Acute Kidney Injury at Hospital Admission for SARS-CoV-2 Infection as a Marker of Poor Prognosis: Clinical Implications for Triage Risk Stratification
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ABSTRACT: Background/Aims
The new severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes a wide spectrum of effects, including acute kidney injury (AKI) in up to 40% of hospitalized patients. Given the established relationship between AKI and poor prognosis, whether AKI might be a prognostic indicator for patients admitted to the hospital for SARS-CoV-2 infection would allow for a straightforward risk stratification of these patients. Methods
We analyzed data of 623 patients admitted to San Raffaele Hospital (Milan, IT) between February 25 and April 19, 2020, for laboratory-confirmed SARS-CoV-2 infection. Incidence of AKI at hospital admission was calculated, with AKI defined according to the KDIGO criteria. Multivariable Cox regression models assessed the association between AKI and overall mortality and admission to the intensive care unit (ICU). Results
Overall, 108 (17%) patients had AKI at hospital admission for SARS-CoV-2 infection. After a median follow-up for survivors of 14 days (interquartile range: 8, 23), 123 patients died, while 84 patients were admitted to the ICU. After adjusting for confounders, patients who had AKI at hospital admission were at increased risk of overall mortality compared to those who did not have AKI (hazards ratio [HR]: 2.00; p = 0.0004), whereas we did not find evidence of an association between AKI and ICU admission (HR: 0.95; p = 0.9). Conclusions
These data suggest that AKI might be an indicator of poor prognosis for patients with SARS-CoV-2 infection, and as such, given its readily availability, it might be used to improve risk stratification at hospital admission.
SUBMITTER: Bravi C
PROVIDER: S-EPMC8805049 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundThe incidence of Acute Kidney Injury (AKI) continues to increase in the UK, with associated mortality rates remaining significant. Approximately one fifth of hospital admissions are associated with AKI and approximately a third of patients with AKI in hospital develop AKI during their time in hospital. A fifth of these cases are considered avoidable. Early risk detection remains key to decreasing AKI in hospitals, where sub-optimal care was noted for half of patients who developed AKI.MethodsElectronic anonymised data for adults admitted into the Royal Cornwall Hospitals Trust (RCHT) between 18th March and 31st December 2015 was trimmed to that collected within the first 24?h of hospitalisation. These datasets were split according to three separate time periods: data used for training the Takagi-Sugeno Fuzzy Logic Systems (FLS) and the multivariable logistic regression (MLR) models; data used for testing; and data from a later patient spell used for validation. Three fuzzy logic models and three MLR models were developed to link characteristics of patients diagnosed with a maximum stage AKI within 7?days of admission: the first models to identify any AKI Stage (FLS I, MLR I), the second for patterns of AKI Stage 2 or 3 (FLS II, MLR II), and the third to identify AKI Stage 3 (FLS III, MLR III). Model accuracy is expressed by area under the curve (AUC).ResultsAccuracy for each model during internal validation was: FLS I and MLR I (AUC 0.70, 95% CI: 0.64-0.77); FLS II (AUC 0.77, 95% CI: 0.69-0.85) and MLR II (AUC 0.74, 95% CI: 0.65-0.83); FLS III and MLR III (AUC 0.95, 95% CI: 0.92-0.98).ConclusionsFLS II and FLS III (and the respective MLR models) can identify with a high level of accuracy patients at high risk of developing AKI in hospital. These two models cannot be properly assessed against prior studies as this is the first attempt at quantifying the risk of developing specific Stages of AKI for a broad cohort of both medical and surgical inpatients. FLS I and MLR I performance is comparable to other existing models.
