Objective To explore the risk factors for noncontiguous spinal fractures(NSFs)in the elderly.Methods The clinical data of 614 elderly patients with spinal fracture from January 2013 to December 2019 were analyzed retrospectively.Patients were divided into the NSFs group and the Non-NSFs group according to whether NSFs occurred or not.Univariate analysis and multivariate Logistic regression analysis were used to screen the risk factors of NSFs.Results Univariate analysis showed that female(P=0.003),high-energy violent injury(P=0.032),osteoporosis(P=0.004),fracture in spring(P=0.020),and previous spinal fracture history(P<0.001)were associated with the occurrence of NSFs.Multivariate Logistic regression analysis showed that fracture in spring(P=0.024),previous spinal fracture history(P<0.001)and high-energy violent injury(P=0.038)were the independent risk factors for the occurrence of NSFs in the elderly.Conclusion High-energy violent injury,fracture in spring and previous spinal fracture history are the independent risk factors for the occurrence of NSFs in the elderly.Therefore,elderly patients with the above risk factors should be examined more carefully and comprehensively to avoid missed diagnosis and delayed diagnosis.In order to reduce the incidence of this disease,corresponding measures should be taken according to the preventable risk factors.

Objective To analyze the risk factors for increased drainage volume after open transforaminal lumbar interbody fusion(TLIF),and to establish a predictive model and then validate it.Methods The clinical data of 680 patients who underwent open TLIF at the General Hospital of Northern Theater Command from January 2016 to December 2019 were collected and the patients were randomly divided into the training group(n=476)and the validation group(n=204).Taking the predictive factors screened out by LASSO regression analysis as independent variables,a multivariate Logistic regression predictive model was constructed.The model was internally validated through the receiver operating characteristic(ROC)curve,Hosmer-Lemeshow goodness-of-fit test,and calibration curve,and its clinical utility was assessed via decision curve analysis(DCA).Results LASSO regression analysis screened out four predictive variables:age,number of surgical segments,operative duration,and intraoperative blood loss.The multivariate Logistic regression predictive model demonstrated that age≥60 years,number of surgical segments≥4,operative duration≥2 hours,and intraoperative blood loss≥200 mL were independent influencing factors for the increased postoperative drainage volume in patients undergoing TLIF(P<0.05).ROC curve analysis revealed an area under the curve(AUC)of 0.816(95%CI:0.798 to 0.867)in the training group and 0.783(95%CI:0.685 to 0.823)in the validation group,indicating that the predictive model had good discriminatory ability.Additionally,the Hosmer-Lemeshow goodness-of-fit test and calibration curve indicated that the predictive model had a good degree of fit,and the predicted probability was basically consistent with the actual probability,demonstrating a good calibration.The DCA results confirmed that this predictive model could be applied in clinical practice.Conclusion The risk factors for increased drainage volume after open TLIF include age,number of surgical segments,operative duration,and intraoperative blood loss.The predictive model established based on these factors demonstrates good performance,and it can be applied in clinical guidance for the selection of drainage tube removal time after TLIF.

Objective To analyze the risk factors for increased drainage volume after open transforaminal lumbar interbody fusion(TLIF),and to establish a predictive model and then validate it.Methods The clinical data of 680 patients who underwent open TLIF at the General Hospital of Northern Theater Command from January 2016 to December 2019 were collected and the patients were randomly divided into the training group(n=476)and the validation group(n=204).Taking the predictive factors screened out by LASSO regression analysis as independent variables,a multivariate Logistic regression predictive model was constructed.The model was internally validated through the receiver operating characteristic(ROC)curve,Hosmer-Lemeshow goodness-of-fit test,and calibration curve,and its clinical utility was assessed via decision curve analysis(DCA).Results LASSO regression analysis screened out four predictive variables:age,number of surgical segments,operative duration,and intraoperative blood loss.The multivariate Logistic regression predictive model demonstrated that age≥60 years,number of surgical segments≥4,operative duration≥2 hours,and intraoperative blood loss≥200 mL were independent influencing factors for the increased postoperative drainage volume in patients undergoing TLIF(P<0.05).ROC curve analysis revealed an area under the curve(AUC)of 0.816(95%CI:0.798 to 0.867)in the training group and 0.783(95%CI:0.685 to 0.823)in the validation group,indicating that the predictive model had good discriminatory ability.Additionally,the Hosmer-Lemeshow goodness-of-fit test and calibration curve indicated that the predictive model had a good degree of fit,and the predicted probability was basically consistent with the actual probability,demonstrating a good calibration.The DCA results confirmed that this predictive model could be applied in clinical practice.Conclusion The risk factors for increased drainage volume after open TLIF include age,number of surgical segments,operative duration,and intraoperative blood loss.The predictive model established based on these factors demonstrates good performance,and it can be applied in clinical guidance for the selection of drainage tube removal time after TLIF.

