Derivation and External Validation of a Simple Prediction Rule for the Development of Respiratory Failure in Hospitalized Patients With Influenza

BACKGROUND: Influenza viruses cause seasonal epidemics worldwide with a significant morbimortality burden. Clinical spectrum of Influenza is wide, being respiratory failure (RF) one of its most severe complications. This study aims to elaborate a clinical prediction rule of RF in hospitalized Influenza patients.METHODS: a prospective cohort study was conducted during two consecutive Influenza seasons (December 2016 - March 2017 and December 2017 - April 2018) including hospitalized adults with confirmed A or B Influenza infection. A prediction rule was derived using logistic regression and recursive partitioning, followed by internal cross-validation. External validation was performed on a retrospective cohort in a different hospital between December 2018 - May 2019. RESULTS: Overall, 707 patients were included in the derivation cohort and 285 in the validation cohort. RF rate was 6.8% and 11.6%, respectively. Chronic obstructive pulmonary disease, immunosuppression, radiological abnormalities, respiratory rate, lymphopenia, lactate dehydrogenase and C-reactive protein at admission were associated with RF. A four category-grouped seven point-score was derived including radiological abnormalities, lymphopenia, respiratory rate and lactate dehydrogenase. Final model area under the curve was 0.796 (0.714-0.877) in the derivation cohort and 0.773 (0.687-0.859) in the validation cohort (p<0.001 in both cases). The predicted model showed an adequate fit with the observed results (Fisher’s test p>0.43). CONCLUSION: we present a simple, discriminating, well-calibrated rule for an early prediction of the development of RF in hospitalized Influenza patients, with proper performance in an external validation cohort. This tool can be helpful in patient´s stratification during seasonal Influenza epidemics.

Simple risk scores to predict hospitalization or death in outpatients with COVID-19

Importance Outpatient physicians need guidance to support their clinical decisions regarding management of patients with COVID-19, in particular whether to hospitalize a patient and if managed as an outpatient, how closely to follow them. Objective To develop and prospectively validate a clinical prediction rule to predict the likelihood of hospitalization for outpatients with COVID-19 that does not require laboratory testing or imaging. Design Derivation and temporal validation of a clinical prediction rule, and prospective validation of two externally derived clinical prediction rules. Setting Primary and Express care clinics in a Pennsylvania health system. Participants Patients 12 years and older presenting to outpatient clinics who had a positive polymerase chain reaction test for COVID-19. Main outcomes and measures Classification accuracy (percentage in each risk group hospitalized) and area under the receiver operating characteristic curve (AUC). Results Overall, 7.4% of outpatients in the early derivation cohort (5843 patients presenting before 3/1/21) and 5.5% in the late validation cohort (3806 patients presenting 3/1/21 or later) were ultimately hospitalized. We developed and temporally validated three risk scores that all included age, dyspnea, and the presence of comorbidities, adding respiratory rate for the second score and oxygen saturation for the third. All had very good overall accuracy (AUC 0.77 to 0.78) and classified over half of patients in the validation cohort as very low risk with a 1.7% or lower likelihood of hospitalization. Two externally derived risk scores identified more low risk patients, but with a higher overall risk of hospitalization (2.8%). Conclusions and relevance Simple risk scores applicable to outpatient and telehealth settings can identify patients with very low (1.6% to 1.7%), low (5.2% to 5.9%), moderate (14.7% to 15.6%), and high risk (32.0% to 34.2%) of hospitalization. The Lehigh Outpatient COVID Hospitalization (LOCH) risk score is available online as a free app: https://ebell-projects.shinyapps.io/LehighRiskScore/.

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

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.

Phenotyping of acute decompensated heart failure with preserved ejection fraction

ObjectiveThe pathophysiological heterogeneity of heart failure with preserved ejection fraction (HFpEF) makes the conventional ‘one-size-fits-all’ treatment approach difficult. We aimed to develop a stratification methodology to identify distinct subphenotypes of acute HFpEF using the latent class analysis.MethodsWe established a prospective, multicentre registry of acute decompensated HFpEF. Primary candidates for latent class analysis were patient data on hospital admission (160 features). The patient subset was categorised based on enrolment period into a derivation cohort (2016–2018; n=623) and a validation cohort (2019–2020; n=472). After excluding features with significant missingness and high degree of correlation, 83 features were finally included in the analysis.ResultsThe analysis subclassified patients (derivation cohort) into 4 groups: group 1 (n=215, 34.5%), characterised by arrythmia triggering (especially atrial fibrillation) and a lower comorbidity burden; group 2 (n=77, 12.4%), with substantially elevated blood pressure and worse classical HFpEF echocardiographic features; group 3 (n=149, 23.9%), with the highest level of GGT and total bilirubin and frequent previous hospitalisation for HF and group 4 (n=182, 29.2%), with infection-triggered HF hospitalisation, high C reactive protein and worse nutritional status. The primary end point—a composite of all-cause death and HF readmission—significantly differed between the groups (log-rank p<0.001). These findings were consistent in the validation cohort.ConclusionsThis study indicated the feasibility of clinical application of the latent class analysis in a highly heterogeneous cohort of patients with acute HFpEF. Patients can be divided into 4 phenotypes with distinct patient characteristics and clinical outcomes.Trial registration numberUMIN000021831.

