Risk prediction models for hospital readmission: a systematic review

Adaptable graph neural networks design to support generalizability for clinical event prediction

OBJECTIVE

While many machine learning and deep learning-based models for clinical event prediction leverage various data elements from electronic healthcare records such as patient demographics and billing codes, such models face severe challenges when tested outside of their institution of training. These challenges are rooted not only in differences in patient population characteristics, but medical practice patterns of different institutions.

METHOD

We propose a solution to this problem through systematically adaptable design of graph-based convolutional neural networks (GCNN) for clinical event prediction. Our solution relies on the unique property of GCNN where data encoded as graph edges is only implicitly used during the prediction process and can be adapted after model training without requiring model re-training.

RESULTS

Our adaptable GCNN-based prediction models outperformed all comparative models during external validation for two different clinical problems, while supporting multimodal data integration. For prediction of hospital discharge and mortality, the comparative fusion baseline model achieved 0.58 [0.52-0.59] and 0.81[0.80-0.82] AUROC on the external dataset while the GCNN achieved 0.70 [0.68-0.70] and 0.91 [0.90-0.92] respectively. For prediction of future unplanned transfusion, we observed even more gaps in performance due to missing/incomplete data in the external dataset - late fusion achieved 0.44[0.31-0.56] while the GCNN model achieved 0.70 [0.62-0.84].

CONCLUSION

These results support our hypothesis that carefully designed GCNN-based models can overcome generalization challenges faced by prediction models.

Factors associated with avoidable 30-day readmissions in patients with cancer: a single institution study

PURPOSE

Unplanned readmissions have profound medical and financial implications for patients and hospitals. Cancer patients are particularly susceptible to readmission and often face complex care needs. This quality improvement project aimed to identify factors associated with avoidable hospitalizations among oncology patients.

METHODS

Hospital discharges of adult cancer patients at Stony Brook University Hospital (June 2021-July 2022) were reviewed to identify unplanned 30-day readmissions. Readmissions were categorized as avoidable or unavoidable. Factors analyzed included patient demographics, cancer characteristics, social factors, outpatient follow-up, and palliative care involvement.

RESULTS

Of the 468 hospitalized cancer patients, 96 (21%) were readmitted within 30 days of discharge. Most readmitted patients had stage IV disease (51%). Fifty-seven percent of patients were symptomatic on index admission compared to 100% on readmission. Pain was the most frequently reported symptom, increasing from 36 patients (38%) on index admission to 54 (56%) on readmission (p < 0.001). Notably, 16 patients (17%) were discharged on comfort-focused care and 11 (12%) died inpatient on readmission. Palliative care was consulted 2.3 times more frequently during readmission compared to index admission. Readmissions were determined to be avoidable for 27 patients (28%). A complaint of failure to thrive on readmission (p < 0.04), no identifiable post-discharge caretaker (p < 0.009), being symptomatic at index admission (p < 0.04), and not attending an outpatient visit prior to readmission (p < 0.05) were associated with avoidable readmissions.

CONCLUSION

Timely outpatient support and early palliative care involvement to manage symptoms and optimize care transitions are readily addressable measures that may reduce avoidable readmissions among advanced-stage cancer patients.

Social Determinants of Health and 30-Day Readmission After Acute Myocardial Infarction in the REGARDS Study

BACKGROUND

Social determinants of health (SDOH) may influence 30-day readmission or emergency department (ED) use following acute myocardial infarction (AMI) hospitalizations. Understanding this relationship will promote the development of interventions and policies to reduce readmissions.

OBJECTIVES

The aim of the study was to test associations between SDOH and readmission after AMI.

METHODS

In this cross-sectional study, we analyzed 753 adults ≥65 years from the Reasons for Geographic and Racial Differences in Stroke study discharged after an AMI between 2003 and 2019. Participants were categorized into 3 groups (0/1, 2, and 3+) based on the number of SDOHs. Poisson models were used to determine relative risks (RRs) and corresponding 95% CI for the associations between SDOH and risk of readmission/ED visit.

RESULTS

Of participants, 39.1% (295/753) were women, 27.5% (207/753) were Black, and the median age was 77 years (72-82 years). There were 219 (29.1%) individuals with readmission/ED visit. Of 612 participants with validated SDOH counts, 273 (44.6%) had 0/1 SDOH, 117 (19.1%) had 2 SDOH, and 222 (36.3%) had 3+ SDOH. After adjusting for age and region, increasing number of SDOHs was associated with elevated readmission/ED visit risk (2 SDOH: RR: 1.15; 95% CI: 0.83-1.60; 3+ SDOH: RR: 1.56; 95% CI: 1.20-2.01; P trend = 0.001). Similar results were observed in the fully adjusted model (2 SDOH: RR: 1.12; 95% CI: 0.81-1.56; 3+ SDOH: RR: 1.37; 95% CI: 1.04-1.80; P trend = 0.026).

CONCLUSIONS

A cumulative burden of SDOHs is associated with an increased risk of readmission/ED visits after AMI hospitalization. SDOH burden may be a useful approach in identifying individuals presenting with AMI who are most vulnerable for readmission.

The role of continuous monitoring in acute-care settings for predicting all-cause 30-day hospital readmission: A pilot study

Background

Accurate prediction and prevention of hospital readmission remains a clinical challenge. The influence of different data sources, including remotely monitored continuous vital signs and activity, on machine learning (ML) models' performances is examined for predicting all-cause unplanned 30-day readmission.

Methods

Patients (n = 354) recruited in the emergency department and admitted to acute care at either hospital or home hospital settings are analyzed. Data sources included continuous vital signs and activity, electronic health record (EHR) data - episodic physiological monitoring of laboratory and vital signs, demographics, hospital utilization history, and quality of life survey measures. Five (5) machine learning classifiers were systematically trained by varying input data sources for readmission. Performances of ML models as well as the standard-of-care HOSPITAL score for readmissions were assessed with area under the receiver operating characteristic curve (AUROC) and area under precision-recall curve (AUPRC) statistics.

Results

There were 29 patients readmitted out of the 354 total included patients (an 8.2 % readmission rate). The average five-fold cross-validation AUROC and AUPRC scores of the five readmission models ranged from 0.76 to 0.84 (P > .05) and 0.23-0.49 (P < .05), respectively. The model input with episodic physiological monitoring (vitals and labs) had an AUPRC of 0.23 ± 0.07, while the model input with continuous vitals and activity data and episodic vitals and laboratory measurements had an AUPRC of as 0.49 ± 0.10 (P < .005). The HOSPITAL score had an AUROC of 0.62 and AUPRC of 0.16 in this pilot study.

Conclusions

The systematic ML modeling and analysis showcased diversity in predictive power and performances of patient data sources for predicting readmission. This pilot study suggests continuous vital signs and activity data, when added to episodic physiological monitoring, boosts performance. The HOSPITAL score shows low predictive power for readmission in this population. Predictive modeling of unplanned 30-day readmission improves with continuous vital signs and activity monitoring.

Using the Six-Minute Walk Test to Evaluate Functional Capacity of Children Undergoing a Surgical Repair of Congenital Heart Disease: Two Case Reports

Congenital heart disease (CHD) presents a range of structural abnormalities in the heart that are present at birth. Advances in surgical techniques have significantly improved outcomes for children with CHD. Common surgical procedures include repair of septal defects, such as atrial septal defects (ASDs) and ventricular septal defects (VSDs), as well as correction of complex anomalies such as tetralogy of Fallot (TOF) and transposition of the great arteries. Despite these advancements, it is crucial to assess the exercise capacity of children with CHD. This evaluation provides insights into their cardiovascular function and helps tailor appropriate exercise recommendations. Children with CHD often exhibit reduced exercise tolerance due to factors such as altered heart function, limited blood flow, or impaired oxygen delivery. Assessing their exercise capacity through standardized tests, such as the six-minute walk test (6MWT) or cardiopulmonary exercise test, helps clinicians gauge their functional abilities and determine any limitations. Understanding a child's exercise capacity guides medical management and also aids in designing personalized exercise programs to promote cardiovascular health and overall well-being. Regular assessments can track changes over time, ensuring optimal care and enhancing the quality of life (QOL) for children living with CHD. These two case reports examine the exercise capacity of two children with CHD who underwent surgery for VSD. Both children participated in the 6MWT, covering 223 and 183 meters, respectively. The physiological responses of these two CHD patients during the exercise test are discussed in this case series. This case series provides information regarding the cardiovascular adjustment during the 6MWT and various causes that affected them to complete the 6MWT.

Fluid status assessment in heart failure patients: pilot validation of the Maastricht Decompensation Questionnaire

BACKGROUND

eHealth products have the potential to enhance heart failure (HF) care by identifying at-risk patients. However, existing risk models perform modestly and require extensive data, limiting their practical application in clinical settings. This study aims to address this gap by validating a more suitable risk model for eHealth integration.

METHODS

We developed the Maastricht Decompensation Questionnaire (MDQ) based on expert opinion to assess HF patients' fluid status using common signs and symptoms. Subsequently, the MDQ was administered to a cohort of HF outpatients at Maastricht University Medical Centre. Patients with ≥ 10 MDQ points were categorised as 'decompensated', patients with < 10 MDQ points as 'not decompensated'. HF nurses, blinded to MDQ scores, served as the gold standard for fluid status assessment. Patients were classified as 'correctly' if MDQ and nurse assessments aligned; otherwise, they were classified as 'incorrectly'.

RESULTS

A total of 103 elderly HF patients were included. The MDQ classified 50 patients as 'decompensated', with 17 of them being correctly classified (34%). Additionally, 53 patients were categorised as 'not decompensated', with 48 of them being correctly classified (90%). The calculated area under the curve was 0.69 (95% confidence interval: 0.57-0.81; p < 0.05). Cronbach's alpha reliability coefficient for the MDQ was 0.85.

CONCLUSIONS

The MDQ helps identify decompensated HF patients through clinical signs and symptoms. Further trials with larger samples are needed to confirm its validity, reliability and applicability. Tailoring the MDQ to individual patient profiles may improve its accuracy.

Improving event prediction using general practitioner clinical judgement in a digital risk stratification model: a pilot study

BACKGROUND

Numerous tools based on electronic health record (EHR) data that predict risk of unscheduled care and mortality exist. These are often criticised due to lack of external validation, potential for low predictive ability and the use of thresholds that can lead to large numbers being escalated for assessment that would not have an adverse outcome leading to unsuccessful active case management. Evidence supports the importance of clinical judgement in risk prediction particularly when ruling out disease. The aim of this pilot study was to explore performance analysis of a digitally driven risk stratification model combined with GP clinical judgement to identify patients with escalating urgent care and mortality events.

