key: cord-0767900-1lirucqj authors: Kieninger, Martin; Sinning, Annemarie; Vadász, Timea; Gruber, Michael; Gronwald, Wolfram; Zeman, Florian; Lunz, Dirk; Dienemann, Thomas; Schmid, Stephan; Graf, Bernhard; Lubnow, Matthias; Müller, Thomas; Holzmann, Thomas; Salzberger, Bernd; Kieninger, Bärbel title: Lower blood pH as a strong prognostic factor for fatal outcomes in critically ill COVID-19 patients at an intensive care unit: A multivariable analysis date: 2021-09-29 journal: PLoS One DOI: 10.1371/journal.pone.0258018 sha: 7426175b3f0a877850552d755d2303a993d5e4c1 doc_id: 767900 cord_uid: 1lirucqj BACKGROUND: Data of critically ill COVID-19 patients are being evaluated worldwide, not only to understand the various aspects of the disease and to refine treatment strategies but also to improve clinical decision-making. For clinical decision-making in particular, prognostic factors of a lethal course of the disease would be highly relevant. METHODS: In this retrospective cohort study, we analyzed the first 59 adult critically ill Covid-19 patients treated in one of the intensive care units of the University Medical Center Regensburg, Germany. Using uni- and multivariable regression models, we extracted a set of parameters that allowed for prognosing in-hospital mortality. RESULTS: Within the cohort, 19 patients died (mortality 32.2%). Blood pH value, mean arterial pressure, base excess, troponin, and procalcitonin were identified as highly significant prognostic factors of in-hospital mortality. However, no significant differences were found for other parameters expected to be relevant prognostic factors, like low arterial partial pressure of oxygen or high lactate levels. In the multivariable logistic regression analysis, the pH value and the mean arterial pressure turned out to be the most influential prognostic factors for a lethal course. Within the cohort, 19 patients died (mortality 32.2%). Blood pH value, mean arterial pressure, base excess, troponin, and procalcitonin were identified as highly significant prognostic factors of in-hospital mortality. However, no significant differences were found for other parameters expected to be relevant prognostic factors, like low arterial partial pressure of oxygen or high lactate levels. In the multivariable logistic regression analysis, the pH value and the mean arterial pressure turned out to be the most influential prognostic factors for a lethal course. Severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) is a new beta coronavirus that was identified as the cause of coronavirus disease 2019 in early 2020. In the spring and summer of 2020, about 10% of those infected with SARS-CoV-2 in Germany required hospital treatment, of whom 14% had to be treated at an intensive care unit (ICU). The mortality rate of ICU patients with COVID-19 was 47% [1, 2] . Although originally assumed to be a 'lung disease,' COVID-19 has a broad organotropism and causes neurological, nephrological, cardiological, and hematological problems. Typical of severe courses are dramatic inflammatory reactions of the monocyte-macrophage system similar to those observed in severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS) [3, 4] . Three recently published studies that included more than 15000 patients with COVID-19 from South Korea, France, Belgium, Switzerland, Denmark, and Great Britain have shown that advanced age as well as pre-existing comorbidities were significantly more common in non-survivors than in survivors [5] [6] [7] . The complication rate depended on both age and comorbidities and characterized the patients who required intensive care treatment [8] . For patients with COVID-19 who require ICU treatment, various parameters have already been suggested as prognostic factors of outcome. However, no consistent overall picture has yet been drawn. Choron et al. found the course of fever to be an important prognostic factor [9] . Pan et al. included lymphocyte percentage, prothrombin time, lactate dehydrogenase, total bilirubin, eosinophil percentage, creatinine, neutrophil percentage, and the albumin level in their model [10] . Thomson et al. found age, obesity, the lowest Horowitz index (P/F ratio) on the first day of admission, and paCO 2 to be independently associated with ICU death [11] . Mesas et al. identified Interleucin 6 (IL-6) together with pre-existing diseases as prognostic factors of a lethal course in patients under the age of 60 and elevated bilirubin and liver enzymes together with decreased albumin in patients older than 60 years [12] . Mennuni et al. found that the dosage of low molecular weight heparin hardly affected outcome [13] . Our study focused on COVID-19 patients who already required high-care intensive medicine. The aim was to identify prognostic factors for fatal outcomes in this population. We evaluated 103 different parameters assumed to be crucial for ICU treatment and performed further analysis using logistic regression models. The study included all patients who had to be treated at the ICUs of the University Medical Center Regensburg, Germany, during the first wave of the COVID-19 pandemic. The study was