Development and Validation of a Prostate Biopsy Risk Calculator in Black Men.

PURPOSE: We sought to develop and validate a prostate biopsy risk calculator for Black men and compare it with the Prostate Cancer Prevention Trial version 2.0, Prostate Biopsy Collaborative Group, and Kaiser Permanente Prostate Cancer Risk Calculators for the detection of Gleason Grade Group (GG) ≥ 2 prostate cancer (PCa). MATERIALS AND METHODS: We prospectively recruited 2 cohorts of men undergoing prostate biopsy from 5 facilities in Chicago. The first cohort was split into development (70%) and internal validation (30%) groups. The second was used for external validation. Iterative logistic regression was used to develop 3 models for predicting GG ≥ 2 PCa. Models were compared for discrimination using the C statistics, calibration curves, and net benefit curves. The frequency of unnecessary biopsies and missed PCas was compared at 10% and 30% risk thresholds. RESULTS: The 2 cohorts included 393 and 292 Black men, respectively. Our first model, Mistry-Sun 1, used serum PSA and prior negative biopsy. Mistry-Sun 2 added abnormal digital rectal exam (DRE) and an interaction term with abnormal DRE and PSA to Mistry-Sun 1. Mistry-Sun 3 added prostate volume, abnormal DRE, and age to Mistry-Sun 1. The C statistics were 0.74, 0.74, and 0.78, respectively, and were similar to or higher than established calculators. At the 10% and 30% risk thresholds our models had the fewest unnecessary biopsies and an appropriate proportion of missed GG ≥ 2 PCas. CONCLUSIONS: Tailoring a risk calculator to detect clinically significant PCa in Black men may improve biopsy decision-making and outcomes compared to tools developed in non-Black populations.

Validation of the Melanoma Institute of Australia's Sentinel Lymph Node Biopsy Risk Prediction Tool for Cutaneous Melanoma.

BACKGROUND: For patients with cutaneous melanoma, sentinel lymph node biopsy (SLNB) is used to stage regional lymph nodes pathologically and inform prognosis, treatment, and surveillance. To reduce unnecessary surgeries, predictive tools aim to identify those at lowest risk for node-positive disease. The Melanoma Institute of Australia (MIA)'s Prediction Tool for Sentinel Node Metastasis Risk estimates risk of a positive SLNB using patient age and primary melanoma Breslow depth, histologic subtype, ulceration, mitotic rate, and lymphovascular invasion. METHODS: A single-institution validation was performed of the MIA Calculator with 982 cutaneous melanoma patients that included all relevant clinicopathologic factors and SLNB pathology outcomes. The study evaluated discrimination via receiver operating characteristic (ROC) curves, calibration via calibration plots, and clinical utility via decision curve analysis of the MIA model in various subgroups. The data were fit to MIA model parameters via a generalized linear model to assess the odds ratio of parameters in our dataset. RESULTS: The Calculator demonstrated limited discrimination based on ROC curves (C-statistic, 0.709) and consistently underestimated risk of SLN positivity. It did not provide a net benefit over SLNB performed on all patients or reduce unnecessary procedures in the risk domain of 0% to 16%. Compared with the original development and validation cohorts, the current study cohort had thinner tumors and a larger proportion of acral melanomas. CONCLUSIONS: The Calculator generally underestimated SLN positivity risk, including assessment in patients who would be counseled to forego SLNB based on a predicted risk lower than 5%. Recognition of the tool's current limitations emphasizes the need to refine it further for use in medical decision-making.

Preoperative predictors of nonhome discharge after fenestrated-branched endovascular repair of complex abdominal and thoracoabdominal aortic aneurysms.