METHODS

Clinically risk stratified cohort study of 6 GP practices in a deprived, multi-ethnic UK city. Initial digital driven risk stratification into Escalated and Non-escalated groups used 7 risk factors. The Escalated group underwent stratification using GP global clinical judgement (GCJ) into Concern and No concern groupings.

RESULTS

3968 out of 31,392 patients were data stratified into the Escalated group and further categorised into No concern (n = 3450 (10.9%)) or Concern (n = 518 (1.7%)) by GPs. The 30-day combined event rate (unscheduled care or death) per 1,000 was 19.0 in the whole population, 67.8 in the Escalated group and 168.0 in the Concern group (p < 0.001). The de-escalation effect of GP assessment into No Concern versus Concern was strongly negatively predictive (OR 0.25 (95%CI 0.19-0.33; p < 0.001)). The whole population ROC for the global approach (Non-escalated, GP No Concern, GP Concern) was 0.614 (0.592-0.637), p < 0.001, and the increase in the ROC area under the curve for 30-day events was all focused here (+ 0.4% (0.3-0.6%, p < 0.001), translating into a specific ROC c-statistic for GP GCJ of 0.603 ((0.565-0.642), p < 0.001).

CONCLUSIONS

The digital only component of the model performed well but adding GP clinical judgement significantly improved risk prediction, particularly by adding negative predictive value.

Assessment tools addressing avoidable care transitions in older adults: a systematic literature review

PURPOSE

The phenomenon of avoidable care transitions has received increasing attention over the last decades due to its frequency and associated burden for the patients and the healthcare system. A number of assessment tools to identify avoidable transitions have been designed and implemented. The selection of the most appropriate tool appears to be challenging and time-consuming. This systematic review aimed to identify and comprehensively describe the assessment tools that can support stakeholders´ care transition decisions on older adults.

METHODS

This study was conducted as part of the TRANS-SENIOR research network. A systematic search was conducted in MEDLINE via PubMed, CINAHL, and CENTRAL. No restrictions regarding publication date and language were applied.

RESULTS

The search in three electronic databases revealed 1266 references and screening for eligibility resulted in 58 articles for inclusion. A total of 48 assessment tools were identified covering different concepts, judgement processes, and transition destinations. We found variation in the comprehensiveness of the tools with regard to dimensions used in the judgement process.

CONCLUSION

All tools are not comprehensive with respect to the dimensions covered, as they address only one or a few perspectives. Although assessment tools can be useful in clinical practice, it is worth it to bear in mind that they are meant to support decision-making and supplement the care professional´s judgement, instead of replacing it. Our review might guide clinicians and researchers in choosing the right tool for identification of avoidable care transitions, and thus support informed decision-making.

Differential behaviour of a risk score for emergency hospital admission by demographics in Scotland-A retrospective study

The Scottish Patients at Risk of Re-Admission and Admission (SPARRA) score predicts individual risk of emergency hospital admission for approximately 80% of the Scottish population. It was developed using routinely collected electronic health records, and is used by primary care practitioners to inform anticipatory care, particularly for individuals with high healthcare needs. We comprehensively assess the SPARRA score across population subgroups defined by age, sex, ethnicity, socioeconomic deprivation, and geographic location. For these subgroups, we consider differences in overall performance, score distribution, and false positive and negative rates, using causal methods to identify effects mediated through age, sex, and deprivation. We show that the score is well-calibrated across subgroups, but that rates of false positives and negatives vary widely, mediated by various causes including variability in demographic characteristics, admission reasons, and potentially differential data availability. Our work assists practitioners in the application and interpretation of the SPARRA score in population subgroups.

Nursing Variables Predicting Readmissions in Patients With a High Risk: A Scoping Review

Unplanned readmission endangers patient safety and increases unnecessary healthcare expenditure. Identifying nursing variables that predict patient readmissions can aid nurses in providing timely nursing interventions that help patients avoid readmission after discharge. We aimed to provide an overview of the nursing variables predicting readmission of patients with a high risk. The authors searched five databases-PubMed, CINAHL, EMBASE, Cochrane Library, and Scopus-for publications from inception to April 2023. Search terms included "readmission" and "nursing records." Eight studies were included for review. Nursing variables were classified into three categories-specifically, nursing assessment, nursing diagnosis, and nursing intervention. The nursing assessment category comprised 75% of the nursing variables; the proportions of the nursing diagnosis (25%) and nursing intervention categories (12.5%) were relatively low. Although most variables of the nursing assessment category focused on the patients' physical aspect, emotional and social aspects were also considered. This study demonstrated how nursing care contributes to patients' adverse outcomes. The findings can assist nurses in identifying the essential nursing assessment, diagnosis, and interventions, which should be provided from the time of patients' admission. This can mitigate preventable readmissions of patients with a high risk and facilitate their safe transition from an acute care setting to the community.

External validation and comparative analysis of the HOSPITAL score and LACE index for predicting readmissions among patients hospitalised with community-acquired pneumonia in Australia

Objective Community-acquired pneumonia (CAP) is a leading cause of emergency hospitalisations globally and is associated with high readmission rates. Specific score systems developed for all medical conditions such as the HOSPITAL score and the LACE index can also usefully predict CAP readmissions. However, there is limited evidence regarding their performance in the Australian healthcare settings. Methods This multicentre retrospective study analysed adult CAP discharges from two metropolitan hospitals in South Australia between 1 January 2018 and 31 December 2023. Data for determining the HOSPITAL score and the LACE index were derived from electronic medical records. Demographic characteristics of patients readmitted within 30 days were compared with those who were not readmitted. The scores were evaluated for overall performance, discriminatory power and calibration, with discriminatory power assessed using the concordance statistic (C-statistic). Results Over 6years, 7245 CAP discharges were recorded, with 1329 (18.3%) readmissions within 30days. The mean (s.d.) age of the cohort was 74.4 (17.8) years. Readmitted patients were more likely to have multiple morbidities and frailty than those not readmitted (P <0.05). They also had a higher mean number of emergency department presentations and hospital admissions in the previous year and a longer initial hospital stay (P <0.05). Overall, the mean (s.d.) HOSPITAL score and LACE index were 3.4 (2.1) and 9.3 (3.6), respectively. Among readmissions, 28.4% occurred in patients with a HOSPITAL score >4 (intermediate and high-risk group), while 25.8% occurred in patients in the high-risk LACE category (LACE index>10). The C-statistic for the HOSPITAL score and LACE index was 0.62 (95% CI 0.61-0.64) and 0.63 (95% CI 0.61-0.65), respectively, with no significant difference in the area under the receiver operating characteristic curves (P >0.05). Conclusions The predictive abilities of the HOSPITAL score and the LACE index for CAP readmissions are modest and comparable in an Australian setting.

Medications for alcohol-use disorder and follow-up after hospitalization for alcohol withdrawal: A multicenter study

BACKGROUND

Alcohol withdrawal is a common reason for admission to acute care hospitals. Prescription of medications for alcohol-use disorder (AUD) and close outpatient follow-up are commonly recommended, but few studies report their effects on postdischarge outcomes.

OBJECTIVES

The objective of this study is to evaluate the effects of medications for AUD and follow-up appointments on readmission and abstinence.

METHODS

This retrospective cohort study evaluated veterans admitted for alcohol withdrawal to medical services at 19 Veteran Health Administration hospitals between October 1, 2018 and September 30, 2019. Factors associated with all-cause 30-day readmission and 6-month abstinence were examined using logistic regression.

RESULTS

Of the 594 patients included in this study, 296 (50.7%) were prescribed medications for AUD at discharge and 459 (78.5%) were discharged with follow-up appointments, including 251 (42.8%) with a substance-use clinic appointment, 191 (32.9%) with a substance-use program appointment, and 73 (12.5%) discharged to a residential program. All-cause 30-day readmission occurred for 150 patients (25.5%) and 103 (17.8%) remained abstinent at 6 months. Medications for AUD and outpatient discharge appointments were not associated with readmission or abstinence. Discharge to residential treatment program was associated with reduced 30-day readmission (adjusted odds ratio [AOR]: 0.39, 95% confidence interval [95% CI]: 0.18-0.82) and improved abstinence (AOR: 2.50, 95% CI: 1.33-4.73).

CONCLUSIONS

Readmission and return to heavy drinking are common for patients discharged for alcohol withdrawal. Medications for AUD were not associated with improved outcomes. The only intervention at the time of discharge that improved outcomes was discharge to residential treatment program, which was associated with decreased readmission and improved abstinence.

Implementing the MIRROR-TCM Randomised Control Trial During the COVID-19 Pandemic: A Mixed-Methods Evaluation

AIM

To evaluate the implementation of the Transitional Care Model (TCM), an evidence-based, advanced practice registered nurse-led multi-component intervention, as part of a randomised controlled trial during the first year of the COVID-19 pandemic.

DESIGN

Parallel convergent mixed-methods approach.

METHODS

Data for this study were collected between June 2020 and February 2021. Data from 78 patients who received the intervention and 68 recorded meetings with system leaders and clinical teams were analysed using descriptive statistics, directed content analysis, and joint display.

RESULTS

Fidelity to delivery of elements of the TCM components was variable, with the Hospital-to-Home visit elements having the widest range (14.3%-100%) and Maintaining Relationships elements having the highest range (97.3%-98.6%). There were 27 identified challenges and 15 strategies for implementing the TCM with fidelity during the pandemic.

CONCLUSION

The COVID-19 pandemic impacted all aspects of the delivery of the TCM across all sites. This historical event highlighted the need for services and support for patients and caregivers transitioning from the hospital to home.

IMPLICATIONS FOR NURSING AND PATIENT CARE

Evidence-based solutions are needed to enhance healthcare delivery and patient outcomes. Findings will guide nurses in implementing proven transitional care interventions.

IMPACT

Findings will inform the implementation and scaling of transitional care and other evidence-based interventions across diverse healthcare settings.

REPORTING METHOD

GRAMMS reporting guidelines.

PATIENT OR PUBLIC CONTRIBUTION

No patient or public contribution.

TRIAL REGISTRATION

ClinicalTrials.gov identifier: NCT04212962. https://www.

CLINICALTRIALS

gov/study/NCT04212962?titles=NCT04212962&rank=1.

