Medical Information Database Research Articles

Diagnostic testing and treatment in Japanese patients with a diagnosis code for chronic inflammatory demyelinating polyradiculoneuropathy: A claims database analysis

AbstractObjectivesReports of real‐world data concerning chronic inflammatory demyelinating polyradiculoneuropathy (CIDP) are limited in Japan. This study aimed to investigate the diagnostic testing and treatment performed in patients diagnosed with CIDP in Japan.MethodsUsing a Japanese commercial medical information database, we analyzed diagnostic tests, differential diseases, and treatments for patients with a diagnosis code for CIDP between April 13, 2008 and August 31, 2018.ResultsOf the 4564 patients with a diagnosis code for CIDP, 1658 patients were confirmed as newly assigned with the diagnosis code during the study period. Diagnostic tests performed before assignment of the diagnosis code for CIDP included nerve conduction studies in 48.1%, magnetic resonance imaging in 40.5%, and cerebrospinal fluid tests in 38.4%. Other diseases with codes that were assigned at the time of the CIDP diagnosis included Guillain–Barré syndrome in 8.1%, Sjögren syndrome in 4.4%, and systemic lupus erythematosus in 3.7%. Initial treatments performed after the assignment of the new diagnosis code for CIDP were intravenous immunoglobulin (IVIg) in 54.3%, corticosteroids (CS) in 45.4%, and plasmapheresis in 2.3%. The event‐free survival rates for predefined specific safety‐related events at 1000 d were 90.0% for any event in patients receiving IVIg monotherapy and 71.7% for any event in patients receiving CS monotherapy.ConclusionDiagnostic testing and treatment performed in clinical practice was shown through the analysis of real‐world data of Japanese patients with a diagnosis code for CIDP. These findings might contribute to developments in clinical practice for Japanese CIDP patients.

Epidemiological Aspects and Basic Directions of the Protective Medications against Monkeypox Development

Relevance. After smallpox eradication, in conditions of population immunity to orthopoxviruses absence, Monkeypox virus became most significant orthopoxvirus, pathogenic for humans. Therefore the generalization of data on the areas of infection outbreaks, human diseases and methods of prevention and treatment of monkey pox is important task. Aim. To characterize the problem of monkeypox in the world based on an analysis of foreign scientific publications over the past 20 years. Materials and methods. The work used publications presented in the main international medical information databases PubMed, Web of Science, Embase, etc. To analyze the publications, the analytical epidemiological method was used. Results and discussion. Monkeypox virus, obtained and identified in 1958, by genetic and phenotypic differences divides on two clades: West-African with lethality 3.6% and Central-African (Congo Basin) with lethality 10%. Monkeypox virus transmission to men happens in two ways, either from animal-to-human or human-to-human. Monkey pox is endemic only on African continent, but In 2003 year the first outbreak, numbering 47 confirmed cases, was occurred in non-endemic country – USA and the largest monkeypox outbreak began in Nigeria in September 2017 year and continue to the present. Comparison of the genome sequences of strains, isolated from patients in non-endemic countries, showed, that it genetically close to West-african strains, belong to II clades and were descended from a common ancestor. Many cases of disease in humans in the current outbreak have been traced to sexual transmission especially among men, who identify ourselves as gay or bisexual. The basis method for identification of agent in present time is PCR-RT targeting on the tumor necrosis factor (TNF) receptor gene. Usually monkeypox of human is mild, self-limiting disease. The symptoms of monkeypox are varied and non-specific. One of the most frequently observed clinical symptoms is lymphadenopathy. Most patients recover during some weeks. However, specific antiviral treatment – tecovirimat (S-246) and brincidofovir (CMX-001) – may be used for seriously ill or immunocompromised individuals. For prophylactic disease in present time are use vaccines JYNNEOSTM, ACAM2000R and Aventis Pasteur (APSV). Conclusion. General vaccination against monkeypox don't develop accordingly to modern recommendations WHO. Ring vaccination is recommended to conduct for suppression of spread virus in nidus of infection among population. Timely international coordination is needed to prevent the global spread of a disease with epidemic potential.

Detection Algorithms for Gastrointestinal Perforation Cases in the Medical Information Database Network (MID-NET®) in Japan.

