Protocol for the derivation and external validation of a 30-day mortality risk prediction model for older patients having emergency general surgery (PAUSE score—Probability of mortality Associated with Urgent/emergent general Surgery in oldEr patients score)

IntroductionPeople 65 years and older represent the fastest growing segment of the surgical population. Older age is associated with doubling of risk when undergoing emergency general surgery (EGS) procedures and often coexists with medical complexity and considerations of end-of-life care, creating prognostic and decisional uncertainty. Combined with the time-sensitive nature of EGS, it is challenging to gauge perioperative risk and ensure that clinical decisions are aligned with the patient values. Current preoperative risk prediction models for older EGS patients have major limitations regarding derivation and validation, and do not address the specific risk profile of older patients. Accurate and externally validated models specific to older patients are needed to inform care and decision making.Methods and analysisWe will derive, internally and externally validate a multivariable model to predict 30-day mortality in EGS patients >65 years old. Our derivation sample will be individuals enrolled in the National Surgical Quality Improvement Program (NSQIP) database between 2012 and 2016 having 1 of 7 core EGS procedures. Postulated predictor variables have been identified based on previous research, clinical and epidemiological knowledge. Our model will be derived using logistic regression penalised with elastic net regularisation and ensembled using bootstrap aggregation. The resulting model will be internally validated using k-fold cross-validation and bootstrap validation techniques and externally validated using population-based health administrative data. Discrimination and calibration will be reported at each step.Ethics and disseminationEthics for NSQIP data use was obtained from the Ottawa Hospital Research Ethics Board; external validation will use routinely collected anonymised data legally exempt from research ethics review. The final risk score will be published in a peer-reviewed journal. We plan to further disseminate the model as an online calculator or application for clinical use. Future research will be required to test the clinical application of the final model.

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Predicting sentinel node positivity in melanoma patients: external validation of a risk‐prediction calculator (the MIA nomogram) using a large European population‐based patient cohort

British Journal of Dermatology ◽

10.1111/bjd.19895 ◽

2021 ◽

Author(s):

M.A. El Sharouni ◽

A.H.R. Varey ◽

A.J. Witkamp ◽

T. Ahmed ◽

V. Sigurdsson ◽

...

Keyword(s):

Sentinel Node ◽

Risk Prediction ◽

External Validation ◽

Population Based ◽

European Population ◽

Patient Cohort ◽

Melanoma Patients

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Emergency General Surgery in Older Patients: A National Comparison of Presentation and Outcomes

Journal of the American College of Surgeons ◽

10.1016/j.jamcollsurg.2014.07.118 ◽

2014 ◽

Vol 219 (3) ◽

pp. S52-S53

Author(s):

Syed Nabeel Zafar ◽

Adil A. Shah ◽

Zain G. Hashmi ◽

Aftab Iqbal ◽

Wendy R. Greene ◽

...

Keyword(s):

General Surgery ◽

Older Patients ◽

Emergency General Surgery ◽

National Comparison

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Review of an emergency general surgery process improvement program at a verified military trauma center

Surgical Endoscopy ◽

10.1007/s00464-018-6303-0 ◽

2018 ◽

Vol 32 (10) ◽

pp. 4321-4328 ◽

Cited By ~ 1

Author(s):

Joseph Bozzay ◽

Matthew Bradley ◽

Angela Kindvall ◽

Ashley Humphries ◽

Elliot Jessie ◽

...

Keyword(s):

Process Improvement ◽

General Surgery ◽

Trauma Center ◽

Emergency General Surgery ◽

Improvement Program ◽

Military Trauma

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Bladder Cancer (NMIBC) in a population-based cohort from Stockholm County with long-term follow-up; A comparative analysis of prediction models for recurrence and progression, including external validation of the updated 2021 E.A.U. model

Urologic Oncology Seminars and Original Investigations ◽

10.1016/j.urolonc.2021.10.008 ◽

2021 ◽

Author(s):

Zachary Dovey ◽

John Pfail ◽

Alberto Martini ◽

Gunnar Steineck ◽

Linda Dey ◽

...