Transitional Care Support for Medicaid-Insured Patients With Serious Mental Illness: Protocol for a Type I Hybrid Effectiveness-Implementation Stepped-Wedge Cluster Randomized Controlled Trial

BACKGROUND

People diagnosed with a co-occurring serious mental illness (SMI; ie, major depressive disorder, bipolar disorder, or schizophrenia) but hospitalized for a nonpsychiatric condition experience higher rates of readmissions and other adverse outcomes, in part due to poorly coordinated care transitions. Current hospital-to-home transitional care programs lack a focus on the integrated social, medical, and mental health needs of these patients. The Thrive clinical pathway provides transitional care support for patients insured by Medicaid with multiple chronic conditions by focusing on posthospitalization medical concerns and the social determinants of health. This study seeks to evaluate an adapted version of Thrive that also meets the needs of patients with co-occurring SMI discharged from a nonpsychiatric hospitalization.

OBJECTIVE

This study aimed to (1) engage staff and community advisors in participatory implementation processes to adapt the Thrive clinical pathway for all Medicaid-insured patients, including those with SMI; (2) examine utilization outcomes (ie, Thrive referral, readmission, emergency department [ED], primary, and specialty care visits) for Medicaid-insured individuals with and without SMI who receive Thrive compared with usual care; and (3) evaluate the acceptability, appropriateness, feasibility, and cost-benefit of an adapted Thrive clinical pathway that is tailored for Medicaid-insured patients with co-occurring SMI.

METHODS

This study will use a prospective, type I hybrid effectiveness-implementation, stepped-wedge, cluster randomized controlled trial design. We will randomize the initiation of Thrive referrals at the unit level. Data collection will occur over 24 months. Inclusion criteria for Thrive referral include individuals who (1) are Medicaid insured, dually enrolled in Medicaid and Medicare, or Medicaid eligible; (2) reside in Philadelphia; (3) are admitted for a medical diagnosis for over 24 hours at the study hospital; (4) are planned for discharge to home; (5) agree to receive home care services; and (6) are aged ≥18 years. Primary analyses will use a mixed-effects negative binomial regression model to evaluate readmission and ED utilization, comparing those with and without SMI who receive Thrive to those with and without SMI who receive usual care. Using a convergent parallel mixed methods design, analyses will be conducted simultaneously for the survey and interview data of patients, clinicians, and health care system leaders. The cost of Thrive will be calculated from budget monitoring data for the research budget, the cost of staff time, and average Medicaid facility fee payments.

RESULTS

This research project was funded in October 2023. Data collection will occur from April 2024 through December 2025. Results are anticipated to be published in 2025-2027.

CONCLUSIONS

We anticipate that patients with and without co-occurring SMI will benefit from the adapted Thrive clinical pathway. We also anticipate the adapted version of Thrive to be deemed feasible, acceptable, and appropriate by patients, clinicians, and health system leaders.

TRIAL REGISTRATION

ClinicalTrials.gov NCT06203509; https://clinicaltrials.gov/ct2/show/NCT06203509.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/64575.

Analysis of Clinical Criteria for Discharge Among Patients Hospitalized for COVID-19: Development and Validation of a Risk Prediction Model

BACKGROUND

Patients hospitalized with COVID-19 can clinically deteriorate after a period of initial stability, making optimal timing of discharge a clinical and operational challenge.

OBJECTIVE

To determine risks for post-discharge readmission and death among patients hospitalized with COVID-19.

DESIGN

Multicenter retrospective observational cohort study, 2020-2021, with 30-day follow-up.

PARTICIPANTS

Adults admitted for care of COVID-19 respiratory disease between March 2, 2020, and February 11, 2021, to one of 180 US hospitals affiliated with the HCA Healthcare system.

MAIN MEASURES

Readmission to or death at an HCA hospital within 30 days of discharge was assessed. The area under the receiver operating characteristic curve (AUC) was calculated using an internal validation set (33% of the HCA cohort), and external validation was performed using similar data from six academic centers associated with a hospital medicine research network (HOMERuN).

KEY RESULTS

The final HCA cohort included 62,195 patients (mean age 61.9 years, 51.9% male), of whom 4704 (7.6%) were readmitted or died within 30 days of discharge. Independent risk factors for death or readmission included fever within 72 h of discharge; tachypnea, tachycardia, or lack of improvement in oxygen requirement in the last 24 h; lymphopenia or thrombocytopenia at the time of discharge; being ≤ 7 days since first positive test for SARS-CoV-2; HOSPITAL readmission risk score ≥ 5; and several comorbidities. Inpatient treatment with remdesivir or anticoagulation were associated with lower odds. The model's AUC for the internal validation set was 0.73 (95% CI 0.71-0.74) and 0.66 (95% CI 0.64 to 0.67) for the external validation set.

CONCLUSIONS

This large retrospective study identified several factors associated with post-discharge readmission or death in models which performed with good discrimination. Patients 7 or fewer days since test positivity and who demonstrate potentially reversible risk factors may benefit from delaying discharge until those risk factors resolve.

Impact of wearable device data and multi-scale entropy analysis on improving hospital readmission prediction

OBJECTIVE

Unplanned readmissions following a hospitalization remain common despite significant efforts to curtail these. Wearable devices may offer help identify patients at high risk for an unplanned readmission.

MATERIALS AND METHODS

We conducted a multi-center retrospective cohort study using data from the All of Us data repository. We included subjects with wearable data and developed a baseline Feedforward Neural Network (FNN) model and a Long Short-Term Memory (LSTM) time-series deep learning model to predict daily, unplanned rehospitalizations up to 90 days from discharge. In addition to demographic and laboratory data from subjects, post-discharge data input features include wearable data and multiscale entropy features based on intraday wearable time series. The most significant features in the LSTM model were determined by permutation feature importance testing.

RESULTS

In sum, 612 patients met inclusion criteria. The complete LSTM model had a higher area under the receiver operating characteristic curve than the FNN model (0.83 vs 0.795). The 5 most important input features included variables from multiscale entropy (steps) and number of active steps per day.

DISCUSSION

Data available from wearable devices can improve ability to predict readmissions. Prior work has focused on predictors available up to discharge or on additional data abstracted from wearable devices. Our results from 35 institutions highlight how multiscale entropy can improve readmission prediction and may impact future work in this domain.

CONCLUSION

Wearable data and multiscale entropy can improve prediction of a deep-learning model to predict unplanned 90-day readmissions. Prospective studies are needed to validate these findings.

Association of Social Determinants of Health With Hospital Readmission and Mortality: A Prospective Cohort Study

BACKGROUND

The relative contributions of common patient-reported social determinants of health on 30- and 90-day post-discharge outcomes among patients with acute coronary syndromes (ACS) is unclear.

OBJECTIVE

The aim of this article is to examine the independent associations of social determinants with readmission or death, accounting for medical history.

METHODS

Participants included adults who were hospitalized with ACS at an academic medical center. Domains measured were social support, health literacy/numeracy, and socioeconomic status (SES) (including education and difficulty paying bills). We employed multivariable Cox proportional hazard models to study associations with time to all-cause readmission or death, up to 30 or 90 days after discharge, and adjusted for demographics and medical history (prior admissions and Elixhauser comorbidity index).

KEY RESULTS

Among 1,168 patients with ACS and no history of heart failure, more prior admissions, and higher comorbidity index (the medical history domain) were associated with higher rates of 30- and 90-day readmission or death (domain p values <.01 and <.0001, respectively). The social support domain was not associated with outcomes. Higher health literacy and numeracy were associated with lower rates of 30- and 90-day readmission or death (domain p values .016 and .002, respectively). Higher education and less difficulty paying bills (the SES domain) was marginally associated with lower rates of 90-day readmission or death (domain, p = .052).

CONCLUSIONS

In addition to medical history, the domain of health literacy and numeracy was independently associated with readmission or death of patients with ACS during the 90 days after hospital discharge. [HLRP: Health Literacy Research and Practice. 2024;8(4):e212-e223.].

Enhancing readmission prediction models by integrating insights from home healthcare notes: Retrospective cohort study

BACKGROUND

Hospital readmission is an important indicator of inpatient care quality and a significant driver of increasing medical costs. Therefore, it is important to explore the effects of postdischarge information, particularly from home healthcare notes, on enhancing readmission prediction models. Despite the use of Natural Language Processing (NLP) and machine learning in prediction model development, current studies often overlook insights from home healthcare notes.

OBJECTIVE

This study aimed to develop prediction models for 30-day readmissions using home healthcare notes and structured data. In addition, it explored the development of 14- and 180-day prediction models using variables in the 30-day model.

DESIGN

A retrospective observational cohort study.

SETTING(S)

This study was conducted at Ajou University School of Medicine in South Korea.

PARTICIPANTS

Data from electronic health records, encompassing demographic characteristics of 1819 participants, along with information on conditions, drug, and home healthcare, were utilized.

METHODS

Two distinct models were developed for each prediction window (30-, 14-, 180-day): the traditional model, which utilized structured variables alone, and the common data model (CDM)-NLP model, which incorporated structured and topic variables extracted from home healthcare notes. BERTopic facilitated topic generation and risk probability, representing the likelihood of documents being assigned to specific topics. Feature selection involved experimenting with various algorithms. The best-performing algorithm, determined using the area under the receiver operating characteristic curve (AUROC), was used for model development. Model performance was assessed using various learning metrics including AUROC.

RESULTS

Among 1819 patients, 251 (13.80 %) experienced 30-day readmission. The least absolute shrinkage and selection operator was used for feature extraction and model development. The 15 structured features were used in the traditional model. Moreover, five additional topic variables from the home healthcare notes were applied in the CDM-NLP model. The AUROC of the traditional model was 0.739 (95 % CI: 0.672-0.807). The AUROC of the CDM-NLP model was high at 0.824 (95 % CI: 0.768-0.880), which indicated an outstanding performance. The topics in the CDM-NLP model included emotional distress, daily living functions, nutrition, postoperative status, and cardiorespiratory issues. In extended prediction model development for 14- and 180-day readmissions, the CDM-NLP consistently outperformed the traditional model.

CONCLUSIONS

This study developed effective prediction models using both structured and unstructured data, thereby emphasizing the significance of postdischarge information from home healthcare notes in readmission prediction.

Paramedic-Assisted Community Evaluation After Discharge: The PACED Intervention

OBJECTIVES

Early rehospitalization of frail older adults after hospital discharge is harmful to patients and challenging to hospitals. Mobile integrated health (MIH) programs may be an effective solution for delivering community-based transitional care. The objective of this study was to assess the feasibility and implementation of an MIH transitional care program.

DESIGN

Pilot clinical trial of a transitional home visit conducted by MIH paramedics within 72 hours of hospital discharge.

SETTING AND PARTICIPANTS

Patients aged ≥65 years discharged from an urban hospital with a system-adapted eFrailty index ≥0.24 were eligible to participate.

