A systematic literature review of predicting patient discharges using statistical methods and machine learning

Discharge planning is integral to patient flow as delays can lead to hospital-wide congestion. Because a structured discharge plan can reduce hospital length of stay while enhancing patient satisfaction, this topic has caught the interest of many healthcare professionals and researchers. Predicting discharge outcomes, such as destination and time, is crucial in discharge planning by helping healthcare providers anticipate patient needs and resource requirements. This article examines the literature on the prediction of various discharge outcomes. Our review discovered papers that explore the use of prediction models to forecast the time, volume, and destination of discharged patients. Of the 101 reviewed papers, 49.5% looked at the prediction with machine learning tools, and 50.5% focused on prediction with statistical methods. The fact that knowing discharge outcomes in advance affects operational, tactical, medical, and administrative aspects is a frequent theme in the papers studied. Furthermore, conducting system-wide optimization, predicting the time and destination of patients after discharge, and addressing the primary causes of discharge delay in the process are among the recommendations for further research in this field.

A Randomized Controlled Trial to Assess the Impact of Enhanced Recovery After Surgery on Patients Undergoing Elective Spine Surgery

OBJECTIVE

To conduct a prospective, randomized controlled trial (RCT) of an enhanced recovery after surgery (ERAS) protocol in an elective spine surgery population.

BACKGROUND

Surgical outcomes such as length of stay (LOS), discharge disposition, and opioid utilization greatly contribute to patient satisfaction and societal healthcare costs. ERAS protocols are multimodal, patient-centered care pathways shown to reduce postoperative opioid use, reduced LOS, and improved ambulation; however, prospective ERAS data are limited in spine surgery.

METHODS

This single-center, institutional review board-approved, prospective RCT-enrolled adult patients undergoing elective spine surgery between March 2019 and October 2020. Primary outcomes were perioperative and 1-month postoperative opioid use. Patients were randomized to ERAS (n=142) or standard-of-care (SOC; n=142) based on power analyses to detect a difference in postoperative opioid use.

RESULTS

Opioid use during hospitalization and the first postoperative month was not significantly different between groups (ERAS 112.2 vs SOC 117.6 morphine milligram equivalent, P =0.76; ERAS 38.7% vs SOC 39.4%, P =1.00, respectively). However, patients randomized to ERAS were less likely to use opioids at 6 months postoperatively (ERAS 11.4% vs SOC 20.6%, P =0.046) and more likely to be discharged to home after surgery (ERAS 91.5% vs SOC 81.0%, P =0.015).

CONCLUSION

Here, we present a novel ERAS prospective RCT in the elective spine surgery population. Although we do not detect a difference in the primary outcome of short-term opioid use, we observe significantly reduced opioid use at 6-month follow-up as well as an increased likelihood of home disposition after surgery in the ERAS group.

Preoperative medical assessment for adult spinal deformity surgery: a state-of-the-art review

INTRODUCTION

The purpose of this study is to provide a state-of-the-art review regarding risk factors for perioperative complications in adult spinal deformity (ASD) surgery. The review includes levels of evidence for risk factors associated with complications in ASD surgery.

METHODS

Using the PubMed database, we searched for complications, risk factors, and adult spinal deformity. The included publications were assessed for level of evidence as described in clinical practice guidelines published by the North American Spine Society, with summary statements generated for each risk factor (Bono et al. in Spine J 9:1046-1051, 2009).

RESULTS

Frailty had good evidence (Grade A) as a risk for complications in ASD patients. Fair evidence (Grade B) was assigned for bone quality, smoking, hyperglycemia and diabetes, nutritional status, immunosuppression/steroid use, cardiovascular disease, pulmonary disease, and renal disease. Indeterminate evidence (Grade I) was assigned for pre-operative cognitive function, mental health, social support, and opioid utilization.

CONCLUSIONS

Identification of risk factors for perioperative complications in ASD surgery is a priority for empowering informed choices for patients and surgeons and managing patient expectations. Risk factors with grade A and B evidence should be identified prior to elective surgery and modified to reduce the risk of perioperative complications.

Post-operative neurosurgery outcomes by race/ethnicity among enhanced recovery after surgery (ERAS) participants

OBJECTIVE

Prior work reveals that Enhanced Recovery After Surgery (ERAS) programs decrease opioid use, improve mobilization, and shorten length of stay (LOS) among patients undergoing spine surgery. The impact of ERAS on outcomes by race/ethnicity is unknown. This study examined outcomes by race/ethnicity among neurosurgical patients enrolled in an ERAS program.

