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Fragmentation of Care after Surgical Discharge: Non-Index Readmission after Major Cancer Surgery.

ABSTRACT: Despite national emphasis on care coordination, little is known about how fragmentation affects cancer surgery outcomes. Our study examines a specific form of fragmentation in post-discharge care-readmission to a hospital different from the location of the operation-and evaluates its causes and consequences among patients readmitted after major cancer surgery.We used the State Inpatient Database of California (2004 to 2011) to identify patients who had major cancer surgery and their subsequent readmissions. Logistic models were used to examine correlates of non-index readmissions and to assess associations between location of readmission and outcomes, measured by in-hospital mortality and repeated readmission.Of 9,233 readmissions within 30 days of discharge after major cancer surgery, 20.0% occurred in non-index hospitals. Non-index readmissions were associated with emergency readmission (odds ratio [OR] = 2.63; 95% CI, 2.26-3.06), rural residence (OR = 1.81; 95% CI, 1.61-2.04), and extensive procedures (eg hepatectomy vs proctectomy; OR = 2.77; CI, 2.08-3.70). Mortality was higher during non-index readmissions than index readmissions independent of patient, procedure, and hospital factors (OR = 1.31; 95% CI, 1.03-1.66), but was mitigated by adjusting for conditions present at readmission (OR = 1.24; 95% CI, 0.98-1.58). Non-index readmission predicted higher odds of repeated readmission within 60 days of discharge from the first readmission (OR = 1.16; 95% CI, 1.02-1.32), independent of all covariates.Non-index readmissions constitute a substantial proportion of all readmissions after major cancer surgery. They are associated with more repeated readmissions and can be caused by severe surgical complications and increased travel burden. Overcoming disadvantages of non-index readmissions represents an opportunity to improve outcomes for patients having major cancer surgery.

SUBMITTER: Zheng C

PROVIDER: S-EPMC5244824 | biostudies-literature | 2016 May

REPOSITORIES: biostudies-literature

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Fragmentation of Care after Surgical Discharge: Non-Index Readmission after Major Cancer Surgery.

Zheng Chaoyi C Habermann Elizabeth B EB Shara Nawar M NM Langan Russell C RC Hong Young Y Johnson Lynt B LB Al-Refaie Waddah B WB

Journal of the American College of Surgeons 20160205 5

<h4>Background</h4>Despite national emphasis on care coordination, little is known about how fragmentation affects cancer surgery outcomes. Our study examines a specific form of fragmentation in post-discharge care-readmission to a hospital different from the location of the operation-and evaluates its causes and consequences among patients readmitted after major cancer surgery.<h4>Study design</h4>We used the State Inpatient Database of California (2004 to 2011) to identify patients who had maj ...[more]

PMID: 27016905

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Project description:ImportanceIncreasing regionalization of cancer surgery has the inadvertent potential to lead to fragmentation of care if readmissions occur at a facility other than the index hospital. The magnitude and adverse effects of readmission to a facility other than the one where the surgery was performed are unclear.ObjectivesTo assess rates of readmission to nonindex hospitals after major cancer surgery and to compare outcomes between index and nonindex hospital readmissions.Design, setting, and participantsIn this multicenter, population-based, nationally representative study of adult patients undergoing a major cancer operation (defined as esophagectomies or gastrectomies, hepaticobiliary resections, pancreatectomies, colorectal resections, or cystectomies), retrospective analyses were performed using the Nationwide Readmissions Database (admissions from January 1 through September 30, 2013). Descriptive analyses were performed to determine 90-day readmission characteristics, including timing, cost, and outcomes. Adjusting for clustering by facility, the study used multivariate logistic regression to identify factors associated with nonindex vs index readmissions. The study also used regression models to identify differences in mortality, major complications, and subsequent readmissions between the 2 groups. Data analysis was performed from January 1 through December 31, 2013.ExposuresReadmission to index vs nonindex hospitals (defined as any hospital other than the hospital where the major cancer operation was performed).Main outcomes and measuresProportion of 90-day readmissions and nonindex readmissions after major cancer surgery, factors associated with nonindex readmissions, and difference between in-hospital mortality, hospital costs, and subsequent readmissions for patients admitted to index vs nonindex hospitals.ResultsA total of 60 970 patients were included in the study (mean [SD] age, 67 [13] years; 7619 [55.6%] male and 6075 [44.4%] female). The 90-day readmission rate was 23.0%. Of the 13 695 first readmissions, 20.1% were to a nonindex hospital. Independent factors associated with readmission to a nonindex hospital included type of procedure, comorbidities (OR, 1.40; 95% CI, 1.15-1.70), elective admission (OR, 1.21; 95% CI, 1.06-1.37), discharge to a nursing facility (OR, 1.20; 95% CI, 1.07-1.36), and surgery at a teaching hospital (OR, 1.16; 95% CI, 1.00-1.34) (all P < .05). After risk adjustment, patients readmitted to nonindex hospitals had 31.2% higher odds of mortality (odds ratio, 1.31; 95% CI, 1.05-1.64) and 27.3% higher odds of having a major complication (odds ratio, 1.27; 95% CI, 1.14-1.42). Subsequent readmissions and hospital costs were not different between the 2 groups.Conclusions and relevanceApproximately one-fifth of readmissions were to a nonindex hospital and were associated with higher mortality and morbidity than readmission to index hospitals. Factors that influence nonindex readmissions have been identified to target interventions.

