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1.
BMJ Open ; 11(9), 2021.
Article in English | ProQuest Central | ID: covidwho-1842724

ABSTRACT

ObjectivesDevelop simple and valid models for predicting mortality and need for intensive care unit (ICU) admission in patients who present at the emergency department (ED) with suspected COVID-19.DesignRetrospective.SettingSecondary care in four large Dutch hospitals.ParticipantsPatients who presented at the ED and were admitted to hospital with suspected COVID-19. We used 5831 first-wave patients who presented between March and August 2020 for model development and 3252 second-wave patients who presented between September and December 2020 for model validation.Outcome measuresWe developed separate logistic regression models for in-hospital death and for need for ICU admission, both within 28 days after hospital admission. Based on prior literature, we considered quickly and objectively obtainable patient characteristics, vital parameters and blood test values as predictors. We assessed model performance by the area under the receiver operating characteristic curve (AUC) and by calibration plots.ResultsOf 5831 first-wave patients, 629 (10.8%) died within 28 days after admission. ICU admission was fully recorded for 2633 first-wave patients in 2 hospitals, with 214 (8.1%) ICU admissions within 28 days. A simple model—COVID outcome prediction in the emergency department (COPE)—with age, respiratory rate, C reactive protein, lactate dehydrogenase, albumin and urea captured most of the ability to predict death. COPE was well calibrated and showed good discrimination for mortality in second-wave patients (AUC in four hospitals: 0.82 (95% CI 0.78 to 0.86);0.82 (95% CI 0.74 to 0.90);0.79 (95% CI 0.70 to 0.88);0.83 (95% CI 0.79 to 0.86)). COPE was also able to identify patients at high risk of needing ICU admission in second-wave patients (AUC in two hospitals: 0.84 (95% CI 0.78 to 0.90);0.81 (95% CI 0.66 to 0.95)).ConclusionsCOPE is a simple tool that is well able to predict mortality and need for ICU admission in patients who present to the ED with suspected COVID-19 and may help patients and doctors in decision making.

2.
Dongelmans, Dave A.; Termorshuizen, Fabian, Brinkman, Sylvia, Bakhshi-Raiez, Ferishta, Sesmu, Arbous M.; de Lange Dylan, W.; van Bussel Bas, C. T.; de Keizer Nicolette, F.; Verbiest, Dirk P.; te Velde Leo, F.; van Driel Erik, M.; Rijpstra, Tom, Elbers, Paul W. G.; Georgieva, Lyuba, Verweij, Eva, de Jong Remko, M.; van Iersel Freya, M.; Koning Dick, T. J. J.; Rengers, Els, Kusadasi, Nuray, Erkamp, Michiel L.; van den Berg, Roy, Jacobs Cretièn, J. M. G.; Epker, Jelle L.; Rijkeboer, Annemiek A.; de Bruin Martha, T.; Spronk, Peter, Draisma, Annelies, Versluis, Dirk Jan, van den Berg Lettie, A. E.; Mos Marissa, Vrolijk-de, Lens, Judith A.; Jannet, Mehagnoul-Schipper D.; Gommers, Diederik, Lutisan, Johan G.; Hoeksema, Martijn, Pruijsten, Ralph V.; Kieft, Hans, Rozendaal, Jan, Nooteboom, Fleur, Boer, Dirk P.; Janssen Inge, T. A.; van Gulik, Laura, Peter, Koetsier M.; Silderhuis, Vera M.; Schnabel, Ronny M.; Drogt, Ioana, de Ruijter, Wouter, Bosman, Rob J.; Frenzel, Tim, Urlings-Strop Louise, C.; Allard, Dijkhuizen, Hené, Ilanit Z.; de Meijer Arthur, R.; Holtkamp Jessica, W. M.; Postma, Nynke, Bindels Alexander, J. G. H.; Wesselink Ronald, M. J.; van Slobbe-Bijlsma Eline, R.; van der Voort Peter, H. J.; Eikemans Bob, J. W.; Barnas Michel, G. W.; Festen-Spanjer, Barbara, van Lieshout, Maarten, Gritters, Niels C.; van Tellingen, Martijn, Brunnekreef, Gert B.; Vandeputte, Joyce, Dormans Tom, P. J.; Hoogendoorn, Marga E.; de Graaff, Mart, Moolenaar, David, Reidinga, Auke C.; Spijkstra Jan, Jaap, de Waal, Ruud.
Annals of Intensive Care ; 12(1), 2022.
Article in English | ProQuest Central | ID: covidwho-1837260

