Using Laboratory Test Results at Hospital Admission to Predict Short-term Survival in Critically Ill Patients With Metastatic or Advanced Cancer

Comparing Clinician Estimates versus a Statistical Tool for Predicting Risk of Death within 45 Days of Admission for Cancer Patients

OBJECTIVES

 While clinical practice guidelines recommend that oncologists discuss goals of care with patients who have advanced cancer, it is estimated that less than 20% of individuals admitted to the hospital with high-risk cancers have end-of-life discussions with their providers. While there has been interest in developing models for mortality prediction to trigger such discussions, few studies have compared how such models compare with clinical judgment to determine a patient's mortality risk.

METHODS

 This study is a prospective analysis of 1,069 solid tumor medical oncology hospital admissions (n = 911 unique patients) from February 7 to June 7, 2022, at Memorial Sloan Kettering Cancer Center. Electronic surveys were sent to hospitalists, advanced practice providers, and medical oncologists the first afternoon following a hospital admission and they were asked to estimate the probability that the patient would die within 45 days. Provider estimates of mortality were compared with those from a predictive model developed using a supervised machine learning methodology, and incorporated routine laboratory, demographic, biometric, and admission data. Area under the receiver operating characteristic curve (AUC), calibration and decision curves were compared between clinician estimates and the model predictions.

RESULTS

 Within 45 days following hospital admission, 229 (25%) of 911 patients died. The model performed better than the clinician estimates (AUC 0.834 vs. 0.753, p < 0.0001). Integrating clinician predictions with the model's estimates further increased the AUC to 0.853 (p < 0.0001). Clinicians overestimated risk whereas the model was extremely well-calibrated. The model demonstrated net benefit over a wide range of threshold probabilities.

CONCLUSION

 The inpatient prognosis at admission model is a robust tool to assist clinical providers in evaluating mortality risk, and it has recently been implemented in the electronic medical record at our institution to improve end-of-life care planning for hospitalized cancer patients.

A prediction model for in-hospital mortality in intensive care unit patients with metastatic cancer

Aim

To identify predictors for in-hospital mortality in patients with metastatic cancer in intensive care units (ICUs) and established a prediction model for in-hospital mortality in those patients.

Methods

In this cohort study, the data of 2,462 patients with metastatic cancer in ICUs were extracted from the Medical Information Mart for Intensive Care III (MIMIC-III) database. Least absolute shrinkage and selection operator (LASSO) regression analysis was applied to identify the predictors for in-hospital mortality in metastatic cancer patients. Participants were randomly divided into the training set (n = 1,723) and the testing set (n = 739). Patients with metastatic cancer in ICUs from MIMIC-IV were used as the validation set (n = 1,726). The prediction model was constructed in the training set. The area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were employed for measuring the predictive performance of the model. The predictive performance of the model was validated in the testing set and external validation was performed in the validation set.

Results

In total, 656 (26.65%) metastatic cancer patients were dead in hospital. Age, respiratory failure, the sequential organ failure assessment (SOFA) score, the Simplified Acute Physiology Score II (SAPS II) score, glucose, red cell distribution width (RDW) and lactate were predictors for the in-hospital mortality in patients with metastatic cancer in ICUs. The equation of the prediction model was ln(P/(1 + P)) = -5.9830 + 0.0174 × age + 1.3686 × respiratory failure + 0.0537 × SAPS II + 0.0312 × SOFA + 0.1278 × lactate - 0.0026 × glucose + 0.0772 × RDW. The AUCs of the prediction model was 0.797 (95% CI,0.776-0.825) in the training set, 0.778 (95% CI, 0.740-0.817) in the testing set and 0.811 (95% CI, 0.789-0.833) in the validation set. The predictive values of the model in lymphoma, myeloma, brain/spinal cord, lung, liver, peritoneum/pleura, enteroncus and other cancer populations were also assessed.

