Hospital-associated Factors Research Articles

Abstract 17953: Predictors of Mortality Among Hospitalized Patients With Chronic Constrictive Pericarditis

Introduction: Studies have been conducted on patients with constrictive pericarditis but there is no literature on the factors associated with mortality among hospitalized patients in this population. We examined the predictors of mortality among patients hospitalized with chronic constrictive pericarditis with or without arrhythmias. Hypothesis: Patient and hospital associated factors can predict mortality among hospitalized patients with chronic constrictive pericarditis. Methods: Retrospective cohort analyses were conducted using data from the National Inpatient Sample (NIS), 2016-2020. Multivariate logistic regression modelling was used to examine the factors associated with mortality among hospitalized patients with chronic constrictive pericarditis with or without arrhythmias. Results: Among those with arrhythmias, African Americans (AOR: 1.83; CI: 1.36-2.48) and Hispanics (AOR: 2.08; CI: 1.43-3.01) had higher odds of mortality compared to non-Hispanic whites. Those admitted electively were 62% (AOR: 0.38; CI: 0.27-0.53) less likely to die than the non-elective cases. Length of stay of more than 5 days, (AOR: 1.78; CI: 1.41-2.24) was associated with higher odds of mortality relative to 5 days or less. Other predictors of mortality were charlson comorbidity index and median household income national quartiles. Furthermore, among those without arrhythmias, older age (AOR: 1.88; CI: 1.37-2.59), and longer length of hospital stay (AOR: 2.87; CI: 2.27-3.63) were associated with higher odds of mortality. Conclusions: Our findings highlight the predictors of mortality among patients hospitalized with chronic constrictive pericarditis. Additional studies are needed to further explore patient and hospital related factors associated with mortality, especially among those with comorbid arrhythmias. This will enable physicians to identify high risk patients who may benefit from closer monitoring.

Current Perioperative Care in Pancreatoduodenectomy: A Step-by-Step Surgical Roadmap from First Visit to Discharge.

Pancreaticoduodenectomy (PD) is a mainstay in the management of periampullary tumors. Treatment algorithms increasingly employ a multimodal strategy, which includes neoadjuvant and adjuvant therapies. However, the successful treatment of a patient is contingent on the execution of a complex operation, whereby minimizing postoperative complications and optimizing a fast and complete recovery are crucial to the overall success. In this setting, risk reduction and benchmarking the quality of care are essential frameworks through which modern perioperative PD care must be delivered. The postoperative course is primarily influenced by pancreatic fistulas, but other patient- and hospital-associated factors, such as frailty and the ability to rescue from complications, also affect the outcomes. A comprehensive understanding of the factors influencing surgical outcomes allows the clinician to risk stratify the patient, thereby facilitating a frank discussion of the morbidity and mortality of PD. Further, such an understanding allows the clinician to practice based on the most up-to-date evidence. This review intends to provide clinicians with a roadmap to the perioperative PD pathway. We review key considerations in the pre-, intra-, and post-operative periods.

Clostridium difficile colitis following geriatric hip fracture surgery: incidence, trends, and risk factors from 45,910 patients.

Clostridium difficile colitis is a serious complication in elderly patients undergoing surgery. The objectives of this study were: (1) to use a nationwide sample of patients to report the incidence and timing of C. difficile colitis in geriatric patients who underwent surgery for hip fractures, (2) to identify preoperative factors associated with developing C. difficile colitis and mortality. This was a retrospective evaluation of the 2016-2019 ACS Targeted Hip Fracture database merged with the ACS-NSQIP database. Patients undergoing surgery for hip fracture were included. Outcomes studied were incidence, preoperative, and postoperative risk factors for occurrence of C. difficile infection and mortality. Chi-squared tests were used to compare demographics between the patients infected (study) and not infected (control). Logistic regression models were utilized to compute the odds ratios (OR) testing for the association of independent factors on developing C. difficile infection postoperatively and mortality. A statistical threshold was set at p < 0.008. The incidence of C. difficile infection within 30days of hip fracture surgery was 0.81%. Fifty percent of infections were diagnosed within 9days postoperatively. Preoperative and hospital-associated factors associated with development of C. difficile infection were ≥ 2days until operation (OR 1.88 [95% CI 1.39-2.55], p < 0.001) and dependent functional status (OR 1.43 [95% CI 1.14-1.79], p = 0.002). After adjusting for multiple comorbidities, increased age, male sex, COPD, CHF, dependent functional status, and C. difficile infection were associated with increased mortality within 30days of surgery (all p < 0.001). Clostridium difficile colitis is a serious infection after hip fracture surgery in geriatric patients with an incidence of about 1%. Patients at increased risk should be targeted with preventative measures to prevent the morbidity from this complication.