METHODS

Participants were enrolled after hospital discharge. Demographic and clinical information were recorded at enrollment and 30 days after discharge from the electronic health record. Data from a comparison group of patients excluded from enrollment due to geographical location was also abstracted. Primary outcomes were intervention feasibility and implementation, which were reported descriptively. Exploratory clinical outcomes included emergency department (ED) visits and rehospitalization within 30 days. Categorical and continuous group comparisons were conducted using χ2 tests and Kruskal-Wallis testing. Binomial regression was used for comparative outcomes.

RESULTS

One hundred of 134 eligible individuals (74.6%) were enrolled (median age 81, 64% female). Forty-seven participants were included in the control group (median age 80, 55.2% female). The complete protocol was performed in 92 (92.0%) visits. Paramedics identified acute clinical problems in 23 (23.0%) visits, requested additional services for participants during 34 (34.0%) encounters, and detected medication errors during 34 (34.0%). The risk of 30-day rehospitalization was lower in the Paramedic-Assisted Community Evaluation after Discharge (PACED) group compared with the control (RR, 0.40; CI, 0.19-0.84; P = .03); there was a trend toward decreased risk of 30-day ED visits (RR, 0.61; CI, 0.37-1.37; P = .23).

CONCLUSIONS AND IMPLICATIONS

This pilot study of an MIH transition care program was feasible with high protocol fidelity. It yields preliminary evidence demonstrating a decreased risk of rehospitalization in frail older adults.

Behavioral, Cognitive, and Functional Risk Factors for Repeat Hospital Episodes Among Medicare-Medicaid Dually Eligible Adults Receiving Long-Term Services and Supports

Repeat hospitalizations adversely impact the well-being of adults dually eligible for Medicare and Medicaid in the United States. This study aimed to identify behavioral, cognitive, and functional characteristics associated with the risk of a repeat hospital episode (HE) among the statewide population of dually eligible adults in Maryland receiving long-term services and supports prior to an HE between July 2018 and May 2020. The odds of experiencing a repeat HE within 30 days after an initial HE were positively associated with reporting difficulty with hearing (adjusted odds ratio, AOR: 1.10 [95% confidence interval: 1.02-1.19]), being easily distractible (AOR: 1.09 [1.00-1.18]), being self-injurious (AOR: 1.33 [1.09-1.63]), and exhibiting verbal abuse (AOR: 1.15 [1.02-1.30]). Conversely, displaying inappropriate public behavior (AOR: 0.62 [0.42-0.92]) and being dependent for eating (AOR: 0.91 [0.83-0.99]) or bathing (AOR: 0.79 [0.67-0.92]) were associated with reduced odds of a repeat HE. We also observed differences in the magnitude and direction of these associations among adults 65 years of age or older relative to younger counterparts.

Impact of time from discharge to readmission on outcomes: an observational study from the US National Readmission Database

BACKGROUND

The Hospital Readmission Reduction Programme (HRRP) was created to decrease the number of hospital readmissions for acute myocardial infarction (AMI), chronic obstructive pulmonary disease (COPD), heart failure (HF), pneumonia (PNA), coronary artery bypass graft (CABG), elective total hip arthroplasty (THA) and total knee arthroplasty.

OBJECTIVES

To analyse the impact of the HRRP on readmission rates from 2010 to 2019 and how time to readmission impacted outcomes.

DESIGN

Population-based retrospective study.

SETTING

All patients included in the US National Readmission database from 2010 to 2019.

PATIENTS

We recorded demographic and clinical variables.

MEASUREMENTS

Using linear regression models, we analysed the association between readmission status and timing with death and length of stay (LOS) outcomes. We transformed LOS and charges into log-LOS and log-charges to normalise the data.

RESULTS

There were 31 553 363 records included in the study. Of those, 4 593 228 (14.55%) were readmitted within 30 days. From 2010 to 2019, readmission rates for COPD (20.8%-19.8%), HF (24.9%-21.9%), PNA (16.4%-15.1%), AMI (15.6%-12.9%) and TKR (4.1%-3.4%) decreased whereas CABG (10.2%-10.6%) and THA (4.2%-5.8%) increased. Readmitted patients were at higher risk of mortality (6% vs 2.8%) and had higher LOS (3 (2-5) vs 4 (3-7)). Patients readmitted within 10 days had a mortality 6.4% higher than those readmitted in 11-20 days (5.4%) and 21-30 days (4.6%). Increased time from discharge to readmission was associated with a lower likelihood of mortality, like LOS.

CONCLUSION

Over the last 10 years, readmission rates decreased for most conditions included in the HRRP except CABG and THA. Patients readmitted shortly after discharge were at higher risk of death.

Using machine learning methods to predict all-cause somatic hospitalizations in adults: A systematic review

AIM

In this review, we investigated how Machine Learning (ML) was utilized to predict all-cause somatic hospital admissions and readmissions in adults.

METHODS

We searched eight databases (PubMed, Embase, Web of Science, CINAHL, ProQuest, OpenGrey, WorldCat, and MedNar) from their inception date to October 2023, and included records that predicted all-cause somatic hospital admissions and readmissions of adults using ML methodology. We used the CHARMS checklist for data extraction, PROBAST for bias and applicability assessment, and TRIPOD for reporting quality.

RESULTS

We screened 7,543 studies of which 163 full-text records were read and 116 met the review inclusion criteria. Among these, 45 predicted admission, 70 predicted readmission, and one study predicted both. There was a substantial variety in the types of datasets, algorithms, features, data preprocessing steps, evaluation, and validation methods. The most used types of features were demographics, diagnoses, vital signs, and laboratory tests. Area Under the ROC curve (AUC) was the most used evaluation metric. Models trained using boosting tree-based algorithms often performed better compared to others. ML algorithms commonly outperformed traditional regression techniques. Sixteen studies used Natural language processing (NLP) of clinical notes for prediction, all studies yielded good results. The overall adherence to reporting quality was poor in the review studies. Only five percent of models were implemented in clinical practice. The most frequently inadequately addressed methodological aspects were: providing model interpretations on the individual patient level, full code availability, performing external validation, calibrating models, and handling class imbalance.

CONCLUSION

This review has identified considerable concerns regarding methodological issues and reporting quality in studies investigating ML to predict hospitalizations. To ensure the acceptability of these models in clinical settings, it is crucial to improve the quality of future studies.

The Clinical Impact of Hospital-Acquired Adverse Drug Reactions in Older Adults: An Australian Cohort Study

INTRODUCTION

Hospital-acquired adverse drug reactions (HA-ADRs) are common in older adults. However, there is limited knowledge regarding the association between HA-ADRs and adverse clinical outcomes.

OBJECTIVE

To investigate the incidence and characteristics of HA-ADRs in older adults, and any association with mortality, length of stay, and readmissions.

DESIGN

Prospective cohort study.

SETTING AND PARTICIPANTS

Flinders Medical Centre, a large tertiary referral hospital in Adelaide, South Australia. Older adults admitted under the General Medicine and Acute Care of the Elderly units with no previous diagnosis of dementia.

METHODS

All patients had a Multidimensional Prognostic Index (MPI) assessment performed within 3 days of the admission. Data collected included age, gender, estimated glomerular filtration rate (eGFR), length of stay, readmissions, and mortality. HA-ADRs were identified by review of individual discharge summaries. Univariate and multivariate analyses were performed to investigate associations with clinical outcomes including mortality, length of stay, and readmissions. Exploratory analyses were performed for HA-ADR groups based on Medical Dictionary for Regulatory Activities System Organ Class and World Health Organization Anatomical Therapeutic Chemical classifications that accounted for ≥10% of all HA-ADRs.

RESULTS

There were 737 patients in the cohort with 72 having experienced a HA-ADRs (incidence = 9.8%). Patients with an HA-ADR had increased length of stay and 30-day readmissions compared with those without an HA-ADR. In multivariate analysis, the number of HA-ADRs was associated with in-hospital mortality and length of stay but not post-discharge mortality or readmissions within 30 days. In exploratory analyses, patients with an HA-ADR to antibacterial drugs had significantly higher rates of in-hospital mortality compared with those without these reactions.

CONCLUSIONS AND IMPLICATIONS

The number of HA-ADRs are associated with in-hospital mortality and length of stay in older Australian inpatients. The occurrence of HA-ADRs may be a trigger to offer advice to prescribers to prevent future ADRs to similar agents and proactively manage disease to improve health outcomes.

A Systematic Review of Recent Studies on Hospital Readmissions of Patients With Diabetes

Hospital readmissions are a major area of concern across the healthcare ecosystem. Diabetes mellitus (DM) and associated complications significantly contributed to hospital readmissions in 2018, placing it among the leading causes alongside septicemia and heart failure. Diabetes is an urgent public health concern that has reached epidemic proportions globally. Compared to the early 2000s, the prevalence of diabetes among individuals aged 20-79 years in the US has significantly increased. This research provides an in-depth examination of diabetes-related hospital readmissions and reviews recent studies (2015-2023) to understand the characteristics, risk factors, and potential outcomes for re-admitted diabetes patients. The study identified 21 articles that met the inclusion criteria to provide valuable insights and analyze risk factors associated with these readmissions. The findings indicated that risk factors such as age, demographics, income, insurance type, severity of illness, and comorbidities among diabetic patients were critical and warranted further investigation. Diabetes awareness, quality of hospital care, involvement of healthcare providers, timely screening, and lifestyle changes were noted as important factors to improve the effectiveness of healthcare delivery, reduce diabetes-related complications, and eventually lower preventable hospital readmissions.

Comparison of machine-learning and logistic regression models for prediction of 30-day unplanned readmission in electronic health records: A development and validation study

It is expected but unknown whether machine-learning models can outperform regression models, such as a logistic regression (LR) model, especially when the number and types of predictor variables increase in electronic health records (EHRs). We aimed to compare the predictive performance of gradient-boosted decision tree (GBDT), random forest (RF), deep neural network (DNN), and LR with the least absolute shrinkage and selection operator (LR-LASSO) for unplanned readmission. We used EHRs of patients discharged alive from 38 hospitals in 2015-2017 for derivation and in 2018 for validation, including basic characteristics, diagnosis, surgery, procedure, and drug codes, and blood-test results. The outcome was 30-day unplanned readmission. We created six patterns of data tables having different numbers of binary variables (that ≥5% or ≥1% of patients or ≥10 patients had) with and without blood-test results. For each pattern of data tables, we used the derivation data to establish the machine-learning and LR models, and used the validation data to evaluate the performance of each model. The incidence of outcome was 6.8% (23,108/339,513 discharges) and 6.4% (7,507/118,074 discharges) in the derivation and validation datasets, respectively. For the first data table with the smallest number of variables (102 variables that ≥5% of patients had, without blood-test results), the c-statistic was highest for GBDT (0.740), followed by RF (0.734), LR-LASSO (0.720), and DNN (0.664). For the last data table with the largest number of variables (1543 variables that ≥10 patients had, including blood-test results), the c-statistic was highest for GBDT (0.764), followed by LR-LASSO (0.755), RF (0.751), and DNN (0.720), suggesting that the difference between GBDT and LR-LASSO was small and their 95% confidence intervals overlapped. In conclusion, GBDT generally outperformed LR-LASSO to predict unplanned readmission, but the difference of c-statistic became smaller as the number of variables was increased and blood-test results were used.

