Contributors to Increased Mortality Associated With Care Fragmentation After Emergency General Surgery

Deaths After Readmissions are Mostly Attributable to Failure-to-Rescue in EGS Patients

INTRODUCTION

We have recently shown that readmission after EGS procedures carries a 4-fold higher mortality rate when compared to those not readmitted. Understanding factors associated with death after readmission is paramount to improving outcomes for EGS patients. We aimed to identify risk factors contributing to failure-to-rescue (FTR) during readmission after EGS. We hypothesized that most post-readmission deaths in EGS are attributable to FTR.

METHODS

A retrospective cohort study using the NSQIP database 2013-2019 was performed. Patients who underwent 1 of 9 urgent/emergent surgical procedures representing 80% of EGS burden of disease, who were readmitted within 30 days post-procedure were identified. The procedures were classified as low- and high-risk. Patient characteristics analyzed included age, sex, BMI, ASA score comorbidities, postoperative complications, frailty, and FTR. The population was assessed for risk factors associated with mortality and FTR by uni- and multivariate logistic regression.

RESULTS

Of 312,862 EGS cases, 16,306 required readmission. Of those, 10,748 (3.4%) developed a postoperative complication. Overall mortality after readmission was 2.4%, with 90.6% of deaths attributable to FTR. Frailty, high-risk procedures, pulmonary complications, AKI, sepsis, and the need for reoperation increased the risk of FTR.

DISCUSSION

Death after a complication is common in EGS readmissions. The impact of FTR could be minimized with the implementation of measures to allow early identification and intervention or prevention of infectious, respiratory, and renal complications.

Collaborative Care for Injured Older Adults: The Trauma Medical Home Randomized Clinical Trial

Importance

Older adults with recent injuries can have impaired long-term biopsychosocial function and may benefit from interventions adapted to their needs.

Objective

To determine if a collaborative care intervention, Trauma Medical Home (TMH), improved the biopsychosocial function of older patients in the year after injury.

Design, Setting, and Participants

This was a single-blinded, randomized clinical trial conducted at 4 level I trauma centers in Indianapolis, Indiana, and Madison, Wisconsin. Between October 2017 and October 2021, patients aged 50 years and older with an Injury Severity Score (ISS) of 9 or greater and without traumatic brain or spinal cord injury were enrolled. Exclusions were significant brain injury or a spinal cord injury with a persistent neurologic deficit at the time of enrollment, extensive burns, pregnancy, incarceration, neurodegenerative disease, visual or auditory impairment that would preclude study participation, a life expectancy of less than 1 year, significant alcohol or drug use history, and acute stroke during admission. Of 10 276 patients screened, 430 were randomized and 299 completed 12-month follow-up. Data were analyzed from March to July 2023.

Intervention

Intervention patients received 6 months of TMH delivered by a nurse care coordinator guided by an interdisciplinary team (trauma surgeon, pulmonary critical care and geriatrician physicians, nurses, and psychologist) in partnership with primary care. The care coordinator used standard protocols to monitor and treat biopsychosocial symptoms.

Main Outcomes and Measures

Primary outcomes were Medical Outcome Study Short Form-36 (SF-36) score and Short Physical Performance Battery (SPPB) score at 12 months. Secondary outcomes were Patient Health Questionnaire-9 (PHQ-9) score, the Generalized Anxiety Disorder scale-7 (GAD-7) score, and health care utilization.

Results

A total of 429 participants (228 [53.1%] female; mean [SD] age, 69.3 [10.8] years; mean [SD] ISS, 12.3 [4.6]) completed baseline assessments and were randomized. Follow-up was 76% (n = 324) at 6 months and 70% (n = 299) at 12 months. There were no differences between the TMH and usual care groups at 12 months in SF-36 Physical Component Summary score (mean [SD], 40.42 [12.82] vs 39.18 [12.43]), SF-36 Mental Component Summary score (mean [SD], 53.92 [10.02] vs 53.21 [10.82]), or SPPB score (mean [SD], 8.00 [3.60] vs 8.28 [3.88]). Secondary outcomes were also no different. Planned subgroup analysis revealed patients with baseline symptoms of anxiety or depression (high GAD-7 and PHQ-9 scores) experienced improvement in the Mental Component Summary score when randomized to the TMH intervention.