Conclusion

The prediction model for in-hospital mortality in ICU patients with metastatic cancer exhibited good predictive ability, which might help identify patients with high risk of in-hospital death and provide timely interventions to those patients.

Correlation between the 72-hour fatality ratios and out-of-hospital cardiac arrest ratios in patients with extremely high outlier values of 57 laboratory test items: A single-center retrospective inception cohort study

The association between extremely high outlier values (EHOV) of laboratory test items (LTIs) and short-term prognosis or out-of-hospital cardiac arrest (OHCA) remains unclear. This retrospective study investigated the correlation between 72-hour fatality ratios and OHCA ratios in patients with the top 100 EHOV of 57 LTIs without focusing on the disease group and which test items were predictors of 72-hour fatality. This single-center retrospective inception cohort study enrolled patients aged ≥ 18 years who underwent any combination of laboratory tests at the Saitama Medical Center, Japan between January 1, 2008, and December 31, 2013. The primary outcome was the correlation between the 72-hour fatality ratios and OHCA ratios in patients with the top 100 EHOV for 57 LTIs without focusing on the disease group. The LTIs included hematology, blood chemistry, erythrocyte sedimentation, blood coagulation, and arterial blood gas test results. The secondary outcome was which of the 57 LTIs with the top 100 EHOV were more likely to associate with the 72-hour fatality. We evaluated the correlation between the 72-hour fatality ratios and the OHCA ratios for each laboratory test item using the Passing-Bablok regression method. The 72-hour fatality ratios for the top 100 EHOV of 57 LTIs were significantly positively correlated with the OHCA ratios. The regression coefficient of the regression line was 0.394, and the correlation coefficient (95% confidence interval) was 0.644 (0.458-0.775, P < .001). These 72-hour fatality ratios tended to be lower than the OHCA ratios. The top 100 EHOV of 13 LTIs including total bilirubin, direct bilirubin, C-reactive protein, base excess, bicarbonate ion, creatine kinase, uric acid, partial pressure of oxygen, sodium, chloride, blood urea nitrogen, aspartate aminotransferase, and lactate dehydrogenase had 72-hour fatality ratios that were above the upper limit of the linear confidence region of the regression line, with higher 72-hour fatality ratios than the OHCA ratios. The 72-hour fatality ratios for the top 100 EHOV of 57 LTIs tended to be lower than the OHCA ratios. The top 100 EHOV of these 13 LTIs were found to be more likely to associate with 72-hour fatality than OHCA.

Prognostic significance of albumin to alkaline phosphatase ratio in critically ill patients with acute kidney injury

OBJECTIVE

No epidemiological evidence has investigated the effect of albumin to alkaline phosphatase ratio (AAPR) on the prognosis among critically ill patients with acute kidney injury (AKI). We aimed to explore the prognostic value of AAPR in these patients.

METHODS

We extracted all clinical data from MIMIC III. ROC curve analysis was used to evaluate the discrimination of AAPR for predicting in-hospital mortality. A generalized additive model was applied to identify a nonlinear association between AAPR and in-hospital mortality. The Cox proportional hazards models were used to determine the association between AAPR and in-hospital and 30-day mortality.

RESULTS

A total of 6894 eligible subjects were enrolled in this study. The relationship between AAPR and in-hospital mortality was nonlinear. Multivariate analysis demonstrated that lower AAPR (AAPR < 0.35) was an independent predictor of in-hospital and 30-day mortality after adjusting for potential confounders (HR 1.74, 95% CI 1.72-2.20, P < 0.001; HR 1.89, 95% CI 1.66-2.14, P < 0.001, respectively).

CONCLUSIONS

AAPR may serve as a potential prognostic biomarker in critically ill patients with AKI and lower AAPR was associated with increased risk of in-hospital and 30-day mortality among these patients.

