Variations in hemoglobin before colorectal cancer diagnosis

Goldshtein, Inbal; Neeman, Uri; Chodick, Gabriel; Shalev, Varda

doi:10.1097/cej.0b013e32833c1be0

Cited by 17 publications

(12 citation statements)

References 4 publications

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“…Previous publications have shown that red blood cell distribution width (RDW) had 84% sensitivity and 88% specificity for right-sided CRC cases; no improved sensitivity in combination with red cell distribution width (RDW), hemoglobin (Hgb), and mean corpuscular volume (MCV) was documented [10]. Goldshtein et al [18] have shown that a minor decrement in the levels of blood Hgb may signify the early development of CRC. Recognition of a change in Hgb levels over time, rather than the most current value alone, has been shown to improve detection of CRC [19].…”

mentioning

confidence: 99%

Early Colorectal Cancer Detected by Machine Learning Model Using Gender, Age, and Complete Blood Count Data

Hornbrook

Goshen²,

Choman³

et al. 2017

Dig Dis Sci

100

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Background Machine learning tools identify patients with blood counts indicating greater likelihood of colorectal cancer and warranting colonoscopy referral. Aims To validate a machine learning colorectal cancer detection model on a US community-based insured adult population. Methods Eligible colorectal cancer cases (439 females, 461 males) with complete blood counts before diagnosis were identified from Kaiser Permanente Northwest Region's Tumor Registry. Control patients (n = 9108) were randomly selected from KPNW's population who had no cancers, received at C1 blood count, had continuous enrollment from 180 days prior to the blood count through 24 months after the count, and were aged 40-89. For each control, one blood count was randomly selected as the pseudo-colorectal cancer diagnosis date for matching to cases, and assigned a ''calendar year'' based on the count date. For each calendar year, 18 controls were randomly selected to match the general enrollment's 10-year age groups and lengths of continuous enrollment. Prediction performance was evaluated by area under the curve, specificity, and odds ratios. Results Area under the receiver operating characteristics curve for detecting colorectal cancer was 0.80 ± 0.01. At 99% specificity, the odds ratio for association of a high-risk detection score with colorectal cancer was 34.7 (95% CI 28.9-40.4). The detection model had the highest accuracy in identifying right-sided colorectal cancers. Conclusions ColonFlag Ò identifies individuals with tenfold higher risk of undiagnosed colorectal cancer at curable stages (0/I/II), flags colorectal tumors 180-360 days prior to usual clinical diagnosis, and is more accurate at identifying right-sided (compared to left-sided) colorectal cancers.

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mentioning

confidence: 99%

Early Colorectal Cancer Detected by Machine Learning Model Using Gender, Age, and Complete Blood Count Data

Hornbrook

Goshen²,

Choman³

et al. 2017

Dig Dis Sci

100

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“…In the 39 studies that used a single FBC test, three used the first set of FBC measurements available from entry into the study, six used the last available FBC set (the one closest to the time of diagnosis), and it was unclear in the remaining 30 studies. Of the three studies that used multiple sets of FBCs, Boursi 2016 [ 20 ] and Kinar 2016 [ 40 ] developed a prediction model and Goldshtein 2010 [ 27 ] did not.…”

Section: Resultsmentioning

confidence: 99%

“…Goldshtein 2010 [27] performed a mixed effects analysis to model changes in haemoglobin levels between those with and without a colorectal cancer diagnosis over 10 years before diagnosis, in an Israeli population. Using a logarithmic transformation of time, the mixed model for those with a diagnosis (slope = 0.3, intercept = 12.72) indicated a greater decline in haemoglobin than those without (slope = 0.04, intercept = 13.111).…”

Section: Repeated Fbc Measuresmentioning

confidence: 99%

The Full Blood Count Blood Test for Colorectal Cancer Detection: A Systematic Review, Meta-Analysis, and Critical Appraisal

Virdee

Marian

Mansouri

et al. 2020

Cancers

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Introduction: A full blood count (FBC) blood test includes 20 components. We systematically reviewed studies that assessed the association of the FBC and diagnosis of colorectal cancer to identify components as risk factors. We reviewed FBC-based prediction models for colorectal cancer risk. Methods: MEDLINE, EMBASE, CINAHL, and Web of Science were searched until 3 September 2019. We meta-analysed the mean difference in FBC components between those with and without a diagnosis and critically appraised the development and validation of FBC-based prediction models. Results: We included 53 eligible articles. Three of four meta-analysed components showed an association with diagnosis. In the remaining 16 with insufficient data for meta-analysis, three were associated with colorectal cancer. Thirteen FBC-based models were developed. Model performance was commonly assessed using the c-statistic (range 0.72–0.91) and calibration plots. Some models appeared to work well for early detection but good performance may be driven by early events. Conclusion: Red blood cells, haemoglobin, mean corpuscular volume, red blood cell distribution width, white blood cell count, and platelets are associated with diagnosis and could be used for referral. Existing FBC-based prediction models might not perform as well as expected and need further critical testing.

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“…Ideally, a patient specific approach would be taken for the interpretation of laboratory results, because historic data from the patient of interest is the best indicator of the normality of the current results [2]. Such a personalized approach would allow small fluctuations to be identified, a desirable characteristic given recent studies showing the predictive power of gradual temporal trends [3, 4]. However, large amounts of patient specific data are rarely available.…”

Section: Introductionmentioning

confidence: 99%

An unsupervised learning method to identify reference intervals from a clinical database

Poole

Schroeder

Shah

2016

Journal of Biomedical Informatics

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Reference intervals are critical for the interpretation of laboratory results. The development of reference intervals using traditional methods is time consuming and costly. An alternative approach, known as an a posteriori method, requires an expert to enumerate diagnoses and procedures that can affect the measurement of interest. We develop a method, LIMIT, to use laboratory test results from a clinical database to identify ICD9 codes that are associated with extreme laboratory results, thus automating the a posteriori method. LIMIT was developed using sodium serum levels, and validated using potassium serum levels, both tests for which harmonized reference intervals already exist. To test LIMIT, reference intervals for total hemoglobin in whole blood were learned, and were compared with the hemoglobin reference intervals found using an existing a posteriori approach. In addition, prescription of iron supplements were used to identify individuals whose hemoglobin levels were low enough for a clinician to choose to take action. This prescription data indicating clinical action was then used to estimate the validity of the hemoglobin reference interval sets. Results show that LIMIT produces usable reference intervals for sodium, potassium and hemoglobin laboratory tests. The hemoglobin intervals produced using the data driven approaches consistently had higher positive predictive value and specificity in predicting an iron supplement prescription than the existing intervals. LIMIT represents a fast and inexpensive solution for calculating reference intervals, and shows that it is possible to use laboratory results and coded diagnoses to learn laboratory test reference intervals from clinical data warehouses.

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Variations in hemoglobin before colorectal cancer diagnosis

Cited by 17 publications

References 4 publications

Early Colorectal Cancer Detected by Machine Learning Model Using Gender, Age, and Complete Blood Count Data

Early Colorectal Cancer Detected by Machine Learning Model Using Gender, Age, and Complete Blood Count Data

The Full Blood Count Blood Test for Colorectal Cancer Detection: A Systematic Review, Meta-Analysis, and Critical Appraisal

An unsupervised learning method to identify reference intervals from a clinical database

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