Predicting unplanned hospitalisations in older adults using routinely recorded general practice data.

BACKGROUND: Unplanned hospitalisations represent a hazardous event for older persons. Timely identification of high-risk individuals using a prediction tool may facilitate preventive interventions. AIM: To develop and validate an easy-to-use prediction model for unplanned hospitalisations in community-dwelling older adults using readily available data to allow rapid bedside assessment by general practitioners. DESIGN AND SETTING: Retrospective study using general practice electronic health records of 243,129 community-dwelling adults aged ≥65 years linked with national administrative data. METHODS: The dataset was geographically split into a development (58.7%) and validation (41.3%) sample to predict unplanned hospitalisations within 6 months. We evaluated the performance of three different models with increasingly smaller selections of candidate predictors (i.e. optimal, readily-available and easy-to-use model, respectively). We used logistic regression with backward selection for model development. The models were validated internally and externally. We assessed predictive performance by area under the curve (AUC) and calibration plots. RESULTS: In both samples, 7.6% had at least one unplanned hospitalisation within 6 months. The discriminative ability of the three models was comparable and remained stable after geographic validation. The easy-to-use model included age, sex, prior hospitalisations, pulmonary emphysema, heart failure and polypharmacy. Its discriminative ability after validation was AUC 0.72 [95% confidence interval: 0.72-0.71]. Calibration plots showed good calibration. CONCLUSION: Our models showed satisfactory predictive ability. Reducing the number of predictors and geographic validation did not impact predictive performance, demonstrating the robustness of the model. We developed an easy-to-use tool that may assist general practitioners in decision-making and targeted preventive interventions.

Predicting unplanned hospitalisations in older adults using routinely recorded general practice data.

Context: Unplanned hospitalisations represent a hazardous event for older persons. Timely identification of high-risk individuals using a prediction tool may facilitate preventive interventions. Objective: To develop and validate an easy-to-use prediction model for unplanned hospitalisations in community-dwelling older adults using readily available data to allow rapid bedside assessment by general practitioners. Study Design and Analysis: retrospective study Setting or Dataset: general practice electronic health records linked with national administrative data Population Studied: 243,129 community dwelling adults aged 65 years and over Intervention/Instrument: The dataset was geographically split into a development (58.7%) and validation (41.3%) sample. The model was developed using logistic regression with backward selection. The models were validated internally and externally. We evaluated the performance of three different models with increasingly smaller selections of candidate predictors (i.e. optimal, readily-available and easy-to-use model, respectively). Predictive performance was assessed by area under the curve (AUC) and calibration plots. Outcome Measures: unplanned hospital admissions within 6 months. Results: In both samples, 7.6% had at least one unplanned hospitalisation within 6 months. The discriminative ability of the three models was comparable and remained stable after geographic validation. The easy-to-use model included age, sex, prior hospitalisations, pulmonary emphysema, heart failure and polypharmacy. Its discriminative ability after validation was AUC 0.72 [95% confidence interval: 0.72-0.71]. Calibration was good. Conclusions: Our models showed satisfactory predictive ability. Reducing the number of predictors and geographic validation did not impact predictive performance, demonstrating the robustness of the model. We developed an easy-to-use tool that may assist general practitioners in decision-making and targeted preventive interventions.

Compression of the optic chiasm is associated with reduced photoentrainment of the central biological clock.

Objective: Pituitary tumours that compress the optic chiasm are associated with long-term alterations in sleep-wake rhythm. This may result from damage to intrinsically photosensitive retinal ganglion cells (ipRGCs) projecting from the retina to the hypothalamic suprachiasmatic nucleus via the optic chiasm to ensure photoentrainment (i.e. synchronisation to the 24-h solar cycle through light). To test this hypothesis, we compared the post-illumination pupil response (PIPR), a direct indicator of ipRGC function, between hypopituitarism patients with and without a history of optic chiasm compression. Design: Observational study, comparing two predefined groups. Methods: We studied 49 patients with adequately substituted hypopituitarism: 25 patients with previous optic chiasm compression causing visual disturbances (CC+ group) and 24 patients without (CC- group). The PIPR was assessed by chromatic pupillometry and expressed as the relative change between baseline and post-blue-light stimulus pupil diameter. Objective and subjective sleep parameters were obtained using polysomnography, actigraphy, and questionnaires. Results: Post-blue-light stimulus pupillary constriction was less sustained in CC+ patients compared with CC- patients, resulting in a significantly smaller extended PIPR (mean difference: 8.1%, 95% CI: 2.2-13.9%, P = 0.008, Cohen's d = 0.78). Sleep-wake timing was consistently later in CC+ patients, without differences in sleep duration, efficiency, or other rest-activity rhythm features. Subjective sleep did not differ between groups. Conclusion: Previous optic chiasm compression due to a pituitary tumour in patients with hypopituitarism is associated with an attenuated PIPR and delayed sleep timing. Together, these data suggest that ipRGC function and consequently photoentrainment of the central biological clock is impaired in patients with a history of optic chiasm compression.

