Prognostic Estimates Research Articles

Prognostic significance of eighth edition TNM stage criteria in combined small-cell lung cancer.

This study aimed to evaluate the prognostic significance of the eighth edition TNM stage criteria in patients with combined small-cell lung cancer (C-SCLC) on a population level. Using the Surveillance, Epidemiology, and End Results (SEER) database, patients diagnosed with C-SCLC (histology code 8245) between the years 2004 and 2015 were identified. We performed a Kaplan-Meier analysis and used the multivariable cox regression proportional hazards model to obtain prognostic overall survival estimates for each group of patients. A total of 477 patients diagnosed with C-SCLC were identified. The T, N, M, TNM, and combined TNM stage status of the eighth edition were all significant prognostic factors for patients' overall survivals, with the best discrimination identified in the combined stages. Surgery was also found to be a prognostic factor (HR =1.95, 95%CI =1.49-2.56, p<0.01) for patients with C-SCLC. The combined eighth edition of the TNM staging criteria shows reliable prognostic significance in patients with C-SCLC. Moreover, surgery might be significant for improving the patients' prognosis.

Prognosis of thyroid carcinoma patients with osseous metastases: an SEER-based study with machine learning.

Osseous metastasis (OM) is the second most common site of thyroid cancer distant metastasis and presents a poor prognosis. Accurate prognostic estimation for OM has clinical significance. Ascertain the risk factors for survival and develop an effective model to predict the 3-year, 5-year overall survival (OS) and cancer-specific survival (CSS) for thyroid cancer patients with OM. We retrieved the information of patients with OMs between 2010 and 2016 from the Surveillance, Epidemiology, and End Result Program. The Chi-square test, and univariate and multivariate Cox regression analyses were performed. Four machine learning (ML) algorithms, which were most commonly used in this field, were applied. A total of 579 patients having OMs were eligible. Advanced age, tumor size ≥ 40mm, combined with other distant metastasis were associated with worse OS in DTC OMs patients. Radioactive iodine (RAI) significantly improved CSS in both males and females. Among four ML models [logistic regression, support vector machines, extreme gradient boosting, and random forest (RF)], RF had the best performance [area under the receiver-operating characteristic curve: 0.9378 for 3-year CSS, 0.9105 for 5-year CSS, 0.8787 for 3-year OS, 0.8909 for 5-year OS]. The accuracy and specificity of RF were also the best. RF model shall be used to establish an accurate prognostic model for thyroid cancer patients with OM, not only from the SEER cohort but also intended for all thyroid cancer patients in the general population, which may be applicable in clinical practice in the future.

The prognostic impacts of frailty on clinical and patient-reported outcomes in patients undergoing coronary artery or valvular surgeries/procedures: A systematic review and meta-analysis.

Frailty is emerging as an important prognostic indicator for patients undergoing cardiac surgeries/procedures. We sought to evaluate the prognostic and differential impacts of frailty on patients undergoing coronary artery or valvular surgical procedures of different levels of invasiveness, and to explore the differential predictability of various frailty measurement models. Eight databases were searched for prospective cohort studies that have adopted validated measure(s) of frailty and reported clinical, healthcare service utilization, or patient-reported outcomes in patients undergoing coronary artery or valvular surgeries/procedures. Sixty-two articles were included (N=16,679). Frailty significantly predicted mortality (short-term [≤ 30 days]: odds ratio [OR]: 2.33, 95% confidence interval [CI]: 1.28-4.26; midterm [6 months to 1 year]: OR: 3.93, 95%CI: 2.65-5.83; long-term [>1 year]: HR: 2.23, 95%CI: 1.60-3.11), postoperative complications (ORs: 2.54-3.57), discharge to care facilities (OR: 5.52, 95%CI: 3.84-7.94), hospital readmission (OR: 2.00, 95%CI: 1.15-3.50), and reduced health-related quality of life (HRQoL; standardized mean difference: -0.74, 95%CI: -1.30 to -0.18). Subgroup analyses showed that frailty exerted a greater impact on short-term mortality in patients undergoing open-heart surgeries than those receiving transcatheter procedures. Multidimensional and physical-aspect-focused frailty measurements performed equally in predicting mortality, but multidimensional measurements were more predictive of hospital readmission than physical-aspect-focused measurements. Frailty was predictive of postoperative mortality, complications, increased healthcare service utilization, and reduced HRQoL. The impact of frailty on short-term mortality was more prominent in patients undergoing open-heart surgeries than those receiving transcatheter procedures. Multidimensional measures of frailty enhanced prognostic risk estimation, especially for hospital readmission.