ABSTRACT

BackgroundTo assess trends in the quality of care for COVID-19 patients at the ICU over the course of time in the Netherlands.MethodsData from the National Intensive Care Evaluation (NICE)-registry of all COVID-19 patients admitted to an ICU in the Netherlands were used. Patient characteristics and indicators of quality of care during the first two upsurges (N = 4215: October 5, 2020–January 31, 2021) and the final upsurge of the second wave, called the ‘third wave’ (N = 4602: February 1, 2021–June 30, 2021) were compared with those during the first wave (N = 2733, February–May 24, 2020).ResultsDuring the second and third wave, there were less patients treated with mechanical ventilation (58.1 and 58.2%) and vasoactive drugs (48.0 and 44.7%) compared to the first wave (79.1% and 67.2%, respectively). The occupancy rates as fraction of occupancy in 2019 (1.68 and 1.55 vs. 1.83), the numbers of ICU relocations (23.8 and 27.6 vs. 32.3%) and the mean length of stay at the ICU (HRs of ICU discharge = 1.26 and 1.42) were lower during the second and third wave. No difference in adjusted hospital mortality between the second wave and the first wave was found, whereas the mortality during the third wave was considerably lower (OR = 0.80, 95% CI [0.71–0.90]).ConclusionsThese data show favorable shifts in the treatment of COVID-19 patients at the ICU over time. The adjusted mortality decreased in the third wave. The high ICU occupancy rate early in the pandemic does probably not explain the high mortality associated with COVID-19.

3.
EuropePMC;
Preprint in English | EuropePMC | ID: ppcovidwho-328824

ABSTRACT

Background: Identification of distinct clinical phenotypes in critically ill COVID-19 patients could improve understanding of the disease heterogeneity and enable prognostic and predictive enrichment facilitating more personalized treatment. However, previous attempts did not take into account temporal dynamics of the disease. By including the dimension of time, we aim to gain further insights into the heterogeneity of COVID-19. Methods: : We used highly granular data from 3202 adult critically ill COVID patients in the multicenter Dutch Data Warehouse that were admitted to one of 25 Dutch ICUs between February 2020 and March 2021. Parameters including demographics, clinical observations, medications, laboratory values, vital signs, and data from life support devices were selected based on relevance and availability. Twenty-one consecutive datasets were created that each covered 24 hours of ICU data for each day of ICU treatment up until day 21. After aggregation and multiple imputation of the temporal data, clinical phenotypes in each dataset were identified by performing multiple cluster analyses. Clinical phenotypes were identified by aggregating values from all patients per cluster. Both evolution of the clinical phenotypes over time and patient allocation to these clusters over time were tracked. Results: : The final patient cohort consisted of 2438 critically ill COVID-19 patients with a registered ICU mortality outcome. Forty-one parameters were chosen for the cluster analysis. On admission, both a mild and a more severe clinical phenotype were found. After day 4, the severe phenotype split into an intermediate and a severe phenotype for 11 consecutive days. Heterogeneity between phenotypes appears to be strongly driven by inflammation and dead space ventilation. During the 21-day period only 8.2% and 4.6% of patients in the initial mild and severe clusters remained assigned to the same phenotype respectively. The clinical phenotype half-life was between 5 and 6 days for the mild and severe phenotypes, and about 3 days for the medium severe phenotype. Conclusions: : Patients typically do not remain in the same cluster throughout intensive care treatment. This may have important implications for prognostic or predictive enrichment. Prominent dissimilarities between clinical phenotypes are predominantly driven by inflammation and dead space ventilation.

