Hosmer-Lemeshow Goodness Of Fit Test Research Articles

Menstrual hygiene management practice and associated factors among secondary school girls in eastern Ethiopia: The influence of water, sanitation and hygiene facilities.

Inadequate water supply, poor sanitation and hygiene (WASH) facilities in schools, insufficient puberty education and a lack of hygienic menstrual hygiene management (MHM) items (absorbents) cause girls in developing countries like Ethiopia to view menstruation as shameful and uncomfortable. However, there was little evidence of female MHM practice and associated factors among secondary school girls in eastern Ethiopia. To assess MHM practice and associated factors among secondary school girls in eastern Ethiopia. Institutional-based cross-sectional study design was conducted. A total of 473 secondary schoolgirls were selected using a stratified sampling technique. Data were collected using a self-administered structured questionnaire, double-entered to Epidata version 3.1 and exported to SPSS version 26 for analysis. Descriptive analysis was computed using frequency, percentage, mean and standard deviations. Multivariable logistic regression was applied to assess the association of the school water, sanitation and hygiene facilities with MHM. Adjusted odd ratio (AOR) with its 95% confidence interval was computed to show the strength of the association between dependent and independent variables. The goodness of fit of the model was tested by the Hosmer-Lemeshow goodness of fit test. Out of 473 study participants, 72.93% of them (95% CI: 68.74-76.76) reported good MHM practices. The study also found that availability of continuous water supply (AOR = 2.40, 95% CI: (1.42-4.01)); types of toilet (AOR = 2.01, 95% CI: (1.20-3.40)), confined space (AOR = 3.02, 95% CI: (1.49-4.76)) and having females' toilets alone inside in the school (AOR = 2.70, 95% CI: (1.20-4.40)) were significantly associated with female students' good MHM practice. The survey revealed that some of the secondary schoolgirls practiced poor menstrual hygiene management (MHM), which needs further improvement. The study also found that the availability of continuous water supply in the school, types of toilet facility in the school, availability of private space to manage periods at school, learning about MHM in schools and availability of female toilets kept locked inside were factors significantly associated with MHM practice of students, which require integration of Zonal Health and education bureau to jointly work towards the improvement of school WASH facilities.

Post-abortion contraceptive utilization and associated factors among women who attended abortion services: A health facility cross-sectional study.

Background: Post-abortion contraceptive utilization prevents unintended pregnancies, reduces the number of abortions, and lowers the morbidity and mortality related to obstetric complications. It plays a central role in improving maternal health and reducing child mortality. However, many women are suffering from unintended pregnancy and its complications after abortion care. Hence, the main objective of this study was to determine the magnitude of post-abortion contraceptive utilization and its associated factors in Bahir Dar City. Methods: Health-facility-based cross-sectional study was conducted in Bahir Dar city health facilities from April 1 to May 30, 2018. A systematic random sampling technique was used to select 354 eligible study participants. A pre-tested semi-structured questionnaire was used to collect the data. Data entry was done using Epi Data version 3.7 software and analyzed by SPSS v23 software. Descriptive statistics were done based on the nature of the data. A simple logistic regression model was used to identify the association and strength of exploratory variables and the outcome variables. Associations were announced at a 95% confidence interval and p-value <0.05 with adjusted odds ratio. Model fitness was checked by the Hosmer-Lemeshow goodness of fit test for logistic regression. Results: In this study, 348 aborted participants were involved with a mean age of 24.37 (±5.73) years. About 40% of the participants were not currently married and 12.9% were unable to read and write. The magnitude of post-abortion contraceptive utilization was 65.8%. The number of alive children (adjusted odds ratio: 7.0, 95% confidence interval: 1.54, 31.95), lower income (adjusted odds ratio: 0.14, 95% confidence interval: 0.03, 0.60), and (adjusted odds ratio: 0.11, 95% confidence interval: 0.02, 0.46), primary school education (adjusted odds ratio: 0.18, 95% confidence interval: 0.03, 0.97), and currently unemployed (adjusted odds ratio: 0.23, 95% confidence interval: 0.06, 0.85) were significantly associated with the post-abortion contraceptive utilization. Conclusion: The level of post-abortion contraceptive utilization is low as per the national plan. The number of alive children, lower income, lower education, and currently unemployed were the identified factors affecting post-abortion contraceptive utilization. Therefore, a collaborative effort is needed among stakeholders to increase the utilization and avoid factors that prevent the utilization of post-abortion contraceptive utilization.

Predictive Value of Troponin I, Creatinine Kinase Isoenzyme and the New Japanese Severity Score in Severe Acute Pancreatitis.