Project description:ObjectiveSurgical site infection (SSI) is one of the most common postoperative complications after vascular reconstruction, producing significant morbidity and hospital readmission. In contrast to SSI that develops while patients are still hospitalized, little is known about the cohort of patients who develop SSI after discharge. In this study, we explore the factors that lead to postdischarge SSI, investigate the differences between risk factors for in-hospital vs postdischarge SSI, and develop a scoring system to identify patients who might benefit from postdischarge monitoring of their wounds.MethodsPatients who underwent major vascular surgery from 2005 to 2012 for aneurysm and lower extremity occlusive disease were identified from the American College of Surgeons National Surgical Quality Improvement Program Participant Use Files. Patients were categorized as having no SSI, in-hospital SSI, or SSI after hospital discharge. Predictors of postdischarge SSI were determined by multivariable logistic regression and internally validated by bootstrap resampling. Risk scores were assigned to all significant variables in the model. Summative risk scores were collapsed into quartile-based ordinal categories and defined as low, low/moderate, moderate/high, and high risk. Multivariable logistic regression was used to determine predictors of in-hospital SSI.ResultsOf the 49,817 patients who underwent major vascular surgery, 4449 (8.9%) were diagnosed with SSI (2.1% in-hospital SSI; 6.9% postdischarge SSI). By multivariable analysis, factors significantly associated with increased odds of postdischarge SSI include female gender, obesity, diabetes, smoking, hypertension, coronary artery disease, critical limb ischemia, chronic obstructive pulmonary disease, dyspnea, neurologic disease, prolonged operative time >4 hours, American Society of Anesthesiology class 4 or 5, lower extremity revascularization or aortoiliac procedure, and groin anastomosis. The model exhibited moderate discrimination (bias-corrected C statistic, 0.691) and excellent internal calibration. The postdischarge SSI rate was 2.1% for low-risk patients, 5.1% for low/moderate-risk patients, 7.8% for moderate/high-risk patients, and 14% for high-risk patients. In a comparative analysis, comorbidities were the primary driver of postdischarge SSI, whereas in-hospital factors (operative time, emergency case status) and complications predicted in-hospital SSI.ConclusionsThe majority of SSIs after major vascular surgery develop following hospital discharge. We have created a scoring system that can select a cohort of patients at high risk for SSI after discharge. These patients can be targeted for transitional care efforts focused on early detection and treatment with the goal of reducing morbidity and preventing readmission secondary to SSI.

