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Objective:To construct a Chinese neonatal model of early-onset sepsis (EOS) using the Kaiser Permanente sepsis risk calculator and laboratory indicators and validate its clinical prediction potential.Methods:Newborns with a gestational age of ≥34 weeks, who were hospitalized in the Department of Neonatology, the First Affiliated Hospital of Zhengzhou University from January 2020 to June 2022 were retrospectively recruited.Their clinical data were collected.Predictors were screened via the multivariate regression analysis, and the Nomogram model was constructed using R software and RStudio software.Hosmer-Lemeshow test, receiver operating characteristic curve, the decision curve analysis (DCA) were used to evaluate the prediction potential of the Nomogram.Results:A total of 769 patients were enrolled, including 107 patients in the EOS group (5 culture-confirmed cases and 102 clinically diagnosed cases), and 662 cases in the non-EOS group.Ten variables were screened and introduced into the Nomogram, including the gestational age, birth weight, body temperature, white blood cell count, C-reactive protein, procalcitonin, premature rupture of membranes≥18 h, infection of Group B Streptococcus, ventilator application, and prenatal antibiotics.The predictive model showed good discrimination and consistency, with the area under the curve of 0.834 (95% CI: 0.771-0.896). The DCA of the prediction model showed that it was effective in clinical application within the effective threshold of 6%-95%, with a net benefit following the application of corresponding treatment measures. Conclusions:A Chinese neonatal model of EOS was created by using the Kaiser Permanente sepsis risk calculator and laboratory indicators, which has been validated effective.It provides references for clinical management and the guidance for the use of antibiotics.
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Objectives To evaluate the complications predicting efficacy of the American College of Surgeons(ACS)National Surgical Quality Improvement Program(NSQIP)surgical risk calculator for cervical cancer patients undergoing open radical hysterectomy in China.Methods This study enrolled the cervical cancer patients(139 cases)undertaken open radical hysterectomy at Women's Hospital of Nanjing Medical University from Janu-ary to December in 2021.Preoperative risk factors were abstracted from medical records and the surgical risk scores were calculated using ACS NSQIP surgical risk calculator.The association between risk scores and actual outcomes were assessed using logistic regression together with the c-statistic(area under ROC)and Brier score.Results The ACSNSQIP calculator did not predict accurately for serious complications,any complications,venous thrombo-embolism(VTE),readmission,return operation room and surgical site infection(SSI)compared with actual out-comes.There was significantly difference in the predicted and actual length of stay(3.93±0.42 days vs.13.11±4.71 days,P<0.001).Conclusions The ACS NSQIP surgical risk calculator failed to predict the postoperative complications and the length of hospital stay for cervical cancer patients undergoing open radical hysterectomy.
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【Objective】 To investigate the value of prostate cancer prevention trial risk calculator （PCPT-RC） combined with biopsy Gleason score for predicting the risk of metastasis in newly diagnosed prostate cancer patients. 【Methods】 We retrospectively collected the data of 74 patients with newly diagnosed prostate cancer confirmed by biopsy from April 2019 to August 2021， concurrent with 18F-PSMA-1007 PET/CT whole body imaging in the same period. Based on this， a binary logistic regression model was established to obtain the high risk probability of PCPT. We calculated the receiver operating characteristic curve （ROC） was drawn and the area under the curve， Yuden index， sensitivity， specificity， positive predictive value and negative predictive value. We compared the predictive value of the prostate cancer prevention trial risk calculator and Gleason score alone or in combination in predicting the risk of prostate cancer metastasis. 【Results】 Based on the PSMA PET/CT results， 74 patients were divided into non-metastatic group （46/74） and metastatic group （28/74）. PCPT high risk probability ［41.14% （16%-67%）］ vs. ［30.89% （5%-65%）］， Gleason score ［8.5（6-10） score］ vs. ［7.7（6-9） score］， tPSA ［26.24（5.70-42.32） ng/mL］ vs. ［19.58（2.47-49.35） ng/mL］， and fPSA ［3.94（0.82-12.00） ng/mL］ vs. ［2.33（0.35-10.20） ng/mL］ were significantly higher in metastatic group than in non-metastatic group. Binary Logistic regression analysis showed that Gleason score and PCPT low risk probability may be independent predictors of prostate cancer metastasis. PCPT low risk probability alone did not predict the risk of prostate cancer metastasis （P=0.172）. The predictive accuracy of Gleason score and high probability of PCPT in predicting prostate cancer metastasis were 0.715 and 0.679， respectively， and the accuracy of the combined prediction was 0.809. 【Conclusion】 PCPT-RC combined with Gleason score is valuable for predicting the metastasis risk of newly diagnosed prostate cancer patients， which has certain guiding significance for clinical individualized treatment.
