Prediction of Postoperative Day 1 Hemoglobin Level after Cesarean Delivery

Objective To create a prediction model for postoperative hemoglobin levels after cesarean delivery, which could reduce routine use of postoperative laboratory test. Study Design This was a secondary analysis of a retrospective cohort study of all women who underwent cesarean delivery (primary or repeat) at or more than 23 weeks' gestation at a single academic center. The cohort was randomly divided into a training cohort to develop a prediction model and a validation cohort to test the model in a 2:1 ratio. Variables with p-value <0.10 were considered for the mixed multivariable linear regression model in a backward stepwise fashion. We obtained the best cutoff point of the predicted hemoglobin level to detect severe anemia (postoperative hemoglobin level less than 7.0 g/dL) in the training cohort. A receiver operating characteristic curve with the area under a curve was created. We calculated the sensitivity and specificity of the model in the validation cohort using the best cutoff point obtained in the training cohort as well as postoperative hemoglobin of 10.0 g/dL. Results Of 2,930 women, 1,954 (66.6%) and 976 (33.3%) were randomly allocated to training and validation cohorts. The final model included preoperative hemoglobin level, preoperative platelet level, quantitative blood loss, height, weight, magnesium administration, labor, and general anesthesia. The best cutoff to predict severe anemia was predicted hemoglobin level of 8.57 g/dL in the training cohort. Using this cutoff, the sensitivity and specificity in the validation cohort were 77% (95% confidence interval [CI]: 56–91%) and 87% (95% CI: 85–89%), respectively. The use of postpartum hemorrhage yielded the sensitivity of 58% (95% CI: 37–77%) and specificity 79% (95% CI: 76–81%), respectively. Conclusion We developed a validated model to predict the postoperative day 1 hemoglobin levels after cesarean delivery that could assist with identifying women who may not need postoperative laboratory tests. Key Points

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Development and Prospective Validation of the Levothyroxine Dose Prediction Model in Primary Hypothyroidism

Hormone and Metabolic Research ◽

10.1055/a-1336-3502 ◽

2021 ◽

Author(s):

Rekha Singh ◽

Ashwani Tandon ◽

Ashish Awasthi

Keyword(s):

Prediction Model ◽

Validation Cohort ◽

Estimation Algorithm ◽

Primary Hypothyroidism ◽

P Value ◽

Dose Estimation ◽

Training Cohort ◽

Predictive Algorithm ◽

Dose Prediction ◽

Age Range

AbstractMultiple visits are needed to achieve euthyroidism on levothyroxine in newly detected primary hypothyroidism. We aimed to develop a levothyroxine dose estimation algorithm for primary hypothyroidism. Adults with newly diagnosed hypothyroidism were enrolled prospectively, first in the training cohort, followed by the validation cohort separated by time and person. We developed a predictive algorithm from Training Cohort and validated the model in Validation Cohort. Training Cohort: In this cohort, 358 subjects (259 women and 99 men) were enrolled. The median duration needed to achieve euthyroidism was 4±0.5 months. The mean levothyroxine daily dose was 60.5±34.1 μg. Data of euthyroid subjects within 6 months of treatment initiation and age range 18–65 years were used for algorithm development. In the multivariable linear regression algorithm, pretreatment serum thyrotropin level, and sex formed the best-fit predictive model (adjusted R2 0.73, p-value <0.001). Validation Cohort: Eighty-four subjects (61 women and 23 men) were enrolled and started on an estimated levothyroxine dose derived from the developed prediction model. On the first follow-up on treatment, 34/50 participants achieved euthyroidism (68%) at 1.5 months. In conclusion, the proposed prediction model for levothyroxine dose estimation effectively achieves early euthyroidism in two-third subjects in the age range of 18–65 years.

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Establishment and validation of prognosis model for patients with cerebral contusion

BMC Neurology ◽

10.1186/s12883-021-02482-4 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Yufeng Zhu ◽

Xiaoqing Jin ◽

Lulu Xu ◽

Pei Han ◽

Shengwu Lin ◽

...

