Racial and ethnic differences in early death among gynecologic malignancy.

BACKGROUND: Racial and ethnic differences in early death after cancer diagnosis have not been well studied in gynecologic malignancy. OBJECTIVE: This study aimed to assess population-level trends and characteristics of early death among patients with gynecologic malignancy based on race and ethnicity in the United States. STUDY DESIGN: The National Cancer Institute's Surveillance, Epidemiology, and End Results Program was queried to examine 461,300 patients with gynecologic malignancies from 2000 to 2020, including uterine (n=242,709), tubo-ovarian (n=119,989), cervical (n=68,768), vulvar (n=22,991), and vaginal (n=6843) cancers. Early death, defined as a mortality event within 2 months of the index cancer diagnosis, was evaluated per race and ethnicity. RESULTS: At the cohort level, early death occurred in 21,569 patients (4.7%), including 10.5%, 5.5%, 2.9%, 2.5%, and 2.4% for tubo-ovarian, vaginal, cervical, uterine, and vulvar cancers, respectively (P<.001). In a race- and ethnicity-specific analysis, non-Hispanic Black patients with tubo-ovarian cancer had the highest early death rate (14.5%). Early death racial and ethnic differences were the largest in tubo-ovarian cancer (6.4% for Asian vs 14.5% for non-Hispanic Black), followed by uterine (1.6% for Asian vs 4.9% for non-Hispanic Black) and cervical (1.8% for Hispanic vs 3.8% to non-Hispanic Black) cancers (all, P<.001). In tubo-ovarian cancer, the early death rate decreased over time by 33% in non-Hispanic Black patients from 17.4% to 11.8% (adjusted odds ratio, 0.67; 95% confidence interval, 0.53-0.85) and 23% in non-Hispanic White patients from 12.3% to 9.5% (adjusted odds ratio, 0.77; 95% confidence interval, 0.71-0.85), respectively. The early death between-group difference diminished only modestly (12.3% vs 17.4% for 2000-2002 [adjusted odds ratio for non-Hispanic White vs non-Hispanic Black, 0.54; 95% confidence interval, 0.45-0.65] and 9.5% vs 11.8% for 2018-2020 [adjusted odds ratio, 0.65; 95% confidence interval, 0.54-0.78]). CONCLUSION: Overall, approximately 5% of patients with gynecologic malignancy died within the first 2 months from cancer diagnosis, and the early death rate exceeded 10% in non-Hispanic Black individuals with tubo-ovarian cancer. Although improving early death rates is encouraging, the difference among racial and ethnic groups remains significant, calling for further evaluation.

Disseminated intravascular coagulation score evolution in 48 h predicts early death in acute promyelocytic leukemia patients.

INTRODUCTION: Early death (ED) is the unsolved issue of acute promyelocytic leukemia (APL). The disseminated intravascular coagulation (DIC) score has been proposed as a marker of bleeding and death in APL; whether its temporal evolution predicts outcomes in APL is unknown. We evaluated whether an increasing score 48 h after diagnosis associates with ED. METHODS: Retrospective, single-center study, including patients with newly diagnosed APL between 2000 and 2023, treated with all-transretinoic acid (ATRA) plus anthracycline or arsenic trioxide (ATO). "DIC score worsening" was defined as ≥1 point increase in the score after 48 h, and ED as death within 30 days of diagnosis. RESULTS: Eighty-six patients were included, with median age of 46 years (17-82). ED patients (26.7%) more frequently had age >60 years and worsening DIC score after 48 h. These were also the only predictors of ED identified in both univariate and multivariate (OR 4.18, p = .011; OR 7.8, p = .005, respectively) logistic regression analysis. CONCLUSION: This is the first study on DIC score evolution in APL-a worsening DIC score 48 h after diagnosis is a strong independent predictive factor of ED. We propose a reduction of the DIC score from diagnosis as a new treatment goal in APL care.

Predictors of 1-year mortality after gastrectomy for gastric cancer.

