scholarly journals Clinical decision-making in older adults following emergency admission to hospital. Derivation and validation of a risk stratification score: OPERA

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0248477
Author(s):  
Khushal Arjan ◽  
Lui G. Forni ◽  
Richard M. Venn ◽  
David Hunt ◽  
Luke Eliot Hodgson
Keyword(s):  
Older Adults ◽  
Decision Making ◽  
Risk Stratification ◽  
Hospital Mortality ◽  
Hospital Stay ◽  
Risk Score ◽  
Clinical Decision Making ◽  
External Validation ◽  
Clinical Decision ◽  
Early Warning Score

Objectives of the study Demographic changes alongside medical advances have resulted in older adults accounting for an increasing proportion of emergency hospital admissions. Current measures of illness severity, limited to physiological parameters, have shortcomings in this cohort, partly due to patient complexity. This study aimed to derive and validate a risk score for acutely unwell older adults which may enhance risk stratification and support clinical decision-making. Methods Data was collected from emergency admissions in patients ≥65 years from two UK general hospitals (April 2017- April 2018). Variables underwent regression analysis for in-hospital mortality and independent predictors were used to create a risk score. Performance was assessed on external validation. Secondary outcomes included seven-day mortality and extended hospital stay. Results Derivation (n = 8,974) and validation (n = 8,391) cohorts were analysed. The model included the National Early Warning Score 2 (NEWS2), clinical frailty scale (CFS), acute kidney injury, age, sex, and Malnutrition Universal Screening Tool. For mortality, area under the curve for the model was 0.79 (95% CI 0.78–0.80), superior to NEWS2 0.65 (0.62–0.67) and CFS 0.76 (0.74–0.77) (P<0.0001). Risk groups predicted prolonged hospital stay: the highest risk group had an odds ratio of 9.7 (5.8–16.1) to stay >30 days. Conclusions Our simple validated model (Older Persons’ Emergency Risk Assessment [OPERA] score) predicts in-hospital mortality and prolonged length of stay and could be easily integrated into electronic hospital systems, enabling automatic digital generation of risk stratification within hours of admission. Future studies may validate the OPERA score in external populations and consider an impact analysis.

scholarly journals Predicting mortality in adults with suspected infection in a Rwandan hospital: an evaluation of the adapted MEWS, qSOFA and UVA scores

BMJ Open ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. e040361
Author(s):  
Amanda Klinger ◽  
Ariel Mueller ◽  
Tori Sutherland ◽  
Christophe Mpirimbanyi ◽  
Elie Nziyomaze ◽  
...  
Keyword(s):  
Decision Making ◽  
Quality Improvement ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Tertiary Hospital ◽  
Early Warning Score ◽  
Receiver Operating Curve ◽  
Predictive Capacity ◽  
Suspected Infection ◽  
Significant Difference

RationaleMortality prediction scores are increasingly being evaluated in low and middle income countries (LMICs) for research comparisons, quality improvement and clinical decision-making. The modified early warning score (MEWS), quick Sequential (Sepsis-Related) Organ Failure Assessment (qSOFA), and Universal Vital Assessment (UVA) score use variables that are feasible to obtain, and have demonstrated potential to predict mortality in LMIC cohorts.ObjectiveTo determine the predictive capacity of adapted MEWS, qSOFA and UVA in a Rwandan hospital.Design, setting, participants and outcome measuresWe prospectively collected data on all adult patients admitted to a tertiary hospital in Rwanda with suspected infection over 7 months. We calculated an adapted MEWS, qSOFA and UVA score for each participant. The predictive capacity of each score was assessed including sensitivity, specificity, positive and negative predictive value, OR, area under the receiver operating curve (AUROC) and performance by underlying risk quartile.ResultsWe screened 19 178 patient days, and enrolled 647 unique patients. Median age was 35 years, and in-hospital mortality was 18.1%. The proportion of data missing for each variable ranged from 0% to 11.7%. The sensitivities and specificities of the scores were: adapted MEWS >4, 50.4% and 74.9%, respectively; qSOFA >2, 24.8% and 90.4%, respectively; and UVA >4, 28.2% and 91.1%, respectively. The scores as continuous variables demonstrated the following AUROCs: adapted MEWS 0.69 (95% CI 0.64 to 0.74), qSOFA 0.65 (95% CI 0.60 to 0.70), and UVA 0.71 (95% CI 0.66 to 0.76); there was no statistically significant difference between the discriminative capacities of the scores.ConclusionThree scores demonstrated a modest ability to predict mortality in a prospective study of inpatients with suspected infection at a Rwandan tertiary hospital. Careful consideration must be given to their adequacy before using them in research comparisons, quality improvement or clinical decision-making.

