scholarly journals Clinical and functional patient characteristics predict medical needs in older patients at risk of functional decline

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Anne-Carina Scharf ◽  
Janine Gronewold ◽  
Christian Dahlmann ◽  
Jeanina Schlitzer ◽  
Andreas Kribben ◽  
...  
Keyword(s):  
At Risk ◽  
Older Patients ◽  
Functional Decline ◽  
Patient Characteristics ◽  
Medical Needs ◽  
Patients At Risk

scholarly journals Clinical and functional patient characteristics predict medical needs in older patients at risk of functional decline

2020 ◽  
Author(s):  
Anne-Carina Scharf ◽  
Janine Gronewold ◽  
Christian Dahlmann ◽  
Jeanina Schlitzer ◽  
Andreas Kribben ◽  
...  
Keyword(s):  
At Risk ◽  
Hospital Stay ◽  
Older Patients ◽  
Functional Decline ◽  
Length Of Hospital Stay ◽  
Patient Characteristics ◽  
Clinical Patient ◽  
Medical Needs ◽  
Patients At Risk ◽  
Cognition Impairment

Abstract Background: The rising number of older multimorbid in-patients has implications for medical care. There is a growing need for the identification of factors predicting the needs of older patients in hospital environments. Our aim was to evaluate the use of clinical and functional patient characteristics for the prediction of medical needs in older hospitalized patients. Methods: 242 in-patients (57.4% male) aged 78.4±6.4 years, who were consecutively admitted to internal medicine departments of the University Hospital Essen between July 2015 and February 2017, were prospectively enrolled. Patients were assessed upon admission using the Identification of Seniors at Risk (ISAR) screening followed by comprehensive geriatric assessment (CGA). The CGA included standardized instruments for the assessment of activities of daily living (ADL), cognition, mobility, and signs of depression upon admission. In multivariable regressions we evaluated the association of clinical patient characteristics, the ISAR score and CGA results with length of hospital stay, number of nursing hours and receiving physiotherapy as indicators for medical needs. We identified clinical characteristics and risk factors associated with higher medical needs. Results: The 242 patients spent [median(Q1;Q3)]:9.0(4.0;16.0) days in the hospital, needed 2.0(1.5;2.7) hours of nursing each day, and 34.3% received physiotherapy. In multivariable regression analyses including clinical patient characteristics, ISAR and CGA domains, the factors age (β=-0.19, 95% confidence interval (CI)=-0.66;-0.13), number of admission diagnoses (β=0.28, 95%CI=0.16;0.41), ADL impairment (B=6.66, 95%CI=3.312;10.01), and signs of depression (B=6.69, 95%CI=1.43;11.94) independently predicted length of hospital stay. ADL impairment (B=1.14, 95%CI=0.67;1.61), cognition impairment (B=0.57, 95%CI=0.07;1.07) and ISAR score (β =0.26, 95%CI=0.01;0.28) independently predicted nursing hours. The number of admission diagnoses (risk ratio (RR)=1.06, 95%CI=1.04;1.08), ADL impairment (RR=3.54, 95%CI=2.29;5.47), cognition impairment (RR=1.77, 95%CI=1.20;2.62) and signs of depression (RR=1.99, 95%CI=1.39;2.85) predicted receiving physiotherapy. Conclusion: Among older in-patients at risk for functional decline, the number of comorbidities, reduced ADL, cognition impairment and signs of depression are important predictors of length of hospital stay, nursing hours, and receiving physiotherapy during hospital stay.

scholarly journals Clinical and functional patient characteristics predict medical needs in older patients at risk of functional decline

2019 ◽  
Author(s):  
Anne-Carina Scharf ◽  
Janine Gronewold ◽  
Christian Dahlmann ◽  
Jeanina Schlitzer ◽  
Andreas Kribben ◽  
...  
Keyword(s):  
At Risk ◽  
Hospital Stay ◽  
Older Patients ◽  
Functional Decline ◽  
Length Of Hospital Stay ◽  
Patient Characteristics ◽  
Clinical Patient ◽  
Medical Needs ◽  
Patients At Risk ◽  
Cognition Impairment

