scholarly journals Prediction modelling of inpatient neonatal mortality in high-mortality settings

2020 ◽  
pp. archdischild-2020-319217
Author(s):  
Jalemba Aluvaala ◽  
Gary Collins ◽  
Beth Maina ◽  
Catherine Mutinda ◽  
Mary Waiyego ◽  
...  
Keyword(s):  
Hospital Mortality ◽  
High Mortality ◽  
Clinical Decision Making ◽  
Clinical Signs ◽  
Prognostic Models ◽  
Routine Practice ◽  
Low Resource ◽  
C Statistic ◽  
Temporal Validation ◽  
Time Of Admission

ObjectivePrognostic models aid clinical decision making and evaluation of hospital performance. Existing neonatal prognostic models typically use physiological measures that are often not available, such as pulse oximetry values, in routine practice in low-resource settings. We aimed to develop and validate two novel models to predict all cause in-hospital mortality following neonatal unit admission in a low-resource, high-mortality setting.Study design and settingWe used basic, routine clinical data recorded by duty clinicians at the time of admission to derive (n=5427) and validate (n=1627) two novel models to predict in-hospital mortality. The Neonatal Essential Treatment Score (NETS) included treatments prescribed at the time of admission while the Score for Essential Neonatal Symptoms and Signs (SENSS) used basic clinical signs. Logistic regression was used, and performance was evaluated using discrimination and calibration.ResultsAt derivation, c-statistic (discrimination) for NETS was 0.92 (95% CI 0.90 to 0.93) and that for SENSS was 0.91 (95% CI 0.89 to 0.93). At external (temporal) validation, NETS had a c-statistic of 0.89 (95% CI 0.86 to 0.92) and SENSS 0.89 (95% CI 0.84 to 0.93). The calibration intercept for NETS was −0.72 (95% CI −0.96 to −0.49) and that for SENSS was −0.33 (95% CI −0.56 to −0.11).ConclusionUsing routine neonatal data in a low-resource setting, we found that it is possible to predict in-hospital mortality using either treatments or signs and symptoms. Further validation of these models may support their use in treatment decisions and for case-mix adjustment to help understand performance variation across hospitals.

scholarly journals NLP Methods for Extraction of Symptoms from Unstructured Data for Use in Prognostic COVID-19 Analytic Models

2021 ◽  
Vol 72 ◽  
pp. 429-474
Author(s):  
Greg M. Silverman ◽  
Himanshu S. Sahoo ◽  
Nicholas E. Ingraham ◽  
Monica Lupei ◽  
Michael A. Puskarich ◽  
...  
Keyword(s):  
Hospital Mortality ◽  
Clinical Decision Making ◽  
Quality Care ◽  
Vital Signs ◽  
Clinical Decision ◽  
Prognostic Models ◽  
P Value ◽  
Rule Based ◽  
Presenting Symptoms ◽  
Increased Risk

Statistical modeling of outcomes based on a patient's presenting symptoms (symptomatology) can help deliver high quality care and allocate essential resources, which is especially important during the COVID-19 pandemic. Patient symptoms are typically found in unstructured notes, and thus not readily available for clinical decision making. In an attempt to fill this gap, this study compared two methods for symptom extraction from Emergency Department (ED) admission notes. Both methods utilized a lexicon derived by expanding The Center for Disease Control and Prevention's (CDC) Symptoms of Coronavirus list. The first method utilized a word2vec model to expand the lexicon using a dictionary mapping to the Uni ed Medical Language System (UMLS). The second method utilized the expanded lexicon as a rule-based gazetteer and the UMLS. These methods were evaluated against a manually annotated reference (f1-score of 0.87 for UMLS-based ensemble; and 0.85 for rule-based gazetteer with UMLS). Through analyses of associations of extracted symptoms used as features against various outcomes, salient risks among the population of COVID-19 patients, including increased risk of in-hospital mortality (OR 1.85, p-value < 0.001), were identified for patients presenting with dyspnea. Disparities between English and non-English speaking patients were also identified, the most salient being a concerning finding of opposing risk signals between fatigue and in-hospital mortality (non-English: OR 1.95, p-value = 0.02; English: OR 0.63, p-value = 0.01). While use of symptomatology for modeling of outcomes is not unique, unlike previous studies this study showed that models built using symptoms with the outcome of in-hospital mortality were not significantly different from models using data collected during an in-patient encounter (AUC of 0.9 with 95% CI of [0.88, 0.91] using only vital signs; AUC of 0.87 with 95% CI of [0.85, 0.88] using only symptoms). These findings indicate that prognostic models based on symptomatology could aid in extending COVID-19 patient care through telemedicine, replacing the need for in-person options. The methods presented in this study have potential for use in development of symptomatology-based models for other diseases, including for the study of Post-Acute Sequelae of COVID-19 (PASC).