BACKGROUND: Nonhome discharge (NHD) has significant implications for patient counseling and discharge planning and is frequently required following fenestrated-branched endovascular aortic repair (FB-EVAR) of complex abdominal aortic aneurysms (CAAA) and thoracoabdominal aortic aneurysms (TAAA). We aimed to identify preoperative predictors of NHD after elective FB-EVAR for CAAA and TAAA and develop a risk calculator able to predict NHD. METHODS: A retrospective review of prospectively collected data on all patients undergoing FB-EVAR between January 2007 and December 2021 at a single institution was performed. Exclusion criteria were admission from a nonhome setting, emergency and repeat FB-EVAR, and discharge to an unknown destination. The cohort was randomly split into separate development (70% of patients) and validation (30%) cohorts to develop a predictive calculator for NHD. Independent variables associated with NHD were assessed in a series of logistic regression analyses from 100 bootstrapped samples of the development set, and a model was developed using the most predictive variables. Resulting parameter estimates were applied to data in the validation set to assess model discrimination and calibration. RESULTS: From the initial cohort of 712 FB-EVAR patients, 644 were included in the study (74% male; mean age, 75.4 ± 7.6 years), including 452 with CAAA (70%) and 192 with TAAA (30%). Early mortality occurred in eight patients (1.2%; 5 in CAAA and 3 in TAAA) and the median hospital stay was 5 days (4 for CAAA and 7 for TAAA). Ninety-seven patients (15%) had a NHD. On multivariable analysis, older age (per year, odds ratio [OR], 1.08; P < .001), female gender (OR, 3.03; P < .001), smoking (OR, 2.86; P = .01), congestive heart failure (OR, 3.05; P = .004), peripheral artery disease (OR, 1.81; P = .07), and extent I (OR, 3.17), II (OR, 2.84), and III (OR, 2.52; all P = .08) TAAAs were associated with an increased likelihood of NHD in the development set. Based on these factors, the risk calculator was developed which accurately predicts NHD in the validation set with an area under the curve of 0.7. CONCLUSIONS: Older, female smokers with congestive heart failure and peripheral artery disease and more extensive aneurysms are at highest risk of NHD after FB-EVAR. Using only preoperative factors, our risk calculator can predict accurately who will have a NHD, allowing enhanced preoperative patient counselling and accelerated hospital discharge.

Improving prediction of psychosis in youth at clinical high-risk: pre-baseline symptom duration and cortical thinning as moderators of the NAPLS2 risk calculator.

BACKGROUND: Clinical implementation of risk calculator models in the clinical high-risk for psychosis (CHR-P) population has been hindered by heterogeneous risk distributions across study cohorts which could be attributed to pre-ascertainment illness progression. To examine this, we tested whether the duration of attenuated psychotic symptom (APS) worsening prior to baseline moderated performance of the North American prodrome longitudinal study 2 (NAPLS2) risk calculator. We also examined whether rates of cortical thinning, another marker of illness progression, bolstered clinical prediction models. METHODS: Participants from both the NAPLS2 and NAPLS3 samples were classified as either 'long' or 'short' symptom duration based on time since APS increase prior to baseline. The NAPLS2 risk calculator model was applied to each of these groups. In a subset of NAPLS3 participants who completed follow-up magnetic resonance imaging scans, change in cortical thickness was combined with the individual risk score to predict conversion to psychosis. RESULTS: The risk calculator models achieved similar performance across the combined NAPLS2/NAPLS3 sample [area under the curve (AUC) = 0.69], the long duration group (AUC = 0.71), and the short duration group (AUC = 0.71). The shorter duration group was younger and had higher baseline APS than the longer duration group. The addition of cortical thinning improved the prediction of conversion significantly for the short duration group (AUC = 0.84), with a moderate improvement in prediction for the longer duration group (AUC = 0.78). CONCLUSIONS: These results suggest that early illness progression differs among CHR-P patients, is detectable with both clinical and neuroimaging measures, and could play an essential role in the prediction of clinical outcomes.

Performance of magnetic resonance imaging-based prostate cancer risk calculators and decision strategies in two large European medical centres.