| S-EPMC6376785 | biostudies-literature
Project description:ObjectiveTo predict hospital admission at the time of ED triage using patient history in addition to information collected at triage.MethodsThis retrospective study included all adult ED visits between March 2014 and July 2017 from one academic and two community emergency rooms that resulted in either admission or discharge. A total of 972 variables were extracted per patient visit. Samples were randomly partitioned into training (80%), validation (10%), and test (10%) sets. We trained a series of nine binary classifiers using logistic regression (LR), gradient boosting (XGBoost), and deep neural networks (DNN) on three dataset types: one using only triage information, one using only patient history, and one using the full set of variables. Next, we tested the potential benefit of additional training samples by training models on increasing fractions of our data. Lastly, variables of importance were identified using information gain as a metric to create a low-dimensional model.ResultsA total of 560,486 patient visits were included in the study, with an overall admission risk of 29.7%. Models trained on triage information yielded a test AUC of 0.87 for LR (95% CI 0.86-0.87), 0.87 for XGBoost (95% CI 0.87-0.88) and 0.87 for DNN (95% CI 0.87-0.88). Models trained on patient history yielded an AUC of 0.86 for LR (95% CI 0.86-0.87), 0.87 for XGBoost (95% CI 0.87-0.87) and 0.87 for DNN (95% CI 0.87-0.88). Models trained on the full set of variables yielded an AUC of 0.91 for LR (95% CI 0.91-0.91), 0.92 for XGBoost (95% CI 0.92-0.93) and 0.92 for DNN (95% CI 0.92-0.92). All algorithms reached maximum performance at 50% of the training set or less. A low-dimensional XGBoost model built on ESI level, outpatient medication counts, demographics, and hospital usage statistics yielded an AUC of 0.91 (95% CI 0.91-0.91).ConclusionMachine learning can robustly predict hospital admission using triage information and patient history. The addition of historical information improves predictive performance significantly compared to using triage information alone, highlighting the need to incorporate these variables into prediction models.
| S-EPMC6054406 | biostudies-literature
Project description:The study entails novel bio-marker discovery of Tumor Aggressive Grade signature (TAGs) genes and their role in recurrence free survival of breast cancer (BC) patients. Current BC dataset was used for co-expression analysis of TAGs genes and their role in BC progression. Additionally, recent findings have suggested an importance of structural organization of sense-antisense gene pairs (SAGPs) for transcription, post-transcriptional and post-translational events and their associations with cancer and disease. We studied SAGPs in which both gene partners are protein encoding genes (coding-coding SAGPs, ccSAGPs), their role in human BC development and demonstrated their potential for BC stratification and prognosis. Based on gene expression and correlation analyses we identified the robust set of breast cancer-relevant ccSAGPs (BCR-ccSAGPs). We isolated and characterized the sense-antisense gene classifier (SAGC) and evaluated its prognostic potential in various gene expression datasets comprising 1161 BC patients. The methods used included the Cox proportional survival analysis, statistical analysis of clinicopathologic parameters and differential gene expression.The SAGC was effective in identification of BC patients with the most aggressive disease. Independently, we validated the SAGC using 58 RNA samples of breast cancer tumors purchased from OriGene Technologies (Rockville, MD). Sixty two total RNA samples from breast tumors and normal breast epithelium have been purchased from OriGene Technologies in March, 2011. Four RNA samples were obtained from normal individuals. Among 58 breast tumors (58 patients) 56 were diagnosed as ductal breast adenocarcinoma, 1 - as lobular breast adenocarcinoma and 1 - as squamous cell carcinoma. Gene expression data for all the samples were quantified by using whole-genome RNA microarrays (HG-U133 plus 2.0, Affymetrix).
2015-05-10 | E-GEOD-61304 | biostudies-arrayexpress
Project description:The study entails novel bio-marker discovery of Tumor Aggressive Grade signature (TAGs) genes and their role in recurrence free survival of breast cancer (BC) patients. Current BC dataset was used for co-expression analysis of TAGs genes and their role in BC progression. Additionally, recent findings have suggested an importance of structural organization of sense-antisense gene pairs (SAGPs) for transcription, post-transcriptional and post-translational events and their associations with cancer and disease. We studied SAGPs in which both gene partners are protein encoding genes (coding-coding SAGPs), their role in human BC development and demonstrated their potential for BC stratification and prognosis. Based on gene expression and correlation analyses we identified the robust set of breast cancer-relevant SAGPs (BCR-SAGPs). We isolated and characterized the sense-antisense gene signature (SAGS) and evaluated its prognostic potential in various gene expression datasets comprising 1161 BC patients. The methods used included the Cox proportional survival analysis, statistical analysis of clinicopathologic parameters and differential gene expression. The SAGS was effective in identification of BC patients with the most aggressive disease. Independently, we validated the SAGS using 58 RNA samples of breast cancer tumors purchased from OriGene Technologies (Rockville, MD).
