Simplified cardiovascular index may be the best comorbidity index for clinical use in prediction of mortality for renal cancer patients.

OBJECTIVE: Understanding the relationship between comorbidities and life expectancy is important in cancer patients who carry risks of cancer and noncancer-related mortality. Comorbidity indices (CI) are tools to provide an objective measure of competing risks of death. We sought to determine which CI might be best incorporated into clinical practice for patients with suspected renal cancer. MATERIALS AND METHODS: 1572 patients diagnosed with renal masses (stage I-IV) between 1998 and 2016 were analyzed for this study. Patient data were gathered from a community-based health center. Comorbidities were evaluated individually, and with 1 of 4 CI: Charlson (CCI), updated CCI (uCCI), age-adjusted CCI (aCCI), and simplified cardiovascular index (CVI). Cox-proportional hazard analysis of all-cause mortality was performed using the four CI, adjusting for the 4 CI, adjusting for age, gender, race, tumor size, and tumor stage. RESULTS: Univariable analyses revealed the four CI were significant predictors of mortality (P < 0.05), as were age, gender, tumor size, and stage. Comorbid conditions at diagnosis included hypertension (47.8%), diabetes mellitus (47.2%), coronary artery disease (41.1%), chronic kidney disease (31.8%), peripheral vascular disease (8.0%), congestive heart failure (5.7%), chronic obstructive pulmonary disease (5.7%), and cerebrovascular disease (2.0%). When analyzing the 4 CI in multivariable survival analyses accounting for factors available at diagnosis, and analyses incorporating pathologic and recurrence data, only CVI score and uCCI remained statistically significant (P < 0.05). Limitations of this work are the retrospective nature of data collection and data from a single institution, limiting the generalizability. CONCLUSION: Increasing comorbidity, age, tumor size, and cM stage are predictors of ACM for suspected renal cancer patients. CVI appears to provide comparable information to various iterations of CCI (uCCI, aCCI) while being the simplest to use. Utilization of CVI may assist clinicians and patients when considering between interventional and noninterventional approaches for suspected renal cancer.

Development and Validation of Dynamic Multivariate Prediction Models of Sexual Function Recovery in Patients with Prostate Cancer Undergoing Radical Prostatectomy: Results from the MUSIC Statewide Collaborative.

Background: Radical prostatectomy (RP) is the most common definitive treatment for men with intermediate-risk prostate cancer and is frequently complicated by erectile dysfunction. Objective: To develop and validate models to predict 12- and 24-month post-RP sexual function. Design setting and participants: Using Michigan Urological Surgery Improvement Collaborative (MUSIC) registry data from 2016 to 2021, we developed dynamic, multivariate, random-forest models to predict sexual function recovery following RP. Model factors (established a priori) included baseline patient characteristics and repeated assessments of sexual satisfaction, and Expanded Prostate Cancer Index Composite 26 (EPIC-26) overall scores and sexual domain questions. Outcome measurements and statistical analysis: We evaluated three outcomes related to sexual function: (1) the EPIC-26 sexual domain score (range 0-100); (2) the EPIC-26 sexual domain score dichotomized at ≥73 for "good" function; and (3) a dichotomized variable for erection quality at 12 and 24 months after RP. A gradient-boosting decision tree was used for the prediction models, which combines many decision trees into a single model. We evaluated the performance of our model using the root mean squared error (RMSE) and mean absolute error (MAE) for the EPIC-26 score as a continuous variable, and the area under the receiver operating characteristic curve (AUC) for the dichotomized EPIC-26 sexual domain score (SDS) and erection quality. All analyses were conducted using R v3.6.3. Results and limitations: We identified 3983 patients at 12 months and 2494 patients at 24 months who were randomized to the derivation cohort at 12 and 24 months, respectively. Using baseline information only, our model predicted the 12-month EPIC-26 SDS with RMSE of 24 and MAE of 20. The AUC for predicting EPIC-26 SDS ≥73 (a previously published threshold) was 0.82. Our model predicted 24-month EPIC-26 SDS with RMSE of 26 and MAE of 21, and AUC for SDS ≥73 of 0.81. Inclusion of post-RP data improved the AUC to 0.91 and 0.94 at 12 and 24 months, respectively. A web tool has also been developed and is available at https://ml4lhs.shinyapps.io/askmusic_prostate_pro/. Conclusions: Our model provides a valid way to predict sexual function recovery at 12 and 24 months after RP. With this dynamic, multivariate (multiple outcomes) model, accurate predictions can be made for decision-making and during survivorship, which may reduce decision regret. Patient summary: Our prediction model allows patients considering prostate cancer surgery to understand their probability before and after surgery of recovering their erectile function and may reduce decision regret.

Predicting persistent opioid use after surgery using electronic health record and patient-reported data.

