Medication-related hospital readmissions within 30 days of discharge-A retrospective study of risk factors in older adults

Enhancing patient participation in discharge medication communication: a feasibility pilot trial

OBJECTIVES

To pilot test a co-designed intervention that enhances patient participation in hospital discharge medication communication.

DESIGN

Pilot randomised controlled trial.

SETTING

One tertiary hospital.

PARTICIPANTS

Patients who were ≥45 years of age; ≥1 chronic illness and ≥1 regularly prescribed medication that they manage at home were recruited between October 2022 and May 2023. Healthcare professionals on participating units completed surveys.

INTERVENTION

The co-designed intervention included three websites: a medication search engine, a medication question builder and tools to facilitate medication management at home. Inpatient posters contained QR codes to provide access to these websites.

PRIMARY AND SECONDARY OUTCOME MEASURES

The primary outcomes were the feasibility of study processes and intervention acceptability. Feasibility of study processes was measured in terms of recruitment, fidelity, retention, missing data and contamination. Patients in the intervention group and healthcare professionals on the wards self-reported intervention acceptability. Secondary outcomes were medication understanding, use, self-efficacy and healthcare utilisation.

RESULTS

60 patients were recruited and randomised; half in each study group. The intervention was largely delivered as intended, and 99.7% of data collected was complete. In total, 16/59 (27.1%) patients were lost to follow-up 28 days after hospital discharge, and 3 patients in the usual care group reported that they saw the intervention poster prior to hospital discharge. 21 of 24 intervention group patients (87.5%) deemed the intervention acceptable, while half of the healthcare professionals (n=5, 50%) thought it was acceptable.

CONCLUSIONS

We demonstrated that in a future definitive trial, intervention fidelity would be high with little missing data, and patients would likely find the intervention acceptable. Thus, a larger trial may be warranted, as the intervention is implementable and approved by patients. However, additional strategies to increase recruitment and retention of eligible participants are needed. Healthcare professionals may require more preparation for the intervention to enhance their perceptions of intervention acceptability.

TRIAL REGISTRATION NUMBER

ACTRN12622001028796.

The impact of discharge letter content on unplanned hospital readmissions within 30 and 90 days in older adults with chronic illness - a mixed methods study

BACKGROUND

Care transitions are high-risk processes, especially for people with complex or chronic illness. Discharge letters are an opportunity to provide written information to improve patients' self-management after discharge. The aim of this study is to determine the impact of discharge letter content on unplanned hospital readmissions and self-rated quality of care transitions among patients 60 years of age or older with chronic illness.

METHODS

The study had a convergent mixed methods design. Patients with chronic obstructive pulmonary disease or congestive heart failure were recruited from two hospitals in Region Stockholm if they were living at home and Swedish-speaking. Patients with dementia or cognitive impairment, or a "do not resuscitate" statement in their medical record were excluded. Discharge letters from 136 patients recruited to a randomised controlled trial were coded using an assessment matrix and deductive content analysis. The assessment matrix was based on a literature review performed to identify key elements in discharge letters that facilitate a safe care transition to home. The coded key elements were transformed into a quantitative variable of "SAFE-D score". Bivariate correlations between SAFE-D score and quality of care transition as well as unplanned readmissions within 30 and 90 days were calculated. Lastly, a multivariable Cox proportional hazards model was used to investigate associations between SAFE-D score and time to readmission.

RESULTS

All discharge letters contained at least five of eleven key elements. In less than two per cent of the discharge letters, all eleven key elements were present. Neither SAFE-D score, nor single key elements correlated with 30-day or 90-day readmission rate. SAFE-D score was not associated with time to readmission when adjusted for a range of patient characteristics and self-rated quality of care transitions.

CONCLUSIONS

While written summaries play a role, they may not be sufficient on their own to ensure safe care transitions and effective self-care management post-discharge.

TRIAL REGISTRATION

Clinical Trials. giv, NCT02823795, 01/09/2016.

