OBJECTIVE: To investigate the predictive value of oxygenation index (PaO₂/FiO₂) at intensive care unit (ICU) admission on 30-day mortality in patients with sepsis. METHODS: A retrospective study was conducted. Patients with sepsis who were hospitalized in the ICU of the Affiliated Hospital of Jining Medical University from April 2015 to October 2023 were enrolled. The demographic information, comorbidities, sites of infection, vital signs and laboratory test indicators at the time of admission to the ICU, disease severity scores within 24 hours of admission to the ICU, treatment process and prognostic indicators were collected. According to the PaO₂/FiO₂ at ICU admission, patients were divided into Q1 group (PaO₂/FiO₂ of 4.1-16.4 cmHg, 1 cmHg ≈ 1.33 kPa), Q2 group (PaO₂/FiO₂ of 16.5-22.6 cmHg), Q3 group (PaO₂/FiO₂ of 22.7-32.9 cmHg), and Q4 group (PaO₂/FiO₂ of 33.0-94.8 cmHg). Differences in the indicators across the four groups were compared. Multifactorial Cox regression analysis was used to assess the relationship between PaO₂/FiO₂ and 30-day mortality of patients with sepsis. The predictive value of PaO₂/FiO₂, sequential organ failure assessment (SOFA) and acute physiology and chronic health evaluation II (APACHE II) on 30-day prognosis of patients with sepsis was analyzed by receiver operator characteristic curve (ROC curve). RESULTS: A total of 1 711 patients with sepsis were enrolled, including 428 patients in Q1 group, 424 patients in Q2 group, 425 patients in Q3 group, and 434 patients in Q4 group. 622 patients died at 30-day, the overall 30-day mortality was 36.35%. There were statistically significant differences in age, body mass index (BMI), history of smoking, history of alcohol consumption, admission heart rate, respiratory rate, APACHE II score, SOFA score, Glasgow coma score (GCS), site of infection, Combined chronic obstructive pulmonary disease (COPD), blood lactic acid (Lac), prothrombin time (PT), albumin (Alb), total bilirubin (TBil), pH, proportion of mechanical ventilation, duration of mechanical ventilation, proportion of vasoactive medication used, and maximal concentration, length of ICU stay, hospital stay, incidence of acute kidney injury, in-hospital mortality, 30-day mortality among the four groups. Multivariate Cox regression analysis showed that after adjusting for confounding factors, for every 1 cmHg increase in PaO₂/FiO₂ at ICU admission, the 30-day mortality risk decreased by 2% [hazard ratio (HR) = 0.98, 95% confidence interval (95%CI) was 0.98-0.99, P < 0.001]. The 30-day mortality risk in the Q4 group was reduced compared with the Q1 group by 41% (HR = 0.59, 95%CI was 0.46-0.76, P < 0.001). The fitted curve showed that a curvilinear relationship between PaO₂/FiO₂ and 30-day mortality after adjustment for confounders. In the inflection point analysis, for every 1 cmHg increase in PaO₂/FiO₂ at PaO₂/FiO₂ < 28.55 cmHg, the risk of 30-day death in sepsis patients was reduced by 5% (HR = 0.95, 95%CI was 0.94-0.97, P < 0.001); when PaO₂/FiO₂ ≥ 28.55 cmHg, there was no statistically significant association between PaO2/FiO2 and the increase in the risk of 30-day death in sepsis (HR = 1.01, 95%CI was 0.99-1.02, P = 0.512). ROC curve analysis showed that the area under the curve (AUC) for the prediction of 30-day mortality by admission PaO₂/FiO₂ in ICU sepsis patients was 0.650, which was lower than the predictive ability of the SOFA score (AUC = 0.698) and APACHE II score (AUC = 0.723). CONCLUSION In patients with sepsis, PaO₂/FiO₂ at ICU admission is strongly associated with 30-day mortality risk, alerting healthcare professionals to pay attention to patients with low PaO₂/FiO₂ for timely interventions.
