scholarly journals Development and validation of the RCOS prognostic index: a bedside multivariable logistic regression model to predict hypoxaemia or death in patients with of SARS-CoV-2 infection

2021 ◽  
Author(s):  
Gerardo Alvarez-Uria ◽  
Sumanth Gandra ◽  
Venkata R Gurram ◽  
Raghu P Reddy ◽  
Manoranjan Midde ◽  
...  
Keyword(s):  
Validation Cohort ◽  
Characteristic Curve ◽  
Prognostic Score ◽  
Prognostic Index ◽  
Prognostic Scores ◽  
Prognostic Models ◽  
Brier Score ◽  
Multivariable Logistic Regression Model ◽  
Neutrophil Lymphocyte Ratio ◽  
Development And Validation

Previous COVID-19 prognostic models have been developed in hospital settings, and are not applicable to COVID-19 cases in the general population. There is an urgent need for prognostic scores aimed to identify patients at high risk of complications at the time of COVID-19 diagnosis. The RDT COVID-19 Observational Study (RCOS) collected clinical data from patients with COVID-19 admitted regardless of the severity of their symptoms in a general hospital in India. We aimed to develop and validate a simple bedside prognostic score to predict the risk of hypoxaemia or death. 4035 patients were included in the development cohort and 2046 in the validation cohort. The primary outcome occurred in 961 (23.8%) and 548 (26.8%) patients in the development and validation cohorts, respectively. The final model included 12 variables: age, systolic blood pressure, heart rate, respiratory rate, aspartate transaminase, lactate dehydrogenase, urea, C-reactive protein, sodium, lymphocyte count, neutrophil count and neutrophil/lymphocyte ratio. In the validation cohort, the area under the receiver operating characteristic curve (AUROCC) was 0.907 (95% CI, 0.892-0.922) and the Brier Score was 0.098. The decision curve analysis showed good clinical utility in hypothetical scenarios where admission of patients was decided according to the prognostic index. When the prognostic index was used to predict mortality in the validation cohort, the AUROCC was 0.947 (95% CI, 0.925-0.97) and the Brier score was 0.0188. If our results are validated in other settings, the RCOS prognostic index could help improve the decision making in the current COVID-19 pandemic, especially in resource limited-settings.

Abstract TMP85: Predictive Score of Hemorrhagic Transformation in Patients Not Submitted to Reperfusion Therapies - PROpHET

Stroke ◽  
2020 ◽  
Vol 51 (Suppl_1) ◽  
Author(s):  
Joao B Andrade ◽  
Gisele S Silva ◽  
Jay P Mohr ◽  
Joao J Carvalho ◽  
Luisa Franciscatto ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
Acute Ischemic Stroke ◽  
Validation Cohort ◽  
Characteristic Curve ◽  
Reperfusion Therapy ◽  
Predictive Score ◽  
Brier Score ◽  
Multivariable Logistic Regression Model ◽  
Derivation Cohort ◽  
Digital Version

Objective: To create an accurate and user-friendly pr edictive sc o re for he morrhagic t ransformation in patients not submitted to reperfusion therapies (PROpHET). Methods: We created a multivariable logistic regression model to assess the prediction of Hemorrhage Transformation (HT) for acute ischemic strokes not treated with reperfusion therapy. One point was assigned for each of gender, cardio-aortic embolism, hyperdense middle cerebral artery sign, leukoaraiosis, hyperglycemia, 2 points for ASPECTS ≤7, and -3 points for lacunar syndrome. Acute ischemic stroke patients admitted to the Fortaleza Comprehensive Stroke Center in Brazil from 2015 to 2017 were randomly selected to the derivation cohort. The validation cohort included similar, but not randomized, cases from 5 Brazilian and one American Comprehensive Stroke Centers. Symptomatic cases were defined as NIHSS ≥4 at 24 hours after the event. Results from the derivation and validation cohorts were assessed with the area under the receiver operating characteristic curve (AUC-ROC). Results: From 2,432 of acute ischemic stroke screened in Fortaleza, 448 were prospectively selected for the derivation cohort and a 7-day follow-up. From 1,847 not selected, 577 underwent reperfusion therapy, 734 were excluded due to inadequate imaging or refusal of consent, and 538 whose data were obtained retrospectively and were selected only for the validation cohort. A score ≥3 had 78% sensitivity and 75% specificity, AUC-ROC 0.82 for all cases of HT, Hosmer-Lemeshow 0.85, Brier Score 0.1, and AUC-ROC 0.83 for those with symptomatic HT. An AUC-ROC of 0.84 was found for the validation cohort of 1,910 from all 6 centers, and a score ≥3 was found in 65% of patients with HT against 11.3% of those without HT. In comparison with 8 published predictive scores of HT, PROpHET was the most accurate (p < 0.01). Conclusions: PROpHET offers a tool simple, quick and easy-to-perform to estimate risk stratification of HT in patients not submitted to RT. A digital version of PROpHET is available in www.score-prophet.com Classification of evidence: This study provides Class I evidence from prospective data acquisition.

