Prediction of progression from pre-diabetes to diabetes: Development and validation of a machine learning model.

AIMS: Identification, a priori, of those at high risk of progression from pre-diabetes to diabetes may enable targeted delivery of interventional programmes while avoiding the burden of prevention and treatment in those at low risk. We studied whether the use of a machine-learning model can improve the prediction of incident diabetes utilizing patient data from electronic medical records. METHODS: A machine-learning model predicting the progression from pre-diabetes to diabetes was developed using a gradient boosted trees model. The model was trained on data from The Health Improvement Network (THIN) database cohort, internally validated on THIN data not used for training, and externally validated on the Canadian AppleTree and the Israeli Maccabi Health Services (MHS) data sets. The model's predictive ability was compared with that of a logistic-regression model within each data set. RESULTS: A cohort of 852 454 individuals with pre-diabetes (glucose ≥ 100 mg/dL and/or HbA1c ≥ 5.7) was used for model training including 4.9 million time points using 900 features. The full model was eventually implemented using 69 variables, generated from 11 basic signals. The machine-learning model demonstrated superiority over the logistic-regression model, which was maintained at all sensitivity levels - comparing AUC [95% CI] between the models; in the THIN data set (0.865 [0.860,0.869] vs 0.778 [0.773,0.784] P < .05), the AppleTree data set (0.907 [0.896, 0.919] vs 0.880 [0.867, 0.894] P < .05) and the MHS data set (0.925 [0.923, 0.927] vs 0.876 [0.872, 0.879] P < .05). CONCLUSIONS: Machine-learning models preserve their performance across populations in diabetes prediction, and can be integrated into large clinical systems, leading to judicious selection of persons for interventional programmes.

Prediction of findings at screening colonoscopy using a machine learning algorithm based on complete blood counts (ColonFlag).

Adenomatous polyps are a common precursor lesion for colorectal cancer. ColonFlag is a machine- learning-based algorithm that uses basic patient information and complete blood cell counts (CBC) to identify individuals at elevated risk of colorectal cancer for intensified screening. The purpose of this study was to determine whether ColonFlag is also able to predict the presence of high risk adenomatous polyps at colonoscopy. This study was conducted at a large colon cancer screening center in Calgary, Alberta. The study population included asymptomatic individuals between the ages of 50 and 75 who underwent a screening colonoscopy between January 2013 and June 2015. All subjects had at least one CBC result within the year prior to colonoscopy. Based on age, sex, red blood cell parameters, inflammatory cells and platelets, the ColonFlag algorithm generated a score from 0 to 100. We compared the ability of the ColonFlag test result to discriminate between individuals who were found to have a high risk polyp and those with a normal colonoscopy. Among the 17,676 individuals who underwent a screening colonoscopy there were 1,014 found to have a high risk precancerous lesion (5.7%) and 60 were found to have colorectal cancer (0.3%). At a specificity of 95%, the odds ratio for a positive ColonFlag was 2.0 for those with an advanced precancerous lesion compared with those with a normal colonoscopy. The odds ratio did not vary according to patient subgroup, colorectal cancer location or stage. ColonFlag is a passive test that can use routine blood test results to help identify individuals at elevated risk for high risk precancerous polyps as well as frank colorectal cancer. These individuals may be targeted in an effort to achieve greater compliance with conventional screening tests.

Computer-Assisted Flagging of Individuals at High Risk of Colorectal Cancer in a Large Health Maintenance Organization Using the ColonFlag Test.

PURPOSE: To evaluate in a sample of adults who had been noncompliant with colorectal cancer (CRC) screening whether screening could be enhanced by an automated patient recall system based on identifying high-risk individuals using the ColonFlag test and an electronic medical record database. METHODS: A total of 79,671 individuals who were determined to be noncompliant with current screening recommendations were identified in the Maccabi Health Services program in Israel. Their cancer risk was determined by ColonFlag using information on age, sex, and CBC results. Doctors of individuals who were flagged as high risk were notified and asked to follow up with their patients. RESULTS: The ColonFlag identified 688 men and women who scored in the highest 0.87 percentile. Of these individuals, 254 had colonoscopies performed by Maccabi physicians, and 19 CRCs (7.5%) were found. An additional 15 cancers primarily identified outside of Maccabi were found through code matching. CONCLUSION: The ColonFlag test is a rapid, efficient, and inexpensive test that can be applied to scan electronic medical records to identify individuals at high risk of CRC who would otherwise avoid screening.

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

The article Early Colorectal Cancer Detected by Machine Learning Model Using Gender, Age, and Complete Blood Count Data, written by Mark C. Hornbrook, Ran Goshen, Eran Choman, Maureen O'Keeffe-Rosetti, Yaron Kinar, Elizabeth G. Liles, and Kristal C. Rust, was originally published Online First without open access.