The Medical Information Database Network (MID-NET®) in Japan is a vast repository providing an essential pharmacovigilance tool. Gastrointestinal perforation (GIP) is a critical adverse drug event, yet no well-established GIP identification algorithm exists in MID-NET®. This study evaluated 12 identification algorithms by combining ICD-10 codes with GIP therapeutic procedures. Two sites contributed 200 inpatients with GIP-suggestive ICD-10 codes (100 inpatients each), while a third site contributed 165 inpatients with GIP-suggestive ICD-10 codes and antimicrobial prescriptions. The positive predictive values (PPVs) of the algorithms were determined, and the relative sensitivity (rSn) among the 165 inpatients at the third institution was evaluated. A trade-off between PPV and rSn was observed. For instance, ICD-10 code-based definitions yielded PPVs of 59.5%, whereas ICD-10 codes with CT scan and antimicrobial information gave PPVs of 56.0% and an rSn of 97.0%, and ICD-10 codes with CT scan and antimicrobial information as well as three types of operation codes produced PPVs of 84.2% and an rSn of 24.2%. The same algorithms produced statistically significant differences in PPVs among the three institutions. Combining diagnostic and procedure codes improved the PPVs. The algorithm combining ICD-10 codes with CT scan and antimicrobial information and 80 different operation codes offered the optimal balance (PPV: 61.6%, rSn: 92.4%). This study developed valuable GIP identification algorithms for MID-NET®, revealing the trade-offs between accuracy and sensitivity. The algorithm with the most reasonable balance was determined. These findings enhance pharmacovigilance efforts and facilitate further research to optimize adverse event detection algorithms.

Exploration of Risk Factors of the Onset of Antibiotics-induced Acute Kidney Injury and Its Transfer to Chronic Kidney Disease Using the Medical Information Database

Drug-induced acute kidney injury (AKI) is a serious adverse drug reaction, which results in a significant decline in renal function and is known to progress to chronic kidney disease (CKD). Therefore, appropriate drug therapy is important to avoid the risk of drug-induced AKI and CKD, which are serious concerns in clinical practice. In this study, using the medical information database of Hamamatsu University Hospital, we investigated the risk factors that accelerate the onset of drug-induced AKI or its progression to CKD in patients who received aminoglycoside antibiotics (AGs) or glycopeptide antibiotics (GPs), which are strongly associated with drug-induced AKI and CKD. We performed logistic regression analysis using patients' background, laboratory test results, and concomitant drug use, among other such factors as explanatory variables and drug-induced AKI or CKD onset as objective variables to explore the risk factors for drug-induced AKI and CKD. Our results showed that co-administration of amphotericin B, piperacillin-tazobactam and other AGs and GPs, increased serum creatinine (Scr) and chloride concentrations, serum lactate dehydrogenase activity, and decreased serum albumin concentration were risk factors for drug-induced AKI onset. Moreover, a reduced blood urea nitrogen:Scr ratio at drug-induced AKI onset served as a risk factor for CKD. These results suggest that careful monitoring of the aforementioned factors is important to ensure appropriate usage of these drugs in patients treated with AGs and GPs.

Positive fluid balance and poor outcomes after initial intensive care unit admission in sepsis resuscitation: a retrospective study.

Fluid resuscitation of patients with sepsis is crucial. This study explored the role of fluid balance in the early resuscitation of sepsis patients in the intensive care unit (ICU). A retrospective study of patients with sepsis using the Peking Union Medical College Hospital Intensive Care Medical Information System and Database from January 2014 to June 2020 was performed. Based on the survival status on day 28, the training cohort was divided into an alive group (n = 1,803) and a deceased group (n = 429). Univariate and multivariate analyses were used to identify risk factors, and the integrated learning XGBoost algorithm was used to construct a model for predicting outcomes. ROC and Kaplan-Meier survival curves were used to evaluate the effectiveness of the model. A verification cohort (n = 433) was used to verify the model. Univariate analysis showed that fluid balance is an important covariate. Based on the scatterplot distribution, a significant difference in mortality was determined between groups stratified with a balance of 1000 ml. There were associations in the multivariate analysis between poor outcomes and sex, PO2/FiO2, serum creatinine, FiO2, platelets, respiratory rate, SPO2, temperature, and total fluid volume (1000 ml). Among these variables, total fluid balance (1000 ml) had an OR of 1.98 (CI: 1.41-2.77, p < 0.001). Therefore, the model was built with these nine factors using XGBoost. Cross validation was used to verify generalizability. This model performed better than the SOFA and APACHE II models. The result was well verified in the verification cohort. A causal forest model suggested that patients with hypoxemia may suffer from positive fluid balance. Sepsis fluid resuscitation in the ICU should be a targeted and goal-oriented treatment. A new prognostic prediction model was constructed and indicated that a 6-hour positive fluid balance after ICU initial admission is a risk factor for poor outcomes in sepsis patients. A 6-hour fluid balance above 1000 ml should be performed with caution.

