scholarly journals Supportive and Palliative Care Indicators Tool prognostic value in older hospitalised patients: a prospective multicentre study

2021 ◽  
pp. bmjspcare-2021-003042
Author(s):  
Ruth Piers ◽  
Isabelle De Brauwer ◽  
Hilde Baeyens ◽  
Anja Velghe ◽  
Lineke Hens ◽  
...  

BackgroundAn increasing number of older patients are hospitalised. Prognostic uncertainty causes hospital doctors to be reluctant to make the switch from cure to care. The Supportive and Palliative Care Indicators Tool (SPICT) has not been validated for prognostication in an older hospitalised population.AimTo validate SPICT as a prognostic tool for risk of dying within one year in older hospitalised patients.DesignProspective multicentre study. Premorbid SPICT and 1-year survival and survival time were assessed.Setting/participantsPatients 75 years and older admitted at acute geriatric (n=209) and cardiology units (CUs) (n=249) of four hospitals.ResultsIn total, 59.3% (124/209) was SPICT identified on acute geriatric vs 40.6% (101/249) on CUs (p<0.001). SPICT-identified patients in CUs reported more functional needs and more symptoms compared to SPICT non-identified patients. On acute geriatric units, SPICT-identified patients reported more functional needs only.The HR of dying was 2.9 (95% CI 1.1 to 8.7) in SPICT-identified versus non-identified after adjustment for hospital strata, age, gender and did not differ between units. One-year mortality was 24% and 22%, respectively, on acute geriatric versus CUs (p=0.488). Pooled average sensitivity, specificity and partial area under the curve differed significantly between acute geriatric and CUs (p<0.001), respectively, 0.82 (95%CI 0.66 to 0.91), 0.49 (95%CI 0.40 to 0.58) and 0.82 in geriatric vs 0.69 (95% CI 0.42 to 0.87), 0.66 (95% CI 0.55 to 0.77) and 0.65 in CUs.ConclusionsSPICT may be used as a tool to identify older hospitalised patients at risk of dying within 1 year and who may benefit from a palliative care approach including advance care planning. The prognostic accuracy of SPICT is better in older patients admitted at the acute geriatric versus the CU.

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Celine Van de Vyver ◽  
Anja Velghe ◽  
Hilde Baeyens ◽  
Jean-Pierre Baeyens ◽  
Julien Dekoninck ◽  
...  

Abstract Background Timely palliative care in frail older persons remains challenging. Scales to identify older patients at risk of functional decline already exist. However, factors to predict short term mortality in older hospitalized patients are scarce. Methods In this prospective study, we recruited patients of 75 years and older at the department of cardiology and geriatrics. The usual gait speed measurement closest to discharge was chosen. We used the risk of dying within 1 year as parameter for starting palliative care. ROC curves were used to determine the best cut-off value of usual gait speed to predict one-year mortality. Time to event analyses were assessed by COX regression. Results On the acute geriatric ward (n = 60), patients were older and more frail (assessed by Katz and iADL) in comparison to patients on the cardiology ward (n = 82); one-year mortality was respectively 27 and 15% (p = 0.069). AUC on the acute geriatric ward was 0.748 (p = 0.006). The best cut-off value was 0.42 m/s with a sensitivity and specificity of 0.857 and 0.643. Slow walkers died earlier than faster walkers (HR 7.456, p = 0.011), after correction for age and sex. On the cardiology ward, AUC was 0.560 (p = 0.563); no significant association was found between usual gait speed and survival time. Conclusions Usual gait speed may be a valuable prognostic factor to identify patients at risk for one-year mortality on the acute geriatric ward but not on the cardiology ward.


2021 ◽  
pp. 082585972110033
Author(s):  
Elizabeth Hamill Howard ◽  
Rachel Schwartz ◽  
Bruce Feldstein ◽  
Marita Grudzen ◽  
Lori Klein ◽  
...  

