Outcome Of Implementation Of A Multidisciplinary Team Approach To The Care Of Patients After Transsphenoidal Surgery

Objective: Transsphenoidal surgery (TS) for sellar lesions is an established and safe procedure, but complications can occur, particularly involving the neuroendocrine system. We hypothesized that postoperative care of TS patients would be optimized when performed by a coordinated team including a pituitary neurosurgeon, endocrinologists, and a specialty nurse.Methods: We implemented a formalized, multidisciplinary team approach and standardized postoperative protocols for the care of adult patients undergoing TS by a single surgeon (J.N.B.) at our institution beginning in July 2009. We retrospectively compared the outcomes of 214 consecutive TS-treated cases: 113 cases prior to and 101 following the initiation of the team approach and protocol implementation. Outcomes assessed included the incidence of neurosurgical and endocrine complications, length of stay (LOS), and rates of hospital readmission and unscheduled clinical visits.Results: The median LOS decreased from 3 days preteam to 2 days postteam (P<.01). Discharge occurred on postoperative day 2 in 46% of the preteam group patients compared to 69% of the postteam group (P<.01). Rates of early postoperative diabetes insipidus (DI) and readmissions within 30 days for syndrome of inappropriate antidiuretic hormone (SIADH) or other complications did not differ between groups.Conclusion: Implementation of a multidisciplinary team approach was associated with a reduction of LOS. Despite earlier discharge, postoperative outcomes were not compromised. The endocrinologist is central to the success of this team approach, which could be successfully applied to care of patients undergoing TS, as well as other types of endocrine surgery at other centers.Abbreviations:CSF = cerebrospinal fluidDDAVP = desmopressinDI = diabetes insipidusLOS = length of stayPOD = postoperative daySIADH = syndrome of inappropriate antidiuretic hormoneTS = transsphenoidal surgery     

Outcomes Associated With Insulin Therapy Disruption After Hospital Discharge Among Patients with Type 2 Diabetes Mellitus who Had Used Insulin Before and During Hospitalization

ObjectiveTo evaluate outcomes associated with insulin therapy disruption after hospital discharge in patients with type 2 diabetes mellitus who had used insulin before and during hospitalization.MethodsIn this observational, retrospective analysis of medical records obtained from a coordinated health system in the United States, patients with type 2 diabetes mellitus who had used insulin 30 days before and during hospitalization were included. Clinical and cost outcomes were compared between patients who continued insulin therapy and those who had disrupted insulin therapy after hospital discharge.ResultsIn total, 2160 records were analyzed (851 patients with continued insulin therapy and 1309 patients with disrupted insulin therapy). Mean baseline glycated hemoglobin A_1c levels were 8.56% and 7.73% in patients who continued insulin therapy and patients who disrupted insulin therapy, respectively (P <.001), suggesting that patients who discontinued insulin therapy had better glycemic control at baseline. Continued insulin therapy was associated with an expected greater reduction in glycated hemoglobin A_1c (P <.001); similar hypoglycemia rates; lower risks of all-cause hospital readmission, diabetesrelated readmission, and all-cause emergency department visits; and improved survival. Continued insulin therapy was associated with $3432 lower total medical service costs than disrupted therapy over the 6-month postdischarge period.ConclusionEnsuring adherence to insulin therapy in patients who require insulin therapy after hospitalization should be a priority for postdischarge patient care programs. However, the clinical implications of this study are limited by the fact that it could not be determined whether all patients required insulin therapy after hospital discharge. (Endocr Pract. 2012;18:651-659)     

Patient Understanding of Discharge Instructions for Home Diabetes Self-Management and Risk for Hospital Readmission and Emergency Department Visits

ObjectiveThe primary objective of this study was to examine the patient comprehension of diabetes self-management instructions provided at hospital discharge as an associated risk of readmission.MethodsNoncritically ill patients with diabetes completed patient comprehension questionnaires (PCQ) within 48 hours of discharge. PCQ scores were compared among patients with and without readmission or emergency department (ED) visits at 30 and 90 days. Glycemic measures 48 hours preceding discharge were investigated. Diabetes Early Readmission Risk Indicators (DERRIs) were calculated for each patient.ResultsOf 128 patients who completed the PCQ, scores were similar among those with 30-day (n = 31) and 90-day (n = 54) readmission compared with no readmission (n = 72) (79.9 ± 14.4 vs 80.4 ± 15.6 vs 82.3 ± 16.4, respectively) or ED visits. Clarification of discharge information was provided for 47 patients. PCQ scores of 100% were achieved in 14% of those with and 86% without readmission at 30 days (P = .108). Of predischarge glycemic measures, glycemic variability was negatively associated with PCQ scores (P = .035). DERRIs were significantly higher among patients readmitted at 90 days but not 30 days.ConclusionThese results demonstrate similar PCQ scores between patients with and those without readmission or ED visits despite the need for corrective information in many patients. Measures of glycemic variability were associated with PCQ scores but not readmission risk. This study validates DERRI as a predictor for readmission at 90 days.     