PURPOSE: One-year mortality is important for referrals to specialist palliative care or advance care planning (ACP). This helps optimize comfort for those who cannot be cured or have a lower life expectancy. Few studies have investigated the risk factors for 1-year mortality after gastrectomy for gastric cancer (GC). METHODS: A total of 1415 patients with gastric cancer (stages I-IV) who underwent gastrectomy between 2005 and 2020 were included. The patients were randomly assigned to the investigation group (n = 850) and validation group (n = 565) in a 3:2 ratio. In the investigation group, significant independent prognostic factors for predicting 1-year survival were identified. A scoring system for predicting 1-year mortality was developed which was validated in the validation group. RESULTS: Multivariate analysis revealed that the following seven variables were significant independent factors for 1-year survival: age ≧78, preoperative comorbidity, total gastrectomy, postoperative complication (Clavien-Dindo classification CD â‰§ 3a), stage III and IV, and R2 resection. While developing a 1-year mortality score (OMS), an age ≧78 was scored 2, preoperative comorbidity, total gastrectomy, and postoperative complication (CD â‰§ 3a) were scored 1, and stage III, IV, and R2-resection were scored 2, 3, and 3, respectively. OMS 3 had a sensitivity of 91% and a specificity of 66% for predicting death within 1 year. In the validation group, OMS 5 had a sensitivity of 55% and a specificity of 93% for predicting death within 1 year. CONCLUSIONS: OMS may provide important information and help surgeons select the timing of ACP in patients with GC.

A convenient nomogram for predicting early death or liver transplantation after the Kasai procedure in patients with biliary atresia.

PURPOSE: Many patients with biliary atresia (BA) after the Kasai procedure (KP) progress to death or require liver transplantation to achieve long-term survival; however, most cases of death/liver transplantation (D/LT) occur in the early period after KP (usually within 1 year). This study was designed to construct a convenient nomogram for predicting early D/LT in patients with BA after KP. METHODS: A BA cohort was established in May 2017, and up to May 2023, 112 patients with 1-5 years of follow-up were enrolled in the study and randomly (ratio, 3:1) divided into a training cohort for constructing a nomogram (n = 84) and a validation cohort (n = 28) for externally validating the discrimination and calibration. The training cohort was divided into two groups: the early D/LT group (patients who died or had undergone LT within 1 year after KP [n = 35]) and the control group (patients who survived through the native liver more than 1 year after KP [n = 49]). Multivariate logistic regression and stepwise regression were applied to detect variables with the best predictive ability for the construction of the nomogram. The discrimination and calibration of the nomogram were internally and externally validated. RESULTS: The Kaplan-Meier (K-M) curve showed an actual 1-year native liver transplantation (NLS) rate of 57.1% and an estimated 2-year NLS rate of 55.2%. By multivariate regression and stepwise regression, age at KP, jaundice clearance (JC) speed 1 month after KP, early-onset PC (initial time < 36.5 days) after KP, sex, aspartate aminotransferase-to-platelet ratio index (APRI), and weight at KP were identified as the independent variables with the best ability to predict early D/LT and were used to construct a nomogram. The developed nomogram based on these independent variables showed relatively good discrimination and calibration according to internal and external validation. CONCLUSION: Most D/LTs were early D/LTs that occurred within 1 year after KP. The established nomogram based on predictors, including sex, weight at the KP, the APRI, age at the KP, JC speed 1 month after the KP, and early PC, may be useful for predicting early D/LT and may be helpful for counseling BA patients about patient prognosis after KP. This study was retrospectively registered at ClinicalTrials.gov (NCT05909033) in June 2023.

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Objective: To construct nomogram models to predict the risk factors for early death in patients with metastatic melanoma (MM). Methods: The study covered 2138 cases from the Surveillance, Epidemiology, and End Results Program (SEER) database and all these patients were diagnosed with MM between 2010 and 2015. Logistic regression was performed to identify independent risk factors affecting early death in MM patients. These risk factors were then used to construct nomograms of all-cause early death and cancer-specific early death. The efficacy of the model was assessed with receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). In addition, external validation of the model was performed with clinicopathologic data of 105 patients diagnosed with MM at Sichuan Cancer Hospital between January 2015 and January 2020. Results: According to the results of logistic regression, marital status, the primary site, N staging, surgery, chemotherapy, bone metastases, liver metastases, lung metastases, and brain metastases could be defined as independent predictive factors for early death. Based on these factors, 2 nomograms were plotted to predict the risks of all-cause early death and cancer-specific early death, respectively. For the models for all-cause and cancer-specific early death, the areas under the curve (AUCs) for the training group were 0.751 (95% confidence interval [CI]: 0.726-0.776) and 0.740 (95% CI: 0.714-0.765), respectively. The AUCs for the internal validation group were 0.759 (95% CI: 0.722-0.797) and 0.757 (95% CI: 0.718-0.780), respectively, while the AUCs for the external validation group were 0.750 (95% CI: 0.649-0.850) and 0.741 (95% CI: 0.644-0.838), respectively. The calibration curves showed high agreement between the predicted and the observed probabilities. DCA analysis indicated high clinical application value of the models. Conclusion: The nomogram models demonstrated good performance in predicting early death in MM patients and can be used to help clinical oncologists develop more individualized treatment strategies.