scholarly journals The development and validation of prognostic models for overall survival in the presence of missing data in the training dataset: a strategy with a detailed example

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Kara-Louise Royle ◽  
David A. Cairns
Keyword(s):  
Overall Survival ◽  
Missing Data ◽  
Prognostic Model ◽  
Prognostic Index ◽  
Risk Groups ◽  
Prognostic Models ◽  
Training Dataset ◽  
Validation Process ◽  
Test Dataset ◽  
Development And Validation

Abstract Background The United Kingdom Myeloma Research Alliance (UK-MRA) Myeloma Risk Profile is a prognostic model for overall survival. It was trained and tested on clinical trial data, aiming to improve the stratification of transplant ineligible (TNE) patients with newly diagnosed multiple myeloma. Missing data is a common problem which affects the development and validation of prognostic models, where decisions on how to address missingness have implications on the choice of methodology. Methods Model building The training and test datasets were the TNE pathways from two large randomised multicentre, phase III clinical trials. Potential prognostic factors were identified by expert opinion. Missing data in the training dataset was imputed using multiple imputation by chained equations. Univariate analysis fitted Cox proportional hazards models in each imputed dataset with the estimates combined by Rubin’s rules. Multivariable analysis applied penalised Cox regression models, with a fixed penalty term across the imputed datasets. The estimates from each imputed dataset and bootstrap standard errors were combined by Rubin’s rules to define the prognostic model. Model assessment Calibration was assessed by visualising the observed and predicted probabilities across the imputed datasets. Discrimination was assessed by combining the prognostic separation D-statistic from each imputed dataset by Rubin’s rules. Model validation The D-statistic was applied in a bootstrap internal validation process in the training dataset and an external validation process in the test dataset, where acceptable performance was pre-specified. Development of risk groups Risk groups were defined using the tertiles of the combined prognostic index, obtained by combining the prognostic index from each imputed dataset by Rubin’s rules. Results The training dataset included 1852 patients, 1268 (68.47%) with complete case data. Ten imputed datasets were generated. Five hundred twenty patients were included in the test dataset. The D-statistic for the prognostic model was 0.840 (95% CI 0.716–0.964) in the training dataset and 0.654 (95% CI 0.497–0.811) in the test dataset and the corrected D-Statistic was 0.801. Conclusion The decision to impute missing covariate data in the training dataset influenced the methods implemented to train and test the model. To extend current literature and aid future researchers, we have presented a detailed example of one approach. Whilst our example is not without limitations, a benefit is that all of the patient information available in the training dataset was utilised to develop the model. Trial registration Both trials were registered; Myeloma IX-ISRCTN68454111, registered 21 September 2000. Myeloma XI-ISRCTN49407852, registered 24 June 2009.

scholarly journals Log Odds of Positive Lymph Node- (LODDS-) Based Competing-Risk Nomogram for Predicting Prognosis of Resected Rectal Cancer: A Development and Validation Study

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Rui-zhe Zheng ◽  
Jiang Xie ◽  
Shui-qiang Zhang ◽  
Wen Li ◽  
Bo Dong ◽  
...  
Keyword(s):  
Rectal Cancer ◽  
Death Rate ◽  
Clinical Decision Making ◽  
Validation Cohort ◽  
Tnm Classification ◽  
Characteristic Curve ◽  
Tnm Stage ◽  
Competing Risk ◽  
Brier Score ◽  
Log Odds

