A Clinical Analysis of 293 FUO Patients, A Diagnostic Model Discriminating infectious Diseases from Non-infectious Diseases

Abstract Objective A diagnostic model was established to discriminate infectious diseases from non-infectious diseases. Methods The clinical data of patients with fever of unknown origin (FUO) hospitalized in Xiangya Hospital Central South University, from January, 2006 to April, 2011 were retrospectively analyzed. Patients enrolled were divided into two groups. The first group was used to develop a diagnostic model: independent variables were recorded and considered in a logistic regression analysis to identify infectious and non-infectious diseases (αin = 0.05, αout = 0.10). The second group was used to evaluate the diagnostic model and make ROC analysis. Results The diagnostic rate of 143 patients in the first group was 87.4%, the diagnosis included infectious disease (52.4%), connective tissue diseases (16.8%), neoplastic disease (16.1%) and miscellaneous (2.1%). The diagnostic rate of 168 patients in the second group was 88.4%, and the diagnosis was similar to the first group. Logistic regression analysis showed that decreased white blood cell count (WBC < 4.0×109/L), higher lactate dehydrogenase level (LDH > 320 U/L) and lymphadenectasis were independent risk factors associated with non-infectious diseases. The odds ratios were 14.74, 5.84 and 5.11 (P ≤ 0.01) , respectively. In ROC analysis, the sensitivity and specificity of the positive predictive values was 62.1% and 89.1%, respectively, while that of negative predicting values were 75% and 81.7%, respectively (AUC = 0.76, P = 0.00). Conclusions The combination of WBC < 4.0×109/L, LDH > 320 U/L and lymphadenectasis may be useful in discriminating infectious diseases from non-infectious diseases in patients hospitalized as FUO.

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Urethral Injury Treatment Challenge. Comparison of Surgical Treatments for Acquired or Iatrogenic Urethral Stenosis and Predictive Values for Failure of Each Surgical Method

Folia Medica ◽

10.3897/folmed.63.e53739 ◽

2021 ◽

Vol 63 (1) ◽

pp. 42-50

Author(s):

Yavuz Güler

Keyword(s):

Logistic Regression ◽

Regression Analysis ◽

Logistic Regression Analysis ◽

Roc Analysis ◽

Multivariate Logistic Regression Analysis ◽

Urethral Injury ◽

Multivariate Logistic Regression ◽

Independent Predictive Factor ◽

Predictive Values ◽

Urethral Stenosis

Introduction: Minimal invasive endo-urologic interventions and prostate surgeries performed through the urethral route are the most commonly used surgeries in the urology field at present. One of the unwanted complications weeks after some of these surgeries is urethral stenosis. Aim: In this study, we aimed to review our treatment methods for patients with urethral stricture occurring after urologic interventions retrospectively to evaluate our success rates and data predicting recurrence and to determine the most feasible method. Materials and methods: From 2008 to 2017, we evaluated retrospectively 234, 85, and 51 patients with urethral stenosis due to iatrogenic causes treated with direct visual internal urethrotomy (DVIU), anastomotic excision urethroplasty, and buccal graft urethro-plasty, respectively. Postoperative Qmax above 15 mL/s and >17 CH cystoscope easily passing the urethra was accepted as success for patients. Group variables were compared using ANOVA. Multivariate logistic regression analysis was performed to determine variables with predictive values. Predictive values significant in regression analysis (p<0.005) were evaluated with ROC analysis. Results: Patients were followed up for mean 48 (16-56) months, 48 (23-60) months, and 50 (32-65) months after DVIU, anastomotic and graft urethroplasty, respectively, and success was obtained for 75 (32.1%), 71 (83.5%) and 42 patients (82.4%). Multivariate logistic regression analysis found age, DM, BMI and stenosis length were independent predictive factors for DVIU success, while stenosis length was the independent predictive factor for excision anastomosis and buccal graft urethroplasty success. Cut-off values were determined as 56.5 years of age, 30.2 kg/m2 BMI and 1.85 mm stenosis length for DVIU patients, while 2.15 mm and 5.9 mm stenosis lengths,  respectively, were determined for excision anastomosis and buccal graft urethroplasty with ROC analysis. Conclusions: DVIU is a failed method for urethral stenosis in the long term. In contrast, resection / anastomosis and graft urethro-plasty are very successful methods.

