Early unplanned readmission of neurosurgical patients after treatment of intracranial lesions: a comparison between surgical and non-surgical intervention group

Machine learning prediction of postoperative unplanned 30-day hospital readmission in older adult

Background: Although unplanned hospital readmission is an important indicator for monitoring the perioperative quality of hospital care, few published studies of hospital readmission have focused on surgical patient populations, especially in the elderly. We aimed to investigate if machine learning approaches can be used to predict postoperative unplanned 30-day hospital readmission in old surgical patients.

Methods: We extracted demographic, comorbidity, laboratory, surgical, and medication data of elderly patients older than 65 who underwent surgeries under general anesthesia in West China Hospital, Sichuan University from July 2019 to February 2021. Different machine learning approaches were performed to evaluate whether unplanned 30-day hospital readmission can be predicted. Model performance was assessed using the following metrics: AUC, accuracy, precision, recall, and F1 score. Calibration of predictions was performed using Brier Score. A feature ablation analysis was performed, and the change in AUC with the removal of each feature was then assessed to determine feature importance.

Results: A total of 10,535 unique surgeries and 10,358 unique surgical elderly patients were included. The overall 30-day unplanned readmission rate was 3.36%. The AUCs of the six machine learning algorithms predicting postoperative 30-day unplanned readmission ranged from 0.6865 to 0.8654. The RF + XGBoost algorithm overall performed the best with an AUC of 0.8654 (95% CI, 0.8484–0.8824), accuracy of 0.9868 (95% CI, 0.9834–0.9902), precision of 0.3960 (95% CI, 0.3854–0.4066), recall of 0.3184 (95% CI, 0.259–0.3778), and F1 score of 0.4909 (95% CI, 0.3907–0.5911). The Brier scores of the six machine learning algorithms predicting postoperative 30-day unplanned readmission ranged from 0.3721 to 0.0464, with RF + XGBoost showing the best calibration capability. The most five important features of RF + XGBoost were operation duration, white blood cell count, BMI, total bilirubin concentration, and blood glucose concentration.

Conclusion: Machine learning algorithms can accurately predict postoperative unplanned 30-day readmission in elderly surgical patients.

The Relation of Surgical Procedures and Diagnosis Groups to Unplanned Readmission in Spinal Neurosurgery: A Retrospective Single Center Study

Background: Unplanned readmission has gained increasing interest as a quality marker for inpatient care, as it is associated with patient mortality and higher economic costs. Spinal neurosurgery is characterized by a lack of epidemiologic readmission data. The aim of this study was to identify causes and predictors for unplanned readmissions related to index diagnoses and surgical procedures. Methods: In this study, from 2015 to 2017, spinal neurosurgical procedures were recorded for surgical and non-surgical treated patients. The main reasons for an unplanned readmission within 30 days following discharge were identified. Multivariate logarithmic regression revealed predictors of unplanned readmission. Results: A total of 1172 patient records were examined, of which 4.27% disclosed unplanned readmissions. Among the surgical patients, the readmission rate was 4.06%, mainly attributable to surgical site infections, while it was 5.06% for the non-surgical patients, attributable to uncontrolled pain. A night-time surgery presented as the independent predictive factor. Conclusion: In the heterogeneous group of spinal neurosurgical patients, stratification into diagnostic groups is necessary for statistical analysis. Degenerative lumbar spinal stenosis and spinal abscesses are mainly affected by unplanned readmission. The surgical procedure dorsal root ganglion stimulation is an independent predictor of unplanned re-hospitalizations, as is the timing of surgery.

Neurosurgical Care during the COVID-19 Pandemic in Central Germany: A Retrospective Single Center Study of the Second Wave

The healthcare system has been placed under an enormous burden by the SARS-CoV-2 (COVID-19) pandemic. In addition to the challenge of providing sufficient care for COVID-19 patients, there is also a need to ensure adequate care for non-COVID-19 patients. We investigated neurosurgical care in a university hospital during the pandemic. We examined the second wave of the pandemic from 1 October 2020 to 15 March 2021 in this retrospective single-center study and compared it to a pre-pandemic period from 1 October 2019 to 15 March 2020. Any neurosurgical intervention, along with patient- and treatment-dependent factors, were recorded. We also examined perioperative complications and unplanned readmissions. A statistical comparison of the study groups was performed. We treated 535 patients with a total of 602 neurosurgical surgeries during the pandemic. This compares to 602 patients with 717 surgeries during the pre-pandemic period. There were 67 fewer patients (reduction to 88.87%) admitted and 115 fewer surgeries (reduction to 83.96%) performed, which were essentially highly elective procedures, such as cervical spinal stenosis, intracranial neurinomas, and peripheral nerve lesions. Regarding complication rates and unplanned readmissions, there was no significant difference between the COVID-19 pandemic and the non-pandemic patient group. Operative capacities were slightly reduced to 88% due to the pandemic. Nevertheless, comprehensive emergency and elective care was guaranteed in our university hospital. This speaks for the sufficient resources and high-quality processes that existed even before the pandemic.

Causes and Predictors of Unplanned Readmission in Cranial Neurosurgery

OBJECTIVE

A better understanding of the risks and reasons for unplanned readmission is an essential component in reducing costs in the health care system and in optimizing patient safety and satisfaction. The reasons for unplanned readmission vary between different disciplines and procedures. The aim of this study was to identify reasons for readmission in view of different diagnoses in cranial neurosurgery.

METHODS

In this single-center retrospective study, adult patients after neurosurgical treatment were analyzed and grouped according to the indication based on International Classification of Diseases and Related Health Problems, Tenth Revision, German Modification diagnosis codes. The main outcome measure was unplanned readmission within 30 days of discharge. Further logistic regression models were performed to identify factors associated with unplanned rehospitalization.

RESULTS

Of the 2474 patients analyzed, 183 underwent unplanned rehospitalization. Readmission rates differed between the diagnosis groups, with 9.19% in neoplasm, 8.26% in hydrocephalus, 5.76% in vascular, 6.13% after trauma, and 8.05% in the functional group. Several causes were considered to be preventable, such as wound healing disorders, seizures, or social reasons. Younger age, length of first stay, surgical treatment, and side diagnoses were predictors for unplanned readmission. Diagnoses with an increased risk of readmission were glioblastoma, traumatic subdural hematoma, or chronic subdural hematoma.

CONCLUSIONS

Reasons and predictors for an unplanned readmission differ considerably among the index diagnosis groups. In addition to well-known reasons for readmission, we identified social indication, meaning a lack of home care, which is particularly prevalent in oncologic and elderly patients. A transitional care program could benefit these vulnerable patients.