The Predictive Value of the HOSPITAL Score and LACE Index for an Adult Neurosurgical Population: A Prospective Analysis

A hybrid approach for risk stratification and predictive modelling of 30-days unplanned readmission of comorbid patients with diabetes

OBJECTIVES

When comorbid patients with diabetes have 30-days Unplanned Readmission (URA), they attract more burdens to the healthcare system due to increased cost of treatment, insurance penalties to hospitals, and unavailable bed spaces for new patients. This paper, therefore, aims to develop a risk stratification and a predictive model for identifying patients at various risk severities of 30-days URA.

METHODS

Patients records of comorbid patients with diabetes treated with different medications were collected from different hospitals and analysed with Principal Component Analysis (PCA) and Multivariate Logistic Regression (MLR) to determine the probability of 30-days URA, which is classified into very low, low, moderate, high, and very high. The risk classes are later modelled using ANOVA feature selection to identify the optimal predictors and the best random forest (RF) hyperparameters for 30-days URA risk stratification. Synthetic Minority Oversampling Technique (SMOTE) was used to balance the risk classes while employing a10-fold cross-validation.

RESULTS

After analysing 17,933 episodes of comorbid diabetes patients' treatment, 10.71% are identified to have 30-days URA with 61.95% of patients at moderate risk, 35.5% at low risk, 2.25% at very low risk, 0.37% at high risk, and 0.08% at very high risk. The predictive accuracy of RF is: - recall: 0.947 ± 0.035, precision: 0.951 ± 0.033, F1-score: 0.947 ± 0.035, AUC: 0.994 ± 0.007 and Average Precision (AP) of 0.99. The predictive accuracies of the risk classes measured with F1-score are: - very low: 0.985 ± 0.019, low risk: 0.871 ± 0.079, moderate: 0.881 ± 0.093, high: 0.999 ± 0.003, and very high: 1.000 ± 0.00.

CONCLUSION

This study identified the risk severity of comorbid patients with diabetes treated with different medications, making it easier to identify those that will be prioritized on hospitalization to minimize 30-days URA. By relying on the technique developed, vulnerable patients to 30-days URA can be given better post-discharge monitoring to build critical self-management skills that will minimize the cost of diabetes care and improve the quality of life.

Enhanced Recovery After Surgery (ERAS) for Cranial Tumor Resection: A Review

Enhanced Recovery After Surgery (ERAS) protocols describe a standardized method of preoperative, perioperative, and postoperative care to enhance outcomes and minimize complication risks surrounding elective surgical intervention. A growing body of evidence is being generated as we learn to apply principles of ERAS standardization to neurosurgical patients. First applied in spinal surgery, ERAS protocols have been extended to cranial neuro-oncological procedures. This review synthesizes recent findings to generate evidence-based guidelines to manage neurosurgical oncology patients with standardized systems and assess ability of these systems to coordinate multidisciplinary, patient-centric care efforts. Furthermore, we highlight the potential utility of multimedia, app-based communication platforms to facilitate patient education, autonomy, and team communication within each of the three settings.

LACE Index to Predict the High Risk of 30-Day Readmission: A Systematic Review and Meta-Analysis

The LACE index accounts for: Length of stay (L), Acuity of admission (A), Comorbidities (C), and recent Emergency department use (E). This study aimed to explore the LACE index to predict the high risk of 30-day readmission in patients with diverse disease conditions by an updated systematic review. A systematic review carried out by electronic databases from 2011−2021. The studies included a LACE index score for 30-day of readmission and patients with all types of diseases and were published in the English language. The meta-analysis was performed by using a random-effects model with a 95% confidence interval. Of 3300 records, a total of 16 studies met the inclusion criteria. The country of publication was primarily the USA (n = 7) and study designs were retrospective and perspective cohorts. The average mean age was 64 years. The C-statistics was 0.55 to 0.81. The pooled random effects of relative risk readmission were overall (RR, 0.20; 95% CI, 0.12−0.34) and it was favorable. The subgroup analysis of the opted disease-based relative risk of readmissions of all causes, cardiovascular and pulmonary diseases, and neurological diseases were consistent and statistically significant at p < 0.001 level. Current evidence of this review suggested that incorporating a high-risk LACE index showed favorable to risk prediction and could be applied to predict 30-day readmission with chronic conditions. Future study would be planned to predict the high risk of 30-day readmission in acute clinical care for utility, and applicability of promising LACE index in South Korean hospitals.

Adverse Events and Bundled Costs after Cranial Neurosurgical Procedures: Validation of the LACE Index Across 40,431 Admissions and Development of the LACE-Cranial Index

OBJECTIVE

Anticipating postdischarge complications after neurosurgery remains difficult. The LACE index, based on 4 hospitalization descriptors, stratifies patients by risk of 30-day postdischarge adverse events but has not been validated in a procedure-specific manner in neurosurgery. Our study sought to explore the usefulness of the LACE index in a population undergoing cranial neurosurgery and to develop an enhanced model, LACE-Cranial.

METHODS

The OptumClinformatics Database was used to identify cranial neurosurgery admissions (2004-2017). Procedures were grouped as trauma/hematoma/intracranial pressure, open vascular, functional/pain, skull base, tumor, or endovascular. Adverse events were defined as postdischarge death/readmission. LACE-Cranial was developed using a logistic regression framework incorporating an expanded feature set in addition to the original LACE components.

RESULTS

A total of 40,431 admissions were included. Predictions of 30-day readmissions was best for skull base (area under the curve [AUC], 0.636) and tumor (AUC, 0.63) admissions but was generally poor. Predictive ability of 30-day mortality was best for functional/pain admissions (AUC, 0.957) and poorest for trauma/hematoma/intracranial pressure admissions (AUC, 0.613). Across procedure types except for functional/pain, a high-risk LACE score was associated with higher postdischarge bundled payment costs. Incorporating features identified to contribute independent predictive value, the LACE-Cranial model achieved procedure-specific 30-day mortality AUCs ranging from 0.904 to 0.98. Prediction of 30-day and 90-day readmissions was also improved, with tumor and skull base cases achieving 90-day readmission AUCs of 0.718 and 0.717, respectively.

CONCLUSIONS

Although the unmodified LACE index shows inconsistent classification performance, the enhanced LACE-Cranial model offers excellent prediction of short-term postdischarge mortality across procedure groups and significantly improved anticipation of short-term postdischarge readmissions.