Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): the TRIPOD Statement

Collins, Gary S.; Reitsma, Johannes B.; Altman, Douglas G.; Moons, Karel G.M.

doi:10.1002/bjs.9736

Cited by 847 publications

(740 citation statements)

References 70 publications

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“…Study results are reported in line with the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement10. In the UK, the South‐East Scotland Research Ethics Service (reference NR/1506AB4) confirmed that ethical review was not required, as this observational study collected only routine, non‐patient‐identifiable data.…”

Section: Methodssupporting

confidence: 63%

Prognostic model to predict postoperative acute kidney injury in patients undergoing major gastrointestinal surgery based on a national prospective observational cohort study

Collaborative¹

2018

BJS Open

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BackgroundAcute illness, existing co‐morbidities and surgical stress response can all contribute to postoperative acute kidney injury (AKI) in patients undergoing major gastrointestinal surgery. The aim of this study was prospectively to develop a pragmatic prognostic model to stratify patients according to risk of developing AKI after major gastrointestinal surgery.MethodsThis prospective multicentre cohort study included consecutive adults undergoing elective or emergency gastrointestinal resection, liver resection or stoma reversal in 2‐week blocks over a continuous 3‐month period. The primary outcome was the rate of AKI within 7 days of surgery. Bootstrap stability was used to select clinically plausible risk factors into the model. Internal model validation was carried out by bootstrap validation.ResultsA total of 4544 patients were included across 173 centres in the UK and Ireland. The overall rate of AKI was 14·2 per cent (646 of 4544) and the 30‐day mortality rate was 1·8 per cent (84 of 4544). Stage 1 AKI was significantly associated with 30‐day mortality (unadjusted odds ratio 7·61, 95 per cent c.i. 4·49 to 12·90; P < 0·001), with increasing odds of death with each AKI stage. Six variables were selected for inclusion in the prognostic model: age, sex, ASA grade, preoperative estimated glomerular filtration rate, planned open surgery and preoperative use of either an angiotensin‐converting enzyme inhibitor or an angiotensin receptor blocker. Internal validation demonstrated good model discrimination (c‐statistic 0·65).DiscussionFollowing major gastrointestinal surgery, AKI occurred in one in seven patients. This preoperative prognostic model identified patients at high risk of postoperative AKI. Validation in an independent data set is required to ensure generalizability.

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Section: Methodssupporting

confidence: 63%

Prognostic model to predict postoperative acute kidney injury in patients undergoing major gastrointestinal surgery based on a national prospective observational cohort study

Collaborative¹

2018

BJS Open

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“…These results indicate that risk models based on local data accurately predict mortality rate; however, risk models based on data from other countries are unable to accurately predict mortality rate. When evaluating the performance of a prediction model in adherence to the transparent reporting of a multivariable prediction model for individual prognosis or diagnosis guideline, 26 investigators should pay attention to the discrepancy, involving the use of participant data collected by another country for external validation.…”

Section: Discussionmentioning

confidence: 99%

Comparison of National Operative Mortality in Gastroenterological Surgery Using Web-based Prospective Data Entry Systems

et al. 2015

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International collaboration is important in healthcare quality evaluation; however, few international comparisons of general surgery outcomes have been accomplished. Furthermore, predictive model application for risk stratification has not been internationally evaluated. The National Clinical Database (NCD) in Japan was developed in collaboration with the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP), with a goal of creating a standardized surgery database for quality improvement. The study aimed to compare the consistency and impact of risk factors of 3 major gastroenterological surgical procedures in Japan and the United States (US) using web-based prospective data entry systems: right hemicolectomy (RH), low anterior resection (LAR), and pancreaticoduodenectomy (PD).Data from NCD and ACS-NSQIP, collected over 2 years, were examined. Logistic regression models were used for predicting 30-day mortality for both countries. Models were exchanged and evaluated to determine whether the models built for one population were accurate for the other population.We obtained data for 113,980 patients; 50,501 (Japan: 34,638; US: 15,863), 42,770 (Japan: 35,445; US: 7325), and 20,709 (Japan: 15,527; US: 5182) underwent RH, LAR, and, PD, respectively. Thirty-day mortality rates for RH were 0.76% (Japan) and 1.88% (US); rates for LAR were 0.43% versus 1.08%; and rates for PD were 1.35% versus 2.57%. Patient background, comorbidities, and practice style were different between Japan and the US. In the models, the odds ratio for each variable was similar between NCD and ACS-NSQIP. Local risk models could predict mortality using local data, but could not accurately predict mortality using data from other countries.We demonstrated the feasibility and efficacy of the international collaborative research between Japan and the US, but found that local risk models remain essential for quality improvement.

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“…Statistical analyses were conducted using R software and SPSS ® version 19.0 (IBM, Armonk, New York, USA). This study was conducted and reported in line with the TRIPOD guidelines16.…”

Section: Methodsmentioning

confidence: 99%

Gene expression profiles for a prognostic immunoscore in gastric cancer

Zeng

Zhou

et al. 2018

British Journal of Surgery

193

185

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BackgroundIncreasing evidence has indicated an association between immune infiltration in gastric cancer and clinical outcome. However, reliable prognostic signatures, based on systematic assessments of the immune landscape inferred from bulk tumour transcriptomes, have not been established. The aim was to develop an immune signature, based on the cellular composition of the immune infiltrate inferred from bulk tumour transcriptomes, to improve the prognostic predictions of gastric cancer.MethodsTwenty‐two types of immune cell fraction were estimated based on large public gastric cancer cohorts from the Gene Expression Omnibus using CIBERSORT. An immunoscore based on the fraction of immune cell types was then constructed using a least absolute shrinkage and selection operator (LASSO) Cox regression model.ResultsUsing the LASSO model, an immunoscore was established consisting of 11 types of immune cell fraction. In the training cohort (490 patients), significant differences were found between high‐ and low‐immunoscore groups in overall survival across and within subpopulations with an identical TNM stage. Multivariable analysis revealed that the immunoscore was an independent prognostic factor (hazard ratio 1·92, 95 per cent c.i. 1·54 to 2·40). The prognostic value of the immunoscore was also confirmed in the validation (210) and entire (700) cohorts.ConclusionThe proposed immunoscore represents a promising signature for estimating overall survival in patients with gastric cancer.

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Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): the TRIPOD Statement

Cited by 847 publications

References 70 publications

Prognostic model to predict postoperative acute kidney injury in patients undergoing major gastrointestinal surgery based on a national prospective observational cohort study

Prognostic model to predict postoperative acute kidney injury in patients undergoing major gastrointestinal surgery based on a national prospective observational cohort study

Comparison of National Operative Mortality in Gastroenterological Surgery Using Web-based Prospective Data Entry Systems

Gene expression profiles for a prognostic immunoscore in gastric cancer

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