To evaluate troponin I, creatine kinase isoenzyme, and the new Japanese Severity Score(JSS) for predicting Severe Acute Pancreatitis-Associated myocardial Injury(SACI). This retrospective study included 136 patients with Severe Acute Pancreatitis, hospitalized in grade-III hospital from June 1, 2015, to October 31, 2022; selected using convenience sampling method and divided into SACI occurrence (n =34) and SACI non-occurrence (n =102) groups. New JSS evaluated predictive value of each SACI index. Binary logistic regression model compared risk factors and constructed a prediction model. Area under receiver operating characteristic curve (AUC) and Hosmer-Lemeshow goodness of fit test evaluated model's prediction efficiency and calibration ability. The incidence of SACI was 25%. Univariate analysis found that troponin I and creatine kinase isoenzyme were significantly different (P < 0.05) and independent risk factors for SACI. The new JSS, troponin I, and creatine kinase isoenzyme were included in the prediction model. The prediction model had a good calibration ability, and its predicted value and the actual observed value were not significantly different (Hosmer-Lemeshow χ2 = 5.408, P = 0.368). AUC of the model was 0.803 (95% CI: 0.689-0.918), and the optimal threshold of the prediction model was 0.318 with the maximum Youden index (0.488). The AUC for internal validation was 0.788 (95% CI: 0.657-0.876), and external validation was 0.761 (95% CI: 0.622-0.832). Troponin I and creatine kinase isoenzymes combined with the new JSS have a high predictive value for SACI, improving the early prediction and treatment of at-risk patients.

Developing a Predictive Model for Minimal or Mild Endometriosis as a Clinical Screening Tool in Infertile Women: Uterosacral Tenderness as a Key Predictor

To develop a noninvasive predictive model based on patients with infertility for identifying minimal or mild endometriosis. A retrospective cohort study. This study was conducted at a tertiary referral center. A total of consecutive 1365 patients with infertility who underwent laparoscopy between January 2013 and August 2020 were divided into a training set (n = 910) for developing the predictive model and a validation set (n = 455) to confirm the model's prediction efficiency. The patients were randomly assigned in a 2:1 ratio. Sensitivities, specificities, area under the curve, the Hosmer-Lemeshow goodness of fit test, Net Reclassification Improvement index, and Integrated Discrimination Improvement index were evaluated in the training set to select the optimum model. In the validation set, the model's discriminations, calibrations, and clinical use were tested for validation. In the training set, there were 587 patients with minimal or mild endometriosis and 323 patients without endometriosis. The combination of clinical parameters in the model was evaluated for both statistical and clinical significance. The best-performing model ultimately included body mass index, dysmenorrhea, dyspareunia, uterosacral tenderness, and serum cancer antigen 125 (CA-125). The nomogram based on this model demonstrated sensitivities of 87.7% and 93.3%, specificities of 68.6% and 66.4%, and area under the curve of 0.84 (95% confidence interval 0.81-0.87) and 0.85 (95% confidence interval 0.80-0.89) for the training and validation sets, respectively. Calibration curves and decision curve analyses also indicated that the model had good calibration and clinical value. Uterosacral tenderness emerged as the most valuable predictor. This study successfully developed a predictive model with high accuracy in identifying infertile women with minimal or mild endometriosis based on clinical characteristics, signs, and cost-effective blood tests. This model would assist clinicians in screening infertile women for minimal or mild endometriosis, thereby facilitating early diagnosis and treatment.

Optimal postoperative delirium prediction after coronary artery bypass grafting surgery: a prospective cohort study.

Postoperative delirium (POD) presents as a serious neuropsychiatric syndrome in patients undergoing off-pump coronary artery bypass grafting (OPCABG) surgery. This is correlated with higher mortality, cognitive decline, and increased costs. The Age-adjusted Charlson Comorbidity Index (ACCI) is recognized as an independent predictor for mortality and survival rate. The purpose of our study is to estimate the predictive value of the ACCI on the POD in patients undergoing OPCABG surgery. This prospective cohort study enrolled patients undergoing OPCABG surgery between December 2020 and May 2021 in Qilu Hospital. Patients were divided into the low-ACCI group (score, 0-3) and the high-ACCI group (score ≥4) according to their ACCI scores. The Confusion Assessment Method for the Intensive Care Unit (CAM-ICU) and CAM were used to diagnose POD within 7 days after surgery. The general, laboratory, and clinical data of the patients were recorded and collected. The characteristic ROC curve was applied to further assess the predictive value of the ACCI for POD in patients following OPCABG surgery. A total of 89 patients were enrolled, including 45 patients in the low-ACCI group and 44 patients in the high-ACCI group. The incidence of POD was higher in the high-ACCI group than in the low-ACCI group (45.5% vs. 15.6%, P = 0.003). Multivariate logistic regression analyses showed that the ACCI (OR, 2.433; 95% CI, 1.468-4.032; P = 0.001) was an independent risk factor for POD. The ACCI accurately predicted POD in patients following OPCABG surgery with an AUC of 0.738, and the Hosmer-Lemeshow goodness of fit test yielded X2 = 5.391 (P = 0.145). The high-ACCI group showed a high incidence of POD. The ACCI was an independent factor associated with POD in patients following OPCABG surgery. In addition, the ACCI could accurately predict POD in patients following OPCABG surgery. ClinicalTrials.gov, identifier CHiCTR2100052811.

