Prediction Model For In-hospital Mortality Research Articles

External validation of the OPALS prediction model for in-hospital mortality in patients with acute decompensated pulmonary hypertension.

In this external validation of the OPALS prediction model for in-hospital mortality in patients with acute decompensated pulmonary hypertension admitted to the intensive care unit, discrimination was very good and similar to the derivation cohort https://bit.ly/3sa8oQy

Reply: External validation of the OPALS prediction model for in-hospital mortality in patients with acute decompensated pulmonary hypertension.

The OPALS score appears to be a promising PH-specific tool for predicting outcomes in medically decompensated patients https://bit.ly/3rTxzbr

Derivation and Validation of a Machine Learning-Based Risk Prediction Model for In-Hospital Mortality in Patients with Acute Heart Failure

Derivation and Validation of a Machine Learning-Based Risk Prediction Model for In-Hospital Mortality in Patients with Acute Heart Failure

External validation of the U-HIP prediction model for in-hospital mortality in geriatric hip fracture patients

IntroductionIdentification of high-risk hip fracture patients in an early stage is vital for guiding surgical management and shared decision making. To objective of this study was to perform an external international validation study of the U-HIP prediction model for in-hospital mortality in geriatric patients with a hip fracture undergoing surgery. Materials and methodsIn this retrospective cohort study, data were used from The American College of Surgeons National Surgical Quality Improvement Program. Patients aged 70 years or above undergoing hip fracture surgery were included. The discrimination (c-statistic) and calibration of the model were investigated. ResultsA total of 25,502 patients were included, of whom 618 (2.4%) died. The mean predicted probability of in-hospital mortality was 3.9% (range 0%-55%). The c-statistic of the model was 0.74 (95% CI 0.72–0.76), which was comparable to the c-statistic of 0.78 (95% CI 0.71–0.85) that was found in the development cohort. The calibration plot indicated that the model was slightly overfitted, with a calibration-in-the-large of 0.015 and a calibration slope of 0.780. Within the subgroup of patients aged between 70 and 85, however, the c-statistic was 0.78 (95% CI 0.75–0.81), with good calibration (calibration slope 0.934). Discussion and conclusionThe U-HIP model for in-hospital mortality in geriatric hip fractures was externally validated in a large international cohort, and showed a good discrimination and fair calibration. This model is freely available online and can be used to predict the risk of mortality, identify high-risk patients and aid clinical decision making.

Clinical features, risk factors and a prediction model for in-hospital mortality among diabetic patients infected with COVID-19: data from a referral centre in Iran

ObjectivesThe aim of this study was to identify risk factors of in-hospital mortality among diabetic patients infected with COVID-19. Study designThis is a retrospective cohort study. MethodsUsing logistic regression analysis, the independent association of potential prognostic factors and COVID-19 in-hospital mortality was investigated in three models. Model 1 included demographic data and patient history; model 2 consisted of model 1, plus vital signs and pulse oximetry measurements at hospital admission; and model 3 included model 2, plus laboratory test results at hospital admission. The odds ratios (ORs) and 95% confidence intervals (95% CIs) were reported for each predictor in the different models. Moreover, to examine the discriminatory powers of the models, a corrected area under the receiver-operating characteristic curve (AUC) was calculated. ResultsAmong 560 patients with diabetes (men = 291) who were hospitalised for COVID-19, the mean age of the study population was 61.8 (standard deviation [SD] 13.4) years. During a median length of hospitalisation of 6 days, 165 deaths (men = 93) were recorded. In model 1, age and a history of cognitive impairment were associated with higher mortality; however, taking statins, oral antidiabetic drugs and beta-blockers was associated with a lower risk of mortality (AUC = 0.76). In model 2, adding the data for respiratory rate (OR 1.07 [95% CI 1.00–1.14]) and oxygen saturation (OR 0.95 [95% CI 0.92–0.98]) slightly increased the AUC to 0.80. In model 3, the data for platelet count (OR 0.99 [95% CI 0.99–1.00]), lactate dehydrogenase (OR 1.002 [95% CI 1.001–1.003]), potassium (OR 2.02 [95% CI 1.33–3.08]) and fasting plasma glucose (OR 1.04 [95% CI 1.02–1.07]) significantly improved the discriminatory power of the model to AUC 0.86 (95% CI 0.83–0.90). ConclusionsAmong patients with type 2 diabetes, a combination of past medical and drug history and pulse oximetry data, with four non-expensive laboratory measures, was significantly associated with in-hospital COVID-19 mortality.

