BACKGROUND

Early-stage endometrial cancer often presents with favorable survival rates, but high-risk factors, including TP53 mutations and high-grade serous pathology, can lead to recurrence and poor prognosis. The standard primary treatment for endometrial cancer is surgical staging, and lymph node metastases significantly impact adjuvant therapy decisions. The subgroup of p53-abnormal (p53abn) indicates the worst prognosis and potential benefits from adjuvant chemotherapy. Molecular classification, while recommended, faces practical challenges due to resource constraints.

OBJECTIVES

The study aimed to assess the incidence of p53 abnormal expression in clinical stage 1 endometrial cancer cases that underwent surgery at a government tertiary hospital, and assess its relationship with clinicopathologic factors and pelvic and paraaortic lymph node metastasis (LNM).

METHODS

A cross-sectional retrospective analysis was conducted on clinical early-stage endometrial cancer cases that underwent surgical primary treatment between January 2018 and December 2022. Patient records were reviewed to gather demographics, surgical information, and pathological evaluations. Preoperative clinical staging was determined through imaging, and surgical staging involved comprehensive lymphadenectomy. Immunohistochemistry studies for p53 were carried out on formalin-fixed paraffin-embedded tissue samples.

RESULTS

A total of 233 endometrial cancer cases were included. The mean age at diagnosis was 53.7 years. Common comorbidities included hypertension (47.2%) and dyslipidemia (20.6%). Most cases were endometrioid histology (82.8%) and low-grade tumors (85.8%). Tumor grade (p=0.010), myometrial invasion (pCONCLUSION

Tumor grade, myometrial invasion, and LVSI were all significantly associated with lymph node involvement. While p53 immunohistochemical stains show promise in predicting metastasis and has been associated with tumor aggressiveness, this should still be correlated with clinicopathological parameters to carry out a more accurate risk stratification of early-stage patients.

Therapeutics ; Survival Rate ; Risk Factors ; Recurrence ; Prognosis ; Pathology ; Endometrial Neoplasms ; Immunohistochemistry ; Tumor Suppressor Protein P53 ; Lymph Node Excision ; Risk Assessment

This study aims to determine the prevalence of erectile dysfunction (ED) and identify its associated risk factors among young and middle-aged Filipino male patients diagnosed with diabetes mellitus. This study utilized a cross-sectional design to investigate the prevalence and associated factors of ED among male patients with diabetes. A total of 423 participants were recruited from clinical settings using purposive sampling. Data were collected using structured interviews and medical records, including demographics, comorbidities and laboratory results. Among 423 male diabetic patients, 78% were found to have ED. Patients with ED were significantly older (median: 49.5 versus 42 years, p<0.001), had higher body mass index (BMI), longer diabetes duration and more comorbidities. Univariable logistic regression showed that age (OR: 1.06, p<0.001), diabetes duration (OR: 1.11, p<0.001), hypertension (OR: 1.62, p = 0.042), dyslipidemia (OR: 1.75, p = 0.022), elevated HbA1c (>9.0%; OR: 3.43, p = 0.034) and serum creatinine (OR: 1.01, p = 0.008) were significantly associated with ED. However, none remained significant in the multivariable model. Male Filipino patients with diabetes have a significant burden of ED. Results of the univariable models show that age, duration of diabetes, hypertension, dyslipidemia, HbA1c and serum creatinine are significant individual predictors of ED.

Human ; Male ; Adolescent: 13-18 Yrs Old ; Young Adult: 19-24 Yrs Old ; Adult: 25-44 Yrs Old ; Middle Aged: 45-64 Yrs Old ; Aged: 65-79 Yrs Old ; Tertiary Care Centers ; Risk Factors ; Risk ; Medical Records ; Erectile Dysfunction ; Diabetes Mellitus ; Philippines

BACKGROUND

Diabetic retinopathy (DR) remains to be the leading cause of blindness worldwide. Traditionally, risk factors such as diabetes duration, HbA1c levels, hypertension and dyslipidemia have been closely linked to the development of this condition. However, recent research suggests that these factors account for only a portion of DR cases. Emerging studies highlight additional potential risk factors including diabetic nephropathy, insulin use, and body mass index -though data on these factors remain limited and at times contradictory. While there have been a few local studies exploring some of these variables, none have comprehensively examined how these factors collectively influence the occurrence and severity of diabetic retinopathy. This study aims to asses both the factors associated with presence and occurrence of diabetic retinopathy.