OBJECTIVES: To compare the performance of currently available biopsy decision support tools incorporating magnetic resonance imaging (MRI) findings in predicting clinically significant prostate cancer (csPCa). PATIENTS AND METHODS: We retrospectively included men who underwent prostate MRI and subsequent targeted and/or systematic prostate biopsies in two large European centres. Available decision support tools were identified by a PubMed search. Performance was assessed by calibration, discrimination, decision curve analysis (DCA) and numbers of biopsies avoided vs csPCa cases missed, before and after recalibration, at risk thresholds of 5%-20%. RESULTS: A total of 940 men were included, 507 (54%) had csPCa. The median (interquartile range) age, prostate-specific antigen (PSA) level, and PSA density (PSAD) were 68 (63-72) years, 9 (7-15) ng/mL, and 0.20 (0.13-0.32) ng/mL2 , respectively. In all, 18 multivariable risk calculators (MRI-RCs) and dichotomous biopsy decision strategies based on MRI findings and PSAD thresholds were assessed. The Van Leeuwen model and the Rotterdam Prostate Cancer Risk Calculator (RPCRC) had the best discriminative ability (area under the receiver operating characteristic curve 0.86) of the MRI-RCs that could be assessed in the whole cohort. DCA showed the highest clinical utility for the Van Leeuwen model, followed by the RPCRC. At the 10% threshold the Van Leeuwen model would avoid 22% of biopsies, missing 1.8% of csPCa, whilst the RPCRC would avoid 20% of biopsies, missing 2.6% of csPCas. These multivariable models outperformed all dichotomous decision strategies based only on MRI-findings and PSAD. CONCLUSIONS: Even in this high-risk cohort, biopsy decision support tools would avoid many prostate biopsies, whilst missing very few csPCa cases. The Van Leeuwen model had the highest clinical utility, followed by the RPCRC. These multivariable MRI-RCs outperformed and should be favoured over decision strategies based only on MRI and PSAD.

External validation of the barcelona magnetic resonance imaging predictive model for detecting significant prostate cancer including men receiving 5-alpha reductase inhibitors.

PURPOSE: To validate the Barcelona-magnetic resonance imaging predictive model (BCN-MRI PM) for clinically significant prostate cancer (csPCa) in Catalonia, a Spanish region with 7.9 million inhabitants. Additionally, the BCN-MRI PM is validated in men receiving 5-alpha reductase inhibitors (5-ARI). MATERIALS AND METHODS: A population of 2,212 men with prostate-specific antigen serum level > 3.0 ng/ml and/or a suspicious digital rectal examination who underwent multiparametric MRI and targeted and/or systematic biopsies in the year 2022, at ten participant centers of the Catalonian csPCa early detection program, were selected. 120 individuals (5.7%) were identified as receiving 5-ARI treatment for longer than a year. The risk of csPCa was retrospectively assessed with the Barcelona-risk calculator 2 (BCN-RC 2). Men undergoing 5-ARI treatment for less than a year were excluded. CsPCa was defined when the grade group was ≥ 2. RESULTS: The area under the curve of the BCN-MRI PM in 5-ARI naïve men was 0.824 (95% CI 0.783-0.842) and 0.849 (0.806-0.916) in those receiving 5-ARI treatment, p 0.475. Specificities at 100, 97.5, and 95% sensitivity thresholds were to 2.7, 29.3, and 39% in 5-ARI naïve men, while 43.5, 46.4, and 47.8%, respectively in 5-ARI users. The application of BCN-MRI PM would result in a reduction of 23.8% of prostate biopsies missing 5% of csPCa in 5-ARI naïve men, while reducing 25% of prostate biopsies without missing csPCa in 5-ARI users. CONCLUSIONS: The BCN-MRI PM has achieved successful validation in Catalonia and, notably, for the first time, in men undergoing 5-ARI treatment.

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.

Optimal extent of lymph node dissection for high-risk gastric cancer stratified by a national clinical database risk calculator.

