Impact of Length of Stay on Readmission in Hospitalized Patients

Single and Unhoused Population at Risk for Self-Inflicted Burn Injury: A Retrospective Analysis of an Urban American Burn Center's Experience

Despite the growing recognition of self-harm as a pressing public health issue, demographic risk factors of self-inflicted burn (SIB) injuries in the United States have not been extensively described. In this retrospective study, we seek to identify demographic risk factors and patterns associated with SIB injuries at an urban burn center. Charts were reviewed of patients admitted to a single American Burn Association-verified burn unit between 2015 and 2023 with a history of SIB injury, identified with ICD10 code X76.XXXA. Descriptive statistics, Welch's t-test of unequal variances, and chi-squared analysis were performed. A total of 3212 patients were admitted to our institution for the management of acute burn injury, with 94 (2.9%) patients presenting with SIB injury. SIB patients were more likely than the control cohort to be male (P = .035), single (P = .008), unhoused (P < .001), live alone (P < 0.001), and have documented psychiatric diagnoses (72.3% vs 2.1%, P < .001). They had larger %TBSA affected (P < .001) and higher rates of inhalation injury (P < .001). The SIB cohort also showed significantly higher rates of positive urine toxicology results, primarily for stimulants and opiates (P < .001). Patients with SIBs had longer hospital stays (21.7 ± 2.6 days vs 12.0 ± 22.1 days, P = .006), higher rates of ICU admission (P < .001), and mechanical ventilation requirement (P < .001). Mental health support services, substance abuse rehabilitation programs, and community outreach need to be prioritized, especially targeting vulnerable populations such as the unhoused.

Integrating Quantitative Data and Qualitative Insights to Understand 30-Day Readmission Rates: A Mixed-Methods Study

The rate of patients readmitted to hospitals within 30 days of discharge is a critical indicator of healthcare quality. This study explored the factors contributing to 30-day hospital readmission rates nationally and at Arrowhead Regional Medical Center (ARMC) through a mixed-methods research design. Quantitative analysis utilized data from the Centers for Medicare & Medicaid Services (CMS) database, focusing on patient demographics, principal diagnoses, length of stay, and hospital characteristics. Multivariate regression and descriptive statistics were employed to identify predictors of 30-day readmission. The qualitative analysis sought to understand the specific medical conditions and patient profiles linked to higher readmission rates. The findings revealed that older age, specific principal diagnoses (e.g., heart failure, pneumonia, chronic obstructive pulmonary disease (COPD)), and longer initial hospital stays were associated with an increased likelihood of 30-day readmission. Gender disparities and hospital size/type also influenced readmission rates. These results provide valuable insights into the complex interplay of individual patient characteristics and hospital attributes in driving readmissions. The study's mixed-methods approach yielded a comprehensive understanding of the quantitative patterns and qualitative factors contributing to 30-day hospital readmission rates, offering important implications for healthcare quality improvement initiatives.

Utilizing a comprehensive machine learning approach to identify patients at high risk for extended length of stay following spinal deformity surgery in pediatric patients with early onset scoliosis

PURPOSE

Early onset scoliosis (EOS) patient diversity makes outcome prediction challenging. Machine learning offers an innovative approach to analyze patient data and predict results, including LOS in pediatric spinal deformity surgery.

METHODS

Children under 10 with EOS were chosen from the American College of Surgeon's NSQIP database. Extended LOS, defined as over 5 days, was predicted using feature selection and machine learning in Python. The best model, determined by the area under the curve (AUC), was optimized and used to create a risk calculator for prolonged LOS.

RESULTS

The study included 1587 patients, mostly young (average age: 6.94 ± 2.58 years), with 33.1% experiencing prolonged LOS (n = 526). Most patients were female (59.2%, n = 940), with an average BMI of 17.0 ± 8.7. Factors influencing LOS were operative time, age, BMI, ASA class, levels operated on, etiology, nutritional support, pulmonary and neurologic comorbidities. The gradient boosting model performed best with a test accuracy of 0.723, AUC of 0.630, and a Brier score of 0.189, leading to a patient-specific risk calculator for prolonged LOS.

CONCLUSIONS

Machine learning algorithms accurately predict extended LOS across a national patient cohort and characterize key preoperative drivers of increased LOS after PSIF in pediatric patients with EOS.

Complications and Healthcare Cost of Total Hip Arthroplasty in Patients with Depressive Disorder

Purpose

The purpose of this study was to determine whether the rates of (1) in-hospital lengths of stay (LOS), (2) readmissions, (3) medical complications, and (4) costs of care are higher for patients with depressive disorder (DD) undergoing primary total hip arthroplasty (THA) for treatment of femoral neck fractures (FNFs).