INTRODUCTION: Cancer patients attending emergency departments (EDs) often present with acute symptoms and are frequently admitted. This study aimed to characterise the profile of oncology patients who were discharged from the ED. METHODS: This was a retrospective audit of patients with cancer-related diagnoses who presented to the ED at the Singapore General Hospital (SGH) over a 6-month period from 1 October 2018 to 31 March 2019 and were directly discharged from the ED. Data was extracted from the hospital's electronic medical record system. RESULTS: Of the 492 participants included in the study, the majority were triaged as Priority 2 (61.4%), while 30.7% were triaged as Priority 3, 6.9% as Priority 1 and 1.0% as Priority 4. There was no statistical difference between the National Early Warning scores across the different triage categories in these patients. The most common complaint was (44.3%), followed by genitourinary symptoms (19.5%) and those related to devices, catheters or stomas (17.3%). More investigations of all types were done for patients being managed in Priority 1 (57.6%) than in the other triage categories (40.1% for Priority 2, 23.2% for Priority 3 and 12.0% for Priority 4). Treatment procedures carried out were mainly symptomatic (analgesics, antiemetics, proton pump inhibitors) for 79.8% of the patients. There were no significant differences in the proportion of patients requiring various treatment modalities among the triage categories. CONCLUSION Selected oncological patients may potentially be managed in an ambulatory setting.
Humans ; Emergency Service, Hospital/statistics & numerical data* ; Retrospective Studies ; Female ; Neoplasms/diagnosis* ; Male ; Singapore ; Patient Discharge/statistics & numerical data* ; Middle Aged ; Aged ; Triage ; Adult ; Emergencies ; Aged, 80 and over

OBJECTIVES: To study genotype-phenotype correlations in children with FGFR3 variants and to improve clinical recognition of related disorders. METHODS: Clinical data of 95 patients aged 0-18 years harboring FGFR3 variants, confirmed by whole‑exome sequencing at Shanghai Children's Medical Center from January 2012 to December 2023, were retrospectively reviewed. Detailed phenotypic characterization was performed for 22 patients with achondroplasia (ACH) and 10 with hypochondroplasia (HCH). RESULTS: Among the 95 patients, 52 (55%) had ACH, 24 (25%) had HCH, 9 (9%) had thanatophoric dysplasia, 3 (3%) had syndromic skeletal dysplasia, 2 (2%) had severe achondroplasia with developmental delay and acanthosis nigricans, and 5 (5%) remained unclassified. A previously unreported FGFR3 variant, c.1663G>T, was identified. All 22 ACH patients presented with disproportionate short stature accompanied by limb dysplasia, commonly with macrocephaly, a depressed nasal bridge, bowed legs, and frontal bossing; complications were present in 17 (77%). The 10 HCH patients predominantly exhibited disproportionate short stature with limb dysplasia and depressed nasal bridge. CONCLUSIONS ACH is the most frequent phenotype associated with FGFR3 variants, and missense variants constitute the predominant variant type. The degree of FGFR3 activation appears to correlate with the clinical severity of skeletal dysplasia.
Humans ; Receptor, Fibroblast Growth Factor, Type 3/genetics* ; Child ; Male ; Child, Preschool ; Female ; Infant ; Adolescent ; Dwarfism/genetics* ; Achondroplasia/genetics* ; Lordosis/genetics* ; Infant, Newborn ; Retrospective Studies ; Genetic Association Studies ; Bone and Bones/abnormalities* ; Phenotype ; Limb Deformities, Congenital

Objective To explore the risk factors for noncontiguous spinal fractures(NSFs)in the elderly.Methods The clinical data of 614 elderly patients with spinal fracture from January 2013 to December 2019 were analyzed retrospectively.Patients were divided into the NSFs group and the Non-NSFs group according to whether NSFs occurred or not.Univariate analysis and multivariate Logistic regression analysis were used to screen the risk factors of NSFs.Results Univariate analysis showed that female(P=0.003),high-energy violent injury(P=0.032),osteoporosis(P=0.004),fracture in spring(P=0.020),and previous spinal fracture history(P<0.001)were associated with the occurrence of NSFs.Multivariate Logistic regression analysis showed that fracture in spring(P=0.024),previous spinal fracture history(P<0.001)and high-energy violent injury(P=0.038)were the independent risk factors for the occurrence of NSFs in the elderly.Conclusion High-energy violent injury,fracture in spring and previous spinal fracture history are the independent risk factors for the occurrence of NSFs in the elderly.Therefore,elderly patients with the above risk factors should be examined more carefully and comprehensively to avoid missed diagnosis and delayed diagnosis.In order to reduce the incidence of this disease,corresponding measures should be taken according to the preventable risk factors.

