Optimism-corrected Area Research Articles

Risk assessment for endometrial cancer in women with abnormal vaginal bleeding: Results from the prospective IETA-1 cohort study.

ObjectiveTo investigate the association between personal history, anthropometric features and lifestyle characteristics and endometrial malignancy in women with abnormal vaginal bleeding.MethodsProspective observational cohort assessed by descriptive and multivariable logistic regression analyses. Three features—age, body mass index (BMI; calculated as weight in kilograms divided by the square of height in meters), and nulliparity—were defined a priori for baseline risk assessment of endometrial malignancy. The following variables were tested for added value: intrauterine contraceptive device, bleeding pattern, age at menopause, coexisting diabetes/hypertension, physical exercise, fat distribution, bra size, waist circumference, smoking/drinking habits, family history, use of hormonal/anticoagulant therapy, and sonographic endometrial thickness. We calculated adjusted odds ratio, optimism‐corrected area under the receiver operating characteristic curve (AUC), R2, and Akaike's information criterion.ResultsOf 2417 women, 155 (6%) had endometrial malignancy or endometrial intraepithelial neoplasia. In women with endometrial cancer median age was 67 years (interquartile range [IQR] 56–75 years), median parity was 2 (IQR 0–10), and median BMI was 28 (IQR 25–32). Age, BMI, and parity produced an AUC of 0.82. Other variables marginally affected the AUC, adding endometrial thickness substantially increased the AUC in postmenopausal women.ConclusionAge, parity, and BMI help in the assessment of endometrial cancer risk in women with abnormal uterine bleeding. Other patient information adds little, whereas sonographic endometrial thickness substantially improves assessment.

Predicting resistance to fluoroquinolones among patients with rifampicin-resistant tuberculosis using machine learning methods.

BackgroundLimited access to drug-susceptibility tests (DSTs) and delays in receiving DST results are challenges for timely and appropriate treatment of multi-drug resistant tuberculosis (TB) in many low-resource settings. We investigated whether data collected as part of routine, national TB surveillance could be used to develop predictive models to identify additional resistance to fluoroquinolones (FLQs), a critical second-line class of anti-TB agents, at the time of diagnosis with rifampin-resistant TB.Methods and findingsWe assessed three machine learning-based models (logistic regression, neural network, and random forest) using information from 540 patients with rifampicin-resistant TB, diagnosed using Xpert MTB/RIF and notified in the Republic of Moldova between January 2018 and December 2019. The models were trained to predict the resistance to FLQs based on demographic and TB clinical information of patients and the estimated district-level prevalence of resistance to FLQs. We compared these models based on the optimism-corrected area under the receiver operating characteristic curve (OC-AUC-ROC). The OC-AUC-ROC of all models were statistically greater than 0.5. The neural network model, which utilizes twelve features, performed best and had an estimated OC-AUC-ROC of 0.87 (0.83,0.91), which suggests reasonable discriminatory power. A limitation of our study is that our models are based only on data from the Republic of Moldova and since not externally validated, the generalizability of these models to other populations remains unknown.ConclusionsModels trained on data from phenotypic surveillance of drug-resistant TB can predict resistance to FLQs based on patient characteristics at the time of diagnosis with rifampin-resistant TB using Xpert MTB/RIF, and information about the local prevalence of resistance to FLQs. These models may be useful for informing the selection of antibiotics while awaiting results of DSTs.

Toward more accurate prediction of future pregnancy outcome in couples with unexplained recurrent pregnancy loss: taking both partners into account

To identify, besides maternal age and the number of previous pregnancy losses, additional characteristics of couples with unexplained recurrent pregnancy loss (RPL) that improve the prediction of an ongoing pregnancy. Hospital-based cohort study in couples who visited specialized RPL units of two academic centers between 2012 and2020. Two academic centers in the Netherlands. Clinical data from 526 couples with unexplained RPL were used in this study. None. The final model to estimate the chance of a subsequent ongoing pregnancy was determined using a backward selection process and internally validated using bootstrapping. Model performance was assessed in terms of calibration and discrimination (area under the receiver operating characteristic curve). Subsequent ongoing pregnancy was achieved in 345 of 526 couples (66%). The number of previous pregnancy losses, maternal age, paternal age, maternal body mass index, paternal body mass index, maternal smoking status, and previous invitro fertilization/intracytoplasmic sperm injection treatment were predictive of the outcome. The optimism-corrected area under the receiver operating characteristic curve was 0.63 compared with 0.57 when using only the number of previous pregnancy losses and maternal age. The identification of additional predictors of a subsequent ongoing pregnancy after RPL, including male characteristics, is significant for both clinicians and couples with RPL. At the same time, we showed that the predictive ability of the current model is still limited and more research is warranted to develop a model that can be used in clinical practice.