A New Risk Score for Patients With Acute Chest Pain and Normal High Sensitivity Troponin

Objective: To investigate a new risk score for patients who suffered from acute chest pain with normal high-sensitivity troponin I (hs-TnI) levels.Methods: In this study, patients with acute chest pain who were admitted to the emergency department (ED) of our hospital had been recruited. Hs-TnI was measured in serum samples drawn on admission to the ED. The end point was the occurrence of major adverse cardiac events (MACE) within 3 months. Predictor variables were selected by logistic regression analysis, and external validity was assessed in this study. Furthermore, validation was performed in an independent cohort, i.e., 352 patients (validation cohort).Results: A total of 724 patients were included in the derivation cohort. The results showed that four predictor variables were significant in the regression analysis—male, a history of chest pain, 60 years of age or older and with three or more coronary artery disease (CAD) risk factors. A total of 105 patients in the validation cohort had serious adverse cardiac events. The validation cohort showed a homogenous pattern with the derivation cohort when patients were stratified by score. The area under the curve (AUC) of the receiver operating characteristic (ROC) in the derivation cohort was 0.80 (95% CI: 0.76–0.83), while in the validation cohort, it was 0.79 (95% CI: 0.75–0.82).Conclusion: A new risk score was developed for acute chest pain patients without known CAD and ST-segment deviation and with normal hs-TnI and may aid MACE risk assessment and patient triage in the ED.

Hospital admission risk stratification of patients with gout presenting to the emergency department

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.

Development And Validation of a Risk Scoring System to Predict Pneumothorax in CT-Guided Percutaneous Transthoracic Needle Biopsy

Objective: To develop and validate a risk scoring system using variables easily obtained for the prediction of pneumothorax in CT-guided percutaneous transthoracic needle biopsy (PTNB).Methods: The derivation cohort was comprised of 1001 patients who underwent CT-guided PTNB. Multivariate logistic regression was used to identify risk factors for pneumothorax, which were treated as the foundation to develop the risk scoring system. To validate the system, a validation cohort group of 230 patients was enrolled.Results: Age, puncture times, puncture depth, smoking index, number of specimens, bleeding from the needle path, and lobular lesion were identified as risk factors in the derivation cohort. A risk scoring system (Hosmer-Lemeshow goodness-of-fit test p =0.33) was developed. The area under the receiver operating characteristic curve (AUROC) was 0.601 by using the risk score system. This risk score system demonstrated a better diagnostic effect with increasing age. In the group of patients older than 80 years, the AUROC was 0.76, showing good predictive power. This risk scoring system was confirmed in the validation cohort with an AUROC of 0.736.Conclusion: This scoring system has a good predictive effect in both derivation and validation cohort.

Temporal Validation of a Predictive Score for Death in Children with Visceral Leishmaniasis

Objectives. To perform a temporal validation of a predictive model for death in children with visceral leishmaniasis (VL). Methods. A temporal validation of a children-exclusive predictive model of death due to VL (Sampaio et al. 2010 model), using a retrospective cohort, hereby called validation cohort. The validation cohort convenience sample was made of 156 patients less than 15 years old hospitalized between 2008 and 2018 with VL. Patients included in the Sampaio et al. 2010 study are here denominated derivation cohort, which was composed of 546 patients hospitalized in the same hospital setting in the period from 1996 to 2006. The calibration and discriminative capacity of the model to predict death by VL in the validation cohort were then assessed through the procedure of logistic recalibration that readjusted its coefficients. The calibration of the updated model was tested using Hosmer–Lemeshow test and Spiegelhalter test. A ROC curve was built and the value of the area under this curve represented the model’s discrimination. Results. The validation cohort found a lethality of 6.4%. The Sampaio et al. 2010 model demonstrated inadequate calibration in the validation cohort (Spiegelhalter test: p = 0.007 ). It also presented unsatisfactory discriminative capacity, evaluated by the area under the ROC curve = 0.618. After the coefficient readjustment, the model showed adequate calibration (Spiegelhalter test, p = 0.988 ) and better discrimination, becoming satisfactory (AUROC = 0.762). The score developed by Sampaio et al. 2010 attributed 1 point to the variables dyspnea, associated infections, and neutrophil count <500/mm3; 2 points to jaundice and mucosal bleeding; and 3 points to platelet count <50,000/mm3. In the recalibrated model, each one of the variables had a scoring of 1 point for each. Conclusion. The temporally validated model, after coefficient readjustment, presented adequate calibration and discrimination to predict death in children hospitalized with VL.