Objective To investigate the population characteristics,distribution of traditional Chinese medicine(TCM)syndromes and influencing factors of long-term prognosis of diarrhea-predominant irritable bowel syndrome(IBS-D),and to provide evidence for the formulation of intervention program for IBS-D patients.Methods A total of 124 patients with IBS-D admitted to the medical institutions of the project team members from July 2020 to August 2022 were selected.According to the scoring results of IBS Quality of Life Measure(IBS-QOL),the patients were divided into the good prognosis group(81 cases)and the poor prognosis group(43 cases).The distribution of TCM syndromes in patients with IBS-D was explored,and the difference of IBS-QOL scores of the patients between good prognosis group and poor prognosis group was compared.Univariate logistic regression analysis and multivariate logistic regression analysis were used to determine the main risk factors for poor prognosis in patients with IBS-D.Results(1)The analysis of population characteristics showed that there was no significant difference in the proportion of male and female patients with IBS-D.The patients with IBS-D were usually middle-aged,and had a large interval span of the course of disease.The severity of their symptoms was mostly moderate.All of the patients with IBS-D had various degrees of anxiety and depression,and had nutritional imbalance.(2)The distribution of TCM syndromes in the patients with IBS-D were shown as the following:78 cases were identified as liver depression and spleen deficiency type,accounting for 62.90％;26 cases were identified as spleen-qi deficiency type,accounting for 20.97％;20 cases were identified as spleen and kidney yang deficiency type,accounting for 16.13％.(3)Analysis of IBS-QOL score showed that compared with the good prognosis group,the items scores of negative emotion,physical function,behavioral disorder,health status,being fastidious about food,social function,sexual behavior and interpersonal relationship of IBS-QOL in the poor prognosis group were significantly lowered(P＜0.01).(4)The univariate analysis showed that the risk of poor prognosis in patients with IBS-D would be increased by the factors of age,education level,course of disease,severity of symptoms,anxiety state,depression state,TCM syndrome types,Acute Physiology and Chronic Health Evaluation scoring system Ⅱ(APACHE 11)score,complication of neurological diseases,hemoglobin level,albumin level and total protein level(P＜0.01).(5)The multivariate Logistic regression analysis showed that the risk factors for poor prognosis of IBS-D patients involved age,education level below junior high school,the severity of symptoms being severe,Self-Rating Anxiety Scale(SAS)score,Self-Rating Depression Scale(SDS)score,TCM syndrome being liver depression and spleen deficiency type,hemoglobin level,albumin level and total protein level(P＜0.01).Conclusion Most of IBS-D patients exert long-term poor prognosis,and their long-term prognosis is affected by the factors of age,education level,severity of symptoms,anxiety and depression state,nutritional imbalance and TCM syndrome being liver depression and spleen deficiency type.The identification of the risk factors of poor prognosis will provide evidence for the formulation and adjustment of clinical intervention programs.

