Examination of unplanned 30-day readmissions to a comprehensive cancer hospital

Unplanned Hospital Admissions in Patients With Solid Tumors in Brazil: Causes and Progressive Disease's Impact on Outcomes

PURPOSE

Most patients with cancer will be hospitalized throughout the disease course. However, most evidence on the causes and outcomes of these hospitalizations comes from administrative data or small retrospective studies from high-income countries.

METHODS

This study is a retrospective cohort of patients with solid tumors hospitalized from February 1, 2021, to December 31, 2021, in a tertiary cancer center in São Paulo, Brazil. We collected data on cancer diagnosis, symptoms at admission, hospitalization diagnosis, and survival clinical outcomes during in-hospital stay (in-hospital mortality) and after discharge (readmission rates and overall survival [OS]). Progressive disease (PD) diagnosis during admission was retrieved from manual chart review if explicitly stated by the attending physician. We modeled in-hospital mortality and postdischarge OS with logistic regression and Cox proportional hazards models, respectively.

RESULTS

A total of 3,726 unique unplanned admissions were identified. The most common symptoms at admission were pain (40.6%), nausea (16.8%), and dyspnea (16.1%). PD (34.0%), infection (31.1%), and cancer pain (13.4%) were the most frequent reasons for admission. The in-hospital mortality rate was 18.9%. Patients with PD had a high in-hospital mortality rate across all tumor groups and higher odds of in-hospital death (odds ratio, 3.5 [95% CI, 3.0 to 4.2]). The 7-, 30-, and 90-day readmission rates were 11.9%, 33.5%, and 54%, respectively. The postdischarge median OS (mOS) was 12.6 months (95% CI, 11.6 to 13.7). Poorer postdischarge survival was observed among patients with PD (mOS, 5 months v 18 months; P < .001; hazard ratio, 2.4 [95% CI, 2.1 to 2.6]).

CONCLUSION

PD is a common diagnosis during unplanned hospitalizations and is associated with higher in-hospital mortality rates and poorer OS after discharge. Oncologists should be aware of the prognostic implications of PD during admission and align goals of care with their patients.

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.

The impact of a multidisciplinary goals-of-care program on unplanned readmission rates at a comprehensive cancer center

PURPOSE

This study examined the 30-day unplanned readmission rate in the medical oncology population before and after the implementation of an institution-wide multicomponent interdisciplinary goals of care (myGOC) program.

METHODS

This retrospective study compared the 30-day unplanned readmission rates in consecutive medical patients during the pre-implementation period (May 1, 2019, to December 31, 2019) and the post-implementation period (May 1, 2020, to December 31, 2020). Secondary outcomes included 7-day unplanned readmission rates, inpatient do-not-resuscitate (DNR) orders, and palliative care consults. We randomly selected a hospitalization encounter for each unique patient during each study period for statistical analysis. A multivariate analysis model was used to examine the association between 30-day unplanned readmission rates and implementation of the myGOC program.

RESULTS

There were 7028 and 5982 unique medical patients during the pre- and post-implementation period, respectively. The overall 30-day unplanned readmission rate decreased from 24.0 to 21.3% after implementation of the myGOC program. After adjusting for covariates, the myGOC program implementation remained significantly associated with a reduction in 30-day unplanned readmission rates (OR [95% CI] 0.85 [0.77, 0.95], p = 0.003). Other factors significantly associated with a decreased likelihood of a 30-day unplanned readmission were an inpatient DNR order, advanced care planning documentation, and an emergent admission type. We also observed a significant decrease in 7-day unplanned readmission rates (OR [95% CI] 0.75 [0.64, 0.89]) after implementation of the myGOC program.

CONCLUSION

The 30-day and 7-day unplanned readmission rates decreased in our hospital after implementation of a system-wide multicomponent GOC intervention.

Association of polypharmacy with all-cause mortality and adverse events among elderly colorectal cancer survivors

BACKGROUND

The risk of inappropriate drug exposure in elderly colorectal cancer (CRC) survivors after the initial cancer treatment has not been well studied. This study investigated the association of polypharmacy (PP) with overall survival, hospitalization, and emergency room (ER) visits among older CRC survivors.

METHODS

A retrospective cohort study was conducted using the Korean National Health Insurance claims data follow-up from 2002 to 2017. Participants comprised those aged ≥65 years who were hospitalized with a diagnosis of CRC received cancer treatment and survived at least 2 years from the initial CRC diagnosis between 2003 and 2012. PP was defined based on the number of individual drugs during the third year, after 2 years of survival since the initial cancer treatment. PP was categorized as follows: non-PP (zero to four prescribed drugs); PP (five to nine drugs), and excessive PP (≥10 drugs). Main outcomes are all-cause mortality, hospitalization, and ER visits.

RESULTS

Of the 55,228 participants, 44.5% died, 83.1% were hospitalized, and 46.1% visited the ER. The PP and excess PP groups showed increased risk of all-cause mortality, hospitalization, and ER visit compared with the low PP group, and was highly associated among groups including patients aged 65 to 74 years and those in low-level frailty groups.

CONCLUSIONS

These risks can be minimized by increasing awareness and enhancing behaviors among health care professionals, especially clinician and pharmacists, to be aware of potential drug interactions, review, and ongoing monitoring.

PLAIN LANGUAGE SUMMARY

The risk of inappropriate drug exposure in older colorectal cancer (CRC) survivors after the initial cancer treatment has not been well studied. Polypharmacy was associated with adverse outcomes, including all-cause mortality, hospitalization, and emergency room visits among older CRC survivors and it was particularly associated with those who were 65 to 75 years and those with low risk of frailty. When prescribing drugs, physicians should be mindful of finding a balance between adequate treatment of diseases and avoiding adverse drug effects in survivors of CRC.

Association Between Postdischarge Medical Oncology Follow-Up Appointments and Downstream Health Care Use: A Single-Institution Experience

PURPOSE:

There is limited understanding of the role of postdischarge medical oncology follow-up during care transition periods. Our study describes the care transition patterns and the association between postdischarge medical oncology appointments and downstream health care use at a tertiary academic center.

METHODS:

We conducted a retrospective cohort study of 25,135 medical oncology admissions between 2018 and 2020 at Yale New Haven Hospital. We examined the association between postdischarge medical oncology appointment timing with 30-day all-cause readmissions and emergency department (ED) visits using multivariable logistic regression models and propensity score–matched analyses.

RESULTS:

Compared with admissions without appointment within 30 days, admissions with postdischarge medical oncology appointment within 30 days were associated with lower rates of all-cause 30-day readmission (odds ratio [OR] = 0.56, 95% CI, 0.52 to 0.59; P < .001) and ED visit (OR = 0.56, 95% CI, 0.52 to 0.59; P < .001). Admissions with appointment ≤ 14 days were associated with lower rates of 30-day readmission (OR = 0.28, 95% CI, 0.25 to 0.32; P < .001) and ED visit (OR = 0.56, 95% CI, 0.52 to 0.63; P < .001) compared with those with appointment within 15-30 days. Similar patterns in health care use were seen with propensity score matching. Subgroup analyses of cancer types with the most admissions observed similar trends between 30-day readmission and ED visits with appointment timing.

CONCLUSION:

Timely postdischarge medical oncology appointments were associated with significantly lower likelihood of 30-day readmission and ED visits, suggesting a potential role for postdischarge follow-up as an intervention to decrease health care use.

The prevalence, characteristics, and risk factors of frequently readmitted patients to an internal medicine service

AIMS

To determine the prevalence, characteristics and risk factors associated with frequent readmissions to an internal medicine service at a tertiary public hospital.