Keyword(s):

Prediction Model ◽

Validation Cohort ◽

Characteristic Curve ◽

Third Ventricle ◽

Curve Analysis ◽

Imaging Features ◽

Lasso Regression ◽

Cerebral Contusion ◽

Decision Curve Analysis ◽

Training Cohort

Abstract Background And Objective Cerebral Contusion (CC) is one of the most serious injury types in patients with traumatic brain injury (TBI). In this study, the baseline data, imaging features and laboratory examinations of patients with CC were summarized and analyzed to develop and validate a prediction model of nomogram to evaluate the clinical outcomes of patients. Methods A total of 426 patients with cerebral contusion (CC) admitted to the People’s Hospital of Qinghai Province and Affiliated Hospital of Qingdao University from January 2018 to January 2021 were included in this study, We randomly divided the cohort into a training cohort (n = 284) and a validation cohort (n = 142) with a ratio of 2:1.At Least absolute shrinkage and selection operator (Lasso) regression were used for screening high-risk factors affecting patient prognosis and development of the predictive model. The identification ability and clinical application value of the prediction model were analyzed through the analysis of receiver operating characteristic curve (ROC), calibration curve, and decision curve analysis (DCA). Results Twelve independent prognostic factors, including age, Glasgow Coma Score (GCS), Basal cistern status, Midline shift (MLS), Third ventricle status, intracranial pressure (ICP) and CT grade of cerebral edema,etc., were selected by Lasso regression analysis and included in the nomogram. The model showed good predictive performance, with a C index of (0.87, 95% CI, 0.026–0.952) in the training cohort and (0.93, 95% CI, 0.032–0.965) in the validation cohort. Clinical decision curve analysis (DCA) also showed that the model brought high clinical benefits to patients. Conclusion This study established a high accuracy of nomogram model to predict the prognosis of patients with CC, its low cost, easy to promote, is especially applicable in the acute environment, at the same time, CSF-glucose/lactate ratio(C-G/L), volume of contusion, and mean CT values of edema zone, which were included for the first time in this study, were independent predictors of poor prognosis in patients with CC. However, this model still has some limitations and deficiencies, which require large sample and multi-center prospective studies to verify and improve our results.

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Development and Validation of Nomogram to Preoperatively Predict Intraoperative Cerebrospinal Fluid Leakage in Endoscopic Pituitary Surgery: A Retrospective Cohort Study

Frontiers in Oncology ◽

10.3389/fonc.2021.719494 ◽

2021 ◽

Vol 11 ◽

Author(s):

Xiangming Cai ◽

Junhao Zhu ◽

Jin Yang ◽

Chao Tang ◽

Feng Yuan ◽

...

Keyword(s):

Risk Factors ◽

Cerebrospinal Fluid ◽

Prediction Model ◽

Calibration Curve ◽

Validation Cohort ◽

Laboratory Data ◽

Pituitary Surgery ◽

Cerebrospinal Fluid Leakage ◽

Training Cohort ◽

Preoperative Prediction

BackgroundPituitary adenomas (PAs) are the most common tumor of the sellar region. PA resection is the preferred treatment for patients with clear indications for surgery. Intraoperative cerebrospinal fluid (iCSF) leakage is a major complication of PA resection surgery. Risk factors for iCSF leakage have been studied previously, but a predictive nomogram has not yet been developed. We constructed a nomogram for preoperative prediction of iCSF leakage in endoscopic pituitary surgery.MethodsA total of 232 patients who underwent endoscopic PA resection at the Department of Neurosurgery in Jinling Hospital between January of 2018 and October of 2020 were enrolled in this retrospective study. Patients treated by a board-certified neurosurgeon were randomly classified into a training cohort or a validation cohort 1. Patients treated by other qualified neurosurgeons were included in validation cohort 2. A range of demographic, clinical, radiological, and laboratory data were acquired from the medical records. The Least Absolute Shrinkage and Selection Operator (LASSO) algorithm and uni- and multivariate logistic regression were utilized to analyze these features and develop a nomogram model. We used a receiver operating characteristic (ROC) curve and calibration curve to evaluate the predictive performance of the nomogram model.ResultsVariables were comparable between the training cohort and validation cohort 1. Tumor height and albumin were included in the final prediction model. The area under the curve (AUC) of the nomogram model was 0.733, 0.643, and 0.644 in training, validation 1, and validation 2 cohorts, respectively. The calibration curve showed satisfactory homogeneity between the predicted probability and actual observations. Nomogram performance was stable in the subgroup analysis.ConclusionsTumor height and albumin were the independent risk factors for iCSF leakage. The prediction model developed in this study is the first nomogram developed as a practical and effective tool to facilitate the preoperative prediction of iCSF leakage in endoscopic pituitary surgery, thus optimizing treatment decisions.

