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author:("Yu, changhang")
1.  Mortality After Prostate Cancer Treatment with Radical Prostatectomy, External-Beam Radiation Therapy, or Brachytherapy in Men Without Comorbidity 
European urology  2013;64(3):372-378.
Medical comorbidity is a confounding factor in prostate cancer (PCa) treatment selection and mortality. Large-scale comparative evaluation of PCa mortality (PCM) and overall mortality (OM) restricted to men without comorbidity at the time of treatment has not been performed.
To evaluate PCM and OM in men with no recorded comorbidity treated with radical prostatectomy (RP), external-beam radiation therapy (EBRT), or brachytherapy (BT).
Design, setting, and participants
Data from 10 361 men with localized PCa treated from 1995 to 2007 at two academic centers in the United States were prospectively obtained at diagnosis and retrospectively reviewed. We identified 6692 men with no recorded comorbidity on a validated comorbidity index. Median follow-up after treatment was 7.2 yr.
Treatment with RP in 4459 men, EBRT in 1261 men, or BT in 972 men.
Outcome measurements and statistical analysis
Univariate and multivariate Cox proportional hazards regression analysis, including propensity score adjustment, compared PCM and OM for EBRT and BT relative to RP as reference treatment category. PCM was also evaluated by competing risks analysis.
Results and limitations
Using Cox analysis, EBRT was associated with an increase in PCM compared with RP (hazard ratio [HR]: 1.66; 95% confidence interval [CI], 1.05–2.63), while there was no statistically significant increase with BT (HR: 1.83; 95% CI, 0.88–3.82). Using competing risks analysis, the benefit of RP remained but was no longer statistically significant for EBRT (HR: 1.55; 95% CI, 0.92–2.60) or BT (HR: 1.66; 95% CI, 0.79–3.46). In comparison with RP, both EBRT (HR: 1.71; 95% CI, 1.40–2.08) and BT (HR: 1.78; 95% CI, 1.37–2.31) were associated with increased OM.
In a large multicenter series of men without recorded comorbidity, both forms of radiation therapy were associated with an increase in OM compared with surgery, but there were no differences in PCM when evaluated by competing risks analysis. These findings may result from an imbalance of confounders or differences in mortality related to primary or salvage therapy.
PMCID: PMC3930076  PMID: 23506834
Prostatic neoplasms; Prostatectomy; Radiation therapy; Comorbidity; Comparative effectiveness research
The Journal of urology  2011;185(3):869-875.
Long-term prostate cancer-specific mortality (PCSM) after radical prostatectomy is poorly defined in the era of widespread screening. An understanding of the treated natural history of screen-detected cancers and the pathological risk factors for PCSM are needed for treatment decision-making.
Using Fine and Gray competing risk regression analysis, the clinical and pathological data and follow-up information of 11,521 patients treated by radical prostatectomy at four academic centers from 1987 to 2005 were modeled to predict PCSM. The model was validated on 12,389 patients treated at a separate institution during the same period.
The overall 15-year PCSM was 7%. Primary and secondary pathological Gleason grade 4–5 (P < 0.001 for both), seminal vesicle invasion (P < 0.001), and year of surgery (P = 0.002) were significant predictors of PCSM. A nomogram predicting 15-year PCSM based on standard pathological parameters was accurate and discriminating with an externally-validated concordance index of 0.92. Stratified by patient age, 15-year PCSM for Gleason score ≤ 6, 3+4, 4+3, and 8–10 ranged from 0.2–1.2%, 4.2–6.5%, 6.6–11%, and 26–37%, respectively. The 15-year PCSM risks ranged from 0.8–1.5%, 2.9–10%, 15–27%, and 22–30% for organ-confined cancer, extraprostatic extension, seminal vesicle invasion, and lymph node metastasis, respectively. Only 3 of 9557 patients with organ-confined, Gleason score ≤ 6 cancers have died from prostate cancer.
The presence of poorly differentiated cancer and seminal vesicle invasion are the prime determinants of PCSM after radical prostatectomy. The risk of PCSM can be predicted with unprecedented accuracy once the pathological features of prostate cancer are known.
PMCID: PMC4058776  PMID: 21239008
prostatic neoplasms; prostatectomy; models; statistical; treatment outcome
3.  Human kallikrein-2 gene and protein expression predicts prostate cancer at repeat biopsy 
SpringerPlus  2014;3:295.
The human kallikrein-2 (hK2) protein and two single nucleotide polymorphism (SNPs) (rs2664155, rs198977) of the gene are associated with prostate cancer risk. We examined whether hK2 protein and gene SNPs predict prostate cancer at the time of repeat biopsy.
