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Development and validation of a haematuria cancer risk score to identify patients at risk of harbouring cancer.

ABSTRACT: BACKGROUND:A lack of consensus exists amongst national guidelines regarding who should be investigated for haematuria. Type of haematuria and age-specific thresholds are frequently used to guide referral for the investigation of haematuria. OBJECTIVES:To develop and externally validate the haematuria cancer risk score (HCRS) to improve patient selection for the investigation of haematuria. METHODS:Development cohort comprise of 3539 prospectively recruited patients recruited at 40 UK hospitals (DETECT 1; ClinicalTrials.gov: NCT02676180) and validation cohort comprise of 656 Swiss patients. All patients were aged >18 years and referred to hospital for the evaluation of visible and nonvisible haematuria. Sensitivity and specificity of the HCRS in the validation cohort were derived from a cut-off identified from the discovery cohort. RESULTS:Patient age, gender, type of haematuria and smoking history were used to develop the HCRS. HCRS validation achieves good discrimination (AUC 0.835; 95% CI: 0.789-0.880) and calibration (calibration slope = 1.215) with no significant overfitting (P = 0.151). The HCRS detected 11.4% (n = 8) more cancers which would be missed by UK National Institute for Health and Clinical Excellence guidelines. The American Urological Association guidelines would identify all cancers with a specificity of 12.6% compared to 30.5% achieved by the HCRS. All patients with upper tract cancers would have been identified. CONCLUSION:The HCRS offers good discriminatory accuracy which is superior to existing guidelines. The simplicity of the model would facilitate adoption and improve patient and physician decision-making.

SUBMITTER: Tan WS

PROVIDER: S-EPMC6446724 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature

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Development and validation of a haematuria cancer risk score to identify patients at risk of harbouring cancer.

Tan W S WS Ahmad A A Feber A A Mostafid H H Cresswell J J Fankhauser C D CD Waisbrod S S Hermanns T T Sasieni P P Kelly J D JD

Journal of internal medicine 20190104 4

<h4>Background</h4>A lack of consensus exists amongst national guidelines regarding who should be investigated for haematuria. Type of haematuria and age-specific thresholds are frequently used to guide referral for the investigation of haematuria.<h4>Objectives</h4>To develop and externally validate the haematuria cancer risk score (HCRS) to improve patient selection for the investigation of haematuria.<h4>Methods</h4>Development cohort comprise of 3539 prospectively recruited patients recruite ...[more]

PMID: 30521125

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Project description:BackgroundUnexpected weight loss (UWL) is a presenting feature of cancer in primary care. Existing research proposes simple combinations of clinical features (risk factors, symptoms, signs, and blood test data) that, when present, warrant cancer investigation. More complex combinations may modify cancer risk to sufficiently rule-out the need for investigation. We aimed to identify which clinical features can be used together to stratify patients with UWL based on their risk of cancer.Methods and findingsWe used data from 63,973 adults (age: mean 59 years, standard deviation 21 years; 42% male) to predict cancer in patients with UWL recorded in a large representative United Kingdom primary care electronic health record between January 1, 2000 and December 31, 2012. We derived 3 clinical prediction models using logistic regression and backwards stepwise covariate selection: Sm, symptoms-only model; STm, symptoms and tests model; Tm, tests-only model. Fifty imputations replaced missing data. Estimates of discrimination and calibration were derived using 10-fold internal cross-validation. Simple clinical risk scores are presented for models with the greatest clinical utility in decision curve analysis. The STm and Tm showed improved discrimination (area under the curve ≥ 0.91), calibration, and greater clinical utility than the Sm. The Tm was simplest including age-group, sex, albumin, alkaline phosphatase, liver enzymes, C-reactive protein, haemoglobin, platelets, and total white cell count. A Tm score of 5 balanced ruling-in (sensitivity 84.0%, positive likelihood ratio 5.36) and ruling-out (specificity 84.3%, negative likelihood ratio 0.19) further cancer investigation. A Tm score of 1 prioritised ruling-out (sensitivity 97.5%). At this threshold, 35 people presenting with UWL in primary care would be referred for investigation for each person with cancer referred, and 1,730 people would be spared referral for each person with cancer not referred. Study limitations include using a retrospective routinely collected dataset, a reliance on coding to identify UWL, and missing data for some predictors.ConclusionsOur findings suggest that combinations of simple blood test abnormalities could be used to identify patients with UWL who warrant referral for investigation, while people with combinations of normal results could be exempted from referral.

Project description:BackgroundPatients suffering from major trauma often experience complications such as sepsis. The early recognition of patients at high risk of sepsis after trauma is critical for precision therapy. We aimed to derive and validate a novel predictive score for sepsis risk using electronic medical record (EMR) data following trauma.Materials and methodsClinical and laboratory variables of 684 trauma patients within 24 h after admission were collected, including 411 patients in the training cohort and 273 in the validation cohort. The least absolute shrinkage and selection operator (LASSO) technique was adopted to identify variables contributing to the early prediction of traumatic sepsis. Then, we constructed a traumatic sepsis score (TSS) using a logistic regression model based on the variables selected in the LASSO analysis. Moreover, we evaluated the discrimination and calibration of the TSS using the area under the curve (AUC) and the Hosmer-Lemeshow (H-L) goodness-of-fit test.ResultsBased on the LASSO, seven variables (injury severity score, Glasgow Coma Scale, temperature, heart rate, albumin, international normalized ratio, and C-reaction protein) were selected for construction of the TSS. Our results indicated that the incidence of sepsis after trauma increased with an increasing TSS (P trend = 7.44 × 10-21 for the training cohort and P trend = 1.16 × 10-13 for the validation cohort). The areas under the receiver operating characteristic (ROC) curve of TSS were 0.799 (0.757-0.837) and 0.790 (0.736-0.836) for the training and validation datasets, respectively. The discriminatory power of our model was superior to that of a single variable and the sequential organ failure assessment (SOFA) score (P < 0.001). Moreover, the TSS was well calibrated (P > 0.05).ConclusionsWe developed and validated a novel TSS with good discriminatory power and calibration for the prediction of sepsis risk in trauma patients based on the EMR data.

Dataset Information

Development and validation of a haematuria cancer risk score to identify patients at risk of harbouring cancer.

Publications

Development and validation of a haematuria cancer risk score to identify patients at risk of harbouring cancer.

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