Prediction of in-hospital mortality of Clostriodiodes difficile infection using critical care database: a big data-driven, machine learning approach

Research objectivesClostriodiodes difficile infection (CDI) is a major cause of healthcare-associated diarrhoea with high mortality. There is a lack of validated predictors for severe outcomes in CDI. The aim of...

Predictive model for in-hospital mortality following cardiogenic shock

Abstract Introduction and objectives Cardiogenic shock is a condition caused by reduced cardiac output and hypotension, resulting in end-organ damage and multiorgan failure. Although prognosis has been improved in recent years, this state is still associated with high morbidity and mortality. The aim of our study was to perform a predictive model for in-hospital mortality that allows stratifying the risk of death in patients with cardiogenic shock. Methods This is a retrospective analysis from a prospective registry, that included 135 patients from one Spanish Universitary Hospital between 2011 and 2020. Multivariate analysis was performed among those variables with significant association with short-term outcome of univariate analysis with a p-value <0.2. Those variables which had a p-value >0.1 in the multivariable analysis were excluded of the final model. Our method was assessed using the area under the ROC-curve (AUC). Goodness of fit was tested using Hosmer-Lemeshow statistic test. Finally, we performed a risk score using the pondered weight of the coefficients of a simplified model created after categorizing the continuous quantitative variables included in the final model, giving a maximum of 16 points and creating three categories of risk. Results The in-hospital mortality rate was 41.5%, the average of age was 74.2 years, 35.6% were females and acute coronary syndrome (ACS) was the main cause of shock (60.7%). Mitral regurgitation (moderate-severe), age, ACS etiology, NT-proBNP, blood hemoglobin and lactate at admission were included in the final model. Risk-adjustment model had good accuracy in predicting in-hospital mortality (AUC 0.85; 95% CI 0,78–0,90) and the goodness of fit test was p-value>0.10. According to the risk score made with the simplified model, these patients were stratified into three categories: low (scores 0–6), intermediate (scores 7–10), and high (scores 11–16) risk with observed mortality of 12.9%, 49.1% and 87.5% respectively (p<0,001). Conclusions Our predictive model using six variables, shows good discernment for in-hospital mortality and the risk score has identified three groups with significant differences in prognosis. This model could help in guiding treatments and clinical decision-making, so it needs external validation and to be compared with other models already published. Funding Acknowledgement Type of funding sources: None. ROC curveRisk Score

Development and validation of the ABC2-SPH risk score for in-hospital mortality in COVID-19 patients

Abstract Introduction Fast and efficient assessment of prognosis of coronavirus disease 19 (COVID-19) is needed to optimize the allocation of health care and human resources, to empower early identification and intervention of patients at higher risk of poor outcome. A proper assessment tool may guide decision making, to develop an appropriate plan of care for each patient. Although different scores have been proposed, the majority of them are limited due to high risk of bias, and there is a lack of reliable prognostic prediction models. Purpose To develop and validate an easy applicable rapid scoring system that employs routinely available clinical and laboratory data at hospital presentation, to predict in-hospital mortality in patients with COVID-19, able to discriminate high vs non-high risk patients. Additionally, we aimed to compare this score with other existing ones. Method Cohort study, conducted in 36 Brazilian hospitals in 17 cities. Consecutive symptomatic patients (≥18 years old) with laboratory confirmed COVID-19 admitted to participating hospitals. Primary outcome was in-hospital mortality. Logistic regression analysis was performed to develop a prediction model for in-hospital mortality, based on the 3978 patients that were admitted between March-July, 2020. The model was then validated in the 1054 patients admitted during August-September, as well as in an external cohort of 474 Spanish patients. Results Median (25th-75th percentile) age of the model-derivation cohort was 60 (48–72) years, 53.8% were men, in-hospital mortality was 20.3%. The validation cohorts had similar age distribution and in-hospital mortality. From 20 potential predictors, seven significant variables were included in the in-hospital mortality risk score: age, blood urea nitrogen, number of comorbidities, C-reactive protein, SpO2/FiO2 ratio, platelet count and heart rate. The model had high discriminatory value (AUROC 0.844, 95% CI 0.829 to 0.859), which was confirmed in the Brazilian (0.859) and Spanish (0.899) validation cohorts. Our ABC2-SPH score showed good calibration in both Brazilian cohorts, but, in the Spanish cohort, mortality was somewhat underestimated in patients with very high (>25%) risk. The ABC2-SPH score is implemented in a freely available online risk calculator. Conclusions We designed and validated an easy-to-use rapid scoring system based on characteristics of COVID-19 patients commonly available at hospital presentation, for early stratification for in-hospital mortality risk of patients with COVID-19. Funding Acknowledgement Type of funding sources: Foundation. Main funding source(s): Minas Gerais State Agency for Research and Development (Fundação de Amparo à Pesquisa do Estado de Minas Gerais - FAPEMIG) [grant number APQ-00208-20], National Institute of Science and Technology for Health Technology Assessment (Instituto de Avaliação de Tecnologias em Saúde – IATS)/ National Council for Scientific and Technological Development (Conselho Nacional de Desenvolvimento Científico e Tecnolόgico - CNPq) [grant number 465518/2014-1]