Objective: To explore chaplains’ ability to identify unmet palliative care (PC) needs in older emergency department (ED) patients. Methods: A palliative chaplain-fellow conducted a retrospective chart review evaluating 580 ED patients, age ≥80 using the Palliative Care and Rapid Emergency Screening (P-CaRES) tool. An emergency medicine physician and chaplain-fellow screened 10% of these charts to provide a clinical assessment. One year post-study, charts were re-examined to identify which patients received PC consultation (PCC) or died, providing an objective metric for comparing predicted needs with services received. Results: Within one year of ED presentation, 31% of the patient sub-sample received PCC; 17% died. Forty percent of deceased patients did not receive PCC. Of this 40%, chaplain screening for P-CaRES eligibility correctly identified 75% of the deceased as needing PCC. Conclusion: Establishing chaplain-led PC screenings as standard practice in the ED setting may improve end-of-life care for older patients.


2018 ◽  
Vol 48 (4) ◽  
pp. 322-325 ◽  
Author(s):  
Mekkattukunnel A Andrews ◽  
Abraham M Ittyachen

Acute febrile illness with varied aetiology but similar symptoms is common in tropical countries. This prospective, multicentre study was conducted in selected centres in the province of Kerala in India principally to analyse the aetiology of acute febrile illnesses in adult patients over the course of one year. Overall, 1324 patients were included in the study. The most common cause was dengue in 576 patients (43.5%). In 396 (29.9%), the exact aetiology could not be identified. Other causes, in order, were leptospirosis, enteric fever, malaria, respiratory tract infection, urinary tract infection and typhus. When such a wide variation with a significant number of ‘indeterminate’ cases exists, especially in such a small area and with limited resources, the onus is on public health authorities to draw up an ‘easy-to-use algorithm’ to tackle epidemics of febrile illness, particularly in the monsoon season.


2020 ◽  
Vol 16 (1) ◽  
pp. 1-9
Author(s):  
Piotr Pieniążek ◽  
Przemysław Nowakowski ◽  
Krzysztof Ziaja ◽  
Adam Kobayashi ◽  
Wojciech Uchto ◽  
...  

2021 ◽  
Author(s):  
Vicent Blanes-Selva ◽  
Ascensión Doñate-Martínez ◽  
Gordon Linklater ◽  
Juan M. García-Gómez

AbstractBackgroundPalliative care (PC) has demonstrated benefits for life-limiting illnesses. Cancer patients have mainly accessed these services, but there is growing consensus about the importance of promoting access for patients with non-malignant disease. Bad survival prognosis and patient’s frailty are usual dimensions to decide PC inclusion.ObjectivesThe main aim of this work is to design and evaluate three quantitative models based on machine learning approaches to predict frailty and mortality on older patients in the context of supporting PC decision making: one-year mortality, survival regression and one-year frailty classification.MethodsThe dataset used in this study is composed of 39,310 hospital admissions for 19,753 older patients (age >= 65) from January 1st, 2011 to December 30th, 2018. All prediction models were based on Gradient Boosting Machines. From the initial pool of variables at hospital admission, 20 were selected by a recursive feature elimination algorithm based on the random forest’s GINI importance criterion. Besides, we run an independent grid search to find the best hyperparameters in each model. The evaluation was performed by 10-fold cross-validation and area under the receiver operating characteristic curve and mean absolute error were reported. The Cox proportional-hazards model was used to compare our proposed approach with classical survival methods.ResultsThe one-year mortality model achieved an AUC ROC of 0.87 ± 0.01; the mortality regression model achieved an MAE of 329.97 ± 5.24 days. The one-year frailty classification reported an AUC ROC of 0.9 ± 0.01. The Spearman’s correlation between the admission frailty index and the survival time was –0.1, while the point-biserial correlation between one-year frailty index and survival time was –0.16.ConclusionsOne-year mortality model performance is at a state-of-the-art level. Frailty Index used in this study behaves coherently with other works in the literature. One-year frailty classifier demonstrated that frailty status within the year could be predicted accurately. To our knowledge, this is the first study predicting one-year frailty status based on a frailty index. We found mortality and frailty as two weakly correlated and complementary PC needs assessment criteria. Predictive models are available online at http://demoiapc.upv.es.


BMJ Open ◽  
2020 ◽  
Vol 10 (10) ◽  
pp. e043651
Author(s):  
Pierachille Santus ◽  
Dejan Radovanovic ◽  
Laura Saderi ◽  
Pietro Marino ◽  
Chiara Cogliati ◽  
...  

