Development and validation of a nomogram to predict the mortality risk in elderly patients with ARF

Background Acute respiratory failure (ARF) is a life-threatening complication in elderly patients. We developed a nomogram model to explore the risk factors of prognosis and the short-term mortality in elderly patients with ARF. Methods A total of 759 patients from MIMIC-III database were categorized into the training set and 673 patients from our hospital were categorized into the validation set. Demographical, laboratory variables, SOFA score and APS-III score were collected within the first 24 h after the ICU admission. A 30-day follow-up was performed for all patients. Results Multivariate logistic regression analysis showed that the heart rate, respiratoryrate, systolic pressure, SPO2, albumin and 24 h urine output were independent prognostic factors for 30-day mortality in ARF patients. A nomogram was established based on above independent prognostic factors. This nomogram had a C-index of 0.741 (95% CI [0.7058–0.7766]), and the C-index was 0.687 (95% CI [0.6458–0.7272]) in the validation set. The calibration curves both in training and validation set were close to the ideal model. The SOFA had a C-index of 0.653 and the APS-III had a C-index of 0.707 in predicting 30-day mortality. Conclusion Our nomogram performed better than APS-III and SOFA scores and should be useful as decision support on the prediction of mortality risk in elderly patients with ARF.

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Development and Validation of a Risk Prediction Model of Linezolid-induced Thrombocytopenia in Elderly Patients

10.21203/rs.3.rs-582799/v1 ◽

2021 ◽

Author(s):

Yan Qin ◽

Zhe Chen ◽

Shuai Gao ◽

Ming Kun Pan ◽

Yu Xiao Li ◽

...

Keyword(s):

Risk Factors ◽

Elderly Patients ◽

Predictive Model ◽

Bacterial Infections ◽

Demographic Data ◽

Multivariate Logistic Regression Analysis ◽

Platelet Counts ◽

Linezolid Therapy ◽

Validation Set ◽

Development And Validation

Abstract Background Linezolid is an oxazolidinone antimicrobial agent developed for treating multi-drug-resistant gram-positive bacterial infections. Objective This study aimed at investigating risk factors of linezolid (LI)-induced thrombocytopenia (LI-TP) and establishing a risk predictive model for LI-TP.Setting ZhongShan Hospital, FuDan University, China. Method A retrospective study was performed in patients aged ≥ 65 years receiving linezolid therapy from January 2015 to April 2021. Clinical characteristics and demographic data were collected and compared between patients with LI-TP and those without.Main outcome measures Incidence and risk factors of LI-TP in elderly patients.Results A total of 343 inpatients were included as the train set from January 2015 to August 2020. Among them, 67 (19.5%) developed LI-TP. Multivariate logistic regression analysis revealed that baseline platelet counts < 150×109·L-1 (OR=3.576; P< 0.001), age ≥ 75 years (OR=2.258; P=0.009), eGFR< 60 mL·(min·1.73m2)-1 (OR=2.553; P=0.002), duration of linezolid therapy ≥ 10 d (OR=3.218; P<0.001), ICU admittance (OR=2.682; P=0.004), and concomitant with piperacillin-tazobactam (PTZ) (OR=3.863; P=0.006) were independent risk factors for LI-TP. The risk predictive model was established and exhibited a moderate discriminative power, with an AUC of 0.795 [95%CI 0.740-0.851] and 0.849 [95%CI 0.760-0.939] in train set (n=343) and validation set (n=90), respectively.Conclusion The risk factors of LI-TP in elderly patients were duration of linezolid therapy, age, eGFR, ICU admittance, baseline platelet counts, and concomitant with PTZ. A risk predictive model based on these risk factors may be useful to identify patients with high risk of LI-TP.

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Development and Validation of a Nomogram to Predict the Mortality Risk in Elderly Patients With ARF：A Retrospective Study in the Mimic-III Database

10.21203/rs.3.rs-53951/v1 ◽

2020 ◽

Author(s):

Chen Qin ◽

Weng Jie ◽

Hou Ruonan ◽

Zhou Xiaoming ◽

Zhou Zhiliang ◽

...