The Association of Diabetes and Hyperglycemia on Inpatient Readmissions

ObjectiveTo evaluate the association between inpatient glycemic control and readmission in individuals with diabetes and hyperglycemia (DM/HG).MethodsTwo data sets were analyzed from fiscal years 2011 to 2013: hospital data using the International Classification of Diseases, Ninth Revision (ICD-9) codes for DM/HG and point of care (POC) glucose monitoring. The variables analyzed included gender, age, mean, minimum and maximum glucose, along with 4 measures of glycemic variability (GV), standard deviation, coefficient of variation, mean amplitude of glucose excursions, and average daily risk range.ResultsOf 66 518 discharges in FY 2011-2013, 28.4% had DM/HG based on ICD-9 codes and 53% received POC monitoring. The overall readmission rate was 13.9%, although the rates for individuals with DM/HG were higher at 18.9% and 20.6% using ICD-9 codes and POC data, respectively. The readmitted group had higher mean glucose (169 ± 47 mg/dL vs 158 ± 46 mg/dL, P < .001). Individuals with severe hypoglycemia and hyperglycemia had the highest readmission rates. All 4 GV measures were consistent and higher in the readmitted group.ConclusionIndividuals with DM/HG have higher 30-day readmission rates than those without. Those readmitted had higher mean glucose, more extreme glucose values, and higher GV. To our knowledge, this is the first report of multiple metrics of inpatient glycemic control, including GV, and their associations with readmission.     

Development and Validation of a Novel Tool to Predict Hospital Readmission Risk Among Patients with Diabetes

Objective: To develop and validate a tool to predict the risk of all-cause readmission within 30 days (30-d readmission) among hospitalized patients with diabetes.Methods: A cohort of 44,203 discharges was retrospectively selected from the electronic records of adult patients with diabetes hospitalized at an urban academic medical center. Discharges of 60% of the patients (n = 26,402) were randomly selected as a training sample to develop the index. The remaining 40% (n = 17,801) were selected as a validation sample. Multivariable logistic regression with generalized estimating equations was used to develop the Diabetes Early Readmission Risk Indicator (DERRI™).Results: Ten statistically significant predictors were identified: employment status; living within 5 miles of the hospital; preadmission insulin use; burden of macrovascular diabetes complications; admission serum hematocrit, creatinine, and sodium; having a hospital discharge within 90 days before admission; most recent discharge status up to 1 year before admission; and a diagnosis of anemia. Discrimination of the model was acceptable (C statistic 0.70), and calibration was good. Characteristics of the validation and training samples were similar. Performance of the DERRI™ in the validation sample was essentially unchanged (C statistic 0.69). Mean predicted 30-d readmission risks were also similar between the training and validation samples (39.3% and 38.7% in the highest quintiles).Conclusion: The DERRI™ was found to be a valid tool to predict all-cause 30-d readmission risk of individual patients with diabetes. The identification of high-risk patients may encourage the use of interventions targeting those at greatest risk, potentially leading to better outcomes and lower healthcare costs.Abbreviations:DERRI™ = Diabetes Early Readmission Risk IndicatorICD-9-CM = International Classification of Diseases, Ninth Revision, Clinical ModificationGEE = generalized estimating equationsROC = receiver operating characteristic     

Thirty-Day Readmission Rates in Orthopedics: A Systematic Review and Meta-Analysis

Background

Hospital readmission rates are being used to evaluate performance. A survey of the present rates is needed before policies can be developed to decrease incidence of readmission. We address three questions: What is the present rate of 30-day readmission in orthopedics? How do factors such as orthopedic specialty, data source, patient insurance, and time of data collection affect the 30-day readmission rate? What are the causes and risk factors for 30-day readmissions?