The safety and efficacy of systemic versus catheter-based therapies: application of a prognostic model by a pulmonary embolism response team.

The decision by pulmonary embolism response teams (PERTs) to utilize anticoagulation (AC) with or without systemic thrombolysis (ST) or catheter-directed therapies (CDT) for pulmonary embolism (PE) is a balance between the desire for a positive outcome and safety. Our primary aim was to develop a predictive model of in-hospital mortality for patients with high- or intermediate-risk PE managed by PERT while externally validating this model. Our secondary aim was to compare the relative safety and efficacy of ST and CDT in this cohort. Consecutive patients hospitalized between June 2014 and January 2020 at the Cleveland Clinic Foundation and The University of Rochester with acute high- or intermediate-risk PE managed by PERT were retrospectively evaluated. Groups were stratified by treatment strategy. The primary outcome was in-hospital mortality, and secondary outcome was major bleeding. A logistic regression model to predict the primary outcome was built using the derivation cohort, with 100-fold bootstrapping for internal validation. External validation was performed and the area under the receiver operating curve (AUC) was calculated. Of 549 included patients, 421 received AC alone, 71 received ST, and 64 received CDT. Predictors of major bleeding include ESC risk category, PESI score, hypoxia, hemodynamic instability, and serum lactate. CDT trended towards lower mortality but with an increased risk of bleeding relative to ST (OR = 0.42; 95% CI [0.15, 1.17] and OR = 2.14; 95% CI [0.9, 5.06] respectively). In the multivariable logistic regression model in the derivation institution cohort, predictors of in-hospital mortality were age, cancer, hemodynamic instability requiring vasopressors, and elevated NT-proBNP (AUC = 0.86). This model was validated using the validation institution cohort (AUC = 0.88). We report an externally-validated model for predicting in-hospital mortality in patients with PE managed by PERT. The decision by PERT to initiate CDT or ST for these patients had no impact on mortality or major bleeding, yet the long-term efficacy of these interventions needs to be elucidated.

ABC2-SPH risk score for in-hospital mortality in COVID-19 patients: development, external validation and comparison with other available scores

ObjectivesThe majority of available scores to assess mortality risk of coronavirus disease 2019 (COVID-19) patients in the emergency department have high risk of bias. Therefore, this cohort aimed to develop and validate a score at hospital admission for predicting in-hospital mortality in COVID-19 patients and to compare this score with other existing ones. MethodsConsecutive patients (≥ 18 years) with confirmed COVID-19 admitted to the participating hospitals were included. Logistic regression analysis was performed to develop a prediction model for in-hospital mortality, based on the 3978 patients admitted between March–July, 2020. The model was validated in the 1054 patients admitted during August–September, as well as in an external cohort of 474 Spanish patients. ResultsMedian (25–75th percentile) age of the model-derivation cohort was 60 (48–72) years, and in-hospital mortality was 20.3%. The validation cohorts had similar age distribution and in-hospital mortality. Seven significant variables were included in the risk score: age, blood urea nitrogen, number of comorbidities, C-reactive protein, SpO2/FiO2 ratio, platelet count, and heart rate. The model had high discriminatory value (AUROC 0.844, 95% CI 0.829–0.859), which was confirmed in the Brazilian (0.859 [95% CI 0.833–0.885]) and Spanish (0.894 [95% CI 0.870–0.919]) validation cohorts, and displayed better discrimination ability than other existing scores. It is implemented in a freely available online risk calculator (https://abc2sph.com/). ConclusionsAn easy-to-use rapid scoring system based on characteristics of COVID-19 patients commonly available at hospital presentation was designed and validated for early stratification of in-hospital mortality risk of patients with COVID-19.