METHODOLOGY

This analytical cross-sectional study included patients with diabetes (n=201, aged 18 years and older) who underwent fundus photography at The Medical City Ortigas between January 1, 2022, and December 31, 2022. Data collection involved a thorough review of patient records, which provided demographic information and details on potential risk factors. The presence and severity of diabetic retinopathy (DR) were assessed based on fundus photography results, as interpreted by ophthalmologists. To evaluate the statistical significance of the association between risk factors and DR status, logistic regression analysis was done

RESULTS

Duration of diabetes (odds ratio [OR] 1.07; 95% CI, 1.01-1.13 per year increase), HBA1c (OR 1.4; 95% CI, 1.1-1.86),
fasting blood sugar (OR 1.4; 95% CI, 0.977-0.998), hypercholesterolemia (OR 5.02; 95% CI 1.67-16.44) and presence of
nephropathy (OR 3.39; 95% CI 1.49-8) were correlated with diabetic retinopathy.

CONCLUSION

The presence of diabetic retinopathy was significantly associated with several clinical factors. Each additional year of diabetes mellitus duration was associated with a 1.07-fold increase in the likelihood of DR. Additionally, each 1% increase in HbA1c was linked to a 1.40-fold increase in DR risk. The presence of diabetic nephropathy and hypercholesterolemia were also strong predictors, associated with a 3.39-fold and 5-fold increase in the likelihood of DR, respectively. Glycemic control, dyslipidemia and nephropathy appear to be associated with more severe forms of diabetic retinopathy. Clinicians handling diabetes patients with this patient profile should be wary of this diabetes complication.

Human ; Male ; Female ; Adolescent: 13-18 Yrs Old ; Young Adult: 19-24 Yrs Old ; Adult: 25-44 Yrs Old ; Diabetic Retinopathy ; Hospitals ; Risk ; Risk Factors ; Tertiary Care Centers

BACKGROUND

Early-stage endometrial cancer often presents with favorable survival rates, but high-risk factors, including TP53 mutations and high-grade serous pathology, can lead to recurrence and poor prognosis. The standard primary treatment for endometrial cancer is surgical staging, and lymph node metastases significantly impact adjuvant therapy decisions. The subgroup of p53-abnormal (p53abn) indicates the worst prognosis and potential benefits from adjuvant chemotherapy. Molecular classification, while recommended, faces practical challenges due to resource constraints.

OBJECTIVES

The study aimed to assess the incidence of p53 abnormal expression in clinical stage 1 endometrial cancer cases that underwent surgery at a government tertiary hospital, and assess its relationship with clinicopathologic factors and pelvic and paraaortic lymph node metastasis (LNM).

METHODS

A cross-sectional retrospective analysis was conducted on clinical early-stage endometrial cancer cases that underwent surgical primary treatment between January 2018 and December 2022. Patient records were reviewed to gather demographics, surgical information, and pathological evaluations. Preoperative clinical staging was determined through imaging, and surgical staging involved comprehensive lymphadenectomy. Immunohistochemistry studies for p53 were carried out on formalin-fixed paraffin-embedded tissue samples.

RESULTS

A total of 233 endometrial cancer cases were included. The mean age at diagnosis was 53.7 years. Common comorbidities included hypertension (47.2%) and dyslipidemia (20.6%). Most cases were endometrioid histology (82.8%) and low-grade tumors (85.8%). Tumor grade (p=0.010), myometrial invasion (pCONCLUSION

Tumor grade, myometrial invasion, and LVSI were all significantly associated with lymph node involvement. While p53 immunohistochemical stains show promise in predicting metastasis and has been associated with tumor aggressiveness, this should still be correlated with clinicopathological parameters to carry out a more accurate risk stratification of early-stage patients.