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A novel nomogram to predict the overall survival in esthesinoeroblastoma

10.21203/rs.3.rs-22163/v2 ◽

2020 ◽

Author(s):

Lijie Jiang ◽

Tengjiao Lin ◽

Yu Zhang ◽

Wenxiang Gao ◽

Jie Deng ◽

...

Keyword(s):

Overall Survival ◽

Proportional Hazards ◽

Proportional Hazards Model ◽

Validation Cohort ◽

Risk Groups ◽

Cox Proportional Hazards ◽

Cox Proportional Hazards Model ◽

Cancer Center ◽

P Value ◽

Training Cohort

Abstract Background Increasing evidence indicates that the pathology and the modified Kadish system have some influence on the prognosis of esthesioneuroblastoma (ENB). However, an accurate system to combine pathology with a modified Kadish system has not been established. Methods This study aimed to set up and evaluate a model to predict overall survival (OS) accurately in ENB, including clinical characteristics, treatment and pathological variables. We screened the information of patients with ENB between January 1, 1976, and December 30, 2016 from the National Cancer Institute Surveillance, Epidemiology, and End Results (SEER) program as a training cohort. The validation cohort consisted of patients with ENB at Sun Yat-sen University Cancer Center and The First Affiliated Hospital of Sun Yat-sen University in the same period, and 87 patients were identified. The Pearson’s chi-squared test was used to assess significance of clinicopathological and demographic characteristics. We used the Cox proportional hazards model to examine univariate and multivariate analyses. The model coefficients were used to calculate the Hazard ratios (HR) with 95% confidence intervals (CI). Prognostic factors with a p- value < 0.05 in multivariate analysis were included in the nomogram. The concordance index (c-index) and calibration curve were used to evaluate the predictive power of the nomogram. Results The c-index of training cohort and validation cohort are 0.737 (95% CI, 0.709 to 0.765) and 0.791 (95% CI, 0.767 to 0.815) respectively. The calibration curves revealed a good agreement between the nomogram prediction and actual observation regarding the probability of 3-year and 5-year survival. We used a nomogram to calculate the 3-year and 5-year growth probability and stratified patients into three risk groups. Conclusions The nomogram provided the risk group information and identified mortality risk and can serve as a reference for designing a reasonable follow-up plan.

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Nomogram to predict primary non-response to infliximab in patients with Crohn’s disease: a multicenter study

Gastroenterology Report ◽

10.1093/gastro/goaa069 ◽

2020 ◽

Author(s):

Xiao-Qi Ye ◽

Jing Cai ◽

Qiao Yu ◽

Xiao-Cang Cao ◽

Yan Chen ◽

...

Keyword(s):

