Against the backdrop of bundled-payment programs, healthcare systems cannot underestimate the importance of monitoring patient health information at the population level.

Methods

Using the data from the Centers for Medicare and Medicaid Services (CMS) administrative claims database, we created a dashboard prototype to enable hospitals to examine the impact of diabetes on their all-cause readmission rates and financial implications if diabetes was present at the index hospitalization. The technical design involved loading the relevant 10th revision of International Classification of Diseases (ICD-10) codes provided by the medical team and flagging diabetes patients at the claim. These patients were then tagged for readmissions within the same database. The odds ratios were determined based on data from two groups: those with diabetes at index hospitalization which include type 1 only, type 2 only, and type 1 and type 2 diabetes, plus those without diabetes at index hospitalization.

Results

The dashboard presents summary data of diabetes readmissions quality metrics at a national level. Users can visualize summary data of each state and compare odds ratios for readmissions as well as raw hospitalization data at their facility. Dashboard users can also view data classified by a diagnosis-related group (DRG) system. In addition to a “national” data view, for users who inquire about data specific to demographic regions, the DRG view can be further stratified at the state level or county level. At the DRG level, users can view data about the cost per readmissions for all index hospitalization with and without diabetes.

Conclusions

The dashboard prototype offers users a virtual interface which displays visual data for quick interpretation, monitors changes at a population level, and enables administrators to benchmark facility data against local and national trends. This is an important step in using data analytics to drive population level decision making to ultimately improve medical systems.

Identification of risk factors for failure in patients with skin and soft tissue infections

PURPOSE

The purpose was to determine significant predictors of treatment failure of skin and soft tissue infections (SSTI) in the inpatient and outpatient setting.

METHODS

A retrospective chart review of patients treated between January 1, 2005 to July 1, 2016 with ICD-9 or ICD-10 code of cellulitis or abscess. The primary outcome was failure defined as an additional prescription or subsequent hospital admission within 30 days of treatment. Risk factors for failure were identified through multivariate logistic regression.

RESULTS

A total of 541 patients were included. Seventeen percent failed treatment. In the outpatient group, 24% failed treatment compared to 9% for inpatients. Overweight/obesity (body mass index (BMI) > 25 kg/m²) was identified in 80%, with 15% having a BMI >40 kg/m². BMI, heart failure, and outpatient treatment were determined to be significant predictors of failure. The unit odds ratio for failure with BMI was 1.04 (95% [Cl] = 1.01 to 1.1, p = 0.0042). Heart failure increased odds by 2.48 (95% [Cl] = 1.3 to 4.7, p = 0.0056). Outpatients were more likely to fail with an odds ratio of 3.36.

CONCLUSION

Patients with an elevated BMI and heart failure were found to have increased odds of failure with treatment for SSTIs. However, inpatients had considerably less risk of failure than outpatients. These risk factors are important to note when making the decision whether to admit a patient who presents with SSTI in the emergency department. Thoughtful strategies are needed with this at-risk population to prevent subsequent admission.

Clinical characteristics affecting length of stay in patients with cellulitis

BACKGROUND

This study aimed to profile the clinical characteristics of patients presenting to Middlemore Hospital with cellulitis in order to identify factors that are associated with an increased length of stay (LOS).

METHODS

Retrospective clinical data were collected for all patients aged 18 and above who were admitted with cellulitis to Middlemore Hospital General Surgical Department between 1 January and 31 March 2014. Comorbidities, laboratory results and medical conditions were included in the investigation.

RESULTS

The study included 201 patients. Significant factors associated with increased LOS include type 2 diabetes mellitus (P < 0.012), obesity (P < 0.001), raised C-reactive protein (P < 0.0001), raised white cell count (P < 0.0001), raised temperature (P < 0.0001), septic shock (P < 0.003), multiorgan failure (P < 0.01), extended-spectrum beta-lactamases or methicillin-resistant Staphylococcus aureus colonization (P < 0.04) and intensive care unit admission (P < 0.0004).

CONCLUSION

This single-centre, retrospective clinical study has identified several factors that are significantly associated with an increased LOS. These factors provide a basis for future studies that may facilitate identification and timely medical optimization of high-risk patients.