Development of Preventive Methods for Drug-induced Cardiotoxicity Using a Large-scale Medical Information Database

Cancer therapies have evolved considerably thereby substantially improving the survival of patients with cancer. However, cardiotoxicity, such as myocarditis and heart failure, induced by anticancer drugs, including immune checkpoint inhibitor(ICI)s and doxorubicin, present serious challenges. Numerous observations have indicated increased risks of cardiotoxicity- and cancer-related mortality in patients with drug-induced cardiotoxicity. Therefore, the prevention and management of drug-induced cardiotoxicity should be prioritized to enable sustainable long-term treatment while preserving patients' quality of life. Recently, medical research has been primarily focused on elucidation of therapeutic benefits and adverse events using medical big data, including worldwide databases of adverse events. The aim of the present study was to establish prevention strategies for drug-induced cardiotoxicity and advance data analytics. A data-driven approach was adopted to comprehensively analyze patient data and drug-induced cardiotoxicity. These data analytics revealed numerous risk factors, leading to the development of drugs that mitigate these factors. Furthermore, many unknown adverse events with molecularly targeted drugs were brought to light. Consequently, the importance of managing adverse events, guided by insights from data science, is predicted to increase. In this symposium review, we introduce our research exemplifying pharmaceutical studies utilizing medical big data. In particular, we discuss in detail the risk factors associated with myocarditis induced by immune checkpoint inhibitors along with prophylactic agents to mitigate doxorubicin-induced cardiotoxicity.

Real-world prescription of anti-COVID-19 drugs in hospitalized patients with COVID-19 in Japan.

Prescription trends and patterns of anti-COVID-19 drugs in hospitalized patients were examined based on real world data to understand the use of anti-COVID-19 drugs in clinical practice in Japan. The longitudinal and cross-sectional study was conducted utilizing data from January 1, 2019 to December 31, 2021 of the MID-NET® medical information database, which stored the electronic medical records, administrative claim data, and diagnosis procedure combination data of patients in Japan. Hospitalized patients with a COVID-19-related diagnosis who received at least one anti-COVID-19 drug between April 1, 2020 and December 31, 2021. The following 14 drugs were included in this study: remdesivir, baricitinib, combination product of casirivimab and imdevimab, favipiravir, dexamethasone, ivermectin, azithromycin, nafamostat mesylate, camostat mesylate, ciclesonide, tocilizumab, sarilumab, combination product of lopinavir and ritonavir, and hydroxychloroquine. We identified 5,717 patients hospitalized with COVID-19 and prescribed at least one anti-COVID-19 drug. The entire cohort generally included patients over 41-50 years and more males. The most common prescription pattern was dexamethasone monotherapy (22.9%), followed by the concomitant use of remdesivir and dexamethasone (15.0%), azithromycin monotherapy (15.0%), remdesivir monotherapy (10.2%), and nafamostat mesylate monotherapy (8.5%). However, an often prescribed anti-COVID-19 drug differed depending on the period. This study revealed the real-world situation of anti-COVID-19 drug prescriptions in hospitalized COVID-19 patients in Japan. A prescribed drug would depend on the latest scientific evidence, such as efficacy, safety, and approval status, at the time of prescription. Understanding the prescription of anti-COVID-19 drugs will be important for providing the most up-to-date treatments to patients and evaluating the benefit and/or risk of anti-COVID-19 drugs based on the utilization of an electronic medical record database.

Prescription trend and lactic acidosis in patients prescribed metformin before and after the revision of package insert for allowing metformin administration to patients with moderately decreased kidney function based on real-world data from MID-NET® in Japan.