4.
EuropePMC;
Preprint in English | EuropePMC | ID: ppcovidwho-326061

ABSTRACT

Despite the recent progress in the field of causal inference, to date there is no agreed upon methodology to glean treatment effect estimation from observational data. The consequence on clinical practice is that, when lacking results from a randomized trial, medical personnel is left without guidance on what seems to be effective in a real-world scenario. This article proposes a pragmatic methodology to obtain preliminary but robust estimation of treatment effect from observational studies, to provide front-line clinicians with a degree of confidence in their treatment strategy. Our study design is applied to an open problem, the estimation of treatment effect of the proning maneuver on COVID-19 Intensive Care patients.

5.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-313097

ABSTRACT

Despite the recent progress in the field of causal inference, to date there is no agreed upon methodology to glean treatment effect estimation from observational data. The consequence on clinical practice is that, when lacking results from a randomized trial, medical personnel is left without guidance on what seems to be effective in a real-world scenario. This article showcases a pragmatic methodology to obtain preliminary estimation of treatment effect from observational studies. Our approach was tested on the estimation of treatment effect of the proning maneuver on oxygenation levels, on a cohort of COVID-19 Intensive Care patients. We modeled our study design on a recent RCT for proning (the PROSEVA trial). Linear regression, propensity score models such as blocking and DR-IPW, BART and two versions of Counterfactual Regression were employed to provide estimates on observational data comprising first wave COVID-19 ICU patient data from 25 Dutch hospitals. 6371 data points, from 745 mechanically ventilated patients, were included in the study. Estimates for the early effect of proning -- P/F ratio from 2 to 8 hours after proning -- ranged between 14.54 and 20.11 mm Hg depending on the model. Estimates for the late effect of proning -- oxygenation from 12 to 24 hours after proning -- ranged between 13.53 and 15.26 mm Hg. All confidence interval being strictly above zero indicated that the effect of proning on oxygenation for COVID-19 patient was positive and comparable in magnitude to the effect on non COVID-19 patients. These results provide further evidence on the effectiveness of proning on the treatment of COVID-19 patients. This study, along with the accompanying open-source code, provides a blueprint for treatment effect estimation in scenarios where RCT data is lacking. Funding: SIDN fund, CovidPredict consortium, Pacmed.

6.
Crit Care ; 25(1): 448, 2021 12 27.
Article in English | MEDLINE | ID: covidwho-1632299

ABSTRACT

INTRODUCTION: Determining the optimal timing for extubation can be challenging in the intensive care. In this study, we aim to identify predictors for extubation failure in critically ill patients with COVID-19. METHODS: We used highly granular data from 3464 adult critically ill COVID patients in the multicenter Dutch Data Warehouse, including demographics, clinical observations, medications, fluid balance, laboratory values, vital signs, and data from life support devices. All intubated patients with at least one extubation attempt were eligible for analysis. Transferred patients, patients admitted for less than 24 h, and patients still admitted at the time of data extraction were excluded. Potential predictors were selected by a team of intensive care physicians. The primary and secondary outcomes were extubation without reintubation or death within the next 7 days and within 48 h, respectively. We trained and validated multiple machine learning algorithms using fivefold nested cross-validation. Predictor importance was estimated using Shapley additive explanations, while cutoff values for the relative probability of failed extubation were estimated through partial dependence plots. RESULTS: A total of 883 patients were included in the model derivation. The reintubation rate was 13.4% within 48 h and 18.9% at day 7, with a mortality rate of 0.6% and 1.0% respectively. The grandient-boost model performed best (area under the curve of 0.70) and was used to calculate predictor importance. Ventilatory characteristics and settings were the most important predictors. More specifically, a controlled mode duration longer than 4 days, a last fraction of inspired oxygen higher than 35%, a mean tidal volume per kg ideal body weight above 8 ml/kg in the day before extubation, and a shorter duration in assisted mode (< 2 days) compared to their median values. Additionally, a higher C-reactive protein and leukocyte count, a lower thrombocyte count, a lower Glasgow coma scale and a lower body mass index compared to their medians were associated with extubation failure. CONCLUSION: The most important predictors for extubation failure in critically ill COVID-19 patients include ventilatory settings, inflammatory parameters, neurological status, and body mass index. These predictors should therefore be routinely captured in electronic health records.