scholarly journals Early Identification of Patients with Acute Gastrointestinal Bleeding using Electronic Health Record Phenotyping

2020 ◽  
Author(s):  
Dennis Shung ◽  
Cynthia Tsay ◽  
Loren Laine ◽  
Prem Thomas ◽  
Caitlin Partridge ◽  
...  
Keyword(s):  
Risk Stratification ◽  
Gastrointestinal Bleeding ◽  
Real Time ◽  
Decision Rule ◽  
Language Processing ◽  
Clinical Decision Making ◽  
External Validation ◽  
Clinical Decision ◽  
Internal Validation ◽  
Electronic Health

Background and AimGuidelines recommend risk stratification scores in patients presenting with gastrointestinal bleeding (GIB), but such scores are uncommonly employed in practice. Automation and deployment of risk stratification scores in real time within electronic health records (EHRs) would overcome a major impediment. This requires an automated mechanism to accurately identify (“phenotype”) patients with GIB at the time of presentation. The goal is to identify patients with acute GIB by developing and evaluating EHR-based phenotyping algorithms for emergency department (ED) patients.MethodsWe specified criteria using structured data elements to create rules for identifying patients, and also developed a natural-language-processing (NLP)-based algorithm for automated phenotyping of patients, tested them with tenfold cross-validation (n=7144) and external validation (n=2988), and compared them with the standard method for encoding patient conditions in the EHR, Systematized Nomenclature of Medicine (SNOMED). The gold standard for GIB diagnosis was independent dual manual review of medical records. The primary outcome was positive predictive value (PPV).ResultsA decision rule using GIB-specific terms from ED triage and from ED review-of-systems assessment performed better than SNOMED on internal validation (PPV=91% [90%-93%] vs. 74% [71%-76%], P<0.001) and external validation (PPV=85% [84%-87%] vs. 69% [67%-71%], P<0.001). The NLP algorithm (external validation PPV=80% [79-82%]) was not superior to the structured-datafields decision rule.ConclusionsAn automated decision rule employing GIB-specific triage and review-of-systems terms can be used to trigger EHR-based deployment of risk stratification models to guide clinical decision-making in real time for patients with acute GIB presenting to the ED.

scholarly journals Personalized risk stratification through attribute matching for clinical decision making in clinical conditions with aspecific symptoms: The example of syncope

PLoS ONE ◽  
2020 ◽  
Vol 15 (3) ◽  
pp. e0228725
Author(s):  
Monica Solbiati ◽  
James V. Quinn ◽  
Franca Dipaola ◽  
Piergiorgio Duca ◽  
Raffaello Furlan ◽  
...  
Keyword(s):  
Decision Making ◽  
Risk Stratification ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Attribute Matching ◽  
Clinical Conditions ◽  
Personalized Risk

scholarly journals Machine Learning for Prediction and Risk Stratification of Lupus Nephritis Renal Flare

2021 ◽  
pp. 1-9
Author(s):  
Yinghua Chen ◽  
Siwan Huang ◽  
Tiange Chen ◽  
Dandan Liang ◽  
Jing Yang ◽  
...  
Keyword(s):  
Decision Making ◽  
Lupus Nephritis ◽  
Risk Stratification ◽  
Clinical Decision Making ◽  
Validation Cohort ◽  
Clinical Decision ◽  
Complement C3 ◽  
Internal Validation ◽  
Renal Flare ◽  
Individualized Management