Objective To analyze factors affecting outcomes of single-dose methotrexate(MTX)treatment in patients with ectopic pregnancy.Methods The data of 416 patients with ectopic pregnancy treated by single-dose MTX injection in Obstetrics and Gynecology Hospital,Fudan University from Jan 2019 to Dec 2022 were retrospectively analyzed.MTX was administered on day 1,and the serum β-human chorionic gonadotrophin(β-hCG)levels were measured on posttreatment day 4 and day 7.If serum β-hCG level decreased less than 15%,it was considered as treatment failure,thus the patient was included in the failure group.If serum β-hCG level decreased more than 15%,it was considered as effective initial treatment and β-hCG levels were measured weekly until back to nonpregnant level,thus the patient was included in the success group.Clinical data and treatment outcomes were collected and outcomes-related risk factors were analyzed.Results There were 70 patients in failure group and 346 patients in success group.The proportion of patients with increased β-humanchorionic gonadotrophin(β-hCG)level before MTX treatment in failure group was significantly higher than that of patients in success group(55.2%vs.35.8%,P=0.007)and the proportion of patients with decreased β-hCG level before MTX treatment in failure group was significantly lower than that of patients in success group(43.1%vs.63.6%,P=0.007).Monocyte counts in failure group was significantly lower than that in success group[(0.43±0.13)×109/L vs.(0.47±0.17)×109/L,t=-2.001,P=0.047)].The differences of basal β-hCG level,change speed of β-hCG level,serum albumin,serum creatinine,white blood cell,neutrocyte,mean platelet volume,neutrocyte-lymphocyte ratio,platelet-lymphocyte ratio and lymphocyte-monocyte ratio between the two groups were not significant.Logistic regression analysis suggested the changing patterns of serum β-hCG level and monocyte counts were associated with treatment outcomes.Decreased β-hCG level(OR=2.313,95%CI:1.222-4.379,P=0.010)and monocyte counts≥0.6×109/L(OR=4.018,95%CI:1.207-13.378,P=0.023)were positively correlated with treatment success.Conclusion Decreased β-hCG level MTX treatment and monocyte counts≥0.6×109/L were two independent risk factors for the successful treatment of ectopic pregnancy.

Objective To explore the efficiency of artificial intelligence and radiomics-assisted X-ray in diagnosis of lumbar osteoporotic vertebral compression fractures(OVCF).Methods The clinical data of 455 patients diagnosed as lumbar OVCF by MRI in our hospital were selected.The patients were divided into the training group(n=364)and the validation group(n=91),X-ray films were extracted,the image delineation,feature extraction and data analysis were carried out,and the artificial intelligence radiomics deep learning was applied to establish a diagnostic model for OVCF.After verifying the effectiveness of the model by receiver operating characteristic(ROC)curve,area under the curve(AUC),calibration curve,and decision curve analysis(DCA),the efficiencies of manual reading,model reading,and model-assisted manual reading of X-ray in the early diagnosis of OVCF were compared.Results The ROC curve,AUC and calibration curve proved that the model had good discrimination and calibration,and excellent diagnostic performance.DCA demonstrated that the model had a higher clinical net benefit.The diagnostic efficiency of the manual reading group:the accuracy rate was 0.89,the recall rate was 0.62.The diagnostic efficiency of the model reading group:the accuracy rate was 0.93,the recall rate was 0.86,the model diagnosis showed good predictive performance,which was significantly better than the manual reading group.The diagnostic efficiency of the model-assisted manual reading group:the accuracy rate was 0.92,the recall rate was 0.72,and the recall rate of the model-assisted manual reading group was higher than that of the manual reading group,but lower than that of the model reading group,indicating the superiority of the model diagnosis.Conclusion The diagnostic model established based on artificial intelligence and radiomics in this study has reached an ideal level of efficacy,with better diagnostic efficacy compared with manual reading,and can be used to assist X-ray in the early diagnosis of OVCF.

Objective To develop a clinical prediction model for predicting risk factors for prolonged hospital stay after anterior cervical discectomy and fusion(ACDF).Methods The clinical data of 914 patients underwent ACDF treatment for cervical spondylotic myelopathy(CSM)were retrospectively analyzed.According to the screening criteria,800 eligible patients were eventually included,and the patients were divided into the development cohort(n=560)and the validation cohort(n=240).LASSO regression was used to screen variables,and multivariate Logistic regression analysis was used to establish a prediction model.The prediction model was evaluated from three aspects:differentiation,calibration and clinical effectiveness.The performance of the model was evaluated by area under the curve(AUC)and Hosmer-Lemeshow test.Decision curve analysis(DCA)was used to evaluate the clinical effectiveness of the model.Results In this study,the five factors that were significantly associated with prolonged hospital stay were male,abnormal BMI,mild-to-moderate anemia,stage of surgery(morning,afternoon,evening),and alcohol consumption history.The AUC of the development cohort was 0.778(95%CI:0.740 to 0.816),with a cutoff value of 0.337,and that of the validation cohort was 0.748(95%CI:0.687 to 0.809),with a cutoff value of 0.169,indicating that the prediction model had good differentiation.At the same time,the Hosmer-Lemeshow test showed that the model had a good calibration degree,and the DCA proved that it was effective in clinical application.Conclusion The prediction model established in this study has excellent comprehensive performance,which can better predict the risk of prolonged hospital stay,and can guide clinical intervention as soon as possible,so as to minimize the postoperative hospital stay and reduce the cost of hospitalization.