Developing indicators for medication-related readmissions based on a Delphi consensus study

BACKGROUND

Medication-related readmissions challenge healthcare systems by burdening patients, increasing costs and straining resources. However, to date, there has been no consensus study on indicators for medication-related readmissions.

OBJECTIVES

This Delphi study aimed to develop a consensus-based set of indicators for detecting patients at risk of medication-related readmission.

METHODS

An expert panel of clinical pharmacists, physicians and nursing experts participated in a two-round Delphi study. In round 1, 31 indicators taken from the literature were rated for relevance on a scale from 1 to 9, with a median rating of 7 or higher suggesting relevance. The RAND/UCLA method was used to determine consensus. In round 2, indicators lacking consensus were re-rated together with a series of new indicators generated by the experts. Additional details were sought for some indicators. The main outcomes were the relevance of, consensus on, and completeness of the proposed indicators for identifying risks of 30-day medication-related readmission.

RESULTS

Thirty-eight experts participated in round 1. Consensus was found for all the indicators, with 25 included and 6 excluded. Thirty-four experts participated in round 2. Consensus was found for all 5 newly suggested indicators, and 4 were included. The expert panel prioritized the following indicators: (1) insufficient communication between different healthcare providers, (2) polypharmacy (≥7 medications), (3) low rates of medication adherence (twice-weekly mistakes or missing administration), (4) complex medication regimens (≥3 doses, ≥2 dosage forms and ≥2 administration routes per day), and (5) multimorbidity (≥3 chronic conditions). The final set comprised 29 indicators.

CONCLUSIONS

The indicator set developed for flagging potential medication-related readmissions could guide priorities for clinical pharmacy services at hospital discharge, improving patient outcomes and resource use. A validation study of these indicators is planned.

Prescribing Benzodiazepines and Opioids and Clinical Characteristics Associated With 30-Day Hospital Return in Patients Aged ≥75 Years: Secondary Data Analysis

PURPOSE

The current study compared prevalence of opioid or benzodiazepine (BZD) prescription and co-prescription of opioids and BZD at discharge and return to a community hospital within 30 days, as well as identified clinical characteristics associated with hospital return in patients aged ≥75 years.

METHOD

A secondary analysis of a database created during implementation of the Safe Transitions for At Risk Patients program at a 400-bed community teaching hospital in south Florida was conducted. Multivariable logistic regression analyses were performed to identify significant demographic and clinical characteristics associated with return to the hospital within 30 days of discharge.

RESULTS

A total of 24,262 participants (52.6% women) with a mean age of 85.3 (SD = 6.42) years were included. More than 20% in each central nervous system prescription group (i.e., opioids only, BZD only, opioids and BZD) returned to the hospital within 30 days of discharge. Demographic and chronic conditions (e.g., congestive heart failure, chronic obstructive pulmonary disease, diabetes) and poly-pharmacy were significant factors of a 30-day return to the hospital.

CONCLUSION

Findings highlight the importance of hospital nurses' role in identifying high-risk patients, educating patients and caregivers, monitoring them closely, communicating with primary care physicians and specialists, and conducting intensive follow up via telephone to avoid 30-day rehospitalization. [Journal of Gerontological Nursing, 50(4), 25-33.].

Impact of medication intensification on 30-day hospital readmissions in a geriatric trauma population: A multicenter cohort study

BACKGROUND

Fall-related injuries are a significant health issue that occur in 25% of older adults and account for a significant number of trauma-related hospitalizations. Although medication intensification may increase the risk of hospital readmissions in non-trauma patients, data on a geriatric trauma population are lacking.

OBJECTIVE

The primary objective was to evaluate the effect of medication intensification on 30-day hospital readmissions in geriatric patients hospitalized for fall-related injuries.

METHODS

This multicenter, retrospective cohort study included patients with geriatric who presented to one of three trauma centers within a large, health-system between January 1, 2018 and December 31, 2020. Patients at least 65 years old admitted with a fall-related injury were eligible for inclusion. Patients were grouped according to medication changes at discharge, which included intensified and non-intensified groups. Medication intensification included increased dose(s) or initiation of new agents. The primary outcome was the 30-day hospital readmission rate.