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Association between Carotid Artery Calcification and Periodontal Disease Progression in Japanese Men and Women: A Cross-Sectional Study

Journal of Clinical Medicine ◽

10.3390/jcm9103365 ◽

2020 ◽

Vol 9 (10) ◽

pp. 3365

Author(s):

Nanae Dewake ◽

Yasuaki Ishioka ◽

Keiichi Uchida ◽

Akira Taguchi ◽

Yukihito Higashi ◽

...

Keyword(s):

Logistic Regression ◽

Regression Analysis ◽

Carotid Artery ◽

Logistic Regression Analysis ◽

Roc Analysis ◽

Multivariate Logistic Regression Analysis ◽

Multivariate Logistic Regression ◽

Panoramic Radiographs ◽

Carotid Artery Calcification ◽

Univariate Analyses

Objective: To evaluate the association between alveolar bone loss (ABL) detected on panoramic radiographs and carotid artery calcification (CAC) detected on computed tomography (CT). Methods: The study subjects included 295 patients (mean age ± SD: 64.6 ± 11.8 years) who visited the Matsumoto Dental University Hospital. The rate of ABL and the number of present teeth were measured on panoramic radiographs. Univariate analyses with t-tests and chi-squared tests were performed to evaluate the differences in age, gender, history of diseases, number of present teeth, and the ABL between subjects, with and without CAC. Moreover, multivariate logistic regression analysis, with forward selection and receiver operating characteristic curve (ROC) analysis, was performed. Results: The number of subjects without and with CAC was 174 and 121, respectively. Univariate analyses revealed that CAC was significantly associated with age, hypertension, osteoporosis, number of present teeth, and ABL. Multivariate logistic regression analysis adjusted for covariates revealed that the presence of CAC was significantly associated with ABL (OR = 1.233, 95% CI = 1.167–1.303). In the ROC analysis for predicting the presence of CAC, the the area under the ROC curve was the highest at 0.932 (95% CI = 0.904–0.960) for ABL, which was significant. Conclusions: Our results suggest that the measurement of ABL on panoramic radiographs may be an effective approach to identifying patients with an increased risk of CAC.

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Predictors for Development of Oxaliplatin-induced Peripheral Neuropathy in Cancer Patients as Determined by Ordered Logistic Regression Analysis

10.21203/rs.3.rs-847675/v2 ◽

2021 ◽

Author(s):

Yuko Kanbayashi ◽

Takeshi Ishikawa ◽

Yoshiaki Kuriu ◽

Yusuke Tabuchi ◽

Eigo Otsuji ◽

...

Keyword(s):

Logistic Regression ◽

Regression Analysis ◽

Peripheral Neuropathy ◽

Cancer Patients ◽

Logistic Regression Analysis ◽

Roc Analysis ◽

Operating Characteristic ◽

Ordered Logistic Regression ◽

Number Of Cycles ◽

Significant Factors

Abstract Purpose This retrospective study aimed to identify predictors for the development of oxaliplatin-induced peripheral neuropathy (OXAIPN). Methods Between January 2017 and March 2021, a total 322 cancer patients at our hospital who were receiving oxaliplatin were enrolled. For the regression analysis of factors associated with oxaliplatin-induced peripheral neuropathy, variables were extracted manually from medical charts. The level of OXAIPN was evaluated using the National Cancer Institute’s Common Terminology Criteria for Adverse Events (version 5). Multivariate ordered logistic regression analysis was performed to identify predictors for the development of OXAIPN. Optimal cut-off thresholds were determined using receiver operating characteristic (ROC) analysis. Values of P <0.05 (2-tailed) were considered significant. Results Significant factors identified included body mass index (BMI) (odds ratio [OR] = 1.06, 95% confidence interval [CI] = 1.00–1.12; P = 0.046), number of cycles (OR = 1.09, 95%CI = 1.05–1.14; P <0.0001), S-1 plus oxaliplatin (SOX) regimen (OR = 0.54, 95%CI = 0.32–0.92; P = 0.023), concomitant use of proton pump inhibitors (PPIs) (OR = 1.64, 95%CI = 1.05–2.58; P = 0.031) and concomitant use of analgesic adjuvant (OR = 3.30, 95%CI = 1.09–9.97; P = 0.035). Conclusion BMI, number of cycles, SOX regimen, concomitant use of PPIs and concomitant use of analgesic drugs were identified as significant predictors for the development of OXAIPN.

