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Predictive model of length of stay in hospital among older patients.

ABSTRACT: BACKGROUND:Most National Health Service (NHS) hospital bed occupants are older patients because of their frequent admissions and prolonged length of stay (LOS). We evaluated demographic and clinical factors as predictors of LOS in a single NHS Trust and derived an equation to estimate LOS. METHODS:Stepwise logistic and linear regressions were used to predict prolonged LOS (upper-quintile LOS >?17 days) and LOS respectively, from demographic factors and acute and pre-existing conditions. RESULTS:Of 374 (men:women?=?127:247) admitted patients (20% to orthogeriatric, 69% to general medical and 11% to surgical wards), median age of 85 years (IQR?=?78-90), 77 had acute first hip fracture; 297 had previous hip fracture (median time since previous fracture?=?2.4 years) and 21 (7.1%) had recurrent hip fracture, with median time since first fracture?=?2.4 years. Median LOS was 6.5 days (IQR?=?1.8-14.8), and 38 (10.2%) died after 4.8 days (IQR?=?1.6-14.3). Prolonged LOS was associated with discharge to places other than usual residence: OR?=?3.1 (95% CI 1.7-5.7), acute stroke: OR?=?10.1 (3.7-26.7), acute first hip fractures: OR?=?6.8 (3.1-14.8), recurrent hip fractures: OR?=?9.5 (3.2-28.7), urinary tract infection/pneumonia: OR?=?4.0 (2.1-8.0), other acute fractures: OR?=?9.8 (3.0-32.3) and malignancy: OR?=?15.0 (3.1-71.8). Predictive equation showed estimated LOS was 11.6 days for discharge to places other than usual residence, 15 days for pre-existing or acute stroke, 9-14 days for acute and recurrent hip fractures, infections, other acute fractures and malignancy; these factors together explained 32% of variability in LOS. CONCLUSIONS:A useful estimate of outcome and LOS can be made by constructing a predictive equation from information on hospital admission, to provide evidence-based guidance for resource requirements and discharge planning.

SUBMITTER: Lisk R

PROVIDER: S-EPMC6589144 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature

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Predictive model of length of stay in hospital among older patients.

Lisk Radcliffe R Uddin Mahir M Parbhoo Anita A Yeong Keefai K Fluck David D Sharma Pankaj P Lean Michael E J MEJ Han Thang S TS

Aging clinical and experimental research 20180906 7

<h4>Background</h4>Most National Health Service (NHS) hospital bed occupants are older patients because of their frequent admissions and prolonged length of stay (LOS). We evaluated demographic and clinical factors as predictors of LOS in a single NHS Trust and derived an equation to estimate LOS.<h4>Methods</h4>Stepwise logistic and linear regressions were used to predict prolonged LOS (upper-quintile LOS > 17 days) and LOS respectively, from demographic factors and acute and pre-existing condi ...[more]

PMID: 30191455

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Project description:BACKGROUND:A comprehensive in-hospital patient management with reasonable and economic resource allocation is arguably the major challenge of health-care systems worldwide, especially in elderly, frail, and polymorbid patients. The need for patient management tools to improve the transition process and allocation of health care resources in routine clinical care particularly for the inpatient setting is obvious. To address these issues, a large prospective trial is warranted. METHODS:The "Integrative Hospital Treatment in Older patients to benchmark and improve Outcome and Length of stay" (In-HospiTOOL) study is an investigator-initiated, multicenter effectiveness trial to compare the effects of a novel in-hospital management tool on length of hospital stay, readmission rate, quality of care, and other clinical outcomes using a time-series model. The study aims to include approximately 35`000 polymorbid medical patients over an 18-month period, divided in an observation, implementation, and intervention phase. Detailed data on treatment and outcome of polymorbid medical patients during the in-hospital stay and after 30 days will be gathered to investigate differences in resource use, inter-professional collaborations and to establish representative benchmarking data to promote measurement and display of quality of care data across seven Swiss hospitals. The trial will inform whether the "In-HospiTOOL" optimizes inter-professional collaboration and thereby reduces length of hospital stay without harming subjective and objective patient-oriented outcome markers. DISCUSSION:Many of the current quality-mirroring tools do not reflect the real need and use of resources, especially in polymorbid and elderly patients. In addition, a validated tool for optimization of patient transition and discharge processes is still missing. The proposed multicenter effectiveness trial has potential to improve interprofessional collaboration and optimizes resource allocation from hospital admission to discharge. The results will enable inter-hospital comparison of transition processes and accomplish a benchmarking for inpatient care quality.

