Does the cost of care differ for patients with fee-for-service vs. capitation of payment? A case–control study in gastroenterology

2013 ◽  
Vol 182 (4) ◽  
pp. 669-672 ◽  
Author(s):  
E. Slattery ◽  
K. X. Clancy ◽  
G. C. Harewood ◽  
F. E. Murray ◽  
S. Patchett
Transfusion ◽  
2002 ◽  
Vol 42 (9) ◽  
pp. 1123-1126 ◽  
Author(s):  
Natalia Volkova ◽  
Ellen Klapper ◽  
Samuel H. Pepkowitz ◽  
Timothy Denton ◽  
Glenn Gillaspie ◽  
...  

1993 ◽  
Vol 25 (4) ◽  
pp. 239-250 ◽  
Author(s):  
R. Coello ◽  
H. Glenister ◽  
J. Fereres ◽  
C. Bartlett ◽  
D. Leigh ◽  
...  

2014 ◽  
Vol 2014 ◽  
pp. 1-4 ◽  
Author(s):  
Navid Zahedi Niaki ◽  
Harmeet Singh ◽  
Sami P. Moubayed ◽  
Rebecca Leboeuf ◽  
Jean-Claude Tabet ◽  
...  

The aim of this study is to evaluate the additional costs associated with calcium monitoring and treatment as well as evaluate the incidence and predictors of postthyroidectomy hypocalcemia. Methods. This case-control study involved thyroidectomy and completion thyroidectomy patients operated on between January 2012 and August 2013. Cases were defined as requiring calcitriol supplementation, and controls did not require supplementation. Patient (age, sex), nodule (cytology, pathology), surgical data (neck dissection, parathyroid identification, and reimplantation), and hospital stay (days hospitalized in total and after drain removal) were compared. Comparisons were made using t-tests and chi-square tests with an alpha of 0.05. The estimated cost associated with the extended stay was then compared with the cost of supplementation. Results. A total of 191 patients were evaluated (61 cases and 130 controls). Predictors of hypocalcemia include female age, neck dissection, and parathyroid reimplantation. Hypocalcemic patients were hospitalized for a longer period of time after drain removal (2.5 versus 0.8 days, P<0.001), and hospitalization costs after neck drain removal were higher in this group as well (8,367.32$ versus 2,534.32$, P<0.001). Conclusion. Postoperative hypocalcemia incurs significant additional health care costs at both the local and health care system levels.


2022 ◽  
Vol 8 ◽  
Author(s):  
Yichi Zhang ◽  
Haige Zhao ◽  
Qun Su ◽  
Cuili Wang ◽  
Hongjun Chen ◽  
...  

Introduction:Acute kidney injury (AKI) after cardiac surgery is independently associated with a prolonged hospital stay, increased cost of care, and increased post-operative mortality. Delayed elevation of serum creatinine (SCr) levels requires novel biomarkers to provide a prediction of AKI after cardiac surgery. Our objective was to find a novel blood biomarkers combination to construct a model for predicting AKI after cardiac surgery and risk stratification.Methods:This was a case-control study. Weighted Gene Co-expression Network Analysis (WGCNA) was applied to Gene Expression Omnibus (GEO) dataset GSE30718 to seek potential biomarkers associated with AKI. We measured biomarker levels in venous blood samples of 67 patients with AKI after cardiac surgery and 59 control patients in two cohorts. Clinical data were collected. We developed a multi-biomarker model for predicting cardiac-surgery-associated AKI and compared it with a traditional clinical-factor-based model.Results:From bioinformatics analysis and previous articles, we found 6 potential plasma biomarkers for the prediction of AKI. Among them, 3 biomarkers, such as growth differentiation factor 15 (GDF15), soluble suppression of tumorigenicity 2 (ST2, IL1RL1), and soluble urokinase plasminogen activator receptor (uPAR) were found to have prediction ability for AKI (area under the curve [AUC] &gt; 0.6) in patients undergoing cardiac surgery. They were then incorporated into a multi-biomarker model for predicting AKI (C-statistic: 0.84, Brier 0.15) which outperformed the traditional clinical-factor-based model (C-statistic: 0.73, Brier 0.16).Conclusion:Our research validated a promising plasma multi-biomarker model for predicting AKI after cardiac surgery.


Author(s):  
A.E. González-Vélez ◽  
M. Romero-Martín ◽  
R. Villanueva-Orbaiz ◽  
C. Díaz-Agero-Pérez ◽  
A. Robustillo-Rodela ◽  
...  

1996 ◽  
Vol 86 (6) ◽  
pp. 809-814 ◽  
Author(s):  
B Luke ◽  
H R Bigger ◽  
S Leurgans ◽  
D Sietsema

2020 ◽  
Author(s):  
Kui Yang ◽  
Ni Zhang ◽  
Chunchen Gao ◽  
Hongyan Qin ◽  
Anhui Wang ◽  
...  

