Probability Of Death Research Articles

Air Pollution and Mortality in India: Investigating the Nexus of Ambient and Household Pollution Across Life Stages.

Air pollution in India is a foremost environmental risk factor that affects human health. This study first investigates the geographical distribution of ambient and household air pollution (HAP) and then examines the associated mortality risk. Data on fine particulate matter (PM2.5) concentration has been extracted from the Greenhouse Gas Air Pollution Interactions and Synergies (GAINS) model. HAP, mortality and socio-demographic data were extracted from the National Family and Health Survey-5, India, 2019-2021. Regression models were applied to see the difference in age-group mortality by different pollution parameters. The districts with PM2.5 concentration above the National Ambient Air Quality Standard (NAAQS) level of 40μg/m3 show a higher risk of neonatal (OR-1.86, CI 1.418-2.433), postneonatal (OR-2.04, CI 1.399-2.971), child (OR-2.19, CI 0.999-4.803) and adult death (OR-1.13, CI 1.060-1.208). The absence of a separate kitchen shows a higher probability of neonatal (OR: 1.18, CI 1.074-1.306) and adult death (OR-1.06, CI 1.027-1.088). The interaction between PM2.5 levels above NAAQS and HAP leads toa substantial rise in mortality observed for neonatal (OR 1.19 CI 1.051-1.337), child (OR 1.17 CI 1.054-1.289), and adult (OR 1.13 CI 1.096-1.168) age groups. This study advocates that there is a strong positive association between ambient and HAP and mortality risk. PM2.5 pollution significantly contributes to the mortality risk in all age groups. Children are more vulnerable to HAP than adults. In India, policymakers should focus on reducing the anthropogenic PM2.5 emission at least to reach the NAAQS, which can substantially reduce disease burden and, more precisely, mortality.

The HIV Care Cascade for Older Adults in Rural South Africa: A Longitudinal Cohort Study (2014-2019).

As people with HIV grow older, stable engagement in care is essential for healthy aging. We evaluate the HIV care cascade for older adults in rural South Africa at 2 time points cross-sectionally and assess movement in the cascade over time. We evaluated the cascade stage at waves 1 (2014-2015) and 2 (2018-2019) of Health and Aging in Africa: A Longitudinal Study of an INDPETH Community in South Africa, a population-based longitudinal cohort study in Mpumalanga Province, South Africa. Biomarker screening defined cascade stages [HIV+/no antiretroviral therapy (ART); ART+/unsuppressed viral load; ART+/suppressed viral load]. Between-wave probability of death, cascade progression, regression, cascade transitions, and sociodemographic predictors were assessed with Poisson regression. The impact of death was considered using the Fine and Gray competing risk model. We observed a higher prevalence of antiretroviral therapy with viral suppression over time (50% in wave 1 vs. 70% in wave 2). Among those alive, the oldest age group (70+ years old) was most likely to have cascade progression [adjusted risk ratio for treatment initiation vs. 40-49 years old: 1.38 (95% confidence interval: 1.02 to 1.86)]. However, there was a significant risk of death and cascade regression. Death between waves reached 40% for 70+-year-olds who were ART+/unsuppressed. In competing risk models, older age was associated with equivalent or less cascade progression. Older age groups who were unsuppressed on treatment and men had poorer cascade outcomes. Improvements observed in HIV treatment coverage over time for older adults must be interpreted in the context of the high risk of death for older HIV-positive adults, especially among those failing treatment.

Life Table Prediction Using the Lee-Carter Model

<span lang="EN-US">Mortality is the state where all signs of life permanently, which can occur at any time after a person is born. Mortality data can be presented in the form of table called a mortality table, which is a table that provides an overview of the life of a population group starting from births at the same time and life of a population group that starts from birth at the same time and slowly decreases due to death. In actuarial studies, this table has a role as one of the important factors to determine the amount of premium costs and premium reserves, especially life insurance. Therefore, it is necessary for continuity in conducting research related to mortality tables. This research aims to forecast the death rate and the probability of death of the population in the future using the Lee-Carter model, which is a mortality forecasting model that combines a demographic model with a time series model. This mortality model shows that the logarithm of mortality rate is the sum of the parameters of the average general mortality rate by age and the multiplication of the trend parameter of mortality rate changes by age with the mortality index parameter. Forecasting process begins with estimating the parameters of the average mortality rate and the trend of mortality rate changes influenced by the mortality index parameter using the singular value decomposition (SVD) method. After that, the mortality index mortality index is forecasted using the ARIMA model and the results of this forecast are then reinserted into the Lee-Carter model to obtain death rate prediction. Based on the results of the mortality rate prediction, it can then be the prediction of the probability of death for the mortality table. The result of this research is a mortality table that contains predictions of the probability of death of the Indonesian population for both male and female gender using the Lee-Carter model from the year 2022 2026. Based on these results, it is concluded that the value of the probability of death for each year increases with increasing age.</span>