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Development and External Validation of Diagnostic Model for Coronary Microvascular Obstruction: Algorithm Development and Validation (Preprint)

10.2196/preprints.23052 ◽

2020 ◽

Author(s):

Yong Li ◽

Shuzheng Lyu

Keyword(s):

Logistic Regression ◽

Regression Analysis ◽

Logistic Regression Analysis ◽

Microvascular Obstruction ◽

External Validation ◽

Diagnostic Model ◽

Total Occlusion ◽

Validation Data ◽

Data Set ◽

Acute Stemi

BACKGROUND Prevention of coronary microvascular obstruction /no-reflow phenomenon(CMVO/NR) is a crucial step in improving prognosis of patients with acute ST segment elevation myocardial infarction (STEMI )during primary percutaneous coronary intervention (PPCI). OBJECTIVE The objective of our study was to develop and externally validate a diagnostic model of CMVO/NR in patients with acute STEMI underwent PPCI. METHODS Design: Multivariate logistic regression of a cohort of acute STEMI patients. Setting: Emergency department ward of a university hospital. Participants: Diagnostic model development: Totally 1232 acute STEMI patients who were consecutively treated with PPCI from November 2007 to December 2013. External validation: Totally 1301 acute STEMI patients who were treated with PPCI from January 2014 to June 2018. Outcomes: CMVO/NR during PPCI. We used logistic regression analysis to analyze the risk factors of CMVO/NR in the development data set. We developed a diagnostic model of CMVO/NR and constructed a nomogram.We assessed the predictive performance of the diagnostic model in the validation data sets by examining measures of discrimination, calibration, and decision curve analysis (DCA). RESULTS A total of 147 out of 1,232 participants (11.9%) presented CMVO/NR in the development dataset.The strongest predictors of CMVO/NR were age, periprocedural bradycardia, using thrombus aspiration devices during procedure and total occlusion of culprit vessel. Logistic regression analysis showed that the differences between two group with and without CMVO/NR in age( odds ratios (OR)1.031; 95% confidence interval(CI), 1.015 ~1.048 ; P <.001), periprocedural bradycardia (OR 2.151;95% CI,1.472~ 3.143 ; P <.001) , total occlusion of the culprit vessel (OR 1.842;95% CI, 1.095~ 3.1 ; P =.021) , and using thrombus aspirationdevices during procedure (OR 1.631; 95% CI, 1.029~ 2.584 ; P =.037).We developed a diagnostic model of CMVO/NR. The area under the receiver operating characteristic curve (AUC) was .6833±.023. We constructed a nomogram. CMVO/NR occurred in 120 out of 1,301 participants (9.2%) in the validation data set. The AUC was .6547±.025. Discrimination, calibration, and DCA were satisfactory. Date of approved by ethic committee:16 May 2019. Date of data collection start: 1 June 2019. Numbers recruited as of submission of the manuscript:2,533. CONCLUSIONS We developed and externally validated a diagnostic model of CMVO/NR during PPCI. CLINICALTRIAL We registered this study with WHO International Clinical Trials Registry Platform on 16 May 2019. Registration number: ChiCTR1900023213. http://www.chictr.org.cn/edit.aspx?pid=39057&htm=4.

