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

2021 ◽  
Author(s):  
Yu Sun ◽  
Masao Iwagami ◽  
Nobuo Sakata ◽  
Tomoko Ito ◽  
Ryota Inokuchi ◽  
...  
Keyword(s):  
High Risk ◽  
Risk Score ◽  
Primary Care Physicians ◽  
Home Visits ◽  
Term Care ◽  
Derivation Cohort ◽  
House Calls ◽  
High Risk Patients ◽  
Frequent Use ◽  
Risk Patients

Abstract 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.

Effect of a Real-Time Risk Score on 30-day Readmission Reduction in Singapore

2021 ◽  
Vol 12 (02) ◽  
pp. 372-382
Author(s):  
Christine Xia Wu ◽  
Ernest Suresh ◽  
Francis Wei Loong Phng ◽  
Kai Pik Tai ◽  
Janthorn Pakdeethai ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Risk ◽  
Real Time ◽  
Risk Score ◽  
Patient Specific ◽  
Learning Models ◽  
Medicine Department ◽  
High Risk Patients ◽  
Risk Patients ◽  
Machine Learning Models

Abstract Objective To develop a risk score for the real-time prediction of readmissions for patients using patient specific information captured in electronic medical records (EMR) in Singapore to enable the prospective identification of high-risk patients for enrolment in timely interventions. Methods Machine-learning models were built to estimate the probability of a patient being readmitted within 30 days of discharge. EMR of 25,472 patients discharged from the medicine department at Ng Teng Fong General Hospital between January 2016 and December 2016 were extracted retrospectively for training and internal validation of the models. We developed and implemented a real-time 30-day readmission risk score generation in the EMR system, which enabled the flagging of high-risk patients to care providers in the hospital. Based on the daily high-risk patient list, the various interfaces and flow sheets in the EMR were configured according to the information needs of the various stakeholders such as the inpatient medical, nursing, case management, emergency department, and postdischarge care teams. Results Overall, the machine-learning models achieved good performance with area under the receiver operating characteristic ranging from 0.77 to 0.81. The models were used to proactively identify and attend to patients who are at risk of readmission before an actual readmission occurs. This approach successfully reduced the 30-day readmission rate for patients admitted to the medicine department from 11.7% in 2017 to 10.1% in 2019 (p < 0.01) after risk adjustment. Conclusion Machine-learning models can be deployed in the EMR system to provide real-time forecasts for a more comprehensive outlook in the aspects of decision-making and care provision.

scholarly journals Does the implementation of a novel intensive care discharge risk score and nurse-led inpatient review tool improve outcome? A prospective cohort study in two intensive care units in the UK

BMJ Open ◽  
2017 ◽  
Vol 7 (12) ◽  
pp. e018322
Author(s):  
Jez Fabes ◽  
William Seligman ◽  
Carolyn Barrett ◽  
Stuart McKechnie ◽  
John Griffiths
Keyword(s):  
Intensive Care ◽  
High Risk ◽  
Risk Score ◽  
Prospective Cohort ◽  
Data Set ◽  
High Risk Patients ◽  
Poor Outcome ◽  
Icu Discharge ◽  
Risk Patients ◽  
Risk Scoring

