Multiple risk factors for unplanned readmissions within 1 month of hospital discharge in acute care hospitals in Japan

AIM

The aim of this study is to analyse the risk factors for unplanned readmissions within 1 month after hospital discharge to develop a seamless support system from discharge to home care.

BACKGROUND

With shorter hospital stay lengths, understanding the characteristics of patients with multiple risk factors is important to prevent rehospitalization.

DESIGN

This is a single-centre retrospective descriptive study.

METHODS

Logistic regression and decision tree analyses were performed using eight items from the records of 3117 patients discharged from a university hospital between April-September 2017 as risk factors.

RESULTS

Unplanned readmission risk was significantly associated with emergency hospitalization (odds ratio [OR]: 3.12, 95% confidence interval [CI]: 2.04-4.77), malignancy (OR: 2.16, 95% CI: 1.44-3.24), non-surgical admission (OR: 1.76, 95% CI: 1.07-2.88), hospital stay of ≥ 15 days (OR: 1.66, 95% CI: 1.14-2.43) and decline in activities of daily living owing to hospitalization (OR: 1.68, 95% CI: 1.06-2.64). The highest risk combinations for rehospitalization were as follows: emergency hospitalization and malignancy; emergency admission, non-malignancy and a hospital stay of ≥15 days; and scheduled hospitalization, no surgery and a hospital stay of ≥15 days.

CONCLUSIONS

Patients with multiple risks for unplanned readmission should be accurately screened and provided with optimal home care.

Thirty-Day Unplanned Hospital Readmissions in Patients With Cancer and the Impact of Social Determinants of Health: A Machine Learning Approach

PURPOSE

Develop a cancer-specific machine learning (ML) model that accurately predicts 30-day unplanned readmissions in patients with solid tumors.

METHODS

The initial cohort included patients 18 years or older diagnosed with a solid tumor. Two distinct cohorts were generated: one with and one without detailed social determinants of health (SDOHs) data. For each cohort, data were temporally partitioned in 70% (training), 20% (validation), and 10% (testing). Tree-based ML models were developed and validated on each cohort. The metrics used to evaluate the model's performance were receiver operating characteristic curve (ROC), area under the ROC curve, precision, recall (R), accuracy, and area under the precision-recall curve.

RESULTS

We included 13,717 patients in this study in two cohorts (5,059 without SDOH data and 8,658 with SDOH data). Unplanned 30-day readmission occurred in 21.3% of the cases overall. The five main non-SDOH factors most highly associated with an unplanned 30-day readmission (R, 0.74; IQR, 0.58-0.76) were: number of previous unplanned readmissions; higher Charlson comorbidity score; nonelective index admission; discharge to anywhere other than home, hospice, or nursing facility; and higher anion gap during the admission. Neighborhood crime index, neighborhood median home values, annual income, neighborhood median household income, and wealth index were the main five SDOH factors important for predicting a high risk for an unplanned hospital readmission (R, 0.66; IQR, 0.56-0.72). The models were not directly comparable.

CONCLUSION

Key drivers of unplanned readmissions in patients with cancer are complex and involve both clinical factors and SDOH. We developed a cancer-specific ML model that with reasonable accuracy identified patients with cancer at high risk for an unplanned hospital readmission.

Derivation and Validation of the Cancer READMIT Score: A Readmission Risk Scoring System for Patients With Solid Tumor Malignancies

PURPOSE

Readmissions for the medical treatment of cancer have traditionally been excluded from readmission measures under the Hospital Readmissions Reduction Program. Patients with cancer often have higher readmission rates and may need heightened support to ensure effective care transitions after hospitalization. Estimating readmission risk before discharge may assist in discharge planning efforts and help promote care coordination at time of discharge.

PATIENTS AND METHODS

We developed and validated a readmission risk scoring system among a cohort of adult cancer patients with solid tumor admitted at a comprehensive cancer center. Multivariate logistic regression analysis was used to develop the model. The model's discriminative capacity was evaluated through a receiver operating characteristic curve analysis. We further compared the performance of the developed score with existing risk scores for 30-day readmission.

