Predicting Hospital Length of Stay at Admission Using Global and Country-Specific Competing Risk Analysis of Structural, Patient, and Nutrition-Related Data from nutritionDay 2007-2015.
Ontology highlight
ABSTRACT:
SUBMITTER: Kiss N
PROVIDER: S-EPMC8624242 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:ObjectiveHypokalaemia and hyperkalaemia ('dyskalaemia') are commonly seen in patients requiring emergency hospital admission. The adverse effect of dyskalaemia on mortality is well described but there are few data for the effect on hospital length of stay. We sought to determine the association of serum potassium concentration with in-hospital length of stay.DesignSystematic review and meta-analysis.Data sourcesA structured search of MEDLINE, PubMed and SCOPUS databases to 19 March 2021.Eligibility criteriaObservational cohort studies defining exposure of interest as serum potassium levels (at admission or within the first 72 hours) and with outcome of interest as length of hospital stay. Studies had to provide estimates of length of stay as a comparison between normokalaemia and defined ranges of hyperkalaemia or hypokalaemia.Data extraction and synthesisWe identified 39 articles published to March 2021 that met the inclusion and exclusion criteria. Study selection, data extraction and quality assessment were carried out by two reviewers working independently and in duplicate, to assessed eligibility and risk of bias, and extract data from eligible studies. Random effects models were used to pool estimates across the included studies. Meta-analyses were performed using Cochrane-RevMan.ResultsFive studies were included in the meta-analysis. Compared with the reference group (3.5-5.0 mmol/L), the pooled raw differences of medians were 4.45 (95% CI 2.71 to 6.91), 1.99 (95% CI 0.03 to 3.94), 0.98 (95% CI 0.91 to 1.05), 1.51 (95% CI 1.03 to 2.0), 1 (95% CI 0.75 to 1.25) and 2.76 (95% CI 1.24 to 4.29) for patients with potassium levels of <2.5, 2.5 to <3.0, 3.0 to <3.5, <5 to 5.5, <5.5 to 6 and >6.0 mmol/L, respectively.ConclusionHospital length of stay follows a U-shaped distribution, with duration of admission being twofold greater at the extremes of the potassium range.
| S-EPMC10450050 | biostudies-literature
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.
| S-EPMC4968081 | biostudies-literature
Project description:IntroductionPredicting length of stay (LOS) is beneficial to patients and the health service. When a prolonged LOS is predicted, it gives the opportunity for focused therapies and allocation of resources to reduce this period. In emergency general surgery (EGS) there has been limited investigation of variables that may be important predictors of LOS. This study examines social characteristics alongside measures of severity of acute illness and co-morbidities in an adult EGS population to establish their contribution to LOS.MethodsData were collected prospectively from patients at admission including medical variables, demographics, and therapeutic requirements. The length of hospital admission was measured, and multiple regression analysis was used to identify variables which predicted the LOS.ResultsData were collected from 105 patients. The regression model gave an R2 of 0.34, p = 0.0006. Barthal index (measure of independence in activities of daily living) was a significant predictor of LOS [logworth 1.649, p0.02243]. Housing status and Level of social support both correlated in one-way analysis with LOS.ConclusionThere are non-surgical variables, measurable at admission which are of significant value in predicting LOS of EGS patients. This warrants further investigation through a larger study to better quantify the contributions of these variables, and establish potential early interventions to reduce the LOS.
| S-EPMC7819802 | biostudies-literature
Project description:The aim of our study was to determine the association between admitting service, medicine or orthopaedics, and length of stay (LOS) for a geriatric hip fracture patient.Retrospective.Urban level 1 trauma center.Six hundred fourteen geriatric hip fracture patients from 2000 to 2009.Orthopaedic surgery for geriatric hip fracture.Patient demographics, medical comorbidities, hospitalization length, and admitting service. Negative binomial regression used to determine association between LOS and admitting service.Six hundred fourteen geriatric hip fracture patients were included in the analysis, of whom 49.2% of patients (n = 302) were admitted to the orthopaedic service and 50.8% (3 = 312) to the medicine service. The median LOS for patients admitted to orthopaedics was 4.5 days compared with 7 days for patients admitted to medicine (P < 0.0001). Readmission was also significantly higher for patients admitted to medicine (n = 92, 29.8%) than for those admitted to orthopaedics (n = 70, 23.1%). After controlling for important patient factors, it was determined that medicine patients are expected to stay about 1.5 times (incidence rate ratio: 1.48, P < 0.0001) longer in the hospital than orthopaedic patients.This is the largest study to demonstrate that admission to the medicine service compared with the orthopaedic service increases a geriatric hip fractures patient's expected LOS. Since LOS is a major driver of cost as well as a measure of quality care, it is important to understand the factors that lead to a longer hospital stay to better allocate hospital resources. Based on the results from our institution, orthopaedic surgeons should be aware that admission to medicine might increase a patient's expected LOS.Therapeutic Level III. See Instructions for Authors for a complete description of levels of evidence.
