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A Multistate Model Predicting Mortality, Length of Stay, and Readmission for Surgical Patients.

ABSTRACT: 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.

SUBMITTER: Clark DE

PROVIDER: S-EPMC4874834 | biostudies-other | 2016 Jun

REPOSITORIES: biostudies-other

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A Multistate Model Predicting Mortality, Length of Stay, and Readmission for Surgical Patients.

Clark David E DE Ostrander Kaitlin R KR Cushing Brad M BM

Health services research 20151020 3

<h4>Objective</h4>Simultaneously evaluate postoperative mortality, length of stay (LOS), and readmission.<h4>Data source</h4>National Surgical Quality Improvement Program (NSQIP).<h4>Design</h4>Retrospective cohort.<h4>Methods</h4>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. Tran ...[more]

PMID: 26481092

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Project description:ImportanceIn 2012, hospital reimbursement in Switzerland changed from a fee-for-service per diem system to a diagnosis-related group (SwissDRG) system. Whether this change in reimbursement is associated with harmful implications for quality of care and patient outcomes remains unclear.ObjectiveTo examine the association of the SwissDRG implementation with length of hospital stay (LOS), in-hospital mortality, and 30-day readmission rates in the overall adult inpatient population and stratified by 5 individual diagnoses.Design, setting, and participantsThis cohort study used administrative data from the Swiss Federal Statistical Office to investigate medical hospitalizations in Switzerland from January 1, 2009, through December 31, 2015. All hospitalizations for adult medical inpatients were included in the main analysis. Patients who presented with 1 of the 5 common medical diagnoses were included in the subanalyses: community-acquired pneumonia, exacerbation of chronic obstructive pulmonary disease, acute myocardial infarction, acute heart failure, and pulmonary embolism. An interrupted time series model was used to determine changes in time trends for risk-adjusted LOS, in-hospital mortality, and 30-day readmission after the implementation of SwissDRG in 2012. Analyses were performed from March 1, 2018, to June 30, 2018, and from November 1, 2018, to December 18, 2018.Main outcomes and measuresMonthly patient-level data for LOS, in-hospital mortality, and 30-day readmission rates.ResultsThe sample included a total of 2 426 722 hospitalized adult patients. Of this total, 1 018 404 patients (41.9%; 531 226 [52.2%] male, median [interquartile range (IQR)] age of 69 [55-80] years) composed the before-SwissDRG period; 1 408 318 patients (58.0%; 730 228 [51.9%] male, median [IQR] age of 70 [56-81] years) composed the after-SwissDRG period. The overall LOS gradually decreased from unadjusted mean (SD) 8.0 (12.7) days in 2009 to 7.2 (17.3) days in 2015. This reduction in LOS, however, was not substantially greater with the implementation of SwissDRG in 2012 (risk-adjusted slope, -0.0166 days; 95% CI, -0.0223 to -0.0110 days), with an adjusted difference in slopes of 0.0000 days (95% CI, -0.0072 to 0.0072 days). Risk-adjusted all-cause in-hospital mortality declined from 4.9% in 2009 to 4.6% in 2015, with a substantially greater decline after implementation of SwissDRG (difference between monthly slopes before and after implementation, -0.0115%; 95% CI, -0.0190% to -0.0039%). In the same period, risk-adjusted 30-day readmission rates increased from 14.4% in 2009 to 15.0% in 2015, with a greater increase after SwissDRG implementation (change in monthly slope, 0.0339%; 95% CI, 0.0254%-0.0423%). Patients with acute myocardial infarction were found to have a substantially greater increase after SwissDRG implementation in 30-day readmission rates (adjusted difference in slopes, 0.1144%; 95% CI, 0.0617%-0.1671%).Conclusions and relevanceAmong medical hospitalizations in Switzerland, SwissDRG implementation appeared to be associated with an increase in readmission rates and a decrease in in-hospital mortality but not with the gradual decrease in LOS observed in the historical control period.

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.