A Prognostic Model for Predicting One-Month Outcomes among Emergency Department Patients with Mild Traumatic Brain Injury and a Presenting Glasgow Coma Scale of Fifteen.

The lack of well-performing prognostic models for early prognostication of outcomes remains a major barrier to improving the clinical care of patients with mild traumatic brain injury (mTBI). We aimed to derive a prognostic model for predicting incomplete recovery at 1-month in emergency department (ED) patients with mTBI and a presenting Glasgow Coma Scale (GCS) score of 15 who were enrolled in the HeadSMART (Head Injury Serum Markers for Assessing Response to Trauma) study. The derivation cohort included 355 participants with complete baseline (day-of-injury) and follow-up data. The primary outcome measure was the Glasgow Outcome Scale Extended (GOSE) at 1-month and incomplete recovery was defined as a GOSE <8. At 1-month post-injury, incomplete recovery was present in 58% (n = 205) of participants. The final multi-variable logistic regression model included six variables: age in years (odds ratio [OR] = 0.98; 95% confidence interval [CI]: 0.97-1.00), positive head CT (OR = 4.42; 95% CI: 2.21-9.33), history of depression (OR = 2.59; 95% CI: 1.47-4.69), and self-report of moderate or severe headache (OR = 2.49; 95% CI: 1.49-4.18), difficulty concentrating (OR = 3.17; 95% CI: 1.53-7.04), and photophobia (OR = 4.17; 95% CI: 2.08-8.92) on the day-of-injury. The model was validated internally using bootstrap resampling (1000 resamples), which revealed a mean over-optimism value of 0.01 and an optimism-corrected area under the curve (AUC) of 0.79 (95% CI: 0.75-0.85). A prognostic model for predicting incomplete recovery among ED patients with mTBI and a presenting GCS of 15 using easily obtainable clinical and demographic variables has acceptable discriminative accuracy. External validation of this model is warranted.

Web-Based Calculator Predicts Surgical-Site Infection After Thoracolumbar Spine Surgery

Surgical-site infection (SSI) after spine surgery leads to increased length of stay, reoperation, and worse patient quality of life. We sought to develop a web-based calculator that computes an individual's risk of a wound infection following thoracolumbar spine surgery. We performed a retrospective review of consecutive patients undergoing elective degenerative thoracolumbar spine surgery at a tertiary-care institution between January 2016 and December 2018. Patients who developed SSI requiring reoperation were identified. Regression analysis was performed and model performance was assessed using receiver operating curve analysis to derive an area under the curve. Bootstrapping was performed to check for overfitting, and a Hosmer-Lemeshow test was employed to evaluate goodness-of-fit and model calibration. In total, 1259 patients were identified; 73% were index operations. The overall infection rate was 2.7%, and significant predictors of SSI included female sex (odds ratio [OR] 3.0), greater body mass index (OR 1.1), active smoking (OR 2.8), worse American Society of Anesthesiologists physical status (OR 2.1), and greater surgical invasiveness (OR 1.1). The prediction model had an optimism-corrected area under the curve of 0.81. A web-based calculator was created: https://jhuspine2.shinyapps.io/Wound_Infection_Calculator/. In this pilot study, we developed a model and simple web-based calculator to predict a patient's individualized risk of SSI after thoracolumbar spine surgery. This tool has a predictive accuracy of 83%. Through further multi-institutional validation studies, this tool has the potential to alert both patients and providers of an individual's SSI risk to improve informed consent, mitigate risk factors, and ultimately drive down rates of SSIs.

Development of a risk prediction model for early discrimination between permanent and transient congenital hypothyroidism.