Background and Aims. Cancer-specific survival (CSS) of rectal cancer (RC) is associated with several factors. We aimed to build an efficient competing-risk nomogram based on log odds of positive lymph nodes (LODDS) to predict RC survival. Methods. Medical records of 8754 patients were collected from the Surveillance, Epidemiology, and End Results (SEER) database, of 4895 patients from SEER during 2011–2014 and of 478 patients from an Eastern center as a development cohort, validation cohort, and test cohort, respectively. Univariate and multivariate competing-risk analyses were performed to build competing-risk nomogram for predicting the CSS of RC patients. Prediction efficacy was evaluated and compared with reference to the 8th TNM classification using the factor areas under the receiver operating characteristic curve (AUC) and Brier score. Results. The competing-risk nomogram was based on 6 variables: size, M stage, LODDS, T stage, grade, and age. The competing-risk nomogram showed a higher AUC value in predicting the 5-year death rate due to RC than the 8th TNM stage in the development cohort (0.81 vs. 0.76), validation cohort (0.85 vs. 0.82), and test cohort (0.71 vs. 0.66). The competing-risk nomogram also showed a higher Brier score in predicting the 5-year death rate due to RC than the 8th TNM stage in the development cohort (0.120 vs. 0.127), validation cohort (0.123 vs. 0.128), and test cohort (0.202 vs. 0.226). Conclusion. We developed and validated a competing-risk nomogram for RC death, which could provide the probability of survival averting competing risk to facilitate clinical decision-making.

scholarly journals Comparison of The Accuracy of Clinicians’ Prediction of Survival and Palliative Prognostic Score: An East Asian Cross-Cultural Study

2021 ◽  
Author(s):  
Yusuke Hiratsuka ◽  
Seok-Joon Yoon ◽  
Sang-Yeon Suh ◽  
Sung-Eun Choi ◽  
David Hui ◽  
...  
Keyword(s):  
Advanced Cancer ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Prognostic Score ◽  
East Asian ◽  
Prognostic Scores ◽  
Term Survival ◽  
Cross Cultural Study ◽  
Prediction Of Survival ◽  
Short Term Survival

Abstract Purpose:No study has been conducted to compare the clinicians’ prediction of survival (CPS) with Palliative Prognostic Scores (PaP) across countries. We aimed to compare the performance of the CPS in PaP (PaP-CPS), the PaP without the CPS, and the PaP total scores in patients with advanced cancer in three East Asian countries.Methods:We compared the discriminative accuracy of the three predictive models (the PaP-CPS [the score of the categorical CPS of PaP], the PaP without the CPS [sum of the scores of only the objective variables of PaP], and the PaP total score) in patients in Japan, Korea, and Taiwan. We calculated the area under the receiver operating characteristic curve (AUROC) for 30-day survival to compare the discriminative accuracy of these three models.Results:We analyzed 2,072 patients from three countries. The AUROC for the PaP total scores was 0.84 in patients in Japan, 0.76 in Korea, and 0.79 in Taiwan. The AUROC of the PaP-CPS was 0.82 in patients in Japan, 0.75 in Korea, and 0.78 in Taiwan. The AUROC of the PaP without the CPS was 0.75 in patients in Japan, 0.66 in Korea, and 0.67 in Taiwan.Conclusion:The PaP total scores and the PaP-CPS consistently showed similar discriminative accuracy in predicting 30-day survival in patients in Japan, Korea, and Taiwan. It may be sufficient for experienced clinicians to use the CPS alone for estimating the short-term survival (less than one month) of patients with far-advanced cancer. The PaP may help to improve prognostic confidence and further reduce subjective variations.

scholarly journals Prognostic impact of nutritional and inflammation-based risk scores in follicular lymphoma in the era of anti-CD20 targeted treatment strategies

2021 ◽  
Author(s):  
Niklas Gebauer ◽  
Britta Mengler ◽  
Svenja Kopelke ◽  
Alex Frydrychowicz ◽  
Alexander Fürschke ◽  
...  
Keyword(s):  
Follicular Lymphoma ◽  
Prognostic Score ◽  
Univariate Analysis ◽  
Prognostic Index ◽  
Glasgow Prognostic Score ◽  
Risk Scores ◽  
Study Cohort ◽  
Prognostic Impact ◽  
Who Grade ◽  
Neutrophil Lymphocyte Ratio