Impact of acute kidney injury response on survival and liver transplant rates in hospitalized patients with cirrhosis awaiting liver transplantation: Results from the HRS-HARMONY consortium.

Acute kidney injury (AKI) frequently complicates the course of hospitalized patients with cirrhosis and negatively affects their prognosis. How AKI response influences the timing of liver transplantation (LT) remains unclear. We sought to assess the impact of AKI response to treatment on survival and LT rates in cirrhosis patients awaiting LT. This was a retrospective multicenter study of cirrhosis patients waitlisted for LT and hospitalized with AKI in 2019. The exposure was AKI response versus no response during hospitalization. Outcomes were 90-day overall and transplant-free survival, and rates of LT with time to transplant. We adjusted for age, sex, race, cirrhosis etiology, site, and MELD-Na score. Among the 317 patients in this study, 170 had AKI response (53.6%), and 147 had no response (46.4%). Compared to non-responders, responders had better 90-day overall survival (89.4% vs. 76.2%, adjusted sHR for mortality 0.34, p=0.001), and transplant-free survival (63.5% vs. 25.2%, aHR for probability of death or transplant 0.35, p<0.001). The LT rate was lower in responders (45.9% vs. 61.2%, adjusted sHR 0.55, p=0.005). 79% of transplants in responders occurred after discharge, at a median of 103 days, while 62% of transplants in non-responders occurred during hospitalization, with the remainder occurring post-discharge at a median of 58 days. In patients with cirrhosis waitlisted for LT who are hospitalized with AKI, AKI response to therapy is associated with improved 90-day survival, despite a reduced LT rate and longer time to LT.

Comparison of Mortality Transition in China and India, 1950–2021

This paper compares mortality transition in China and India in the period 1950–2021, highlighting similarities and differences. Mortality transition has been inconsistent in both countries, but differences remain. In China, the transition has been spread evenly across the age range, while in India, it has primarily been confined to younger ages, being markedly slow in ages 35–90. This difference in the older ages appears to explain the main difference between the respective mortality transition in the two countries. To address its ongoing mortality transition, the paper concludes, India needs to reinvigorate its health-care delivery system to meet the health care needs of the old people. The paper also emphasises using geometric mean of the age-specific probabilities of death as an appropriate measure to analyse mortality transition.

Usefulness of Dynamic Assessment of Clinical and Laboratory Factors in Severe Acute Pancreatitis.

Background/Objectives: Early identification of patients at risk of developing severe acute pancreatitis (SAP) is still an issue. Dynamic assessment of clinical and laboratory parameters within the first 48 h of admission may offer valuable insights into the prediction of unfavorable outcomes such as SAP and death. Methods: A prospective observational study was conducted on a cohort of patients admitted for AP at a tertiary referral hospital. Clinical and laboratory data were collected on admission and at 48 h. Patients were classified based on the Revised Atlanta classification. Logistic regression analysis was performed to identify independent risk factors for SAP. Likelihood ratios and post-test probabilities were calculated to assess the clinical usefulness of predictive markers. Results: 227 patients were included, with biliary etiology being the most common and a prevalence of SAP and death of 10.7% and 5.7%, respectively. BISAP ≥ 2 on admission, presence of SIRS after 48 h, rise in heart rate over 20 bpm, and any increase in BUN after 48 h were independent risk factors for SAP. The combination of these factors increased the post-test probability of SAP and death, with BISAP ≥ 2 combined with the presence of SIRS after 48 h showing the highest probability (82% and 73%, respectively). Conclusions: Dynamic assessment of BUN, heart rate, and SIRS within the first 48 h of admission can aid in predicting the development of SAP and death in patients with AP. These findings underscore the importance of continuous monitoring, although multicenter studies are warranted to refine predictive models for SAP.