Machine Learning to Predict In-Hospital Mortality in COVID-19 Patients Using Computed Tomography-Derived Pulmonary and Vascular Features.

Pulmonary parenchymal and vascular damage are frequently reported in COVID-19 patients and can be assessed with unenhanced chest computed tomography (CT), widely used as a triaging exam. Integrating clinical data, chest CT features, and CT-derived vascular metrics, we aimed to build a predictive model of in-hospital mortality using univariate analysis (Mann–Whitney U test) and machine learning models (support vectors machines (SVM) and multilayer perceptrons (MLP)). Patients with RT-PCR-confirmed SARS-CoV-2 infection and unenhanced chest CT performed on emergency department admission were included after retrieving their outcome (discharge or death), with an 85/15% training/test dataset split. Out of 897 patients, the 229 (26%) patients who died during hospitalization had higher median pulmonary artery diameter (29.0 mm) than patients who survived (27.0 mm, p < 0.001) and higher median ascending aortic diameter (36.6 mm versus 34.0 mm, p < 0.001). SVM and MLP best models considered the same ten input features, yielding a 0.747 (precision 0.522, recall 0.800) and 0.844 (precision 0.680, recall 0.567) area under the curve, respectively. In this model integrating clinical and radiological data, pulmonary artery diameter was the third most important predictor after age and parenchymal involvement extent, contributing to reliable in-hospital mortality prediction, highlighting the value of vascular metrics in improving patient stratification.

Institutional case volume-incorporated mortality risk prediction model for cardiac surgery

Most risk prediction models predicting short-term mortality after cardiac surgery incorporate patient characteristics, laboratory data, and type of surgery, but do not account for surgical experience. Considering the impact of case volume on patient outcome after high-risk procedures, we attempted to develop a risk prediction model for mortality after cardiac surgery that incorporates institutional case volume. Adult patients who underwent cardiac surgery from 2009 to 2016 were identified. Patients who underwent cardiac surgery (n=57,804) were randomly divided into the derivation cohort (n=28,902) or the validation cohorts (n=28,902). A risk prediction model for in-hospital mortality and 1-year mortality was developed from the derivation cohort and the performance of the model was evaluated in the validation cohort. The model demonstrated fair discrimination (c-statistics, 0.76 for in-hospital mortality in both cohorts; 0.74 for 1-year mortality in both cohorts) and acceptable calibration. Hospitals were classified based on case volume into 50 or less, 50-100, 100-200, or more than 200 average cardiac surgery cases per year and case volume was a significant variable in the prediction model. A new risk prediction model that incorporates institutional case volume and accurately predicts in-hospital and 1-year mortality after cardiac surgery was developed and validated.

Two-stage prediction model for in-hospital mortality of patients with influenza infection

BackgroundInfleunza is a challenging issue in public health. The mortality and morbidity associated with epidemic and pandemic influenza puts a heavy burden on health care system. Most patients with influenza can be treated on an outpatient basis but some required critical care. It is crucial for frontline physicians to stratify influenza patients by level of risk. Therefore, this study aimed to create a prediction model for critical care and in-hospital mortality.MethodsThis retrospective cohort study extracted data from the Chang Gung Research Database. This study included the patients who were diagnosed with influenza between 2010 and 2016. The primary outcome of this study was critical illness. The secondary analysis was to predict in-hospital mortality. A two-stage-modeling method was developed to predict hospital mortality. We constructed a multiple logistic regression model to predict the outcome of critical illness in the first stage, then S1 score were calculated. In the second stage, we used the S1 score and other data to construct a backward multiple logistic regression model. The area under the receiver operating curve was used to assess the predictive value of the model.ResultsIn the present study, 1680 patients met the inclusion criteria. The overall ICU admission and in-hospital mortality was 10.36% (174 patients) and 4.29% (72 patients), respectively. In stage I analysis, hypothermia (OR = 1.92), tachypnea (OR = 4.94), lower systolic blood pressure (OR = 2.35), diabetes mellitus (OR = 1.87), leukocytosis (OR = 2.22), leukopenia (OR = 2.70), and a high percentage of segmented neutrophils (OR = 2.10) were associated with ICU admission. Bandemia had the highest odds ratio in the Stage I model (OR = 5.43). In stage II analysis, C-reactive protein (OR = 1.01), blood urea nitrogen (OR = 1.02) and stage I model’s S1 score were assocaited with in-hospital mortality. The area under the curve for the stage I and II model was 0.889 and 0.766, respectively.ConclusionsThe two-stage model is a efficient risk-stratification tool for predicting critical illness and mortailty. The model may be an optional tool other than qSOFA and SIRS criteria.