METHOD

A retrospective observational study was conducted at an internal medicine service in a tertiary teaching hospital between 1st January 2010 and the 30th June 2016. Frequent readmission was defined as four or more readmissions within 12 months of discharge from the index admission. Demographic and clinical characteristics, and potential risk factors were evaluated.

RESULTS

50 515 patients were included, 1657 (3.3%) had frequent readmissions and were associated with nearly 2.5 times higher in 12-month mortality rates. They were older, had higher rates of Indigenous Australians (3.2%), more disadvantaged status (Index of Relative Socio-Economic Disadvantage decile of 5.3), and more comorbidities (mean Charlson comorbidity index 1.4) in comparison, to infrequent readmission group. The mean length of hospital stay during the index admission was 6 days for frequent readmission group (21.4% staying more than 7 days) with higher incidence of discharge against medical advice (2.0% higher). Intensive care unit admission rate was 6.6% for frequent readmission group compared to 3.9% for infrequent readmission group. Multivariate analysis showed mental disease and disorders, neoplastic, and alcohol/drug use and alcohol/drug induced organic mental disorders are associated with frequent readmission.

CONCLUSION

The risk factors associated with frequent readmission were older age, indigenous status, being socially disadvantaged, having higher comorbidities, and discharging against medical advice. Conditions that lead to frequent readmissions were mental disorders, alcohol/drug use and alcohol/drug induced organic mental disorders, and neoplastic disorders.

Reflections of Healthcare Experiences of African Americans With Sickle Cell Disease or Cancer: A Qualitative Study

BACKGROUND

The experiences of African American adult patients before, during, and after acute care utilization are not well characterized for individuals with sickle cell disease (SCD) or cancer.

OBJECTIVE

To describe the experiences of African Americans with SCD or cancer before, during, and after hospitalization for pain control.

METHODS

We conducted a qualitative study among African American participants with SCD (n = 15; 11 male; mean age, 32.7 ± 10.9 years; mean pain intensity, 7.8 ± 2.6) or cancer (n = 15; 7 male; mean age, 53.7 ± 15.2 years; mean pain intensity, 4.9 ± 3.7). Participants completed demographic questions and pain intensity using PAINReportIt and responded to a 7-item open-ended interview, which was recorded and transcribed verbatim. We used content analysis to identify themes in the participants' responses.

RESULTS

Themes identified included reason for admission, hospital experiences, and discharge expectations. Pain was the primary reason for admission for participants with SCD (n = 15) and for most participants with cancer (n = 10). Participants of both groups indicated that they experienced delayed treatment and a lack of communication. Participants with SCD also reported accusations of drug-seeking behavior, perceived mistreatment, and feeling of not being heard or believed. Participants from both groups verbalized concerns about well-being after discharge and hopeful expectations.

CONCLUSIONS

Race-concordant participants with SCD but not with cancer communicated perceived bias from healthcare providers.

IMPLICATIONS FOR PRACTICE

Practice change interventions are needed to improve patient-provider interactions, reduce implicit bias, and increase mutual trust, as well as facilitate more effective pain control, especially for those who with SCD.

Adjuvant chemotherapy versus observation following neoadjuvant therapy and surgery for resectable stages I–II pancreatic cancer

Aim:

This National Cancer Database (NCDB) analysis was performed to evaluate the outcomes of adjuvant chemotherapy (AC) versus observation for resected pancreatic adenocarcinoma treated with neoadjuvant therapy (NT).

Materials and methods:

The NCDB was queried for primary stages I–II cT1-3N0-1M0 resected pancreatic adenocarcinoma treated with NT (2004–2015). Baseline patient, tumour and treatment characteristics were extracted. The primary end point was overall survival (OS). With a 6-month conditional landmark, Kaplan–Meier analysis, multivariable Cox proportional hazards method and 1:1 propensity score matching was used to analyse the data.

Results:

A total of 1,737 eligible patients were identified, of which 1,247 underwent post-operative observation compared to 490 with AC. The overall median follow-up was 34·7 months. The addition of AC showed improved survival on the multivariate analysis (HR 0·78, p < 0·001). AC remained statistically significant for improved OS, with a median OS of 26·3 months versus 22·3 months and 2-year OS of 63·9% versus 52·9% for the observation cohort (p < 0·001). Treatment interaction analysis showed OS benefit of AC for patients with smaller tumours.

Findings:

Our findings suggest a survival benefit for AC compared to observation following NT and surgery for resectable pancreatic adenocarcinoma, especially in patients with smaller tumours.

Evaluation of early unplanned readmissions and predisposing factors in an oncology clinic

BACKGROUND

Unplanned readmission in the first 30 days after discharge is an important medical problem, although the data on cancer patients is limited. So we planned to evaluate the rates and causes of early readmissions and the predisposing factors.

METHODS

Patients hospitalized in Hacettepe University Oncology services between August 2018 and July 2019 were included. The demographic features, tumor stages, regular drugs, last laboratory parameters before discharge, and readmissions in the first 30 days after discharge were recorded. The predisposing features were evaluated with univariate and multivariate analyses.

RESULTS

A total of 562 hospitalizations were included. The mean age of the patients was 58.5 ± 14.5 years. Almost 2/3 of the hospitalizations were due to symptom palliation and infections. Eighty-three percent of the patients had advanced disease, and over 60% had an ECOG score of 2 and above. In the first 30 days after discharge, 127 patients were readmitted (22.6%). Advanced stage disease, presence of polypharmacy (5 or more regular drugs), hospitalization setting (emergency department (ED) vs. outpatient clinic), and hypoalbuminemia (< 3 gr/dL) were associated with a statistically significant increase in the risk of readmission. Among these factors, advanced-stage disease (HR: 2.847, 95% CI: 1.375-5.895), hospitalization from ED (HR: 1.832, 95% CI: 1.208-2.777), and polypharmacy (HR: 1.782, 95% CI: 1.173-2.706) remained significant in multivariate analyses.

CONCLUSIONS

In this study, 22% of cancer patients had early readmissions. The readmission risk increased in patients with advanced disease, hospitalization from ED, and polypharmacy. The optimal post-discharge plan may reduce readmissions in all oncology patients, with priority for these patient groups.

Factors associated with healthcare utilisation during first year after cancer diagnose-a population-based study

Background

Improved cancer treatments and models of care (such as early palliative care) has developed during recent years. Aspects of healthcare utilisation—unplanned care have been used for evaluation of coordination and quality. The aim was to explore factors associated with cancer healthcare utilisation, during the first year after a cancer diagnosis.

Methods

Population‐based registry and patient‐reported data, (The European Organisation of Research and Treatment of Cancer (EORTC), QLQ‐ C30 questionnaire and study‐specific questions) were collected. Descriptive statistics and multivariate regression models were performed.

Results

The sample consists of 1718 patients (haematological, gynaecological, upper gastrointestinal and head and neck cancers). Living alone were associated with unplanned hospital admissions (OR 1.35; 95% CI [1.15, 1.59], p < 0.001). Patients with specialised palliative home care had a higher likelihood of unplanned hospital admissions, (OR 4.35; 95% CI [3.22‐5.91], p < 0.001) and re‐admissions within 30 days, (OR, 5.8; 95% CI [4.12‐8.19], p < 0.001).

Conclusions

Sociodemographic and clinical factors, such as living alone and disease stage, is associated with healthcare utilisation. Patients with specialised palliative home care report lower levels of HRQoL and higher levels of unplanned care, and our findings stresses the importance of a holistic view when planning care.

The hospital care of patients with cancer: a retrospective analysis of the characteristics of their hospital stay in comparison with other medical conditions

PURPOSE

Hospital admission is a frequent occurrence among patients with cancer, and a significant proportion of patients admitted to medical units have cancer. Their hospital stay has features that may be different compared with patients without cancer. We performed a retrospective analysis of the characteristics of patients with cancer admitted for medical conditions.