Conclusions and Relevance

The TMH intervention did not significantly influence quality of life, depressive and anxiety symptoms, or physical function of older adults with injury at 12 months. Subgroup analysis showed positive impact in patients with a high burden of anxiety and depression symptoms at enrollment. Collaborative care interventions may improve long-term outcomes of select patients, but further research is needed.

Trial Registration

ClinicalTrials.gov Identifier: NCT03108820.

Fragmentation of Care After Geriatric Trauma: A Nationwide Analysis of outcomes and Predictors

The health care system for the elderly is fragmented, that is worsened when readmission occurs to different hospitals. There is limited investigation into the impact of fragmentation on geriatric trauma patient outcomes. The aim of this study was to compare the outcomes following readmissions after geriatric trauma. The Nationwide Readmissions Database (2016-2017) was queried for elderly trauma patients (aged ≥65 years) readmitted due to any cause. Patients were divided into 2 groups according to readmission: index vs non-index hospital. Outcomes were 30 and 180-day complications, mortality, and the number of subsequent readmissions. Multivariable logistic regression was performed to analyze the independent predictors of fragmentation of care. A total of 36,176 trauma patients were readmitted, of which 3856 elderly patients (aged ≥65 years) were readmitted: index hospital (3420; 89%) vs non-index hospital (436; 11%). Following 1:2 propensity matching, elderly with non-index hospital readmission had higher rates of death and MI within 180 days (P = .01 and .02, respectively). They had statistically higher 30 and 180-day pneumonia (P < .01), CHF (P < .01), arrhythmias (P < .01), MI (P < .01), sepsis (P < .01), and UTI (P < .01). On multivariable binary logistic regression analysis, pneumonia (OR 1.70, P = .03), congestive heart failure (CHF) (OR 1.80, P = .03), female gender (OR .72, P = .04), and severe Head and Neck trauma (AIS≥3) (OR 1.50, P < .01) on index admission were independent predictors of fragmentation of care. While the increase in time to readmission (OR 1.01, P < .01) was also associated independently with non-index hospital admission. Fragmented care after geriatric trauma could be associated with higher mortality and complications.

Readmission to a non-index hospital following total joint replacement

Aims

It is unclear whether mortality outcomes differ for patients undergoing total hip arthroplasty (THA) or total knee arthroplasty (TKA) surgery who are readmitted to the index hospital where their surgery was performed, or to another hospital.

Methods

We analyzed linked hospital and death records for residents of New South Wales, Australia, aged ≥ 18 years who had an emergency readmission within 90 days following THA or TKA surgery between 2003 and 2022. Multivariable modelling was used to identify factors associated with non-index readmission and to evaluate associations of readmission destination (non-index vs index) with 90-day and one-year mortality.

Results

Of 394,248 joint arthroplasty patients (THA = 149,456; TKA = 244,792), 9.5% (n = 37,431) were readmitted within 90 days, and 53.7% of these were admitted to a non-index hospital. Non-index readmission was more prevalent among patients who underwent surgery in private hospitals (60%). Patients who were readmitted for non-orthopaedic conditions (62.8%), were more likely to return to a non-index hospital compared to those readmitted for orthopaedic complications (39.5%). Factors associated with non-index readmission included older age, higher socioeconomic status, private health insurance, and residence in a rural or remote area. Non-index readmission was significantly associated with 90-day (adjusted odds ratio (aOR) 1.69; 95% confidence interval (CI) 1.39 to 2.05) and one-year mortality (aOR 1.31; 95% CI 1.16 to 1.47). Associations between non-index readmission and mortality were similar for patients readmitted with orthopaedic and non-orthopaedic complications (90-day mortality aOR 1.61; 95% CI 0.98 to 2.64, and aOR 1.67; 95% CI 1.35 to 2.06, respectively).