Methods/Findings

A review was first registered with Prospero (CRD42014010293, 6/17/2014) and a meta-analysis was performed to assess the current 30-day readmission rate in orthopedics. Studies published after 2006 were retrieved, and 24 studies met the inclusion criteria. The 30-day readmission rate was extrapolated from each study along with the orthopedic subspecialty, data source, patient insurance, time of collection, patient demographics, and cause of readmission. A sensitivity analysis was completed on the stratified groups. The overall 30-day readmission rate across all orthopedics was 5.4 percent (95% confidence interval: 4.8,6.0). There was no significant difference between subspecialties. Studies that retrieved data from a multicenter registry had a lower 30-day readmission rate than those reporting data from a single hospital or a large national database. Patient populations that only included Medicare patients had a higher 30-day readmission rate than populations of all insurance. The 30-day readmission rate has decreased in the past ten years. Age, length of stay, discharge to skilled nursing facility, increased BMI, ASA score greater than 3, and Medicare/Medicaid insurance showed statistically positive correlation with increased 30-day readmissions in greater than 75 percent of studies. Surgical site complications accounted for 46 percent of 30-day readmissions.

Conclusions

This meta-analysis shows the present rate of 30-day readmissions in orthopedics. Demonstrable heterogeneity between studies underlines the importance of uniform collection and reporting of readmission rates for hospital evaluation and reimbursement.     

Hospital Readmission Prevalence and Analysis of Those Potentially Avoidable in Southern Italy

Background

One quality indicator of hospital care, which can be used to judge the process of care, is the prevalence of hospital readmission because it reflects the impact of hospital care on the patient’s condition after discharge. The purposes of the study were to measure the prevalence of hospital readmissions, to identify possible factors that influence such readmission and to measure the prevalence of readmissions potentially avoidable in Italy.

Methods

A sample of 2289 medical records of patients aged 18 and over admitted for medical or surgical illness at one 502-bed community non-teaching hospital were randomly selected.

Results

A total of 2252 patients were included in the final analysis, equaling a response rate of 98.4%. The overall hospital readmission prevalence within 30 days of discharge was 10.2%. Multivariate logistic regression analysis revealed that the proportion of patients readmitted within 30 days of discharge significantly increased regardless of Charlson et al. comorbidity score, among unemployed or retired patients, and in patients in general surgery. A total of 43.7% hospital readmissions were judged to be potentially avoidable. Multivariate logistic regression analysis showed that potentially avoidable readmissions were significantly higher in general surgery, in patients referred to hospital by an emergency department physician, and in those with a shortened time between discharge and readmission.

Conclusion

Additional research on intervention or bundle of interventions applicable to acute inpatient populations that aim to reduce potentially avoidable readmissions is strongly needed, and health care providers are urged to implement evidence-based programs for more cost-effective delivery of health care.     

Development,Validation and Deployment of a Real Time 30 Day Hospital Readmission Risk Assessment Tool in the Maine Healthcare Information Exchange

ObjectivesIdentifying patients at risk of a 30-day readmission can help providers design interventions, and provide targeted care to improve clinical effectiveness. This study developed a risk model to predict a 30-day inpatient hospital readmission for patients in Maine, across all payers, all diseases and all demographic groups.MethodsOur objective was to develop a model to determine the risk for inpatient hospital readmission within 30 days post discharge. All patients within the Maine Health Information Exchange (HIE) system were included. The model was retrospectively developed on inpatient encounters between January 1, 2012 to December 31, 2012 from 24 randomly chosen hospitals, and then prospectively validated on inpatient encounters from January 1, 2013 to December 31, 2013 using all HIE patients.ResultsA risk assessment tool partitioned the entire HIE population into subgroups that corresponded to probability of hospital readmission as determined by a corresponding positive predictive value (PPV). An overall model c-statistic of 0.72 was achieved. The total 30-day readmission rates in low (score of 0–30), intermediate (score of 30–70) and high (score of 70–100) risk groupings were 8.67%, 24.10% and 74.10%, respectively. A time to event analysis revealed the higher risk groups readmitted to a hospital earlier than the lower risk groups. Six high-risk patient subgroup patterns were revealed through unsupervised clustering. Our model was successfully integrated into the statewide HIE to identify patient readmission risk upon admission and daily during hospitalization or for 30 days subsequently, providing daily risk score updates.ConclusionsThe risk model was validated as an effective tool for predicting 30-day readmissions for patients across all payer, disease and demographic groups within the Maine HIE. Exposing the key clinical, demographic and utilization profiles driving each patient’s risk of readmission score may be useful to providers in developing individualized post discharge care plans.     

