Surgeon Performance as a Predictor for Patient-Reported Outcomes After Arthroscopic Partial Meniscectomy.

Background: Surgeon performance has been investigated as a factor affecting patient outcomes after orthopaedic procedures to improve transparency between patients and providers. Purpose/Hypothesis: The purpose of this study was to identify whether surgeon performance influenced patient-reported outcomes (PROMs) 1 year after arthroscopic partial meniscectomy (APM). It was hypothesized that there would be no significant difference in PROMs between patients who underwent APM from various surgeons. Study Design: Case-control study; Level of evidence, 3. Methods: A prospective cohort of 794 patients who underwent APM between 2018 and 2019 were included in the analysis. A total of 34 surgeons from a large multicenter health care center were included. Three multivariable models were built to determine whether the surgeon-among demographic and meniscal pathology factors-was a significant variable for predicting the Knee injury and Osteoarthritis Outcome Score (KOOS)-Pain subscale, the Patient Acceptable Symptom State (PASS), and a 10-point improvement in the KOOS-Pain at 1 year after APM. Likelihood ratio (LR) tests were used to determine the significance of the surgeon variable in the models. Results: The 794 patients were identified from the multicenter hospital system. The baseline KOOS-Pain score was a significant predictor of outcome in the 1-year KOOS-Pain model (odds ratio [OR], 2.1 [95% CI, 1.77-2.48]; P < .001), the KOOS-Pain 10-point improvement model (OR, 0.57 [95% CI, 0.44-0.73), and the 1-year PASS model (OR, 1.42 [95% CI, 1.15-1.76]; P = .002) among articular cartilage pathology (bipolar medial cartilage) and patient-factor variables, including body mass index, Veterans RAND 12-Item Health Survey-Mental Component Score, and Area Deprivation Index. The individual surgeon significantly impacted outcomes in the 1-year KOOS-Pain mixed model in the LR test (P = .004). Conclusion: Patient factors and characteristics are better predictors for patient outcomes 1 year after APM than surgeon characteristics, specifically baseline KOOS-Pain, although an individual surgeon influenced the 1-Year KOOS-Pain mixed model in the LR test. This finding has key clinical implications; surgeons who wish to improve patient outcomes after APM should focus on improving patient selection rather than improving the surgical technique. Future research is needed to determine whether surgeon variability has an impact on longer-term patient outcomes.

An Accelerated Failure Time Model to Predict Cause-Specific Survival and Prognostic Factors of Lung and Bronchus Cancer Patients with at Least Bone or Brain Metastases: Development and Internal Validation Using a SEER-Based Study.

BACKGROUND: This study addresses the significant challenge of low survival rates in patients with cause-specific lung cancer accompanied by bone or brain metastases. Recognizing the critical need for an effective predictive model, the research aims to establish survival prediction models using both parametric and non-parametric approaches. METHODS: Clinical data from lung cancer patients with at least one bone or brain metastasis between 2000 and 2020 from the SEER database were utilized. Four models were constructed: Cox proportional hazard, Weibull accelerated failure time (AFT), log-normal AFT, and Zografos-Balakrishnan log-normal (ZBLN). Independent prognostic factors for cause-specific survival were identified, and model fit was evaluated using Akaike's and Bayesian information criteria. Internal validation assessed predictive accuracy and discriminability through the Harriel Concordance Index (C-index) and calibration plots. RESULTS: A total of 20,412 patients were included, with 14,290 (70%) as the training cohort and 6122 (30%) validation. Independent prognostic factors selected for the study were age, race, sex, primary tumor site, disease grade, total malignant tumor in situ, metastases, treatment modality, and histology. Among the accelerated failure time (AFT) models considered, the ZBLN distribution exhibited the most robust model fit for the 3- and 5-year survival, as evidenced by the lowest values of Akaike's information criterion of 6322 and 79,396, and the Bayesian information criterion of 63,495 and 79,396, respectively. This outperformed other AFT and Cox models (AIC = [156,891, 211,125]; BIC = [158,848, 211,287]). Regarding predictive accuracy, the ZBLN AFT model achieved the highest concordance C-index (0.682, 0.667), a better performance than the Cox model (0.669, 0.643). The calibration curves of the ZBLN AFT model demonstrated a high degree of concordance between actual and predicted values. All variables considered in this study demonstrated significance at the 0.05 level for the ZBLN AFT model. However, differences emerged in the significant variations in survival times between subgroups. The study revealed that patients with only bone metastases have a higher chance of survival compared to only brain and those with bone and brain metastases. CONCLUSIONS: The study highlights the underutilized but accurate nature of the accelerated failure time model in predicting lung cancer survival and identifying prognostic factors. These findings have implications for individualized clinical decisions, indicating the potential for screening and professional care of lung cancer patients with at least one bone or brain metastasis in the future.