Conclusion

Non-index readmission was associated with increased mortality, irrespective of whether the readmission was for orthopaedic complications or other conditions.

Care Fragmentation After Hospitalization for Acute Myocardial Infarction

Care fragmentation (CF), or readmission at a nonindex hospital, has been linked to inferior clinical and financial outcomes for patients. However, its impact on patients with acute myocardial infarction (AMI) is unclear. This study investigated the prevalence and impact of CF on the outcomes of patients with AMI. All US adult (≥18 years) hospitalizations for AMI from January 2010 to November 2019 were identified using the Nationwide Readmissions Database. Patients were stratified by readmission at an index or nonindex center. Multivariable models were developed to evaluate factors associated with CF, and independent associations with mortality, complications, and resource utilization. A total of 413,819 patients with AMI requiring nonelective readmission within 30 days of discharge were included for analysis. Of these, 25.4% (n = 104,966) experienced CF. The incidence of CF increased from 2010 to 2019 (nptrend <0.001). After adjustment, patients insured by Medicaid faced higher odds of nonindex readmission. CF was associated with in-hospital mortality (adjusted odds ratio [AOR] 1.09, 95% confidence interval [CI] 1.01 to 1.18), and cardiac (AOR 1.12, 95% CI 1.03 to 1.22), respiratory (AOR 1.14, 95% CI 1.12 to 1.26), and infectious complications (AOR 1.14, 95% CI 1.07 to 1.22). Further, CF was linked to increased odds of nonhome discharge (AOR 1.18, 95% CI 1.11 to 1.24) and an additional ∼$5,000 in per-patient hospitalization costs (95% CI 4,260 to 5,100). Approximately 25% of AMI patients experienced CF, which was independently associated with excess mortality, complications, and expenditures. Given the growing national burden of cardiovascular disease, new efforts are needed to mitigate the significant clinical and financial implications of nonindex readmissions and improve value-based healthcare.

The Influence of Rural Healthcare Systems and Communities on Surgery and Recovery: A Qualitative Study

INTRODUCTION

Successful recovery after surgery is complex and highly individual. Rural patients encounter greater barriers to successful surgical recovery than urban patients due to varying healthcare and community factors. Although studies have previously examined the recovery process, rural patients' experiences with recovery have not been well-studied. The rural socioecological context can provide insights into potential barriers or facilitators to rural patient recovery after surgery.

METHODS

We conducted semi-structured qualitative interviews with a purposeful sample of 30 adult general surgery patients from rural areas in the Mountain West region of the United States. We used the socioecological framework to analyze their responses. Interviews focused on rural participants' experiences accessing healthcare and the impact of family and community support during postoperative recovery. Interviews were transcribed verbatim and coded using content and thematic analysis.

RESULTS

All participants commented on the quality of their rural healthcare systems and its influence on postoperative care. Some enjoyed the trust developed through long-standing relationships with providers in their communities. However, participants described community providers' lack of money, equipment, and/or knowledge as barriers to care. Following surgery, participants recognized that there are advantages and disadvantages to receiving family and community support. Some participants worried about being stigmatized or judged by their community.

CONCLUSIONS

Future interventions aimed at improving access to and recovery from surgery for rural patients should take into account the unique perspectives of rural patients. Addressing the socioecological factors surrounding rural surgery patients, such as healthcare, family, and community resources, will be key to improving postoperative recovery.