Crohn’S Disease ◽

Crohn's Disease ◽

Validation Cohort ◽

Characteristic Curve ◽

Mucosal Healing ◽

Clinical Usefulness ◽

Secondary Outcome ◽

Training Cohort ◽

Graphical Tool ◽

Therapeutic Decision Making

Abstract Background Infliximab (IFX) is effective at inducing and maintaining clinical remission and mucosal healing in patients with Crohn’s disease (CD); however, 9%–40% of patients do not respond to primary IFX treatment. This study aimed to construct and validate nomograms to predict IFX response in CD patients. Methods A total of 343 patients diagnosed with CD who had received IFX induction from four tertiary centers between September 2008 and September 2019 were enrolled in this study and randomly classified into a training cohort (n = 240) and a validation cohort (n = 103). The primary outcome was primary non-response (PNR) and the secondary outcome was mucosal healing (MH). Nomograms were constructed from the training cohort using multivariate logistic regression. Performance of nomograms was evaluated by area under the receiver-operating characteristic curve (AUC) and calibration curve. The clinical usefulness of nomograms was evaluated by decision-curve analysis. Results The nomogram for PNR was developed based on four independent predictors: age, C-reactive protein (CRP) at week 2, body mass index, and non-stricturing, non-penetrating behavior (B1). AUC was 0.77 in the training cohort and 0.76 in the validation cohort. The nomogram for MH included four independent factors: baseline Crohn’s Disease Endoscopic Index of Severity, CRP at week 2, B1, and disease duration. AUC was 0.79 and 0.72 in the training and validation cohorts, respectively. The two nomograms showed good calibration in both cohorts and were superior to single factors and an existing matrix model. The decision curve indicated the clinical usefulness of the PNR nomogram. Conclusions We established and validated nomograms for the prediction of PNR to IFX and MH in CD patients. This graphical tool is easy to use and will assist physicians in therapeutic decision-making.

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The Prognostic Significance of Tumor Deposit Count for Colorectal Cancer Patients after Radical Surgery

Gastroenterology Research and Practice ◽

10.1155/2020/2052561 ◽

2020 ◽

Vol 2020 ◽

pp. 1-8 ◽

Cited By ~ 2

Author(s):

Kuo Zheng ◽

Nanxin Zheng ◽

Cheng Xin ◽

Leqi Zhou ◽

Ge Sun ◽

...

Keyword(s):

Colorectal Cancer ◽

Prognostic Value ◽

Validation Cohort ◽

Cutoff Point ◽

Disease Specific Survival ◽

Optimal Cutoff ◽

Training Cohort ◽

Tumor Deposit ◽

Optimal Cutoff Point ◽

Disease Specific

Background. The prognostic value of tumor deposit (TD) count in colorectal cancer (CRC) patients has been rarely evaluated. This study is aimed at exploring the prognostic value of TD count and finding out the optimal cutoff point of TD count to differentiate the prognoses of TD-positive CRC patients. Method. Patients diagnosed with CRC from Surveillance, Epidemiology, and End Results (SEER) database from January 1, 2010, to December 31, 2012, were analyzed. X-tile program was used to identify the optimal cutoff point of TD count in training cohort, and a validation cohort was used to test this cutoff point after propensity score matching (PSM). Univariate and multivariate Cox proportional hazard models were used to assess the risk factors of survival. Results. X-tile plots identified 3 (P<0.001) as the optimal cutoff point of TD count to divide the patients of training cohort into high and low risk subsets in terms of disease-specific survival (DSS). This cutoff point was validated in validation cohort before and after PSM (P<0.001, P=0.002). More TD count, which was defined as more than 3, was validated as an independent risk prognostic factor in univariate and multivariate analysis (P<0.001). Conclusion. More TD count (TD count≥4) was significantly associated with poor disease-specific survival in CRC patients.

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A urine exosomal circRNA classifier for detection of high-grade prostate cancer at initial biopsy: A multicenter, retrospective study.

Journal of Clinical Oncology ◽

10.1200/jco.2020.38.15_suppl.5522 ◽

2020 ◽

Vol 38 (15_suppl) ◽

pp. 5522-5522

Author(s):

Liaoyuan Li ◽

Wen Tao ◽

Yadi He ◽

Tao He ◽

Qing Li ◽

...

Keyword(s):