2015-05-10 | GSE61304 | GEO
Project description:We aimed to cluster patients with acute kidney injury at hospital admission into clinically distinct subtypes using an unsupervised machine learning approach and assess the mortality risk among the distinct clusters. We performed consensus clustering analysis based on demographic information, principal diagnoses, comorbidities, and laboratory data among 4289 hospitalized adult patients with acute kidney injury at admission. The standardized difference of each variable was calculated to identify each cluster's key features. We assessed the association of each acute kidney injury cluster with hospital and one-year mortality. Consensus clustering analysis identified four distinct clusters. There were 1201 (28%) patients in cluster 1, 1396 (33%) patients in cluster 2, 1191 (28%) patients in cluster 3, and 501 (12%) patients in cluster 4. Cluster 1 patients were the youngest and had the least comorbidities. Cluster 2 and cluster 3 patients were older and had lower baseline kidney function. Cluster 2 patients had lower serum bicarbonate, strong ion difference, and hemoglobin, but higher serum chloride, whereas cluster 3 patients had lower serum chloride but higher serum bicarbonate and strong ion difference. Cluster 4 patients were younger and more likely to be admitted for genitourinary disease and infectious disease but less likely to be admitted for cardiovascular disease. Cluster 4 patients also had more severe acute kidney injury, lower serum sodium, serum chloride, and serum bicarbonate, but higher serum potassium and anion gap. Cluster 2, 3, and 4 patients had significantly higher hospital and one-year mortality than cluster 1 patients (p < 0.001). Our study demonstrated using machine learning consensus clustering analysis to characterize a heterogeneous cohort of patients with acute kidney injury on hospital admission into four clinically distinct clusters with different associated mortality risks.
| S-EPMC8544570 | biostudies-literature
Project description:To characterise gout patients at high risk of hospitalisation and to develop a web-based prognostic model to predict the likelihood of gout-related hospital admissions. This was a retrospective single-centre study of 1417 patients presenting to the emergency department (ED) with a gout flare between 2015 and 2017 with a 1-year look-back period. The dataset was randomly divided, with 80% forming the derivation and the remaining forming the validation cohort. A multivariable logistic regression model was used to determine the likelihood of hospitalisation from a gout flare in the derivation cohort. The coefficients for the variables with statistically significant adjusted odds ratios were used for the development of a web-based hospitalisation risk estimator. The performance of this risk estimator model was assessed via the area under the receiver operating characteristic curve (AUROC), calibration plot, and brier score. Patients who were hospitalised with gout tended to be older, less likely male, more likely to have had a previous hospital stay with an inpatient primary diagnosis of gout, or a previous ED visit for gout, less likely to have been prescribed standby acute gout therapy, and had a significant burden of comorbidities. In the multivariable-adjusted analyses, previous hospitalisation for gout was associated with the highest odds of gout-related admission. Early identification of patients with a high likelihood of gout-related hospitalisation using our web-based validated risk estimator model may assist to target resources to the highest risk individuals, reducing the frequency of gout-related admissions and improving the overall health-related quality of life in the long term. KEY POINTS : • We reported the characteristics of gout patients visiting a tertiary hospital in Singapore. • We developed a web-based prognostic model with non-invasive variables to predict the likelihood of gout-relatedhospital admissions.
