OBJECTIVE: The aim of this study was to assess the risk factors associated with 30-day hospital readmissions after a cholecystectomy.
METHODS: We conducted a case-control study, with data obtained from UC-Christus from Santiago, Chile. All patients who underwent a cholecystectomy between January 2015 and December 2019 were included in the study. We identified all patients readmitted after a cholecystectomy and compared them with a randomized control group. Univariate and multivariate analyses were conducted to identify risk factors.
RESULTS: Of the 4866 cholecystectomies performed between 2015 and 2019, 79 patients presented 30-day hospital readmission after the surgical procedure (1.6%). We identified as risk factors for readmission in the univariate analysis the presence of a solid tumor at the moment of cholecystectomy (OR = 7.58), high pre-operative direct bilirubin (OR = 2.52), high pre-operative alkaline phosphatase (OR = 3.25), emergency admission (OR = 2.04), choledocholithiasis on admission (OR = 4.34), additional surgical procedure during the cholecystectomy (OR = 4.12), and post-operative complications. In the multivariate analysis, the performance of an additional surgical procedure during cholecystectomy was statistically significant (OR = 4.24).
CONCLUSIONS: Performing an additional surgical procedure during cholecystectomy was identified as a risk factor associated with 30-day hospital readmission.
OBJECTIVE: El objetivo de este estudio fue evaluar los factores de riesgo asociados al reingreso hospitalario en los primeros 30 días post colecistectomía.
UNASSIGNED: Estudio de casos-controles con datos obtenidos del Hospital Clínico de la UC-Christus, Santiago, Chile. Se ­incluyeron las colecistectomías realizadas entre los años 2015-2019. Se consideraron como casos aquellos pacientes que reingresaron en los 30 primeros días posterior a una colecistectomía. Se realizó un análisis univariado y multivariado de diferentes posibles factores de riesgo.
RESULTS: De un total de 4866 colecistectomías, 79 pacientes presentaron reingreso hospitalario. Los resultados estadísticamente significativos en el análisis univariado fueron; tumor sólido al momento de la colecistectomía (OR = 7.58) bilirrubina directa preoperatoria alterada (OR = 2.52), fosfatasa alcalina preoperatoria alterada (OR = 3.25), ingreso de urgencia (OR = 2.04), coledocolitiasis al ingreso (OR = 4.34) realización de otros procedimientos (OR = 4.12) y complicaciones postoperatorias. En el análisis multivariado sólo la realización de otro procedimiento durante la colecistectomía fue estadísticamente significativa (OR = 4.24).
UNASSIGNED: La realización de otros procedimientos durante la colecistectomía es un factor de riesgo de reingreso hospitalario en los 30 días posteriores a la colecistectomía.

BACKGROUND: Emergency interhospital transfers from inpatient subacute care to acute care occur in 8% to 17.4% of admitted patients and are associated with high rates of acute care readmission and in-hospital mortality. Serious adverse events in subacute care (rapid response team or cardiac arrest team calls) and increased nursing surveillance are the strongest known predictors of emergency interhospital transfer from subacute to acute care hospitals. However, the epidemiology of clinical deterioration across sectors of care, and specifically in subacute care is not well understood.
OBJECTIVE: To explore the trajectory of clinical deterioration in patients who did and did not have an emergency interhospital transfer from subacute to acute care; and develop an internally validated predictive model to identify the role of vital sign abnormalities in predicting these emergency interhospital transfers.
METHODS: This prospective, exploratory cohort study is a subanalysis of data derived from a larger case-time-control study.
METHODS: Twenty-two wards of eight subacute care hospitals in five major health services in Victoria, Australia. All subacute care hospitals were geographically separate from their health services\' acute care hospitals.
METHODS: All patients with an emergency transfer from inpatient rehabilitation or geriatric evaluation and management unit to an acute care hospital within the same health service were included. Patients receiving palliative care were excluded.
METHODS: Study data were collected between 22 August 2015 and 30 October 2016 by medical record audit. The Cochran-Mantel-Haenszel test and bivariate logistic regression analysis were used to compare cases with controls and to account for health service clustering effect.
RESULTS: Data were collected on 603 transfers (557 patients) and 1160 controls. Adjusted for health service, ≥2 vital sign abnormalities in subacute care (adjusted odds ratio=8.81, 95% confidence intervals:6.36-12.19, p<0.001) and serious adverse events during the first acute care admission (adjusted odds ratio=1.28, 95% confidence intervals:1.08-1.99, p=0.015) were the clinical factors associated with increased risk of emergency interhospital transfer. An internally validated predictive model showed that vital sign abnormalities can fairly predict emergency interhospital transfers from subacute to acute care hospitals.
CONCLUSIONS: Serious adverse events in acute care should be a key consideration in decisions about the location of subacute care delivery. During subacute care, 15.7% of cases had vital signs fulfilling organisational rapid response team activation criteria, yet missed rapid response team activations were common suggesting that further consideration of the criteria and strategies to optimise recognition and response to clinical deterioration in subacute care are needed.