Novel Image-Based Rapid RUL Prediction for Li-Ion Batteries Using a Capsule Network and Transfer Learning

The recent popularity boost in electric vehicles created a large demand for lithium-ion batteries, but current recycling methods are not ready to carry the weight of the years to come. Because of this, the main goal of this study is to optimize the speed and accuracy of lithium-ion batteries’ remaining useful life (RUL) prediction methods to make them a viable option for second-life classification applications. This article develops a capsule network architecture for rapid battery RUL prediction using transfer learning techniques. The proposed method can accurately predict the RUL of a cell using a single charging and discharging cycle, making it one of the fastest methods available to date. This novel image-based health prognostic estimation method reduces the preprocessing labor and, consequently, the amount of human-induced bias in the dataset. Not only are complete charging and discharging cycles shown in a single image, but also even numerical data are added and taught to be recognized by the network. This rapid prediction model will have uses in the fast characterization of battery cells for second-life classification purposes, for researchers developing health-conscious charging protocols, and even for battery management system implementations.

US and Dutch Perspectives on the Use of COVID-19 Clinical Prediction Models: Findings from a Qualitative Analysis.

Clinical prediction models (CPMs) for coronavirus disease 2019 (COVID-19) may support clinical decision making, treatment, and communication. However, attitudes about using CPMs for COVID-19 decision making are unknown. Online focus groups and interviews were conducted among health care providers, survivors of COVID-19, and surrogates (i.e., loved ones/surrogate decision makers) in the United States and the Netherlands. Semistructured questions explored experiences about clinical decision making in COVID-19 care and facilitators and barriers for implementing CPMs. In the United States, we conducted 4 online focus groups with 1) providers and 2) surrogates and survivors of COVID-19 between January 2021 and July 2021. In the Netherlands, we conducted 3 focus groups and 4 individual interviews with 1) providers and 2) surrogates and survivors of COVID-19 between May 2021 and July 2021. Providers expressed concern about CPM validity and the belief that patients may interpret CPM predictions as absolute. They described CPMs as potentially useful for resource allocation, triaging, education, and research. Several surrogates and people who had COVID-19 were not given prognostic estimates but believed this information would have supported and influenced their decision making. A limited number of participants felt the data would not have applied to them and that they or their loved ones may not have survived, as poor prognosis may have suggested withdrawal of treatment. Many providers had reservations about using CPMs for people with COVID-19 due to concerns about CPM validity and patient-level interpretation of the outcome predictions. However, several people who survived COVID-19 and their surrogates indicated that they would have found this information useful for decision making. Therefore, information provision may be needed to improve provider-level comfort and patient and surrogate understanding of CPMs. While clinical prediction models (CPMs) may provide an objective means of assessing COVID-19 prognosis, provider concerns about CPM validity and the interpretation of CPM predictions may limit their clinical use.Providers felt that CPMs may be most useful for resource allocation, triage, research, or educational purposes for COVID-19.Several survivors of COVID-19 and their surrogates felt that CPMs would have been informative and may have aided them in making COVID-19 treatment decisions, while others felt the data would not have applied to them.

Fluorescence-Enhanced Dual-Driven "OR-AND" DNA Logic Platform for Accurate Cell Subtype Identification.

Developing an endogenous stimuli-responsive and ultrasensitive DNA sensing platform that contains a logic gate biocomputation for precise cell subtype identification holds great potential for disease diagnosis and prognostic estimation. Herein, a fluorescence-enhanced "OR-AND" DNA logic platform dual-driven by intracellular apurinic/apyrimidinic endonuclease 1 (APE 1) or a DNA strand anchored on membrane protein Mucin 1 (MUC 1) for sensitive and accurate cell subtype identification was rationally designed. The recognition toehold of the traditional activated probe (TP) was restrained by introducing a blocking sequence containing an APE 1 cleavable site (AP-site) that can be either cleaved by APE 1 or replaced by Mk-apt, ensuring the "OR-AND" gated molecular imaging for cell subtype identification. It is worth noting that this "OR-AND" gated design can effectively avoid the missing logical computation caused by membrane protein heterogeneous spatial distribution as a single input. In addition, a benefit from the excellent plasmon-enhanced fluorescence (PEF) ability of Au NSTs is that the detection limit can be decreased by nearly 165 times. Based on this, not only different kinds of MCF-7, HepG2, and L02 cells, but also different breast cancer cell subtypes, including malignant MCF-7, metastatic MDA-MB-231, and nontumorigenic MCF-10A cells, can be accurately identified by the proposed "OR-AND" gated DNA logic platform, indicating the prospect of this simple and universal design in accurate cancer screening.

