Predictors of hospital readmission among older adults with cancer

Effect of comprehensive geriatric assessment on hospitalizations in older adults with frailty initiating curatively intended oncologic treatment: The PROGNOSIS-RCT study

INTRODUCTION

Frailty constitutes a risk for unplanned hospitalizations in older adults with cancer. This study examines whether comprehensive geriatric assessment (CGA) as an add-on to standard oncologic care can prevent unplanned hospitalizations in older adults with frailty and cancer who initiate curative oncological treatment.

MATERIALS AND METHODS

This randomized controlled trial included older adults aged ≥70 with frailty (Geriatric 8 [G8] ≤14), and solid cancers who initiated curative oncological treatment. Participants were randomized 1:1 to either standard oncologic care (control) or standard oncologic care supplemented with CGA-guided interventions (intervention). Baseline characteristics were retrieved prior to randomization. The primary endpoint, the between-group rate ratio of unplanned hospitalizations within six months of treatment initiation, was analyzed using negative binominal regression. Analyses were performed using an intention-to-treat approach, followed by per-protocol analysis, including participants receiving CGA within 30 days of randomization, and preplanned subgroup analyses based on treatment modality and Geriatric 8 screening. Secondary endpoints included acute hospital contacts, treatment adherence, and toxicity.

RESULTS

From November 1, 2020 to May 31, 2023, 173 participants were enrolled. Median age was 75 (interquartile range 72-79), 51.5% were female, 58% had a G8 score > 12, and 84% had Eastern Cooperative Oncology Group performance status 0-1. The most common cancer sites were lung (23%), upper gastrointestinal (15%), and breast (13%). The rate (per person-years) of unplanned hospitalization was 1.32 in the intervention group and 1.81 in the control group, with a between-group rate ratio of 0.74 (95% confidence interval [CI] 0.45-1.23, P = 0.25) favoring the intervention. The between-group rate ratio increased in the per-protocol analysis (0.64 [95% CI 0.37-1.10, P = 0.10]). Similarly, no significant between group differences were found in treatment adherence, rate of acute hospital contacts, or toxicity.

DISCUSSION

In this study, CGA did not significantly reduce the rate of unplanned hospitalizations. Furthermore, no between-group differences were found in treatment adherence, toxicity lead hospitalizations, or treatment completion in older adults with cancer and frailty. However, per-protocol analysis suggests that increasing adherence to CGA may improve the outcome. Larger studies ensuring higher CGA adherence are warranted to confirm our findings.

Predictive analytics support for complex chronic medical conditions: An experience-based co-design study of physician managers' needs and preferences

PURPOSE

The literature suggests predictive technology applications in health care would benefit from physician and manager input during design and development. The aim was to explore the needs and preferences of physician managers regarding the role of predictive analytics in decision support for patients with the highly complex yet common combination of multiple chronic conditions of cardiovascular (Heart) and kidney (Nephrology) diseases and diabetes (HND).

METHODS

This qualitative study employed an experience-based co-design model comprised of three data gathering phases: 1. Patient mapping through non-participant observations informed by process mining of electronic health records data, 2. Semi-structured experience-based interviews, and 3. A co-design workshop. Data collection was conducted with physician managers working at or collaborating with the HND center, Danderyd University Hospital (DSAB), in Stockholm, Sweden. HND center is an integrated practice unit offering comprehensive person-centered multidisciplinary care to stabilize disease progression, reduce visits, and develop treatment strategies that enables a transition to primary care.

RESULTS

Interview and workshop data described a complex challenge due to the interaction of underlying pathophysiologies and the subsequent need for multiple care givers that hindered care continuity. The HND center partly met this challenge by coordinating care through multiple interprofessional and interdisciplinary shared decision-making interfaces. The large patient datasets were difficult to operationalize in daily practice due to data entry and retrieval issues. Predictive analytics was seen as a potentially effective approach to support decision-making, calculate risks, and improve resource utilization, especially in the context of complex chronic care, and the HND center a good place for pilot testing and development. Simplicity of visual interfaces, a better understanding of the algorithms by the health care professionals, and the need to address professional concerns, were identified as key factors to increase adoption and facilitate implementation.

CONCLUSIONS

The HND center serves as a comprehensive integrated practice unit that integrates different medical disciplinary perspectives in a person-centered care process to address the needs of patients with multiple complex comorbidities. Therefore, piloting predictive technologies at the same time with a high potential for improving care represents an extreme, demanding, and complex case. The study findings show that health care professionals' involvement in the design of predictive technologies right from the outset can facilitate the implementation and adoption of such technologies, as well as enhance their predictive effectiveness and performance. Simplicity in the design of predictive technologies and better understanding of the concept and interpretation of the algorithms may result in implementation of predictive technologies in health care. Institutional efforts are needed to enhance collaboration among the health care professionals and IT professionals for effective development, implementation, and adoption of predictive analytics in health care.