We prospectively offered a repeat biopsy to men with a negative prostate biopsy performed for a PSA >4.0 ng/mL or abnormal Digital Rectal Exam (DRE) between 2001–2005. We genotyped and measured serum hK2 levels in 941 men who underwent a repeat prostate biopsy. Logistic regression analyses were conducted to determine the significance of KLK2 SNPs and hK2 levels for predicting cancer at repeat biopsy.
Of the 941 patients, 180 (19.1%) were found to have cancer. The rs198977 SNP was positively associated with cancer at repeat biopsy (OR variant T allele = 1.8, 95% CI: 1.04-3.13, p = 0.049). When combined, the odds ratio for prostate cancer for patients with high hK2 levels and the variant T-allele of rs198977 was 3.77 (95% CI: 1.94-7.32, p < 0.0001), compared to patients with low hK2 levels and the C-allele. The addition of hK2 levels and KLK2 rs198977 to the baseline predictive model did not significantly increase the area under the curve from a baseline model of 0.67 to 0.69 (p = 0.6).
The KLK2 SNP rs198977 was positively associated with hK2 levels and predicts prostate cancer at the time of repeat prostate biopsy. Further characterization of the KLK2 gene will be needed to determine its clinical utility.
PMCID: PMC4162525  PMID: 25279276
Human Kallikrein-2; Nomogram; Prostate cancer; Single nucleotide polymorphisms
4.  Development of a Nomogram Model Predicting Current Bone Scan Positivity in Patients Treated with Androgen-Deprivation Therapy for Prostate Cancer 
Frontiers in Oncology  2014;4:296.
Purpose: To develop a nomogram predictive of current bone scan positivity in patients receiving androgen-deprivation therapy (ADT) for advanced prostate cancer; to augment clinical judgment and highlight patients in need of additional imaging investigations.
Materials and methods: A retrospective chart review of bone scan records (conventional 99mTc-scintigraphy) of 1,293 patients who received ADT at the Memorial Sloan-Kettering Cancer Center from 2000 to 2011. Multivariable logistic regression analysis was used to identify variables suitable for inclusion in the nomogram. The probability of current bone scan positivity was determined using these variables and the predictive accuracy of the nomogram was quantified by concordance index.
Results: In total, 2,681 bone scan records were analyzed and 636 patients had a positive result. Overall, the median pre-scan prostate-specific antigen (PSA) level was 2.4 ng/ml; median PSA doubling time (PSADT) was 5.8 months. At the time of a positive scan, median PSA level was 8.2 ng/ml; 53% of patients had PSA <10 ng/ml; median PSADT was 4.0 months. Five variables were included in the nomogram: number of previous negative bone scans after initiating ADT, PSA level, Gleason grade sum, and history of radical prostatectomy and radiotherapy. A concordance index value of 0.721 was calculated for the nomogram. This was a retrospective study based on limited data in patients treated in a large cancer center who underwent conventional 99mTc bone scans, which themselves have inherent limitations.
Conclusion: This is the first nomogram to predict current bone scan positivity in ADT-treated prostate cancer patients, providing high predictive accuracy.
PMCID: PMC4209823  PMID: 25386410
non-steroidal anti-androgens; radionuclide imaging; nomogram; prostatic neoplasms; androgen-deprivation therapy; bone scan positivity
5.  Adding propensity scores to pure prediction models fails to improve predictive performance 
PeerJ  2013;1:e123.
Background. Propensity score usage seems to be growing in popularity leading researchers to question the possible role of propensity scores in prediction modeling, despite the lack of a theoretical rationale. It is suspected that such requests are due to the lack of differentiation regarding the goals of predictive modeling versus causal inference modeling. Therefore, the purpose of this study is to formally examine the effect of propensity scores on predictive performance. Our hypothesis is that a multivariable regression model that adjusts for all covariates will perform as well as or better than those models utilizing propensity scores with respect to model discrimination and calibration.
Methods. The most commonly encountered statistical scenarios for medical prediction (logistic and proportional hazards regression) were used to investigate this research question. Random cross-validation was performed 500 times to correct for optimism. The multivariable regression models adjusting for all covariates were compared with models that included adjustment for or weighting with the propensity scores. The methods were compared based on three predictive performance measures: (1) concordance indices; (2) Brier scores; and (3) calibration curves.
Results. Multivariable models adjusting for all covariates had the highest average concordance index, the lowest average Brier score, and the best calibration. Propensity score adjustment and inverse probability weighting models without adjustment for all covariates performed worse than full models and failed to improve predictive performance with full covariate adjustment.
Conclusion. Propensity score techniques did not improve prediction performance measures beyond multivariable adjustment. Propensity scores are not recommended if the analytical goal is pure prediction modeling.
PMCID: PMC3740143  PMID: 23940836
Prediction; Propensity score; Calibration curve; Concordance index; Multivariable regression
6.  A nomogram for individualized estimation of survival among patients with brain metastasis 
Neuro-Oncology  2012;14(7):910-918.