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43 Pulmonary Artery dopplers for early prediction of Bronchopulmonary Dysplasia (BPD) in Extremely Low Birth Weight babies (ELBW): A forgotten tale

Paediatrics & Child Health ◽

10.1093/pch/pxaa068.042 ◽

2020 ◽

Vol 25 (Supplement_2) ◽

pp. e18-e19

Author(s):

Renjini Lalitha ◽

Eyad Bitar ◽

Abbas Hyderi ◽

Matthew Hicks ◽

Kumar Kumaran

Keyword(s):

At Risk ◽

Mechanical Ventilation ◽

Logistic Regression ◽

Regression Analysis ◽

Pulmonary Artery ◽

Logistic Regression Analysis ◽

Subsequent Development ◽

Observer Agreement ◽

Predictive Values ◽

Tertiary Center

Abstract Introduction/Background BPD is a chronic lung disease that affects ELBW babies and contributes significantly to their morbidity and mortality. The early abnormal vasoreactivity observed in babies at risk of BPD increases the pulmonary vascular resistance and can be assessed non-invasively using Time to Peak Velocity: Right Ventricular Ejection Time ratio (TPV: RVET) that is calculated from pulmonary artery doppler waveform. We postulate that screening for this condition early may be useful to predict BPD in this cohort, which may provide prognostic information as well as early clinical management of the ELBW at risk of developing BPD and therefore can potentially present a window of opportunity for therapeutic intervention. Objectives 1. To determine utility of TPV/RVET ratio in predicting the risk for BPD in ELBW babies in a tertiary center. 2. To determine utility of TPV/RVET(c) ratio in predicting the risk for BPD in ELBW babies in a tertiary center. Design/Methods This is a retrospective cohort study of ELBW babies <29 weeks admitted to the Neonatal Intensive Care Unit at Stollery Children Hospital (SCH) over a 4 year time period and had early echocardiograms performed between 7-21 days of life. These babies were further identified to have BPD or no BPD at 36 weeks CGA. TPV/RVET ratios were measured by two reviewers from pulmonary artery doppler waveforms and were corrected for heart rate. The predictive ability of TPV/RVET and TPV/RVET(c) for subsequent development of BPD were analyzed using ROC curve. Inter observer agreement was evaluated. Logistic regression analysis was performed to derive a model that can be used in the 2nd to 3rd weeks of life, to predict the subsequent development of BPD at 36 weeks CGA. Infants with congenital heart disease (other than PDA, persistent foramen ovale/atrial septal defect and Ventricular Septal Defects), congenital lung malformations, multiple congenital anomalies and chromosomal anomalies were excluded. Results Out of 589 ELBW babies <29 weeks admitted to SCH NICU, 207 infants were eligible with early echocardiograms done at a mean age of 12.6 days (SD 4.1). One-hundred-and-twenty-five babies (60.4%) were found to have BPD. Babies with BPD were of lower gestational age (25.6 Vs 26.4, p-0.0001), sicker at birth (SNAPPE II 42.3 Vs 33.2, p-0.0024), had higher incidence of PDA needing surgical ligation (18.4% Vs 4.9%, p-0.005) and had spent more days on mechanical ventilation (39.8 Vs 12.5, p<0.0001) than those without BPD. Both TPV, TPV/RVET and its corrected ratios were significantly lower in ELBW babies with BPD compared to non-BPD babies (p<0.0001). The TPV/RVET ratio (cut off 0.34) and TPV/RVET(c) (cut off 0.54) had Sensitivities [76.8% (95%CI 68.4-83.9) and 72% (95%CI 63.3-79.7)], Specificities [85.4% (95%CI 75.8- 92.2) and 84.1% (95%CI 74.4-91.3)], Positive Predictive Values [88.9% (95%CI 81.4-94.1) and 87.4% (95%CI 79.4-93.1)], Negative Predictive values [70.7% (95%CI 60.7- 79) and 66.3% (95%CI 56.4-75.3)] and ROC area [0.811 (95% CI 0.757-0.864) and 0.781 (95% CI 0.725-0.837)] respectively. Multi variant logistic regression analysis showed Odds Ratio(OR) for having BPD at TPV/RVET cut off 0.34 and TPV/RVET(c) cut off 0.54 to be 19.9(95%CI 8.19-48.34) and 16.3(95% 6.78-39.33) respectively and the OR increased by 1.07(95%CI 1.05-1.09) and 1.08(95% CI 1.05-1.10) with every additional days of mechanical ventilation respectively. TPV/RVET ratio had 92.75% inter-observer agreement with kappa 0.83. Conclusion TPV/RVET and its corrected ratio are good and reliable early screening tools for subsequent development of BPD in ELBW babies with substantial inter-observer agreement. Two variable model namely TPV/RVET<0.34 and mechanical ventilation or TPV/RVET(c) <0.54 and mechanical ventilation can be used in the 2nd to 3rd week of life to predict subsequent development of BPD at 36 weeks CGA in ELBW babies.