RESULTS

Of the 870 patients included (median [interquartile range, IQR] age, 82 [74-89] years, 522 (60%) female, and 220 (25%) with a previous fall), there were 471 (54%) and 399 (46%) patients in the intensified and non-intensified groups, respectively. The intensified group had a higher 30-day hospital readmission rate (21% intensified vs. 16% non-intensified, p = 0.043; number needed to harm 20) based on an unweighted analysis. According to a weighted propensity score logistic regression, medication intensification was associated with higher 30-day hospital readmissions (24% [95% confidence interval [CI] 19-31%] intensified vs. 15% [95% CI 11-20%] non-intensified, p = 0.018). These results were consistent within competing risk models accounting for death (cause-specific model: hazard ratio [HR] 1.63 [95% CI 1.07-2.49], p = 0.023; Fine-Gray model: HR 1.64 [95% CI 1.07-2.50], p = 0.022).

CONCLUSIONS

In a geriatric trauma population hospitalized after a fall, intensification of medications may pose an increased risk of 30-day hospital readmission.

Prediction of 30-day unplanned hospital readmission through survival analysis

Background and Objective

Unplanned hospital readmissions are a severe and recurrent problem that affects all health systems. Estimating the risk of being readmitted the following days after discharge is difficult since many heterogeneous factors can influence this. The extensive work concerning this problem proposes solutions mostly based on classification machine-learning models. Survival analysis methods could make a better match with the assessment of readmission risk and are yet to become well-established in this field.

Methods

We compare different statistical and machine learning survival analysis models trained with right-censored all-cause hospital admission data with covariates available at the moment of discharge. The main focus is on tree-ensemble regression methods based on the assumption of proportional hazards. These models are more thoroughly evaluated at a 30-day time period after discharge, although the actual prediction could be set to any time up to 90 days.

Results

The mean performance obtained by each of the proposed survival models ranges from 0.707 to 0.716 C-Index and 0.709 to 0.72 ROC-AUC at a 30-day time period after discharge. The model with the lower performance on both metrics was Cox Proportional Hazards, while the model marking the upper end on both ranges is an XGBoost Regression model with a Cox objective function.

Conclusions

Our findings indicate that survival models perform well addressing the hospital readmission problem, machine-learning models getting the edge over statistical methods. There seems to be an improvement over classification models when attempting to predict at a 30-day period since discharge, perhaps due to a better handling of cases nearing the 30-day boundary. Some preprocessing steps, such as limiting the observation period to 90 days after discharge, are also highlighted since they resulted in a performance boost.

Elderly patients whose hospitalization was medication-related were more likely to receive medication recommendations by clinical pharmacist than patients whose hospitalization was unlikely medication-related in non-geriatric units

BACKGROUND

Elderly patients are often polymedicated, and drug-related hospitalizations are common in this population. In our hospital, pharmacists from the mobile geriatric team (MGT) coordinate medication reviews (MR) for elderly patients hospitalized in non-geriatric wards, to prevent iatrogenic.

OBJECTIVE

The aim of this work is to determine whether the drug-related origin of hospitalizations can be considered as a targeting criterion for performing MRs.

MATERIAL AND METHOD

We conducted a retrospective study of data from patients who received a MGT's MR between March 2021 and December 2022, from a single center of more than 1000 beds. The drug-related origin of the hospitalization was estimated as probable or unlikely by the AT-HARM10 tool. Between the two groups, we compared the number of potentially inappropriate prescriptions detected by the PIM-check and START/STOPP tools, drug-drug interactions (DI), unintended discrepancies (UDI) at entry reconciliation, the drug burden index (DBI), and the number of drug-related problems (DRP) i.e., START/STOPP score + DI + UDI. Linear regression of the number of DRP by AT-HARM10 score was computed.