Purpose: An estimated 24%–45% of patients with cancer develop brain metastases. Individualized estimation of survival for patients with brain metastasis could be useful for counseling patients on clinical outcomes and prognosis. Methods: De-identified data for 2367 patients with brain metastasis from 7 Radiation Therapy Oncology Group randomized trials were used to develop and internally validate a prognostic nomogram for estimation of survival among patients with brain metastasis. The prognostic accuracy for survival from 3 statistical approaches (Cox proportional hazards regression, recursive partitioning analysis [RPA], and random survival forests) was calculated using the concordance index. A nomogram for 12-month, 6-month, and median survival was generated using the most parsimonious model. Results: The majority of patients had lung cancer, controlled primary disease, no surgery, Karnofsky performance score (KPS) ≥ 70, and multiple brain metastases and were in RPA class II or had a Diagnosis-Specific Graded Prognostic Assessment (DS-GPA) score of 1.25–2.5. The overall median survival was 136 days (95% confidence interval, 126–144 days). We built the nomogram using the model that included primary site and histology, status of primary disease, metastatic spread, age, KPS, and number of brain lesions. The potential use of individualized survival estimation is demonstrated by showing the heterogeneous distribution of the individual 12-month survival in each RPA class or DS-GPA score group. Conclusion: Our nomogram provides individualized estimates of survival, compared with current RPA and DS-GPA group estimates. This tool could be useful for counseling patients with respect to clinical outcomes and prognosis.
PMCID: PMC3379797  PMID: 22544733
brain metastases; nomogram; prediction; prognosis; survival
7.  Prediction of morbidity and mortality in patients with type 2 diabetes 
PeerJ  2013;1:e87.
Introduction. The objective of this study was to create a tool that accurately predicts the risk of morbidity and mortality in patients with type 2 diabetes according to an oral hypoglycemic agent.
Materials and Methods. The model was based on a cohort of 33,067 patients with type 2 diabetes who were prescribed a single oral hypoglycemic agent at the Cleveland Clinic between 1998 and 2006. Competing risk regression models were created for coronary heart disease (CHD), heart failure, and stroke, while a Cox regression model was created for mortality. Propensity scores were used to account for possible treatment bias. A prediction tool was created and internally validated using tenfold cross-validation. The results were compared to a Framingham model and a model based on the United Kingdom Prospective Diabetes Study (UKPDS) for CHD and stroke, respectively.
Results and Discussion. Median follow-up for the mortality outcome was 769 days. The numbers of patients experiencing events were as follows: CHD (3062), heart failure (1408), stroke (1451), and mortality (3661). The prediction tools demonstrated the following concordance indices (c-statistics) for the specific outcomes: CHD (0.730), heart failure (0.753), stroke (0.688), and mortality (0.719). The prediction tool was superior to the Framingham model at predicting CHD and was at least as accurate as the UKPDS model at predicting stroke.
Conclusions. We created an accurate tool for predicting the risk of stroke, coronary heart disease, heart failure, and death in patients with type 2 diabetes. The calculator is available online at under the heading “Type 2 Diabetes” and entitled, “Predicting 5-Year Morbidity and Mortality.” This may be a valuable tool to aid the clinician’s choice of an oral hypoglycemic, to better inform patients, and to motivate dialogue between physician and patient.
PMCID: PMC3685323  PMID: 23781409
Type 2 diabetes mellitus; Prediction; Propensity; Coronary heart disease; Heart failure; Stroke; Mortality; Electronic health record; Hypoglycemic agents
8.  Preoperative nomograms incorporating magnetic resonance imaging and spectroscopy for prediction of insignificant prostate cancer 
Bju International  2011;109(9):1315-1322.
• To validate previously published nomograms for predicting insignificant prostate cancer (PCa) that incorporate clinical data, percentage of biopsy cores positive (%BC+) and magnetic resonance imaging (MRI) or MRI/MR spectroscopic imaging (MRSI) results.
• We also designed new nomogram models incorporating magnetic resonance results and clinical data without detailed biopsy data.
• Nomograms for predicting insignificant PCa can help physicians counsel patients with clinically low-risk disease who are choosing between active surveillance and definitive therapy.
Patients and methods
• In total, 181 low-risk PCa patients (clinical stage T1c–T2a, prostate-specific antigen level < 10 ng/mL, biopsy Gleason score of 6) had MRI/MRSI before surgery.
• For MRI and MRI/MRSI, the probability of insignificant PCa was recorded prospectively and independently by two radiologists on a scale from 0 (definitely insignificant) to 3 (definitely significant PCa).
• Insignificant PCa was defined on surgical pathology.
• There were four models incorporating MRI or MRI/MRSI and clinical data with and without %BC+ that were compared with a base clinical model without %BC and a more comprehensive clinical model with %BC+.