Dark papillary muscles sign: a novel prognostic marker for cardiac magnetic resonance

ObjectivesThe prognostic role of left ventricular (LV) papillary muscle abnormalities in patients with preserved LV systolic ejection fraction (LVEF) is unknown. We sought to evaluate the prognosis role of LV papillary muscle abnormalities by CMR in patients with ventricular arrhythmias, preserved LVEF with no cardiac disease.MethodsA total of 391 patients with > 500/24 h premature ventricular complexes and/or with non-sustained ventricular tachycardia (NSVT), preserved LVEF, and no cardiac disease were enrolled. Different features of LV papillary muscles were considered: supernumerary muscles, papillary thickness, the attachment, late gadolinium enhancement (LGE). Dark-Paps was defined as end-systolic signal hypointensity of both papillary muscles in early post-contrast cine CMR images. Mitral valve prolapse, mitral annular disjunction (MAD), and myocardial LGE were considered.ResultsDark-Paps was found in 79 (20%) patients and was more frequent in females. It was associated with higher prevalence of mitral valve prolapse and MAD. During a median follow-up of 2534 days, 22 hard cardiac events occurred. At Kaplan-Meier curve analysis, patients with Dark-Paps were at higher risk of events than those without (p < 0.0001). Dark-Paps was significantly associated with hard cardiac events in all the multivariate models. Dark-Paps improved prognostic estimation when added to NSVT (p = 0.0006), to LGE (p = 0.005) and to a model including NSVT+LGE (p = 0.014). Dark-Paps allowed a significant net reclassification when added to NSVT (NRI 0.30, p = 0.03), to LGE (NRI 0.25, p = 0.04), and to NSVT + LGE (NRI 0.32, p = 0.02).ConclusionsIn LV papillary muscles, Dark-Paps is a novel prognostic marker in patients with ventricular arrhythmias and preserved ejection fraction.Key Points• Papillary muscle abnormalities are seen in patients with ventricular arrhythmias and preserved left ventricular ejection fraction.• Early post-contrast hypointensity of papillary muscles in end-systolic cine images (Dark-Paps) is a novel prognostic marker in patients with ventricular arrhythmias and preserved ejection fraction.• Dark-Paps had an additive prognostic role over late gadolinium enhancement and non-sustained ventricular tachycardia.

Prognosis of adults and children following a first unprovoked seizure.

Prognosis of adults and children following a first unprovoked seizure.

To Treat or Not to Treat? Dilemmas when Deciding on Antineoplastic Treatment in Patients With Far Advanced Cancers.

Patients with advanced cancers and their oncologists are often faced with difficult treatment decisions, especially when there are borderline situations of expected benefit or increased risk of complications. In this narrative review, we will explore the decision-making process for patients with advanced cancers and provide insights on how to approach this complex task, while didactically dividing the oncologist's assessments according to a mnemonic rule of the ABCDE of therapeutic decision-making. Part A (advanced cancer) recalls that the rule is to be used specifically for advanced cancers. Parts B (potential benefits) and C (clinical conditions and risks) represents the traditional risk vs benefit scale. In Part D, we discuss ways to identify and understand patients' desires, values, preferences, and beliefs. The prognostic estimation, from Part E, may function as an "adjust" for the antineoplastic treatment decision-making. Treatment decisions need to be conducted by skilled oncologists, in a patient-centered care, aiming to promote valuable oncology with lower rates of aggressive care.

