Acute exacerbation of rheumatoid arthritis-associated interstitial lung disease: mortality and its prediction model.

BACKGROUND: Patients with rheumatoid arthritis-associated interstitial lung disease (RA-ILD), like those with idiopathic pulmonary fibrosis (IPF), might develop an unexpected acute exacerbation (AE)-a rapidly progressing and deadly respiratory decline. Although AE incidence and risk factors in RA-ILD patients are known, their post-AE clinical course remains unknown owing to the rarity of AE-RA-ILD. This multicentre retrospective study evaluated post-AE mortality and prognostic variables in AE-RA-ILD patients and created a mortality prediction model for AE-RA-ILD. METHODS: This research comprised 58 patients with AE-RA-ILD and 96 with AE-IPF (a control disease). Multivariate Cox regression analysis was performed to identify prognostic variables. A prediction model was created with recursive partitioning (decision tree). RESULTS: The post-AE 90-day mortality rate in the overall AE-RA-ILD group was 48.3%; percent predicted forced vital capacity within 12 months before AE onset (baseline %FVC) and PaO2/FiO2 ratio at AE onset (P/F at AE) were independent predictors of mortality. Post-AE 90-day mortality rates were 40.6% and 43.8%, respectively, in AE-RA-ILD and AE-IPF patients propensity score-matched for age, sex, baseline %FVC and P/F at AE (P = 1.0000). In AE-RA-ILD patients, C-indices of baseline %FVC and P/F at AE to predict post-AE 90-day mortality were 0.604 and 0.623, respectively. A decision tree model based on these prognostic factors classified AE-RA-ILD patients into mild, moderate and severe groups (post-AE 90-day mortality rates: 20.8%, 64.0% and 88.9%, respectively; P = 0.0002); the C-index improved to 0.775. CONCLUSIONS: Post-AE mortality was high in AE-RA-ILD patients similar to AE-IPF patients. The discovered prognostic factors and our mortality prediction model may aid in the management of AE-RA-ILD patients.

Do functional status and Medicare claims data improve the predictive accuracy of an electronic health record mortality index? Findings from a national Veterans Affairs cohort.

BACKGROUND: Electronic health record (EHR) prediction models may be easier to use in busy clinical settings since EHR data can be auto-populated into models. This study assessed whether adding functional status and/or Medicare claims data (which are often not available in EHRs) improves the accuracy of a previously developed Veterans Affairs (VA) EHR-based mortality index. METHODS: This was a retrospective cohort study of veterans aged 75 years and older enrolled in VA primary care clinics followed from January 2014 to April 2020 (n = 62,014). We randomly split participants into development (n = 49,612) and validation (n = 12,402) cohorts. The primary outcome was all-cause mortality. We performed logistic regression with backward stepwise selection to develop a 100-predictor base model using 854 EHR candidate variables, including demographics, laboratory values, medications, healthcare utilization, diagnosis codes, and vitals. We incorporated functional measures in a base + function model by adding activities of daily living (range 0-5) and instrumental activities of daily living (range 0-7) scores. Medicare data, including healthcare utilization (e.g., emergency department visits, hospitalizations) and diagnosis codes, were incorporated in a base + Medicare model. A base + function + Medicare model included all data elements. We assessed model performance with the c-statistic, reclassification metrics, fraction of new information provided, and calibration plots. RESULTS: In the overall cohort, mean age was 82.6 years and 98.6% were male. At the end of follow-up, 30,263 participants (48.8%) had died. The base model c-statistic was 0.809 (95% CI 0.805-0.812) in the development cohort and 0.804 (95% CI 0.796-0.812) in the validation cohort. Validation cohort c-statistics for the base + function, base + Medicare, and base + function + Medicare models were 0.809 (95% CI 0.801-0.816), 0.811 (95% CI 0.803-0.818), and 0.814 (95% CI 0.807-0.822), respectively. Adding functional status and Medicare data resulted in similarly small improvements among other model performance measures. All models showed excellent calibration. CONCLUSIONS: Incorporation of functional status and Medicare data into a VA EHR-based mortality index led to small but likely clinically insignificant improvements in model performance.

Mortality Prediction Score for Hirschsprung's Disease-Associated Enterocolitis: A Novel Mortality Prediction Model.