• Prediction accuracy was assessed using areas under receiver–operator characteristic curves.
• At pathology, 27% of patients had insignificant PCa, and the Gleason score was upgraded in 56.4% of patients.
• For both readers, all magnetic resonance models performed significantly better than the base clinical model (P ≤ 0.05 for all) and similarly to the more comprehensive clinical model.
• Existing models incorporating magnetic resonance data, clinical data and %BC+ for predicting the probability of insignificant PCa were validated.
• All MR-inclusive models performed significantly better than the base clinical model.
PMCID: PMC3270152  PMID: 21933336
magnetic resonance imaging; magnetic resonance spectroscopic imaging; nomograms; prostate neoplasms
9.  Predictive models for short- and long-term adverse outcomes following discharge in a contemporary population with acute coronary syndromes 
Although numerous risk-prediction models exist in patients presenting with acute coronary syndromes (ACS), they are subject to important short-comings, including lack of contemporary information. Short-term models are frequently biased by in-hospital events. Accordingly, we sought to create contemporary risk-prediction models for clinical outcomes following ACS up to 1 year following discharge. Models were constructed for death at 30 days and 1 year, death/myocardial infarction (MI)/revascularization at 30 days and death/MI at 1 year in consecutive patients presenting with ACS at our institution between 2006 and 2008, and discharged alive. Logistic regression was used to model the 30 day outcomes and Cox proportional hazards were used to model the 1 year outcomes. No linearity assumptions were made for continuous variables. The final model coefficients were used to create a prediction nomogram, which was incorporated into an online risk calculator. A total of 2,681 patients were included, of which about 9.5% presented with ST-elevation MI. All-cause mortality was 2.6% at 30 days and 13% at 1 year. Demographic, past medical history, laboratory, pharmacological and angiographic parameters were identified as being predictive of adverse ischemic outcomes at 30 days and 1 year. The c-indices for these models ranged from 0.73 to 0.82. Our study thus identified risk factors that are predictive of short- and long-term ischemic and revascularization outcomes in contemporary patients with ACS, and incorporated them into an easy-to-use online calculator, with equal or better discriminatory power than currently available models.
PMCID: PMC3584647  PMID: 23467552
Mortality; myocardial infarction; predictors; registry; revascularization
10.  Nomogram to Predict Prostate Cancer Diagnosis on Primary Transrectal Ultrasound-Guided Prostate Biopsy in a Contemporary Series 
Current Urology  2012;6(3):141-145.
Transrectal ultrasound-guided biopsy (TRUSB) remains the mainstay for prostate cancer (CaP) diagnosis. Numerous variables have shown associations with development of CaP. We present a nomogram that predicts the probability of detecting CaP on TRUSB.
After obtaining institutional review board approval, all patients undergoing primary TRUSB for CaP detection at a single center at our institution between 2/2000 and 9/2007 were reviewed. Patients undergoing repeat biopsies were excluded, and only the first biopsy was included in the analysis. Variables included age at biopsy, race, clinical stage, prostate specific antigen (PSA), number of cores removed, TRUS prostate volume (TRUSPV), body mass index, family history of CaP, and pathology results. S-PLUS 2000 statistical software was utilized with p < 0.05 considered significant. Cox proportional hazards regression models with restricted cubic splines were utilized to construct the nomogram. Validation utilized bootstrapping, and the concordance index was calculated based on these predictions.
A total of 1,542 consecutive patients underwent primary TRUSB with a median age of 64.2 years (range 34.9–89.2 years), PSA of 5.7 ng/ml (range 0.3–3,900 ng/ml), number of cores removed of 8.0 (range 1– 22) and TRUSPV of 36.4 cm3 (range 9.6–212.0 cm3). CaP was diagnosed in 561 (36.4%) patients. A nomogram was constructed incorporating age at biopsy, race, PSA, body mass index, clinical stage, TRUSPV, number of cores removed, and family history of CaP. The concordance index when validated internally was 0.802.
We have developed and internally validated a model predicting cancer detection in men undergoing TRUSB in a contemporary series. This model may assist clinicians in risk-stratifying potential candidates for TRUSB, potentially avoiding unnecessary or low-probability TRUSB.
PMCID: PMC3783299  PMID: 24917732
Nomograms; Prostatic neoplasm; Risk factors; Outcomes assessment; Predictive factors; Prostate biopsy
11.  New variants at 10q26 and 15q21 are associated with aggressive prostate cancer in a genome-wide association study from a prostate biopsy screening cohort 
Cancer Biology & Therapy  2011;12(11):997-1004.
Purpose: To identify and examine polymorphisms of genes associated with aggressive and clinical significant forms of prostate cancer among a screening cohort.