An Improved Generic Hybrid Prognostic Method for RUL Prediction Based on PF-LSTM Learning

Accurate estimation and prediction of the State-of-Health (SOH) and Remaining Useful Life (RUL) are fundamental to optimal maintenance strategies formulation for Prognostics and Health Management (PHM) of degraded equipment. However, the performance assessment of health state prognostics and RUL prediction is strongly dependent on the errors and uncertainties in physical measurements, and heterogeneous degradation of equipment in time-varying operating conditions. The objective of the paper is to provide a hybrid prognostic framework that integrates a two-phases clustering scheme and a PF-LSTM learning algorithm based on Particle Filter (PF) and Long Short-Term Memory (LSTM) networks for dynamic classification of SOH and long-term RUL prediction in the absence of future observations. The proposed generic hybrid PF-LSTM prognostic approach is demonstrated and compared with other adaptive learning and machine learning methods such as Unscented Particle Filter (UPF) and Radial Basis Function Network (RBFN) on the degradation modeling and RUL prediction for Lithium-ion batteries. The comparison results show that robust prediction performance can be obtained by the hybrid PF-LSTM prognostic approach with accurate characterization of equipment degradation states based on the integrated subtractive-fuzzy clustering analysis. The more accuracy on prognostic estimations in Probability Density Function (PDF) of prior and posterior distributions of battery capacity and RUL that are achieved by particle filtering can gain extensive insights to predictive maintenance action guide.

An Immunogenic Cell Death-Related Gene Signature Reflects Immune Landscape and Predicts Prognosis in Melanoma Independently of BRAF V600E Status.

Immunogenic cell death (ICD) is a type of regulated cell death that can activate adaptive immune response, and its ability to reshape the tumor microenvironment via multiple mechanisms may contribute to immunotherapy. The treatment options for patients with skin cutaneous melanoma (SKCM) vary based on BRAF V600E statuses. However, all standard treatments include immunotherapy. Therefore, it is critical to identify ICD-associated signatures that can help classify patients according to benefits from ICD immunotherapy. In this study, data on melanoma samples with BRAF V600E mutation (BRAF V600E-mutant melanoma) and melanoma samples with wild-type BRAF V600E alleles (BRAF V600E WT melanoma) were collected from The Cancer Genome Atlas (TCGA) database. The ICD-related (ICD-high and ICD-low) subgroups of patients with BRAF V600E WT melanoma were established via consensus clustering. The analyses of survival, differentially expressed genes (DEGs), functional annotation, and immune landscape were performed in these two subgroups. Results showed that ICD-high subgroup was correlated with a positive overall survival (OS) and active tumor immune landscape. A model comprising seven prognosis ICD-related gene biomarkers was developed. Survival analysis and receiver operating characteristic (ROC) curve evaluation in both cohorts with BRAF V600E WT and BRAF V600E-mutant melanoma showed an accurate prognostic estimation of ICD-related risk signature. There was a correlation between immune cell infiltration and immunotherapy response and risk score. Thus, the ICD risk signature was closely associated with the tumor's immune microenvironment. Our results may provide insights to further individualize and improve precision therapeutic decision-making in BRAF V600E-mutant and WT melanoma.

Identification and Prognostication of End-of-Life State Using a Japanese Guideline-Based Diagnostic Method: A Diagnostic Accuracy Study

Prognostic uncertainty can be a barrier to providing palliative care. Accurate prognostic estimation for patients at the end of life is challenging. This study aimed to evaluate the accuracy of end-of-life diagnosis using our unique diagnostic method. A retrospective longitudinal observational study was conducted through collaboration among three medical facilities in a rural super-aged community in Japan. In 2007, we established a unique end-of-life diagnostic process comprising (1) physicians' judgement, (2) disclosure to patients, and (3) discussion at an end-of-life case conference (EOL-CC), based on Japanese end-of-life-related guidelines. Research subjects were consecutive patients discussed in EOL-CC between January 1, 2010, and September 30, 2017. The primary outcome was mortality within 6 months after the initial EOL-CC decision. Sensitivity, specificity, and diagnostic odds ratio were calculated using EOL-CC diagnosis (end-of-life or non-end-of-life) as an index test and overall survival (<6 months or ≥6 months) as a reference standard. In total, 315 patients were eligible for survival analysis (median age 89, range 54-107). The study population was limited to patients with severe conditions such as advanced cancer, organ failures, advanced dementia with severe deterioration in functioning. EOL-diagnosis by our methods was associated with much lower survival rate at 6 months after EOL-CC than non-EOL-diagnosis (6.9% vs 43.5%; P < 0.001). Of the patients, 297 were eligible for diagnostic accuracy analysis (median age 89, range 54-107). The EOL-diagnosis showed high sensitivity (0.95; 95% confidence interval [CI] 0.92-0.97) but low specificity (0.35; 95% CI 0.20-0.53) against the outcomes. It also showed a high diagnostic odds ratio (10.32; 95% CI 4.08-26.13). The diagnostic process using the Japanese end-of-life guidelines had tolerable accuracy in identification and prognostication of end of life.