Materials and Methods

A retrospective query of a national administrative claims database for patients undergoing primary THA from 2006 to 2014 was conducted. Patients with DD undergoing THA for treatment of FNF were 1:5 ratio propensity score matched to a cohort (DD=6,758, controls=33,708). Primary endpoints included LOS, 90-day medical complications, 90-day readmissions, and healthcare reimbursements. A P-value less than 0.05 was considered statistically significant.

Results

Longer LOS were observed for patients with DD compared to those without DD (5.6 days vs. 5.4 days, P<0.001). Similar readmission rates (29.9% vs. 25.0%, odds ratio [OR] 1.03, P=0.281) were observed between groups. The odds of 90-day medical complications were higher for patients with DD compared to control subjects (60.6% vs. 21.4%, OR 1.57, P<0.0001). Within the 90-day episode of care interval, patients with a history of DD incurred significantly higher healthcare expenditures ($21,382 vs. $19,781, P<0.001).

Conclusion

Our findings showed longer LOS, higher odds of 90-day medical complications, and higher healthcare expenditures within the 90-day episode of care following a primary THA for treatment of FNF for patients with DD compared to the matched cohort. Thus, accordingly, patients with DD should receive counseling prior to undergoing surgery.

Interventions and impact of pharmacist-delivered services in perioperative setting on clinically important outcomes: a systematic review and meta-analysis

Background

The perioperative arena is a unique and challenging environment that requires coordination of the complex processes and involvement of the entire care team. Pharmacists' scope of practice has been evolving to be patient-centered and to expand to variety of settings including perioperative settings.

Objectives

To critically appraise, synthesize, and present the available evidence of the characteristics and impact of pharmacist-led interventions on clinically important outcomes in the perioperative settings.

Design

A systematic review and meta-analysis.

Methods

We searched PubMed, Embase, and CINAHL from index inception to September 2023. Included studies compared the effectiveness of pharmacist-led interventions on clinically important outcomes (e.g. length of stay, readmission) compared to usual care in perioperative settings. Two independent reviewers extracted the data using the DEPICT-2 (Descriptive Elements of Pharmacist Intervention Characterization Tool) and undertook quality assessment using the Crowe Critical Appraisal (CCAT). A random-effect model was used to estimate the overall effect [odds ratio (OR) for dichotomous and standard mean difference (SMD) for continuous data] with 95% confidence intervals (CIs).

Results

Twenty-five studies were eligible, 20 (80%) had uncontrolled study design. Most interventions were multicomponent and continuous over the perioperative period. The intervention components included clinical pharmacy services (e.g. medication management/optimization, medication reconciliation, discharge counseling) and education of healthcare professionals. While some studies provided a minor description in regards to the intervention development and processes, only one study reported a theoretical underpinning to intervention development. Pooled analyses showed a significant impact of pharmacist care compared to usual care on length of stay (11 studies; SMD -0.09; 95% CI -0.49 to -0.15) and all-cause readmissions (8 studies; OR 0.60; 95% CI 0.39-0.91). The majority of included studies (n = 21; 84%) were of moderate quality.

Conclusion

Pharmacist-led interventions are effective at improving clinically important outcomes in the perioperative setting; however, most studies were of moderate quality. Studies lacked the utilization of theory to develop interventions; therefore, it is not clear whether theory-derived interventions are more effective than those without a theoretical element. Future research should prioritize the development and evaluation of multifaceted theory-informed pharmacist interventions that target the whole surgical care pathway.

Social determinants of health and disparities in spine surgery: a 10-year analysis of 8,565 cases using ensemble machine learning and multilayer perceptron

BACKGROUND CONTEXT

The influence of SDOH on spine surgery is poorly understood. Historically, researchers commonly focused on the isolated influences of race, insurance status, or income on healthcare outcomes. However, analysis of SDOH is becoming increasingly more nuanced as viewing social factors in aggregate rather than individually may offer more precise estimates of the impact of SDOH on healthcare delivery.

PURPOSE

The aim of this study was to evaluate the effects of patient social history on length of stay (LOS) and readmission within 90 days following spine surgery using ensemble machine learning and multilayer perceptron.

STUDY DESIGN

Retrospective chart review.

PATIENT SAMPLE

8,565 elective and emergency spine surgery cases performed from 2013 to 2023 using our institution's database of longitudinally collected electronic medical record information.

OUTCOMES MEASURES

Patient LOS, discharge disposition, and rate of 90-day readmission.

