Weimin Zhang’s research while affiliated with First Affiliated Hospital of China Medical University and other places

Fluid management remains a critical challenge in the treatment of septic shock, with individualized approaches lacking. This study aims to develop a statistical model based on transcriptomics to identify subgroups of septic shock patients with varied responses to fluid strategy. The study encompasses 494 septic shock patients. A benefit score is derived from the transcriptome space, with higher values indicating greater benefits from restrictive fluid strategy. Adherence to the recommended strategy is associated with a hazard ratio of 0.82 (95% confidence interval: 0.64–0.92). When applied to the baseline hospital mortality rate of 16%, adherence to the recommended fluid strategy could potentially lower this rate to 13%. A proteomic signature comprising six proteins is developed to predict the benefit score, yielding an area under the curve of 0.802 (95% confidence interval: 0.752–0.846) in classifying patients who may benefit from a restrictive strategy. In this work, we develop a proteomic signature with potential utility in guiding fluid strategy for septic shock patients.

We developed an interpretable machine learning algorithm that prospectively predicts the risk of thrombocytopenia in older critically ill patients during their stay in the intensive care unit (ICU), ultimately aiding clinical decision-making and improving patient care. Data from 2286 geriatric patients who underwent surgery and were admitted to the ICU of Dongyang People’s Hospital between 2012 and 2021 were retrospectively analyzed. Integrated algorithms were developed, and four machine-learning algorithms were used. Selected characteristics included common demographic data, biochemical indicators, and vital signs. Eight key variables were selected using the Least Absolute Shrinkage and Selection Operator and Random Forest Algorithm. Thrombocytopenia occurred in 18.2% of postoperative geriatric patients, with a higher mortality rate. The C5.0 model showed the best performance, with an area under the receiver operating characteristic curve close to 0.85, along with unparalleled accuracy, precision, specificity, recall, and balanced accuracy scores of 0.88, 0.98, 0.89, 0.98, and 0.85, respectively. The support vector machine model excelled at predictively assessing thrombocytopenia severity, demonstrating an accuracy rate of 0.80 in the MIMIC database. Thus, our machine learning-based models have considerable potential in effectively predicting the risk and severity of postoperative thrombocytopenia in geriatric ICU patients for better clinical decision-making and patient care.

Investigating cytokine levels in ICU patients reveals their potential in predicting the occurrence of Persistent Inflammatory Response-Immunosuppression-Catabolic Syndrome (PICS). Our study encompassed clinical data from ICU patients admitted between December 2020 and January 2022. The cohort was divided based on the incidence of PICS, and a comparative analysis was conducted on their clinical data. Using logistic regression, we identified independent factors influencing PICS. Among 132 patients meeting our inclusion criteria, 39 (31.70%) developed PICS. Significant differences were observed between the PICS and non-PICS groups in terms of average age, APACHE II scores, hospital stay duration, mortality, and infection rates. Notably, laboratory parameters indicated lower pre-albumin and IL-4 levels, alongside higher IL-6, IL-10, IL-17, and IFN-y levels in the PICS group. Multivariate analysis pinpointed pre-albumin, IL-4, IL-6, and IL-10 as independent risk factors for PICS in ICU settings. Our findings underscore the importance of IL-4, IL-6, and IL-10 as key cytokines in the early detection and management of PICS, offering significant insights for clinical practice.

Asthma, a chronic respiratory ailment, affects millions worldwide. Extracorporeal membrane oxygenation (ECMO) has gained traction as a life-saving intervention for patients with severe asthma who are unresponsive to conventional treatments. However, complications associated with ECMO, including electrolyte imbalances and hemorrhage, can have significant clinical implications. This case report highlights a 49 years-old male patient with severe asthma who developed pronounced hypokalemia and hemorrhage following venovenous ECMO (VVECMO) therapy. Despite potassium supplementation, serum potassium levels continued declining before normalizing after 24 h. The patient subsequently experienced gastrointestinal bleeding, cerebral hemorrhage, and extensive cerebral infarction, ultimately resulting in a deep coma. Hypokalemia during ECMO therapy can result from a rapid reduction of carbon dioxide, β-receptor agonist use, corticosteroid use, and diuretic administration. Hemorrhage is another common ECMO complication, often linked to heparin anticoagulation therapy. Clinicians should be aware of potential complications and adopt appropriate prevention and management strategies when using ECMO in patients with severe asthma.

Rationale Mitochondrial encephalomyopathy, lactic acidosis, and stroke-like episodes (MELAS) syndrome is a subset of rare mitochondrial diseases characterized by diverse clinical manifestations, which often complicates its diagnosis. Patient concerns This report chronicles the experiences of a 14-year-old female patient who underwent multiple misdiagnoses before the eventual identification of MELAS syndrome. Her journey began with symptoms that included growth retardation, hypertrophic cardiomyopathy, and epilepsy. Diagnosis The definitive diagnosis of MELAS syndrome was established through genetic confirmation, revealing a mutation in the MT-TL1 gene (m.3242A > G). Interventions Upon diagnosis, the patient received targeted symptomatic treatment, which led to pronounced improvements in her symptoms. Outcomes The patient’s condition stabilized with the administered treatments, and she exhibited significant symptom relief, emphasizing the importance of accurate diagnosis and timely intervention. Lessons This case underscores the imperative for heightened clinical vigilance and thorough differential diagnosis in the face of complex clinical presentations, such as those seen in MELAS syndrome, to ensure timely and appropriate interventions.

