Optimization of the Classic Transfer-Stacking Model Migration Algorithm: A Way to Solve Time-Varying Performance Degradation of Acute Kidney Injury Clinical Prediction Model

Background: Several biomarkers of acute kidney injury (AKI) have been examined for their ability to predict AKI before serum creatinine. Few studies have focused on using kidney biomarkers to better predict major adverse kidney events (MAKE), an increasingly used composite outcome in critical care nephrology research. Methods: Single-center prospective study collecting blood and urine samples from critically ill patients with AKI Kidney Disease: Improving Global Outcomes stage 2 or above, and matched controls from a single, tertiary care intensive care unit. Samples were collected at 24-48 hours after AKI diagnosis (cases) or ICU admission (controls), 5-7 days later, and 4-6 weeks following discharge for AKI patients. The primary outcome of interest was MAKE at hospital discharge (MAKE-DC), consisting of the composite endpoint of death, renal replacement therapy dependence, or a decrease in estimated glomerular filtration to <75% of baseline. Results: Serum/urinary neutrophil gelatinase-associated lipocalin (NGAL), serum/urinary cystatin C, and urinary kidney injury molecule-1 early in the AKI or ICU course were all significantly higher in patients with MAKE-DC compared to those not experiencing MAKE-DC. Additionally, serum/urinary NGAL and serum cystatin C measurements at the first time point remained significantly associated with MAKE events at 3, 6, and 12 months. Serum cystatin C, and to a lesser extent serum NGAL, significantly improved upon a logistic regression clinical prediction model of MAKE-DC (AUROC 0.94 and 0.87 vs. 0.83; p= 0.001 and 0.019, respectively). Patients without MAKE-DC experienced a greater decline in serum NGAL from first to second measurement than those patients experiencing MAKE-DC. Conclusion: Early measures of kidney biomarkers in critically ill patients are associated with MAKE-DC. This relationship appears to be greatest with serum NGAL and cystatin C, which display additive utility to a clinical prediction model. Trending serum NGAL may also have utility in predicting MAKE-DC.

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Kidney Biomarkers and Major Adverse Kidney Events in Critically Ill Patients

10.1101/2020.06.06.20124354 ◽

2020 ◽

Author(s):

Alexander H. Flannery ◽

Katherine Bosler ◽

Victor Ortiz-Soriano ◽

Fabiola Gianella ◽

Victor Prado ◽

...

Keyword(s):

Prediction Model ◽

Hospital Discharge ◽

Critically Ill ◽

Cystatin C ◽

Critically Ill Patients ◽

Tertiary Care ◽

Kidney Injury ◽

Clinical Prediction ◽

Clinical Prediction Model ◽

Major Adverse Kidney Events

AbstractBackgroundSeveral biomarkers of acute kidney injury (AKI) have been examined for their ability to predict AKI earlier than serum creatinine. Few studies have focused on using kidney biomarkers to better predict major adverse kidney events (MAKE), an increasingly used composite outcome in critical care nephrology research.MethodsSingle-center prospective study collecting blood and urine samples from critically ill patients with AKI KDIGO stage 2 or above, and matched controls from a single, tertiary care intensive care unit. Samples were collected at 24-48 hours after AKI diagnosis (cases) or ICU admission (controls), 5-7 days later, and 4-6 weeks following discharge for AKI patients. The primary outcome of interest was MAKE at hospital discharge.ResultsSerum/urinary neutrophil gelatinase-associated lipocalin, serum/urinary cystatin C, and urinary kidney injury molecule-1 early in the AKI or ICU course were all significantly higher in patients with MAKE compared to those not experiencing MAKE at hospital discharge. Serum cystatin C, and to a lesser extent serum NGAL, significantly improved upon a clinical prediction model of MAKE as assessed by the area under the receiver operating characteristic curve.Patients without MAKE experienced a greater decline in serum NGAL from initial measurement to second measurement than those patients experiencing MAKE.ConclusionEarly measures of kidney biomarkers in critically ill patients are associated with MAKE. This relationship appears to be greatest with serum NGAL and cystatin C, which display additive utility to a clinical prediction model. Trending serum NGAL may also have utility in predicting MAKE.

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Localized Osteosarcoma in Upper Egypt: A Clinical Prediction Model

Case Medical Research ◽

10.31525/ct1-nct03839095 ◽

2000 ◽

Author(s):

Keyword(s):

Prediction Model ◽

Clinical Prediction ◽

Clinical Prediction Model ◽

Upper Egypt

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Minimum sample size for external validation of a clinical prediction model with a binary outcome

Statistics in Medicine ◽

10.1002/sim.9025 ◽

2021 ◽

Author(s):

Richard D. Riley ◽

Thomas P. A. Debray ◽

Gary S. Collins ◽

Lucinda Archer ◽

Joie Ensor ◽

...