BACKGROUND: For patients with gastric cancer, a well-balanced treatment that considers both oncological aspects and surgical risk is demanded. This study aimed to explore the optimal extent of lymph node dissection (LND) for patients with gastric cancer according to surgical risk, stratified by the risk calculator system produced by the Japan National Clinical Database (NCD). PATIENTS AND METHODS: We retrospectively evaluated 187 patients who underwent radical gastrectomy for gastric cancer. Using the median predicted anastomotic leak rate obtained by the NCD risk calculator as the cutoff value, we classified 97 and 90 patients as having high and low risks, respectively. RESULTS: In low-risk patients, although limited LND reduced the postoperative intraabdominal infectious complications (IAIC), multivariate analysis revealed standard LND as an independent prognostic factor that improved Relapse-free survival (RFS). In high-risk patients, the rates of postoperative IAIC and RFS were similar between standard and limited LND. Pancreatic fistula was not observed in the limited dissection group. CONCLUSION: Limited LND might be the optimal treatment strategy for patients with gastric cancer with high surgical risk.

Evaluation of the American College of Surgeons risk calculator in hepatectomy for metastatic colorectal cancer in a Southeast Asian population.

PURPOSE: This study evaluated the accuracy of the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) calculator in predicting outcomes after hepatectomy for colorectal cancer (CRC) liver metastasis in a Southeast Asian population. METHODS: Predicted and actual outcomes were compared for 166 patients undergoing hepatectomy for CRC liver metastasis identified between 2017 and 2022, using receiver operating characteristic curves with area under the curve (AUC) and Brier score. RESULTS: The ACS-NSQIP calculator accurately predicted most postoperative complications (AUC > 0.70), except for surgical site infection (AUC = 0.678, Brier score = 0.045). It also exhibited satisfactory performance for readmission (AUC = 0.818, Brier score = 0.011), reoperation (AUC = 0.945, Brier score = 0.002), and length of stay (LOS, AUC = 0.909). The predicted LOS was close to the actual LOS (5.9 vs. 5.0 days, P = 0.985). CONCLUSION: The ACS-NSQIP calculator demonstrated generally accurate predictions for 30-day postoperative outcomes after hepatectomy for CRC liver metastasis in our patient population.

Development and Validation of a Risk Calculator for Intensive Care Unit Admission After Total Hip Arthroplasty.

BACKGROUND: The purpose of this study was to develop and validate a risk stratification calculator to determine the risk of a patient requiring intensive care unit (ICU) admission following primary and revision total hip arthroplasty (THA). METHODS: Using a database of 12,342 THA procedures, with 132 ICU admissions, from 2005 to 2017, we developed models of ICU admission risk based on previously identified preoperative factors including age, heart disease, neurologic disease, renal disease, unilateral versus bilateral surgery, preoperative hemoglobin, blood glucose, and smoking status. Prior to developing the calculator, a set of logistic regressions were analyzed to determine weight and scoring for each variable. Once developed, we validated the risk calculator using a second independent institution. RESULTS: A separate risk calculator was developed for primary and revision THA. The area under the curve (AUC) for primary THA was 0.808 (95% confidence interval 0.740 to 0.876) and revision THA was AUC 0.795 (confidence interval 0.740 to 0.850). As an example, the primary THA risk calculator had a Total Points scale of 220, with 50 points associated with a 0.1% chance of ICU admission and 205 points associated with a 95% chance of ICU admission. Validation with an external cohort demonstrated satisfactory AUCs, sensitivities, and specificities for both primary THA (AUC 0.794, sensitivity 0.750, and specificity 0.722) and revision THA (AUC 0.703, sensitivity 0.704, and specificity 0.671) CONCLUSION: The externally validated risk calculators developed in this study can accurately predict ICU admission following primary and revision THA based on a number of readily available preoperative factors.

Assessing, communicating and managing cardiovascular disease risk: a practical summary of the 2023 guideline.

