Predictors of Extended Length of Stay Following Treatment of Unruptured Adult Cerebral Aneurysms: A Study of The National Inpatient Sample

Statistical Analysis of the Factors that Affect Postoperative Length of Hospital Stay after Unruptured Intracranial Aneurysm Treatment in Japan: A 20-year Nationwide Multicenter Study

Treatment strategies for unruptured intracranial aneurysms (UIAs) should be carefully considered with reference to rupture and complication rates. It is also important to minimize the length of hospital stay (LOS) and to ensure a high quality of medical care. In this study, we aim to clarify the factors that affect the LOS of patients treated for UIAs using the Inpatient Clinico-Occupational Database of the Rosai Hospital Group (ICOD-R). This was a nationwide-multicenter study based on ICOD-R data from 2000 to 2019. Patients diagnosed with UIAs who were treated with clipping or coiling were included in the study. Multivariate analysis was performed to identify the factors affecting LOS. LOS was also compared between groups classified by surgical procedure or treatment period. We identified 3294 patients on the database who underwent clipping or coiling of UIAs during the study period. Multivariate analysis revealed hospital admission during the early 2000s and the late 2010s, age, and treating institution to be significantly correlated with LOS (p < 0.05). There was a significant difference between the mean LOS of the clipping group (20.3 days) and the coiling group (9.65 days) (p < 0.001). Compared by treatment period, LOS significantly shortened over time. Our results suggest that the type of treatment, time of treatment, patient age, and the treating institution affect postoperative LOS for UIAs. Although coiling was found to lead to a lower average LOS than clipping, treatment selection should take the characteristics of each patient's aneurysm into consideration.

Preadmission assessment of extended length of hospital stay with RFECV-ETC and hospital-specific data

Background

Patients who exceed their expected length of stay in the hospital come at a cost to stakeholders in the healthcare sector as bed spaces are limited for new patients, nosocomial infections increase and the outcome for many patients is hampered due to multimorbidity after hospitalization.

Objectives

This paper develops a technique for predicting Extended Length of Hospital Stay (ELOHS) at preadmission and their risk factors using hospital data.

Methods

A total of 91,468 records of patient’s hospital information from a private acute teaching hospital were used for developing a machine learning algorithm relaying on Recursive Feature Elimination with Cross-Validation and Extra Tree Classifier (RFECV-ETC). The study implemented Synthetic Minority Oversampling Technique (SMOTE) and tenfold cross-validation to determine the optimal features for predicting ELOHS while relying on multivariate Logistic Regression (LR) for computing the risk factors and the Relative Risk (RR) of ELOHS at a 95% confidence level.

Results

An estimated 11.54% of the patients have ELOHS, which increases with patient age as patients < 18 years, 18–40 years, 40–65 years and ≥ 65 years, respectively, have 2.57%, 4.33%, 8.1%, and 15.18% ELOHS rates. The RFECV-ETC algorithm predicted preadmission ELOHS to an accuracy of 89.3%. Age is a predominant risk factors of ELOHS with patients who are > 90 years—PAG (> 90) {RR: 1.85 (1.34–2.56), P:  < 0.001} having 6.23% and 23.3%, respectively, higher likelihood of ELOHS than patient 80–90 years old—PAG (80–90) {RR: 1.74 (1.34–2.38), P:  < 0.001} and those 70–80 years old—PAG (70–80) {RR: 1.5 (1.1–2.05), P: 0.011}. Those from admission category—ADC (US1) {RR: 3.64 (3.09–4.28, P:  < 0.001} are 14.8% and 70.5%, respectively, more prone to ELOHS compared to ADC (UC1) {RR: 3.17 (2.82–3.55), P:  < 0.001} and ADC (EMG) {RR: 2.11 (1.93–2.31), P:  < 0.001}. Patients from SES (low) {RR: 1.45 (1.24–1.71), P:  < 0.001)} are 13.3% and 45% more susceptible to those from SES (middle) and SES (high). Admission type (ADT) such as AS2, M2, NEWS, S2 and others {RR: 1.37–2.77 (1.25–6.19), P:  < 0.001} also have a high likelihood of contributing to ELOHS while the distance to hospital (DTH) {RR: 0.64–0.75 (0.56–0.82), P:  < 0.001}, Charlson Score (CCI) {RR: 0.31–0.68 (0.22–0.99), P:  < 0.001–0.043} and some VMO specialties {RR: 0.08–0.69 (0.03–0.98), P:  < 0.001–0.035} have limited influence on ELOHS.

Conclusions

Relying on the preadmission assessment of ELOHS helps identify those patients who are susceptible to exceeding their expected length of stay on admission, thus, making it possible to improve patients’ management and outcomes.

Characteristics of Patients Selected for Surgical Treatment of Spinal Meningioma

BACKGROUND

Spinal meningiomas are benign extra-axial tumors that can present with neurological deficits. Treatment partly depends on the degree of disability as there is no agreed-upon patient selection algorithm at present. We aimed to elucidate general patient selection patterns in patients undergoing surgery for spinal meningioma.

METHODS

Data for patients with spinal tumors admitted between 2016 and 2019 were extracted from the U.S. Nationwide Inpatient Sample. We identified patients with a primary diagnosis of spinal meningioma (using International Classification of Disease, 10^th revision codes) and divided them into surgical and nonsurgical treatment groups. Patient characteristics were evaluated for intergroup differences.

RESULTS

Of 6395 patients with spinal meningioma, 5845 (91.4%) underwent surgery. Advanced age, nonwhite race, obesity, diabetes mellitus, chronic renal failure, and anticoagulant/antiplatelet use were less prevalent in the surgical group (all P < 0.001). The only positive predictor of surgical treatment was elective admission status (odds ratio, 3.166; P < 0.001); negative predictors were low income, Medicaid insurance, anxiety, obesity, and plegia. Patients with bowel-bladder dysfunction, plegia, or radiculopathy were less likely to undergo surgical treatment. The surgery group was less likely to experience certain complications (deep vein thrombosis, P < 0.001; pulmonary embolism, P = 0.002). Increased total hospital charges were associated with nonwhite race, diabetes, depression, obesity, myelopathy, plegia, and surgery.

CONCLUSIONS

Patients treated surgically had a decreased incidence of complications, comorbidities, and Medicaid payer status. A pattern of increased utilization of health care resources and spending was also observed in the surgery group. The results indicate a potentially underserved population of patients with spinal meningioma.