To develop a risk prediction model for early discrimination between transient and permanent congenital hypothyroidism (CH). In a retrospective cohort, 1047 confirmed CH neonates, from 15 randomly selected provinces in Iran, were entered to the study. Clinical and biochemical information of transient and permanent cases, distinct at the age of 3 years were retrospectively gathered. Among CH neonates, the overall prevalence of permanent CH was 57.1%. Using forward stepwise multivariable logistic regression analysis, confirmatory venous TSH, total T4 < 8.2 ng/dl, requiring levothyroxine dosage increase, venous TSH ≥ 10 mU/l between 6 and 12 months of age, parental consanguinity and family history of thyroid diseases were associated with increased risk of permanent CH. The prediction model achieved a very good power in discriminating patients with transient and permanent CH with an optimism-corrected area under the ROC curve of 0.86 (95% CI:0.84-0.88) with a very good calibration. Integrated discrimination improvement (IDI) test indicated significantly greater diagnostic performance of the model compared to serum TSH alone. Using several potential predictors for permanent CH, we developed a relatively powerful risk prediction model as a cost-saving screening tool in order to avoid unnecessary long-term treatment of transient cases which might empower clinicians for prognostication of the CH course and tailoring treatment up to 1 year of age.

Personal Factors Associated With Postconcussion Symptoms 3 Months After Mild Traumatic Brain Injury

ObjectiveTo describe personal factors in patients with mild traumatic brain injury (MTBI) and 2 control groups and to explore how such factors were associated with postconcussion symptoms (PCSs). DesignProspective cohort study. SettingLevel 1 trauma center and outpatient clinic. ParticipantsParticipants (N=541) included patients with MTBI (n=378), trauma controls (n=82), and community controls (n=81). Main Outcome MeasuresData on preinjury health and work status, personality, resilience, attention deficit/hyperactivity, and substance use. Computed tomography (CT) findings and posttraumatic amnesia were recorded. Symptoms were assessed at 3 months with the British Columbia Postconcussion Symptom Inventory and labeled as PCS+ if ≥3 symptoms were reported or the total score was ≥13. Predictive models were fitted with penalized logistic regression using the least absolute shrinkage and selection operator (lasso) in the MTBI group, and model fit was assessed with optimism-corrected area under the curve (AUC) of the receiver operating characteristic curve. ResultsThere were few differences in personal factors between the MTBI group and the 2 control groups without MTBI. Rates of PCS+ were 20.8% for the MTBI group, 8.0% for trauma controls, and 1.3% for community controls. In the MTBI group, there were differences between the PCS+ and PCS− group on most personal factors and injury-related variables in univariable comparisons. In the lasso models, the optimism-corrected AUC for the full model was 0.79, 0.73 for the model only including personal factors, and 0.63 for the model only including injury variables. Working less than full time before injury, having preinjury pain and poor sleep quality, and being female were among the selected predictors, but also resilience and some personality traits contributed in the model. Intracranial abnormalities on CT were also a risk factor for PCS. ConclusionsPersonal factors convey important prognostic information in patients with MTBI. A vulnerable work status and preinjury health problems might indicate a need for follow-up and targeted interventions.

Ventilation parameters and early graft function in double lung transplantation

Currently, the primary graft dysfunction (PGD) score is used to measure allograft function in the early post-lung transplant period. Although PGD grades at later time points (T48 hours and T72 hours) are useful to predict mid- and long-term outcomes, their predictive value is less relevant within the first 24 hours after transplantation. This study aimed to evaluate the capability of PGD grades to predict prolonged mechanical ventilation (MV) and compare it with a model derived from ventilation parameters measured on arrival at the intensive care unit (ICU). A retrospective single-center analysis of 422 double lung transplantations (LTxs) was performed. PGD was assessed 2 hours after arrival at ICU, and grades were associated with length of MV (LMV). In addition, peak inspiratory pressure (PIP), ratio of the arterial partial pressure of oxygen to fraction of inspired oxygen (P/F ratio), and dynamic compliance (cDyn) were collected, and a logistic regression model was created. The predictive capability for prolonged MV was calculated for both (the PGD score and the model). In a second step, the created model was externally validated using a prospective, international multicenter cohort including 102 patients from the lung transplant centers of Vienna, Toronto, and Budapest. In the retrospective cohort, a high percentage of extubated patients was reported at 24 hours (35.1%), 48 hours (68.0%), and 72 hours (80.3%) after transplantation. At T0 (time point defined as 2 hours after arrival at the ICU), patients with PGD grade 0 had a shorter LMV with a median of 26 hours (interquartile range [IQR]: 16-47 hours) than those with PGD grade 1 (median: 42 hours, IQR: 27-50 hours), PGD grade 2 (median: 37.5 hours, IQR: 15.5-78.5 hours), and PGD grade 3 (median: 46 hours, IQR: 27-86 hours). However, IQRs largely overlapped for all grades, and the value of PGD to predict prolonged MV was poor. A total of 3 ventilation parameters (PIP, cDyn, and P/F ratio), determined at T0, were chosen on the basis of clinical reasoning. A logistic regression model including these parameters predicted prolonged MV (>72 hours) with an optimism-corrected area under the curve (AUC) of 0.727. In the prospective validation cohort, the model proved to be stable and achieved an AUC of 0.679. The prediction model reported in this study combines 3 easily obtainable variables. It can be employed immediately after LTx to quantify the risk of prolonged MV, an important early outcome parameter.

