Predicting length of stay in patients admitted to stroke rehabilitation with severe and moderate levels of functional impairments.
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ABSTRACT:
SUBMITTER: Garcia-Rudolph A
PROVIDER: S-EPMC7581132 | biostudies-literature | 2020 Oct
REPOSITORIES: biostudies-literature
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Predicting length of stay in patients admitted to stroke rehabilitation with severe and moderate levels of functional impairments.
García-Rudolph Alejandro A Cegarra Blanca B Opisso Eloy E Tormos Josep María JM Bernabeu Montserrat M Saurí Joan J
Medicine 20201001 43
Severe stroke patients are known to be associated with larger rehabilitation length of stay (LOS) but other factors besides severity may be contributing. We aim to identify LOS predictors within a population of mostly severe patients and analyze the impact of socioeconomic situation in functionality at admission.A retrospective observational cohort study was conducted including 172 inpatients admitted to a rehabilitation center between 2007 and 2019. Associations with LOS were examined among 30 ...[more]
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Project description:BackgroundEmergency department length of stay (ED-LOS) has been associated with worse outcomes after various medical conditions. However, there is a relative paucity of data for ischemic stroke patients. We sought to determine whether a longer ED-LOS is associated with a poor 90-day outcome after ischemic stroke.MethodsThis study is a retrospective analysis of a single-center cohort of consecutive ischemic stroke patients (n = 325). Multivariable linear and logistic regression models were constructed to determine factors independently associated with ED-LOS as well as a poor 90-day outcome (modified Rankin Scale [mRS] score >2), respectively.ResultsThe median ED-LOS in the cohort was 5.8 hours. For patients admitted to the inpatient stroke ward (n = 160) versus the neuroscience intensive care unit (NICU; n = 165), the median ED-LOS was 8.2 hours versus 3.7 hours, respectively. On multivariable linear regression, NICU admission (P <.001), endovascular stroke therapy (P = .001), and thrombolysis (P = .021) were inversely associated with the ED-LOS. Evening shift presentation was associated with a longer ED-LOS (P = .048). On multivariable logistic regression, a greater admission National Institutes of Health Stroke Scale score (P <.001), worse preadmission mRS score (P = .001), hemorrhagic conversion (P = .041), and a shorter ED-LOS (P = .016) were associated with a poor 90-day outcome. Early initiation of statin therapy (P = .049), endovascular stroke therapy (P = .041), NICU admission (P = .029), and evening shift presentation (P = .035) were associated with a good 90-day outcome.ConclusionsIn contrast to prior studies, a shorter ED-LOS was associated with a worse 90-day functional outcome, possibly reflecting prioritized admission of more severely affected patients who are at high risk of a poor functional outcome.
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Project description:Background The incidence of stroke is a challenge in China, as stroke imposes a heavy burden on families, national health services, social services, and the economy. The length of hospital stay (LOS) is an essential indicator of utilization of medical services and is usually used to assess the efficiency of hospital management and patient quality of care. This study established a prediction model based on a machine learning algorithm to predict ischemic stroke patients’ LOS. Methods A total of 18,195 ischemic stroke patients’ electronic medical records and 28 attributes were extracted from electronic medical records in a large comprehensive hospital in China. The prediction of LOS was regarded as a multi classification problem, and LOS was divided into three categories: 1–7 days, 8–14 days and more than 14 days. After preprocessing the data and feature selection, the XGBoost algorithm was used to build a machine learning model. Ten fold cross-validation was used for model validation. The accuracy (ACC), recall rate (RE) and F1 measure were used to evaluate the performance of the prediction model of LOS of ischemic stroke patients. Finally, the XGBoost algorithm was used to identify and remove irrelevant features by ranking all attributes based on feature importance. Results Compared with the naive Bayesian algorithm, logistic region algorithm, decision tree classifier algorithm and ADaBoost classifier algorithm, the XGBoot algorithm has higher ACC, RE and F1 measure. The average ACC, RE and F1 measure were 0.89, 0.89 and 0.89 under the 10-fold cross-validation. According to the analysis of the importance of features, the LOS of ischemic stroke patients was affected by demographic characteristics, past medical history, admission examination features, and operation characteristics. Finally, the features in terms of hemiplegia aphasia, MRS, NIHSS, TIA, Operation or not, coma index etc. were found to be the top features in importance in predicting the LOS of ischemic stroke patients. Conclusions The XGBoost algorithm was an appropriate machine learning method for predicting the LOS of patients with ischemic stroke. Based on the prediction model, an intelligent medical management prediction system could be developed to predict the LOS based on ischemic stroke patients’ electronic medical records. Supplementary Information The online version contains supplementary material available at 10.1186/s12911-023-02140-4