Construction and evaluation of a nomogram prediction model for periprosthetic fractures after total hip arthroplasty

To construct and evaluate nomogram prediction model for periprosthetic fractures in patients undergoing total hip arthroplasty (THA). A total of 538 patients who underwent THA from April 2013 to February 2019 were selected as the research subjects, including 318 males and 220 females, aged 40 to 60 years old with an average age of (50.79±6.37) years old. All patients with THA were divided into non-fracture group (506 patients) and fracture group (32 pathents) according to the 3-year follow-up results. Univariate and multivariate Logistic regression analyses were performed to analyze the influencing factors of postoperative periprosthetic fractures in patients with THA. A nomogram prediction model for periprosthetic fractures in patients undergoing THA was constructed, and the validity and discrimination of the prediction model were evaluated. The proportion of patients with osteoporosis, trauma history, and hip revision in the fracture group were higher than those in the non-fracture group(P<0.05), and the proportion of bone cement prosthesis was lower than that in the non-fracture group(P<0.05). The osteoporosis status[OR=4.177, 95%CI(1.815, 9.617), P<0.05], trauma history[OR=7.481, 95%CI(3.104, 18.031), P<0.05], and hip revision[OR=11.371, 95%CI(3.220, 40.153, P<0.05] were independent risk factors for postoperative periprosthetic fractures in patients undergoing THA, cemented prosthesis [OR=0.067, 95%CI(0.019, 0.236), P<0.05] was an independent protective factor for postoperative periprosthetic fractures in patients undergoing THA(P<0.05). Hosmer-Lemeshow goodness of fit test showed that χ2=7.864, P=0.325;the area under the curve (AUC) for periprosthetic fractures in patients undergoing THA was 0.892 with a sensitivity of 87.5% and a specificity of 77.7% by receiver operating characteristic(ROC) curve. The nomogram prediction model for periprosthetic fractures after THA constructed in this study has good discrimination, which is beneficial to clinical prediction of periprosthetic fractures in patients undergoing THA, and facilitates individualized fracture prevention.

Risk factors and construction of a nomogram model for cirrhotic portal vein thrombosis combined with esophagogastric variceal bleeding

Objective: To investigate the risk factors and construct a nomogram model for predicting the occurrence of cirrhotic portal vein thrombosis in patients combined with esophagogastric variceal bleeding (EVB). Methods: Clinical data on 416 cirrhotic PVT cases was collected from the First Hospital of Lanzhou University between January 2016 and January 2022. A total of 385 cases were included after excluding 31 cases for retrospective analysis. They were divided into an esophagogastric variceal bleeding group and a non-esophagogastric variceal bleeding group based on the clinical diagnosis. The esophagogastric variceal group was then further divided into an EVB group and a non-bleeding group. All patients underwent gastroscopy, serology, and imaging examinations. The risk factors of PVT combined with EVB were identified by univariate analysis using SPSS 26. The prediction model of cirrhotic PVT in patients combined with EVB was constructed by R 4.0.4. The prediction efficiency and clinical benefits of the model were evaluated by the C-index, area under the receiver operating characteristic curve, calibration plots, and decision curve. The measurement data were examined by a t-test or Mann-Whitney U test. The counting data were tested using the χ(2) test or the Fisher exact probability method. Results: There were statistically significant differences in the etiology, Child-Pugh grade,erythrocyte count, hematocrit, globulin, and serum lipids between the esophageal and non-esophageal varices groups (P < 0.05). There were statistically significant differences in etiology, erythrocyte count, hemoglobin, hematocrit, neutrophil percentage, total protein, globulin, albumin/globulin, urea, high-density lipoprotein cholesterol, calcium, and neutrophil lymphocyte ratio (NLR) between the EVB and non-bleeding groups (P < 0.05). Multivariate logistic regression analysis showed that etiology (OR = 3.287, 95% CI: 1.497 ~ 7.214), hematocrit (OR = 0.897, 95% CI: 0.853 ~ 0.943), and high-density lipoprotein cholesterol (OR = 0.229, 95% CI: 0.071 ~ 0.737) were independent risk factors for cirrhotic PVT patients combined with EVB. The constructed normogram model predicted the probability of bleeding in patients. The nomogram model had shown good consistency and differentiation (AUC = 0.820, 95% CI: 0.707 ~ 0.843), as verified by 10-fold cross-validation (C-index = 0.799) and the Hosmer-Lemeshow goodness of fit test (P = 0.915). The calibration plot and the decision curve suggested that the prediction model had good stability and clinical practicability. Conclusion: The risk factors for EVB occurrence include etiology, erythrocyte, hemoglobin, hematocrit, percentage of neutrophils, total protein, globulin, albumin/globulin, urea, high-density lipoprotein cholesterol, calcium, and NLR in patients with cirrhotic liver. The constructed prediction model has good predictive value, and it can provide a reference for medical personnel to screen patients with high bleeding risk for targeted treatment.

Prediction model of stress ulcer after laparoscopic surgery for colorectal cancer established by machine learning algorithm.

Patients with colorectal cancer (CRC) are prone to stress ulcer after laparoscopic surgery. The analysis of risk factors for stress ulcer (SU) in patients with CRC is important to reduce mortality and improve patient prognosis. To identify risk factors for SU after laparoscopic surgery for CRC, and develop a nomogram model to predict the risk of SU in these patients. Among the 135 patients with CRC, 23 patients had postoperative SU, with an incidence of 17.04%. The SU group had higher levels of heat shock protein (HSP) 70, HSP90, and gastrin (GAS) than the non-SU group. Age, lymph node metastasis, HSP70, HSP90, and GAS levels were statistically different between the two groups, but other indicators were not statistically different. Logistic regression analysis showed that age ≥ 65 years, lymph node metastasis, and increased levels of HSP70, HSP90 and GAS were all risk factors for postoperative SU in patients with CRC (P < 0.05). According to these five risk factors, the area under the ROC curve for the nomogram model was 0.988 (95%CI: 0.971-1.0); the calibration curve demonstrated excellent agreement between predicted and actual probabilities, and the Hosmer-Lemeshow goodness of fit test revealed that the difference was not statistically significant (χ2 = 0.753, P = 0.999), suggesting that the nomogram model had good discrimination, calibration, and stability. Patients with CRC aged ≥ 65 years, with lymph node metastasis and elevated HSP70, HSP90, GAS levels, are prone to post-laparoscopic surgery SU. Our nomogram model shows good predictive value.