A convolutional neural network-based, quantitative complete blood count scattergram-mapping framework promptly screens acute promyelocytic leukemia with high sensitivity.

BACKGROUND: Acute promyelocytic leukemia (APL) is a subtype of acute myeloid leukemia (AML) characterized by its rapidly progressive and fatal clinical course if untreated, although it is curable if treated in a timely manner. Promptly screening patients who have results that are suspicious for APL is vital to overcome early death. METHODS: The authors developed an innovative framework consisting of ResNet-18, a convolutional neural network architecture, with the objective of quantitatively mapping a complete blood count (CBC) scattergram to quickly and robustly indicate a probable susceptibility to APL. Three hundred and twenty scattergrams of the white blood cell differential channel from 51 patients with APL, 510 scattergrams from 105 patients who had non-APL AML, and 320 scattergrams from 320 healthy controls were randomly stratified at a ratio of 4:1 and split into training and testing data sets to accomplish five-fold cross-validation. RESULTS: Both the area under the curve and the average precision of >0.99 were achieved in each fold. Three hundred four of the 320 APL scattergrams (95%) were correctly flagged by the model, which outcompeted the CBC review rules recommended by the International Society of Laboratory Hematology (all p < .001). External validation based on an independent testing data set that included 56 scattergrams from 31 patients with APL, 56 scattergrams from 55 patients with non-APL AML, and 64 scattergrams from 64 healthy controls also confirmed the sensitivity and specificity of the framework. CONCLUSIONS: To the authors' knowledge, their convolutional neural network-based framework is the first to use scattergram output from routine CBC analysis to map suspicious APL early with outstanding sensitivity, specificity, and precision. The authors also describe a new CBC workflow incorporating this framework upstream of the morphologic review, which would provide the earliest flag for APL. PLAIN LANGUAGE SUMMARY: The authors propose an innovative way to visualize complete blood counts (CBCs) by mapping the difference in white blood cell counts using automated CBC analysis to identify potential acute promyelocytic leukemia (APL) using a convolutional neural network (CNN), which can eliminate the potential pitfalls of manual observation. Analyses of an unprecedented, realistic data set validated that the quantitative relationship between the CBC scattergram and an APL abnormality is highly consistent. This is the first study to date focusing on screening for APL using scattergrams of the difference in white blood cell counts from routine CBC tests and has significant clinical relevance. The authors recommend using this method even before analyzing cell images, which could provide the earliest way to screen for APL in a sensitive and accurate way.

Modified Rapid Emergency Medicine Score-Lactate (mREMS-L) performance to screen non-anticipated 30-day-related-mortality in emergency department.

BACKGROUND: The aim of this study was to compare the ability to predict 30-day in-hospital mortality of lactate versus the modified Rapid Emergency Medicine Score (mREMS) versus the arithmetic sum of the mREMS plus the numerical value of lactate (mREMS-L). METHODS: A prospective, multicentric, emergency department delivery, pragmatic study was conducted. To determine the predictive capacity of the scales, lactate was measured and the mREMS and mREMS-L were calculated in adult patients (aged>18 years) transferred with high priority by ambulance to the emergency department in five hospitals of Castilla y Leon between 1 January 2020 and 31 December 2021. The area under the receiver operating characteristic (ROC) curve of each of the scales was calculated in terms of mortality for 30 days. RESULTS: A total of 5371 participants were included, and the in-hospital mortality rate at 30 days was of 11.4% (615 cases). The best cut-off point determined in the mREMS was 7.0 points (sensitivity of 67% and specificity of 84%), and for lactate, the cut-off point was 1.4 mmol/L (sensitivity of 88% and specificity of 67%). Finally, the combined mREMS-L showed a cut-off point of 7.9 (sensitivity of 83% and a specificity of 83%). The area under the ROC curve of the mREMS, lactate and mREMS-L for 30-day mortality was 0.851, 0.853, and 0.903, respectively (p < 0.001 in all cases). CONCLUSIONS: The new score generated, mREMS-L, obtained better statistical results than its components (mREMS and lactate) separately.

Predictors of very early death in acute promyelocytic leukemia: a retrospective real-world cohort study.