Background: Renal flare of lupus nephritis (LN) is strongly associated with poor kidney outcomes, and predicting renal flare and stratifying its risk are important for clinical decision-making and individualized management to reduce LN flare. Methods: We randomly divided 1,694 patients with biopsy-proven LN, who had achieved remission after treatment, into a derivation cohort (n = 1,186) and an internal validation cohort (n = 508), at a ratio of 7:3. The risk of renal flare 5 years after remission was predicted using an eXtreme Gradient Boosting (XGBoost) method model, developed from 59 variables, including demographic, clinical, immunological, pathological, and therapeutic characteristics. A simplified risk score prediction model (SRSPM) was developed from important variables selected by XGBoost model using stepwise Cox regression for practical convenience. Results: The 5-year relapse rates were 39.5% and 38.2% in the derivation and internal validation cohorts, respectively. Both the XGBoost model and the SRSPM had good predictive performance, with a C-index of 0.819 (95% confidence interval [CI]: 0.774–0.857) and 0.746 (95% CI: 0.697–0.795), respectively, in the validation cohort. The SRSPM comprised 6 variables, including partial remission and endocapillary hypercellularity at baseline, age, serum Alb, anti-dsDNA, and serum complement C3 at the point of remission. Using Kaplan-Meier analysis, the SRSPM identified significant risk stratification for renal flares (p < 0.001). Conclusions: Renal flare of LN can be readily predicted using the XGBoost model and the SRSPM, and the SRSPM can also stratify flare risk. Both models are useful for clinical decision-making and individualized management in LN.

scholarly journals Using Big Data for Clinical Decision Making

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 174-174
Author(s):  
Diana Woods ◽  
Maria Yefimova
Keyword(s):  
Older Adults ◽  
Decision Making ◽  
Chronic Conditions ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Sensor Data ◽  
Innovative Technology ◽  
Multiple Chronic Conditions ◽  
Daily Routine ◽  
Frail Older Adults

Abstract The current workforce is ill prepared for the rise in Americans 65 and older from 46.3 million in 2010 to 98.2 million by 2050, a national increase of 112.2 % accompanied by increasing chronic conditions. The increase in older Americans, the prevalence of those with dementia, accompanied by behavioral symptoms of dementia (BSD) is increasing. Innovative technology may alert health providers to early signs of decline in frail older adults with multiple chronic conditions. Remote monitoring in the home and community living spaces can address complex care needs for older adults. Monitoring may identify and predict deviations in a person’s daily routine that herald a change in a chronic condition. We present two examples that can potentially assist in clinical decision making. The first exemplar used 24/7 sensor data to identify changes, potentially clinically significant, such that early intervention may prevent hospitalizations; the second exemplar presents the use of pattern recognition software (THEME TM) for temporal pattern analysis, to identify and quantify behavior patterns with regard to intensity, frequency and complexity, such that interventions may be individually tailored and timed. Clinical researchers and technology developers need to collaborate early in the process to consider the sources and frequency of clinical measures for meaningful predictions. One major challenge lies in the interpretation of the vast amounts of within individual data. Our insights strive to improve future interdisciplinary development of monitoring systems to support aging in place and support clinical decisions for timely and effective care for frail older adults.

Cost-effective clinical decision making

PEDIATRICS ◽  
1977 ◽  
Vol 60 (5) ◽  
pp. 756-759
Author(s):  
Duncan Neuhauser
Keyword(s):  
Decision Making ◽  
Hospital Stay ◽  
Clinical Decision Making ◽  
Cost Effective ◽  
Clinical Decision ◽  
Inappropriate Therapy ◽  
X Ray ◽  
Scarce Resources ◽  
Chest X Ray ◽  
The Cost

Are pediatric preoperative chest x-ray examinations worth doing? Sane et al. (p. 669) conclude that they are "medically and economically justified and essential." To answer this question requires at least the following information. What is the cost of the examination (dollars, radiation exposure, cost of further testing and perhaps inappropriate therapy resulting therefrom, and possible prolongation of hospital stay)? What is the benefit? What percent of patients have unsuspected findings; of these, the percent with modified treatment; and of these, what percent benefited therefrom? What was the cost per unit of benefit received? Is there some other use of these scarce resources that would have yielded greater benefits?

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