Keyword(s):

Risk Factors ◽

Logistic Regression ◽

Prognostic Factors ◽

Elderly Patients ◽

Mortality Risk ◽

Sofa Score ◽

Multivariate Logistic Regression ◽

Validation Set ◽

Mimic Iii ◽

Better Than

Abstract Background: To explore the risk factors of prognosis in elderly patients with acute respiratory failure (ARF), and to develop a nomogram model to predict the short-term mortality risk of ARF.Methods: A total of 1432 patients were included in this study from MIMIC-III database. 759 patients were categorized into the training set and 673 patients were categorized into the validation set. Demographical, laboratory variables, SOFA score and APS-III score were collected within the first 24 h after the ICU admission. The univariate and multivariate logistic regression were used to identify risk factors from the training data set. A nomogram model was developed to predict the mortality risk of ARF patients within 30 days according to the risk factors.Results: Multivariate logistic regression analysis showed that the heart rate, respiratory rate, systolic pressure, SPO2, albumin and 24 h urine output were independent prognostic factors for 30-day mortality in ARF patients. A nomogram was established based on above independent prognostic factors. This nomogram had C-index of 0.741 (95% CI: 0.7058–0.7766), and the C-index was 0.687 (95%CI: 0.6458-0.7272) in the validation set. The calibration curves both in training and validation set were close to the ideal model. The SOFA had a C-index of 0.653 and the APS-III had a C-index of 0.707 in predicting 30-day mortality. The predictive performance of our nomogram is better than the SOFA score and APS-III score. Conclusions: Our nomogram performed better than APS-III and SOFA scores and should be useful as decision support on the prediction of mortality risk in elderly patients with ARF.

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

Frontiers in Medicine ◽

10.3389/fmed.2020.609769 ◽

2021 ◽

Vol 7 ◽

Author(s):

Kai Zhang ◽

Shufang Zhang ◽

Wei Cui ◽

Yucai Hong ◽

Gensheng Zhang ◽

...

Keyword(s):

Intensive Care ◽

Mortality Risk ◽

External Validation ◽

Model Development ◽

Mortality Prediction ◽

Sepsis Mortality ◽

Severity Scores ◽

Validation Set ◽

Development And Validation ◽

Risk Stratification Score

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

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Augmented intelligence to predict 30-day mortality in patients with cancer

Future Oncology ◽

10.2217/fon-2021-0302 ◽

2021 ◽

Author(s):

Ajeet Gajra ◽

Marjorie E Zettler ◽

Kelly A Miller ◽

Sibel Blau ◽

Swetha S Venkateshwaran ◽

...

Keyword(s):

Mortality Risk ◽

Decision Tool ◽

Short Term ◽

Algorithm Performance ◽

Patients With Cancer ◽

Care Services ◽

Short Term Mortality ◽

Palliative Care Services ◽

Oncology Practice ◽

Development And Validation

Aim: An augmented intelligence tool to predict short-term mortality risk among patients with cancer could help identify those in need of actionable interventions or palliative care services. Patients & methods: An algorithm to predict 30-day mortality risk was developed using socioeconomic and clinical data from patients in a large community hematology/oncology practice. Patients were scored weekly; algorithm performance was assessed using dates of death in patients’ electronic health records. Results: For patients scored as highest risk for 30-day mortality, the event rate was 4.9% (vs 0.7% in patients scored as low risk; a 7.4-times greater risk). Conclusion: The development and validation of a decision tool to accurately identify patients with cancer who are at risk for short-term mortality is feasible.