Sepsis, a life-threatening condition caused by an inflammatory response to systemic infection, results in a significant social burden and healthcare costs. This study aimed to investigate the relationship between the C-reactive protein (CRP) trajectories of patients with sepsis in the intensive care unit (ICU) and the in-hospital mortality rate. We reviewed 1464 patients with sepsis treated in the ICU of Dongyang People’s Hospital from 2010 to 2020 and used latent growth mixture modeling to divide the patients into four classes according to CRP trajectory (intermediate, gradually increasing, persistently high, and persistently low CRP levels). We found that patients with intermediate and persistently high CRP levels had the lowest (18.1%) and highest (32.6%) in-hospital mortality rates, respectively. Multiple logistic regression analysis showed that patients with persistently high (odds ratio [OR] = 2.19, 95% confidence interval [CI] = 1.55–3.11) and persistently low (OR = 1.41, 95% CI = 1.03–1.94) CRP levels had a higher risk of in-hospital mortality than patients with intermediate CRP levels. In conclusion, in-hospital mortality rates among patients with sepsis differ according to the CRP trajectory, with patients with intermediate CRP levels having the lowest mortality rate. Further research on the underlying mechanisms is warranted.

Introduction The causes of thrombocytopenia (TP) in critically ill patients are numerous and heterogeneous. Currently, subphenotype identification is a popular approach to address this problem. Therefore, this study aimed to identify subphenotypes that respond differently to therapeutic interventions in patients with TP using routine clinical data and to improve individualized management of TP. Methods This retrospective study included patients with TP admitted to the intensive care unit (ICU) of Dongyang People’s Hospital during 2010–2020. Subphenotypes were identified using latent profile analysis of 15 clinical variables. The Kaplan–Meier method was used to assess the risk of 30-day mortality for different subphenotypes. Multifactorial Cox regression analysis was used to analyze the relationship between therapeutic interventions and in-hospital mortality for different subphenotypes. Results This study included a total of 1,666 participants. Four subphenotypes were identified by latent profile analysis, with subphenotype 1 being the most abundant and having a low mortality rate. Subphenotype 2 was characterized by respiratory dysfunction, subphenotype 3 by renal insufficiency, and subphenotype 4 by shock-like features. Kaplan–Meier analysis revealed that the four subphenotypes had different in-30-day mortality rates. The multivariate Cox regression analysis indicated a significant interaction between platelet transfusion and subphenotype, with more platelet transfusion associated with a decreased risk of in-hospital mortality in subphenotype 3 [hazard ratio (HR): 0.66, 95% confidence interval (CI): 0.46–0.94]. In addition, there was a significant interaction between fluid intake and subphenotype, with a higher fluid intake being associated with a decreased risk of in-hospital mortality for subphenotype 3 (HR: 0.94, 95% CI: 0.89–0.99 per 1 l increase in fluid intake) and an increased risk of in-hospital mortality for high fluid intake in subphenotypes 1 (HR: 1.10, 95% CI: 1.03–1.18 per 1 l increase in fluid intake) and 2 (HR: 1.19, 95% CI: 1.08–1.32 per 1 l increase in fluid intake). Conclusion Four subphenotypes of TP in critically ill patients with different clinical characteristics and outcomes and differential responses to therapeutic interventions were identified using routine clinical data. These findings can help improve the identification of different subphenotypes in patients with TP for better individualized treatment of patients in the ICU.

Acute kidney injury (AKI) is a common postoperative complication among patients in the neurological intensive care unit (NICU), often resulting in poor prognosis and high mortality. In this retrospective cohort study, we established a model for predicting AKI following brain surgery based on an ensemble machine learning algorithm using data from 582 postoperative patients admitted to the NICU at the Dongyang People's Hospital from March 1, 2017, to January 31, 2020. Demographic, clinical, and intraoperative data were collected. Four machine learning algorithms (C5.0, support vector machine, Bayes, and XGBoost) were used to develop the ensemble algorithm. The AKI incidence in critically ill patients after brain surgery was 20.8%. Intraoperative blood pressure; postoperative oxygenation index; oxygen saturation; and creatinine, albumin, urea, and calcium levels were associated with the postoperative AKI occurrence. The area under the curve value for the ensembled model was 0.85. The accuracy, precision, specificity, recall, and balanced accuracy values were 0.81, 0.86, 0.44, 0.91, and 0.68, respectively, indicating good predictive ability. Ultimately, the models using perioperative variables exhibited good discriminatory ability for early prediction of postoperative AKI risk in patients admitted to the NICU. Thus, the ensemble machine learning algorithm may be a valuable tool for forecasting AKI.

Sepsis, a life-threatening condition caused by an inflammatory response to systemic infection, results in a significant social burden and healthcare costs. This study aimed to investigate the relationship between the C-reactive protein (CRP) trajectories of patients with sepsis in the intensive care unit (ICU) and the in-hospital mortality rate. We reviewed 1,464 patients with sepsis treated in the ICU of Dongyang People's Hospital from 2010 to 2020 and used latent growth mixture modeling to divide the patients into four classes according to CRP trajectory (intermediate, gradually increasing, persistently high, and persistently low CRP levels). We found that patients with intermediate and persistently high CRP levels had the lowest (18.1%) and highest (32.6%) in-hospital mortality rates, respectively. Multiple logistic regression analysis showed that patients with persistently high (odds ratio [OR] = 2.19, 95% confidence interval [CI] = 1.55–3.11) and persistently low (OR = 1.41, 95% CI = 1.03–1.94) CRP levels had a higher risk of in-hospital mortality than patients with intermediate CRP levels. In conclusion, in-hospital mortality rates among patients with sepsis differ according to the CRP trajectory, with patients with intermediate CRP levels having the lowest mortality rate. Further research on the underlying mechanisms is warranted.