Humans ; Sepsis/mortality* ; Intensive Care Units ; Retrospective Studies ; Prognosis ; Hospital Mortality ; Oxygen ; Male ; Predictive Value of Tests ; Female ; Middle Aged ; Aged

Objective To investigate the value of MRI of the sternal end of clavicle in bone age assessment in Chinese population,especially its applicability in the determination of criminal responsible age.Methods A total of 431 patients aged from 10.00 to 29.99 years with neck or chest MRI were retrospectively collected.According to the Schmeling grading method,the epiphyseal development of the clavicle MRI was divided into five grades.The consistency of methods was evaluated.The correlation and general descriptive analysis between MRI grades and age was analyzed.The sex difference was analyzed.Curve fitting was used to establish a nonlinear model between age and grades.Results The grades of clavicle MRI showed a significant age-related trend(Figure 2),and the correlation was 0.861(0.887 in males and 0.840 in females).Except for grade 1,there was no significant difference between males and females in other grades.The minimum age of male grade 3 was greater than 14 years old,and the minimum age of female grade 3 was greater than 16 years old.The minimum age in grade 4 and grade 5 was over 18 years old in both sexes.The best curve fitting model was cubic model for both sexes(R2=0.805 for men and 0.722 for women).Conclusion Clavicle MRI can be used for the assessment of bone age in Chinese population.Complete epiphyseal plate closure can be used as a reliable indicator for the determination of age at 18 years old,and it is expected to achieve radiation-free forensic bone age assessment.

Objective To investigate the value of MRI of the sternal end of clavicle in bone age assessment in Chinese population,especially its applicability in the determination of criminal responsible age.Methods A total of 431 patients aged from 10.00 to 29.99 years with neck or chest MRI were retrospectively collected.According to the Schmeling grading method,the epiphyseal development of the clavicle MRI was divided into five grades.The consistency of methods was evaluated.The correlation and general descriptive analysis between MRI grades and age was analyzed.The sex difference was analyzed.Curve fitting was used to establish a nonlinear model between age and grades.Results The grades of clavicle MRI showed a significant age-related trend(Figure 2),and the correlation was 0.861(0.887 in males and 0.840 in females).Except for grade 1,there was no significant difference between males and females in other grades.The minimum age of male grade 3 was greater than 14 years old,and the minimum age of female grade 3 was greater than 16 years old.The minimum age in grade 4 and grade 5 was over 18 years old in both sexes.The best curve fitting model was cubic model for both sexes(R2=0.805 for men and 0.722 for women).Conclusion Clavicle MRI can be used for the assessment of bone age in Chinese population.Complete epiphyseal plate closure can be used as a reliable indicator for the determination of age at 18 years old,and it is expected to achieve radiation-free forensic bone age assessment.

Objective:To explore the current situation and influencing factors of quality of life of septic patients in intensive care unit (ICU) after discharge, and to provide theoretical basis for clinical early psychological intervention and continuity of care.Methods:A prospective observational study was conducted. The septic patients who were hospitalized in the department of critical care medicine of the Affiliated Hospital of Jining Medical University and discharged with improvement from January 1 to December 31, 2022 were selected as the research objects. The demographic information, basic diseases, infection site, vital signs at ICU admission, severity scores of the condition within 24 hours after ICU admission, various biochemical indexes, treatment process, and prognostic indexes of all the patients were recorded. All patients were assessed by questionnaire at 3 months of discharge using the 36-item short-form health survey scale (SF-36 scale), the activities of daily living scale (ADL scale), and the Montreal cognitive assessment scale (MoCA scale). Multiple linear regression was used to analyze the factors influencing the quality of life of septic patients after discharge from the hospital.Results:A total of 200 septic patients were discharged with improvement and followed up at 3 months of discharge, of which 150 completed the questionnaire. Of