Prostate Cancer ◽

Validation Cohort ◽

Characteristic Curve ◽

Specific Antigen ◽

Digital Rectal Examination ◽

Circular Rna ◽

High Grade ◽

Training Cohort ◽

Potential Biomarker ◽

Initial Biopsy

5522 Background: The low specificity of prostate-specific antigen (PSA) has resulted in the overdiagnosis and overtreatment of clinically indolent prostate cancer (PCa). We aimed to identify a urine exosomal circular RNA (circRNA) classifier that could detect high-grade (Gleason score [GS]7 or greater) PCa. Methods: We did a three-stage study that enrolled eligible participants, including PCa-free men, 45 years or older, scheduled for an initial prostate biopsy due to suspicious digital rectal examination findings and/or PSA levels (limit range, 2.0-20.0 ng/mL), from four hospitals in China. We used RNA sequencing and digital droplet polymerase chain reaction to identify 18 candidate urine exosomal circRNAs that were increased in 11 patients with high-grade PCa compared with 11 case-matched patients with benign prostatic hyperplasia. Using a training cohort of eligible participants, we built a urine exosomal circRNA classifier (Ccirc) to detect high-grade PCa. We then evaluated the classifier in discrimination of GS7 or greater from GS6 and benign disease on initial biopsy in two independent cohorts. We used the sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) to evaluate diagnostic performance, and compared Ccirc with standard of care (SOC) (ie, PSA level, age, race, and family history). Results: Between June 1, 2016, and July 31, 2019, we recruited 356 participants to the training cohort, and 442 and 325 participants to the two independent validation cohorts. We identified a Ccirc containing five differentially expressed circRNAs (circ_0049335, circ_0056536, circ_0004028, circ_0008475, and circ_0126027) that could detect high-grade PCa. Ccirc showed higher accuracy than SOC to distinguish individuals with high-grade PCa from controls in both the training cohort and the validation cohorts. (AUC 0.831 [95% CI 0.765-0.883] vs 0.724 [0.705-0.852], P = 0.032 in the training cohort; 0.823 [0.762-0.871] vs 0.706 [0.649-0.762], P = 0.007 in validation cohort 1; and 0.878 [0.802-0.943] vs 0.785 [0.701-0.890], P = 0.021 for validation cohort 2). In all three cohorts, Ccirc had higher sensitivity (range 71.6-87.2%) and specificity (82.3-90.7%) than did SOC (sensitivity, 42.3-68.2%; specificity, 40.1-62.3%) to detect high-grade PCa. Using a predefined cut point, 202 of 767 (26.3%) biopsies would have been avoided, missing only 6% of patients with dominant pattern 4 high-risk GS 7 disease. Conclusions: Ccirc is a potential biomarker for high-grade PCa among suspicious men.

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Application of an index derived from the area under a neutrophil curve as a predictor of surgical site infection after spinal surgery

BMC Surgery ◽

10.1186/s12893-021-01345-6 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Hiroyuki Inose ◽

Yutaka Kobayashi ◽

Shingo Morishita ◽

Yu Matsukura ◽

Masato Yuasa ◽

...

Keyword(s):

Surgical Site Infection ◽

Sensitivity And Specificity ◽

Spinal Surgery ◽

Operating Characteristic ◽

Characteristic Curve ◽

Objective Assessment ◽

Cutoff Point ◽

Control Group ◽

Site Infection ◽

Neutrophil Percentage

Abstract Background Patients with prolonged and intense neutrophilia after spinal surgery are at high risk of developing surgical site infection (SSI). To date, there is no standard method for the objective assessment of the intensity and duration of neutrophilia. Thus, this retrospective observational study aimed to test a new index (I-index), developed by combining the duration and intensity of neutrophilia, as a predictor of SSI. Methods I-index was calculated based on the postoperative neutrophil percentage. A total of 17 patients with SSI were enrolled as cases, and 51 patients without SSI were selected as controls. The groups were matched at a ratio of 1:3 by age, sex, and surgery type. The differences in the I-index were compared between the groups. Moreover, we checked the cumulative I-index (c-I-index), which we defined as the area under the neutrophil curve from postoperative day 1 until the first clinical manifestation of SSI in each case. Furthermore, a cutoff for SSI was defined using the receiver operating characteristic curve. Results The median I-index-7, I-index-14, and c-I-index were significantly higher in the SSI group than those in the control group. For a cutoff point of 42.1 of the I-index-7, the sensitivity and specificity were 0.706 and 0.882, respectively. For a cutoff point of 45.95 of the I-index-14, the sensitivity and specificity were 0.824 and 0.804, respectively. For a cutoff point of 45.95 of the c-I-index, the sensitivity and specificity were 0.824 and 0.804, respectively. Conclusion We devised a new indicator of infection, i.e., the I-Index and c-I-index, and confirmed its usefulness in predicting SSI.

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Establishment of the Radiologic Tumor Invasion Index Based on Radiomics Splenic Features and Clinical Factors to Predict Serous Invasion of Gastric Cancer

Frontiers in Oncology ◽

10.3389/fonc.2021.682456 ◽

2021 ◽

Vol 11 ◽

Author(s):

Bujian Pan ◽

Weiteng Zhang ◽

Wenjing Chen ◽

Jingwei Zheng ◽

Xinxin Yang ◽

...

