Registered report protocol:A scoping review to identify potential predictors as features for developing automated estimation of the probability of being frail in secondary care

INTRODUCTION: The impact of frailty surges, as the prevalence increases with age and the population age is rising. Frailty is associated with adverse health outcomes and increased healthcare costs. Many validated instruments to detect frailty have been developed. Using these in clinical practice takes time. Automated estimation of the probability of being frail using routinely collected data from hospital electronic health records (EHRs) would circumvent that. We aim to identify potential predictors that could be used as features for modeling algorithms on the basis of routine hospital EHR data to incorporate in an automated tool for estimating the probability of being frail. METHODS: PubMed (MEDLINE), CINAHL Plus, Embase, and Web of Science will be searched. The studied population consists of older people (≥65 years). The first step is searching articles published ≥2018. Second, we add two published literature reviews (and the articles included therein) [Bery 2020; Bouillon, 2013] to our search results. In these reviews, articles on potential predictor variables in frailty screening tools were included from inception until March 2018. The goal is to identify and extract all potential predictors of being frail. Domain experts will be consulted to evaluate the results. DISCUSSION: The results of the intended study will increase the quality of the developed algorithms to be used for automated estimation of the probability of being frail in secondary care. This is a promising perspective, being less labor-intensive compared to screening each individual patient by hand. Also, such an automated tool may raise awareness of frailty, especially in those patients who would not be screened for frailty by hand because they seem robust. CONCLUSION: The identified potential predictors of being frail can be used as evidence-based input for machine learning based automated estimation of the probability of being frail using routine EHR data in the near future

Impact of geriatric co-management on outcomes in hospitalised cardiology patients aged 85 and over

OBJECTIVE: Cardiovascular disease and frailty are common among the population aged 85+. We hypothesised these patients might benefit from geriatric co-management, as has been shown in other frail patient populations. However, there is limited evidence supporting geriatric co-management in older, hospitalised cardiology patients. METHODS: A retrospective cohort study was performed in a large teaching hospital in the Netherlands. We compared patients aged 85 and over admitted to the cardiology ward before (control group) and after the implementation of standard geriatric co-management (intervention group). Data on readmission, mortality, length of stay, number of consultations, delirium, and falls were analysed. RESULTS: The data of 1163 patients were analysed (n = 542 control, n = 621 intervention). In the intervention group, 251 patients did not receive the intervention because of logistic reasons or the treating physician's decision. Baseline characteristics were comparable in the intervention and control groups. Patients in the intervention group had a shorter length of stay (-1 day, p = 0.01) and were more often discharged to a geriatric rehabilitation facility (odds ratio [OR] 1.97, 95% confidence interval [CI] 1.10-3.54, p = 0.02) compared with the control patients. Other outcomes were not significantly different between the groups. CONCLUSIONS: After implementation of standard geriatric co-management for hospitalised cardiology patients aged 85 and over, the length of hospital stay shortened and the number of patients discharged to a geriatric rehabilitation facility increased. The adherence to geriatric team recommendations was high. Geriatric co-management would appear to optimise care for older hospitalised patients with cardiac disease

The perspectives of patients with lithium-induced end-stage renal disease

Abstract Background Lithium is the treatment of choice for patients suffering from bipolar disorder (BD) but prolonged use induces renal dysfunction in at least 20% of patient. Intensive monitoring of kidney functioning helps to reveal early decline in renal failure. This study investigates the views and experiences of BD patients who have developed end-stage renal disease and were receiving renal replacement therapy. Results The patients overall reported not to have been offered alternative treatment options at the start of lithium therapy or when renal functions deteriorated. All indicated to have lacked sound information and dialogue in accordance with shared decision making. Kidney monitoring was inadequate in many cases and decision making rushed. Conclusions Retrospectively, the treatment and monitoring of lithium and the information process were inadequate in many cases. We give suggestions on how to inform patients taking lithium for their BD timely and adequately on the course of renal function loss in the various stages of their treatment

Registered report protocol: A scoping review to identify potential predictors as features for developing automated estimation of the probability of being frail in secondary care

Introduction The impact of frailty surges, as the prevalence increases with age and the population age is rising. Frailty is associated with adverse health outcomes and increased healthcare costs. Many validated instruments to detect frailty have been developed. Using these in clinical practice takes time. Automated estimation of the probability of being frail using routinely collected data from hospital electronic health records (EHRs) would circumvent that. We aim to identify potential predictors that could be used as features for modeling algorithms on the basis of routine hospital EHR data to incorporate in an automated tool for estimating the probability of being frail. Methods PubMed (MEDLINE), CINAHL Plus, Embase, and Web of Science will be searched. The studied population consists of older people (≥65 years). The first step is searching articles published ≥2018. Second, we add two published literature reviews (and the articles included therein) [Bery 2020; Bouillon, 2013] to our search results. In these reviews, articles on potential predictor variables in frailty screening tools were included from inception until March 2018. The goal is to identify and extract all potential predictors of being frail. Domain experts will be consulted to evaluate the results. Discussion The results of the intended study will increase the quality of the developed algorithms to be used for automated estimation of the probability of being frail in secondary care. This is a promising perspective, being less labor-intensive compared to screening each individual patient by hand. Also, such an automated tool may raise awareness of frailty, especially in those patients who would not be screened for frailty by hand because they seem robust. Conclusion The identified potential predictors of being frail can be used as evidence-based input for machine learning based automated estimation of the probability of being frail using routine EHR data in the near future

Integrating healthcare for follow‐up of adult COVID‐19 patients in an outpatient clinic: A matter of cooperation

Abstract Rationale, aims, and objectives A large number of patients infected with SARS‐CoV‐2 (COVID‐19) need outpatient follow‐up after hospitalization. As these patients may experience a broad range of symptoms, as do patients infected with the related SARS‐CoV‐1 virus, we set up a multidisciplinary outpatient clinic involving pulmonologists, internists, and geriatricians. Patients were allocated to a specialist based on symptoms reported on a self‐developed questionnaire of expected symptoms of COVID‐19. This study aimed to evaluate the effectiveness of this outpatient clinic. Methods In this retrospective study, the medical records of patients who presented to the outpatient clinic for follow‐up after hospitalization for COVID‐19 up to 31 August 2020, were reviewed. Results In total, 266 patients were seen at the outpatient clinic at least once. Overall, 100 patients were seen by a pulmonologist, 97 by an internist, and 65 by a geriatrician. A referral between these 3 medical specialists was needed for only 14 patients (5.3%). Fifty patients were seen by a psychologist, mostly those with a HADS score >10. Only 5 (2.2%) of the 221 patients who were not directly referred to a psychologist based on triage needed psychological support. Forty‐eight patients (18%) were also seen by a physiatrist. Conclusion Identifying which medical specialist (pulmonologist, internist, and/or geriatrician) should see patients attending a post‐COVID outpatient clinic based on patient‐reported symptoms proved an effective approach to managing the flow of post‐COVID patients