Abstract Background The composition of the tumor microenvironment (TME) is conditioned by immunity and the inflammatory response. Nutritional and inflammation-based risk scores have emerged as relevant predictors of survival outcome across a variety of hematological malignancies. Methods In this retrospective multicenter trial, we ascertained the prognostic impact of established nutritional and inflammation-based risk scores [Glasgow Prognostic Score (GPS), C-reactive–protein/albumin ratio (CAR), neutrophil–lymphocyte ratio (NLR), prognostic nutritional index (PNI), and prognostic index (PI)] in 209 eligible patients with histologically confirmed CD20+ follicular lymphoma (FL) of WHO grade 1 (37.3%), 1–2 (16.3%), 2 (26.8%) or 3A (19.8%) admitted to the participating centers between January 2000 and December 2019. Characteristics significantly associated with overall or progression-free survival (OS, PFS) upon univariate analysis were subsequently included in a Cox proportional hazard model. Results In the study cohort, the median age was 63 (range 22–90 years). The median follow-up period covered 99 months. The GPS and the CAR were identified to predict survival in FL patients. The GPS was the only independent predictor of OS (p < 0.0001; HR 2.773; 95% CI 1.630–4.719) and PFS (p = 0.001; HR 1.995; 95% CI 1.352–2.944) upon multivariate analysis. Additionally, there was frequent occurrence of progression of disease within 24 months (POD24) in FL patients with a calculated GPS of 2. Conclusion The current results indicate that the GPS predicts especially OS in FL patients. Moreover, GPS was found to display disease-specific effects in regard to FL progression. These findings and potential combinations with additional established prognosticators should be further validated within prospective clinical trials.

scholarly journals Simple Scoring Tool to Estimate Risk of Hospitalization and Mortality in Ambulatory and Emergency Department Patients with COVID-19

2021 ◽  
Author(s):  
Brandon J. Webb ◽  
Nicholas M. Levin ◽  
Nancy Grisel ◽  
Samuel M. Brown ◽  
Ithan D. Peltan ◽  
...  
Keyword(s):  
Monoclonal Antibodies ◽  
High Risk ◽  
Validation Cohort ◽  
Characteristic Curve ◽  
External Validation ◽  
Receiver Operator Characteristic Curve ◽  
Multivariable Logistic Regression Model ◽  
White Race ◽  
Derivation Cohort ◽  
Scoring Tool

AbstractBackgroundAccurate methods of identifying patients with COVID-19 who are at high risk of poor outcomes has become especially important with the advent of limited-availability therapies such as monoclonal antibodies. Here we describe development and validation of a simple but accurate scoring tool to classify risk of hospitalization and mortality.MethodsAll consecutive patients testing positive for SARS-CoV-2 from March 25-October 1, 2020 within the Intermountain Healthcare system were included. The cohort was randomly divided into 70% derivation and 30% validation cohorts. A multivariable logistic regression model was fitted for 14-day hospitalization. The optimal model was then adapted to a simple, probabilistic score and applied to the validation cohort and evaluated for prediction of hospitalization and 28-day mortality.Results22,816 patients were included; mean age was 40 years, 50.1% were female and 44% identified as non-white race or Hispanic/Latinx ethnicity. 6.2% required hospitalization and 0.4% died. Criteria in the simple model included: age (0.5 points per decade); high-risk comorbidities (2 points each): diabetes mellitus, severe immunocompromised status and obesity (body mass index≥30); non-white race/Hispanic or Latinx ethnicity (2 points), and 1 point each for: male sex, dyspnea, hypertension, coronary artery disease, cardiac arrythmia, congestive heart failure, chronic kidney disease, chronic pulmonary disease, chronic liver disease, cerebrovascular disease, and chronic neurologic disease. In the derivation cohort (n=16,030) area under the receiver-operator characteristic curve (AUROC) was 0.82 (95% CI 0.81-0.84) for hospitalization and 0.91 (0.83-0.94) for 28-day mortality; in the validation cohort (n=6,786) AUROC for hospitalization was 0.8 (CI 0.78-0.82) and for mortality 0.8 (CI 0.69-0.9).ConclusionA prediction score based on widely available patient attributes accurately risk stratifies patients with COVID-19 at the time of testing. Applications include patient selection for therapies targeted at preventing disease progression in non-hospitalized patients, including monoclonal antibodies. External validation in independent healthcare environments is needed.