Subject(s)
Airway Extubation , COVID-19 , Treatment Failure , Adult , COVID-19/therapy , Critical Illness , Humans , Machine Learning
7.
Front Endocrinol (Lausanne) ; 12: 747732, 2021.
Article in English | MEDLINE | ID: covidwho-1598924

ABSTRACT

Objective: To evaluate the association between overweight and obesity on the clinical course and outcomes in patients hospitalized with COVID-19. Design: Retrospective, observational cohort study. Methods: We performed a multicenter, retrospective, observational cohort study of hospitalized COVID-19 patients to evaluate the associations between overweight and obesity on the clinical course and outcomes. Results: Out of 1634 hospitalized COVID-19 patients, 473 (28.9%) had normal weight, 669 (40.9%) were overweight, and 492 (30.1%) were obese. Patients who were overweight or had obesity were younger, and there were more women in the obese group. Normal-weight patients more often had pre-existing conditions such as malignancy, or were organ recipients. During admission, patients who were overweight or had obesity had an increased probability of acute respiratory distress syndrome [OR 1.70 (1.26-2.30) and 1.40 (1.01-1.96)], respectively and acute kidney failure [OR 2.29 (1.28-3.76) and 1.92 (1.06-3.48)], respectively. Length of hospital stay was similar between groups. The overall in-hospital mortality rate was 27.7%, and multivariate logistic regression analyses showed that overweight and obesity were not associated with increased mortality compared to normal-weight patients. Conclusion: In this study, overweight and obesity were associated with acute respiratory distress syndrome and acute kidney injury, but not with in-hospital mortality nor length of hospital stay.


Subject(s)
Acute Kidney Injury/complications , COVID-19/mortality , Hospital Mortality , Hospitalization , Obesity/complications , Respiratory Distress Syndrome/complications , Aged , Female , Humans , Intensive Care Units , Length of Stay , Male , Middle Aged , Patient Discharge , Respiration, Artificial , Retrospective Studies , Treatment Outcome
8.
Emerg Med J ; 38(12): 901-905, 2021 Dec.
Article in English | MEDLINE | ID: covidwho-1495501

ABSTRACT

OBJECTIVE: Validated clinical risk scores are needed to identify patients with COVID-19 at risk of severe disease and to guide triage decision-making during the COVID-19 pandemic. The objective of the current study was to evaluate the performance of early warning scores (EWS) in the ED when identifying patients with COVID-19 who will require intensive care unit (ICU) admission for high-flow-oxygen usage or mechanical ventilation. METHODS: Patients with a proven SARS-CoV-2 infection with complete resuscitate orders treated in nine hospitals between 27 February and 30 July 2020 needing hospital admission were included. Primary outcome was the performance of EWS in identifying patients needing ICU admission within 24 hours after ED presentation. RESULTS: In total, 1501 patients were included. Median age was 71 (range 19-99) years and 60.3% were male. Of all patients, 86.9% were admitted to the general ward and 13.1% to the ICU within 24 hours after ED admission. ICU patients had lower peripheral oxygen saturation (86.7% vs 93.7, p≤0.001) and had a higher body mass index (29.2 vs 27.9 p=0.043) compared with non-ICU patients. National Early Warning Score 2 (NEWS2) ≥ 6 and q-COVID Score were superior to all other studied clinical risk scores in predicting ICU admission with a fair area under the receiver operating characteristics curve of 0.740 (95% CI 0.696 to 0.783) and 0.760 (95% CI 0.712 to 0.800), respectively. NEWS2 ≥6 and q-COVID Score ≥3 discriminated patients admitted to the ICU with a sensitivity of 78.1% and 75.9%, and specificity of 56.3% and 61.8%, respectively. CONCLUSION: In this multicentre study, the best performing models to predict ICU admittance were the NEWS2 and the Quick COVID-19 Severity Index Score, with fair diagnostic performance. However, due to the moderate performance, these models cannot be clinically used to adequately predict the need for ICU admission within 24 hours in patients with SARS-CoV-2 infection presenting at the ED.


