Factors Associated with the Treatment Costs within the First Year after Pacemaker Implantation or Pulse Generator Replacement. / Fatores Associados aos Custos do Tratamento no Primeiro Ano após Implantes Iniciais ou Trocas de Geradores de Pulsos de Marca-passos Cardíacos.

BACKGROUND: The use of artificial cardiac pacemakers has grown steadily in line with the aging population. OBJECTIVES: To determine the rates of hospital readmissions and complications after pacemaker implantation or pulse generator replacement and to assess the impact of these events on annual treatment costs from the perspective of the Unified Health System (SUS). METHODS: A prospective registry, with data derived from clinical practice, collected during index hospitalization and during the first 12 months after the surgical procedure. The cost of index hospitalization, the procedure, and clinical follow-up were estimated according to the values reimbursed by SUS and analyzed at the patient level. Generalized linear models were used to study factors associated with the total annual treatment cost, adopting a significance level of 5%. RESULTS: A total of 1,223 consecutive patients underwent initial implantation (n=634) or pulse generator replacement (n=589). Seventy episodes of complication were observed in 63 patients (5.1%). The incidence of hospital readmissions within one year was 16.4% (95% CI 13.7% - 19.6%) after initial implants and 10.6% (95% CI 8.3% - 13.4%) after generator replacements. Chronic kidney disease, history of stroke, length of hospital stays, need for postoperative intensive care, complications, and hospital readmissions showed a significant impact on the total annual treatment cost. CONCLUSIONS: The results confirm the influence of age, comorbidities, postoperative complications, and hospital readmissions as factors associated with increased total annual treatment cost for patients with pacemakers.

FUNDAMENTO: O uso de marca-passos cardíacos artificiais tem crescido constantemente, acompanhando o envelhecimento populacional. OBJETIVOS: Determinar as taxas de readmissões hospitalares e complicações após implante de marca-passo ou troca de gerador de pulsos e avaliar o impacto desses eventos nos custos anuais do tratamento sob a perspectiva do Sistema Único de Saúde (SUS). MÉTODOS: Registro prospectivo, com dados derivados da prática clínica assistencial, coletados na hospitalização índice e durante os primeiros 12 meses após o procedimento cirúrgico. O custo da hospitalização índice, do procedimento e do seguimento clínico foram estimados de acordo com os valores reembolsados pelo SUS e analisados ao nível do paciente. Modelos lineares generalizados foram utilizados para estudar fatores associados ao custo total anual do tratamento, adotando-se um nível de significância de 5%. RESULTADOS: No total, 1.223 pacientes consecutivos foram submetidos a implante inicial (n= 634) ou troca do gerador de pulsos (n= 589). Foram observados 70 episódios de complicação em 63 pacientes (5,1%). A incidência de readmissões hospitalares em um ano foi de 16,4% (IC 95% 13,7% - 19,6%) após implantes iniciais e 10,6% (IC 95% 8,3% - 13,4%) após trocas de geradores. Doença renal crônica, histórico de acidente vascular encefálico, tempo de permanência hospitalar, necessidade de cuidados intensivos pós-operatórios, complicações e readmissões hospitalares mostraram um impacto significativo sobre o custo anual total do tratamento. CONCLUSÕES: Os resultados confirmam a influência da idade, comorbidades, complicações pós-operatórias e readmissões hospitalares como fatores associados ao incremento do custo total anual do tratamento de pacientes com marca-passo.

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.

Hospital Cost of Complications After Kidney Transplant.

BACKGROUND: Kidney transplant is considered the best treatment of rehabilitation for chronic kidney disease, but clinical and/or surgical complications may occur after transplant. The study aimed to assess the cost of complications after kidney transplant. MATERIALS AND METHODS: This is a descriptive, retrospective, and exploratory study that used data from Hospital Information System ("Sistema de Informação Hospitalar"). We identified the patients with records of kidney transplant in the states of northern and northeastern Brazil in 2013. These patients were followed up through the records, specifically from 2013 to 2017. The variables analyzed were sex, age, and period after transplant of readmissions (early, intermediate, or late), the main complications within 4 years after the kidney transplant, and cost of hospital admissions. RESULTS: There were 893 patients with records of kidney transplant in the regions of the study. During the follow-up period, 319 patients had complications. Most hospital readmissions involved male patients (63.6%; n = 203). Mean age was 45 (SD, 15.14) years. Patients developed complications mainly in the early period after transplant (70.22%; n = 224). The number of hospital admissions was 758. The main complications were regarding urinary tract (72.02%; n = 546), infections (19.79%; n = 150), and vascular and/or pulmonary (2.90%; n = 22). The total cost for the treatment of these complications was US $528,329.51. CONCLUSIONS: By analyzing the data it was possible to identify that there is a significant cost involved in the treatment of complications after kidney transplant.

