Dataset Information

Early unplanned readmission of neurosurgical patients after treatment of intracranial lesions: a comparison between surgical and non-surgical intervention group.

ABSTRACT: BACKGROUND:Recent health care policy making has highlighted the necessity for understanding factors that influence readmission. To elucidate the rate, reason, and predictors of readmissions in neurosurgical patients, we analyzed unscheduled readmissions to our neurosurgical department after treatment for cranial or cerebral lesions. METHODS:From 2015 to 2017, all adult patients who had been discharged from our Department of Neurosurgery and were readmitted within 30 days were included into the study cohort. The patients were divided into a surgical and a non-surgical group. The main outcome measure was unplanned inpatient admission within 30 days of discharge. RESULTS:During the observation period, 183 (7.4%) of 2486 patients had to be readmitted unexpectedly within 30 days after discharge. The main readmission causes were surgical site infection (34.4 %) and seizure (16.4%) in the surgical group, compared to natural progression of the original diagnosis (38.2%) in the non-surgical group. Most important predictors for an unplanned readmission were younger age, presence of malignoma (OR: 2.44), and presence of cardiovascular side diagnoses in the surgical group. In the non-surgical group, predictors were length of stay (OR: 1.07) and the need for intensive care (OR: 5.79). CONCLUSIONS:We demonstrated that reasons for readmission vary between operated and non-operated patients and are preventable in large numbers. In addition, we identified treatment-related partly modifiable factors as predictors of unplanned readmission in the non-surgical group, while unmodifiable patient-related factors predominated in the surgical group. Further patient-related risk adjustment models are needed to establish an individualized preventive strategy in order to reduce unplanned readmissions.

SUBMITTER: Sander C

PROVIDER: S-EPMC7550291 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Early unplanned readmission of neurosurgical patients after treatment of intracranial lesions: a comparison between surgical and non-surgical intervention group.

Sander Caroline C Oppermann Henry H Nestler Ulf U Sander Katharina K von Dercks Nikolaus N Meixensberger Jürgen J

Acta neurochirurgica 20200815 11

<h4>Background</h4>Recent health care policy making has highlighted the necessity for understanding factors that influence readmission. To elucidate the rate, reason, and predictors of readmissions in neurosurgical patients, we analyzed unscheduled readmissions to our neurosurgical department after treatment for cranial or cerebral lesions.<h4>Methods</h4>From 2015 to 2017, all adult patients who had been discharged from our Department of Neurosurgery and were readmitted within 30 days were incl ...[more]

PMID: 32803369

Similar Datasets

Project description:ObjectiveHospital readmissions result in increased health care costs and are associated with worse outcomes after neurosurgical intervention. Understanding factors associated with readmissions will inform future studies aimed at improving quality of care in those with epilepsy.MethodsPatients of all ages with epilepsy who underwent a neurosurgical intervention were identified in the 2014 Nationwide Readmissions Database, a nationally representative dataset containing data from roughly 17 million US hospital discharges. Diagnosis of epilepsy was based on International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM)-based case definitions. Neurosurgical interventions for epilepsy: resective/disconnective surgery, responsive neurostimulation/deep brain stimulation, vagus nerve stimulation, radiosurgery, and intracranial electroencephalography were identified using ICD-9-CM procedure codes. Primary outcome was all-cause 30-day readmission following discharge from the index hospitalization.ResultsThere were a total of 2284 index surgical admissions. Overall, 10.83% (n = 251) of patients following an index epilepsy surgery admission were readmitted within 30 days. Factors independently associated with 30-day readmission for all epilepsy surgery admissions were: Medicare insurance (P < .01), discharge disposition that was not home (P < .01), higher Elixhauser comorbidity indexes (P < .01), longer length of stay (P < .01), and adverse events of surgical and medical care during index stay (P = .04). In the multivariate model, Medicare insurance (hazard ratio [HR] 1.81 [1.29-2.53], P < .01) and length of stay (HR 1.02 [1.01-1.04], P < .01) remained significant independent predictors for 30-day readmission. The most common primary reason for readmissions was epilepsy/convulsions accounting for 22.85%.SignificanceOur results suggest that careful management of postoperative seizures and discharge planning after epilepsy surgery may be important to optimize outcomes and reduce the risk of readmission, particularly for patients on Medicare.

Project description:BackgroundExisting readmission reduction solutions tend to focus on complementing inpatient care with enhanced care transition and postdischarge interventions. These solutions are initiated near or after discharge, when clinicians' impact on inpatient care is ending. Preventive intervention during hospitalization is an underexplored area that holds potential for reducing readmission risk. However, it is challenging to predict readmission risk at the early stage of hospitalization because few data are available.ObjectiveThe objective of this study was to build an early prediction model of unplanned 30-day hospital readmission using a large and diverse sample. We were also interested in identifying novel readmission risk factors and protective factors.MethodsWe extracted the medical records of 96,550 patients in 205 participating Cerner client hospitals across four US census regions in 2016 from the Health Facts database. The model was built with index admission data that can become available within 24 hours and data from previous encounters up to 1 year before the index admission. The candidate models were evaluated for performance, timeliness, and generalizability. Multivariate logistic regression analysis was used to identify readmission risk factors and protective factors.ResultsWe developed six candidate readmission models with different machine learning algorithms. The best performing model of extreme gradient boosting (XGBoost) achieved an area under the receiver operating characteristic curve of 0.753 on the development data set and 0.742 on the validation data set. By multivariate logistic regression analysis, we identified 14 risk factors and 2 protective factors of readmission that have never been reported.ConclusionsThe performance of our model is better than that of the most widely used models in US health care settings. This model can help clinicians identify readmission risk at the early stage of hospitalization so that they can pay extra attention during the care process of high-risk patients. The 14 novel risk factors and 2 novel protective factors can aid understanding of the factors associated with readmission.