RESULTS

110 patients were included. 56 hospitalizations were estimated MRH and 54 non-MRH. Mean age (85.1 ± 7.0), ADL (3.8 ± 1.9), IADL (2.0 ± 1.6), and number of medications at entry (8.9 ± 3.8) were comparable in the 2 groups. Compared with non-MRH group, MRH group had a higher number of START/STOPP criteria (5.7 ± 3.5 vs 3.0 ± 2.6; p < 0.05), PIM-check overuses (2.1 ± 1.7 vs 1.4 ± 1.4; p < 0.05), DI (8.4 ± 9.0 vs 4.7 ± 4.7; p < 0.05), UDI at entry (4.0 ± 3.34 vs 2.2 ± 2.1; p < 0.05), and higher DBI score (0.9 ± 0.7 vs 0.3 ± 0.4; p < 0.05). The number of DRP was higher in group P (17.6 ± 10.8 vs 9.8 ± 6.3; p < 0.00.5). Linear regression showed a positive correlation between AT-HARM10 score and the number of DRP (r = 0.5, p < 0.05) with a coefficient of 7.7 (CI95% = [4.3; 11.1]) and an intercept of 9.8.

DISCUSSION

These results allow us to consider AT-HARM10 score as a targeting criterion for performing MR for elderly patients, as part of a curative approach to drug iatrogenic for these patients.

Risk factors for medication-related short-term readmissions in adults - a scoping review

BACKGROUND

Hospital readmissions due to medication-related problems occur frequently, burdening patients and caregivers emotionally and straining health care systems economically. In times of limited health care resources, interventions to mitigate the risk of medication-related readmissions should be prioritized to patients most likely to benefit. Focusing on general internal medicine patients, this scoping review aims to identify risk factors associated with drug-related 30-day hospital readmissions.

METHODS

We began by searching the Medline, Embase, and CINAHL databases from their inception dates to May 17, 2022 for studies reporting risk factors for 30-day drug-related readmissions. We included all peer-reviewed studies, while excluding literature reviews, conference abstracts, proceeding papers, editorials, and expert opinions. We also conducted backward citation searches of the included articles. Within the final sample, we analyzed the types and frequencies of risk factors mentioned.

RESULTS

After deduplication of the initial search results, 1159 titles and abstracts were screened for full-text adjudication. We read 101 full articles, of which we included 37. Thirteen more were collected via backward citation searches, resulting in a final sample of 50 articles. We identified five risk factor categories: (1) patient characteristics, (2) medication groups, (3) medication therapy problems, (4) adverse drug reactions, and (5) readmission diagnoses. The most commonly mentioned risk factors were polypharmacy, prescribing problems-especially underprescribing and suboptimal drug selection-and adherence issues. Medication groups associated with the highest risk of 30-day readmissions (mostly following adverse drug reactions) were antithrombotic agents, insulin, opioid analgesics, and diuretics. Preventable medication-related readmissions most often reflected prescribing problems and/or adherence issues.

CONCLUSIONS

This study's findings will help care teams prioritize patients for interventions to reduce medication-related hospital readmissions, which should increase patient safety. Further research is needed to analyze surrogate social parameters for the most common drug-related factors and their predictive value regarding medication-related readmissions.

Identifying older adults at increased risk of medication-related readmission to hospital within 30 days of discharge: development and validation of a risk assessment tool

OBJECTIVE

Developing and validating a risk assessment tool aiming to identify older adults (≥65 years) at increased risk of possibly medication-related readmission to hospital within 30 days of discharge.

DESIGN

Retrospective cohort study.

SETTING

The risk score was developed using data from a hospital in southern Sweden and validated using data from four hospitals in the mid-eastern part of Sweden.

PARTICIPANTS

The development cohort (n=720) was admitted to hospital during 2017, whereas the validation cohort (n=892) was admitted during 2017-2018.