the 150 patients, 57 had sepsis and 93 had septic shock. The total SF-36 scale score of septic patients at 3 months of discharge was 81.4±23.0, and the scores of dimensions were, in descending order, role-emotional (83.4±23.0), mental health (82.9±23.6), bodily pain (82.8±23.3), vitality (81.6±23.2), physical function (81.4±23.5), general health (81.1±23.3), role-physical (79.5±27.0), and social function (78.8±25.2). There was no statistically significant difference in the total SF-36 scale score between the patients with sepsis and septic shock (82.6±22.0 vs. 80.7±23.6, P > 0.05). Incorporating the statistically significant indicators from linear univariate analysis into multiple linear regression analysis, and the results showed that the factors influencing the quality of life of septic patients at 3 months after discharge included ADL scale score at 3 months after discharge [ β= 0.741, 95% confidence interval (95% CI) was 0.606 to 0.791, P < 0.001], length of ICU stay ( β= -0.209, 95% CI was -0.733 to -0.208, P = 0.001), duration of mechanical ventilation ( β= 0.147, 95% CI was 0.122 to 0.978, P = 0.012), total dosage of norepinephrine ( β= -0.111, 95% CI was -0.044 to -0.002, P = 0.028), mean arterial pressure (MAP) at ICU admission ( β= -0.102, 95% CI was -0.203 to -0.007, P = 0.036) and body weight ( β= 0.097, 95% CI was 0.005 to 0.345, P = 0.044). Conclusions:The quality of life of patients with sepsis at 3 months after discharge is at a moderately high level. The influencing factors of the quality of life of patients with sepsis at 3 months after discharge include the ADL scale score at 3 months after discharge, the length of ICU stay, the duration of mechanical ventilation, the total dosage of norepinephrine, MAP at ICU admission and body weight, and healthcare professionals should enhance the treatment and care of the patients during their hospitalization based on the above influencing factors, and pay attention to early psychological intervention and continued care for such patients.

AIM: To compare the binocular and monocular accommodation among normal group, premyopia group and mild myopia group, and to study the characteristics of accommodation in the premyopia group, thus providing clinical evidence for the delay/prevention of myopia and the effective decrease of the incidence of myopia.METHODS: Cross-sectional descriptive study. A total of 179 children who had abnormal/high-risk visual acuity indicated by the vision screening in school from October 2021 to February 2023 were selected, including 92 males and 87 females, aged from 6 to 12(mean 8.55±1.66)years old, then they were referred to the Juvenile Myopia Prevention and Control Center in Cuizu Community Health Service Center. They were divided into normal group(+0.75 D<SE≤+2.00 D), the premyopia group(-0.50 D<SE≤+0.75 D)and the mild myopia group(-3.00 D≤SE≤-0.50 D)according to the diopters after cycloplegia, and binocular myopia grouping is defined by the eye with lower diopter. Binocular positive relative accommodation(PRA), negative relative accommodation(NRA), accommodative facility(AF), and monocular AF and amplitude of accommodation(AA)were examined. The age, binocular and monocular accommodation of different groups were compared.RESULTS: There were no difference in the sex ratio of different groups(χ2=0.167, P=0.920). There was no difference in age between the normal group and the premyopia group(P=0.310), but there were differences between the mild myopia group and the normal group and premyopia group(P=0.018, <0.01); Binocular NRA, PRA, and AF had significance between the normal group and the premyopia group(P<0.01), while there was no significance between the premyopia group and the mild myopia(P>0.05). Monocular AF had significance between the normal group and the premyopia group(P<0.01), while there was no significance between the premyopia group and the mild myopia group(P>0.05); The monocular AA had significance among the three groups(P<0.05).CONCLUSION: Although the diopters was normal, binocular NRA, PRA, monocular and binocular AF had significantly decreased in the premyopia group, and there was no significant difference compared with mild myopia group; monocular AA had decreased in the premyopia group and it was also significantly different from the mild myopia group. The accommodation function should be examined in premyopic children. Recovering the abnormal visual function through visual training may be a way to prevent and control premyopia from progressing to myopia.