Personalized Prediction of Hospital Mortality in COVID-19-Positive Patients.

ObjectiveTo develop predictive models for in-hospital mortality and length of stay (LOS) for coronavirus disease 2019 (COVID-19)–positive patients.Patients and MethodsWe performed a multicenter retrospective cohort study of hospitalized COVID-19–positive patients. A total of 764 patients admitted to 14 different hospitals within the Cleveland Clinic from March 9, 2020, to May 20, 2020, who had reverse transcriptase-polymerase chain reaction–proven coronavirus infection were included. We used LightGBM, a machine learning algorithm, to predict in-hospital mortality at different time points (after 7, 14, and 30 days of hospitalization) and in-hospital LOS. Our final cohort was composed of 764 patients admitted to 14 different hospitals within our system.ResultsThe median LOS was 5 (range, 1-44) days for patients admitted to the regular nursing floor and 10 (range, 1-38) days for patients admitted to the intensive care unit. Patients who died during hospitalization were older, initially admitted to the intensive care unit, and more likely to be white and have worse organ dysfunction compared with patients who survived their hospitalization. Using the 10 most important variables only, the final model’s area under the receiver operating characteristics curve was 0.86 for 7-day, 0.88 for 14-day, and 0.85 for 30-day mortality in the validation cohort.ConclusionWe developed a decision tool that can provide explainable and patient-specific prediction of in-hospital mortality and LOS for COVID-19–positive patients. The model can aid health care systems in bed allocation and distribution of vital resources.

A Scoring Model to Predict In-Hospital Mortality in Patients With Budd-Chiari Syndrome.

A model that can predict short-term mortality in patients with the Budd-Chiari syndrome (BCS) with a high degree of accuracy is currently lacking. The primary objective of our study was to develop an easy-to-use in-hospital mortality prediction model in patients with BCS using easily available clinical variables. Data were extracted from the National Inpatient Sample to identify all adult patients with a listed diagnosis of BCS from 2008 to 2017 using ICD-9 or ICD-10 codes. After identifying independent risk factors of in-hospital mortality, we developed a prediction model using logistic regression analysis. The model was built and validated in a training and a validation data set, respectively. Using the model, we risk stratified patients into low-, intermediate-, and high-risk groups. Between 2008 and 2017, we identified a total of 5,306 (weighted sample size 26,110) discharge diagnosis of patients with BCS, with an overall in-hospital mortality of 7.14%. The independent risk factors that predicted mortality were age of 50 years or older, ascites, sepsis, acute respiratory failure, acute liver failure, hepatorenal syndrome, and cancers. The mortality prediction model that incorporated these risk factors had an area under the receiver operating characteristic curve of 0.87 (95% CI 0.85-0.95) for the training data and 0.89 (95% CI 0.86-0.92) for the validation data. Patients with low-, intermediate-, and high-risk scores had a predicted in-patient mortality of 4%, 30%, and 66%, respectively. Using a national administrative database, we developed a reliable in-patient mortality prediction model with an excellent accuracy. The model was able to risk stratify patients into low-, intermediate-, and high-risk groups.

Development and Validation of Risk Prediction Model for In-hospital Mortality Among Patients Hospitalized With Acute Exacerbation Chronic Obstructive Pulmonary Disease Between 2015 and 2019.