Early death (ED) is still the major obstacle to cure in acute promyelocytic leukemia (APL). Most studies focus on 30-day ED; however, little is known on predictors of death before starting APL treatment (very early death - VED) and on predictors of 7-day ED, the period with most deaths due to thrombohemorrhagic diathesis. We hypothesized whether the severity of the coagulopathy of APL could predict VED and 7-day ED. We also aimed to evaluate other characteristics associated with these outcomes. We undertook a retrospective, single-center observational study including newly diagnosed APL patients admitted to our institution between January 2000 and November 2022. Baseline demographical, clinical, and laboratorial data were collected. Statistical analysis was performed using Stata. One hundred four patients were included. The VED rate was 4.8%. A DIC Score ≥ 7 (p = 0.045), serum creatinine > 1.5 mg/dL (p < 0.001%), a DIC Score ≥ 6 within 24 h (p = 0.009), and mechanical ventilation (p < 0.001) were associated with VED. The 7-day ED rate was 12.5%. High-risk (p = 0.007) and hypogranular APL (p = 0.029), DIC Score at diagnosis (p = 0.047), DIC Score ≥ 7 (p = 0.043), DIC Score ≥ 6 within 24 h (p = 0.025), PT prolongation > 6 s (p = 0.002), and creatinine > 1.5 mg/dL (p = 0.004) were associated with 7-day ED. However, only elevated creatinine emerged as an independent predictor of 7-day ED (OR 21.4; p = 0.008). Our study shows that in patients with APL, an elevated creatinine at diagnosis strongly predicts for 7-day ED. A DIC Score ≥ 7 and a Score that remains ≥ 6 within 24 h and a serum creatinine > 1.5 mg/dL significantly associated with VED.

Risk factors and prognosis of early death in secondary hemophagocytic lymphohistiocytosis.

The aim of our study was to summarize the clinical characteristics of early death patients with newly diagnosed secondary hemophagocytic lymphohistiocytosis (sHLH), analyze the risk factors of early death, and analyze the survival of patients. The clinical characteristics of 324 newly diagnosed sHLH patients admitted to the First Affiliated Hospital of Zhejiang University Medical College and Zhejiang Provincial Cancer Hospital from January 2014 to February 2021 were analyzed retrospectively. Analyze the independent risk factors of early death, compare the secondary diseases and treatment methods of patients with early death group and non early death group, and analyze the survival of all patients with sHLH. Among the 324 newly diagnosed patients with sHLH, 134 died early, with an early mortality rate of 41.4%. Comparing the clinical characteristics of patients with early death group and patients with non early death group, logistic regression model was used to conduct multifactor analysis. Age > 60 years, Plt ≤ 20.0 × 109/L, APTT > 36.0 s and LDH > 1000.0 U/L were independent risk factors for early death of newly diagnosed sHLH patients (P < 0.05). Comparing the secondary diseases and treatment methods between early death group and non early death group, the proportion of sHLH patients secondary to lymphoma was higher in early death group than that in non early death group (P < 0.05). The proportion of sHLH patients secondary to connective tissue disease and infection was lower in early death group than that in non early death group (P < 0.05), and the proportion of sHLH patients used hormone combined chemotherapy was lower in early death group than that in non early death group (P < 0.05). The median follow-up time of all patients was 12.0 (1-65) months. The 5-year OS rates of patients with age > 60 years and age ≤ 60 years were 25.8% and 49.6% respectively (P < 0.001); The 5-year OS rates of patients with Plt > 20.0 × 109/L and Plt ≤ 20.0 × 109/L were 52.5% and 25.5% respectively (P < 0.001); The 5-year OS rates of patients with APTT > 36.0 s and APTT ≤ 36.0 s were 34.5% and 57.4% respectively (P < 0.001); The 5-year OS rates of patients with LDH > 1000.0 U/L and LDH ≤ 1000.0 U/L were 23.3% and 56.3% respectively (P < 0.001). Age > 60 years, Plt ≤ 20.0 × 109/L, APTT > 36.0 s and LDH > 1000.0 U/L are independent risk factors for early death of sHLH patients. The early mortality of lymphoma associated HLH (LA-HLH) patients is high, and early use of hormone combined chemotherapy can reduce the early mortality.

A novel risk prediction nomogram for early death in patients with resected synchronous multiple primary colorectal cancer based on the SEER database.