RESULTS

The 30-day unplanned readmission rate in the total cohort was 16.0% (n = 1,078 of 6,720). After multivariate analysis, Cancer site, Recent emergency room visit within 30 days, non-English primary language, Anemia defined as hemoglobin < 10 g/dL, > 4 Days length of stay during the index admission, unmarried Marital status, Increased white blood cell count > 11 × 109/L, and distant Tumor spread were significantly associated with risk of unplanned 30-day readmission. The derived score, which we call the Cancer READMIT score, had modest discriminatory performance in predicting readmissions (area under the curve for the model receiver operating characteristic curve = 0.647).

CONCLUSION

The Cancer READMIT score was able to predict 30-day unplanned readmissions to our institution with fairly modest performance. External validation of our derived risk scoring system is recommended.

Explainable Tree-Based Predictions for Unplanned 30-Day Readmission of Patients With Cancer Using Clinical Embeddings

Thirty-day unplanned readmission is one of the key components in measuring quality in patient care. Risk of readmission in oncology patients may be associated with a wide variety of specific factors including laboratory results and diagnoses, and it is hard to include all such features using traditional approaches such as one-hot encoding in predictive models.

Toward Using Wearables to Remotely Monitor Cognitive Frailty in Community-Living Older Adults: An Observational Study

Physical frailty together with cognitive impairment (Cog), known as cognitive frailty, is emerging as a strong and independent predictor of cognitive decline over time. We examined whether remote physical activity (PA) monitoring could be used to identify those with cognitive frailty. A validated algorithm was used to quantify PA behaviors, PA patterns, and nocturnal sleep using accelerometer data collected by a chest-worn sensor for 48-h. Participants (N = 163, 75 ± 10 years, 79% female) were classified into four groups based on presence or absence of physical frailty and Cog: PR-Cog-, PR+Cog-, PR-Cog+, and PR+Cog+. Presence of physical frailty (PR-) was defined as underperformance in any of the five frailty phenotype criteria based on Fried criteria. Presence of Cog (Cog-) was defined as a Mini-Mental State Examination (MMSE) score of less than 27. A decision tree classifier was used to identify the PR-Cog- individuals. In a univariate model, sleep (time-in-bed, total sleep time, percentage of sleeping on prone, supine, or sides), PA behavior (sedentary and light activities), and PA pattern (percentage of walk and step counts) were significant metrics for identifying PR-Cog- (p < 0.050). The decision tree classifier reached an area under the curve of 0.75 to identify PR-Cog-. Results support remote patient monitoring using wearables to determine cognitive frailty.

Vital Sign Abnormalities on Discharge Do Not Predict 30-Day Readmission

OBJECTIVE

Hospital readmissions are common and expensive. Risk factors for hospital readmission may include vital sign abnormalities (VSA) at the time of discharge. The study aimed to validate VSA at the time of discharge as a useful predictor of hospital readmission within 30 days of discharge. VSA was compared to the validated HOSPITAL score and LACE index readmission risk prediction models.

DESIGN

All adult medical patients discharged from internal medicine hospitalist service were studied retrospectively. Variables such as age, gender, diagnoses, vital signs at discharge, 30-day hospital readmission, and components for the HOSPITAL score and LACE index were extracted from the electronic health record for analysis.

SETTINGS

A 507-bed university-affiliated tertiary care center.

PARTICIPANTS

During the 2-year study period, a cohort of 1,916 discharges for the hospitalist service were evaluated. The final analysis was based on the data from 1,781 hospital discharges that met the inclusion criteria.

RESULTS

VSA was found in 13% of the study population. Only one abnormal vital sign was present in a higher proportion readmitted to the hospital within 30 days of discharge. No discharges had three or more unstable vital signs. Receiver operating characteristic (ROC) comparisons of the HOSPITAL score (C statistic of 0.67, P < 0.001), LACE index (C statistic of 0.61, P < 0.001), and VSA (C statistic of 0.52, P = 0.318) indicated that VSA at time of discharge was not a useful predictor of hospital readmission within 30 days of discharge.

CONCLUSION

Our study indicated that VSA at the time of discharge is not a useful predictor of 30-day hospital readmission at a university-affiliated teaching hospital. The more complex and validated HOSPITAL score and LACE index were useful predictors of hospital readmission in this patient population.