Project description:BackgroundReducing the 30-day unplanned hospital readmission rate is a goal for physicians and policymakers in order to improve quality of care. However, data on the readmission rate of critically ill patients in Japan and knowledge of the predictors associated with readmission are lacking. We investigated predictors associated with 30-day rehospitalization for medical and surgical adult patients separately.MethodsPatient data from 502 acute care hospitals with intensive care unit (ICU) facilities in Japan were retrospectively extracted from the Japanese Diagnosis Procedure Combination (DPC) database between April 2012 and February 2014. Factors associated with unplanned hospital readmission within 30 days of hospital discharge among medical and surgical ICU survivors were identified using multivariable logistic regression analysis.ResultsOf 486,651 ICU survivors, we identified 5583 unplanned hospital readmissions within 30 days of discharge following 147,423 medical hospitalizations (3.8% readmitted) and 11,142 unplanned readmissions after 339,228 surgical hospitalizations (3.3% readmitted). The majority of unplanned hospital readmissions, 60.9% of medical and 63.1% of surgical case readmissions, occurred within 15 days of discharge. For both medical and surgical patients, the Charlson comorbidity index score; category of primary diagnosis during the index admission (respiratory, gastrointestinal, and metabolic and renal); hospital length of stay; discharge to skilled nursing facilities; and having received a packed red blood cell transfusion, low-dose steroids, or renal replacement therapy were significantly associated with higher unplanned hospital readmission rates.ConclusionsFrom patient data extracted from a large Japanese national database, the 30-day unplanned hospital readmission rate after ICU stay was 3.4%. Further studies are required to improve readmission prediction models and to develop targeted interventions for high-risk patients.

Project description:ImportanceFrail patients are known to have poor perioperative outcomes. There is a paucity of literature investigating how the Modified Frailty Index (mFI), a validated measure of frailty, is associated with unplanned readmission among military veterans following surgery.ObjectiveTo understand the association between frailty and 30-day postoperative unplanned readmission.Design, setting, and participantsA retrospective cohort study was conducted among adult patients who underwent surgery and were discharged alive from Veterans Affairs hospitals for orthopedic, general, and vascular conditions between October 1, 2007, and September 30, 2014, with a postoperative length of stay between 2 and 30 days.ExposureFrailty, as calculated by the 11 variables on the mFI.Main outcomes and measuresThe primary outcome of interest is 30-day unplanned readmission. Secondary outcomes included any 30-day predischarge or postdischarge complication, 30-day postdischarge mortality, and 30-day emergency department visit.ResultsThe study sample included 236 957 surgical procedures (among 223 877 men and 13 080 women; mean [SD] age, 64.0 [11.3] years) from high-volume surgical specialties: 101 348 procedures (42.8%) in orthopedic surgery, 92 808 procedures (39.2%) in general surgery, and 42 801 procedures (18.1%) in vascular surgery. The mFI was associated with readmission (odds ratio [OR], 1.11; 95% CI, 1.10-1.12; R2 = 10.3%; C statistic, 0.71). Unadjusted rates of overall 30-day readmission (26 262 [11.1%]), postdischarge emergency department visit (34 204 [14.4%]), any predischarge (13 855 [5.9%]) or postdischarge (14 836 [6.3%]) complication, and postdischarge mortality (1985 [0.8%]) varied by frailty in a dose-dependent fashion. In analysis by individual mFI components using Harrell ranking, impaired functional status, identified as nonindependent functional status (OR, 1.16; 95% CI, 1.11-1.21; P < .01) or having a residual deficit from a prior cerebrovascular accident (OR, 1.17; 95% CI, 1.11-1.22; P < .01), contributed most to the ability of the mFI to anticipate readmission compared with the other components. Acutely impaired sensorium (OR, 1.12; 95% CI, 0.99-1.27; P = .08) and history of a myocardial infarction within 6 months (OR, 0.93; 95% CI, 0.81-1.06; P = .28) were not significantly associated with readmission.Conclusions and relevanceThe mFI is associated with poor surgical outcomes, including readmission, primarily due to impaired functional status. Targeting potentially modifiable aspects of frailty preoperatively, such as improving functional status, may improve perioperative outcomes and decrease readmissions.