СЕРЕДНІЙ РІЧНИЙ СТІК ВОДИ РІЧОК УКРАЇНСЬКИХ КАРПАТ У БАГАТОВОДНІ ТА МАЛОВОДНІ ФАЗИ ВОДНОСТІ

he assessment of the average annual runoff of the rivers of the Ukrainian Carpathians in the high-water and low-water phases of water was carried out on the basis of a stochastic analysis and the identification of characteristic patterns of alternation of such periods within the boundaries of the Tisza, Dniester, Prut and Siret river systems. The study was carried out using a methodological base based on statistical means of generalization, data systematization, methods for estimating temporal random sequences, methods for analyzing the long-term variability of time series of water runoff characteristics and identifying their structure. As a result of the study, the variability in the structure of the time series of the average annual water discharge of the rivers of the Ukrainian Carpathians is clearly revealed. Such variability is similar for rivers within the studied river systems and is characterized by common patterns of stochastic relationships and cyclic fluctuations. The high reliability of cycles with periods of 29 ± 2 years indicates a stable recurrence of periods of high and low water content. The graphs of the relationships between the average annual water discharges during the periods of high and low water content and their average long-term values for the rivers of the Tisza, Prut and Siret river systems on the right bank of the Dniester showed that the qualitative characteristics of such relationships are assessed as very high. The estimates of water discharge in the water phase for the rivers of the Tisza, Prut, Siret and the right bank of the Dniester basins were checked using regression equations in comparison with empirical data and their high reliability (the probability of not exceeding probable deviations in dry periods was 77%, in high-water periods – 95%), which made it possible to generalize the probable expected average water discharges in the high-water and low-water phases of the cycle, depending on the average long-term values for the rivers of the studied basins of the Ukrainian Carpathians. Such generalizations can be used for prognostic estimates of possible values of water discharge in high-water and low-water phases for any river of the Ukrainian Carpathians. At the same time, it is necessary to take into account in which system of rivers it is located, to know the average long-term water discharge and the time frame and the chronology of the water phases.

Identification and Development of a 4-Gene Ferroptosis Signature Predicting Overall Survival for Diffuse Large B-Cell Lymphoma.

Background: Diffuse large B-cell lymphoma (DLBCL) is a well-differentiated disease, which makes the diagnosis and therapeutic strategy a difficult problem. While ferroptosis, as an iron-dependent form of regulated cell death, it plays an important role in causing several types of cancer. This study is aimed at exploring the prognostic value of ferroptosis-related genes in DLBCL. Methods: In our study, mRNA expression and matching clinical data of DLBCL patients were derived from Gene Expression Omnibus (GEO) database. First, multivariate cox regression model and nomogram which can predict the DLBCL patients' prognosis were built and validated. The multigene signature was constructed and optimized by the least absolute shrinkage and selection operator (LASSO) cox regression model. Also, ferroptosis-related subtypes were developed by consistent cluster. Last but not least, we explored the association between categories of infiltrating immune cells and model genes' expression. Results: Our results showed that 27 gene expressions were correlated with overall survival (OS) in the univariate cox regression analysis. A 4-gene signature was constructed through these genes to stratify patients into high-low risk groups using risk score derived from model (model 1:gene expression model). The OS of patients in the high-risk group was shorter than that of patients in the low-risk group in the TNM stage and clinically distinct subtypes (activated B cell [ABC], germinal center B cell [GCB]) (P < .001). Furthermore, it was shown that the risk score was an independent factor in clinical cox regression model for OS (model 2:clinical model) (HR>1, P < .010). Besides, in consistent cluster analysis, ferroptosis prognosis status was different among 3 subtypes. Moreover, the correlation analysis between 4-gene with immune cells showed dendritic cells may be significantly associated with DLBCL. Conclusion: This research constructed an innovative ferroptosis-related gene signature for prognostic estimation of DLBCL patients. Solutions targeting ferroptosis could be an important therapeutic intervention for DLBCL.