| S-EPMC4841672 | biostudies-literature
Project description:BackgroundRadical prostatectomy (RP) is a leading treatment option for localised prostate cancer. Although hospital in-patient stays accounts for much of the costs of treatment, little is known about population-level trends in length-of-stay (LOS). We investigated factors predicting hospital LOS and readmissions in men who had RP following prostate cancer.MethodsIncident prostate cancers (ICD-O3: C61), diagnosed January 2002-December 2008 in men < 70 years, were identified from the Irish Cancer Registry, and linked to public hospital episodes. For those who had RP (ICD-9 CM procedure codes 60.3, 60.4, 60.5, 60.62) the associated hospital episode was identified. LOS was calculated as the number of days from date of admission to date of discharge. Patient-, tumour-, and health service-related factors predicting longer LOS (upper quartile, >9 days) were investigated using logistic regression. Patterns in day-case and in-patient readmissions within 28 days of discharge following RP were explored.ResultsOver the study period 9096 prostate cancers were diagnosed in men under 70, 26.5% of whom had RP by end of follow-up 31/12/2009. Two of eight public hospitals and eight of forty surgeons carried out 50% of all public-service RPs. Median LOS was 8 days (10th-90th percentile = 6-13 days) and fell significantly over time (2002, 9 days; 2008, 7 days; p < 0.001). In adjusted analyses men who were not married (OR = 1.71, 95% CI 1.25-2.34), had co-morbidities (OR = 1.64, 95% CI 1.25-2.16) or stage III-IV cancer (OR = 2.19, 95% CI 1.44-3.34) were significantly more likely to have prolonged LOS. Those treated in higher volume hospitals (annual median >49 RPs) or by higher volume surgeons (annual median >17 RPs) were significantly less likely to have prolonged LOS (OR = 0.34, 95% CI 0.26-0.45; OR = 0.55, 95% CI 0.42-0.71 respectively).ConclusionMedian LOS after RP decreased between 2002 and 2008 in Ireland but it remains higher than in both England and the US. Although volumes of RPs conducted in Ireland are low, there is considerable variation between hospitals and surgeons. Hospital and surgeon volume were strong predictors of shorter LOS, after adjusting for other variables. These factors point to a need for a comprehensive review of prostate cancer service provision.
| S-EPMC3750445 | biostudies-literature
Project description:Although patients undergoing maintenance hemodialysis have exceptionally high hospitalization rates, the risk factors for hospitalizations are unclear. This study sought to examine hospitalization rates among hemodialysis patients in the United States according to both race/ethnicity and age.US Renal Data System data on 563,281 patients beginning maintenance hemodialysis between 1995 and 2009 were analyzed. Rates of hospital admission and number of hospital days for all-cause and cause-specific hospitalizations during the first year of dialysis were compared among blacks, whites, and Hispanics in the entire cohort and subgroups stratified by age.After multiple adjustments, compared with whites, Hispanics overall had lower rates of both all-cause hospital days (adjusted rate ratio [aRR], 0.91; 95% confidence interval [95% CI], 0.90 to 0.93; P<0.001) and hospital admissions (aRR, 0.89; 95% CI, 0.88 to 0.90; P<0.001), whereas blacks had a lower rate of all-cause admissions (aRR, 0.95; 95% CI, 0.94 to 0.96; P<0.001). The racial/ethnic differences, however, varied by age. Hispanics exhibited the lowest rates of hospital days and admissions for all age groups?70 years, but those >80 years had higher rates than their white counterparts. The adjusted black-to-white rate ratios exhibited a U-shaped pattern with age, with higher rates for blacks in the younger and older age groups. Hospitalization rates for dialysis-related infections were markedly higher in blacks and Hispanics than whites, which were consistent in all age groups for blacks (aRRs for hospital days ranged from 1.09 to 1.36) and all ages>60 years for Hispanics (aRRs ranged from 1.20 to 1.38).There are significant racial/ethnic differences in hospitalization rates within first year of dialysis, which are not consistent across the age groups and also differ by causes of hospitalization. Overall, blacks and Hispanics had lower rates of all-cause hospital admissions than whites. However, younger and older blacks and older Hispanics were at greatest risk.