ObjectivesCOVID-19 causes lung parenchymal and endothelial damage that lead to hypoxic acute respiratory failure (hARF). The influence of hARF severity on patients’ outcomes is still poorly understood.DesignObservational, prospective, multicentre study.SettingThree academic hospitals in Milan (Italy) involving three respiratory high dependency units and three general wards.ParticipantsConsecutive adult hospitalised patients with a virologically confirmed diagnosis of COVID-19. Patients aged <18 years or unable to provide informed consent were excluded.InterventionsAnthropometrical, clinical characteristics and blood biomarkers were assessed within the first 24 hours from admission. hARF was graded as follows: severe (partial pressure of oxygen to fraction of inspired oxygen ratio (PaO2/FiO2) <100 mm Hg); moderate (PaO2/FiO2 101–200 mm Hg); mild (PaO2/FiO2 201–300 mm Hg) and normal (PaO2/FiO2 >300 mm Hg).Primary and secondary outcome measuresThe primary outcome was the assessment of clinical characteristics and in-hospital mortality based on the severity of respiratory failure. Secondary outcomes were intubation rate and application of continuous positive airway pressure during hospital stay.Results412 patients were enrolled (280 males, 68%). Median (IQR) age was 66 (55–76) years with a PaO2/FiO2 at admission of 262 (140–343) mm Hg. 50.2% had a cardiovascular disease. Prevalence of mild, moderate and severe hARF was 24.4%, 21.9% and 15.5%, respectively. In-hospital mortality proportionally increased with increasing impairment of gas exchange (p<0.001). The only independent risk factors for mortality were age ≥65 years (HR 3.41; 95% CI 2.00 to 5.78, p<0.0001), PaO2/FiO2 ratio ≤200 mm Hg (HR 3.57; 95% CI 2.20 to 5.77, p<0.0001) and respiratory failure at admission (HR 3.58; 95% CI 1.05 to 12.18, p=0.04).ConclusionsA moderate-to-severe impairment in PaO2/FiO2 was independently associated with a threefold increase in risk of in-hospital mortality. Severity of respiratory failure is useful to identify patients at higher risk of mortality.Trial registration numberNCT04307459


2010 ◽  
Vol 26 (2) ◽  
pp. 92-99 ◽  
Author(s):  
P. Hartvig ◽  
J.O. Roaldset ◽  
T.A. Moger ◽  
B. Østberg ◽  
S. Bjørkly

AbstractBackgroundInstruments for evaluating the risk of violence towards others have mostly been developed for assessment of risk for recidivism into violent crime in forensic psychiatry. In general psychiatry there is a considerable need for specialised, brief and structured assessment tools to inform risk decisions.MethodThe study aimed to validate a brief structured clinical risk assessment screen of inpatient violence (V-RISK-10), a 10-item structured clinical checklist with a good vignette-based interrater reliability (ICC=0.87). In this study it was used for risk assessment of a one-year sample of patients (N = 1.017) admitted to two acute psychiatric units. Risk assessments at admission were compared to prospective records of aggressive and violent acts during the hospital stay.ResultsResults showed a base rate for aggression of 9%. The predictive validity of the V-RISK-10 was estimated by Receiver Operating Characteristics (ROC). It yielded an area under the curve (AUC) of 0.83, with sensitivity/specificity of 0.81/0.73 and corresponding positive and negative predictive values (PPV/NPV) of 0.24/0.97. The screen was easy-to-use and showed a short completion time.ConclusionDespite promising results further validation studies are required before the V-RISK-10 is adopted into routine clinical practise.


2010 ◽  
Vol 183 (4S) ◽  
Author(s):  
Shahin Tabatabaei ◽  
Benjamin Choi ◽  
Edward Collins ◽  
Alexander Bachmann ◽  
Fernando Gomez Sancha ◽  
...  

Blood ◽  
2019 ◽  
Vol 134 (Supplement_1) ◽  
pp. 1310-1310
Author(s):  
Corentin Orvain ◽  
Aline Tanguy-Schmidt ◽  
Sylvain Thepot ◽  
Anne Bouvier ◽  
Benedicte Ribourtout ◽  
...  