Subject(s)
COVID-19/diagnosis , Critical Illness , Early Warning Score , Adult , Aged , Aged, 80 and over , COVID-19/classification , Female , Humans , Intensive Care Units , Male , Middle Aged , Patient Admission , Predictive Value of Tests , ROC Curve , Triage
9.
Crit Care Explor ; 3(10): e0555, 2021 Oct.
Article in English | MEDLINE | ID: covidwho-1475865

ABSTRACT

OBJECTIVES: As coronavirus disease 2019 is a novel disease, treatment strategies continue to be debated. This provides the intensive care community with a unique opportunity as the population of coronavirus disease 2019 patients requiring invasive mechanical ventilation is relatively homogeneous compared with other ICU populations. We hypothesize that the novelty of coronavirus disease 2019 and the uncertainty over its similarity with noncoronavirus disease 2019 acute respiratory distress syndrome resulted in substantial practice variation between hospitals during the first and second waves of coronavirus disease 2019 patients. DESIGN: Multicenter retrospective cohort study. SETTING: Twenty-five hospitals in the Netherlands from February 2020 to July 2020, and 14 hospitals from August 2020 to December 2020. PATIENTS: One thousand two hundred ninety-four critically ill intubated adult ICU patients with coronavirus disease 2019 were selected from the Dutch Data Warehouse. Patients intubated for less than 24 hours, transferred patients, and patients still admitted at the time of data extraction were excluded. MEASUREMENTS AND MAIN RESULTS: We aimed to estimate between-ICU practice variation in selected ventilation parameters (positive end-expiratory pressure, Fio2, set respiratory rate, tidal volume, minute volume, and percentage of time spent in a prone position) on days 1, 2, 3, and 7 of intubation, adjusted for patient characteristics as well as severity of illness based on Pao2/Fio2 ratio, pH, ventilatory ratio, and dynamic respiratory system compliance during controlled ventilation. Using multilevel linear mixed-effects modeling, we found significant (p ≤ 0.001) variation between ICUs in all ventilation parameters on days 1, 2, 3, and 7 of intubation for both waves. CONCLUSIONS: This is the first study to clearly demonstrate significant practice variation between ICUs related to mechanical ventilation parameters that are under direct control by intensivists. Their effect on clinical outcomes for both coronavirus disease 2019 and other critically ill mechanically ventilated patients could have widespread implications for the practice of intensive care medicine and should be investigated further by causal inference models and clinical trials.

10.
Acta Anaesthesiol Scand ; 66(1): 65-75, 2022 01.
Article in English | MEDLINE | ID: covidwho-1462715

ABSTRACT

BACKGROUND: The prediction of in-hospital mortality for ICU patients with COVID-19 is fundamental to treatment and resource allocation. The main purpose was to develop an easily implemented score for such prediction. METHODS: This was an observational, multicenter, development, and validation study on a national critical care dataset of COVID-19 patients. A systematic literature review was performed to determine variables possibly important for COVID-19 mortality prediction. Using a logistic multivariable model with a LASSO penalty, we developed the Rapid Evaluation of Coronavirus Illness Severity (RECOILS) score and compared its performance against published scores. RESULTS: Our development (validation) cohort consisted of 1480 (937) adult patients from 14 (11) Dutch ICUs admitted between March 2020 and April 2021. Median age was 65 (65) years, 31% (26%) died in hospital, 74% (72%) were males, average length of ICU stay was 7.83 (10.25) days and average length of hospital stay was 15.90 (19.92) days. Age, platelets, PaO2/FiO2 ratio, pH, blood urea nitrogen, temperature, PaCO2, Glasgow Coma Scale (GCS) score measured within +/-24 h of ICU admission were used to develop the score. The AUROC of RECOILS score was 0.75 (CI 0.71-0.78) which was higher than that of any previously reported predictive scores (0.68 [CI 0.64-0.71], 0.61 [CI 0.58-0.66], 0.67 [CI 0.63-0.70], 0.70 [CI 0.67-0.74] for ISARIC 4C Mortality Score, SOFA, SAPS-III, and age, respectively). CONCLUSIONS: Using a large dataset from multiple Dutch ICUs, we developed a predictive score for mortality of COVID-19 patients admitted to ICU, which outperformed other predictive scores reported so far.