Adult Cancer Patients' Perceptions of Factors That Influence Hospital Admissions

PURPOSE/AIMS

To explore cancer patients' perceptions of factors that influence hospital readmissions.

DESIGN

A cross-sectional, prospective design was employed utilizing a 1-time survey and brief interviews to measure patients' perceptions and unplanned hospital admissions.

METHODS AND VARIABLES

The principal investigator collected data from medical record review, the Hospital Admission Survey, and interviews to measure patient characteristics and perceptions of influencing factors that contributed to an unplanned hospital admission upon admission. Data were analyzed using descriptive statistics to categorize patient perceptions of influencing factors of unplanned hospital admissions.

RESULTS

The top reasons for admission were symptoms of uncontrolled gastrointestinal, pain, fever, and respiratory problems. The majority perceived the admission was unavoidable and wanted to avoid an admission. Perceived influencing factors were related to survey categories of 1) communication (ie, cannot reach physician anytime, cannot get a next-day appointment, medical problems are out of control, advised to go to the emergency department) and 2) home environment (ie, unable to adequately manage symptoms at home and hospital admission is the best place for care). Other survey categories of patient education and palliative care were not perceived as influencing or contributing factors.

CONCLUSIONS

These findings highlight opportunities for clinical nurse specialists to target these vulnerable patients and provide expert consultation to address potential barriers and gaps in utilization of appropriate supportive services that may reduce unplanned hospital admissions.

Improvement of scoring system used before discharge to predict 30-day all-cause unplanned readmission in geriatric population: a prospective cohort study

BACKGROUND

Data taken from tertiary referral hospitals in Indonesia suggested readmission rate in older population ranging between 18.1 and 36.3%. Thus, it is crucial to identify high risk patients who were readmitted. Our previous study found several important predictors, despite unsatisfactory discrimination value.

METHODS

We aimed to investigate whether comprehensive geriatric assessment (CGA) -based modification to the published seven-point scoring system may increase the discrimination value. We conducted a prospective cohort study in July-September 2022 and recruited patients aged 60 years and older admitted to the non-surgical ward and intensive coronary care unit. The ROC curve was made based on the four variables included in the prior study. We conducted bivariate and multivariate analyses, and derived a new scoring system with its discrimination value.

RESULTS

Of 235 subjects, the incidence of readmission was 32.3% (95% CI 26-38%). We established a new scoring system consisting of 4 components. The scoring system had maximum score of 21 and incorporated malignancy (6 points), delirium (4 points), length of stay ≥ 10 days (4 points), and being at risk of malnutrition or malnourished (7 points), with a good calibration test. The C-statistic value was 0.835 (95% CI 0.781-0.880). The optimal cut-off point was ≥ 8 with a sensitivity of 90.8% and a specificity of 54.7%.

CONCLUSIONS

Malignancy, delirium, length of stay ≥ 10 days, and being at risk of malnutrition or malnourished are predictors for 30-day all-cause unplanned readmission. The sensitive scoring system is a strong model to identify whether an individual is at higher risk for readmission. The new CGA-based scoring system had higher discrimination value than that of the previous seven-point scoring system.

Understanding Potentially Preventable 7-day Readmission Rates in Hospital Medicine Patients at a Comprehensive Cancer Center

This study aimed to describe the potentially preventable 7-day unplanned readmission (PPR) rate in medical oncology patients. A retrospective analysis of all unplanned 7-day readmissions within Hospital Medicine at MD Anderson Cancer Center from September 1, 2020 to February 28, 2021, was performed. Readmissions were independently analyzed by 2 randomly selected individuals to determine preventability. Discordant reviews were resolved by a third reviewer to reach a consensus. Statistical analysis included 138 unplanned readmissions. The estimated PPR rate was 15.94%. The median age was 62.50 years; 52.90% were female. The most common type of cancer was noncolon GI malignancy (34.06%). Most patients had stage 4 cancer (69.57%) and were discharged home (64.93%). Premature discharge followed by missed opportunities for goals of care discussions were the most cited reasons for potential preventability. These findings highlight areas where care delivery can be improved to mitigate the risk of readmission within the medical oncology population.

Fall risk in older adults hospitalized with tumours: Contributing factors and prediction model

AIM

Rates vary widely across hospitals globally and typically range from 3 to 11 falls per 1000 bed days and as 7-11 in Affiliated Hospital of Nantong University. This study determined to explore contributing factors and poor prognosis of fall in elderly tumour patients in China.

DESIGN

A cross-sectional study.

