Length of hospital stay after craniotomy for tumor: a National Surgical Quality Improvement Program analysis

Dasenbrock, Hormuzdiyar H.; Liu, Kevin X.; Devine, Christopher A.; Chavakula, Vamsidhar; Smith, Timothy R.; Gormley, William B.; Dunn, Ian F.

doi:10.3171/2015.10.focus15386

Cited by 146 publications

(124 citation statements)

References 69 publications

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“…Based on previous studies of LOS in the CFBT population, the primary outcome for the LOS model was defined as greater than 7 inpatient days versus less than or equal to 7 inpatient days. 6 According to this definition, patients with extended LOS made up approximately one third of the CFBT population.…”

Section: Methodsmentioning

confidence: 99%

“…Previous work has demonstrated the existence of racial disparities for these outcomes following CFBT – Curry and colleagues showed that black patients have more non-home discharge after CFBT, 5 while Dasenbrock et al saw that black or Hispanic patients were more likely to have extended LOS (>7 days). 6 We expand on this work in three important ways: (1) we use a novel guided ML ensemble technique to validate findings ascertained using more traditional statistical methods; (2) we compare findings in the NIS CFBT population to the NIS population as a whole to determine whether our findings are system-wide or specific to the CFBT population; and (3) we explore the interplay between discharge disposition and LOS.…”

Section: Introductionmentioning

confidence: 99%

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The Impact of Race on Discharge Disposition and Length of Hospitalization After Craniotomy for Brain Tumor

et al. 2017

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Background Racial disparities exist in healthcare, frequently resulting in unfavorable outcomes for minority patients. Here, we use guided machine learning (ML) ensembles to model the impact of race on discharge disposition and length of stay (LOS) following brain tumor surgery from the HCUP National Inpatient Sample (NIS). Methods We performed a retrospective cohort study of 41,222 patients who underwent craniotomies for brain tumors from 2002–2011 and were registered in the NIS. 26 ML algorithms were trained on pre-hospitalization variables to predict non-home discharge and extended LOS (>7 days) following brain tumor resection, and the most predictive algorithms combined to create ensemble models. Partial dependence analysis was performed to measure the independent impact of race on the ensembles. Results The guided ML ensembles predicted non-home disposition (AUC = .796) and extended LOS (AUC = .824) with good discrimination. Partial dependence analysis demonstrated that black race increases the risk of non-home discharge and extended LOS over white race by 6.9% and 6.5%, respectively. Other, non-black race increases the risk of extended LOS over white race by 6.0%. The impact of race on these outcomes is not seen when analyzing the general inpatient or general operative population. Conclusions Minority race independently increases the risk of extended LOS and black race increases the risk of non-home discharge in patients undergoing brain tumor resection, a finding not mimicked in the general inpatient or operative population. Recognition of the influence of race on discharge and LOS could generate interventions that may improve outcomes in this population.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Impact of Race on Discharge Disposition and Length of Hospitalization After Craniotomy for Brain Tumor

et al. 2017

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“…Patients enrolled in the NSQIP are followed for 30 days postoperatively, and all complications (among those collected in the NSQIP algorithm) that occurred during this period are recorded; there is no administrative censoring or loss to follow-up of patients entered. The accuracy of data collection by the NSQIP (including cumulative incidence and reasons for readmission) has been externally validated, 84 and this data source has been previously used to evaluate patients undergoing neurosurgical intervention, 4,8,12,14,15,28,29,45,54,83 including spine surgery. 2,6,9,10,25,40,53,59,61,82,91 Our institutional review board has exempted the de-identified NSQIP data set from individual review.…”

Section: Data Sourcementioning

confidence: 99%

Thirty-day readmission and reoperation after surgery for spinal tumors: a National Surgical Quality Improvement Program analysis