Early Hospital Readmission (EHR) in kidney transplantation: a review article.

Early hospital readmission (EHR), defined as all readmissions within 30 days of initial hospital discharge, is a health care quality measure. It is influenced by the demographic characteristics of the population at risk, the multidisciplinary approach for hospital discharge, the access, coverage, and comprehensiveness of the health care system, and reimbursement policies. EHR is associated with higher morbidity, mortality, and increased health care costs. Monitoring EHR enables the identification of hospital and outpatient healthcare weaknesses and the implementation of corrective interventions. Among kidney transplant recipients in the USA, EHR ranges between 18 and 47%, and is associated with one-year increased mortality and graft loss. One study in Brazil showed an incidence of 19.8% of EHR. The main causes of readmission were infections and surgical and metabolic complications. Strategies to reduce early hospital readmission are therefore essential and should consider the local factors, including socio-economic conditions, epidemiology and endemic diseases, and mobility.

Targeting malnutrition: Nutrition programs yield cost savings for hospitalized patients.

BACKGROUND & AIMS: Between 30 and 50% of Colombian patients are malnourished or at-risk of malnutrition on hospital admission. Malnutrition is associated with poor outcomes and increased costs. We used cost modeling to estimate savings that could be derived from implementation of a nutrition therapy program for patients at malnutrition risk. METHODS: The budget impact analysis was performed using previously-published outcomes data. Outcomes included length of stay, 30-day readmissions, and infectious/non-infectious complications. We developed a Markov model that compared patients who were assigned to receive early nutrition therapy (started within 24-48 h of hospital admission) with those assigned to receive standard nutrition therapy (not started early). Our model used a 60-day time-horizon and estimated event probabilities based on published data. RESULTS: Average total costs over 60 days were $3770 US dollars for patients with delayed nutrition therapy vs $2419 for patients with early nutrition therapy-a savings of $1351 (35.8% decrease) per nutrition-treated patient. Cost differences between the groups were: $2703 vs $1600 for hospital-associated costs; $883 vs $665 for readmissions; and $176 vs $94 for complications. Taken broadly, the potential costs savings from a nutrition care program for an estimated 638,318 hospitalized Colombian patients at malnutrition risk is $862.6 million per year. CONCLUSIONS: Our budget impact analysis demonstrated the potential for hospital-based nutrition care programs to improve health outcomes and reduce healthcare costs for hospitalized patients in Colombia. These findings provide a rationale for implementing comprehensive nutrition care in Colombian hospitals.

The LACE+ Index as a Predictor of 30-Day Patient Outcomes in a Urologic Surgery Population: A Coarsened Exact Match Study.

OBJECTIVE: To examine the potential of LACE+ scores, in patients undergoing urologic surgery, to predict short-term undesirable outcomes. METHODS: Coarsened exact matching was used to assess the predictive value of the LACE+ index among all urologic surgery cases over a 2-year period (2016-2018) at 1 health system (n = 9824). Study subjects were matched on characteristics not assessed by LACE+, including duration of surgery and race, among others. For comparison of outcomes, matched populations were compared by LACE+ quartile with Q4 as the referent group: Q4 vs Q1, Q4 vs Q2, Q4 vs Q3. RESULTS: Seven hundred and twenty-two patients were matched for Q1-Q4; 1120 patients were matched for Q2-Q4; 2550 patients were matched for Q3-Q4. Escalating LACE+ score significantly predicted increased readmission (2.86% vs 4.91% for Q2 vs Q4; P = .012) and Emergency Room (ER) visits at 30 days postop (5.69% vs 11.37% for Q1 vs Q4, 4.11% vs 11.45% for Q2 vs Q4, 8.29% vs 13.32% for Q3 vs Q4; P <.001 for all). Increasing LACE score did not predict reoperation within 30 days or rate of death over follow-up within 30 postoperative days. CONCLUSION: The results of this study suggest that the LACE+ index is suitable as a prediction model for important patient outcomes in a urologic surgery population including unanticipated readmission and ER evaluation.