Keyword(s):

Risk Factors ◽

Gastric Cancer ◽

Prediction Model ◽

Tumor Invasion ◽

Training Group ◽

P Value ◽

Serosal Invasion ◽

Invasion Risk ◽

Training Cohort ◽

Invasion Index

BackgroundCurrently, there are shortcomings in diagnosing gastric cancer with or without serous invasion, making it difficult for patients to receive appropriate treatment. Therefore, we aimed to develop a radiomic nomogram for preoperative identification of serosal invasion.MethodsWe selected 315 patients with gastric cancer, confirmed by pathology, and randomly divided them into two groups: the training group (189 patients) and the verification group (126 patients). We obtained patient splenic imaging data for the training group. A p-value of <0.05 was considered significant for features that were selected for lasso regression. Eight features were chosen to construct a serous invasion prediction model. Patients were divided into high- and low-risk groups according to the radiologic tumor invasion risk score. Subsequently, univariate and multivariate regression analyses were performed with other invasion-related factors to establish a visual combined prediction model.ResultsThe diagnostic accuracy of the radiologic tumor invasion score was consistent in the training and verification groups (p<0.001 and p=0.009, respectively). Univariate and multivariate analyses of invasion risk factors revealed that the radiologic tumor invasion index (p=0.002), preoperative hemoglobin <100 (p=0.042), and the platelet and lymphocyte ratio <92.8 (p=0.031) were independent risk factors for serosal invasion in the training cohort. The prediction model based on the three indexes accurately predicted the serosal invasion risk with an area under the curve of 0.884 in the training cohort and 0.837 in the testing cohort.ConclusionsRadiological tumor invasion index based on splenic imaging combined with other factors accurately predicts serosal invasion of gastric cancer, increases diagnostic precision for the most effective treatment, and is time-efficient.

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The Risk Prediction Model for Healthcare-Associated Infections among Elderly Patients Underwent Pancreaticoduodenectomy

10.21203/rs.3.rs-138219/v1 ◽

2021 ◽

Author(s):

Yuejing Feng ◽

Hongwu Yao ◽

Jie Li ◽

Mingmei Du ◽

Xibao Gao ◽

...

Keyword(s):

Elderly Patients ◽

Prediction Model ◽

Sensitivity And Specificity ◽

Prediction Accuracy ◽

Urinary Catheter ◽

Characteristic Curve ◽

Univariate Analysis ◽

Healthcare Associated Infections ◽

Healthcare Associated ◽

Significant Factors

Abstract Background: The elderly are a high-risk group of healthcare-associated infections (HAIs) after pancreaticoduodenectomy (PD), and the effective prediction model may be beneficial to HAIs control. Methods: The data were obtained from Hospital Infection Surveillance System. The BP-ANN model was conducted according to univariate analysis. The receiver operating characteristic curve (ROC), the prediction accuracy, sensitivity and specificity were used to estimate the predicted performance. The final weight coefficients were calculated to illustrate the relative importance of indicators. Results: Of 688 elderly patients underwent PD, 83 (12.06%) were diagnosed with HAIs. 9 significant factors (P<0.05) including weight, fever, continuous fever for more than three days, blood routine abnormal percentage, ever livered in intensive care unit (ICU), antibacterial combination, postoperative antibacterial use days, ventilator use and urinary catheter use days were included into prediction model. The prediction accuracy in testing sets was 93.79%, and the sensitivity and specificity were 0.67 and 0.97. The contribution level of 9 significant factors were 10.65%, 8.54%, 10.17%, 9.64%, 9.26%, 10.02%, 12.53%, 11.90% and 17.29%, respectively. Conclusions: The 9-9-1 BP-ANN prediction model underpinned by complex factors in this study has relatively excellent performance for HAIs prediction among elderly patients after PD. Urinary catheter use days, postoperative antibacterial use days, ventilator use, weight and continuous fever for more than three days are the top five contribution indicators for HAIs prediction, which should be fully taken into consideration when developing HAIs prediction for the PD patients.

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