Incidence of potentially avoidable urgent readmissions and their relation to all-cause urgent readmissions

Background:

Urgent, unplanned hospital readmissions are increasingly being used to gauge the quality of care. We reviewed urgent readmissions to determine which were potentially avoidable and compared rates of all-cause and avoidable readmissions.

Methods:

In a multicentre, prospective cohort study, we reviewed all urgent readmissions that occurred within six months among patients discharged to the community from 11 teaching and community hospitals between October 2002 and July 2006. Summaries of the readmissions were reviewed by at least four practising physicians using standardized methods to judge whether the readmission was an adverse event (poor clinical outcome due to medical care) and whether the adverse event could have been avoided. We used a latent class model to determine whether the probability that each readmission was truly avoidable exceeded 50%.

Results:

Of the 4812 patients included in the study, 649 (13.5%, 95% confidence interval [CI] 12.5%–14.5%) had an urgent readmission within six months after discharge. We considered 104 of them (16.0% of those readmitted, 95% CI 13.3%–19.1%; 2.2% of those discharged, 95% CI 1.8%–2.6%) to have had a potentially avoidable readmission. The proportion of patients who had an urgent readmission varied significantly by hospital (range 7.5%–22.5%; χ² = 92.9, p < 0.001); the proportion of readmissions deemed avoidable did not show significant variation by hospital (range 1.2%–3.7%; χ² = 12.5, p < 0.25). We found no association between the proportion of patients who had an urgent readmission and the proportion of patients who had an avoidable readmission (Pearson correlation 0.294; p = 0.38). In addition, we found no association between hospital rankings by proportion of patients readmitted and rankings by proportion of patients with an avoidable readmission (Spearman correlation coefficient 0.28, p = 0.41).

Interpretation:

Urgent readmissions deemed potentially avoidable were relatively uncommon, comprising less than 20% of all urgent readmissions following hospital discharge. Hospital-specific proportions of patients who were readmitted were not related to proportions with a potentially avoidable readmission.Urgent, unplanned hospital readmissions are increasingly being used to measure institutional or regional quality of care.^1–4 The public reporting of readmissions and their use in considerations for funding suggest a belief that readmissions indicate the quality of care provided by particular institutions. However, urgent readmissions are an informative metric only if we know what proportion of them are avoidable. If they are rarely avoidable, they would be a poor gauge of the quality of patient care.Current estimates of the proportion of urgent readmissions that are avoidable are unreliable. In a systematic review of 34 studies that reviewed how many readmissions were avoidable, 3 of the studies relied solely on combinations of administrative diagnostic codes, and most used undefined or subjective criteria.⁵ In addition, most of the studies were conducted at a single centre and used only one reviewer. The proportion of readmissions deemed avoidable varied widely, from 5.1%⁶ to 78.9%,⁷ which reflected in part the lack of standardized and reliable methods to identify avoidable readmissions.We conducted a multicentre prospective cohort study to elicit judgments from multiple practising physicians who used standard implicit review methods to determine whether urgent readmissions were potentially avoidable. We analyzed these judgments using a latent class analysis. We also measured the proportion of readmissions deemed avoidable and compared hospital-specific proportions of all-cause and avoidable readmissions.     

Predictors for Adverse Outcomes in Diabetic Ketoacidosis in a Multihospital Health System