METHODS

Ensemble machine learning and multilayer perceptron were employed to predict LOS and readmission within 90 days following spine surgery. All other subsequent statistical analysis was performed using SPSS version 28. To further assess correlations among variables, Pearson's correlation tests and multivariate linear regression models were constructed. Independent sample t-tests, paired sample t-tests, one-way analysis of variance (ANOVA) with post-hoc Bonferroni and Tukey corrections, and Pearson's chi-squared test were applied where appropriate for analysis of continuous and categorical variables.

RESULTS

Black patients demonstrated a greater LOS compared to white patients, but race and ethnicity were not significantly associated with 90-day readmission rates. Insured patients had a shorter LOS and lower readmission rates compared to non-insured patients, as did privately insured patients compared to publicly insured patients. Patients discharged home had lower LOS and lower readmission rates, compared to patients discharged to other facilities. Marriage decreased both LOS and readmission rates, underweight patients showcased increased LOS and readmission rates, and religion was shown to impact LOS and readmission rates. When utilizing patient social history, lab values, and medical history, machine learning determined the top 5 most-important variables for prediction of LOS -along with their respective feature importances-to be insurance status (0.166), religion (0.100), ICU status (0.093), antibiotic use (0.061), and case status: elective or urgent (0.055). The top 5 most-important variables for prediction of 90-day readmission-along with their respective feature importances-were insurance status (0.177), religion (0.123), discharge location (0.096), emergency case status (0.064), and history of diabetes (0.041).

CONCLUSIONS

This study highlights that SDOH is influential in determining patient length of stay, discharge disposition, and likelihood of readmission following spine surgery. Machine learning was utilized to accurately predict LOS and 90-day readmission with patient medical history, lab values, and social history, as well as social history alone.

Multiple risk factors for unplanned readmissions within 1 month of hospital discharge in acute care hospitals in Japan

AIM

The aim of this study is to analyse the risk factors for unplanned readmissions within 1 month after hospital discharge to develop a seamless support system from discharge to home care.

BACKGROUND

With shorter hospital stay lengths, understanding the characteristics of patients with multiple risk factors is important to prevent rehospitalization.

DESIGN

This is a single-centre retrospective descriptive study.

METHODS

Logistic regression and decision tree analyses were performed using eight items from the records of 3117 patients discharged from a university hospital between April-September 2017 as risk factors.

RESULTS

Unplanned readmission risk was significantly associated with emergency hospitalization (odds ratio [OR]: 3.12, 95% confidence interval [CI]: 2.04-4.77), malignancy (OR: 2.16, 95% CI: 1.44-3.24), non-surgical admission (OR: 1.76, 95% CI: 1.07-2.88), hospital stay of ≥ 15 days (OR: 1.66, 95% CI: 1.14-2.43) and decline in activities of daily living owing to hospitalization (OR: 1.68, 95% CI: 1.06-2.64). The highest risk combinations for rehospitalization were as follows: emergency hospitalization and malignancy; emergency admission, non-malignancy and a hospital stay of ≥15 days; and scheduled hospitalization, no surgery and a hospital stay of ≥15 days.

CONCLUSIONS

Patients with multiple risks for unplanned readmission should be accurately screened and provided with optimal home care.

Predictors of unplanned hospital readmission after non-cardiac surgery in Singapore: a 2-year retrospective review

INTRODUCTION

Unplanned hospital readmissions after surgery contribute significantly to healthcare costs and potential complications. Identifying predictors of readmission is inherently complex and involves an intricate interplay between medical factors, healthcare system factors and sociocultural factors. Therefore, the aim of this study was to elucidate the predictors of readmissions in an Asian surgical patient population.

METHODS

A two-year single-institution retrospective cohort study of 2744 patients was performed in a university-affiliated tertiary hospital in Singapore, including patients aged 45 and above undergoing intermediate or high-risk non-cardiac surgery. Unadjusted analysis was first performed, followed by multivariable logistic regression.

RESULTS

Two hundred forty-nine patients (9.1%) had unplanned 30-day readmissions. Significant predictors identified from multivariable analysis include: American Society of Anaesthesiologists (ASA) Classification grades 3 to 5 (adjusted OR 1.51, 95% CI 1.10-2.08, p = 0.01), obesity (adjusted OR 1.66, 95% CI 1.18-2.34, p = 0.04), asthma (OR 1.70, 95% CI 1.03-2.81, p = 0.04), renal disease (OR 2.03, 95% CI 1.41-2.92, p < 0.001), malignancy (OR 1.68, 95% CI 1.29-2.37, p < 0.001), chronic obstructive pulmonary disease (OR 2.46, 95% CI 1.19-5.11, p = 0.02), cerebrovascular disease (OR 1.73, 95% CI 1.17-2.58, p < 0.001) and anaemia (OR 1.45, 95% CI 1.07-1.96, p = 0.02).