BACKGROUND: Synchronous multiple primary colorectal cancer (SMPCC) involves the simultaneous occurrence of 2 or more independent primary malignant tumors in the colon or rectum. Although SMPCC is rare, it results in a higher incidence of postoperative complications and mortality compared to patients with single primary colorectal cancer (SPCRC). METHODS: The clinical factors and survival outcomes of SMPCC patients registered on the Surveillance, Epidemiology, and End Results (SEER) database between 2000 and 2017 were extracted. The patients were divided into the training and validation cohorts using a ratio of 7:3. Univariate and multivariate logistic regression analyses were used to identify the independent risk factors for early death. The performance of the nomogram was evaluated using the concordance index (C-index), calibration curves, and the area under the curve (AUC) of a receiver operating characteristics curve (ROC). A decision curve analysis (DCA) was used to evaluate the clinical utility of the nomogram and standard TNM system. RESULTS: A total of 4386 SMPCC patients were enrolled in the study and randomly assigned to the training (n = 3070) and validation (n = 1316) cohorts. The multivariate logistic analysis identified age, chemotherapy, radiotherapy, T stage, N stage, and M stage as independent risk factors for all-cause and cancer-specific early death. The marital status was associated with all-cause early death, and the tumor grade was associated with cancer-specific early death. In the training cohort, the nomogram achieved a C-index of 0.808 (95% CI, 0.784-0.832) and 0.843 (95% CI, 0.816-0.870) for all-cause and cancer-specific early death, respectively. Following validation, the C-index was 0.797 (95% CI, 0.758-0.837) for all-cause early death and 0.832 (95% CI, 0.789-0.875) for cancer-specific early death. The ROC and calibration curves indicated that the model had good stability and reliability. The DCA showed that the nomogram had a better clinical net value than the TNM staging system. CONCLUSION: Our nomogram can provide a simple and accurate tool for clinicians to predict the risk of early death in SMPCC patients undergoing surgery and could be used to optimize the treatment according to the patient's needs.

Early-death weeks associated with COVID-19: a comparison among France, the UK and the USA.

BACKGROUND: Years of life lost (YLL) is recently used as a more insightful indicator to assess the mortality impact of COVID-19. However, this indicator still has methodological limits. This study aims to propose an alternative approach and new index, early-death weeks. METHODS: The natural mortality and social mortality laws were employed to support two essential assumptions: the sequential and translational early-mortality patterns of COVID-19. This approach was then used with the data related to COVID-19 to calculate early-death weeks associated with COVID-19 in France, the UK and the USA. RESULTS: As of week 20 of 2021, the rate of the total number of early-death weeks per the population of the USA is nearly two times compared to that of France and the UK, with 0.004% to 0.0021 and 0.0023%, respectively. The average numbers of early-death weeks after converting to units of years are 1.2, 1.0 and 1.3 years in France, the UK and the USA, respectively. CONCLUSIONS: The new approach is significantly different from death counts, excess deaths and YLL. The early-death week index provides more insights into COVID-19 and can be applied promptly at any time as well as anywhere once excess deaths have occurred.

A Web-Based Calculator to Predict Early Death Among Patients With Bone Metastasis Using Machine Learning Techniques: Development and Validation Study.

BACKGROUND: Patients with bone metastasis often experience a significantly limited survival time, and a life expectancy of <3 months is generally regarded as a contraindication for extensive invasive surgeries. In this context, the accurate prediction of survival becomes very important since it serves as a crucial guide in making clinical decisions. OBJECTIVE: This study aimed to develop a machine learning-based web calculator that can provide an accurate assessment of the likelihood of early death among patients with bone metastasis. METHODS: This study analyzed a large cohort of 118,227 patients diagnosed with bone metastasis between 2010 and 2019 using the data obtained from a national cancer database. The entire cohort of patients was randomly split 9:1 into a training group (n=106,492) and a validation group (n=11,735). Six approaches-logistic regression, extreme gradient boosting machine, decision tree, random forest, neural network, and gradient boosting machine-were implemented in this study. The performance of these approaches was evaluated using 11 measures, and each approach was ranked based on its performance in each measure. Patients (n=332) from a teaching hospital were used as the external validation group, and external validation was performed using the optimal model. RESULTS: In the entire cohort, a substantial proportion of patients (43,305/118,227, 36.63%) experienced early death. Among the different approaches evaluated, the gradient boosting machine exhibited the highest score of prediction performance (54 points), followed by the neural network (52 points) and extreme gradient boosting machine (50 points). The gradient boosting machine demonstrated a favorable discrimination ability, with an area under the curve of 0.858 (95% CI 0.851-0.865). In addition, the calibration slope was 1.02, and the intercept-in-large value was -0.02, indicating good calibration of the model. Patients were divided into 2 risk groups using a threshold of 37% based on the gradient boosting machine. Patients in the high-risk group (3105/4315, 71.96%) were found to be 4.5 times more likely to experience early death compared with those in the low-risk group (1159/7420, 15.62%). External validation of the model demonstrated a high area under the curve of 0.847 (95% CI 0.798-0.895), indicating its robust performance. The model developed by the gradient boosting machine has been deployed on the internet as a calculator. CONCLUSIONS: This study develops a machine learning-based calculator to assess the probability of early death among patients with bone metastasis. The calculator has the potential to guide clinical decision-making and improve the care of patients with bone metastasis by identifying those at a higher risk of early death.