Therapeutics ; Survival Rate ; Risk Factors ; Recurrence ; Prognosis ; Pathology ; Endometrial Neoplasms ; Immunohistochemistry ; Tumor Suppressor Protein P53 ; Lymph Node Excision ; Risk Assessment

OBJECTIVE: To explore the relationship between serum sodium level and the risk of delirium in patients with sepsis. METHODS: Based on the Medical Information Mart for Intensive Care-IV (MIMIC-IV), adult patients with sepsis in the intensive care unit (ICU) were enrolled. The serum sodium level prior to the onset of sepsis during hospitalization was used as the exposure variable. Delirium was assessed using the ICU-confusion assessment method (ICU-CAM) as the primary outcome. Patients were divided into delirium and non-delirium groups based on the occurrence of delirium. The relationship between serum sodium level and delirium risk was described using restricted cubic spline (RCS) to determine the optimal reference range for serum sodium. Logistic regression analysis was used to evaluate the effect of blood sodium levels on delirium in sepsis patients. Subgroup analyses were performed to explore potential interactions and further validate the robustness of the results. Receiver operator characteristic curve (ROC curve) analysis was performed to assess the predictive value of serum sodium level for delirium occurrence in patients with sepsis. RESULTS: A total of 13 889 patients with sepsis were included, of which 4 831 experienced delirium. The maximum and mean serum sodium values were significantly higher in the delirium group compared to the non-delirium group, while there were no statistically significant differences in terms of initial and minimum serum sodium values between the two groups. Compared with the non-delirium group, the delirium group had a higher mortality and longer hospital stay. The RCS curve showed that a "U"-shaped relationship between serum sodium level and delirium risk in patients with sepsis, with the optimal reference range for average serum sodium was 135.3-141.3 mmol/L. Group based on this reference range, compared to the group with 135.3 mmol/L ≤ serum sodium ≤ 141.3 mmol/L, the delirium incidence and mortality were significantly higher, and the hospital stay was longer in the groups with serum sodium < 135.3 mmol/L and serum sodium ≥ 141.3 mmol/L [delirium incidence: 36.92%, 40.88% vs. 31.22%; 28-day mortality: 23.08%, 20.15% vs. 13.39%; 90-day mortality: 30.75%, 24.81% vs. 18.26%; in-hospital mortality: 19.53%, 17.48% vs. 11.61%; ICU mortality: 14.35%, 14.05% vs. 9.00%; hospital length of stay (days): 10.1 (6.1, 17.7), 9.4 (5.4, 17.0) vs. 8.9 (5.5, 15.4), length of ICU stay (days): 3.7 (2.1, 7.1), 4.0 (2.1, 8.9) vs. 3.2 (1.9, 6.8); all P < 0.01]. Logistic regression analysis showed that, in the initial model and each factor-adjusted models, compared to the reference group with 135.3 mmol/L ≤ serum sodium < 141.3 mmol/L, serum sodium < 135.3 mmol/L increased the risk of delirium in septic patients by 21% to 29% [odds ratio (OR) was 1.21-1.29, all P < 0.01], while serum sodium ≥ 141.3 mmol/L increased the delirium risk by 28%-52% (OR was 1.28-1.52, all P < 0.01). Subgroup analyses based on gender, age, race, diuretic use, and sequential organ failure assessment (SOFA) score revealed there was no significant interactions between subgroup variables and serum sodium, and the results supported that both serum sodium < 135.3 mmol/L and serum sodium ≥ 141.3 mmol/L were risk factors for delirium in septic patients. ROC curve analysis showed that the area under the curve (AUC) for predicting delirium in septic patients based on serum sodium was 0.614, with a cut-off value of 139.5 mmol/L yielding a specificity of 67.5% and sensitivity of 50.9%. CONCLUSIONS The risk of delirium in patients with sepsis is associated with serum sodium level in a "U"-shaped manner. Both high and low serum sodium levels are associated with increased risk of delirium, higher all-cause mortality, and prolonged hospital stays in patients with sepsis. Abnormal serum sodium levels may have predictive value for sepsis-associated delirium and could serve as an early biomarker for identifying delirium in septic patients, although further validation is needed.
Humans ; Delirium/etiology* ; Sepsis/complications* ; Sodium/blood* ; Intensive Care Units ; Risk Factors ; Male ; Middle Aged ; Female ; Aged ; Logistic Models ; Adult