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Prognostic Nomogram for PJP Patients With HIV and NHIV

10.21203/rs.3.rs-404009/v1 ◽

2021 ◽

Author(s):

Qiuyue Feng ◽

Jingjing Hao ◽

Ang Li ◽

Zhaohui Tong

Keyword(s):

Risk Factors ◽

Confidence Interval ◽

Mortality Risk ◽

Multivariate Logistic Regression Analysis ◽

Area Under The Curve ◽

Operating Characteristics ◽

Prediction Of Mortality ◽

Precise Prediction ◽

Independent Risk Factors ◽

Good Calibration

Abstract Background: This study was to create nomogram models for precise prediction of mortality risk of NHIV-PJP and HIV-PJP cases.Methods: A retrospective study was performed over a 10-year period to evaluate the clinical characteristics and outcomes of NHIV-PJP at Beijing Chaoyang Hospital and HIV-PJP at Beijing Ditan Hospital in China from 2010 to 2019. Univariate and multivariate logistic regression analysis were used to screen out mortality risk factors for creating nomograms. Nomogram models were evaluated by using a bootstrapped concordance index, calibration plots and receiver operating characteristics (ROCs) curve.Results: A total of 167 NHIV-PJP cases and 193 HIV-PJP cases were included in the study. Pneumothorax, febrile days after admission, CD4+ T cells≤100cells/ul and sulfa combine CAS treatment were identified as independent risk factors that could be combined for accurate prediction of mortality result in NHIV-PJP group. We created a nomogram for mortality risk by using these variables. The area under the curve was 0.865 (95% confidence interval 0.799-0.931). The nomogram had a C-index of 0.865 and was well calibrated. Independent risk factors contained in the nomogram in HIV-PJP group included pneumothorax, PLT≤80×109/L, HGB≤90g/L, ALB, CMV co-infection and sulfa combine CAS treatment. The nomogram showed good discrimination, with a C-index of 0.904 and good calibration. The area under the curve was 0.910 (95% confidence interval 0.850-0.970). Conclusions: Our nomograms were useful tools for evaluating the poor prognosis in both NHIV-PJP and HIV-PJP cases.

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Development and validation of a nomogram for predicting early stress urinary incontinence following endoscopic enucleation of the prostate

World Journal of Urology ◽

10.1007/s00345-021-03592-x ◽

2021 ◽

Author(s):

Xuanhao Li ◽

Fei He ◽

Cong Huang ◽

Liangshuo Zhang ◽

Qiang Liu ◽

...

Keyword(s):

Urinary Incontinence ◽

Stress Urinary Incontinence ◽

Operating Characteristic ◽

Multivariate Logistic Regression Analysis ◽

Clinical Tool ◽

Logistics Regression ◽

Early Stress ◽

Validation Set ◽

Endoscopic Enucleation ◽

Development And Validation

Abstract Purpose To develop and validate a predictive nomogram for early stress urinary incontinence (SUI) after endoscopic enucleation of the prostate (EEP) in patients with benign prostatic hyperplasia (BPH). Methods The records of 458 patients who underwent plasmakinetic- or diode-based EEP at our center from March 2016 to December 2019 were reviewed. Among these, 326 and 132 cases were randomly assigned to the training and validation set, respectively. A predictive nomogram was constructed based on multivariate logistic regression analysis. Receiver operating characteristic (ROC) analysis and calibration curves were employed to evaluate its performance. Results 65 years ≤ age < 70 years, 75 years ≤ age, 25 kg/m2 ≤ BMI < 30 kg/m2, 30 kg/m2 ≤ BMI, 5 years ≤ LUTS duration, and 75 ml ≤ prostate volume were finally selected as independent predictors of early SUI into the multivariate logistics regression model. It was visualized as a concise nomogram with satisfactory discrimination and accuracy in both training and validation sets. Conclusions A concise nomogram was developed and validated as a useful clinical tool for predicting early SUI post-EEP.

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Development and Validation of a Nomogram to Predict Deteriorating Trajectory in Patients with COVID-19 Infection: A Population-Based Prospective Study

10.21203/rs.3.rs-30640/v1 ◽

2020 ◽

Author(s):

Wei Chen ◽

Menglin Zhu ◽

Jian Li ◽

Cuiping Pan ◽

Demian Zhao ◽

...