ObjectiveTo develop a clinical prediction model for poor outcome after intensive care unit (ICU) discharge in a large observational data set and couple this to an acute post-ICU ward-based review tool (PIRT) to identify high-risk patients at the time of ICU discharge and improve their acute ward-based review and outcome.DesignRetrospective patient cohort of index ICU admissions between June 2006 and October 2011 receiving routine inpatient review. Prospective cohort between March 2012 and March 2013 underwent risk scoring (PIRT) which subsequently guided inpatient ward-based review.SettingTwo UK adult ICUs.Participants4212 eligible discharges from ICU in the retrospective development cohort and 1028 patients included in the prospective intervention cohort.InterventionsMultivariate analysis was performed to determine factors associated with poor outcome in the retrospective cohort and used to generate a discharge risk score. A discharge and daily ward-based review tool incorporating an adjusted risk score was introduced. The prospective cohort underwent risk scoring at ICU discharge and inpatient review using the PIRT.OutcomesThe primary outcome was the composite of death or readmission to ICU within 14 days of ICU discharge following the index ICU admission.ResultsPIRT review was achieved for 67.3% of all eligible discharges and improved the targeting of acute post-ICU review to high-risk patients. The presence of ward-based PIRT review in the prospective cohort did not correlate with a reduction in poor outcome overall (P=0.876) or overall readmission but did reduce early readmission (within the first 48 hours) from 4.5% to 3.6% (P=0.039), while increasing the rate of late readmission (48 hours to 14 days) from 2.7% to 5.8% (P=0.046).ConclusionPIRT facilitates the appropriate targeting of nurse-led inpatient review acutely after ICU discharge but does not reduce hospital mortality or overall readmission rates to ICU.

scholarly journals A Tumor Progression Related 7-Gene Signature Indicates Prognosis and Tumor Immune Characteristics of Gastric Cancer

2021 ◽  
Vol 11 ◽  
Author(s):  
Fen Liu ◽  
Zongcheng Yang ◽  
Lixin Zheng ◽  
Wei Shao ◽  
Xiujie Cui ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
High Risk ◽  
Cancer Progression ◽  
Risk Score ◽  
Risk Model ◽  
Cox Regression ◽  
Hub Genes ◽  
Cox Regression Analysis ◽  
High Risk Patients ◽  
Risk Patients

BackgroundGastric cancer is a common gastrointestinal malignancy. Since it is often diagnosed in the advanced stage, its mortality rate is high. Traditional therapies (such as continuous chemotherapy) are not satisfactory for advanced gastric cancer, but immunotherapy has shown great therapeutic potential. Gastric cancer has high molecular and phenotypic heterogeneity. New strategies for accurate prognostic evaluation and patient selection for immunotherapy are urgently needed.MethodsWeighted gene coexpression network analysis (WGCNA) was used to identify hub genes related to gastric cancer progression. Based on the hub genes, the samples were divided into two subtypes by consensus clustering analysis. After obtaining the differentially expressed genes between the subtypes, a gastric cancer risk model was constructed through univariate Cox regression, least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox regression analysis. The differences in prognosis, clinical features, tumor microenvironment (TME) components and immune characteristics were compared between subtypes and risk groups, and the connectivity map (CMap) database was applied to identify potential treatments for high-risk patients.ResultsWGCNA and screening revealed nine hub genes closely related to gastric cancer progression. Unsupervised clustering according to hub gene expression grouped gastric cancer patients into two subtypes related to disease progression, and these patients showed significant differences in prognoses, TME immune and stromal scores, and suppressive immune checkpoint expression. Based on the different expression patterns between the subtypes, we constructed a gastric cancer risk model and divided patients into a high-risk group and a low-risk group based on the risk score. High-risk patients had a poorer prognosis, higher TME immune/stromal scores, higher inhibitory immune checkpoint expression, and more immune characteristics suitable for immunotherapy. Multivariate Cox regression analysis including the age, stage and risk score indicated that the risk score can be used as an independent prognostic factor for gastric cancer. On the basis of the risk score, we constructed a nomogram that relatively accurately predicts gastric cancer patient prognoses and screened potential drugs for high-risk patients.ConclusionsOur results suggest that the 7-gene signature related to tumor progression could predict the clinical prognosis and tumor immune characteristics of gastric cancer.

scholarly journals The risk-treatment paradox in acute coronary syndrome patients: insights from the FORCE-ACS registry

2021 ◽  
Vol 42 (Supplement_1) ◽  
Author(s):  
J Azzahhafi ◽  
N M R Van Der Sangen ◽  
D R P P Chan Pin Yin ◽  
J P Simao Henriques ◽  
W J Kikkert ◽  
...  
Keyword(s):  
High Risk ◽  
Risk Score ◽  
Mortality Risk ◽  
Recommended Care ◽  
High Risk Patients ◽  
Grace Risk Score ◽  
Risk Patients ◽  
Risk Treatment ◽  
One Year ◽  
Risk Categories

