Predictors of 90-Day Readmission in Children Undergoing Spinal Cord Tumor Surgery: A Nationwide Readmissions Database Analysis

Applicability of predictive models for 30-day unplanned hospital readmission risk in paediatrics: a systematic review

OBJECTIVES

To summarise multivariable predictive models for 30-day unplanned hospital readmissions (UHRs) in paediatrics, describe their performance and completeness in reporting, and determine their potential for application in practice.

DESIGN

Systematic review.

DATA SOURCE

CINAHL, Embase and PubMed up to 7 October 2021.

ELIGIBILITY CRITERIA

English or German language studies aiming to develop or validate a multivariable predictive model for 30-day paediatric UHRs related to all-cause, surgical conditions or general medical conditions were included.

DATA EXTRACTION AND SYNTHESIS

Study characteristics, risk factors significant for predicting readmissions and information about performance measures (eg, c-statistic) were extracted. Reporting quality was addressed by the 'Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis' (TRIPOD) adherence form. The study quality was assessed by applying six domains of potential biases. Due to expected heterogeneity among the studies, the data were qualitatively synthesised.

RESULTS

Based on 28 studies, 37 predictive models were identified, which could potentially be used for determining individual 30-day UHR risk in paediatrics. The number of study participants ranged from 190 children to 1.4 million encounters. The two most common significant risk factors were comorbidity and (postoperative) length of stay. 23 models showed a c-statistic above 0.7 and are primarily applicable at discharge. The median TRIPOD adherence of the models was 59% (P₂₅-P₇₅, 55%-69%), ranging from a minimum of 33% to a maximum of 81%. Overall, the quality of many studies was moderate to low in all six domains.

CONCLUSION

Predictive models may be useful in identifying paediatric patients at increased risk of readmission. To support the application of predictive models, more attention should be placed on completeness in reporting, particularly for those items that may be relevant for implementation in practice.

Analysis of Short-Term versus Long-Term Readmission-Free Survival After Metastatic Spine Tumor Surgery

BACKGROUND

Readmission-free survival (ReAFS) is a novel clinical and quality metric after metastatic spine tumor surgery (MSTS). We believe that factors influencing ReAFS after index MSTS vary based on time. We considered 2 time frames and defined short-term ReAFS as survival without an unplanned hospital readmission up to 90 days and long-term ReAFS as survival without unplanned hospital readmission up to 1 year after MSTS.

METHODS

We retrospectively analyzed 266 patients who underwent MSTS between 2005 and 2016. All relevant oncologic, surgical and follow-up data were collected. Multivariate logistic regression analysis was used to analyze prognostic factors associated with higher probability of short-term ReAFS and long-term ReAFS.

RESULTS

Multivariate analysis showed that Eastern Cooperative Oncology Group score ≤2 (P = 0.011), preoperative hemoglobin (Hb) level >12 g/dL (P = 0.008), ≤3 comorbidities (P = 0.052), shorter index length of stay ≤10 days (P = 0.007), and absence of neurologic/hematologic complications during index stay (P = 0.015) significantly increased the probability of short-term ReAFS, whereas preoperative Hb level >12 g/dL (P = 0.003) or tumor primaries with advanced treatment modalities such as breast (P = 0.012), hematologic (P = 0.006), prostate (P = 0.004), and renal/thyroid (P = 0.038) as opposed to aggressive lung tumor primaries were associated with significantly higher probability of long-term ReAFS.

CONCLUSIONS

Patient and treatment factors predominantly influence ReAFS up to 90 days, whereas primary tumor-related factors alongside general health influence ReAFS beyond 90 days after index MSTS. Awareness of these factors may help oncologists and surgeons optimize treatment planning. The clinical significance of this study will continue to evolve, because we have been witnessing over the past decade that patients are becoming more involved in both their general health and understanding the natural history of the diseases that affect them.