scholarly journals Development and validation of the MMCD score to predict kidney replacement therapy in COVID-19 patients

2022 ◽  
Author(s):  
Flavio Azevedo Figueiredo ◽  
Lucas Emanuel Ferreira Ramos ◽  
Rafael Tavares Silva ◽  
Magda Carvalho Pires ◽  
Daniela Ponce ◽  
...  
Keyword(s):  
Mechanical Ventilation ◽  
Replacement Therapy ◽  
Demographic Data ◽  
Characteristic Curve ◽  
Prognostic Score ◽  
Additive Models ◽  
Brier Score ◽  
Derivation Cohort ◽  
Icu Admission ◽  
Kidney Replacement Therapy

Background: Acute kidney injury (AKI) is frequently associated with COVID–19 and the need for kidney replacement therapy (KRT) is considered an indicator of disease severity. This study aimed to develop a prognostic score for predicting the need for KRT in hospitalized COVID–19 patients. Methods: This study is part of the multicentre cohort, the Brazilian COVID–19 Registry. A total of 5,212 adult COVID–19 patients were included between March/2020 and September/2020. We evaluated four categories of predictor variables: (1) demographic data; (2) comorbidities and conditions at admission; (3) laboratory exams within 24 h; and (4) the need for mechanical ventilation at any time during hospitalization. Variable selection was performed using generalized additive models (GAM) and least absolute shrinkage and selection operator (LASSO) regression was used for score derivation. The accuracy was assessed using the area under the receiver operating characteristic curve (AUCROC). Risk groups were proposed based on predicted probabilities: non-high (up to 14.9%), high (15.0 to 49.9%), and very high risk (≥ 50.0%). Results: The median age of the model–derivation cohort was 59 (IQR 47–70) years, 54.5% were men, 34.3% required ICU admission, 20.9% evolved with AKI, 9.3% required KRT, and 15.1% died during hospitalization. The validation cohort had similar age, sex, ICU admission, AKI, required KRT distribution and in–hospital mortality. Thirty–two variables were tested and four important predictors of the need for KRT during hospitalization were identified using GAM: need for mechanical ventilation, male gender, higher creatinine at admission, and diabetes. The MMCD score had excellent discrimination in derivation (AUROC = 0.929; 95% CI 0.918–0.939) and validation (AUROC = 0.927; 95% CI 0.911–0.941) cohorts an good overall performance in both cohorts (Brier score: 0.057 and 0.056, respectively). The score is implemented in a freely available online risk calculator (https://www.mmcdscore.com/). Conclusion: The use of the MMCD score to predict the need for KRT may assist healthcare workers in identifying hospitalized COVID–19 patients who may require more intensive monitoring, and can be useful for resource allocation.

scholarly journals Prognostic value of inflammation-based markers in advanced or metastatic neuroendocrine tumours

2019 ◽  
Vol 26 (1) ◽  
Author(s):  
J. Zou ◽  
Q. Li ◽  
F. Kou ◽  
Y. Zhu ◽  
M. Lu ◽  
...  
Keyword(s):  
Neuroendocrine Tumours ◽  
Multivariate Analyses ◽  
Prognostic Score ◽  
Univariate Analysis ◽  
White Blood Cells ◽  
Prognostic Index ◽  
Glasgow Prognostic Score ◽  
High Sensitivity ◽  
Prognostic Scores ◽  
Inflammatory Factors

Background The role of systemic inflammation–based markers remains uncertain in advanced or metastatic neuroendocrine tumours (nets).Methods Systemic inflammatory factors, such as levels of circulating white blood cells and other blood components, were combined to yield inflammation-based prognostic scores [high-sensitivity inflammation-based Glasgow prognostic score (hsgps), neutrophil:lymphocyte ratio (nlr), platelet:lymphocyte ratio (plr), high-sensitivity inflammation-based prognostic index (hspi), and prognostic nutritional index (pni)], whose individual values as prognostic markers were retrospectively determined. Univariate and multivariate analyses were used to examine the association of inflammatory markers with overall survival (os).Results The study included 135 patients. Univariate analysis revealed that elevated white blood cell count, elevated neutrophil count, low serum albumin, elevated high-sensitivity C-reactive protein, and elevated hspi, hsgps, and nlr scores were significantly associated with worse os. Multivariate analyses demonstrated that, apart from pathology grade and original site of the tumour, elevated hspi (p = 0.004) was an independent prognostic factor for worse os.Conclusions In the present study, elevated pretreatment hspi was observed to be an independent predictor of shorter os in patients with inoperable advanced or metastatic net. The hspi might thus provide additional guidance for therapeutic decision-making in such patients.

scholarly journals Predicting survival in patients with advanced cancer in the last weeks of life: How accurate are prognostic models compared to clinicians’ estimates?