PATIENTS AND METHODS

We studied the administrative data of patients with solid cancer admitted to the medical department of a large referral hospital over a 12-month period and compared them with those of patients without cancer.

RESULTS

Seven thousand eight hundred two consecutive admissions were analysed, of which 1099 (14.1%) had a principal or associated diagnosis of cancer. Admissions were distributed across 12 units, with 44% concentrated in the medical oncology unit and 56% in other units. Patients with cancer were more frequently men and were younger than patients without cancer. Admission less frequently involved the emergency department (ED), while discharge was more frequently assisted. The in-hospital death rate was higher, as was the readmission rate. Length of stay was longer (11.3 days vs. 9.8 days; p < 0.0001). Patients with cancer admitted to the medical oncology unit used the ED even less, and their length of stay was shorter than that of patients with cancer admitted in other units.

CONCLUSIONS

The in-hospital pathway of patients with cancer displays specific issues and adds complexity to hospital stay of patients with medical conditions. The medical oncology unit plays a role in reducing ED use and in providing efficient care. The evidence gathered should help in shaping new models of care and in improving adequate clinical competencies.

Risk factors for unplanned hospital readmissions within 30 days of discharge among medical oncology patients: A retrospective medical record review

PURPOSE

This study aimed to identify the risk factors for unplanned hospital readmissions (UHR) within 30 days of discharge among medical oncology patients at a tertiary hospital in Singapore.

METHODS

This study is a retrospective, case-control medical record review of patients admitted to a medical oncology unit at a tertiary hospital between 1 June and October 31, 2017. During the study period, there were 1559 adult patients discharged alive from the medical oncology unit. Of this, 359 patients had experienced at least a 30-day UHR (cases). The cases were matched to those without a 30-day UHR (controls) by their primary reason for index admission and discharge date. After matching, 312 medical records (cases: 156; controls: 156) were analysed.

RESULTS

Of the 156 cases with a 30-day UHR, 46.2% (n = 72) were readmitted within the first 10 days of discharge. The top reasons contributing to the UHR were non-neutropenic infection (n = 41) and pain (n = 23). Multivariate analyses identified three independent risk factors that were associated with the 30-day UHR: (1) single marital status, (2) emergency department visit(s) in the past six months, and (3) recent decline in activities of daily living.

CONCLUSION

The study results can guide risk stratification to identify medical oncology patients at high risk for 30-day UHR. In addition, the results warrant the need to refine the inpatient assessments and discharge planning, as well as ensure the accurate referral to and allocation of community and outpatient resources so as to reduce the risk of UHR.

Genesis of Quality Measurements to Improve the Care Delivered to Patients With Brain Metastases

PURPOSE

High-value and high-quality health care requires outcome measurements to inform treatment decisions, but, to our knowledge, no standardized measurements exist to evaluate brain metastases (BMs) care. We propose a set of measurements and report on their implementation in the care of patients with BMs.

METHODS

On the basis of a stakeholders' needs assessment and review of the literature, we identified outcome and process measurements to assess the care of patients with BMs according to treatment modality. Retrospectively, we applied these indicators of care to all patients diagnosed and treated at our institution over 2 years.

RESULTS

We ascertained 5 outcome and 6 process measurements of relevance in the care of BMs. When applied to 209 patients (89.7%) who received cancer treatment, 77% were alive > 90 days after diagnosis. The proportion alive at 90 days after surgery, whole-brain radiation therapy (WBRT), and stereotactic radiosurgery (SRS) was 82%, 59%, and 81%, respectively. Other performance measurements included 30-day postoperative readmission rate (6%), SRS within 30 days of surgery (79%), use of memantine with WBRT (41%), advance directives within 6 months of diagnosis (53%), and palliative care consultation for patients with poor prognosis or receiving WBRT (45%). Measurements for the 24 patients (10.3%) receiving best supportive care were advance directives documentation (67%) and referral to palliative or hospice care (83%).

CONCLUSION

We propose a set of measurements to apprise quality improvement efforts, inform treatment decision-making, and to use in evaluation of the performance of interdisciplinary BMs programs. Their refinement can potentially enhance the quality and value of care delivered to patients with BMs.

Personalized predictions of patient outcomes during and after hospitalization using artificial intelligence

Hospital systems, payers, and regulators have focused on reducing length of stay (LOS) and early readmission, with uncertain benefit. Interpretable machine learning (ML) may assist in transparently identifying the risk of important outcomes. We conducted a retrospective cohort study of hospitalizations at a tertiary academic medical center and its branches from January 2011 to May 2018. A consecutive sample of all hospitalizations in the study period were included. Algorithms were trained on medical, sociodemographic, and institutional variables to predict readmission, length of stay (LOS), and death within 48-72 h. Prediction performance was measured by area under the receiver operator characteristic curve (AUC), Brier score loss (BSL), which measures how well predicted probability matches observed probability, and other metrics. Interpretations were generated using multiple feature extraction algorithms. The study cohort included 1,485,880 hospitalizations for 708,089 unique patients (median age of 59 years, first and third quartiles (QI) [39, 73]; 55.6% female; 71% white). There were 211,022 30-day readmissions for an overall readmission rate of 14% (for patients ≥65 years: 16%). Median LOS, including observation and labor and delivery patients, was 2.94 days (QI [1.67, 5.34]), or, if these patients are excluded, 3.71 days (QI [2.15, 6.51]). Predictive performance was as follows: 30-day readmission (AUC 0.76/BSL 0.11); LOS > 5 days (AUC 0.84/BSL 0.15); death within 48-72 h (AUC 0.91/BSL 0.001). Explanatory diagrams showed factors that impacted each prediction.

Causes of Cancer Re-Admissions: A Patient-Centered Approach

PURPOSE

Patients with a cancer diagnosis have a high risk of re-admission during the 30 days after discharge. Clinicians, payers, and patients consider this to be an indicator of care quality. The causes of re-admission remain poorly understood. Retrospective chart reviews, used in most re-admission research, provide limited information regarding the causes of, and methods to reduce, re-admissions. This project sought to elicit the insights of those most affected by re-admission: the patients themselves.

METHODS

We interviewed patients with cancer who were re-admitted to 2 urban teaching hospitals when they were hospitalized during their second admission. Trained interviewers used a semistructured interview guide to gather information on events just before the second admission, the patients' understanding of the cause of re-admission, and the patients' views about their readiness for discharge at the previous admission. Interviews were transcribed and analyzed, and themes were identified and explored.

RESULTS

Three major themes were identified. First, most patients saw their re-admission as caused by problems that could not be treated in an outpatient setting. Second, the patients felt that they did not have sufficient resources at home to manage their care. Furthermore, the patients did not see the outpatient care team as a resource that they could call on for assistance. As a result, most of the decisions to return to the hospital were made by the patients themselves.

CONCLUSION

The decision that leads to re-admission often happens at home, in response to unmanageable needs. Strengthening the bond between the care team and the patient, with the aim of providing care in the most appropriate setting, could decrease re-admissions in patients with cancer. Possible interventions include home visits, enhanced discharge planning, and telehealth services.

Relationship between polypharmacy and inpatient hospitalization among older adults with cancer treated with intravenous chemotherapy

OBJECTIVES

Polypharmacy (≥5 concurrent medications) is common among older patients with cancer (48%-80%) and associated with increased frailty, morbidity, and mortality. This study examined the relationship between polypharmacy and inpatient hospitalization among older adults with cancer treated with intravenous (IV) chemotherapy.