A clinical calculator for predicting intraoperative blood loss and transfusion risk in spine tumor patients

BACKGROUND CONTEXTSurgery for vertebral column tumors is commonly associated with intraoperative blood loss (IOBL) exceeding 2 liters and the need for transfusion of allogeneic blood products. Transfusion of allogeneic blood, while necessary, is not benign, and has been associated with increased rates of wound complication, venous thromboembolism, delirium, and death. PURPOSETo develop a prediction tool capable of predicting IOBL and risk of requiring allogeneic transfusion in patients undergoing surgery for vertebral column tumors. STUDY DESIGN/SETTINGRetrospective, single-center study. PATIENT SAMPLEConsecutive series of 274 patients undergoing 350 unique operations for primary or metastatic spinal column tumors over a 46-month period at a comprehensive cancer center OUTCOME MEASURESIOBL (in mL), use of intraoperative blood products, and intraoperative blood products transfused. METHODSWe identified IOBL and transfusions, along with demographic data, preoperative laboratory data, and surgical procedures performed. Independent predictors of IOBL and transfusion risk were identified using multivariable regression. RESULTSMean age at surgery was 57.0±13.6 years, 53.1% were male, and 67.1% were treated for metastatic lesions. Independent predictors of IOBL included en bloc resection (p<.001), surgical invasiveness (β=25.43 per point; p<0.001), and preoperative albumin (β=−244.86 per g/dL; p=0.011). Predictors of transfusion risk included preoperative hematocrit (odds ratio [OR]=0.88 per %; 95% confidence interval [CI, 0.84, 0.93]; p<0.001), preoperative MCHgb (OR=0.88 per pg; 95% CI [0.78, 1.00]; p=0.048), preoperative red cell distribution width (OR=1.32 per %; 95% CI [1.13, 1.55]; p<0.001), en bloc resection (OR=3.17; 95%CI [1.33, 7.54]; p=0.009), and surgical invasiveness (OR=1.08 per point; [1.06; 1.11]; p<0.001). The transfusion model showed a good fit of the data with an optimism-corrected area under the curve of 0.819. A freely available, web-based calculator was developed for the transfusion risk model (https://jhuspine3.shinyapps.io/TRUST/). CONCLUSIONSHere we present the first clinical calculator for intraoperative blood loss and transfusion risk in patients being treated for primary or metastatic vertebral column tumors. Surgical invasiveness and preoperative microcytic anemia most strongly predict transfusion risk. The resultant calculators may prove clinically useful for surgeons counseling patients about their individual risk of requiring allogeneic transfusion.

A predictive model for prostate cancer incorporating PSA molecular forms and age

The diagnostic specificity of prostate specific antigen (PSA) is limited. We aimed to characterize eight anti-PSA monoclonal antibodies (mAbs) to assess the prostate cancer (PCa) diagnostic utility of different PSA molecular forms, total (t) and free (f) PSA and PSA complexed to α1-antichymotrypsin (complexed PSA). MAbs were obtained by immunization with PSA and characterized by competition studies, ELISAs and immunoblotting. With them, we developed sensitive and specific ELISAs for these PSA molecular forms and measured them in 301 PCa patients and 764 patients with benign prostate hyperplasia, and analyzed their effectiveness to discriminate both groups using ROC curves. The free-to-total (FPR) and the complexed-to-total PSA (CPR) ratios significantly increased the diagnostic yield of tPSA. Moreover, based on model selection, we constructed a multivariable logistic regression model to predictive PCa that includes tPSA, fPSA, and age as predictors, which reached an optimism-corrected area under the ROC curve (AUC) of 0.86. Our model outperforms the predictive ability of tPSA (AUC 0.71), used in clinical practice. In conclusion, The FPR and CPR showed better diagnostic yield than tPSA. In addition, the PCa predictive model including age, fPSA and complexed PSA, outperformed tPSA detection efficacy. Our model may avoid unnecessary biopsies, preventing harmful side effects and reducing health expenses.