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Potential Role of the Renal Arterial Resistance Index in the Differential Diagnosis of Diabetic Kidney Disease

Frontiers in Endocrinology ◽

10.3389/fendo.2021.731187 ◽

2022 ◽

Vol 12 ◽

Author(s):

Haiyang Li ◽

Yunzhu Shen ◽

Zhikai Yu ◽

Yinghui Huang ◽

Ting He ◽

...

Keyword(s):

Logistic Regression ◽

Differential Diagnosis ◽

Regression Analysis ◽

Kidney Disease ◽

Logistic Regression Analysis ◽

Diabetic Kidney Disease ◽

Potential Role ◽

Multivariate Logistic Regression Analysis ◽

Diagnostic Model ◽

Diabetic Kidney

AimsTo investigate the potential role of renal arterial resistance index (RI) in the differential diagnosis between diabetic kidney disease (DKD) and non-diabetic kidney disease (NDKD) and establish a better-quantified differential diagnostic model.Materials and MethodsWe consecutively reviewed 469 type 2 diabetes patients who underwent renal biopsy in our center. According to the renal biopsy results, eligible patients were classified into the DKD group and the NDKD group. The diagnostic significance of RI was evaluated by receiver operating characteristic (ROC) curve analysis. Logistic regression analysis was used to search for independent risk factors associated with DKD. Then a novel diagnostic model was established using multivariate logistic regression analysis.ResultsA total of 332 DKD and 137 NDKD patients were enrolled for analysis. RI was significantly higher in the DKD group compared with those in the NDKD group (0.70 vs. 0.63, p< 0.001). The optimum cutoff value of RI for predicting DKD was 0.66 with sensitivity (69.2%) and specificity (80.9%). Diabetic retinopathy, diabetes duration ≥ 60 months, HbA1c ≥ 7.0(%), RI ≥ 0.66, and body mass index showed statistical significance in the multivariate logistic regression analysis. Then, we constructed a new diagnostic model based on these results. And the validation tests indicated that the new model had good sensitivity (81.5%) and specificity (78.6%).ConclusionsRI has a potential role in discriminating DKD from NDKD. The RI-based predicting model can be helpful for differential diagnosis of DKD and NDKD.

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Predictors for development of palbociclib-induced neutropenia in breast cancer patients as determined by ordered logistic regression analysis

10.21203/rs.3.rs-679084/v1 ◽

2021 ◽

Author(s):

Yuko Kanbayashi ◽

Koichi Sakaguchi ◽

Takeshi Ishikawa ◽

Koichi Takayama ◽

Tetsuya Taguchi

Keyword(s):