Re-Admission in Patients with Necrotizing Soft Tissue Infections: Continuity of Care Matters

Background: Necrotizing soft tissue infections (NSTIs) are rapidly progressive infections with high mortality and complication rates. The incidence of NSTIs has been increasing steadily whereas mortality has decreased; survivors have a high risk of re-hospitalization. We hypothesized that re-admission to the index hospital where the first admission occurred would be associated with better clinical outcomes compared with re-admission to a non-index hospital. Patients and Methods: We identified patients from the 2017 Nationwide Readmissions Database with an index admission for NSTIs and examined all-cause re-admissions within 90 days of discharge. We noted whether re-admission occurred at the index or a non-index hospital. Survey-weighted logistic regression identified factors associated with death at the first re-admission and re-admission to index hospital. We also compared patient outcomes between patients admitted to index versus non-index hospitals. Results: We identified 27,051 NSTI survivors, of whom 6,954 (25.7%) had an unplanned re-admission within 90 days. A large proportion of re-admission occurred at non-index hospitals (28.3%; n = 1,966). Factors associated with non-index re-admission included prolonged index length of stay, discharge to short-term hospital, and leaving against medical advice. Patients re-admitted to index hospitals had a lower mortality rate (4.7% vs. 6.7%; p = 0.003), lower admission costs (in $1000; 45 [23-88] vs. 50 [24-104]; p = 0.004) and higher discharge rate to home (55.7% vs. 48.6%; p < 0.001). Conclusions: More than one-quarter of re-admissions among NSTI survivors were to non-index hospitals. Continuity of care is important because re-admission to the index hospital was associated with better patient outcomes.

Association Between Mental Health Conditions and Outpatient Care Fragmentation: a National Study of Older High-Risk Veterans

BACKGROUND

Healthcare fragmentation may lead to adverse consequences and may be amplified among older, sicker patients with mental health (MH) conditions.

OBJECTIVE

To determine whether older Veterans with MH conditions have more fragmented outpatient non-MH care, compared with older Veterans with no MH conditions.

DESIGN

Retrospective cohort study using FY2014 Veterans Health Administration (VHA) administrative data linked to Medicare data.

PARTICIPANTS

125,481 VHA patients ≥ 65 years old who were continuously enrolled in Medicare Fee-for-Service Parts A and B and were at high risk for hospitalization.

MAIN OUTCOME AND MEASURES

The main outcome was non-MH care fragmentation as measured by (1) non-MH provider count and (2) Usual Provider of Care (UPC), the proportion of care with the most frequently seen non-MH provider. We tested the association between no vs. any MH conditions and outcomes using Poisson regression and fractional regression with logit link, respectively. We also compared Veterans with no MH condition with each MH condition and combinations of MH conditions, adjusting for sociodemographics, comorbidities, and drive-time to VHA specialty care.

KEY RESULTS

In total, 47.3% had at least one MH condition. Compared to those without MH conditions, Veterans with MH conditions had less fragmented care, with fewer non-MH providers (IRR = 0.96; 95% CI: 0.96-0.96) and more concentrated care with their usual provider (OR = 1.08 for a higher UPC; 95% CI: 1.07, 1.09) in adjusted models. Secondary analyses showed that those with individual MH conditions (e.g., depression) had fewer non-MH providers (IRR range: 0.86-0.98) and more concentrated care (OR range: 1.04-1.20). A similar pattern was observed when examining combinations of MH conditions (IRR range: 0.80-0.90; OR range: 1.16-1.30).

CONCLUSIONS

Contrary to expectations, having a MH condition was associated with less fragmented non-MH care among older, high-risk Veterans. Further research will determine if this is due to different needs, underuse, or appropriate use of healthcare.

Socioeconomic disadvantage is associated with greater mortality after high-risk emergency general surgery

BACKGROUND

Socioeconomic disadvantage is associated with worse outcomes after elective surgery, but the effect on emergency general surgery (EGS) remains unclear. We examined the association of socioeconomic disadvantage and outcomes after EGS procedures and investigated whether admission to hospitals with comprehensive clinical and social resources mitigated this effect.