A prediction model of risk factors of poor wound healing after craniocerebral surgery.

To explore the independent risk factors of poor wound healing after craniocerebral surgery, and to generate a risk prediction model. A single-center retrospective observational analysis of 160 patients who underwent craniocerebral surgery in The 904th Hospital of the Joint Logistics Support Force of the PLA from February 2018 to February 2021 was carried out. Patients were divided into Group-A (n=70) and Group-B (n=90) according to postoperative wound healing outcome. Logistic regression was used to analyze the independent risk factors, and a nomogram prediction model was constructed using R software. The receiver operating characteristic (ROC) curve was used to test the predictive ability of the model, and the fitting effect was verified by Hosmer Lemeshow. The duration of operation, surgical site infection, diabetes mellitus, and the time of intubation in Group-B were significantly lower than Group-A (P<0.05). Serum albumin (ALB) and hemoglobin (HGB) in Group-B were significantly higher than those in Group-A (P<0.05). Logistic regression analysis showed that long operation duration, surgical site infection, duration of drainage tube, ALB <35g/L, and abnormal HGB were independent risk factors for poor wound healing (P<0.05). The area under the ROC curve (AUC) predicted by the model was 0.932, 95%CI (0.862~1.000). The Hosmer-Lemeshow goodness of fit test showed that the expected probability calculated by the model matched the actual probability (P>0.05). Long operation duration, surgical site infection, duration of drainage tube, ALB <35g/L, and abnormal HGB were risk factors for poor wound healing. The nomograph model based on these factors showed good discrimination, calibration, and clinical effectiveness in predicting poor wound healing.

P11.05.A EXTERNAL VALIDATION OF THE LUNG-MOLGPA TO PREDICT SURVIVAL AFTER STEREOTACTIC RADIOSURGERY FOR BRAIN METASTASES OF NON-SMALL CELL LUNG CANCER

Abstract BACKGROUND Accurate prediction of survival in patients with brain metastases is important for shared decision making. An updated version of the Lung-molGPA prognostic index estimates survival in patients with brain metastases of non-small cell lung cancer (NSCLC). This version includes programmed death ligand 1 (PDL1) expression as a prognostic factor. As this version has not yet been externally validated, we investigated whether the Lung-molGPA accurately predicts survival in a cohort of patients treated with stereotactic radiosurgery (SRS) for brain metastases. MATERIAL AND METHODS All patients (n = 140) treated with SRS for brain metastases of NSCLC (adenocarcinoma) between 2015 and 2020 were included in this retrospective study. GPA score was calculated for each patient based on: Karnofsky Performance Score (0 points for ≤70, 0.5 for 80, 1.0 for 90-100), age (0 points for ≥ 70 years, 0.5 for &amp;lt; 70), number of brain metastases (0 points for ≥ 5, 0.5 for &amp;lt; 5), extracranial metastases (0 points for present, 1.0 for absent), expression of either epidermal growth factor receptor (EGFR) or anaplastic lymphoma kinase (ALK) (0 points for negative or unknown, 0.5 for positive in either), and lastly expression of PDL1 (0 points for &amp;lt; 1% or unknown, 0.5 for ≥ 1%). A higher GPA score predicts improved survival. Kaplan-Meier analysis evaluated overall survival and hazard ratios were estimated by a Cox regression model. Predictive performance was evaluated using discrimination (C-statistic or area under the curve) and calibration (Hosmer-Lemeshow test). RESULTS Median age at diagnosis was 64 years (range 37-83) and median survival was 13 months. The cohort was divided into four prognostic groups, with a higher GPA score corresponding with favorable survival. The proportion of patients into each prognostic group was 12% (n = 17) with a GPA score of 0-1.0, 39% (n = 54) with a GPA score of 1.5-2.0, 43% (n = 59) with a GPA score of 2.5-3.0, and 5% (n = 8) with a GPA score of 3.5-4.0. Patients with a higher GPA score had better overall survival compared with lower GPA scores (p-values &amp;lt; 0.05 for all pairwise comparisons except for the most favorable prognostic group). Predicted and observed median survival were: 6 versus 7 months (GPA score 0-1.0), 15 versus 11 months (GPA score 1.5-2.0), 30 versus 30 months (GPA score 2.5-3.0), and 52 versus 24 months (GPA score 3.5-4.0). In the external validation, the GPA had a C-statistic of 0.70 (95% CI 0.59-0.82) and passed the Hosmer-Lemeshow goodness of fit test (p-value = 0.55). CONCLUSION The Lung-molGPA was externally validated in our population. The Lung-molGPA accurately predicted survival in our cohort of patients treated with SRS for brain metastases of NSCLC (adenocarcinoma) except for the small proportion of patients in the most favorable prognostic group. This prognostic tool is useful for counseling of patients in clinical practice.