scholarly journals Development and validation of new poisoning mortality score system for patients with acute poisoning at the emergency department

Critical Care ◽  
2021 ◽  
Vol 25 (1) ◽  
Author(s):  
Kap Su Han ◽  
Su Jin Kim ◽  
Eui Jung Lee ◽  
Joong Ho Shin ◽  
Ji Sung Lee ◽  
...  
Keyword(s):  
Emergency Department ◽  
Hospital Mortality ◽  
Prediction Model ◽  
Scoring System ◽  
Goodness Of Fit ◽  
Clinical Decision Making ◽  
Acute Poisoning ◽  
Goodness Of Fit Test ◽  
Temporal Validation ◽  
Probability Of Death

Abstract Background A prediction model of mortality for patients with acute poisoning has to consider both poisoning-related characteristics and patients’ physiological conditions; moreover, it must be applicable to patients of all ages. This study aimed to develop a scoring system for predicting in-hospital mortality of patients with acute poisoning at the emergency department (ED). Methods This was a retrospective analysis of the Injury Surveillance Cohort generated by the Korea Center for Disease Control and Prevention (KCDC) during 2011–2018. We developed the new-Poisoning Mortality Scoring system (new-PMS) to generate a prediction model using the derivation group (2011–2017 KCDC cohort). Points were computed for categories of each variable. The sum of these points was the new-PMS. The validation group (2018 KCDC cohort) was subjected to external temporal validation. The performance of new-PMS in predicting mortality was evaluated using area under the receiver operating characteristic curve (AUROC) for both the groups. Results Of 57,326 poisoning cases, 42,568 were selected. Of these, 34,352 (80.7%) and 8216 (19.3%) were enrolled in the derivation and validation groups, respectively. The new-PMS was the sum of the points for each category of 10 predictors. The possible range of the new-PMS was 0–137 points. Hosmer–Lemeshow goodness-of-fit test showed adequate calibration for the new-PMS with p values of 0.093 and 0.768 in the derivation and validation groups, respectively. AUROCs of the new-PMS were 0.941 (95% CI 0.934–0.949, p < 0.001) and 0.946 (95% CI 0.929–0.964, p < 0.001) in the derivation and validation groups, respectively. The sensitivity, specificity, and accuracy of the new-PMS (cutoff value: 49 points) were 86.4%, 87.2%, and 87.2% and 85.9%, 89.5%, and 89.4% in the derivation and validation groups, respectively. Conclusions We developed a new-PMS system based on demographic, poisoning-related variables, and vital signs observed among patients at the ED. The new-PMS showed good performance for predicting in-hospital mortality in both the derivation and validation groups. The probability of death increased according to the increase in the new-PMS. The new-PMS accurately predicted the probability of death for patients with acute poisoning. This could contribute to clinical decision making for patients with acute poisoning at the ED.

scholarly journals Prediction of Mortality in a Patient With Acute Poisoning

2020 ◽  
Author(s):  
Kap Su Han ◽  
Su Jin Kim ◽  
Eui Jung Lee ◽  
Joong Ho Shin ◽  
Ji Sung Lee ◽  
...  
Keyword(s):  
High Risk ◽  
Hospital Mortality ◽  
Scoring System ◽  
Clinical Decision Making ◽  
Risk Group ◽  
Acute Poisoning ◽  
High Risk Group ◽  
Clinical Settings ◽  
Prediction Of Mortality ◽  
Temporal Validation

Abstract Objective: This study aimed to develop a scoring system for predicting the in-hospital mortality of acute poisoning patients at the emergency department (ED). Methods: This was a retrospective analysis of the Injury Surveillance Cohort generated by the Korea Center for Disease Control and Prevention (KCDC) from 2011–2018. We developed the new-Poisoning Mortality Scoring system (new-PMS) to generate a prediction model using the derivation group (2011–2017 KCDC cohort). Points were computed for each category of each variable. The sum of these points was the new-PMS. The validation group (2018 KCDC cohort) was subjected to external temporal validation. The performance of new-PMS in predicting mortality was evaluated using receiver operating characteristic (ROC) curves for both groups. For simple interpretation in clinical settings, risk groups were categorized as very low, low, intermediate, and high according to the new-PMS; we suggested the mortality curve according to new-PMS. Results: Of 57326 poisoning cases, 42568 were selected. Of these, 34352 (80.7%) and 8216 (19.3%) were enrolled in the derivation and validation groups, respectively. New-PMS was the sum of points for each category of 10 predictors. The range of new-PMS was -20 to 3420 points. The area under the ROC curve of new-PMS was 0.942 (95% CI: 0.934–0.949) and 0.946 (95% CI: 0.930–0.963) for the derivation and validation groups, respectively. The mean predicted mortality and the observed mortalities of the high-risk group (new-PMS ≥1048) were 9.7% (95% CI: 9.3 – 10.0) and 10.0% for the derivation group and 8.4% (95% CI: 7.7 – 9.1) and 7.4% for the validation groups, respectively. Conclusions: New-PMS showed good performance in predicting in-hospital mortality for both groups. As mortality sharply increased with the high risk-group of the new-PMS, early hemodynamic stabilization of acute poisoning patients at the ED may improve their clinical outcomes. New-PMS contributes to clinical decision-making for acute poisoning patients in clinical settings.