Introduction: Enterocolitis associated with Hirschsprung's disease is a fatal and serious complication. Number of scoring systems are in vogue to grade the severity of Hirschsprung's disease associated with enterocolitis (HDAEC), but none of these scoring systems help predict mortality. Hence, we attempt to develop a mortality prediction model (MPM) for HDAEC. Materials and Methods: A retrospective analysis of all cases of HDAEC encountered was analyzed. We also used the parameters of Elhalaby et al. for data collection. A total number of 71 cases were analyzed with regard to mortality in relation to each parameter. Sensitivity and specificity were calculated by statistician, and based on these values, a scoring model was proposed. All those with predicted mortality were given score 2 and those who did not were given score 1. Results: A total score of more than 16 predicted mortality, a score of <10 predicted survival, and a score between 11 and 15 predicted survival with morbidity. Conclusion: A MPM for HDAEC is being proposed.

Development and Validation of a 30-Day In-hospital Mortality Model Among Seriously Ill Transferred Patients: a Retrospective Cohort Study.

BACKGROUND: Predicting the risk of in-hospital mortality on admission is challenging but essential for risk stratification of patient outcomes and designing an appropriate plan-of-care, especially among transferred patients. OBJECTIVE: Develop a model that uses administrative and clinical data within 24 h of transfer to predict 30-day in-hospital mortality at an Academic Health Center (AHC). DESIGN: Retrospective cohort study. We used 30 putative variables in a multiple logistic regression model in the full data set (n = 10,389) to identify 20 candidate variables obtained from the electronic medical record (EMR) within 24 h of admission that were associated with 30-day in-hospital mortality (p < 0.05). These 20 variables were tested using multiple logistic regression and area under the curve (AUC)-receiver operating characteristics (ROC) analysis to identify an optimal risk threshold score in a randomly split derivation sample (n = 5194) which was then examined in the validation sample (n = 5195). PARTICIPANTS: Ten thousand three hundred eighty-nine patients greater than 18 years transferred to the Indiana University (IU)-Adult Academic Health Center (AHC) between 1/1/2016 and 12/31/2017. MAIN MEASURES: Sensitivity, specificity, positive predictive value, C-statistic, and risk threshold score of the model. KEY RESULTS: The final model was strongly discriminative (C-statistic = 0.90) and had a good fit (Hosmer-Lemeshow goodness-of-fit test [X2 (8) =6.26, p = 0.62]). The positive predictive value for 30-day in-hospital death was 68%; AUC-ROC was 0.90 (95% confidence interval 0.89-0.92, p < 0.0001). We identified a risk threshold score of -2.19 that had a maximum sensitivity (79.87%) and specificity (85.24%) in the derivation and validation sample (sensitivity: 75.00%, specificity: 85.71%). In the validation sample, 34.40% (354/1029) of the patients above this threshold died compared to only 2.83% (118/4166) deaths below this threshold. CONCLUSION: This model can use EMR and administrative data within 24 h of transfer to predict the risk of 30-day in-hospital mortality with reasonable accuracy among seriously ill transferred patients.

Financial Estimation of the Uncertainty in Medicine Using Present Value of Medical Fees and a Mortality Risk Prediction Model: a Retrospective Cohort Study.

This study aimed to develop a method to enable the financial estimation of each patient's uncertainty without focusing on healthcare technology. We define financial uncertainty (FU) as the difference between an actual amount of claim (AC) and the discounted present value of the AC (DAC). DAC can be calculated based on a discounted present value calculated using a cash flow, a period of investment, and a discount rate. The present study considered these three items as AC, the length of hospital stay, and the predicted mortality rate. The mortality prediction model was built using typical data items in standard level electronic medical records such as sex, age, and disease information. The performance of the prediction model was moderate because an area under curve was approximately 85%. The empirical analysis primarily compares the FU of the top 20 diseases with the actual AC using a retrospective cohort in the University of Miyazaki Hospital. The observational period is 5 years, from April 1, 2013, to March 31, 2018. The analysis demonstrates that the proportion of FU to actual AC is higher than 20% in low-weight children, patients with leukemia, brain tumor, myeloid leukemia, or non-Hodgkin's lymphoma. For these diseases, patients cannot avoid long hospitalization; therefore, the medical fee payment system should be designed based on uncertainty. Our method is both practical and generalizable because it uses a small number of data items that are required in standard electronic medical records. This method contributes to the decision-making processes of health policymakers.

The 4-Hour Cairns Sepsis Model: A novel approach to predicting sepsis mortality at intensive care unit admission.