approved by and conducted according to the guidelines of the Ethics Committee of the University of Regensburg (approval number 20-1790-104, 'S1 Appendix'). In accordance with European law, consent to participate was not required due to retrospective analysis of anonymized patient data. All data were anonymized before being accessed. between 14 March 2020 and 2 June 2020. All patients were treated according to our in-house standard described in 'S1 Table. ' The cohort consisted of patients who were either directly admitted to the University Medical Center Regensburg or who were transferred from a non-tertiary hospital in the surrounding area for higher-care therapy. Three patients were transferred from the Bergamo area in Italy. There were no standardized criteria for admission of the patients to the ICU at the University Medical Center Regensburg from which the present study's data has been drawn. Forty patients were discharged from the ICU alive; 19 had died (mortality 32.2%). Fig 1 shows the Kaplan-Meier estimator for all patients. In each case, we examined the first two weeks of ICU treatment or the time until the patient died or was discharged from the ICU. For the investigated period, we collected parameters from the following categories: (1) baseline and demographic data, (2) pre-medication, (3) preexisting comorbidities, (4) vital signs, (5) dosage of catecholamines, (6) dosage of analgosedation, (7) anticoagulation and antithrombotic medication, (8) laboratory blood diagnostics and microbiological diagnostics, (9) extracorporeal membrane oxygenation (ECMO), prone positioning, and ICU scores, (10) airway, respirator therapy, and pulmonary gas exchange and (11) complications during ICU treatment. A complete list of all the parameters we examined is provided in 'S2 Table. ' Some parameters were recorded daily. It should be noted that not all patients survived the observation period and that some patients were discharged from the ICU before the end of the observation period; therefore, not all data sets are complete. Complete data sets consisted of 883 values per patient. Data were extracted from the in-house patient data management systems of the ICU (MetaVisionSuite1, version V6.9.0.23, iMDsoft1, Tel Aviv, Israel; SAP1 Enterprise resource planning, version 6.0 EHP7 SP21, SAP SE, Walldorf, German; SWISSLAB1 Laborinformationssysteme, version 2.18.3.00, NEXUS SWISSLAB GmbH, Berlin, Germany). The patients were grouped according to their outcome ('died:' death in intensive care, 'survived:' transferred to a rehabilitation or normal care unit); the outcome was determined on the day of discharge from the ICU. Statistical analysis was conducted using IBM SPSS Statistics TM 26 (IBM, Armonk, USA). Statistical tests were two-sided, and the level of significance was set to p<0.050. Categorical parameters are presented as absolute and relative frequencies and illustrated by bar charts. Survivors and non-survivors were compared by using the Chi-square test of independence. Continuous data are shown as median and minimum/maximum, and differences between survivors and non-survivors were assessed with the Mann-Whitney-U Test. Continuous parameters with values for each day of examination were analyzed in two steps. First, we investigated the course of the parameters within the first 14 days of ICU treatment. We calculated the median and interquartile range for survivors and non-survivors on a daily basis and compared both groups using the two-sided Mann-Whitney-U Test. The results are visualized by box plots. Second, we investigated the parameters with apparently relevant differences between the two groups on several days during the observation period. For this purpose, we calculated mean values and, if reasonable from a clinical point of view, minimum and maximum values for each patient over the course of time, leading to a total of 92 parameters, which were then compared between survivors and non-survivors using the Mann-Whitney-U Test. To account and correct for multiple comparisons, Bonferroni correction was applied and a new significance level p � = 5.43 � 10 −4 was determined. In a next step, all parameters with p

1 � 10 6 copies at least once in the observation period hardly differed between the two groups (non-survivors: 10 out of 19, survivors: 17 out of 40, p = 0.579). Microbiological diagnostics were checked for further viral, bacterial, as well as fungal infections. Three temporal categories were formed: positive results at admission, positive results on days 1 to 7, and positive results on days 8 to 14. The number of patients per group was determined in each case. However, no significant differences could be found between the two groups. ECMO, prone positioning, and ICU scores ('S6 Fig') . 