Clostridium difficile colitis in patients undergoing surgery for hip fractures: an analysis of 17,474 patients.

We compiled evidence from a large national surgical database to identify the incidence, risk factors and postoperative impact of Clostridium difficile infection (CDI) in patients undergoing hip fracture repair. We identified 17,474 patients who underwent hip fracture repairs in 2015 using the ACS-NSQIP database. Interventions studied were open reduction/Internal fixation, total hip arthroplasty or hemiarthroplasty being performed for traumatic hip fractures. Outcomes studied were incidence, preoperative and postoperative risk factors for occurrence of CDI were studied using descriptive and statistical analysis. A total of 92 patients (0.53%) developed CDI within 30 days of the operation. Following adjustment using multi-variate logistic regression, preoperative and hospital-associated factors associated with development of CDI were smoking (OR 1.75 [95% CI 1.03-2.99]), hypertension (OR 1.70 [95% CI 1.01-2.85]), hyponatraemia (OR 1.65 [95% CI 1.04-2.63]), prior systemic inflammatory response syndrome (SIRS) (OR 2.18 [95% CI 1.32-3.59]) and a length of stay >7 days (OR 1.98 [95% CI 1.11-3.53]. Postoperative factors associated with occurrence of CDI were occurrence of a deep surgical site infection (SSI) (OR 5.89 [95% CI 1.31-26.6]), a stay in the hospital >30 days (OR 6.56 [95% CI 2.56-16.9]) and unplanned reoperations (OR 2.78 [95% CI 1.29-5.99]). As we move toward an era of bundled-payment models, identification of risk factors associated with the occurrence of postoperative complications, such as CDI, will help curb excess healthcare utilisation and costs associated with the management of this complication.

Hospital-acquired conditions: predictors and implications for outcomes following spine tumor resection

OBJECTIVE Hospital-acquired conditions (HACs) significantly compromise patient safety, and have been identified by the Centers for Medicare and Medicaid Services as events that will be associated with penalties for surgeons. The mitigation of HACs must be an important consideration during the postoperative management of patients undergoing spine tumor resection. The purpose of this study was to identify the risk factors for HACs and to characterize the relationship between HACs and other postoperative adverse events following spine tumor resection. METHODS The 2008-2014 American College of Surgeons' National Surgical Quality Improvement Program database was used to identify adult patients undergoing the resection of intramedullary, intradural extramedullary, and extradural spine lesions via current procedural terminology and ICD-9 codes. Demographic, comorbidity, and operative variables were evaluated via bivariate statistics before being incorporated into a multivariable logistic regression model to identify the independent risk factors for HACs. Associations between HACs and other postoperative events, including death, readmission, prolonged length of stay, and various complications were determined through multivariable analysis while controlling for other significant variables. The c-statistic was computed to evaluate the predictive capacity of the regression models. RESULTS Of the 2170 patients included in the study, 195 (9.0%) developed an HAC. Only 2 perioperative variables, functional dependency and high body mass index, were risk factors for developing HACs (area under the curve = 0.654). Hospital-acquired conditions were independent predictors of all examined outcomes and complications, including death (OR 2.26, 95% CI 1.24-4.11, p = 0.007), prolonged length of stay (OR 2.74, 95% CI 1.98-3.80, p < 0.001), and readmission (OR 9.16, 95% CI 6.27-13.37, p < 0.001). The areas under the curve for these models ranged from 0.750 to 0.917. CONCLUSIONS The comorbidities assessed in this study were not strongly predictive of HACs. Other variables, including hospital-associated factors, may play a role in the development of these conditions. The presence of an HAC was found to be an independent risk factor for a variety of adverse events. These findings highlight the need for continued development of evidence-based protocols designed to reduce the incidence and severity of HACs.

Modeling Carbapenem Resistance Among Gram-Negative Pathogens Using Commonly Available Clinical Data