Background: In patients with chronic obstructive pulmonary disease (COPD), acute exacerbations affect patients' health and can lead to death. This study was aimed to develop a prediction model for in-hospital mortality in patients with acute exacerbations of COPD (AECOPD).Method: A retrospective study was performed in patients hospitalized for AECOPD between 2015 and 2019. Patients admitted between 2015 and 2017 were included to develop model and individuals admitted in the following 2 years were included for external validation. We analyzed variables that were readily available in clinical practice. Given that death was a rare outcome in this study, we fitted Firth penalized logistic regression. C statistic and calibration plot quantified the model performance. Optimism-corrected C statistic and slope were estimated by bootstrapping. Accordingly, the prediction model was adjusted and then transformed into risk score.Result: Between 2015 and 2017, 1,096 eligible patients were analyzed, with a mean age of 73 years and 67.8% male. The in-hospital mortality was 2.6%. Compared to survivors, non-survivors were older, more admitted from emergency, more frequently concomitant with respiratory failure, pneumothorax, hypoxic-hypercarbic encephalopathy, and had longer length of stay (LOS). Four variables were included into the final model: age, respiratory failure, pneumothorax, and LOS. In internal validation, C statistic was 0.9147, and the calibration slope was 1.0254. Their optimism-corrected values were 0.90887 and 0.9282, respectively, indicating satisfactory discrimination and calibration. When externally validated in 700 AECOPD patients during 2018 and 2019, the model demonstrated good discrimination with a C statistic of 0.8176. Calibration plot illustrated a varying discordance between predicted and observed mortality. It demonstrated good calibration in low-risk patients with predicted mortality rate ≤10% (P = 0.3253) but overestimated mortality in patients with predicted rate >10% (P < 0.0001). The risk score of 20 was regarded as a threshold with an optimal Youden index of 0.7154.Conclusion: A simple prediction model for AECOPD in-hospital mortality has been developed and externally validated. Based on available data in clinical setting, the model could serve as an easily used instrument for clinical decision-making. Complications emerged as strong predictors, underscoring an important role of disease management in improving patients' prognoses during exacerbation episodes.

A Retrospective Review of Outcomes in Intensive Care Unit Patients Infected With SARS-Cov2 in Correlation to Admission Acute Physiologic Assessment and Chronic Health Evaluation II Scores.

IntroductionCoronavirus disease 2019 (COVID-19) has emerged as a global pandemic that has placed an unprecedented burden on intensive care services worldwide. Identification of a reliable risk-stratification tool for COVID-19 patients is necessary for appropriate resource allocation, selection of clinical management pathways, and guidance of goals of care conversations with families and caregivers in the critical care setting. The Acute Physiologic Assessment and Chronic Health Evaluation (APACHE) II scoring system is one of several predictive models used to classify illness severity and estimate mortality risk on admission to the intensive care unit (ICU). Our retrospective study sought to evaluate the prognostic ability of the APACHE II score in COVID-19 patients according to endpoints of mortality and length of stay (LOS) as well as unfavorable clinical outcomes, including development of acute renal failure (ARF) requiring renal replacement therapy (RRT) and acute venous thromboembolic events (VTE).MethodsThis multicenter retrospective cohort study evaluated a randomized sample of 3,102 patients with confirmed COVID-19 disease admitted to the ICU from January 2020 to May 2020. A total of 395 patients with complete data points for appropriate APACHE II score calculation, absence of the preexisting comorbidities end-stage renal disease, and history of VTE were included. Linear and logistic regression models were employed to evaluate primary outcomes of mortality and LOS as well as secondary outcomes of VTE and ARF requiring continuous renal replacement therapy (CRRT) or hemodialysis (HD).Key resultsAmong the 395 patients enrolled, total percent mortality and mean LOS were 37.0% and 12.92 days, respectively. Primary outcome analysis revealed a statistically significant increase in odds of mortality as well as in mean LOS with every additional point increase in APACHE II score from a baseline of zero. Specifically, for every point increase in the APACHE II score, odds of mortality increased by 12% (p value < 0.001), and average LOS increased by 0.2 days (p value < 0.001). In our secondary outcome analysis, 14.43% and 62.2% of the total sample population developed ARF requiring RRT and VTE, respectively. For every additional point increase in APACHE II score from a baseline of zero, odds of requiring CRRT or HD increased by 10% on average (95% CI (1.06, 1.15); p value < 0.001). Similarly, for every additional point increase in the APACHE II score from a baseline of zero, there was a corresponding increase in odds of VTE by 19% (95% CI (1.14, 1.24); p value < 0.001).ConclusionsThe APACHE II score is an effective predictive model of in-hospital mortality and unfavorable clinical outcomes, including prolonged LOS, ARF requiring CRRT or HD, and development of VTE. As therapeutic interventions for COVID-19 evolve, application of this risk-stratification tool may guide clinical management decisions in the critical care setting.

Development and validation of a predictive model of in-hospital mortality in COVID-19 patients.