Development of Prehospital Factors for Predicting Massive Blood Transfusion in Trauma Patients

Background: Massive blood loss is the primary cause of immediate death in trauma patients. In prehospital care, where laboratory and investigation resources are limited, a massive blood transfusion (MBT) scoring system could activate blood bank preparation of blood components and facilitate EMS decision-making regarding the appropriate trauma center destination. This is particularly significant in urban environments where motor vehicle accidents are highly prevalent. Materials and Methods: A retrospective cohort study was conducted using data from prehospital trauma patients. Predictive parameters, including vital signs, mechanism of injury, and serious injury body parts, were analyzed, and the significant parameters identified by a multivariable analysis were used to develop a clinical scoring system. Discrimination was evaluated by the area under the receiver operating characteristic (AuROC) curve, calibration was demonstrated with the Hosmer-Lemeshow goodness of fit test, and internal validation was performed. Results: Among 511 trauma patients, 72 (14.1%) received MBT. The prehospital factors that significantly predicted massive MBT included hypotension with a SBP of less than 90 mmHg, penetrating object injuries, serious injuries to the face, thorax, abdomen, extremities including the pelvis, and the use of life-saving interventions such as advanced airway management and pelvic binding. The AuROC was 0.943 (95% CI 0.914 to 0.972, p&lt;0.001). The probability of receiving massive transfusion was 94.79% in patients with score of 5 or higher. Conclusion: The prehospital MBT score demonstrates good performance and discrimination for predicting MBT using simple and rapidly obtainable parameters in a prehospital setting. Keywords: Massive transfusion; Massive bleeding; Prehospital care

Constructing a prognostic tool for predicting the risk of non-adherence to antiplatelet therapy in discharged patients with coronary heart disease: a retrospective cohort study.

To investigate the incidence and influencing factors affecting the non-adherence behavior of patients with coronary heart disease (CHD) to antiplatelet therapy after discharge and to construct a personalized predictive tool. In this retrospective cohort study, 289 patients with CHD who were admitted to the Department of Cardiology of The First Affiliated Hospital of the University of Science and Technology of China between June 2021 and September 2021 were enrolled. The clinical data of all patients were retrospectively collected from the hospital information system, and patients were followed up for 1 year after discharge to evaluate their adherence level to antiplatelet therapy, analyze their present situation and influencing factors for post-discharge adherence to antiplatelet therapy, and construct a nomogram model to predict the risk of non-adherence. Based on the adherence level to antiplatelet therapy within 1 year after discharge, the patients were divided into the adherence (n=216) and non-adherence (n=73) groups. Univariate analysis revealed statistically significant differences between the two groups in terms of variable distribution, including age, education level, medical payment method, number of combined risk factors, percutaneous coronary intervention, duration of antiplatelet medication, types of drugs taken at discharge, and CHD type (P<0.05). Furthermore, multivariate logistic regression analysis revealed that, except for the medical payment method, all the seven abovementioned variables were independent risk factors for non-adherence to antiplatelet therapy (P<0.05). The areas under the receiver operating characteristic curve before and after the internal validation of the predictive tool based on the seven independent risk factors and the nomogram were 0.899 (95% confidence interval [CI]: 0.858-0.941) and 0.89 (95% CI: 0.847-0.933), respectively; this indicates that the tool has good discrimination ability. The calibration curve and Hosmer-Lemeshow goodness of fit test revealed that the tool exhibited good calibration and prediction consistency (χ2=5.17, P=0.739). In this retrospective cohort study, we investigated the incidence and influencing factors affecting the non-adherence behavior of patients with CHD after discharge to antiplatelet therapy. For this, we constructed a personalized predictive tool based on seven independent risk factors affecting non-adherence behavior. The predictive tool exhibited good discrimination ability, calibration, and clinical applicability. Overall, our constructed tool is useful for predicting the risk of non-adherence behavior to antiplatelet therapy in discharged patients with CHD and can be used in personalized intervention strategies to improve patient outcomes.

Establishment and validation of prognostic nomogram in acute leukemia with trisomy 8

ABSTRACTObjectiveThis study aims to construct a nomogram model for predicting poor prognosis in acute leukemia with trisomy 8.MethodsA retrospective analysis was conducted on 244 patients with primary acute leukemia with trisomy 8 who received treatment in our hospital, and they were randomly divided into the modeling group (122 cases, including 78 cases with good prognosis and 44 cases with poor prognosis) and the verification group (122 cases). R software was used to construct a nomogram model for predicting poor prognosis in acute leukemia with trisomy 8.ResultsThe results of multivariate analysis showed that age >51 years old, and white blood cell count ≤20 × 109/L were risk factors for poor prognosis in acute leukemia patients with trisomy 8 (P < 0.05), and chemotherapy + transplantation was a protective factor for poor prognosis in acute leukemia patients with trisomy 8 (P < 0.05). The nomogram for poor prognosis of acute leukemia patients with trisomy 8 was constructed using the above four risk predictors. The Hosmer-Lemeshow goodness of fit test was = 6.371, P = 0.497, and the area under the ROC curve was 0.817 (95%CI: 0.742-0.892). The slope of the calibration curve of external validation was close to 1, Hosmer-Lemeshow goodness of fit test was χ2 = 6.507, P = 0.448, and the area under the ROC curve was 0.834 (95%CI: 0.748-0.921).ConclusionThe nomogram model constructed in this study for predicting poor prognosis among acute leukemia patients with trisomy 8 demonstrates excellent discrimination and consistency.