Abstract Background Acute coronary syndrome (ACS) patients at high risk might benefit most from guideline-recommended interventions. However, it is well recognized that the delivery of guideline-directed care is inversely related to the estimated mortality risk, the so called risk-treatment paradox. Purpose To assess the existence of the risk-treatment paradox in a contemporary cohort of ACS patients and its possible association with one-year mortality. Methods The study population consisted patients enrolled in the FORCE-ACS registry who survived their initial admission. All ACS patients were stratified into low, intermediate or high mortality risk based on the Global Registry of Acute Coronary Events (GRACE) risk score. Optimal guideline-recommended care was defined as undergoing coronary angiography during initial hospital admission and receiving all outpatient medications with a class I guideline recommendation (i.e. aspirin, P2Y12-inhibitor, beta-blocker, angiotensin-converting enzyme inhibitor or angiotensin receptor blocker and cholesterol-lowering drug). Aspirin and/or a P2Y12-inhibitor on top of an oral anticoagulant was also considered as optimal guideline-recommended care. The cumulative incidence of one-year mortality between optimal and suboptimal managed patients, within each GRACE risk score stratum, was estimated. Results In total, 2,524 patients who were enrolled between January 2015 and June 2018 were included. Based on the GRACE risk score, 46.9% of patients were classified as low-risk, 37.6% as intermediate-risk and 15.5% as high-risk. Overall, 49.8% of patients received optimal guideline-recommended care. Among the different risk strata, 54.9% of the low-risk, 49.1% of the intermediate-risk and 36.1% of the high-risk patients received optimal guideline-recommended care (Table 1). DAPT or DAT treatment (95.3% overall) did not differ between the risk categories. Beta-blockers were prescribed less frequently (69.6% overall), butprescription rates did not differ between the risk categories. ACE-inhibitors/ARBs were prescribed in 74.1% of all patients, but less often in high risk patients. Cholesterol lowering-drugs were prescribed in almost all patients (94.9% overall), but less frequently in high risk patients. Overall, 93.9% of patients underwent coronary angiography (CAG), high-risk patients had a statistically significant lower likelihood of undergoing CAG. In all risk categories, optimal guideline-recommended care was associated with a lower one-year mortality as compared to sub-optimal treatment (5.7% vs. 15.6% in high-risk) (Fig. 1). Conclusion Patients at higher estimated mortality risk, based on the GRACE-risk score, are less likely to receive guideline-recommended care. Although, the absolute benefit from guideline-recommended care appears to be greater in high-risk patients. Receiving guideline-recommended care was associated with a statistically significant better prognosis in intermediate- and high-risk patients. FUNDunding Acknowledgement Type of funding sources: Public grant(s) – National budget only. Main funding source(s): ZonMW Netherlands TopZorgSt. Antonius Research funds Figure 1. All-cause mortality

scholarly journals Revised-MALT-IPI: A New Predictive Model That Identifies High-Risk Patients with Extranodal Marginal Zone Lymphoma (EMZL)

Blood ◽  
2019 ◽  
Vol 134 (Supplement_1) ◽  
pp. 4010-4010
Author(s):  
Juan Pablo Alderuccio ◽  
Isildinha M Reis ◽  
Thomas M. Habermann ◽  
Brian K. Link ◽  
Catherine Thieblemont ◽  
...  
Keyword(s):  
High Risk ◽  
Family Member ◽  
Board Of Directors ◽  
Risk Score ◽  
Research Funding ◽  
Stage Iii ◽  
Advisory Committees ◽  
High Risk Patients ◽  
Disease Characteristics ◽  
Risk Patients