Subject(s)
COVID-19 , Adult , Aged , Critical Care , Hospital Mortality , Humans , Intensive Care Units , Male , Multicenter Studies as Topic , Observational Studies as Topic , Patient Acuity , Prognosis , Retrospective Studies , SARS-CoV-2
11.
Crit Care ; 25(1): 304, 2021 08 23.
Article in English | MEDLINE | ID: covidwho-1370943

ABSTRACT

BACKGROUND: The Coronavirus disease 2019 (COVID-19) pandemic has underlined the urgent need for reliable, multicenter, and full-admission intensive care data to advance our understanding of the course of the disease and investigate potential treatment strategies. In this study, we present the Dutch Data Warehouse (DDW), the first multicenter electronic health record (EHR) database with full-admission data from critically ill COVID-19 patients. METHODS: A nation-wide data sharing collaboration was launched at the beginning of the pandemic in March 2020. All hospitals in the Netherlands were asked to participate and share pseudonymized EHR data from adult critically ill COVID-19 patients. Data included patient demographics, clinical observations, administered medication, laboratory determinations, and data from vital sign monitors and life support devices. Data sharing agreements were signed with participating hospitals before any data transfers took place. Data were extracted from the local EHRs with prespecified queries and combined into a staging dataset through an extract-transform-load (ETL) pipeline. In the consecutive processing pipeline, data were mapped to a common concept vocabulary and enriched with derived concepts. Data validation was a continuous process throughout the project. All participating hospitals have access to the DDW. Within legal and ethical boundaries, data are available to clinicians and researchers. RESULTS: Out of the 81 intensive care units in the Netherlands, 66 participated in the collaboration, 47 have signed the data sharing agreement, and 35 have shared their data. Data from 25 hospitals have passed through the ETL and processing pipeline. Currently, 3464 patients are included in the DDW, both from wave 1 and wave 2 in the Netherlands. More than 200 million clinical data points are available. Overall ICU mortality was 24.4%. Respiratory and hemodynamic parameters were most frequently measured throughout a patient's stay. For each patient, all administered medication and their daily fluid balance were available. Missing data are reported for each descriptive. CONCLUSIONS: In this study, we show that EHR data from critically ill COVID-19 patients may be lawfully collected and can be combined into a data warehouse. These initiatives are indispensable to advance medical data science in the field of intensive care medicine.


Subject(s)
COVID-19/epidemiology , Critical Illness/epidemiology , Data Warehousing/statistics & numerical data , Electronic Health Records/statistics & numerical data , Hospitalization/statistics & numerical data , Intensive Care Units/statistics & numerical data , Critical Care , Humans , Netherlands
12.
BMJ Open ; 11(7): e047347, 2021 07 19.
Article in English | MEDLINE | ID: covidwho-1318029

ABSTRACT

OBJECTIVE: Develop and validate models that predict mortality of patients diagnosed with COVID-19 admitted to the hospital. DESIGN: Retrospective cohort study. SETTING: A multicentre cohort across 10 Dutch hospitals including patients from 27 February to 8 June 2020. PARTICIPANTS: SARS-CoV-2 positive patients (age ≥18) admitted to the hospital. MAIN OUTCOME MEASURES: 21-day all-cause mortality evaluated by the area under the receiver operator curve (AUC), sensitivity, specificity, positive predictive value and negative predictive value. The predictive value of age was explored by comparison with age-based rules used in practice and by excluding age from the analysis. RESULTS: 2273 patients were included, of whom 516 had died or discharged to palliative care within 21 days after admission. Five feature sets, including premorbid, clinical presentation and laboratory and radiology values, were derived from 80 features. Additionally, an Analysis of Variance (ANOVA)-based data-driven feature selection selected the 10 features with the highest F values: age, number of home medications, urea nitrogen, lactate dehydrogenase, albumin, oxygen saturation (%), oxygen saturation is measured on room air, oxygen saturation is measured on oxygen therapy, blood gas pH and history of chronic cardiac disease. A linear logistic regression and non-linear tree-based gradient boosting algorithm fitted the data with an AUC of 0.81 (95% CI 0.77 to 0.85) and 0.82 (0.79 to 0.85), respectively, using the 10 selected features. Both models outperformed age-based decision rules used in practice (AUC of 0.69, 0.65 to 0.74 for age >70). Furthermore, performance remained stable when excluding age as predictor (AUC of 0.78, 0.75 to 0.81). CONCLUSION: Both models showed good performance and had better test characteristics than age-based decision rules, using 10 admission features readily available in Dutch hospitals. The models hold promise to aid decision-making during a hospital bed shortage.