Development and Validation of a Risk Score to Predict the Frequent use of Emergency House Calls among Older People who Receive Regular Home Visits

Background: Demand for home care services is increasing in Japan, and a 24-hour on-call system could be a burden for primary care physicians. Identifying high-risk patients who need frequent emergency house calls could help physicians prepare and allocate medical resources. The aim of the present study was to develop a risk score to predict the frequent use of emergency house calls in patients who receive regular home visits.Methods: We conducted a retrospective cohort study with linked medical and long-term care claims data from two Japanese cities. Participants were ≥65 years of age and had newly started regular home visits between July 2014 and March 2018 in Tsukuba city and between July 2012 and March 2017 in Kashiwa city. A total of 4,888 eligible patients were randomly divided into a derivation cohort (n=3,259) and a validation cohort (n=1,629). The primary outcome was the frequent use of emergency house calls, defined as the use once per month or more on average during each observation period. We considered pre-specified variables, such as age, gender, medical procedure performed in home health care, long-term care need level, and medical diagnosis at the start of the regular home visit. We used the least absolute shrinkage and selection operator (Lasso) method to select predictor variables. Results: The frequent use of emergency house calls was observed in 13.0% participants (424/3,259) in the derivation cohort and 12.9% participants (210/1,629) in the validation cohort. The risk score included three variables with the following point assignments: home oxygen therapy (4 points); care need level 4-5 (2 point); cancer (5 point). The area under the curve (AUC) in the derivation cohort was 0.708, whereas the AUC of a model that included all pre-specified variables was 0.729. The AUC in the derivation cohort was 0.708, showing moderate discrimination. Conclusions: This easy-to-use risk score would be useful for assessing high-risk patients and would allow the burden on primary care physicians to be reduced through measures such as clustering high-risk patients in well-equipped medical facilities.

Posterior National Institutes of Health Stroke Scale Improves Prognostic Accuracy in Posterior Circulation Stroke

Background and Purpose: The National Institutes of Health Stroke Scale (NIHSS) underestimates clinical severity in posterior circulation stroke and patients presenting with low NIHSS may be considered ineligible for reperfusion therapies. This study aimed to develop a modified version of the NIHSS, the Posterior NIHSS (POST-NIHSS), to improve NIHSS prognostic accuracy for posterior circulation stroke patients with mild-moderate symptoms. Methods: Clinical data of consecutive posterior circulation stroke patients with mild-moderate symptoms (NIHSS <10), who were conservatively managed, were retrospectively analyzed from the Basilar Artery Treatment and Management registry. Clinical features were assessed within 24 hours of symptom onset; dysphagia was assessed by a speech therapist within 48 hours of symptom onset. Random forest classification algorithm and constrained optimization were used to develop the POST-NIHSS in the derivation cohort. The POST-NIHSS was then validated in a prospective cohort. Poor outcome was defined as modified Rankin Scale score ≥3 at 3 months. Results: We included 202 patients (mean [SD] age 63 [14] years, median NIHSS 3 [interquartile range, 1–5]) in the derivation cohort and 65 patients (mean [SD] age 63 [16] years, median NIHSS 2 [interquartile range, 1–4]) in the validation cohort. In the derivation cohort, age, NIHSS, abnormal cough, dysphagia and gait/truncal ataxia were ranked as the most important predictors of functional outcome. POST-NIHSS was calculated by adding 5 points for abnormal cough, 4 points for dysphagia, and 3 points for gait/truncal ataxia to the baseline NIHSS. In receiver operating characteristic analysis adjusted for age, POST-NIHSS area under receiver operating characteristic curve was 0.80 (95% CI, 0.73–0.87) versus NIHSS area under receiver operating characteristic curve, 0.73 (95% CI, 0.64–0.83), P =0.03. In the validation cohort, POST-NIHSS area under receiver operating characteristic curve was 0.82 (95% CI, 0.69–0.94) versus NIHSS area under receiver operating characteristic curve 0.73 (95% CI, 0.58–0.87), P =0.04. Conclusions: POST-NIHSS showed higher prognostic accuracy than NIHSS and may be useful to identify posterior circulation stroke patients with NIHSS <10 at higher risk of poor outcome.