METHODS

Patients undergoing elective spine or peripheral nerve surgeries at a multi-hospital university health system from April 2017 to November 2020 were enrolled in an ERAS program that involves preoperative, perioperative, and postoperative phases focused on improving outcomes through measures such as specialty consultations for co-morbidities, multimodal analgesia, early mobilization, and wound care education. The following outcomes for ERAS patients were compared by race/ethnicity: length of stay, discharge disposition, complications, readmission, pain level at discharge, and post-operative health rating. We estimated the association between race/ethnicity and the outcomes using linear and logistic regression models adjusting for age, sex, insurance, BMI, comorbid conditions, and surgery type.

RESULTS

Among participants (n = 3449), 2874 (83.3%) were White and 575 (16.7%) were Black, Indigenous, and people of color (BIPOC). BIPOC patients had significantly longer mean length of stay compared to White patients (3.8 vs. 3.4 days, p = 0.005) and were significantly more likely to be discharged to a rehab or subacute nursing facility compared to White patients (adjusted odds ratio (95% CI): 3.01 (2.26-4.01), p < 0.001). The complication rate did not significantly differ between BIPOC and White patients (13.7% vs. 15.5%, p = 0.29). BIPOC patients were not significantly more likely to be readmitted within 30 days compared to White patients in the adjusted model (adjusted odds ratio (95% CI): 1.30 (0.91-1.86), p = 0.15) CONCLUSION: BIPOC as compared to White ERAS participants in ERAS undergoing neurosurgical procedures had significantly longer hospital stays and were significantly less likely to be discharged home. ERAS protocols present an opportunity to provide consistent high quality post-operative care, however while there is evidence that it improves care in aggregate, our results suggest significant disparities in outcomes by patient race/ethnicity despite enrollment in ERAS. Future inquiry must identify contributors to these disparities in the recovery pathway.

Risk factors for extended length of stay and non-home discharge in adults treated with multi-level fusion for lumbar degenerative pathology and deformity

PURPOSE

To identify independent risk factors, including the Risk Assessment and Prediction Tool (RAPT) score, associated with extended length of stay (eLOS) and non-home discharge following elective multi-level instrumented spine fusion operations for diagnosis of adult spinal deformity (ASD) and lumbar degenerative pathology.

METHODS

Adults who underwent multi-level ([Formula: see text] segments) instrumented spine fusions for ASD and lumbar degenerative pathology at a single institution (2016-2021) were reviewed. Presence of a pre-operative RAPT score was used as an inclusion criterion. Excluded were patients who underwent non-elective operations, revisions, operations for trauma, malignancy, and/or infections. Outcomes were eLOS (> 7 days) and discharge location (home vs. non-home). Predictor variables included demographics, comorbidities, operative information, Surgical Invasiveness Index (SII), and RAPT score. Fisher's exact test was used for univariate analysis, and significant variables were implemented in multivariate binary logistic regression, with generation of 95% percent confidence intervals (CI), odds ratios (OR), and p-values.

RESULTS

Included for analysis were 355 patients. Post-operatively, 36.6% (n = 130) had eLOS and 53.2% (n = 189) had a non-home discharge. Risk factors significant for a non-home discharge were older age (> 70 years), SII > 36, pre-op RAPT < 10, DMII, diagnosis of depression or anxiety, and eLOS. Risk factors significant for an eLOS were SII > 20, RAPT < 6, and an ASA score of 3.

CONCLUSION

The RAPT score and SII were most important significant predictors of eLOS and non-home discharges following multi-level instrumented fusions for lumbar spinal pathology and deformity. Preoperative optimization of the RAPT's individual components may provide a useful strategy for decreasing LOS and modifying discharge disposition.

Simple and actionable preoperative prediction of postoperative healthcare needs of single-level lumbar fusion patients

OBJECTIVE

Preoperative prediction of a patient's postoperative healthcare utilization is challenging, and limited guidance currently exists. The objective of the present study was to assess the capability of individual risk-related patient characteristics, which are available preoperatively, that may predict discharge disposition prior to lumbar fusion.

METHODS

In total, 1066 consecutive patients who underwent single-level, posterior-only lumbar fusion at a university health system were enrolled. Patients were prospectively asked 4 nondemographic questions from the Risk Assessment and Prediction Tool during preoperative office visits to evaluate key risk-related characteristics: baseline walking ability, use of a gait assistive device, reliance on community supports (e.g., Meals on Wheels), and availability of a postoperative home caretaker. The primary outcome was discharge disposition (home vs skilled nursing facility/acute rehabilitation). Logistic regression was performed to analyze the ability of each risk-related characteristic to predict likelihood of home discharge.