Merkel cell carcinoma recurrence risk estimation is improved by integrating factors beyond cancer stage: A multivariable model and web-based calculator.

BACKGROUND: Merkel cell carcinoma (MCC) recurs in 40% of patients. In addition to stage, factors known to affect recurrence risk include: sex, immunosuppression, unknown primary status, age, site of primary tumor, and time since diagnosis. PURPOSE: Create a multivariable model and web-based calculator to predict MCC recurrence risk more accurately than stage alone. METHODS: Data from 618 patients in a prospective cohort were used in a competing risk regression model to estimate recurrence risk using stage and other factors. RESULTS: In this multivariable model, the most impactful recurrence risk factors were: American Joint Committee on Cancer stage (P < .001), immunosuppression (hazard ratio 2.05; P < .001), male sex (1.59; P = .003) and unknown primary (0.65; P = .064). Compared to stage alone, the model improved prognostic accuracy (concordance index for 2-year risk, 0.66 vs 0.70; P < .001), and modified estimated recurrence risk by up to 4-fold (18% for low-risk stage IIIA vs 78% for high-risk IIIA over 5 years). LIMITATIONS: Lack of an external data set for model validation. CONCLUSION/RELEVANCE: As demonstrated by this multivariable model, accurate recurrence risk prediction requires integration of factors beyond stage. An online calculator based on this model (at merkelcell.org/recur) integrates time since diagnosis and provides new data for optimizing surveillance for MCC patients.

Prospective observational study to validate a next-generation sequencing blood RNA signature to predict early kidney transplant rejection.

The objective of this study was to validate the performance of Tutivia, a peripheral blood gene expression signature, in predicting early acute rejection (AR) post-kidney transplant. Recipients of living or deceased donor kidney transplants were enrolled in a nonrandomized, prospective, global, and observational study (NCT04727788). The main outcome was validation of the area under the curve (AUC) of Tutivia vs serum creatinine at biopsy alone, or Tutivia + serum creatinine at biopsy. Of the 151 kidney transplant recipients, the mean cohort age was 53 years old, and 64% were male. There were 71% (107/151) surveillance/protocol biopsies and 29% (44/151) for-cause biopsies, with a 31% (47/151) overall rejection rate. Tutivia (AUC 0.69 [95% CI: 0.59-0.77]) and AUC of Tutivia + creatinine at biopsy (0.68 [95% CI: 0.59-0.77]) were greater than the AUC of creatinine at biopsy alone (0.51.4 [95% CI: 0.43-0.60]). Applying a model cut-off of 50 (scale 0-100) generated a high- and low-risk category for AR with a negative predictive value of 0.79 (95% CI: 0.71-0.86), a positive predictive value of 0.60 (95% CI: 0.45-0.74), and an odds ratio of 5.74 (95% CI: 2.63-12.54). Tutivia represents a validated noninvasive approach for clinicians to accurately predict early AR, beyond the current standard of care.

Analysis of the Larissa Heart Failure Risk Score: Predictive Value in 9207 Patients Hospitalized for Heart Failure from a Single Center.

Early risk stratification is of outmost clinical importance in hospitalized patients with heart failure (HHF). We examined the predictive value of the Larissa Heart Failure Risk Score (LHFRS) in a large population of HHF patients from the Cleveland Clinic. A total of 13,309 admissions for heart failure (HF) from 9207 unique patients were extracted from the Cleveland Clinic's electronic health record system. For each admission, components of the 3-variable simple LHFRS were obtained, including hypertension history, myocardial infarction history, and red blood cell distribution width (RDW) ≥ 15%. The primary outcome was a HF readmission and/or all-cause mortality at one year, and the secondary outcome was all-cause mortality at one year of discharge. For both outcomes, all variables were statistically significant, and the Kaplan-Meier curves were well-separated and in a consistent order (Log-rank test p-value < 0.001). Higher LHFRS values were found to be strongly related to patients experiencing an event, showing a clear association of LHFRS with this study outcomes. The bootstrapped-validated area under the curve (AUC) for the logistic regression model for each outcome revealed a C-index of 0.64 both for the primary and secondary outcomes, respectively. LHFRS is a simple risk model and can be utilized as a basis for risk stratification in patients hospitalized for HF.