Experimental Design: We conducted a genome-wide association study among patients with aggressive forms of prostate cancer and biopsy-proven normal controls ascertained from a prostate cancer screening program. We then examined significant associations of specific polymorphisms among a prostate cancer screened cohort to examine their predictive ability in detecting prostate cancer.
Results: We found significant associations between aggressive prostate cancer and five single nucleotide polymorphisms (SNPs) in the 10q26 (rs10788165, rs10749408, and rs10788165, p value for association 1.3 × 10−10 to 3.2 × 10−11) and 15q21 (rs4775302 and rs1994198, p values for association 3.1 × 10−8 to 8.2 × 10−9) regions. Results of a replication study done in 3439 patients undergoing a prostate biopsy, revealed certain combinations of these SNPs to be significantly associated not only with prostate cancer but with aggressive forms of prostate cancer using an established classification criterion for prostate cancer progression (odds ratios for intermediate to high-risk disease 1.8–3.0, p value 0.003–0.001). These SNP combinations were also important clinical predictors for prostate cancer detection based on nomogram analysis that assesses prostate cancer risk.
Conclusions: Five SNPs were found to be associated with aggressive forms of prostate cancer. We demonstrated potential clinical applications of these associations.
PMCID: PMC3280918  PMID: 22130093
12.  Preoperative and Postoperative Nomograms Incorporating Surgeon Experience for Clinically Localized Prostate Cancer 
Cancer  2009;115(5):1005-1010.
Accurate preoperative and postoperative risk assessment has been critical for counseling patients regarding radical prostatectomy for clinically localized prostate cancer. In addition to other treatment modalities, neoadjuvant or adjuvant therapies have been considered. The growing literature suggested that the experience of the surgeon may affect the risk of prostate cancer recurrence. The purpose of this study was to develop and internally validate nomograms to predict the probability of recurrence, both preoperatively and postoperatively, with adjustment for standard parameters plus surgeon experience.
The study cohort included 7724 eligible prostate cancer patients treated with radical prostatectomy by 1 of 72 surgeons. For each patient, surgeon experience was coded as the total number of cases conducted by the surgeon before the patient’s operation. Multivariable Cox proportional hazards regression models were developed to predict recurrence. Discrimination and calibration of the models was assessed following bootstrapping methods, and the models were presented as nomograms.
In this combined series, the 10-year probability of recurrence was 23.9%. The nomograms were quite discriminating (preoperative concordance index, 0.767; postoperative concordance index, 0.812). Calibration appeared to be very good for each. Surgeon experience seemed to have a quite modest effect, especially postoperatively.
Nomograms have been developed that consider the surgeon’s experience as a predictor. The tools appeared to predict reasonably well but were somewhat little improved with the addition of surgeon experience as a predictor variable.
PMCID: PMC3391599  PMID: 19156928
prostate cancer; surgeon experience; recurrence; predictive value; nomogram
13.  Pre-Existing Diseases of Patients Increase Susceptibility to Hypoxemia during Gastrointestinal Endoscopy 
PLoS ONE  2012;7(5):e37614.
Hypoxemia is the most common adverse event that happened during gastrointestinal endoscopy. To estimate risk of hypoxemia prior to endoscopy, American Society of Anesthesiology (ASA) classification scores were used as a major predictive factor. But the accuracy of ASA scores for predicting hypoxemia incidence was doubted here, considering that the classification system ignores much information about general health status and fitness of patient that may contribute to hypoxemia. In this retrospective review of clinical data collected prospectively, the data on 4904 procedures were analyzed. The Pearson’s chi-square test or the Fisher exact test was employed to analyze variance of categorical factors. Continuous variables were statistically evaluated using t-tests or Analysis of variance (ANOVA). As a result, only 245 (5.0%) of the enrolled 4904 patients were found to present hypoxemia during endoscopy. Multivariable logistic regressions revealed that independent risk factors for hypoxemia include high BMI (BMI 30 versus 20, Odd ratio: 1.52, 95% CI: 1.13–2.05; P = 0.0098), hypertension (Odd ratio: 2.28, 95% CI: 1.44–3.60; P = 0.0004), diabetes (Odd ratio: 2.37, 95% CI: 1.30–4.34; P = 0.005), gastrointestinal diseases (Odd ratio: 1.77, 95% CI: 1.21–2.60; P = 0.0033), heart diseases (Odd ratio: 1.97, 95% CI: 1.06–3.68; P = 0.0325) and the procedures that combined esophagogastroduodenoscopy (EGD) and colonoscopy (Odd ratio: 4.84, 95% CI: 1.61–15.51; P = 0.0292; EGD as reference). It is noteworthy that ASA classification scores were not included as an independent predictive factor, and susceptibility of youth to hypoxemia during endoscopy was as high as old subjects. In conclusion, some certain pre-existing diseases of patients were newly identified as independent risk factors for hypoxemia during GI endoscopy. High ASA scores are a confounding predictive factor of pre-existing diseases. We thus recommend that youth (≤18 yrs), obese patients and those patients with hypertension, diabetes, heart diseases, or GI diseases should be monitored closely during sedation endoscopy.