OBJECTIVE: To identify the optimal machine learning algorithm for predicting post-stroke epilepsy (PSE) within one year following acute stroke, establish a nomogram model based on this algorithm, and perform external validation to achieve accurate prediction of secondary epilepsy. METHODS: A total of 870 acute stroke patients admitted to the emergency department of Xiang'an Hospital of Xiamen University from June 2019 to June 2023 were enrolled for model development (model group). An external validation cohort of 435 acute stroke patients admitted to the Fifth Hospital of Xiamen during the same period was used to validate the machine learning algorithms and nomogram model. Patients were classified into control and epilepsy groups based on the development of PSE within one year. Clinical and laboratory data, including baseline characteristics, stroke location, vascular status, complications, hematologic parameters, and National Institutes of Health Stroke Scale (NIHSS) score, were collected for analysis. Nine machine learning algorithms such as logistic regression, CN2 rule induction, K-nearest neighbors, adaptive boosting, random forest, gradient boosting, support vector machine, naive Bayes, and neural network were applied to evaluate predictive performance. The area under the curve (AUC) of receiver operator characteristic curve (ROC curve) was used to identify the optimal algorithm. Logistic regression was used to screen risk factors for PSE, and the top 10 predictors were selected to construct the nomogram model. The predictive performance of the model was evaluated using the ROC curve in both the model and validation groups. RESULTS: Among the 870 patients in the model group, 29 developed PSE within one year. Among the nine algorithms tested, logistic regression demonstrated the best performance and generalizability, with an AUC of 0.923. Univariate logistic regression identified several risk factors for PSE, including platelet count, white blood cell count, red blood cell count, glycated hemoglobin (HbA1c), C-reactive protein (CRP), triglycerides, high-density lipoprotein (HDL), aspartate aminotransferase (AST), alanine aminotransferase (ALT), activated partial thromboplastin time (APTT), thrombin time, D-dimer, fibrinogen, creatine kinase (CK), creatine kinase-MB (CK-MB), lactate dehydrogenase (LDH), serum sodium, lactic acid, anion gap, NIHSS score, brain herniation, periventricular stroke, and carotid artery plaque. Further multivariate logistic regression analysis showed that white blood cell count, HDL, fibrinogen, lactic acid and brain herniation were independent risk factors [odds ratio (OR) were 1.837, 198.039, 47.025, 11.559, 70.722, respectively, all P < 0.05]. In the external validation group, univariate logistic regression analysis showed that platelet count, white blood cell count, CRP, triacylglycerol, APTT, D-dimer, fibrinogen, CK, CK-MB, LDH, NIHSS score, and cerebral herniation were risk factors for PSE one year after acute stroke. Further multiple logistic regression analysis showed that APTT and cerebral herniation were independent predictors (OR were 0.587 and 116.193, respectively, both P < 0.05). The nomogram model, constructed using 10 key variables-brain herniation, periventricular stroke, carotid artery plaque, white blood cell count, triglycerides, thrombin time, D-dimer, serum sodium, lactic acid, and NIHSS score-achieved an AUC of 0.908 in the model group and 0.864 in the external validation group. CONCLUSIONS The logistic regression-based prediction model for epilepsy one year after acute stroke, developed using machine learning algorithms, showed optimal predictive performance. The nomogram model based on the logistic regression-derived predictors showed strong discriminative power and was successfully validated externally, suggesting favorable clinical applicability and generalizability.
Humans ; Machine Learning ; Stroke/complications* ; Nomograms ; Epilepsy/etiology* ; Algorithms ; Male ; Female ; Logistic Models ; Middle Aged ; Aged ; Risk Factors ; Bayes Theorem