MEASURES

The risk assessment tool aims to predict possibly medication-related readmission to hospital within 30 days of discharge. Variables known at first admission and individually associated with possibly medication-related readmission were used in development. The included variables were assigned points, and Youden's index was used to decide a threshold score. The risk score was calculated for all individuals in both cohorts. Area under the receiver operating characteristic (ROC) curve (c-index) was used to measure the discrimination of the developed risk score. Sensitivity, specificity and positive and negative predictive values were calculated using cross-tabulation.

RESULTS

The developed risk assessment tool, the Hospitalisations, Own home, Medications, and Emergency admission (HOME) Score, had a c-index of 0.69 in the development cohort and 0.65 in the validation cohort. It showed sensitivity 76%, specificity 54%, positive predictive value 29% and negative predictive value 90% at the threshold score in the development cohort.

CONCLUSION

The HOME Score can be used to identify older adults at increased risk of possibly medication-related readmission within 30 days of discharge. The tool is easy to use and includes variables available in electronic health records at admission, thus making it possible to implement risk-reducing activities during the hospital stay as well as at discharge and in transitions of care. Further studies are needed to investigate the clinical usefulness of the HOME Score as well as the benefits of implemented activities.

Prevalence and clinical implications of polypharmacy and potentially inappropriate medication in elderly patients with heart failure: results of six months' follow-up

OBJECTIVES

To investigate the prevalence of polypharmacy and potentially inappropriate medication (PIM) in elderly patients with heart failure (HF) and their impact on readmission and mortality.

METHODS

We conducted a study of 274 participants aged 60 years or older with HF. The prevalence of polypharmacy (defined as the use of five or more medications) was calculated, and the 2019 American Geriatrics Society Beers criteria were applied to access PIMs. Medications and PIMs were characterized at admission and discharge, and changes in prescriptions during hospitalization were compared. The impact of polypharmacy and PIM on readmission and mortality were investigated.

RESULTS

The median age of this study population was 68 years old. The median number of prescribed drugs was 7 at admission and 10 at discharge. At discharge, 99.27% of all patients were taking five or more drugs. The incidence of composite endpoint and cardiovascular readmission increased with the number of polypharmacy within 6 months. The use of guideline-directed medical therapy reduced the incidence of composite endpoint events and cardiovascular readmission, while the use of non-cardiovascular medications increased the composite endpoint events. The frequency of PIMs was 93.79% at discharge. The incidence of composite endpoint events increased with the number of PIMs. "PIMs in older adults with caution" increased cardiovascular readmission and "PIMs based on kidney function" increased cardiovascular mortality. Several comorbidities were associated with cardiovascular mortality or non-cardiovascular readmission.

CONCLUSIONS

Polypharmacy and PIM were highly prevalent in elderly patients with HF, and their use was associated with an increased risk of composite endpoint events, readmission and mortality. Non-cardiovascular medications, "PIMs in older adults with caution", "PIMs based on kidney function" and several comorbidities were important factors associated with hospital readmission and mortality. Our findings highlight the importance of medication optimization in the management of HF in elderly patients.

Evaluation of potentially inappropriate medications in older patients admitted to the cardiac intensive care unit according to the 2019 Beers criteria, STOPP criteria version 2 and Chinese criteria

WHAT IS KNOWN AND OBJECTIVES

Potential inappropriate medications (PIMs) can increase the risk of medication-induced harm. However, there are no studies regarding PIMs in older and critically ill patients with cardiovascular diseases in China. Therefore, studies evaluating PIMs in these patients can help in the implementation of more effective interventions to reduce the risk of drug use. Our objective was to analyse the prevalence of PIMs in elderly patients admitted to the cardiac intensive care unit (CICU) comparing the 2019 Beers criteria (Beers criteria), Screening Tool of Older People's Potentially Inappropriate Prescriptions (STOPP) criteria version 2 (STOPP criteria) and criteria of potentially inappropriate medications for older adults in China (Chinese criteria); and analyse the factors influencing the PIMs.

METHODS

This cross-sectional and retrospective study was performed with elderly patients (≥65 years) admitted to the CICU of the Beijing Tongren Hospital in China from January 2019 to June 2020. The PIMs were identified based on the Chinese, STOPP and Beers criteria at admission and discharge. The three criteria were compared using the Kappa statistic. Multiple regression analysis was used to investigate the influencing factors associated with PIMs.