A case vignette study to refine the target group of an intermediate care model: the Acute Geriatric Community Hospital

PURPOSE

To refine the admission criteria of the Acute Geriatric Community Hospital (AGCH) by defining its target group boundaries with (geriatric) hospital care and other bed-based intermediate care models in the Netherlands.

METHODS

A qualitative study consisting of a three-phase refinement procedure with case vignettes. Physicians, medical specialists, nurse practitioners, and physician assistants in hospitals (n = 10) or intermediate care facilities (n = 10) in the Netherlands participated. They collected case vignettes from clinical practice (phase one). The referral considerations and decisions for each case were then documented through surveys (phase two) and two focus groups (phase 3). For thematic data analysis, inductive and deductive approaches were used.

RESULTS

The combination of medical specialist care (MSC) and medical generalist care (MGC), is unique for the AGCH compared to other intermediate care models in the Netherlands. Compared to (geriatric) hospital care, the AGCH offers a more limited scope of MSC. Based on these findings, 13 refined admission criteria were developed such as 'The required diagnostic tests to monitor the effectiveness of treatment are available at the AGCH'. Besides admission criteria, additional clinical and organizational considerations played a role in referral decision-making; 10 themes were identified.

CONCLUSION

This case vignette study defined the target group boundaries between the AGCH and other care models, allowing us to refine the AGCH admission criteria. Our findings may help to determine the required competencies of the interdisciplinary AGCH team and to develop triage instruments. The identified consideration themes can be used as conceptual framework in further research. The findings may also be of interests for healthcare systems outside the Netherlands who aspire to design integrated care for older people closer to home.

Predicting Outcomes in Frail Older Community-Dwellers in Western Australia: Results from the Community Assessment of Risk Screening and Treatment Strategies (CARTS) Programme

Understanding risk factors for frailty, functional decline and incidence of adverse healthcare outcomes amongst community-dwelling older adults is important to plan population-level health and social care services. We examined variables associated with one-year risk of institutionalisation, hospitalisation and death among patients assessed in their own home by a community-based Aged Care Assessment Team (ACAT) in Western Australia. Frailty and risk were measured using the Clinical Frailty Scale (CFS) and Risk Instrument for Screening in the Community (RISC), respectively. Predictive accuracy was measured from the area under the curve (AUC). Data from 417 patients, median 82 ± 10 years, were included. At 12-month follow-up, 22.5% (n = 94) were institutionalised, 44.6% (n = 186) were hospitalised at least once and 9.8% (n = 41) had died. Frailty was common, median CFS score 6/9 ± 1, and significantly associated with institutionalisation (p = 0.001), hospitalisation (p = 0.007) and death (p < 0.001). Impaired activities of daily living (ADL) measured on the RISC had moderate correlation with admission to long-term care (r = 0.51) and significantly predicted institutionalisation (p < 0.001) and death (p = 0.01). The RISC had the highest accuracy for institutionalisation (AUC 0.76). The CFS and RISC had fair to good accuracy for mortality (AUC of 0.69 and 0.74, respectively), but neither accurately predicted hospitalisation. Home assessment of community-dwelling older patients by an ACAT in Western Australia revealed high levels of frailty, ADL impairment and incident adverse outcomes, suggesting that anticipatory care planning is imperative for these patients.

Multimodal Machine Learning for Prediction of 30-Day Readmission Risk in Elderly Population

BACKGROUND

Readmission within 30 days is a prevalent issue among elderly patients, linked to unfavorable health outcomes. Our objective was to develop and validate multimodal machine learning models for predicting 30-day readmission risk in elderly patients discharged from internal medicine departments.

METHODS

This was a retrospective cohort study which included elderly patients aged 75 or older, who were hospitalized at the Hadassah Medical Center internal medicine departments between 2014 and 2020. Three machine learning algorithms were developed and employed to predict 30-day readmission risk. The primary measures were predictive model performance scores, specifically area under the receiver operator curve (AUROC), and average precision.

RESULTS

This study included 19,569 admissions. Of them, 3258 (16.65%) resulted in 30-day readmission. Our 3 proposed models demonstrated high accuracy and precision on an unseen test set, with AUROC values of 0.87, 0.89, and 0.93, respectively, and average precision values of 0.76, 0.78, and 0.81. Feature importance analysis revealed that the number of admissions in the past year, history of 30-day readmission, Charlson score, and admission length were the most influential variables. Notably, the natural language processing score, representing the probability of readmission according to a textual-based model trained on social workers' assessment letters during hospitalization, ranked among the top 10 contributing factors.

CONCLUSIONS

Leveraging multimodal machine learning offers a promising strategy for identifying elderly patients who are at high risk for 30-day readmission. By identifying these patients, machine learning models may facilitate the effective execution of preventive actions to reduce avoidable readmission incidents.

Impact of a patient risk-scoring tool pilot on prioritization of pharmacy-conducted medication histories

BACKGROUND

Approximately 50% to 70% of patients have at least 1 medication discrepancy in their initial medication history. These discrepancies can lead to errors on admission and discharge orders and have the potential to cause patient harm and incur added costs associated with increased length of stay and readmission rates. Several studies have demonstrated improved medication history accuracy with pharmacy-conducted services, but variations in practice exist due to challenges with workflow and resources.

OBJECTIVE

This study aims to assess the impact of implementing a patient risk-scoring tool for the prioritization of medication history review by pharmacy staff.

METHODS

This quasi-experimental, single-center study was conducted at a 948-bed academic medical center as a pilot study with the medication history team which consists of pharmacists and certified pharmacy technicians in the emergency department. The endpoints assessed included pharmacy completion rate of patients in the high-risk category, overall pharmacy conducted medication history rate, and the proportion of medication discrepancies identified after reconciliation.

RESULTS

The number of medication histories completed by pharmacy (n=849) decreased by 5.7% in the postintervention period (P = 0.002). Between the preintervention and postintervention period, there were fewer low-risk patients being captured by pharmacy (89.7% to 59.9%, respectively). There was also an increase in the number of medium-risk (Δ=25.4%) and high-risk patients (Δ=4.4%) being captured by pharmacy staff (P < 0.017, α = 0.017).

CONCLUSION

Use of a risk-scoring tool allowed pharmacy staff to prioritize workflow and capture more high-risk patients for medication history.

How artificial intelligence could transform emergency care

Artificial intelligence (AI) in healthcare is the ability of a computer to perform tasks typically associated with clinical care (e.g. medical decision-making and documentation). AI will soon be integrated into an increasing number of healthcare applications, including elements of emergency department (ED) care. Here, we describe the basics of AI, various categories of its functions (including machine learning and natural language processing) and review emerging and potential future use-cases for emergency care. For example, AI-assisted symptom checkers could help direct patients to the appropriate setting, models could assist in assigning triage levels, and ambient AI systems could document clinical encounters. AI could also help provide focused summaries of charts, summarize encounters for hand-offs, and create discharge instructions with an appropriate language and reading level. Additional use cases include medical decision making for decision rules, real-time models that predict clinical deterioration or sepsis, and efficient extraction of unstructured data for coding, billing, research, and quality initiatives. We discuss the potential transformative benefits of AI, as well as the concerns regarding its use (e.g. privacy, data accuracy, and the potential for changing the doctor-patient relationship).

Targeted care coordination towards patients with a history of multiple readmissions effectively reduces readmissions

BACKGROUND

To decrease hospital readmission rates, clinical practices create a transition of care (TOC) process to assess patients and coordinate care postdischarge. As current evidence suggests lack of universal benefit, this study's objectives are to determine what patient and process factors associate with hospital readmissions, as well as construct a model to decrease 30-day readmissions.

METHODS

Three months of retrospective discharged patient data (n = 123) were analysed for readmission influences including: patient-specific comorbidities, admission-specific diagnoses, and TOC components. A structured intervention of weekly contact, the Care Coordination Cocoon (CCC), was created for multiply readmitted patients (MRPs), defined as ≥2 readmissions. Three months of postintervention data (n = 141) were analysed. Overall readmission rates and patient- and process-specific characteristics were analysed for associations with hospital readmission.

RESULTS

Standard TOC lacked significance. Patient-specific comorbidities of cancer (odds ratio [OR] 6.27; 95% confidence interval [CI] 1.73-22.75) and coronary artery disease (OR 6.71; 95% CI 1.84-24.46), and admission-specific diagnoses within pulmonary system admissions (OR 7.20; 95% CI 1.96-26.41) were associated with readmissions. Post-CCC data demonstrated a 48-h call (OR 0.21; 95% CI 0.09-0.50), answered calls (OR 0.16; CI 0.07-0.38), 14-day scheduled visit (OR 0.20; 95% CI 0.07-0.54), and visit arrival (OR 0.39; 95% CI 0.17-0.91) independently associated with decreased readmission rate. Patient-specific (hypertension-OR 3.65; CI 1.03-12.87) and admission-specific (nephrologic system-OR 3.22; CI 1.02-10.14) factors associated with readmissions which differed from the initial analysis.

CONCLUSIONS

Targeting a practice's MRPs with CCC resources improves the association of TOC components with readmissions and rates decreased. This is a more efficient use of TOC resources.

Development of a prediction model of postpartum hospital use using an equity-focused approach

BACKGROUND

Racial inequities in maternal morbidity and mortality persist into the postpartum period, leading to a higher rate of postpartum hospital use among Black and Hispanic people. Delivery hospitalizations provide an opportunity to screen and identify people at high risk to prevent adverse postpartum outcomes. Current models do not adequately incorporate social and structural determinants of health, and some include race, which may result in biased risk stratification.

OBJECTIVE

This study aimed to develop a risk prediction model of postpartum hospital use while incorporating social and structural determinants of health and using an equity approach.