Project description:Length-of-hospital stay (LOS) after lumbar spine surgery (LSS) can be affected by many factors. However, few studies have evaluated predictors of LOS, and all have used limited number of variables as predictors.The purpose of the study was to identify presurgical, surgical, and postsurgical predictors of LOS after LSS.Retrospective review of consecutive patients who had LSS at the University of Kansas Hospital from October 2008 to April, 2012.Five hundred ninety-three patients underwent LSS consisting of laminotomy, laminectomy, or arthrodesis.LOS. Multiple presurgical, surgical, and postsurgical variables were extracted from the patients' medical records and considered as possible predictors (independent variables) of LOS.Potential predictors that were significantly correlated with LOS were used as indicators to construct three latent factors presurgical, surgical, and postsurgical, which were in turn used to predict LOS in a structural equation model.The average LOS was 4.01±2.73 days. The presurgical factor was indicated by age (61.97±14.49 years), previous level of function (60.5% were totally independent), previous hemoglobin level (13.70±1.36 mg/dL), and use of assistive devices (60% were assistive device users). The surgical factor was indicated by severity of illness (50.2% had minor disease severity), presence of complications (1.9%), and stay in an intensive care unit (4.0%). The postsurgical factor was indicated by postsurgical walking distance (166.43±175.75 ft), level of assistance during walking (5.18±0.81 out of 7 points), balance scores (6.18±1.82 out of 10 points), and bed mobility and transfer dependency scores (9.81±1.99 out of 14 points). These three latent factors explained 47% of variation in LOS.Postsurgical factors predicted the highest variation in LOS in comparison with presurgical and surgical factors and should be taken into consideration for discharge planning. Postsurgical factors are related to the patient's function, modifiable with rehabilitation, and can be improved to shorten LOS. Inclusion of more reliable and standardized presurgical variables could improve the predictability of the model.

Project description:BackgroundThe increasing number of patients treated with peritoneal dialysis (PD) and their consistently high rate of hospital admissions have placed a large burden on the health care system. Early clinical interventions and optimal management of patients at a high risk of prolonged length of stay (pLOS) may help improve the medical efficiency and prognosis of PD-treated patients. If timely clinical interventions are not provided, patients at a high risk of pLOS may face a poor prognosis and high medical expenses, which will also be a burden on hospitals. Therefore, physicians need an effective pLOS prediction model for PD-treated patients.ObjectiveThis study aimed to develop an optimal data-driven model for predicting the pLOS risk of PD-treated patients using basic admission data.MethodsPatient data collected using the Hospital Quality Monitoring System (HQMS) in China were used to develop pLOS prediction models. A stacking model was constructed with support vector machine, random forest (RF), and K-nearest neighbor algorithms as its base models and traditional logistic regression (LR) as its meta-model. The meta-model used the outputs of all 3 base models as input and generated the output of the stacking model. Another LR-based pLOS prediction model was built as the benchmark model. The prediction performance of the stacking model was compared with that of its base models and the benchmark model. Five-fold cross-validation was employed to develop and validate the models. Performance measures included the Brier score, area under the receiver operating characteristic curve (AUROC), estimated calibration index (ECI), accuracy, sensitivity, specificity, and geometric mean (Gm). In addition, a calibration plot was employed to visually demonstrate the calibration power of each model.ResultsThe final cohort extracted from the HQMS database consisted of 23,992 eligible PD-treated patients, among whom 30.3% had a pLOS (ie, longer than the average LOS, which was 16 days in our study). Among the models, the stacking model achieved the best calibration (ECI 8.691), balanced accuracy (Gm 0.690), accuracy (0.695), and specificity (0.701). Meanwhile, the stacking and RF models had the best overall performance (Brier score 0.174 for both) and discrimination (AUROC 0.757 for the stacking model and 0.756 for the RF model). Compared with the benchmark LR model, the stacking model was superior in all performance measures except sensitivity, but there was no significant difference in sensitivity between the 2 models. The 2-sided t tests revealed significant performance differences between the stacking and LR models in overall performance, discrimination, calibration, balanced accuracy, and accuracy.ConclusionsThis study is the first to develop data-driven pLOS prediction models for PD-treated patients using basic admission data from a national database. The results indicate the feasibility of utilizing a stacking-based pLOS prediction model for PD-treated patients. The pLOS prediction tools developed in this study have the potential to assist clinicians in identifying patients at a high risk of pLOS and to allocate resources optimally for PD-treated patients.

Project description:Current outcome predictors for illness and injury are measured at a single time point-admission. However, patient prognosis often changes during hospitalization, limiting the usefulness of those predictions. Accurate depiction of the dynamic interaction between competing events during hospitalization may enable real-time outcome assessment.To determine how the effects of burn outcome predictors (ie, age, total body surface area burn, and inhalation injury) and the outcomes of interest (ie, mortality and length of stay) vary as a function of time throughout hospitalization.In this retrospective study, we used the American Burn Association's National Burn Repository, containing outcomes and patient and injury characteristics, to identify 95?579 patients admitted with an acute burn injury to 80 tertiary American Burn Association burn centers from 2000 through 2009. We applied competing risk statistical methods to analyze patient outcomes.We estimated the cause-specific hazard rates for death and discharge to assess how the instantaneous risk of these events changed across time. We further evaluated the varying effects of patient age, total body surface area burn, and inhalation injury on the probability of discharge and death across time.Maximum length of stay among patients who died was 270 days and 731 days among those discharged. Total body surface area, age, and inhalation injury had significant effects on the subdistribution hazard for discharge (P?<?.001); these effects varied across time (P?<?.002). Burn size (coefficient -0.046) determined early outcomes, while age (coefficient -0.034) determined outcomes later in the hospitalization. Inhalation injury (coefficient -0.622) played a variable role in survival and hospital length of stay.Real-time measurement of dynamic interrelationships among burn outcome predictors using competing risk analysis demonstrated that the key factors influencing outcomes differed throughout hospitalization. Further application of this analytic technique to other injury or illness types may improve assessment of outcomes.

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Predictive model of length of stay in hospital among older patients.

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Predictive model of length of stay in hospital among older patients.

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