We retrospectively evaluated 2879 hospitalized COVID-19 patients from four hospitals to evaluate the ability of demographic data, medical history, and on-admission laboratory parameters to predict in-hospital mortality. Association of previously published risk factors (age, gender, arterial hypertension, diabetes mellitus, smoking habit, obesity, renal failure, cardiovascular/ pulmonary diseases, serum ferritin, lymphocyte count, APTT, PT, fibrinogen, D-dimer, and platelet count) with death was tested by a multivariate logistic regression, and a predictive model was created, with further validation in an independent sample. A total of 2070 hospitalized COVID-19 patients were finally included in the multivariable analysis. Age 61-70 years (p<0.001; OR: 7.69; 95%CI: 2.93 to 20.14), age 71-80 years (p<0.001; OR: 14.99; 95%CI: 5.88 to 38.22), age >80 years (p<0.001; OR: 36.78; 95%CI: 14.42 to 93.85), male gender (p<0.001; OR: 1.84; 95%CI: 1.31 to 2.58), D-dimer levels >2 ULN (p = 0.003; OR: 1.79; 95%CI: 1.22 to 2.62), and prolonged PT (p<0.001; OR: 2.18; 95%CI: 1.49 to 3.18) were independently associated with increased in-hospital mortality. A predictive model performed with these parameters showed an AUC of 0.81 in the development cohort (n = 1270) [sensitivity of 95.83%, specificity of 41.46%, negative predictive value of 98.01%, and positive predictive value of 24.85%]. These results were then validated in an independent data sample (n = 800). Our predictive model of in-hospital mortality of COVID-19 patients has been developed, calibrated and validated. The model (MRS-COVID) included age, male gender, and on-admission coagulopathy markers as positively correlated factors with fatal outcome.

Model Prediction for In-Hospital Mortality in Patients with COVID-19: A Case-Control Study in Isfahan, Iran.

ABSTRACTThe COVID-19 pandemic has now imposed an enormous global burden as well as a large mortality in a short time period. Although there is no promising treatment, identification of early predictors of in-hospital mortality would be critically important in reducing its worldwide mortality. We aimed to suggest a prediction model for in-hospital mortality of COVID-19. In this case–control study, we recruited 513 confirmed patients with COVID-19 from February 18 to March 26, 2020 from Isfahan COVID-19 registry. Based on extracted laboratory, clinical, and demographic data, we created an in-hospital mortality predictive model using gradient boosting. We also determined the diagnostic performance of the proposed model including sensitivity, specificity, and area under the curve (AUC) as well as their 95% CIs. Of 513 patients, there were 60 (11.7%) in-hospital deaths during the study period. The diagnostic values of the suggested model based on the gradient boosting method with oversampling techniques using all of the original data were specificity of 98.5% (95% CI: 96.8–99.4), sensitivity of 100% (95% CI: 94–100), negative predictive value of 100% (95% CI: 99.2–100), positive predictive value of 89.6% (95% CI: 79.7–95.7), and an AUC of 98.6%. The suggested model may be useful in making decision to patient’s hospitalization where the probability of mortality may be more obvious based on the final variable. However, moderate gaps in our knowledge of the predictors of in-hospital mortality suggest further studies aiming at predicting models for in-hospital mortality in patients with COVID-19.

An Artificial Neural Networks Model for Early Predicting In-Hospital Mortality in Acute Pancreatitis in MIMIC-III.

Background Early and accurate evaluation of severity and prognosis in acute pancreatitis (AP), especially at the time of admission is very significant. This study was aimed to develop an artificial neural networks (ANN) model for early prediction of in-hospital mortality in AP. Methods Patients with AP were identified from the Medical Information Mart for Intensive Care-III (MIMIC-III) database. Clinical and laboratory data were utilized to perform a predictive model by back propagation ANN approach. Results A total of 337 patients with AP were analyzed in the study, and the in-hospital mortality rate was 11.2%. A total of 12 variables that differed between patients in survivor group and nonsurvivor group were applied to construct ANN model. Three independent variables were identified as risk factors associated with in-hospital mortality by multivariate logistic regression analysis. The predictive performance based on the area under the receiver operating characteristic curve (AUC) was 0.769 for ANN model, 0.607 for logistic regression, 0.652 for Ranson score, and 0.401 for SOFA score. Conclusion An ANN predictive model for in-hospital mortality in patients with AP in MIMIC-III database was first performed. The patients with high risk of fatal outcome can be screened out easily in the early stage of AP by our model.