STUDY DESIGN

We conducted a retrospective cohort study using 2016-2018 linked birth certificate and hospital discharge data for live-born infants in New York City. We included deliveries from 2016 to 2017 in model development, randomly assigning 70%/30% of deliveries as training/test data. We used deliveries in 2018 for temporal model validation. We defined "Composite postpartum hospital use" as at least 1 readmission or emergency department visit within 30 days of the delivery discharge. We categorized diagnosis at first hospital use into 14 categories based on International Classification of Diseases-Tenth Revision diagnosis codes. We tested 72 candidate variables, including social determinants of health, demographics, comorbidities, obstetrical complications, and severe maternal morbidity. Structural determinants of health were the Index of Concentration at the Extremes, which is an indicator of racial-economic segregation at the zip code level, and publicly available indices of the neighborhood built/natural and social/economic environment of the Child Opportunity Index. We used 4 statistical and machine learning algorithms to predict "Composite postpartum hospital use", and an ensemble approach to predict "Cause-specific postpartum hospital use". We simulated the impact of each risk stratification method paired with an effective intervention on race-ethnic equity in postpartum hospital use.

RESULTS

The overall incidence of postpartum hospital use was 5.7%; the incidences among Black, Hispanic, and White people were 8.8%, 7.4%, and 3.3%, respectively. The most common diagnoses for hospital use were general perinatal complications (17.5%), hypertension/eclampsia (12.0%), nongynecologic infections (10.7%), and wound infections (8.4%). Logistic regression with least absolute shrinkage and selection operator selection retained 22 predictor variables and achieved an area under the receiver operating curve of 0.69 in the training, 0.69 in test, and 0.69 in validation data. Other machine learning algorithms performed similarly. Selected social and structural determinants of health features included the Index of Concentration at the Extremes, insurance payor, depressive symptoms, and trimester entering prenatal care. The "Cause-specific postpartum hospital use" model selected 6 of the 14 outcome diagnoses (acute cardiovascular disease, gastrointestinal disease, hypertension/eclampsia, psychiatric disease, sepsis, and wound infection), achieving an area under the receiver operating curve of 0.75 in training, 0.77 in test, and 0.75 in validation data using a cross-validation approach. Models had slightly lower performance in Black and Hispanic subgroups. When simulating use of the risk stratification models with a postpartum intervention, identifying high-risk individuals with the "Composite postpartum hospital use" model resulted in the greatest reduction in racial-ethnic disparities in postpartum hospital use, compared with the "Cause-specific postpartum hospital use" model or a standard approach to identifying high-risk individuals with common pregnancy complications.

CONCLUSION

The "Composite postpartum hospital use" prediction model incorporating social and structural determinants of health can be used at delivery discharge to identify persons at risk for postpartum hospital use.

al-BERT: a semi-supervised denoising technique for disease prediction

BACKGROUND

Medical records are a valuable source for understanding patient health conditions. Doctors often use these records to assess health without solely depending on time-consuming and complex examinations. However, these records may not always be directly relevant to a patient's current health issue. For instance, information about common colds may not be relevant to a more specific health condition. While experienced doctors can effectively navigate through unnecessary details in medical records, this excess information presents a challenge for machine learning models in predicting diseases electronically. To address this, we have developed 'al-BERT', a new disease prediction model that leverages the BERT framework. This model is designed to identify crucial information from medical records and use it to predict diseases. 'al-BERT' operates on the principle that the structure of sentences in diagnostic records is similar to regular linguistic patterns. However, just as stuttering in speech can introduce 'noise' or irrelevant information, similar issues can arise in written records, complicating model training. To overcome this, 'al-BERT' incorporates a semi-supervised layer that filters out irrelevant data from patient visitation records. This process aims to refine the data, resulting in more reliable indicators for disease correlations and enhancing the model's predictive accuracy and utility in medical diagnostics.

METHOD

To discern noise diseases within patient records, especially those resembling influenza-like illnesses, our approach employs a customized semi-supervised learning algorithm equipped with a focused attention mechanism. This mechanism is specifically calibrated to enhance the model's sensitivity to chronic conditions while concurrently distilling salient features from patient records, thereby augmenting the predictive accuracy and utility of the model in clinical settings. We evaluate the performance of al-BERT using real-world health insurance data provided by Taiwan's National Health Insurance.

RESULT

In our study, we evaluated our model against two others: one based on BERT that uses complete disease records, and another variant that includes extra filtering techniques. Our findings show that models incorporating filtering mechanisms typically perform better than those using the entire, unfiltered dataset. Our approach resulted in improved outcomes across several key measures: AUC-ROC (an indicator of a model's ability to distinguish between classes), precision (the accuracy of positive predictions), recall (the model's ability to find all relevant cases), and overall accuracy. Most notably, our model showed a 15% improvement in recall compared to the current best-performing method in the field of disease prediction.

CONCLUSION

The conducted ablation study affirms the advantages of our attention mechanism and underscores the crucial role of the selection module within al-BERT.

Nursing diagnoses and hospital readmission of patients with respiratory diseases: Findings from a case-control study

AIM

The rate of readmission after hospitalisation for respiratory diseases has become a common and challenging clinical problem. Social and functional patient variables could help identify cases at high risk of readmission. The aim was to identify the nursing diagnoses that were associated with readmission after hospitalisation for respiratory disease in Spain.

DESIGN

Case-control study within the cohort of patients admitted for respiratory disease during 2016-19 in a tertiary public hospital in Spain (n = 3781).

METHODS

Cases were patients who were readmitted within the first 30 days of discharge, and their controls were the remaining patients. All nursing diagnoses (n = 130) were collected from the electronic health record. They were then grouped into 29 informative diagnostic categories. Clinical confounder-adjusted odds ratios (ORs) and 95% confidence intervals (95% CIs) were calculated using logistic regression models.

RESULTS

The readmission rate was 13.1%. The nursing diagnoses categories 'knowledge deficit' (OR: 1.61; 95%CI: 1.13-2.31), 'impaired skin integrity and risk of ulcer infection' (OR: 1.45; 95%CI: 1.06-1.97) and 'activity intolerance associated with fatigue' (OR: 1.56; 95%CI: 1.21-2.01) were associated with an increased risk of suffering an episode of hospital readmission rate at 30% after hospital discharge, and this was independent of sociodemographic background, care variables and comorbidity.

PATIENT OR PUBLIC CONTRIBUTION

The nursing diagnoses assigned as part of the care plan of patients during hospital admission may be useful for predicting readmissions.

Predictive Models to Assess Risk of Persistent Opioid Use, Opioid Use Disorder, and Overdose

BACKGROUND

This systematic review summarizes the development, accuracy, quality, and clinical utility of predictive models to assess the risk of opioid use disorder (OUD), persistent opioid use, and opioid overdose.

METHODS

In accordance with Preferred Reporting Items for a Systematic Review and Meta-analysis guidelines, 8 electronic databases were searched for studies on predictive models and OUD, overdose, or persistent use in adults until June 25, 2023. Study selection and data extraction were completed independently by 2 reviewers. Risk of bias of included studies was assessed independently by 2 reviewers using the Prediction model Risk of Bias ASsessment Tool (PROBAST).

RESULTS

The literature search yielded 3130 reports; after removing 199 duplicates, excluding 2685 studies after abstract review, and excluding 204 studies after full-text review, the final sample consisted of 41 studies that developed more than 160 predictive models. Primary outcomes included opioid overdose (31.6% of studies), OUD (41.4%), and persistent opioid use (17%). The most common modeling approach was regression modeling, and the most common predictors included age, sex, mental health diagnosis history, and substance use disorder history. Most studies reported model performance via the c statistic, ranging from 0.507 to 0.959; gradient boosting tree models and neural network models performed well in the context of their own study. One study deployed a model in real time. Risk of bias was predominantly high; concerns regarding applicability were predominantly low.

CONCLUSIONS

Models to predict opioid-related risks are developed using diverse data sources and predictors, with a wide and heterogenous range of accuracy metrics. There is a need for further research to improve their accuracy and implementation.

Evaluating Algorithmic Bias in 30-Day Hospital Readmission Models: Retrospective Analysis

BACKGROUND

The adoption of predictive algorithms in health care comes with the potential for algorithmic bias, which could exacerbate existing disparities. Fairness metrics have been proposed to measure algorithmic bias, but their application to real-world tasks is limited.

OBJECTIVE

This study aims to evaluate the algorithmic bias associated with the application of common 30-day hospital readmission models and assess the usefulness and interpretability of selected fairness metrics.

METHODS

We used 10.6 million adult inpatient discharges from Maryland and Florida from 2016 to 2019 in this retrospective study. Models predicting 30-day hospital readmissions were evaluated: LACE Index, modified HOSPITAL score, and modified Centers for Medicare & Medicaid Services (CMS) readmission measure, which were applied as-is (using existing coefficients) and retrained (recalibrated with 50% of the data). Predictive performances and bias measures were evaluated for all, between Black and White populations, and between low- and other-income groups. Bias measures included the parity of false negative rate (FNR), false positive rate (FPR), 0-1 loss, and generalized entropy index. Racial bias represented by FNR and FPR differences was stratified to explore shifts in algorithmic bias in different populations.

RESULTS

The retrained CMS model demonstrated the best predictive performance (area under the curve: 0.74 in Maryland and 0.68-0.70 in Florida), and the modified HOSPITAL score demonstrated the best calibration (Brier score: 0.16-0.19 in Maryland and 0.19-0.21 in Florida). Calibration was better in White (compared to Black) populations and other-income (compared to low-income) groups, and the area under the curve was higher or similar in the Black (compared to White) populations. The retrained CMS and modified HOSPITAL score had the lowest racial and income bias in Maryland. In Florida, both of these models overall had the lowest income bias and the modified HOSPITAL score showed the lowest racial bias. In both states, the White and higher-income populations showed a higher FNR, while the Black and low-income populations resulted in a higher FPR and a higher 0-1 loss. When stratified by hospital and population composition, these models demonstrated heterogeneous algorithmic bias in different contexts and populations.

CONCLUSIONS

Caution must be taken when interpreting fairness measures' face value. A higher FNR or FPR could potentially reflect missed opportunities or wasted resources, but these measures could also reflect health care use patterns and gaps in care. Simply relying on the statistical notions of bias could obscure or underplay the causes of health disparity. The imperfect health data, analytic frameworks, and the underlying health systems must be carefully considered. Fairness measures can serve as a useful routine assessment to detect disparate model performances but are insufficient to inform mechanisms or policy changes. However, such an assessment is an important first step toward data-driven improvement to address existing health disparities.

The Early HOSPITAL Score to Predict 30-Day Readmission Soon After Hospitalization: a Prospective Multicenter Study

BACKGROUND

The simplified HOSPITAL score is an easy-to-use prediction model to identify patients at high risk of 30-day readmission before hospital discharge. An earlier stratification of this risk would allow more preparation time for transitional care interventions.

OBJECTIVE

To assess whether the simplified HOSPITAL score would perform similarly by using hemoglobin and sodium level at the time of admission instead of discharge.