Machine Learning Improves Risk Stratification in Myelofibrosis: An Analysis of the Spanish Registry of Myelofibrosis.

Myelofibrosis (MF) is a myeloproliferative neoplasm (MPN) with heterogeneous clinical course. Allogeneic hematopoietic cell transplantation remains the only curative therapy, but its morbidity and mortality require careful candidate selection. Therefore, accurate disease risk prognostication is critical for treatment decision-making. We obtained registry data from patients diagnosed with MF in 60 Spanish institutions (N = 1386). These were randomly divided into a training set (80%) and a test set (20%). A machine learning (ML) technique (random forest) was used to model overall survival (OS) and leukemia-free survival (LFS) in the training set, and the results were validated in the test set. We derived the AIPSS-MF (Artificial Intelligence Prognostic Scoring System for Myelofibrosis) model, which was based on 8 clinical variables at diagnosis and achieved high accuracy in predicting OS (training set c-index, 0.750; test set c-index, 0.744) and LFS (training set c-index, 0.697; test set c-index, 0.703). No improvement was obtained with the inclusion of MPN driver mutations in the model. We were unable to adequately assess the potential benefit of including adverse cytogenetics or high-risk mutations due to the lack of these data in many patients. AIPSS-MF was superior to the IPSS regardless of MF subtype and age range and outperformed the MYSEC-PM in patients with secondary MF. In conclusion, we have developed a prediction model based exclusively on clinical variables that provides individualized prognostic estimates in patients with primary and secondary MF. The use of AIPSS-MF in combination with predictive models that incorporate genetic information may improve disease risk stratification.

Poynting Vector Method Combined with Weighted Least Square Regression for Enhanced Prognostic Electric Field Strength Modelling and Estimation

Poynting Vector Method Combined with Weighted Least Square Regression for Enhanced Prognostic Electric Field Strength Modelling and Estimation

Performance of administrative database frailty instruments in predicting clinical outcomes and cost for patients undergoing transcatheter aortic valve implantation: a historical cohort study.

Frailty instrumentsmay improve prognostic estimates for patients undergoing transcatheter aortic valve implantation (TAVI). Few studies have evaluated and compared the performance of administrative database frailty instruments for patients undergoing TAVI. This study aimed to examine the performance of administrative database frailty instruments in predicting clinical outcomes and costs in patients who underwent TAVI. We conducted a historical cohort study of 3,848 patients aged 66 yr or older who underwent a TAVI procedure in Ontario, Canada from 1 April 2012 to 31 March 2018. We used the Johns Hopkins Adjusted Clinical Group (ACG) frailty indicator and the Hospital Frailty Risk Score (HFRS) to assign frailty status. Outcomes of interest were in-hospital mortality, one-year mortality, rehospitalization, and healthcare costs. We compared the performance of the two frailty instruments with that of a reference model that adjusted baseline covariates and procedural characteristics. Accuracy measures included c-statistics, Akaike information criterion (AIC), Bayesian information criterion (BIC), integrated discrimination improvement (IDI), net reclassification index (NRI), bias, and accuracy of cost estimates. A total of 863 patients (22.4%) were identified as frail using the Johns Hopkins ACG frailty indicator and 865 (22.5%) were identified as frail using the HFRS. Although agreement between the frailty instruments was fair (Kappa statistic = 0.322), each instrument classified different subgroups as frail. Both the Johns Hopkins ACG frailty indicator (rate ratio [RR], 1.13; 95% confidence interval [CI], 1.06 to 1.20) and the HFRS (RR, 1.14; 95% CI, 1.07 to 1.21) were significantly associated with increased one-year costs. Compared with the reference model, both the Johns Hopkins ACG frailty indicator and HFRS significantly improved NRI for one-year mortality (Johns Hopkins ACG frailty indicator: NRI, 0.160; P < 0.001; HFRS: NRI, 0.146; P = 0.001) and rehospitalization (Johns Hopkins ACG frailty indicator: NRI, 0.201; P < 0.001; HFRS: NRI, 0.141; P = 0.001). These improvements in NRI largely resulted from classification improvement among those who did not experience the event. With one-year mortality, there was a significant improvement in IDI (IDI, 0.003; P < 0.001) with the Johns Hopkins ACG frailty indicator. This improvement in performance resulted from an increase in the mean probability of the event among those with the event. Preoperative frailty assessment may add some predictive value for TAVI outcomes. Use of administrative database frailty instruments may provide small but significant improvements in case-mix adjustment when profiling hospitals for certain outcomes.