A predictive model for early recurrence of colorectal-cancer liver metastases based on clinical parameters

Background

The prognosis for patients with colorectal-cancer liver metastases (CRLM) after curative surgery remains poor and shows great heterogeneity. Early recurrence, defined as tumor recurrence within 6 months of curative surgery, is associated with poor survival, requiring earlier detection and intervention. This study aimed to develop and validate a bedside model based on clinical parameters to predict early recurrence in CRLM patients and provide insight into post-operative surveillance strategies.

Material and methods

A total of 202 consecutive CRLM patients undergoing curative surgeries between 2012 and 2019 were retrospectively enrolled and randomly assigned to the training (n = 150) and validation (n = 52) sets. Baseline information and radiological, pathological, and laboratory findings were extracted from medical records. Predictive factors for early recurrence were identified via a multivariate logistic-regression model to develop a predictive nomogram, which was validated for discrimination, calibration, and clinical application.

Results

Liver-metastases number, lymph-node suspicion, neurovascular invasion, colon/rectum location, albumin and post-operative carcinoembryonic antigen, and carbohydrate antigen 19–9 levels (CA19–9) were independent predictive factors and were used to construct the nomogram for early recurrence after curative surgery. The area under the curve was 0.866 and 0.792 for internal and external validation, respectively. The model significantly outperformed the clinical risk score and Beppu’s model in our data set. In the lift curve, the nomogram boosted the detection rate in post-operative surveillance by two-fold in the top 30% high-risk patients.

Conclusion

Our model for early recurrence in CRLM patients after curative surgeries showed superior performance and could aid in the decision-making for selective follow-up strategies.

Increased in-hospital mortality and emergent cases in patients with stage IV cancer

BACKGROUND

Cancer patients in the USA are still being treated with aggressive, life-prolonging interventions. Palliative care services remain vastly underutilized despite surges in both quality and quantity of programs. We evaluated surgical outcomes of metastatic cancer patients to question whether palliative care may be a better option.

STUDY DESIGN

We queried the 2014 National Surgical Quality Improvement Program database (NSQIP) for patients with a diagnosis of malignancy (ICD 9 Codes 145.00 to 200.00). Cases were divided into metastatic and non-metastatic cancer. Demographic data including preoperative, intraoperative, and postoperative factors, as well as complications and comorbidities were compared between these two groups. Independent t testing was used to compare continuous variables. Chi-square testing was used to compare categorical variables. Multiple logistic regression was used to assess for predictors of mortality in metastatic cancer.

RESULTS

A total of 80,275 cancer patients were analyzed, 11.8% (9423) of whom had metastatic disease. In-hospital mortality rate was found to be 4 times higher among patients with metastatic cancer (2.1% vs. 0.5%; P = < 0.0001). Of those metastatic cancer patients that died while in hospital, 18.5% had an emergency surgery performed. After adjusting for confounders, dyspnea at rest/moderate exertion (OR 5.7/2.4; 95% CI 2.7/1.6 to 11.9/3.7; P < 0.0001) was found to be the most significant predictor of in hospital mortality in stage IV cancer patients.

CONCLUSION

Aggressive treatment in advanced cancer patients contributes to alarmingly high in-hospital mortality. Improved, deliberate communication of palliative care options with patients is exceedingly conducive to enhancing end-of-life cancer care.

Acute palliative care unit-initiated interventions for advanced cancer patients at the end of life: prediction of impending death based on Glasgow Prognostic Score

PURPOSE

Unexpected death occurred in an unexpectedly high proportion of advanced cancer patients in the acute palliative care unit (APCU) setting and associated with fewer signs of impending death. Recognition of patients at high risk of approaching death, especially immediately after admitting APCU among clinicians, can improve the end-of-life trajectory. Our objective was accurate prognostication within a few days of admission.