Objective:To investigate the risk factors of lower extremity deep venous thrombosis (LEDVT) in patients with sepsis during hospitalization in intensive care unit (ICU), and to construct a nomogram prediction model of LEDVT in sepsis patients in the ICU based on the critical care scores combined with inflammatory markers, and to validate its effectiveness in early prediction.Methods:726 sepsis patients admitted to the ICU of the Affiliated Hospital of Jining Medical University from January 2015 to December 2021 were retrospectively included as the training set to construct the prediction model. In addition, 213 sepsis patients admitted to the ICU of the Affiliated Hospital of Jining Medical University from January 2022 to June 2023 were retrospectively included as the validation set to verify the performance of the prediction model. Clinical data of patients were collected, such as demographic information, vital signs at the time of admission to the ICU, underlying diseases, past history, various types of scores within 24 hours of admission to the ICU, the first laboratory indexes of admission to the ICU, lower extremity venous ultrasound results, treatment, and prognostic indexes. Lasso regression analysis was used to screen the influencing factors for the occurrence of LEDVT in sepsis patients, and the results of Logistic regression analysis were synthesized to construct a nomogram model. The nomogram model was evaluated by receiver operator characteristic curve (ROC curve), calibration curve, clinical impact curve (CIC) and decision curve analysis (DCA).Results:The incidence of LEDVT after ICU admission was 21.5% (156/726) in the training set of sepsis patients and 21.6% (46/213) in the validation set of sepsis patients. The baseline data of patients in both training and validation sets were comparable. Lasso regression analysis showed that seven independent variables were screened from 67 parameters to be associated with the occurrence of LEDVT in patients with sepsis. Logistic regression analysis showed that the age [odds ratio ( OR) = 1.03, 95% confidence interval (95% CI) was 1.01 to 1.04, P < 0.001], body mass index (BMI: OR = 1.05, 95% CI was 1.01 to 1.09, P = 0.009), venous thromboembolism (VTE) score ( OR = 1.20, 95% CI was 1.11 to 1.29, P < 0.001), activated partial thromboplastin time (APTT: OR = 0.98, 95% CI was 0.97 to 0.99, P = 0.009), D-dimer ( OR = 1.03, 95% CI was 1.01 to 1.04, P < 0.001), skin or soft-tissue infection ( OR = 2.53, 95% CI was 1.29 to 4.98, P = 0.007), and femoral venous cannulation ( OR = 3.72, 95% CI was 2.50 to 5.54, P < 0.001) were the independent influences on the occurrence of LEDVT in patients with sepsis. The nomogram model was constructed by combining the above variables, and the ROC curve analysis showed that the area under the curve (AUC) of the nomogram model for predicting the occurrence of LEDVT in patients with sepsis was 0.793 (95% CI was 0.746 to 0.841), and the AUC in the validation set was 0.844 (95% CI was 0.786 to 0.901). The calibration curve showed that its predicted probability was in good agreement with the actual probabilities were in good agreement, and both CIC and DCA curves suggested a favorable net clinical benefit. Conclusion:The nomogram model based on the critical illness scores combined with inflammatory markers can be used for early prediction of LEDVT in ICU sepsis patients, which helps clinicians to identify the risk factors for LEDVT in sepsis patients earlier, so as to achieve early treatment.

Objective:To explore the risk factors of septic acute kidney injury (sAKI) in patients with sepsis, construct a predictive model for sAKI, verify the predictive value of the model, and develop a dynamic nomogram to help clinical doctors identify patients with high-risk sAKI earlier and more easily.Methods:A cross-sectional study was conducted. A total of 245 patients with sepsis admitted to intensive care unit (ICU) of the Affiliated Hospital of Jining Medical University from May 2013 to November 2023 were enrolled as the research subjects. The patients were divided into sAKI group and non-sAKI group based on whether they suffered from sAKI during ICU hospitalization. The differences of the demographic, clinical and laboratory indicators of patients between the two groups were compared. Logistic ordinal regression analysis was performed to analyze the imbalanced variables between the two groups, and to construct a sAKI predictive model. The predictive value of the sAKI predictive model was evaluated through 5-fold cross validation, calibration curve, and decision curve analysis (DCA), and to develop an online dynamic nomogram for the predictive model.Results:A total of 245 patients were enrolled in the final analysis. 