26% of non-survivors and 30% of survivors received ECMO therapy. For further examination, patients were divided into the Lower blood pH as a strong prognostic factor for fatal outcomes in critically ill COVID-19 patients following categories: cannulation start of ECMO prior to admission to our hospital, start of ECMO on day 1 to 7, start of ECMO on day 8 to 14, duration of ECMO treatment <5 days, and duration of ECMO treatment �5 days. The comparison of survivors and non-survivors showed that outcomes were neither influenced by the day of starting the therapy nor by the length of ECMO treatment. Therapy with prone positioning was recorded as metric data in hours per day. The duration of prone positioning did not differ between the two groups over the entire observation period. The therapeutic intervention scoring system (TISS) score and the simplified acute physiology score (SAPS) were calculated daily for each patient. Although survivors and non-survivors differed in the TISS score on less than 7 days of the observation period, SAPS values were lower in survivors from day 2 to day 14. The comparison of the overall mean values for TISS and SAPS yielded a highly significant difference between the two groups (p<0.001). Airway and respiratory therapy ('S7 Fig') . None of the patients underwent extubation during the first week of the observation period; however, 20% of survivors could be extubated in the second week. The two groups did not differ in the timing of tracheostomy. Interestingly, fraction of inspired oxygen (FiO 2 ), positive endexpiratory pressure (PEEP), and tidal volume did not differ at all between survivors and non-survivors. Driving pressure was significantly lower in survivors only on two days of the first week (day 3 and day 4) but did not differ in the other days of the observation period. The P/F ratio never differed between the two groups. Complications during ICU treatment (' S8 Fig') . The rate of serious complications within the first two weeks of ICU treatment was rather low for both survivors and non-survivors, except for acute kidney injury (AKI). Seventeen out of 19 non-survivors and 23 out of 40 survivors developed AKI (p = 0.017). Renal replacement therapy (RRT) was required in 63% of the non-survivors and in 30% of the survivors, corresponding to a p-value of 0.023. RRT was therefore significantly more often necessary in non-survivors. Twelve out of 19 non-survivors and only 7 out of 40 survivors had required RRT for at least 5 days (p = 0.001). A notable finding was that intracerbral hemorrhage (ICH) was diagnosed in 4 non-survivors and in 1 survivor during the first two weeks of ICU treatment. Other bleeding complications occurred in 2 non-survivors and 3 survivors. No further statistical analyses could be performed due to the heterogeneity of the events (see 'S3 Table' ) and the low number of cases. Selection of the parameters for the model. All parameters with explicit differences between survivors and non-survivors with p<5.43 � 10 −4 ('S4 Table' ) were screened regarding their usefulness of inclusion in a model for prognosing fatal outcomes in critically ill COVID-19 ICU patients. SAPS and TISS values, which would both have met this criterion of usefulness, were excluded because both scores are already a combination of several parameters. PCT was also not considered because very high values of >100 ng/mL are only reported as ' = 100 ng/mL' by the laboratory system, which factually excludes this metric parameter from use in a mathematical model. Univariable logistic regression model. The following parameters were identified as highly significant prognostic factors of survival or death and used for the calculation of logistic regression models and the estimation of eligibility for multivariable model calculation: (1) mean MAP during the 14-day observation period for each patient, (2) mean, maximum, and minimum blood pH during the 14-day observation period for each patient, (3) mean, maximum, and minimum BE during the 14-day observation period for each patient, and (4) mean troponin T during the 14-day observation period for each patient. The best result could be achieved with the mean pH value over time (pHmean). The calculation provides the following formula of the probability of non-survival: Characteristics of the calculation for univariable analysis can be found in 'S5 Table' . Table 1 shows the relative mortalities corresponding to the individual parameters identified as highly significant prognostic factors for non-surviving. Multivariable logistic regression model. We identified blood pH as a minimum value per patient (pHmin) and MAP as an averaged value per patient (MAPmean) as the best set of parameters for a logistic regression model. Following these determinations, the following formula for calculating the probability for fatal outcome resulted: Probability of non survival ¼ 1 1 þ e À x with x ¼ 225:508 À 28:435 � pHmin À 0:238 � MAPmean A model accuracy of 84.7% was calculated by LOOCV. Fig 4 shows the comparison of ROC analysis for the multivariable model using MAPmean and pHmin and with the univariable models using MAPmean, pHmin and pHmean. Further parameters of the calculation for the multivariable analysis can be found in 'S6 Table. ' The aim of the present study was to find prognosis factors for fatal outcomes in critically ill COVID-19 patients by investigating the first two weeks of treatment at a high-care ICU of a university medical center. For this purpose, we first screened the current literature for any