PMCID: PMC3358262  PMID: 22629430
14.  A nomogram to predict the probability of passing the American Board of Internal Medicine examination 
Medical Education Online  2012;17:10.3402/meo.v17i0.18810.
Although the American Board of Internal Medicine (ABIM) certification is valued as a reflection of physicians’ experience, education, and expertise, limited methods exist to predict performance in the examination.
The objective of this study was to develop and validate a predictive tool based on variables common to all residency programs, regarding the probability of an internal medicine graduate passing the ABIM certification examination.
The development cohort was obtained from the files of the Cleveland Clinic internal medicine residents who began training between 2004 and 2008. A multivariable logistic regression model was built to predict the ABIM passing rate. The model was represented as a nomogram, which was internally validated with bootstrap resamples. The external validation was done retrospectively on a cohort of residents who graduated from two other independent internal medicine residency programs between 2007 and 2011.
Of the 194 Cleveland Clinic graduates used for the nomogram development, 175 (90.2%) successfully passed the ABIM certification examination. The final nomogram included four predictors: In-Training Examination (ITE) scores in postgraduate year (PGY) 1, 2, and 3, and the number of months of overnight calls in the last 6 months of residency. The nomogram achieved a concordance index (CI) of 0.98 after correcting for over-fitting bias and allowed for the determination of an estimated probability of passing the ABIM exam. Of the 126 graduates from two other residency programs used for external validation, 116 (92.1%) passed the ABIM examination. The nomogram CI in the external validation cohort was 0.94, suggesting outstanding discrimination.
A simple user-friendly predictive tool, based on readily available data, was developed to predict the probability of passing the ABIM exam for internal medicine residents. This may guide program directors’ decision-making related to program curriculum and advice given to individual residents regarding board preparation.
PMCID: PMC3475012  PMID: 23078794
board examination; in-training examination; internal medicine; residents; program directors
15.  The REDUCE metagram: a comprehensive prediction tool for determining the utility of dutasteride chemoprevention in men at risk for prostate cancer 
Frontiers in Oncology  2012;2:138.
Introduction: 5-alpha reductase inhibitors can reduce the risk of prostate cancer (PCa) but can be associated with significant side effects. A library of nomograms which predict the risk of clinical endpoints relevant to dutasteride treatment may help determine if chemoprevention is suited to the individual patient. Methods: Data from the REDUCE trial was used to identify predictive factors for 9 endpoints relevant to dutasteride treatment. Using the treatment and placebo groups from the biopsy cohort, Cox proportional hazards (PH) and competing risks regression (CRR) models were used to build 18 nomograms, whose predictive ability was measured by concordance index (CI) and calibration plots. Results: A total of 18 nomograms assessing the risks of cancer, high grade cancer, high grade prostatic intraepithelial neoplasia (HGPIN), atypical small acinar proliferation (ASAP), erectile dysfunction (ED), acute urinary retention (AUR), gynecomastia, urinary tract infection (UTI) and BPH-related surgery either on or off dutasteride were created. The nomograms for cancer, high grade cancer, ED, AUR, and BPH-related surgery demonstrated good discrimination and calibration while those for gynecomastia, UTI, HGPIN, and ASAP predicted no better than random chance. Conclusions: To aid patients in determining whether the benefits of dutasteride use outweigh the risks, we have developed a comprehensive metagram that can generate individualized risks of 9 outcomes relevant to men considering chemoprevention. Better models based on more predictive markers are needed for some of the endpoints but the current metagram demonstrates potential as a tool for patient counseling and decision-making that is accessible, intuitive, and clinically relevant.
PMCID: PMC3468831  PMID: 23087901
prostatic neoplasms; nomogram; chemoprevention; prediction
16.  The Risk of Overall Mortality in Patients With Type 2 Diabetes Receiving Glipizide, Glyburide, or Glimepiride Monotherapy 
Diabetes Care  2010;33(6):1224-1229.
Sulfonylureas have historically been analyzed as a medication class, which may be inappropriate given the differences in properties inherent to the individual sulfonylureas (hypoglycemic risk, sulfonylurea receptor selectivity, and effects on myocardial ischemic preconditioning). The purpose of this study was to assess the relationship of individual sulfonylureas and the risk of overall mortality in a large cohort of patients with type 2 diabetes.