Objective: To determine predictors of prolonged length of stay (LOS), 30-day readmission, and 30-day mortality in a multihospital health system.Methods: We performed a retrospective review of 531 adults admitted with diabetic ketoacidosis (DKA) to a multihospital health system between November 2015 and December 2016. Demographic and clinical data were collected. Linear regression was used to calculate odds ratios (ORs) for predictors and their association with prolonged LOS (3.2 days), 30-day readmission, and 30-day mortality.Results: Significant predictors for prolonged LOS included: intensive care unit (ICU) admission (OR, 2.12; 95% confidence interval [CI], 1.38 to 3.27), disease duration (nonlinear) (OR, 1.28; 95% CI, 1.10 to 1.49), non-white race (OR, 1.73; 95% CI, 1.15 to 2.60), age at admission (OR, 1.03; 95% CI, 1.01 to 1.04), and Elixhauser index (EI) (OR, 1.21; 95% CI, 1.13 to 1.29). Shorter time to consult after admission (median [Q1, Q3] of 11.3 [3.9, 20.7] vs. 14.8 [7.4, 37.3] hours, P<.001) was associated with a shorter LOS. Significant 30-day readmission predictors included: Medicare insurance (OR, 2.35; 95% CI, 1.13 to 4.86) and EI (OR, 1.31; 95% CI, 1.21 to 1.41). Endocrine consultation was associated with reduced 30-day readmission (OR, 0.51; 95% CI, 0.28 to 0.92). A predictive model for mortality was not generated because of low event rates.Conclusion: EI, non-white race, disease duration, age, Medicare, and ICU admission were associated with adverse outcomes. Endocrinology consultation was associated with lower 30-day readmission, and earlier consultation resulted in a shorter LOS.Abbreviations: CI = confidence interval; DKA = diabetic ketoacidosis; EI = Elixhauser index; HbA1c = hemoglobin A1c; ICD = International Classification of Diseases; ICU = intensive care unit; LOS = length of stay; OR = odds ratio; Q = quartile     

The Effects of Perioperative Dexamethasone on Glycemic Control and Postoperative Outcomes

Objective: Perioperative glucocorticoids are commonly given to reduce pain and nausea in patients undergoing surgery. However, the glycemic effects of steroids and the potential effects on morbidity and mortality have not been systematically evaluated. This study investigated the association between perioperative dexamethasone and postoperative blood glucose, hospital length of stay (LOS), readmission rates, and 90-day survival.Methods: Data from 4,800 consecutive orthopedic surgery patients who underwent surgery between 2000 and 2016 within a single health system were analyzed retrospectively.Results: Patients with and without diabetes mellitus (DM) who were given a single dose of dexamethasone had higher rates of hyperglycemia during the first 24 hours after surgery as compared to those who did not receive dexamethasone (hazard ratio &lsqb;HR] was 1.81, and 95% confidence interval &lsqb;CI] was &lsqb;1.46, 2.24] for the DM cohort; HR 2.34, 95% CI &lsqb;1.66, 3.29] for the nonDM cohort). LOS was nearly 1 day shorter in patients who received dexamethasone (geometric mean ratio &lsqb;GMR] 0.79, 95% CI &lsqb;0.75, 0.83] for patients with DM; GMR 0.75, 95% CI &lsqb;0.72, 0.79] for patients without DM), and there was no difference in 90-day readmission rates. In patients without DM, dexamethasone was associated with a higher 90-day overall survival (99.07% versus 96.90%; P = .004).Conclusion: In patients with and without DM who undergo orthopedic surgery, perioperative dexamethasone was associated with a transiently higher risk of hyperglycemia. However, dexamethasone treatment was associated with a shorter LOS in patients with and without DM, and a higher overall 90-day survival rate in patients without DM, compared to patients who did not receive dexamethasone.Abbreviations: BMI = body mass index; CAD = coronary artery disease; CI = confidence interval; DM = diabetes mellitus; GMR = geometric mean ratio; HR = hazard ratio; IV = intravenous; LOS = length of stay; POD = postoperative day     

An Individualized Inpatient Diabetes Education and Hospital Transition Program for Poorly Controlled Hospitalized Patients with Diabetes

ObjectiveTo evaluate predictors of outcomes associated with an inpatient diabetes education and discharge support program for hospitalized patients with poorly controlled diabetes (glycated hemoglobin [HbA1c]>9%).MethodsPatients participated in individualized diabetes education conducted by a certified diabetes educator (CDE) that included an exploration of barriers and goal setting during hospitalization with telephone follow-up and communication with primary providers at discharge. Predictors of HbA1c reduction, successful follow-up, and readmission were analyzed.ResultsThere were 82 subjects, and 48% were insulin naïve. Patients with type 2 diabetes (T2D, n = 58) had a significant decrease in HbAlc at follow-up (-2.8%, P < .0001), while those with type 1 diabetes (T1D, n = 19) did not (+ 0.02%, P = .96). However, after adjustment for other factors, only increasing age, higher baseline HbA1c, earlier education, and initiation of basal insulin were significant predictors of reduction in HbA1c. Higher area level income and empowerment and earlier education were significant predictors of outpatient follow-up within 30 days. While 28% were admitted for severe hyperglycemia, only 1 patient was readmitted with severe hyperglycemia. Successful phone contact was 77% and 57% with and without the support of non-CDE assistants respectively, but all outcomes were similar.ConclusionThe study suggests that an individualized inpatient diabetes education and transition program is associated with a significant reduction in HbA1c that is dependent on baseline HbA1c, older age, initiation of insulin, and earlier enrollment. Additional interventions are needed to ensure better continuity of care. (Endocr Pract. 2014;20:1265-1273)     