| S-EPMC4123407 | biostudies-other
Project description:PurposeMalnutrition, as determined by the Controlling Nutritional Status (CONUT), has an effect on the 3-month and long-term prognosis of stroke patients. The association between malnutrition and in-hospital mortality has not been well established. We aimed to investigate the relationship between the CONUT score on admission and in-hospital mortality and length of stay (LOS) in elderly patients with acute ischemic stroke (AIS).MethodsThis study analyzed controls and patients with AIS. Malnutrition was determined using the CONUT score. A CONUT score of 5-12 was defined as undernutrition status. Based on the CONUT scores, the patients were divided into the low CONUT (0-4) and high CONUT (5-12) groups.ResultsIn total, 1079 participants were recruited, comprising 288 controls and 791 AIS patients. Among the 791 patients, 64 (8.1%) had malnutrition and 63 (7.9%) had an in-hospital death. Compared to the controls, the AIS patients presented higher CONUT scores, higher proportion of in-hospital mortality (8.0%), and longer length of stay. Malnutrition was independently associated with in-hospital mortality in the AIS patients (adjusted odds ratio: 3.77, 95% confidence interval [CI]: 1.55-9.15; p = 0.003). The general linear models showed an association between the CONUT score and LOS (β = 0.574, 95% CI: 0.208-0.934; p = 0.002). Furthermore, the effect of the interaction between infection and nutrition status on in-hospital mortality showed borderline statistical significance (p = 0.06).ConclusionsMalnutrition estimated by the CONUT score on admission can be a predictor of in-hospital mortality and increased LOS in elderly AIS patients.
| S-EPMC8761426 | biostudies-literature
Project description:Hospital length of stay (LOS) after burn injury is commonly estimated as 1 day per percent burn, but LOS often exceeds that estimate. The purpose of this study is to develop a novel method for estimating burn hospital LOS at any time during hospitalization. The authors used the American Burn Association National Burn Repository from 2000 to 2009 to directly estimate the median residual LOS (MRLOS) of patients hospitalized for burn injuries and who survived to discharge. The MRLOS is the median of how many more days a burn patient will be hospitalized given that the person has been in hospital for a specified time period. The authors also estimated the 25th and 75th percentiles of residual life and quantified the relationship between MRLOS and LOS with ordinary least squares for all burn patients, by burn size and by presence of inhalation injury. MRLOS increased with increasing LOS, confirming that discharge estimates change over time. Patients with inhalation injury had longer MRLOS than patients without inhalation injury in the first 100 hospital days. Patients with large burns (>25%) had large MRLOS consistent with prolonged hospitalization, but patients with small burns (<25%) also had steadily increasing MRLOS during hospitalization (ie, the longer the patient was in the hospital, the longer the predicted LOS regardless of initial LOS estimate). Estimating remaining hospital LOS at any time during admission has been problematic; MRLOS can be used to provide an estimate of remaining hospital LOS and resource utilization to families, administrators, and other medical professionals.
| S-EPMC5014656 | biostudies-literature
Project description: Not available
| S-EPMC1184244 | biostudies-literature
Project description:BackgroundIn the early stages of the COVID-19 pandemic our institution was interested in forecasting how long surgical patients receiving elective procedures would spend in the hospital. Initial examination of our models indicated that, due to the skewed nature of the length of stay, accurate prediction was challenging and we instead opted for a simpler classification model. In this work we perform a deeper examination of predicting in-hospital length of stay.MethodsWe used electronic health record data on length of stay from 42,209 elective surgeries. We compare different loss-functions (mean squared error, mean absolute error, mean relative error), algorithms (LASSO, Random Forests, multilayer perceptron) and data transformations (log and truncation). We also assess the performance of two stage hybrid classification-regression approach.ResultsOur results show that while it is possible to accurately predict short length of stays, predicting longer length of stay is extremely challenging. As such, we opt for a two-stage model that first classifies patients into long versus short length of stays and then a second stage that fits a regresssor among those predicted to have a short length of stay.DiscussionThe results indicate both the challenges and considerations necessary to applying machine-learning methods to skewed outcomes.ConclusionsTwo-stage models allow those developing clinical decision support tools to explicitly acknowledge where they can and cannot make accurate predictions.
| S-EPMC9035272 | biostudies-literature