Newer technologies such as matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF) are providing clinicians rapid identification of pathogen species. However, this technology cannot provide antibiotic susceptibility. Infection with carbapenem resistance (CR) among Gram-negative rods (GNRs) species is now extremely common in hospitalized patients and associated with higher mortality. We sought to create a model to predict high-risk CR among GNR pathogens. We performed an analysis of all patients admitted from 2006 to 2016 to two-medical centers, one academic and one community, that shared a common clinical microbiology laboratory. Routine susceptibility testing was conducted with gold standard broth micro-dilution. We examined all admissions with positive cultures of Klebsiella spp. and Pseudomonas aeruginosa and correlated CR with commonly collected data from the medical records such as demographics, diagnoses, and clinical data. We created bivariate and multivaritate logistic models to predict CR. Data were analyzed for complete cases only and were analyzed once per hospital admission. CR was seen in 5.7% (232/4,043) of admissions where Klebsiella was identified and 29.7% (1,102/3,709) for Pseudomonas. Independent predictors for CR among Klebsiella included male gender, younger age, skilled nursing residency, and more co-morbidities. Independent predictors for CR among Pseudomonas included male gender, younger age, longer duration of hospitalization, skilled nursing residency, more co-morbidities, renal failure, cystic fibrosis, and lack of solid organ transplant. Both models had very good predictive ability (AUC of 0.76 and 0.73, respectively) We developed a simple algorithm to identify select GNR pathogens associated with a high-risk of CR in our medical system that had good predictive ability. Among Klebsiella CR is more associated with factors present on admission, where as CR among Pseudomonas is associated with longer hospitalization and hospital associated factors. Findings from this model can be tested in real-time situations and validated in other medical care systems. L. G. Miller, Sage Products: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product. Xttrium: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product. Clorox: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product. 3M: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product.

An Outbreak of Clostridium difficile Ribotype 027 Associated with Length of Stay in the Intensive Care Unit and Use of Selective Decontamination of the Digestive Tract: A Case Control Study.

BackgroundAn outbreak of Clostridium difficile ribotype 027 infection (CDI) occurred at an university hospital, involving 19 departments. To determine what hospital-associated factors drove the outbreak of this particular strain we performed a case-control study.MethodsCases (n = 79), diagnosed with CDI due to C. difficile ribotype 027 were matched for age and treating medical specialty to four control patients (n = 316). Patients diagnosed with CDI due to other ribotypes were included as a second control group. A random selection of C. difficile ribotype 027 strains (n = 10) was genotyped by Whole Genome Sequencing (WGS).FindingsWGS showed the outbreak was likely caused by a single strain of C. difficile (two or less single-nucleotide variants between isolates). Ninety-five percent of cases had used antibiotics, compared to 56% of controls. Previous admission to the intensive care unit (ICU) (OR: 2.4, 95% CI 1.0–5.6), longer length of stay (LOS), and recent hospital admission were associated with CDI ribotype 027. Cases were less likely to have been admitted to a ward with a known isolated CDI patient (OR: 0.2, 95% CI 0.1–0.6).Analysis of patients who stayed at the ICU (35 cases; 51 controls), indicated that the use of selective decontamination of the digestive tract (SDD) and a longer LOS in the ICU were associated with CDI risk.InterpretationIn this large outbreak, any antibiotic use, including SDD use, appeared as a prerequisite for acquisition of the outbreak strain. The role of use of SDD and prolonged stay on the ICU could not be disentangled, but both factors can play a biologically plausible role in C. difficile acquisition and infection.

Institution specific risk factors for 30 day readmission at a community hospital: a retrospective observational study

BackgroundAs of October 1, 2012, hospitals in the United States with excess readmissions based on the Centers for Medicare and Medicaid Services (CMS) risk-adjusted ratio began being penalized. Given the impact of high readmission rates to hospitals nationally, it is important for individual hospitals to identify which patients may be at highest risk of readmission. The objective of this study was to assess the association of institution specific factors with 30-day readmission.MethodsThe study is a retrospective observational study using administrative data from January 1, 2009 through December 31, 2010 conducted at a 257 bed community hospital in Massachusetts. The patients included inpatient medical discharges from the hospitalist service with the primary diagnoses of congestive heart failure, pneumonia or chronic obstructive pulmonary disease. The outcome was 30-day readmission rates. After adjusting for known factors that impact readmission, provider associated factors (i.e. hours worked and census on the day of discharge) and hospital associated factors (i.e. floor of discharge, season) were compared.ResultsOver the study time period, there were 3774 discharges by hospitalists, with 637 30-day readmissions (17% readmission rate). By condition, readmission rates were 19.6% (448/2284) for congestive heart failure, 13.0% (141/1083) for pneumonia, and 14.7% (200/1358) for chronic obstructive lung disease. After adjusting for known risk factors (gender, age, length of stay, Elixhauser sum score, admission in the previous year, insurance, disposition, primary diagnosis), we found that patients discharged in the winter remained significantly more likely to be readmitted compared to the summer (OR 1.54, p = 0.0008). Patients discharged from the cardiac floor had a trend toward decreased readmission compared a medical/oncology floor (OR 0.85, p = 0.08). Hospitalist work flow factors (census and hours on the day of discharge) were not associated with readmission.ConclusionsWe found that 30 day hospital readmissions may be associated with institution specific risk factors, even after adjustment for patient factors. These institution specific risk factors may be targets for interventions to prevent readmissions.