2019 ◽  
Vol 34 (1) ◽  
pp. 126-133 ◽  
Author(s):  
David Hui ◽  
Jeremy Ross ◽  
Minjeong Park ◽  
Rony Dev ◽  
Marieberta Vidal ◽  
...  
Keyword(s):  
Palliative Care ◽  
Advanced Cancer ◽  
Prognostic Score ◽  
Prognostic Index ◽  
Prognostic Scores ◽  
Concordance Index ◽  
Prognostic Accuracy ◽  
Prediction Of Survival ◽  
Palliative Prognostic Index ◽  
Performance Scale

Background: It is unclear if validated prognostic scores such as the Palliative Performance Scale, Palliative Prognostic Index, and Palliative Prognostic Score are more accurate than clinician prediction of survival in patients admitted to an acute palliative care unit with only days of survival. Aim: We compared the prognostic accuracy of Palliative Performance Scale, Palliative Prognostic Index, Palliative Prognostic Score, and clinician prediction of survival in this setting. Design: This is a pre-planned secondary analysis of a prospective study. Setting/participants: We assessed Palliative Performance Scale, Palliative Prognostic Index, Palliative Prognostic Score, and clinician prediction of survival at baseline. We computed their prognostic accuracy using the Concordance index and area under the receiver operating characteristics curve for 7-, 14-, and 30-day survival. Results: A total of 204 patients were included with a median overall survival of 10 days (95% confidence interval: 8–11 days). The Concordance index for Palliative Performance Scale, Palliative Prognostic Index, Palliative Prognostic Score, and clinician prediction of survival were 0.74, 0.71, 0.70, and 0.75, respectively. The areas under the curve for these approaches were 0.82–0.87 for 30-day survival, 0.75–0.80 for 14-day survival, and 0.74–0.81 for 7-day survival. The four prognostic approaches had similar accuracies, with the exception of 7-day survival in which clinician prediction of survival was significantly more accurate than Palliative Prognostic Score (difference: 7%) and Palliative Prognostic Index (difference: 8%). Conclusion: In patients with advanced cancer with days of survival, clinician prediction of survival and Palliative Performance Scale alone were as accurate as Palliative Prognostic Score and Palliative Prognostic Index. These four approaches may be useful for prognostication in acute palliative care units. Our findings highlight how patient population may impact the accuracy of prognostic scores.

scholarly journals Examination of a Short-Term, Prognostic Predictive Method for Terminal Cancer Patients Using the Barthel Index

Reports ◽  
2020 ◽  
Vol 3 (3) ◽  
pp. 26
Author(s):  
Masahiro Okada ◽  
Kazuko Okazaki ◽  
Fumiyoshi Murakami ◽  
Shinya Okamoto ◽  
Hiroki Sugihara ◽  
...  
Keyword(s):  
Cancer Patients ◽  
Barthel Index ◽  
Characteristic Curve ◽  
Prognostic Score ◽  
Prognostic Index ◽  
Glasgow Prognostic Score ◽  
Terminal Cancer ◽  
Short Term ◽  
Blood Tests ◽  
Terminal Cancer Patients

For the estimation of short-term prognosis in terminal cancer patients, it is important to establish a prognostic index that does not involve blood tests. We compared the prognostic ability of the Barthel Index (BI) with the Glasgow Prognostic Score (GPS). Ninety-seven inpatients with terminal cancer at Onomichi Municipal Hospital who died between 2018 and 2019 were retrospectively analyzed. The sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUROC) were compared between the BI and GPS. For predicting the 15 day prognosis, the BI showed higher specificity, accuracy, and AUROC than the GPS. For predicting the 30 day prognosis, the BI showed higher sensitivity, accuracy, and AUROC than the GPS. The BI can predict the 15 or 30 day prognosis in terminal cancer patients. As the BI does not require blood tests, it may be an option for prognostic prediction in terminal cancer patients.

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