110 (44.9%) patients developed sAKI during ICU hospitalization and 135 (55.1%) patients did not develop sAKI. Compared with the non-sAKI group, the patients in the sAKI group had higher ratios of female, hypertension, invasive mechanical ventilation (IMV), renal replacement therapy (RRT), vasopressin usage, and neutrophil count (NEU), aspartate aminotransferase (AST), blood urea nitrogen (BUN), serum creatinine (SCr), uric acid (UA), Na +, K +, procalcitonin (PCT), acute physiology and chronic health evaluation Ⅱ (APACHEⅡ) score, and sequential organ failure assessment (SOFA) score. Multivariate Logistic ordinal regression analysis showed that female [odd ratio ( OR) = 2.208, 95% confidence interval (95% CI) was 1.073-4.323, P = 0.020], hypertension ( OR = 2.422, 95% CI was 1.255-5.073, P = 0.012), vasopressin usage ( OR = 2.888, 95% CI was 1.380-6.679, P = 0.002), and SCr ( OR = 1.015, 95% CI was 1.009-1.024, P < 0.001) were independent risk factors for sAKI in septic patients, and a sAKI predictive model was constructed: ln[ P/(1+ P)] = -4.665+0.792×female+0.885×hypertension+1.060×vasopressin usage+0.015×SCr. The 5-fold cross validation showed that the average area under the receiver operator characteristic curve (AUC) was 0.860, indicating the sAKI predictive model had a good performance. The calibration curve analysis showed that the calibration degree of the sAKI predictive model was good. DCA showed that the net profit of the sAKI predictive model was relatively high. A static nomogram and an online dynamic nomogram were constructed for the sAKI predictive model. Compared with the static nomogram, the dynamic nomogram allowed for manual selection of corresponding patient characteristics and viewing the corresponding sAKI risk directly. Conclusions:Female, hypertension, vasopressin usage, and SCr are the main risk factors for sAKI in patients with sepsis. The sAKI predictive model constructed based on these factors can help clinical doctors identifying high-risk patients as early as possible, and intervene in a timely manner to provide preventive effects. Compared with the common static nomogram, online dynamic nomogram can make predictive models clearer, more intuitive, and easier.

OBJECTIVE: To analyze the risk factors of in-hospital death in patients with sepsis in the intensive care unit (ICU) based on machine learning, and to construct a predictive model, and to explore the predictive value of the predictive model. METHODS: The clinical data of patients with sepsis who were hospitalized in the ICU of the Affiliated Hospital of Jining Medical University from April 2015 to April 2021 were retrospectively analyzed,including demographic information, vital signs, complications, laboratory examination indicators, diagnosis, treatment, etc. Patients were divided into death group and survival group according to whether in-hospital death occurred. The cases in the dataset (70%) were randomly selected as the training set for building the model, and the remaining 30% of the cases were used as the validation set. Based on seven machine learning models including logistic regression (LR), K-nearest neighbor (KNN), support vector machine (SVM), decision tree (DT), random forest (RF), extreme gradient boosting (XGBoost) and artificial neural network (ANN), a prediction model for in-hospital mortality of sepsis patients was constructed. The receiver operator characteristic curve (ROC curve), calibration curve and decision curve analysis (DCA) were used to evaluate the predictive performance of the seven models from the aspects of identification, calibration and clinical application, respectively. In addition, the predictive model based on machine learning was compared with the sequential organ failure assessment (SOFA) and acute physiology and chronic health evaluation II (APACHE II) models. RESULTS: A total of 741 patients with sepsis were included, of which 390 were discharged after improvement, 351 died in hospital, and the in-hospital mortality was 47.4%. There were significant differences in gender, age, APACHE II score, SOFA score, Glasgow coma score (GCS), heart rate, oxygen index (PaO₂/FiO₂), mechanical ventilation ratio, mechanical ventilation time, proportion of norepinephrine (NE) used, maximum NE, lactic acid (Lac), activated partial thromboplastin time (APTT), albumin (ALB), serum creatinine (SCr), blood urea nitrogen (BUN), blood uric acid (BUA), pH value, base excess (BE), and K⁺ between the death group and the survival group. ROC curve analysis showed that the area under the curve (AUC) of RF, XGBoost, LR, ANN, DT, SVM, KNN models, SOFA score, and APACHE II score for predicting in-hospital mortality of sepsis patients were 0.871, 0.846, 0.751, 0.747, 0.677, 0.657, 0.555, 0.749 and 0.760, respectively. Among all the models, the RF model had the highest precision (0.750), accuracy (0.785), recall (0.773), and F1 score (0.761), and best discrimination. The calibration curve showed that the RF model performed best among the seven machine learning models. DCA curve showed that the RF model exhibited greater net benefit as well as threshold probability compared to other models, indicating that the RF model was the best model with good clinical utility. CONCLUSIONS The machine learning model can be used as a reliable tool for predicting in-hospital mortality in sepsis patients. RF models has the best predictive performance, which is helpful for clinicians to identify high-risk patients and implement early intervention to reduce mortality.