Establishment and evaluation of early in-hospital death prediction model for patients with acute pancreatitis in intensive care unit

To investigate the death risk prediction factors of acute pancreatitis (AP) patients in intensive care unit (ICU), and to establish a death prediction model and evaluate its efficacy. A retrospective cohort study was conducted using the data in the Medical Information Mart for Intensive Care-III (MIMIC-III). The clinical data of 285 AP patients admitted to the ICU in the database were collected, including age, gender, blood routine and blood biochemical indicators, comorbidities, simplified acute physiology score III (SAPS III) and hospital prognosis. By using univariate analysis, the differences in the clinical data of the patients were compared between the two groups. Binary multivariate Logistic regression analysis was used to screen out independent predictors of in-hospital death in AP patients. A death prediction model was established, and the nomogram was drawn. The receiver operator characteristic curve (ROC curve) was plotted, and the area under the ROC curve (AUC) was used to test the discrimination of the prediction model. In addition, the prediction model was compared with the SAPS III score in predicting in-hospital death. The calibration ability of the prediction model was evaluated by the Hosmer-Lemeshow goodness of fit test, and a calibration map was drawn to show the calibration degree of the prediction model. Among 285 patients with AP, 29 patients died in the hospital and 256 patients survived. Univariate analysis showed that the patients in the death group were older than those in the survival group (years old: 70±17 vs. 58±16), and had higher white blood cell count (WBC), total bilirubin (TBil), serum creatinine (SCr), blood urea nitrogen (BUN), red blood cell volume distribution width (RDW), proportion of congestive heart failure and SAPS III score [WBC (×109/L): 18.5 (13.9, 24.3) vs. 13.2 (9.3, 17.9), TBil (μmol/L): 29.1 (15.4, 66.7) vs. 16.2 (10.3, 29.1), SCr (μmol/L): 114.9 (88.4, 300.6) vs. 79.6 (53.0, 114.9), BUN (mmol/L): 13.9 (9.3, 17.8) vs. 6.1 (3.7, 9.6), RDW: 0.152 (0.141, 0.165) vs. 0.141 (0.134, 0.150), congestive heart failure: 34.5% vs. 14.8%, SAPS III score: 66 (52, 90) vs. 39 (30, 48), all P < 0.05]. Multivariate Logistic regression analysis showed that age [odds ratio (OR) = 1.038, 95% confidence interval (95%CI) was 1.005-1.073], WBC (OR = 1.103, 95%CI was 1.038-1.172), TBil (OR = 1.247, 95%CI was 1.066-1.459), BUN (OR = 1.034, 95%CI was 1.014-1.055) and RDW (OR = 1.344, 95%CI was 1.024-1.764) were independent risk predictors of in-hospital death in patients with AP. Logistic regression model was established: Logit(P) = 0.037×age+0.098×WBC+0.221×TBil+0.033×BUN+0.296×RDW-12.133. ROC curve analysis showed that the AUC of the Logistic regression model for predicting the in-hospital death of patients with AP was 0.870 (95%CI was 0.794-0.946), the sensitivity was 86.2%, and the specificity was 78.5%, indicating that the model had good predictive performance, and it was superior to the SAPS III score [AUC was 0.831 (95%CI was 0.754-0.907), the sensitivity was 82.8%, and the specificity was 75.4%]. A nomogram model was established based on the result of multivariate Logistic regression analysis. The calibration map showed that the calibration curve of the nomogram model was very close to the standard curve, with the goodness of fit test: χ2 = 6.986, P = 0.538, indicating that the consistency between the predicted death risk of the nomogram model and the actual occurrence risk was relatively high. The older the AP patient is, the higher the WBC, TBil, BUN, and RDW, and the greater the risk of hospital death. The death prediction Logistic regression model and nomogram model constructed based on the above indicators have good discrimination ability and high accuracy for high-risk patients with hospital death, which can accurately predict the probability of death in AP patients and provide a basis for prognosis judgment and clinical treatment of AP patients.

Risk factors for readmission after isolated coronary bypass graft surgery: context matters

Abstract Funding Acknowledgements Type of funding sources: None. Background Risk prediction scores are primarily based on preoperative factors and most applicable to their country of origin. Factors that contribute to these scores are commonly used to predict risk of readmission that is also highly variable. A recent systematic review of risk factors for unplanned readmission after first time isolated coronary artery bypass grafting (CABG)1 included cohort studies from the USA alone. Purpose To contrast risk factors for unplanned readmission in an Australian cohort with those identified in the systematic review. Methods A single centre, retrospective cohort analysis of adult patients from a major metropolitan tertiary teaching hospital having first time isolated CABG (N = 6155) between August 2002 and 2022. Unplanned inpatient readmission within 30-days of surgery was the primary outcome measure. Direct entry logistic regression modelling was used to identify risk factors for readmission in this cohort. Results The rate of readmission was 10.6% (n = 655). Female sex (OR 1.0, 95%CI 1-1.01), preoperative dialysis (OR 3.3, 95%CI 2.19-5.12), preoperative atrial fibrillation (OR 1.4, 95%CI 1.06-1.87), diabetes (OR 1.22, 95%CI 1.03-1.45), and peripheral vascular disease (OR 1.3, 95%CI 1.05-1.67) were the only significant predictors retained in the full model (c2(27, N = 6155) = 111.28, p &amp;lt;0.001) that correctly classified 89.3% of cases but only explained 3.6% (Nagelkerke R squared) of variation in readmission rate. The Hosmer-Lemeshow goodness of fit test indicated good support for the model despite this (p 0.841). Conclusion(s): Risk factors predicting readmission in this patient cohort were not consistent with those identified in the review of studies from the USA. Risk prediction is context specific. Research investigating factors associated with risk needs to capture variables that impact on context.