OBJECTIVE: To explore the relationship between the geriatric nutritional risk index (GNRI) and cognitive function. METHODS: A cross-sectional study method was conducted. People aged ≥ 60 years from the National Health and Nutrition Examination Survey (NHANES) databases from 1999 to 2002 and 2011 to 2014 were included as study subjects. The participants were divided into three groups based on their GNRI scores: a medium-high risk group (82 ≤ GNRI < 92), a low risk group (92 ≤ GNRI < 98), and a no-risk group (GNRI ≥ 98). Demographic characteristics (gender, age, race, education), chronic diseases [chronic bronchitis, emphysema, thyroid problems, coronary heart disease, angina pectoris, stroke, hypertension, diabetes mellitus, and depression score on the patient health questionnaire (PHQ-9)], lifestyle habits (history of smoking, hours of sleep), etc., were collected. Cognitive function was assessed using the consortium to establish a registry for Alzheimer's disease word learning subtest (CERAD-WL), animal fluency test (AFT), and digit symbol substitution test (DSST) for the 2011-2014 data, while only the DSST was used for the 1999-2002 data. Differences in the above information among the GNRI cohorts were compared. Factors affecting cognitive function in the population were analyzed using multifactorial Logistic regression. RESULTS: 2 653 participants from 2011 to 2014 and 2 380 participants from 1999 to 2002 were enrolled, with a total of 5 033 participants in the study. There were statistically significant differences in age, stroke, diabetes mellitus, DSST score, AFT score, CERAD score test 1 recall (Cst1), and CERAD score test 2 recall (Cst2) among the GNRI groups. Multifactorial Logistic regression analysis of data from 2011 to 2014 showed that in model 3 (DSST score, age, gender, race, marriage, education, hours of sleep, history of smoking, emphysema, thyroid problems, chronic bronchitis, coronary heart disease, angina pectoris, hypertension, diabetes mellitus, depression score on the PHQ-9, and stroke) adjusted for all covariates, GNRI was a protective factor for DSST [odds ratio (OR) = 1.03, 95% confidence interval (95%CI) was 1.00 to 1.05, P = 0.03]; Logistic regression analyse for 1999 to 2002 and 2011 to 2014 showed a significant association even after adjustment for covariates (OR = 1.02, 95%CI was 1.00 to 1.03, P = 0.02). Subgroup Logistic regression analyses of the total population from 2011 to 2014 showed a significant association between GNRI and DSST scores (OR = 1.02, 95%CI was 1.01 to 1.03, P < 0.001), with significant associations in the age subgroups of 60 to 64 years old, across gender, non-Hispanic Whites and Blacks, by education, and by marital status associations were significant (all P < 0.05). Subgroup Logistic regression analyse of the total populations from 1999 to 2002 and 2011 to 2014 showed a significant association between the GNRI and DSST score (OR = 1.01, 95%CI was 1.01 to 1.02, P < 0.001), but did not show a significant year difference (interaction P = 0.503), and the newly found in the smoking population the association was also more significant (P < 0.01). CONCLUSION The GNRI correlates with the presence of cognitive functions related to processing speed, sustained attention, and executive function, and may be able to serve as an indicator for the assessment or prediction of related cognitive functions.
Humans ; Cross-Sectional Studies ; Aged ; Middle Aged ; Nutrition Surveys ; Cognition ; Female ; Male ; Nutritional Status ; Risk Factors ; Geriatric Assessment