Keyword(s):

Bladder Cancer ◽

Comparative Analysis ◽

Prediction Models ◽

External Validation ◽

Population Based ◽

Stockholm County ◽

Long Term Follow Up

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Abstract 196: Development and Validation of Machine Learning-based Race-specific Models to Predict 10-year Risk of Heart Failure: A Multi-cohort Analysis

Circulation ◽

10.1161/circ.142.suppl_3.196 ◽

2020 ◽

Vol 142 (Suppl_3) ◽

Author(s):

Matthew W Segar ◽

Byron Jaeger ◽

Kershaw V Patel ◽

Vijay Nambi ◽

Chiadi E Ndumele ◽

...

Keyword(s):

Machine Learning ◽

Risk Factors ◽

Heart Failure ◽

Risk Prediction ◽

Prediction Models ◽

External Validation ◽

Superior Performance ◽

Risk Models ◽

Black And White ◽

White Adults

Introduction: Heart failure (HF) risk and the underlying biological risk factors vary by race. Machine learning (ML) may improve race-specific HF risk prediction but this has not been fully evaluated. Methods: The study included participants from 4 cohorts (ARIC, DHS, JHS, and MESA) aged > 40 years, free of baseline HF, and with adjudicated HF event follow-up. Black adults from JHS and white adults from ARIC were used to derive race-specific ML models to predict 10-year HF risk. The ML models were externally validated in subgroups of black and white adults from ARIC (excluding JHS participants) and pooled MESA/DHS cohorts and compared to prior established HF risk scores developed in ARIC and MESA. Harrell’s C-index and Greenwood-Nam-D’Agostino chi-square were used to assess discrimination and calibration, respectively. Results: In the derivation cohorts, 288 of 4141 (7.0%) black and 391 of 8242 (4.7%) white adults developed HF over 10 years. The ML models had excellent discrimination in both black and white participants (C-indices = 0.88 and 0.89). In the external validation cohorts for black participants from ARIC (excluding JHS, N = 1072) and MESA/DHS pooled cohorts (N = 2821), 131 (12.2%) and 115 (4.1%) developed HF. The ML model had adequate calibration and demonstrated superior discrimination compared to established HF risk models (Fig A). A consistent pattern was also observed in the external validation cohorts of white participants from the MESA/DHS pooled cohorts (N=3236; 100 [3.1%] HF events) (Fig A). The most important predictors of HF in both races were NP levels. Cardiac biomarkers and glycemic parameters were most important among blacks while LV hypertrophy and prevalent CVD and traditional CV risk factors were the strongest predictors among whites (Fig B). Conclusions: Race-specific and ML-based HF risk models that integrate clinical, laboratory, and biomarker data demonstrated superior performance when compared to traditional risk prediction models.

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Vulnerable Elderly Surgical Patients: Is It the Time for Geriatric Emergency General Surgery Quality Improvement Program?

Journal of the American College of Surgeons ◽

10.1016/j.jamcollsurg.2018.07.263 ◽

2018 ◽

Vol 227 (4) ◽

pp. S125-S126

Author(s):

Kimberly B. Golisch ◽

Muhammad Zeeshan ◽

El Rasheid Zakaria ◽

Faisal Jehan ◽

Narong Kulvatunyou ◽

...