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| S-EPMC4587953 | biostudies-literature
Project description:Acute kidney injury (AKI) is associated with higher hospital mortality. However, the relationship between geriatric AKI and in-hospital complications is unclear. We prospectively enrolled elderly patients (≥65 years) from general medical wards of National Taiwan University Hospital, part of whom presented AKI at admission. We recorded subsequent in-hospital complications, including catastrophic events, incident gastrointestinal bleeding, hospital-associated infections, and new-onset electrolyte imbalances. Regression analyses were utilized to assess the associations between in-hospital complications and the initial AKI severity. A total of 163 elderly were recruited, with 39% presenting AKI (stage 1: 52%, stage 2: 23%, stage 3: 25%). The incidence of any in-hospital complication was significantly higher in the AKI group than in the non-AKI group (91% vs. 68%, p < 0.01). Multiple regression analyses indicated that elderly patients presenting with AKI had significantly higher risk of developing any complication (Odds ratio [OR] = 3.51, p = 0.01) and new-onset electrolyte imbalance (OR = 7.1, p < 0.01), and a trend toward more hospital-associated infections (OR = 1.99, p = 0.08). The risk of developing complications increased with higher AKI stage. In summary, our results indicate that initial AKI at admission in geriatric patients significantly increased the risk of in-hospital complications.
| S-EPMC4564739 | biostudies-other
Project description:BackgroundDD was found to be associated with acute myocardial infarction (AMI) and renal insufficiency. However, it is uncertain whether DD is an independent risk factor of CI-AKI in patients undergoing pPCI.MethodsWe prospectively enrolled 550 consecutive patients with STEMI undergoing pPCI between January 2012 and December 2016. The predictive value of admission DD for CI-AKI was assessed by receiver operating characteristic (ROC) and multivariable logistic regression analysis. CI-AKI was defined as an absolute serum creatinine increase ≥0.3 mg/dl or a relative increase in serum creatinine ≥50% within 48 h of contrast medium exposure.ResultsOverall, the incidence of CI-AKI was 13.1%. The ROC analysis showed that the cutoff point of DD was 0.69 μg/ml for predicting CI-AKI with a sensitivity of 77.8% and a specificity of 57.3%. The predictive value of DD was similar to the Mehran score for CI-AKI (AUCDD = 0.729 vs AUCMehran = 0.722; p = 0.8298). Multivariate logistic regression analysis indicated that DD > 0.69 μg/ml was an independent predictor of CI-AKI (odds ratio [OR] = 3.37,95% CI:1.80-6.33, p < 0.0001). Furthermore, DD > 0.69 μg/ml was associated with an increased risk of long-term mortality during a mean follow-up period of 16 months (hazard ratio = 3.41, 95%CI:1.4-8.03, p = 0.005).ConclusionAdmission DD > 0.69 μg/ml was a significant and independent predictor of CI-AKI and long-term mortality in patients undergoing pPCI.
| S-EPMC7063708 | biostudies-literature
Project description:BACKGROUND:Given the aging of the population, nephrologists are ever more frequently assisting nonagenarians with acute kidney injury (AKI). The management of these patients presents unique characteristics, including bioethical dilemmas, such as the utilization of renal replacement therapy (RRT) at this extreme age. METHODS:We conducted a retrospective cohort study at a tertiary hospital. Over a 10-year period, 832 nonagenarians were hospitalized for two or more days. A random sample of 461 patients was obtained; 25 subjects were excluded due to lack of essential data. AKI was defined and staged according to the Kidney Disease Improving Global Outcomes (KDIGO) criteria. RESULTS:We analyzed data from 436 patients, mean age 93.5?±?3.3?years, 74.3% female; 76.4% required intensive care unit (ICU). The incidence of AKI was 45%. Length of hospital stay, ICU admission, vasopressors, and mechanical ventilation (MV) were independent predictors of AKI. Overall in-hospital mortality was 43.1%. Mortality was higher in the AKI compared to the no AKI group (66.8% vs. 23.8%, p <?0.001). Only 13 patients underwent RRT; all were critically ill, requiring vasopressors and 76.9% in MV. Mortality for this RRT group was 100% but not significantly higher than that observed in 26 non-RRT controls (96.1%, p =?1.0) obtained by proportional random sampling, matched by variables related to illness severity. In multivariable analysis, age, Charlson's score, vasopressors, MV, and AKI - but not RRT - were independent predictors of mortality. CONCLUSIONS:AKI is common in hospitalized nonagenarians and carries a grave prognosis, especially in those who are critically iil. The use of RRT was not able to change the fatal prognosis of this subgroup of patients. Our data may help guide informed decisions about the utility of RRT in this scenario.
| S-EPMC6993395 | biostudies-literature