Myeloid-derived suppressor cells at diagnosis may discriminate between benign and malignant ovarian tumors

BackgroundThe behavior of the immune system as a driver in the progression of ovarian cancer has barely been studied. Our knowledge is mainly limited to the intra-tumoral adaptive immune system....

213-LB: Developing a Prognostic Model to Assess Dysglycemia Risk in Canadians Aged 18-39

In Canada, the prevalence of diabetes has seen the greatest relative increase in young adulthood, where the disorder is severely pathological compared to later-onset. Still, few prognostic models have been developed to screen young adults for dysglycemia risk and boost early identification and intervention. We sought to establish predictors of dysglycemia risk among young Canadian adults (aged 18-39) and evaluate their utility in identifying high-risk individuals. The Canadian Diabetes Risk Questionnaire (CANRISK) study collected questionnaire, anthropometric, and oral glucose tolerance test (OGTT) data from a large, multiethnic convenience sample of Canadians over two phases. Young adults with diagnosed diabetes, missing OGTT data, or pregnant were excluded. Potential factors that modestly predicted (p&amp;lt;0.20) dysglycemia status (FPG≥6.1mmol/L or 2h-PG≥7.8mmol/L) were entered into a lenient stepwise function, producing a young adult-specific model; risk scores were developed from adjusted odds ratios. Discriminatory ability was assessed by optimism-corrected area under the curve (AUC) via bootstrapping and goodness-of-fit by Hosmer-Lemeshow (H-L) test and calibration plot. More than half of the 3334 participants were female (62.4%), non-white (79.2%), less than 25kg/m2 (50.7%), and reported a family history of diabetes (55.4%); based on OGTT results, 7.3% were dysglycemic. The young adult-specific model displayed an adjusted AUC of 72.9%, and reasonable goodness-of-fit (H-L p=0.49). Model performance was similar when run sex-specifically (males: unadjusted AUC of 72.1%, H-L p=0.67; females: 73.6%, p=0.67). Employing a cut-point of 22, the tool displayed high sensitivity (78.8%) but low specificity (54.0%). Only 3% of those identified as low risk by the tool were misclassified. This young adult-specific risk score shows promise to identify high-risk individuals in a multiethnic Canadian sample. Additional studies are needed to assess its generalizability to new datasets. Disclosure S.A. Srugo: None. Y. Jiang: None. H.I. Morrison: None. M.M. deGroh: None.

Stress urinary incontinence after vaginal prolapse repair: development and internal validation of a prediction model with and without the stress test.

To develop a prediction model for stress urinary incontinence (SUI) after vaginal prolapse repair (postoperative stress urinary incontinence [POSUI]) and assess the value of a preoperative stress test. Secondary analysis of two trials in which women were randomised for prolapse repair with or without a midurethral sling (MUS). The trials included women with (CUPIDO-1, n = 134) and without (CUPIDO-2, n = 225) coexisting SUI. POSUI was defined as bothersome SUI one year after surgery and/or treatment of SUI in the first postoperative year. Logistic regression analysis was used to define a reference model, which was extended with the preoperative stress test. The stress test was performed with and without reduction of the prolapse. Missing values were imputed 20 times, with bootstrap resampling for internal validation of discriminatory ability. Three hundred fifty-six women could be included. POSUI occurred in 17% of the women (n = 61). The reference model included age (<55years), point Ba of the pelvic organ prolapse quantification system (<-1), vaginal parity (≤3), subjective urinary incontinence, and MUS. The stress test had an odds ratio of 2.4 (95% confidence interval [CI], 1.2-4.6) in the extended model, which increased the optimism-corrected area under the receiver-operating curve from 0.74 to 0.76. The stress test was especially valuable in women with a 10% to 30% POSUI risk, where a stress test substantially impacted the POSUI risk. In more than 50% of the women, the stress test had no additional value in predicting POSUI. A preoperative stress test is not valuable for women at low risk of SUI after vaginal prolapse repair.

Modeling risk assessment for breast cancer in symptomatic women: a Saudi Arabian study.