External Validation of the Breast Cancer Surgery Risk Calculator (BCSRc): A Predictive Model for Postoperative Complications.

The breast cancer surgical risk calculator (BCSRc) is a prognostic tool that determines a breast cancer patient's unique risk of acute complications following each possible surgical intervention. When used in the preoperative setting, it can help to stratify patients with an increased complication risk and enhance the patient-physician informed decision-making process. The objective of this study was to externally validate the four models used in the BCSRc on a large cohort of patients who underwent breast cancer surgery. The BCSRc was developed by using a retrospective cohort from the National Surgical Quality Improvement Program database from 2005 to 2018. Four models were built by using logistic regression methods to predict the following composite outcomes: overall, infectious, hematologic, and internal organ complications. This study obtained a new cohort of patients from the National Surgical Quality Improvement Program by utilizing participant user files from 2019 to 2020. The area under the curve, brier score, and Hosmer-Lemeshow goodness of fit test measured model performance, accuracy, and calibration, respectively. A total of 192,095 patients met inclusion criteria in the development of the BCSRc, and the validation cohort included 60,144 women. The area under the curve during external validation for each model was approximately 0.70. Accuracy, or Brier scores, were all between 0.04 and 0.003. Model calibration using the Hosmer-Lemeshow statistic found all p-values > 0.05. All of these model coefficients will be updated on the web-based BCSRc platform: www.breastcalc.org . The BCSRc continues to show excellent external-validation measures. Collectively, this prognostic tool can enhance the decision-making process, help stratify patients with an increased complication risk, and improve expectant management.

Magnitude and associated factors of acute kidney injury among preterm neonates admitted to public hospitals in Bahir Dar city, Ethiopia 2022: cross-sectional study

BackgroundAcute kidney injury is an independent risk factor for morbidity and mortality in critically ill neonates. Although the magnitude of preterm neonates is high and a major risk for acute kidney injury, there is a paucity of information regarding the magnitude and associated factors of acute kidney injury among preterm neonates in the study area. Therefore, the aim of this study was to assess magnitude and associated factors of acute kidney injury among preterm neonates admitted to public hospitals in Bahir Dar city, Ethiopia, 2022.MethodsAn institutional-based cross-sectional study was conducted among 423 preterm neonates admitted to public hospitals in Bahir Dar city from May 27 to June 27, 2022. Data were entered into Epi Data Version 4.6.0.2 transferred to Statistical Package and Service Solution version 26 for analysis. Descriptive and inferential statistics were employed. A binary logistic regression analysis was done to identify factors associated with acute kidney injury. Model fitness was checked through Hosmer-Lemeshow goodness of fit test. Variables with a p-value < 0.05 were considered as statistically significant in the multiple binary logistic regression analysis.ResultOut of 423 eligible, 416 neonatal charts were reviewed with a response rate of 98.3%.This study revealed that the magnitude of acute kidney injury was 18.27% (95% CI = 15–22). Very low birth weight (AOR = 3.26; 95% CI = 1.18–9.05), perinatal asphyxia (AOR = 2.84; 95%CI = 1.55–5.19), dehydration (AOR = 2.30; 95%CI = 1.29–4.09), chest compression (AOR = 3.79; 95%CI = 1.97–7.13), and pregnancy-induced hypertension (AOR = 2.17; 95%CI = 1.20–3.93) were factors significantly associated with the development of neonatal acute kidney injury.ConclusionAlmost one in five admitted preterm neonates developed acute kidney injury. The odds of acute kidney injury were high among neonates who were very low birth weight, perinataly asphyxiated, dehydrated, recipients of chest compression, and born to pregnancy-induced hypertensive mothers. Therefore, clinicians have to be extremely cautious and actively monitor renal function in those neonatal population in order to detect and treat acute kidney injury as early as possible.

Women's Satisfaction with Abortion Care and Associated Factors in Public Health Facilities of Mojo Town, East Ethiopia.