OBJECTIVE: To analyze the risk factors for ventilator-associated pneumonia (VAP) and its prognosis in patients with severe craniocerebral injury. METHODS: A prospective observational study was conducted. Patients with severe craniocerebral injury admitted to the Second Affiliated Hospital of Xingtai Medical College from January 2020 to December 2022 were enrolled as the study subjects. Patients were divided into VAP group and non-VAP group based on the occurrence of VAP. VAP patients were further stratified into low-risk group [sequential organ failure assessment (SOFA) score 0-5], moderate-risk group (SOFA score 6-8), and high-risk group (SOFA score ≥ 9). General data, serological indicators [interleukin-6 (IL-6), interleukin-1β (IL-1β), tumor necrosis factor-α (TNF-α), and signal transducer and activator of transcription 3 (STAT3)], and 28-day prognosis (with mortality as the endpoint event) were compared. Multivariate Logistic regression was used to identify risk factors for VAP and 28-day mortality. Linear regression was applied to analyze the correlations between risk factors and outcomes. RESULTS: A total of 140 patients with severe craniocerebral injury were enrolled, including 49 in the VAP group and 91 in the non-VAP group. The primary cause of injury was traffic accidents, followed by falls and heavy object impacts. Among VAP patients, 38 survived and 11 died within 28 days; 112 were classified as low-risk, 25 as moderate-risk, and 12 as high-risk. Significant differences were observed in age, body mass index (BMI), smoking history, hypertension, diabetes, hyperlipidemia, length of hospital stay, duration of mechanical ventilation, serum albumin levels, and frequency of sputum suction among different subgroups. Serologically, IL-1β, TNF-α, IL-6, and STAT3 mRNA expression levels in the VAP group were significantly higher than those in the non-VAP group. Deceased VAP patients exhibited higher IL-1β, TNF-α, IL-6, and STAT3 mRNA levels compared to survivors. These biomarkers progressively increased from low-risk to high-risk subgroups. Multivariate Logistic regression identified age [odds ratio (OR) were 0.328 and 0.318], BMI (OR were 0.340 and 0.268), hypertension (OR were 0.275 and 0.245), diabetes (OR were 0.319 and 0.307), hyperlipidemia (OR were 0.228 and 0.235), smoking history (OR were 0.255 and 0.240), length of hospital stay (OR were 0.306 and 0.230), duration of mechanical ventilation (OR were 0.247 and 0.219), frequency of sputum suction (OR were 0.325 and 0.228), IL-1β (OR were 0.231 and 0.259), TNF-α (OR were 0.308 and 0.235), IL-6 (OR were 0.298 and 0.277), and STAT3 (OR were 0.259 and 0.265) as independent risk factors for both VAP occurrence and 28-day mortality (all P < 0.05). Correlation analysis revealed that serum albumin levels were negatively correlated with VAP occurrence and mortality (all P < 0.01), while other factors showed positive correlations (all P < 0.01). CONCLUSIONS Age, BMI, length of hospital stay, duration of mechanical ventilation, frequency of sputum suction, hypertension, diabetes, hyperlipidemia, smoking history, IL-1β, TNF-α, and IL-6/STAT3 signaling pathway activation are significantly associated with VAP development and poor prognosis in patients with severe craniocerebral injury, providing a scientific basis for targeted clinical interventions.
Humans ; Risk Factors ; Pneumonia, Ventilator-Associated ; Prognosis ; Prospective Studies ; Craniocerebral Trauma/complications* ; Interleukin-6/blood* ; Male ; Female ; STAT3 Transcription Factor/blood* ; Interleukin-1beta/blood* ; Tumor Necrosis Factor-alpha/blood* ; Middle Aged ; Adult ; Logistic Models

OBJECTIVE: To develop and compare risk prediction models for in-hospital post-cardiac arrest brain injury (PCABI) in critically ill patients using nomograms and random forest algorithms, aiming to identify the optimal model for early identification of high-risk PCABI patients and providing evidence for precise treatment. METHODS: A retrospective cohort study was used to collect the first-time in-hospital cardiac arrest (IHCA) patients admitted to the intensive care unit (ICU) from 2008 to 2019 in the Medical Information Mart for Intensive Care-IV (MIMIC-IV) as the study population, and the patients' age, gender, body mass, health insurance utilization, first vital signs and laboratory tests within 24 hours of ICU admission, mechanical ventilation, and critical care scores were extracted. Independent influencing factors of PCABI were identified through univariate and multivariate Logistic regression analyses. The included patients were randomly divided into a training cohort and an internal validation cohort in a 7:3 ratio, and the PCABI risk prediction model was constructed by the nomogram and random forest algorithm, respectively, and the model was evaluated by receiver operator characteristic curve (ROC curve), the calibration curve, and the decision curve analysis (DCA), and after the better model was selected, 179 patients admitted to Tianjin Medical University General Hospital as the external validation cohort for external evaluation were collected by using the same inclusion and exclusion criteria. RESULTS: A total of 1 419 patients with without traumatic brain injury who had their first-time IHCA were enrolled, including 995 in the training cohort (including 176 PCABI and 819 non-PCABI) and 424 in the internal validation cohort (including 74 PCABI and 350 non-PCABI). Univariate and multivariate analysis showed that age, potassium, urea nitrogen, sequential organ failure assessment (SOFA), acute physiology and chronic health evaluation III (APACHE III), and mechanical ventilation were independent influences on the occurrence of PCABI in patients with IHCA (all P < 0.05). Combining the above variables, we constructed a nomogram model and a random forest model for comparison, and the results show that the nomogram model has better predictive efficacy than the random forest model [nomogram model: area under the ROC curve (AUC) of the training cohort = 0.776, with a 95% credible interval (95%CI) of 0.741-0.811; internal validation cohort AUC = 0.776, with a 95%CI of 0.718-0.833; random forest model: AUC = 0.720, with a 95%CI of 0.653-0.787], and they performed similarly in terms of calibration curves, but the nomogram performed better in terms of decision curve analysis (DCA); at the same time, the nomogram model was robust in terms of external validation cohort (external validation cohort AUC = 0.784, 95%CI was 0.692-0.876). CONCLUSIONS A nomogram risk prediction model for the occurrence of PCABI in critically ill patients was successfully constructed, which performs better than the random forest model, helps clinicians to identify the risk of PCABI in critically ill patients at an early stage and provides a theoretical basis for early intervention.
Humans ; Critical Illness ; Retrospective Studies ; Heart Arrest/complications* ; Nomograms ; Brain Injuries/etiology* ; Intensive Care Units ; Algorithms ; Male ; Female ; Middle Aged ; ROC Curve ; Risk Factors ; Risk Assessment ; Logistic Models ; Aged