Prognostic factors in patients in the terminal phase of haematological malignancies who are receiving home medical care.

Although there are many prognostic models for patients in the terminal phase of solid tumours, a reliable prognostic scoring system in patients in the terminal phase of haematological malignancies (HM) has not been established. We retrospectively evaluated 180 patients in the terminal phase of HM who were receiving home medical care (HMC). Multivariate analyses revealed that clinician's estimate, consciousness, loss of appetite, dyspnoea, neutrophil count, lymphocyte count, and lactate dehydrogenase were associated with overall survival (OS). Based on this result, we developed a novel prognostic scoring system, the Japan palliative haematological oncology prognostic estimates, in which four risk groups were shown to clearly differ in survival (p < 0.001): a low-risk group (n=41, median OS of 434 days), an intermediate-low-risk group (n=80, median OS of 112 days), an intermediate-high-risk group (n=38, median OS of 31.5 days), and a high-risk group (n=21, median OS of 10 days). This is the first investigation of prognostic factors that influence the OS of patients in the terminal phase of HM who are receiving HMC. Providing patients with reliable information about their prognosis is important for them to consider how to spend their remaining life.

Prevalence and characteristics of unipolar mania in a low-income country setting: population-based data from the Butajira cohort, rural Ethiopia

PurposePrevious research suggests unipolar mania, i.e., bipolar disorder without depression, to be more common in low-income countries. However, longitudinal population-based studies on unipolar mania from low-income countries are lacking. This study therefore examined unipolar mania, in Butajira, Ethiopia, and associations with possible determinants.MethodsKey informants and 68,378 screenings with the Composite International Diagnostic Interviews (CIDI 2.1) identified suspected cases of bipolar disorder. Diagnosis was confirmed using the Schedules for Clinical Assessment in Neuropsychiatry (SCAN 2.1) (n = 2,285). 315 participants with bipolar disorder were recruited and followed up for an average of 2.5 years. Unipolar mania was defined when illness episodes consisted of at least two manic relapses. 240 cases had sufficient data to ascertain course of disorder.Results41.7% (100 of 240 cases) of participants had unipolar mania. Unipolar mania was associated with less suicidal ideation (0% vs. 26.4%, p < 0.001), less suicidal thoughts (occasionally/often: 1%/3% vs. 19.6%/21%, p < 0.001), and less history of suicide attempt (2% vs. 11.6%, p = 0.01). The participants with unipolar mania tended to have better social functioning (OR = 2.05, p = 0.07) and less alcohol use (20.8% vs. 31.4%, p = 0.07). The study was partly based on retrospective data liable to recall bias. Some cases defined as unipolar mania in our study may later develop depression.ConclusionPrevious cross-sectional studies finding high proportions of unipolar mania in low-income countries appear supported. Unipolar mania trended towards better social functioning and was associated with lower suicidality. Future unipolar mania specifications could inform treatment and prognostic estimates of bipolar disorder.

The essential changes of demographic processes in the Vologda Oblast

The demographic situation is one of markers of well-being of society. The purpose of the study was to establish main trends in demographic process in the Vologda Oblast. The Rosstat data on the Vologda Oblast for 2008-2019 were analyzed using analytical, parametric and non-parametric statistics, descriptive statistics and prognostic estimate, calculation of intensive and extensive indicators. It is demonstrated that demographic situation in the Vologda Oblast is characterized by decreasing of fertility coefficient (-20.0%) and increasing of negative natural growth of population. In rural areas, at low fertility, high mortality of population is registered and its related mostly to suicides. The relationship of random drowning with suicides and murders (R = +0.66 and R = +0.78, respectively), random alcohol poisoning with suicides (R = +0.57) and suicides with murders (R = +0.76) is established. The strong feedback between murder and random alcohol poisoning was revealed too.