Introduction: The prognosis of older patients treated for acute myeloid leukemia (AML) relies on cytogenetic/molecular classifications as well as their ability to tolerate intensive induction therapy for which comorbidities have an important impact. A prognostic model has been elaborated to incorporate these two variables, cytogenetic/molecular risk and comorbidities evaluated by the Hematopoietic Cell Transplantation - Comorbidity Index (HCT-CI) (Sorror et al. 2017). The AML-composite model (AML-CM), which also incorporates hypoalbuminemia and high LDH levels at diagnosis, has been recently updated to incorporate the new European LeukemiaNet (ELN) 2017 classification (Sorror et al. 2019). In this study, we aimed to confirm the predictive impact of the revised AML-CM in an independent cohort and to explore which parameters were the most relevant for predicting early mortality and relapse. Patients and Methods: All patients (pts) older than 60 years diagnosed with AML who received intensive induction therapy in our department between 2004 and 2017 were included. Patients with acute promyelocytic leukemia were excluded. They received induction therapy with idarubicin 8 mg/m2 for 5 days and cytarabine 100 mg/m2 for 7 days with or without lomustine 200 mg/m2 on day 1. Patients in first complete remission (CR1) were to receive six consolidation courses with idarubicin 8 mg/m2 for one day and cytarabine 100 mg/m2 for 5 days and maintenance therapy with oral methotrexate and mercaptopurine. The HCT-CI and the revised AML-CM were calculated as previously described (Sorror et al. 2005; Sorror et al. 2019) using individual patient medical records. Early mortality was defined as death within one month after the start of induction therapy. The ELN 2017 classification, the HCT-CI, and the revised AML-CM were considered to determine which parameters - comorbidities or cytogenetic/molecular risk - were associated with each outcome. Results: Ninety-nine pts were included in the study with a median age of 66 years-old (range: 60 - 82). Twenty-seven pts (27%) had secondary AML (prior solid tumor requiring chemotherapy and/or radiation therapy in 11 pts and prior hematological malignancy in 16 patients). According to the ELN 2017 classification, 24 (24%) were favorable, 53 (54%) were intermediate, and 22 (22%) were adverse. The most frequent comorbidities included liver disease (30 pts, low/moderate, 5 pts, severe), previous cancer (22 pts), and arrythmia (22 pts). Thus, 13 (13%), 25 (25%), 44 (45%), and 17 (17%) pts had a revised AML-CM score of 1-4, 5-6, 7-9, and &gt;10, respectively. 78 pts (79%) achieved CR1, with 10 early deaths (10%) due to toxicity (early mortality) and 11 induction failures (11%). Fifteen pts received allogeneic SCT. 54 (55%) relapsed, and 72 (73%) died with a median follow-up of 18 months (range: 0 - 167). Both the HCT-CI and the AML-CM were associated with increased early mortality (OR: 1.4, 95% CI: 1 - 1.8, p=0.03 for HCT-CI and OR: 1.3, 95% CI: 1-1.7, p=0.03 for AML-CM) whereas the ELN 2017 classification had no impact. The predictive value of the AML-CM for early mortality was not superior to the HCT-CI (area under the curve - AUC: 0.76, p=0.01, and 0.76, p=0.01, respectively). The risk of relapse was only associated with an unfavorable ELN 2017 classification (OR: 8.8, 95% CI: 1.1 - 70, p=0.04). It was the most predictive parameter for relapse, in comparison to the HCT-CI and the revised AML-CM, but this did not reach statistical significance (AUC: 0.62, p=0.08). The AML-CM clearly distinguishes 4 groups with different prognosis (Figure, p&lt;0.001 for log rank test). One-year OS rates were 92%, 68%, 61%, and 29% among pts with a score of 1-4, 5-6, 7-9, and &gt; 10, respectively (OR: 1.4, 95% CI: 1.2 - 1.8, p&lt;0.01). The revised AML-CM (AUC: 0.73, p&lt;0.001) was more reliable for predicting one-year survival than the HCT-CI (AUC: 0.70, p=0.001) or the ELN 2017 classification (AUC: 0.66, p=0.01). Cox analysis confirms the prognostic value of the revised AML-CM on overall survival (HR: 1.2 for each additional point, 95% CI: 1.1 - 1.3, p&lt;0.001). Conclusion: The revised AML-CM is an effective tool for predicting overall survival in older pts with AML receiving intensive induction therapy. It is a better prognostic system compared to the HCT-CI and the ELN 2017 classification as it combines the evaluation of comorbidities, which predicts early mortality, and cytogenetic/molecular risk, which predicts relapse. Figure Disclosures Orvain: Novartis: Honoraria; Incyte: Honoraria.


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