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Background: Gestational diabetes is defined as impaired glucose tolerance with onset or first recognition during pregnancy. Undiagnosed or inadequately treated gestational diabetes can lead to significant maternal and fetal complications. Even though there are guidelines for diagnosis of GDM (gestational diabetes mellitus) by the Government of India, there is poor penetration of the implementation throughout the nation.Methods: The study was conducted in A. J. Institute of Medical Sciences between April to June 2019. 56 patients were evaluated with the fetal medicine foundation GDM risk calculator to assess the risk for gestational diabetes in a retrospective approach.Results: Incidence of GDM in the study was 15.9%. At a cut-off of 1/80, the calculator predicted increased risk for 37 out of 56 patients. There was a sensitivity of 91.6% and specificity of 63.6% with a negative predictive value of 96.5% and positive predictive value of 29.5%. there was a false positive rate of 43.2%.Conclusions: The fetal medicine foundation GDM risk calculator will prove to be an invaluable tool to predict high risk patients who need closer monitoring of blood glucose into the third trimester.
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Risk prediction models including the Prostate Health Index (phi) for prostate cancer have been well established and evaluated in the Western population. The aim of this study is to build phi-based risk calculators in a prostate biopsy population and evaluate their performance in predicting prostate cancer (PCa) and high-grade PCa (Gleason score ≥7) in the Chinese population. We developed risk calculators based on 635 men who underwent initial prostate biopsy. Then, we validated the performance of prostate-specific antigen (PSA), phi, and the risk calculators in an additional observational cohort of 1045 men. We observed that the phi-based risk calculators (risk calculators 2 and 4) outperformed the PSA-based risk calculator for predicting PCa and high-grade PCa in the training cohort. In the validation study, the area under the receiver operating characteristic curve (AUC) for risk calculators 2 and 4 reached 0.91 and 0.92, respectively, for predicting PCa and high-grade PCa, respectively; the AUC values were better than those for risk calculator 1 (PSA-based model with an AUC of 0.81 and 0.82, respectively) (all P < 0.001). Such superiority was also observed in the stratified population with PSA ranging from 2.0 ng ml-1to 10.0 ng ml-1. Decision curves confirmed that a considerable proportion of unnecessary biopsies could be avoided while applying phi-based risk calculators. In this study, we showed that, compared to risk calculators without phi, phi-based risk calculators exhibited superior discrimination and calibration for PCa in the Chinese biopsy population. Applying these risk calculators also considerably reduced the number of unnecessary biopsies for PCa.
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Aged , Humans , Male , Asian People/statistics & numerical data , Biopsy , China , Neoplasm Grading , Prostate/pathology , Prostate-Specific Antigen/blood , Prostatic Neoplasms/pathology , Risk Assessment/methodsABSTRACT
Risk prediction models including the Prostate Health Index (phi) for prostate cancer have been well established and evaluated in the Western population. The aim of this study is to build phi-based risk calculators in a prostate biopsy population and evaluate their performance in predicting prostate cancer (PCa) and high-grade PCa (Gleason score ≥7) in the Chinese population. We developed risk calculators based on 635 men who underwent initial prostate biopsy. Then, we validated the performance of prostate-specific antigen (PSA), phi, and the risk calculators in an additional observational cohort of 1045 men. We observed that the phi-based risk calculators (risk calculators 2 and 4) outperformed the PSA-based risk calculator for predicting PCa and high-grade PCa in the training cohort. In the validation study, the area under the receiver operating characteristic curve (AUC) for risk calculators 2 and 4 reached 0.91 and 0.92, respectively, for predicting PCa and high-grade PCa, respectively; the AUC values were better than those for risk calculator 1 (PSA-based model with an AUC of 0.81 and 0.82, respectively) (all P < 0.001). Such superiority was also observed in the stratified population with PSA ranging from 2.0 ng ml-1to 10.0 ng ml-1. Decision curves confirmed that a considerable proportion of unnecessary biopsies could be avoided while applying phi-based risk calculators. In this study, we showed that, compared to risk calculators without phi, phi-based risk calculators exhibited superior discrimination and calibration for PCa in the Chinese biopsy population. Applying these risk calculators also considerably reduced the number of unnecessary biopsies for PCa.