Erratum: Addressing Therapeutic Inertia: Development and Implementation of an Electronic Health Record-Based Diabetes Intensification Tool. Diabetes Spectrum 2023;36:161-170 (https://doi.org/10.2337/ds22-0031).

[This corrects the article DOI: 10.2337/ds22-0031.].

Image-guided superficial radiation therapy has superior 2-year recurrence probability to Mohs micrographic surgery.

Introduction: Non-melanoma skin cancers (NMSCs) are the most common cancers in the USA, and their incidence is rising. Mohs micrographic surgery (MMS) is commonly performed to excise NMSCs. MMS replaced superficial radiotherapy (SRT) as a first line treatment, given its superior efficacy. Image-guided superficial radiation therapy (IGSRT) was invented to improve the precision of SRT. This study investigates how the 2-year recurrence probability of IGSRT-treated NMSCs compares to that of MMS-treated lesions. Methods: This retrospective cohort study compared the 2-year recurrence probability of early stage NMSCs (squamous and basal cell carcinomas (SCCs and BCCs)) treated by IGSRT (2,286 lesions) to data on NMSCs treated by MMS (5,391 lesions) via one sample proportion tests. Medical Subject Headings were used to search PubMed for reports of 2-year recurrence probability rates of NMSCs treated by MMS. Seventeen studies were screened; 14 studies were excluded for lack of 2-year time to event analysis, or irrelevant patient population (non-BCC/SCC study, advanced disease), leaving 3 studies for comparison. Results: IGSRT-treated NMSCs have a statistically significantly improved 2-year recurrence probability than those treated by MMS, P < 0.001 for pooled data. Conclusion: The 2-year recurrence probability IGSRT-treated NMSCs is superior to MMS-treated and supports IGSRT as an effective treatment option for individuals with early stage NMSCs.

A clinicogenomic model including GARD predicts outcome for radiation treated patients with HPV+ oropharyngeal squamous cell carcinoma.

Background: Treatment decision-making in oropharyngeal squamous cell carcinoma (OPSCC) includes clinical stage, HPV status, and smoking history. Despite improvements in staging with separation of HPV-positive and -negative OPSCC in AJCC 8th edition (AJCC8), patients are largely treated with a uniform approach, with recent efforts focused on de-intensification in low-risk patients. We have previously shown, in a pooled analysis, that the genomic adjusted radiation dose (GARD) is predictive of radiation treatment benefit and can be used to guide RT dose selection. We hypothesize that GARD can be used to predict overall survival (OS) in HPV-positive OPSCC patients treated with radiotherapy (RT). Methods: Gene expression profiles (Affymetrix Clariom D) were analyzed for 234 formalin-fixed paraffin-embedded samples from HPV-positive OPSCC patients within an international, multi-institutional, prospective/retrospective observational study including patients with AJCC 7th edition stage III-IVb. GARD, a measure of the treatment effect of RT, was calculated for each patient as previously described. In total, 191 patients received primary RT definitive treatment (chemoradiation or RT alone, and 43 patients received post-operative RT. Two RT dose fractionations were utilized for primary RT cases (70 Gy in 35 fractions or 69.96 Gy in 33 fractions). Median RT dose was 70 Gy (range 50.88-74) for primary RT definitive cases and 66 Gy (range 44-70) for post-operative RT cases. The median follow up was 46.2 months (95% CI, 33.5-63.1). Cox proportional hazards analyses were performed with GARD as both a continuous and dichotomous variable and time-dependent ROC analyses compared the performance of GARD with the NRG clinical nomogram for overall survival. Results: Despite uniform radiation dose utilization, GARD showed significant heterogeneity (range 30-110), reflecting the underlying genomic differences in the cohort. On multivariable analysis, each unit increase in GARD was associated with an improvement in OS (HR = 0.951 (0.911, 0.993), p = 0.023) compared to AJCC8 (HR = 1.999 (0.791, 5.047)), p = 0.143). ROC analysis for GARD at 36 months yielded an AUC of 80.6 (69.4, 91.9) compared with an AUC of 73.6 (55.4, 91.7) for the NRG clinical nomogram. GARD≥64.2 was associated with improved OS (HR = 0.280 (0.100, 0.781), p = 0.015). In a virtual trial, GARD predicts that uniform RT dose de-escalation results in overall inferior OS but proposes two separate genomic strategies where selective RT dose de-escalation in GARD-selected populations results in clinical equipoise. Conclusions: In this multi-institutional cohort of patients with HPV-positive OPSCC, GARD predicts OS as a continuous variable, outperforms the NRG nomogram and provides a novel genomic strategy to modern clinical trial design. We propose that GARD, which provides the first opportunity for genomic guided personalization of radiation dose, should be incorporated in the diagnostic workup of HPV-positive OPSCC patients.