OBJECTIVE: To analyse, hierarchically, factors associated with hospital readmissions for acute coronary syndrome.
BACKGROUND: Hospital readmissions have risen, especially in patients with multiple comorbidities, which are most often chronic. The leading causes of hospital readmission include acute coronary syndrome, which is costly and often preventable. Determining clinical and nonclinical variables that increase the chances of readmission is important to assess and evaluate patients hospitalised for coronary heart diseases.
METHODS: A case-control study whose dependent variable was hospital readmission for acute coronary syndrome.
METHODS: The study included 277 inpatients, of whom 132 were in their first hospitalisation and 145 had already been hospitalised for acute coronary syndrome. The independent variables for this hierarchical model were sociodemographic conditions, life habits, access to health services and physical health measures. Data were obtained by interviews, anthropometric measurements and patient records. Logistic regression analysis was performed using the stepwise technique, with Microsoft Excel and R version 3.2.3. The research was reported via strengthening the reporting of observational studies in epidemiology (STROBE).
RESULTS: In the final hierarchical logistic model, the following risk factors were associated with readmission for acute coronary syndrome: inadequate drug therapy adherence, stress, history of smoking for 30 years or more, and the lack of use of primary healthcare services.
CONCLUSIONS: Clinical and nonclinical variables are related to hospital readmission for acute coronary syndrome and can increase the chance of readmission by up to six times.
CONCLUSIONS: The predictive model can be used to avoid readmission for acute coronary syndrome, and it represents an advance in the prediction of the occurrence of the outcome. This implies the need for a reorientation of the network for postdischarge care in the first hospitalisation for acute coronary syndrome.

UNASSIGNED: We aimed to gain a better understanding of how standardization of laboratory data can impact predictive model performance in multi-site datasets. We hypothesized that standardizing local laboratory codes to logical observation identifiers names and codes (LOINC) would produce predictive models that significantly outperform those learned utilizing local laboratory codes.
UNASSIGNED: We predicted 30-day hospital readmission for a set of heart failure-specific visits to 13 hospitals from 2008 to 2012. Laboratory test results were extracted and then manually cleaned and mapped to LOINC. We extracted features to summarize laboratory data for each patient and used a training dataset (2008-2011) to learn models using a variety of feature selection techniques and classifiers. We evaluated our hypothesis by comparing model performance on an independent test dataset (2012).
UNASSIGNED: Models that utilized LOINC performed significantly better than models that utilized local laboratory test codes, regardless of the feature selection technique and classifier approach used.
UNASSIGNED: We quantitatively demonstrated the positive impact of standardizing multi-site laboratory data to LOINC prior to use in predictive models. We used our findings to argue for the need for detailed reporting of data standardization procedures in predictive modeling, especially in studies leveraging multi-site datasets extracted from electronic health records.