BackgroundDespite the continuing increase in the breast cancer incidence rate among Saudi Arabian women, no breast cancer risk-prediction model is available in this population. The aim of this research was to develop a risk-assessment tool to distinguish between high risk and low risk of breast cancer in a sample of Saudi women who were screened for breast cancer.MethodsA retrospective chart review was conducted on symptomatic women who underwent breast mass biopsies between September 8, 2015 and November 8, 2017 at King Abdulaziz Medical City, Riyadh, Saudi Arabia.ResultsA total of 404 (63.8%) malignant breast biopsies and 229 (36.2%) benign breast biopsies were analyzed. Women ≥40 years old (aOR: 6.202, CI 3.497–11.001, P=0.001), hormone-replacement therapy (aOR 24.365, 95% CI 8.606–68.987, P=0.001), postmenopausal (aOR 3.058, 95% CI 1.861–5.024, P=0.001), and with a family history of breast cancer (aOR 2.307, 95% CI 1.142–4.658, P=0.020) were independently associated with an increased risk of breast cancer. This model showed an acceptable fit and had area under the receiver-operating characteristic curve of 0.877 (95% CI 0.851–0.903), with optimism-corrected area under the curve of 0.865.ConclusionThe prediction model developed in this study has a high ability in predicting increased breast cancer risk in our facility. Combining information on age, use of hormone therapy, postmenopausal status, and family history of breast cancer improved the degree of discriminatory accuracy of breast cancer prediction. Our risk model may assist in initiating population-screening programs and prompt clinical decision making to manage cases and prevent unfavorable outcomes.

Improving risk prediction for pancreatic cancer in symptomatic patients: a Saudi Arabian study.

BackgroundImaging tests used in our center are usually inadequate to confirm the high risk for pancreatic cancer. We aimed to use a combination of potential predictors including imaging tests to quantify the risk of pancreatic cancer and evaluate its utility.MethodsThis was a retrospective cohort study of patients who were suspected as having pancreatic cancer and underwent biopsy examination of pancreatic mass at King Abdulaziz Medical City, Riyadh, Saudi Arabia, between January 1, 2013, and December 31, 2016. We retrieved data on demographics, clinical history, imaging tests, and final pancreatic diagnosis from medical records.ResultsOf the 206 who underwent pancreatic biopsies, the mean age was 63.6 years; 54.4% were male. Of all the biopsies, 57.8% were malignant and 42.2% were benign masses. Nine factors contributed significantly to the risk of pancreatic cancer and were noted: older age (adjusted odds ratio [aOR] =1.048; P=0.010), male gender (aOR =4.670; P=0.008), weight loss (aOR =14.810; P=0.001), abdominal pain (aOR =7.053; P=0.0.001), blood clots (aOR =20.787; P=0.014), pancreatitis (aOR =4.473; P=0.021), jaundice (aOR =7.446; P=0.003), persistent fatigue (aOR =22.015; P=0.015), and abnormal imaging tests (aOR =67.124; P=0.001). The model yielded powerful calibration (P=0.953), excellent predictive utility (area under the receiver operating characteristic curve 96.3%; 95% CI =94.1, 98.6), with optimism-corrected area under the curve bootstrap resampling of 94.9%. An optimal cut-off risk probability of 0.513 yielded a sensitivity of 94% and specificity of 84.7% for risk classification.ConclusionThe study developed and validated a risk model for quantifying the risk of pancreatic cancer. Nine characteristics were associated with increased risk of pancreatic cancer. This risk assessment model is feasible and highly sensitive and could be useful to improve screening performance and the decision-making process in clinical settings in Saudi Arabia.

Development of a risk grading system to identify patients with acute promyelocytic leukemia at high risk of early death.