Diabetes, Obesity, and Pathological Upstaging in Renal Cell Carcinoma: Results From a Large Multi-institutional Consortium.

PURPOSE: We sought to determine whether clinical risk factors and morphometric features on preoperative imaging can be utilized to identify those patients with cT1 tumors who are at higher risk of upstaging (pT3a). MATERIALS AND METHODS: We performed a retrospective international case-control study of consecutive patients treated surgically with radical or partial nephrectomy for nonmetastatic renal cell carcinoma (cT1 N0) conducted between January 2010 and December 2018. Multivariable logistic regression models were used to study associations of preoperative risk factors on pT3a pathological upstaging among all patients, as well as subsets with those with preoperative tumors ≤4 cm, renal nephrometry scores, tumors ≤4 cm with nephrometry scores, and clear cell histology. We also examined association with pT3a subsets (renal vein, sinus fat, perinephric fat). RESULTS: Among the 4,092 partial nephrectomy and 2,056 radical nephrectomy patients, pathological upstaging occurred in 4.9% and 23.3%, respectively. Among each group independent factors associated with pT3a upstaging were increasing preoperative tumor size, increasing age, and the presence of diabetes. Specifically, among partial nephrectomy subjects diabetes (OR=1.65; 95% CI 1.17, 2.29), male sex (OR=1.62; 95% CI 1.14, 2.33), and increasing BMI (OR=1.03; 95% CI 1.00, 1.05 per 1 unit BMI) were statistically associated with upstaging. Subset analyses identified hilar tumors as more likely to be upstaged (partial nephrectomy OR=1.91; 95% CI 1.12, 3.16; radical nephrectomy OR=2.16; 95% CI 1.44, 3.25). CONCLUSIONS: Diabetes and higher BMI were associated with pathological upstaging, as were preoperative tumor size, increased age, and male sex. Similarly, hilar tumors were frequently upstaged.

Diagnostic and prognostic value of cardiac stress testing before major noncardiac surgery-A cohort study.

OBJECTIVE: To assess the incremental contribution of preoperative stress test results toward a diagnosis of obstructive coronary artery disease (CAD), prediction of mortality, or prediction of perioperative myocardial infarction in patients considering noncardiac, nonophthalmologic surgery. DESIGN, SETTING, PARTICIPANTS: A retrospective cohort study of visits to a preoperative risk assessment and optimization clinic in a large health system between 2008 and 2018. MEASUREMENTS: To assess diagnostic information of preoperative stress testing, we used the Begg and Greenes method to calculate test characteristics adjusted for referral bias, with a gold standard of angiography. To assess prognostic information, we first created multiply-imputed logistic regression models to predict 90-day mortality and perioperative myocardial infarction (MI), starting with two tools commonly used to assess perioperative cardiac risk, Revised Cardiac Risk Index (RCRI) and Myocardial Infarction or Cardiac Arrest (MICA). We then added stress test results and compared the discrimination for models with and without stress test results. MAIN RESULTS: Among 136,935 visits by patients without an existing diagnosis of CAD, the decision to obtain preoperative stress testing identified around 4.0% of likely new diagnoses. Stress testing increased the likelihood of CAD (likelihood ratio: 1.31), but for over 99% of patients, stress testing should not change a decision on whether to proceed to angiography. In 117,445 visits with subsequent noncardiac surgery, stress test results failed to improve predictions of either perioperative MI or 90-day mortality. Reweighting the models and adding hemoglobin improved the prediction of both outcomes. CONCLUSIONS: Cardiac stress testing before noncardiac, nonophthalmologic surgery does not improve predictions of either perioperative mortality or myocardial infarction. Very few patients considering noncardiac, nonophthalmologic surgery have a pretest probability of CAD in a range where stress testing could usefully select patients for angiography. Better use of existing patient data could improve predictions of perioperative adverse events without additional patient testing.