OBJECTIVE: To explore the predictive efficacy of the early lactic acid/albumin ratio (LAR) for the occurrence of acute skin failure (ASF) in patients with sepsis. METHODS: A retrospective study was conducted to collect the clinical data of 115 patients with sepsis admitted to the intensive care unit (ICU) of the First Affiliated Hospital of Dalian Medical University from June 2022 to March 2024. The patients' gender, age, length of ICU stay, past medical history, and severity scores, use of mechanical ventilation or vasoactive drugs, albumin (Alb), lactic acid (Lac), mean arterial pressure (MAP), and blood gas analysis indicators within 24 hours of ICU admission were collected, and LAR was calculated. The patients were divided into two groups based on whether they developed ASF, and the clinical data between the two groups were compared. Multivariate Logistic regression analysis was used to screen the risk factors for the occurrence of ASF in patients with sepsis. The receiver operator characteristic curve (ROC curve) was drawn to analyze the predictive value of LAR for the occurrence of ASF in patients with sepsis. RESULTS: A total of 115 patients with sepsis were enrolled in the final analysis, among whom 35 developed ASF and 80 did not. The incidence of ASF was 30.43%. Univariate analysis showed that compared with the non-ASF group, the ASF group had higher acute physiology and chronic health evaluation II (APACHE II) score, proportion of using vasoactive drugs, Lac, and LAR as well as lower Alb and MAP, with statistically significant differences. Multivariate Logistic regression analysis was conducted on the factors with statistical significance in the univariate analysis, and the results showed that Alb [odds ratio (OR) = 0.639, 95% confidence interval (95%CI) was 0.474-0.862, P = 0.003], Lac (OR = 17.228, 95%CI was 1.517-195.641, P = 0.022), MAP (OR = 0.905, 95%CI was 0.855-0.959, P = 0.001), and LAR (OR < 0.001, 95%CI was < 0.001-0.005, P = 0.033) were independent risk factors for the occurrence of ASF in patients with sepsis. ROC curve analysis showed that the area under the ROC curve (AUC) of LAR for predicting the occurrence of ASF in patients with sepsis was 0.867 (95%CI was 0.792-0.943), which was superior to Alb, Lac, and MAP [AUC (95%CI) was 0.739 (0.648-0.829), 0.844 (0.760-0.929), and 0.860 (0.783-0.937), respectively]. When the optimal cut-off value of LAR was 0.11, the sensitivity was 65.7%, the specificity was 96.3%, and the Youden index was 0.620. Patients were grouped based on the optimal cut-off value of LAR, and the results showed that the incidence of ASF in the LAR > 0.11 group was significantly higher than that in the LAR ≤ 0.11 group [88.89% (24/27) vs. 12.50% (11/88), P < 0.05]. CONCLUSIONS LAR has early predictive value for the occurrence of ASF in patients with sepsis, and its efficacy is superior to that of Lac or Alb alone.
Humans ; Sepsis/blood* ; Retrospective Studies ; Lactic Acid/blood* ; Male ; Female ; Intensive Care Units ; Middle Aged ; Risk Factors ; Predictive Value of Tests ; Serum Albumin/analysis* ; ROC Curve ; Aged