Keyword(s):

Multivariate Logistic Regression Analysis ◽

Area Under The Curve ◽

Population Based ◽

Clinical Prediction ◽

Single Center ◽

Multivariate Logistic Regression ◽

Training Set ◽

Clinical Prediction Model ◽

Validation Set ◽

Development And Validation

Abstract Background Most of the patients with COVID-19 infection are mild to moderate initially. However, there is no effective prediction for the patients to develop into severe or extremely severe. This study aims to develop an effective clinical prediction model.Methods A single-center, retrospective, observational study conducted. A nomogram was conducted based on the results of multivariate logistic regression analysis. Results A total of 483 patients diagnosed mild to moderate were included, among these patients 62 developed severe or extremely critical illness. Seven variables including hyperlipidemia, vomiting, diarrhea, lymphocyte, imaging and mentality were associated with deteriorating trajectory. The ROC curve showed that model was robust, for which the area under the curve of the training set and the validation set are 0.873 and 0.813.Conclusions For patients with mild to moderate COVID-19 infection, nomogram score can effectively predict the possibility of patients developing into severe or extremely critical.

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Revascularization and functional outcomes after mechanical thrombectomy for acute ischemic stroke in elderly patients

Journal of Neurosurgery ◽

10.3171/2018.12.jns182399 ◽

2020 ◽

Vol 132 (4) ◽

pp. 1182-1187 ◽

Cited By ~ 1

Author(s):

Carrie E. Andrews ◽

Nikolaos Mouchtouris ◽

Evan M. Fitchett ◽

Fadi Al Saiegh ◽

Michael J. Lang ◽

...

Keyword(s):

Ischemic Stroke ◽

Elderly Patients ◽

Acute Ischemic Stroke ◽

Mechanical Thrombectomy ◽

Multivariate Logistic Regression Analysis ◽

Standard Of Care ◽

Stroke Severity ◽

Vessel Occlusion ◽

Younger Patients ◽

Success Of Treatment

OBJECTIVEMechanical thrombectomy (MT) is now the standard of care for acute ischemic stroke (AIS) secondary to large-vessel occlusion, but there remains a question of whether elderly patients benefit from this procedure to the same degree as the younger populations enrolled in the seminal trials on MT. The authors compared outcomes after MT of patients 80–89 and ≥ 90 years old with AIS to those of younger patients.METHODSThe authors retrospectively analyzed records of patients undergoing MT at their institution to examine stroke severity, comorbid conditions, medical management, recanalization results, and clinical outcomes. Univariate and multivariate logistic regression analysis were used to compare patients < 80 years, 80–89 years, and ≥ 90 years old.RESULTSAll groups had similar rates of comorbid disease and tissue plasminogen activator (tPA) administration, and stroke severity did not differ significantly between groups. Elderly patients had equivalent recanalization outcomes, with similar rates of readmission, 30-day mortality, and hospital-associated complications. These patients were more likely to have poor clinical outcome on discharge, as defined by a modified Rankin Scale (mRS) score of 3–6, but this difference was not significant when controlled for stroke severity, tPA administration, and recanalization results.CONCLUSIONSOctogenarians, nonagenarians, and centenarians with AIS have similar rates of mortality, hospital readmission, and hospital-associated complications as younger patients after MT. Elderly patients also have the capacity to achieve good functional outcome after MT, but this potential is moderated by stroke severity and success of treatment.

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Successful incorporation of single reviewer assessments during systematic review screening: development and validation of sensitivity and work-saved of an algorithm that considers exclusion criteria and count

Systematic Reviews ◽

10.1186/s13643-021-01632-6 ◽

2021 ◽

Vol 10 (1) ◽

Author(s):

Nassr Nama ◽

Mirna Hennawy ◽

Nick Barrowman ◽

Katie O’Hearn ◽

Margaret Sampson ◽

...

