Hospital and medical care days in pancreatic cancer

Clinical Data Prediction Model to Identify Patients With Early-Stage Pancreatic Cancer

PURPOSE

Pancreatic cancer is an aggressive malignancy with patients often experiencing nonspecific symptoms before diagnosis. This study evaluates a machine learning approach to help identify patients with early-stage pancreatic cancer from clinical data within electronic health records (EHRs).

MATERIALS AND METHODS

From the Optum deidentified EHR data set, we identified early-stage (n = 3,322) and late-stage (n = 25,908) pancreatic cancer cases over 40 years of age diagnosed between 2009 and 2017. Patients with early-stage pancreatic cancer were matched to noncancer controls (1:16 match). We constructed a prediction model using eXtreme Gradient Boosting (XGBoost) to identify early-stage patients on the basis of 18,220 features within the EHR including diagnoses, procedures, information within clinical notes, and medications. Model accuracy was assessed with sensitivity, specificity, positive predictive value, and the area under the curve.

RESULTS

The final predictive model included 582 predictive features from the EHR, including 248 (42.5%) physician note elements, 146 (25.0%) procedure codes, 91 (15.6%) diagnosis codes, 89 (15.3%) medications, and 9 (1.5%) demographic features. The final model area under the curve was 0.84. Choosing a model cut point with a sensitivity of 60% and specificity of 90% would enable early detection of 58% late-stage patients with a median of 24 months before their actual diagnosis.

CONCLUSION

Prediction models using EHR data show promise in the early detection of pancreatic cancer. Although widespread use of this approach on an unselected population would produce high rates of false-positive tests, this technique may be rapidly impactful if deployed among high-risk patients or paired with other imaging or biomarker screening tools.

Time Spent Away from Home in the Year Following High-Risk Cancer Surgery in Older Adults

OBJECTIVES

To understand where older adults spend time (at home, in the hospital, or in a nursing home) in the year following high-risk cancer surgery.

DESIGN

Retrospective cohort study.

SETTING

Medicare beneficiaries using data from Medicare Inpatient claims to ascertain hospital days and the Minimum Data Set to ascertain nursing home days.

PARTICIPANTS

Beneficiaries who underwent high-risk cancer surgery (cystectomy, pancreaticoduodenectomy, gastrectomy, or esophagectomy) were identified to determine cumulative time spent away from home in the year following surgery.

MEASUREMENTS

Adjusted percentages of time spent away from home (ie, days in a hospital or nursing home) were modeled for the year following surgery.

RESULTS

A total of 37 748 beneficiaries underwent high-risk cancer surgery during the study period, and 28.3% died within 1 year. Overall, beneficiaries spent 13.9 ± 26.2 days in the hospital (over 1.5 ± 2.0 hospital readmissions) and 37.2 ± 50.6 days in the nursing home (over 1.5 ± 1.0 admissions) in the year following surgery. Among beneficiaries who were alive and dead at 1 year, 18.5% and 30.1% of time was spent away from home, respectively. Beneficiaries who were initially discharged to a facility following surgery and died within 1 year spent 44.4% of their final year away from home.

CONCLUSION

Time spent away from home in the hospital and/or nursing home in the year following high-risk cancer surgery is substantial among Medicare beneficiaries. This information is crucial in counseling patients on postoperative expectations and may additionally influence preoperative decision making. J Am Geriatr Soc 68:505-510, 2020.

Postresection surveillance for pancreatic cancer performance status, imaging, and serum markers

Approximately 15% of patients with a diagnosis of pancreatic adenocarcinoma are candidates for potentially curative surgery. However, most patients who undergo such surgery will die from recurrent disease, most within the first few years, whereas nearly all succumb by 5 to 7 years from diagnosis. Currently, there is a lack of high-level evidence to guide consensus recommendations as to the optimal surveillance strategy after resection. There is considerable variability in clinical practice, ranging from frequent clinical follow-up, with serial Ca 19-9 measurement and routine computed tomographic imaging on a 3- to 6-monthly basis, to a practice of no routine serum or imaging follow-up after surgery. In most part, this divergence in practice reflects a lack of data to define optimal practice. The argument in favor of limited surveillance presumably stems from the relatively uniform poor outcomes after recurrence and the absence of evidence indicating that early detection of local, regional, or metastatic recurrence improves outcomes. However, recent advancements in the treatment of metastatic disease offer hope that earlier detection and initiation of treatment for recurrent disease may positively impact clinical outcomes and at least urges review of the topic. One advantage to the development of defined guidelines would be greater consistency in the setting of both routine clinical follow-up and follow-up after adjuvant therapy on trial.