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Objective@#The Prostate Cancer Prevention Trial risk calculator (PCPT-RC) is an online tool for assessing the risk of prostate cancer (PCa) based on age, race, serum PSA, biopsy history, family history, and other factors. This study aimed to investigate the value, sensitivity and specificity of the PCPT-RC 2.0 in assessing the risk of PCa in the Chinese high-risk population.@*METHODS@#This study included 622 patients with the high risk of PCa characterized by high serum PSA (PSA >3 μg/L) or abnormality in digital rectal examination or imaging of the prostate. According to the results of prostate biopsy, we divided the patients into a PCa and a non-PCa group and used the PCPT-RC 2.0 for evaluation of all the cases followed by statistical analysis.@*RESULTS@#PCa was detected in 264 (42.4%) of the 622 patients, including 126 cases of high-grade malignancy. Compared with the non-PCa group, the PCa patients showed a significantly older age (［68.40 ± 7.30］ vs ［72.80 ± 7.20］ yr, P 0.05).@*CONCLUSIONS@#The PCPT risk score is valuable in predicting the risk of PCa in China, which may play a better role than the serum PSA level in screening PCa and avoid unnecessary prostate biopsy, though its advantage is not so obvious in identifying high-grade malignancy. A prediction tool needs to be established for evaluating the risk of PCa in the Chinese population.
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Aged , Humans , Male , Age Factors , Asian People , Biopsy , China , Racial Groups , Digital Rectal Examination , Prostate , Pathology , Prostate-Specific Antigen , Blood , Prostatic Neoplasms , Blood , Pathology , ROC Curve , Risk Assessment , Methods , Risk FactorsABSTRACT
The performances of the Prostate Cancer Prevention Trial (PCPT) risk calculator and other risk calculators for prostate cancer (PCa) prediction in Chinese populations were poorly understood. We performed this study to build risk calculators (Huashan risk calculators) based on Chinese population and validated the performance of prostate-specific antigen (PSA), PCPT risk calculator, and Huashan risk calculators in a validation cohort. We built Huashan risk calculators based on data from 1059 men who underwent initial prostate biopsy from January 2006 to December 2010 in a training cohort. Then, we validated the performance of PSA, PCPT risk calculator, and Huashan risk calculators in an observational validation study from January 2011 to December 2014. All necessary clinical information were collected before the biopsy. The results showed that Huashan risk calculators 1 and 2 outperformed the PCPT risk calculator for predicting PCa in both entire training cohort and stratified population (with PSA from 2.0 ng ml-1 to 20.0 ng m). In the validation study, Huashan risk calculator 1 still outperformed the PCPT risk calculator in the entire validation cohort (0.849 vs 0.779 in area under the receiver operating characteristic curve [AUC] and stratified population. A considerable reduction of unnecessary biopsies (approximately 30%) was also observed when the Huashan risk calculators were used. Thus, we believe that the Huashan risk calculators (especially Huashan risk calculator 1) may have added value for predicting PCa in Chinese population.
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BACKGROUND AND PURPOSE: There is a variety of stroke risk factors, and engaging individuals in reducing their own personal risk is hugely relevant and could be an optimal dissemination strategy. The aim of the present study was to estimate the stroke risk for specific combinations of health- and lifestyle-related factors, and to develop a personalized stroke-risk assessment tool for health professionals and the general population (called the MyRisk_Stroke Calculator). METHODS: This population-based, longitudinal study followed a historical cohort formed from the 1992 or 1998 Sante Quebec Health Surveys with information for linkage to health administrative databases. Stroke risk factors were ascertained at the time of survey, and stroke was determined from hospitalizations and death records. Cox proportional hazards models were used, modeling time to stroke in relationship to all variables. RESULTS: A total of 358 strokes occurred among a cohort of 17805 persons (men=8181) who were followed for approximately 11 years (i.e., -200000 person-years). The following regression parameters were used to produce 10-year stroke-risk estimates and assign risk points: for age (1 point/year after age 20 years), male sex (3 points), low education (4 points), renal disease (8 points), diabetes (7 points), congestive heart failure (5 points), peripheral arterial disease (2 points), high blood pressure (2 points), ischemic heart disease (1 point), smoking (8 points), >7 alcoholic drinks per week (3 points), low physical activity (2 points), and indicators of anger (4 points), depression (4 points), and anxiety (3 points). According to MyRisk_Stroke Calculator, a person with 75%, respectively. CONCLUSIONS: The MyRisk_Stroke Calculator is a simple method of disseminating information to the general population about their stroke risk.