Derivation and Validation of a Model to Predict 30-Day Readmission in Surgical Patients Discharged to Skilled Nursing Facility

Using Machine Learning Models to Identify Factors Associated With 30-Day Readmissions After Posterior Cervical Fusions: A Longitudinal Cohort Study

OBJECTIVE

Readmission rates after posterior cervical fusion (PCF) significantly impact patients and healthcare, with complication rates at 15%-25% and up to 12% 90-day readmission rates. In this study, we aim to test whether machine learning (ML) models that capture interfactorial interactions outperform traditional logistic regression (LR) in identifying readmission-associated factors.

METHODS

The Optum Clinformatics Data Mart database was used to identify patients who underwent PCF between 2004-2017. To determine factors associated with 30-day readmissions, 5 ML models were generated and evaluated, including a multivariate LR (MLR) model. Then, the best-performing model, Gradient Boosting Machine (GBM), was compared to the LACE (Length patient stay in the hospital, Acuity of admission of patient in the hospital, Comorbidity, and Emergency visit) index regarding potential cost savings from algorithm implementation.

RESULTS

This study included 4,130 patients, 874 of which were readmitted within 30 days. When analyzed and scaled, we found that patient discharge status, comorbidities, and number of procedure codes were factors that influenced MLR, while patient discharge status, billed admission charge, and length of stay influenced the GBM model. The GBM model significantly outperformed MLR in predicting unplanned readmissions (mean area under the receiver operating characteristic curve, 0.846 vs. 0.829; p < 0.001), while also projecting an average cost savings of 50% more than the LACE index.

CONCLUSION

Five models (GBM, XGBoost [extreme gradient boosting], RF [random forest], LASSO [least absolute shrinkage and selection operator], and MLR) were evaluated, among which, the GBM model exhibited superior predictive performance, robustness, and accuracy. Factors associated with readmissions impact LR and GBM models differently, suggesting that these models can be used complementarily. When analyzing PCF procedures, the GBM model resulted in greater predictive performance and was associated with higher theoretical cost savings for readmissions associated with PCF complications.

Identification of Factors Associated With 30-day Readmissions After Posterior Lumbar Fusion Using Machine Learning and Traditional Models: A National Longitudinal Database Study

STUDY DESIGN

A retrospective cohort study.

OBJECTIVE

To identify the factors associated with readmissions after PLF using machine learning and logistic regression (LR) models.

SUMMARY OF BACKGROUND DATA

Readmissions after posterior lumbar fusion (PLF) place significant health and financial burden on the patient and overall health care system.

MATERIALS AND METHODS

The Optum Clinformatics Data Mart database was used to identify patients who underwent posterior lumbar laminectomy, fusion, and instrumentation between 2004 and 2017. Four machine learning models and a multivariable LR model were used to assess factors most closely associated with 30-day readmission. These models were also evaluated in terms of ability to predict unplanned 30-day readmissions. The top-performing model (Gradient Boosting Machine; GBM) was then compared with the validated LACE index in terms of potential cost savings associated with the implementation of the model.

RESULTS

A total of 18,981 patients were included, of which 3080 (16.2%) were readmitted within 30 days of initial admission. Discharge status, prior admission, and geographic division were most influential for the LR model, whereas discharge status, length of stay, and prior admissions had the greatest relevance for the GBM model. GBM outperformed LR in predicting unplanned 30-day readmission (mean area under the receiver operating characteristic curve 0.865 vs. 0.850, P <0.0001). The use of GBM also achieved a projected 80% decrease in readmission-associated costs relative to those achieved by the LACE index model.

CONCLUSIONS

The factors associated with readmission vary in terms of predictive influence based on standard LR and machine learning models used, highlighting the complementary roles these models have in identifying relevant factors for the prediction of 30-day readmissions. For PLF procedures, GBM yielded the greatest predictive ability and associated cost savings for readmission.

LEVEL OF EVIDENCE

3.

Geriatric surgical service interventions in older emergency general surgery patients: Preliminary results

BACKGROUND

Older adults comprise an increasing proportion of emergency general surgery (EGS) admissions and face high morbidity and mortality. We created a geriatric surgical service with geriatric and palliative expertise to mitigate risks of hospitalization most hazardous to older patients. We sought to identify geriatric surgical service interventions most relevant to EGS patients.

METHODS

We prospectively identified patients ≥75 years admitted to the EGS service at an urban tertiary care hospital from January 2020-March 2021 who screened positive for frailty (FRAIL score ≥3 [scale 0-5, higher being worse]) or with cognitive impairment. A pilot geriatric surgical service, led by a dually-board certified geriatric and palliative care specialist, conducted a comprehensive geriatric assessment and modified Rockwood Frailty Index calculation for each eligible patient. Patient, hospital admission, and geriatric consultation characteristics were collected via chart review.

RESULTS

Fifty consecutive patients (median age 82 years [IQR 78-90], 56% female) received geriatric consultation (median time 3 days [IQR 1-6] from admission). The most common admission diagnosis was bowel obstruction (32%). Sixty-four percent of patients underwent ≥1 surgical procedure. Using the Frailty Index, 64% were moderately or severely frail. Interventions most frequently performed by the geriatric team included delirium prevention and management (66%), consideration of swallowing function (52%), individualized pain management (50%), and facilitation of serious illness conversations (58%).

CONCLUSIONS

Geriatric service involvement addresses a high burden of both geriatric and palliative care needs in older EGS patients. Geriatric recommendations may direct interventions for surgical education in fundamental geriatric and palliative care knowledge to maximize geriatric resources for the most high-risk patients.

Data Science Methods for Nursing-Relevant Patient Outcomes and Clinical Processes: The 2019 Literature Year in Review

Data science continues to be recognized and used within healthcare due to the increased availability of large data sets and advanced analytics. It can be challenging for nurse leaders to remain apprised of this rapidly changing landscape. In this article, we describe our findings from a scoping literature review of papers published in 2019 that use data science to explore, explain, and/or predict 15 phenomena of interest to nurses. Fourteen of the 15 phenomena were associated with at least one paper published in 2019. We identified the use of many contemporary data science methods (eg, natural language processing, neural networks) for many of the outcomes. We found many studies exploring Readmissions and Pressure Injuries. The topics of Artificial Intelligence/Machine Learning Acceptance, Burnout, Patient Safety, and Unit Culture were poorly represented. We hope that the studies described in this article help readers: (1) understand the breadth and depth of data science's ability to improve clinical processes and patient outcomes that are relevant to nurses and (2) identify gaps in the literature that are in need of exploration.