MATERIALS AND METHODS

The main data source was the Surveillance, Epidemiology, and End Results-Medicare linked files. Patients (≥65 years) were included if they were diagnosed with prostate (n = 1430), breast (n = 5490), or lung cancer (n = 7309) in 1991-2013 and received IV chemotherapy in 2011-2014. The number of medications during the six-month window pre-IV chemotherapy initiation determined polypharmacy status. Negative binomial models were used to assess the association between polypharmacy and post-chemotherapy inpatient hospitalization. The results were presented as incidence rate ratios.

RESULTS

We identified 13,959 patients with prostate, breast, or lung cancer treated with IV chemotherapy. The median number of prescription medications during the six-month window pre-IV chemotherapy initiation was high: ten among patients with prostate cancer, nine among patients with breast cancer, and eleven among patients with lung cancer. Compared to patients taking <5 prescriptions, post-chemotherapy hospitalization rate for patients with prostate cancer was 42%, 75%, and 114% higher among those taking 5-9, 10-14, and 15+ medications, respectively. Patients with breast and lung cancer demonstrated similar patterns.

CONCLUSION

This large population-based study found that polypharmacy during the six-month window pre-IV chemotherapy is highly predictive of post-chemotherapy inpatient hospitalization. Further studies are needed to evaluate whether medication management interventions can reduce post-chemotherapy inpatient hospitalization among older patients with cancer.

Towards a patient journey perspective on causes of unplanned readmissions using a classification framework: results of a systematic review with narrative synthesis

Background

Several literature reviews have been published focusing on the prevalence and/or preventability of hospital readmissions. To our knowledge, none focused on the different causes which have been used to evaluate the preventability of readmissions. Insight into the range of causes is crucial to understand the complex nature of readmissions.

With this review we aim to: 1) evaluate the range of causes of unplanned readmissions in a patient journey, and 2) present a cause classification framework that can support future readmission studies.

Methods

A literature search was conducted in PUBMED and EMBASE using “readmission” and “avoidability” or “preventability” as key terms. Studies that specified causes of unplanned readmissions were included. The causes were classified into eight preliminary root causes: Technical, Organization (integrated care), Organization (hospital department level), Human (care provider), Human (informal caregiver), Patient (self-management), Patient (disease), and Other. The root causes were based on expert opinions and the root cause analysis tool of PRISMA (Prevention and Recovery Information System for Monitoring and Analysis). The range of different causes were analyzed using Microsoft Excel.

Results

Forty-five studies that reported 381 causes of readmissions were included. All studies reported causes related to organization of care at the hospital department level. These causes were often reported as preventable. Twenty-two studies included causes related to patient’s self-management and 19 studies reported causes related to patient’s disease. Studies differed in which causes were seen as preventable or unpreventable. None reported causes related to technical failures and causes due to integrated care issues were reported in 18 studies.

Conclusions

This review showed that causes for readmissions were mainly evaluated from a hospital perspective. However, causes beyond the scope of the hospital can also play a major role in unplanned readmissions. Opinions regarding preventability seem to depend on contextual factors of the readmission. This study presents a cause classification framework that could help future readmission studies to gain insight into a broad range of causes for readmissions in a patient journey.

In conclusion, we aimed to: 1) evaluate the range of causes for unplanned readmissions, and 2) present a cause classification framework for causes related to readmissions.

Electronic supplementary material

The online version of this article (10.1186/s12874-019-0822-9) contains supplementary material, which is available to authorized users.

Association of Timing of Adjuvant Therapy With Survival in Patients With Resected Stage I to II Pancreatic Cancer

IMPORTANCE

Surgery followed by adjuvant chemotherapy or chemoradiation is widely used to treat resectable pancreatic cancer. Although studies suggest initiation of adjuvant therapy within 12 weeks of surgery, there is no clear time interval associated with better survival.

OBJECTIVE

To evaluate the ideal timing of adjuvant therapy for patients with stage I to II resected pancreatic cancer.

DESIGN, SETTING, AND PARTICIPANTS

This cohort study included 7548 patients with stage I to II resected pancreatic cancer (5453 with adjuvant therapy; 2095 without adjuvant therapy) from the National Cancer Database from 2004 to 2015. Data were collected from January 2014 to December 2015 and analyzed from December 2018 to May 2019.

EXPOSURES

Adjuvant chemotherapy or chemoradiation at various time intervals.

MAIN OUTCOMES AND MEASURES

Overall survival (OS).

RESULTS

A total of 7548 patients (3770 male [49.9%]; median [interquartile range] age, 67 [59-74] years) were identified from the National Cancer Database. Among 5453 patients with adjuvant therapy, a Cox model with restricted cubic splines identified the lowest mortality risk when adjuvant therapy was started 28 to 59 days after surgery. Patients were divided into early (n = 269, adjuvant therapy initiated within <28 days), reference (n = 3048, adjuvant therapy initiated within 28-59 days), and late (n = 2136, adjuvant therapy initiated after >59 days) interval cohorts. Median (interquartile range) overall follow-up was 38.6 (24.6-62.0) months. Compared with the reference interval cohort on multivariable analysis, both the early cohort (hazard ratio, 1.17; 95% CI, 1.02-1.35; P = .03) and the late cohort (hazard ratio, 1.09; 95% CI, 1.02-1.17; P = .008) were associated with worse mortality. Similarly, the reference interval cohort had improved OS compared with the early cohort in 268 propensity-matched pairs (2-year OS, 52.5% [95% CI, 46.7%-59.0%] vs 45.1% [95% CI, 39.5%-51.6%]; P = .02) and compared with the late cohort in 2042 propensity-matched pairs (2-year OS, 51.3% [95% CI, 49.1%-53.6%] vs 45.4% [95% CI, 43.3%-47.7%]; P = .01). Patients who received adjuvant therapy more than 12 weeks after surgery (n = 683) had improved OS compared with surgery alone in both multivariable analysis (hazard ratio, 0.75; 95% CI, 0.66-0.85; P < .001) and 655 propensity-matched pairs (2-year OS, 47.2% [95% CI, 43.5%-51.3%] vs 38.0% [95% CI, 34.4%-42.0%]; P < .001).

CONCLUSIONS AND RELEVANCE

Patients with stage I to II pancreatic cancer who commenced adjuvant therapy within 28 to 59 days after primary surgical resection had improved survival outcomes compared with those with adjuvant therapy before 28 days or after 59 days. Patients who recovered slowly from surgery still benefited from delayed adjuvant therapy initiated more than 12 weeks after surgery compared with patients who underwent surgery only.

Association of pre-operative medication use with unplanned 30-day hospital readmission after surgery in oncology patients receiving comprehensive geriatric assessment

BACKGROUND

This study aimed to determine whether pre-operative medication use is associated with unplanned 30-day readmission in elderly people undergoing cancer surgery.

METHODS

Patients aged 65 years or older who were scheduled for cancer surgery and presented for comprehensive geriatric assessment were included. Comparisons of variables between patients with readmission and those without readmission were performed by univariate and multivariate analyses.

RESULTS

A total of 473 patients were included. Multivariate analysis showed that pre-operative discontinuation-requiring medications (PDRMs) and gastrointestinal/hepato-pancreato-biliary (GI/HPB) cancer were significant factors for 30-day readmission. PDRM increased the risk of readmission by about 2.2-fold. Attributable risk of PDRM to readmission was around 55%. The adjusted odds ratio and attributable risk for GI/HPB surgery was 3.4 (95% CI 1.0-11.5) and 70.8%, respectively.

CONCLUSIONS

Medication use has an impact on unplanned 30-day readmission in geriatric oncology patients, further highlighting the importance of medication optimization for elderly patients with cancer surgery.