RESULTS

Regression analysis demonstrated that improved baseline walking ability (OR 3.17), ambulation without a gait assistive device (OR 3.13), and availability of a postoperative home caretaker (OR 1.99) each significantly predicted an increased likelihood of home discharge (all p < 0.0001). However, reliance on community supports did not significantly predict discharge disposition (p = 0.94).

CONCLUSIONS

Patient mobility and the availability of a postoperative caretaker, when determined preoperatively, strongly predict a patient's healthcare utilization in the setting of single-level, posterior lumbar fusion. These findings may help surgeons to streamline preoperative clinic workflow and support the patients at highest risk in a targeted fashion.

RAPT score and preoperative factors to predict discharge location following adult spinal deformity surgery

PURPOSE

To assess factors, including RAPT score, predictive of non-home discharges following adult spinal deformity (ASD) operations.

METHODS

Adults who underwent thoracolumbar instrumented fusions to the pelvis for ASD (1/2019-1/2020) were reviewed. Patient demographics, RAPT metrics, hospital length of stay (LOS), operative details, and complications were compared between patients discharged home and non-home. Univariate and multivariate analyses were performed using logistic regression to determine the relative risk of non-home discharge. Area Under the Receiver Operating Characteristic curve (AUROC) for RAPT score and non-home discharge was also determined.

RESULTS

Ninety-nine patients (average age 68 ± 9 years; female-64; average RAPT 8.6 ± 2.2) were analyzed. Operations had the following characteristics: average # levels fused 11 ± 3, revisions 54%, anterior-posterior 70%, 3-column osteotomies 23%. Average LOS was 8.5 ± 3.6 days. The majority of patients (75.8%) had non-home discharges. Non-home discharges had significantly lower RAPT scores (8.3 vs. 9.6; p = 0.02), more advanced age (70 vs. 63 years; p = 0.01), and higher Charlson Comorbidity Index (CCI) scores (3.6 vs. 2.5; p < 0.01) compared to home discharges. On univariate analysis, factors significantly associated with non-home discharge were older age [relative risk (RR) 1.09, p < 0.01], higher CCI (RR 1.73, p = 0.01), total # levels fused (RR 1.24, p = 0.04), and lower RAPT scores (RR 0.71, p = 0.01). RAPT score < 8 was most predictive of non-home discharge (RR 4.87, p = 0.04). An AUROC relating RAPT scores and non-home discharge was 0.7.

CONCLUSIONS

Non-home discharges after ASD operations are common. Of the four factors associated with non-home discharges (elderly age, higher CCI, total number of levels fused, RAPT score), a RAPT score < 8 was most predictive. The RAPT score holds promising utility for pre-operative patient counseling and discharge planning for adults undergoing operations for spinal deformity.

Association of Patient Frailty With Vestibular Schwannoma Resection Outcomes and Machine Learning Development of a Vestibular Schwannoma Risk Stratification Score

BACKGROUND

Patient frailty is predictive of higher neurosurgical morbidity and mortality. However, existing frailty measures are hindered by lack of specificity to neurosurgery.

OBJECTIVE

To analyze the association between 3 risk stratification scores and outcomes for nationwide vestibular schwannoma (VS) resection admissions and develop a custom VS risk stratification score.

METHODS

We identified all VS resection admissions in the National Inpatient Sample (2002-2017). Three risk stratification scores were analyzed: modified Frailty Index-5, modified Frailty Index-11(mFI-11), and Charlson Comorbidity Index (CCI). Survey-weighted multivariate regression evaluated associations between frailty and inpatient outcomes, adjusting for patient demographics, hospital characteristics, and disease severity. Subsequently, we used k-fold cross validation and Akaike Information Criterion-based model selection to create a custom risk stratification score.

RESULTS

We analyzed 32 465 VS resection admissions. High frailty, as identified by the mFI-11 (odds ratio [OR] = 1.27, P = .021) and CCI (OR = 1.72, P < .001), predicted higher odds of perioperative complications. All 3 scores were also associated with lower routine discharge rates and elevated length of stay (LOS) and costs (all P < .05). Our custom VS-5 score (https://skullbaseresearch.shinyapps.io/vs-5_calculator/) featured 5 variables (age ≥60 years, hydrocephalus, preoperative cranial nerve palsies, diabetes mellitus, and hypertension) and was predictive of higher mortality (OR = 6.40, P = .001), decreased routine hospital discharge (OR = 0.28, P < .001), and elevated complications (OR = 1.59, P < .001), LOS (+48%, P < .001), and costs (+23%, P = .001). The VS-5 outperformed the modified Frailty Index-5, mFI-11, and CCI in predicting routine discharge (all P < .001), including in a pseudoprospective cohort (2018-2019) of 3885 admissions.