The outdated cardiovascular disease risk calculator has been reported to overestimate cardiovascular disease risk for a contemporary Australian population, and does not include relevant variables, such as socioeconomic disadvantage, which has been shown to increase the incidence of both heart attack and stroke. The 2023 Australian Guideline for Assessing and Managing Cardiovascular Disease Risk marks a major milestone as the first update to Australia's cardiovascular disease prevention guideline in over a decade. The new guideline may help to refine and recategorise risk estimates, hence improving the discriminatory and predictive value of the new calculator. The new Australian Cardiovascular Disease Risk Calculator expresses risk scores as a percentage estimate of a person's probability of dying or being hospitalised due to cardiovascular disease within the next 5 years. The new calculator expresses risk scores as low (less than 5%), intermediate (5% to less than 10%), or high (10% or higher) risk over 5 years. Reclassification factors built into the new calculator are designed to help clinicians individualise risk estimates. These factors include ethnicity (e.g. First Nations status), family history of premature cardiovascular disease, severe mental illness, kidney disease and coronary artery calcium score. The new calculator also uses optional diabetes-specific variables (supporting a more granular cardiovascular disease risk assessment of people with type 2 diabetes). People who meet the clinically determined high-risk criteria (chronic kidney disease, familial hypercholesterolaemia) should not progress through the Australian Cardiovascular Disease risk calculator, but move straight to management. For a person with a cardiovascular disease risk score recorded from the outdated calculator, clinicians may want to reassess their risk using the new calculator the next time the person attends.

[Advances in clinical management of neonatal sepsis]. / 新生儿败血症的临床管理进展.

Neonatal sepsis, as a significant cause of various complications and adverse outcomes in neonates, remains a serious health burden both domestically and internationally. Strategies such as antibiotic prophylaxis during delivery, the utilization of early-onset sepsis risk calculators, and quality improvement initiatives in neonatal wards are beneficial in alleviating the disease burden of neonatal sepsis. This paper provides a review of the epidemiology, risk factors, and recent advances in clinical management of neonatal sepsis.

Early Detection of Prostate Cancer: AUA/SUO Guideline Part I: Prostate Cancer Screening.

PURPOSE: The summary presented herein covers recommendations on the early detection of prostate cancer and provides a framework to facilitate clinical decision-making in the implementation of prostate cancer screening, biopsy, and follow-up. This is Part I of a two-part series that focuses on prostate cancer screening. Please refer to Part II for discussion of initial and repeat biopsies as well as biopsy technique. MATERIALS AND METHODS: The systematic review utilized to inform this guideline was conducted by an independent methodological consultant. The systematic review was based on searches in Ovid MEDLINE and Embase and Cochrane Database of Systematic Reviews (January 1, 2000-November 21, 2022). Searches were supplemented by reviewing reference lists of relevant articles. RESULTS: The Early Detection of Prostate Cancer Panel developed evidence- and consensus-based guideline statements to provide guidance in prostate cancer screening, initial and repeat biopsy, and biopsy technique. CONCLUSIONS: Prostate-specific antigen (PSA)-based prostate cancer screening in combination with shared decision-making (SDM) is recommended. Current data regarding risk from population-based cohorts provide a basis for longer screening intervals and tailored screening, and the use of available online risk calculators is encouraged.

Transperineal template saturation and conventional biopsy for stage prediction in prostate cancer.