A retrospective cohort study was conducted using an academic health center enterprise-wide electronic health record (EHR) system to identify 11,141 patients with type 2 diabetes (4,279 initiators of monotherapy with glyburide, 4,325 initiators of monotherapy with glipizide, and 2,537 initiators of monotherapy with glimepiride), ≥18 years of age with and without a history of coronary artery disease (CAD) and not on insulin or a noninsulin injectable at baseline. The patients were followed for mortality by documentation in the EHR and Social Security Death Index. Multivariable Cox models were used to compare cohorts.
No statistically significant difference in the risk of overall mortality was observed among these agents in the entire cohort, but we did find evidence of a trend toward an increased overall mortality risk with glyburide versus glimepiride (hazard ratio 1.36 [95% CI 0.96–1.91]) and glipizide versus glimepiride (1.39 [0.99–1.96]) in those with documented CAD.
Our results did not identify an increased mortality risk among the individual sulfonylureas but did suggest that glimepiride may be the preferred sulfonylurea in those with underlying CAD.
PMCID: PMC2875427  PMID: 20215447
17.  A novel nonsense mutation in the NDP gene in a Chinese family with Norrie disease 
Molecular Vision  2010;16:2653-2658.
Norrie disease (ND), a rare X-linked recessive disorder, is characterized by congenital blindness and, occasionally, mental retardation and hearing loss. ND is caused by the Norrie Disease Protein gene (NDP), which codes for norrin, a cysteine-rich protein involved in ocular vascular development. Here, we report a novel mutation of NDP that was identified in a Chinese family in which three members displayed typical ND symptoms and other complex phenotypes, such as cerebellar atrophy, motor disorders, and mental disorders.
We conducted an extensive clinical examination of the proband and performed a computed tomography (CT) scan of his brain. Additionally, we performed ophthalmic examinations, haplotype analyses, and NDP DNA sequencing for 26 individuals from the proband’s extended family.
The proband’s computed tomography scan, in which the fifth ventricle could be observed, indicated cerebellar atrophy. Genome scans and haplotype analyses traced the disease to chromosome Xp21.1-p11.22. Mutation screening of the NDP gene identified a novel nonsense mutation, c.343C>T, in this region.
Although recent research has shown that multiple different mutations can be responsible for the ND phenotype, additional research is needed to understand the mechanism responsible for the diverse phenotypes caused by mutations in the NDP gene.
PMCID: PMC3002970  PMID: 21179243
18.  Predicting 6-Year Mortality Risk in Patients With Type 2 Diabetes  
Diabetes Care  2008;31(12):2301-2306.
OBJECTIVE—The objective of this study was to create a tool that predicts the risk of mortality in patients with type 2 diabetes.
RESEARCH DESIGN AND METHODS—This study was based on a cohort of 33,067 patients with type 2 diabetes identified in the Cleveland Clinic electronic health record (EHR) who were initially prescribed a single oral hypoglycemic agent between 1998 and 2006. Mortality was determined in the EHR and the Social Security Death Index. A Cox proportional hazards regression model was created using medication class and 20 other predictor variables chosen for their association with mortality. A prediction tool was created using the Cox model coefficients. The tool was internally validated using repeated, random subsets of the cohort, which were not used to create the prediction model.
RESULTS—Follow-up in the cohort ranged from 1 day to 8.2 years (median 28.6 months), and 3,661 deaths were observed. The prediction tool had a concordance index (i.e., c statistic) of 0.752.
CONCLUSIONS—We successfully created a tool that accurately predicts mortality risk in patients with type 2 diabetes. The incorporation of medications into mortality predictions in patients with type 2 diabetes should improve treatment decisions.
PMCID: PMC2584185  PMID: 18809629
19.  Competing Causes of Death From a Randomized Trial of Extended Adjuvant Endocrine Therapy for Breast Cancer 
Older women with early-stage breast cancer experience higher rates of non—breast cancer-related death. We examined factors associated with cause-specific death in a large cohort of breast cancer patients treated with extended adjuvant endocrine therapy.
In the MA.17 trial, conducted by the National Cancer Institute of Canada Clinical Trials Group, 5170 breast cancer patients (median age = 62 years; range = 32–94 years) who were disease free after approximately 5 years of adjuvant tamoxifen treatment were randomly assigned to treatment with letrozole (2583 women) or placebo (2587 women). The median follow-up was 3.9 years (range = 0–7 years). We investigated the association of 11 baseline factors with the competing risks of death from breast cancer, other malignancies, and other causes. All statistical tests were two-sided likelihood ratio criterion tests.
During follow-up, 256 deaths were reported (102 from breast cancer, 50 from other malignancies, 100 from other causes, and four from an unknown cause). Non—breast cancer deaths accounted for 60% of the 252 known deaths (72% for those ≥70 years and 48% for those <70 years). Two baseline factors were differentially associated with type of death: cardiovascular disease was associated with a statistically significant increased risk of death from other causes (P = .002), and osteoporosis was associated with a statistically significant increased risk of death from other malignancies (P = .05). An increased risk of breast cancer—specific death was associated with lymph node involvement (P < .001). Increased risk of death from all three causes was associated with older age (P < .001).