Abstract Background : The rising number of older multimorbid in-patients has implications for medical care. There is a growing need for the identification of factors predicting the needs of older patients in hospital environments. Our aim was to evaluate the use of clinical and functional patient characteristics for the prediction of medical needs in older hospitalized patients. Methods : 242 in-patients (57.4% male) aged 78.4±6.4 years, who were consecutively admitted to internal medicine departments of the University Hospital Essen between July 2015 and February 2017, were prospectively enrolled. Patients were assessed upon admission using Identification of Seniors at Risk (ISAR) screening followed by comprehensive geriatric assessment (CGA). CGA included standardized instruments for the assessment of activities of daily living (ADL), cognition, mobility, and signs of depression upon admission. In multivariable regressions we evaluated the association of clinical patient characteristics, ISAR score and CGA results with length of hospital stay and number of nursing hours and receiving physiotherapy as indicators for medical needs. We identified clinical characteristics and risk factors associated with higher medical needs. Results: The 242 patients spent [median(Q1;Q3)]:9.0(4.0;16.0) days in the hospital, needed 2.0(1.5;2.7) hours of nursing each day, and 34.3% received physiotherapy. In multivariable regression analyses including clinical patient characteristics, ISAR and CGA domains, the factors age (β=-0.19, 95% confidence interval (CI)=-0.66;-0.13), number of admission diagnoses (β=0.28, 95%CI=0.16;0.41), ADL impairment (B=6.66, 95%CI=3.312;10.01), signs of depression (B=6.69, 95%CI=1.43;11.94) independently predicted length of hospital stay. ADL impairment (B=1.14, 95%CI=0.67;1.61) cognition impairment (B=0.57, 95%CI=0.07;1.07) and ISAR score (β =0.26, 95%CI=0.01;0.28) independently predicted nursing hours. The number of admission diagnoses (risk ratio (RR)=1.06, 95%CI=1.04;1.08), ADL impairment (RR=3.54, 95%CI=2.29;5.47), cognition impairment (RR=1.77, 95%CI=1.20;2.62) and signs of depression (RR=1.99, 95%CI=1.39;2.85) predicted receiving physiotherapy. Conclusion : Among older in-patients at risk for functional decline, the number of comorbidities, reduced ADL, cognition impairment and signs of depression are important predictors of length of hospital stay, nursing hours, and receiving physiotherapy during hospital stay.

scholarly journals Real-time utilisation of administrative data in the ED to identify older patients at risk: development and validation of the Dynamic Silver Code

BMJ Open ◽  
2019 ◽  
Vol 9 (12) ◽  
pp. e033374 ◽  
Author(s):  
Daniela Balzi ◽  
Giulia Carreras ◽  
Francesco Tonarelli ◽  
Luca Degli Esposti ◽  
Paola Michelozzi ◽  
...  
Keyword(s):  
At Risk ◽  
Cohort Study ◽  
Real Time ◽  
Administrative Data ◽  
Older Patients ◽  
Clinical Decision Making ◽  
External Validation ◽  
Clinical Decision ◽  
Patients At Risk ◽  
Ed Visits

ObjectiveIdentification of older patients at risk, among those accessing the emergency department (ED), may support clinical decision-making. To this purpose, we developed and validated the Dynamic Silver Code (DSC), a score based on real-time linkage of administrative data.Design and settingThe ‘Silver Code National Project (SCNP)’, a non-concurrent cohort study, was used for retrospective development and internal validation of the DSC. External validation was obtained in the ‘Anziani in DEA (AIDEA)’ concurrent cohort study, where the DSC was generated by the software routinely used in the ED.ParticipantsThe SCNP contained 281 321 records of 180 079 residents aged 75+ years from Tuscany and Lazio, Italy, admitted via the ED to Internal Medicine or Geriatrics units. The AIDEA study enrolled 4425 subjects aged 75+ years (5217 records) accessing two EDs in the area of Florence, Italy.InterventionsNone.Outcome measuresPrimary outcome: 1-year mortality. Secondary outcomes: 7 and 30-day mortality and 1-year recurrent ED visits.ResultsAdvancing age, male gender, previous hospital admission, discharge diagnosis, time from discharge and polypharmacy predicted 1-year mortality and contributed to the DSC in the development subsample of the SCNP cohort. Based on score quartiles, participants were classified into low, medium, high and very high-risk classes. In the SCNP validation sample, mortality increased progressively from 144 to 367 per 1000 person-years, across DSC classes, with HR (95% CI) of 1.92 (1.85 to 1.99), 2.71 (2.61 to 2.81) and 5.40 (5.21 to 5.59) in class II, III and IV, respectively versus class I (p<0.001). Findings were similar in AIDEA, where the DSC predicted also recurrent ED visits in 1 year. In both databases, the DSC predicted 7 and 30-day mortality.ConclusionsThe DSC, based on administrative data available in real time, predicts prognosis of older patients and might improve their management in the ED.

scholarly journals Can routine clinical data identify older patients at risk of poor healthcare outcomes on admission to hospital?