scholarly journals Development of Clinical Prognostic Models for Postoperative Survival and Quality of Life in Patients With Surgically Treated Metastatic Epidural Spinal Cord Compression

Neurosurgery ◽  
2019 ◽  
Vol 66 (Supplement_1) ◽  
Author(s):  
Anick Nater ◽  
Junior Chuang ◽  
Kuan Liu ◽  
Nasir A Quraishi ◽  
Dritan Pasku ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Spinal Cord ◽  
Spinal Cord Compression ◽  
Clinical Decision Making ◽  
Cord Compression ◽  
Prognostic Models ◽  
Physical Component Score ◽  
C Statistic ◽  
Sf 36

Abstract INTRODUCTION Surgery is generally considered for patients with metastatic epidural spinal cord compression (MESCC) with life expectancy >3 mo. No existing clinical prognostic models (CPMs) of survival are consistently used, and no CPMs exist which predict quality of life (QoL) following surgical treatment. These knowledge gaps are important given the challenges involved in managing MESCC. METHODS Using TRIPOD guidelines and data from 258 patients (AOSpine North America (NA) MESCC study and Nottingham MESCC registry), we created 1-yr survival and QoL CPMs using Cox model and logistic regression with manual backward elimination. The outcome measure for QoL was the minimal clinical important difference (MCID) in EQ5D scores. Internal validation involved 200 bootstrap iterations; calibration and discrimination were evaluated. RESULTS Higher SF-36 physical component score (PCS) (HR: 0.96) was associated with longer survival whereas primary tumor other than breast, thyroid, and prostate (unfavorable, HR: 2.57; others, HR: 1.20), organ metastasis (HR: 1.51), male sex (HR: 1.58), and preoperative radiotherapy (HR: 1.53) were associated with shorter survival (c-statistic: 0.69, 95% CI: 0.64-0.73). KPS < 70% (OR: 2.50), living in NA (OR: 4.06), SF-36 PCS (OR: 0.95) and mental component (OR: 0.96) were associated with the likelihood of achieving a MCID improvement in EQ-5D at 3 mo (c-statistic: 0.74, 95% CI: 0.68-0.79). Calibration for both CPMs was very good. CONCLUSION We developed and internally validated the first CPMs of survival and QoL at 3 mo postoperatively in patients with MESCC using TRIPOD guidelines. A web-based calculator is available (http://spine-met.com) to assist clinical decision-making in this complex patient population.

scholarly journals Inflammatory and Hypercoagulable Biomarkers and Clinical Outcomes in COVID-19 Patients

2021 ◽  
Vol 10 (14) ◽  
pp. 3086
Author(s):  
Hiroki Kitakata ◽  
Shun Kohsaka ◽  
Shunsuke Kuroda ◽  
Akihiro Nomura ◽  
Takeshi Kitai ◽  
...  
Keyword(s):  
Hospital Mortality ◽  
Risk Estimation ◽  
Interquartile Range ◽  
Proportional Hazard ◽  
Multicenter Observational Study ◽  
Cox Proportional Hazard ◽  
Cox Proportional Hazard Models ◽  
Time Of Admission ◽  
Mortality Hazard Ratio ◽  
D Dimer