Breast Cancer ◽

Logistic Regression ◽

Regression Analysis ◽

Cancer Patients ◽

Logistic Regression Analysis ◽

Roc Analysis ◽

Operating Characteristic ◽

Breast Cancer Patients ◽

Ordered Logistic Regression ◽

Significant Factors

Abstract This retrospective study aimed to identify predictors for the development of palbociclib-induced neutropenia. This study retrospectively analysed 78 breast cancer patients who had received palbociclib at our hospital between January 2018 and May 2020. For the regression analysis of factors associated with palbociclib-induced neutropenia, variables were extracted manually from medical charts. The level of palbociclib-induced neutropenia was evaluated using the National Cancer Institute’s Common Terminology Criteria for Adverse Events (version 5). Multivariate ordered logistic regression analysis was performed to identify predictors for the development of neutropenia. Optimal cut-off thresholds were determined using receiver operating characteristic (ROC) analysis. Values of P < 0.05 (2-tailed) were considered significant. Significant factors identified included concomitant use of statin (odds ratio [OR] = 0.104, 95% confidence interval [CI] = 0.018–0.598; P = 0.011] and body mass index (BMI) (OR = 1.118, 95% CI = 1.007–1.241; P = 0.037). ROC analysis revealed that neutropenia (grade 4) was more likely to occur with a BMI ≥ 22.3 kg/m2. In conclusion, no concomitant use of statins and high BMI were identified as significant predictors for the development of palbociclib-induced neutropenia.

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Can Patient-Reported Outcomes Predict the Need for Secondary Surgeries After Hip Arthroscopy?

The American Journal of Sports Medicine ◽

10.1177/0363546520974374 ◽

2020 ◽

Vol 49 (1) ◽

pp. 97-103 ◽

Cited By ~ 1

Author(s):

Jacob Shapira ◽

Rishika Bheem ◽

Cynthia Kyin ◽

Philip J. Rosinsky ◽

Mitchell B. Meghpara ◽

...

Keyword(s):

Logistic Regression ◽

Regression Analysis ◽

Logistic Regression Analysis ◽

Hip Arthroscopy ◽

Patient Reported Outcomes ◽

Roc Analysis ◽

Multivariate Logistic Regression Analysis ◽

Multivariate Logistic Regression ◽

Secondary Surgery ◽

Patient Reported

Background: Patient-reported outcomes (PROs) capture the postoperative period and reflect the patient’s perspective of one’s own recovery. However, it is unknown if PROs can reflect and predict the need for secondary surgeries after a primary hip arthroscopy. Purpose: To examine if PROs at 3 months and 1 year after primary hip arthroscopy were correlated with future reoperations and determine the critical thresholds for significant PROs utilizing a multivariate logistic regression analysis and receiver operator characteristic (ROC) analysis. Study Design: Case-control study; Level of evidence, 3. Methods: Data on consecutive patients who underwent primary hip arthroscopy between February 2008 and August 2018 was retrospectively reviewed. Patients were included for analysis if they had the following PROs preoperatively and at 3 months and 1 year postoperatively: modified Harris Hip Score (mHHS), Nonarthritic Hip Score (NAHS), and visual analog scale (VAS) for pain. Patients were split into 2 groups: those who underwent secondary surgery and those who did not. Patient variables, intraoperative labral treatment, preoperative PROs, and postoperative PROs were compared between the 2 groups. A multivariate logistic regression analysis and ROC analysis were deployed to evaluate the correlation between PROs and the need for future surgery. Results: A total of 911 primary arthroscopy cases were included in this study. While age, body mass index, labral treatment, and 3-month and 1-year follow-up mHHS, NAHS, and VAS were significant in the bivariate analysis, the multivariate logistic regression analysis only found 1-year mHHS to be significant in the final model ( P < .05). The ROC curve for 1-year mHHS demonstrated acceptable discrimination between patients requiring secondary surgery and patients not requiring secondary surgery with an area under the curve of 0.73. Using the Youden index, a threshold of 80.5 was determined for the 1-year mHHS. Conclusion: The risk for secondary procedures may be evaluated with mHHS at 1 year after primary hip arthroscopy. Surpassing a score of 80.5 may be associated with a 74.4% reduction in risk for either a revision hip arthroscopy or a conversion to hip replacement.

