Predictive factors of hospital length of stay in patients with operatively treated ankle fractures.
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ABSTRACT: Background
Operative fixation of ankle fractures is common. However, as reimbursement plans evolve with the potential for bundled payments, it is critical that orthopedic surgeons better understand factors influencing the postoperative length of stay (LOS) in patients undergoing these procedures to negotiate appropriate reimbursement. We sought to identify factors influencing the postoperative LOS in patients with operatively treated ankle fractures.Materials and methods
Six hundred twenty-two patients with ankle fractures between January 1st, 2004 and December 31st, 2010 were identified retrospectively. Charts were reviewed for gender, length of operative procedure, method of fixation, American Society of Anesthesiologists (ASA) physical status score, medical comorbidities, and postoperative LOS. Both univariate and multivariate models were developed to determine predictors of patient LOS. Financial data for an average 24-h inpatient stay were obtained from financial services.Results
Six hundred twenty-two patients were included. In a linear regression analysis, a statistically significant relationship was demonstrated between ASA status and LOS (P < 0.001). Multiple regression analysis further characterized the relationship between ASA and LOS: a 1-U increase in ASA classification conferred a 3.42-day increase in LOS on average (P < 0.001). Based on an average per-day inpatient cost of $4,503, each unit increase in ASA status led to a $15,490 increase in cost.Conclusions
Our study demonstrates that ASA status is a powerful predictor of LOS in patients undergoing operative fixation of ankle fractures. More complete understanding of these factors will lead to better risk adjustment models for measuring outcomes, determining fair reimbursement, and potential improvements to the efficiency of patient care.Level of evidence
Level III retrospective comparative study regressing length of stay with many variables, including ASA physical status.
SUBMITTER: McDonald MR
PROVIDER: S-EPMC4244567 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:ObjectiveTo determine patient-specific and injury-specific factors that may predict infection and other adverse clinical results in the setting of tibial pilon fractures.DesignRetrospective chart review.SettingLevel 1 academic trauma center.PatientsTwo hundred forty-eight patients who underwent operative treatment for tibial pilon fractures between 2010 and 2020.InterventionExternal fixation and/or open reduction and internal fixation.Main outcome measurementsFracture-related infection rates and specific bacteriology, risk factors associated with development of a fracture-related infection, and predictors of adverse clinical results.ResultsTwo hundred forty-eight patients were enrolled. There was an infection rate of 21%. The 3 most common pathogens cultured were methicillin-resistant Staphylococcus aureus (20.3%), Enterobacter cloacae (16.7%), and methicillin-resistant Staphylococcus aureus (15.5%). There was no significant difference in age, sex, race, body mass index, or smoking status between those who developed an infection and those who did not. Patients with diabetes mellitus ( P = 0.0001), open fractures ( P = 0.0043), and comminuted fractures (OTA/AO 43C2 and 43C3) ( P = 0.0065) were more likely to develop a fracture-related infection. The presence of a polymicrobial infection was positively associated with adverse clinical results ( P = 0.006). History of diabetes was also positively associated with adverse results ( P = 0.019).ConclusionsHistory of diabetes and severe fractures, such as those that were open or comminuted fractures, were positively associated with developing a fracture-related infection after the operative fixation of tibial pilon fractures. History of diabetes and presence of a polymicrobial infection were independently associated with adverse clinical results.Level of evidencePrognostic Level III. See Instructions for Authors for a complete description of levels of evidence.