Systemic inflammation and hypercoagulopathy are known pathophysiological processes of coronavirus disease 2019 (COVID-19), particularly in patients with known cardiovascular disease or its risk factors (CVD). However, whether a cumulative assessment of these biomarkers at admission could contribute to the prediction of in-hospital outcomes remains unknown. The CLAVIS-COVID registry was a Japanese nationwide retrospective multicenter observational study, supported by the Japanese Circulation Society. Consecutive hospitalized patients with pre-existing CVD and COVID-19 were enrolled. Patients were stratified by the tertiles of CRP and D-dimer values at the time of admission. Multivariable Cox proportional hazard models were constructed. In 461 patients (65.5% male; median age, 70.0), the median baseline CRP and D-dimer was 58.3 (interquartile range, 18.2–116.0) mg/L and 1.5 (interquartile range, 0.8–3.0) mg/L, respectively. Overall, the in-hospital mortality rate was 16.5%, and the rates steadily increased in concordance with both CRP (5.0%, 15.0%, and 28.2%, respectively p < 0.001) and D-dimer values (6.8%, 19.6%, and 22.5%, respectively p = 0.001). Patients with the lowest tertiles of both biomarkers (CRP, 29.0 mg/L; D-dimer, 1.00 mg/L) were at extremely low risk of in-hospital mortality (0% until day 50, and 1.4% overall). Conversely, the elevation of both CRP and D-dimer levels was a significant predictor of in-hospital mortality (Hazard ratio, 2.97; 95% confidence interval, 1.57–5.60). A similar trend was observed when the biomarker threshold was set at a clinically relevant threshold. In conclusion, the combination of these abnormalities may provide a framework for rapid risk estimation for in-hospital COVID-19 patients with CVD.

scholarly journals Understanding the usefulness of prognostic models in clinical decision-making

2017 ◽  
Vol 63 (2) ◽  
pp. 121-125 ◽  
Author(s):  
Adrian C Traeger ◽  
Markus Hübscher ◽  
James H McAuley
Keyword(s):  
Decision Making ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Prognostic Models

scholarly journals Legal Aspects in Obstetric Anesthesia

2016 ◽  
Vol 10 (2) ◽  
pp. 3-9
Author(s):  
S Chattopadhyay ◽  
A Rudra ◽  
M Ray ◽  
S Sengupta ◽  
S Goswami
Keyword(s):  
High Risk ◽  
General Anesthesia ◽  
Obstetric Anesthesia ◽  
Legal Aspects ◽  
Routine Practice ◽  
Good Doctor ◽  
Surgical Safety ◽  
Valid Consent ◽  
Headache Pain ◽  
Time Of Admission

Obstetric anesthesia is a particularly high-risk sub-specialty of anesthesia and may lead to serious morbidities and even mortality. Good doctor-patient relation from the time of admission till discharge is the most important factor to avert future litigations. Any procedure done or planned should be clearly documented. Documentation should start with a valid consent in the patient’s own language, and have all three components of voluntariness, capacity and knowledge. A ‘Surgical Safety’ checklist is particularly helpful in documentation and decreasing errors. Safety of the mother (and her child) is paramount. Both regional as well as general anesthesia, either inadvertently or if not administered properly may be associated with morbidities like headache, pain and emotional distress. However, deaths do occur and general anesthesia is associated with care should be routine practice and inculcated by everyone involved in patient care.

scholarly journals Inpatient Outcomes and Rates of Venous Thromboembolism and Gastrointestinal Bleeding in Patients with Hepatocellular Carcinoma: Results from Nationwide Inpatient Sample Database 2007-2016

Blood ◽  
2019 ◽  
Vol 134 (Supplement_1) ◽  
pp. 2162-2162
Author(s):  
Kamelah Abushalha ◽  
Sawsan Abulaimoun ◽  
Ryan Walters ◽  
Sara Albagoush ◽  
Hussain I Rangoonwala ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Venous Thromboembolism ◽  
Gastrointestinal Bleeding ◽  
Hospital Mortality ◽  
Hospital Cost ◽  
Clinical Decision Making ◽  
Conflicts Of Interest ◽  
Increased Risk ◽  
Increasing Trend ◽  
Increasing Trends