This study was conducted to understand the impact of package insert (PI) revision in Japan on 18 June 2019 to allow metformin use for patients with moderately decreased kidney function (30 ≤ estimated glomerular filtration rate (eGFR) < 60 mL/min/1.73 m2). A new user cohort design was employed to examine the prescription trend and the occurrence of lactic acidosis in patients prescribed metformin before and after PI revision using the Medical Information Database Network (MID-NET®). From 12 May 2016 to 31 March 2020, 5,874 patients (before, n = 4,702; after, n = 1,172) were identified as new metformin users, including 1,145 patients (before, n = 914; after, n = 231) with moderately decreased kidney function. Although no marked changes in metformin prescription were observed before and after PI revision, the daily metformin dose at the first prescription decreased after PI revision. For both before and after PI revision, less than 10 cases of lactic acidosis occurred in all patients prescribed metformin, and no lactic acidosis was observed in patients with moderately decreased kidney function. The results of this study are useful for understanding the safety of metformin use in patients with decreased kidney function and suggest no worse impacts of PI revision in Japan, indicating no further safety concerns on metformin use in patients with moderately decreased kidney function under the situation with careful use and safety monitoring of metformin.

Методические аспекты диагностики когнитивных расстройств у пациен¬тов с нарушениями речи

Aim. To analyze the prevalence and significance of speech disorders in patients after stroke and to study the theoretical aspects of the diagnosis of cognitive disorders in patients with post-stroke speech disorders. Key points. We have processed data from 1 500 information sources on domestic (Elibrary.ru, Rucml.ru) and foreign (PubMed, Medlineplus, The Cochrane Collaboration) medical information databases. Information was obtained on the prevalence and significance of speech disorders in the structure of post-stroke defect. Their influence on social adaptation, disintegration of the psyche, and the effectiveness of neurorehabilitation both in relation to aphasia itself and in relation to its concomitant disorders is shown. Various mechanisms of formation of aphatic disorders as one of the manifestations of general brain dysfunction are considered. The problems that arise when conducting psychodiagnostic measures in patients with speech disorders using standard screening techniques are outlined. Conclusion. Based on the results of studying the diagnostic capabilities of existing scales for assessing cognitive functions in patients with speech disorders, recommendations are proposed for the formation of a new universal standardized Russian-language screening tool covering the entire field of cognitive status, applicable also in patients with speech disorders and allowing to obtain a single score corresponding to the accepted parameters for building an individual rehabilitation route. Keywords: cognitive disorders, speech deficits, aphasia, stroke, neurorehabilitation.

Deep Forest-based Disease Prediction and Diagnosis under the Concept of Digital Health

Abstract With the continuous development of digital technology and the continuous improvement of medical information databases, effective mining methods for potentially useful information behind medical data have become one of the research focuses of digital development in the medical field. In this paper, based on the deep forest model, a deep forest classifier framework based on the federated learning algorithm is constructed for the privacy protection of medical data and optimized by gradient boosting decision tree. Then the feature vector of cardiovascular diseases is constructed from engineering features, and feature selection is carried out through the constructed classifier algorithm to realize the prediction and diagnosis of epidemic diseases, followed by the experimental analysis of the method of this paper. The empirical analysis shows that the diagnostic accuracy of this paper’s model in seven common cardiovascular diseases is higher than 80%. Among them, the accuracy rate of heart valve disease is as high as 87%, and the diagnostic accuracy rate of arrhythmia and coronary heart disease is tied for second place with 83%. It shows that the predictive diagnosis model based on deep forest in this paper has good performance, can meet the actual needs of predictive diagnosis of cardiovascular diseases, and provides an effective reference for the development of auxiliary diagnosis in the digital era.

Illicit drug use in very young patients with acute myocardial infarction: Temporal trends from a nationwide hospital medical information database

Illicit drug use in very young patients with acute myocardial infarction: Temporal trends from a nationwide hospital medical information database

Phenotyping of heart failure with preserved ejection faction using electronic health records and echocardiography.

Patients presenting symptoms of heart failure with preserved ejection fraction (HFpEF) are not a homogenous population. Different phenotypes can differ in prognosis and optimal management strategies. We sought to identify phenotypes of HFpEF by using the medical information database from a large university hospital centre using machine learning. We explored the use of clinical variables from electronic health records in addition to echocardiography to identify different phenotypes of patients with HFpEF. The proposed methodology identifies four phenotypic clusters based on both clinical and echocardiographic characteristics, which have differing prognoses (death and cardiovascular hospitalization). This work demonstrated that artificial intelligence-derived phenotypes could be used as a tool for physicians to assess risk and to target therapies that may improve outcomes.