Background: Examination of readmission data is a standard method for evaluating health service outcomes. Limited work has evaluated the efficacy of mental health rehabilitation, despite the need for evidence-based approaches.Aims: To evaluate the impact of inpatient mental health rehabilitation using metrics of psychiatric readmissions routinely collected at occasions of service.Methods: Consumers (n = 252) of a nonacute inpatient mental health rehabilitation unit were case matched with normative clients from community mental health services. The impact of inpatient care was measured on: (1) the occurrence of a psychiatric readmission within 12 months of discharge; (2) the total number of psychiatric readmissions within 12 months of discharge; and (3) the number of days to a psychiatric readmission after discharge.Results: The proportion of consumers experiencing a readmission significantly decreased following inpatient care, comparable to the normative group. The number of readmissions also significantly decreased, approaching normative group levels, except in consumers with comorbid bipolar, substance use, or personality disorder. Time to a readmission significantly increased following inpatient care, approximating normative group values, and was related to the number of previous admissions.Conclusion: Routinely collected service data demonstrated nonacute inpatient mental health rehabilitation reduces re-hospitalization, which will have benefits for both consumers and health services.

The identification of patients at high risk of unplanned readmission is an important component of discharge planning strategies aimed at preventing unwanted returns to hospital. The aim of this study was to investigate the factors associated with unplanned readmission in a Sydney hospital. We developed and compared validated readmission risk scores using routinely collected hospital data to predict 7-day, 30-day and 60-day all-cause unplanned readmission.
A combination of gradient boosted tree algorithms for variable selection and logistic regression models was used to build and validate readmission risk scores using medical records from 62,235 live discharges from a metropolitan hospital in Sydney, Australia.
The scores had good calibration and fair discriminative performance with c-statistic of 0.71 for 7-day and for 30-day readmission, and 0.74 for 60-day. Previous history of healthcare utilization, urgency of the index admission, old age, comorbidities related to cancer, psychosis, and drug-abuse, abnormal pathology results at discharge, and being unmarried and a public patient were found to be important predictors in all models. Unplanned readmissions beyond 7 days were more strongly associated with longer hospital stays and older patients with higher number of comorbidities and higher use of acute care in the past year.
This study demonstrates similar predictors and performance to previous risk scores of 30-day unplanned readmission. Shorter-term readmissions may have different causal pathways than 30-day readmission, and may, therefore, require different screening tools and interventions. This study also re-iterates the need to include more informative data elements to ensure the appropriateness of these risk scores in clinical practice.

Improving mid-term and long-term outcomes after solid organ transplantation is imperative, and requires both state-of-the-art transplant surgery and optimization of routine, evidence-based aftercare. This randomized, controlled trial assessed the effectiveness of standard aftercare versus telemedically supported case management, an innovative aftercare model, in 46 living-donor renal transplant recipients during the first posttransplant year. The model includes three components: (i) chronic care case management initiated after discharge, (ii) case management initiated in emerging acute care situations, and (iii) a telemedically equipped team comprising a transplant nurse case manager and two senior transplant physicians (nephrologist, surgeon). Analyses revealed a reduction of unplanned inpatient acute care, with considerable cost reductions, in the intervention group. The prevalence of nonadherence over the 1-year study period was 17.4% in the intervention group versus 56.5% in the standard aftercare group (p = 0.013). Only the intervention group achieved their pre-agreed levels of adherence, disease-specific quality of life, and return to employment. This comparative effectiveness study provides the basis for multicenter study testing of telemedically supported case management with the aim of optimizing posttransplant aftercare. The trial was registered with the German Clinical Trials Register (www.DRKS.de), DKRS00007634.