parameters to be used as possible prognosis factors of good or bad outcomes in patients with COVID-19; furthermore, we added parameters that we considered crucial in everyday intensive care treatment. Consequently, a panel of 883 distinct parameters was generated for each patient if a complete dataset was available. The number of patients to be included was rather low, because we focused on those COVID-19 patients who had required the highest level of ICU therapy (41 out of 59 patients included in the present study had been transferred from an ICU of a non-tertiary hospital to an ICU at our university medical center). Despite this special selection, the cohort of patients included in the present study did not differ to the extent that could have been expected in terms of baseline and demographic data as well as pre-existing comorbidities [1, 5] . In a first step, we checked all parameters suitable for prognosing patient outcome. Some results were in line with published data, and some results were unexpected. For example, in contrast to data published by Choron et al., fever was not a predictor of higher mortality in our study [9] . A possible reason for this apparent discrepancy may be different definitions of fever. We classified each day with a peak temperature of � 38˚C to be a 'fever day,' while Choron et al. had considered the absolute peak temperature levels for comparing survivors and non-survivors. In the context of vital signs, MAP was a highly significant prognosis factor of patient outcome with lower values for non-survivors in our study. This finding is per se not surprising and in line with an obviously higher need for cardiocirculatory support of non-survivors who also required higher dosages of norepinephrine. Serious courses of COVID-19 seem to be associated with a higher risk of venous thrombosis [13, 14] . In our cohort, higher prophylactical dosages of unfractionated or low molecular weight heparin were more often administered to survivors than to non-survivors. However, the heterogeneity of different dosages of UFH and LMWH in the study's context with the overall small number of patients in our cohort does not allow any further conclusions on specific therapy recommendations regarding the intensity of anticoagulation. Initial recommendations [15, 16] suggested that COVID-19 patients should not be treated with steroids. This guideline was applied when generating an initial in-house standard for the ICU treatment of COVID-19 patients during the first wave of the disease ('S1 Table' ). As the patients included in the present study had been treated according to this initial standard and had therefore hardly received any steroids, we did not consider treatment with steroids to be a reasonable parameter for prognosing outcomes in our cohort. Meanwhile, the efficacy of dexamethasone in the treatment of COVID-19 patients with respiratory support has been demonstrated [17] . Analysis of the laboratory diagnostics showed lower blood pH, BE, and bicarbonate values for non-survivors. Lactate, chloride, paO 2 and paCO 2 , however, did not significantly differ in a relevant manner between the two groups. Choron et al. found acidosis to be a predictor of mortality in mechanically ventilated COVID-19 patients [9] . Calculation of the daily medians of metric parameters and the presentation as box plots for the 14-day observation period gives a visual impression of the question of whether there is a difference between survivors and non-survivors. Such daily comparisons, however, may be falsified, because patients were admitted to our ICU at different stages of their course of disease. 'Day 1' may be the day at which a patient first required ICU therapy but could also be the first day at our ICU after many days of ICU treatment in an external hospital. Therefore, mean or extreme values for each patient should be more conveniently considered in a multivariable analysis. In addition, the use of mean or extreme values does not imply that patients who died earlier than 14 days after ICU admission must be excluded from further calculation. To account for the rather small size (59 patients) of our cohort for multivariable analysis and to avoid overfitting, the number of parameters selected for multivariable analysis was deliberately kept very low. We therefore only considered parameters that showed highly significant differences between the two groups for further examination. The derived formula correctly prognoses fatal outcome with a very high model accuracy of 84.7%, which underlines the practicality of the chosen approach. Fig 2 ( for MAP) and Fig 3 (for pH) show the remarkable difference in both parameters between survivors and non-survivors over the entire observation period, indicating the high stability of the model. MAPmean and pHmin do not reflect any progressive divergence of the two parameters over time caused by the degeneration of patient status in non-survivors or the improvement of patient status in survivors. Calculation of the linear regression model with only one parameter for pHmin, MAPmean and pHmean showed a highly prognostic value for pHmean that almost reached