Humans ; Hospital Mortality ; Retrospective Studies ; ROC Curve ; Prognosis ; Sepsis/diagnosis* ; Intensive Care Units

Objective:To analyze and explore the independent risk factors of 28-day mortality in patients with septic myocardial injury.Methods:A retrospective cohort study was conducted to collect clinical data of 505 patients diagnosed with sepsis related myocardial injury admitted to the intensive care unit (ICU) of the Affiliated Hospital of Jining Medical University from January 2015 to December 2020. According to the 28-day survival status of patients, they were divided into survival group and death group. COX multivariate regression analysis was used to analyze the influencing factors of the 28-day mortality rate of sepsis related myocardial injury patients, and receiver operating characteristic (ROC) curves were drawn to evaluate the effectiveness of independent risk factors in predicting the 28-day mortality rate of sepsis related myocardial injury patients.Results:A total of 505 patients with sepsis myocardial injury were included, of which 282 survived on 28 days and 223 died, with a mortality rate of 44.16%. COX multivariate regression analysis showed that Sequential Organ Failure Assessment (SOFA) score, Acute Physiology and Chronic Health Evaluation Ⅱ (APACHE Ⅱ) score, blood lactate (LAC), oxygenation index (PaO 2/FiO 2), admission heart rate, and albumin were independent risk factors for sepsis associated myocardial injury mortality at 28 days (all P<0.05). ROC curve analysis showed that the area under the ROC curve (AUC) of SOFA score was 0.766 2, and the 95% confidence interval (95% CI) was 0.724 5-0.807 9; The predictive value of 28-day mortality in sepsis associated myocardial injury patients was superior to APACHE Ⅱ score, LAC, PaO 2/FiO 2, admission heart rate, and albumin [The AUC values were 0.754 1(0.711 5-0.796 7), 0.752 6(0.710 1-0.795 1), 0.697 0(0.649 7-0.744 2), 0.623 2(0.573 7-0.672 7), and 0.620 3(0.570 8-0.669 7), respectively]. Conclusions:SOFA score, APACHE Ⅱ score, LAC, PaO 2/FiO 2, admission heart rate, and albumin are independent risk factors for the 28-day mortality rate of sepsis related myocardial injury. Clinical practice should identify these factors early, intervene early, and improve patient prognosis.

Objective:To construct a nomogram model predicting the occurrence of acute kidney injury (AKI) in patients with sepsis in the intensive care unit (ICU), and to verify its validity for early prediction.Methods:Sepsis patients admitted to the ICU of the Affiliated Hospital of Jining Medical University from April 2015 to December 2021 were retrospectively included, and those who met the inclusion criteria were randomly divided into training and validation sets at a ratio of 7:3. Univariate and multivariate logistic regression models were used to identify independent risk factors for AKI in patients with sepsis, and a nomogram was constructed based on the independent risk factors. Calibration curve, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA) were used to evaluate the nomogram model.Results:741 patients with sepsis were included in the study, 335 patients developed AKI within 7 d of ICU admission, with an AKI incidence of 45.1%. Randomization was performed in the training set ( n=519) and internal validation set ( n=222). Multivariate logistic analysis revealed that acute physiology and chronic health status score Ⅱ, sequential organ failure score, serum lactate, calcitoninogen, norepinephrine dose, urea nitrogen, and neutrophil percentage were independent factors influencing the occurrence of AKI, and a nomogram model was constructed by combining these variables. In the training set, the AUC of the nomogram model ROC was 0.875 (95% CI: 0.767-0.835), the calibration curve showed consistency between the predicted and actual probabilities, and the DCA showed a good net clinical benefit. In the internal validation set, the nomogram model had a similar predictive value for AKI (AUC=0.871, 95% CI: 0.734-0.854). Conclusions:A nomogram model constructed based on the critical care score at admission combined with inflammatory markers can be used for the early prediction of AKI in sepsis patients in the ICU. The model is helpful for clinicians early identify AKI in sepsis patients.