RESULTS AND DISCUSSION

A total of 369 patients who met the inclusion/exclusion criteria were included in this study. According to the three criteria used to evaluate the PIMs, the prevalence was 78.3% and 72.6% at admission and discharge, respectively. The prevalence rate of PIMs determined by the Chinese criteria was 62.1% at admission versus 56.6% at discharge (p = 0.134); the Beers criteria was 53.9% at admission versus 46.9% at discharge (p = 0.056); by the STOPP criteria was 20.6% at admission versus 13.8% at discharge (p = 0.015). Moreover, 28.9% (STOPP criteria), 56.8% (Beers criteria) and 73.4% (Chinese criteria) of patients taking PIMs on admission still had the same problem at discharge. The most common PIMs screened by the Beers, STOPP and Chinese criteria were diuretics, benzodiazepines and clopidogrel, respectively. Besides, the three criteria showed poor agreement. Finally, the stronger predictor of PIMs was the increased number of medications (p < 0.05).

WHAT IS NEW AND CONCLUSION

The prevalence of PIMs in elderly patients admitted to the CICU was high. The Chinese, STOPP and Beers criteria are effective screening tools to detect PIMs, but the consistency between them was poor. The increased number of medications was a significant predictor of PIMs.

The Association Between Medication Non-Adherence and Early and Late Readmission Rates for Patients with Acute Coronary Syndrome

Purpose

Unplanned hospital readmission forms costly, but preventable burdens on healthcare system. This study was designed to evaluate cardiovascular-related readmission rate after discharge of acute coronary syndrome (ACS) patients and its relationship with medication adherence at a university hospital, Saudi Arabia.

Methods

A total of 370 consecutive patients presenting with ACS were involved. The inclusion criteria were clinical and coronary angiography diagnostic data of ACS. Exclusion criteria included heart valve disease, myocarditis, hepatic disease, and history of acute infection during the previous two weeks. Patients were divided into index admission group (n = 291) and unplanned readmission group (n = 79). Readmission and medication adherence rates were evaluated during 1–30, 31–180, 181–365, and 366–548 days post-ACS discharge. Medication adherence was estimated with a (yes/no) questionnaire.

Results

The overall readmission rate was 21.4%; individual rates were 30.4%, 38.0%, 27.8%, and 3.8% and the overall medication adherence rate was 62.03%, while individual rates were 54.2%, 70.0%, 63.6%, and 33.3% during the four periods, respectively. There were strong correlations between medication non-adherence and readmission rates. Heart failure, ST-elevated myocardial infarction, unstable angina, cerebrovascular accident, and arrhythmia represented the top causes. Body mass index was higher in readmission group. There were significant correlations between smoking, hypertension, cerebrovascular accident, ischemic heart disease, previous stent, instent restenosis, and LDL-cholesterol as predictor factors and readmission rate.

Conclusion

The cardiovascular-related unplanned readmission rate post-ACS discharge was 21.4%, and medication non-adherence rate was 37.97%. There were strong correlations between them in the time frames from 1-month to 1.5-year post-discharge. The individual rates decreased by time, but the first month showed lower rates than the following 5 months and this indicated the role of factors other than medication non-adherence in readmission. The rates are generally consistent with the international levels but utilizing technology can further improve medication adherence and reduce readmission rates.

A Participatory Sensing Study to Understand the Problems Older Adults Faced in Developing Medication-Taking Habits

Past research has demonstrated that older adults tend to use daily activities as cues to remember to take medications. However, they may still experience medication non-adherence because they did not select adequate contextual cues or face situations that interfere with their medication routines. This work addresses two research questions: (1) How does the association that older adults establish between their daily routines and their medication taking enable them to perform it consistently? (2) What problems do they face in associating daily routines with medication taking? For 30 days, using a mixed-methods approach, we collected quantitative and qualitative data from four participants aged 70–73 years old about their medication taking. We confirm that older adults who matched their medication regimens to their habitual routines obtained better results on time-based consistency measures. The main constraints for using daily routines as contextual cues were the insertion of medication taking into broad daily routines, the association of multiple daily routines with medication taking, the lack of strict daily routines, and the disruption of daily routines. We argue that the strategies proposed by the literature for forming medication-taking habits should support their formulation by measuring patients’ dosage patterns and generating logs of their daily activities.