Reduced Impact of Diabetes Clinic Referral on High-Frequency Emergency Department Users

Objective: The patterns of emergency department (ED) visits in patients with diabetes are not well understood. The Emergency Department Diabetes Rapid-referral Program (EDRP) allows direct booking of ED patients presenting with urgent diabetes needs into a diabetes specialty clinic within 1 day of ED discharge. The objective of this secondary analysis was to examine characteristics of patients with diabetes who have frequent ED visits and determine reasons for revisits.Methods: A single-center analysis was conducted comparing patients referred to the EDRP (n = 420) to historical unexposed controls (n = 791). The primary outcome was the proportion of patients in each frequency group of ED revisits (none, 1 to 3 [infrequent], 4 to 10 [frequent], or >10 [superfrequent]) in the year after the ED index visit. Secondary outcomes were hospitalization rates and International Classification of Diseases–Ninth Revision (ICD-9) diagnoses at ED revisits.Results: Superfrequent users, responsible for >20% of total ED visits, made up small but not significantly different proportions of EDRP and control populations, 3.6% and 5.2%, respectively. Superfrequent groups had lower hospital admission rates at ED revisits compared to frequent groups. Mental health disorders (including substance abuse) were the primary, secondary, or tertiary ICD-9 codes in 30.6% (95% confidence interval [CI], 27.7% to 33.5%) and 6.6% (95% CI, 5.1% to 8.2%) in the superfrequent and infrequent groups, respectively.Conclusion: Direct access to diabetes specialty care from the ED is effective in reducing ED recidivism but not amongst a small subgroup of superfrequent ED users. This group was more likely to have mental health disorders recorded at ED revisits, suggesting that more comprehensive approaches are needed for this population.Abbreviations: EDRP = Emergency Department Diabetes Rapid-referral Program; ED = emergency department; HbA1c = hemoglobin A1c; ICD-9 = International Classification of Diseases–Ninth Revision     

Impact of Glucose Management Team on Outcomes of Hospitalization in Patients With Type 2 Diabetes Admitted to the Medical Service

Objective: To improve glycemic control of hospitalized patients with diabetes and hyperglycemia, many medical centers have established dedicated glucose management teams (GMTs). However, the impact of these specialized teams on clinical outcomes has not been evaluated.Methods: We conducted a retrospective study of 440 patients with type 2 diabetes admitted to the medical service for cardiac or infection-related diagnosis. The primary endpoint was a composite outcome of several well-recognized markers of morbidity, consisting of: death during hospitalization, transfer to intensive care unit, initiation of enteral or parenteral nutrition, line infection, new in-hospital infection or infection lasting more than 20 days of hospitalization, deep venous thrombosis or pulmonary embolism, rise in plasma creatinine, and hospital re-admissions.Results: Medical housestaff managed the glycemia in 79% of patients (usual care group), while the GMT managed the glycemia in 21% of patients (GMT group). The primary outcome was similar between cohorts (0.95 events per patient versus 0.99 events per patient in the GMT and usual care cohorts, respectively). For subanalysis, the subjects in both groups were stratified into those with average glycemia of <180 mg/dL versus those with glycemia >180 mg/dL. We found a significant beneficial impact of glycemic management by the GMT on the composite outcome in patients with average glycemia >180 mg/dL during their hospital stay. The number of patients who met primary outcome was significantly higher in the usual care group (40 of 83 patients, 48%) than in the GMT-treated cohort (8 of 33 patients, 25.7%) (P<.02).Conclusion: Our data suggest that GMTs may have an important role in managing difficult-to-control hyperglycemia in the inpatient setting.Abbreviations:BG = blood glucoseGMT = glucose management teamHbA1c = hemoglobin A1cICU = intensive care unitPOC = point of careT2D = type 2 diabetes     

Are readmissions avoidable?