INTRODUCTION: EMZL is a heterogeneous disease with variable risk for relapse and progression. Based on age ≥70 years, stage III-IV and elevated LDH, Thieblemont et al (Blood. 2017) developed the MALT-IPI to identify high-risk patients. In this index, disease characteristics (stage and LDH) account for 66% while a disease nonspecific characteristic (age) for 33% of the index score. We reported (Am J Hematol. 2019) that EMZL with multiple mucosal sites (MMS) at diagnosis is characterized by shorter survival and increased incidence of higher grade transformation. To better recognize disease-attributable high-risk patients, we developed a new EMZL prognosis score chiefly based on patient's disease characteristics. METHODS: The revised (R)-MALT-IPI was developed using a retrospective data set of 405 EMZL patients treated at the University of Miami (UM) from 1995 to 2017. Cox proportional hazards regression analysis was conducted to evaluate the effect of the potential prognostic variables on progression-free survival (PFS) and overall survival (OS) and to develop the new index R-MALTI-IPI based on PFS. Model validation was performed in two independent cohorts of EMZL patients from the University of Iowa/Mayo Clinic Molecular Epidemiology Resource (MER) database (n=297) and the IELSG-19 study (n=400) used for the development of MALT-IPI. Performance of various prognostic indices was compared using AIC statistics, and concordance c-statistics by Harrell (CH) and by Gonen and Heller (CGH). RESULTS: Among the candidate variables tested in univariable analysis, the following were statistically significant predictors of shorter PFS: age >60, age ≥70, anemia (Hb<12g/dL), stage III-IV, ECOG PS ≥2, elevated serum LDH, number of extranodal sites >1, number of nodal sites >4, and presence of MMS at diagnosis, defined as EMZL with ≥2 different extranodal sites excluding spleen and bone marrow. A stepwise Cox regression analysis yielded a multivariable model with four independent predictors of shorter PFS: age >60 (HR=1.53, p=0.010), elevated LDH (HR=1.73, p=0.004), stage III-IV (HR=2.03, p=0.0003) and presence of MMS (HR=2.78, p<0.0001). Based on this, a new index R-MALT-IPI was developed with scores ranging from 0 to 5, calculated as a sum of 1 point for age >60, elevated LDH, stage III-IV, and 2 points for MMS. The R-MALT-IPI defined 4 risk groups: low-risk (score 0 (35%), reference group), low-medium risk (score 1 (39%), HR=1.91, p=0.005), medium-high risk (score 2 (13%), HR=3.77, p<0.0001), and high-risk (score 3+ (13%), HR=8.54, p<0.0001). When compared with MALT-IPI, R-MALT-IPI better stratifies and separates high risk patients (26%) into medium-high risk and high-risk patients with a median PFS of 5.8 years (2.9-9.1) and 1.8 years (1.3-2.6) respectively, compared to 2.6 years (1.8-4.7) in the high-risk MALT-IPI patients (16.8%). The R-MALT-IPI index also distinguished patients with different OS. For validation, we analyzed R-MALT-IPI index performance in independent Iowa/Mayo Clinic MER and IELSG-19 cohorts. Both R-MALT-IPI and MALT-IPI were useful in distinguishing PFS and OS in all the cohorts. In the UM training cohort, the concordance c-statistics' values for the two indices were similar: for PFS, CH=0.6893 and CGH=0.6611 for R-MALT-IPI, and CH=0.6551 and CGH=0.6367 for MALT-IPI; for OS, CH=0.7017 and CGH=0.6813 for R-MALT-IPI, and CH=0.7029 and CGH=0.67715 for MALT-IPI. In the validation cohorts, the concordance c-statistics' values for the two indices were also similar, but slightly lower than in the UM cohort for PFS. When comparing medium-high to high-risk R-MALT-IPI groups, there was a reduction of 4 years in median PFS in the UM cohort, and reduction in median EFS of 5.6 years in the MER cohort, an important difference between these risk groups identified by the R-MALT-IPI index. CONCLUSIONS: R-MALT-IPI is a new index for EMZL centered principally on disease characteristics. Overall, there is a similar prediction of PFS (EFS) by R-MALT-IPI and MALT-IPI indexes; however, R-MALT-IPI better recognizes a high-risk group accounting for 13% of EMZL patients with short median PFS and thus obviates the waiting period needed to recognize patients with shorter EFS24. Collaborative studies addressing best treatment approach for these high-risk EMZL patients are eagerly needed. Disclosures Alderuccio: Agios: Other: Immediate family member; Foundation Medicine: Other: Immediate family member; OncLive: Consultancy; Targeted Oncology: Honoraria; Puma Biotechnology: Other: Immediate family member; Inovio Pharmaceuticals: Other: Immediate family member. Thieblemont:Cellectis: Membership on an entity's Board of Directors or advisory committees; Kyte: Honoraria; Janssen: Honoraria; Celgene: Honoraria; Roche: Honoraria, Research Funding; Gilead: Honoraria; Novartis: Honoraria. Cerhan:Celgene: Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees; NanoString: Research Funding. Zucca:Kite, A Gilead Company: Membership on an entity's Board of Directors or advisory committees; Roche: Membership on an entity's Board of Directors or advisory committees, Other: Travel Grant, Research Funding; AstraZenaca: Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Research Funding; Merck: Research Funding; Celltrion Helathcare: Membership on an entity's Board of Directors or advisory committees; Abbvie: Other: Travel Grant. Lossos:NIH: Research Funding; Seattle Genetics: Membership on an entity's Board of Directors or advisory committees; Janssen Scientific: Membership on an entity's Board of Directors or advisory committees.