DESIGN

Prospective national multicentric cohort study.

PARTICIPANTS

In total, 934 consecutively discharged medical inpatients from internal general services.

MAIN MEASURES

We measured the composite of the first unplanned readmission or death within 30 days after discharge of index admission and compared the performance of the simplified score with lab at discharge (simplified HOSPITAL score) and lab at admission (early HOSPITAL score) according to their discriminatory power (Area Under the Receiver Operating characteristic Curve (AUROC)) and the Net Reclassification Improvement (NRI).

KEY RESULTS

During the study period, a total of 3239 patients were screened and 934 included. In total, 122 (13.2%) of them had a 30-day unplanned readmission or death. The simplified and the early versions of the HOSPITAL score both showed very good accuracy (Brier score 0.11, 95%CI 0.10-0.13). Their AUROC were 0.66 (95%CI 0.60-0.71), and 0.66 (95%CI 0.61-0.71), respectively, without a statistical difference (p value 0.79). Compared with the model at discharge, the model with lab at admission showed improvement in classification based on the continuous NRI (0.28; 95%CI 0.08 to 0.48; p value 0.004).

CONCLUSION

The early HOSPITAL score performs, at least similarly, in identifying patients at high risk for 30-day unplanned readmission and allows a readmission risk stratification early during the hospital stay. Therefore, this new version offers a timely preparation of transition care interventions to the patients who may benefit the most.

Hospital Readmissions for Fluid Overload among Individuals with Diabetes and Diabetic Kidney Disease: Risk Factors and Multivariable Prediction Models

AIMS

Hospital readmissions due to recurrent fluid overload in diabetes and diabetic kidney disease can be avoided with evidence-based interventions. We aimed to identify at-risk patients who can benefit from these interventions by developing risk prediction models for readmissions for fluid overload in people living with diabetes and diabetic kidney disease.

METHODS

This was a single-center retrospective cohort study of 1,531 adults with diabetes and diabetic kidney disease hospitalized for fluid overload, congestive heart failure, pulmonary edema, and generalized edema between 2015 and 2017. The multivariable regression models for 30-day and 90-day readmission for fluid overload were compared with the LACE score for discrimination, calibration, sensitivity, specificity, and net reclassification index (NRI).

RESULTS

Readmissions for fluid overload within 30 days and 90 days occurred in 8.6% and 17.2% of patients with diabetes, and 8.2% and 18.3% of patients with diabetic kidney disease, respectively. After adjusting for demographics, comorbidities, clinical parameters, and medications, a history of alcoholism (HR 3.85, 95% CI: 1.41-10.55) and prior hospitalization for fluid overload (HR 2.50, 95% CI: 1.26-4.96) were independently associated with 30-day readmission in patients with diabetic kidney disease, as well as in individuals with diabetes. Additionally, current smoking, absence of hypertension, and high-dose intravenous furosemide were also associated with 30-day readmission in individuals with diabetes. Prior hospitalization for fluid overload (HR 2.43, 95% CI: 1.50-3.94), cardiovascular disease (HR 1.44, 95% CI: 1.03-2.02), eGFR ≤45 mL/min/1.73 m2 (HR 1.39, 95% CI: 1.003-1.93) was independently associated with 90-day readmissions in individuals with diabetic kidney disease. Additionally, thiazide prescription at discharge reduced 90-day readmission in diabetic kidney disease, while the need for high-dose intravenous furosemide predicted 90-day readmission in diabetes. The clinical and clinico-psychological models for 90-day readmission in individuals with diabetes and diabetic kidney disease had better discrimination and calibration than the LACE score. The NRI for the clinico-psychosocial models to predict 30- and 90-day readmissions in diabetes was 22.4% and 28.9%, respectively. The NRI for the clinico-psychosocial models to predict 30- and 90-day readmissions in diabetic kidney disease was 5.6% and 38.9%, respectively.

CONCLUSION

The risk models can potentially be used to identify patients at risk of readmission for fluid overload for evidence-based interventions, such as patient education or transitional care programs to reduce preventable hospitalizations.

Development and Validation of Inpatient Hypoglycemia Models Centered Around the Insulin Ordering Process

BACKGROUND

The insulin ordering process is an opportunity to provide clinicians with hypoglycemia risk predictions, but few hypoglycemia models centered around the insulin ordering process exist.

METHODS

We used data on adult patients, admitted in 2019 to non-ICU floors of a large teaching hospital, who had orders for subcutaneous insulin. Our outcome was hypoglycemia, defined as a blood glucose (BG) <70 mg/dL within 24 hours after ordering insulin. We trained and evaluated models to predict hypoglycemia at the time of placing an insulin order, using logistic regression, random forest, and extreme gradient boosting (XGBoost). We compared performance using area under the receiver operating characteristic curve (AUCs) and precision-recall curves. We determined recall at our goal precision of 0.30.

RESULTS

Of 21 052 included insulin orders, 1839 (9%) were followed by a hypoglycemic event within 24 hours. Logistic regression, random forest, and XGBoost models had AUCs of 0.81, 0.80, and 0.79, and recall of 0.44, 0.49, and 0.32, respectively. The most significant predictor was the lowest BG value in the 24 hours preceding the order. Predictors related to the insulin order being placed at the time of the prediction were useful to the model but less important than the patient's history of BG values over time.

CONCLUSIONS

Hypoglycemia within the next 24 hours can be predicted at the time an insulin order is placed, providing an opportunity to integrate decision support into the medication ordering process to make insulin therapy safer.

An overview of reviews and synthesis across 440 studies examines the importance of hospital readmission predictors across various patient populations

OBJECTIVES

The scientific literature contains an abundance of prediction models for hospital readmissions. However, no review has yet synthesized their predictors across various patient populations. Therefore, our aim was to examine predictors of hospital readmissions across 13 patient populations.

STUDY DESIGN AND SETTING

An overview of systematic reviews was combined with a meta-analytical approach. Two thousand five hundred four different predictors were categorized using common ontologies to pool and examine their odds ratios and frequencies of use in prediction models across and within different patient populations.

RESULTS

Twenty-eight systematic reviews with 440 primary studies were included. Numerous predictors related to prior use of healthcare services (odds ratio; 95% confidence interval: 1.64; 1.42-1.89), diagnoses (1.41; 1.31-1.51), health status (1.35; 1.20-1.52), medications (1.28; 1.13-1.44), administrative information about the index hospitalization (1.23; 1.14-1.33), clinical procedures (1.20; 1.07-1.35), laboratory results (1.18; 1.11-1.25), demographic information (1.10; 1.06-1.14), and socioeconomic status (1.07; 1.02-1.11) were analyzed. Diagnoses were frequently used (in 37.38%) and displayed large effect sizes across all populations. Prior use of healthcare services showed the largest effect sizes but were seldomly used (in 2.57%), whereas demographic information (in 13.18%) was frequently used but displayed small effect sizes.

CONCLUSION

Diagnoses and patients' prior use of healthcare services showed large effects both across and within different populations. These results can serve as a foundation for future prediction modeling.

Consensus methodology to investigate the crucial referral criteria to pharmacist-led counseling clinics in Makkah City

Aim

Identifying and prioritizing criteria for referring patients to a counseling clinic managed by hospital pharmacists in the tertiary care setting in Saudi Arabia (SA).

Method

A two-phase consensus Delphi methodological approach was adopted in this study. Data was collected from physicians and pharmacists from different specialties working in different hospitals in Makkah City. In Phase 1, semi-structured interviews were conducted with physicians and pharmacists to discuss and develop the initial list of potential referral criteria for post-discharge counseling. Phase 2 consisted of two rounds of online surveys where participants were asked to independently rank the referral criteria using a 5-point Likert Scale.

Results

In Phase 1, four participants undertook the interviews (two physicians and two pharmacists). Overall, no major comments were given on the suggested criteria. In Phase 2, most suggested referral criteria to the counseling clinic reached participants' consensus agreement of >70 % in both rounds for all three domains. Among all criteria that achieved consensus agreement, two demographic criteria were top-ranked by the participants; the elderly patients (100 %) and those who needed help with their devices (96 %). These were followed by five medication-related criteria, which are medication-related problems, polypharmacy, medication that needs monitoring, high-risk medication, and medication with special formulations. All had a consensus agreement of 96 %.

Conclusion

This study suggests that a counseling clinic led by pharmacists is particularly advisable for the elderly, individuals requiring assistance with their devices, and those encountering medication issues. It is essential to prioritize specific patient demographics when contemplating the extensive establishment and integration of such clinics across various hospitals in SA.

Early repeat hospitalization for fluid overload in individuals with cardiovascular disease and risks: a retrospective cohort study

AIMS

Fluid overload is a common manifestation of cardiovascular and kidney disease and a leading cause of hospitalizations. To identify patients at risk of recurrent severe fluid overload, we evaluated the incidence and risk factors associated with early repeat hospitalization for fluid overload among individuals with cardiovascular disease and risks.

METHODS

Single-center retrospective cohort study of 3423 consecutive adults with an index hospitalization for fluid overload between January 2015 and December 2017 and had cardiovascular risks (older age, diabetes mellitus, hypertension, dyslipidemia, kidney disease, known cardiovascular disease), but excluded if lost to follow-up or eGFR < 15 ml/min/1.73 m2. The outcome was early repeat hospitalization for fluid overload within 30 days of discharge.

RESULTS

The mean age was 73.9 ± 11.6 years and eGFR was 54.1 ± 24.6 ml/min/1.73 m2 at index hospitalization. Early repeat hospitalization for fluid overload occurred in 291 patients (8.5%). After adjusting for demographics, comorbidities, clinical parameters during index hospitalization and medications at discharge, cardiovascular disease (adjusted odds ratio, OR 1.66, 95% CI 1.27-2.17), prior hospitalization for fluid overload within 3 months (OR 2.52, 95% CI 1.17-5.44), prior hospitalization for any cause in within 6 months (OR 1.33, 95% CI 1.02-1.73) and intravenous furosemide use (OR 1.58, 95% CI 1.10-2.28) were associated with early repeat hospitalization for fluid overload. Higher systolic BP on admission (OR 0.992, 95% 0.986-0.998) and diuretic at discharge (OR 0.50, 95% CI 0.26-0.98) reduced early hospitalization for fluid overload.

CONCLUSION

Patients at-risk of early repeat hospitalization for fluid overload may be identified using these risk factors for targeted interventions.

Patients with repeated acute admissions to somatic departments: sociodemographic characteristics, disease burden, and contact with primary healthcare sector - a retrospective register-based case-control study

BACKGROUND

Healthcare systems face escalating capacity challenges and patients with repeated acute admissions strain hospital resources disproportionately. However, studies investigating the characteristics of such patients across all public healthcare providers in a universal healthcare system are lacking.