Keyword(s):

Prediction Model ◽

Sample Size ◽

External Validation ◽

Minimum Sample Size ◽

Binary Outcome ◽

Clinical Prediction ◽

Clinical Prediction Model ◽

Minimum Sample

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Develop a Clinical Prediction Model for Postoperative Cognitive Dysfunction after Major Noncardiac Surgery in Elderly Patients: A Protocol for a Prospective Observational Study

Gerontology ◽

10.1159/000517511 ◽

2021 ◽

pp. 1-8

Author(s):

Yang Shen ◽

Xianchen Li ◽

Junyan Yao

Keyword(s):

Observational Study ◽

Prediction Model ◽

Cognitive Decline ◽

Cognitive Dysfunction ◽

Prospective Observational Study ◽

Postoperative Cognitive Dysfunction ◽

Noncardiac Surgery ◽

Clinical Prediction ◽

Clinical Prediction Model ◽

Postoperative Cognitive Decline

Perioperative neurocognitive disorders (PNDs) refer to cognitive decline identified in the preoperative or postoperative period. It has been reported that the incidence of postoperative neurocognitive impairment after noncardiac surgery in patients older than 65 at 1 week was 25.8∼41.4%, and at 3 months 9.9∼12.7%. PNDs will last months or even develop to permanent dementia, leading to prolonged hospital stays, reduced quality of life, and increased mortality within 1 year. Despite the high incidence and poor prognosis of PNDs in the aged population, no effective clinical prediction model has been established to predict postoperative cognitive decline preoperatively. To develop a clinical prediction model for postoperative neurocognitive dysfunction, a prospective observational study (Clinical trial registration number: ChiCTR2000036304) will be performed in the Shanghai General Hospital during January 2021 to October 2022. A sample size of 675 patients aged >65 years old, male or female, and scheduled for elective major noncardiac surgery will be recruited. A battery of neuropsychological tests will be used to test the cognitive function of patients at 1 week, 1 month, and 3 months postoperatively. We will evaluate the associations of PNDs with a bunch of candidate predictors including general characteristics of patients, blood biomarkers, indices associated with anesthesia and surgery, retinal nerve-fiber layer thickness, and frailty index to develop the clinical prediction model by using multiple logistic regression analysis and least absolute shrinkage and the selection operator (LASSO) method. The <i>k</i>-fold cross-validation method will be utilized to validate the clinical prediction model. In conclusion, this study was aimed to develop a clinical prediction model for postoperative cognitive dysfunction of old patients. It is anticipated that the knowledge gained from this study will facilitate clinical decision-making for anesthetists and surgeons managing the aged patients undergoing noncardiac surgery.

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Predicting severe pneumonia in the emergency department: a global study of the Pediatric Emergency Research Networks (PERN)—study protocol

BMJ Open ◽

10.1136/bmjopen-2020-041093 ◽

2020 ◽

Vol 10 (12) ◽

pp. e041093

Author(s):

Todd Adam Florin ◽

Daniel Joseph Tancredi ◽

Lilliam Ambroggio ◽

Franz E Babl ◽

Stuart R Dalziel ◽

...

Keyword(s):

Emergency Department ◽

Prediction Model ◽

Severe Disease ◽

Pediatric Emergency ◽

Global Network ◽

Clinical Prediction ◽

Emergency Research ◽

Research Networks ◽

Clinical Prediction Model ◽

Severity Scores

IntroductionPneumonia is a frequent and costly cause of emergency department (ED) visits and hospitalisations in children. There are no evidence-based, validated tools to assist physicians in management and disposition decisions for children presenting to the ED with community-acquired pneumonia (CAP). The objective of this study is to develop a clinical prediction model to accurately stratify children with CAP who are at risk for low, moderate and severe disease across a global network of EDs.Methods and analysisThis study is a prospective cohort study enrolling up to 4700 children with CAP at EDs at ~80 member sites of the Pediatric Emergency Research Networks (PERN; https://pern-global.com/). We will include children aged 3 months to <14 years with a clinical diagnosis of CAP. We will exclude children with hospital admissions within 7 days prior to the study visit, hospital-acquired pneumonias or chronic complex conditions. Clinical, laboratory and imaging data from the ED visit and hospitalisations within 7 days will be collected. A follow-up telephone or text survey will be completed 7–14 days after the visit. The primary outcome is a three-tier composite of disease severity. Ordinal logistic regression, assuming a partial proportional odds specification, and recursive partitioning will be used to develop the risk stratification models.Ethics and disseminationThis study will result in a clinical prediction model to accurately identify risk of severe disease on presentation to the ED. Ethics approval was obtained for all sites included in the study. Cincinnati Children’s Hospital Institutional Review Board (IRB) serves as the central IRB for most US sites. Informed consent will be obtained from all participants. Results will be disseminated through international conferences and peer-reviewed publications. This study overcomes limitations of prior pneumonia severity scores by allowing for broad generalisability of findings, which can be actively implemented after model development and validation.

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