Subject(s)
COVID-19 , Cohort Studies , Humans , Logistic Models , Retrospective Studies , SARS-CoV-2
13.
J Diabetes Metab Disord ; 20(2): 1155-1160, 2021 Dec.
Article in English | MEDLINE | ID: covidwho-1286208

ABSTRACT

Purpose: Inhibition of dipeptidyl peptidase (DPP-)4 could reduce coronavirus disease 2019 (COVID-19) severity by reducing inflammation and enhancing tissue repair beyond glucose lowering. We aimed to assess this in a prospective cohort study. Methods: We studied in 565 patients with type 2 diabetes in the CovidPredict Clinical Course Cohort whether use of a DPP-4 inhibitor prior to hospital admission due to COVID-19 was associated with improved clinical outcomes. Using crude analyses and propensity score matching (on age, sex and BMI), 28 patients using a DPP-4 inhibitor were identified and compared to non-users. Results: No differences were found in the primary outcome mortality (matched-analysis = odds-ratio: 0,94 [95% confidence interval: 0,69 - 1,28], p-value: 0,689) or any of the secondary outcomes (ICU admission, invasive ventilation, thrombotic events or infectious complications). Additional analyses comparing users of DPP-4 inhibitors with subgroups of non-users (subgroup 1: users of metformin and sulphonylurea; subgroup 2: users of any insulin combination), allowing to correct for diabetes severity, did not yield different results. Conclusions: We conclude that outpatient use of a DPP-4 inhibitor does not affect the clinical outcomes of patients with type 2 diabetes who are hospitalized because of COVID-19 infection.

14.
Intensive Care Med Exp ; 9(1): 32, 2021 Jun 28.
Article in English | MEDLINE | ID: covidwho-1282270

ABSTRACT

BACKGROUND: The identification of risk factors for adverse outcomes and prolonged intensive care unit (ICU) stay in COVID-19 patients is essential for prognostication, determining treatment intensity, and resource allocation. Previous studies have determined risk factors on admission only, and included a limited number of predictors. Therefore, using data from the highly granular and multicenter Dutch Data Warehouse, we developed machine learning models to identify risk factors for ICU mortality, ventilator-free days and ICU-free days during the course of invasive mechanical ventilation (IMV) in COVID-19 patients. METHODS: The DDW is a growing electronic health record database of critically ill COVID-19 patients in the Netherlands. All adult ICU patients on IMV were eligible for inclusion. Transfers, patients admitted for less than 24 h, and patients still admitted at time of data extraction were excluded. Predictors were selected based on the literature, and included medication dosage and fluid balance. Multiple algorithms were trained and validated on up to three sets of observations per patient on day 1, 7, and 14 using fivefold nested cross-validation, keeping observations from an individual patient in the same split. RESULTS: A total of 1152 patients were included in the model. XGBoost models performed best for all outcomes and were used to calculate predictor importance. Using Shapley additive explanations (SHAP), age was the most important demographic risk factor for the outcomes upon start of IMV and throughout its course. The relative probability of death across age values is visualized in Partial Dependence Plots (PDPs), with an increase starting at 54 years. Besides age, acidaemia, low P/F-ratios and high driving pressures demonstrated a higher probability of death. The PDP for driving pressure showed a relative probability increase starting at 12 cmH2O. CONCLUSION: Age is the most important demographic risk factor of ICU mortality, ICU-free days and ventilator-free days throughout the course of invasive mechanical ventilation in critically ill COVID-19 patients. pH, P/F ratio, and driving pressure should be monitored closely over the course of mechanical ventilation as risk factors predictive of these outcomes.