Generating Mortality Rate Intensity for Life Insurance Applications through Novel Method of Successive Differencing Under the Parsimonious Generalised Makeham’s Framework

Developing and implementing age dependent mortality rates in functional forms have presented critical modelling problems for life insurance and annuity firms thereby creating serious research gaps. A recurring problem with the existing life tables is that their underlying generating functions are not capable of showing any evidence that mortality at 10 declines. This observed irregularity in the age pattern of mortality is the prime motivation in the search for analytical functions which fully capture the observed variations of mortality with age. To fill the gaps identified, the study aims to develop specific life table function under the Generalised Makeham’s framework. The objectives of this study are to (i) compute the mortality rate intensities (ii) compute the curve of deaths densities (iii) compute the probability of deaths and then compare the common domain of definition of these measures. From the method of successive differencing employed to model, the male ageing parameter value fall within the globally accepted interval for the. This method is superior to the method of maximum likelihood estimation which mostly violates the permissible interval of validity. Computational evidence from our analysis proves that under the Generalised Makeham’s law, the mortality rate intensity declines at .

Substitution of Methods in Suicide Deaths - Firearm Injury and Hanging.

Background: Prevention strategies for suicide emphasize restrictions on firearm access. These restrictions may lose efficacy if individuals substitute other lethal suicide methods. Aims: The objective of this study is to determine the extent to which individuals who die by suicide in the United States substitute hanging for firearm injury. Methods: This study is a repeated cross-sectional analysis of suicide deaths in the United States from 2003 to 2021. Multiple regression was used to estimate the effect of firearm access proxies on individual suicide methods (hanging vs. firearm injury). Results: The probability of death by hanging was significantly and negatively associated with proxies of firearm access. Limitations: This study does not compare crude rates of suicide by state, which rise on average with rates of firearm ownership. The National Violent Death Reporting System expanded over the sample period, so early years have incomplete ascertainment. Rates of gun ownership and gun safety law scales are measured as proxies. Conclusions: Although means restriction around firearm access is a critical tool for suicide prevention, complementary strategies for prevention around hanging merit further study.

Invited Commentary: Influence of Incomplete Death Information on Cumulative Risk Estimates.

Censoring at death is the only feasible option if death is not recorded and individuals who died simply no longer contribute visits, such as in the setting of Barberio et al (Am J Epidemiol. 2024) before they acquired access to mortality information. Censoring at death is known to lead to biased estimates of the probability of the event of interest before time $t$. Barberio et al show through simulations that this bias increases with increasing mortality. However, when analyzing claims data it is often important to not exclude individuals with shorter life expectancies: an important strength of observational studies is that they allow estimating treatment effects in more varied populations than typically included in randomized clinical trials. We derive an analytic expression for the bias, and provide two upper bounds for the bias. The bounds inform the usefulness of obtaining mortality information. If the probability of death before the event is known to be small, wider confidence intervals can be created using the first bound on the bias; we provide an algorithm. If the bias is large, obtaining mortality information is important. Barberio et al show that obtaining mortality information can be essential in practice.

Worldwide application and valuation of extracorporeal membrane oxygenation support during the COVID-19 pandemic (WAVES).

The outcomes of COVID-19 patients on venovenous extracorporeal membrane oxygenation (VV-ECMO) varied. We aim to investigate the variability concerning location and timeframe. We conducted a retrospective analysis of data from 351 institutions in 53 countries. The primary outcome was survival to hospital discharge or death up to 90 days from ECMO start. The associations between calendar time (month and year) of ECMO initiation and the primary outcome were examined by Cox regression modeling. Multivariable survival analyses were adjusted for the time of ECMO start, age, body mass index, APACHE II, SOFA, and the duration of mechanical ventilation before ECMO. 1060 adult COVID-19 patients enrolled in the COVID-19 Critical Care Consortium (COVID Critical) international registry and required VV-ECMO support. The study period is from January 2020 to December 2021. The median age was 51 years old, and 70% were male patients. Most patients were from Europe (39.3%) and North America (37.4%). The in-hospital mortality of the entire cohort was 47.12%. In North America and Europe, there was an increased probability of death from May 2020 through February 2021. Latin America showed a steady rate of survival until late in the study. South Asia, the Middle East, and Africa showed an increased chance of mortality around May 2020. In the Asian-Pacific region, after February 2021, there was an increased probability of death. The time of ECMO initiation and advanced patient age were associated with increased mortality. Variability in the outcomes of COVID-19 patients on VV-ECMO existed within different regions. This variability reflects the differences in resources, policies, patient selection, management, and possibly COVID-19 virus subtypes. Our findings might help guide global response in the future by early adoption of patient selection protocols, worldwide policies, and delivery of resources.

Associations of hypertension and antenatal care-seeking with perinatal mortality: A nested case-control study in rural Bangladesh.

Maternal hypertension may be an underrecognized but important risk factor for perinatal death in low resource settings. We investigated the association of maternal hypertension and perinatal mortality in rural Bangladesh. This nested, matched case-control study used data from a 2019 cross-sectional survey and demographic surveillance database in Baliakandi, Bangladesh. We randomly matched each pregnancy ending in perinatal death with five pregnancies in which the neonate survived beyond seven days based on maternal age, education, and wealth quintile. We estimated associations of antenatal care-seeking and self-reported hypertension with perinatal mortality using conditional logistic regression and used median and interquartile ranges to assess the mediation of antenatal care by timing or frequency. Among 191 cases and 934 matched controls, hypertension prevalence was 14.1% among cases and 7.7% among controls. Compared with no diagnosis, the probability of perinatal death was significantly higher among women with a pre-gestational hypertension diagnosis (OR 2.90, 95% CI 1.29, 6.57), but not among women with diagnosis during pregnancy (OR 1.68, 95% CI 0.98, 2.98). We found no association between the number of antenatal care contacts and perinatal death (p = 0.66). Among women with pre-gestational hypertension who experienced a perinatal death, 78% had their first antenatal contact in the sixth or seventh month of gestation. Hypertension was more common among rural women who experience a perinatal death. Greater effort to prevent hypertension prior to conception and provide early maternity care to women with hypertension could improve perinatal outcomes in rural Bangladesh.

Cervicocerebral Atherosclerosis Burden Increases Long-Term Mortality in Patients With Ischemic Stroke or Transient Ischemic Attack.

The influence of burden of atherosclerosis in the brain supplying arteries on mortality in patients with acute ischemic stroke or transient ischemic attack is poorly known. We assessed whether total burden of atherosclerosis within cervicocerebral arteries is associated with long-term mortality. A total of 406 patients (median age, 71.8 years; 57.9% male) with acute ischemic stroke or transient ischemic attack were included and their cervicocerebral arteries imaged with computed tomography angiography. The presence of atherosclerotic findings was scored for 25 artery segments and points were summed as a Cervicocerebral Atherosclerosis Burden (CAB) score, analyzed as quartiles. Data on all-cause mortality came from Statistics Finland. After a median follow-up of 7.3 years, 147 (33.5%) patients had died. Compared with surviving patients, those who died had a higher median CAB score (5, interquartile range 2-10 versus 11, 7-16; P<0.001). Cumulative mortality increased from 8.9% (95% CI, 7.0-10.8) in the lowest to 61.4% (95% CI, 55.4-67.4) in the highest quartile of CAB score. Adjusted for demographics, cardiovascular risk factors, secondary preventive medication, and admission National Institute of Health Stroke Scale score, every CAB score point increased probability of death by 3%. Analyzed in quartiles, the highest CAB quartile was associated with a 2.5-fold likelihood of all-cause mortality. The main findings of our study were the increasing mortality with the total burden of computed tomography angiography-defined atherosclerosis in the brain supplying arteries after ischemic stroke or transient ischemic attack and that the CAB score-integrating this pathology-independently increased all-cause mortality.

Functional Assessment of >18 Years Old Patients at Internal Medicine Ward—Relationship with In-Hospital and 30-Day Mortality

ObjectiveTo evaluate the connection between the items included in the AVD-DezIs score (a questionnaire about basic and instrumental activities of daily living and other topics related to social and personal life) and in-hospital and 30-day mortality after discharge. MethodsProspective cohort study of hospitalizations in the Internal Medicine ward from 2014 to 2020, including >18 years old patients with a fully completed AVD-DezIs. To identify in-hospital and 30 days mortality, univariate and multivariate logistic models were applied, including random effects if justified. ResultsA total of 19,771 episodes of hospitalization were included. In the univariate analysis, except for the presence of isolation and financial insufficiency, all the items were predictors of mortality in-hospital or within 30 days after discharge. In multivariate analysis, older age, male sex, longer hospital stay, higher Charlson score, deficiency in all four activities of daily living, deficiency in meal preparation and housekeeping, presence of pain/depression, immobility, and malnutrition are associated with a higher probability of in-hospital death whereas older age, male gender, higher Charlson score, longer length of hospital stay, deficiency in personal hygiene, ambulation, and eating habits, as well as the presence of incontinence and malnutrition, are associated with a higher probability of 30 days after discharge death. Discussion/ConclusionExcept for isolation and financial insufficiency, all items were individually associated with the outcomes. When they are considered in conjunction and taking into account sex, age, comorbidities and length of stay, the predictive ability of in-hospital and 30 days mortality differed.