Predicting Hospital Readmissions from Health Insurance Claims Data: A Modeling Study Targeting Potentially Inappropriate Prescribing

BACKGROUND

 Numerous prediction models for readmissions are developed from hospital data whose predictor variables are based on specific data fields that are often not transferable to other settings. In contrast, routine data from statutory health insurances (in Germany) are highly standardized, ubiquitously available, and would thus allow for automatic identification of readmission risks.

OBJECTIVES

 To develop and internally validate prediction models for readmissions based on potentially inappropriate prescribing (PIP) in six diseases from routine data.

METHODS

 In a large database of German statutory health insurance claims, we detected disease-specific readmissions after index admissions for acute myocardial infarction (AMI), heart failure (HF), a composite of stroke, transient ischemic attack or atrial fibrillation (S/AF), chronic obstructive pulmonary disease (COPD), type-2 diabetes mellitus (DM), and osteoporosis (OS). PIP at the index admission was determined by the STOPP/START criteria (Screening Tool of Older Persons' Prescriptions/Screening Tool to Alert doctors to the Right Treatment) which were candidate variables in regularized prediction models for specific readmission within 90 days. The risks from disease-specific models were combined ("stacked") to predict all-cause readmission within 90 days. Validation performance was measured by the c-statistics.

RESULTS

 While the prevalence of START criteria was higher than for STOPP criteria, more single STOPP criteria were selected into models for specific readmissions. Performance in validation samples was the highest for DM (c-statistics: 0.68 [95% confidence interval (CI): 0.66-0.70]), followed by COPD (c-statistics: 0.65 [95% CI: 0.64-0.67]), S/AF (c-statistics: 0.65 [95% CI: 0.63-0.66]), HF (c-statistics: 0.61 [95% CI: 0.60-0.62]), AMI (c-statistics: 0.58 [95% CI: 0.56-0.60]), and OS (c-statistics: 0.51 [95% CI: 0.47-0.56]). Integrating risks from disease-specific models to a combined model for all-cause readmission yielded a c-statistics of 0.63 [95% CI: 0.63-0.64].

CONCLUSION

 PIP successfully predicted readmissions for most diseases, opening the possibility for interventions to improve these modifiable risk factors. Machine-learning methods appear promising for future modeling of PIP predictors in complex older patients with many underlying diseases.

Drug-related emergency department visits in older patients: an applicability and reliability study of an existing assessment tool

BACKGROUND

AT-HARM10 is a research tool to identify possible drug-related hospital admissions. It is unclear whether the tool can be applied to emergency department visits as well.

AIM

The aim of this study was to investigate the applicability and reliability to identify drug-related emergency department visits in older patients with AT-HARM10.

METHOD

A random sample of 400 patients aged 65 years or older from a clinical trial in four Swedish hospitals was selected. All patients' emergency department visits within 12 months after discharge were assessed with AT-HARM10. The main outcome measures were the percentage of successfully assessed visits for applicability and the interrater reliability (Cohen's kappa).

RESULTS

Of the initial sample (n = 400), 113 patients [median age (interquartile range): 81 (76-88) years] had at least one emergency department visit within 12 months. The patients had in total 184 visits, of which 179 (97%) were successfully assessed. Fifty-three visits (29%) were possibly drug-related. The Cohen's kappa value was 0.70 (substantial).

CONCLUSION

It seems applicable and reliable to identify possible drug-related emergency department visits in addition to hospital admissions in older patients with AT-HARM10. As a consequence, the tool has been updated to support its novel use in clinical research.