A Nurse Navigator Program Is Effective in Reducing Episode-of-Care Costs Following Primary Hip and Knee Arthroplasty.

BACKGROUND: Alternative payment models for total hip arthroplasty (THA) and total knee arthroplasty (TKA) have incentivized providers to deliver higher quality care at a lower cost, prompting some institutions to develop formal nurse navigation programs (NNPs). The purpose of this study was to determine whether a NNP for primary THA and TKA resulted in decreased episode-of-care (EOC) costs. METHODS: We reviewed a consecutive series of primary THA and TKA patients from 2015-2016 using claims data from the Centers for Medicare and Medicaid Services and Medicare Advantage patients from a private insurer. Three nurse navigators were hired to guide discharge disposition and home needs. Ninety-day EOC costs were collected before and after implementation of the NNP. To control for confounding variables, we performed a multivariate regression analysis to determine the independent effect of the NNP on EOC costs. RESULTS: During the study period, 5275 patients underwent primary TKA or THA. When compared with patients in the prenavigator group, the NNP group had reduced 90-day EOC costs ($19,116 vs $20,418 for Medicare and $35,378 vs $36,961 for private payer, P < .001 and P < .012, respectively). Controlling for confounding variables in the multivariate analysis, the NNP resulted in a $1575 per Medicare patient (P < .001) and a $1819 per private payer patient cost reduction (P = .005). This translates to a cost savings of at least $5,556,600 per year. CONCLUSION: The implementation of a NNP resulted in a marked reduction in EOC costs following primary THA and TKA. The cost savings significantly outweighs the added expense of the program. Providers participating in alternative payment models should consider using a NNP to provide quality arthroplasty care at a reduced cost.

A validated risk model for prediction of early readmission in patients with hepatic encephalopathy.

INTRODUCTION AND AIM: Hepatic encephalopathy (HE) is a common complication in cirrhotics and is associated with an increased healthcare burden. Our aim was to study independent predictors of 30-day readmission and develop a readmission risk model in patients with HE. Secondary aims included studying readmission rates, cost, and the impact of readmission on mortality. MATERIALS AND METHODS: We utilized the 2013 Nationwide Readmission Database (NRD) for hospitalized patients with HE. A risk assessment model based on index hospitalization variables for predicting 30-day readmission was developed using multivariate logistic regression and validated with the 2014 NRD. Patients were stratified into Low Risk and High Risk groups. Cox regression models were fit to identify predictors of calendar-year mortality. RESULTS: Of 24,473 cirrhosis patients hospitalized with HE, 32.4% were readmitted within 30 days. Predictors of readmission included presence of ascites (OR: 1.19; 95% CI: 1.06-1.33), receiving paracentesis (OR: 1.43; 95% CI: 1.26-1.62) and acute kidney injury (OR: 1.11; 95% CI: 1.00-1.22). Our validated model stratified patients into Low Risk and High Risk of 30-day readmissions (29% and 40%, respectively). The cost of the first readmission was higher than index admission in the 30-day readmission cohort ($14,198 vs. $10,386; p-value <0.001). Thirty-day readmission was the strongest predictor of calendar-year mortality (HR: 4.03; 95% CI: 3.49-4.65). CONCLUSIONS: Nearly one-third of patients with HE were readmitted within 30 days, and early readmission adversely impacted healthcare utilization and calendar-year mortality. With our proposed simple risk assessment model, patients at high risk for early readmissions can be identified to potentially avert poor outcomes.

72-hour hospital readmission of older people after hospital discharge with home care services.

In this retrospective cohort study in Argentina, risk factors for hospital readmission of older adults, within 72 hours after hospital discharge with home care services, were analyzed. Fifty-three percent of unplanned emergency room visits within 72 hours after hospital discharge resulted in hospital readmissions, 65% of which were potentially avoidable. By multivariate logistic regression, low functionality, pressure ulcers, and age over 83 years predicted hospital readmission among emergency room attendees. It is important to identify and analyze barriers in current home care services and the high-risk population of hospital readmission to improve the strategies to avoid adverse outcomes.