Client satisfaction is an important and commonly used indicator for measuring the quality of health care as it affects clinical outcomes, patient retention, and medical malpractice claims. To limit unintended pregnancies and avoid repeated abortions promoting abortion care services is crucial. In Ethiopia, problems related to abortion were neglected, and access to quality abortion care was very limited. Similarly, information related to abortion care service, particularly clients' satisfaction, and associated factors are limited in the study area that the study will be going to fill. A facility-based cross-sectional study design was employed on 255 women who came for abortion service in public health facilities of Mojo town and were included consecutively. The data was coded and entered into Epi info version 7 software and exported to SPSS version 20 for analysis. Bivariable and multivariable logistic regression models were applied to identify the associated factors. Model fitness and multicollinearity were checked by using the Hosmer-Lemeshow goodness of fit test and the variance inflation factor (VIF). Adjusted odds ratios and their 95% confidence were reported. A total of 255 study subjects were included in this study with a 100% response rate. The study depicted that 56.5% (95% CI: 51.3, 61.7 of the clients were satisfied with abortion care service. Having college and above educational level (AOR: 0.27; 95% CI: (0.14, 0.95), employee occupation (AOR: 1.86; 95% CI: (1.41, 2.93), medical abortion as a type of uterine evacuation (AOR: 3.93; 95% CI: (1.75, 8.83), and natural method of family planning users (AOR: 0.36; 95% CI: (0.08, 0.60) were factors associated with women's satisfaction. The overall satisfaction towards abortion care was considerably lower. Waiting time, cleanness of rooms, lack of laboratory service, and availability of service providers are mentioned factors for client dissatisfaction.

A166 RISK STRATIFICATION OF EARLY RE-HOSPITALIZATION IN PERSONS WITH INFLAMMATORY BOWEL DISEASES USING MULTIVARIABLE MODELS

Abstract Background Hospitalization for persons with inflammatory bowel disease (IBD), including Crohn’s disease (CD) and ulcerative colitis (UC), is a significant contributor to morbidity and health care costs in Canada. Recognition of individuals at high risk of re-hospitalization could help inform targeted outpatient interventions that mitigate this risk. Purpose The aim of our study is to derive prediction models of risk of early (90-day) re-hospitalization among persons with IBD. Method We conducted a retrospective cohort study of all adult persons with IBD admitted to The Ottawa Hospital, Canada, for an acute IBD-related indication between April 2009 - March 2016. Demographic, clinical, and health services variables were obtained through chart review. Persons were linked to population-based health administrative datasets to identify historical and future IBD-related hospitalizations across the greater Ottawa region. Multivariable logistic regression models of 90-day re-hospitalization in persons with CD and UC were derived, and candidate predictors that demonstrated an independent association with the outcome at a p-value of 0.1 were retained. Bootstrap internal validation (200 iterations) was performed on the final models. Model performance and calibration were evaluated using the optimism-corrected c-statistic value and Hosmer-Lemeshow goodness of fit test, respectively. Adjusted odds ratios are reported with 95% confidence intervals (CI). Optimal probability cut points for re-hospitalization were selected to optimize sensitivity, specificity, and the J (Youden’s) index. Result(s) There were 524 CD and 248 UC hospitalizations during the study period. Of these, 57 (10.9%) CD and 27 (10.9%) UC hospitalizations were associated with re-hospitalization within 90 days of discharge. Forty-two candidate predictors were tested among CD hospitalizations, and 35 were tested among UC hospitalizations. Four variables were retained in each of the final models. Model performance and calibration for each variable are described in Table 1. The optimal range of probability cut points allowed for a sensitivity/positive predictive value (PPV)/false positive rate (FPR) of 0.72/0.23/0.29 (maximum J-index of 0.43) in the model for CD, and 0.78/0.33/0.19 (maximum J-index of 0.59) in the model for UC, respectively. Image Conclusion(s) Demographic, clinical, and health services variables at the time of discharge have the potential to help identify persons with IBD at risk of early re-hospitalization, thereby permitting targeted outpatient intervention. Application of the models to our reference cohorts would earmark 1/3 or less of patients for early post-discharge intervention, with the potential to benefit more than 70% of patients destined for early re-hospitalization. Although the PPVs of our models were low, the models incorrectly predicted early re-hospitalization in less than 30% of patients. We are in process of externally validating these models in other jurisdictions across Ontario to test their generalizability. Please acknowledge all funding agencies by checking the applicable boxes below None Disclosure of Interest None Declared

Development and validation of an ultrasonography and clinicopathological features-based nomogram for non-sentinel lymph node metastasis.

With the development of precise treatment for breast cancer, the current trend of clinical treatment aims to limit axillary surgery as much as possible. At present, there is an unmet need to predict the probability of patients with a low risk of non-sentinel lymph node (SLN) metastasis and determine whether the omission of axillary lymph node dissection (ALND) is appropriate. We retrospectively analyzed the data of patients with breast cancer who underwent sentinel lymph node biopsy (SLNB) and ALND. The patients were randomly assigned to training and validation sets. The associations between non-SLN metastasis (NSLNM) and ultrasonography and clinicopathological characteristics were assessed by multivariate logistic regression. Then, a nomogram model was constructed and validated using the calibration curve and the receiver operating characteristic curve. Vascular infiltration, positive SLN number, negative SLN number, human epidermal growth factor receptor 2 (HER2) status, and lymph node shape were identified as independent predictive factors for positive NSLNM. The areas under the curve of the nomogram model to predict NSLNM were 0.793 and 0.780 in the training and validation sets, respectively, and P=0.161 and P=0.768 in the Hosmer-Lemeshow goodness of fit test, respectively. A nomogram model based on ultrasonography and clinicopathological features predicting NSLNM was established in our study, which is helpful for accurately assessing the risk of NSLNM in invasive breast cancer and providing evidence for individual surgical procedures involving axillary lymph nodes.