Readmissions After Complex Cancer Surgery: Analysis of the Nationwide Readmissions Database

PURPOSE

Hospital readmissions after surgery are a focus of quality improvement efforts. Although some reflect appropriate care, others are potentially preventable readmissions (PPRs). We aim to describe the burden, timing, and factors associated with readmissions after complex cancer surgery.

METHODS

The Nationwide Readmissions Database (2013) was used to select patients undergoing a complex oncologic resection, which was defined as esophagectomy/gastrectomy, hepatectomy, pancreatectomy, colorectal resection, lung resection, or cystectomy. Readmissions within 30 days from discharge were analyzed. International Classification of Diseases (9th revision) primary diagnosis codes were reviewed to identify PPRs. Multivariable logistic regression analyses identified demographic, clinical, and hospital factors associated with readmissions.

RESULTS

Of the 59,493 eligible patients, 14% experienced a 30-day readmission, and 82% of these were deemed PPRs. Half of the readmissions occurred within the first 8 days of discharge. Infections (26%), GI complications (17%), and respiratory conditions (10%) accounted for most readmissions. Factors independently associated with an increased likelihood of readmission included Medicaid versus private insurance (odds ratio [OR], 1.32; 95% CI, 1.17 to 1.48), higher comorbidity score (OR, 1.5; 95% CI, 1.33 to 1.63), discharge to a facility (OR, 1.39; 95% CI, 1.29 to 1.51), prolonged length of stay (OR, 1.42; 95% CI, 1.32 to 1.52), and occurrence of a major in-hospital complication (OR, 1.24; 95% CI, 1.16 to 1.34).

CONCLUSION

One in seven patients undergoing complex cancer surgery suffered a readmission within 30 days. We identified common causes of these and identified patients at high risk for such an event. These data can be used by physicians, administrators, and policymakers to develop strategies to decrease readmissions.

How do studies assess the preventability of readmissions? A systematic review with narrative synthesis

Background

A large number of articles examined the preventability rate of readmissions, but comparison and interpretability of these preventability rates is complicated due to the large heterogeneity of methods that were used.

To compare (the implications of) the different methods used to assess the preventability of readmissions by means of medical record review.

Methods

A literature search was conducted in PUBMED and EMBASE using “readmission” and “avoidability” or “preventability” as key terms. A consensus-based narrative data synthesis was performed to compare and discuss the different methods.

Results

Abstracts of 2504 unique citations were screened resulting in 48 full text articles which were included in the final analysis. Synthesis led to the identification of a set of important variables on which the studies differed considerably (type of readmissions, sources of information, definition of preventability, cause classification and reviewer process). In 69% of the studies the cause classification and preventability assessment were integrated; meaning specific causes were predefined as preventable or not preventable. The reviewers were most often medical specialist (67%), and 27% of the studies added interview as a source of information.

Conclusion

A consensus-based standardised approach to assess preventability of readmission is warranted to reduce the unwanted bias in preventability rates. Patient-related and integrated care related factors are potentially underreported in readmission studies.

Electronic supplementary material

The online version of this article (10.1186/s12874-019-0766-0) contains supplementary material, which is available to authorized users.

Thirty-Day Readmissions in Patients With Metastatic Cancer: Room for Improvement?

Purpose:

Cancer, with readmission rates as high as 27%, has thus far been excluded from most readmission reduction efforts. However, some readmissions for patients with advanced disease may be avoidable. We assessed the prevalence of potentially preventable readmissions and associated factors in patients with metastatic cancer.

Patients and Methods:

Using a merged longitudinal data set of New York State hospital discharges and vital records, we measured 30-day readmissions for anemia, dehydration, diarrhea, emesis, fever, nausea, neutropenia, pain, pneumonia, and sepsis among patients with metastatic cancer between 2012 and 2014. We used competing-risk models to assess the effects of demographics, comorbidities, hospital type, payer, and discharge disposition.

Results:

A total of 11,275 patients had 19,307 hospitalizations. The 30-day readmission rate was 24.5%; 11.9% (n = 565) of readmissions were potentially preventable. Higher readmission rates occurred in black (hazard rate [HR], 1.26; 95% CI, 1.17 to 1.35), Hispanic (HR, 1.19; 95% CI, 1.09 to 1.31), and younger patients (HR per 10 years, 0.94; 95% CI, 0.90 to 0.97). Lower rates were associated with female sex (HR, 0.95; 95% CI, 0.91 to 0.99), private insurance (HR, 0.87; 95% CI, 0.87 to 0.81), teaching hospitals, and hospice discharge (HR, 0.62; 95% CI, 0.42 to 0.91). Discharge home with services (HR, 1.21; 95% CI, 1.14 to 1.27) or to a skilled nursing facility (HR, 1.11; 95% CI, 1.01 to 1.23) increased readmission likelihood. Potentially preventable readmissions were associated with younger age (HR per 10 years, 0.98; 95% CI, 0.98 to 0.99) and discharge home with services (HR, 1.25; 95% CI, 1.04 to 1.50). Likelihood decreased if care was received at a teaching hospital (HR, 0.76; 95% CI, 0.59 to 0.99). Payer, sex, race, and comorbidities did not contribute.

Conclusion:

Although the overall rate of potentially preventable readmissions among patients with metastatic cancer is low, higher readmission rates among those discharged home with help suggest that services supplied may not be sufficient to address health needs.

Characteristics of hospitalizations among patients receiving immune checkpoint inhibitors at a community teaching hospital

PURPOSE

As immune checkpoint inhibitors continue to acquire new indications, it is important to understand the impact their use has on patients. This study adds to current literature by presenting an analysis of hospitalizations in this population. The primary objective was to assess the reasons for an emergency department visit or hospital admission in patients who receive immune checkpoint inhibitors. Secondary objectives included identifying the frequency of suspected or confirmed immune related adverse events, types of immune related adverse events, number of preventable admissions, duration of immunotherapy, and length of stay.

METHODS

This study was a retrospective, multi-center, chart review of patients hospitalized after receiving an immune checkpoint inhibitor. The population included patients aged 18 and above who received at least one dose of an immune checkpoint inhibitor at a network facility and had a documented admission within one year following the initiation of immunotherapy. Descriptive statistics were performed along with inferential comparisons and a Poisson regression to determine if the immune checkpoint blocker or cancer type predicted admission or reason for admission.

RESULTS

The 99 patients who met inclusion criteria had a total of 202 admissions. Of these patients, 56 (56.6%) had multiple admissions within the year following initiation of immunotherapy. The most common diagnoses on initial admissions were shortness of breath, pain, and pneumonia. A total of 104 admissions (51.5%) were considered potentially preventable. Suspected or confirmed immune related adverse events were identified in 15.6% of all admissions. There were no significant predictors of admissions or reason for admission.

CONCLUSION

Reasons for admission in the study population were comparable to those identified in the general cancer population, with immune related adverse events being associated with a minority of both total and potentially preventable admissions.

Patient and Facility Demographics Related Outcomes in Early-Stage Non-Small Cell Lung Cancer Treated with Radiofrequency Ablation: A National Cancer Database Analysis

PURPOSE

To determine facility and patient demographics associated with survival in early-stage non-small cell lung cancer (NSCLC) treated with radiofrequency (RF) ablation.

MATERIALS AND METHODS

The National Cancer Database was queried for cases of stage 1a NSCLC treated with RF ablation without chemotherapy or radiotherapy from 2004 to 2014. High-volume centers (HVCs) were defined as the top 95^th percentile of facilities by number of procedures performed. Overall survival (OS) was estimated with the Kaplan-Meier method, and comparisons between survival curves were performed with the log-rank test. Propensity score-matched cohort analysis was performed. P values less than .05 were considered statistically significant.