METHODS

Patients admitted to an APCU of the NTT Medical Center Tokyo, Tokyo, Japan, between April 2009 and December 2016 were retrospectively examined. The Glasgow Prognostic Score (GPS) was optimized with concomitant neutrophilia, lymphocytopenia, thrombocytopenia, anemia, and monocytosis. Kaplan-Meier survival curves were estimated, and independent predictors for 3-day mortality were identified using univariate and multivariate analyses. The sensitivity, specificity, and likelihood ratios (LRs) associated with imminent death were also assessed.

RESULTS

Nine hundred ninety-one patients were included; 52.9% was male. The median age was 72 years. The median survival was 13 days (IQ range 6 to 26), and 11.7% died within 3 days of admission. Significant difference in survival with a GPS of 2 was observed in GPS optimized with concomitant thrombocytopenia, and it was the only significant predictor associated with 3-day mortality (p = 0.004), which had high specificity (> 95%) and high positive LR (> 5).

CONCLUSION

The prognostic value of the GPS was enhanced by adding thrombocytopenia. The concurrent use of the GPS and platelet count improved the prognostication of limited time of survival and could assist in the personal and clinical decisions for advanced cancer patients.

Survival time after marked reduction in oral intake in terminally ill noncancer patients: A retrospective study

Background

Methods

Results

Conclusion

Prognostic factors in patients with advanced extrahepatic cholangiocarcinoma: A single center experience

Extrahepatic cholangiocarcinoma (ECC) is an aggressive malignancy causing a lot of fatalities and comorbidities. Endoscopic biliary stenting (EBS) is mostly needed for ECC. In this study, we aimed to investigate the prognostic factors for the overall survival (OS) and the factors predicting the patients eligible for chemotherapy after EBS in ECC.We retrospectively screened 153 advanced ECC patients who underwent EBS for jaundice to make the patients eligible for chemotherapy. Patient's clinical and laboratory parameters were recorded. OS was estimated by the Kaplan-Meier method. All parameters were assessed by binary logistic regression analysis to predict which patients are eligible for chemotherapy.The median OS of all patients was 12.0 months (10.1-13.8). The median OS of the patients treated with chemotherapy was 13.0 months (12.0-14.0), while it was 4.0 months (2.3-5.7) for patients unable for chemotherapy after EBS. Albumin, aspartate aminotransferase (ALT) and carbohydrate antigen 19-9 (CA 19-9) values were independent prognostic factors for OS. Higher albumin and lower prothrombin time (PT) levels were independent parameters to predict the patients eligible for chemotherapy after EBS.Being suitable for chemotherapy was the main determinant for prolonged survival and albumin and PT levels were independent predictors for chemotherapy eligibility after EBS. Albumin, ALT, and CA 19-9 values were independent prognostic factors for OS in ECC.

Prevalence of Laboratory Critical Results in Eye Patients from an Eye Hospital in Southern China

OBJECTIVES

To investigate the prevalence of laboratory critical results (CRs) and associated risk factors in patients with eye diseases in a tertiary eye hospital.

METHODS

Blood samples were collected from both inpatients and outpatients at Zhongshan Ophthalmic Center, Guangzhou, China, from June 1, 2012, to May 31, 2014, and samples were sent to the hospital's clinical laboratory for blood routine, biochemistry, and blood coagulation tests. Laboratory CRs for blood glucose, sodium, potassium, white blood cell count, platelet count, prothrombin time, fibrinogen, international normalized ratio, and activated partial thromboplastin time were included in the current analysis.

RESULTS

A total of 60403 subjects were enrolled in the current analysis. CRs were identified in 339 tests from 336 patients with a prevalence of 5.7‰. Age was positively associated with the presence of CRs. Compared to patients with lens diseases, patients with strabismus, oculoplastics, and ocular trauma were less likely to have CRs (P < 0.05), while patients with tumors were more likely to have CRs (P < 0.001).

CONCLUSIONS

The prevalence of CRs in eye patients is low but calls for medication attention. It is important for medical personnel, especially ophthalmologists, to increase awareness of the importance, as well as the prevalence and risk factors of CRs.