Background: Patients with hepatocellular carcinoma (HCC) are at an increased risk for developing venous thromboembolism (VTE)- mainly portal venous thrombosis (PVT). Malignancy and liver cirrhosis ( 80%-90% of HCC cases are related to cirrhosis) are conditions that can perturb the hemostatic balance towards a prothrombotic state. Also, these patients with HCC are at high risk for gastrointestinal bleeding (GIB), making thromboprophylaxis and anticoagulation a treatment challenge. Additional information regarding the outcomes and severity of both VTE and GIB in patients with HCC would be useful to guide clinical decision-making Aim: To determine the rates, inpatient mortality, length of stay (LOS) and hospital cost of VTE and GIB-related admissions in patients with hepatocellular carcinoma. Method: We used ICD-9-CM and ICD-10-CM codes to identify hospitalizations from 2007 to 2016 that included HCC with primary discharge diagnoses of GIB or VTE. Linear trends in the rate of GIB and VTE, as well as in-hospital mortality, LOS, and inflation-adjusted hospital cost (in 2016 US dollars), were evaluated using Daniel's test; piecewise slopes were used as needed. All analyses accounted for the NIS sampling design with updated hospital trend weights used as appropriate. SAS v. 9.4 was used for all analyses. Results: Between 2007 and 2016, we identified 6,527,871 hospitalizations with HCC and a primary discharge diagnosis of GIB (3,517,059; 53.9%) or VTE (3,010,812; 46.1%). From 2007 to 2010, a decreasing trend was observed in the rate of GIB diagnoses (55.5% to 51.6%, ptrend < .001), whereas an increasing trend was observed for VTE diagnoses (44.5% to 48.4%, ptrend < .001). By contrast, from 2010 to 2016, an increasing trend was observed in GIB (51.6% to 55.2%, ptrend < .001), whereas a decreasing trend was observed in VTE (48.4% to 44.8%, ptrend < .001). For in-hospital mortality, a decreasing trend was observed for GIB (2.3% to 1.9%, ptrend < .001), whereas a decreasing trend was observed in VTE until 2012 (1.8% to 1.5%, ptrend < .001), after which no trend was indicated (1.5% to 1.6%, ptrend = .337). Although decreasing trends in LOS were observed for GIB (3.4 days to 3.2 days, ptrend < .001) and VTE (4.3 days to 3.3 days, ptrend < .001), increasing trends were observed for inflation-adjusted hospital cost for both GIB ($6,996 to $7,707, ptrend < .001) and VTE ($7,283 to $7,584, ptrend = .048). Conclusion: In this NIS cohort of hospitalized patients with HCC, GIB was more frequently observed than VTE. Trends observed in the rates of GIB and VTE went in opposite directions. In general decreasing trends were observed in in-hospital mortality and LOS for both VTE and GIB. By contrast, increasing trends were observed in the hospital cost for both diagnoses. Clinicians should balance benefits against risks when deciding VTE prophylaxis and treatment in patients with HCC. Future studies are needed to determine the ideal agent and specific dosages to treat HCC-associated VTE. Disclosures No relevant conflicts of interest to declare.

scholarly journals Characteristics and Outcomes of Frail Patients with COVID-19 Admitted to ICU: An Individual Patient Data Meta-Analysis

2021 ◽  
Author(s):  
Ashwin Subramaniam ◽  
Christopher Anstey ◽  
J Randall Curtis ◽  
Sushma Ashwin ◽  
Mallikarjuna PONNAPA REDDY ◽  
...  
Keyword(s):  
Hospital Mortality ◽  
Clinical Decision Making ◽  
Individual Patient Data ◽  
Meta Analysis ◽  
Multivariable Analysis ◽  
Patient Data ◽  
Clinical Frailty Scale ◽  
Risk Of Death ◽  
Frail Patients ◽  
Bed Days

Abstract Purpose: Frailty is often used in clinical decision-making for patients with COVID-19, yet studies have found variable influence of frailty on outcomes in those admitted to the intensive care unit (ICU). In this individual patient data meta-analysis, we evaluated the characteristics, and outcomes of frail patients admitted to ICU with COVID-19.Methods: We contacted the corresponding authors of sixteen eligible studies published between December 1st 2019 and February 28th 2021 reporting the clinical frailty scale (CFS) in patients with confirmed COVID-19 admitted to ICU. Individual patient data was obtained from 7 studies. We classified patients as non-frail (CFS=1-4) or frail (CFS=5-8). The primary outcome was hospital mortality. We also compared the use of mechanical ventilation (MV) and the proportion of ICU bed-days between frailty categories. Results: Of the 2001 patients admitted to ICU, 388 (19.4%) were frail. Increasing age and sequential organ failure assessment (SOFA) score, CFS ≥4, use of MV, vasopressors, renal replacement therapy and hyperlactatemia were risk factors for death in a multivariable analysis. Hospital mortality was higher in frail patients (65.2% vs. 41.8%; p<0.001), with adjusted mortality increasing with a rising CFS score beyond 3. Younger and non-frail patients were more likely to receive MV. Frail patients spent less time on MV (median days [IQR] 9 [5-16] vs. 11 [6-18]; p=0.012) and accounted for only 12.3% of total ICU bed-days. Conclusion: Frail patients with COVID-19 were commonly admitted to ICU and had greater hospital mortality but spent relatively fewer days in ICU when compared with non-frail patients. Frail patients receiving MV were at greater risk of death than non-frail patients. Systematic review registration: Registration protocol in PROSPERO (CRD42020224255).

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