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:OBJECTIVES:To understand from a patient and carer perspective: (1) what features of the discharge process could be improved to avoid early unplanned hospital readmission (within 72?hours of acute care discharge) and (2) what elements of discharge planning could have enhanced the discharge experience. DESIGN:A qualitative descriptive design was used. Study data were collected using semi-structured interviews that were transcribed verbatim and analysed using inductive thematic analysis. Data related to participant characteristic were collected by medical record audit and summarised using descriptive statistics. SETTING:Three acute care hospitals from one health service in Australia. PARTICIPANTS:Patients who had an early unplanned hospital readmission and/or their carers, if present during the interviews and willing to participate, with patient permission. FINDINGS:Thirty interviews were conducted (23 patients only; 6 patient and carer dyads; 1 carer only). Five themes were constructed: 'experiences of care', 'hearing and being heard', 'what's wrong with me', 'not just about me' and 'all about going home'. There was considerable variability in patients' and carers' experiences of hospital care, discharge processes and early unplanned hospital readmission. Features of the discharge process that could be improved to potentially avoid early unplanned hospital readmission were better communication, optimal clinical care including ensuring readiness for discharge and shared decision-making regarding discharge timing and goals on returning home. The discharge experience could have been enhanced by improved communication between patients (and carers) and the healthcare team, not rushing the discharge process and a more coordinated approach to patient transport home from hospital. CONCLUSIONS:The study findings highlight the complexities of the discharge process and the importance of effective communication, shared decision-making and carer engagement in optimising hospital discharge and reducing early unplanned hospital readmissions.

Project description:OBJECTIVES:Unplanned readmissions to the intensive care unit (ICU) are highly undesirable, increasing variance in care, making resource planning difficult and potentially increasing length of stay and mortality in some settings. Identifying patients who are likely to suffer unplanned ICU readmission could reduce the frequency of this adverse event. SETTING:A single academic, tertiary care hospital in the UK. PARTICIPANTS:A set of 3326 ICU episodes collected between October 2014 and August 2016. All records were of patients who visited an ICU at some point during their stay. We excluded patients who were ?16 years of age; visited ICUs other than the general and neurosciences ICU; were missing crucial electronic patient record measurements; or had indeterminate ICU discharge outcomes or very early or extremely late discharge times. After exclusion, 2018 outcome-labelled episodes remained. PRIMARY AND SECONDARY OUTCOME MEASURES:Area under the receiver operating characteristic curve (AUROC) for prediction of unplanned ICU readmission or in-hospital death within 48?hours of first ICU discharge. RESULTS:In 10-fold cross-validation, an ensemble predictor was trained on data from both the target hospital and the Medical Information Mart for Intensive Care (MIMIC-III) database and tested on the target hospital's data. This predictor discriminated between patients with the unplanned ICU readmission or death outcome and those without this outcome, attaining mean AUROC of 0.7095 (SE 0.0260), superior to the purpose-built Stability and Workload Index for Transfer (SWIFT) score (AUROC=0.6082, SE 0.0249; p=0.014, pairwise t-test). CONCLUSIONS:Despite the inherent difficulties, we demonstrate that a novel machine learning algorithm based on transfer learning could achieve good discrimination, over and above that of the treating clinicians or the value added by the SWIFT score. Accurate prediction of unplanned readmission could be used to target resources more efficiently.

Project description:OBJECTIVES:To identify modifiable factors leading to unplanned readmission and characterize differences in adjusted unplanned readmission rates across hospitals. DESIGN:Retrospective cohort study using prospectively collected clinical registry data SETTING:: Pediatric Cardiac Critical Care Consortium clinical registry. PATIENTS:Patients admitted to a pediatric cardiac ICU at Pediatric Cardiac Critical Care Consortium hospitals. INTERVENTIONS:None. MEASUREMENTS AND MAIN RESULTS:We examined pediatric cardiac ICU encounters in the Pediatric Cardiac Critical Care Consortium registry from October 2013 to March 2016. The primary outcomes were early (< 48?hr from pediatric cardiac ICU transfer) and late (2-7 d) unplanned readmission. Generalized logit models identified independent predictors of unplanned readmission. We then calculated observed-to-expected ratios of unplanned readmission and identified higher-than or lower-than-expected unplanned readmission rates for those with an observed-to-expected ratios greater than or less than 1, respectively, and a 95% CI that did not cross 1. Of 11,301 pediatric cardiac ICU encounters (16 hospitals), 62% were surgical, and 18% were neonates. There were 175 (1.6%) early unplanned readmission, and 300 (2.7%) late unplanned readmission, most commonly for respiratory (31%), or cardiac (28%) indications. In multivariable analysis, unique modifiable factors were associated with unplanned readmission. Although shorter time between discontinuation of vasoactive infusions and pediatric cardiac ICU transfer was associated with early unplanned readmission, nighttime discharge was independently associated with a greater likelihood of late unplanned readmission. Two hospitals had lower-than-expected unplanned readmission in both the early and late categories, whereas two other hospitals were higher-than-expected in both. CONCLUSIONS:This analysis demonstrated time from discontinuation of critical care therapies to pediatric cardiac ICU transfer as a significant, modifiable predictor of unplanned readmission. We identified two hospitals with lower-than-expected adjusted rates of both early and late unplanned readmission, suggesting that their systems are well designed to prevent unplanned readmission. This offers the possibility of disseminating best practices to other hospitals through collaborative learning.

Dataset Information

Early unplanned readmission of neurosurgical patients after treatment of intracranial lesions: a comparison between surgical and non-surgical intervention group.

Publications

Early unplanned readmission of neurosurgical patients after treatment of intracranial lesions: a comparison between surgical and non-surgical intervention group.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets