In the following maps, the U.S. states are divided into groups based on the rates at which people developed or died from cancer in 2013, the most recent year for which incidence data are available.

The rates are the numbers out of 100,000 people who developed or died from cancer each year.

Incidence Rates by State The number of people who get cancer is called cancer incidence. In the United States, the rate of getting cancer varies from state to state.

*Rates are per 100,000 and are age-adjusted to the 2000 U.S. standard population.

‡Rates are not shown if the state did not meet USCS publication criteria or if the state did not submit data to CDC.

†Source: U.S. Cancer Statistics Working Group. United States Cancer Statistics: 1999–2013 Incidence and Mortality Web-based Report. Atlanta (GA): Department of Health and Human Services, Centers for Disease Control and Prevention, and National Cancer Institute; 2016. Available at: http://www.cdc.gov/uscs.

Death Rates by State Rates of dying from cancer also vary from state to state.

*Rates are per 100,000 and are age-adjusted to the 2000 U.S. standard population.

†Source: U.S. Cancer Statistics Working Group. United States Cancer Statistics: 1999–2013 Incidence and Mortality Web-based Report. Atlanta (GA): Department of Health and Human Services, Centers for Disease Control and Prevention, and National Cancer Institute; 2016. Available at: http://www.cdc.gov/uscs.

Source: https://www.cdc.gov/cancer/dcpc/data/state.htm

Mortality Rates for Lake County, Illinois. Explanation of field attributes: Average Age of Death – The average age at which a people in the given zip code die. Cancer Deaths – Cancer deaths refers to individuals who have died of cancer as the underlying cause. This is a rate per 100,000. Heart Disease Related Deaths – Heart Disease Related Deaths refers to individuals who have died of heart disease as the underlying cause. This is a rate per 100,000. COPD Related Deaths – COPD Related Deaths refers to individuals who have died of chronic obstructive pulmonary disease (COPD) as the underlying cause. This is a rate per 100,000.

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The "Cancer and Deaths Dataset: 1990~2019 Globally" is a comprehensive dataset containing information on cancer incidence and mortality rates across the world from 1990 to 2019.

The dataset is an excellent resource for researchers, healthcare professionals, and policymakers who are interested in understanding the global burden of cancer and its impact on populations.

[ Total 9 file , 160 columns, All the countries, 30Years data ]

>In 2017, 9.6 million people are estimated to have died from the various forms of cancer. Every sixth death in the world is due to cancer, making it the second leading cause of death – second only to cardiovascular diseases.1

Progress against many other causes of deaths and demographic drivers of increasing population size, life expectancy and — particularly in higher-income countries — aging populations mean that the total number of cancer deaths continues to increase. This is a very personal topic to many: nearly everyone knows or has lost someone dear to them from this collection of diseases.

## Data vastness of this dataset: 01. annual-number-of-deaths-by-cause data. 02. total-cancer-deaths-by-type data. 03. cancer-death-rates-by-age data. 04. share-of-population-with-cancer-types data. 05. share-of-population-with-cancer data. 06. number-of-people-with-cancer-by-age data. 07. share-of-population-with-cancer-by-age data. 08. disease-burden-rates-by-cancer-types data. 09. cancer-deaths-rate-and-age-standardized-rate-index data.

Rank, number of deaths, percentage of deaths, and age-specific mortality rates for the leading causes of death, by age group and sex, 2000 to most recent year.

Context

COVID-19 Cases and Deaths by Race/Ethnicity

Content

COVID-19 cases and associated deaths that have been reported among Connecticut residents, broken down by race and ethnicity. All data in this report are preliminary; data for previous dates will be updated as new reports are received and data errors are corrected. Deaths reported to either the Office of the Chief Medical Examiner (OCME) or Department of Public Health (DPH) are included in the COVID-19 update.

The following data show the number of COVID-19 cases and associated deaths per 100,000 population by race and ethnicity. Crude rates represent the total cases or deaths per 100,000 people. Age-adjusted rates consider the age of the person at diagnosis or death when estimating the rate and use a standardized population to provide a fair comparison between population groups with different age distributions. Age-adjustment is important in Connecticut as the median age of among the non-Hispanic white population is 47 years, whereas it is 34 years among non-Hispanic blacks, and 29 years among Hispanics. Because most non-Hispanic white residents who died were over 75 years of age, the age-adjusted rates are lower than the unadjusted rates. In contrast, Hispanic residents who died tend to be younger than 75 years of age which results in higher age-adjusted rates.

The age-adjusted rates are directly standardized using the 2018 ASRH Connecticut population estimate denominators (available here: https://portal.ct.gov/DPH/Health-Information-Systems--Reporting/Population/Annual-State--County-Population-with-Demographics).

Rates are standardized to the 2000 US Millions Standard population (data available here: https://seer.cancer.gov/stdpopulations/). Standardization was done using 19 age groups (0, 1-4, 5-9, 10-14, ..., 80-84, 85 years and older). More information about direct standardization for age adjustment is available here: https://www.cdc.gov/nchs/data/statnt/statnt06rv.pdf Categories are mutually exclusive. The category “multiracial” includes people who answered ‘yes’ to more than one race category. Counts may not add up to total case counts as data on race and ethnicity may be missing. Age-adjusted rates calculated only for groups with more than 20 deaths. Abbreviation: NH=Non-Hispanic.

This dataset will be updated on a daily basis. Data are subject to future revision as reporting changes.

Starting in July 2020, this dataset will be updated every weekday.

Additional notes: A delay in the data pull schedule occurred on 06/23/2020. Data from 06/22/2020 was processed on 06/23/2020 at 3:30 PM. The normal data cycle resumed with the data for 06/23/2020.

A network outage on 05/19/2020 resulted in a change in the data pull schedule. Data from 5/19/2020 was processed on 05/20/2020 at 12:00 PM. Data from 5/20/2020 was processed on 5/20/2020 8:30 PM. The normal data cycle resumed on 05/20/2020 with the 8:30 PM data pull. As a result of the network outage, the timestamp on the datasets on the Open Data Portal differs from the timestamp in DPH's daily PDF reports.

Acknowledgements

Thanks to catalog.data.gov.

Smoking is so common, and feels so familiar, that it can be hard to grasp just how large the impact is. Every year, around 8 million people die prematurely as a result of smoking.1 This means that about one in seven deaths worldwide are due to smoking.2 Millions more live in poor health because of it.

Smoking primarily contributes to early deaths through heart diseases and cancers. Globally, more than one in five cancer deaths are attributed to smoking.

This means tobacco kills more people every day than terrorism kills in a year.

Smoking is a particularly large problem in high-income countries. There, cigarette smoking is the most important cause of preventable disease and death. This is especially true for men: they account for almost three-quarters of deaths from smoking.

The impact of smoking is devastating on the individual level. In case you need some motivation to stop smoking: The life expectancy of those who smoke regularly is about 10 years lower than that of non-smokers.

It’s also devastating on the aggregate level. In the past 30 years more than 200 million have died from smoking. Looking into the future, epidemiologists Prabhat Jha and Richard Peto estimate that “If current smoking patterns persist, tobacco will kill about 1 billion people this century.”

It is on us to prevent this.

Context

Coronavirus disease (COVID-19) is an infectious disease caused by a newly discovered coronavirus. Most people infected with COVID-19 virus will experience mild to moderate respiratory illness and recover without requiring special treatment. Older people, and those with underlying medical problems like cardiovascular disease, diabetes, chronic respiratory disease, and cancer are more likely to develop serious illness. During the entire course of the pandemic, one of the main problems that healthcare providers have faced is the shortage of medical resources and a proper plan to efficiently distribute them. In these tough times, being able to predict what kind of resource an individual might require at the time of being tested positive or even before that will be of immense help to the authorities as they would be able to procure and arrange for the resources necessary to save the life of that patient.

The main goal of this project is to build a machine learning model that, given a Covid-19 patient's current symptom, status, and medical history, will predict whether the patient is in high risk or not.

content

The dataset was provided by the Mexican government (link). This dataset contains an enormous number of anonymized patient-related information including pre-conditions. The raw dataset consists of 21 unique features and 1,048,576 unique patients. In the Boolean features, 1 means "yes" and 2 means "no". values as 97 and 99 are missing data.

• sex: 1 for female and 2 for male.
• age: of the patient.
• classification: covid test findings. Values 1-3 mean that the patient was diagnosed with covid in different degrees. 4 or higher means that the patient is not a carrier of covid or that the test is inconclusive.
• patient type: type of care the patient received in the unit. 1 for returned home and 2 for hospitalization.
• pneumonia: whether the patient already have air sacs inflammation or not.
• pregnancy: whether the patient is pregnant or not.
• diabetes: whether the patient has diabetes or not.
• copd: Indicates whether the patient has Chronic obstructive pulmonary disease or not.
• asthma: whether the patient has asthma or not.
• inmsupr: whether the patient is immunosuppressed or not.
• hypertension: whether the patient has hypertension or not.
• cardiovascular: whether the patient has heart or blood vessels related disease.
• renal chronic: whether the patient has chronic renal disease or not.
• other disease: whether the patient has other disease or not.
• obesity: whether the patient is obese or not.
• tobacco: whether the patient is a tobacco user.
• usmr: Indicates whether the patient treated medical units of the first, second or third level.
• medical unit: type of institution of the National Health System that provided the care.
• intubed: whether the patient was connected to the ventilator.
• icu: Indicates whether the patient had been admitted to an Intensive Care Unit.
• date died: If the patient died indicate the date of death, and 9999-99-99 otherwise.

Age-standardised proportion of adults (16+) who met the recommended guidelines of consuming five or more portions of fruit and vegetables a day by gender. To help reduce the risk of deaths from chronic diseases such as heart disease, stroke, and cancer. The Five-a-day programme was introduced to increase fruit and vegetable consumption within the general population. Its central message is that people should eat at least five portions of fruit and vegetables a day; that a variety of fruit and vegetables should be consumed and that fresh, frozen, canned and dried fruit, vegetables and pulses all count in making up these portions. The programme includes educational initiatives to increase awareness of the Five-a-day message and the benefits of fruit and vegetable consumption, along with more direct schemes to increase access to fruit and vegetables, such as the school fruit scheme and community initiatives. Monitoring of fruit and vegetable consumption is key to evaluating the success of the policy, both at the level of individual schemes and at a more general level. The England average, at the 95% confidence level (LCL = lower confidence interval; UCL = upper confidence interval). Related to: National Indicator Library - NHS England Digital (editor note: was https://indicators.ic.nhs.uk/webview/)

Overview This dataset contains historical daily prices for Pfizer which is largest pharmaceutical company, headquartered at United States. Pfizer primarily develops medication to treat various diseases including cancer.

It contains stock prices from 15th May 2019 till present date (5 years data).

Data Structure:

1) Date - specifies the trading date 2) Open - opening price for that date 3) High - maximum price during the day 4) Low - minimum price during the day 5) Close - close price during the day 6) Adj Close - close price adjusted for both dividends and splits 6) Volume - the number of shares transaction during a given day

Kindly use the dataset for exploring the data and analyzing.

The National Cancer Registration and Analysis Service (NCRAS) at Public Health England supplies cancer registration data to NHS Digital. This data is available to be linked to other data held by NHS Digital in order to provide notifications on an individual's cancer status, be available to support research studies and to identify potential research participants for clinical trials.

NCRAS is the population-based cancer registry for England. It collects, quality assures and analyses data on all people living in England who are diagnosed with malignant and pre-malignant neoplasms, with national coverage since 1971.

The Cancer Registration dataset comprises England data to the present day, and Welsh data up to April 2017.

Timescales for dissemination of agreed data can be found under 'Our Service Levels' at the following link: https://digital.nhs.uk/services/data-access-request-service-dars/data-access-request-service-dars-process Standard response

Although the link between sugar-sweetened beverages (SSB) and pancreatic cancer has been suggested for its insulin-stimulating connection, most epidemiological studies showed inconclusive relationship. Whether the result was limited by sample size is explored. This prospective study followed 491,929 adults, consisting of 235,427 men and 256,502 women (mean age: 39.9, standard deviation: 13.2), from a health surveillance program and there were 523 pancreatic cancer deaths between 1994 and 2017. The individual identification numbers of the cohort were matched with the National Death file for mortality, and Cox models were used to assess the risk. The amount of SSB intake was recorded based on the average consumption in the month before interview by a structured questionnaire. We classified the amount of SSB intake into 4 categories: 0–<0.5 serving/day, ≥0.5–<1 serving per day, ≥1–<2 servings per day, and ≥2 servings per day. One serving was defined as equivalent to 12 oz and contained 35 g added sugar. We used the age and the variables at cohort enrolment as the reported risks of pancreatic cancers. The cohort was divided into 3 age groups, 20–39, 40–59, and ≥60. We found young people (age <40) had higher prevalence and frequency of sugar-sweetened beverages than the elderly. Those consuming 2 servings/day had a 50% increase in pancreatic cancer mortality (HR = 1.55, 95% CI: 1.08–2.24) for the total cohort, but a 3-fold increase (HR: 3.09, 95% CI: 1.44–6.62) for the young. The risk started at 1 serving every other day, with a dose–response relationship. The association of SSB intake of ≥2 servings/day with pancreatic cancer mortality among the total cohort remained significant after excluding those who smoke or have diabetes (HR: 2.12, 97% CI: 1.26–3.57), are obese (HR: 1.57, 95% CI: 1.08–2.30), have hypertension (HR: 1.90, 95% CI: 1.20–3.00), or excluding who died within 3 years after enrollment (HR: 1.67, 95% CI: 1.15–2.45). Risks remained in the sensitivity analyses, implying its independent nature. We concluded that frequent drinking of SSB increased pancreatic cancer in adults, with highest risk among young people.

Context

Delhi is the capital of India and Second most populous city of the country. This is city situated in northern part of country and home of nearly 2 million people. Delhi is AQI is consider as hazardous category, this city suffering from poor AQI. Now poor air quality impact people health and daily activities. Almost 25000 people died because of poor air quality, people suffering from asthma, lung cancer and many disease. Government of India putting great efforts to tackle this problem, Let's show analysis skill that help agencies to understand the data better ways.

Data Content Hourly data for last one year (1-sep-2021 to 1-sep-2022). dataset contains various parameters of AQI such PM 2.5, PM 10, CO, CO2 etc. This data collected on IGI Delhi center.

Acknowledgement I would like to thank Central Control Room for Air Quality Management - All India for providing data & They are actual owner. Please provide reference in case of any external usage.

Image credit Unsplash by amir hosseini

Inspiration 1. Understand the trend, seasons other factor in given time series. 2. Which parameter of AQI is more dangerous 3. can we forecast the AQI parameter in advance so that agencies can prepare accordingly.

Let's do analysis and find the insights.

This dataset reports the rate at which people under the age of 75 years have died from disease of the liver, respiratory system, cardiovascular system and/or cancer. The mortality rates are directly standardized by age and sex to the England population.

BackgroundPalliative endobiliary drainage is the mainstay treatment for unresectable malignant biliary obstruction (MBO). Despite optimal drainage, the survival benefit is arguable. This study aimed to identify factors predicting post-endoscopic drainage mortality and develop and validate a mortality prediction model.MethodsWe retrospectively analyzed data for 451 patients with unresectable pancreatobiliary cancers undergoing first endoscopic retrograde cholangiopancreatography (ERCP)-guided endobiliary stent placement between 2007 and 2017. We randomly assigned patients in a 3:1 fashion into a derivation cohort (n=339) and validation cohort (n=112). Predictors for 90-day mortality post-stenting were identified from the derivation cohort. A prediction model was subsequently developed and verified with the validation cohort.ResultsThe overall 90-day mortality rate of the derivation cohort was 46.9%, and the mean age was 64.2 years. The 2 most common diagnoses were cholangiocarcinoma (53.4%) and pancreatic cancer (35.4%). In all, 34.2% had liver metastasis. The median total bilirubin (TB) level was 19.2 mg/dL, and the mean serum albumin was 3.2 g/dL. A metallic stent was used for 64.6% of the patients, and the median stent patency time was 63 days. A total of 70.8% had TB improvement of more than 50% within 2 weeks after stenting, and 14.5% were eligible for chemotherapy. Intrahepatic obstruction (OR=5.69; P=0.023), stage IV cancer (OR=3.01; P=0.001), pre-endoscopic serum albumin (OR=0.48; P=0.001), TB improvement within 2 weeks after stenting (OR=0.57; P=0.036), and chemotherapy after ERCP (OR=0.11; P<0.001) were associated with 90-day mortality after stenting. The prediction model was developed to identify the risk of death within 90 days post-stent placement. The AUROC was 0.76 and 0.75 in derivation and validation cohorts. Patients with a score ≥ 1.40 had a high likelihood of death, whereas those scoring < -1.50 had a low likelihood of death. Additionally, a score ≥ 0.58 provided a 75.2% probability of death, which highlights the usability of the model.ConclusionsThis study proposes a useful validated prediction model to forecast the 90-day mortality of unresectable MBO patients after stenting. The model permits physicians to stratify the death risk and may be helpful to provide a proper palliative strategy.

ObjectiveColon cancer with retroperitoneal abscess is a rare and easily misdiagnosed disease and has only been reported via case. There is an urgent need to conduct a dataset analysis for such patients, which is crucial to improving the survival rate and quality of life of these patientsMethodsPatients with colon cancer associated with retroperitoneal abscess were extracted from our hospital and the PubMed, EMBASE and Web of Science databases. Clinical information, including the patients’ basic characteristics, clinical symptoms, laboratory tests, imaging examinations, treatment methods and prognosis was analyzed.ResultsSixty-one patients were analyzed, with an average age of 65 years. The proportions of right and left colon cancers were 63.9% and 36.1%, respectively. A total of 98.0% of the patients had adenocarcinoma. Many patients have insidious symptoms such as fever and weight loss. At the first medical visit, pain was the most common symptom (71%), with pain in the thigh (21.8%), abdomen (21.8%), and waist and back (14.5%) ranking among the top three. The misdiagnosis rate of the patients referred to our department was 75%, while the overall misdiagnosis rate in the literature was 43.9%. Laboratory tests show that these patients often have elevated white blood cells and anemia. CT examination showed that 87.2% of patients had an iliopsoas muscle abscess, and tumors were not simultaneously detected in 37.2%. A total of 33.9% of patients had local abscesses of the iliopsoas muscle, 26.4% had drainage into the subcutaneous tissue of the waist and upper buttocks, and 22.6% had drainage around the adductor muscle group of the thigh. These patients have a variety of treatments, and many patients have undergone multiple and unnecessary treatments. Thirteen patients died after surgery, and 6 died in the hospital, of whom four were patients undergoing direct surgery, and the other 7 died after discharge due to cachexia.ConclusionColorectal cancer with retroperitoneal abscess is a relatively rare and easily misdiagnosed subtype of colon cancer. It is more likely to occur in right-sided colon adenocarcinoma. The main clinical symptom is pain caused by the drainage of pus to the corresponding areas of the waist, abdomen, and legs. CT is the preferred diagnostic method. Actively treating the abscess and then transitioning to standard colon cancer treatment can prevent patient death and improve treatment quality.

BackgroundVery few studies have been published on the causes of death of upper tract urothelial carcinoma (UTUC). We sought to explore the mortality patterns of contemporary UTUC survivors.MethodsWe performed a retrospective cohort study involving patients with upper urinary tract carcinoma from the National Cancer Institute’s Surveillance, Epidemiology, and End Results (SEER) database (2000 and 2015). We used standardized mortality ratios (SMRs) to compare death rates among patients with UTUC in the general population and excess absolute risks (EARs) to quantify the disease-specific death burden.ResultsA total of 10,179 patients with UTUC, including 7,133 who died, were included in our study. In total, 302 (17.17%) patients with the localized disease died of UTUC; however, patients who died from other causes were 4.8 times more likely to die from UTUC (n = 1,457 [82.83%]). Cardiovascular disease was the most common non-cancer cause of death (n = 393 [22.34% of all deaths]); SMR, 1.22; 95% confidence intervals [CI], 1.1–1.35; EAR, 35.96). A total of 4,046 (69.99%) patients with regional stage died within their follow-up, 1,413 (34.92%) of whom died from UTUC and 1,082 (26.74%) of whom died from non-cancer causes. UTUC was the main cause of death (SMR, 242.48; 95% CI, 230–255.47; EAR, 542.47), followed by non-tumor causes (SMR, 1.18; 95% CI, 1.11–1.25; EAR, 63.74). Most patients (94.94%) with distant stage died within 3 years of initial diagnosis. Although UTUC was the leading cause of death (n = 721 [54.29%]), these patients also had a higher risk of death from non-cancer than the general population (SMR, 2.08; 95% CI, 1.67–2.56; EAR, 288.26).ConclusionsNon-UTUC deaths accounted for 82.48% of UTUC survivors among those with localized disease. Patients with regional/distant stages were most likely to die of UTUC; however, there is an increased risk of dying from non-cancer causes that cannot be ignored. These data provide the latest and most comprehensive assessment of the causes of death in patients with UTUC.

BackgroundEarly-onset colorectal cancer (EOCRC) has an alarmingly increasing trend and arouses increasing attention. Causes of death in EOCRC population remain unclear.MethodsData of EOCRC patients (1975–2018) were extracted from the Surveillance, Epidemiology, and End Results database. Distribution of death was calculated, and death risk of each cause was compared with the general population by calculating standard mortality ratios (SMRs) at different follow-up time. Univariate and multivariate Cox regression models were utilized to identify independent prognostic factors for overall survival (OS).ResultsThe study included 36,013 patients, among whom 9,998 (27.7%) patients died of colorectal cancer (CRC) and 6,305 (17.5%) patients died of non-CRC causes. CRC death accounted for a high proportion of 74.8%–90.7% death cases within 10 years, while non-CRC death (especially cardiocerebrovascular disease death) was the major cause of death after 10 years. Non-cancer death had the highest SMR in EOCRC population within the first year after cancer diagnosis. Kidney disease [SMR = 2.10; 95% confidence interval (CI), 1.65–2.64] and infection (SMR = 1.92; 95% CI, 1.48–2.46) were two high-risk causes of death. Age at diagnosis, race, sex, year of diagnosis, grade, SEER stage, and surgery were independent prognostic factors for OS.ConclusionMost of EOCRC patients died of CRC within 10-year follow-up, while most of patients died of non-CRC causes after 10 years. Within the first year after cancer diagnosis, patients had high non-CRC death risk compared to the general population. Our findings help to guide risk monitoring and management for US EOCRC patients.

BackgroundSedentary behavior is thought to pose different risks to those attributable to physical inactivity. However, few studies have examined the association between physical activity and sitting time with cancer incidence within the same population.MethodsWe followed 38,154 healthy Norwegian adults in the Nord-Trøndelag Health Study (HUNT) for cancer incidence from 1995–97 to 2014. Cox proportional hazards regression was used to estimate risk of site-specific and total cancer incidence by baseline sitting time and physical activity.ResultsDuring the 16-years follow-up, 4,196 (11%) persons were diagnosed with cancer. We found no evidence that people who had prolonged sitting per day or had low levels of physical activity had an increased risk of total cancer incidence, compared to those who had low sitting time and were physically active. In the multivariate model, sitting ≥8 h/day was associated with 22% (95% CI, 1.05–1.42) higher risk of prostate cancer compared to sitting <8 h/day. Further, men with low physical activity (≤8.3 MET-h/week) had 31% (95% CI, 1.00–1.70) increased risk of colorectal cancer (CRC) and 45% (95% CI, 1.01–2.09) increased risk of lung cancer compared to participants with a high physical activity (>16.6 MET-h/week). The joint effects of physical activity and sitting time the indicated that prolonged sitting time increased the risk of CRC independent of physical activity in men.ConclusionsOur findings suggest that prolonged sitting and low physical activity are positively associated with colorectal-, prostate- and lung cancer among men. Sitting time and physical activity were not associated with cancer incidence among women. The findings emphasizing the importance of reducing sitting time and increasing physical activity.

This study is open to adults with different types of advanced cancer (solid tumours). The study is also open to patients with diffuse large B-cell lymphoma in whom previous treatment was not successful. In some countries, adolescents who are at least 15 years old and who are diagnosed with NUT carcinoma can also participate. No standard treatment exists for this rare and aggressive form of cancer.The purpose of this study is to find out the highest dose of BI 894999 that people can tolerate.BI 894999 is tested for the first time in humans. Participants take tablets once daily. The study also tests whether participants can tolerate BI 894999 better when taken continuously or with breaks in between.Participants can stay in the study as long as they benefit from the treatment and can tolerate it.The doctors also regularly check the general health of the participants.

BackgroundImmune checkpoint inhibitor (ICI) therapy has improved survivals with a favorable toxicity profile in a variety of cancer patients. We hypothesized that hospitalized cancer patients who have acute or chronic comorbidities may have suppressed immune systems and poor clinical outcomes to ICIs. The objective of this study was to explore clinical outcomes and predictive factors of hospitalized cancer patients who received ICI therapy at an NCI-designated Comprehensive Cancer Center.MethodsA retrospective review of electronic medical records was conducted for adult cancer patients who received an FDA-approved ICI during admission from 08/2016 to 01/2022. For each patient we extracted demographics, cancer histology, comorbidities, reasons for hospitalization, ICI administered, time from treatment to discharge, time from treatment to progression or death, and complete blood counts. Progression-free survival (PFS) and overall survival (OS) were estimated using the Kaplan–Meier method and compared using the log-rank test. The 95% confidence interval for survival was calculated using the exact binomial distribution. Statistical significance was defined as 2-sided p<0.05.ResultsOf 37 patients identified, 2 were excluded due to lack of complete blood counts on admission. Average hospital stay was 24.2 (95% CI 16.5, 31.9) days. Ten (27.0%) patients died during the same hospitalization as treatment. Of those who followed up, 22 (59.5%) died within 90 days of inpatient therapy. The median PFS was 0.86 (95% CI 0.43, 1.74) months and median OS was 1.55 (95% CI 0.76, 3.72) months. Patients with ≥3 comorbidities had poorer PFS (2.4 vs. 0.4 months; p=0.0029) and OS (5.5 vs. 0.6 months; p=0.0006). Pre-treatment absolute lymphocyte counts (ALC) <600 cells/µL were associated with poor PFS (0.33 vs. 1.35 months; p=0.0053) and poor OS (0.33 vs. 2.34 months; p=0.0236). Pre-treatment derived neutrophil to lymphocyte ratio (dNLR) <4 was associated with good median PFS (1.6 vs. 0.4 months; p=0.0157) and OS (2.8 vs. 0.9 months; p=0.0375).ConclusionsAdministration of ICI therapy was associated with poor clinical outcomes and high rates of both inpatient mortality and 90-day mortality after inpatient ICI therapy. The presence of ≥3 comorbidities, ALC <600/μL, or dNLR >4 in hospitalized patients was associated with poor survival outcomes.