Post-Radiotherapy Exosomal Non-Coding RNA and Hemograms for Early Death Prediction in Patients with Cervical Cancer.

Concurrent chemo-radiotherapy (CCRT) is linked with accelerated disease progression and early death (ED) in various cancers. This study aimed to assess the association of plasma levels of exosomal non-coding ribonucleic acid (RNA) (ncRNA) and blood cell dynamics with ED prediction in patients with cervical cancer undergoing CCRT. Using propensity score matching, a comparison of complete blood counts (CBCs) was performed among 370 CCRT-treated patients. Differences in ncRNA and messenger RNA (mRNA) expression before and after CCRT in 84 samples from 42 patients (cohort 2) were represented as logarithmic fold change (log2FC). Networks were constructed to link the CBCs to the RNAs whose expression correlated with ED. From the key RNAs selected using multiple regression of all RNA combinations in the network, CBC dynamics-associated ncRNAs were functionally characterized using an enrichment analysis. Cohort 1 (120 patients) exhibited a correlation between elevated absolute neutrophil counts (ANC) and ED. Cohort 2 exhibited a prevalence of microRNA (miR)-574-3p and long intergenic non-protein coding (LINC)01003 ncRNA, whose expression correlated with ANC and hemoglobin values, respectively. Conversely, acyl-coenzyme A thioesterase 9 (ACOT9) mRNA was relevant to all CBC components. An integrative analysis of post-CCRT ncRNA levels and CBC values revealed that the patients with miR-574-3p-LINC01003-ACOT9 log2FC) < 0 had a better prospect of 30-month disease-specific survival. These findings indicate that miR-574-3p and LINC01003 could serve as ED prognostic biomarkers.

Haemoglobin and C-reactive protein levels are efficient at identifying neoplasms as the most common early mortality of chronic diseases in emergency department patients.

Introduction: Extensive research has focused on emergency department (ED) and post-admission deaths, seeking to understand their frequency and causative factors. With the rising prevalence of advanced diseases, it is crucial to identify patients in need of end-of-life care and ensure its high quality. In this epidemiological study, we analyse routine ED blood tests to identify early warning signs of deteriorating patients with common non-traumatic and non-infectious (chronic) conditions. Material and methods: We conducted a retrospective single-centre study for the years 2016-2019 using medical records and electronic data from the Multi-Specialistic Hospital in Gorzów Wielkopolski, Poland. We examined 8971 unique patients with circulatory, neoplastic, and endocrine diseases. We assessed the impact of 2 grouping variables (survivors and non-survivors) on a continuous outcome variable, including age and 37 routine blood tests. Results: Two-way analysis of variance revealed that haemoglobin (Hb), haematocrit (Hct), and C-reactive protein (CRP) are the best differentiating biomarkers for early death in ED patients with cardiovascular, oncological, and endocrine diseases (excluding Hct due to its strong correlation with Hb). The Marczewski-Steinhaus taxonomy highlighted that oncological patients had the shortest survival time, averaging just 2 days from admission among ED non-survivors. Conclusions: Among routinely tested ED biomarkers, Hb and CRP levels are efficient at identifying neoplasms as the most common early mortality of chronic diseases in ED patients.

Predictive factors associated with induction-related death in acute myeloid leukemia in a resource-constrained setting.