the level of the above model including MAPmean and pHmin (AUC 0.901 for pHmean alone vs. AUC 0.945 for MAPmean and pHmin). This finding was not expected and underlines the predictive strength of the pH value. Nine patients from the non-survivors died during the 2-week observation period. One could assume that a relevant drop in blood pH value and MAP could have occurred within the last days before death due to multiple organ failure, thus causing explicit differences in pHmin and MAPmean between both groups and falsification of the model. However, exclusion of the last 3 days before death in all non-survivors for calculation does not result in relevant differences regarding the prognostication of fatal outcomes ('S7 Table' ). The overall number of patients included in the present study is rather low, thus making multivariable analysis difficult. To avoid overfitting, we considered only parameters for calculation that had clearly yielded highly significant differences between the two groups over time and restricted the number of parameters included in the model. We used a classical significance and regression analysis to examine parameters important in intensive care after an observation period of 2 weeks. The aim was to identify parameters that could help discriminate the survivor group from the non-survivor group in the further course. However, a multimodal prognostication that not only considers the results of statistical calculations but also performs a critical evaluation of the previous course of ICU treatment and the results of different kinds of diagnostics will be indispensable prior to withdrawal of life-sustaining treatment. As the cohort of patients included in the present study was treated with the highest level of ICU therapy at a university hospital, our findings will possibly not be transferrable one to one to situations for patients treated at ICUs at smaller hospitals with more limited resources and logistical options. We could only include 59 patients in our study, which may explain why some anticipated associations, such as the association between co-morbidities and ICU mortality, were not found. Our study cohort consisted exclusively of patients from the first wave of the COVID-19 pandemic. Patients from the second and third wave may have different characteristics from those of the patients included in the present study. We have begun a follow-up study that additionally includes the patients of the second and third wave in Germany for reevaluation of our findings. A large number of parameters were screened as possible prognostic factors of fatal outcomes within a highly selective population of critically ill COVID-19 patients who required high-care ICU therapy in the first wave of the pandemic. MAP and blood pH were identified as prognostic factors because both parameters showed highly significant differences between survivors and non-survivors over the entire observation period. Both parameters can be easily derived from patient data management systems. Other parameters which were expected to be crucial for the further course of ICU treatment, like low paO 2 or high lactate values, were not identified as relevant for the prognostication of a fatal outcome. A follow-up study including a higher number of patients and patients from the second and third pandemic waves will be necessary to reevaluate our findings. Disease severity of the first COVID-19 wave in Germany using reporting data from the national notification system Beschluss der STIKO zur 1. Aktualisierung der COVID-19-Impfempfehlung und die dazugehörige wissenschaftliche Begrü ndung Pathological inflammation in patients with COVID-19: a key role for monocytes and macrophages Developing and validating COVID-19 adverse outcome risk prediction models from a bi-national European cohort of 5594 patients Prediction of patients requiring intensive care for COVID-19: development and validation of an integer-based score using data from Centers for Disease Control and Prevention of South Korea Clinical characteristics and day-90 outcomes of 4244 critically ill adults with COVID-19: a prospective cohort study. Intensive care medicine Fever in the ICU: A Predictor of Mortality in Mechanically Ventilated COVID-19 Patients Prognostic Assessment of COVID-19 in the Intensive Care Unit by Machine Learning Methods: Model Development and Validation Clinical characteristics and outcomes of critically ill patients with COVID-19 admitted to an intensive care unit in London: A prospective observational cohort study Predictors of in-hospital COVID-19 mortality: A comprehensive systematic review and meta-analysis exploring differences by age, sex and health conditions Clinical outcome with different doses of low-molecular-weight heparin in patients hospitalized for COVID-19 Thrombosis and Coagulopathy in COVID-19 Coronavirus disease 2019 (COVID-19): update for anesthesiologists and intensivists Clinical management of severe acute respiratory infection when novel coronavirus (nCoV) infection is suspected Dexamethasone in Hospitalized Patients with Covid-19 Supporting information S1 Appendix. Ethics approval document. (PDF) S1