CONCLUSION

Patient frailty predicted poorer inpatient outcomes after VS surgery. Our custom VS-5 score outperformed earlier risk stratification scores.

Brain tumor craniotomy outcomes for dual-eligible medicare and medicaid patients: a 10-year nationwide analysis

INTRODUCTION

Dual-eligible (DE) patients, simultaneous Medicare and Medicaid beneficiaries, have been shown to have poorer clinical outcomes while incurring higher resource utilization. However, neurosurgical oncology outcomes for DE patients are poorly characterized. Accordingly, we examined the impact of DE status on perioperative outcomes following glioma, meningioma, or metastasis resection.

METHODS

We identified all admissions undergoing a craniotomy for glioma, meningioma, or metastasis resection in the National Inpatient Sample from 2002 to 2011. Assessed outcomes included inpatient mortality, complications, discharge disposition, length of stay (LOS), and hospital costs. Multivariable regression adjusting for 13 patient, severity, and hospital characteristics assessed the association between DE status and outcomes, relative to four reference insurance groups (Medicare-only, Medicaid-only, private insurance, self-pay).

RESULTS

Of 195,725 total admissions analyzed, 3.0% were dual-eligible beneficiaries (n = 5933). DEs were younger than Medicare admissions (P < 0.001) but older than Medicaid, private, and self-pay admissions (P < 0.001). Relative to other insurance groups, DEs also exhibited higher severity of illness, risk of mortality, and Charlson Comorbidity Index scores as well as treatment at low-volume hospitals (all P < 0.001). DEs had lower mortality than self-pay admissions (odds ratio [OR] 0.47, P = 0.017). Compared to Medicare, Medicaid, private, and self-pay admissions, DEs had lower rates of discharge disposition (OR 0.53, 0.50, 0.34, and 0.27, respectively, all P < 0.001). DEs also had higher complications (OR 1.23 and 1.20, respectively, both P < 0.05) and LOS (β = 1.06 and 1.13, respectively, both P < 0.01) than Medicare and private insurance beneficiaries. Differences in discharge disposition remained significant for all three tumor subtypes, but only glioma DE admissions continued to exhibit higher complications and LOS.

CONCLUSIONS

DEs undergoing definitive craniotomy for brain tumor had higher rates of unfavorable discharge disposition compared to all other insurance groups and, especially for glioma surgery, had higher inpatient complication rates and LOS. Practice and policy reforms to improve outcomes for this vulnerable clinical population are warranted.

Challenges in Predicting Discharge Disposition for Trauma and Emergency General Surgery Patients

BACKGROUND

Changes in discharge disposition and delays in discharge negatively impact the patient and hospital system. Our objectives were¹ to determine the accuracy with which trauma and emergency general surgery (TEGS) providers could predict the discharge disposition for patients and² determine the factors associated with incorrect predictions.

METHODS

Discharge dispositions and barriers to discharge for 200 TEGS patients were predicted individually by members of the multidisciplinary TEGS team within 24 h of patient admission. Univariate analyses and multivariable logistic least absolute shrinkage and selection operator regressions determined the associations between patient characteristics and correct predictions.

RESULTS

A total of 1,498 predictions of discharge disposition were made by the multidisciplinary TEGS team for 200 TEGS patients. Providers correctly predicted 74% of discharge dispositions. Prediction accuracy was not associated with clinical experience or job title. Incorrect predictions were independently associated with older age (OR 0.98; P < 0.001), trauma admission as compared to emergency general surgery (OR 0.33; P < 0.001), higher Injury Severity Scores (OR 0.96; P < 0.001), longer lengths of stay (OR 0.90; P < 0.001), frailty (OR 0.43; P = 0.001), ICU admission (OR 0.54; P < 0.001), and higher Acute Physiology and Chronic Health Evaluation II scores (OR 0.94; P = 0.006).

CONCLUSION

The TEGS team can accurately predict the majority of discharge dispositions. Patients with risk factors for unpredictable dispositions should be flagged to better allocate appropriate resources and more intensively plan their discharges.