Despite advances in supportive measures, acute myeloid leukemia (AML) remission induction still has a high mortality rate in real-world studies as compared to prospective reports. We analyzed data from 206 AML adult patients treated with conventional chemotherapy. The primary endpoint was the 60-day mortality rate, aiming to find risk factors and to examine the role of anti-infection prophylaxis. The 60-day mortality rate was 26%, raising to 41% among those older than 60 years. Complete response was documented at the end of induction in 49%. The final survival model showed that age > 60 years (HR 3.2), Gram-negative colonization (HR 3), monocytic AML (HR 1.8), C-reactive protein (CRP) > 15 mg/dL (HR 10), and an adverse risk in the genetic stratification (HR 3) were associated with induction death. Multidrug-resistant bacteria colonization, thrombosis, and AKI were documented in 71%, 12%, and 66% of the cohort, respectively. Antibacterial and antifungal prophylaxis did not improve outcomes in this study. Our report corroborated the higher mortality during AML induction compared to real-world data from the USA and Europe. In line with other publications, age and cytogenetic stratification influenced early death in this cohort. Noticeably, Gram-negative colonization, monocytic AML, and CRP were also significant to early mortality.

Analysis of risk factors for early death in patients with acute promyelocytic leukaemia treated with arsenic trioxide.

To date, no specific studies have evaluated early death (ED) in patients with acute promyelocytic leukaemia (APL) homogeneously treated with arsenic trioxide induction therapy and investigated according to the white blood cell (WBC) count at onset. Such patients were retrospectively analysed in this study, including 314 patients with a WBC count ≤ 10 × 109/L (standard-risk (SR) group) and 144 with a WBC count > 10 × 109/L (high-risk (HR) group). The baseline clinical characteristics and risk factors for ED were compared between the two groups. The incidence of fibrinogen < 1.0 g/L and elevated serum uric acid, aspartate aminotransferase and creatinine levels were higher in the HR group than in the SR group (P = 0.001; P < 0.001; P < 0.001; P = 0.044, respectively). The ED rate was significantly higher in the HR group than in the SR group (29.17% vs. 10.83%, P < 0.001). The main cause of ED was bleeding, followed by infection and differentiation syndrome (DS) in the HR group, while it was bleeding, followed by DS and infection in the SR group. Male sex, age > 50 years old, and fibrinogen < 1.0 g/L were independent risk factors for ED in the SR group. Increased serum creatinine levels, decreased albumin levels, and fibrinogen < 1.0 g/L were independent risk factors for ED in the HR group. Overall, the incidence of ED was higher in the HR group, and the baseline clinical characteristics, causes, times, and predictors of ED in the HR group differed from those in the SR group.

The evaluation of risk factors leading to early deaths in patients with acute promyelocytic leukemia: a retrospective study.

Acute promyelocytic leukemia (APL) differs from other forms of acute myeloid leukemia (AML), including coagulopathy, hemorrhage, disseminated intravascular coagulation (DIC), and treatment success with all-trans retinoic acid (ATRA). Despite ATRA, early deaths (ED) are still common in APL. Here, we evaluated factors associated with ED and applicability of scoring systems used to diagnose DIC. Ninety-one APL patients (55 females, 36 males, and median age 40 years) were included. ED was defined as deaths attributable to any cause between day of diagnosis and following 30th day. DIC was assessed based on DIC scoring system released by the International Society of Thrombosis and Hemostasis (ISTH) and Chinese Diagnostic Scoring System (CDSS). Patients' median follow-up time was 49.2 months, and ED developed in 14 (15.4% of) cases. Patients succumbing to ED had higher levels of the Eastern Cooperative Oncology Group Performance Status (ECOG PS), lactate dehydrogenase (LDH), and ISTH DIC, and lower fibrinogen levels (p <0.05). In multivariate Cox regression analysis, age >55 and ECOG PS ≥2 rates were revealed to be associated with ED. Based on ISTH and CDSS scores, DIC was reported in 47.3 and 58.2% of the patients, respectively. Despite advances in APL, ED is still a major obstacle. Besides the prompt recognition and correction of coagulopathy, those at high ED risk are recommended to be detected rapidly. Implementation of local treatment plans and creating awareness should be achieved in hematological centers. Common utilization of ATRA and arsenic trioxide (ATO) may be beneficial to overcome ED and coagulopathy in APL patients.

Predictive Model of Early Death of Resectable Pancreatic Ductal Adenocarcinoma After Curative Resection: A SEER-Based Study.