OBJECTIVE: To evaluate the performance of risk calculators (RCs) predicting lymph node invasion (LNI) and extraprostatic extension (EPE) in men undergoing transperineal magnetic resonance imaging/transrectal ultrasound (TRUS)-fusion template saturation biopsy (TTSB) and conventional systematic TRUS-guided biopsy (SB). PATIENTS AND METHODS: The RCs were tested in a consecutive cohort of 645 men undergoing radical prostatectomy with extended pelvic LN dissection between 2005 and 2019. TTSB was performed in 230 (35.7%) and SB in 415 (64.3%) men. Risk of LNI and EPE was calculated using the available RCs. Discrimination, calibration, and clinical usefulness stratified by different biopsy techniques were assessed. RESULTS: Lymph node invasion was observed in 23 (10%) and EPE in 73 (31.8%) of cases with TTSB and 53 (12.8%) and 158 (38%) with SB, respectively. RCs showed an excellent discrimination and acceptable calibration for prediction of LNI based on TTSB (area under the curve [AUC]/risk estimation: Memorial Sloan Kettering Cancer Center [MSKCC]-RC 0.79/-4%, Briganti (2012)-RC 0.82/-4%, Gandaglia-RC 0.81/+6%). These were comparable in SB (MSKCC-RC 0.78/+2%; Briganti (2012)-RC 0.77/-3%). Decision curve analysis (DCA) revealed a net benefit at threshold probabilities between 3% and 6% when TTSB was used. For prediction of EPE based on TTSB an inferior discrimination and variable calibration were observed (AUC/risk estimation: MSKCC-RC 0.71/+8% and Martini (2018)-RC 0.69/+2%) achieving a net benefit on DCA only at risk thresholds of >17%. Performance of RCs for prediction of LNI and EPE based on SB showed comparable results with a better performance in the DCA for LNI (risk thresholds 1-2%) and poorer performance for EPE (risk threshold >20%). This study is limited by its retrospective single-institution design. CONCLUSIONS: The potentially more accurate grading ability of TTSB did not result in improved performance of preoperative RCs. Prediction tools for LNI proved clinical usefulness while RCs for EPE did not.

Application of machine learning to predict postoperative gastrointestinal bleed in bariatric surgery.

BACKGROUND: Postoperative gastrointestinal bleeding (GIB) is a rare but serious complication of bariatric surgery. The recent rise in extended venous thromboembolism regimens as well as outpatient bariatric surgery may increase the risk of postoperative GIB or lead to delay in diagnosis. This study seeks to use machine learning (ML) to create a model that predicts postoperative GIB to aid surgeon decision-making and improve patient counseling for postoperative bleeds. METHODS: The Metabolic and Bariatric Surgery Accreditation and Quality Improvement Program (MBSAQIP) database was used to train and validate three types of ML methods: random forest (RF), gradient boosting (XGB), and deep neural networks (NN), and compare them with logistic regression (LR) regarding postoperative GIB. The dataset was split using fivefold cross-validation into training and validation sets, in an 80/20 ratio. The performance of the models was assessed using area under the receiver operating characteristic curve (AUROC) and compared with the DeLong test. Variables with the strongest effect were identified using Shapley additive explanations (SHAP). RESULTS: The study included 159,959 patients. Postoperative GIB was identified in 632 (0.4%) patients. The three ML methods, RF (AUROC 0.764), XGB (AUROC 0.746), and NN (AUROC 0.741) all outperformed LR (AUROC 0.709). The best ML method, RF, was able to predict postoperative GIB with a specificity and sensitivity of 70.0% and 75.4%, respectively. Using DeLong testing, the difference between RF and LR was determined to be significant with p < 0.01. Type of bariatric surgery, pre-op hematocrit, age, duration of procedure, and pre-op creatinine were the 5 most important features identified by ML retrospectively. CONCLUSIONS: We have developed a ML model that outperformed LR in predicting postoperative GIB. Using ML models for risk prediction can be a helpful tool for both surgeons and patients undergoing bariatric procedures but more interpretable models are needed.

Evaluation of the ACS NSQIP surgical risk calculator in patients undergoing common bile duct exploration.