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

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 ≥1 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® 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.

The ABC7 regimen: a new approach to metastatic breast cancer using seven common drugs to inhibit epithelial-to-mesenchymal transition and augment capecitabine efficacy.

Breast cancer metastatic to bone has a poor prognosis despite recent advances in our understanding of the biology of both bone and breast cancer. This article presents a new approach, the ABC7 regimen (Adjuvant for Breast Cancer treatment using seven repurposed drugs), to metastatic breast cancer. ABC7 aims to defeat aspects of epithelial-to-mesenchymal transition (EMT) that lead to dissemination of breast cancer to bone. As add-on to current standard treatment with capecitabine, ABC7 uses ancillary attributes of seven already-marketed noncancer treatment drugs to stop both the natural EMT process inherent to breast cancer and the added EMT occurring as a response to current treatment modalities. Chemotherapy, radiation, and surgery provoke EMT in cancer generally and in breast cancer specifically. ABC7 uses standard doses of capecitabine as used in treating breast cancer today. In addition, ABC7 uses 1) an older psychiatric drug, quetiapine, to block RANK signaling; 2) pirfenidone, an anti-fibrosis drug to block TGF-beta signaling; 3) rifabutin, an antibiotic to block beta-catenin signaling; 4) metformin, a first-line antidiabetic drug to stimulate AMPK and inhibit mammalian target of rapamycin, (mTOR); 5) propranolol, a beta-blocker to block beta-adrenergic signaling; 6) agomelatine, a melatonergic antidepressant to stimulate M1 and M2 melatonergic receptors; and 7) ribavirin, an antiviral drug to prevent eIF4E phosphorylation. All these block the signaling pathways - RANK, TGF-beta, mTOR, beta-adrenergic receptors, and phosphorylated eIF4E - that have been shown to trigger EMT and enhance breast cancer growth and so are worthwhile targets to inhibit. Agonism at MT1 and MT2 melatonergic receptors has been shown to inhibit both breast cancer EMT and growth. This ensemble was designed to be safe and augment capecitabine efficacy. Given the expected outcome of metastatic breast cancer as it stands today, ABC7 warrants a cautious trial.

Predicting the presence of colon cancer in members of a health maintenance organisation by evaluating analytes from standard laboratory records.

BACKGROUND: A valid risk prediction model for colorectal cancer (CRC) could be used to identify individuals in the population who would most benefit from CRC screening. We evaluated the potential for information derived from a panel of blood tests to predict a diagnosis of CRC from 1 month to 3 years in the future. METHODS: We abstracted information on 1755 CRC cases and 54 730 matched cancer-free controls who had one or more blood tests recorded in the electronic records of Maccabi Health Services (MHS) during the period 30-180 days before diagnosis. A scoring model (CRC score) was constructed using the study subjects' blood test results. We calculated the odds ratio for being diagnosed with CRC after the date of blood draw, according to CRC score and time from blood draw. RESULTS: The odds ratio for having CRC detected within 6 months for those with a score of four or greater (vs three or less) was 7.3 (95% CI: 6.3-8.5) for men and was 7.8 (95% CI: 6.7-9.1) for women. CONCLUSIONS: Information taken from routine blood tests can be used to predict the risk of being diagnosed with CRC in the near future.

Performance analysis of a machine learning flagging system used to identify a group of individuals at a high risk for colorectal cancer.

Individuals with colorectal cancer (CRC) have a tendency to intestinal bleeding which may result in mild to severe iron deficiency anemia, but for many colon cancer patients hematological abnormalities are subtle. The fecal occult blood test (FOBT) is used as a pre-screening test whereby those with a positive FOBT are referred to colonscopy. We sought to determine if information contained in the complete blood count (CBC) report coud be processed automatically and used to predict the presence of occult colorectal cancer (CRC) in the setting of a large health services plan. Using the health records of the Maccabi Health Services (MHS) we reviewed CBC reports for 112,584 study subjects of whom 133 were diagnosed with CRC in 2008 and analysed these with the MeScore tool. The odds ratio for being diagnosed with CRC in 2008 was calculated with regards to the MeScore, using cutoff levels of 97% and 99% percentiles. For individuals in the highest one percentile, the odds ratio for CRC was 21.8 (95% CI 13.8 to 34.2). For the majority of the individuals with cancer, CRC was not suspected at the time of the blood draw. Frequent use of anticoagulants, the presence of other gastrointestinal pathologies and non-GI malignancies were assocaitged with false positive MeScores. The MeScore can help identify individuals in the population who would benefit most from CRC screening, including those with no clinical signs or symptoms of CRC.