How Much Does a Readmission Cost the Bundle Following Primary Hip and Knee Arthroplasty?

BACKGROUND: As alternative payment models increase in popularity for total joint arthroplasty (TJA), providers and hospitals now share the financial risk associated with unexpected readmissions. While studies have identified postacute care as a driver for costs in a bundle, the fiscal burden associated with specific causes of readmission is unclear. The purpose of this study is to quantify the additional costs associated with each of the causes of readmission following primary TJA. METHODS: We reviewed a consecutive series of primary TJA patients at our institution from 2015 to 2016 using claims data from the Centers for Medicare and Medicaid Services and Medicare Advantage patients from a single private insurer. We collected demographic data, medical comorbidities, 90-day episode-of-care costs, and readmissions for all patients. Medical records for each readmission were reviewed and classified into 1 of 11 categories. We then compared the mean facility readmission costs, postacute care costs, and overall 90-day episode-of-care costs between the reasons for readmission. RESULTS: Of the 4704 patients, there were 325 readmissions in 286 patients (6.1%), with 50% being readmitted to a different facility than their index surgery hospital. The mean additional cost was $8588 per readmission. Medical reasons accounted for the majority of readmissions (n = 257, 79.1%). However, patients readmitted for revision surgery (n = 68, 20.9%) had the highest mean readmission cost ($15,356, P < .001). Furthermore, readmissions for revision surgery had the highest mean postacute care ($37,207, P = .002) and overall episode-of-care costs ($52,162, P = .003). Risk factors for readmission included age >75 years (odds ratio [OR], 1.85; P < .001), body mass index >35 kg/m2 (OR, 1.63; P = .004), history of congestive heart failure (OR, 2.47; P = .002), diabetes mellitus (OR, 2.0; P < .001), and renal disease (OR, 2.28; P = .005). CONCLUSION: Providers participating in alternative payment models should be cognizant of the increased bundle costs attributed to readmissions, especially due to revision surgery. Improved communication with patients and close postoperative monitoring may help minimize the large percentage of readmissions at different facilities.

Cost Determinants in the 90-Day Management of Isolated Ankle Fractures at a Large Urban Academic Hospital.

OBJECTIVES: To determine the independent risk factors associated with increasing costs and unplanned hospital readmissions in the 90-day episode of care (EOC) for isolated operative ankle fractures at our institution. DESIGN: Retrospective cohort study. SETTING: Level I Trauma Center. PATIENTS: Two hundred ninety-nine patients undergoing open reduction internal fixation for the treatment of an acute, isolated ankle fracture between 2010 and 2015. INTERVENTION: None. MAIN OUTCOME MEASURES: Independent risk factors for increasing 90-day EOC costs and unplanned hospital readmission rates. RESULTS: Orthopaedic (64.9%) and podiatry (35.1%) patients were included. The mean index admission cost was $14,048.65 ± $5,797.48. Outpatient cases were significantly cheaper compared to inpatient cases ($10,164.22 ± $3,899.61 vs. $15,942.55 ± $5,630.85, respectively, P < 0.001). Unplanned readmission rates were 5.4% (16/299) and 6.7% (20/299) at 30 and 90 days, respectively, and were often (13/20, 65.0%) due to surgical site infections. Independent risk factors for unplanned hospital readmissions included treatment by the podiatry service (P = 0.024) and an American Society of Anesthesiologists score of ≥3 (P = 0.017). Risk factors for increasing total postdischarge costs included treatment by the podiatry service (P = 0.011) and male gender (P = 0.046). CONCLUSIONS: Isolated operative ankle fractures are a prime target for EOC cost containment strategy protocols. Our institutional cost analysis study suggests that independent financial clinical risk factors in this treatment cohort includes podiatry as the treating surgical service and patients with an American Society of Anesthesiologists score ≥3, with the former also independently increasing total postdischarge costs in the 90-day EOC. Outpatient procedures were associated with about a one-third reduction in total costs compared to the inpatient subgroup.