OBJECTIVE

To investigate characteristics of patients with repeated acute admissions (three or more acute admissions within a calendar year) in regard to sociodemographic characteristics, disease burden, and contact with the primary healthcare sector.

METHODS

This matched register-based case-control study investigated repeated acute admissions from 1 January 2014 to 31 December 2018, among individuals, who resided in four Danish municipalities. The study included 6169 individuals with repeated acute admissions, matched 1:4 to individuals with no acute admissions and one to two acute admissions, respectively. Group comparisons were conducted using conditional logistic regression.

RESULTS

Receiving social benefits increased the odds of repeated acute admissions 9.5-fold compared with no acute admissions (odds ratio (OR) 9.5; 95% confidence interval (CI) 8.5; 10.6) and 3.4-fold compared with one to two acute admissions (OR 3.4; 95% CI 3.1; 3.7). The odds of repeated acute admissions increased with the number of used medications and chronic diseases. Having a mental illness increased the odds of repeated acute admissions 5.8-fold when compared with no acute admissions (OR 5.7; 95% CI 5.2; 6.4) and 2.3-fold compared with one to two acute admissions (OR 2.3; 95% CI 2.1; 2.5). Also, high use of primary sector services (e.g. nursing care) increased the odds of repeated acute admissions when compared with no acute admissions and one to two acute admissions.

CONCLUSIONS

This study pinpointed key factors encompassing social status, disease burden, and healthcare utilisation as pivotal markers of risk for repeated acute admissions, thus identifying high-risk patients and facilitating targeted intervention.

A study protocol for a predictive model to assess population-based avoidable hospitalization risk: Avoidable Hospitalization Population Risk Prediction Tool (AvHPoRT)

INTRODUCTION

Avoidable hospitalizations are considered preventable given effective and timely primary care management and are an important indicator of health system performance. The ability to predict avoidable hospitalizations at the population level represents a significant advantage for health system decision-makers that could facilitate proactive intervention for ambulatory care-sensitive conditions (ACSCs). The aim of this study is to develop and validate the Avoidable Hospitalization Population Risk Tool (AvHPoRT) that will predict the 5-year risk of first avoidable hospitalization for seven ACSCs using self-reported, routinely collected population health survey data.

METHODS AND ANALYSIS

The derivation cohort will consist of respondents to the first 3 cycles (2000/01, 2003/04, 2005/06) of the Canadian Community Health Survey (CCHS) who are 18-74 years of age at survey administration and a hold-out data set will be used for external validation. Outcome information on avoidable hospitalizations for 5 years following the CCHS interview will be assessed through data linkage to the Discharge Abstract Database (1999/2000-2017/2018) for an estimated sample size of 394,600. Candidate predictor variables will include demographic characteristics, socioeconomic status, self-perceived health measures, health behaviors, chronic conditions, and area-based measures. Sex-specific algorithms will be developed using Weibull accelerated failure time survival models. The model will be validated both using split set cross-validation and external temporal validation split using cycles 2000-2006 compared to 2007-2012. We will assess measures of overall predictive performance (Nagelkerke R2), calibration (calibration plots), and discrimination (Harrell's concordance statistic). Development of the model will be informed by the Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD) statement.

ETHICS AND DISSEMINATION

This study was approved by the University of Toronto Research Ethics Board. The predictive algorithm and findings from this work will be disseminated at scientific meetings and in peer-reviewed publications.

Characteristics and Outcome of ICU Unplanned Readmission in Trauma Patients During the Same Hospitalization

Objective

This study aimed to determine the rate of readmission for trauma patients in ICUs, as well as the factors that predict this outcome.

Methods

This retrospective cohort study was conducted at Emtiaz Hospital, a level I referral trauma center (Shiraz, Iran). It analyzed the ICU readmission rates among trauma patients over three years. The required data were extracted from the Iranian Intensive Care Registry (IICUR), which included patient demographics, injury severity, physiological parameters, and clinical outcomes. Statistical analysis was performed using SPSS version 25.0. Descriptive statistics and different statistical tests, such as T-tests, Mann-Whitney tests, Chi-square tests, and logistic binary regression test were utilized.

Results

Among the 5273 patients discharged from the ICU during the study period, 195 (3.7%) were readmitted during the same hospitalization. Patients readmitted to the ICU had a significantly higher mean age (54.83±22.73 years) than those who were not readmitted (47.08 years, p<0.001). Lower Glasgow Coma Scale (GCS) scores at admission and discharge were associated with ICU readmission, implying that neurological status and readmission risk were correlated with each other. Furthermore, respiratory challenges were identified as the leading cause of unexpected readmission, including respiratory failure, hypoxic respiratory failure, respiratory distress, and respiratory infections such as pneumonia. Injury patterns analysis revealed a higher frequency of poly-trauma and head and neck injuries among patients readmitted to the ICU.

Conclusion

This study underscored the importance of ICU readmission among trauma patients, with a high readmission rate during the same hospitalization. By developing comprehensive guidelines and optimizing discharge processes, healthcare providers could potentially mitigate ICU readmissions and associated complications, ultimately enhancing patient outcomes and resource utilization in trauma ICU settings. This research provided valuable insights to inform evidence-based practices and improve the quality of care delivery for trauma patients in intensive care settings.

Prediction of patient admission and readmission in adults from a Colombian cohort with bipolar disorder using artificial intelligence

Introduction

Bipolar disorder (BD) is a chronically progressive mental condition, associated with a reduced quality of life and greater disability. Patient admissions are preventable events with a considerable impact on global functioning and social adjustment. While machine learning (ML) approaches have proven prediction ability in other diseases, little is known about their utility to predict patient admissions in this pathology.

Aim

To develop prediction models for hospital admission/readmission within 5 years of diagnosis in patients with BD using ML techniques.

Methods

The study utilized data from patients diagnosed with BD in a major healthcare organization in Colombia. Candidate predictors were selected from Electronic Health Records (EHRs) and included sociodemographic and clinical variables. ML algorithms, including Decision Trees, Random Forests, Logistic Regressions, and Support Vector Machines, were used to predict patient admission or readmission. Survival models, including a penalized Cox Model and Random Survival Forest, were used to predict time to admission and first readmission. Model performance was evaluated using accuracy, precision, recall, F1 score, area under the receiver operating characteristic curve (AUC) and concordance index.

Results

The admission dataset included 2,726 BD patients, with 354 admissions, while the readmission dataset included 352 patients, with almost half being readmitted. The best-performing model for predicting admission was the Random Forest, with an accuracy score of 0.951 and an AUC of 0.98. The variables with the greatest predictive power in the Recursive Feature Elimination (RFE) importance analysis were the number of psychiatric emergency visits, the number of outpatient follow-up appointments and age. Survival models showed similar results, with the Random Survival Forest performing best, achieving an AUC of 0.95. However, the prediction models for patient readmission had poorer performance, with the Random Forest model being again the best performer but with an AUC below 0.70.

Conclusion

ML models, particularly the Random Forest model, outperformed traditional statistical techniques for admission prediction. However, readmission prediction models had poorer performance. This study demonstrates the potential of ML techniques in improving prediction accuracy for BD patient admissions.

Development of a risk prediction model to predict the risk of hospitalization due to exacerbated asthma among adult asthma patients in a lower middle-income country

BACKGROUND

Asthma patients experience higher rates of hospitalizations due to exacerbations leaving a considerable clinical and economic burden on the healthcare system. The use of a simple, risk prediction tool offers a low-cost mechanism to identify these high-risk asthma patients for specialized care. The study aimed to develop and validate a risk prediction model to identify high-risk asthma patients for hospitalization due to exacerbations.

METHODS

Hospital-based, case-control study was carried out among 466 asthma patients aged ≥ 20 years recruited from four tertiary care hospitals in a district of Sri Lanka to identify risk factors for asthma-related hospitalizations. Patients (n = 116) hospitalized due to an exacerbation with respiratory rate > 30/min, pulse rate > 120 bpm, O2 saturation (on air) < 90% on admission, selected consecutively from medical wards; controls (n = 350;1:3 ratio) randomly selected from asthma/medical clinics. Data was collected via a pre-tested Interviewer-Administered Questionnaire (IAQ). Logistic Regression (LR) analyses were performed to develop the model with consensus from an expert panel. A second case-control study was carried out to assess the criterion validity of the new model recruiting 158 cases and 101 controls from the same hospitals. Data was collected using an IAQ based on the newly developed risk prediction model.

RESULTS

The developed model consisted of ten predictors with an Area Under the Curve (AUC) of 0.83 (95% CI: 0.78 to 0.88, P < 0.001), sensitivity 69.0%, specificity 86.1%, positive predictive value (PPV) 88.6%, negative predictive value (NPV) 63.9%. Positive and negative likelihood ratios were 4.9 and 0.3, respectively.

CONCLUSIONS

The newly developed model was proven valid to identify adult asthma patients who are at risk of hospitalization due to exacerbations. It is recommended as a simple, low-cost tool for identifying and prioritizing high-risk asthma patients for specialized care.

Factors associated with readmission in chronic kidney disease: Systematic review and meta-analysis

BACKGROUND

Risk factors associated with all-cause hospital readmission are poorly characterised in patients with chronic kidney disease.

OBJECTIVE

A systematic review and meta-analysis were conducted to identify risk factors and protectors of hospital readmission in chronic kidney disease.

DESIGN, PARTICIPANTS & MEASUREMENTS

Studies involving adult patients were identified from four databases from inception to 31/03/2020. Random-effects meta-analyses were conducted to determine factors associated with all-cause 30-day hospital readmission in general chronic kidney disease, in dialysis and in kidney transplant recipient groups.

RESULTS

Eighty relevant studies (chronic kidney disease, n = 14 studies; dialysis, n = 34 studies; and transplant, n = 32 studies) were identified. Meta-analysis revealed that in both chronic kidney disease and transplant groups, increasing age in years and days spent at the hospital during the initial stay were associated with a higher risk of 30-day readmission. Other risk factors identified included increasing body mass index (kg/m2 ) in the transplant group, and functional impairment and discharge destination in the dialysis group. Within the chronic kidney disease ​​​​group, having an outpatient follow-up appointment with a nephrologist within 14 days of discharge was protective against readmission but this was not protective if provided by a primary care provider or a cardiologist.

CONCLUSION

Risk-reduction interventions that can be implemented include a nephrologist appointment within 14 days of hospital discharge, rehabilitation programme for functional improvement in the dialysis group and meal plans in the transplant group. Future risk analysis should focus on modifiable factors to ensure that strategies can be tested and implemented in those who are more at risk.