BackgroundEarly death (ED) rate in acute promyelocytic leukemia (APL) remains high. Some studies have identified prognostic factors capable of predicting ED, whereas no risk rating system for ED has been reported in the literature. In this study, a risk classification system was built to identify subgroup at high risk of ED among patients with APL.MethodsTotally, 364 consecutive APL patients who received arsenic trioxide as induction therapy were included. Ten baseline clinical characteristics were selected for analysis, and they were de novo/relapse, age, sex, white blood cell count, platelet count, serum fibrinogen, creatinine, uric acid, aspartate aminotransferase, and albumin. Using a training cohort (N=275), a multivariable logistic regression model was constructed, which was internally validated by the bootstrap method and externally validated using an independent cohort (N=89). Based on the model, a risk classification system was designed. Then, all patients were regrouped into de novo (N=285) and relapse (N=79) cohorts and the model and risk classification system were applied to both cohorts.ResultsThe constructed model included 8 variables without platelet count and sex. The model had excellent discriminatory ability (optimism-corrected area under the receiver operator characteristic curve=0.816±0.028 in the training cohort and area under the receiver operator characteristic curve=0.798 in the independent cohort) and fit well for both the training and independent data sets (Hosmer–Lemeshow test, P=0.718 and 0.25, respectively). The optimism-corrected calibration slope was 0.817±0.12. The risk classification system could identify a subgroup comprising ~25% of patients at high risk of ED in both the training and independent cohorts (OR=0.140, P<0.001 and OR=0.224, P=0.027, respectively). The risk classification system could effectively identify patient subgroups at high risk of ED in not only de novo but also relapse cohorts (OR=0.233, P<0.001 and OR=0.105, P=0.001, respectively).ConclusionAll the results highlight the high practical value of the risk classification system.

Development and Validation of a Practical Two-Step Prediction Model and Clinical Risk Score for Post-Thrombotic Syndrome.

Post-thrombotic syndrome (PTS) is a common chronic consequence of deep vein thrombosis that affects the quality of life and is associated with substantial costs. In clinical practice, it is not possible to predict the individual patient risk. We develop and validate a practical two-step prediction tool for PTS in the acute and sub-acute phase of deep vein thrombosis. Multivariable regression modelling with data from two prospective cohorts in which 479 (derivation) and 1,107 (validation) consecutive patients with objectively confirmed deep vein thrombosis of the leg, from thrombosis outpatient clinic of Maastricht University Medical Centre, the Netherlands (derivation) and Padua University hospital in Italy (validation), were included. PTS was defined as a Villalta score of ≥ 5 at least 6 months after acute thrombosis. Variables in the baseline model in the acute phase were: age, body mass index, sex, varicose veins, history of venous thrombosis, smoking status, provoked thrombosis and thrombus location. For the secondary model, the additional variable was residual vein obstruction. Optimism-corrected area under the receiver operating characteristic curves (AUCs) were 0.71 for the baseline model and 0.60 for the secondary model. Calibration plots showed well-calibrated predictions. External validation of the derived clinical risk scores was successful: AUC, 0.66 (95% confidence interval [CI], 0.63-0.70) and 0.64 (95% CI, 0.60-0.69). Individual risk for PTS in the acute phase of deep vein thrombosis can be predicted based on readily accessible baseline clinical and demographic characteristics. The individual risk in the sub-acute phase can be predicted with limited additional clinical characteristics.

Prediction of Response to Neoadjuvant Chemotherapy and Radiation Therapy with Baseline and Restaging 18F-FDG PET Imaging Biomarkers in Patients with Esophageal Cancer.

Purpose To assess the value of baseline and restaging fluorine 18 (18F) fluorodeoxyglucose (FDG) positron emission tomography (PET) radiomics in predicting pathologic complete response to neoadjuvant chemotherapy and radiation therapy (NCRT) in patients with locally advanced esophageal cancer. Materials and Methods In this retrospective study, 73 patients with histologic analysis-confirmed T1/N1-3/M0 or T2-4a/N0-3/M0 esophageal cancer were treated with NCRT followed by surgery (Chemoradiotherapy for Esophageal Cancer followed by Surgery Study regimen) between October 2014 and August 2017. Clinical variables and radiomic features from baseline and restaging 18F-FDG PET were selected by univariable logistic regression and least absolute shrinkage and selection operator. The selected variables were used to fit a multivariable logistic regression model, which was internally validated by using bootstrap resampling with 20 000 replicates. The performance of this model was compared with reference prediction models composed of maximum standardized uptake value metrics, clinical variables, and maximum standardized uptake value at baseline NCRT radiomic features. Outcome was defined as complete versus incomplete pathologic response (tumor regression grade 1 vs 2-5 according to the Mandard classification). Results Pathologic response was complete in 16 patients (21.9%) and incomplete in 57 patients (78.1%). A prediction model combining clinical T-stage and restaging NCRT (post-NCRT) joint maximum (quantifying image orderliness) yielded an optimism-corrected area under the receiver operating characteristics curve of 0.81. Post-NCRT joint maximum was replaceable with five other redundant post-NCRT radiomic features that provided equal model performance. All reference prediction models exhibited substantially lower discriminatory accuracy. Conclusion The combination of clinical T-staging and quantitative assessment of post-NCRT 18F-FDG PET orderliness (joint maximum) provided high discriminatory accuracy in predicting pathologic complete response in patients with esophageal cancer. © RSNA, 2018 Online supplemental material is available for this article.

