Development and validation of a machine-learning prediction model to improve abdominal aortic aneurysm screening.

OBJECTIVE: Despite recommendations by the United States Preventive Services Task Force and the Society for Vascular Surgery, adoption of screening for abdominal aortic aneurysms (AAAs) remains low. One challenge is the low prevalence of AAAs in the unscreened population, and therefore a low detection rate for AAA screenings. We sought to use machine learning to identify factors associated with the presence of AAAs and create a model to identify individuals at highest risk for AAAs, with the aim of increasing the detection rate of AAA screenings. METHODS: A machine-learning model was trained using longitudinal medical records containing lab results, medications, and other data from our institutional database. A retrospective cohort study was performed identifying current or past smoking in patients aged 65 to 75 years and stratifying the patients by sex and smoking status as well as determining which patients had a confirmed diagnosis of AAA. The model was then adjusted to maximize fairness between sexes without significantly reducing precision and validated using six-fold cross validation. RESULTS: Validation of the algorithm on the single-center institutional data utilized 18,660 selected patients over 2 years and identified 314 AAAs. There were 41 factors identified in the medical record included in the machine-learning algorithm, with several factors never having been previously identified to be associated with AAAs. With an estimated 100 screening ultrasounds completed monthly, detection of AAAs is increased with a lift of 200% using the algorithm as compared with screening based on guidelines. The increased detection of AAAs in the model-selected individuals is statistically significant across all cutoff points. CONCLUSIONS: By utilizing a machine-learning model, we created a novel algorithm to detect patients who are at high risk for AAAs. By selecting individuals at greatest risk for targeted screening, this algorithm resulted in a 200% lift in the detection of AAAs when compared with standard screening guidelines. Using machine learning, we also identified several new factors associated with the presence of AAAs. This automated process has been integrated into our current workflows to improve screening rates and yield of high-risk individuals for AAAs.

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.

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.

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.

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.

Age and indication for referral to capsule endoscopy significantly affect small bowel transit times: the given database.

The purpose of this study was to examine the effect of age and selected indications for capsule endoscopy on small bowel transit times. Data on 67 clinical studies (790 subjects with different gastrointestinal pathologies [49.5% males; mean age, 51.9 +/- 18.33 years; range, 18-91 years] and 87 healthy volunteers) were retrieved from the company (Given Imaging, Ltd.)-sponsored database. All subjects swallowed the PillCam SB Capsule after a 12-hr fast. The capsule reached the cecum in all 877 participants. Indications for referral for capsule endoscopy were as follows: 372 obscure gastrointestinal bleeding, 96 suspected Crohn's disease, 65 celiac disease, 54 irritable bowel syndrome, and 116 familial adenomatous polyposis, intestinal lymphoma, or ulcerative colitis. One group consisted of patients <40 years old (n = 235), and the other patients 40 years old (n = 555). The younger group, volunteers, and Crohn's disease patients had significantly shorter small bowel transit times than the others (P < 0.001). Gastric emptying indirectly influenced capsule transit time.