Keyword(s):

Quality Improvement ◽

General Surgery ◽

Quality Improvement Program ◽

Surgical Patients ◽

Emergency General Surgery ◽

Improvement Program ◽

Vulnerable Elderly

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Risk Prediction Models of Locoregional Failure After Radical Cystectomy for Urothelial Carcinoma: External Validation in a Cohort of Korean Patients

International Journal of Radiation Oncology*Biology*Physics ◽

10.1016/j.ijrobp.2014.04.049 ◽

2014 ◽

Vol 89 (5) ◽

pp. 1032-1037 ◽

Cited By ~ 12

Author(s):

Ja Hyeon Ku ◽

Myong Kim ◽

Chang Wook Jeong ◽

Cheol Kwak ◽

Hyeon Hoe Kim

Keyword(s):

Urothelial Carcinoma ◽

Radical Cystectomy ◽

Risk Prediction ◽

Prediction Models ◽

External Validation ◽

Risk Prediction Models ◽

Korean Patients ◽

Locoregional Failure

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Comparison of prognostic models to predict the occurrence of colorectal cancer in asymptomatic individuals: a systematic literature review and external validation in the EPIC and UK Biobank prospective cohort studies

Gut ◽

10.1136/gutjnl-2017-315730 ◽

2018 ◽

Vol 68 (4) ◽

pp. 672-683 ◽

Cited By ~ 6

Author(s):

Todd Smith ◽

David C Muller ◽

Karel G M Moons ◽

Amanda J Cross ◽

Mattias Johansson ◽

...

Keyword(s):

Colorectal Cancer ◽

Systematic Review ◽

Prediction Models ◽

Large Population ◽

External Validation ◽

Population Based ◽

Prognostic Models ◽

Uk Biobank ◽

Asymptomatic Individuals ◽

The Uk

ObjectiveTo systematically identify and validate published colorectal cancer risk prediction models that do not require invasive testing in two large population-based prospective cohorts.DesignModels were identified through an update of a published systematic review and validated in the European Prospective Investigation into Cancer and Nutrition (EPIC) and the UK Biobank. The performance of the models to predict the occurrence of colorectal cancer within 5 or 10 years after study enrolment was assessed by discrimination (C-statistic) and calibration (plots of observed vs predicted probability).ResultsThe systematic review and its update identified 16 models from 8 publications (8 colorectal, 5 colon and 3 rectal). The number of participants included in each model validation ranged from 41 587 to 396 515, and the number of cases ranged from 115 to 1781. Eligible and ineligible participants across the models were largely comparable. Calibration of the models, where assessable, was very good and further improved by recalibration. The C-statistics of the models were largely similar between validation cohorts with the highest values achieved being 0.70 (95% CI 0.68 to 0.72) in the UK Biobank and 0.71 (95% CI 0.67 to 0.74) in EPIC.ConclusionSeveral of these non-invasive models exhibited good calibration and discrimination within both external validation populations and are therefore potentially suitable candidates for the facilitation of risk stratification in population-based colorectal screening programmes. Future work should both evaluate this potential, through modelling and impact studies, and ascertain if further enhancement in their performance can be obtained.

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Childhood obesity: how long should we wait to predict weight?

Journal of Pediatric Endocrinology and Metabolism ◽

10.1515/jpem-2018-0110 ◽

2018 ◽

Vol 31 (5) ◽

pp. 497-501 ◽

Cited By ~ 3

Author(s):

Éadaoin M. Butler ◽

José G.B. Derraik ◽

Rachael W. Taylor ◽

Wayne S. Cutfield

Keyword(s):

Early Childhood ◽

Childhood Obesity ◽

Prediction Models ◽

Population Based ◽

Future Research ◽

Clinical Prediction ◽

Obesity Risk ◽

Clinical Prediction Models ◽

Predictive Capacity ◽

Early Childhood Obesity

AbstractObesity is highly prevalent in children under the age of 5 years, although its identification in infants under 2 years remains difficult. Several clinical prediction models have been developed for obesity risk in early childhood, using a number of different predictors. The predictive capacity (sensitivity and specificity) of these models varies greatly, and there is no agreed risk threshold for the prediction of early childhood obesity. Of the existing models, only two have been practically utilized, but neither have been particularly successful. This commentary suggests how future research may successfully utilize existing early childhood obesity prediction models for intervention. We also consider the need for such models, and how targeted obesity intervention may be more effective than population-based intervention.

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