METHODS

Adults undergoing 1 of the 10 most burdensome high- and low-risk EGS procedures were identified in six 2014 State Inpatient Databases. Socioeconomic disadvantage was assessed using Area Deprivation Index (ADI) of patient residence. Multivariable logistic regression models adjusting for patient and hospital factors were used to evaluate the association between ADI quartile (high >75 percentile vs. low <25 percentile), and 30-day readmission, in-hospital mortality, and discharge disposition. Effect modification between ADI and (a) level 1 trauma center and (b) safety-net hospital status was tested.

RESULTS

A total of 103,749 patients were analyzed: 72,711 low-risk (70.1%) and 31,038 high-risk procedures (29.9%). Patients from neighborhoods with high socioeconomic disadvantage had a higher proportion with ≥3 comorbidities (41.9% vs. 32.0%), minority race/ethnicity (66.3% vs. 42.4%), and Medicaid (28.8% vs. 14.7%) and were less likely to be treated at level 1 trauma centers (18.3% vs. 27.7%; p < 0.001 for all). Adjusting for competing factors, high socioeconomic disadvantage was associated with increased in-hospital mortality after high-risk procedures (odd ratio, 1.30; 95% confidence interval, 1.01-1.66; p = 0.04) and higher odds of non-home discharge (odd ratio, 1.15; 95% confidence interval, 1.02-1.30; p = 0.03) for low-risk procedures. Socioeconomic disadvantage was not associated with 30-day readmission for either procedure group. Level 1 trauma status and safety-net hospital did not meaningfully mitigate effect of ADI for any outcome.

CONCLUSION

Socioeconomic disadvantage is associated with increased mortality after high-risk procedures and higher odds of non-home discharge after low-risk procedures. This effect was not mitigated by either level 1 trauma or safety-net hospitals. Interventions that specifically address the needs of socially vulnerable communities will be required to significantly improve EGS outcomes for this population.

LEVEL OF EVIDENCE

Prognostic and Epidemiologic, level III.

Emergency general surgery transfer to lower acuity facility: The role of right-sizing care in emergency general surgery regionalization

BACKGROUND

Regionalization of emergency general surgery (EGS) has primarily focused on expediting care of high acuity patients through interfacility transfers. In contrast, triaging low-risk patients to a nondesignated trauma facility has not been evaluated. This study evaluates a 16-month experience of a five-surgeon team triaging EGS patients at a tertiary care, Level I trauma center (TC) to an affiliated community hospital 1.3 miles away.

METHODS

All EGS patients who presented to the Level I TC emergency department from January 2020 to April 2021 were analyzed. Patients were screened by EGS surgeons covering both facilities for transfer appropriateness including hemodynamics, resource need, and comorbidities. Patients were retrospectively evaluated for disposition, diagnosis, comorbidities, length of stay, surgical intervention, and 30-day mortality and readmission.

RESULTS

Of 987 patients reviewed, 31.5% were transferred to the affiliated community hospital, 16.1% were discharged home from the emergency department, and 52.4% were admitted to the Level I TC. Common diagnoses were biliary disease (16.8%), bowel obstruction (15.7%), and appendicitis (14.3%). Compared with Level I TC admissions, Charlson Comorbidity Index was lower (1.89 vs. 4.45, p < 0.001) and length of stay was shorter (2.23 days vs. 5.49 days, p < 0.001) for transfers. Transfers had a higher rate of surgery (67.5% vs. 50.1%, p < 0.001) and lower readmission and mortality (8.4% vs. 15.3%, p = 0.004; 0.6% vs. 5.0%, p < 0.001). Reasons not to transfer were emergency evaluation, comorbidity burden, operating room availability, and established care. No transfers required transfer back to higher care (under-triage). Bed days saved at the Level I TC were 693 (591 inpatients). Total operating room minutes saved were 24,008 (16,919, between 7:00 am and 5:00 pm).