Causes, Preventability, and Cost of Unplanned Rehospitalizations Within 30 Days of Discharge After Lung Transplantation.

BACKGROUND: Unplanned rehospitalizations (UR) within 30 days of discharge are common after lung transplantation. It is unknown whether UR represents preventable gaps in care or necessary interventions for complex patients. The objective of this study was to assess the incidence, causes, risk factors, and preventability of UR after initial discharge after lung transplantation. METHODS: This was a single-center prospective cohort study. Subjects completed a modified short physical performance battery to assess frailty at listing and at initial hospital discharge after transplantation and the State-Trait Anxiety Inventory at discharge. For each UR, a study staff member and the patient's admitting or attending clinician used an ordinal scale (0, not; 1, possibly; 2, definitely preventable) to rate readmission preventability. A total sum score of 2 or higher defined a preventable UR. RESULTS: Of the 90 enrolled patients, 30 (33.3%) had an UR. The single most common reasons were infection (7 [23.3%]) and atrial tachyarrhythmia (5 [16.7%]). Among the 30 URs, 9 (30.0%) were deemed preventable. Unplanned rehospitalization that happened before day 30 were more likely to be considered preventable than those between days 30 and 90 (30.0% versus 6.2%, P = 0.04). Discharge frailty, defined as short physical performance battery less than 6, was the only variable associated with UR on multivariable analysis (odds ratio, 3.4; 95% confidence interval, 1.1-11.8; P = 0.04). CONCLUSIONS: Although clinicians do not rate the majority of UR after lung transplant as preventable, discharge frailty is associated with UR. Further research should identify whether modification of discharge frailty can reduce UR.

Return Visit Admissions May Not Indicate Quality of Emergency Department Care for Children.

OBJECTIVE: The objective was to test the hypothesis that in-hospital outcomes are worse among children admitted during a return ED visit than among those admitted during an index ED visit. METHODS: This was a retrospective analysis of ED visits by children age 0 to 17 to hospitals in Florida and New York in 2013. Children hospitalized during an ED return visit within 7 days were classified as "ED return admissions" (discharged at ED index visit and admitted at return visit) or "readmissions" (admission at both ED index and return visits). In-hospital outcomes for ED return admissions and readmissions were compared to "index admissions without return admission" (admitted at ED index visit without 7-day return visit admission). RESULTS: Among 1,886,053 index ED visits to 321 hospitals, 75,437 were index admissions without return admission, 7,561 were ED return admissions, and 1,333 were readmissions. ED return admissions had lower intensive care unit admission rates (11.0% vs. 13.6%; adjusted odds ratio = 0.78; 95% confidence interval [CI] = 0.71 to 0.85), longer length of stay (3.51 days vs. 3.38 days; difference = 0.13 days; incidence rate ratio = 1.04; 95% CI = 1.02 to 1.07), but no difference in mean hospital costs (($7,138 vs. $7,331; difference = -$193; 95% CI = -$479 to $93) compared to index admissions without return admission. CONCLUSIONS: Compared with children who experienced index admissions without return admission, children who are initially discharged from the ED who then have a return visit admission had lower severity and similar cost, suggesting that ED return visit admissions do not involve worse outcomes than do index admissions.

Interactive Cost-benefit Analysis: Providing Real-World Financial Context to Predictive Analytics.

Objective: Clinical implementation of predictive analytics that assess risk of high-cost outcomes are presumed to save money because they help focus interventions designed to avert those outcomes on a subset patients who are most likely to benefit from the intervention. This premise may not always be true. A cost-benefit analysis is necessary to show if a strategy of applying the predictive algorithm is truly favorable to alternative strategies. Methods: We designed and implemented an interactive web-based cost-benefit calculator, enabling specification of accuracy parameters for the predictive model and other clinical and financial factors related to the occurrence of an undesirable outcome. We use the web tool, populated with real-world data to illustrate a cost-benefit analysis of a strategy of applying predictive analytics to select a cohort of high-risk patients to receive interventions to avert readmissions for Congestive Heart Failure (CHF). Results: Application of predictive analytics in clinical care may not always be a cost-saving strategy compared with intervening on all patients. Improving the accuracy of a predictive model may lower costs, but other factors such as the prevalence and cost of the outcome, and the cost and effectiveness of the intervention designed to avert the outcome may be more influential in determining the favored strategy. Conclusion: An interactive cost-benefit analyses provides insights regarding the financial implications of a clinical strategy that implements predictive analytics.