CONCLUSION

Several significant predictors of unplanned readmissions identified in this Asian surgical population corroborate well with findings from Western studies. Further research will require future prospective studies and development of predictive risk modelling to further address and mitigate this phenomenon.

The impact of including a medication review in an integrated care pathway: A pilot study

AIM

The objective of the present study was to measure the impact of the intervention of combining a medication review with an integrated care approach on potentially inappropriate medications (PIMs) and hospital readmissions in frail older adults.

METHODS

A cohort of hospitalized older adults enrolled in the French PAERPA integrated care pathway (the exposed cohort) was matched retrospectively with hospitalized older adults not enrolled in the pathway (unexposed cohort) between January 1st, 2015, and December 31st, 2018. The study was an analysis of French health administrative database. The inclusion criteria for exposed patients were admission to an acute care department in a general hospital, age 75 years or over, at least three comorbidities or the prescription of diuretics or oral anticoagulants, discharge alive and performance of a medication review.

RESULTS

For the study population (n = 582), the mean ± standard deviation age was 82.9 ± 4.9 years, and 380 (65.3%) were women. Depending on the definition used, the overall median number of PIMs ranged from 2 [0;3] on admission to 3 [0;3] at discharge. The intervention was not associated with a significant difference in the mean number of PIMs. Patients in the exposed cohort were half as likely to be readmitted to hospital within 30 days of discharge relative to patients in the unexposed cohort.

CONCLUSION

Our results show that a medication review was not associated with a decrease in the mean number of PIMs. However, an integrated care intervention including the medication review was associated with a reduction in the number of hospital readmissions at 30 days.

Efficiency of the Japanese Hospitalist System for Patients with Urinary Tract Infection: A Propensity-matched Analysis

Objective The hospitalist system in the United States has been considered successful in terms of the quality of care and cost effectiveness. In Japan, however, its efficacy has not yet been extensively examined. This study examined the impact of the hospitalist system on the quality of care and healthcare economics in a Japanese population using treatment of urinary tract infection as an example. Methods We analyzed 271 patients whose most resource-consuming diagnosis at admission was urinary tract infection between April 2017 and March 2019. Propensity-matched analyses were performed to compare health care economics and the quality of care between the hospitalist system and the conventional system. Results In matched pairs, care by the hospitalist system was associated with a significantly shorter length of stay than that by the conventional system. The quality of care (oral antibiotics switch rate, rate of appropriate antibiotics change based on urine or blood culture results, detection rate of urinary tract infection etiology and the number of laboratory tests) was also considered to be favorably impacted by the hospitalist system. Although not statistically significant, hospital costs tended to be lower with the hospitalist system than with the conventional system. The mortality rate and 30-day readmission were also not significantly different between the groups. Conclusion The hospitalist system had a favorable impact on the quality of care and length of stay without increasing readmission in patients with urinary tract infection. This study is further evidence of the strong potential for the positive impact of an implemented hospitalist system in Japan.

Association Between Racial Disparities in Hospital Length of Stay and the Hospital Readmission Reduction Program

Background:

On average Black patients have longer LOS than comparable White patients. Longer hospital length of stay (LOS) may be associated with higher readmission risk. However, evidence suggests that the Hospital Readmission Reduction Program (HRRP) reduced overall racial differences in 30-day adjusted readmission risk. Yet, it is unclear whether the HRRP narrowed these LOS racial differences.

Objective:

We examined the relationship between Medicare-insured Black-White differences in average, adjusted LOS (ALOS) and the HRRP’s implementation and evaluation periods.

Methods:

Using 2009-2017 data from State Inpatient Dataset from New York, New Jersey, and Florida, we employed an interrupted time series analysis with multivariate generalized regression models controlling for patient, disease, and hospital characteristics. Results are reported per 100 admissions.

Results:

We found that for those discharged home, Black-White ALOS differences significantly widened by 4.15 days per 100 admissions (95% CI: 1.19 to 7.11, P < 0.001) for targeted conditions from before to after the HRRP implementation period, but narrowed in the HRRP evaluation period by 1.84 days per 100 admissions for every year-quarter (95% CI: −2.86 to −0.82, P < 0.001); for those discharged to non-home destinations, there was no significant change between HRRP periods, but ALOS differences widened over the study period. Black-White ALOS differences for non-targeted conditions remained unchanged regardless of HRRP phase and discharge destination.

Conclusion:

Increased LOS for Black patients may have played a role in reducing Black-White disparities in 30-day readmission risks for targeted conditions among patients discharged to home.