Evaluating the utility and quality of large administrative databases in pediatric spinal neurosurgery research

PURPOSE

The purpose of this study was to assess the quality of articles utilizing large administrative databases to answer questions related to pediatric spinal neurosurgery by quantifying their adherence to standard reporting guidelines.

METHODS

A systematic literature search was conducted with search terms including "pediatric" and "neurosurgery," associated neurosurgical diagnoses, and the names of known databases. Study abstracts were reviewed to identify clinical studies involving pediatric populations, spine-related pathology or procedures, and large administrative databases. Included studies were graded using the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) criteria.

RESULTS

A total of 28 papers of the initial 1496 identified met inclusion criteria. These papers involved 10 databases and had a mean study period of 11.46 ± 12.27 years. The subjects of these research papers were undergoing treatment of scoliosis (n = 5), spinal cord injury (n = 5), spinal cord tumors (n = 9), and spine surgery in general (n = 9). The mean STROBE score was 19.41 ± 2.02 (out of 22).

CONCLUSION

Large administrative databases are commonly used within pediatric spine-related neurosurgical research to cover a broad spectrum of research questions and study topics. The heterogeneity of research to this point encourages the continued use of large databases to better understand treatment and diagnostic trends, perioperative and long-term outcomes, and rare pathologies within pediatric spinal neurosurgery.

Using machine learning to predict paediatric 30-day unplanned hospital readmissions: a case-control retrospective analysis of medical records, including written discharge documentation

Objectives To assess whether adding clinical information and written discharge documentation variables improves prediction of paediatric 30-day same-hospital unplanned readmission compared with predictions based on administrative information alone. Methods A retrospective matched case-control study audited the medical records of patients discharged from a tertiary paediatric hospital in Western Australia (WA) between January 2010 and December 2014. A random selection of 470 patients with unplanned readmissions (out of 3330) were matched to 470 patients without readmissions based on age, sex, and principal diagnosis at the index admission. Prediction utility of three groups of variables (administrative, administrative and clinical, and administrative, clinical and written discharge documentation) were assessed using standard logistic regression and machine learning. Results Inclusion of written discharge documentation variables significantly improved prediction of readmission compared with models that used only administrative and/or clinical variables in standard logistic regression analysis (χ2 17=29.4, P=0.03). Highest prediction accuracy was obtained using a gradient boosted tree model (C-statistic=0.654), followed closely by random forest and elastic net modelling approaches. Variables highlighted as important for prediction included patients' social history (legal custody or patient was under the care of the Department for Child Protection), languages spoken other than English, completeness of nursing admission and discharge planning documentation, and timing of issuing discharge summary. Conclusions The variables of significant social history, low English language proficiency, incomplete discharge documentation, and delay in issuing the discharge summary add value to prediction models. What is known about the topic? Despite written discharge documentation playing a critical role in the continuity of care for paediatric patients, limited research has examined its association with, and ability to predict, unplanned hospital readmissions. Machine learning approaches have been applied to various health conditions and demonstrated improved predictive accuracy. However, few published studies have used machine learning to predict paediatric readmissions. What does this paper add? This paper presents the findings of the first known study in Australia to assess and report that written discharge documentation and clinical information improves unplanned rehospitalisation prediction accuracy in a paediatric cohort compared with administrative data alone. It is also the first known published study to use machine learning for the prediction of paediatric same-hospital unplanned readmission in Australia. The results show improved predictive performance of the machine learning approach compared with standard logistic regression. What are the implications for practitioners? The identified social and written discharge documentation predictors could be translated into clinical practice through improved discharge planning and processes, to prevent paediatric 30-day all-cause same-hospital unplanned readmission. The predictors identified in this study include significant social history, low English language proficiency, incomplete discharge documentation, and delay in issuing the discharge summary.

Thirty- and 90-day Readmissions After Spinal Surgery for Spine Metastases: A National Trend Analysis of 4423 Patients

STUDY DESIGN

Retrospective cohort study.