The Burden of Hospital Readmissions among Pediatric Patients with Inflammatory Bowel Disease.

OBJECTIVE: To evaluate the burden and predictors of hospital readmissions among pediatric patients with inflammatory bowel disease using the Nationwide Readmissions Database. STUDY DESIGN: We performed a retrospective cohort study using 2013 Nationwide Readmissions Database. International Classification of Diseases, Ninth Revision, Clinical Modification codes were used to identify patients <18 years with diagnoses of ulcerative colitis (UC) or Crohn's disease (CD). Demographic factors and details of hospitalizations were evaluated using survey procedures in SAS v 9.4 (SAS Institute, Cary, North Carolina). Multivariable logistic regression was used to estimate ORs and 95% CIs of readmission. RESULTS: Among 2733 hospitalizations (63% CD, 37% UC), 611 (22%) patients were readmitted within 90 days of the index hospitalization. Readmission resulted in weighted estimates of 11 440 excess days of hospitalization and total charges of over $107 million. For CD, male sex (aOR 1.36, 95% CI 1.03-1.81) and co-existing anxiety or depression (aOR 1.89, 95% CI 1.06-3.40) were associated with increased readmissions, while patients who underwent surgery had decreased readmissions (aOR 0.40, 95% CI 0.24-0.65). In patients with UC, an index admission of >7 days was associated with increased readmissions (aOR 1.69, 95% CI 1.09-2.62). CONCLUSIONS: Readmission occurs frequently in children with inflammatory bowel disease and is associated with significant cost and resource burdens. Among patients with CD, psychiatric comorbidities such as anxiety and depression are apparent drivers of readmission.

Financial Performance of Hospitals in the Mississippi Delta Region Under the Hospital Readmissions Reduction Program and Hospital Value-based Purchasing Program.

BACKGROUND: Previous studies showed that the Hospital Readmissions Reduction Program (HRRP) and the Hospital Value-based Purchasing Program (HVBP) disproportionately penalized hospitals caring for the poor. The Mississippi Delta Region (Delta Region) is among the most socioeconomically disadvantaged areas in the United States. The financial performance of hospitals in the Delta Region under both HRRP and HVBP remains unclear. OBJECTIVE: To compare the differences in financial performance under both HRRP and HVBP between hospitals in the Delta Region (Delta hospitals) and others in the nation (non-Delta hospitals). RESEARCH DESIGN: We used a 7-year panel dataset and applied difference-in-difference models to examine operating and total margin between Delta and non-Delta hospitals in 3 time periods: preperiod (2008-2010); postperiod 1 (2011-2012); and postperiod 2 (2013-2014). RESULTS: The Delta hospitals had a 0.89% and 4.24% reduction in operating margin in postperiods 1 and 2, respectively, whereas the non-Delta hospitals had 1.13% and 1% increases in operating margin in postperiods 1 and 2, respectively. The disparity in total margins also widened as Delta hospitals had a 1.98% increase in postperiod 1, but a 0.30% reduction in postperiod 2, whereas non-Delta hospitals had 1.27% and 2.28% increases in postperiods 1 and 2, respectively. CONCLUSIONS: The gap in financial performance between Delta and non-Delta hospitals widened following the implementation of HRRP and HVBP. Policy makers should modify these 2 programs to ensure that resources are not moved from the communities that need them most.

Influence of Socioeconomic Context on the Rehospitalization Rates of Infants Born Preterm.