Biomarkers of AKI Progression after Pediatric Cardiac Surgery.

Background As children progress to higher stages of AKI, the risk for adverse outcomes dramatically increases. No reliable methods exist to predict AKI progression in hospitalized children. To determine if biomarkers of inflammation and kidney injury can predict AKI progression, we conducted a three-center prospective cohort study of children undergoing cardiopulmonary bypass.Methods On the first day of serum creatinine-defined AKI, we measured urine biomarkers (neutrophil gelatinase-associated lipocalin [NGAL], IL-18, kidney injury molecule 1, liver fatty acid binding protein [L-FABP], albumin, and cystatin C) and plasma biomarkers (IFN, IL-1, IL-2, IL-4, IL-6, IL-8, IL-10, IL-12, IL-13, TNF-α, NGAL, and cystatin C). We defined AKI progression as a worsening of AKI stage or persisting stage 3 AKI (≥2 consecutive days).Results In all, 176 of 408 (43%) children developed postoperative AKI. Among the children with AKI, we diagnosed stages 1, 2, and 3 AKI in 145 (82.5%), 25 (14%), and six (3.5%) children, respectively, on the first day of AKI; 28 (7%) children had AKI progression. On the first day of AKI, nine of 17 biomarkers were significantly higher in patients with than without AKI progression. Urine L-FABP (among injury biomarkers) and plasma IL-8 (among inflammatory biomarkers) had the highest discrimination for AKI progression: optimism-corrected area under the curve, 0.70; 95% confidence interval, 0.58 to 0.81 and optimism-corrected area under the curve, 0.80; 95% confidence interval, 0.69 to 0.91, respectively.Conclusions If validated in additional cohorts, plasma IL-8 could be used to improve clinical care and guide enrollment in therapeutic trials of AKI.

Development of a Pediatric Risk Assessment Score to Predict Perioperative Mortality in Children Undergoing Noncardiac Surgery.

Although there have been numerous attempts to predict perioperative mortality in adults, an objective model to predict mortality in children has not been developed. In this study, we aimed to develop a Pediatric Risk Assessment (PRAm) score to predict perioperative mortality in children undergoing noncardiac surgery. We included all children recorded in the 2012 and 2013 American College of Surgeons National Surgical Quality Improvement Program Pediatric databases in a derivation cohort and those from the 2014 database in a validation cohort. The primary outcome was the incidence of in-hospital mortality. A total of 115,229 (63%) were included in the derivation cohort and 68,194 (37%) in the validation cohort. We used multivariable logistic regression to determine the predictors for mortality and designed the PRAm score. On the basis of the multivariable regression model, we created a simplified risk assessment tool (PRAm score) ranging from 0 to ≥9, including the presence of any comorbidities, factors of critical illness, age <12 months, the requirement for an urgent procedure, and the diagnosis of a neoplasm. The PRAm score showed an excellent discriminative ability with an apparent "optimistic" area under the receiver operating characteristic curve (AUC) of 0.950 (95% confidence interval [CI], 0.942-0.957) in the derivation cohort. In the validation cohort, we observed similar performances with an area under the "naive" receiver operating characteristic curve of 0.950 (95% CI, 0.938-0.961). The AUC was also calculated from a bootstrap procedure and then applied to the original derivation sample to estimate "optimism" for each bootstrap sample with an AUC of 0.943 (95% CI, 0.929-0.9956). The optimism in apparent performance was 0.007, corresponding to an optimism-corrected area of 0.943. Calibration was assessed graphically by plotting the observed outcome against the predicted mortality (Pearson correlation coefficient = 0.995, calibration in the large = 0.001 [P = .974], calibration slope = 0.927). In this study, we developed a simplified PRAm tool (PRAm score) as a predictor of perioperative mortality in children undergoing noncardiac surgery. The PRAm score has excellent accuracy. In patients assigned American Society of Anesthesiologists physical status classification ≥4, there is wide variability in objectively obtained PRAm scores.