Delay in Diagnosis of Pulmonary Thromboembolism in Emergency Department

Pulmonary embolism (PE) is a common and serious disease that can result in death unless emergent diagnosis is made and treatment is initiated. In this study, we aimed to identify whether there is still a delay in the diagnosis of PE and to identify the time to delay in diagnosis and factors leading to this delay. This is a prospective observational cohort study performed in an emergency department (ED) of a tertiary care university hospital between September 2008 and September 2010. The rate and cause of delay in diagnosis were analyzed among patients with PE. The "delay" was defined as diagnosing after first 24 hours of symptom onset. Among the 53 patients who were diagnosed with PE, a delay in diagnosis was present in 49 (93%) of them. Total delay time was 6.8 ± 7.7 days. In 33 (62%) patients, there was a delay of 4.6 ± 6.5 days due to patient-related factors. Delay in diagnosis after admission to hospital was 2.2 ± 2.9 days in 40 (75%) patients. In multivariate regression analysis, being female and having chest pain and cough were identified as significant factors causing patient-related delay. Unilateral leg edema, recent operation, and previous venous thromboembolism (VTE) history were the significant factors causing PE diagnosis without a delay. On the other hand, systemic hypertension as comorbidity was the only factor leading to physician-related delay. The delay in diagnosis of PE in EDs still remains as an important problem. While being female and having chest pain and cough are significantly and independently associated with patient delay in diagnosis, the unilateral leg edema, recent operation, and previous VTE history cause physicians to diagnose on time. On the other hand, having hypertension as comorbidity may lead to physician delay. In order to prevent the delay in diagnosis, hospital-associated factors must be elucidated totally and more interventions must be made to increase public and professional awareness of the disease.

A comparison of risk factors associated with community-associated methicillin-resistant and -susceptibleStaphylococcus aureusinfections in remote communities

In this case-control study, cases [community-associated methicillin-resistant Staphylococcus aureus (CA-MRSA), n=79] and controls [community-associated methicillin-susceptible S. aureus (CA-MSSA), n=36] were defined as a laboratory-confirmed infection in a patient with no previous hospital-associated factors. Skin and soft tissue were the predominant sites of infection, both for cases (67.1%) and controls (55.6%). Most of the cases (79.7%) and controls (77.8%) were aged <30 years. Investigations did not reveal any significant statistical differences in acquiring a CA-MRSA or CA-MSSA infection. The most common shared risk factors included overcrowding, previous antibiotic usage, existing skin conditions, household exposure to someone with a skin condition, scratches/insect bites, and exposure to healthcare workers. Similar risk factors, identified for both CA-MRSA and CA-MSSA infections, suggest standard hygienic measures and proper treatment guidelines would be beneficial in controlling both CA-MRSA and CA-MSSA in remote communities.

Hospital-Based Factors Predict Outcome After Carotid Endarterectomy

Patient, hospital, and surgeon factors affect outcome after carotid endarterectomy (CEA). The nature and importance of hospital-specific factors, especially those unrelated to procedural volume, that affect post-CEA outcome remains poorly defined. We used a statewide database to determine the impact of several hospital-associated factors on outcome after CEA. Hospital factors were established by telephone survey and validated by repeated survey as well as by the Connecticut Hospital Association, Connecticut state, and individual hospital internet Websites. All CEA in Connecticut non-federal hospitals between 1991 and 2002 were examined, including perioperative mortality, stroke, and cardiac complications. Multivariable logistic regression was used to analyze data. There were 14,288 CEAs performed with 0.5% mortality, 1.3% stroke, and 2.4% cardiac complications. The only hospital factor independently predictive of perioperative mortality was few number of hospital beds (less than 132 beds; odds ratio (OR) 2.78, P = 0.032). Factors predictive of perioperative stroke included few number of beds (OR 1.96, P = 0.001) and absence of a critical pathway (OR 1.39, P = 0.038). Factors predictive of perioperative cardiac complications included few number of beds (OR 3.01, P = 0.003), absence of a critical pathway (OR 1.50, P = 0.001), and absence of dedicated vascular recovery beds (OR 1.35, P = 0.03). Combined mortality, stroke, or cardiac complications were independently predicted by few hospital beds (OR 4.58, P = 0.002), absence of a critical pathway (OR 1.81, P < or = 0.0001), or inability to perform cardiac angiography (OR 3.92, P = 0.024). Hospital-based factors, such as greater bed capacity, use of critical pathways, ability to perform cardiac angiography, or presence of a dedicated vascular recovery unit predict reduced perioperative mortality, stroke and cardiac complications from CEA. These results suggest that hospital-associated factors do impact surgical outcome and that surgeons need to optimize these factors, extrinsic to the patient and surgeon, to provide maximal quality of care.