Logistic and CSG Growth Models for Predicting Life Expectancy

The tables that allow calculating the probability of death at a certain age by recording the number of births/deaths in a population are called life tables. The concept of life expectancy, which is a measure that determines how long a creature will live, is also determined by mortality rates obtained from life tables. It is also possible to model the expected lifetime with some nonlinear mathematical functions. One of the functions that is often used in modeling mortality rates is the logistic growth function. The aim of this study is to propose a model that can be used as an alternative to the logistic growth model and to interpret the mortality rates of countries. In this study, the life expectancy of males and females in Turkey, Singapore, Norway, and China was modeled using the logistic and the CSG growth model, which was newly introduced to the literature. When modeling the life expectancy of countries, adjusted graph was drawn in accordance with the data of each growth model. Then, the performances of the logistic growth model and the CSG growth model were compared with R^2, RMSE, and MAPE statistical criteria. As a result of the comparison, it was revealed that the CSG growth model is more suitable than the logistic model for estimating life expectancy for overall data and for each gender. The originality of this study is the CSG model which is very new nonlinear model that predicts life expectancy effectively for related datasets.

Enfermedades cardiovasculares y mortalidad hospitalaria por COVID-19 en establecimientos de salud del departamento de Cajamarca, año 2020

Introduction. Cardiovascular diseases are one of the most frequently reported comorbidities in COVID-19 cases. Objective. To evaluate the relationship between the presence of cardiovascular risk diseases and death from COVID-19 in a cohort of hospitalized patients in the Cajamarca Region, during the year 2020. Methods. Retrospective cohort of 576 patients hospitalized for COVID-19 between July and December 2020, from four hospitals in the Cajamarca Region. A survival analysis was carried out using Cox regression to establish the relationship between death from COVID-19 and the presence of arterial hypertension (HTA), type 2 diabetes, obesity, and multimorbidity, using models adjusted for sociodemographic and clinical factors seeking to identify possible effect modifying variables. Results. The mortality from COVID-19 was 45.1%. In the bivariate analysis, the history of HTA was the only cardiovascular disease that was related to a higher probability of death from COVID-19 (HR: 1.43; 95% CI: 1.12-1.85). Two subgroup evaluations were developed:a) by age group and b) by oxygen saturation on admission (SatO2). An association was found between death from COVID-19 and DM2 in those under 60 years of age (HR: 4.56; 95% CI: 2.32-8.95); as well as with HTA in patients with SatO2 less than 80% on admission (HR: 1.67; 95% CI: 1.04-2.69). Conclusion. A relationship was found between death from COVID-19 and cardiovascular diseases such as HTA or type 2 diabetes, in specific subgroups (patients with SatO2 less than 80% on admission or age less than 60 years respectively) of patients who were hospitalized in health establishments of the Cajamarca Region.

Multi Organ Failure (MOF) and a Promising Novel Device

Acutely ill hospitalized patients are at risk of cascading failure of vital organs leading to prolonged stay in the hospital, persistent dysfunction of vital organs and higher probability of death. It is estimated that 50 to 75% of ICU patients have failure of one or more vital organs and as more organs are involved the risk of death increases [1, 2]. For example in Acute on Chronic Liver Failure (AOCLF) the 28 day probability of death increases from 20% with one vital organ failure to 90% or higher if 3 or more organs are involved. Dysfunction of brain, lungs, heart and kidneys are most commonly seen, kidney involvement is usually poor prognostic sign.

Старение и надежность: как вариации во времени интенсивности отказов влияют на форму кривых дожития стареющих организмов

The article considers the regularities of the theory of reliability, which determine the constancy of the shape of the curves of organisms’ survival. The assumption that the probability of natural death is determined by the probability of accidental failure of a part of a series of homogeneous elements (cells) allows to obtain, analytically and through numerical experiments, survival curves similar to those observed in nature. The use of com-puter modelling methods makes it possible to identify the survival curve parameters determined by the dynamics of the probability of failure of an individual element. The value of one of these parameters, namely the coordinates of the inflection point of human survival curves, indicates that, apparently, both in long-lived species in general and in humans specifically, the ageing promoting mechanism is the progressive deterioration of reliability - the dilapidation of the elements (cells) crucial for ageing.

Generating Complex Explanations for Artificial Intelligence Models: An Application to Clinical Data on Severe Mental Illness.

We present an explainable artificial intelligence methodology for predicting mortality in patients. We combine clinical data from an electronic patient healthcare record system with factors relevant for severe mental illness and then apply machine learning. The machine learning model is used to predict mortality in patients with severe mental illness. Our methodology uses class-contrastive reasoning. We show how machine learning scientists can use class-contrastive reasoning to generate complex explanations that explain machine model predictions and data. An example of a complex class-contrastive explanation is the following: "The patient is predicted to have a low probability of death because the patient has self-harmed before, and was at some point on medications such as first-generation and second-generation antipsychotics. There are 11 other patients with these characteristics. If the patient did not have these characteristics, the prediction would be different". This can be used to generate new hypotheses, which can be tested in follow-up studies. Diuretics seemed to be associated with a lower probability of mortality (as predicted by the machine learning model) in a group of patients with cardiovascular disease. The combination of delirium and dementia in Alzheimer's disease may also predispose some patients towards a higher probability of predicted mortality. Our technique can be employed to create intricate explanations from healthcare data and possibly other areas where explainability is important. We hope this will be a step towards explainable AI in personalized medicine.

Deep learning model for the prediction of all-cause mortality among long term care people in China: a prospective cohort study

This study aimed to develop a deep learning model to predict the risk stratification of all-cause death for older people with disability, providing guidance for long-term care plans. Based on the government-led long-term care insurance program in a pilot city of China from 2017 and followed up to 2021, the study included 42,353 disabled adults aged over 65, with 25,071 assigned to the training set and 17,282 to the validation set. The administrative data (including baseline characteristics, underlying medical conditions, and all-cause mortality) were collected to develop a deep learning model by least absolute shrinkage and selection operator. After a median follow-up time of 14 months, 17,565 (41.5%) deaths were recorded. Thirty predictors were identified and included in the final models for disability-related deaths. Physical disability (mobility, incontinence, feeding), adverse events (pressure ulcers and falls from bed), and cancer were related to poor prognosis. A total of 10,127, 25,140 and 7086 individuals were classified into low-, medium-, and high-risk groups, with actual risk probabilities of death of 9.5%, 45.8%, and 85.5%, respectively. This deep learning model could facilitate the prevention of risk factors and provide guidance for long-term care model planning based on risk stratification.

Integrated analysis of histone modification features in clear cell renal cancer for risk stratification and therapeutic prediction

Clear cell renal cell carcinoma (ccRCC) is a common urological malignancy that is involved in tumor genesis and development. However, few studies have focused on the predictive role of the global histone modification status in ccRCC. A total of 621 patients with complete transcript information and corresponding clinical profiles were obtained from TCGA-KIRC, GSE22541, and EMTAB3267 cohorts. A total of 122 histone modification relevant pathways were derived from MSigDB, and their activation status was quantified using GSVA. Differentially expressed genes (DEGs) were identified and filtrated using univariate Cox regression analysis. The signature was built relied on the least absolute shrinkage and selection operator (LASSO) regression analysis, and evaluated from survival difference, chemotherapy response, and activated pathways. A novel nomogram was established to quantify the probability of death in different patients. Seven risky and fifty-eight protective genes were used in LASSO analysis, and six genes were used to build the histone modification gene (HiMG) signature, which showed significant independent prognostic potential in all three cohorts. The nomogram showed acceptable incremental predictions. CKS2 (p = 0.004) and PD1 (p = 0.002) expression were significantly higher in grade 3 ccRCC than in grades 1–2. CKS2 siRNA in renal cancer cells caused reductions in cellular proliferation, migration, and invasion. Patients with low HiMG may be potential responders to rapamycin, erlotinib and FH535, while AZD6482 and CHIR-99,021 may be more suitable for patients with high HiMG levels. ccRCC histone modification distribution and a clinical signature for prognosis prediction, clinical decision making, and molecular mechanism exploration, were established for risk stratification and personalized treatments.