BACKGROUND: Sepsis commonly causes intensive care unit (ICU) mortality, yet early identification of adults with sepsis at risk of dying in the ICU remains a challenge. OBJECTIVE: The aim of the study was to derive a mortality prediction model (MPM) to assist ICU clinicians and researchers as a clinical decision support tool for adults with sepsis within 4 h of ICU admission. METHODS: A cohort study was performed using 500 consecutive admissions between 2014 and 2018 to an Australian tertiary ICU, who were aged ≥18 years and had sepsis. A total of 106 independent variables were assessed against ICU episode-of-care mortality. Multivariable backward stepwise logistic regression derived an MPM, which was assessed on discrimination, calibration, fit, sensitivity, specificity, and predictive values and bootstrapped. RESULTS: The average cohort age was 58 years, the Acute Physiology and Chronic Health Evaluation III-j severity score was 72, and the case fatality rate was 12%. The 4-Hour Cairns Sepsis Model (CSM-4) consists of age, history of renal disease, number of vasopressors, Glasgow Coma Scale, lactate, bicarbonate, aspartate aminotransferase, lactate dehydrogenase, albumin, and magnesium with an area under the receiver operating characteristic curve of 0.90 (95% confidence interval = 0.84-0.95, p < 0.00001), a Nagelkerke R2 of 0.51, specificity of 0.94, a negative predictive value of 0.98, and almost identical odds ratios during bootstrapping. The CSM-4 outperformed existing MPMs tested on our data set. The CSM-4 also performed similar to existing MPMs in their derivation papers whilst using fewer, routinely collected, and inexpensive variables. CONCLUSIONS: The CSM-4 is a newly derived MPM for adults with sepsis at ICU admission. It displays excellent discrimination, calibration, fit, specificity, negative predictive value, and bootstrapping values whilst being easy to use and inexpensive. External validation is required.

Mortality prediction models in the adult critically ill: A scoping review.

BACKGROUND: Mortality prediction models are applied in the intensive care unit (ICU) to stratify patients into different risk categories and to facilitate benchmarking. To ensure that the correct prediction models are applied for these purposes, the best performing models must be identified. As a first step, we aimed to establish a systematic review of mortality prediction models in critically ill patients. METHODS: Mortality prediction models were searched in four databases using the following criteria: developed for use in adult ICU patients in high-income countries, with mortality as primary or secondary outcome. Characteristics and performance measures of the models were summarized. Performance was presented in terms of discrimination, calibration and overall performance measures presented in the original publication. RESULTS: In total, 43 mortality prediction models were included in the final analysis. In all, 15 models were only internally validated (35%), 13 externally (30%) and 10 (23%) were both internally and externally validated by the original researchers. Discrimination was assessed in 42 models (98%). Commonly used calibration measures were the Hosmer-Lemeshow test (60%) and the calibration plot (28%). Calibration was not assessed in 11 models (26%). Overall performance was assessed in the Brier score (19%) and the Nagelkerke's R2 (4.7%). CONCLUSIONS: Mortality prediction models have varying methodology, and validation and performance of individual models differ. External validation by the original researchers is often lacking and head-to-head comparisons are urgently needed to identify the best performing mortality prediction models for guiding clinical care and research in different settings and populations.

Identifying predictors for comorbidities related mortality versus pancreatic cancer related mortality in patients with intraductal papillary mucinous neoplasm.

BACKGROUNDS: Few data are available for surveillance decisions focusing on factors related to mortality, as the primary outcome, in intraductal papillary mucinous neoplasm (IPMN) patients. AIMS: We aimed to identify imaging features and patient backgrounds associated with mortality risks by comparing pancreatic cancer (PC) and comorbidities. METHODS: We retrospectively conducted a multicenter long-term follow-up of 1864 IPMN patients. Competing risk analysis was performed for PC- and comorbidity-related mortality. RESULTS: During the median follow-up period of 5.5 years, 14.0% (261/1864) of patients died. Main pancreatic duct ≥5 mm and mural nodules were significantly related to all-cause and PC-related mortality, whereas cyst ≥30 mm did not relate. In 1730 patients without high-risk imaging features, 48 and 180 patients died of PC and comorbidity. In the derivation cohort, a prediction model for comorbidity-related mortality was created, comprising age, cancer history, diabetes mellitus complications, chronic heart failure, stroke, paralysis, peripheral artery disease, liver cirrhosis, and collagen disease in multivariate analysis. If a patient had a 5 score, 5- and 10-year comorbidity-related mortality is estimated at 18.9% and 50.2%, respectively, more than 7 times higher than PC-related mortality. The model score was also significantly associated with comorbidity-related mortality in a validation cohort. CONCLUSIONS: This study demonstrates main pancreatic duct dilation and mural nodules indicate risk of PC-related mortality, identifying patients who need periodic examination. A comorbidity-related mortality prediction model based on the patient's age and comorbidities can stratify patients who do not require regular tests, especially beyond 5 years, among IPMN patients without high-risk features. CLINICAL TRIAL REGISTRATION: T2022-0046.