Project description:BackgroundThe population of persons seeking medical care is linguistically diverse in the United States. Language barriers can adversely affect a patient's ability to explain their symptoms. Among hospitalized patients, these barriers may lead to higher readmission rates and longer hospitalizations. Trained interpreters help overcome communication barriers; however, interpreter usage among patients is suboptimal.ObjectiveTo investigate differences among patients with limited English proficiency (LEP) in their length of stay (LOS) and 30-day readmission rate associated with their receiving professional interpretation at admission or discharge.DesignWe analyzed the rates of interpretation at admission and discharge of all LEP patients admitted to a tertiary care hospital over a three-year period. We calculated length of stay in days and as log of LOS. We also examined 30-day readmission. Using multivariable regression models, we explored differences among patients who received interpretation at admission, discharge, or both, controlling for patient characteristics, including age, illness severity, language, and gender.ParticipantsAll LEP patients admitted between May 1, 2004 and April 30, 2007.Main measuresLength of hospital stay as related to use of professional interpreters; readmission to the hospital within 30 days.Key resultsOf the 3071 patients included in the study, 39 % received language interpretation on both admission and discharge date. Patients who did not receive professional interpretation at admission or both admission/discharge had an increase in their LOS of between 0.75 and 1.47 days, compared to patients who had an interpreter on both day of admission and discharge (P<0.02). Patients receiving interpretation at admission and/or discharge were less likely than patients receiving no interpretation to be readmitted with 30 days.ConclusionsThe length of a hospital stay for LEP patients was significantly longer when professional interpreters were not used at admission or both admission/discharge.

Project description:BackgroundIn-hospital mortality, prolonged length of stay (LOS), and 30-day readmission are common outcomes in the intensive care unit (ICU). Traditional scoring systems and machine learning models for predicting these outcomes usually ignore the characteristics of ICU data, which are time-series forms. We aimed to use time-series deep learning models with the selective combination of three widely used scoring systems to predict these outcomes.Materials and methodsA retrospective cohort study was conducted on 40,083 patients in ICU from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database. Three deep learning models, namely, recurrent neural network (RNN), gated recurrent unit (GRU), and long short-term memory (LSTM) with attention mechanisms, were trained for the prediction of in-hospital mortality, prolonged LOS, and 30-day readmission with variables collected during the initial 24 h after ICU admission or the last 24 h before discharge. The inclusion of variables was based on three widely used scoring systems, namely, APACHE II, SOFA, and SAPS II, and the predictors consisted of time-series vital signs, laboratory tests, medication, and procedures. The patients were randomly divided into a training set (80%) and a test set (20%), which were used for model development and model evaluation, respectively. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and Brier scores were used to evaluate model performance. Variable significance was identified through attention mechanisms.ResultsA total of 33 variables for 40,083 patients were enrolled for mortality and prolonged LOS prediction and 36,180 for readmission prediction. The rates of occurrence of the three outcomes were 9.74%, 27.54%, and 11.79%, respectively. In each of the three outcomes, the performance of RNN, GRU, and LSTM did not differ greatly. Mortality prediction models, prolonged LOS prediction models, and readmission prediction models achieved AUCs of 0.870 ± 0.001, 0.765 ± 0.003, and 0.635 ± 0.018, respectively. The top significant variables co-selected by the three deep learning models were Glasgow Coma Scale (GCS), age, blood urea nitrogen, and norepinephrine for mortality; GCS, invasive ventilation, and blood urea nitrogen for prolonged LOS; and blood urea nitrogen, GCS, and ethnicity for readmission.ConclusionThe prognostic prediction models established in our study achieved good performance in predicting common outcomes of patients in ICU, especially in mortality prediction. In addition, GCS and blood urea nitrogen were identified as the most important factors strongly associated with adverse ICU events.

Dataset Information

A Multistate Model Predicting Mortality, Length of Stay, and Readmission for Surgical Patients.

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A Multistate Model Predicting Mortality, Length of Stay, and Readmission for Surgical Patients.

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