OBJECTIVE

The aim of this study was to investigate differences in 30- and 90-day readmissions for spine metastases treated with decompression and/or fusion spine surgery in a nationwide readmission database.

SUMMARY OF BACKGROUND DATA

Patients with metastases to the spine represent a particularly vulnerable patient group that may encounter frequent readmissions. However, the 30- and 90-day rates for readmission following surgery for spine metastases have not been well described.

METHODS

The Nationwide Readmission Database years 2013 to 2015 was queried. Patients were grouped by no readmission (non-R), readmission within 30 days (30-R), and readmission within 31 to 90 days (90-R). Weighted multivariate analysis assessed impact of treatment approach and clinical factors associated with 30- and 90-day readmissions.

RESULTS

There were a total of 4423 patients with a diagnosis of spine metastases identified who underwent spine surgery, of which 1657 (37.5%) encountered either a 30-or 90-day unplanned readmission (30-R: n = 1068 [24-.1%]; 90-R: n = 589 [13.3%]; non-R: n = 2766). The most prevalent inpatient complications observed were postoperative infection (30-R: 16.3%, 90-R: 14.3%, non-R: 11.5%), acute post-hemorrhagic anemia (30-R: 13.4%, 90-R: 14.2%, non-R: 14.5%), and genitourinary complication (30-R: 5.7%, 90-R: 2.9%, non-R: 6.2%). The most prevalent 30-day and 90-day reasons for admission were sepsis (30-R: 10.2%, 90-R: 10.8%), postoperative infection (30-R: 13.7%, 90-R: 6.5%), and genitourinary complication (30-R: 3.9%, 90-R: 4.1%). On multivariate regression analysis, surgery type, age, hypertension, and renal failure were independently associated with 30-day readmission; rheumatoid arthritis/collagen vascular diseases, and coagulopathy were independently associated with 90-day readmission.

CONCLUSION

In this study, we demonstrate several patient-level factors independently associated with unplanned hospital readmissions after surgical treatment intervention for spine metastases. Furthermore, we find that the most common reasons for readmission are sepsis, postoperative infection, and genitourinary complications.Level of Evidence: 3.

Predictors of 30-day readmission in Stevens-Johnson syndrome and toxic epidermal necrolysis: A cross-sectional database study

BACKGROUND

The predictors of readmission in Stevens-Johnson syndrome/toxic epidermal necrolysis (SJS/TEN) have not been characterized.

OBJECTIVE

To determine the variables predictive of 30-day readmission after SJS/TEN hospitalization.

METHODS

We performed a cross-sectional study of the 2010-2014 Nationwide Readmissions Database. Bivariate and multivariable logistic regression was used to evaluate associations of patient demographics, comorbidities, and hospital characteristics with readmission. Aggregate and per-readmission costs were calculated.

RESULTS

There were 8837 index admissions with SJS/TEN reported; of these, 910 (10.3%) were readmitted, with diagnoses including systemic infection (22.0%), SJS/TEN (20.6%), and cutaneous infection (9.1%). Associated characteristics included age 45 to 64 years (odds ratio [OR], 1.88; 95% confidence interval [CI], 1.43-2.49), Medicaid insurance (OR, 1.83; 95% CI, 1.48-2.27), and nonmetropolitan hospital admission (OR, 1.67; 95% CI, 1.31-2.13). Associated comorbidities included HIV/AIDS (OR, 2.48; 95% CI, 1.63-3.75), collagen vascular disease (OR, 2.38; 95% CI, 1.88-3.00), and metastatic cancer (OR, 2.16; 95% CI, 1.35-3.46). The median per-readmission cost was $10,019 (interquartile range, $4,788-$16,485).

LIMITATIONS

The Nationwide Readmissions Database lacks the ability to track the same patient across calendar years. The diagnostic code lacks specificity for hospitalizations <3 days.

CONCLUSIONS

Thirty-day readmissions after SJS/TEN hospitalizations are common. Dedicated efforts to identify at-risk patients may improve peridischarge continuity.