15.
PLoS One ; 16(4): e0249920, 2021.
Article in English | MEDLINE | ID: covidwho-1186609

ABSTRACT

OBJECTIVE: To establish whether one can build a mortality prediction model for COVID-19 patients based solely on demographics and comorbidity data that outperforms age alone. Such a model could be a precursor to implementing smart lockdowns and vaccine distribution strategies. METHODS: The training cohort comprised 2337 COVID-19 inpatients from nine hospitals in The Netherlands. The clinical outcome was death within 21 days of being discharged. The features were derived from electronic health records collected during admission. Three feature selection methods were used: LASSO, univariate using a novel metric, and pairwise (age being half of each pair). 478 patients from Belgium were used to test the model. All modeling attempts were compared against an age-only model. RESULTS: In the training cohort, the mortality group's median age was 77 years (interquartile range = 70-83), higher than the non-mortality group (median = 65, IQR = 55-75). The incidence of former/active smokers, male gender, hypertension, diabetes, dementia, cancer, chronic obstructive pulmonary disease, chronic cardiac disease, chronic neurological disease, and chronic kidney disease was higher in the mortality group. All stated differences were statistically significant after Bonferroni correction. LASSO selected eight features, novel univariate chose five, and pairwise chose none. No model was able to surpass an age-only model in the external validation set, where age had an AUC of 0.85 and a balanced accuracy of 0.77. CONCLUSION: When applied to an external validation set, we found that an age-only mortality model outperformed all modeling attempts (curated on www.covid19risk.ai) using three feature selection methods on 22 demographic and comorbid features.


Subject(s)
COVID-19/mortality , Age Factors , Aged , Aged, 80 and over , Belgium/epidemiology , COVID-19/diagnosis , COVID-19/epidemiology , Cohort Studies , Communicable Disease Control , Comorbidity , Electronic Health Records , Female , Hospitalization , Humans , Male , Middle Aged , Netherlands/epidemiology , Prognosis , Risk Assessment , Risk Factors , SARS-CoV-2/isolation & purification
16.
BMJ Open ; 11(2): e045482, 2021 02 22.
Article in English | MEDLINE | ID: covidwho-1096995

ABSTRACT

OBJECTIVES: Recent reports suggest a high prevalence of hypertension and diabetes in COVID-19 patients, but the role of cardiovascular disease (CVD) risk factors in the clinical course of COVID-19 is unknown. We evaluated the time-to-event relationship between hypertension, dyslipidaemia, diabetes and COVID-19 outcomes. DESIGN: We analysed data from the prospective Dutch CovidPredict cohort, an ongoing prospective study of patients admitted for COVID-19 infection. SETTING: Patients from eight participating hospitals, including two university hospitals from the CovidPredict cohort were included. PARTICIPANTS: Admitted, adult patients with a positive COVID-19 PCR or high suspicion based on CT-imaging of the thorax. Patients were followed for major outcomes during the hospitalisation. CVD risk factors were established via home medication lists and divided in antihypertensives, lipid-lowering therapy and antidiabetics. PRIMARY AND SECONDARY OUTCOMES MEASURES: The primary outcome was mortality during the first 21 days following admission, secondary outcomes consisted of intensive care unit (ICU) admission and ICU mortality. Kaplan-Meier and Cox regression analyses were used to determine the association with CVD risk factors. RESULTS: We included 1604 patients with a mean age of 66±15 of whom 60.5% were men. Antihypertensives, lipid-lowering therapy and antidiabetics were used by 45%, 34.7% and 22.1% of patients. After 21-days of follow-up; 19.2% of the patients had died or were discharged for palliative care. Cox regression analysis after adjustment for age and sex showed that the presence of ≥2 risk factors was associated with increased mortality risk (HR 1.52, 95% CI 1.15 to 2.02), but not with ICU admission. Moreover, the use of ≥2 antidiabetics and ≥2 antihypertensives was associated with mortality independent of age and sex with HRs of, respectively, 2.09 (95% CI 1.55 to 2.80) and 1.46 (95% CI 1.11 to 1.91). CONCLUSIONS: The accumulation of hypertension, dyslipidaemia and diabetes leads to a stepwise increased risk for short-term mortality in hospitalised COVID-19 patients independent of age and sex. Further studies investigating how these risk factors disproportionately affect COVID-19 patients are warranted.


Subject(s)
COVID-19 , Heart Disease Risk Factors , Aged , COVID-19/therapy , Female , Hospitalization , Humans , Intensive Care Units , Male , Middle Aged , Prospective Studies , Treatment Outcome
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