2017 ◽  
Vol 10 (1) ◽  
Author(s):  
Kinda Ibrahim ◽  
Charlotte Owen ◽  
Harnish P. Patel ◽  
Carl May ◽  
Mark Baxter ◽  
...  
Keyword(s):  
At Risk ◽  
Clinical Data ◽  
Older Patients ◽  
Healthcare Outcomes ◽  
Patients At Risk

scholarly journals The association of kidney function and cognitive decline in older patients at risk of cardiovascular disease: a longitudinal data analysis

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Laurien E. Zijlstra ◽  
Stella Trompet ◽  
Simon P. Mooijaart ◽  
Marjolijn van Buren ◽  
Naveed Sattar ◽  
...  
Keyword(s):  
Cardiovascular Disease ◽  
At Risk ◽  
Data Analysis ◽  
Longitudinal Data ◽  
Cognitive Decline ◽  
Kidney Function ◽  
Older Patients ◽  
Longitudinal Data Analysis ◽  
Patients At Risk

scholarly journals Nutritional Advice in Older Patients at Risk of Malnutrition during Treatment for Chemotherapy: A Two-Year Randomized Controlled Trial

PLoS ONE ◽  
2014 ◽  
Vol 9 (9) ◽  
pp. e108687 ◽  
Author(s):  
Isabelle Bourdel-Marchasson ◽  
Christelle Blanc-Bisson ◽  
Adélaïde Doussau ◽  
Christine Germain ◽  
Jean-Frédéric Blanc ◽  
...  
Keyword(s):  
At Risk ◽  
Randomized Controlled Trial ◽  
Older Patients ◽  
Controlled Trial ◽  
Randomized Controlled ◽  
Nutritional Advice ◽  
Patients At Risk

scholarly journals Characterising the severity of treatment resistance in unipolar and bipolar depression

BJPsych Open ◽  
2021 ◽  
Vol 7 (6) ◽  
Author(s):  
Rachael W. Taylor ◽  
Rebecca Strawbridge ◽  
Allan H. Young ◽  
Roland Zahn ◽  
Anthony J. Cleare
Keyword(s):  
Risk Factors ◽  
At Risk ◽  
Bipolar Depression ◽  
Antidepressant Treatment ◽  
Age At Onset ◽  
Treatment Resistance ◽  
Patient Characteristics ◽  
Patients At Risk ◽  
Resistant Depression ◽  
Depressive Episodes

Background Treatment-resistant depression (TRD) is classically defined according to the number of suboptimal antidepressant responses experienced, but multidimensional assessments of TRD are emerging and may confer some advantages. Patient characteristics have been identified as risk factors for TRD but may also be associated with TRD severity. The identification of individuals at risk of severe TRD would support appropriate prioritisation of intensive and specialist treatments. Aims To determine whether TRD risk factors are associated with TRD severity when assessed multidimensionally using the Maudsley Staging Method (MSM), and univariately as the number of antidepressant non-responses, across three cohorts of individuals with depression. Method Three cohorts of individuals without significant TRD, with established TRD and with severe TRD, were assessed (n = 528). Preselected characteristics were included in linear regressions to determine their association with each outcome. Results Participants with more severe TRD according to the MSM had a lower age at onset, fewer depressive episodes and more physical comorbidities. These associations were not consistent across cohorts. The number of episodes was associated with the number of antidepressant treatment failures, but the direction of association varied across the cohorts studied. Conclusions Several risk factors for TRD were associated with the severity of resistance according to the MSM. Fewer were associated with the raw number of inadequate antidepressant responses. Multidimensional definitions may be more useful for identifying patients at risk of severe TRD. The inconsistency of associations across cohorts has potential implications for the characterisation of TRD.

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