RESULTS

In the final cohort, 967 cases were included. Estimated median survival and follow-up were 33.1 and 62.5 months, respectively. Of 305 facilities, 15 were determined to be HVCs, treating 13 or more patients from 2004 to 2014. A total of 335 cases (34.6%) were treated at HVCs. On multivariate Cox regression analysis, treatment at an HVC was independently associated with improved OS (hazard ratio [HR] = 0.766; P = .006). After propensity score adjustment, 1-, 3-, and 5-year OS was 89.8%, 51.2%, and 27.7%, respectively, for patients treated at HVCs, compared to 85.2%, 41.5%, and 19.6%, respectively, for patients treated at non-HVCs (P = .015). Increasing age (HR = 1.012; P = .013) and higher T-classification (HR = 1.392; P < .001) were independently associated with worse OS.

CONCLUSION

Patients with early-stage NSCLC treated with RF ablation at HVCs experienced a significant increase in OS, suggesting regionalization of lung cancer management as a means of improving outcomes.

Causes and correlates of 30 day and 180 day readmission following discharge from a Medicine for the Elderly Rehabilitation unit

Background

Recently hospitalized patients experience a period of generalized risk of adverse health events. This study examined reasons for, and predictors of, readmission to acute care facilities within 30 and 180 days of discharge from an inpatient rehabilitation unit for older people.

Methods

Routinely collected, linked clinical data on admissions to a single inpatient rehabilitation facility over a 13-year period were analysed. Data were available regarding demographics, comorbid disease, admission and discharge Barthel scores, length of hospital stay, and number of medications on discharge. Discharge diagnoses for the index admission and readmissions were available from hospital episode statistics. Univariate and multivariate Cox regression analyses were performed to identify baseline factors that predicted 30 and 180-day readmission.

Results

A total of 3984 patients were included in the analysis. The cohort had a mean age of 84.1 years (SD 7.4), and 39.7% were male. Overall, 5.6% (n = 222) and 23.2% (n = 926) of the patients were readmitted within 30 days and 180 days of discharge respectively. For patients readmitted to hospital, 26.6% and 21.1% of patients were readmitted with the same condition as their initial admission at 30 days and 180 respectively. For patients readmitted within 30 days, 13.5% (n = 30) were readmitted with the same condition with the most common diagnoses associated with readmission being chest infection, falls/immobility and stroke. For patients readmitted within 180 days, 12.4% (n = 115) of patients were readmitted with the same condition as the index condition with the most common diagnoses associated with readmission being falls/immobility, cancer and chest infections. In multivariable Cox regression analyses, older age, male sex, length of stay and heart failure predicted 30 or 180-day readmission. In addition, discharge from hospital to patients own home predicted 30-day readmission, whereas diagnoses of cancer, previous myocardial infarction or chronic obstructive pulmonary disease predicted 180-day readmission.

Conclusion

Most readmissions of older people after discharge from inpatient rehabilitation occurred for different reasons to the original hospital admission. Patterns of predictors for early and late readmission differed, suggesting the need for different mitigation strategies.

Patient Handoff Processes: Implementation and Effects of Bedside Handoffs, the Teach-Back Method, and Discharge Bundles on an Inpatient Oncology Unit

BACKGROUND

Bedside handoffs, the teach-back method, and discharge bundles have been shown to contribute to empowering patients to actively engage in their treatment.

OBJECTIVES

The objectives were to identify patient activation scores, patient readmission rates, and nursing staff satisfaction before and after implementing bedside handoffs, the teach-back method, and discharge bundles on an inpatient oncology unit at a large military treatment facility.

METHODS

A series of three cycles using the Plan-Do-Study-Act framework guided implementation of the multifaceted approach. Patient activation scores, readmission rates, staff satisfaction, and anecdotal feedback from patients and nursing staff were collected prior to and following implementation.

FINDINGS

The sample of patients with cancer had high patient activation scores. After implementation of the multifaceted approach, readmission rates decreased from 32% to 25%, and staff satisfaction improved.

Impact of Post-Discharge Disposition on Risk and Causes of Readmission Following Liver and Pancreas Surgery

BACKGROUND

The relationship between the post-discharge settings and the risk of readmission has not been well examined. We sought to identify the association between discharge destinations and readmission rates after liver and pancreas surgery.

METHODS

The 2013-2015 Medicare-Provider Analysis and Review (MEDPAR) database was reviewed to identify liver and pancreas surgical patients. Patients were subdivided into three groups based on discharge destination: home/self-care (HSC), home with home health assistance (HHA), and skilled nursing facility (SNF). The association between post-acute settings, readmission rates, and readmission causes was assessed.

RESULTS

Among 15,141 liver or pancreas surgical patients, 60% (n = 9046) were HSC, 26.9% (n = 4071) were HHA, and 13.4% (n = 2024) were SNF. Older, female patients and patients with ≥ 2 comorbidities, ≥ 2 previous admissions, an emergent index admission, an index complication, and ≥ 5-day length of stay were more likely to be discharged to HHA or SNF compared to HSC (all P < 0.001). Compared to HSC, HHA and SNF patients had a 34 and a 67% higher likelihood of 30-day readmission, respectively. The HHA and SNF settings were also associated with a 33 and a 69% higher risk of 90-day readmission. There was no association between discharge destination and readmission causes.

CONCLUSION

Among liver and pancreas surgical patients, HHA and SNF patients had a higher risk of readmission within 30 and 90 days. There was no difference in readmission causes and discharge settings. The association between discharge setting and the higher risk of readmission should be further evaluated as the healthcare system seeks to reduce readmission rates after surgery.

Readmission Patterns After GI Cancer Hospitalizations: The Medical Versus Surgical Patient

PURPOSE

Readmission within 30 days has been used as a metric for quality of care received at hospitals for certain diagnoses. In the era of accountability, value-based care, and increasing cancer costs, policymakers are looking into cancer readmissions as well. It is important to describe the readmission profile of patients with cancer in the most clinically relevant approach to inform policy and health care delivery that can positively impact patient outcomes.

PATIENTS AND METHODS

We conducted a retrospective cohort study using linked Texas Cancer Registry and Medicare claims data. We included elderly Texas residents diagnosed with GI cancer and identified risk factors for unplanned readmission using generalized estimating equations, comparing medical with surgical cancer-related hospitalizations.

RESULTS

We analyzed 69,693 hospitalizations from 31,736 patients. The unplanned readmission rate was higher after medical hospitalizations than after surgical hospitalizations (21.6% v 13.4%, respectively). Shared risk factors for readmission after medical and surgical hospitalizations included advanced disease stage, high comorbidity index, and emergency room visit and radiation therapy within 30 days before index hospitalization. Several other associated factors and reasons for readmission were noted to be unique to medical or surgical hospitalizations alone.

CONCLUSION

Unplanned readmissions among elderly patients with GI cancer are more common after medical hospitalizations compared with surgical hospitalizations. There are shared risk factors and unique risk factors for these hospitalizations that can inform policy, health care delivery, and interventions to reduce readmissions. Other findings underscore the importance of care coordination and comorbidity management in this patient population.

Predicting 7-day, 30-day and 60-day all-cause unplanned readmission: a case study of a Sydney hospital

BACKGROUND

The identification of patients at high risk of unplanned readmission is an important component of discharge planning strategies aimed at preventing unwanted returns to hospital. The aim of this study was to investigate the factors associated with unplanned readmission in a Sydney hospital. We developed and compared validated readmission risk scores using routinely collected hospital data to predict 7-day, 30-day and 60-day all-cause unplanned readmission.