Keyword(s):

Systematic Review ◽

A Priori ◽

Research Centre ◽

Exclusion Criteria ◽

Conference Abstract ◽

Pediatric Health Care ◽

Average Loss ◽

Validation Set ◽

Abstract Screening ◽

Development And Validation

Abstract Background Accepted systematic review (SR) methodology requires citation screening by two reviewers to maximise retrieval of eligible studies. We hypothesized that records could be excluded by a single reviewer without loss of sensitivity in two conditions; the record was ineligible for multiple reasons, or the record was ineligible for one or more specific reasons that could be reliably assessed. Methods Twenty-four SRs performed at CHEO, a pediatric health care and research centre in Ottawa, Canada, were divided into derivation and validation sets. Exclusion criteria during abstract screening were sorted into 11 specific categories, with loss in sensitivity determined by individual category and by number of exclusion criteria endorsed. Five single reviewer algorithms that combined individual categories and multiple exclusion criteria were then tested on the derivation and validation sets, with success defined a priori as less than 5% loss of sensitivity. Results The 24 SRs included 930 eligible and 27390 ineligible citations. The reviews were mostly focused on pediatrics (70.8%, N=17/24), but covered various specialties. Using a single reviewer to exclude any citation led to an average loss of sensitivity of 8.6% (95%CI, 6.0–12.1%). Excluding citations with ≥2 exclusion criteria led to 1.2% average loss of sensitivity (95%CI, 0.5–3.1%). Five specific exclusion criteria performed with perfect sensitivity: conference abstract, ineligible age group, case report/series, not human research, and review article. In the derivation set, the five algorithms achieved a loss of sensitivity ranging from 0.0 to 1.9% and work-saved ranging from 14.8 to 39.1%. In the validation set, the loss of sensitivity for all 5 algorithms remained below 2.6%, with work-saved between 10.5% and 48.2%. Conclusions Findings suggest that targeted application of single-reviewer screening, considering both type and number of exclusion criteria, could retain sensitivity and significantly decrease workload. Further research is required to investigate the potential for combining this approach with crowdsourcing or machine learning methodologies.

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The association between hypoglycemia and glycemic variability in elderly patients with type 2 diabetes: a prospective observational study

Diabetology & Metabolic Syndrome ◽

10.1186/s13098-021-00656-1 ◽

2021 ◽

Vol 13 (1) ◽

Author(s):

Takahisa Handa ◽

Akinobu Nakamura ◽

Aika Miya ◽

Hiroshi Nomoto ◽

Hiraku Kameda ◽

...

Keyword(s):

Type 2 Diabetes ◽

Logistic Regression ◽

Regression Analysis ◽

Observational Study ◽

Elderly Patients ◽

Prospective Observational Study ◽

Multivariate Logistic Regression Analysis ◽

Glycemic Variability ◽

Multivariate Logistic Regression

Abstract Background This study aimed to explore predictive factors of time below target glucose range (TBR) ≥ 1% among patients’ characteristics and glycemic variability (GV) indices using continuous glucose monitoring data in elderly patients with type 2 diabetes. Methods We conducted a prospective observational study on 179 (71 female) Japanese outpatients with type 2 diabetes aged ≥ 65 years. The characteristics of the participants with TBR ≥ 1% were evaluated by multivariate logistic regression analysis. Receiver-operating characteristic (ROC) curve analyses of GV indices, comprising coefficient of variation (CV), standard deviation, and mean amplitude of glycemic excursions, were performed to identify the optimal index for the identification of patients with TBR ≥ 1%. Results In the multivariate logistic regression analysis, none of the clinical characteristics, including HbA1c and C-peptide index, were independent markers for TBR ≥ 1%, while all three GV indices showed significant associations with TBR ≥ 1%. Among the three GV indices, CV showed the best performance based on the area under the curve in the ROC curve analyses. Conclusions Among elderly patients with type 2 diabetes, CV reflected TBR ≥ 1% most appropriately among the GV indices examined. Trial registration UMIN-CTR: UMIN000029993. Registered 16 November 2017

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