Consequences of preoperative cardiac stress testing-A cohort study.

OBJECTIVE: To understand the consequences of functional cardiac stress testing among patients considering noncardiac nonophthalmologic surgery. DESIGN: A retrospective cohort study of 118,552 patients who made 159,795 visits to a dedicated preoperative risk assessment and optimization clinic between 2008 and 2018. SETTING: A large integrated health system. PATIENTS: Patients who visited a dedicated preoperative risk assessment and optimization clinic before noncardiac nonophthalmologic surgery. MEASUREMENTS: To assess changes to care delivered, we measured the probability of completing additional cardiac testing, cardiac surgery, or noncardiac surgery. To assess outcomes, we measured time-to-mortality and total one-year mortality. MAIN RESULTS: In causal inference models, preoperative stress testing was associated with increased likelihood of coronary angiography (relative risk: 8.6, 95% CI 6.1-12.1), increased likelihood of percutaneous coronary intervention (RR: 4.1, 95% CI: 1.8-9.2), increased likelihood of cardiac surgery (RR: 6.8, 95% CI 4.9-9.4), decreased likelihood of noncardiac surgery (RR: 0.77, 95% CI 0.75-0.79), and delayed noncardiac surgery for patients completing noncardiac surgery (mean 28.3 days, 95% CI: 23.1-33.6). The base rate of downstream cardiac testing was low, and absolute risk increases were small. Stress testing was associated with higher mortality in unadjusted analysis but was not associated with mortality in causal inference analyses. CONCLUSIONS: Preoperative cardiac stress testing likely induces coronary angiography and cardiac interventions while decreasing use of noncardiac surgery and delaying surgery for patients who ultimately proceed to noncardiac surgery. Despite changes to processes of care, our results do not support a causal relationship between stress testing and postoperative mortality. Analyses of care cascades should consider care that is avoided or substituted in addition to care that is induced.

A preoperative nomogram incorporating CT to predict the probability of ovarian clear cell carcinoma.

OBJECTIVES: To evaluate clinical, laboratory, and radiological variables from preoperative contrast-enhanced computed tomography (CECT) for their ability to distinguish ovarian clear cell carcinoma (OCCC) from non-OCCC and to develop a nomogram to preoperatively predict the probability of OCCC. METHODS: This IRB-approved, retrospective study included consecutive patients who underwent surgery for an ovarian tumor from 1/1/2000 to 12/31/2016 and CECT of the abdomen and pelvis ≤90 days before primary debulking surgery. Using a standardized form, two experienced oncologic radiologists independently analyzed imaging features and provided a subjective 5-point impression of the probability of the histological diagnosis. Nomogram models incorporating clinical, laboratory, and radiological features were created to predict histological diagnosis of OCCC over non-OCCC. RESULTS: The final analysis included 533 patients with surgically confirmed OCCC (n = 61) and non-OCCC (n = 472); history of endometriosis was more often found in patients with OCCC (20% versus 3.6%; p < 0.001), while CA-125 was significantly higher in patients with non-OCCC (351 ng/mL versus 70 ng/mL; p < 0.001). A nomogram model incorporating clinical (age, history of endometriosis and adenomyosis), laboratory (CA-125) and imaging findings (peritoneal implant distribution, morphology, laterality, and diameter of ovarian lesion and of the largest solid component) had an AUC of 0.9 (95% CI: 0.847, 0.949), which was comparable to the AUCs of the experienced radiologists' subjective impressions [0.8 (95% CI: 0.822, 0.891) and 0.9 (95% CI: 0.865, 0.936)]. CONCLUSIONS: A presurgical nomogram model incorporating readily accessible clinical, laboratory, and CECT variables was a powerful predictor of OCCC, a subtype often requiring a distinctive treatment approach.

Methodologic Issues Specific to Prediction Model Development and Evaluation.