scholarly journals Catheter Management and Risk Stratification of Patients With in Inpatient Treatment Due to Acute Epididymitis

2020 ◽  
Vol 7 ◽  
Author(s):  
Mike Wenzel ◽  
Marina Deuker ◽  
Maria N. Welte ◽  
Benedikt Hoeh ◽  
Felix Preisser ◽  
...  
Keyword(s):  
Risk Factors ◽  
High Risk ◽  
Risk Stratification ◽  
Risk Score ◽  
Inpatient Treatment ◽  
Low Risk ◽  
High Risk Patients ◽  
Catheter Group ◽  
Risk Patients ◽  
Acute Epididymitis

Objective: This study aims to evaluate catheter management in acute epididymitis (AE) patients requiring inpatient treatment and risk factors predicting severity of disease.Material and Methods: Patients with diagnosed AE and inpatient treatment between 2004 and 2019 at the University Hospital Frankfurt were analyzed. A risk score, rating severity of AE, including residual urine &gt; 100 ml, fever &gt; 38.0°C, C-reactive protein (CRP) &gt; 5 mg/dl, and white blood count (WBC) &gt; 10/nl was introduced.Results: Of 334 patients, 107 (32%) received a catheter (transurethral (TC): n = 53, 16%, suprapubic (SPC): n = 54, 16%). Catheter patients were older, exhibited more comorbidities, and had higher CRP and WBC compared with the non-catheter group (NC). Median length of stay (LOS) was longer in the catheter group (7 vs. 6 days, p &lt; 0.001), whereas necessity of abscess surgery and recurrent epididymitis did not differ. No differences in those parameters were recorded between TC and SPC. According to our established risk score, 147 (44%) patients exhibited 0–1 (low-risk) and 187 (56%) 2–4 risk factors (high-risk). In the high-risk group, patients received a catheter significantly more often than with low-risk (TC: 22 vs. 9%; SPC: 19 vs. 12%, both p ≤ 0.01). Catheter or high-risk patients exhibited positive urine cultures more frequently than NC or low-risk patients. LOS was comparable between high-risk patients with catheter and low-risk NC patients.Conclusion: Patients with AE who received a catheter at admission were older, multimorbid, and exhibited more severe symptoms of disease compared with the NC patients. A protective effect of catheters might be attributable to patients with adverse risk constellations or high burden of comorbidities. The introduced risk score indicates a possibility for risk stratification.

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