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Diagnostic Model of in-Hospital Mortality in Patients with Acute ST-Segment Elevation Myocardial Infarction: Algorithm Development and Validation (Preprint)

10.2196/preprints.25644 ◽

2020 ◽

Author(s):

Yong Li

Keyword(s):

Logistic Regression ◽

Regression Analysis ◽

Hospital Mortality ◽

Logistic Regression Analysis ◽

Diagnostic Model ◽

Validation Data ◽

Data Set ◽

St Segment Elevation ◽

Acute Stemi ◽

Development Data

BACKGROUND Coronary heart disease, including ST-segment elevation myocardial infarction (STEMI), remains the main cause of death. OBJECTIVE The objective of our research was to develop and externally validate a diagnostic model of in-hospital mortality in acute STEMI patients. METHODS We performed multiple logistic regression analysis on a cohort of hospitalized acute STEMI patients. Participants: From January 2002 to December 2011, a total of 2,183 inpatients with acute STEMI were admitted for development.The external validation data set of this model comes from 7,485 hospitalized patients with acute STEMI from January 2012 to August 2019.We used logistic regression analysis to analyze the risk factors of in-hospital mortality in the development data set.We developed a diagnostic model of in-hospital mortality and constructed a nomogram. We evaluated the predictive performance of the diagnostic model in the validation data set by examining the measures of discrimination, calibration, and decision curve analysis (DCA). RESULTS In the development data set, 61 of the 2,183 participants (2.8%) experienced in-hospital mortality. The strongest predictors of in-hospital mortality were advanced age and high Killip classification. Logistic regression analysis showed the difference between the two groups with and without in-hospital mortality (odds ratio [OR] 1.058, 95% CI 1.029-1.088; P <.001), Killip III (OR 8.249, 95% CI 3.502-19.433; P <.001) and Killip IV (OR 39.234, 95% CI 18.178-84.679; P <.001). We had developed a diagnostic model of in-hospital mortality. The area under the receiver operating characteristic curve (AUC) was 0.9126 (SD 0.0166, 95% CI 0.88015-0.94504). We constructed a nomogram based on age and Killip classification. In-hospital mortality occurred in 127 of 7,485 participants(1.7%) in the validation data set. The AUC was 0 .9305(SD 0.0113, 95% CI 0. 90827-0. 95264). CONCLUSIONS We had developed and externally validated a diagnostic model of in-hospital mortality in acute STEMI patients. It was found that the discrimination, calibration and DCA of this model were satisfactory. CLINICALTRIAL ChiCTR.org ChiCTR1900027129; http://www.chictr.org.cn/edit.aspx?pid=44888&htm=4.

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Predictors for development of palbociclib-induced neutropenia in breast cancer patients as determined by ordered logistic regression analysis

Scientific Reports ◽

10.1038/s41598-021-99504-5 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Yuko Kanbayashi ◽

Koichi Sakaguchi ◽

Takeshi Ishikawa ◽

Koichi Takayama ◽

Tetsuya Taguchi

Keyword(s):

Breast Cancer ◽

Logistic Regression ◽

Regression Analysis ◽

Cancer Patients ◽

Logistic Regression Analysis ◽

Roc Analysis ◽

Operating Characteristic ◽

Breast Cancer Patients ◽

Ordered Logistic Regression ◽

Significant Factors

AbstractThis retrospective study aimed to identify predictors for the development of palbociclib-induced neutropenia. This study retrospectively analysed 78 breast cancer patients who had received palbociclib at our hospital between January 2018 and May 2020. For the regression analysis of factors associated with palbociclib-induced neutropenia, variables were extracted manually from medical charts. The level of palbociclib-induced neutropenia was evaluated using the National Cancer Institute’s Common Terminology Criteria for Adverse Events (version 5). Multivariate ordered logistic regression analysis was performed to identify predictors for the development of neutropenia. Optimal cut-off thresholds were determined using receiver operating characteristic (ROC) analysis. Values of P < 0.05 (2-tailed) were considered significant. Significant factors identified included concomitant use of statin (odds ratio [OR] = 0.104, 95% confidence interval [CI] = 0.018–0.598; P = 0.011) and body mass index (BMI) (OR = 1.118, 95% CI = 1.007–1.241; P = 0.037). ROC analysis revealed that neutropenia (grade 4) was more likely to occur with a BMI ≥ 22.3 kg/m2. In conclusion, no concomitant use of statins and high BMI were identified as significant predictors for the development of palbociclib-induced neutropenia.

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