Combining the Hospital Frailty Risk Score With the Charlson and Elixhauser Multimorbidity Indices to Identify Older Patients at Risk of Poor Outcomes in Acute Care.

The Hospital Frailty Risk Score (HFRS) can be applied to medico-administrative datasets to determine the risks of 30-day mortality and long length of stay (LOS) in hospitalized older patients. The objective of this study was to compare the HFRS with Charlson and Elixhauser comorbidity indices, used separately or combined. A retrospective analysis of the French medical information database. The HFRS, Charlson index, and Elixhauser index were calculated for each patient based on the index stay and hospitalizations over the preceding 2 years. Different constructions of the HFRS were considered based on overlapping diagnostic codes with either Charlson or Elixhauser indices. We used mixed logistic regression models to investigate the association between outcomes, different constructions of HFRS, and associations with comorbidity indices. 743 hospitals in France. All patients aged 75 years or older hospitalized as an emergency in 2017 (n=1,042,234).Main outcome measures: 30-day inpatient mortality and LOS >10 days. The HFRS, Charlson, and Elixhauser indices were comparably associated with an increased risk of 30-day inpatient mortality and long LOS. The combined model with the highest c-statistic was obtained when associating the HFRS with standard adjustment and Charlson for 30-day inpatient mortality (adjusted c-statistics: HFRS=0.654; HFRS + Charlson = 0.676) and with Elixhauser for long LOS (adjusted c-statistics: HFRS= 0.672; HFRS + Elixhauser =0.698). Combining comorbidity indices and HFRS may improve discrimination for predicting long LOS in hospitalized older people, but adds little to Charlson's 30-day inpatient mortality risk.

Comparison of the efficacy of anti-diabetic medications as add-on to metformin in type 2 diabetes mellitus from a real-world database

BackgroundMetformin is recommended as a first-line drug in the guidelines of the treatment for type 2 diabetes mellitus. However, high-quality evidence from clinical trials directly comparing the degree of hypoglycemic effect of combination therapy of metformin and a hypoglycemic agent with a different mechanism of action with that of monotherapy of a hypoglycemic drug is lacking. We aimed to examine whether combination therapy of hypoglycemic agents with metformin showed antagonism, addition, or synergism compared to monotherapy with hypoglycemic agents other than metformin regarding hemoglobin A1c levels.MethodsThis retrospective cohort study used a medical information database in Japan. Non-insulin anti-hyperglycemic agents with different mechanisms of action were classified into eight drug classes. A monotherapy cohort and a combination therapy added to the metformin cohort were defined. The change in hemoglobin A1c levels was evaluated to compare the treatment effect between the cohorts.ResultsA total of 13,359 patients with type 2 diabetes mellitus in the monotherapy cohort and 1,064 in the metformin combination therapy cohort were identified. A comparison of the change from baseline HbA1c level by drug class between the two cohorts showed a similar trend. Among those treated with dipeptidyl peptidase-4 inhibitor and sodium-glucose co-transporter-2 inhibitor, no clinically significant difference was observed between the two cohorts (0.00% and -0.07% for unadjusted, 0.15% and -0.03% for propensity score matching-adjusted, and 0.09% and -0.01% for inverse probability treatment weighting-adjusted analysis).ConclusionsAccording to the results of this study, the effect of dipeptidyl peptidase-4 inhibitor or sodium-glucose co-transporter-2 inhibitor added to metformin seems to be additive with respect to the reduction in hemoglobin A1c.

Illicit drug use in very young patients with acute myocardial infarction: temporal trends from a nationwide hospital medical information database

Abstract Introduction Illicit drug use, including cannabis, opioid and cocaine, which has been associated with increased risk of acute myocardial infarction (MI), has dramatically increase in young European people over the last decade. Aim From a French administrative database, we aimed to examine the rate and temporal trend in illicit drugs use in younger patients hospitalized for an acute MI between 2010 and 2022. Methods From a nationwide medical information system, all adult patients hospitalized for acute MI in the 1546 French healthcare facilities from the 1st January 2010 to 31st December 2022 were included in the analyse. Rate of Illicit drug use was compared in very young patients (18 to 25 y) vs older (&amp;gt;25y) patients. Results Over the 12y study period, 1,011,706 patients were analyzed. The percentage of very young patients remains stable from 2010 to 2022 (0.12% to 0.13%). When compared with older (≥25y), illicit drug use was much more frequent in very young patients, and showed a huge increase, by ≈50%, in drug use over the period (9.7% to 14.9%) (Figure). The rate of drug also increased in older (0.3% to 1.1%). Meanwhile, the prevalence of smoking remains stable in very young (33.1% to 32.2%). Early CV mortality was low in very young, especially in recent years (3.7% to 0.8%). Conclusion This large nationwide observational study reports an alarming increase rate of illicit drug among patients with acute MI over the last years. Very young adults (&amp;lt;25y) are particularly at risk, suggesting the need for systematical screening and urinary toxic identification.Trends in illicit drug use