Development and Validation of a Nomogram for Predicting 28-Day Mortality on Admission in Elderly Patients with Severe Community-Acquired Pneumonia.

Introduction: There were few studies on the mortality of severe community-acquired pneumonia (SCAP) in elderly people. Early prediction of 28-day mortality of hospitalized patients will help in the clinical management of elderly patients (age ≥65 years) with SCAP, but a prediction model that is reliable and valid is still lacking. Methods: The 292 elderly patients with SCAP met the criteria defined by the American Thoracic Society from 33 hospitals in China. Clinical parameters were analyzed by the use of univariable and multivariable logistic regression analysis. A nomogram to predict the 28-day mortality in elderly patients with SCAP was constructed and evaluated using the area under the receiver operating characteristic curve (AUC) and internally verified using the Bootstrap method. Results: A total of 292 elderly patients (227 surviving and 65 died within 28 days) were included in the analysis. Age, Glasgow score, blood platelet, and blood urea nitrogen values were found to be significantly associated with 28-day mortality in elderly patients with SCAP. The AUC of the nomogram was 0.713 and the calibration curve for 28-day mortality also showed high coherence between the predicted and actual probability of mortality. Conclusion: This study provides a nomogram containing age, Glasgow score, blood platelet, and blood urea nitrogen values that can be conveniently used to predict 28-day mortality in elderly patients with SCAP. This model has the potential to assist clinicians in evaluating prognosis of patients with SCAP.

Individual outcome prediction models for patients with COVID-19 based on their first day of admission to the intensive care unit.

BACKGROUND: Currently, good prognosis and management of critically ill patients with COVID-19 are crucial for developing disease management guidelines and providing a viable healthcare system. We aimed to propose individual outcome prediction models based on binary logistic regression (BLR) and artificial neural network (ANN) analyses of data collected in the first 24 h of intensive care unit (ICU) admission for patients with COVID-19 infection. We also analysed different variables for ICU patients who survived and those who died. METHODS: Data from 326 critically ill patients with COVID-19 were collected. Data were captured on laboratory variables, demographics, comorbidities, symptoms and hospital stay related information. These data were compared with patient outcomes (survivor and non-survivor patients). BLR was assessed using the Wald Forward Stepwise method, and the ANN model was constructed using multilayer perceptron architecture. RESULTS: The area under the receiver operating characteristic curve of the ANN model was significantly larger than the BLR model (0.917 vs 0.810; p < 0.001) for predicting individual outcomes. In addition, ANN model presented similar negative predictive value than the BLR model (95.9% vs 94.8%). Variables such as age, pH, potassium ion, partial pressure of oxygen, and chloride were present in both models and they were significant predictors of death in COVID-19 patients. CONCLUSIONS: Our study could provide helpful information for other hospitals to develop their own individual outcome prediction models based, mainly, on laboratory variables. Furthermore, it offers valuable information on which variables could predict a fatal outcome for ICU patients with COVID-19.

Reliable and Interpretable Mortality Prediction With Strong Foresight in COVID-19 Patients: An International Study From China and Germany.

Cohort-independent robust mortality prediction model in patients with COVID-19 infection is not yet established. To build up a reliable, interpretable mortality prediction model with strong foresight, we have performed an international, bi-institutional study from China (Wuhan cohort, collected from January to March) and Germany (Würzburg cohort, collected from March to September). A Random Forest-based machine learning approach was applied to 1,352 patients from the Wuhan cohort, generating a mortality prediction model based on their clinical features. The results showed that five clinical features at admission, including lymphocyte (%), neutrophil count, C-reactive protein, lactate dehydrogenase, and α-hydroxybutyrate dehydrogenase, could be used for mortality prediction of COVID-19 patients with more than 91% accuracy and 99% AUC. Additionally, the time-series analysis revealed that the predictive model based on these clinical features is very robust over time when patients are in the hospital, indicating the strong association of these five clinical features with the progression of treatment as well. Moreover, for different preexisting diseases, this model also demonstrated high predictive power. Finally, the mortality prediction model has been applied to the independent Würzburg cohort, resulting in high prediction accuracy (with above 90% accuracy and 85% AUC) as well, indicating the robustness of the model in different cohorts. In summary, this study has established the mortality prediction model that allowed early classification of COVID-19 patients, not only at admission but also along the treatment timeline, not only cohort-independent but also highly interpretable. This model represents a valuable tool for triaging and optimizing the resources in COVID-19 patients.