Non—breast cancer-related deaths were more common than breast cancer—specific deaths in this cohort of 5-year breast cancer survivors, especially among older women.
PMCID: PMC2745611  PMID: 18270335
20.  Clinical and genetic features of a dominantly-inherited microphthalmia pedigree from China 
Molecular Vision  2009;15:949-954.
To evaluate the clinical, histopathologic, and genetic characteristics of a microphthalmia pedigree.
A five-generation Chinese family with microphthalmia was recruited. Clinical and histological examinations were performed in the affected patients and their family members. Cyrillic software was used to map the pedigree. Genomic DNA was extracted from peripheral blood, and linkage analysis was performed using short tandem repeat polymorphism markers. Two-point LOD scores were calculated using the MLINK program.
Microphthalmia was inherited in an autosomal dominant manner in this family. All nine affected members had hyperopia (mean: +8.00 diopters) and physiologically reduced axis oculi (mean: 19.29 mm) with a visual acuity of less than 0.5. Refractory angle-closure glaucoma occurred in three of them and atrophia bulbi in two. Histological examination showed diffuse degenerated collagen fibers in the scleral stroma. Two-point LOD score linkage analysis excluded all known genetic loci associated with simple microphthalmia in all patients.
Simple microphthalmia was dominantly inherited in this Chinese pedigree with typical phenotypes, which resulted in severe visual deterioration by middle age. A novel locus is predicted to be responsible for the microphthalmia in this family, which may prove a high genetic heterogeneity in microphthalmia.
PMCID: PMC2683027  PMID: 19452014
21.  Disease-specific survival for limited-stage small-cell lung cancer affected by statistical method of assessment 
BMC Cancer  2007;7:31.
In general, prognosis and impact of prognostic/predictive factors are assessed with Kaplan-Meier plots and/or the Cox proportional hazard model. There might be substantive differences from the results using these models for the same patients, if different statistical methods were used, for example, Boag log-normal (cure-rate model), or log-normal survival analysis.
Cohort of 244 limited-stage small-cell lung cancer patients, were accrued between 1981 and 1998, and followed to the end of 2005. The endpoint was death with or from lung cancer, for disease-specific survival (DSS). DSS at 1-, 3- and 5-years, with 95% confidence limits, are reported for all patients using the Boag, Kaplan-Meier, Cox, and log-normal survival analysis methods. Factors with significant effects on DSS were identified with step-wise forward multivariate Cox and log-normal survival analyses. Then, DSS was ascertained for patients with specific characteristics defined by these factors.
The median follow-up of those alive was 9.5 years. The lack of events after 1966 days precluded comparison after 5 years. DSS assessed by the four methods in the full cohort differed by 0–2% at 1 year, 0–12% at 3 years, and 0–1% at 5 years. Log-normal survival analysis indicated DSS of 38% at 3 years, 10–12% higher than with other methods; univariate 95% confidence limits were non-overlapping. Surgical resection, hemoglobin level, lymph node involvement, and superior vena cava (SVC) obstruction significantly impacted DSS. DSS assessed by the Cox and log-normal survival analysis methods for four clinical risk groups differed by 1–6% at 1 year, 15–26% at 3 years, and 0–12% at 5 years; multivariate 95% confidence limits were overlapping in all instances.
Surgical resection, hemoglobin level, lymph node involvement, and superior vena cava (SVC) obstruction all significantly impacted DSS. Apparent DSS for patients was influenced by the statistical methods of assessment. This would be clinically relevant in the development or improvement of clinical management strategies.
PMCID: PMC1805760  PMID: 17311683
22.  A Novel CRYGD Mutation (p.Trp43Arg) Causing Autosomal Dominant Congenital Cataract in a Chinese Family 
Human Mutation  2011;32(1):E1939-E1947.
To identify the genetic defect associated with autosomal dominant congenital nuclear cataract in a Chinese family, molecular genetic investigation via haplotype analysis and direct sequencing were performed Sequencing of the CRYGD gene revealed a c.127T>C transition, which resulted in a substitution of a highly conserved tryptophan with arginine at codon 43 (p.Trp43Arg). This mutation co-segregated with all affected individuals and was not observed in either unaffected family members or in 200 normal unrelated individuals. Biophysical studies indicated that the p.Trp43Arg mutation resulted in significant tertiary structural changes. The mutant protein was much less stable than the wild-type protein, and was more prone to aggregate when subjected to environmental stresses such as heat and UV irradiation. © 2010 Wiley-Liss, Inc.
PMCID: PMC3035819  PMID: 21031598
CRYGD; autosomal dominant congenital cataract; ADCC); structure

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