PURPOSE: The American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) surgical risk calculator is a risk stratification tool to help predict risks of postoperative complications, which is important for informed decision-making. The purpose of this study was to evaluate the accuracy of the calculator in predicting postoperative complications in patients undergoing common bile duct (CBD) exploration. METHODS: A retrospective chart review was completed for 305 patients that underwent open and laparoscopic CBD exploration at a single institution from 2010 to 2018. Patient demographics and preoperative risk factors were entered into the calculator, and the predicted complication risks were compared with observed complication rates. Brier score, C-statistic, and Hosmer-Lemeshow regression analysis were used to assess discrimination and calibration. RESULTS: The observed rate exceeded the predicted rate for any complication (35.1% vs. 21%), return to operating room (5.9% vs. 3.6%), death (3.3% vs. 1%), and sepsis (3% vs. 2.4%). The model performed best in predicting serious complication (Brier 0.087, C-statistic 0.818, Hosmer-Lemeshow 0.695), surgical site infection (Brier 0.068, C-statistic 0.670, Hosmer-Lemeshow 0.292), discharge to rehabilitation facility (Brier 0.041, C-statistic 0.907, Hosmer-Lemeshow 0.638), and death (Brier 0.028, C-statistic 0.898, Hosmer-Lemeshow 0.004). In multivariable analysis, there was no statistically significant predicted complication type that affected the type of surgery. CONCLUSION: The calculator was accurate in predicting serious complication, surgical site infection, discharge to rehabilitation facility, and death. However, the model displayed poor predictive ability in all other complications that were analyzed.

Performance and usability of pre-operative prediction models for 30-day peri-operative mortality risk: a systematic review.

Estimating pre-operative mortality risk may inform clinical decision-making for peri-operative care. However, pre-operative mortality risk prediction models are rarely implemented in routine clinical practice. High predictive accuracy and clinical usability are essential for acceptance and clinical implementation. In this systematic review, we identified and appraised prediction models for 30-day postoperative mortality in non-cardiac surgical cohorts. PubMed and Embase were searched up to December 2022 for studies investigating pre-operative prediction models for 30-day mortality. We assessed predictive performance in terms of discrimination and calibration. Risk of bias was evaluated using a tool to assess the risk of bias and applicability of prediction model studies. To further inform potential adoption, we also assessed clinical usability for selected models. In all, 15 studies evaluating 10 prediction models were included. Discrimination ranged from a c-statistic of 0.82 (MySurgeryRisk) to 0.96 (extreme gradient boosting machine learning model). Calibration was reported in only six studies. Model performance was highest for the surgical outcome risk tool (SORT) and its external validations. Clinical usability was highest for the surgical risk pre-operative assessment system. The SORT and risk quantification index also scored high on clinical usability. We found unclear or high risk of bias in the development of all models. The SORT showed the best combination of predictive performance and clinical usability and has been externally validated in several heterogeneous cohorts. To improve clinical uptake, full integration of reliable models with sufficient face validity within the electronic health record is imperative.

Avoiding Prostate Biopsies in Patients at Low Risk for Prostate Cancer: A Prospective Evaluation of a PSA-Density-Based Safety Net.

INTRODUCTION AND OBJECTIVES: Decision-making to perform prostate biopsy should include individual risk assessment. Patients classified as low risk by the Rotterdam Prostate Cancer Risk Calculator are advised to forego biopsy (PBx). There is concern about missing clinically significant prostate cancer (csPCa). A clear pathway for follow-up is needed. MATERIAL AND METHODS: Data for 111 consecutive patients were collected. Patients were encouraged to adhere to a PSA-density-based safety net after PBx was omitted. Cut off values indicating a re-evaluation were PSA density >0.15 ng/mL/ccm in PBx-naïve patients and >0.2 ng/mL/ccm in men with past-PBx. Primary endpoint was whether men had their PSA taken regularly. Secondary endpoint was whether a new multiparametric MRI was performed when PSA-density increased. Tertiary endpoint was whether biopsy was performed when risk stratification revealed an increased risk. RESULTS: Median follow-up was 12 months (IQR 9-15 months). The primary endpoint was reached by 97.2% (n = 106). The secondary endpoint was reached by 30% (n = 3). The tertiary endpoint was reached by 50% (n = 2). Histopathologic analyses revealed csPCa in none of these cases. Risk stratification did not change (p = 0.187) with the majority of patients (89.2%, n = 99). CONCLUSION: The concern of missing csPCa when omitting PBx in the risk-stratified pathway may be negated. Changes in risk stratification during follow-up should lead to subsequent PBx. We suggest implementing a safety net based on PSA density and digital rectal examination (DRE).