METHODS

A combination of gradient boosted tree algorithms for variable selection and logistic regression models was used to build and validate readmission risk scores using medical records from 62,235 live discharges from a metropolitan hospital in Sydney, Australia.

RESULTS

The scores had good calibration and fair discriminative performance with c-statistic of 0.71 for 7-day and for 30-day readmission, and 0.74 for 60-day. Previous history of healthcare utilization, urgency of the index admission, old age, comorbidities related to cancer, psychosis, and drug-abuse, abnormal pathology results at discharge, and being unmarried and a public patient were found to be important predictors in all models. Unplanned readmissions beyond 7 days were more strongly associated with longer hospital stays and older patients with higher number of comorbidities and higher use of acute care in the past year.

CONCLUSIONS

This study demonstrates similar predictors and performance to previous risk scores of 30-day unplanned readmission. Shorter-term readmissions may have different causal pathways than 30-day readmission, and may, therefore, require different screening tools and interventions. This study also re-iterates the need to include more informative data elements to ensure the appropriateness of these risk scores in clinical practice.

Patterns of Hospital Performance on the Hospital-Wide 30-Day Readmission Metric: Is the Playing Field Level?

BACKGROUND

Hospital performance on the 30-day hospital-wide readmission (HWR) metric as calculated by the Centers for Medicare and Medicaid Services (CMS) is currently reported as a quality measure. Focusing on patient-level factors may provide an incomplete picture of readmission risk at the hospital level to explain variations in hospital readmission rates.

OBJECTIVE

To evaluate and quantify hospital-level characteristics that track with hospital performance on the current HWR metric.

DESIGN

Retrospective cohort study.

SETTING/PATIENTS

A total of 4785 US hospitals.

METRICS

We linked publically available data on individual hospitals published by CMS on patient-level adjusted 30-day HWR rates from July 1, 2011, through June 30, 2014, to the 2014 American Hospital Association annual survey. Primary outcome was performance in the worst CMS-calculated HWR quartile. Primary hospital-level exposure variables were defined as: size (total number of beds), safety net status (top quartile of disproportionate share), academic status [member of the Association of American Medical Colleges (AAMC)], National Cancer Institute Comprehensive Cancer Center (NCI-CCC) status, and hospital services offered (e.g., transplant, hospice, emergency department). Multilevel regression was used to evaluate the association between 30-day HWR and the hospital-level factors.

RESULTS

Hospital-level characteristics significantly associated with performing in the worst CMS-calculated HWR quartile included: safety net status [adjusted odds ratio (aOR) 1.99, 95% confidence interval (95% CI) 1.61-2.45, p < 0.001], large size (> 400 beds, aOR 1.42, 95% CI 1.07-1.90, p = 0.016), AAMC alone status (aOR 1.95, 95% CI 1.35-2.83, p < 0.001), and AAMC plus NCI-CCC status (aOR 5.16, 95% CI 2.58-10.31, p < 0.001). Hospitals with more critical care beds (aOR 1.26, 95% CI 1.02-1.56, p = 0.033), those with transplant services (aOR 2.80, 95% CI 1.48-5.31,p = 0.001), and those with emergency room services (aOR 3.37, 95% CI 1.12-10.15, p = 0.031) demonstrated significantly worse HWR performance. Hospice service (aOR 0.64, 95% CI 0.50-0.82, p < 0.001) and having a higher proportion of total discharges being surgical cases (aOR 0.62, 95% CI 0.50-0.76, p < 0.001) were associated with better performance.

LIMITATION

The study approach was not intended to be an alternate readmission metric to compete with the existing CMS metric, which would require a re-examination of patient-level data combined with hospital-level data.

CONCLUSION

A number of hospital-level characteristics (such as academic tertiary care center status) were significantly associated with worse performance on the CMS-calculated HWR metric, which may have important health policy implications. Until the reasons for readmission variability can be addressed, reporting the current HWR metric as an indicator of hospital quality should be reevaluated.

A supportive care in cancer unit reduces costs and hospitalizations for transfusions in a comprehensive cancer center

PURPOSE

Among patients with solid or hematologic malignancies undergoing oncologic therapies, blood product transfusions (BPT) are a relevant reason for planned/unplanned hospitalizations, as well as a possible cause of delay in administration of the oncologic therapies. Furthermore, they create additional costs for the healthcare system (HCS). The aim of this study was to compare the costs of performing BPT (erythrocytes and platelets) in medical units/wards to the costs derived from the administration of BPT in a dedicated outpatient supportive care in cancer unit (SCCU).

METHODS

Costs were analyzed from June 3, 2009 (when the SCCU started), until December 2013. Four inpatient oncologic units (bone marrow transplantation, radiotherapy, medical oncology I and II) were compared to the SCCU. Data regarding the transfusions performed by the SCCU of the patients who were previously hospitalized for transfusions were extracted, checked, and analyzed through a cross-check on the tax codes. Therefore, patients were considered suitable for the analysis if they had received BPT in the SCCU after a previous hospitalization for transfusion in one of the 4 units/wards. The average daily cost deriving from blood product units and from the hospitalization in each ward (irrespective of pharmaceutical expenses) was compared with the average daily cost deriving from blood product units and from the management of patients in the SCCU.

RESULTS

We analyzed 227 patients (112 female) with a mean age of 60 years (range 20-90) with hematologic malignancies in 79% of cases. The number of transfusions performed by the SCCU has grown constantly and consistently over the years, reaching 1,402 transfusions in 2013, thus exceeding the other considered units. The total savings for the HCS was €282.204.71, €151.182.85 in 2013 only. We saved €124.319,26 for each patient transfused at the SCCU.

CONCLUSIONS

A dedicated outpatient SCCU, aimed at monitoring and treating cancer therapy-related toxicities and comorbidities and in which it is also possible to perform BPT promptly and effectively, reduces the number of hospitalizations and provides an economical benefit for HCS.

Assessing the Performance of a Modified LACE Index (LACE-rt) to Predict Unplanned Readmission After Discharge in a Community Teaching Hospital

BACKGROUND

The LACE index was designed to predict early death or unplanned readmission after discharge from hospital to the community. However, implementing the LACE tool in real time in a teaching hospital required practical unavoidable modifications.

OBJECTIVE

The purpose of this study was to validate the implementation of a modified LACE index (LACE-rt) and test its ability to predict readmission risk using data in a hospital setting.

METHODS

Data from the Canadian Institute for Health Information's Discharge Abstract Database (DAD), the National Ambulatory Care Reporting System (NACRS), and the hospital electronic medical record for one large community hospital in Toronto, Canada, were used in this study. A total of 3855 admissions from September 2013 to July 2014 were analyzed (N=3855) using descriptive statistics, regression analysis, and receiver operating characteristic analysis. Prospectively collected data from DAD and NACRS were linked to inpatient data.

RESULTS

The LACE-rt index was a fair test to predict readmission risk (C statistic=.632). A LACE-rt score of 10 is a good threshold to differentiate between patients with low and high readmission risk; the high-risk patients are 2.648 times more likely to be readmitted than those at low risk. The introduction of LACE-rt had no significant impact on readmission reduction.

CONCLUSIONS

The LACE-rt is a fair tool for identifying those at risk of readmission. A collaborative cross-sectoral effort that includes those in charge of providing community-based care is needed to reduce readmission rates. An eHealth solution could play a major role in streamlining this collaboration.