Prediction models for the prediction of unplanned hospital admissions in community-dwelling older adults: A systematic review.

BACKGROUND: Identification of community-dwelling older adults at risk of unplanned hospitalizations is of importance to facilitate preventive interventions. Our objective was to review and appraise the methodological quality and predictive performance of prediction models for predicting unplanned hospitalizations in community-dwelling older adults. METHODS AND FINDINGS: We searched MEDLINE, EMBASE and CINAHL from August 2013 to January 2021. Additionally, we checked references of the identified articles for the inclusion of relevant publications and added studies from two previous reviews that fulfilled the eligibility criteria. We included prospective and retrospective studies with any follow-up period that recruited adults aged 65 and over and developed a prediction model predicting unplanned hospitalizations. We included models with at least one (internal or external) validation cohort. The models had to be intended to be used in a primary care setting. Two authors independently assessed studies for inclusion and undertook data extraction following recommendations of the CHARMS checklist, while quality assessment was performed using the PROBAST tool. A total of 19 studies met the inclusion criteria. Prediction horizon ranged from 4.5 months to 4 years. Most frequently included variables were specific medical diagnoses (n = 11), previous hospital admission (n = 11), age (n = 11), and sex or gender (n = 8). Predictive performance in terms of area under the curve ranged from 0.61 to 0.78. Models developed to predict potentially preventable hospitalizations tended to have better predictive performance than models predicting hospitalizations in general. Overall, risk of bias was high, predominantly in the analysis domain. CONCLUSIONS: Models developed to predict preventable hospitalizations tended to have better predictive performance than models to predict all-cause hospitalizations. There is however substantial room for improvement on the reporting and analysis of studies. We recommend better adherence to the TRIPOD guidelines.

Predicting unplanned hospital visits in older home care recipients: a cross-country external validation study.

BACKGROUND: Accurate identification of older persons at risk of unplanned hospital visits can facilitate preventive interventions. Several risk scores have been developed to identify older adults at risk of unplanned hospital visits. It is unclear whether risk scores developed in one country, perform as well in another. This study validates seven risk scores to predict unplanned hospital admissions and emergency department (ED) visits in older home care recipients from six countries. METHODS: We used the IBenC sample (n = 2446), a cohort of older home care recipients from six countries (Belgium, Finland, Germany, Iceland, Italy and The Netherlands) to validate four specific risk scores (DIVERT, CARS, EARLI and previous acute admissions) and three frailty indicators (CHESS, Fried Frailty Criteria and Frailty Index). Outcome measures were unplanned hospital admissions, ED visits or any unplanned hospital visits after 6 months. Missing data were handled by multiple imputation. Performance was determined by assessing calibration and discrimination (area under receiver operating characteristic curve (AUC)). RESULTS: Risk score performance varied across countries. In Iceland, for any unplanned hospital visits DIVERT and CARS reached a fair predictive value (AUC 0.74 [0.68-0.80] and AUC 0.74 [0.67-0.80]), respectively). In Finland, DIVERT had fair performance predicting ED visits (AUC 0.72 [0.67-0.77]) and any unplanned hospital visits (AUC 0.73 [0.67-0.77]). In other countries, AUCs did not exceed 0.70. CONCLUSIONS: Geographical validation of risk scores predicting unplanned hospital visits in home care recipients showed substantial variations of poor to fair performance across countries. Unplanned hospital visits seem considerably dependent on healthcare context. Therefore, risk scores should be validated regionally before applied to practice. Future studies should focus on identification of more discriminative predictors in order to develop more accurate risk scores.

NADP+ -dependent IDH1 R132 mutation and its relevance for glioma patient survival.

The isocitrate dehydrogenase 1 (IDH1) mutation occurs in high frequency in glioma and secondary glioblastoma (GBM). Mutated IDH1 produces the oncometabolite 2-hydroxyglutarate rather than α-ketoglutarate or isocitrate. The oncometabolite is considered to be the major cause of the association between the IDH1 mutation and gliomagenesis. On the other hand, the IDH1 mutation in GBM is associated with prolonged patient survival. This association is not well understood yet but IDH1 involvement in epigenetic silencing of O-6-methylguanine-DNA methyltransferase (MGMT), a DNA repair enzyme is considered to be an important mechanism. However, it was shown recently that the IDH1 mutation and MGMT silencing are independent prognostic factors. Here, we hypothesize that the IDH1 mutation reduces the capacity to produce NADPH and thus reduces the capacity to scavenge reactive oxygen species that are generated during irradiation and chemotherapy. IDH1 activity is responsible for two-thirds of the NADPH production capacity in normal brain, whereas the IDH1 mutation reduces this capacity by almost 40%. Therefore, we hypothesize that the reduced NADPH production capacity due to the IDH1 mutation renders GBM cells more vulnerable to irradiation and chemotherapy thus prolonging survival of the patients.