BACKGROUND: More than 100 million surgeries take place annually in the United States, and more than 90% of surgical patients receive an opioid prescription. A sizable minority of these patients will go on to use opioids long-term, contributing to the national opioid epidemic. METHODS: The objective of this study was to develop and validate a model to predict persistent opioid use after surgery. Participants included surgical patients (≥18 years old) enrolled in a cohort study at an academic medical center between 2015 and 2018. Persistent opioid use was defined as filling opioid prescriptions in postdischarge days 4 to 90 and 91 to 180. Predictors included electronic health record data, state prescription drug monitoring data, and patient-reported measures. Three models were developed: a full, a restricted, and a minimal model using a derivation and validation cohort. RESULTS: Of 24,040 patients, 4,879 (20%) experienced persistent opioid use. In the validation cohort, the full, restricted, and minimal model had C-statistics of 0.87 (95% CI 0.86-0.88), 0.86 (0.85-0.88), and 0.85 (0.84-0.87), respectively. All models performed better among patients with preoperative opioid use compared to opioid-naive patients (P < .001). The models slightly overpredicted risk in the validation cohort. The net benefit of using the restricted model to refer patients for preoperative counseling was 0.072 to 0.092, which is superior to evaluating no patients (net benefit of 0) or all patients (net benefit of -0.22 to -0.63). CONCLUSION: This study developed and validated a prediction model for persistent opioid use using accessible data resources. The models achieved strong performance, outperforming prior published models.

Development and Validation of Models to Predict Pathological Outcomes of Radical Prostatectomy in Regional and National Cohorts.

PURPOSE: Prediction models are recommended by national guidelines to support clinical decision making in prostate cancer. Existing models to predict pathological outcomes of radical prostatectomy (RP)-the Memorial Sloan Kettering (MSK) models, Partin tables, and the Briganti nomogram-have been developed using data from tertiary care centers and may not generalize well to other settings. MATERIALS AND METHODS: Data from a regional cohort (Michigan Urological Surgery Improvement Collaborative [MUSIC]) were used to develop models to predict extraprostatic extension (EPE), seminal vesicle invasion (SVI), lymph node invasion (LNI), and nonorgan-confined disease (NOCD) in patients undergoing RP. The MUSIC models were compared against the MSK models, Partin tables, and Briganti nomogram (for LNI) using data from a national cohort (Surveillance, Epidemiology, and End Results [SEER] registry). RESULTS: We identified 7,491 eligible patients in the SEER registry. The MUSIC model had good discrimination (SEER AUC EPE: 0.77; SVI: 0.80; LNI: 0.83; NOCD: 0.77) and was well calibrated. While the MSK models had similar discrimination to the MUSIC models (SEER AUC EPE: 0.76; SVI: 0.80; LNI: 0.84; NOCD: 0.76), they overestimated the risk of EPE, LNI, and NOCD. The Partin tables had inferior discrimination (SEER AUC EPE: 0.67; SVI: 0.76; LNI: 0.69; NOCD: 0.72) as compared to other models. The Briganti LNI nomogram had an AUC of 0.81 in SEER but overestimated the risk. CONCLUSIONS: New models developed using the MUSIC registry outperformed existing models and should be considered as potential replacements for the prediction of pathological outcomes in prostate cancer.

Hybrid bag of approaches to characterize selection criteria for cohort identification.

OBJECTIVE: The 2018 National NLP Clinical Challenge (2018 n2c2) focused on the task of cohort selection for clinical trials, where participating systems were tasked with analyzing longitudinal patient records to determine if the patients met or did not meet any of the 13 selection criteria. This article describes our participation in this shared task. MATERIALS AND METHODS: We followed a hybrid approach combining pattern-based, knowledge-intensive, and feature weighting techniques. After preprocessing the notes using publicly available natural language processing tools, we developed individual criterion-specific components that relied on collecting knowledge resources relevant for these criteria and pattern-based and weighting approaches to identify "met" and "not met" cases. RESULTS: As part of the 2018 n2c2 challenge, 3 runs were submitted. The overall micro-averaged F1 on the training set was 0.9444. On the test set, the micro-averaged F1 for the 3 submitted runs were 0.9075, 0.9065, and 0.9056. The best run was placed second in the overall challenge and all 3 runs were statistically similar to the top-ranked system. A reimplemented system achieved the best overall F1 of 0.9111 on the test set. DISCUSSION: We highlight the need for a focused resource-intensive effort to address the class imbalance in the cohort selection identification task. CONCLUSION: Our hybrid approach was able to identify all selection criteria with high F1 performance on both training and test sets. Based on our participation in the 2018 n2c2 task, we conclude that there is merit in continuing a focused criterion-specific analysis and developing appropriate knowledge resources to build a quality cohort selection system.