Time-related association between fluid balance and prognosis in sepsis patients: a cohort study based on MIMIC-IV database

To investigate time-related association between fluid balance and prognosis in sepsis patients. A retrospective cohort study was conducted based on the data of sepsis patients in the Medical Information Database for Intensive Care-IV 2.0 (MIMIC-IV 2.0) from 2008 to 2019. Sepsis patients aged ≥ 18 years who were admitted to intensive care unit (ICU) for at least 2 days were included. The daily fluid balance and cumulative fluid balance (CFB) were calculated from days 1 to 7 after ICU admission. According to CFB,the patients were divided into negative fluid balance group (CFB% < 0%), fluid balance group (0% ≤ CFB% ≤ 10%), and fluid overload group (CFB% > 10%). In-hospital mortality was the primary outcome. Multifactorial Logistic regression was used to analyze time-related association between different CFB and the risk of in-hospital mortality in patients with sepsis during 7 days after ICU admission. In addition, subgroup analysis was performed on patients with septic shock and patients with sepsis who stayed in the ICU for 7 days or longer. A total of 11 437 patients with sepsis were included, of which 6 595 were male and 4 842 were female. The mean age was (64.4±16.4) years. A total of 10 253 patients (89.6%) survived and 1 184 patients (10.4%) died during hospitalization. Compared with the survival group, patients in the death group were older, lighter, had higher sequential organ failure assessment (SOFA), simplified acute physiology score II (SAPS II), longer ICU stay, higher incidence of septic shock, and higher proportion of invasive mechanical ventilation, renal replacement therapy (RRT) and vasoactive drugs. In terms of comorbidities, congestive heart failure, renal disease, liver disease, and malignancy were more common in the death group. The death group had a higher daily fluid balance than the survival group during 7 days after ICU admission, the CFB in the two groups gradually increased with length of ICU stay. After adjusting variables such as age, gender, race, SOFA score, SAPS II score, comorbidities, and the use of invasive mechanical ventilation, RRT and vasoactive drugs, multivariate Logistic regression analysis showed that fluid overload on day 1 after ICU admission was a protective factor for the reduced risk of in-hospital mortality in sepsis patients [odds ratio (OR) = 0.74, 95% confidence interval (95%CI) was 0.64-0.86, P = 0.001]. However, fluid overload on day 3 was a risk factor for in-hospital mortality in sepsis patients (OR = 1.70, 95%CI was 1.47-1.97, P < 0.001) and the risk of in-hospital mortality was significantly increased from day 4 to day 7. Furthermore, the same results were obtained in patients with septic shock and sepsis patients who stayed in the ICU for 7 days or longer. Fluid overload on day 1 was associated with reduced in-hospital mortality. However, from the third day, fluid overload increases the risk of in-hospital mortality. Thus, managing fluid balance at different times may improve prognosis.

Triglyceride-glucose index as a valuable predictor for aged 65-years and above in critical delirium patients: evidence from a multi-center study

BackgroundThe triglyceride-glucose index (TyG), an established indicator of insulin resistance, is closely correlated with the prognosis of several metabolic disorders. This study aims to investigate the association between the TyG index and the incidence of critical delirium in patients aged 65 years and older.MethodsWe focused on evaluating patients aged 65 years and older diagnosed with critical delirium. Data were obtained from the Medical Information Database for Intensive Care (MIMIC-IV) and the eICU Collaborative Research Database (eICU-CRD). Multivariate logistic regression and restricted cubic spline (RCS) regression were used to determine the relationship between the TyG index and the risk of delirium.ResultsParticipants aged 65 years and older were identified from the MIMIC-IV (n = 4,649) and eICU-CRD (n = 1,844) databases. Based on optimal thresholds derived from RCS regression, participants were divided into two cohorts: Q1 (< 8.912), Q2 (≥ 8.912). The logistic regression analysis showed a direct correlation between the TyG index and an increased risk of critical delirium among ICU patients aged 65 and older. These findings were validated in the eICU-CRD dataset, and sensitivity analysis further strengthened our conclusions. In addition, the subgroup analysis revealed certain differences.ConclusionThis study highlights a clear, independent relationship between the TyG index and the risk of critical delirium in individuals aged 65 years and older, suggesting the importance of the TyG index as a reliable cardio-cerebrovascular metabolic marker for risk assessment and intervention.