Abstract Background: Hospital-acquired influenza A brings hospitalized patients an additional cost of care and considerable mortality, but risk factors for hospital-acquired influenza A are unknown. We aimed to describe the characteristics of patients vulnerable for hospital-acquired influenza A and to identify its risk factors. This knowledge would help clinicians to control hospital-acquired infection and reduce the burden of treatment.Methods: A case-control study was conducted in hospitalized patients aged ≥18 years in a tertiary level teaching hospital during the 2018–2019 influenza A season. Patient data were retrieved from hospital-based electronic medical records. Hospital-acquired influenza A was defined as a case of influenza A diagnosed 7 days or more after admission, in a patient who had no evidence of viral respiratory infection on admission. The controls without influenza were selected among patients exposed to the same setting during the same time period. We identified risk factors using conditional logistic regression and described characteristics of patients with hospital-acquired influenza A by comparing the clinical data of the influenza patients and the controls.Results: Of 412 hospitalized patients with influenza A from all departments of the study hospital, 93 (22.6%) cases were classified as hospital-acquired. The most common comorbidities of the 93 cases were hypertension (41.9%), coronary heart disease (21.5%) and cerebrovascular disease (20.4%). Before the onset of hospital-acquired influenza A, patients presented more lymphocytopenia (51.6% vs 35.5%, P=0.027), hypoalbuminemia (78.5% vs 57.0%, P=0.002) and pleural effusion (26.9% vs 9.7%, P=0.002) than matched controls. Notably, infected patients had a longer hospital stay (18 days vs 14 days, P=0.002), and higher mortality (10.8% vs 2.2%, P=0.017). Lymphocytopenia (odds ratio [OR]: 3.11; 95% confidence interval [CI]: 1.24–7.80; P =0.016), hypoalbuminemia (OR: 2.24; 95% CI: 1.10–4.57; P =0.027) and pleural effusion (OR: 3.09; 95% CI: 1.26–7.58; P =0.014) were independently associated with hospital-acquired influenza A.Conclusions: Lymphocytopenia, hypoalbuminemia and pleural effusion were independent risk factors that could help identify patients at high risk of hospital-acquired influenza A, which might extend hospital stay and is associated with a high mortality.


2020 ◽  
Author(s):  
Kui Yang ◽  
Ni Zhang ◽  
Chunchen Gao ◽  
Hongyan Qin ◽  
Anhui Wang ◽  
...  

Abstract Background: Nosocomial influenza A brings hospitalized patients additional cost of care and considerable mortality, but predictors for hospital-acquired influenza A at the early stage remained unidentified. We aimed to describe the characteristics of patients vulnerable for hospital-acquired influenza A and identify its risk factors, which would help clinicians control nosocomial infection and ease the burden of treatment. Methods: A case-control study was conducted in hospitalized patients aged ≥ 18 years in a level A tertiary teaching hospital during the 2018-2019 influenza A season. Information of patients was retrieved from hospital-based medical records system. Hospital-acquired influenza A was defined as cases diagnosed 7 days or more after admission, who had no signs of viral respiratory infection on admission. The controls with no influenza infection were selected by the following criterion. Namely, patients were exposed to the same setting in the same period of time. We identified risk factors using conditional logistic regression and described characteristics of hospital-acquired influenza A through comparing the clinical data between influenza infected patients and controls. Results: Of 412 hospitalized patients with influenza A from all departments of the investigated hospital, 93 (22.6%) cases were classified as hospital-acquired influenza A. Older age (>65 years old) accounted for 34.4%. Hypertension (41.9%), coronary heart disease (21.5%) and cerebrovascular disease (20.4%) were the most common comorbidities. Before the infection of hospital-acquired influenza A, patients presented more lymphocytopenia (51.6% VS 35.5%, P=0.027), hypoalbuminemia (78.5% VS 57.0%, P=0.002) and pleural effusion (26.9% VS 9.7%, P=0.002) than matched controls. Notably, infected patients had a longer hospital stay [18(12-27.5) days VS 14(11-20) days, P=0.002], and higher mortality (10.8% VS 2.2%, P=0.017 ). Lymphocytopenia (OR: 3.107; 95% CI 1.238-7.796; P =0.016), hypoalbuminemia (OR: 2.241; 95% CI 1.099-4.570; P =0.027) and pleural effusion (OR: 3.094; 95% CI 1.263-7.583; P =0.014) were independently associated with hospital-acquired influenza A. Conclusions: Lymphocytopenia, hypoalbuminemia and pleural effusion were independent risk factors that could help identify patients at high risk of hospital-acquired influenza A, which extended hospital stay and was associated with high mortality.


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