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Project description:Increased emphasis is being laid on ensuring that health resources are efficiently utilized, especially in resource-constrained settings such as in Nigeria. One of the main indices of how efficiently a health institution is being run is Length of Stay (LOS), which is likely to be higher in chronic diseases such as stroke and diabetes. Stroke is a chronic disease that is currently on the rise in Low and Middle income countries (LMICs) who are also characterized by constraint of health resources. This study seeks to determine the LOS of stroke patients as well as factors that affect it.A retrospective analysis of health records of stroke victims admitted into the medical wards of the University College Hospital, Ibadan between January 2012 and December 2014 was conducted. Data on sociodemographic information, comorbidities and risk factors were extracted while LOS was calculated by counting the number of days the patient was admitted. Analysis was carried to using SPSS.A total of 143 records were used in the final analysis with 53.1% of them being males and having a mean age of 61.5 ± 14.2 years. More than half (53.8%) of the cases were ischemic strokes. The average length of stay was 13.7 ± 8.9 days while bivariate analysis showed that a greater proportion of cases who consumed alcohol, had diabetes and hypertension had LOS of over 7 days than those who did not. However, these differences in proportions were not statistically significant (0.310<p<0.883).LOS of stroke patients in Nigeria was shown to be prolonged especially when compared to similar settings in West Africa. The high prevalence of some of the risk factors of stroke such as diabetes mellitus indicates that policy and advocacy to drive changes in lifestyle are necessary to reduce the incidence of stroke and its consequent burden on health systems.
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Project description:STUDY DESIGN: Retrospective cohort study. BACKGROUND: Several studies focus on the long-term results of anterior cervical discectomy and fusion (ACDF) surgeries, but little information exists regarding how various patient-related, procedure-related, and payer-related variables may affect postoperative hospital length of stay (LOS). OBJECTIVE: To determine what factors, if any, contribute to increased hospital LOS in patients who have had an ACDF. METHODS: Retrospective cohort study of 108 consecutive patients who underwent elective ACDF at a Midwest academic medical center. Extensive preoperative, intraoperative, and postoperative data were abstracted and analyzed to identify prognostic factors for an increased LOS. Multivariate analysis was performed to analyze the effects of patient and hospital characteristics on hospital LOS. RESULTS: 103 patients met inclusion and exclusion criteria. The mean LOS for patients undergoing ACDF was 1.98 (±1.6) days. Only 29% of patients had one level fused. The mean blood loss during surgery was 87.4 ± 99.6 mL. One subject lost 700 mL of blood. Complications, though rare, included uncontrolled postoperative pain (13%), cardiac (6%), pulmonary (4%), and urinary (3%). Covariates included in the final model were age, sex, cardiac complication, urinary complication, and pulmonary complication. Factors that contributed to increased LOS and their associated adjusted mean days were: ≥50 years of age (2.5 ± 1.2 days), female gender (2.3 ± 1.2 days), and three particular types of complications. The complications that had the largest effect on increased LOS from least to most severe were cardiac (3.5 ± 1.3 days), urinary (4.7 ± 1.3 days), and pulmonary (5.3 ± 1.3 days). CONCLUSIONS: The information presented in this study may be useful for patients, clinicians, and insurance companies, including precertification and case-management services. Our results can be instrumental in designing future prospective studies using more detailed analyses with more patients, more surgeons, and multiple institutions. [Table: see text].
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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.
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Project description:BackgroundNeutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) are widely accepted indices positively correlated with disease severity, progression, and mortality. In this study, we tested whether NLR and PLR could predict mortality and length of hospital stay (LOS) after cardiac surgery.MethodsNLR and PLR were calculated on days 0, 3, 5, and 7 postoperatively. A ROC curve was generated to assess their prognostic value; multivariate logistic analysis identified independent risk factors for 90-day mortality.ResultsAnalysis was performed on 179 patients' data, 11 of whom (6.15%) died within 90 days. The discriminatory performance for predicting 90-day mortality was better for NLR7 (AUC = 0.925, 95% CI:0.865-0.984) with the optimal cut-off point being 7.10. NLR5 and PLR3 also exhibited a significant strong discriminative performance. Similarly, a significant discriminative performance was prominent for PLR3, NLR5, and NLR7 with respect to LOS. Moreover, NLR7 (OR: 2.143, 95% CI: 1.076-4.267, p = 0.030) and ICU LOS (OR:1.361, 95% CI: 1.045-1.774, p = 0.022) were significant independent risk factors for 90-day mortality.ConclusionsNLR and PLR are efficient predictive factors for 90-day mortality and LOS in cardiac surgery patients. Owing to the simplicity of determining NLR and PLR, their postoperative monitoring may offer a reliable predictor of patients' outcomes in terms of LOS and mortality.
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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.
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