Project description:BACKGROUND:Restrictive transfusion strategies supported by large randomized trials are resulting in decreased blood utilization in cardiac surgery. What remains to be determined, however, is the impact of lower discharge hemoglobin (Hb) levels on readmission rates. We assessed patients with higher versus lower Hb levels on discharge to compare 30-day readmission rates after coronary artery bypass grafting (CABG). METHODS:We retrospectively evaluated 1552 patients undergoing isolated CABG at our institution from January 2013 to May 2016. We evaluated 2 Hb cohorts: "high" (above) and "low" (below) the mean discharge Hb level of 9.4 g/dL, comparing patient characteristics, blood utilization, and clinical outcomes including 30-day readmission rates. We further evaluated the effects of the lowest (<8 g/dL) discharge Hb levels on 30-day readmission rates by dividing the patients into 4 anemia cohorts based on discharge Hb levels: "no anemia" (>12 g/dL), "mild anemia" (10-11.9 g/dL), "moderate anemia" (8-9.9 g/dL), and "severe anemia" (<8 g/dL). Risk adjustment accounted for age, sex, Charlson comorbidity index, preoperative comorbidities, revision sternotomy, and patient blood management program implementation. RESULTS:The "high" and "low" groups had similar patient characteristics except for Hb levels (mean discharge Hb was 10.4 ± 0.9 vs 8.5 ± 0.6 g/dL, respectively). Notably, no evidence for a difference in 30-day readmission rates was noted between the "high" (76/746; 10.2%) and "low" (97/806; 12.0%) (P = .25) Hb cohorts. The 4 anemia cohorts had differences in age, revision sternotomy incidence, Hb levels, certain patient comorbidities, and time to readmission. On multivariable analysis, the risk-adjusted odds of readmission in the "low" Hb cohort (odds ratio, 1.16; 95% confidence interval, 0.84-1.61; P = .36) was not significant compared to the "high" Hb cohort. Compared to patients with discharge Hb ?8 g/dL, patients with Hb <8 g/dL had a higher incidence of readmission (22/129; 17.1% vs 151/1423; 10.6%; P = .036). On multivariable analysis, Hb <8 g/dL on discharge was predictive of readmission (odds ratio, 1.77; 95% confidence interval, 1.05-2.88; P = .03). The most common reason for readmission was volume overload, followed by infection and arrhythmias. CONCLUSIONS:A discharge Hb level below the institution mean for CABG patients does not provide evidence for an association with an increased 30-day readmission rate. In the small number of patients discharged with Hb <8 g/dL, there is a suggestion of increased risk for readmission and larger more controlled studies are needed to verify or refute this finding.

Project description:Background Proximal humerus fractures (PHFs) are generally surgically treated with open reduction internal fixation (ORIF), hemiarthroplasty (HA), or total shoulder arthroplasty (TSA). Diverse fracture patterns and a high prevalence in the elderly population make it difficult to establish objective guidelines for the decision to undergo surgical treatment. The purpose of this study was to investigate risk factors associated with readmission, reoperation, and nonhome discharge following ORIF, HA, and TSA for PHFs. Methods Data on all patients who underwent ORIF, TSA, or HA for treatment of closed PHF between 2015 and 2017 were obtained by querying the American College of Surgeons National Surgical Quality Improvement database. Rates of postoperative readmission, nonhome discharge, and reoperation within 30 days were collected. Multivariate logistic regression was employed to identify predictors of readmission, nonhome discharge, and reoperation. Results A total of 2825 patients were included in this study: 1829 underwent ORIF, 707 underwent TSA, and 289 underwent HA. The significant predictors for readmission were having an American Society of Anesthesiologists class ≥ 3 (odds ratio [OR] 1.95, P = .003) and being of dependent functional status (OR 3.15, P < .001). The significant predictors for reoperation were male sex (OR 2.41, P < .001) and dependent functional status (OR 2.92, P = .006). The significant predictors for nonhome discharge were age 66-80 years (OR 7.00, P < .001), age ≥ 81 years (OR 16.31, P < .001), American Society of Anesthesiologists ≥3 (OR 2.34, P < .001), dependent functional status (OR 2.48, P < .001), and inpatient status (OR 3.32, P < .001). TSA showed slightly higher rates of nonhome discharge than HA and ORIF. Conclusion Significant risk factors for readmission, reoperation, and nonhome discharge within 30 days following surgical treatment for PHF were identified. Additionally, TSA was significantly associated with nonhome discharge compared with HA and ORIF.

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Fragmentation of Care after Surgical Discharge: Non-Index Readmission after Major Cancer Surgery.

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Fragmentation of Care after Surgical Discharge: Non-Index Readmission after Major Cancer Surgery.

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