Karhade

Vasudeva

Dasenbrock

et al. 2016

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OBJECTIVE The goal of this study was to use a large national registry to evaluate the 30-day cumulative incidence and predictors of adverse events, readmissions, and reoperations after surgery for primary and secondary spinal tumors. METHODS Data from adult patients who underwent surgery for spinal tumors (2011–2014) were extracted from the prospective National Surgical Quality Improvement Program (NSQIP) registry. Multivariable logistic regression was used to evaluate predictors of reoperation, readmission, and major complications (death, neurological, cardiopulmonary, venous thromboembolism [VTE], surgical site infection [SSI], and sepsis). Variables screened included patient age, sex, tumor location, American Society of Anesthesiologists (ASA) physical classification, preoperative functional status, comorbidities, preoperative laboratory values, case urgency, and operative time. Additional variables that were evaluated when analyzing readmission included complications during the surgical hospitalization, hospital length of stay (LOS), and discharge disposition. RESULTS Among the 2207 patients evaluated, 51.4% had extradural tumors, 36.4% had intradural extramedullary tumors, and 12.3% had intramedullary tumors. By spinal level, 20.7% were cervical lesions, 47.4% were thoracic lesions, 29.1% were lumbar lesions, and 2.8% were sacral lesions. Readmission occurred in 10.2% of patients at a median of 18 days (interquartile range [IQR] 12–23 days); the most common reasons for readmission were SSIs (23.7%), systemic infections (17.8%), VTE (12.7%), and CNS complications (11.9%). Predictors of readmission were comorbidities (dyspnea, hypertension, and anemia), disseminated cancer, preoperative steroid use, and an extended hospitalization. Reoperation occurred in 5.3% of patients at a median of 13 days (IQR 8–20 days) postoperatively and was associated with preoperative steroid use and ASA Class 4–5 designation. Major complications occurred in 14.4% of patients: the most common complications and their median time to occurrence were VTE (4.5%) at 9 days (IQR 4–19 days) postoperatively, SSIs (3.6%) at 18 days (IQR 14–25 days), and sepsis (2.9%) at 13 days (IQR 7–21 days). Predictors of major complications included dependent functional status, emergency case status, male sex, comorbidities (dyspnea, bleeding disorders, preoperative systemic inflammatory response syndrome, preoperative leukocytosis), and ASA Class 3–5 designation (p < 0.05). The median hospital LOS was 5 days (IQR 3–9 days), the 30-day mortality rate was 3.3%, and the median time to death was 20 days (IQR 12.5–26 days). CONCLUSIONS In this NSQIP analysis, 10.2% of patients undergoing surgery for spinal tumors were readmitted within 30 days, 5.3% underwent a reoperation, and 14.4% experienced a major complication. The most common complications were SSIs, systemic infections, and VTE, which often occurred late (after discharge from the surgical hospitalization). Patients were primarily readmitted for new complications that developed following discharge rather than exacerbation of complications from the surgical hospital stay. The strongest predictors of adverse events were comorbidities, preoperative steroid use, and higher ASA classification. These models can be used by surgeons to risk-stratify patients preoperatively and identify those who may benefit from increased surveillance following hospital discharge.

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“…In the absence of traditional high-level medical evidence (i.e., randomized controlled trials) for outcomes after many neurosurgical procedures, analysis of large prospective registries is becoming increasingly important in determining the exact risks and benefits of surgical intervention [16,18,20,22].…”

Section: Discussionmentioning

confidence: 99%

“…The data are collected by trained surgical reviewers employed at each participating site. The NSQIP dataset has been previously validated [18], and has been used to evaluate outcomes for a variety of neurosurgical procedures [15,16,[18][19][20][21][22][23][24][25][26].…”

Section: Data Sourcementioning

confidence: 99%

Risk factors for post-operative respiratory failure among 94,621 neurosurgical patients from 2006 to 2013: a NSQIP analysis

et al. 2016

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Length of hospital stay after craniotomy for tumor: a National Surgical Quality Improvement Program analysis

Cited by 146 publications

References 69 publications

The Impact of Race on Discharge Disposition and Length of Hospitalization After Craniotomy for Brain Tumor

The Impact of Race on Discharge Disposition and Length of Hospitalization After Craniotomy for Brain Tumor

Thirty-day readmission and reoperation after surgery for spinal tumors: a National Surgical Quality Improvement Program analysis

Risk factors for post-operative respiratory failure among 94,621 neurosurgical patients from 2006 to 2013: a NSQIP analysis

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