Frank Stinchfield Award: Creation of a Patient-Specific Total Hip Arthroplasty Periprosthetic Fracture Risk Calculator.

BACKGROUND: Many risk factors have been described for periprosthetic femur fracture (PPFFx) following total hip arthroplasty (THA), yet a patient-specific risk assessment tool remains elusive. The purpose of this study was to develop a high-dimensional, patient-specific risk-stratification nomogram that allows dynamic risk modification based on operative decisions. METHODS: We evaluated 16,696 primary nononcologic THAs performed between 1998 and 2018. During a mean 6-year follow-up, 558 patients (3.3%) sustained a PPFFx. Patients were characterized by individual natural language processing-assisted chart review on nonmodifiable factors (demographics, THA indication, and comorbidities), and modifiable operative decisions (femoral fixation [cemented/uncemented], surgical approach [direct anterior, lateral, and posterior], and implant type [collared/collarless]). Multivariable Cox regression models and nomograms were developed with PPFFx as a binary outcome at 90 days, 1 year, and 5 years, postoperatively. RESULTS: Patient-specific PPFFx risk based on comorbid profile was wide-ranging from 0.4-18% at 90 days, 0.4%-20% at 1 year, and 0.5%-25% at 5 years. Among 18 evaluated patient factors, 7 were retained in multivariable analyses. The 4 significant nonmodifiable factors included the following: women (hazard ratio (HR) = 1.6), older age (HR = 1.2 per 10 years), diagnosis of osteoporosis or use of osteoporosis medications (HR = 1.7), and indication for surgery other than osteoarthritis (HR = 2.2 for fracture, HR = 1.8 for inflammatory arthritis, HR = 1.7 for osteonecrosis). The 3 modifiable surgical factors were included as follows: uncemented femoral fixation (HR = 2.5), collarless femoral implants (HR = 1.3), and surgical approach other than direct anterior (lateral HR = 2.9, posterior HR = 1.9). CONCLUSION: This patient-specific PPFFx risk calculator demonstrated a wide-ranging risk based on comorbid profile and enables surgeons to quantify risk mitigation based on operative decisions. LEVEL OF EVIDENCE: Level III, Prognostic.

External validation of cone-beam computed tomography- and panoramic radiography-featured prediction models for inferior alveolar nerve injury after lower third molar removal: proposal of a risk calculator.

We previously developed basic and extended models to predict inferior alveolar nerve injuries (IANI) after lower third molar (LM3) removal based on cone-beam computed tomography (CBCT) images. Although these models comprised predictors, including increased age and inferior alveolar canal-related CBCT factors, external validations were lacking. Therefore, this study externally validated these models and compared them with other related models based on their performance. Original and newly validated samples included patients who underwent LM3 removal following CBCT. Subsequently, 39 and 25 patients with IANI, then 457 and 295 randomly selected patients without IANI were chosen of the observed 1573 and 1052 patients, respectively. CBCT- and panoramic radiograph (PAN)-featured models were validated. Then, models' discrimination and calibration abilities were assessed using C-statistics and calibration plots, respectively. Brier scores were also quantified, after which logistic recalibration was achieved to optimize calibration, and a risk calculator was developed. During the external validation, the extended model exhibited the best C-statistic (0.822) and Brier score (0.064), whereas two CBCT- and two PAN-featured models showed lower performances with C-statistics (0.764, 0.706, 0.584, and 0.627) and Brier scores (0.069, 0.074, 0.075, and 0.072). Besides, all models showed a tendency to overpredict its high-risk range. However, recalibration of the extended model resulted in excellent calibration performance. CBCT-featured models, especially the extended model, conclusively showed a superior predictive performance to PAN models. Therefore, the risk calculator on the extended CBCT model is proposed to be a clinical decision-aid tool that preoperatively predicts IANI risk.