OBJECTIVE: This study aims to determine the factors that predict early death and establish a predictive model for early death by analyzing clinical characteristics of patients with resectable pancreatic ductal adenocarcinoma (R-PDAC) who die early after radical surgery. MATERIALS AND METHODS: This was a retrospective study of patients who underwent radical surgical resection for R-PDAC in the Surveillance, Epidemiology, and End Results (SEER) database. Patients with overall survival ≤ 12 months were assigned as early death group and above 1 year as the late death group. Univariate and multivariate logistic regression was conducted to identify factors significantly associated with early death. An early death predictive model was constructed based on the identified independent risk factors. RESULTS: A total of 9695 patients were analyzed, and the total incidence of early death was 30.72%. Multivariable analysis showed that factors significantly associated with early death included age at diagnosis, race, marital status, tumor location, tumor size, tumor grade, number of positive lymph nodes, number of examined lymph nodes, positive lymph node ratio, chemotherapy, and radiotherapy. The predictive model showed good discrimination with a C-index of 0.722 (95% confidence interval: 0.711-0.733) and convincing calibration. CONCLUSIONS: We developed a predictive model that may be easily applied to patients with R-PDAC after radical resection to predict the chance of death within 1 year. For patients with high risk of early death, neoadjuvant therapy should be considered. Even after radical resection, more aggressive adjuvant chemotherapy (with or without combined radiotherapy) must be used to minimize the chance of early death.

Models for Predicting Early Death in Patients With Stage IV Esophageal Cancer: A Surveillance, Epidemiology, and End Results-Based Cohort Study.

BACKGROUND: Despite enormous progress in the stage IV esophageal cancer (EC) treatment, some patients experience early death after diagnosis. This study aimed to identify the early death risk factors and construct models for predicting early death in stage IV EC patients. METHODS: Stage IV EC patients diagnosed between 2010 and 2015 in the Surveillance, Epidemiology, and End Results (SEER) database were selected. Early death was defined as death within 3 months of diagnosis, with or without therapy. Early death risk factors were identified using logistic regression analyses and further used to construct predictive models. The concordance index (C-index), calibration curves, and decision curve analyses (DCA) were used to assess model performance. RESULTS: Out of 4411 patients enrolled, 1779 died within 3 months. Histologic grade, therapy, the status of the bone, liver, brain and lung metastasis, marriage, and insurance were independent factors for early death in stage IV EC patients. Histologic grade and the status of the bone and liver metastases were independent factors for early death in both chemoradiotherapy and untreated groups. Based on these variables, predictive models were constructed. The C-index was .613 (95% confidence interval (CI), [.573-.653]) and .635 (95% CI, [.596-.674]) in the chemoradiotherapy and untreated groups, respectively, while calibration curves and DCA showed moderate performance. CONCLUSIONS: More than 40% of stage IV EC patients suffered from an early death. The models could help clinicians discriminate between low and high risks of early death and strategize individually-tailed therapeutic interventions in stage IV EC patients.

Risk factors and predictive nomograms for early death of patients with advanced hepatocellular carcinoma: a large retrospective study based on the SEER database.

BACKGROUND: Hepatocellular carcinoma (HCC) is a kind of tumor with high invasiveness, and patients with advanced HCC have a higher risk of early death. The aim of the present study was to identify the risk factors of early death in patients with advanced HCC and establish predictive nomograms. METHODS: Death that occurred within 3 months of initial diagnosis is defined as early death. Patients diagnosed with stage IV HCC between 2010 and 2015 were collected from the Surveillance, Epidemiology, and End Results database for model establishment and verification. Univariable and multivariable logistic regression analyses were used to identify the risk factors. Predictive nomograms were constructed and an internal validation was performed. Decision curve analysis (DCA) was used to verify the true clinical application value of the models. RESULTS: Of 6603 patients (57% age > 60, 81% male, 70% white, 46% married), 21% and 79% had stage IVA and IVB, respectively. On the multivariable analyses, risk factors for early deaths in patients with stage IVA were age, tumor size, histological grade, alpha-fetoprotein (AFP), fibrosis score, tumor stage (T stage), surgery, radiotherapy, and chemotherapy, and that in stage IVB were age, histological grade, AFP, T stage, node stage (N stage), bone metastasis, lung metastasis, surgery, radiotherapy, and chemotherapy. The areas under the curves (AUCs) were 0.830 (95% CI 0.809-0.851) and 0.789 (95% CI 0.768-0.810) in stage IVA and IVB, respectively. Nomograms comprising risk factors with the concordance indexes (C-indexes) were 0.820 (95% CI 0.799-0.841) in stage IVA and 0.785 (95% CI 0.764-0.0.806) in stage IVB for internal validation (Bootstrapping, 1000re-samplings). The calibration plots of the nomograms show that the predicted early death was consistent with the actual value. The results of the DCA analysis show that the nomograms had a good clinical application. CONCLUSION: The nomograms can be beneficial for clinicians in identifying the risk factors for early death of patients with advanced HCC and predicting the probability of early death, so as to allow for individualized treatment plans to be accurately selected.