Geographic variation in cost of care for pituitary tumor surgery

Lee, Charles C.; Kimmell, Kristopher T.; Lalonde, Amy; Salzman, Peter; Miller, Matthew C.; Calvi, Laura M.; Manuylova, Ekaterina; Shafiq, Ismat; Vates, G. Edward

doi:10.1007/s11102-016-0738-x

Cited by 15 publications

(13 citation statements)

References 33 publications

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“…5,7,8 Lee et al showed that there is significant variation in charges and costs for transsphenoidal surgery within New York. 9 While they studied hospital costs and charges and cost-to-charge ratios, they did not evaluate patient and hospital characteristics and their relation to hospital charge. By studying these factors, we can better understand variations in health care costs and thereby, instrument approaches to reduce disparities in hospital charge.…”

Section: Discussionmentioning

confidence: 99%

Hospital Charge Variability across New York State: Sociodemographic Factors in Pituitary Surgery

et al. 2019

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Introduction Significant charge disparities exist across New York State (NYS). Race and income are associated with increased charges. To determine risk factor, we correlate hospital charges for pituitary surgery with socioeconomic factors. Additionally, we identify patients at risk for increased hospital charges and provide insight into cost-effective practices. Methods Retrospective cohort study of the Statewide Planning and Research Cooperative System (SPARCS) database from the NYS Department of Health was conducted. The SPARCS database was reviewed. Patients who underwent transsphenoidal pituitary surgery from 1995 to 2015 were identified. Income and urban status were referenced from U.S. census data. Linear regression was performed to analyze the effect of sociodemographic factors, comorbidities, and complications on hospital charges while controlling for length of stay. Results A total of 9,373 patients were identified. Black (10.8%, p < 0.001) and Asian (14.5%, p < 0.001) had higher hospital charges. Patients from nonurban cities (13.4%, p < 0.001), Medicaid (13.8%, p < 0.001), and those from the 0 to 25th (9.1%, p < 0.001) and 25 to 50th (11.7%, p < 0.001) income quartile had lower hospital charges. Patients with postoperative cerebrospinal fluid leak (24.0%, p < 0.001), diabetes insipidus (22.1%, p < 0.001), smoking history (11.8%, p < 0.001), hypertension (7.4%, p < 0.001), and hypothyroidism (6.9%, p < 0.001) had higher hospital charges. Conclusion Patients incurring higher chargers were more likely to have a smoking history, hypertension, hypothyroidism, and comorbidities. The determinants of this analysis may provide insight into barriers to patient access and cost improvement strategies. In addition, this emphasizes the need for future studies to create a risk stratification model, similar to those in other fields.

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Section: Discussionmentioning

confidence: 99%

Hospital Charge Variability across New York State: Sociodemographic Factors in Pituitary Surgery

et al. 2019

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“…6,11 Endoscopic transphenoidal pituitary surgery is a commonly performed skull base operation and is associated with significant cost variability among patients, hospitals, and regions. [12][13][14] While prior work has examined costeffectiveness of various approaches to management of pituitary adenomas, [14][15][16][17][18][19][20][21][22][23][24] less is known regarding the patient and clinical factors that drive in-patient hospital costs following endoscopic transphenoidal pituitary surgery.…”

Section: Introductionmentioning

confidence: 99%

Drivers of In‐Hospital Costs Following Endoscopic Transphenoidal Pituitary Surgery

et al. 2020

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Objective: To characterize the patient and clinical factors that determine variability in hospital costs following endoscopic transphenoidal pituitary surgery. Methods: All endoscopic transphenoidal pituitary surgeries performed from January 1, 2015, to October 24, 2017, with complete data were evaluated in this retrospective single-institution study. The electronic medical record was reviewed for patient factors, tumor characteristics, and cost variables during each hospital stay. Multivariate linear regression was performed using Stata software. Results: The analysis included 190 patients and average length of stay was 4.71 days.

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“…Healthcare spending in the United States in general, and for TSS specifically, varies widely by geography. 5,6,13,20,23 It is not always the case, however, that higher costs in different geographic regions correspond to better health outcomes. 26 Understanding and reconciling regional differences in practice and referral patterns is thus an important step in reducing wasteful charges for TSS.…”

Section: Geographymentioning

confidence: 99%

“…Some have also advocated for greater transparency of hospital charges, which can help patients make more informed decisions about where they want to receive care, which may in turn introduce a level of competition that incentivizes charge reduction. 20…”

Section: Geographymentioning

confidence: 99%

Machine learning ensemble models predict total charges and drivers of cost for transsphenoidal surgery for pituitary tumor

Muhlestein

Akagi

McManus

et al. 2019

Journal of Neurosurgery

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OBJECTIVEEfficient allocation of resources in the healthcare system enables providers to care for more and needier patients. Identifying drivers of total charges for transsphenoidal surgery (TSS) for pituitary tumors, which are poorly understood, represents an opportunity for neurosurgeons to reduce waste and provide higher-quality care for their patients. In this study the authors used a large, national database to build machine learning (ML) ensembles that directly predict total charges in this patient population. They then interrogated the ensembles to identify variables that predict high charges.METHODSThe authors created a training data set of 15,487 patients who underwent TSS between 2002 and 2011 and were registered in the National Inpatient Sample. Thirty-two ML algorithms were trained to predict total charges from 71 collected variables, and the most predictive algorithms combined to form an ensemble model. The model was internally and externally validated to demonstrate generalizability. Permutation importance and partial dependence analyses were performed to identify the strongest drivers of total charges. Given the overwhelming influence of length of stay (LOS), a second ensemble excluding LOS as a predictor was built to identify additional drivers of total charges.RESULTSAn ensemble model comprising 3 gradient boosted tree classifiers best predicted total charges (root mean square logarithmic error = 0.446; 95% CI 0.439–0.453; holdout = 0.455). LOS was by far the strongest predictor of total charges, increasing total predicted charges by approximately $5000 per day.In the absence of LOS, the strongest predictors of total charges were admission type, hospital region, race, any postoperative complication, and hospital ownership type.CONCLUSIONSML ensembles predict total charges for TSS with good fidelity. The authors identified extended LOS, nonelective admission type, non-Southern hospital region, minority race, postoperative complication, and private investor hospital ownership as drivers of total charges and potential targets for cost-lowering interventions.

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Geographic variation in cost of care for pituitary tumor surgery

Cited by 15 publications

References 33 publications

Hospital Charge Variability across New York State: Sociodemographic Factors in Pituitary Surgery

Hospital Charge Variability across New York State: Sociodemographic Factors in Pituitary Surgery

Drivers of In‐Hospital Costs Following Endoscopic Transphenoidal Pituitary Surgery

Machine learning ensemble models predict total charges and drivers of cost for transsphenoidal surgery for pituitary tumor

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