OBJECTIVE--To examine the possible use of readmission rates as an outcome indicator of hospital inpatient care by investigating avoidability of unplanned readmissions within 28 days of discharge. DESIGN--Retrospective analysis of a stratified random sample of case notes of patients with an unplanned readmission between July 1987 and June 1988 by nine clinical assessors (263 assessments) and categorisation of the readmission as avoidable, unavoidable, or unclassifiable. SETTING--District in North East Thames region. 481 General medical, geriatric, and general surgical inpatients with a readmission at 0-6 days or 21-27 days after the first (index) discharge between July 1987 and June 1988 from whom 100 case notes were selected randomly and of which 74 were available for study. MAIN OUTCOME MEASURES--Assessment of readmissions as avoidable, unavoidable, unclassifiable, variability of assessment within cases and variability among assessors according to specialty and duration to readmission. RESULTS--General medical and geriatric readmissions and surgical readmissions at 0-6 days after discharge were more likely to be assessed as avoidable than those at 21-27 days (medical readmissions 32 v 6%, surgical admissions 49 v 19%). General surgical readmissions were significantly more frequently assessed as avoidable than general medical and geriatric readmissions. The extent of agreement between doctors varied, with general medical and geriatric readmissions at 21-27 days after first discharge causing the greatest variability of judgment. CONCLUSIONS--Differences were apparent in the extent of avoidability of readmissions in different groups of admissions. However, assessors rated only 49.3% of the group with the highest proportion of avoidable admissions (surgical readmissions at 0-6 days) as avoidable. The remainder were thought to be unavoidable except for 2%, which could not be classified. The use of readmission rates as an outcome indicator of hospital inpatient care should be avoided.     

Risk factors associated with 31-day unplanned readmission in 50,912 discharged patients after stroke in China

Background

Unplanned readmission within 31?days of discharge after stroke is a useful indicator for monitoring quality of hospital care. We evaluated the risk factors associated with 31-day unplanned readmission of stroke patients in China.

Methods

We identified 50,912 patients from 375 hospitals in 29 provinces, municipalities or autonomous districts across China who experienced an unplanned readmission after stroke between 2015 and 2016, and extracted data from the inpatients’ cover sheet data from the Medical Record Monitoring Database. Patients were grouped into readmission within 31?days or beyond for analysis. Chi-squared test was used to analyze demographic information, health system and clinical process-related factors according to the data type. Multilevel logistic modeling was used to examine the effects of patient (level 1) and hospital (level 2) characteristics on an unplanned readmission ≤31?days.

Results

Among 50,912 patients, 14,664 (28.8%) were readmitted within 31?days after discharge. The commonest cause of readmissions were recurrent stroke (34.8%), hypertension (22.94%), cardio/cerebrovascular disease (13.26%) and diabetes/diabetic complications (7.34%). Higher risks of unplanned readmissions were associated with diabetes (OR?=?1.089, P?=?0.001), use of clinical pathways (OR?=?1.174, P?<?0.001), and being discharged without doctor’s advice (OR?=?1.485, P?<?0.001). Lower risks were associated with basic medical insurances (OR ranging from 0.225 to 0.716, P?<?0.001) and commercial medical insurance (OR?=?0.636, P?=?0.021), compared to self-paying for medical services. And patients aged 50?years old and above (OR ranging from 0.650 to 0.985, P?<?0.05), with haemorrhagic stroke (OR?=?0.467, P?<?0.001), with length of stay more than 7?days in hospital (OR ranging from 0.082 to 0.566, P?<?0.001), also had lower risks.

Conclusions

Age, type of stroke, medical insurance status, type of discharge, use of clinical pathways, length of hospital stay and comorbidities were the most influential factors for readmission within 31?days.

     

Impact of a COPD Discharge Care Bundle on Readmissions following Admission with Acute Exacerbation: Interrupted Time Series Analysis

ObjectivesWe evaluated the impact of a COPD discharge care bundle on readmission rates following hospitalisation with an acute exacerbation.DesignInterrupted time series analysis, comparing readmission rates for COPD exacerbations at nine trusts that introduced the bundle, to two comparison groups; (1) other NHS trusts in London and (2) all other NHS trusts in England. Care bundles were implemented at different times for different NHS trusts, ranging from October 2009 to April 2011.SettingNine NHS acute trusts in the London, England.ParticipantsPatients aged 45 years and older admitted to an NHS acute hospital in England for acute exacerbation of COPD. Data come from Hospital Episode Statistics, April 2002 to March 2012.ResultsIn hospitals introducing the bundle readmission rates were rising before implementation and falling afterwards (e.g. readmissions within 28 days +2.13% per annum (pa) pre and -5.32% pa post (p for difference in trends = 0.012)). Following implementation, readmission rates within 7 and 28 day were falling faster than among other trusts in London, although this was not statistically significant (e.g. readmissions within 28 days -4.6% pa vs. -3.2% pa, p = 0.44). Comparisons with a national control group were similar.ConclusionsThe COPD discharge care bundle appeared to be associated with a reduction in readmission rate among hospitals using it. The significance of this is unclear because of changes to background trends in London and nationally.     