Developing and evaluating statistical prediction models is challenging, and many pitfalls can arise. This article identifies what the authors believe are some common methodologic concerns that may be encountered. We describe each problem and make suggestions regarding how to address them. The hope is that this article will result in higher-quality publications of statistical prediction models.

Guidelines for Reporting Observational Research in Urology: The Importance of Clear Reference to Causality.

Observational studies often dance around the issue of causality. We propose guidelines to ensure that papers refer to whether or not the study aim is to investigate causality, and suggest language to use and language to avoid.

Clinical Nomogram Using Novel Computed Tomography-Based Radiomics Predicts Survival in Patients With Non-Small-Cell Lung Cancer Treated With Stereotactic Body Radiation Therapy.

PURPOSE: Improved survival prediction and risk stratification in non-small-cell lung cancer (NSCLC) would lead to better prognosis counseling, adjuvant therapy selection, and clinical trial design. We propose the persistent homology (PHOM) score, the radiomic quantification of solid tumor topology, as a solution. MATERIALS AND METHODS: Patients diagnosed with stage I or II NSCLC primarily treated with stereotactic body radiation therapy (SBRT) were selected (N = 554). The PHOM score was calculated for each patient's pretreatment computed tomography scan (October 2008-November 2019). PHOM score, age, sex, stage, Karnofsky Performance Status, Charlson Comorbidity Index, and post-SBRT chemotherapy were predictors in the Cox proportional hazards models for OS and cancer-specific survival. Patients were split into high- and low-PHOM score groups and compared using Kaplan-Meier curves for overall survival (OS) and cumulative incidence curves for cause-specific death. Finally, we generated a validated nomogram to predict OS, which is publicly available at Eashwarsoma.Shinyapps. RESULTS: PHOM score was a significant predictor for OS (hazard ratio [HR], 1.17; 95% CI, 1.07 to 1.28) and was the only significant predictor for cancer-specific survival (1.31; 95% CI, 1.11 to 1.56) in the multivariable Cox model. The median survival for the high-PHOM group was 29.2 months (95% CI, 23.6 to 34.3), which was significantly worse compared with the low-PHOM group (45.4 months; 95% CI, 40.1 to 51.8; P < .001). The high-PHOM group had a significantly greater chance of cancer-specific death at post-treatment month 65 (0.244; 95% CI, 0.192 to 0.296) compared with the low-PHOM group (0.171; 95% CI, 0.123 to 0.218; P = .029). CONCLUSION: The PHOM score is associated with cancer-specific survival and predictive of OS. Our developed nomogram can be used to inform clinical prognosis and assist in making post-SBRT treatment considerations.

Addressing Therapeutic Inertia: Development and Implementation of an Electronic Health Record-Based Diabetes Intensification Tool.

Objective: To assess whether an electronic health record (EHR)-based diabetes intensification tool can improve the rate of A1C goal attainment among patients with type 2 diabetes and an A1C ≥8%. Methods: An EHR-based tool was developed and sequentially implemented in a large, integrated health system using a four-phase, stepped-wedge design (single pilot site [phase 1] and then three practice site clusters [phases 2-4]; 3 months/phase), with full implementation during phase 4. A1C outcomes, tool usage, and treatment intensification metrics were compared retrospectively at implementation (IMP) sites versus nonimplementation (non-IMP) sites with sites matched on patient population characteristics using overlap propensity score weighting. Results: Overall, tool utilization was low among patient encounters at IMP sites (1,122 of 11,549 [9.7%]). During phases 1-3, the proportions of patients achieving the A1C goal (<8%) were not significantly improved between IMP and non-IMP sites at 6 months (range 42.9-46.5%) or 12 months (range 46.5-53.1%). In phase 3, fewer patients at IMP sites versus non-IMP sites achieved the goal at 12 months (46.7 vs. 52.3%, P = 0.02). In phases 1-3, mean changes in A1C from baseline to 6 and 12 months (range -0.88 to -1.08%) were not significantly different between IMP and non-IMP sites. Times to intensification were similar between IMP and non-IMP sites. Conclusion: Utilization of a diabetes intensification tool was low and did not influence rates of A1C goal attainment or time to treatment intensification. The low level of tool adoption is itself an important finding highlighting the problem of therapeutic inertia in clinical practice. Testing additional strategies to better incorporate, increase acceptance of, and improve proficiency with EHR-based intensification tools is warranted.