Predictive Value of the Risk Assessment and Prediction Tool (RAPT) Score for Primary Hip and Knee Arthroplasty Patients: A Single-Center Study

The Risk Assessment and Prediction Tool (RAPT) was developed to predict patient discharge destination for arthroplasty operations. However, since Enhanced Recovery After Surgery (ERAS) programs have been utilized in the UK, the RAPT score has not been validated for use. The aim of the current study was to evaluate the predictive validity of the RAPT score in an ERAS environment with short length of stay. Data were compiled from 545 patients receiving a primary elective total hip or total knee arthroplasty in a district general hospital over 12 months. RAPT scores, length of stay, and discharge destinations were recorded. Patients were classified as low, intermediate, or high risk as per their RAPT score. Length of stay was significantly different between groups (p = 0.008), with low-risk patients having shorter length of stay. However, RAPT scores did not predict discharge destination; the overall correct prediction was only 31.9%. Furthermore, the most likely discharge destination was directly home in ≤3 days in all groups (68.5%, 60.2%, and 40% for the low-, intermediate-, and high-risk groups, respectively). The RAPT score is not an adequate tool to predict the discharge disposition following primary total knee and hip replacement surgery in a UK hospital with a standardized modern ERAS program. Alternative predictive tools are required.

Predictive Model and Online Calculator for Discharge Disposition in Brain Tumor Patients

BACKGROUND

In the era of value-based payment models, it is imperative for neurosurgeons to eliminate inefficiencies and provide high-quality care. Discharge disposition is a relevant consideration with clinical and economic ramifications in brain tumor patients. We developed a predictive model and online calculator for postoperative non-home discharge disposition in brain tumor patients that can be incorporated into preoperative workflows.

METHODS

We reviewed all brain tumor patients at our institution from 2017 to 2019. A predictive model of discharge disposition containing preoperatively available variables was developed using stepwise multivariable logistic regression. Model performance was assessed using receiver operating characteristic curves and calibration curves. Internal validation was performed using bootstrapping with 2000 samples.

RESULTS

Our cohort included 2335 patients who underwent 2586 surgeries with a 16% non-home discharge rate. Significant predictors of non-home discharge were age >60 years (odds ratio [OR], 2.02), African American (OR, 1.73) or Asian (OR, 2.05) race, unmarried status (OR, 1.48), Medicaid insurance (OR, 1.90), admission from another health care facility (OR, 2.30), higher 5-factor modified frailty index (OR, 1.61 for 5-factor modified frailty index ≥2), and lower Karnofsky Performance Status (increasing OR with each 10-point decrease in Karnofsky Performance Status). The model was well calibrated and had excellent discrimination (optimism-corrected C-statistic, 0.82). An open-access calculator was deployed (https://neurooncsurgery.shinyapps.io/discharge_calc/).

CONCLUSIONS

A strongly performing predictive model and online calculator for non-home discharge disposition in brain tumor patients was developed. With further validation, this tool may facilitate more efficient discharge planning, with consequent improvements in quality and value of care for brain tumor patients.

The Risk Assessment and Prediction Tool Accurately Predicts Discharge Destination After Revision Hip and Knee Arthroplasty

BACKGROUND

The Risk Assessment and Prediction Tool (RAPT) was developed and validated to predict discharge disposition after primary total hip and knee arthroplasty (THA/TKA). To date, there are no studies evaluating the applicability and accuracy of RAPT for revision THA/TKA. This study aims to determine the predictive accuracy of the RAPT for revision THA/TKA.

METHODS

Prospectively collected data from a single tertiary academic medical center were retrospectively analyzed for patients undergoing revision THA/TKA between January 2016 and July 2019. RAPT score was used to predict their postoperative discharge destination and its predictive accuracy was calculated. Patient risk (low, intermediate, and high) for postoperative inpatient rehabilitation facilities or skilled nursing facilities were determined based on the predictive accuracy of each RAPT score. Other factors evaluated included patient-reported discharge expectation, body mass index, and American Society of Anesthesiologists scores.

RESULTS

A total of 716 consecutive revision THA/TKA episodes were analyzed. Overall, predictive accuracy of RAPT for discharge disposition was 83%. RAPT scores <3 and >8 were deemed high and low risk of discharge to a post-acute care facility, respectively. RAPT scores of 4 to 7 were still accurate 65%-71% of the time and were deemed to be intermediate-risk. RAPT score and patient-reported discharge expectation had the strongest correlation with actual discharge disposition.

CONCLUSION

The RAPT has high predictive accuracy for discharge planning in revision THA/TKA patients. Patient-expected discharge destination is a powerful modulator of the RAPT score and we suggest that it be taken into consideration for preoperative discharge planning.