Risk Factors for Readmission in Patients With Cancer Comanaged by Hospitalists

Little is known about which variables put patients with cancer at risk for 30-day hospital readmission. Comanagement of this often complex patient population by specialists and hospitalists has become increasingly common. This retrospective study examined inpatients with cancer comanaged by hospitalists, hematologists, and oncologists to determine the rate of readmission and factors associated with readmission. Patients in this cohort had a readmission rate of 23%. Patients who were discharged to a skilled nursing facility (odds ratio [OR] = 0.34) or hospice (OR = 0.11) were less likely to have 30-day readmissions, whereas patients who had surgery (OR = 3.16) during their index admission were more likely. Other factors, including patient demographics, cancer types, and hospitalization interventions and events, did not differ between patients who were readmitted and those who were not. These findings contribute to a growing body of literature identifying risk factors for readmission in medical oncology and hematology patients.

Geriatric assessment factors are associated with mortality after hospitalization in older adults with cancer

PURPOSE

Survival in older adults with cancer varies given differences in functional status, comorbidities, and nutrition. Prediction of factors associated with mortality, especially in hospitalized patients, allows physicians to better inform their patients about prognosis during treatment decisions. Our objective was to analyze factors associated with survival in older adults with cancer following hospitalization.

METHODS

Through a retrospective cohort study, we reviewed 803 patients who were admitted to Barnes-Jewish Hospital's Oncology Acute Care of Elders (OACE) unit from 2000 to 2008. Data collected included geriatric assessments from OACE screening questionnaires as well as demographic and medical history data from chart review. The primary end point was time from index admission to death. The Cox proportional hazard modeling was performed.

RESULTS

The median age was 72.5 years old. Geriatric syndromes and functional impairment were common. Half of the patients (50.4 %) were dependent in one or more activities of daily living (ADLs), and 74 % were dependent in at least one instrumental activity of daily living (IADLs). On multivariate analysis, the following factors were significantly associated with worse overall survival: male gender; a total score <20 on Lawton's IADL assessment; reason for admission being cardiac, pulmonary, neurologic, inadequate pain control, or failure to thrive; cancer type being thoracic, hepatobiliary, or genitourinary; readmission within 30 days; receiving cancer treatment with palliative rather than curative intent; cognitive impairment; and discharge with hospice services.

CONCLUSIONS

In older adults with cancer, certain geriatric parameters are associated with shorter survival after hospitalization. Assessment of functional status, necessity for readmission, and cognitive impairment may provide prognostic information so that oncologists and their patients make more informed, individualized decisions.

Elevated neutrophil to lymphocyte ratio predicts mortality in medical inpatients with multiple chronic conditions

Neutrophil to lymphocyte ratio (NLR) is an easy measurable laboratory marker used to evaluate systemic inflammation. Elevated NLR is associated with poor survival and increased morbidity in cancer and cardiovascular disease. However, the usefulness of NLR to predict morbidity and mortality in a hospital setting for patients with multiple chronic conditions has not been previously examined. In this study, we investigate the association between NLR and mortality in multimorbid medical inpatients. Two hundred thirty medical in-patients with chronic conditions were selected from a single academic medical center in Taiwan. Retrospective NLRs were calculated from routine full blood counts previously obtained during the initial hospital admission and at the time of discharge. Self-rated health (using a single-item question), medical disorders, depressive symptoms, and medical service utilization over a 1-year period were included in the analyses. Mortality outcomes were ascertained by reviewing electronic medical records and follow-up. The mortality rate at 2-year follow-up was 23%. Depression (odds ratio [OR] 1.9 [95% CI 1.0-3.7]), poor self-rated health (OR 2.1 [95% CI 1.1-3.9]), being hospitalized 2 or more times in the previous year (OR 2.3 [95% CI 1.2-4.6]), metastatic cancer (OR 4.7 [95% CI 2.3-9.7]), and chronic liver disease (OR 4.3 [95% CI 1.5-12.1]) were associated with 2-year mortality. The median (interquartile range) NLR at admission and discharge were 4.47 (2.4-8.7) and 3.65 (2.1-6.5), respectively. Two-year mortality rates were higher in patients with an elevated NLR at admission (NLR <3 = 15.5%, NLR >3 = 27.6%) and discharge (NLR < 3 = 14.7%, NLR >3 = 29.1%). Multivariate logistic regression demonstrated that an elevated NLR >3.0 at admission (OR 2.3 [95% CI 1.0-5.2]) and discharge (OR 2.3 [95% CI 1.1-5.0]) were associated with mortality independent of baseline age, sex, education, metastatic cancer, liver disease, depression, and previous hospitalization. Increased NLR is associated with mortality among medical inpatients with multiple chronic conditions. NLR may provide added value to predict both risk of mortality for the inpatients with chronic conditions, in addition to allowing predictions of likely hospital service needs such as re-admissions that are associated with long-term mortality.

Reducing Unplanned Medical Oncology Readmissions by Improving Outpatient Care Transitions: A Process Improvement Project at the Cleveland Clinic

Purpose:

Reducing 30-day unplanned hospital readmissions is a national policy priority. We examined the impact of a quality improvement project focused on reducing oncology readmissions among patients with cancer who were admitted to palliative and general medical oncology services at the Cleveland Clinic.

Methods:

Baseline rates of readmissions were gathered during the period from January 2013 to April 2014. A quality improvement project designed to improve outpatient care transitions was initiated during the period leading to April 1, 2014, including: (1) provider education, (2) postdischarge nursing phone calls within 48 hours, and (3) postdischarge provider follow-up appointments within 5 business days. Nursing callback components included symptom management, education, medication review/compliance, and follow-up appointment reminder.

Results:

During the baseline period, there were 2,638 admissions and 722 unplanned 30-day readmissions for an overall readmission rate of 27.4%. Callbacks and 5-day follow-up appointment monitoring revealed a mean monthly compliance of 72% and 78%, respectively, improving over time during the study period. Readmission rates declined by 4.5% to 22.9% (P < .01; relative risk reduction, 18%) during the study period. The mean direct cost of one readmission was $10,884, suggesting an annualized cost savings of $1.04 million with the observed reduction in unplanned readmissions.

Conclusion:

Modest readmission reductions can be achieved through better systematic transitions to outpatient care (including follow-up calls and early provider visits), thereby leading to a reduction in use of inpatient resources. These data suggest that efforts focused on improving outpatient care transition were effective in reducing unplanned oncology readmissions.

Unplanned 30-Day Readmissions in a General Internal Medicine Hospitalist Service at a Comprehensive Cancer Center

PURPOSE

Hospital readmissions are considered by the Centers for Medicare and Medicaid as a metric for quality of health care delivery. Robust data on the readmission profile of patients with cancer are currently insufficient to determine whether this measure is applicable to cancer hospitals as well. To address this knowledge gap, we estimated the unplanned readmission rate and identified factors influencing unplanned readmissions in a hospitalist service at a comprehensive cancer center.

METHODS

We retrospectively analyzed unplanned 30-day readmission of patients discharged from the General Internal Medicine Hospitalist Service at a comprehensive cancer center between April 1, 2012, and September 30, 2012. Multiple independent variables were studied using univariable and multivariable logistic regression models, with generalized estimating equations to identify risk factors associated with readmissions.

RESULTS

We observed a readmission rate of 22.6% in our cohort. The median time to unplanned readmission was 10 days. Unplanned readmission was more likely in patients with metastatic cancer and those with three or more comorbidities. Patients discharged to hospice were less likely to be readmitted (all P values < .01).

CONCLUSION

We observed a high unplanned readmission rate among our population of patients with cancer. The risk factors identified appear to be related to severity of illness and open up opportunities for improving coordination with primary care physicians, oncologists, and other specialists to manage comorbidities, or perhaps transition appropriate patients to palliative care. Our findings will be instrumental for developing targeted interventions to help reduce readmissions at our hospital. Our data also provide direction for appropriate application of readmission quality measures in cancer hospitals.