OBJECTIVE: To investigate the impact of social inequalities on the risk of rehospitalization in the first year after discharge from the neonatal unit in a population of preterm-born children. STUDY DESIGN: Preterm infants were included if they were born between 2006 and 2013 at ≤32 + 6 weeks of gestation and who received follow-up in a French regional medical network with a high level of healthcare. Socioeconomic context was estimated using a neighborhood-based socioeconomic deprivation index. Univariate and logistic regression analyses were used to identify risk factors associated with rehospitalization. RESULTS: For the 2325 children, the mean gestational age was 29 ± 2 weeks and the mean birth weight was 1315 ± 395 g. In the first year, 22% were rehospitalized (n = 589); respiratory diseases were the primary cause (44%). The multiple rehospitalization rate was 18%. Multivariable analysis showed that living in the most deprived neighborhoods (socioeconomic deprivation index of 5) was associated with overall rehospitalization (OR, 2.2; 95% CI, 1.5-3.6; P <.001), and multiple rehospitalizations (OR, 2.5; 95% CI, 1.2-4.9; P <.01); with socioeconomic deprivation index of 1 (least deprived) as reference. Deprivation was associated with all causes of hospitalization. Female sex (P <.001) and living in an urban area (P = .001) were protective factors. CONCLUSIONS: Despite regional routine follow-up for all children, rehospitalization after very preterm birth was higher for children living in deprived neighborhoods. Families' social circumstances need to be considered when evaluating the health consequences of very preterm birth.

Readmission to the Intensive Care Unit: Incidence, Risk Factors, Resource Use, and Outcomes. A Retrospective Cohort Study.

RATIONALE: Readmission to the intensive care unit (ICU) is associated with poor clinical outcomes, increased length of ICU and hospital stay, and higher costs. Nevertheless, knowledge of epidemiology of ICU readmissions, risk factors, and attributable outcomes is restricted to developed countries. OBJECTIVES: To determine the effect of ICU readmissions on in-hospital mortality, determine incidence of ICU readmissions, identify predictors of ICU readmissions and hospital mortality, and compare resource use and outcomes between readmitted and nonreadmitted patients in a developing country. METHODS: This retrospective single-center cohort study was conducted in a 40-bed, open medical-surgical ICU of a private, tertiary care hospital in São Paulo, Brazil. The Local Ethics Committee at Hospital Israelita Albert Einstein approved the study protocol, and the need for informed consent was waived. All consecutive adult (≥18 yr) patients admitted to the ICU between June 1, 2013 and July 1, 2015 were enrolled in this study. RESULTS: Comparisons were made between patients readmitted and not readmitted to the ICU. Logistic regression analyses were performed to identify predictors of ICU readmissions and hospital mortality. Out of 5,779 patients admitted to the ICU, 576 (10%) were readmitted to the ICU during the same hospitalization. Compared with nonreadmitted patients, patients readmitted to the ICU were more often men (349 of 576 patients [60.6%] vs. 2,919 of 5,203 patients [56.1%]; P = 0.042), showed a higher (median [interquartile range]) severity of illness (Simplified Acute Physiology III score) at index ICU admission (50 [41-61] vs. 42 [32-54], respectively, for readmitted and nonreadmitted patients; P < 0.001), and were more frequently admitted due to medical reasons (425 of 576 [73.8%] vs. 2,998 of 5,203 [57.6%], respectively, for readmitted and nonreadmitted patients; P < 0.001). Simplified Acute Physiology III score (P < 0.001), ICU admission from the ward (odds ratio [OR], 1.907; 95% confidence interval [CI], 1.463-2.487; P < 0.001), vasopressors need during index ICU stay (OR, 1.391; 95% CI, 1.130-1.713; P = 0.002), and length of ICU stay (P = 0.001) were independent predictors of ICU readmission. After adjusting for severity of illness, ICU readmission (OR, 4.103; 95% CI, 3.226-5.518; P < 0.001), admission source, presence of cancer, use of vasopressors, mechanical ventilation or renal replacement therapy, length of ICU stay, and nighttime ICU discharge were associated with increased risk of in-hospital death. CONCLUSIONS: Readmissions to the ICU were frequent and strongly related to poor outcomes. The degree to which ICU readmissions are preventable as well as the main causes of preventable ICU readmissions need to be further determined.

National Database Research in Spine Surgery: Limitations in the Current Literature.

Many of the studies in the spine surgical literature using national databases have been directed at examining adverse events, readmission rates, cost, and risk factors for poorer outcomes. Although such studies allow for assessment of large cohorts taken from multiple institutions, they are limited by data collection methods, short-term follow-up, and minimal assessment of functional outcomes. Furthermore, few studies are directed at producing actionable practice changes to improve patient care. Recent work aimed at producing databases with more relevance to spine surgery represent exciting developments to the rapidly growing field of health outcomes research.