METHODS

161 older adults were invited to participate in this study and completed a self-reported questionnaire, took blood tests, and received the exam of musculoskeletal ultrasound.

RESULTS

Among 161 patients, falls occurred in 41 cases, accounting for 24.8%. 51.6% of older adults suffered from intermediate-to-high risk of falls. Fall history, reduced self-care ability, sleep disturbance, hearing impairment, hyperkyphosis, chronic disease, platelet count, and the thickness of left muscle rectus femoris (LF-MLT), and left cross-sectional area (LF-CSA) were all contributing factors of fall, and higher risk of fall indicating lower quality of life. A fall prediction model was established in this study based on above contributing factors with good prediction efficiency (AUC = 0.920).

PATIENT OR PUBLIC CONTRIBUTION

The patient volunteers participated in this study and provided valuable data for the final analysis and the acquisition of conclusion.

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.

Nutritional Intake, Hospital Readmissions and Length of Stay in Hospitalised Oncology Patients

BACKGROUND

Poor food intake is an independent risk factor for malnutrition in oncology patients, and achieving adequate nutrition is essential for optimal clinical and health outcomes. This study investigated interrelationships between nutritional intake and clinical outcomes in hospitalised adult oncology patients.

METHODS

Estimated nutrition intake data were obtained from patients admitted to a 117-bed tertiary cancer centre during May-July 2022. Clinical healthcare data, including length of stay (LOS) and 30-day hospital readmissions, were obtained from patient medical records. Statistical analysis, including multivariable regression analysis, assessed whether poor nutritional intake was predictive of LOS and readmissions.

RESULTS

No relationships between nutritional intake and clinical outcomes were evident. Patients at risk of malnutrition had lower mean daily energy (-898.9 kJ, p = 0.001) and protein (-10.34 g, p = 0.015) intakes. Increased malnutrition risk at admission prolonged LOS (1.33 days, p = 0.008). Hospital readmission rates were 20.2%, and associated with age (r = -0.133, p = 0.015), presence of metastases (r = 0.125, p = 0.02) and longer LOS (1.34 days, r = 0.145, p = 0.02). Sarcoma (43.5%), gynaecological (36.8%) and lung (40.0%) cancers had the highest readmission rates.

CONCLUSIONS

Despite research showing the benefits of nutritional intake during hospitalisation, evidence continues to emerge on the relationship between nutritional intake and LOS and readmissions that may be confounded by malnutrition risk and cancer diagnosis.

Toward Predicting 30-Day Readmission Among Oncology Patients: Identifying Timely and Actionable Risk Factors

PURPOSE

Predicting 30-day readmission risk is paramount to improving the quality of patient care. In this study, we compare sets of patient-, provider-, and community-level variables that are available at two different points of a patient's inpatient encounter (first 48 hours and the full encounter) to train readmission prediction models and identify possible targets for appropriate interventions that can potentially reduce avoidable readmissions.

METHODS

Using electronic health record data from a retrospective cohort of 2,460 oncology patients and a comprehensive machine learning analysis pipeline, we trained and tested models predicting 30-day readmission on the basis of data available within the first 48 hours of admission and from the entire hospital encounter.

RESULTS

Leveraging all features, the light gradient boosting model produced higher, but comparable performance (area under receiver operating characteristic curve [AUROC]: 0.711) with the Epic model (AUROC: 0.697). Given features in the first 48 hours, the random forest model produces higher AUROC (0.684) than the Epic model (AUROC: 0.676). Both models flagged patients with a similar distribution of race and sex; however, our light gradient boosting and random forest models were more inclusive, flagging more patients among younger age groups. The Epic models were more sensitive to identifying patients with an average lower zip income. Our 48-hour models were powered by novel features at various levels: patient (weight change over 365 days, depression symptoms, laboratory values, and cancer type), hospital (winter discharge and hospital admission type), and community (zip income and marital status of partner).

CONCLUSION

We developed and validated models comparable with the existing Epic 30-day readmission models with several novel actionable insights that could create service interventions deployed by the case management or discharge planning teams that may decrease readmission rates over time.

Interrelationships between Dietary Outcomes, Readmission Rates and Length of Stay in Hospitalised Oncology Patients: A Scoping Review

BACKGROUND

Poor food intake is an independent risk factor for malnutrition in oncology patients, and achieving adequate nutrition is essential for optimal clinical and health outcomes. This review investigated the interrelationships between dietary intakes, hospital readmissions and length of stay in hospitalised adult oncology patients.

METHODOLOGY

Three databases, MEDLINE, Web of Science and PubMed were searched for relevant publications from January 2000 to the end of August 2022.