CONCLUSION

Transfer of appropriate patients maintains high quality care and outcomes, while improving operating room and bed capacity and resource utilization at a tertiary care, Level I TC. Emergency general surgery regionalization should consider triage of both high-risk and low-risk patients.

LEVEL OF EVIDENCE

Prospective comparative cohort study, Level II.

The hidden burden of unplanned readmission after emergency general surgery

BACKGROUND

There are no national studies of nonelective readmissions after emergency general surgery (EGS) diagnoses that track nonindex hospital readmission. We sought to determine the rate of overall and nonindex hospital readmissions at 30 and 90 days after discharge for EGS diagnoses, hypothesizing a significant portion would be to nonindex hospitals.

METHODS

The 2013 to 2014 Nationwide Readmissions Database was queried for all patients 16 years or older admitted with an EGS primary diagnosis and survived index hospitalization. Multivariable logistic regression identified risk factors for nonelective 30- and 90-day readmission to index and nonindex hospitals.

RESULTS

Of 4,171,983 patients, 13% experienced unplanned readmission at 30 days. Of these, 21% were admitted to a nonindex hospital. By 90 days, 22% experienced an unplanned readmission, of which 23% were to a nonindex hospital. The most common reason for readmission was infection. Publicly insured or uninsured patients accounted for 67% of admissions and 77% of readmissions. Readmission predictors at 30 days included leaving against medical advice (odds ratio [OR], 2.51 [2.47-2.56]), increased length of stay (4-7 days: OR, 1.42 [1.41-1.43]; >7 days: OR, 2.04 [2.02-2.06]), Charlson Comorbidity Index ≥2 (OR, 1.72 [1.71-1.73]), public insurance (Medicare: OR, 1.45 [1.44-1.46]; Medicaid: OR, 1.38 [1.37-1.40]), EGS patients who fell into the "Other" surgical category (OR, 1.42 [1.38-1.48]), and nonroutine discharge. Risk factors for readmission remained consistent at 90 days.

CONCLUSION

Given that nonindex hospital EGS readmission accounts for nearly a quarter of readmissions and often related to important benchmarks such as infection, current EGS quality metrics are inaccurate. This has implications for policy, benchmarking, and readmission reduction programs.

LEVEL OF EVIDENCE

Epidemiological study, level III.

Influence of Geographic Access on Surgical Center Readmissions After Index Congenital Heart Surgery

OBJECTIVE

To assess the impact of geographic access to surgical center on readmission risk and burden in children after congenital heart surgery.

STUDY DESIGN

Children <6 years old at discharge after congenital heart surgery (Risk Adjustment for Congenital Heart Surgery-1 score 2-6) were identified using Pediatric Health Information System data (46 hospitals, 2004-2015). Residential distance from the surgery center, calculated using ZIP code centroids, was categorized as <15, 15-29, 30-59, 60-119, and ≥120 miles. Rurality was defined using rural-urban commuting area codes. Geographic risk factors for unplanned readmissions to the surgical center and associated burden (total hospital length of stay [LOS], costs, and complications) were analyzed using multivariable regression.

RESULTS

Among 59 696 eligible children, 19 355 (32%) had ≥1 unplanned readmission. The median LOS was 9 days (IQR 22) across the entire cohort. In those readmitted, median total costs were $31 559 (IQR $90 176). Distance from the center was inversely related but rurality was positively related to readmission risk. Among those readmitted, increased distance was associated with longer LOS, more complications, and greater costs. Compared with urban patients, highly rural patients were more likely to have an unplanned readmission but had fewer average readmission days.

CONCLUSIONS

Geographic measures of access differentially affect readmission to the surgery center. Increased distance from the center was associated with fewer unplanned readmissions but more complications. Among those readmitted, the most isolated patients had the greatest readmission costs. Understanding the contribution of geographic access will aid in developing strategies to improve care delivery to this population.