The Japanese Intensive care PAtient Database (JIPAD): A national intensive care unit registry in Japan.

PURPOSE: The Japanese Intensive care PAtient Database (JIPAD) was established to construct a high-quality Japanese intensive care unit (ICU) database. MATERIALS AND METHODS: A data collection structure for consecutive ICU admissions in adults (≥16 years) and children (≤15 years) has been established in Japan since 2014. We herein report a current summary of the data in JIPAD for admissions between April 2015 and March 2017. RESULTS: There were 21,617 ICU admissions from 21 ICUs (217 beds) including 8416 (38.9%) for postoperative or procedural monitoring, defined as adult admissions following elective surgery or for procedures and discharged alive within 24 h, 11,755 (54.4%) critically ill adults other than monitoring, and 1446 (6.7%) children. The standardized mortality ratios (SMRs) based on the Acute Physiology and Chronic Health Evaluation (APACHE) III-j, APACHE II, and Simplified Acute Physiology Score II scores in adults ranged from 0.387 to 0.534, whereas the SMR based on the Paediatric Index of Mortality 2 in children was 0.867. CONCLUSION: The data revealed that the SMRs based on general severity scores in adults were low because of high proportions of elective and monitoring admission. The development of a new mortality prediction model for Japanese ICU patients is needed.

Development and Validation of a Sepsis Mortality Risk Score for Sepsis-3 Patients in Intensive Care Unit.

Background: Many severity scores are widely used for clinical outcome prediction for critically ill patients in the intensive care unit (ICU). However, for patients identified by sepsis-3 criteria, none of these have been developed. This study aimed to develop and validate a risk stratification score for mortality prediction in sepsis-3 patients. Methods: In this retrospective cohort study, we employed the Medical Information Mart for Intensive Care III (MIMIC III) database for model development and the eICU database for external validation. We identified septic patients by sepsis-3 criteria on day 1 of ICU entry. The Least Absolute Shrinkage and Selection Operator (LASSO) technique was performed to select predictive variables. We also developed a sepsis mortality prediction model and associated risk stratification score. We then compared model discrimination and calibration with other traditional severity scores. Results: For model development, we enrolled a total of 5,443 patients fulfilling the sepsis-3 criteria. The 30-day mortality was 16.7%. With 5,658 septic patients in the validation set, there were 1,135 deaths (mortality 20.1%). The score had good discrimination in development and validation sets (area under curve: 0.789 and 0.765). In the validation set, the calibration slope was 0.862, and the Brier value was 0.140. In the development dataset, the score divided patients according to mortality risk of low (3.2%), moderate (12.4%), high (30.7%), and very high (68.1%). The corresponding mortality in the validation dataset was 2.8, 10.5, 21.1, and 51.2%. As shown by the decision curve analysis, the score always had a positive net benefit. Conclusion: We observed moderate discrimination and calibration for the score termed Sepsis Mortality Risk Score (SMRS), allowing stratification of patients according to mortality risk. However, we still require further modification and external validation.

Patient Management Assisted by a Neural Network Reduces Mortality in an Intermediate Care Unit.

INTRODUCTION: Mortality risk prediction for Intermediate Respiratory Care Unit's (IRCU) patients can facilitate optimal treatment in high-risk patients. While Intensive Care Units (ICUs) have a long term experience in using algorithms for this purpose, due to the special features of the IRCUs, the same strategics are not applicable. The aim of this study is to develop an IRCU specific mortality predictor tool using machine learning methods. METHODS: Vital signs of patients were recorded from 1966 patients admitted from 2007 to 2017 in the Jiménez Díaz Foundation University Hospital's IRCU. A neural network was used to select the variables that better predict mortality status. Multivariate logistic regression provided us cut-off points that best discriminated the mortality status for each of the parameters. A new guideline for risk assessment was applied and mortality was recorded during one year. RESULTS: Our algorithm shows that thrombocytopenia, metabolic acidosis, anemia, tachypnea, age, sodium levels, hypoxemia, leukocytopenia and hyperkalemia are the most relevant parameters associated with mortality. First year with this decision scene showed a decrease in failure rate of a 50%. CONCLUSIONS: We have generated a neural network model capable of identifying and classifying mortality predictors in the IRCU of a general hospital. Combined with multivariate regression analysis, it has provided us with an useful tool for the real-time monitoring of patients to detect specific mortality risks. The overall algorithm can be scaled to any type of unit offering personalized results and will increase accuracy over time when more patients are included to the cohorts.