RESULTS

Eleven studies investigating the effects of dietary intakes on length of stay (LOS) and hospital readmissions in cohorts of hospitalised patients that included oncology patients were identified. Heterogenous study design, nutritional interventions and study populations limited comparisons; however, a meta-analysis of two randomised controlled trials comparing dietary interventions in mixed patient cohorts including oncology patients showed no effect on LOS: mean difference -0.08 (95% confidence interval -0.64-0.49) days (p = 0.79).

CONCLUSIONS

Despite research showing the benefits of nutritional intake during hospitalisation, evidence is emerging that the relationship between intakes, LOS and hospital readmissions may be confounded by nutritional status and cancer diagnosis.

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.

Risk of 30-day hospital readmission associated with medical conditions and drug regimens of polymedicated, older inpatients discharged home: a registry-based cohort study

OBJECTIVES

The present study analysed 4 years of a hospital register (2015-2018) to determine the risk of 30-day hospital readmission associated with the medical conditions and drug regimens of polymedicated, older inpatients discharged home.

DESIGN

Registry-based cohort study.

SETTING

Valais Hospital-a public general hospital centre in the French-speaking part of Switzerland.

PARTICIPANTS

We explored the electronic records of 20 422 inpatient stays by polymedicated, home-dwelling older adults held in the hospital's patient register. We identified 13 802 hospital readmissions involving 8878 separate patients over 64 years old.

OUTCOME MEASURES

Sociodemographic characteristics, medical conditions and drug regimen data associated with risk of readmission within 30 days of discharge.

RESULTS

The overall 30-day hospital readmission rate was 7.8%. Adjusted multivariate analyses revealed increased risk of hospital readmission for patients with longer hospital length of stay (OR=1.014 per additional day; 95% CI 1.006 to 1.021), impaired mobility (OR=1.218; 95% CI 1.039 to 1.427), multimorbidity (OR=1.419 per additional International Classification of Diseases, 10th Revision condition; 95% CI 1.282 to 1.572), tumorous disease (OR=2.538; 95% CI 2.089 to 3.082), polypharmacy (OR=1.043 per additional drug prescribed; 95% CI 1.028 to 1.058), and certain specific drugs, including antiemetics and antinauseants (OR=3.216 per additional drug unit taken; 95% CI 1.842 to 5.617), antihypertensives (OR=1.771; 95% CI 1.287 to 2.438), drugs for functional gastrointestinal disorders (OR=1.424; 95% CI 1.166 to 1.739), systemic hormonal preparations (OR=1.207; 95% CI 1.052 to 1.385) and vitamins (OR=1.201; 95% CI 1.049 to 1.374), as well as concurrent use of beta-blocking agents and drugs for acid-related disorders (OR=1.367; 95% CI 1.046 to 1.788).

CONCLUSIONS

Thirty-day hospital readmission risk was associated with longer hospital length of stay, health disorders, polypharmacy and drug regimens. The drug regimen patterns increasing the risk of hospital readmission were very heterogeneous. Further research is needed to explore hospital readmissions caused solely by specific drugs and drug-drug interactions.

Unplanned emergency department visits and hospital admissions of older adults under treatment for cancer in the ambulatory/community setting

PURPOSE

This study aims to identify the incidence and risk/protective factors for (1) unplanned emergency department (ED) visits and hospital admissions (HA) and (2) nausea/vomiting/dehydration (NVD) at time of treatment in older adults under treatment for cancer.

MATERIALS AND METHODS

This is a exploratory retrospective cohort study of adults (60 and older) with cancer. Adults were included if they had a new cancer diagnosis and were being treated with chemotherapy. Study outcomes included the number of ED visits and HA (cycles 1-4) and NVD at the time of receiving chemotherapy (cycles 2-4). Repeated measures, Poisson regression was used to obtain risk ratios with 95% confidence intervals for independent predictors of outcomes.

RESULTS

Of 402 study participants, 20% experienced an ED visit, and 18% experienced a HA. Common reasons for ED visits were pain (23.5%) and NVD (20.4%). Common reasons for HA were infection (34.4%) and NVD (22.2%). Multivariate analysis showed risk factors for ED visits included chemotherapy cycle 1, having esophageal cancer, being treated with ≥ 3 chemotherapy agents, and increasing levels of functional impairment. Risk factors for HA included chemotherapy cycle 1, increasing levels of functional impairment, intravenous fluids between treatment, and being prescribed antiemetics for home use. Predictors of NVD at time of chemotherapy treatment included Hispanic ethnicity, insurance status, cancer type, chemotherapy emetic potent, treatment frequency, intravenous fluids between cycles, and number of home antiemetics.

CONCLUSION

Unplanned ED visits and HA occur in older adults under treatment for cancer due to numerous treatment-related side effects. Helping older adults identify and manage side effects early may reduce the number of unplanned admissions.