The Synergy to Enable Glycemic Control Following Emergency Department Discharge Program for Adults with Type 2 Diabetes: Step-Diabetes

Objective: To evaluate a diabetes (DM) care delivery model among hyperglycemic adults with type 2 DM being discharged from the emergency department (ED) to home. The primary hypothesis was that a focused education and medication management intervention would lead to a greater short-term improvement in glycemic control compared to controls.Methods: A 4-week, randomized controlled trial provided antihyperglycemic medications management using an evidence-based algorithm plus survival skills diabetes self-management education (DSME) for ED patients with blood glucose (BG) levels ≥200 mg/dL. The intervention was delivered by endocrinologist-supervised certified diabetes educators. Controls received usual ED care.Results: Among 101 participants (96% Black, 54% female, 62.3% Medicaid and/or Medicare insurance), 77% completed the week 4 visit. Glycated hemoglobin A1C (A1C) went from 11.8 ± 2.4 to 10.5 ± 1.9% (P<.001) and 11.5 ± 2.0 to 11.1 ± 2.1% in the intervention and control groups, respectively (P = .012). At 4 weeks, the difference in A1C reduction between groups was 0.9% (P = .01). Mean BG decreased for both groups (P<.001), with a higher percentage of intervention patients (65%) reaching a BG <180 mg/dL compared to 29% of controls (P = .002). Hypoglycemia rates did not differ by group, and no severe hypoglycemia was reported. Medication adherence (Modified Morisky Score©) improved from low to medium (P<.001) among intervention patients and did not improve among controls.Conclusions: This study provides evidence that a focused diabetes care delivery intervention can be initiated in the ED among adults with type 2 diabetes and hyperglycemia and safely and effectively completed in the ambulatory setting. Improvement in short-term glycemic outcomes and medication adherence were observed.Abbreviations: A1C = glycated hemoglobin A1C BG = blood glucose BMI = body mass index CDE = certified diabetes educator CI = confidence interval DM = diabetes mellitus DSME = diabetes self-management education ED = emergency departmentMMAS-8 = Modified Morisky Medication Scale PCP = primary care provider POC = point of care SQ = subcutaneous     

Glycemic Control After Hospital Discharge in Insulin-Treated Type 2 Diabetes: A Randomized Pilot Study of Daily Remote Glucose Monitoring

ObjectiveLittle is known about glycemic control in type 2 diabetes patients treated with insulin in the high-risk period between hospital discharge and follow-up. We sought to assess the impact of remote glucose monitoring on postdischarge glycemic control and insulin titration.MethodsWe randomly assigned 28 hospitalized type 2 diabetes patients who were discharged home on insulin therapy to routine specialty care (RSC) or RSC with daily remote glucose monitoring (RGM). We compared the primary outcome of mean blood glucose and exploratory outcomes of hypoglycemia/hyperglycemia rates, change in hemoglobin A_1c and glycated albumin, and insulin titration frequency between groups.ResultsMean blood glucose was not significantly different between the treatment arms (144 ± 34 mg/dL in the RSC group and 172 ± 41 mg/dL in the RGM group; not significant), nor were there significant differences in any of the other measures of glycemia during the month after discharge. Hypoglycemia (glucometer reading < 60 mg/dL) was common, occurring in 46% of subjects, with no difference between groups. In as-treated analysis, insulin dose adjustments (29% with an increase and 43% with decrease in insulin dose) occurred more frequently in the patients who used RGM (average of 2.8 vs. 1.2 dose adjustments; P = .03).ConclusionIn this pilot trial in insulin-treated type 2 diabetes, RGM did not affect glycemic control after hospital discharge; however, the high rate of hypoglycemia in the postdischarge transition period and the higher frequency of insulin titration in patients who used RGM suggest a safety role for such monitoring in the transition from hospital to home. (Endocr Pract. 2015;21:115-121)