OBJECTIVE Despite persisting questions regarding its appropriateness, 30-day readmission is an increasingly common quality metric used to influence hospital compensation in the United States. However, there is currently insufficient evidence to identify which patients are at highest risk for readmission after aneurysmal subarachnoid hemorrhage (SAH). The objective of this study was to identify predictors of 30-day readmission after SAH, to focus preventative efforts, and to provide guidance to funding agencies seeking to risk-adjust comparisons among hospitals. METHODS The authors performed a case-control study of 30-day readmission among aneurysmal SAH patients treated at a single center between 2003 and 2013. To control for geographic distance from the hospital and year of treatment, the authors randomly matched each case (30-day readmission) with approximately 2 SAH controls (no readmission) based on home ZIP code and treatment year. They evaluated variables related to patient demographics, socioeconomic characteristics, comorbidities, presentation severity (e.g., Hunt and Hess grade), and clinical course (e.g., need for gastrostomy or tracheostomy, length of stay). Conditional logistic regression was used to identify significant predictors, accounting for the matched design of the study. RESULTS Among 82 SAH patients with unplanned 30-day readmission, the authors matched 78 patients with 153 nonreadmitted controls. Age, demographics, and socioeconomic factors were not associated with readmission. In univariate analysis, multiple variables were significantly associated with readmission, including Hunt and Hess grade (OR 3.0 for Grade IV/V vs I/II), need for gastrostomy placement (OR 2.0), length of hospital stay (OR 1.03 per day), discharge disposition (OR 3.2 for skilled nursing vs other disposition), and Charlson Comorbidity Index (OR 2.3 for score ≥ 2 vs 0). However, the only significant predictor in the multivariate analysis was discharge to a skilled nursing facility (OR 3.2), and the final model was sensitive to criteria used to enter and retain variables. Furthermore, despite the significant association between discharge disposition and readmission, less than 25% of readmitted patients were discharged to a skilled nursing facility. CONCLUSIONS Although discharge disposition remained significant in multivariate analysis, most routinely collected variables appeared to be weak independent predictors of 30-day readmission after SAH. Consequently, hospitals interested in decreasing readmission rates may consider multifaceted, cost-efficient interventions that can be broadly applied to most if not all SAH patients.

OBJECTIVE: To assess factors predictive of all-cause, 30 day hospital readmission among patients with type 2 diabetes in the United States.
METHODS: A retrospective, case-control study using deidentified Humedica electronic health record data was conducted to identify patients ≥18 years old with ≥6 months of data prior to index hospitalization (pre-period) and ≥30 days of data after discharge (post-period). Combined methods of bootstrap resampling and stepwise logistic regression were used to identify factors associated with readmission.
RESULTS: Among 52,070 patients with type 2 diabetes and an initial hospitalization for any reason, 5201 (10.0%) patients were readmitted within 30 days and 46,869 (90.0%) patients showed no evidence of readmission. Diabetic treatment escalation; race; type 2 diabetes diagnosis prior to the index stay; pre-period heart failure; and number of pre-period, inpatient healthcare visits were among the strongest predictors of 30 day readmission. From a receiver-operating characteristic plot (mean area under curve of 0.693), the predictive accuracy of the final logistic regression model is considered modest. This result might be due to the unavailability of some variables or data.
CONCLUSIONS: These results highlight the importance of the appropriate recognition of and treatment for type 2 diabetes, prior to and during hospitalization and following discharge, in order to impact a subsequent hospitalization. In our analysis, escalation of diabetic treatments (especially those escalated from having no records of anti-diabetic medications to treatment with insulin) was the strongest predictor of 30 day readmission. Limitations of this study include the fact that hospitalizations and other encounters, outside the Humedica network, were not captured in this analysis.

OBJECTIVE: To examine the effects of a nurse-led case management programme for hospital-discharged older adults with co-morbidities.
BACKGROUND: The most significant chronic conditions today involve diseases of the cardiovascular, respiratory, endocrine and renal systems. Previous studies have suggested that a nurse-led case management approach using either telephone follow-ups or home visits was able to improve clinical and patient outcomes for patients having a single, chronic disease, while the effects for older patients having at least two long-term conditions are unknown. A self-help programme using motivation and empowerment approaches is the framework of care in the study.
METHODS: Randomized controlled trial.
METHODS: The study was conducted from 2010-2012. Older patients having at least two chronic diseases were included for analysis. The participants were randomized into three arms: two study groups and one control group. Data were collected at baseline and at 4 and 12 weeks later.
RESULTS: Two hundred and eighty-one patients completed the study. The interventions demonstrated significant differences in hospital readmission rates within 84 days post discharge. The two intervention groups had lower readmission rates than the control group. Patients in the two study arms had significantly better self-rated health and self-efficacy. There was significant difference between the groups in the physical composite score, but no significant difference in mental component score in SF-36 scale.
CONCLUSIONS: The postdischarge interventions led by the nurse case managers on self-management of disease using the empowerment approach were able to provide effective clinical and patient outcomes for older patients having co-morbidities.