Lower Risks of Gastrointestinal Perforation and Intestinal Obstruction in Patients with Atypical Antipsychotics in Comparison with Typical Antipsychotics Based on Real-World Data from the MID-NET® in Japan

Intestinal perforation and obstruction are known to be one of the adverse events caused by antipsychotics; however, warning information on package inserts varies among antipsychotics. To investigate the risks of gastrointestinal perforation and intestinal obstruction in patients prescribed atypical antipsychotics compared with those in patients prescribed typical antipsychotics, a nested case–control study was conducted utilizing real-world data from the MID-NET® medical information database in Japan. The study period spanned from January 1, 2009, to December 31, 2018. We found that the risks of gastrointestinal perforation and intestinal obstruction in patients prescribed atypical antipsychotics were significantly lower than those in patients prescribed typical antipsychotics (adjusted odds ratio, 0.48; 95% confidence interval, 0.29–0.80). This finding was supported with prolonged periods for the exposure definition in the sensitivity analyses. In addition, no major differences in the risks of atypical antipsychotics, such as risperidone, quetiapine, olanzapine, and aripiprazole, were identified in this study. The safety profile regarding the lower risks of gastrointestinal perforation and intestinal obstruction in patients prescribed atypical antipsychotics should be considered when choosing antipsychotics in clinical practice in terms of the proper use of such drugs.

Association between initial microcirculation disturbance patients and mortality in patients who are critically ill: A retrospective cohort study.

Impact of microcirculation status from mortality of critically ill population has been investigated for decades, but the prognosis of early initial microcirculation disturbance in critically ill population in the intensive care unit remains to be explored. The cohort study was conducted using the medical information database for intensive care IV. Critically ill adult in intensive care unit have been enrolled and categorized by early microcirculation status. Cox Proportional-Hazards models have been utilized for testing intermediaries and assess the relationship between combined early initial microcirculation disturbance and mortality. Several 2286 patients were initially screened. Some patients with a highest lactate level >2.2 mmol/L on the firstly day of admission (n = 1468) were then extracted for further analysis. 735 patients received in the initial microcirculation disturbance group as well as 733 patients were in the hyperlactatemia group. In those with elevated lactate, the 28-day mortality of early microcirculation disturbance was higher than that of hyperlactatemia alone (7-day mortality [16.19% vs 12.68%; Adjusted hazard ratio 1.35, 95% confidence intervals 1.03 to 1.78, P = .029], 28-day mortality [33.33% vs 27.28%; adjusted HR 1.34, 95% confidence interval 1.11 to 1.67, P = .002]). Early microcirculatory disturbances (increased PV-ACO2/CA-VO2 ratio and higher initial blood lactate level) were more reliable predictors of in-hospital mortality than early isolated lactate elevation.

Phenotyping of Heart Failure with preserved ejection faction using electronic health records and echocardiography

Abstract Funding Acknowledgements Type of funding sources: Public hospital(s). Main funding source(s): CHU de Rennes Aims Patients presenting heart failure (HF) symptoms with preserved left ventricular ejection fraction (HFpEF) are not a homogenous population. Different phenotypes can differ in prognosis and optimal management strategies. We sought to identify phenotypes of HFpEF by using the medical information database from a large University Hospital Center using machine learning. Methods and results We explored the use of clinical variables from electronic health records (EHR) in addition to echocardiography to identify different phenotypes of patients with heart failure and preserved ejection fraction. The proposed methodology identifies 4 phenotypic clusters based on both clinical and echocardiographic characteristics which have differing prognoses (death and cardio-vascular hospitalization). Conclusion This work demonstrated that AI derived phenotypes could be used as a tool for physicians to assess risk and to target therapies that may improve outcomes.