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 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 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 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 Endometrial Cancer Molecular Characterization: The Key to Identifying High-Risk Patients and Defining Guidelines for Clinical Decision-Making?

Cancers ◽  
2021 ◽  
Vol 13 (16) ◽  
pp. 3988
Author(s):  
Regina Esi Mensimah Baiden-Amissah ◽  
Daniela Annibali ◽  
Sandra Tuyaerts ◽  
Frederic Amant
Keyword(s):  
Decision Making ◽  
High Risk ◽  
Risk Stratification ◽  
Clinical Decision Making ◽  
Treatment Decision ◽  
Risk Groups ◽  
Clinical Decision ◽  
The Cancer Genome Atlas ◽  
Type I ◽  
Molecular Features

Endometrial carcinomas (EC) are the sixth most common cancer in women worldwide and the most prevalent in the developed world. ECs have been historically sub-classified in two major groups, type I and type II, based primarily on histopathological characteristics. Notwithstanding the usefulness of such classification in the clinics, until now it failed to adequately stratify patients preoperatively into low- or high-risk groups. Pieces of evidence point to the fact that molecular features could also serve as a base for better patients’ risk stratification and treatment decision-making. The Cancer Genome Atlas (TCGA), back in 2013, redefined EC into four main molecular subgroups. Despite the high hopes that welcomed the possibility to incorporate molecular features into practice, currently they have not been systematically applied in the clinics. Here, we outline how the emerging molecular patterns can be used as prognostic factors together with tumor histopathology and grade, and how they can help to identify high-risk EC subpopulations for better risk stratification and treatment strategy improvement. Considering the importance of the use of preclinical models in translational research, we also discuss how the new patient-derived models can help in identifying novel potential targets and help in treatment decisions.

scholarly journals Development and internal validation of a prediction model of prostate cancer on initial transperineal template-guided prostate biopsy

BMC Urology ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Yuliang Chen ◽  
Zhien Zhou ◽  
Yi Zhou ◽  
Xingcheng Wu ◽  
Yu Xiao ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Decision Making ◽  
Prediction Model ◽  
Prostate Biopsy ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Good Discrimination ◽  
Internal Validation ◽  
Discrimination Ability ◽  
Guided Biopsy

Abstract Background Due to the invasiveness of prostate biopsy, a prediction model of the individual risk of a positive biopsy result could be helpful to guide clinical decision-making. Most existing models are based on transrectal ultrasonography (TRUS)-guided biopsy. On the other hand, transperineal template-guided prostate biopsy (TTPB) has been reported to be more accurate in evaluating prostate cancer. The objective of this study is to develop a prediction model of the detection of high-grade prostate cancer (HGPC) on initial TTPB. Result A total of 1352 out of 3794 (35.6%) patients were diagnosed with prostate cancer, 848 of whom had tumour with Grade Group 2–5. Age, PSA, PV, DRE and f/t PSA are independent predictors of HGPC with p < 0.001. The model showed good discrimination ability (c-index 0.886) and calibration during internal validation and good clinical performance was observed through decision curve analysis. The external validation of CPCC-RC, an existing model, demonstrated that models based on TRUS-guided biopsy may underestimate the risk of HGPC in patients who underwent TTPB. Conclusion We established a prediction model which showed good discrimination ability and calibration in predicting the detection of HGPC by initial TTPB. This model can be used to aid clinical decision making for Chinese patients and other Asian populations with similar genomic backgrounds, after external validations are conducted to further confirm its clinical applicability.

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