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Project description:BackgroundAnaemia is frequently recorded during preoperative screening and has been suggested to affect outcomes after surgery negatively.ObjectivesThe objectives were to assess the frequency of moderate to severe anaemia and its association with length of hospital stay.DesignPost hoc analysis of the international observational prospective 'Local ASsessment of VEntilatory management during General Anaesthesia for Surgery' (LAS VEGAS) study.Patients and settingThe current analysis included adult patients requiring general anaesthesia for non-cardiac surgery. Preoperative anaemia was defined as a haemoglobin concentration of 11 g dl-1 or lower, thus including moderate and severe anaemia according to World Health Organisation criteria.Main outcome measuresThe primary outcome was length of hospital stay. Secondary outcomes included hospital mortality, intra-operative adverse events and postoperative pulmonary complications (PPCs).ResultsHaemoglobin concentrations were available for 8264 of 9864 patients. Preoperative moderate to severe anaemia was present in 7.7% of patients. Multivariable analysis showed that preoperative moderate to severe anaemia was associated with an increased length of hospital stay with a mean difference of 1.3 ((95% CI 0.8 to 1.8) days; P < .001). In the propensity-matched analysis, this association remained present, median 4.0 [IQR 1.0 to 5.0] vs. 2.0 [IQR 0.0 to 5.0] days, P = .001. Multivariable analysis showed an increased in-hospital mortality (OR 2.9 (95% CI 1.1 to 7.5); P = .029), and higher incidences of intra-operative hypotension (36.3 vs. 25.3%; P < .001) and PPCs (17.1 vs. 10.5%; P = .001) in moderately to severely anaemic patients. However, this was not confirmed in the propensity score-matched analysis.ConclusionsIn this international cohort of non-cardiac surgical patients, preoperative moderate to severe anaemia was associated with a longer duration of hospital stay but not increased intra-operative complications, PPCs or in-hospital mortality.Trial registrationThe LAS VEGAS study was registered at Clinicaltrials.gov, NCT01601223.
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Project description:Simultaneously evaluate postoperative mortality, length of stay (LOS), and readmission.National Surgical Quality Improvement Program (NSQIP).Retrospective cohort.Data from elective general surgical patients were obtained from the 2012 NSQIP Participant Use File. For each postoperative day, each patient's state was classified as index hospitalization, discharged home, discharged to long-term care (LTC), readmitted, or dead. Transition rates were estimated using exponential regression, assuming constant rates for specified time periods. These estimates were combined into a multistate model, simulated results of which were compared to observed outcomes.Age, comorbidities, more complex procedures, and longer index LOS were associated with lower rates of discharge home and higher rates of death, discharge to LTC, and readmission. The longer patients had been discharged, the less likely they were to die or be readmitted. The model predicted 30-day mortality 0.38 percent (95 percent CI: 0.36-0.41), index LOS 2.85 days (95 percent CI: 2.83-2.86), LTC discharge 2.76 percent (95 percent CI: 2.69-2.82), and readmissions 5.53 percent (95 percent CI: 5.43-5.62); observed values were 0.39 percent, 2.82 days, 2.87 percent, and 5.70 percent, respectively.Multistate models can simultaneously predict postoperative mortality, LOS, discharge destination, and readmissions, which allows multidimensional comparison of surgical outcomes.
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Project description:Aims/hypothesisHospital admissions for hypoglycaemia represent a significant burden on individuals with diabetes and have a substantial economic impact on healthcare systems. To date, no prognostic models have been developed to predict outcomes following admission for hypoglycaemia. We aimed to develop and validate prediction models to estimate risk of inpatient death, 24 h discharge and one month readmission in people admitted to hospital for hypoglycaemia.MethodsWe used the Hospital Episode Statistics database, which includes data on all hospital admission to National Health Service hospital trusts in England, to extract admissions for hypoglycaemia between 2010 and 2014. We developed, internally and temporally validated, and compared two prognostic risk models for each outcome. The first model included age, sex, ethnicity, region, social deprivation and Charlson score ('base' model). In the second model, we added to the 'base' model the 20 most common medical conditions and applied a stepwise backward selection of variables ('disease' model). We used C-index and calibration plots to assess model performance and developed a calculator to estimate probabilities of outcomes according to individual characteristics.ResultsIn derivation samples, 296 out of 11,136 admissions resulted in inpatient death, 1789/33,825 in one month readmission and 8396/33,803 in 24 h discharge. Corresponding values for validation samples were: 296/10,976, 1207/22,112 and 5363/22,107. The two models had similar discrimination. In derivation samples, C-indices for the base and disease models, respectively, were: 0.77 (95% CI 0.75, 0.80) and 0.78 (0.75, 0.80) for death, 0.57 (0.56, 0.59) and 0.57 (0.56, 0.58) for one month readmission, and 0.68 (0.67, 0.69) and 0.69 (0.68, 0.69) for 24 h discharge. Corresponding values in validation samples were: 0.74 (0.71, 0.76) and 0.74 (0.72, 0.77), 0.55 (0.54, 0.57) and 0.55 (0.53, 0.56), and 0.66 (0.65, 0.67) and 0.67 (0.66, 0.68). In both derivation and validation samples, calibration plots showed good agreement for the three outcomes. We developed a calculator of probabilities for inpatient death and 24 h discharge given the low performance of one month readmission models.Conclusions/interpretationThis simple and pragmatic tool to predict in-hospital death and 24 h discharge has the potential to reduce mortality and improve discharge in people admitted for hypoglycaemia.
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